As the automotive industry undergoes a transformative phase driven by technological advancements, the integration of driving simulators stands out as an important tool for research and development. These simulators offer a controlled environment for studying driver behavior; however, the alignment of data remains a complex aspect that warrants thorough investigation. This research investigates driver state classification using a dataset obtained from real-road and simulated conditions, recorded through JINS MEME ES_R smart glasses. The data set encompasses electrooculography signals, with a focus on standardizing and processing the data for subsequent analysis. For this purpose, we used a recurrent neural network model, which yielded 95.21% accuracy on testing dataset. The findings of this study indicate the plausible use of proposed methodology in practical scenarios, providing possibility for developments in intelligent transportation systems and driver monitoring technology.

This article uses machine learning analysis of wearable sensor data to explore the topic of driver differentiation. The study focuses on using detailed analyzes of data collected from wearable sensors to identify patterns that differentiate skilled drivers from inexperienced ones. On a predetermined driving route, participants experienced a variety of driving conditions, including parking, navigating cities, driving on highways and driving through residential neighborhoods. The gathered data was analyzed using machine learning techniques, yielding a classification accuracy of 94% overall. The results highlight important differences in sensor data between inexperienced and experienced drivers, providing insight into possible uses for improving safety protocols, driver education, and personalized feedback systems. The article fosters improvements in traffic safety and driver education by offering insightful information on the link between driver behavior analysis and machine learning.

The working environment of mining electric locomotives is wet and muddy coal mine 1 roadway. There may be idling or slipping between the wheels and rails of mining electric locomotives 2 due to low friction between the wheel and rail and insufficient utilization of creep rate. Therefore, it 3 is necessary to control the creep rate within a reasonable range. In this paper, the autonomous control 4 algorithm for mining electric locomotives based on improved ε-greedy is theoretically proven to be 5 convergent and effective firstly. Secondly, after analyzing the contact state between the wheel and rail 6 under wet and slippery road conditions, it was concluded that the value of creep rate is an important 7 factor affecting the autonomous driving of mining electric locomotives. Therefore, the autonomous 8 control method for mining electric locomotives based on creep control is proposed in this paper. 9 Finally, the effectiveness of the proposed method was verified through simulation. The problem of 10 wheel slipping and idling caused by insufficient friction of mining electric locomotives in coal mining 11 environments is effectively suppressed. Autonomous operation of vehicles with optimal driving 12 efficiency can be achieved through quantitative control and utilization of the creep rate between 13 wheels and rails

.Various synthetic nematicides are currently used effectively in the control of plant parasitic nematodes. However, these nematicides have adverse effects on humans and the environment. Neem oil is eco-friendly alternatives to synthetic nematicides. The effectiveness of repeated neem oil application in suppressing the root-knot nematodes infestation in figs seedlings was therefore examined in this study, both in its natural form (10 ml/L) and in its nano-emulsion form (5 and 7 ml/L). The recorded reduction parameters of M. incognita were J2/250g soil, galls and egg masses / 5g roots of infected fig seedlings were measured at that time, and the effects of these natural additives were compared with the Fosthiazate (Krenkel 75%EC). The impact of such soil amendments on some typical plant parameters, the total microbial count of soil, and the overall enzymatic activity of the fig rhizosphere were also included. Obtained results revealed that the soil population density of root knot nematode suppressed by 80.3% , reduction of nematode egg masses (60.4%) due to the application of nano-emulsion of neem oil (at 7ml/L). Present work recommend the use of nano-emulsion of neem oil as one of the effective and eco-friendly means to reduce the root- knot nematode on fig .

Tunnel-interchange connecting sections pose significant safety challenges on mountainous expressways due to their high incidence of accidents. Improving road safety necessitates a comprehensive understanding of driver behavior in such areas. This study explores the influences of road characteristics, signage information volume, and traffic conditions on drivers' car-following and lane-changing behavior in tunnel-interchange diverging areas. Utilizing driving data from 25 subjects of 72 simulated road models, driving performance is assessed using Friedman rank test and multivariate variance analysis. The results highlight the significant influence of both connection distance and signage information load on driving behavior. In tunnel-interchange scenarios, the reduction of velocity increased by 62.61%, and speed variability surged by 61.11%, indicating potential adverse effects on driving stability due to the environmental transitions. Decreased connection distances are associated with reduced lane-changing durations, larger steering angles, and increased failure rates. Furthermore, every two units increase in signage information leads to a 13.16% rise in maximum deceleration and a 5% increase in time headway. Notably, the signage information volume shows a significant interaction with connection distance (F=1.60, P=0.045) for most car-following indicators. Hence, the study recommends a maximum connection distance of 700 m and signage information not exceeding nine units for optimal safety and stability.

The article studies and analyzes a hybrid power source system, lithium battery, and fuel cell assisted by flexible photovoltaic panels for hybrid electric vehicles (HEVs). The new configuration operates based on optimizing the performance of the dual power source to maximize the available energy and operating time. The operational mode simulates specific driving conditions in urban, peripheral, and intercity routes under variable driving patterns and traffic conditions. The simulation results show that the new configuration increases the hybrid electric vehicle performance, providing higher reliability despite the more complex design. The proposed system reduces the external power supply dependence and avoids unexpected sudden stops due to energy exhaustion. The new configuration also extends the vehicle driving range depending on driving mode and route conditions. The vehicle driving range extension may reach up to 276.3 km. The hybridization of fuel cells with lithium battery and PV panel assistance enhances battery management, reducing battery servicing and increasing lifespan. The proposed topology is cheaper than a single fuel cell power system by 17.2%, reducing the total cost related to the single battery power system if we consider the battery recharge for its entire lifespan. The cost saving is 2.6% for the American market and 14.8% for the European one.

Cement production contributes to about 7% of CO2 emissions into the atmosphere. Despite our best efforts, we cannot find a competitive substitute that is both reliable and environmentally friendly. The simplest way to solve the problem is to rationalize the resources and try to minimize its use by substitute them with other materials. A concrete that contains a substantial amount of fly ash is already available, but its use is limited by the current shortage in the market and reduced initial resistance. In this context, ternary mixtures have been used to conduct an experiment. A significant volume of cement has been replaced with fly ash and metakaolin, which serves as a correction factor for the performance. In this work the main advantages and disadvantages of the simultaneous use of these two additions, which may reveal to be very interesting with high volumes of substitution will be presented. The performance of this ternary mixture is very promising. Provide a wide range of possibilities for replacing cement, maintaining or even improving its mechanical and durability properties. A solution for an ecological concrete with enhanced performance for general use in construction as an alternative to conventional concrete.

Despite the partial disclosure of driving scenario knowledge graphs, they still fail to meet the comprehensive needs of intelligent connected vehicles for driving knowledge. Current issues include the high complexity of pattern layer construction, insufficient accuracy of information extraction and fusion, and limited performance of knowledge reasoning models. To address these challenges, a hybrid knowledge graph method was adopted in the construction of the Driving Scenario Knowledge Graph (DSKG). Firstly, core concepts in the field were systematically sorted and classified, laying the foundation for the construction of a multi-level classified knowledge graph top-level ontology. Subsequently, by deeply exploring and analyzing the Traffic Genome data, 34 entities and 51 relations were extracted and integrated with the ontology layer, achieving the expansion and updating of the knowledge graph. Then, in terms of knowledge reasoning models, an analysis of the training results of the TransE, Complex, Distmult, and Rotate models in the entity linking prediction task of DSKG revealed that the Distmult model performed the best in metrics such as hit rate, making it more suitable for inference in DSKG. Finally, a standardized and widely applicable Driving Scenario Knowledge Graph was proposed. The DSKG and related materials have been publicly released for use by industry and academia.

Reducing GHG emissions is a crucial challenge in urban areas, characterized by high energy consumption and reduced exposure to nature. In this context, the urban green system could play a pivotal role. In the literature, scholars analyzed both the ability of species-specific and layout-specific green infrastructures to increase carbon sequestration and the best location sites of new green infrastructures, to increase the provision of overall ecosystem services. There is a lack of studies helping green urban planners and designers choose where and which green infrastructure to implement, based on vegetation species-specific performance and local carbon emissions of city components. This paper uses tree inventory data from a medium-sized city in central Italy (Perugia) to develop a spatial analysis of the urban parks' performance in carbon sequestration. Then, the method evaluates the carbon emission of a public city building to generate a spatialized balance between buildings’ demand and trees’ supply, to support local decisions about the best locations for new green infrastructures and the choice between species. The paper contributes to GIS-based tools that vary the recommended location sites and species for new green infrastructures based on the demanded ecosystem service.

Abstract: This article aims to better understand the mechanisms that connect climate change perceptions and general willingness to engage in pro-environmental behavior using Spanish cross-sectional data (N = 403). To do this, we first developed and validated the General Willingness for Environmental Behavior Scale (GWEBS), which includes the classical approach of voluntarily doing new actions but also actions implying not doing things (degrowth) and actions forced by social constraints. The exploratory and confirmatory factor analysis showed a good fit for the one-factor structure, which had adequate validity based on their relationship with other variables. Additionally, the GWEBS distinguished between women and men, left- and right-oriented people, and people who belonged to pro-environmental groups and people who did not. In the second place, we tested the parallel mediator role of eco-anxiety and trust in science in the relationship between climate change perceptions and the GWEBS. The results showed that eco-anxiety, fully, and trust in science partially mediated such a relationship, making them crucial to mobilize the intention to act following the perception. This study contributes to our understanding of the psychological mechanisms that eventually drive pro-environmental behaviors and provides a clear direction for future research.

The process involved in preparing driving simulator tests is a labor intensive one, and the nature of this complexity depends on the sophistication of the tests to be carried out, as well as the number of participants. Which has an impact on the duration of the research itself. The very solution of using driving simulators is a research tool that has been in use for many years and with great success. They are used in a very wide range of areas of human functioning. They replicate in a very realistic way the conditions that can occur in the external environment, offer the possibility of repetition of particular conditions and insight into certain behaviors that could be very difficult to initiate in real life. This article also describes the dis- ease of using simulators to conduct research activities, along with the workflow from assumptions to finalizing the research. An algorithm is also presented for the study of individual participants, with examples for separate research scenarios concerning driver behavior in the vicinity of pedestrian crossings.

The increasing growth in road users who are unfamiliar with traffic rules and regulations provides a serious safety problem for Pakistani traffic conditions. This study has two main objectives. First, it seeks to validate the reliability and applicability of the Dula Dangerous Driving Index (DDDI) in Pakistan. Second, it intends to investigate the effects of DDDI, sociodemographic characteristics, seat belt use, and driving training on the occurrence of traffic accidents. A total of 623 Pakistani individuals completed a questionnaire as part of this study. The questionnaire consists of items related to dangerous driving behaviors, sociodemographic characteristics, seat belt use, driving instruction, and accident involvement. The DDDI's accuracy was evaluated by comparing it to self-reported traffic incidents and seat belt compliance. To discover predictors within the DDDI dimensions, generalized linear models were used. A binary logistic regression analysis was also performed to evaluate the factors that influence traffic accidents. DDDI dimensions, demographic characteristics, seat belt usage, and driving training were all considered in this study. The use of seat belts while driving reduced the likelihood of an accident by 25%. On the other hand, aggressive driving and risky driving increased the chances of getting involved in traffic accidents by 36%% and 20%. Furthermore, the chances of being involved in a traffic accident decreased by 21% as the drivers age increased. The results also illustrate that Pakistani drivers are more aggressive and risk taker compared to Chinese drivers who are more disciplined. This study suggests that lack of comprehensive and standardized driving training and education programs, in consistent enforcement of traffic laws, the quality of road infrastructure and lack of public awareness campaigns can be attributed to adverse road safety situations in country.

In an effort to enhance the efficiency and safety of open-pit mines, this study explores the optimization of end slope road parameters and slope structures, specifically focusing on unmanned driving lanes.A significant aspect of the study is the development of a truck trajectory offset model, which considers the different reaction times between automated sensors and human drivers in adapting to environmental changes. To test these concepts, the study uses numerical simulations to confirm the stability of the proposed end slope designs. Using Victory West Mine No. 1 as a case study, the research determines the optimized width for unmanned driving lanes and the maximum angle for safe steepening of end slopes. The findings indicate that the optimized unmanned lane width for NTE240 mining dump trucks is 1743 mm, allowing for a 2-degree increase in the slope angle at the south end slope. This optimization leads to a steep mining stripping volume of 3.2735 million m³ and a coal output of 2.49628 million tons, maintaining a stripping ratio of 1.31m³/t. These results demonstrate that the unmanned driving road width optimization not only ensures slope safety but also significantly boosts the economic benefits of steep mining in open-pit mines.

Vision impairment may delay responses to hazards when driving. In a proof-of-concept driving simulator study, we evaluated a hazard warning device designed for vision impaired drivers. Three groups participated: 11 persons with central vision loss (CVL; median age 60 years), 12 with homonymous field loss (HFL; 52 years) and 11 with normal vision (NV; 60 years). CVL is characterised by impaired visual acuity and contrast sensitivity while HFL is a loss of vision in the same half of the visual field (blind side) in both eyes. Participants completed drives with and without the device which gave a directional vibro-tactile warning for an approaching pedestrian hazard when collision risk exceeded a pre-defined threshold. Warnings significantly (p< 0.001) reduced brake response times for CVL (∆= 0.54s) and HFL (∆= 0.30s), but not NV participants. For HFL participants, the reduction was greater (p = 0.02) for hazards on the blind (∆= 0.57s) than seeing side (∆= 0.10s). The warnings significantly reduced collision rates for CVL participants from 11 to 0% (all hazards) and for HFL participants from 30 to 1.7% (blind side hazards). Thus, hazard warnings may be beneficial in improving safety of vision impaired drivers; further research is warranted.

The global textile sector, pivotal to economic development, significantly contributes to environmental degradation, prompting a shift towards sustainable raw material sources. Embracing circular economy principles, this industry is exploring the valorization of agricultural by-products. This study introduces an innovative approach to repurposing mango waste into valuable textile fibers, a byproduct of the fruit processing industry, predominantly generated during juice and jam production. The focus is on the lignocellulosic constituents of mango waste, specifically the peel, seed, and fibrous material, traditionally considered as refuse. Previous research has predominantly utilized mango peel in pectin extraction for food applications, whereas the potential of seed and fibrous content remains underexplored. This investigation endeavors to transform these fibrous by-products into high-value materials suitable for textile manufacturing. The process involved washing, drying, and comprehensive physicochemical characterization of mango fibers, emphasizing lignin quantification, revealing a composition of 12.2% lignin. An experimental design was employed to elucidate the impact of variables such as liquid-to-solid ratio, reaction duration, and sodium hydroxide concentration on lignin dissolution through alkaline hydrolysis. Optimal conditions—10% NaOH concentration, 30 mL/g liquid-to-solid ratio over six hours—achieved an 83% reduction in lignin content. The resulting fibers exhibited properties akin to conventional natural fibers, including a favorable aspect ratio indicative of their suitability for textile applications. A subsequent softening process using propylene glycol enhanced the fibers' spinability, producing a durable, textured fabric ideal for eco-conscious fashion applications, including bags, footwear, and accessories. This research demonstrates the feasibility of converting mango waste into a textile-worthy material and underscores the importance of waste revalorization in promoting sustainability within the circular economy framework.

Distracted driving is a major cause of road traffic crashes in Yaoundé. Partly due to scarcity of enforcement, lack of evidence and investigation of distraction safety performance indicator (SPI), hindering evidence-based intervention. This study aimed to address this evidence gap by evaluating the distraction SPI using proven methodology. Data on distracted driving (handheld mobile device; interaction; eating/smoking/drinking) were collected from roadside observation on 36 randomly selected road sections carefully spread to cover the city. SPI were computed and weighted with traffic volume to ensure representativeness of values. A total of 41,004 drivers were observed (38,248 in car; 1,116 in van; 977 in truck; 663 in bus). The prevalence of distracted driving in Yaoundé is 13.69% for the three distractions type combined. The prevalence is 7.84% for interaction, 4.89% for handled mobile device usage and 0.96% for eating/smoking/drinking. Leveraging these insights, a seven year (2024 - 2030) fighting strategy aiming at halving the prevalence was developed. The strategy contains intervention including on legislation/enforcement, which have been proven effective. This study, pioneer in Yaoundé, provide stakeholders with evidence of the issue and measures to implement and can also serve when developing road safety strategy.Future research should consider investigation at national level.

This study utilises NaOH to treat composite cementitious materials containing aeolian sand and zeolite powders, which were used to replace 50% of the cement in aeolian-sand concrete (ASC). The mechanical properties of treated ASC considerably improved, especially when the NaOH dosage was 4% by mass. After curing this sample (denoted as AAZC-4) for 28 d, its compressive strength improved by 17.2% and its split tensile was increased by 16.3%. Potassium feldspar and montmorillonite in zeolite powder and SiO2 in the sand were decomposed by OH− and combined with other elements to generate various silicate gels and A-type potassium zeolite crystals inside the concrete. Microscopic examination showed that the gels and crystals intertwined to fill the pores, decreasing (increasing) the percentage of large (small) pores, thus optimising the pore structure. This substantially improved the mechanical properties of ASC. Freeze–thaw salt-intrusion tests showed that the extent of mass loss, degree of damage and loss of compressive strength of AAZC-4 were similar to those of ordinary concrete but were reduced by 36.8%, 19% and 52.1%, respectively, compared with those of ASC. Therefore, AAZC-4 has sustainable working performance in the chloride ion permeable environment in cold and arid areas.

In the area of dental healthcare services, where patients and professionals deal with multiple auditory stimuli, this cross-sectional study reports on the intricate dynamics of sound preferences, noise obstacles, and the profound impact of music on anxiety reduction within dental office set-tings. The sample comprises of 134 dental patients, primarily females (56.7%), with varying age groups, education levels, and therapy durations in the university clinics of the department of Dentistry, National and Kapodistrian University of Athens and four private dental offices at the metropolitan area of Athens, Greece. Notably, 92.5% of the participants report no hearing loss, and 56.7% exhibit some level of noise sensitivity. The study reveals a moderate to good perceived health status (M=3.84, SD=0.95) among participants, with low perceived noise disturbance in university dental clinics. Preferences for instrumental music, foreign pop, and classical music are prominent during waiting times. Potential irritations include impersonal treatment (44%) and staff behavior (41.8%). Music player usage within the settings of the dental clinic involves radios (49.3%) and headphones (41.0%). Correlations and predictors indicate associations between health status, music preferences, and noise disturbances. More specifically, visiting the university dental clinics (β = .300, p = .005) as well as younger age (β = .300, p = .034) were significant predictors of better perceived health status. Clearer perception of external sounds while using the music player (β = .190, p = .039) was a significant predictor of better perceived health status, while poorer health was predicted by the preference of Greek folk songs (β = -.200, p = .030). Also, the use of smartphones for playing music was related to poorer health (β = -.370, p < .001). Following, younger age (β = -.270, p = .015), lower education (β = -.210, p = .035), higher noise sensitivity (β = .190, p = .027), as well as feelings of anxiety and nervousness due to ambient noise from people and machines (β = .510, p = .027) were significant predictors of noise disturbance from machines. Also, visiting private clinics (β = .240, p = .018), preferences of specific playlist (β = .310, p = .012), no music (β = .280, p = .016) and use of smartphones to play music (β = .230, p = .029) were significant predictors of noise disturbance from patients or staff. Finally, the preference of listening to a specific playlist was related to better health status (r = .190, p < .05). Findings aim to optimize eco-soundscapes in dental offices, con-tributing to enhanced patient well-being. The research underscores the potential of music inter-ventions to alleviate dental anxiety and improve the overall dental experience and quality care.

Autonomous driving navigation relies on diverse approaches, each with advantages and limitations depending on various factors. For HD maps, modular systems excel, while end-to-end methods dominate mapless scenarios. However, few leverage the strengths of both. This paper innovates by proposing a hybrid architecture that seamlessly integrates modular perception and control modules with data-driven path planning. This innovative design leverages the strengths of both approaches, enabling a clear understanding and debugging of individual components while simultaneously harnessing the learning power of end-to-end approaches. Our proposed architecture achieved 1st and 2nd place in the 2023 CARLA Autonomous Driving Challenge’s SENSORS and MAP tracks, respectively. These results demonstrate the architecture’s effectiveness in both map-based and mapless navigation.

The expansion of copper mining on the hyper-arid pacific slope of southern Peru has precipitated growing concern for scarce water resources in the region. Located in the headwaters of the Torata river, in the department of Moquegua, the Cuajone mine, owned by Southern Copper, provides a unique opportunity, in a little-studied region, to examine the relative impact of the landscape-scale mining on water resources in the region. Principal component analysis and cluster analysis of the water chemistry data from 16 sites, collected over three seasons during 2017 and 2018, show distinct statistical groupings indicating that, above the settlement of Torata, water geochemistry is a function of chemical weathering processes acting upon underlying geological units, and confirming that the Cuajone mine does not significantly affect water quality in the Torata river. Impact mitigation strategies that firstly divert channel flow around the mine and secondly divert mine waste to the Toquepala river and tailings dam at Quebrada Honda, below the Toquepala mine, in the department of Tacna, secure water quality in the Torata river for the foreseeable future. In the greater Moquegua river catchment, our results further suggest that water quality has been more significantly impacted by urban effluents and agricultural runoff than the Cuajone mine. The increase in total dissolved solids in the waters of the lower catchment reflects the cumulative addition of dissolved ions through chemical weathering of the underlying geological units, supplemented by rapid recharge of surface waters contaminated by residues associated with agricultural and urban runoff through the porous alluvial aquifer. Concentrations in some of the major ions exceeded internationally recommended maxima for agricultural use, especially in the coastal region. Occasionally, arsenic and manganese contamination also reached unsafe levels for domestic consumption. In the lower catchment, below the Cuajone mine, data and multivariate analyses point to urban effluents and agricultural run-off rather than weathering of exposed rock units, natural or otherwise, as the main cause of contamination.

The global focus on geopolymer binder production has increased due to the adoption of waste materials and industrial byproducts. Given the gradual decline of the availability of fly ash and ground granular blast furnace slag (GGBFS) resulting from the decarbonization process in electricity and steel productions, waste clay brick powder (WCBP), could be a viable substitute of these pozzolanic by-products. This research is dedicated to thoroughly investigate the techno-eco-efficiency performance of WCBP when utilized as a geopolymer precursor. The favorable mechanical characteristics exhibited by fly ash-GGBFS-WCBP-based geopolymer binder emphasize the importance of assessing its sustainability alongside its technical viability. The study employed life cycle analysis (LCA), following ISO 14040-44 standards, and using the Simapro software, to evaluate the environmental implications of the use of WCBP-based geopolymer mixtures. Human toxicity emerged as the primary impact. Moreover, the analysis of life cycle costs highlighted key financial factors, with around 65-70% attributed to alkaline activators of the total cost. The production of alkaline activators was identified as a critical point for both environmental impact and economic considerations due to energy consumption. While WCBP-rich samples exhibit a 1.7-0.7% higher environmental impact compared to the control mix (CM), their high mechanical strength and cost-effectiveness make them technologically and economically efficient geopolymer mixes. In conclusion, the portfolio analysis for techno-eco-efficiency affirms that mixes containing 40%, 30%, and 20% WCBP are more efficient than those using 10% and 0% WCBP, respectively.

In-depth insights into the profound impacts of climate change and human activities on water resources are garnered through the dynamic changes in surface water, a crucial aspect of effective water resource management and the preservation of aquatic ecosystems. This paper introduces an innovative approach employing the random forest algorithm for the systematic extraction and monitoring of surface water at large regional or national scales. This method integrates spectral bands, spectral indices, and digital elevation model data, offering a novel perspective on this critical task. A data filling model is proposed to mitigate the impact of missing data due to cloud cover. Leveraging the capabilities of the Google Earth Engine (GEE), detailed information on surface water dynamics during the rainy and dry seasons in the Yangtze River Basin (YRB) from 1991 to 2021 is extracted using Landsat time series imagery. The analysis encompasses spatial-temporal variation characteristics and trends, with a specific focus on the intricate interplay between the areal extent of surface water and hydro-meteorological factors in each sub-basin of the YRB. Importantly, this includes considerations of potential groundwater contributions to surface water. Key findings from our research include: (1) Achieving a remarkable overall accuracy of 0.96 ± 0.03 in obtaining reliable surface water datasets with the support of GEE. (2) Identifying significant trends, such as a noteworthy increase in rainy season surface water bodies (+248.0 km2·yr−1) and a concerning decrease in surface ice/snow cover during both rainy and dry seasons, with change rates of −39.7 km2·yr−1 and −651.3 km2·yr−1, respectively. (3) Uncovering the driving mechanisms behind these changes, revealing positive correlations between the areal extent of rainy season surface water bodies and precipitation, as well as negative correlations between surface ice/snow cover area and average surface skin temperature. It is crucial to note that these driving factors exhibit variation among secondary river systems, underscoring the complexity of surface water dynamics. Furthermore, comparative analyses with existing surface water products are conducted, contributing to a deeper understanding of the advantages and uncertainties inherent in our proposed extraction method. The proposed method for large-scale surface water extraction not only enhances the monitoring of spatio-temporal surface water dynamics in the YRB but also provides valuable insights for the sustainable utilization and protection of water resources, considering the potential role of groundwater in supplementing surface water.

Driver’s distractions are more prone to occur with automated driving vehicles, which might turn into cancelling some of their advantages. Traffic reports, naturalistic observations and in-lab tests are carried out to determine the influence of driver’s distractions in car crashes and to develop technology to detect and prevent them from happening. One of the best strategies to cope with this problem is education. In this work we propose a simulation environment to design laboratory experiments to study driver’s distractions as well as simple games to train drivers to take over the control of the vehicle when required. The result framework enables drivers to experience driving highly automated cars in their neighborhoods or in places of their choice with different levels of events to check their ability in taking over the control when required.

Understanding the spatiotemporal characteristics of merging behavior is crucial for the ad-vancement of autonomous driving technology. This study aims to analyze on-ramp vehicle merg-ing patterns, and investigate how various factors, such as merging scenarios and vehicle types, in-fluence driving behavior. Initially, a framework based on High-Definition (HD) Map is devel-oped to extract trajectory information in a meticulous manner. Subsequently, eight distinct merg-ing patterns (Pattern A to H) are identified, with a thorough examination of their behavioral characteristics from both temporal and spatial perspectives. Merging behaviors are examined temporally, encompassing the sequence of events from approaching the on-ramp to completing the merge. This study specifically analyzes the target lane's spatial characteristics, evaluates the merging distance (ratio), investigates merging speed distributions, compares merging patterns and identifying high-risk situations. Our novel findings reveal that Patterns C and F, with Time-to-Collision (TTC) values less than 2.5 seconds, pose a significantly higher risk than other patterns. In traffic simulations, Patterns B and E can be integrated, as they show negligible differences in speed, distance traveled, and duration. Additionally, the practice of truck platooning has a signif-icant impact on vehicle merging behavior. This study enhances the understanding of merging be-havior, facilitating autonomous vehicles' ability to comprehend and adapt to merging scenarios. Furthermore, this research is significant in improving driving safety, optimizing traffic manage-ment, and enabling the effective integration of autonomous driving systems with human drivers.

Digital agricultural technology extension service and sustainable food production have attracted attention from academia. However, the association between digital agricultural technology extension service and sustainable food production has not yet been fully investigated. This research investigates the average and heterogeneous impacts of digital agricultural technology extension service use on eco-efficiency among 1302 maize producing farmers from Northeast China major maize producing area in 2022 to fill this knowledge gap. The slack-based measure model with undesirable outputs is applied to calculate the eco-efficiency of maize production. To obtain an unbiased estimation of the average effect, the self-selection problem generated by observable and unobservable factors is solved by the endogenous switching regression model. Quantile regression is utilized to analyze the heterogeneous effect. Notably, the mediated effects model is utilized to examine the potential mechanism between them. Our findings indicate that digital agricultural technology extension service use can increase maize production's eco-efficiency. Digital agricultural technology extension service users would reduce eco-efficiency by 0.148 (21.11%) if they had not used the digital agricultural technology extension service. Digital agricultural technology extension service nonusers would improve eco-efficiency by 0.214 (35.20%) if they had used it. The robustness check reconfirms the results. Moreover, digital agricultural technology extension service use is more helpful to maize farmers who have lower eco-efficiency than those who have higher eco-efficiency. Digital agricultural technology extension service use can improve eco-efficiency of maize production through the application of organic fertilizers, green pesticides and biodegradable agricultural films.

This paper focuses on the impact that urban traffic has on the environment. The study characterizes the global effect of GHG emissions, including the ecologic evaluation and the characterization, normalization, and evaluation factor. The work makes a detailed survey of the different modes of driving and their influence on engine performance as one of the principal causes of gas emissions during the combustion process. The article analyzes six types of vehicles equipped with different engine configurations: diesel and gasoline, GLP and GNC, hybrid electric, and plug-in hybrid electric. Simulation of the driving mode under various operational conditions for every type of engine results in energy consumption, thus, in GHG emissions, carbon dioxide and monoxide, nitrogen oxides, and Sulphur dioxide. The study concludes that a reduction in vehicle speed, thus in the engine revolutions, has positive effects on engine combustion and gasses emissions, which is reduced by 27.5%. The study also concludes that the limitation in driving mode, avoiding sharp and sudden acceleration, may reduce up to 45% of GHG emissions. The changes applied in the driving mode improve the air quality in the urban environment, reducing the content of GHG from 39% to 61%.

The thermal degradation of brakes has a significant impact on the stability of pure electric commercial vehicles during long downhill braking. Based on road slope and using fuzzy control method, design strategies for motor regenerative braking force and friction braking force distribution, with the aim of reducing friction braking force, improving braking stability, and recovering braking energy. By establishing driving conditions on roads with different slopes and using numerical analysis methods to verify the proposed control strategy. the results show that under the 6% constant slope driving condition, the vehicle maintains a constant speed of 30km/h downhill driving, and the braking energy recovery rate reaches 50.93%; Under the condition of 3% -6% gradient driving, the vehicle speed fluctuates slightly, but it can still be maintained at a constant speed of 30km/h for downhill driving through the adjustment of the motor's reverse drag characteristic, with a braking energy recovery rate of 49.97%; During the process of driving downhill at a constant speed, the frictional braking force does not participate in braking; The braking energy recovery rate is determined by the slope and braking deceleration.

Sustainability has been playing a major role on the world stage. As with everything in the be-ginning, it was aligned to purely environmental contexts, but with the passing of the years and the new way of producing, marketing, and consuming, this concept has encompassed other as-pects which are the way of producing products and services. In this article, to obtain information on the implementation of sustainable innovation, a search equation was formulated in the SCOPUS database, contemplating the period from 2018 to 2023 as a time interval, with a total of 62 articles. Once the search of the published articles was carried out, 7 categories were formed to classify the research topic of each of the publications with Bibliometrix software, according to the main ob-jective and methodology used in each one. In this sense, 44% of the articles published are related to open innovation, and 26% of the articles are focused on sustainable business models. At the end of the analysis of the implementation of innovation according to the articles consulted, it was possible to establish that sustainable innovation began an important work, in the mitigation of the adverse impacts.

The realization of value in eco-products represents a rapidly advancing research theme. However, the clarity of relevant concepts, types and operational mechanisms remains elusive. A comprehensive review of current-stage research outcomes in eco-product value realization not only provides a novel approach to rejuvenating regional economies but also proactively contributes to the enhancement of local ecological environments. This article conducts a systematic literature review on the fundamental concepts, research scope and pathways for realizing the value of eco-products. The conclusions are as follows: (1) To clarify the basic concepts of ecosystem services and eco-products, the interrelationship between the two and the research scope of eco-products. (2) To elaborate the method for accounting the value of eco-products. (3) To demonstrate governmental path, market path and collaborative path for realizing the value of eco-products. (4)To review cases pertaining to the realization of value in public eco-products, quasi-public eco-products and operational eco-products both domestically and internationally. Building on this research foundation, the article identifies shortcomings in past research results and proposes key directions for future research progress.

This research aims to understand the Cambodian green economy transition and analyze its strengths, weaknesses, opportunities, and threats (SWOT). The research showed that Cambodia has been significantly transitioning its green economy since 2009 as evidenced by the National Green Growth Roadmap. Cambodia also established the National Council for Green Growth in 2012 and upgraded it to the National Council for Sustainable Development in 2015 in alignment with 2030 Agenda for Sustainable Development of the United Nations. The members of the Council are from all the government’s ministries and relevant inter-ministerial councils/committees, which are significant for policymaking. All governors of the capital and provincial administrations are also its members, which are significant for supporting and implementing the works at the subnational levels. Under the structure of the Council’s Secretariat, Department of Green Economy is playing an important role in promoting green economic development in Cambodia by looking at national policies and strategic plans, undertaking projects, and mobilizing resources. Through coordination between this Department and UNIDO, Cambodia in 2022 became a Member of the United Nations Partnership for Action on Green Economy. The findings showed that Cambodia is transitioning its green economy at the right time, especially the new government’s Pentagonal Strategy strongly supported this green economy transition with a clear and strong political commitment. The national inter-ministerial council for sustainable development is also very significant for cross sectoral policy-making and implementing levels, inclusively for promoting green economic development.

The present study identified the influence of green advertising and eco-labels on the attitude and environmental awareness of millennials who purchase ecological products in shopping centers in Ecuador. The research was quantitative, correlational and with a cross-sectional design. A total of 430 millennials participated in the study. A 20-question survey was applied in person outside the shopping centers of Quito and Guayaquil - Ecuador. The convergent and discriminant validity of the research model was verified using Confirmatory Factor Analysis (CFA) and Structural Equation Models (SEM). SPSS 20 and AMOS 24 programs were used for statistical analysis. The results show that green advertising influences environmental attitudes (β: 0,245) and awareness (β: 0,110) and specifically influences purchasing behaviour (β: 0,154) for green products. Although eco-labels do not directly influence purchasing behaviour (β: 0,128), they influence the attitudes (β: 0,406) and ecological awareness (β: 0,277) of millennials who buy organic products. This article is one of the first studies to identify the relationship between green advertising elements and green product purchasing behaviors among millennials in a developing country, concluding that marketing strategies based on green advertising and eco-labels have an impact on the attitude and environmental awareness of millennials, but only advertising directly affects purchasing behaviour while eco-labels do not. This research has social implications since it corroborates that millennials are concerned about environmental problems and are willing to contribute to sustainability.

Harnessing the Remarkable Bio-Remediation Potential of Vernonia spp. for the Sustainable Restoration of Hydrocarbon-Polluted Clay Soil in Ogoni Land, Nigeria This study explores the potential of utilizing Vernonia spp. for eco-friendly bio-remediation of hydrocarbon-polluted clay soil in Ogoni Land, Nigeria. The aim is to find a sustainable and effective solution that promotes the restoration of the polluted soil while minimizing the use of external additives. The process of bio-remediation can occur naturally, through natural attenuation or intrinsic bio-remediation. However, it has been observed that in certain cases, the addition of fertilizers, oxygen, or organic matter is required to enhance the effectiveness of bio-remediation. In this research, we focus on the bio-remediation potential of Vernonia spp., a native plant species known for its strong phytoremediation abilities. By harnessing the remarkable bio-remediation properties of Vernonia spp., we seek to restore the hydrocarbon-polluted clay soil in Ogoni Land in an environmentally friendly and sustainable manner. Our methodology involves conducting greenhouse experiments to assess the bio-remediation efficiency of Vernonia spp. in different soil conditions. We will analyze the soil's physical and chemical properties, as well as the degradation of hydrocarbons over time. The experimental results will provide valuable insights into the effectiveness of Vernonia spp. in removing hydrocarbon pollutants from clay soil. Through this study, we aim to develop a cost-effective and sustainable approach to restore hydrocarbon-polluted clay soil in Ogoni Land. By leveraging the natural bio-remediation potential of Vernonia spp., we can potentially reduce the reliance on external additives, thus minimizing the environmental impact associated with traditional remediation methods. The research article explores methods to promote the growth of pollution-eating microbes in order to enhance bio-remediation. Bio-remediation refers to the use of naturally occurring organisms to break down harmful substances into less toxic or non-toxic forms. In situ bio-remediation involves treating the contaminated material directly at the site, while ex-situ bio-remediation involves removing the contaminated material for treatment elsewhere. This study specifically investigates ex-situ bio-remediation techniques for hydrocarbon-contaminated clay soil. The researchers found that the application of room dry and wet blended bitter leaf showed promising results in the bio-remediation of hydrocarbons in the clay soil. Using Vernonia galamensis, a concentration as high as 0.55 ug/ml was achieved, while Vernonia amygdalina yielded a concentration as high as 0.67 ug/ml when applied at 35g and 40g respectively, based on the wet blended approach. These findings highlight the effectiveness of wet blended Vernonia species in the bio-remediation process.During the remediation process of the clay soil, the pH levels showed a trend of increasing from acidic to normal to alkaline. This can be attributed to the remediation of excessive metals present in the soil. After 40 days, the pH of the clay soil reached 6.97 when treated with 40 grams of Vernonia galamensis, and 7.00 when treated with 40 grams of Vernonia amygdalina. Interestingly, while the remediation efficiency of HC (hydrocarbon) decreased with increasing mass of Vernonia galamensis, the remediation efficiency of HC increased when using Vernonia amygdalina. These observations highlight the varying effects of different Vernonia species on hydrocarbon remediation.The highest remediation values were observed when using Vernonia galamensis at 35g and Vernonia amygdalina at 40g. These particular amounts of these Vernonia species demonstrated effective remediation of the targeted pollutants. However, in comparison to other metals, the remediation effects were relatively lower for zinc (Zn), with only approximately 0.25 ug/ml being remediated. This suggests that the remediation potential of the Vernonia species may vary depending on the specific contaminant.The higher remediation effects observed for Zn metal in the soil can be attributed to the wet blended preparation method of the Vernonia species. In contrast, the room dried Vernonia species exhibited the lowest performance in remediating the soil, with approximately 0.17 ug/ml and 0.10 ug/ml remediation for galamensis and amygdalina, respectively. Both Vernonia leaf extracts achieved a remediation of 0.5 ug/ml for chromium. The sun dried and room dried methods also showed considerable remediation potential, with values above 0.4 ug/ml. The level of significance for the model was attained at 0.05, and the r2 value was appreciable. These findings indicate the effectiveness of different preparation methods and Vernonia species in soil remediation

This research explores the predictive capabilities of autonomous driving systems by integrating human factor considerations within the context of safety-critical events. Recognizing the significance of human behaviors in influencing driving dynamics, the study employs advanced modeling techniques to enhance the accuracy of predictions in scenarios that demand heightened safety measures. Traditional rule-based systems and monotonic logic often fall short in addressing the complexities of safety-critical events. To overcome these limitations, the research proposes the application of non-monotonic logic, allowing for flexible and adaptive reasoning that accommodates exceptions and context-specific information.The study emphasizes the importance of incorporating individual differences among drivers, such as risk-taking tendencies, reaction times, decision-making processes, and driving styles. By considering these human factors, the research aims to develop realistic and accurate autonomous driving models that capture the nuances of real-world driving scenarios, especially in safety-critical situations. The predictive model takes into account both internal and external factors, enabling the autonomous system to anticipate and respond effectively to unforeseen events.The primary goal is to provide autonomous vehicles with the capability to make plausible inferences, handle conflicting data, and adapt their behavior in real-time during safety-critical events. The proposed model integrates personalized cognitive agents for each driver, incorporating their unique preferences, characteristics, and needs. This personalized approach aims to optimize the safety and efficiency of autonomous driving, contributing to the ongoing evolution of intelligent transportation systems.In conclusion, this research contributes to advancing the field of autonomous driving by introducing a predictive model that leverages human factor considerations to enhance safety in safety-critical events. The incorporation of non-monotonic logic and individualized cognitive agents signifies a comprehensive approach to address the challenges associated with predicting autonomous driving behavior, paving the way for safer and more reliable autonomous vehicles in dynamic and unpredictable environments.

This research delves into the integration of green roofs with Building Information Modeling (BIM), a pivotal advancement in sustainable urban construction. Aligning with the United Nations Sustainable Development Goals, particularly Goal 11, the study explores how this integration can address global challenges like climate change and resource depletion. Using the Dynamo Visual Programming Language within Autodesk Revit, the research develops a computational modeling approach for green roofs, focusing on their thermal and structural characteristics under varied environmental conditions. Key findings demonstrate the significant influence of substrate and drainage material combinations on green roofs’ thermal performance, highlighting the need for tailored designs based on climatic conditions. The study also emphasizes the importance of considering structural performance in both dry and saturated conditions for overall building integrity. The research identifies gaps in current practices, such as limited focus on specific green roof materials and reliance on certain software tools, suggesting the need for broader material selection and software adaptability. Future research directions include expanding material selections, exploring diverse environmental conditions, and integrating green roofs with various BIM software platforms. The study's implications extend to stakeholders in the construction industry, offering a framework for architects, urban planners, and policymakers to design and implement green roofs aligned with environmental goals. This work contributes to the field by proposing a novel approach to sustainable construction, integrating ecological design with digital technology, and setting a new benchmark in the integration of green roofs with BIM. By addressing these aspects, the research paves the way for future developments in sustainable urban construction, enhancing the efficiency, sustainability, and resilience of urban communities in line with global sustainability objectives.

In the sustainable economy, consumer preferences are gradually coming to prioritise environmentally friendly products and services. A sustainable economy is directly linked to the growth of consumer welfare and environmental culture. The success of environmental projects is largely determined by approaches to the management of complex interconnected objects. Technological integration and the value chain from raw material extraction to the marketing of environmentally friendly products add to the growth factors. In this regard, we formulate the purpose of this study: to generalise the approaches and improve the mechanism of the ecological branding of the industrial complex of a region through conscious ESG transformation. For this purpose, we studied statistics and principles of management systems of Kazakh companies and their stakeholders. We analysed the situation of industrial complexes and their partners in the external environment to identify barriers to the development and implementation of eco-branding tools. The theoretical significance of the study helps to reveal the influence of ecological principles of modern production organisation on the speed of transition to green technologies. Its practical significance is seen in the formation of a system for measuring the level of readiness of companies to promote the ecological branding of a region’s industry outside its country.

An advanced driver-assistance system (ADAS) is critical to driver-vehicle-interaction systems. Driving behaviour modelling and control significantly improves the global performance of ADAS. A haptic assistive system assists the driver by providing a specific torque on the steering wheel according to the driving-vehicle-road profile to improve the steering control. However, the main problem is designing a compensator dealing with the high-level uncertainties in different driving scenarios with haptic driver assistance, where different personalities and diverse perceptions of drivers are considered. If not properly accounted for, these differences can lead to poor driving performance. This paper focuses on designing a data-driven model-free compensator considering various driving behaviours with a haptic feedback system. A back propagation neural network (BPNN) models driving behaviour based on real driving data (speed, acceleration, vehicle orientation, and current steering angle). Then using the genetic algorithm (GA) and the integral time absolute error (ITEA) criterion to optimize multiple PID compensation parameters for various driving behaviour (such as speeding/braking, lane-keeping and turning), which are then be combined by the fuzzy logic to provide different driving commands. An experiment was conducted with 5 participants in a driving simulator. During the second experiment, seven participants drove in the simulator to evaluate the robustness of the proposed combined GA-PID offline and the Fuzzy-PID controller applied online. The experiment results evaluated the ITAE of lateral displacement and yaw angle, during various driving behaviour. The results validated the proposed method by significantly enhancing the driving performance.

Equipping autonomous agents for dynamic interaction and navigation is a significant challenge in intelligent transportation systems. This study aims to address this by implementing a brain-inspired model for decision-making in autonomous vehicles. We employ active inference, a Bayesian approach that models decision-making processes similar to the human brain, focusing on the agent's preferences and the principle of free energy. This approach is combined with imitation learning to enhance the vehicle's ability to adapt to new observations and make human-like decisions. The research involved developing a multi-modal self-awareness architecture for autonomous driving systems and testing this model in driving scenarios, including abnormal observations. The results demonstrated the model's effectiveness in enabling the vehicle to make safe decisions, particularly in unobserved or dynamic environments. The study concludes that the integration of active inference with imitation learning significantly improves the performance of autonomous vehicles, offering a promising direction for future developments in intelligent transportation systems.

In this paper, the overall performance of Cu-(In,Ga)-Se₂ (CIGSe) solar cells was improved in the ultraviolet (UV)–visible wavelength region by two-segment process. In which, adjusting DC sputtering powers (20–40 W) for absorbers (segment I), and metal-alloy compositions (CdS, ZnO/CdS, ZnMgO/CdS and ZnMgO) for buffers (segment II) were explored and characterized. Upon choosing the optimal-30-W CIGSe-absorber (with 0.95-CGI ratio) sputtered by the segment-I process, the Cu-rich film can distinctly boost grain growth, thus reducing the trap state density. After segment-II process as the toxic-CdS alternative, the optimal Zn_0.9Mg_0.1O-alloy buffer can reach the best conversion efficiency (η = 8.70%) was attained for the environmental protection. Meanwhile, and the overall internal/external quantum efficiencies (IQE/EQE) were improved by 13.15%, respectively for the 2.48–3.62-eV bandgap (short wavelength) range. The developed photovoltaic (PV) module (with 9 optimum-CIGSe cells) exhibits acceptable stability with a variance within ±5% over the 60-day experiment. This discovery in PV-device research contributes to a new scientific understanding of renewable energy. Furthermore, this study undeniably enhances the progress of practical applications for PV-modules in alignment with sustainable development goals. It also actively supports the development of eco-friendly communities.

We introduce the Transformation Flower Approach (TFA), a Theory of Change that attends to multiple value creation and institutional change as a dual design challenge. We highlight how the TFA integrates social scientific theories and models relevant for transformative change (in particular focusing on pathways, leverage points, governance, power, and values) and demonstrate its practical value by an application to the ongoing transformation of the Dutch food system. By providing a holistic, transdisciplinary and practically relevant approach that aims to support new social contract formation, the TFA goes beyond other transformative change approaches. Based on the notion of pathways, it offers a toolbox that aids in working towards desired futures, involving both incumbents and challengers in an effort to harness untapped yet proximal potentials in a forward-looking way. By embracing an innovation approach, it not only promises to circumvent resistance to change, but also serves as a step-by-step approach to identify options for multiple value creation and effective cooperation. We demonstrate the analytical and practical value of the TFA by discussing action perspectives at various levels and scales in the context of the Dutch food system transition, including (1) area-oriented approaches, (2) acceleration agendas for specific transformation pathways, and (3) actor-specific transformation flowers. In developing these, we emphasize the importance of interdependencies between leverage points. Our approach helps to identify opportunities to link transformative options (the what), actors (the who) and levers (the how) in dynamic interaction to embark on transformative pathways.

Since sensor-based perception systems are used in autonomous vehicle applications, validating such systems is imperative to guarantee the robustness of the systems before they are being put to use. In this study, a comprehensive corruption-related simulation-based robustness verification and enhancement process for sensor-based perception systems is proposed. Firstly, we present a methodology and scenario-based corruption generation tools for creating diverse simulated test scenarios that can analogously represent real-world traffic environments, especially considering corruption types related to safety concern. Then, an effective corruption similarity filtering algorithm is proposed to remove corruption types with high similarity and identify the representative corruption types to represent all considered corruption types. As a result, we can generate efficient corruption-related robustness test scenarios with less testing time and good scenario coverage. Subsequently, we perform the vulnerability analysis of object detection models to identify model weaknesses and construct an effective training dataset for model vulnerability enhancement. This enhances the tolerance of object detection models to weather and noise-related corruptions, ultimately improving the robustness of the perception system. We employ case studies to demonstrate the feasibility and effectiveness of the proposed robustness verification and enhancement procedures. Additionally, we explore the impact of different "similarity overlap threshold" parameter settings on scenario coverage, effectiveness, scenario complexity (size of training and testing datasets), and time costs.

Micro-plastics (MPs) become significantly important to society since their commercial manufacture began in the 1950s because of their low production costs, stability, light weight, and adaptability. Plastic was produced in 400 million tons worldwide in 2020, compared to 1.5 million tons just 60 years earlier. Scientists predict that by 2050, plastic production would increase to 33 billion tons. MPs are a rising scientific and societal issue in the environment as a new type of contaminant that is widely disseminated in aquatic ecosystems and organisms. Aquatic species that come into contact with MPs for an extended period of time experience ecotoxicity, which includes oxidative stress, genotoxicity, neurotoxicity, and reproductive issues. In aquatic environments, MPs not only transport chemicals within animals but also serve as mediators for chemicals or other contaminants. As a result of co-exposure of MPs and chemical contaminants, various toxicity incidences involving aquatic organisms have been reported. The final exposure of MPs occurs in humans through biomagnification of aquatic edible products. This study presents an important overview of the literature on MP ingestion by aquatic biota. It covers topics such as the present scenario of plastic pollution worldwide, the pathways that MPs take to reach humans and aquatic species, the eco-toxicological impacts of MPs on aquatic species, the relation between MPs and other environmental pollution and the ensuing effects on aquatic organisms, and the MP pollution status in Bangladesh.

Ecological changes evoke many felt losses and types of grief. These affect sustainability efforts in profound ways. Scholarship on the topic is growing, but the relationship between general grief research and ecological grief has received surprisingly little attention. This interdisciplinary article applies theories of grief, loss, and bereavement to ecological grief. Special attention is given to research about “non-death loss” and other broad frameworks about grief. Dynamics related to both local and global ecological grief are discussed. Kinds of potential losses arising out of ecological issues are clarified with the frameworks of tangible/intangible loss, ambiguous loss, nonfinite loss, and shattered assumptions. Various possible types of ecological grief are illuminated by discussing the frameworks of chronic sorrow and anticipatory grief/mourning. Earlier scholarship about disenfranchised ecological grief is augmented by further distinctions about various forms it may take. The difficulties in defining complicated or prolonged grief in ecological context are discussed, and four types of “complicated ecological grief” are explored. Based on the findings, three special forms of ecological loss and grief are named and discussed: transitional loss and grief, lifeworld loss, and shattered dreams. The implications of the results for ecological grief scholarship, counselling and coping are briefly discussed. The results can be used by psychological and healthcare professionals and researchers, but also by members of the public who wish to reflect on their eco-emotions.

Partially automated driving functions (SAE Level 2) can control a vehicle's longitudinal and lateral movements. However, taking over the driving task involves automation risks that the driver must manage. In severe accidents, the driver's ability to avoid a collision must be assessed, considering their expected reaction behavior. The primary goal of this study is to generate essential data on driver reaction behavior in case of malfunctions in partially automated driving functions for use in legal affairs. A simulator study with two scenarios involving 32 subjects was conducted for this purpose. The first scenario investigated driver reactions to system limitations during cornering. The second scenario examined driver responses to phantom braking caused by the AEBS. As a result, the first scenario shows that none of the subjects could control the situation safely. Due to partial automation, we could also identify a new part of the reaction time, the hands-on time, which leads to increased steering reaction times of 1.18 to 1.74 seconds. In the second scenario, we found that 25 of the 32 subjects could not override the phantom braking by pressing the accelerator pedal, although 16 subjects were informed about the system analog to the actual vehicle manuals. Overall, the study suggests that the current legal perspective on vehicle control and the expected driver reaction behavior for accident avoidance should be reconsidered.

The article is dedicated to addressing the current challenge of augmenting the load- bearing ca-pability of pile foundations. This predicament is most effectively addressed by employing piles with unconventional geometries, along with atypical methodologies for installing these foun-dation piles. The primary objective of research is to examine the impact of various fill materials (including both soil and rigid substances) on the energy consumption during pile driving and the resistance to static loads by piles with multiple shaft expansions. The findings derived from model-based investigations demonstrate that the load-bearing capacity of piles with shaft ex-pansions, installed with bulk material filling, surpasses that of conventional piles (prismatic and pyramidal) by a factor ranging from 1.5 to 4.6. Furthermore, the research outcomes also indicate greater energy consumption (1.14-1.66 times) and enhanced load-bearing capacity (1.15-1.68 times) for piles with shaft expansions driven with backfill, in comparison to piles installed without backfill. It is noteworthy that the type of backfill material significantly influences the energy consumption during pile driving and their stability under axial static loads. The correla-tion relationships can be applied to approximate projection of the energy-related and structural parameters of piles with shaft expansions embedded with the addition of bulk materials.

Abstract: High power semiconductor laser is a kind of photoelectric device with high efficiency and high stability, the performance of its drive system directly affects its output characteristics and service life. In order to solve the problems of stability and robustness of the output power of semiconductor laser, a semiconductor laser driving power supply with high efficiency, low ripple and strong anti-interference ability was developed. The power supply adopts full-bridge LCC resonant power topology. Firstly, a mathematical model is established to analyze the relationship between LCC resonator parameters and output current gain. Secondly, a LCC resonator parameter design method is proposed to reduce the current stress of components, and the variable frequency phase shift (PFM-PWM) composite control strategy and linear active disturbance rejection control (LADRC) algorithm are proposed, which not only ensures the zero voltage (ZVS) conduction of MOS tube, but also reduces the on-off loss of MOS tube. Improved power efficiency and suppressed output current ripple; At the same time, the instability of the output current is ensured due to the input voltage, load and parasitic parameter change of the circuit. Finally, the simulation and experimental results show that the power supply can be continuously adjustable in the output current range of 0-40A, the current ripple is less than 0.8%, and the working efficiency is up to 92%. It has the characteristics of high stability, small ripple, high efficiency, low cost and good robustness.

Object detection is important in many applications, such as autonomous driving. While 2D images are lack of depth information and are sensitive to environmental conditions, 3D point cloud can provide accurate depth information and a more descriptive environment. However, sparsity is always a challenge in single-frame point cloud object detection. This paper introduces a two-stage proposal-based feature fusion method for object detection using multiple frames. The proposed method, called proposal features fusion (PFF), utilizes a cosine-similarity approach to associate proposals from multiple frames and employs an attention weighted fusion module to merge features from these proposals. It allows for feature fusion specific to individual objects and offers lower computational complexity while achieving higher precision. The experimental results on the nuScenes dataset demonstrate the effectiveness of our approach, achieving a mAP of 46.7%, which is 1.3% higher than the state-of-the-art 3D object detection method.

Lateral vehicle control is a high importance in automated vehicles as it directly influences the vehicle’s performance and safety during operation. The Linear Quadratic Regulator (LQR) controller stands out due to its high-performance characteristics and is used in the open source for self driving functions. However, a notable limitation of the current approach is the manual calibration of LQR controllers based on the experience and intuition of the designers, leading to empirical uncertainties. To address this issue and enhance the lateral control performance, this paper concentrates on refining the LQR by employing three optimization algorithms: Artificial Bee Colony Optimization (ABC), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). These algorithms aim to overcome the reliance on empirical methods and enable a data-driven approach to LQR calibration. By comparing the outcomes of these optimization algorithms to the manual LQR controller within an offline multibody simulation as testing platform, the study highlights the superiority of the best-performing optimization approach. Following this, the optimal algorithm is implemented on a real-time system for the full vehicle level, revealing the Model-in-the-Loop and the Hardware-in-the-Loop gap up to 78,89% with lateral velocity when we use Relative Error Criterion (REC) method to validate and 2.35m with lateral displacement when considering by maximum absolute value method.

Road collisions are among the world’s critical issues causing many casualties, deaths, and economic losses. To deal with this scenario, precise analysis is required. In this paper, we use Machine Learning (ML) techniques to assess and predict road traffic collisions. An ensemble machine learning model with two layers was developed to analyze simulation data from the driving simulator. The first (base) layer integrates supervised learning techniques namely, k- Nearest Neighbors (k-NN) AdaBoost, Naive Bayes (NB), and Decision Trees (DT). The second layer predicts road collisions by combining the base layer outputs by employing the stacking ensemble method using logistic regression as a meta-classifier. The data for this study were imbalanced; therefore, we performed a SMOTE resampling strategy to handle the dataset imbalance. To simplify the model, the PSO algorithm was used to select the most important features in our dataset. The two-layer ensemble model proposed had the best outcomes with an accuracy of 88%, F1 score of 83%, and an AUC of 86% as compared with k-NN, DT, NB, and AdaBoost.

The ongoing conflict in Syria and Iraq has caused many residents of these countries to flee. This study explored how these refugees cope with traumatic experiences from pre-migration to post-migration phases and how they develop resilience experiences at the individual, familial, and community levels. Data were collected through individual interviews and analyzed using Interpretative Phenomenological Analysis. A discussion of the findings revealed (1) a cumulative effect of prolonged adversity from pre-migration to resettlement phases; (2) the impacts of post-migration context as it facilitates refugees’ adjustment or leads to re-traumatization through increased marginalization; and (3) finally, an overview of refugees’ ongoing survival. Clinical implications are presented along with policy implications and future directions for research.

In this paper, we propose a method for the generation of simulation test scenarios for autonomous driving. Based on the requirements of standard regulatory test scenarios, we can generate virtual simulation scenarios and functional scenario libraries for autonomous driving, which can be used for simulation verification of different ADAS functions. Firstly, the design operation domain (ODD) of the functional scenario is selected and the weight values of the ODD elements taken are calculated; then a combination test algorithm based on parameter weights is improved to generate autonomous driving virtual simulation test cases for the ODD elements, which can effectively reduce the number of generated test cases compared with the traditional combination test algorithm; then the traffic participant elements under each test case are Then, the values of the subelements under the traffic participant element in each test case are sampled and clustered to obtain hazard specific scenarios. Finally, the specific scenarios are applied to the automatic emergency braking (AEB) system on the model-in-the-loop (MIL) testbed to verify the effectiveness of this scenario generation method.

This comprehensive review explores the principles, applications, and future directions of human-powered energy generation technologies. It begins by providing a detailed examination of viable human energy sources, elucidating the physical principles behind various energy harvesting devices. The review then compiles and assesses existing products in the field of human power generation, while also outlining the opportunities and challenges it presents. Finally, an innovative concept of an eco-village system is introduced to demonstrate a future potential application scenario for human-generated electricity. It outlines two developmental stages for this system and underscores its significance in the construction of the harmonious and sustainable communities. This forward-thinking approach could pave the way for broader discussions on sustainable living and eco-friendly energy generation in a world increasingly focused on environmental concerns and social stability.

Ultrasonic sensors are inexpensive and can take measurements with high accuracy, even with simple hardware configurations, so they are used in various fields. When multiple ultrasonic sensors exist in the measurement space, crosstalk occurs due to other nodes, which causes measurement errors. Crosstalk includes not only receiving homogeneous signals from other nodes, but also overlap by other signals, and interference by heterogeneous signals. This paper proposes a method that uses frequency sweep keying modulation to be robust against overlap and a faster region-based convolutional neural networks (R-CNN)-based demodulator to reduce the interference caused by heterogeneous signals. The demodulator works by training faster R-CNN with the spectrograms of various received signals and classifying the received signals using a faster R-CNN. Experiments implementing an ultrasonic crosstalk environment showed that compared to on-off keying (OOK), phase-shift keying (PSK), and frequency-shift keying (FSK), the proposed method can implement CDMA even with shorter codes, and is robust against overlap. Compared to correlation-based frequency sweep keying, the time-of-flight error was reduced by approximately 75%. While existing demodulators did not consider heterogeneous signals, the proposed method ignored approximately 99% of the OOK and PSK signals, and approximately 79% of the FSK signals. The proposed method performs better than the existing methods and is expected to be used in various applications.

This research investigates the utilization of human driving models in autonomous vehicles, particularly in scenarios with minimal or no interactions with other vehicles. Human driving models provide valuable insights into driver behavior and play a crucial role in shaping the behavior of autonomous vehicles, enhancing their performance and user experience. The primary focus of the study is the creation of a planning model for autonomous vehicles when navigating roundabouts in the absence of traffic. This model seeks to emulate human driving behavior, ensuring predictability, safety, optimization of traffic flow, and adaptation to various roundabout geometries. To achieve this, the research introduces a trajectory model that takes into account geometric attributes and speed variations within roundabouts. The model is calibrated using empirical data and generalizes parameters through statistical regression methodologies. This model is referred to as "MRoundabout" and is evaluated for its consistency in generating plans that closely mimic human driving behavior within roundabouts. While the study presents a promising approach, it acknowledges limitations related to the model's reliance on geometric attributes and its inability to account for external factors like weather conditions. This research underscores the importance of bridging the gap between theoretical research and practical application, with the aim of enhancing safety and the overall user experience in real-world driving scenarios.

Autonomous shuttles have been used as end mile solutions for smart mobility in smart cities. The urban driving conditions of smart cities with many other actors sharing the road and the presence of intersections have posed challenges to the use of autonomous shuttles. Round intersections are more challenging as it is more difficult to perceive the other vehicles in and near the intersection. Thus, this paper focuses on decision making of autonomous vehicles for handling round intersections. The round intersection is introduced first, followed by introductions of the Markov Decision Process (MDP), the Partially Observable Markov Decision Process (POMDP) and the Object Oriented Partially Observable Markov Decision Process (OOPOMDP) which are used for decision making with uncertain knowledge of the motion of the other vehicles. The Partially Observable Monte-Carlo Planning (POMCP) algorithm is used as the solution method and OOPOMDP is applied to decision making for autonomous vehicles in round intersections. Decision making is formulated first as a POMDP problem, and the penalty function is formulated and set accordingly. This is followed by improvement of decision making with policy prediction. Augmented objective state and policy-based state transition are introduced and simulations are used to demonstrate the effectiveness of the proposed method for collision free handling of round intersections by the ego vehicle.

Based on the panel data from 31 provinces in China from 2012 to 2020, this paper uses the TOPSIS method to measure the development level of the digital economy and environmental quality. Then, a coupled coordination degree model is used to analyse the relationship between the digital economy and environmental quality. Kernel density estimation and the Dagum Gini coefficient method were used to reveal the dynamic evolution trend of the coupled coordination level between the digital economy and environmental quality and the regional trends of coupled indicators. Finally, the dynamics of the above relations were explored with an econometric model. The results show that the coupled coordination degree of the digital economy and environmental quality displays obvious regional heterogeneity, and the national coordination level exhibits dynamic convergence characteristics. The heterogeneity of the coupled coordination degree of the two systems in the eastern region has decreased year by year, except in the western region, the central region and the northeast region, where an upwards trend is observed. Overall, the Gini coefficient of the coupled coordination degree between the digital economy and environmental quality displays a certain downwards trend. The level of economic development, technological innovation level, industrial structure, degree of opening to the outside world, degree of environmental regulation, marketization index and financial development level have a positive influence on the coupled coordination of the digital economy and environmental quality. Environmental regulation is the core driver of the coupling of the two systems, and the economic development level, technological innovation level, industrial structure and degree of openness are the other major drivers. Therefore, in the processes of digital economy transformation and environmental governance, all regions should focus on balanced development between regions to optimize the overall efficiency of the two systems, consider external influence, enhance internal capabilities, and promote the coordination and co-development of the two systems.

While studies that examined risk perceptions in various environments have been previously conducted, the relationship between risk perceptions measured in such environments had not been examined. Such a relationship, if found, may indicate that when one learns to identify risks in one environment, they may identify risks more easily and quickly in another.This study examined the relationship between measures of risk perception in several separate environments. The findings suggest that when learning to identify and assess risks in a particular environment, one may better identify and assess risks in a similar environment – for example, learning to identify and assess risks while driving makes it easier to identify and assess risks when crossing a road. Conversely, the findings also indicate that learning to identify and assess risks in one environment does not make it possible to better identify and assess risks in a disparate environment – for example, learning to identify and assess risks while driving does not make it easier to identify and assess risks in a workplace environment. This may also suggest that while disparate environments such as the road environment and the workplace environment are perceived as two separate environments, the road environment is experienced as one single environment by both drivers and pedestrians.

The software design of autonomous vehicles (AVs) incorporates artificial intelligence (AI) characteristics to enhance their safety and overall driving performance. Central to vehicle’s operation is the ability to reason effectively in complex and uncertain environments. However, traditional logical systems, such as monotonic logic, often struggle to handle the inherent uncertainties and exceptions encountered in real-world scenarios. This paper proposes the utilization of non-monotonic logic in order to enhance the reasoning capabilities of autonomous vehicles. By incorporating non-monotonic reasoning, vehicles can navigate intricate traffic scenarios, make plausible inferences, and adapt their decisions when faced with conflicting information. This research aims to provide a comprehensive review of non-monotonic logic's application in autonomous vehicles, highlighting its advantages over traditional logical systems and its potential impact on safety and performance. Additionally, through this research, we seek to contribute to the advancement of autonomous driving technology by enhancing the reasoning capabilities of vehicles in various scenarios, such as car- following related to critical safety events. The personalized cognitive agent is proposed in driving behavior to consider particularly in their assumptions of homogeneous drivers. The personalized cognitive agent is incorporating heterogeneous driving behaviors, based on individual user preferences, characteristics, and needs. Driving behavior is a complex interplay of various factors, encompassing both human and external elements. Human factors, including age, experience, and gender, contribute significantly to how individuals navigate the roads. These factors influence decisions, reactions, and risk-taking tendencies on the part of drivers. Additionally, external factors such as weather conditions further compound this intricate dynamic, requiring drivers to adapt their behavior to the prevailing environment. The goal of a personalized cognitive agent is to provide tailored and customized experiences to cognitive vehicles, taking into account the unique requirements and individual preferences of occupants inside autonomous vehicles.

The eco-efficiency of cultivated land use (ECLU) is an important indicator for the construction of ecological civilization in China. Exploring the spatiotemporal dynamic evolution of the ECLU is helpful for sustainable use of arable land, ensuring food security and ecological security. However, previous studies have mostly focused on the use of a slacks-based measure (SBM) model for ECLU measurement, ignoring the more accurate epsilon-based measure (EBM) model. Therefore, in this study, firstly, we explored the conceptual framework of ECLU, and then, based on the panel data of the counties in the region around Beijing and Tianjin from 2005 to 2020, we investigated the spatial and temporal evolution of ECLU by using the Super-EBM model, kernel density estimation method, and spatial Markov chain model. Results displayed: (1) From 2005 to 2020, the ECLU in the region around Beijing and Tianjin displayed an increasing state, but the average value was only 0.55. (2) The time evolution of the ECLU has gradually polarized, the internal gap has widened, but it tends to stabilize. (3) The ECLU in the region around Beijing-Tianjin was more inclined to keep it the same and there was a "club convergence" phenomenon, which was meaningfully affected by the background of neighboring areas. In the light of local conditions, the government should reasonably formulate the path to optimize the ECLU, strengthen the linkage with the surrounding cities, and bring into play the positive spillover effect.

Advanced composite materials have drawn significant interest in the last years as an alternative to traditional materials due to their higher performance. However, industry struggle to provide low-cost, higher occupational safety, lower footprint with composites, making them suitable for holistic analyses. Therefore, the material design becomes an essential element that can impact the competitiveness, particularly in terms of productivity, circularity, safety, sustainability, and quality of the value chain. The Material Design-for-eXcellence is a state-of-art methodology for material performance multi-dimensional assessment along its life cycle phases, either useful to support ma-terial selection for new products or also to new material design support optimizing resource effi-ciency. In this methodology, the material behaviour and its multiple characteristics assessment, and the manufacturing processes efficiency are evaluated. The framework considers the analogy of product design holistic approaches, as Lean Design-for-X, to organize and assess the mul-ti-dimensional performance for each “X” Material Property. In this work, it was possible to observe that the bio-based composites solutions could be a good sustainable alternative for several sectors. Every day researchers are creating more new materials, having a diversity of properties at different scales, Material Design-for-eXcellence must also in the near future consider other factors. Hence, additional studies are thus foreseen to explore and develop this new tool.

Climate changes challenges people’s life and sustainability. Environmental problems seem to derive from human behavior. Dentistry has a high environmental footprint that needs to be controlled worldwide. Thus, the change of behavior of dentists is a necessary condition to improve the environmental situation. In this study we use a system dynamics approach to analyze pro-environmental behavior in dentists. Systemic modelling in this case, allow us to make a clear prediction of how the population of non-eco dentists will change in the future if certain factors will be affected and how much time will be needed by the system of a group of dental professionals to become eco- dentists. The ECODENT model was designed with Vensim software. It was based on the influence of WOM (word of mouth) spread of information among colleagues and stakeholders (e.g. patients, auxiliary staff and community) and other factors such as dental income, state support, CaPex and OpEx, education hours, level of urbanization and time to react. By changing each one of them we can observe the changes in the groups of non-eco and eco dentists as well as the flow of the phenomenon. It is tested that by augmenting WOM parameter, state economic support or education hours, while diminishing CaPex and OpEx, changes in pro-environmental behaviors of dentist will be reported in a shorter time. The ECODENT model can be used to further study pro-environmentalism in different dental groups and associations and design relevant eco-friendly educational programs.

For autonomous driving, perception is a primary and essential element that fundamentally deals with the insight into the ego vehicle’s environment through sensors. Perception is a challenging task suffering from dynamic objects and continuous environmental changes. The issue gets worse due to interrupting the quality of perception by adverse weather like snow, rain, fog, night light, sand storm, strong daylight, etc. In this work, we have tried to improve camera-based perception accuracy, such as autonomous driving-related object detection in adverse weather. We proposed the improvement of YOLOv8-based object detection in adverse weather through transfer learning using merged data from various harsh weather datasets. Two prosperous open-source datasets (ACDC and DAWN) and their merged dataset were used to detect primary objects on the road in harsh weather. A set of training weights were collected from training on the individual datasets, their merged version, and several subsets of those datasets according to their characteristics. A comparison between the training weights also occurred by evaluating the detection performance on the above-mentioned datasets and their subsets. The evaluation revealed that using custom datasets for training significantly improves the detection performance compared to the YOLOv8 base weights. And using more images through the feature-related data merging technique steadily increases the object detection performance.

The creation of nano-zero-valent iron (nZVI) by implementing plant extracts is an environmentally friendly process. High antioxidant capacity and phenol content indicated the possibility of oak-nZVI synthesis using oak leaf extract as a stable material with minimal agglomeration. Simultaneously removal of Cd and phosphates and Ni and phosphates was optimized by a statistically designed experiment with a definitive screening design. In terms of significance, 4 input parameters on process productivity were monitored: initial metal concentration (1 - 9 mgL-1), initial ion concentration (1- 9 mgL-1), pH value (2 - 10) and oak-nZVI dosage (2 - 16 ml). Phosphate removal efficiency, in the presence of cadmium, was the most influenced by oak-nZVI dose and cadmium concentration, while pH gave three statistically significant interactions with oak-nZVI dosage, cadmium concentration, and phosphate concentration. Phosphate removal efficiency, in the presence of Ni, is the most influenced by nickel concentration, phosphate concentration, pH, two-factor interaction between nZVI dose and Ni concentration, and quadratic phosphate interaction. The process optimization yielded the highest simultaneous removal efficiency of 98.99% and 87.30% for cadmium and phosphate ions, respectively. Also, the highest simultaneous removal efficiency of nickel and phosphates ion was 93.44% for 96.75%, respectively. The optimization process fits into the confidence intervals, confirming the assumption that the adopted regression model describes the process well. This work demonstrated an enormous potential and prosperous application for the Cd(II), Ni(II), and phosphate removal from water matrices.

Road marking recognition is of great importance for the deployment of automated driving systems and for already existing driver line-keeping assistance systems. For proper and reliable function of machine vision systems that include LiDAR and camera, road markings must be visible to them to facilitate planning of driving trajectory. In this study, LiDAR intensity and the number of LiDAR points collected from a field analysis of two road markings systems reflectorised with premium and standard glass beads were analysed. In the lateral direction, it was demonstrated that the use of premium glass beads for road markings lead to 56% higher LiDAR intensity and 14% more LiDAR points captured than in case of standard road markings. In the longitudinal direction, premium road markings showed less distance attenuation, which is seen as advantageous for trajectory planning by machine version systems. The outcomes of this study offer insights for road infrastructure decision-makers, developers of automated driving systems, and researchers in the field of transportation technology.

Land use and land cover (LULC) datasets for Jinan in 1992, 1998, 2002, 2006, 2011, 2017, and 2022 were developed from Landsat images using the Random Forest (RF) classification approach. The relationships between social-economic, political factors and time-series LULC data were exam-ined for the periods between 1992 and 2022. The results showed the effectiveness of using the RF classification method for LULC classification with time series of Landsat images. Combined with driving forces analysis, our research can effectively explain the detailed LULC change tra-jectories corresponding to different stages and give new insights into Jinan LULC change pat-terns. The results show a significant increase in impervious surface which opposite change to bare land which experienced a huge decline declined by 95%, due to urbanization and rapid in-crease of population. The driving forces behind these changes are related to population growth, economic development, and climate change. Moreover, the present research employed Principal Components Analysis (PCA) methodology in order to understand the relative significance of disparate driving factors. The analysis results prove that the economy (population, GDP) and climate change were the primary factors that have an obvious impact on land use/land cover changes and that the driving factors for impervious surface, bare land, woodland, farmland, and water were distinct. Government policies also have a substantial impact on LULC change as well, such as the Construction of Harmonious Jinan (COHJ). The results were helpful for better understanding the mechanisms of LULC change and can provide useful knowledge for effective land resource management and planning.

The rapid development of the regional economy in China has led to the rise of local ecological risks. It is very important to provide enjoyable ecosystem services to residents while reducing ecological risks. In order to understand spatial relationships between ecosystem services and ecological risks, we took Chongqing as an example in this study to assess the spatial relationship between ecosystem services and ecological risks at the county scale based on the ES-DPSIR system. The main findings include: (1) significant variation in the spatial distribution of the comprehensive ecosystem service index, where the lowest ecosystem service index (0.013) was found in the main urban area of Chongqing and the scores gradually increased outward from this center, reaching 0.689 in the outermost areas, (2) the increase of the comprehensive ecological risk index from east to west, ranging from -0.134 to 0.333, (3) the spatial relationship between ecosystem services and ecological risks was prominent, with 52.63% of the districts and counties being imbalanced or mild imbalanced, and (4) the significant differences between development trends of ecosystem services-ecological risks, including 60.53% being imbalanced districts and 30.47% mildly balanced. Overall, it was necessary to improve the relationship between ecosystem services and ecological risks in Chongqing by reducing ecological risks., and these research results could provide effective approaches and technical support for improving regional ecological security and enhancing ecosystem service capabilities.

Nodule bacteria (rhizobia) represent a suitable model to address a range of evolutionary prob-lems including the tradeoff between genetic polymorphism and natural selection. Rhizobia possess the complicated genomes in which symbiotically specialized (sym) genes differ in their natural histories from core genes encoding for housekeeping functions. Diversification of sym genes is responsible for the rhizobia microevolution which depends on the host-induced natural selection. For the rhizobia speciation, diversification of core genes is responsible for which the selective factors are unclear. In this paper we demonstrate that in the goats’ rue rhizobia (Neorhizobium galegae) populations collected at North Caucasus and composed of two host-specific biovars orientalis and officianalis (N2-fixing symbionts of Galega orientalis and G. officinalis, respectively), evolutionary mechanisms are different for core and sym genes. In both N. galegae biovars, core genes are more polymorphic than sym genes. In bv. orientalis, evolution of core genes occurs under the impacts of driving se-lection (dN/dS > 1), while evolution of sym genes is close to neutral (dN/dS ≈ 1). In bv. officinalis, evolution of core genes is neutral, while for sym genes, it is dependent on purifying selection (dN/dS < 1). A marked phylogenetic congruence of core and sym genes revealed using the ANI analysis may be due to a low intensity of gene transfer within and between N. galegae biovars. Polymorphism of both gene groups and the impacts of driving selection on the core gene evolu-tion are more pronounced in bv. orientalis than in bv. officianalis reflecting the diversities of re-spective host plant species. In bv. orientalis, highly significant (P0 < 0,001) positive correlation was revealed between the p-distance and dN/dS values for core genes, while in bv. officinalis this cor-relation is lowly significant (0,05 < P0 < 0,10). For sym genes, correlation between the p-distance and dN/dS values is negative in bv. officinalis but is not revealed in bv. orientalis. These data along with the functional annotation of core genes implemented using the Gene Ontology tools sug-gests that evolution of bv. officinalis is based mostly on adaptation for in planta niches while in bv. orientalis evolution presumable depends on adaptation for soil niches. New insights into the tradeoff between natural selection and genetic diversity are presented suggesting that the gene polymorphism may be extended by driving selection only in the ecologically versatile organ-isms capable to support a broad spectrum of gene alleles in their genepools.

In autonomous driving systems, high-speed and real-time image processing, along with object recognition, are crucial technologies. This paper builds upon the research achievements in in-dustrial item sorting systems and proposes an object recognition and sorting system for auton-omous driving. In industrial sorting lines, goods sorting robots often need to work at high speeds to efficiently sort large volumes of items. This poses a challenge to the robot's real-time vision and sorting capabilities, making it both practical and economically viable to implement a real-time and low-cost sorting system in a real-world industrial sorting line. Existing sorting systems have limitations such as high cost, high computing resource consumption, and high power consump-tion. These issues lead to the fact that existing sorting systems are typically used only in large industrial plants. In this paper, we design a high-speed, low-cost, low-resource-consumption FPGA (Field-Programmable Gate Array) based item sorting system that achieves similar perfor-mance to current mainstream sorting systems at a lower cost and consumption than existing sorting systems. The recognition part employs a morphological recognition method, which segments the image using a frame difference algorithm and then extracts the color and shape features of the items. To handle sorting, a six-degree-of-freedom robotic arm is introduced in the sorting segment. The improved cubic B-spline interpolation algorithm is employed to plan the motion trajectory and consequently control the robotic arm to execute the corresponding actions. Through a series of experiments, this system achieves an average recognition delay of 25.26ms, ensures smooth operation of the gripping motion trajectory, minimizes resource consumption, and reduces implementation costs.

Wildlife tourism and wildlife trade may appear juxtaposed, but are two, potentially aligning, in-come generators that could benefit conservation in developing countries. Utilising data sets collated from Madagascar’s Ministère du Tourisme and CITES respectively for the period 2007 to 2018, this study estimated levels of income from wildlife tourism and wildlife trade for Madagascar. Between 2007-2018, tourism reported yearly incomes ranging from a low of US \$1.4 million up to a high of US \$15.7 million. However, it was unclear what percentage of this figure flowed to benefit local communities. Alternatively, using reported networks for the live wildlife trade, the estimated eco-nomic value reaching collectors and/or intermediaries on Madagascar was US $72,299.80 for the period 2007 to 2018. Both revenue generators operated within different geographical areas, with tourism opportunities presenting themselves to communities adjacent NPs, while wildlife trade networks were not restricted to National Parks and operated sporadically across Madagascar. Hence, the economic benefits reached different Malagasy participants across the country. The management of both activities needs great care to ensure that environmental impacts and sus-tainability are core measures on any such activities. Whilst this study shines light on economic values and novel perspectives regarding these two trade types, it also highlighted knowledge gaps. Thus, indicating where much greater research attentions was required to allow better understanding of the specific benefits and risks from engaging with both trade types for local Malagasy people and their environments.

Water is one of the most critical factors affecting environmental health. For this reason, estimating and monitoring the behavior of water in nature and preventing water pollution before it occurs are advantageous to avoid problems that may occur in terms of environmental health. In order to predict the behavior of water, the hydrological cycle needs to be evaluated on a basin basis. At this point, hydrological models can be used to make mathematical representations of hydrologi-cal processes. These models play an essential role in predicting and monitoring problems such as poor water quality, pollution, particle and sediment transport, and proliferation of oil and petro-leum derivatives among frequently encountered issues in environmental health. In this study, a 2D surface water model was created using soil and water assessment tool (SWAT) to simulate the lotic ecosystem and existing water quality in Tatlıçay Basin and to propose solutions for im-proving environmental health in Cankiri provincial center in Türkiye. The accuracy of the input data and the validity of the model were checked with the calibration and validation studies by using the monthly or 3-monthly observation data of the General Directorate of State Hydraulic Works’ flow observation & water quality stations from 2016 to 2020. The aim was to provide the opportunity to produce fast, accurate, and practical solutions in the face of negative scenarios with this model. This modelling study obtained successful results in the calibration and valida-tion process with very few observed data. With this study, a significant contribution to the litera-ture on environmental health is made, as there aren't many examples of hydrological modeling from the Middle East.

The vegetation types on the Qinghai-Tibet Plateau are complex and diverse, and the ecological environment is fragile and sensitive. It is very important to study the dynamic changes in vegetation and the main factors related to these changes to grasp the present state of the regional ecosystem, maintain the balance of the ecosystem and promote the sustainable development of the ecosystem. Therefore, this paper is based on SPOT/VEG NDVI (normalized difference vegetation index) data, land use data, topographic data and meteorological data from 1999 to 2019. The spatiotemporal variation in the NDVI over the Tibetan Plateau in the last 21 years and its response to different driving factors were investigated by using the Sen slope method, Mann–Kendall mutation test, partial correlation analysis and geographical detector method. The results showed that (1) the vegetation coverage on the Qinghai-Tibet Plateau showed an increasing trend from 1999 to 2019, with increases in approximately 67.00% of the plateau area. (2) The spatial differences in vegetation coverage were large; notably, low-density vegetation areas decreased obviously, moderate-density vegetation areas accounted for approximately 50% of the total area, high-density vegetation areas were the least common, and the overall growth rate was significant. (3) The NDVI was positively correlated with temperature and precipitation, and a positive correlation was observed in more than 66% of the region. (4) The order of the influence of single driving factors on the NDVI was as follows: precipitation > soil type > altitude > temperature > gradient > slope > population density > GDP. (5) The effect of multiple factors was significantly higher than that of single driving factors, with a notable nonlinear influence. The interactions between meteorological factors, such as precipitation, and topographic factors, such as altitude, were important, with a q value over 0.79.

Efficient train driving plays a vital role in reducing the overall energy consumption in the railway sector. An energy minimising control strategy can be computed using the framework given by optimal control theory, in particular the Pontryagin maximum principle. Our optimisation approach is based on an algorithm presented by Khmelnitsky that considers electric trains equipped with regenerative braking. A derivation of Khmelnitsky’s theory from a more general formulation of the maximum principle is given in this article, and a complete list of switching cases between different driving regimes has been included that is essential for practical application. A number of numerical examples are added to visualise the various switching cases. Energy consumption data from real-life operation of passenger trains are compared to the calculated energy minimum. In the presented study, the optimised strategy was able to save 37 percent of the average energy demand of the train in operation.

Reinforcement Learning (RL) has demonstrated considerable potential in solving challenges across various domains, notably in autonomous driving. Nevertheless, implementing RL in autonomous driving comes with its own set of difficulties, such as the overestimation phenomenon, extensive learning time, and sparse reward problems. Although solutions like Hindsight Experience Replay (HER) have been proposed to alleviate these issues, the direct utilization of RL in autonomous vehicles remains constrained due to the intricate fusion of information and the possibility of system failures during the learning process. In this paper, we present a novel RL-based autonomous driving system technology that combines Obstacle Dependent Gaussian (ODG) RL, Soft Actor-Critic (SAC), and meta-learning algorithms. Our approach addresses key issues in RL, including the overestimation phenomenon and sparse reward problems, by incorporating prior knowledge derived from the ODG algorithm. We evaluated our proposed algorithm on official F1 circuits, using high-fidelity racing simulations with complex dynamics. The results demonstrate exceptional performance, with our method achieving up to 89% faster learning speed compared to existing algorithms in these environments.

Amidst the strong demand for wood-based products, and the clamor of environmental consciousness, more functional and green solutions arise to meet both of these goals. Pursuant to this, the study looked into the potential of a bio-waste taken from Cocos nucifera tree known as phellem—the tree’s outermost skin made of dead tissues, to be a main component of a new construction material. This study builds on existing research showing phellem is impermeable, buoyant, elastic, has a slow burn rate, and repels water during precipitation, which supports the hypothesis that phellem may be a potential substitute material for dry wall boards. To test this, three (3) formulations were developed as experimental setups to test the physico-mechanical properties of coconut palm phellem (COPAP): Formulation A (40% COPAP and 60% cement); Formulation B ( 25% COPAP and 75% cement); and lastly, Formulation C that was (10% COPAP and 90% cement). The study tested (1) which among these formulations exhibited the best physico-mechanical properties, then it (2) compared the formulations with common dry wall products available in the market, and subsequently, (3) identified the possible architectural applications of COPAP board as a new building material. Of the three setups, Formulation C exhibited the best physico-mechanical properties in terms of compressive and flexural strength, fire resistivity, and water absorption. It fared better than the particle board in terms of compressive strength, better than wafer and particle board in fire resistivity, performed best in the water absorption test, and was almost equivalent in flexural strength to fiber cement board. With these findings, the study concluded that COPAP board can be used for projects that entail a high number of interior partitions, such as offices, BPO firms and commercial stalls in shopping malls, etc. The findings were conclusive only for interior applications, as the results clearly suggest that the COPAP boards are not load-bearing. Future investigations may explore and assess other properties of COPAP boards like thermal and sound insulation, resistance to rot and other properties that may prove its competitive advantage as a construction material.

The A.C.Q.U.A. (Advisable Conscious Quality Use from Assisi) project, promoted by the Climate and Energy and Heritage Design courses of the Planet Life Design Master Program, addresses the theme of the recovery and regeneration of ancient wash-houses in the context of energy, environmental sustainability and innovation, a way of understanding cultural heritage in the wider sense of heritage community through the active participation of all the actors involved: universities, institutions, businesses, students and citizens. The proposal, tested in the municipalities of Assisi and Ruviano (ITALY), involves the creation of a "Community Wash House", a new way of carrying out the usual domestic act of washing clothes in the open air, next to the places where this rite was traditionally performed, in technologically innovative constructions that use renewable energy sources and encourage a reduction in household consumption of water and energy. This project is part of the training of professionals in the new inter-university course that combines knowledge of the tools of technical and scientific design with historical and cultural perspectives in a perspective of sustainable redevelopment of existing structures in the area and the use of alternative energy sources with low climate impact, calculated using the statistics of the Copernicus CDS.

In this paper, the performance of Cu-(In,Ga)-S2 (CIGS2) solar cells with adjusting composite CGI-ratio absorber is explored and compared through an improved three-stage co-evaporation technique. For co-evaporating CIGS2 absorber as a less toxic alternative to Cd-containing film, we analyse the effect of the CGI-ratio stoichiometry and crystallinity, and explore its opto-electric sensing characteristic of individual solar cell. The results of this research signify the potential of high-performance CIGS2-absorption solar cells for photovoltaic (PV)-module industrial applications. For the optimal CIGS2-absorption film (CGI=0.95), the Raman main-phase signal (A1) falls at 291 cm-1, which is excited by the 532-nm line of Ar+-laser. Using photo-luminescence (PL) spectroscopy, the corresponding main-peak bandgaps measured is 1.59 eV at the same CGI-ratio film. Meanwhile, the best conversion efficiency (=3.212%) and the average external quantum efficiency (EQE=51.1% in the visible-wavelength region) of photo-electric properties were achieved for the developed CIGS2-solar cells (CGI=0.95). The discoveries of this CIGS2-absorption PV research provide a new scientific understanding of solar cells. Moreover, this research undeniably contributes to a major advancement towards practical PV-module applications and can help more to build an eco-friendly community.

The objective of this study was to apply simulation and computational intelligence techniques using artificial intelligence and genetic algorithm for economic and environmental optimization of the reverse network (manufacturers, waste managers and recyclers in São Paulo, Brazil) of waste electrical and electronic equipment (WEEE) to promote circular economy. For the economic evaluation, the reduction of: fuel, drivers, insurance, depreciation, maintenance and charges was considered and for the environmental evaluation, the environmental impact was measured in the abiotic, biotic, water, land, air and greenhouse gases compartments. It is concluded that the optimized structure of the WEEE reverse chain for São Paulo, Brazil reduced the number of collections, making the most of the cubage. It also generated economic and environmental gains, contributing to the strategic actions of the circular economy. Thus, the proposed simulation allows replication in organizational practice, mainly to meet the 2030 agenda on reducing the carbon footprint generated in transport in large cities. Thus, this study can guide companies on structuring the reverse WEEE chain in São Paulo, Brazil for economic and environmental optimization, a relevant aspect considering the exponential generation of WEEE, requiring the implementation of the national solid waste policy, and subsequently the signature of the electronics sector agreement in São Paulo.

The concept of "Eco-authoritarianism", although appeared in the 1970s, through the writings of Robert Heilbroner, William Ophuls etc., but Eco-authoritarianism as a theory has emerged very recently [1]. In this era of global environmental degradation and rampant defacement of green space in the rapidly expanding cities of developing countries especially in India, historical sites of green belts, or eco-space developed by royal dynasties or colonial rulers are still preserved by eco-authoritarianism and cultural praxis. A lot of such heritage sites and built-up eco-spaces have faded out over time but at the same time, huge numbers are still present due to the imposition of restrictions by the state or national government or statutory organizations. Built up eco-space under Burdwan Raj Dynasty during the 18th and 19th centuries is still preserved almost in its original form due to the eco-authoritarianism outlook of the present stakeholder (The University authority mainly and the Forest Department). People of this urban unit not only feel proud of such built up eco-space; they also give credit to the university authority for maintaining the legacy of the Raj Dynasty. This investigation attempts to capture not only the role of authoritarian legacy for the preservation and conservation of eco-space in the urban context but also clarify the benefits of this eco-space regarding the cultural ecosystem services and outlook of urbanites towards nature. To portray this, both the qualitative and quantitative methods have been applied to glean the whole perspective.

This article presents a state-of-the-art literature review to understand people’s perceptions and opinions of Autonomous Vehicles (AVs) and the factors that influence AV adoption. A strategic literature search was conducted to select articles for this review. Most of the articles were published within the last five years and they used a household questionnaire survey to collect data. Mostly, they used statistical and econometric methods to evaluate the factors that affect people’s intentions to adopt AVs. The results show that psychological factors often appear as the most important internal factors of people’s willingness to adopt AVs. Additionally, other internal factors such as the socioeconomic profile of individuals and their household, and knowledge and familiarity with AV technologies would affect AV adoption tendencies. User attributes also indirectly affect AV adoption by influencing the psychological factors of users. We identify several critical external factors such as opportunities (e.g., safety and security, low congestion, energy use) and challenges (e.g., system failure, privacy breach, and legal issues), while another influential group includes transportation factors (e.g., travel mode, distance, and time), urban form (e.g., urban/rural, density, land use diversity), affinity to new technology, and the institutional regulatory environment. We discuss some recommendations for policy makers, auto industries, and private stakeholders to formulate policies and strategies to increase market share of AVs. Finally, we identify some limitations of previous studies and provide a blueprint for future research on AV adoption.

With the rapid development of urbanization, the chemical evolution of groundwater is significantly affected by human activities. However, the driving mechanisms of groundwater chemical evolution at different stages of urbanization remain unclear, severely impacting the implementation of groundwater protection. This study investigated the driving mechanisms of groundwater chemical evolution based on the long-term series (from 1985 to 2015) of hydrochemical data from 19 groundwater monitoring sites in rapidly urbanizing areas (Shijiazhuang, Hebei Province, China). The results show that the concentrations of various chemical components in groundwater gradually increase with the acceleration of urbanization process, especially NO3-, which has increased from 13.7 mg/L in the primary stage of urbanization (PSU) to 65.1mg/L in the advanced stage of urbanization (ASU), exceeding the WHO drinking water standard (50mg/L), indicating that the groundwater chemistry has been significantly affected by human activities. It is interesting to find that the main hydrochemical types have changed from HCO3•SO4-Ca•Mg type water in the PSU to SO4•HCO3-Ca•Mg type water in the ASU. It is worth noting that there were obvious differences in driving factors of groundwater chemical evolution at different urbanization stages. In the PSU, the driving factors of groundwater chemical evolution were carbonate and rock salt dissolution, cation exchange, and industrial activities. However, in the intermediate stage and advanced stage, the driving factors of groundwater chemical evolution were changed to carbonate and gypsum dissolution, groundwater over-exploitation, agricultural fertilization, and domestic sewage. Based on the above conclusions, it is suggested that future groundwater management should control the amount of agricultural fertilizers, apply scientific fertilization, and prohibit the discharge of various types of non-compliant sewage, while strengthening the supervision of groundwater extraction to reduce the impact of urbanization development on the groundwater chemical evolution process.

Autonomous driving vehicles rely on sensors for the robust perception of surroundings. Such vehicles are equipped with multiple perceptive sensors with a high level of redundancy to ensure safety and reliability in any driving condition. However, multi-sensor, such as camera, LiDAR and radar, systems bring up the requirements related to sensor calibration and synchronization, which are the fundamental blocks of any autonomous system. On the other hand, sensor fusion and integration have become important aspects of autonomous driving research and directly determine the efficiency and accuracy of advanced functions such as object detection and path planning. Classical model-based estimation and data-driven models are two mainstream approaches to achieving such integration. Most recent research is shifting to the latter, showing high robustness in real-world applications but requiring large quantities of data to be collected, synchronized, and properly categorized. To generalize the implementation of the multi-sensor perceptive system, we introduce an end-to-end generic sensor dataset collection framework that includes both hardware deploying solutions and sensor fusion algorithms. The framework prototype integrates a diverse set of sensors, such as cameras, LiDAR, and radar. Furthermore, we present a universal toolbox to calibrate and synchronize three types of sensors based on their characteristics. The framework also includes the fusion algorithms, which utilize the merits of three sensors , namely, camera, LiDAR and radar, and fuse their sensory information in a manner that is helpful for object detection and tracking research. The generality of this framework makes it applicable in any robotic or autonomous applications, also suitable for quick and large-scale practical deployment.

In the context of partial autonomy, where autonomous vehicles and humans share control of the vehicle, bringing out-of-the-loop drivers back into the loop is a significant challenge. While warning signal design guidelines are commonly used to provide alerts, few studies have examined each signal in depth with an emphasis on the autonomous environment. This study aims to fill this gap by investigating visual, auditory, and tactile stimuli and modifying their sub-attributes to explore variations related to age, gender, and other individual backgrounds. A driving simulator was utilized to create a realistic driving environment and measure participants' reaction times in takeover request situations. Analysis of the data revealed correlations between age and reaction times for auditory and tactile signals, with interaction effects observed between age and sub-attribute intensity. Additionally, participants exhibited varying reaction time patterns in response to different sub-attribute intensities. By evaluating individual differences in perception based on modality characteristics, often overlooked in prior research, this study serves as a foundational contribution to future research in the field.

By shifting the focus from aggregate-level analysis to individual-level analysis, we believe that the DAD model can contribute to a more comprehensive understanding of driving behavior. Combing DAD with a conflict identification (CIM) model can potentially enhance the effectiveness of Advanced Driver Assistance Systems (ADAS) in terms of crash evasion capabilities. This paper is part of our research titled Automatic Safety Diagnosis in Connected Vehicle Environment, which received funding from the Southeastern Transportation Research, Innovation, Development, and Education Center.

The need to address climate change and establish sustainable urban environments has driven increased efforts in Europe to develop climate-neutral cities. This study highlights the importance of integrating solar energy technologies and nature-based solutions as key strategies to achieve climate neutrality. By examining current practices, emerging trends, and case examples, it explores the benefits, challenges, and prospects associated with the integration of solar energy and nature-based solutions in urban contexts. The study presents a pioneering approach to assess the urban heat and climate change mitigation benefits of combining building-integrated photovoltaics and nature-based solutions specifically within the European context. The results emphasize the synergistic relationship between nature-based components and solar conversion technology, identifying effective combinations for different climatic zones. In warmer regions of Southern Europe, strategies like rooftop photovoltaics on cool roofs, photovoltaics shadings, green walls, and urban trees have demonstrated effectiveness. Conversely, mid- and high-latitude European cities have seen positive impacts through the integration of rooftop photovoltaics and photovoltaics facades with green roofs and green spaces. As solar cell conversion efficiency improves, the environmental impact of photovoltaics is expected to decrease, facilitating their integration into urban environments. The study emphasizes the importance of incorporating water bodies, cool pavements, spaces with high sky-view factors, and effective planning in urban design to maximize resilience benefits. It also highlights the significance of prioritizing mitigation actions in low-income regions and engaging citizens in the development of social photovoltaics positive energy houses, resilient neighbourhoods, and green spaces. By adopting these recommendations, European cities can lead the way in creating climate-neutral urban environments that prioritize clean energy, nature-based solutions, and the overall well-being of residents. The findings underscore the need for a multidisciplinary approach that combines technological innovation, urban planning strategies, and policy frameworks to effectively achieve climate neutrality in European cities.

Land use is an embodiment of human socioeconomic activities and represents a bridge between these activities and natural systems. Through social activities, humans transform land use to promote social and economic development and improve production, living conditions, and eco-logical functions of land. Rural residential land represents a space for rural residents to reside in and exhibits spatial characteristics that evolve over time, which is proof of rural socioeconomic development. This paper observes rural residential land in 21 cities on the Grand Canal and analyses its spatial differentiation. Then, it explores the driving factors of this land using spatial grid analysis and the geographic detector model. Lastly, it proposes three different forms of rural residential land based on the results. The study found that: (1) the change of rural residential land in the northern part of the Grand Canal was more volatile than that in the southern part. The change of rural residential land from 1990 to 2020 conformed to the pattern of cultivated land - rural residential land - urban construction land; (2) Based on the driving factors of rural residen-tial land, the land is divided into one-dimensional cities, two-dimensional cities and three-dimensional cities. Circular, linear, and scattered cities of different sizes were affected by socioeconomic factors, transportation accessibility, and the natural environment, respectively; and (3) Finally, according to the spatial differentiation characteristics and the driving factors of rural residential land, the study proposes the construction of three types of villages through the strate-gies of constructing large-scale villages, relocating and reconstructing new villages, and con-structing high-quality villages, respectively. Enhancing the scientific planning of rural residential land and its efficiency can offer the protection of agricultural land and the integration of urban and rural areas in the new era.

As a part of the European Green Deal, the Corporate Sustainable Reporting Directive will apply to over 50,000 companies in Europe, meant to advance the quality of sustainability reporting in the EU, and reduce global emissions and emission distribution inequality. Part of the requirements centre around organisational emission quantification, which will bring much attention to the methodological aspects and resulting decision support capability, which calls for synthesis and argues for clarity. Currently, quantifying the emissions embedded in global transactions mostly focus on direct value-chain attributes, and do not consider unintended consequences beyond the scope of assessment. To achieve genuine reductions, estimated emissions must be considered from a systems perspective to accurately reflect their true impact. While emission inventories serve several purposes, incorrect application limits their potential. The CSRD aligns itself with the GHG Protocol, which does not explicitly facilitate this distinction, although new Land Sector and Removals Guidance draft does, in part. With first CSRD reports expected in 2025, and the GHG protocol entering a revisions period, it presents an opportunity to marry systems thinking with carbon literacy, thereby equipping the expected surge of activity with the sufficient tools for an accelerated transition.

Eco-labels are an instrument for enabling informed food choices and supporting a demand-sided change towards a more and urgently needed sustainable food system. Lately, novel eco-labels that depict a product’s environmental life-cycle assessment on a multi-level scale are being tested across Europe’s retailers. This study elicits consumers preferences and willingness to pay (WTP) for a multi-level eco-label. A Discrete Choice Experiment was conducted in Austria. Individual partworth utilities were estimated by means of Hierarchical Bayes. Results show higher WTP for a positive evaluated multi-level label, revealing consumers’ perceived benefits of colorful multi-level labels over binary black-and-white designs. Even a negative evaluated multi-level label showed higher WTP compared to no label, pointing towards limited effectiveness of eco-labels. Respondents’ preferences for eco-labels were independent from their subjective eco-label knowledge, health consciousness, and environmental concern. The attribute “protein source” was most important, and preferences for animal-based protein source (beef) was strongly correlated with consumers’ meat attachment, implying that a shift towards more sustainable protein sources is challenging and sustainability labels have only a small impact on the meat product choice of average consumers.

This paper proposes an approach for image data extraction and driving behavior analysis using geographic information and driving data. Information derived from geographic and global positioning systems was used for image data extraction. In addition, we used an onboard diagnostic II and a controller area network bus logger to record driving data for driving behavior analysis. Driving behavior was analyzed using sparse automatic encoders and data exploration to detect abnormal and aggressive behavior. A regression analysis was used to derive the relationship between aggressive driving behavior and road facilities. Several traffic improvements were proposed for specific intersections and roads. The results indicate that lane ratios without lane markings and with straight lane markings are important features that affect aggressive driving behaviors.

The increased demand and use of autonomous vehicles and advanced driver-assistance systems has been constrained by an incidence of accidents involving errors with the perception layer’s functionality. In tandem, recent papers have noted the lack of standardized, independent testing formats and insufficient methods with which to analyze, verify and qualify LiDAR-based data and categorization. While camera-based approaches benefit from an ample amount of research, camera images can be unreliable in situations with impaired visibility such as dim lighting and fog. This paper aims to introduce a novel method based entirely on LiDAR data with the capability to detect anomalous patterns as well as complementing other performance evaluators using a Copula-based approach. With a promising set of preliminary results, this methodology may be used to evaluate an algorithm’s confidence score, the impact conditions may have on LiDAR data and detect cases in which LiDAR data may be insufficient or otherwise unusable.

3D object detection is essential for an accurate and reliable autonomous driving system. Currently, the methods used by the state-of-the-art two-stage detectors are not flexible enough and their fea-ture extraction capabilities are very limited to cope effectively with the disorder and irregularity of point clouds. In this paper, we combine the advantages of both PV-RCNN and PAConv (Position Adaptive Convolution) to create a completely new network, FANet, in order to overcome the ir-regularity and disorder of point clouds. The convolution in our network builds convolutional ker-nels from a basic weight matrix, whose combined coefficients are learned adaptively by LearnNet from relative points. This network allows for flexible modeling of complex spatial variations and geometric structures in the 3D point cloud, enabling better extraction of point cloud features and producing high-quality 3D proposal boxes. Compared to other methods, FANet is superior in terms of 3D object detection accuracy. Extensive experiments on the KITTI dataset have shown a signif-icant improvement in our approach.

The key to ensuring the sustainability of transportation operations on the roads is to manage traffic flows homogeneously. By homogenizing the behavior of various drivers, traffic operation can be optimized, and traffic safety can be improved. However, the advent of autonomous vehicles is expected to have a major impact on the homogeneity of sustainable transportation operations. In this study, a method of driving in harmony with surrounding vehicles was studied to minimize the impact of autonomous vehicles on current traffic operation. In particular, in this study, a methodology was developed to optimize the driving behavior of autonomous vehicles by analyzing the driving behavior of following vehicles using spectrum analysis. Specifically, a method for calculating three indicators that can analyze the driving behavior of a following vehicle, such as reaction time, stimulus adaptation index, and collision risk avoidance index, was proposed. These indices produced consistent and robust results for all traffic conditions. If these indicators are used, it is expected that sustainable traffic management will be possible even when autonomous vehicles and human drivers are mixed on the road.

Autonomous vehicles (AVs) may not only reduce parking demand, but also free up on-street parking spaces. From the perspective of bus priority, converting on-street parking lots into dedicated bus lanes can both ensure bus priority and accommodate non-bus traffic, helping to reduce congestion and pollution. In order to study the impact of conversion of on-street parking lots to bus lanes on traffic flow, a microscopic simulation framework based on VISSIM is proposed in this paper. Data was collected using the floating car method and video recording method to quantify the factors affecting traffic flow due to the conversion of on-street parking. The VISSIM simulation model was then calibrated. According to the proportion of the on-street parking lots into a bus lane, the on-street parking lots conversion simulation model of different scenarios was established, and the influence of different on-street parking lots conversion ratio on traffic efficiency and pollution emission was quantitatively analyzed. Finally, the optimization method based on bus lane network connectivity is discussed. The results indicate that: When the on-street parking lots conversion ratio is approximately 100%, bus speed increases by 21.03%, bus delays decrease by 14.27%, and bus parking instances decrease by 11.31%. Total emissions are negatively correlated with vehicle speed. As the conversion rate of on-street parking increases, the emissions of road segments and the entire road network gradually decrease. At a conversion ratio of 100%, the average total emissions for road segments have the highest reduction rate, decreasing by 4.12%. At a conversion ratio of 50%, the average emission total for the road network shows the largest reduction, falling by 2.88%.

In the face of the current energy and environment problems, the full use of biomass resources instead of fossil energy to produce a series of high-value chemicals has great application prospects. 5-hydroxymethylfurfural (HMF), which can be synthesized from lignocellulose as raw material, is an important biological platform molecule. Its preparation and catalytic oxidation of subsequent products have important research significance and practical value. In the actual production process, porous organic polymer (POPs) catalysts are highly suitable for biomass catalytic conversion due to its high efficiency, low cost, good designability, and environmentally friendly features. Here, we briefly described the application of various types of POPs (including COFs、PAFs、HCPs、CMPs) in the preparation and catalytic conversion of HMF from lignocellulosic biomass, and analyzed the influence of the structural properties of catalysts for the catalytic performance. Finally, we summarized some challenges that POPs catalysts will face in biomass catalytic conversion, and prospected the important research directions in future. This review would provide valuable references for the efficient conversion of biomass resources into high-value chemicals in practical applications.

Navigation systems are considered as a fast and efficient source of road information to drivers. However, they can distract drivers with more potential accidents on the road. This study examined the effect of navigation systems on driver distraction and visual search while driving in different driving conditions. An eye-tracking system was used to collect visual search data from twenty young drivers while using a driving simulator. Several factors were investigated, including the driving environment (urban and rural), the illumination level (day and night), and the display of the navigation system (large and small) as well as their interactions. Several measures related to eye movements were used in this experiment, including percentage of total Global Positioning System (GPS) fixation duration, average duration of GPS fixation, GPS fixation frequency, the percentage of total dwell duration for the road ahead, the frequency of dwelling on the mirrors and driver’s right and left side windows, and the percentage of dwellings on the mirrors and driver’s right and left side windows. Statistical analysis of the collected data was performed with repeated measures analysis of variance (ANOVA). The experiment revealed that the small GPS display creates more distraction in terms of average gaze duration and total gaze duration. Moreover, daytime driving conditions increase distraction. Regarding the driver’s visual search, the study showed that the visual search area is wider and more spread out during the day, which leads to better driving performance. This study will compare small and large navigation displays to determine which one is more effective in reducing driver distractions, and contribute to understanding driver distraction and visual search while using the navigation system display.

Tourism sustainability is a significant approach to forming a synergistic model of industry and ecology in ecologically vulnerable areas. Scientifically detecting the effect mechanism of tourism development (TDI) on eco-environment resilience (ERI) is important in achieving regional social-ecological system sustainability. Empirical exploration is conducted on the levels of TDI and ERI in the Yangtze River Economic Belt (YREB) to study the spatiotemporal heterogeneity of TDI's effect on ERI. The results indicate a significant growth in TDI in the YREB, with the formation of tourist clusters around Shanghai and Chongqing as the core. Although ERI typically exhibits a declining trend, the rate of decline has notably slowed, forming a "high at the sides and low in the middle" spatial pattern. TDI and ERI are spatially dependent in the YREB, with predominantly high-high (HH) and low-high (LH) clusters in Shanghai, Zhejiang, and Jiangsu. Conversely, upstream regions with strong eco-environmental foundations exhibit low-low (LL) and high-low (HL) clusters. In general, TDI promotes ERI, but there is significant spatiotemporal heterogeneity in the YREB. Positive impact regions are expanding, while negative impact regions are shrinking. These results could provide scientific evidence for differentiated classification and control policies in the YREB.

The rapid development of Deep Learning brought novel methodologies for 3D Object Detection using LiDAR sensing technology. These improvements in precision and inference speed performances lead to notable high performance and real-time inference, which is especially important for self-driving purposes. However, the developments carried by these approaches overwhelm the research process in this area since new methods, new technologies, and software versions lead to different project necessities, specifications and requirements. Moreover, the improvements brought by the new methods may be due to improvements in newer versions of deep learning frameworks and not just the novelty and innovation of the model architecture. Thus, it became crucial to create a framework with the same software versions, specifications and requirements that accommodate all these methodologies and allow the easy introduction of new methods and models. A framework is proposed that abstracts the implementation, reusing and building of novel methods and models. The main idea is to facilitate the representation of state-of-the-art (SoA) approaches and simultaneously encourage the implementation of new approaches by reusing, improving and innovating modules in the proposed framework, which has the same software specifications to allow a fair comparison. This makes it possible to determine if the key innovation approach outperforms the current SoA by comparing models in a framework with the same software specifications and requirements.

Understanding the hydrological behavior of watersheds and their driving factors is crucial for sustainable water resources management. However, at large scales, this task remains challenging due to the spatial heterogeneity in landscapes, topography, land use, geology, and soil properties. In this context, the aim of this study was to identify the key factors that influence the hydrological system of four watersheds: Ankavia (WS1: 55% forest cover), Ankaviabe (WS2: 77% forest cover), Sahafihitry (WS3: 41% forest cover), and Antsahovy (WS4: 48% forest cover), over a 10-month study period. These catchments are located within the SAVA region of northeastern Madagascar and have a humid-tropical climate. We investigated the relationship between selected catchment descriptors (CD) and hydrological signatures (HS) by using a Pearson coefficient-based correlation matrix. More specifically, CD extracted from topography/morphology (T), land use (LU), soil (S), and geological characteristics (G) were correlated with HS, including base flow index (BFI), runoff coefficient (rc), peak flow (Qp), runoff event time scales (ts), high flows (Q5), low flows (Q95), and mean discharge (q_mean). The analysis revealed that land use, soil properties, and geology seem to be the best predictors for BFI and Q95, while soil properties mainly govern rc, Qp, Q5, ts, and q_mean. These findings provide valuable insights into the key drivers of hydrologic behavior that can inform water resource management strategies. In particular, WS2 has better flood buffering capacity but suffers from lower base flows in the dry season potentially due to higher evapotranspiration. Conversely, WS3 and WS4 (and to a lesser extent WS1) have lower flood buffering capacity, but these watersheds experience less pronounced low flows in the dry season due to higher base flow index resulting from lower evapotranspiration. The results emphasize the importance of sustainable land use practices and conservation efforts, which are essential for the sustainable development of the region. By incorporating these practices into water management strategies, we can help ensure a more stable and reliable water supply for communities and ecosystems within the region.

Recent empirical work has revealed that drivers have different lane change strategies. These strategies help to understand driver heterogeneity and decision stochasticity, which are both often lacking in microscopic models. In this paper we generalize the lane change strategies in a theory for social interactions. The theory introduces a mechanism by which two new driver traits, ego-speed sensitivity and socio-speed sensitivity, endogenously influence other driver traits that govern lane change behaviour such as desired speed, desired headway and lane change desire. A key concept in this mechanism is that drivers are not just affected by what happens in front of them, but also by what they observe in their rear-view mirrors. The idea is that drivers reduce their headways to exert (social) pressure onto leading drivers (on the same or adjacent lanes), which may force them to increase speed, change lanes, or conversely, to refrain from changing lanes. We refer to this reduction of the headway as tailgating. Through an example implementation of the theory, we demonstrate that these social interactions may have a profound impact on several key (mesoscopic) traffic flow characteristics. Our results show that these new driver interactions influence the number of lane changes, platoon lengths, and headway variance. As such, the theory improves our description and understanding of traffic flow and consequently it paves the path for more realistic traffic models.

The main goal of the study is to assess the Decision criteria, and the determining factor for the sustainability of entrepreneurial startups in order to contribute towards social inclusion and capacity building. Both concepts are in the development phase and are the outcome of entrepreneurial ecosystem and individual behavior and traits. The current study observed the research problem as entrepreneurship and the entrepreneurial startups are the continuous phenomena required for every economy. The lack of an efficient ecosystem and incompetent trait of an entrepreneur drags the entrepreneurial startup to a failure. Therefore, an assessment on decision criteria and determining factors categorizing them with their importance may provide requisites to lead a successful entrepreneurial startup contributing to the social inclusion and capacity building. The study solved the research problem by statistical assessment of decision criteria and determining factors and categorizing them with their importance may provide requisites to lead a successful entrepreneurial startup. The research is built on research questions, objectives, conceptual model, and hypothesis which are tested based on the data collected. The collection of data was done through a survey questionnaire on a sample of established entrepreneurs. The study concludes that five components of decision criteria are Region, Competition, Funding Opportunities, Tax System, and Country Economic Situation, whereas the eight components Consciousness and Reliable, Pursuit Results, Flexibility, Stress Resistance, Skills Identification Exploitation Potential Market Opportunities, Leadership, Creativity and Innovation, and Delegation Decision Making of determining factor are required for a successful entrepreneurial startup to be able to work towards social inclusion and capacity building.

The lithium-ion batteries are widely used as a power source for portable devices, including cell phones. The useful life is about 2 years or 500 cycles, contributing to the generation of waste from electrical and electronic equipment (WEEE). Mining of lithium and cobalt damages the environment and is onerous; therefore, sustainable alternatives, such as obtaining these elements from secondary sources as recycling of lithium-ion batteries, are essential to provide the inputs used in the sector. However, the metallurgical route which will used to recovering them must be considered, due to this work aims for a more environmentally favorable process using DL-malic acid 1.5 M and instead of compared with sulfuric acid 2 M, heat pretreatment of 1 h and 3 h, and for all conditions, experiments were carried out with and without adding the oxidizing agent hydrogen peroxide. The best yields occurred in presence of H2O2 10 % v/v, and heat pretreatment of 1 h: 33.49 % Co and 4.63 % Li, and 29.78 % Co e 3.44 % Li were recovered by sulfuric acid and DL-malic acid, respectively.

To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring the cognitive capabilities of drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to primary activities in driving, including crossroad, parking, roundabout was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93–0.94). Furthermore, using the same algorithm, it was possible to distinguish four secondary activities related to activities of daily life and secondary when driving a car.