The genetic potentials of rice cultivars will need to be expressed to their fullest if global rice production is to be expanded enough by 2050 to meet the increased demand of expanding population while the availability of land and water per capita dwindles. New and ‘improved’ rice varieties have contributed greatly to increased production over the past 50 years, but the rate of rice yield increase based on genetic changes has declined in recent decades compared with the early years of the Green Revolution. In fact, many rice consumers continue to prefer to consume ‘traditional’ rice varieties (referred to also as local, native, unimproved, or indigenous) because of their taste, aroma, texture, and other qualities. Further, many farmers prefer to cultivate these varieties because of their better adaptation to local climatic and soil conditions and their evolved resistance to endemic stresses. The practices that comprise the System of Rice Intensification (SRI), including transplanting rice seedlings at a young age, wide spacing between plants, keeping the soil well-aerated rather than inundated, and enhancing soil organic matter, provide traditional rice varieties with micro-environments that are more favorable for the expression of their genetic and agronomic potentials. Interactions among rice plants, soil characteristics, water, energy, and other inputs improve the phenotypic and physiological performance of rice plants. This paper considers how the cultivation of traditional rice varieties with SRI methods can raise yields, reduce farmers’ costs of production, and generate higher incomes, while contributing to the conservation of rice biodiversity.

Rice provides ~20% of human dietary energy and, for many people, a similar share of their protein. Rice cultivation, however, produces significant greenhouse gas (GHG) emissions, comparable to those from the aviation sector. The main GHG from rice production is methane, mostly a result of conventional rice cultivation (CRC) keeping rice fields continuously flooded during the crop cycle. There is extensive evidence that alternate wetting and drying (AWD) of rice fields substantially reduces methane emissions. AWD is one component of the System of Rice Intensification (SRI), an agroecological approach to the management of plants, water, soil and nutrients practiced by millions of farmers in both lowland irrigated rice and upland rainfed cultivation. Thirteen countries have included SRI in their Nationally Determined Contributions to GHG reduction or climate change mitigation. This article reviews 16 field studies of the net reduction in GHG emissions from the adoption of AWD, eight from the adoption of SRI, and two that compared SRI and AWD. Where available, the review includes data on yield and therefore on carbon dioxide-equivalent GHG emissions per kilogram of rice produced. The evidence indicates that AWD and SRI offer a similar and substantial reduction (~35–41%) in GHG emissions per hectare compared with conventional rice cultivation. However, SRI offers ~66% greater yield than CRC, and therefore greater reduction in emissions per kilogram of rice, ~54% more than AWD. The limited data directly comparing SRI and AWD support this finding. SRI also appears to have greater potential to sequester carbon in the soil. SRI lowers rice farmers’ costs of production, adds to their income and can make climate-friendly methods more attractive. Both AWD and SRI are greatly preferable to current conventional practices, but SRI offers opportunities to contribute to food security while directly addressing the drivers of climate change.

Initial evaluations of the System of Rice Intensification in India and elsewhere have mainly fo-cused on its impact on yield and income and have usually covered just one or two seasons. Re-searchers at the ICAR-Indian Institute of Rice Research conducted a more comprehensive evalua-tion of SRI methods over six years (six wet and six dry seasons), comparing these methods with three other rice crop management systems: modified, partially mechanized SRI (MSRI); direct-seeded rice (DSR); and normal transplanting with inundated fields (NTP). SRI grain yield was found to be about 50% higher than with NTP (6.35 t ha-1 vs 4.27 t ha-1), while the MSRI yield was essentially the same (6.34 t ha-1) and 16% higher than with DSR (5.45 t ha-1). Compared to NTP, SRI methods significantly enhanced soil microbial populations over time, bacteria by 12%, fungi by 8%, and actinomycetes by 20%. Indicators of biological activity in the rhizosphere were also higher, dehydrogenase by 8.5% and FDA enzymes by 16%. Glucosidase activity, an indicator of soil organic matter, was 78% higher. Relative to normal transplanting methods, SRI reduced GHG emissions by 21%, while DSR reduced them by 23% and MSRI by 13%, which indicated pos-itive effects of the alternate and improved methods over normal transplanting. Economic analy-sis showed that both gross and net economic returns to be higher with SRI than with MSRI and the other management systems evaluated. While the six-year study documented many ad-vantages of SRI crop management, the MSRI version evaluated is a promising adaptation that provide similar benefits but with lower labor requirements.

In Lubumbashi, the capital of Haut Katanga in the Democratic Republic of the Congo (DR Congo), diesel power plants are a common source of electricity. The need to utilise local renewable energy sources in DR Congo has increased due to the unreliability of the state grid and the rising cost of running Diesel generators. Solar photovoltaic (PV) panels and batteries, in particular, have recently seen significant price drops. Therefore, it is important for operators and suppliers to choose optimal generators together with a renewable energy system to lessen the energy deficit. Diesel generators are still widely used in DRC, but their efficiency pales in contrast to that of more recent power facilities. Consuming fossil fuels results in high expenses for upkeep and operation, in addition to severe environmental damage. This study assessed the feasibility of using local weather and technical data to evaluate the efficiency of a diesel power plant hybridized with a PV system. Hybrid Optimization Model for Electric Renewable (HOMER) simulations suggest that the hybrid system schedule is preferable due to its many economic and environmental advantages for the local community and its inhabitants. The promotion of such a hybrid system, then, may encourage the sustainable economic development of a stable source of electricity for the Congo Region.

Cempo Ireng (Oryza sativa L. var. Cempo Ireng) is one of the black rice variety in Indonesia that consumed as functional food, not as a staple food because of the black rice contains anthocyanin pigment which has beneficial to human health. High concentration of anthocyanin reaching 393.93 ppm cause black color in the pericarp layer of Cempo Ireng which make this variety as an antioxidant source, low glycemic index, and free of gluten, leading to have higher price in the rice market. However, most of the farmers are not cultivate Cempo Ireng due to the longer vegetative phase up to 150 days compared to the white rice varieties which only takes 90 days, the taller phenotype of Cempo Ireng cause problems in cultivation, and also the purple color of Cempo Ireng attract birds and earhead bug (Leptocorisa oratorius) to feed on black rice caryopsis that cause severe yield loss. Transgenic methods, mutation technique, intensification systems, and physical treatment have been applied in Cempo Ireng to overcome those lack characteristics. In the future, Cempo Ireng also has a big potential to become a parent in cross breeding program because of the positive characters. The aim of this review is to describe the potential of Cempo Ireng in rice breeding program for improving food sustainability.

Spatio-temporal variability and dynamics in Sahelian agro-pastoral zones make each local situation a special case. These specificities must be considered to guide the dissemination of agricultural options with a view to sustainable development. The territorial scale of municipalities is not sufficient for this necessary contextualization; the scale of the “village terroir” seems to be a better option. This is the hypothesis put forward by the Global Collaboration for Resilient Food Systems program. By analyzing the links between farm managers and their cultivated land, as well as the spatio-temporal dynamics of “village terroirs” in three regions of Niger (Maradi, Dosso and Tillabéri), this study provides evidence of the existence and functional usefulness of the village terroir for farmers, their land management and their activities. It demonstrates the usefulness of contextualizing agricultural options at this scale. It is based on data collected through participatory mapping and surveys. Their analysis elucidates the links between “terroirs village” and the specific functioning of the agro-socio-ecosystems acting on each of them, thus laying the systemic and geographical foundations for a model of the spatio-temporal dynamics of “village terroirs”. This initial work has opened up new perspectives in modeling and sustainable development.

Rojolele is an Indonesian traditional rice variety from Klaten (Central Java) that classified as tropical japonica and low land rice which become a premium rice variety and have higher price in the market due to a delicious taste and a fragrance aroma. The agronomic characteristics of Rojolele are having high plant stature with sturdy stems and upright plant shape, thick and rough leaves, strong and deeper root systems, long panicle length, and long duration of life. Rojolele rice cultivation is facing important challenges from drought, diseases, long duration, and lodging. Thus, breeding Rojolele cultivars with resistance to drought, diseases, lodging, and short duration is the major focus for Rojolele rice improvement. Indonesian farmer have learned to successfully cultivate Rojolele by applying practical skills, leading to increase the rice productivity. Molecular breeding program, including quantitative trait loci (QTL), genome-wide association study (GWAS), genomic selection, and genome editing can be applied to improve Rojolele characteristics. In this review, important agronomic and quality traits, intensification system for irrigation and pest control, mutation breeding, transgenic lines, and also future perspectives for Rojolele research were presented. Rojolele is useful for rice breeding program in order to guarantee the food security to overcome increasing population and climate change.

Rice wheat cropping system prevailed in more than fifty percent areas of rice belt of central and upper Punjab. In this area yield of wheat is curtailed every year due to late harvesting of rice crop. In addition to this, lack of machinery for direct sowing of wheat into combine harvested rice is a major limitation for timely wheat sowing due to presence of heavy straw load dropped by the combine harvester. A field experiment was planned and executed during two consecutive years 2017-18 and 2018-19 at Agronomic Research Station Farooqabad to evaluate the various stubble management techniques for the sowing of wheat in a rice-wheat cropping system. The Experiment consist of four treatments Happy seeder (Zero tillage), conventional sowing without burning of rice stubble, Rotavator two times, and Conventional sowing with the burning of rice stubble. The trail was arranged as a Randomized Complete Block Design with four replications having a net plot size of 8 m x 20 m. The Treatments have significant effects on Plant height, Number of productive tillers, 1000- grain weight, and grain yield. Maximum plant height (94.74 and 94.25 cm), numbers of productive tillers/m2 (324.00 and 322.25), 1000-grain weight (36.35 and 36.20 g), grain yield (3780 and 3613 kg/ha), and benefit-cost ratio (2.54 and 2.43) were recorded from the treatment happy seeder (Zero tillage) during both years respectively. It is concluded that the sowing of wheat with a happy seeder (Zero tillage) is an appropriate technique in the rice-wheat cropping system by protecting the environment and soil health from the burning of rice stubbles.

The article presents the results of a morpho-physiological assessment of rice samples by the strength of growth, as well as an assessment of the degree of development of the conducting system of the flag leaves of rice plants. The resistance of varieties to flooding is very relevant in the fight against weeds due to the deep layer of water that weeds cannot overcome. Currently, there are no zoned varieties in Russia that would meet these requirements. Therefore, the problem of creating such rice varieties is urgent, as it will reduce production costs, reduce grain losses during harvesting, improve the quality of the products obtained, and also reduce the pesticide load on the ecosystem. As a result of research, a number of studied samples revealed the potential to quickly lengthen the first leaves, overcome a large layer of water and accumulate vegetative mass. As a result of the evaluation of the conducting system of the flag leaf of rice plants, it was found that in plants with the flood resistance gene Sub1A, which stops growth under water, the conductive beams were smaller in comparison with other fast-growing samples with the AG, Sk genes. Consequently, fast-growing samples have bigger sizes of the conducting system as a resistance mechanism.

Climate projections show that southern Indonesia such as West Nusa Tenggara is projected to experience a lower precipitation and higher temperatures. To date, research on climate change impact on Indonesian rice production yield is limited. As climate change is projected to decrease rainfall and to increase temperatures, this paper offers a qualitative analysis using system archetypes to understand the impacts of climate change on rice production. Two system archetypes are identified including Limits to Growth and Success to Successful. Both archetypes explain that rice production is hampered by high minimum temperature as photosynthesis output is decreased by increasing respiration. This paper shows that using a simple tool, system archetypes, we can describe the impacts of climate change on rice production. The outputs of this study such as a causal loop diagram and system archetypes can be a basis to develop a simulation model in understanding the impacts of climate change on main crops.

In Korea, rice is a major staple grain and is mainly cultivated using various agricultural machinery. Air pollutants emitted from agricultural machinery have their origins mainly from the exhaustion of internal combustion engines. In this study, emission characteristics of five main air pollutants by European Environment Agency's Tier 1 method for rice cultivation was analyzed. Diesel is a main fuel for agricultural machinery and gasoline is generally used only for rice transplanters as a fuel in Korea. Tractors consume 46% of total fuel consumption and 56% of diesel fuel consumption. Gasoline used for rice transplanters accounts for 17% of total fuel consumption each year. Tractors and rice transplanters are emitting 82% of all total pollutants. From 2011 to 2019, the total amount of air pollutant emissions was decrease by 15%. That accounted for the reduction of rice cultivation fields in those periods. Rice transplanting operation was in charge of 42% of total emissions. Then, harrowing, harvesting, tilling, leveling, and pest control operations generated 10%, 10%, 8%, 8% and 7% of total emissions, respectively. The contribution of each air pollutant held 54% of CO, 39% of NOx, 5% of NMVOC, and 2% of TSP from the total emission inventory. The three major regions emitting air pollutants from mechanized agricultural practices were Jeollanam-do, Chungcheongnam-do, and Jeollabuk-do, which consume 55% of total fuel usage in rice farming. The total amount of air pollutant emissions from rice cultivation practices in 2019 was calculated as 8,448 Mg in Korea.

Grading of rice grains has gain attentions due its requirement of quality assessment during import or export. Rice grain quality depends on milling operation, where rice hull is removed with a huller system followed by whitening operation. In such process, adjustment of rollers, control, and operation is important in terms of quality of milled rice. Especially, the basmati rice needed more quality assurance as it is not parboiled rice and exported globally with a high product value. In this present work, the basic problem of quality assessment in rice industry is addressed with digital image processing based technique. Machine vision and digital image processing provide an alternative with the automated, nondestructive, cost-effective, and fast approach as compared with traditional method which is done manually by human inspectors. A model of quality grade testing and identification is built based on morphological features using digital image processing and knowledge based adaptive neuro-fuzzy inference system (ANFIS). The qualities of rice kernels are determined with the help of shape descriptors and geometric features using the sample images of milled rice. The adopted technique has been tested on a sufficient number of training images of basmati rice grain. The proposed method gives a promising result in an evaluation of rice quality with 100% classification accuracy for broken and whole grain. The milling efficiency is also assessed using the ratio between head rice and broken rice percentage and it is 77.27% for the test sample. The overall results of the adopted methodology are promising in terms of classification accuracy and efficiency.

Rice production is important for global food security but given its large water footprint, efficient irrigation management strategies need to be developed. Expansion of rice growing area is larger than any other crop in Africa due to increasing demand for rice. Three rice irrigation management alternatives with the system of rice intensification (SRI) were field-evaluated against the conventional continuously flooded system (CF) in Tanzania. Production systems included: (1) CF (50 mm ponding depth for the entire season); (2) SRI (40 mm ponding for 3 days and no irrigation for next 5 days); (3) 80% SRI (80% of the SRI ponding); and (4) 50% SRI (50% of the SRI ponding). Experimental evaluation of the four systems was conducted for both wet and dry seasons. For the dry season, the SRI and 80% SRI produced higher yields of 9.68 tons/ha and 11.45 tons/ha and saved 26% and 35% of water, respectively compared to the CF (8.69 tons/ha). The yield advantage of the 80% SRI and SRI over the CF was less during the wet season with 6.01 tons/ha and 5.99 tons/ha of production, and water savings of 30% and 14%, respectively compared to the CF (5.64 tons/ha). The 50% SRI had lowest yield of all for both seasons, 7.48 tons/ha and 4.99 tons/ha for the dry and wet seasons, respectively. Statistically, the 80% SRI treatment outperformed all other treatments over the two seasons with an additional yield of 1.57 tons/ha and 33% (345 mm) water savings compared to the CF. Economic productivity of water (US$/ha-cm) over two seasons was highest for the 80% SRI ($20.27/ha-cm), while it was lowest for the CF ($12.89/ha-cm). Water saved by converting from the CF to the 80% SRI (1.98 million ha-cm) can support a 50% expansion in the current rice irrigated area in Tanzania. Even without irrigation expansion, the 80% SRI can increase rice production by 1.5 million tons annually while enhancing water availability for industrial and environmental uses (e.g., ecological preserves) and help achieve food security in Tanzania and the greater sub-Saharan Africa.

Farmers’ conventional tillage (CT) and residue removal practices in rice-maize systems in South Asia’s Eastern Gangetic Plain (EGP) are input-intensive, costly and soil degradative. We conducted a rice-maize-mungbean (R-M-MB) system experiment with six tillage and three residue management treatments in Bangladesh representing the EGP. Maize yields were significantly (p≤0.05) higher under permanent (PB) or fresh (FB) beds and strip tillage (ST) than CT but no differences in mungbean yields. Rice yields under PB, FB and CT were similar, but significantly higher than under zero or minimum tillage and ST. Yields of all crops increased significantly (p≤0.05) with residue retention compared to no retention. Total system productivity was highest under PB followed by FB and ST. Compared with CT, gross margins in PB, FB and ST increased by 18, 13 and 11%, and soil organic matter (SOM) and total N contents across tillage treatments increased by 11-16% and 12-24%, respectively. After three years, SOM and total N and available P and S contents increased significantly (p≤0.05) by residue retention. Results demonstrate the potential of PB, FB and ST with residue retention, for improving the productivity, profitability and soil health under R-M-MB systems in Bangladesh and similar soils in the EGP.

Being a chilling-sensitive staple crop, rice (Oryza sativa L.) is vulnerable to climate change. The competence of rice to withstand chilling stress should, therefore, be enhanced through technological tools. The present study employed chemical intervention like application of sodium nitroprusside (SNP) as nitric oxide (NO) donor and elucidated the underlying molecular mechanisms of NO-mediated chilling tolerance in rice. At germination stage, germination indicators were interrupted by chilling stress (5.0 ± 1.0°C for 8 h day‒1), while pretreatment with 100 μM SNP markedly improved the indicators. At seedling stage (14-day-old), chilling stress caused stunted growth with visible toxicity along with alteration of biochemical markers, for example, increase in oxidative stress markers (superoxide, hydrogen peroxide, and malondialdehyde) and osmolytes (total soluble sugar; proline and soluble protein content, SPC), and decrease in chlorophyll (Chl), relative water content (RWC), and antioxidants. However, NO application attenuated toxicity symptoms with improving growth performance which might be attributed to enhanced activities of antioxidants, mineral contents, Chl, RWC and SPC. Furthermore, principal component analysis indicated that water imbalance and increased oxidative damage were the main contributors to chilling injury, whereas NO-mediated mineral homeostasis and antioxidant defense were the critical determinants for chilling tolerance in rice. Collectively, our findings revealed that NO protects against chilling stress through valorizing cellular defense mechanisms, suggesting that exogenous application of NO could be a potential tool to evolve cold tolerance as well as climate resilience in rice.

This paper presents a novel approach to power management techniques in energy storage system (ESS) interoperability with the energy power system (EPS) by inserting a SCADA controlled directional protection relay for improving the mode switching sequence. According with latest European standards, all necessary measures for re-synchronization and reconnection for health work of the system must be taken. The proposed approach intends to improve the “interface” between storage systems and electric power grid by using a directional relay which provides interoperability improvement by automatic recognizing and differentiating the switching sequence of the mode of operation from the fault mode. Simulated results from a small scale electrical generating system are provided. The results demonstrate that the proposed power management solution is technically ideal to achieve high quality energy while retaining the stability of the power grid network. The interoperability of PHS systems integrated with the electric power grid usually leads to perturbations in terms of frequency of the generated voltage, and for improving it we designed an automatic treating and re-connecting software module. This work regards the importance of producing electricity from green and renewable energy sources, while retaining the stability and reliability of the power grid. ESS systems are widely used to help balancing of intermittent forms of load demand on the electrical power grid at specific key moments. In this article, the pumped hydro storage (PHS) technologies are proposed as an economical way to utilize smoothly the off-peak energy or surplus energy, by pumping water to a reservoir at a higher level. During peak load periods, the stored water is discharged through the reversible pump-turbine to generate electricity to meet the peak demand controlled by designed SCADA application.

This research aims to explore factors that influence sustainable music learning system from psychological perspective, and innovatively constructs a combination of qualitative and quantitative approaches. For first time, combines nonlinear system and identifies relevant factors affecting music learning by establishing data system for questionnaire answering and a quantitative model for actual academic performance. Existing research mainly looks at several factors that affect fragmented music learning system from perspective of teachers' teaching rules, emphasizing phased teaching state and learning goals of teachers as main body, thus ignoring music learning students, who are a large group of music learning objects. The research fills gap in this type of research object, focusing on sustainable music learning system from emotional perspective and students who occupy a dominant position in music learning, so as to more accurately explore impact of their emotional influencing factors on music learning system. The most prominent innovation of this study lies in construction of stochastic nonlinear system for psychological influencing factors of student music learning and a quantitative model for actual learning effect. Through analysis of nonlinear system, weight values of several negative emotions on music learning system are obtained. The comprehensive impact of emotional factors and practical learning on music learning system can be obtained by weighting them together.

This research aims to explore factors that influence sustainable music learning system from psychological perspective, and innovatively constructs a combination of qualitative and quantitative approaches. For first time, combines nonlinear system and identifies relevant factors affecting music learning by establishing data system for questionnaire answering and a quantitative model for actual academic performance. Existing research mainly looks at several factors that affect fragmented music learning system from perspective of teachers' teaching rules, emphasizing phased teaching state and learning goals of teachers as main body, thus ignoring music learning students, who are a large group of music learning objects. The research fills gap in this type of research object, focusing on sustainable music learning system from emotional perspective and students who occupy a dominant position in music learning, so as to more accurately explore impact of their emotional influencing factors on music learning system. The most prominent innovation of this study lies in construction of stochastic nonlinear system for psychological influencing factors of student music learning and a quantitative model for actual learning effect. Through analysis of nonlinear system, weight values of several negative emotions on music learning system are obtained. The comprehensive impact of emotional factors and practical learning on music learning system can be obtained by weighting them together.

In graduate programs such as social work, field education is the signature pedagogy of education. As such, student placement with an appropriate field education agency is critical to ensure academic success and career readiness. A variety of Learning Management System (LMS) and Integrated Planning and Advising Service (IPAS) technologies have been developed to fully integrate technology into the educational system and streamline and improve the learning experience for students, educators, and administrators. Few (if any) of the existing solutions have capabilities to match students with field educators on the basis of an individual student’s completed coursework and area of specialization, as well as field educator needs and opportunities. This paper describes our experience developing a custom LMS/IPAS system—the School of Social Work information System (SSWiS)—that was designed specifically for student learning, faculty advising, and academic administration within our social work graduate program. We present the challenges that motivated the design of the SSWiS before describing the architecture and functionality of our solution. We then discuss our preliminary evaluation results. We conclude with a discussion of the benefits and limitations of our system in the context of today’s technical needs in graduate education in social work and other fields.

It is not plausible to predict disasters; however, still the least that may be done is to get ready for that. In disaster circumstances, nowadays, the existing internet of things (IoT) technology is rather developed and capable of being highly beneficial. For many countries, the early warning system for natural catastrophes is a crucial challenge, especially for high seismic hazard countries. Primary function of an early warning system is to assist individuals and authorities to save lives and reduce secondary damage once a disaster occurs. Even though such systems have been defined for the earthquake on the basis of IoT, the least attention has been focused on the post decisions for substructure monitoring systems which can control the gas and electric lifelines given the critical time. Combination of Fuzzy system with IoT can improve the decision making process after the disasters occur. This program minimizes costs and destruction in cities. Tehran is a high seismic hazard area which contains an expanded network of the gas transmission system. Hence, a major seismic event may cause serious loss directly and indirectly owing to the damage to the gas and electric transmission network. The proposed early warning system contains a grid of strong ground motion stations. Each station contains five sensors to collect accurate data and send the data to the cloud server, then the server executes relevant scenarios.

Runway Safety Assistant Foreseeing Excursions (RUN.S.A.F.E.) is a complete embedded system solution that predicts a potential runway overrun of a civil aviation aircraft, during the Take off and Landing. This work examines the feasibility of such a system, through the algorithms and computations that predict the overruns. The system executes both static and dynamic calculations, the former being dependent, while the latter independent to the user’s inputs. Both outcomes and the runway’s length continuously estimate in real time if the process will end up in an overrun. All inputs are specifically selected, to either be available to the pilots, or to be retrieved from the existing avionics systems of the cockpit. A performance evaluation is conducted on both static and dynamic calculations, and metrics unveil the accuracy of the predictions and the time needed to converge to a reliable result. The solution is adapted for a Boeing 737-800 aircraft, with CFM56-7B engines, yet the calculations also apply for similar aicrafts, equipped with a tricycle Landing gear and turbofan engines, namely the whole Boeing 737 family, the Airbus A320 family etc.. The system is aligned with current standards and certification specifications, where applicable.

As marine traffic is contributing to pollution and mostly have predictable route with repetitive load profiles, to reduce their impact on environment, hybrid system with proton exchange membrane fuel cells (PEMFC-s) and battery pack, is promising replacement. For this purpose, the numerical model of repowered existing vessel that have two diesel engines with 300 kWe is developed. The goal of this paper is to develop numerical model which analyzes and determine equivalent hybrid ship propulsion system for known traveling route. The developed numerical model consists of an interconnected system with PEMFC stack and battery pack as power sources. Numerical model is developed and optimized to meet minimal required power demand for successful route demand, which has variable loads and daily sails six-time same voyage- in total 54 nautical miles. The results have shown that equivalent hybrid power system consists of 300 kWe PEMFC stack and battery pack with 424 kWh battery and state of charge varying between 20 and 87%. To power this new hybrid power system, hydrogen tank of 7200 l holding 284.7 kg at pressure of 700 bar is needed, compared to previous system that spend 1524 kg of diesel and generate 4886 kg of CO2.

The intrusion of seawater in coastal aquifers in Greece is a particularly widespread phenomenon. The coastline of Greece has a length of 15,147 km. Once groundwater resources are contaminated, remediation strategies are difficult, costly and time consuming. In this paper three coastal aquifer systems of Peloponnese are studied. Initially the main ions and cations were calculated for the three aquifers. According to the results from hydrochemical analyzes Na+,Mg2+, Ca2+, SO4-2, Cl- have high values and are affected by the intrusion of seawater into the coastal aquifers. It estimates the vulnerability of groundwater to the intrusion of seawater. Using Geographical Information Systems (GIS) software, maps are prepared for the presentation of each parameter of the GALDIT method. The main factors that affect the seawater intrusion are basically presented in the 6 letters of the acronym GALDIT. The analysis of each parameter is as follows: Groundwater occurrence (aquifer type: unconfined, confined, and leaky confined), Aquifer hydraulic conductivity, depth to groundwater Level above the sea, Distance from the shore (inland distance perpendicular from shoreline), Impact of existing status of sea water intrusion in the area and Thickness of the aquifer. The final map that emerged from the study of the three groundwater systems shows the vulnerability of seawater intrusion in Peloponnese. The maps show that the groundwater system of Larissos presents lower vulnerability to the seawater intrusion compared to the other two groundwater systems. This is because the occurrence of flysch in the northern part of the area acts as a barrier to the penetration of seawater.

In this paper, we investigated the formation isolation regulation issue regarding multiple autonomous underwater vehicles (AUVs) characterized by a "leader-follower" framework. Considering the cooperative-competitive relationship among the follower AUVs and the impact of unknown external disturbances, an extended state observer was designed based on backstepping to mitigate these disturbances, and an event-triggered control scheme was designed to realize the two-part consensus control within the multi-AUV system. Through rigorous theoretical analysis, it is shown that the system achieves asymptotic steadiness and is free from zeno behavior under the proposed event-triggered control scheme. Finally, numerical simulations confirm the efficiency of the regulation strategy in achieving formation separation within the multi-AUV, where the trajectory tracking errors of individual AUVs gather in a compact vicinity close to the source and the structure converges was achieved, with the absence of zeno behavior also demonstrated.

Effective waste elimination is critical for the success of construction projects. Although several studies have focused on various aspects of construction waste, limited efforts have investigated the dynamic effect of Making-Do (MD) practices on productivity, rework, defects, and materials wastage. From a lean construction perspective, this study aims to address MD waste using the Last Planner System (LPS) and BIM. First, the casual structure that can cause MD in construction projects was expressed in a casual loop diagram (CLD), and thematic analysis uncovered the strategies of LPS-BIM to eliminate MD identified by reviewing the literature. Secondly, twenty-five strategies from the LPS and BIM strategies to address MD using Structural Equation Modeling (SEM) were assessed. Subsequently, a system dynamics model (SDM) for investigating LPS-BIM strategies on MD decisions in a construction project was formulated based on the underlying causal loop diagrams and the mathematical relations among the variables. Finally, the model was applied to three projects, and simulations for four LPS-BIM scenarios were carried out. The findings show that dynamic interactions among diverse production planning and control factors are critical in evaluating MD impacts on a construction project. The results demonstrate that the LPS-BIM approach resulted in an average 43.8% reduction in the tasks performed with MD, 45.3% of constraints, 66.5% of construction waste, increasing 13.7% completion rate, and 29.3% cost reduction, demonstrating that LPS-BIM as a more efficient solution for MD mitigation and construction planning.

Cancer remains one of the main causes of death in the world due to its increasing incidence and treatment difficulties. Although several progresses have been done in this field, innovative approaches are needed to reduce tumor incidence, progression and spreading. In particular, the development of cancer vaccines is currently ongoing like both preventive and therapeutic strategy. This concept is not new, but few vaccines have been approved in oncology. Antigen-based vaccination emerges as a promising strategy, leveraging specific tumor antigens to activate the immune system response. However, challenges persist in finding suitable delivery systems and antigen preparation methods. Exosomes (EXs) are highly heterogeneous bilayered vesicles which carry several molecule types in the extracellular space. The peculiarity is that they may be released from different cells and may be able to induce a direct or indirect stimulation of immune system. In particular, EX-based vaccines may cause anti-tumor immune attack or produce memory cells recognizing cancer antigens and inhibit disease appearance. This review delves into EXs composition, biogenesis, and immune-modulating properties exploring their role as tool for prevention and therapy in solid tumors. Finally, here we describe future research directions to optimize vaccine efficacy and realize the full potential of EX-based cancer immunotherapy.

Prolylcarboxypeptidase (PRCP, PCP, Lysosomal Pro-X-carboxypeptidase, Angiotensinase C) controls angiotensin - and kinin- induced cell signaling. Elevation of PRCP appear to be activated in chronic inflammatory diseases (cardiovascular disease (CVD), diabetes) in proportion to severity. Vascular endothelial cell senescence and mitochondrial dysfunction have consistently been shown in models of CVD in aging. The cellular senescence, a driver of age-related dysfunction, can differentially alter the expression of lysosomal enzymes due to lysosomal membrane permeability. There is a lack of data demostrating the effect of age-related dysfunction on the expression and function of PRCP. To explore the changes in PRCP, PRCP-dependent prekallikrein (PK) pathway was charcterized in early – and late-passage human pulmonary artery endothelial cells (HPAECs). Detailed kinetic analysis of cells treated with high molecular weight kininogen (HK), a precursor of bradykinin (BK), and PK revealed a mechansim by which senescent HPAECs activate the generation of kallikrein upon the assembly of the HK-PK complex on HPAECs in parallel with an upregulation of PRCP and endothelial nitric oxide (NO) synthase (eNOS) and NO formation. The NO production and expression of both PRCP and eNOS increased in early-passage HPAECs while decreased in late-passage HPAECs. Low activity of PRCP in late passage HPAECs was associated with rapid decreased telomerase reverse transcriptase mRNA levels. We also found that, with an increase in the passage number of HPAECs, reduced PRCP altered respiration rate. These results indicated that aging dysregulates PRCP protein expression, and further studies will shed light into the complexity of PRCP-dependent signling pathway in aging.

This paper investigates the energy dissipation level of a nonlinear vehicle suspension system. The suspension system is modelled as a mass-spring-damper system, which is designed using the output frequency response function (OFRF) method. This method enables the derivation of an explicit relationship, between the system output spectrum (or Performance metric) and the system nonlinear parameters of interest. The OFRF polynomial, of the energy dissipation level, was derived in terms of the nonlinear parameters of interest. The results obtained show that the OFRF model, of the energy dissipation level, could be used to estimate the amount of heat dissipated, for a set of design parameters. This has potentials of providing some insights, in the design of vibration-based energy harvesters, as some of the wasted heat energy could be converted to electrical energy.

Twelve clinically healthy and sexually mature New Zealand White rabbits were studied. The non-contrast imaging included T1-weighted and T2-weighted spin echo and gradient echo sequences in transverse, sagittal and dorsal planes. Transverse MRI (Т2-weighted image) through L1 demonstrated only the right kidney. The transverse T2-weighted image through L2 showed both kidneys. The cranial part of the urinary bladder on T1-weighted transverse scans through L4 was flexed to the left. The T2-weighted sagittal image 30 mm to the right of the median plane showed the right kidney, the right ureter and the urinary bladder. The T2-weighted sagittal image 30 mm to the left of the median plane showed part of the left kidney, the left ureter and the urinary bladder. The T2-weighted sagittal image 45 mm to the left of the median plane presented the lateral part of the left kidney. The dorsal MRI image (Т2-weighted sequence) through the horizontal plane 30 mm ventral to the spine demonstrated the whole organs. The data will be useful in imaging anatomy and diagnostic studies of various pathologies of the excretory system in rabbits and other mammalian species.

Caffeine is the most popular and widely-consumed behaviourally-active substance in the world. This review focuses on the influence of caffeine on the cardiovascular system, with a special focus on blood platelets. For many years, caffeine was thought to have a negative effect on the cardiovascular system mainly due to increasing blood pressure. However, more recent data suggests that habitual caffeine consumption may reduce the risk of cardiovascular disease and hypertension. This could be a significant finding as cardiovascular disease is the leading cause of death worldwide. Caffeine is known to inhibit A1 adenosine receptors, through which it is believed to modulate inter alia coronary blood flow, total peripheral resistance, diuresis and heart rate. Although few publications have reported the effects of caffeine on platelet function, it seems reasonable to conclude that caffeine has antiplatelet activity. Platelet activation is known to play a crucial role in the development of arterial thrombotic diseases such as coronary heart disease and stroke, and hence there is a need to develop novel platelet inhibitors. One promising strategy to develop effective antiplatelet therapy involves targeting multiple pathways of platelet activation. Therefore, this review also examines the concept of dual antiplatelet therapy and the potential benefits associated with the therapeutic use of combined P2Y12 antagonists and adenosine receptor agonists in cardioprotection.

Soil microorganisms plays an important role on plant development and the homogenization of soil microbiome is agri-environmental harmful. It is essential that agricultural practices are carried out taking soil microbiome preservation in consideration. Agroforestry systems are one of the most environmentally friendly agrosystems and its plant diversity influence directly the soil microbiome diversity. In addition, many technologies have been developed and used by applying inoculants in agriculture. However, most of the inoculants are developed with only one or a little number of microbial species to supply a specific plant nutritional demand. In this study we tested the efficacy of the microbial consortium (MC) obtained from compost and the cyanobacteria Arthrospira platensis (AP) comparing with the application of the vermicompost tea (VT) and bokashi (BK) in arugula, lettuce, beetroot, and carrot in an agroforestry system in two seasons. Soil changes on chemical properties and β-glucosidase and arylsulfatase were measured over time. The vegetables yield was measured and for arugula and lettuce the nutritional traits and plant root system were also evaluated. Greater plant yield, nutritional values, and plant root development was observed in the CM treated plants, AP and BK had similar results. Our results show that both CM and AP have potential to become a sustainable product for agricultural production

The sustainable development of an area is highly dependable on reliable electrical energy supply. Microgrids are important in integrating distributed energy resources (DERs) using power electronic converters. However, microgrid control becomes challenging with the increasing number of distributed generators and loads. With the conventional droop control method, power contributions from DER converters cannot be accurately shared due to a mismatch of line impedances. In this paper, an augmented hierarchical control mechanism is proposed to solve the issues mentioned above. This hierarchical control mechanism consists of primary and secondary controllers. The primary stage utilized the droop controller to improve optimal power flow, mainly for the resistive network. The secondary stage is based on an improved methodology to compensate for the voltage and frequency variations during small and large signal disturbances. Moreover, the modelling and analysis for PMSG-based wind energy conversion systems are also presented. The response of the primary controller for the active and reactive power sharing is investigated. The analysis emphasizes the demonstration of optimal power-sharing under normal and abnormal conditions for the considered load. Finally, the suggested robust controller's performance is evaluated in MATLAB environment and simulation results show the proposed scheme's superiority under different operating conditions.

With the fast growth of smartphones and digital cameras, massive images are generated every day in the world. They are easily transmitted on the insecure channel-Internet. It has been a hot spot for protecting sensitive images during communication. A new cryptosystem is proposed using a six dimensional (6D) hyperchaotic system and DNA computing. The hash value is obtained with the function of SHA-512. It keeps the encrypted result closely connected with the original image. The initial values of the cryptosystem are produced with the hash value and the secret key. The pixel is divided into four parts, and a large matrix is formed. Scrambling is performed on the new image. DNA coding, modern DNA complementary rules, DNA computing, and DNA decoding are performed on the new image. Diffusion is also executed, and ciphered image is achieved finally. The experimental result reveals the outcome of the proposed algorithm. Security analysis shows that the designed algorithm has a huge secret key space, low correlation, and high sensitivity. It also signifies that the designed algorithm could protect against common attacks and is more secure than some existing methods.

In recent years, the widespread introduction of bike-sharing systems in China has had a profound impact on the daily lives of Chinese citizens and the development of the urban transport system. This article attempts to analyse the impact of this phenomenon on sustainability. The gradual improvement of related monitoring measures has facilitated the maturation of the bike-sharing industry from the initial stage of uncontrolled growth to the current stage of standardised management. By tracing the global development of bike-sharing systems with a special focus on China, this study sheds light on the platformisation of bicycles and its multiple impacts on technical, environmental, cultural, economic and social sustainability. Furthermore, the study provides a comprehensive analysis of the transformation of bicycles in China and highlights the diverse impacts of platform-based bike sharing on various facets of Chinese society. The development of different bike-sharing systems in China is a unique and crucial case to interpret the current situation of bike-sharing and imagine future scenarios. In contrast to the prevailing and uniform approach derived from the experiences of Northern European countries, the massive and widespread experimentation with different bike-sharing schemes in China reveals not only potentials and aspects of sustainability, innovation and urban and social regeneration, but also some hidden shadows similar to those in small-scale contexts such as Northern Europe. Furthermore, the study emphasises the crucial role of sustainable development principles in addressing urban challenges specific to China.

Leishmaniasis, comprising cutaneous and mucocutaneous forms, is a parasitic disease caused by Leishmania species via sandfly bites, leading to skin ulcers and mucosal damage. Traditional treatments include antiparasitic drugs such as pentavalent antimonials, amphotericin B, and miltefosine, with immunotherapy and chemotherapy being explored for enhanced efficacy and reduced relapse. Mucocutaneous leishmaniasis (ML) balances IFN-γ and IL-10, affecting severity, while cutaneous leishmaniasis (CL) shows varied IFN-γ/IL-10 ratios influencing prognosis. Cytokines and chemokines are diagnostic and prognostic markers in CL. Research into neutrophils and the NLRP3 inflammasome informs Th1/Th2 response regulation. Live attenuated LdCen-/- parasites and genetically modified vaccines show promise, with miR-21 as a potential efficacy biomarker. Understanding cytokine networks is crucial for new treatment strategies.

The article presents several innovative systems developed through student laboratory projects, including two autonomous vehicles, a four-legged walking robot, an active ankle-foot orthosis, a ball on beam balancing mechanism, a ball on plate system, and a manipulator arm—all powered by pneumatic artificial muscles (PAMs). Fluidic muscles offer significant potential in various mechatronic systems, robots, and manipulators due to their flexible, lightweight nature and compliance. Their ability to produce smooth and adaptable motions is particularly beneficial in applications requiring natural and human-like movement, such as rehabilitation and assistive devices. Although PAMs powered control systems face challenges with nonlinear phenomena, their biologically inspired design holds promise for future applications across various industries. These may include innovative applicability in industrial, automotive, and aerospace sectors, as well as use in sports, medical aids, entertainment, and animatronics. Incorporating self-made laboratory systems actuated by PAMs into control systems education offers a comprehensive learning opportunity that combines theoretical concepts with hands-on experience. This approach develops the necessary skills of future engineers, expanding their understanding of technical principles and preparing them for future challenges in engineering practice.

The process of modelling energy systems is accompanied by challenges inherently connected with mathematical modelling. However, due to modern realities in the 21st century, existing challenges are gaining in magnitude and are supplemented with new ones. Modellers are confronted with a rising complexity of energy systems and high uncertainties on different levels. In addition, interdisciplinary modelling is necessary for getting insight in mechanisms of an integrated world. At the same time models need to meet scientific standards as public acceptance becomes increasingly important. In this intricate environment model application as well as result communication and interpretation is also getting more difficult. In this paper we present the open energy modelling framework (oemof) as a novel approach for energy system modelling and derive its contribution to existing challenges. Therefore, based on literature review, we outline challenges for energy system modelling as well as existing and emerging approaches. Based on a description of the philosophy and elementary structural elements of oemof, a qualitative analysis of the framework with regard to the challenges is undertaken. Inherent features of oemof such as the open source, open data, non-proprietary and collaborative modelling approach are preconditions to meet modern realities of energy modelling. Additionally, a generic basis with an object-oriented implementation allows to tackle challenges related to complexity of highly integrated future energy systems and sets the foundation to address uncertainty in the future. Experiences from the collaborative modelling approach can enrich interdisciplinary modelling activities. Our analysis concludes that there are remaining challenges that can neither be tackled by a model nor a modelling framework. Among these are problems connected to result communication and interpretation.

Food composition data in the EMR countries are often lacking, obsolete or unreliable. The study aims to provide reliable and up-to-date nutrient data of selected food products consumed in Oman in order to evaluate consistency of nutrition labelling and update the current used food composition database. Contents of total fat, fatty acids, carbohydrates, total nitrogen, sugars and sodium were chemically analyzed in 221 foods and beverages purchased in 2022 and label and corresponding laboratory values were compared using the tolerance levels of the European Union. Food items were classified according their nutritional composition, the extent of processing and coded according FoodEx2 system. Results indicate that the nutrient values stated on a label align with the values obtained in the laboratory with the exception of a 6.3% discrepancy in TFAs content, where the reported values are higher than the appropriate reference values. The category with the highest frequency (71.5%) consisted of ultra-processed foods. In terms of inconsistencies in the nutrition claims, 5.1% of food products with claims did not comply with the statement “sugar free” or “low salt”. These findings underscore the significance of vigilant monitoring of nutritional labels as a means to implement measures that promote the well-being of consumers.

Sustainable soil quality management is one of the most important approaches in ensuring high agricultural productivity and safe quality produce. With the current rising application of inorganic fertilizers and chemical pesticides, degradation of agricultural soils is becoming a global phenomenon, attracting attention of many researchers. The present study hypothesized that with the increasing chemical pesticide and fertilizer application under various cropping systems, there is potential for increased toxicity of heavy metals residues above background concentrations and their associated complexes and intermedia transfer between environmental compartments in large-scale farms in Kenya. Using standard methods, 204 soil samples collected from five distinct cropping systems were analyzed for PH, Electrical Conductivity, exchangeable base cations (Na+, Mg2+, K+, Ca2+), Cu, Pb, Zn, Cd, Fe, Cr, Total Organic Carbon and Total N and data analyzed using Genstart, version 23.1. The concentrations of Zn, Cu, Pb and Fe were lower than the globally acceptable levels hence less soil pollution threat (NPI<1). However, Cd concentration exceeded permitted levels (NPI>1). Among the cropping systems considered, Maize and sugarcane cropping systems were the most susceptible to heavy metal pollution. In conclusion, the rank order of the pollution potential associated with heavy metal contamination in the study area is Cd>Zn>Pb>Cu>Cr>Fe.

We present a partition of quantum observables in an open quantum system which is inherited from the division of the underlying Hilbert space or configuration space. It is shown that this partition leads to the definition of an inhomogeneous continuity equation for generic, non-local observables. This formalism is employed to describe the local evolution of the von Neumann entropy of a system of independent quantum particles out of equilibrium. Crucially, we find that all local fluctuations in the entropy are governed by an entropy current operator, implying that the production of entanglement entropy is not measured by this partitioned entropy. For systems linearly perturbed from equilibrium, it is shown that this entropy current is equivalent to a heat current, provided that the system-reservoir coupling is partitioned symmetrically. Finally, we show that any other partition of the coupling leads directly to a divergence of the von Neumann entropy. Thus, we conclude that Hilbert-space partitioning is the only partition of the von Neumann entropy which is consistent with the Laws of Thermodynamics.

This study presents an advanced Simulated Shearer Machine Cutting Experiment System enhanced with digital twin technology. Central to this system is a simulated shearer drum, designed based on similarity theory to mirror the operational dynamics of actual mining cutters accurately. The setup incorporates a modified machining center equipped with sophisticated sensors that monitor various parameters such as cutting states, forces, torque, vibration, temperature and sound. These sensors are crucial for precisely simulating the shearer cutting actions. The integration of digital twin technology is pivotal, featuring a real-time data management layer, a dynamic simulation mechanism model layer, and an application service layer that facilitates virtual experiments and algorithm refinement. This multifaceted approach allows for in-depth analysis of simulated coal cutting, utilizing sensor data to comprehensively evaluate the shearer's performance. The study also includes tests on simulated coal samples. The system effectively conducts experiments and captures cutting condition signals via the sensors. Through time-domain analysis of these signals, gathered while cutting materials of varying strengths, it is determined that the cutting force signal characteristics are particularly distinct. By isolating the cutting force signal as a key feature, the system can effectively distinguish between different cutting modes. This capability provides a robust experimental basis for coal-rock identification research, offering significant insights into the nuances of shearer operation.

The article focusses on detecting the unbalance of a mechanical component in the electric drive system of a two-mass servomechanism with a PMSM motor, which is connected to the load via a long, flexible shaft. In the example analysed, the degree of unbalance was determined using the reference current signal from the speed controller of the FOC control system. The authors pre-sented a two-mass model with an unbalanced mechanical system. The STFT transform was used to analyse the symptoms of unbalance and an artificial neural network MLP was used for system state inference. The effectiveness of the presented analysis, based on the reference current signal from the sensor embedded in the control system, was experimentally confirmed.

Hail causes damage to property, including roofs, automobiles, and crops, with an average annual loss of $850 million. In residential structures, tile roofing systems are common in the southern U.S. due to their resistance to hail impact and long service life. Additionally, the commercial buildings and some portions of the residential structures have low-sloped or flat roof systems. Commercial low-slope roof systems are equally prone to hail-strike damages as steep residential roof systems. The objectives of this paper are to present a literature review, inspection protocol, and case studies on a comparative assessment of hail threshold for built-up roof (BUR) and tile roof (TR) systems. The published papers determining the hail impact assessment of different roofing systems from 1969 through 2023 were studied and analyzed. This study develops a comparative hail damage assessment table between BUR and TR systems, and the hail threshold for various built-up roof composition systems. In addition, the different failure modes and their causes, the characteristics of hail impacts, and the variables influencing the impact resistance of these roofing systems were examined using field studies. To better understand the effects, it is recommended that an intelligent model be developed to predict the hail resistance threshold of various configurations of BUR and TR systems with critical variables.

Bisphenol A production plant possesses considerable potential risks in the top of the methanol separation column, as pressurized acetone, methanol and water are processed at an elevated temperature, especially in the event of an abnormal pressure increase due to sudden power outage. This study assesses the potential risks in the methanol separation column through hazard and operability assessment and evaluates the damages in the case of fire and explosion accident scenarios. The study chooses three leakage scenarios: a 5-mm puncture on the methanol separation column, a 50-mm diameter fracture of a discharge pipe and a catastrophic rupture, and simulates using Phast (Ver. 6.531) the concentration distribution of scattered methanol, thermal radiation distribution of fires and overpressure distribution of vapor cloud explosions. Implementation of safety instrumented system equipped with two-out-of-three voting as a safety measure can detect overpressure at the top of the column and shut down the main control valve and the emergency shutoff valve simultaneously, all at the same time. By applying safety integrity level of three, the maximal release volume of the safety relief valve can be reduced and therefore, the design capacity of the flare stack can also be reduced. Such integration will lead to improved safety at a reduced cost.

Objectives: To assess the concordance of the preoperative application of the #Enzian classification (#ENZIANi) with the postoperative result (#ENZIANs) using surgical findings as the reference standard. Methods: This retrospective study included 282 consecutive patients with deep endometriosis undergoing surgical treatment. Preoperative assessment with transvaginal sonography and magnetic resonance imaging was compared with postoperative assessment. Concordance and diagnostic test evaluation were calculated. Results: The highest concordance was observed in the F (abdominal wall endometriosis) with k Cohen of 0.837, following the values for pelvic locations, with 0.795 for T left, 0.791 for T right, 0.776 for F (adenomyosis), 0.766 for C (rectum), and 0.75 and 0.72 for O right k and O left, respectively. The highest sensitivity was demonstrated for the P compartment *(98%), T compartment (both sides 97%), and A, B, C (94%- 96%), corresponding with deep endometriosis. Conclusions: Preoperative assessment using TVS/TAS+MRI with the ENZIANi score correlates well with the ENZIANs postoperative score and demonstrates good concordance in the detection and localization of deep endometriosis, thereby minimizing false negative results and ensuring accurate preoperative staging. The ENZIAN classification is well-suited to surgeons' needs and benefits from continuous development. Future improvements, such as adding the expanded C module, may be considered in the next edition.

This article investigates and studies the current situation of implementing the credit system in ordinary high schools in Zhejiang Province, and puts forward relevant suggestions. Firstly, analyze the background and objectives of implementing the credit system in ordinary high schools in Zhejiang Province. Then, through a questionnaire survey method, a sample survey was conducted on the current status of the implementation of the credit system in ordinary high schools in Zhejiang Province. The results showed that there are some problems in the management of the credit system in schools, such as unreasonable credit course settings, inaccurate recording and statistics of credits, etc. Further analysis reveals that students and parents have biases and utilitarian motives in their understanding and evaluation of the credit system.

In this article the problem of detection and isolation (FDI) in wheeled mobile robot is 1 treated. first of all we consider a master-slave robot system in which the master robot has to follow a 2 desired trajectory given by a well-determined control law, then the slave is controlled by the master. 3 However in real environment there is no ideality because faults can appear in the master or slave 4 or even the both and affect the system and may lead to unacceptable results so to ensure a well 5 functioning an extended kalman filter is tuned to diagnosis eventual faults and to generate residuals 6 in order to detect and isolate them. The performance of an estimator based on an EKF is simulated 7 based MATLAB Simulink simulations.

The increasing demand for energy, driven by technological advances, population growth, and economic expansion, has intensified the focus on efficient energy management. Tri-generation systems, such as Combined Cooling, Heating, and Power (CCHP) systems, are of particular interest due to their efficiency and sustainability. Integrating renewable energy sources like solar power with traditional fossil fuels further optimizes CCHP systems. This study presents a novel method for enhancing CCHP system efficiency by identifying optimal design parameters and assisting decision-makers in selecting the best geometric configurations. A mathematical programming model using the Harris Hawks optimizer was developed to maximize net power and exergy efficiency while minimizing CO2 emissions in a solar-assisted CCHP system. The optimization resulted in 100 Pareto optimal solutions, offering various choices for performance improvement. This method achieved higher net power output, satisfactory exergy efficiency, and lower CO2 emissions compared to similar studies. The study shows that maximum net power and exergy efficiency, with reduced CO2 emissions, can be achieved with a system having a low compression ratio and low combustion chamber inlet temperature. The proposed approach surpassed the response surface method, achieving at least a 4.2% reduction in CO2 emissions and improved exergy values.

Time evolution of the quantum correlation of the hybrid qubit-qutrit system in multilocal and global dephasing under the effect of a classical noises environment is presented. We considered the qutrit in z direction and the qubit x direction. In addition, our system presents two configurations: the different and the common environment and our discussion involves three types of noises: A Static Noise (SN), Random Telegraph Noise (RTN) and Ornstein- Uhlenbeck noise (OU). By using the density matrix approach to determine the evolution of the state of the system, the found that the static noise is fatal for the quantum entanglement, the quantum correlation and for RTN and OU noises the quantum entanglement, the quantum correlation decreases monotonically with a sufficiently long time. Therefore, we can say that, entanglement and the quantum correlation are more robust to the noise precisely those Random telegraph noise and Ornstein-Uhlenbeck noise.

Building a new-type power system is the key core of the green and low-carbon development of energy, the restructuring of the power system will inevitably have a linked impact on the coordinated development of the economy-energy-environment (3E) system. This paper constructs a "Dual-Triangle" theoretical framework consisting of the “Energy Trilemma” and the "Sustainable Development Triangle", by combining historical statistical analysis with scenario predictive analysis, analyzes the historical characteristics of the "Dual Triangle" and future development trends under different scenarios, and studies the coupled interaction relationship of the "Dual-Triangle" to explore the optimal transformation path of the power system that is most beneficial to the coordinated development of the 3E system. The conclusions are as follows: (1) The energy system gradually inclined towards economy and cleanliness from 2010 to 2022, the level of coordinated development of the 3E system fluctuated upwards, and the operation status of the "Dual Triangle" was stable. (2) Positive coupling relationship between "Dual Triangle" of scenario S1 is close, which can be regarded as the preferred path for the reconstruction of the power system. Due to phased development has clear development priorities, scenario S2 will artificially break the balance of the “Energy Trilemma”, which is a development model that dynamically mitigates the challenges of the “Energy Trilemma”.(3)The contradiction between economy and cleanliness is continuously weakened, and security has become the core contradiction of the "Impossible Triangle". The combination of renewable energy and energy storage is the key to resolving this contradiction. This paper conducts an integrated and innovative application of the "Dual-Triangle" theory, and the results provide a case basis for how to achieve the coupled interaction of the "Dual-Triangle", promote high-quality energy development, and support the sustainable development of the economy and society.

The inherent capabilities of the K-means++ algorithm to approximate system dynamics within complex systems are subjected by constructing a network structure that captures interconnections among identified components, extending its original purpose as a data clustering method and transforming it into a tool for systems analysis. Leveraging advanced measurement technologies and sophisticated data collection systems, the K-means++ algorithm unveils hidden relationships among components and identifies critical elements. This study explored the algorithm's potential, facilitating the identification of critical components to enhance system operation, control, optimization, and decision-making and examining the practicality and resiliency of the method in real-world application with noisy and limited data. A case study conducted on power systems (IEEE 39-bus and IEEE 300-bus systems) exemplifies K-means++'s capacity to accurately identify critical components and approximate system dynamics, supported by performance metrics affirming its effectiveness and robustness in system analysis through measurements of bus similarity within clusters based on standard deviation and comparison of net tie-line flows in equivalent networks with the original network across scenarios. Performance metrics, including the Silhouette Score, Davies-Bouldin Index, and Variation of Information (VI) score, further validated K-means++'s performance, yielding reliable and consistent results.

Nanoparticles have been used as a novel drug delivery system. Drug-incorporated nanoparticles for local delivery might optimize the efficacy and minimize the side effects of drugs. The efficacy and safety of intratracheal administration of prostacyclin analog (beraprost)-incorporated nanoparticles and imatinib, a PDGF-receptor tyrosine kinase inhibitor, -incorporated nanoparticles in Sugen-hypoxia-normoxia or monocrotaline rat models of PAH and in human PAH-pulmonary arterial smooth muscle cells have been reported. The use of inhaled drug-incorporated nanoparticles might be a novel approach for treatment of PAH.

This research investigates the energy consumption of buildings managed by traditional Building Management Systems (BMS) and proposes the integration of Internet of Things (IoT) technology to enhance energy efficiency. Conventional BMS often suffer from significant energy wastage and safety hazards due to sensor failures or malfunctions. These issues arise when building systems continue to operate under unknown conditions while the BMS is offline, leading to increased energy consumption and operational risks. The study demonstrates that integrating IoT systems with existing BMS can substantially improve energy efficiency in smart buildings. The research involved designing a system architecture prototype, performing MATLAB simulations, and real-life case study which revealed that IoT devices are effective in reducing energy waste, particularly in Heating, Ventilation, and Air Conditioning (HVAC) systems and lighting. Additionally, an auxiliary bypass system was incorporated in parallel with the IoT system to enhance reliability in the event of IoT system failures. Preliminary findings indicate that the integration of IoT systems with traditional BMS significantly boosts energy efficiency and safety in smart buildings. This integration offers a promising solution for advancing energy management practices and policies, thereby improving both operational performance and sustainability in building management.

This work investigates the stability conditions for linear systems with time-varying delays via augmented Lyapunov-Krasovskii functional(LKF). Two types of augmented LKFs with cross terms in integral are suggested to improve the stability conditions for interval time-varying linear systems. Through this work, the compositions of the LKFs are considered to enhance the feasible region of stability criterion for linear systems. Mathematical tools such as Wirtinger-based integral inequality(WBII), zero equalities, reciprocally convex approach, and Finsler’s lemma are utilized to solve the problem of stability criteria. Two sufficient conditions are derived to guarantee the asymptotic stability of the systems using linear matrix inequality(LMI). First, asymptotic stability criteria are induced by constructing the new augmented LKFs in Theorem 1. And then simplified LKFs in Corollary 1 are proposed to show the effectiveness of Theorem 1. Second, asymmetric LKFs are shown to reduce the conservatism and the number of decision variables in Theorem 2. Finally, the advantages of the proposed criteria are verified by comparing maximum delay bounds in two examples. Two numerical examples show that the proposed Theorem 1 and 2 obtained less conservative results than existing outcomes. Also, Example 2 shows that the asymmetric LKF methods of Theorem 2 can provide larger delay bounds and fewer decision variables than Theorem 1’s in some specific systems.

Due to the vast conformational space proteins can adopt, accurate and efficient prediction of protein structure remains still a challenging task, coupled with the intricacies of interatomic interactions and the limitations of current computational models in effectively navigating this complex molecular landscape. Additionally, the lack of comprehensive experimental data for all protein structures further exacerbates the difficulty in reliable machine learning-based prediction of the three-dimensional conformations of the proteios building block of life. Geometrically, Cartesian coordinate system (CCS, X, Y and Z) and spherical coordinate system (SCS, ρ, θ and φ) are two interconvertible coordinate systems, and are like two sides of one coin. Since the beginning of Protein Data Bank (PDB) in 1971, CCS has been the default approach to specify atomic positions with X, Y and Z in PDB. In this manuscript, therefore, I present a novel method for the reversible spherical geometric conversion of protein backbone structure coordinate matrices to three independent vectors: ρ, θ and φ. This reversible conversion facilitates lossless extraction of essential structural features from protein backbone structural data, enabling the development of advanced novel algorithms for protein structure prediction in future. In short, this inter-atomic SCS approach offers a comprehensive yet efficient means of representing protein backbone geometry, leveraging spherical coordinates to capture spatial relationships in a compact and intuitive inter-atomic manner, and to provide a robust framework for reversible feature extraction for the ongoing efforts in advancing the field of protein structure prediction, the holy grail of computational structural biology.

Conditions under which compositions of component systems form a well-defined system-of-systems here are formulated at a fundamental level. Statement of what defines a well-defined composition and sufficient conditions guaranteeing such a result offers insight into exemplars that can be found in special cases such as differential equation and discrete event systems. For any given global state of a composition, two requirements can be stated informally as: 1) the system can leave this state, i.e., there is at least one trajectory defined that starts from the state, and 2) the trajectory evolves over time without getting stuck at a point in time. Considered for every global state, these conditions determine whether the resultant is a well-defined system and if so, whether it is non-deterministic or deterministic. We formulate these questions within the framework of iterative specifications for mathematical system models that are shown to be behaviorally equivalent to the Discrete Event System Specification (DEVS) formalism. This formalization supports definitions and proofs of the afore-mentioned conditions. Implications are drawn at the fundamental level of existence where the emergence of a system from an assemblage of components can be characterized. We focus on systems with feedback coupling where existence and uniqueness of solutions is problematic.

This case study originates as a design experiment for a sustainable housing system built on-site. The context is Niamey, the capital of Niger. The study takes into account the environmental issues in the construction sector and aims to find a solution capable of meeting housing, environmental, and economic needs. In the field of earthen construction, the most important developments have been achieved in manufacturing methods. In particular, the use of an additive digital manufacturing system, such as large-scale 3D-printing, allows the construction of complex shapes derived from structural and thermal studies, maintaining a high degree of automation in the construction process, reducing construction times and labor costs. This paper investigates the possibility of responding to housing and environmental needs with a settlement system made almost entirely of printed earth, maintaining the highest possible degree of automation. Starting from a study on the state of the art of 3D-printing in architecture and printable earthen compounds, the design choices of similar cases are analyzed to understand the construction techniques, potentials, and limitations of the medium. Finally, a design proposal is developed based on the definition of a fully printable functional module, which, upon aggregation, determines the characteristics of the final settlement. This implies a radical change of approach compared to previous prototyping of 3D-printed earthen buildings, as the design of the single functional module is not an exercise that finds completion in itself, but is oriented to the scale of the settlement right from the definition of its basic geometric characteristics. In other words, the settlement is no longer the result of the serial aggregation of independent basic units, but arises spontaneously from the juxtaposition of functional modules designed to interact with each other and merge into a single residential complex. The settlement is therefore the large-scale replication of the alternation between full and empty spaces that characterizes the single functional module and, even more importantly, the replication can take multiple forms. In fact, the full and empty spaces of the functional module are planned to allow multiple combinations of aggregation. This introduces a certain degree of customization into the growth dynamics of the settlement, a factor that is entirely new compared to previous proposals by repeatable modules. No less important are the environmental implications, as designing for the scale of the settlement allows the low carbon footprint typical of earth-based construction to be extended from the single building to the entire settlement.

This article deals with the denoising of signals from sensors (current and voltage) in a photovoltaic conversion chain using wavelet theory. The idea is to reduce the noise as much as possible without distorting or losing the original signal information. To see the reliability of this proposed method, a comparative study between our proposed method (WDD) and the classic low-pass filter method was presented. The contribution in this work is the improvement of the DC-DC boost converter via the use of wavelet theory, however, that’s why we set two objectives: the first is to obtain reliable control of the DC-DC boost converter via the proposed WDD and the second to increase the overall system efficiency. The experimental results show a very great improvement in the quality of the signals of the proposed method compared to the classic method of filtering by low-pass filter.

It is challenging to differentiate central nervous system (CNS) virus infections and neurological autoimmune diseases in the emergency department. Considering their different pathogenesis, we assume they differ in neuropsychiatric symptoms and laboratory results. 80 patients were included, 50 with CNS virus infections and 30 with CNS autoimmune diseases, confirmed by polymerase chain reaction (PCR) of cerebrospinal fluid (CSF). A binary logistic regression model and receiver operating characteristic (ROC) curve were conducted to examine the discrimination between the two diseases based on neuropsychiatric symptoms and laboratory results. Compared to patients with neurological autoimmune diseases, patients with CNS virus infections had higher incidence of abnormal behavior (p=0.026) and abnormal sensation/thought (p=0.029), higher total (p = 0.005), direct (p = 0.004), and indirect bilirubin (p = 0.004), increased CSF cell (p = 0.01) and CSF white cell counts (p = 0.01). ROC analysis showed that patients with disturbance of consciousness and abnormal sensation/thought were 7.79-fold and 5.07-fold more likely to be diagnosed with CNS virus infections (OR = 7.79, p = 0.008; OR = 5.07, p = 0.032). Each unit increase in blood indirect bilirubin concentration and CSF white cell counts increased risk of developing CNS virus infections by 1.25-fold and 1.01-fold (OR = 1.25, p = 0.016; OR = 1.01, p = 0.011). Our study found that patients with CNS viral infections tend to have higher blood indirect bilirubin concentration, CSF leukocyte count, frequency of disorders of consciousness, and abnormal sensation and thought, which may help differentiate them from neurological autoimmune diseases.

The inverse system identification toolbox named INVSID 1.0 for MATLAB, which is used to identify the inversion of single-input single-output systems, is developed. The complete process of developing the toolbox is demonstrated. Afterwards, a numerical example is illustrated to describe how the toolbox can be used to solve inverse identification problems. Simulation results demonstrate the effectiveness of the toolbox.

Preventing or treating infections at implantation sites where the risk of bacterial contamination is high requires the development of intelligent drug delivery systems. In this work, honeycomb structures as a potential drug carrier were obtained for the first time in a fast, one-step anodization process of titanium grade 4, lasting only 60 seconds. Fourth generation oxide nanotube layers were obtained from an aqueous solution of 0.5% AgNO3 in the voltage range from 50 to 80 at 15(1) °C. The FE-SEM method showed the length of the oxide nanotubes from 1.02(2) µm at 50 V to 230(8) nm at 80 V. The EDS analysis revealed that during anodizing, silver particles are deposited on the Ti G4 up to 2.2(5) wt.%. Electrochemical measurements by open circuit potential and anodic polarization curves methods proved the improvement of in vitro corrosion resistance as a result of Ti G4 anodizing. Assessment of the anodized Ti G4 as gentamicin sulfate carrier carried out using ATR-FTIR and UV-VIS absorption spectroscopy revealed that the obtained oxide nanotube layers can be used for application of drugs directly to the tissue around the implant. This method of application allows full use of the therapeutic dose of the antibiotic used.

This paper addresses the approach of fully automated intelligent control systems in production organizations, focusing on intelligent production systems research. The overview highlights a gap in control operations ahead of assembly as single-purpose control machines are frequently employed, which is precisely their drawback. Conversely, there are accurate multipurpose measurement centers that do not provide total quality control over the manufactured parts because of the cost and length of the measuring procedure. The study's primary objective was to create an intelligent modular control system, whose concept enables it to control various components. The purpose of the modular intelligent control system is to keep an eye on each module's quality during the pre-assembly phase. This system ensures exact control over the production process of modules by integrating sophisticated sensors, diagnostic tools, and intelligent control mechanisms. The technology makes it possible to monitor several parameters and important quality features. Integrated sensors and diagnostic technologies identify possible inaccuracies and disparities, guaranteeing prompt diagnosis of issues in specific modules. The system's intelligent control algorithms streamline the production process and guarantee synchronization between individual modules to achieve consistent quality and performance. One advantage of implementing the solution is that, on average, inspection time is reduced by 40 to 60% as compared to the prior manual inspection approach. In addition, full measurement data archiving was made available, and finally, the human element—which introduced a substantial error rate into this process—was removed. This system also helps to increase the project's overall efficiency, predictability, and safety while allowing for quick adjustments by standards and requirements.

Three Seas Initiative (3SI) is still an underresearched area and is particularly important due to historical circumstances and economic backwardness. A study was carried out to assess the impact of renewable energy and production generated by the agricultural sector on CO2 emissions in 3SI countries between 2008 and 2020. The study used panel data analysis based on the two-step system generalised method of moments (GMM) and the Dumitrescu-Hurlin panel causality test. The results show that a 1% increase in the value-added generated by agriculture increases CO2 emissions in the countries studied by 0.11%. In contrast, a 1% increase in GDP leads to a 0.29% increase in CO2 emissions. Conversely, when renewable energy consumption increases by 1%, CO2 emissions fall by 0.25% in the countries studied. One way to reduce CO2 emissions from agricultural production in the short term is to increase the share of renewables, which, incidentally, is in line with EU action.

The Digital electronics collaborative enhanced learning (DECEL) project has recently developed an international collaborative education course. Its main objective is to enhance the digital electronics skills of international students by working on a complex, multidisciplinary applied problem using a mixed digital architecture. We have developed a logic level synthesis and dedicated software layers on the Red Pitaya FPGA platform. The diversity of digital concepts to be implemented, from hardware description language (HDL) to high-level languages such as Python or Matlab, forced the students to work together and rapidly improve their skills. Their motivation was fueled by the curiosity of controlling an ultrasound probe to obtain ultrasound signatures. This particular physics, little known to the students, was an additional source of curiosity. The goal of forming an image in a liquid medium was an additional motivating factor for them. The students reported that they learned a lot from the experiment. Thus, the technical parts and pedagogical results are documented in this work for reproducibility.

Household waste is the primary source of environmental pollution due to global population growth compared to other sources of waste. This research aims to develop a smart and integrated household waste management system framework. The resulting framework not only focuses on dimensions of information technology but also links it with other integrated dimensions. This research attempts to conduct a Systematic Literature Review (SLR) using the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) protocol to produce a smart and integrated household waste management framework. The framework's design is carried out by identifying the type of household waste management process based on the Integrated Sustainable Waste Management (ISWM) framework, dimensions that support smart household waste management, and stakeholders involved. The SLR results, namely dimensions and subdimensions that support the framework of a smart and integrated household waste management system, were validated by a resource person from the Indonesian Ministry of Environment and Forestry. The smart and integrated household waste management framework that has been developed in this research contains five main dimensions: Information Technology, Operational Infrastructure, Governance, Economy, and Social-culture. This framework also addresses stakeholder engagement to support smart household waste management systems and identifies waste management processes based on the ISWM framework. This research uses the PRISMA technique to provide a framework for smart and integrated household waste management systems in the initial stage. The proposed framework has been validated and can be further developed as a smart and integrated household waste management system. This research also contributes to the involvement of various dimensions identified to deal with waste problems.

Mycoviruses are highly genetically diverse, can significantly change their fungal host’s phenotype, yet, they are generally under-described in genotypic and biological studies. We propose Botrytis cinerea as a model mycovirus system in which to develop a deeper understanding of mycovirus epidemiology including diversity, impact, and the associated cellular biology of the host and virus interaction. Over 100 mycoviruses have been described in this fungal host. B. cinerea is an ideal model fungus for mycovirology as it has highly tractable characteristics – it is easy to culture, has a worldwide distribution, infects a wide range of host plants, can be transformed and gene-edited, and has an existing depth of biological resources including annotated genomes, transcriptomes, and isolates with gene knockouts. Focus on a model system for mycoviruses will enable the research community to address deep research questions that cannot be answered in a non-systematic manner. Since B. cinerea is a major plant pathogen, new insights may have immediate utility as well as creating new knowledge that complements and extends the knowledge of mycovirus interactions in other fungi, alone or with their respective plant hosts. In this review we set out some of the critical steps required to develop B. cinerea as a model mycovirus system and how this may be used in the future.

The aim of this study is to assess of rare earth elements (REE) content in hair samples in children, living in Lovozero village, near REE mining site, and possible effects of REEs on the prevalence of nervous system diseases in the Lovozersky district (Murmansk region, Kola Peninsula). School-age children (53 persons) were recruited for analysis of REE content in their hair samples. REEs (Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu) content was estimated by inductively coupled plasma mass spectrometry (ICP-MS). Analysis of REE content in the hair of children living in the Russia, Kazakhstan and China indicates REEs intake from environment. A possible contribution of REEs to the nervous system disorders is supported by the link between REE content in the hair samples of children, living near REE mining (China) and by manifesting cognitive disorders in these children. It also turn out that highest prevalence of the nervous system diseases in children 15-17 years old manifested in the Lovozersky district then in other area in the Murmansk region. The paper discusses the possible contribution of REEs to the prevalence of episodic paroxysmal disorders (G40-G47), cerebral palsy (G80-G83), epilepsy and status epilepticus (G40-G41).

Background:Tourette syndrome (TS) is a characterized by motor and vocal tics, often accompanied by obsessive-compulsive disorder (OCD) and attention-deficit/hyperactivity disorder (ADHD). Cannabis-based medicines (CBM) present a promising avenue for future therapeutic intervention owing to their interaction with the endocannabinoid system, potentially mitigating tics and associated symptoms. Unlike conventional treatments such as antipsychotics, CBM exhibits improved tolerability and reduced side effects. While the available evidence is still limited, ongoing research underscores the potential of CBM to enhance the quality of life for patients, establishing it as a pivotal area for future therapeutic advancements in TS. Aim: In our systematic review and meta-analysis, we aim to assess the effectiveness of cannabis-based medicine (CBM) in the treatment of Tourette Syndrome. Methods: We searched in electronic databases (PubMed, Google Scholar, Science Direct, Cochrane Collaboration Database of Randomized Trials) was performed to identify cohort studies and randomized controlled trials (RCTs) up to 18th June 2024. Data extraction focused on baseline characteristics of the included studies and efficacy outcomes, including scores on the Yale Global Tic Severity Scale (YGTSS) and Premonitory Urge for Tics Scale (PUTS). We conducted the meta-analysis using Review Manager version 5.4. software. We compared the measurements before and after CBM intake using mean difference (MD) and 95% confidence interval (CI). Result: In total, 1105 articles were identified for screening, with eight studies included in the systematic review and seven in the meta-analysis. These studies involved 306 adult patients with TS treated with cannabis. YGTSS revealed a significant reduction in total scores (MD = -13.29, 95% CI [-21.67 to -4.91], P = 0.002) and PUTS revealed a significant decrease in scores (MD = -4.09, 95% CI [-7.24 to -0.93], P = 0.01). Conclusion: The research emphasizes the potential of cannabis-based medication in alleviating tics and premonitory urges associated with Tourette syndrome. While the initial results are promising, they are based on limited-scale studies. It is imperative to conduct larger, placebo-controlled trials encompassing diverse demographic groups to substantiate the effectiveness, ensure safety, and optimize dosages for long-term therapeutic advantages.

The aim of this retrospective study was to compare the clinical outcomes of treating isolated surgical neck fractures of the humerus using either the intramedullary nail or the MIROS (Minimally Invasive Reduction and Osteosynthesis System®) system. The study included 42 patients who underwent surgical treatment at the Polyclinic from January 2014 to June 2020. The results indicated that both treatments were effective; however, the MIROS system exhibited a higher complication rate. Intramedullary nailing was more reliable, thus recommended as the primary treatment method. This study underscores the importance of selecting the appropriate fixation technique to minimize complications and enhance patient outcomes.

Efficient processing of end-of-life lithium-ion batteries of electric vehicles is important and a pressing challenge for a circular economy. Regardless of whether the processing strategy is recycling, repurposing or remanufacturing, the first processing step would usually involve disassembly. As battery disassembly is a dangerous task, efforts have been made to robotise it. In this paper, a robotic disassembly platform using four industrial robots is proposed to automate the non-destructive disassembly of a plug-in hybrid electric vehicle battery pack into modules. The work was conducted as a case study to demonstrate the concept of autonomous disassembly of an electric vehicle battery pack. A two-step object localisation method based on visual information is used to overcome positional uncertainties from different sources and is validated by experiments. Also, the unscrewing system is highlighted, and its functions, such as handling untightened fasteners, loosening jammed screws, and changing the nutrunner adapters with square drives, are detailed. Furthermore, the time required for each operation is compared with that taken by human operators. Finally, the limitations of the platform are reported and future research directions are suggested.

This study aims to assess the operational capabilities of disaster and emergency management re-sources (DEMRs), which are pivotal in mitigating risks and minimizing losses amid the increas-ing frequency of disasters. The research constructs an effective operational capability evaluation system for DEMRs, encompassing four key aspects: resource planning, organizational manage-ment capability, resource support capability, and information processing capability. It focuses on identifying the factors that influence the effective operational capability of DEMRs in China and Korea, comparing and analyzing the relative importance and priority of each evaluation domain and indicator within these countries to enhance DEMRs' operational effectiveness. The method-ology involves calculating and ranking the weight of the four domains, with findings indicating that organizational management capability is most significant in China, whereas resource sup-port capability is prioritized in Korea. A comparative analysis of the local weight of indicators within each domain revealed the largest discrepancy between China and Korea in the infor-mation processing capability domain. The study concludes by calculating global weights, identi-fying fast response capability and resource allocation capability as the most impactful factors on DEMRs' operational capability, highlighting their critical role in disaster and emergency man-agement.

Finding a good MIMO system model also major issue in Wireless Communication system. It is facing with so many problem, one of the major problem is finding good system model in terms of capacity and transmitting antenna system. In this paper, we analyze the channel capacity of various MIMO system model with some constant SNR level and outage probability. We establish a novel idea for MIMO system models as consider as 2^N- MIMO system model and find-out change in channel capacity when different transmitting antennas with constant SNR and outage probability. The channel capacity ratio CC_R is presented here on the basis of 2^N- MIMO channel capacity model. Number of transmitted antenna presented in MIMO system is increases is well-known however paper shows change in capacity in simple form.

Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with various sensors are expensive and not suitable for mountainous roads due to poor conditions. To address these challenges, this study proposes a customized Unmanned Aerial Vehicles (UAV) inspection system designed for automatic crack detection. The system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method is proposed to enhance the autonomous navigation of UAV flights by generating path planning in mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the YOLOv8 algorithm is introduced to conduct autonomous crack detection from UAV imagery in the offboard module. To validate our UAV inspection system's performance, we conducted several experiments to assess its accuracy, robustness, and efficiency. The experimental results demonstrate that our UAS inspection system accurately estimates the flight trajectory along the mountainous road for autonomous navigation. Furthermore, the experimental detection results also demonstrate that the YOLOv8 algorithm achieves higher recognition accuracy compared to the YOLOv5, and YOLOv7-tiny algorithms, exceeding 3.1%,and 6.8% of mAP, respectively. In summary, the UAV inspection system proposed in this study can provide a useful tool and technological guidance for the regular inspection of mountainous roads.

With the trend of public infrastructure becoming more sustainable, large multi-purpose sports venues (LMPSVs) have become one of the world's most complex architectural projects and one of the landmark buildings of an international city. LMPSVs will lead the world in sustainable design, construction and operational innovations, and play a crucial role in raising awareness of sustainability around the world. System Thinking (ST), System Dynamics (SD) and Sustainable System Development (SSD) theories are three interrelated concepts that together form a methodological framework for solving complex system problems and driving strategies. The purpose of this study is to examine how LMPSVs can implement environmental, social, governance, and innovation (ESGI) enterprise transformation strategy (ETS) based on a sustainable systems development (SSD) framework. In view of the current global environmental challenges and social responsibility requirements, the motivation of this study is to provide an integrated transformation pathway for LMPSVs to achieve the goal of sustainable development and to contribute to the field of related disciplines. The methodology of this research adopts a combination of literature review and case study analysis, and the theoretical basis is mainly drawn from cutting-edge research in the fields of sustainable development, corporate social responsibility (CSR) and environmental management. Then, by analyzing successful transformation cases in China, we extract data and information to explore the application and effectiveness of these strategies in practice. The results of the study show that by implementing ESGI-based transformation strategy, LMPSVs can not only make significant progress in terms of environmental protection, social responsibility, and governance structure, but also drive long-term corporate growth and competitiveness through innovation. Specifically, these results are realized in terms of increased efficiency in energy use, improved waste management, community involvement and increased stakeholder satisfaction. The study concludes by emphasizing that the SSD-based ESGI Transformation Strategic framework provides a sustainable pathway for LMPSVs. In the future, the extension of this transformation strategy will help a wider range of industry sectors to achieve sustainable development goals, and has wide application potential and far-reaching social significance.

Protection suit is vital for fireman’ safety. Traditional protection suit just physically protects fireman from empyrosis, but can not locate the body injury position from the impact debris. Herein, we present a wearable impact debris positioning system for fireman protection suit alarming based on accelerometer array. The wearable piezoelectric accelerometers with distribute regularly on the suit to detect the vibration on different body parts, which is conducive to positioning injured body parts. In addition, the injured parts can be displayed on the dummy body model on the upper computer with a higher localization accuracy of 4 cm. The positioning alarm system has a rapid response time of 0.11ms attributing to smart signal processing method. This work provides a reliable and smart method for locating and assessing the body injury position from the impact debris, which is significant for fire disaster commander to rescue injured fireman in time.

AbstractBackground: In digitalization era, health care system was conditioned with the health worker’s ability to adapt media of digital technology. Digital technology media was used to strengthening and fasted the health care for patient and community. Aims: This study aimed to explore the accurate information of health professional utilization digital technology during doing the job at the health facilities in Timor-Leste. Methods: This study was used correctional study with 56 respondents was participated in the study. Was collected the data from 1 to 30 February 2023. Was used questioner of google form based for data collection. Data collection was distributed via online with google form to social media as WhatsApp’s, email and face book and twitter to completed by respondents. The inclusive Criteria are: health professional living in Timor-Leste, to able response the questioner. Was used descriptive analysis in this study. Finding: This study was indicated that majority of health professional was participated in the study as 54,2% man then women have 45,8%. About 100% health professional used hand phone to facilitate their daily activities including related with their work activities. Other side indicated in digital technology tools use are able to use Microsoft word is 78,3%, able to use Microsoft Excel is 76,7% and able to use Power Point is 81,7%. E-mail use about 89,2%, WhatsApp’s use 81,7%, About 74,2% was used zoom/webinars/Google meet application, about 72,5% sad that they were used computer in the work place until now. Around 96.4% need to improve or deepen their knowledge and skills in using ICT in the future to facilitate they are activity. Conclusion: Majority of health professional in Timor-Leste was used the computer, internet, hand phone in daily activities. Majority need to gate the training to improve their knowledge, attitude and psychomotor in the future.

The growing use of proton-exchange membrane fuel cells (PEMFC) in hybrid propulsion systems, aimed at replacing traditional internal combustion engines and reducing greenhouse gas emis-sions. Effective power distribution between the fuel cell and the energy storage system (ESS) is crucial, which has led to a growing emphasis on developing energy management systems (EMS) to efficiently implement this integration. To address this goal, this study examines the performance of a fuzzy logic rule-based strategy for a hybrid fuel cell propulsion system in a small hydro-gen-powered passenger vessel. The primary objective is to optimize fuel efficiency, with particular attention to reducing hydrogen consumption. The analysis is carried out under typical operating conditions encountered during a river trip. Comparisons between the proposed strategy with other approaches —control-based, optimization-based and deterministic rule-based— are con-ducted to verify the effectiveness of the proposed strategy. Simulation results indicated that EMS based on fuzzy logic mechanisms was the most successful in reducing fuel consumption. The superior performance of this method stems from its ability to adaptively manage power distri-bution between the fuel cell and energy storage systems.

In this work, we introduce the design a 16-channel in-pixel neural analog front-end which employs a current-based summing approach to establish a common-mode feedback loop. The primary aim of this novel structure is to enhance both the system common-mode rejection ratio (SCMRR) and the common-mode interference (CMI) range. Compared to more conventional designs, the hereby proposed front-end utilizes DC-coupled inverter-based main amplifiers, which significantly reduce the occupied on-chip area. Additionally, the current-based implementation of the CMFB loop obviates the need for voltage buffers, replacing them with simple common-gate transistors and in turn decreasing both area occupancy and power consumption. The proposed architecture is further examined from an analytical standpoint, providing a comprehensive evaluation through design equations of its performance in terms of gain, common-mode rejection and noise power. A 50μm×65μm compact layout of the pixel amplifiers that make up the recording channels of the front-end was designd in a 180 nm CMOS process. Simulations conducted in Cadence Virtuoso reveal an SCMRR of 80.5 dB and a PSRR of 72.58 dB, with a differential gain of 44 dB and a bandwidth that fully encompasses the frequency range of the bio-signals that can be theoretically captured by the neural probe. The noise integrated in the range between 1 Hz and 7.5 kHz results in an IRN of 4.04 μVrms. Power consumption is also tested, with a measured value of 3.77 μW per channel, corresponding to an overall consumpution of about 60 μW. To test its robustness with respect to PVT and mismatch variations, the front-end is evaluated by means of extensive parametric simulations and Monte Carlo simulations, revealing favorable results.

The foot-mounted inertial navigation system is an important application of pedestrian navigation as it in principle does not rely any external assistance. A real-time range decomposition constraint method is proposed in this paper to combine the information of dual foot-mounted inertial navigation systems. It is well known that low-cost inertial sensors with ZUPT (zero-velocity update) and range decomposition constraint perform better than in either single way. This paper recommends that the distance of separation between the position estimates of feet-mounted inertial navigation systems be restricted in the ellipsoidal constraint which relates to the maximum step and leg height. The performance of the proposed method is studied utilizing experimental data. The results indicate that the method can effectively correct the dual navigation systems’ position over the existing spherical constraint.

As modern technologies, particularly home assistant devices, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information in 15-minute or even 5-minute intervals, including weather forecasts, outputs from Renewable Energy Sources (RES)-based systems, appliance schedules and the current energy balance, which details any deficits or surpluses along with their quantities and the predicted prices on the Local Energy Market (LEM). The goal for these prosumers is to reduce costs while ensuring their home’s comfort levels are maintained. However, given the complexity and the rapid decision-making required in managing this information, the need for a supportive system is evident. This is particularly true given the routine nature of these decisions, highlighting the potential for a system that provides personalized recommendations to optimize energy consumption, whether that involves adjusting the load or engaging in transactions with the LEM. In this context, we propose a recommendation system powered by Large Language Models (LLM), Scikit-llm and Zero-Shot Classifiers, designed to evaluate specific scenarios and offer tailored advice for prosumers based on the available data at any given moment. Two scenarios for a prosumer of 5.9 kW are assessed using candidate labels, such as Decrease, Increase, Sell and Buy.

The use of pesticides such as rotenone can lead to toxicity, dysfunction and neuronal death in humans. The Copaifera Reticulata Ducke Oil-Resin (CPOR) is widely used by the natives of the Amazon, however, studies on its potential neuroprotective effect are lacking. This study investigated the cytoprotective effect of CPOR on total cell cultures of the ventral midbrain (VMC), hippocampus (HC) and cerebral cortex (CCC) exposed to rotenone at different concentrations. The total cell cultures of VMC, HC and CCC were exposed for 72 or 24 hours to rotenone, CPOR and rotenone + CPOR. Cell viability was measured using the MTT test and compared using one-way ANOVA. Exposure to rotenone reduced the cell viability of CMV, HC and CCC. Exposure to CPOR reduced the cell viability of CMV. The cell viability of CMV exposed simultaneously to rotenone and CPOR was higher compared to rotenone alone. The cell viability of HC exposed simultaneously to rotenone and CPOR was higher than that exposed to rotenone. CMVs are more vulnerable to rotenone toxicity and more sensitive to CPOR. Considering simultaneous exposure, CPOR revealed a possible neuroprotective effect against rotenone damage only for VMC and HC.

Drones have experienced rapid technological advancements, leading to the proliferation of small, low-cost, remotely controlled, and autonomous aerial vehicles with diverse applications, from package delivery to personal transportation. However, integrating these drones into the existing air traffic management (ATM) system poses significant challenges. The current ATM infrastructure, designed primarily for traditional manned aircraft, requires enhanced capacity, workforce, and cost-effectiveness to coordinate the large numbers of drones expected to operate at low altitudes in complex urban environments. To address this critical issue, this study aims to develop an intelligent, highly automated drone management system for integration into smart city transportation networks. The key objectives include: i) Developing a conceptual framework for an intelligent total transportation management system tailored for future smart cities, focusing on incorporating drone operations; ii) Designing an advanced air traffic management and flight control system capable of managing individual drones and drone swarms in complex urban environments; iii) Improving drone management methods by leveraging drone-following models and emerging technologies such as the Internet of Things (IoT) and the Internet of Drones (IoD); and iv) Investigating the landing processes and protocols for unmanned aerial vehicles (UAVs) to enable safe and efficient operations.

Wind energy is a kind of renewable energy with great potential for development. This study mainly investigated the application of shock waves generated by high-pressure gases (wind energy) for generating energy. In this study, we designed a new supersonic shock wave generator that can be reused without disassembling and assembling bolts and developed a supersonic shock wave monitoring system. This system monitored the velocity of the generated shock waves, which was Mach 3-5, and the output pressure was more than 100 times higher than the input pressure of the shock wave generator tank.Then, we developed a power generation system driven by supersonic shock waves based on the characteristics of the new shock wave generator, which can generate high-pressure and high-speed blast waves and can be reused. The shock wave generator can repeatedly generate high-pressure waves to drive the Tesla turbine and then the magnetic energy generator for power generation. We established a complete control process for power generation driven by shock waves by controlling the valve stem. The system can detect the tank pressure, output pressure, gas flow, rotation speed, voltage, and current during the control process for the performance analysis of power generation.

Exploring for green and affordable protection of perishable fruit like cherry tomato is of significance. Here the protective efficacy and its underpinnings of coating with oleaster gum, alone or incorporated with cuminal, on cherry tomatoes stored at ambient temperature were investigated. The composite coating of oleaster gum with 0.1% cuminal reduced decay, respiration rate, weight loss, and softening of the fruit and decelerated their decreases of total soluble solids, titratable acidity, and soluble proteins, and therefore maintained their marketability. Furthermore, it reduced the accumulation of O•− 2and H2O2 in fruit, and mitigated cell membrane lipid oxidation and permeabilization, thereby retarding their senescence. Instrumentally, it elevated the activities of superoxide dismutase, catalase, peroxidase, and ascorbate peroxidase and the levels of ascorbic acid and glutathione. This potentiation of the fruit antioxidant system makes the composite coating a promising approach to keeping the postharvest quality of perishable fruit.

Mechanical ventilation is a life-saving treatment for critically ill patients who are struggling to breathe independently due to injury or disease. Globally, large numbers of Individuals have always required mechanical ventilation per year. The Covid-19 pandemic elevated the significance of the mechanical ventilation which have played a significant role in sustaining Covid 19 infected critically ill patients who could not breathe on their own. The pandemic drew the attention of the world to the shortage of ventilators globally.This research work presents the formulation of a detailed Port Hamiltonian model of a mechanical ventilator integrated to the human respiratory system. The interconnection and coupling conditions for the various subsystem within the mechanical ventilator and the coupling between the mechanical ventilator and the human respiratory system is also presented.A structure preservation discretization is provided along side numerical simulations and results. The obtained results are found to be comparable to results presented in literature.The future work will include the design of suitable controllers for system.

Opossums, native to the Americas, are unique marsupials with over 100 species. The Common Opossum and Virginia Opossum are notable species. They are nocturnal, omnivorous, and adapt to various habitats. Their reproduction involves a short gestation and marsupial pouch development. Despite their ecological benefits like seed dispersal and insect control, their proximity to human habitats exposes them to pathogens, posing public health risks. These include protozoa, helminths, and arthropods that can spread zoonotic parasites and vector-borne diseases. The marsupial immune system, including opossums, is complex, rivaling eutherian mammals. Key immune genes were identified in the grey short-tailed opossum genome. CD1 protein and VpreB surrogate light chain were also found. Marsupials are proposed as ideal models for studying developmental immunology. A West Indian opossum (Didelphis marsupialis) was found and examined. Blood tests revealed abnormalities, including a low white blood cell count and signs of anemia. The results contribute to understanding the species’ health and adaptability in the wild.

The application of AI technology in urban planning covers multiple levels, such as data analysis, decision support, and automated planning. Urban research relies on AI technology to understand and summarize the law of urban growth and improve the analysis of the evolution trend of urban space. Planning and design use AI technology to explore the relevant factors affecting urban development and their weights and discuss the critical role of green building technology in the sustainable development of the construction industry. With the increase in global energy consumption and carbon emissions, traditional building methods can no longer meet environmental protection requirements and efficient use of resources. As a sustainable development solution, green building technology has been paid more and more attention to and adopted by people. These technologies focus not only on the energy efficiency and environmental impact of buildings but also on the resource utilization and environmental load of green buildings over their entire life cycle driven by machine learning. This paper details the basic principles and applications of green building technologies, including AI-driven reduction of negative environmental impacts, improvement of occupant health, efficient use of resources, and optimization of indoor environmental quality. This paper focuses on the critical role of the LEED assessment system developed by the U.S. Green Building Council in advancing green building practices. In addition, the paper analyzes vital points such as water use in green building design, machine learning-driven wind environment optimization, solar technology application, and practical application cases of these technologies on a global scale.

To assess the community health workers’ (CHWs) knowledge, attitude, and practices (KAP) in type-2 diabetes (T2D) and hypertension (HTN) management. We thus also sought to assess the potential health system barriers for incorporating CHWs in T2D and HTN management. Mixed-method study comprised (1) a survey among 153 active CHWs to assess their KAP of T2D and HTN, and (2) semi-structured interviews with key informants to understand challenges of the health system and proposed solutions in incorporating CHWs in T2D and HTN management. The majority of CHWs correctly answered general knowledge questions on NCDs, risk factors, and prevention; however, only one-quarter correctly answered questions on smoking, family history, or tobacco use as risk factors. Most respondents appeared to have positive attitudes and have been practicing some activities related to T2D and HTN. Both financial and non-financial resource constraints were cited as challenges of the health system; therefore, re-structuring the role definition of CHWs and task of responsibilities, and assessment the need and workload have been proposed as ways forwards to effectively incorporate CHWs in T2D and HTN care. CHWs in Cambodia have shown their potential in T2D and HTN management; however, a well-designed strategy including technical training, clearly defined roles and responsibilities, and strong supporting structure are important to maximize their potential in the health system.

This paper explores Ghana's two-decade journey in developing a digital land information system (LIS), exemplified by Ghana Enterprise Land Information System (GELIS) and Enterprise Land Information System (ELIS). Despite advancements by Ghana’s Lands Commission, manual processes persist and coexist with digital ones. Our study uses a qualitative approach to assess the effectiveness of the LIS implemented in Accra. The study found that Accra’s LIS is highly aligned with clear institutional mandates, roles, and responsibilities as well as the availability of laws and policies to support analogue to digital conversion. Furthermore, a robust approach for protecting data, operating system and software underpins the system. Subsequently, the integration of a digital LIS has enhanced service delivery and accountability. Nonetheless, Accra’s LIS is still at the beginning of a comprehensive learning and development curve. To sustain the LIS requires furtherance in the implementation plan, funding, law implementation, ICT strategy, divisional integration, work processes, data quality, and communication strategy. These findings will inform the next steps of improvement for Accra's LIS and guide its nationwide scaling, contributing to discussions on technology acceptance dynamics in predominantly analogue environments.

Automatic Milking Systems (AMS) are revolutionizing the dairy industry by boosting herd effi-ciency, primarily through increased milk yield per cow and reduced labor costs. The performance of milking machines, whether traditional or automated, can be evaluated using advanced vacuum meters through dynamic testing. This process involves scrutinizing the system and milking rou-tine to identify critical points, utilizing the VaDia™ logger (BioControl AS, Rakkestad, NO). Vacuum recordings were downloaded and analyzed using the VaDia Suite™ software under the guidance of a milking specialist. Access to data from AMS across various manufacturers and herds facilitated a retrospective study aimed at describing and comparing key milk emission parameters for different AMS brands while identifying potential mastitis risk factors. Using proper statistical procedures of SPSS 29.1 (IBM Corp, USA), researchers analyzed data from 4,878 individual quarter milkings from cows in 48 dairy herds. Results indicated significant variability in milking pa-rameters associated to quarter milk yield and AMS brand. Notably, despite AMS standardizing teat preparation and stimulation, the study revealed a surprisingly high frequency of two major mastitis risk factors—bimodality and irregular vacuum fluctuations—occurring more frequently than in conventional milking systems. This study, one of the few comparing different AMS brands and their performance, highlights the crucial role of dynamic testing in evaluating AMS perfor-mance under real-world conditions.

The interest in virtual fencing, as opposed to traditional physical barriers, has increased since it makes it easier to move the boundaries of animal enclosures for agricultural and rewilding purposes. Despite this, the implementation of virtual fence systems, such as the Nofence© system, requires large financial investments. The necessity of individual collars also makes it more difficult to implement this system on a larger scale. If a herd of cattle tend to maintain cohesive social groups around dominant individuals, it might be possible to use fewer collars thereby making the system more cost efficient. This study investigates the pattern in spatial distribution in a herd of 17 Angus cows on Fanø in Denmark with GPS locations, using a Nofence© system. The aim of this paper is to determine how individuals position themselves in a herd, spatially, and identify a pattern in ranks. The method used in this study examines the distances between an individual to the rest of the herd mates using nearest neighbour and distribution plots. Contrary to the expectations, this study did not show a distinct pattern in herd distribution. While some tendencies in spatial distribution patterns were observed, only a low concordance could be found (W=0.15,p=3.02e−16). Therefore, interactions among individuals should be considered in future studies, rather than only investigating the herd as one unit. A cumulative curves of the ranks estimated over the entire periods allowed however a rough estimation of the hierarchy and allowed the identification of the highest ranked cows. Therefore it might be possible to use fewer collars if the high ranked cows are discovered, but the estimation of the hierarchy will require a period of observation overall several days as the hierarchical positions are fluctuating too much from days to days. Therefore the cumulative rank curves seems to be a valid solution.

Crops obtained by genome editing (GE) technologies represent a notable and disruptive shift from genetically modified or transgenic plants, outpacing public perception and regulatory frame-works. Multiple aspects of GE are contributing to the advancement of modern agriculture, in-fluencing the market structure by introducing specific changes within the existing genetic blue-print of an organism. Generally, the GE toolbox includes distinct technologies able to incorporate site-specific and pre-determined genomic modifications, exploiting well-established genetic modification strategies. For these reasons, GE-derived approaches are considered new plant breeding methods, known as new breeding techniques (NBTs). Particularly, CRISPR/Cas-based technologies represent a considerable improvement over prior techniques, being more precise, accurate and straightforward for targeted gene editing, in a reliable and reproducible way, with numerous applications. CRISPR/Cas system exploitation for non-editing purposes has also been widely documented, ranging from cell imaging to expression regulation and DNA assembly. Recently, CRISPR/Cas systems have been employed for target detection and could be used to ef-ficiently identify multiple organic chemicals. High-sensitivity and high-specificity detection are two main points of the CRISPR/Cas technology, without needing complex expertise or technical pipelines. This manuscript aims to provide an overview of the main advances of CRISPR/Cas-based systems into new frontiers of non-editing. The associated implications (e.g., molecular traceability) and their relative impacts are also presented and discussed.

Presynaptic Ca²⁺-influx through voltage-gated Ca²⁺ channels (VGCC) is a key signal for synaptic vesicle release. Synaptic neurexins can partially determine the strength of transmission by regulating VGCCs. However, it is unknown whether neurexins modulate Ca²⁺-influx via all VGCC subtypes similarly. Here, we performed live cell imaging of synaptic boutons from primary hippocampal neurons with a Ca²⁺-indicator. We used the expression of inactive and active Cre recombinase to compare control to conditional knockout neurons lacking either all or selected neurexin variants. We found that reduced total presynaptic Ca²⁺-transients caused by the deletion of all neurexins were primarily due to the reduced contribution of P/Q-type VGCCs. The deletion of neurexin1α alone also reduced the total presynaptic Ca²⁺-influx but increased Ca²⁺-influx via N-type VGCCs. Moreover, we tested whether the decrease of Ca²⁺-influx induced by activation of cannabinoid receptor 1 (CB1-receptor) is modulated by neurexins. Unlike earlier observations emphasizing a role for β-neurexins, we found that the decrease of presynaptic Ca²⁺-transients induced by CB1-receptor activation depended more strongly on the presence of α-neurexins in hippocampal neurons. Together, our results suggest that neurexins have unique roles in the modulation of presynaptic Ca²⁺-influx through VGCC subtypes and that different neurexin variants may affect specific VGCCs.

Large-scale deep learning models have produced significant results in underwater visual image target detection. However, deploying these models on underwater embedded devices poses a series of issues and challenges. Firstly, underwater devices have limited communication capabilities and carry relatively short supply of energy, resulting in constrained computing power for edge devices. Secondly, the large scale of deep neural networks creates a conflict in power demands with unmanned underwater equipment. Thirdly, insufficient underwater light and turbid water quality will lead to degradation problems in the images acquired by the device. In this paper, we build an offline system to complete the task of underwater scene target recognition. This paper introduces several innovations: 1) The new YOLO-TN model is developed by compressing the target recognition model based on YOLO-V5. The model use a model compression algorithm replace the YOLO-V5’s backbone network with a lightweight network, then perform parameter pruning on the model detection head, while reduce the size of the input image. The model uses a Teacher-guided Neural Architecture Search method based on YOLO-V5, named as YOLO-TN. 2)A method for constructing and processing real underwater environment data sets is designed. The datasets are all captured using underwater unmanned vehicles in real underwater environments, addressing issues present in existing underwater datasets, such as unbalanced target volume and single-image environment. Experimental results demonstrate that the YOLO-TN model significantly reduces the model size while maintaining the original recognition accuracy. Furthermore, it achieves 28.6 FPS on embedded devices, which is 12 times that of the original model.

Recent interest in intestinal hormones has risen with the idea that they modulate glucose tolerance and food intake through a variety of mechanisms, and such hormones like peptide YY (PYY), ghrelin, glucagon-like peptide (GLP)-1 and 2, and cholecystokinin (CKK) are therefore excellent therapeutic candidates for the treatment of diabetes and obesity. Furthermore, in the recent years, multiple studies suggest that the microbiota is critically important for normal host functions, while impaired host microbiota interactions contribute to the pathogenesis of numerous common metabolic disorders. In this study, we considered the nutraceutical effects of β-glucans added to pasta at the concentration of 6g\100g. Ten participants have been recruited and hematochemical analyses and intestinal hormones tests have been performed before and after 30 days of pasta intake. Stool specimens have been studied for Lactobacillus Fermentum, Lactobacillus acidophilus, Lactobacillus salivarius, Bifidobacterium longum, and Enterococcus faecium presence before and after 30 days of nutritional intervention. After 30 days of regular intake of pasta enriched by β-glucans results have been evaluated. In conclusion, pasta prepared from barley flour enriched with β-glucans at 6% exhibit promising responses on glucose metabolism, on intestinal hormones responses and on microbiota modification.

Ghana, blessed with abundant solar resources and has strategically invested in solar photovoltaic (PV) technologies to diversify its energy mix and reduce the environmental impacts of traditional energy technologies. The 50 MW solar PV installation by the Bui Power Authority exemplifies the nation's dedication to utilizing clean energy for sustainable growth. This study seeks to close the knowledge gap by providing a detailed analysis of the system's performance under different weather conditions, particularly on days with abundant sunshine and those with cloudy skies. The research consists of one-year monitoring data of the climatic and AC energy output fed into the grid. This data was used to analyze PV performance on each month's sunniest and cloudiest days. The results show that the total amount of AC energy output fed into the grid each month on the sunniest day varies between 229.3 MWh in December and 278.0 MWh in November, while the total amount of AC energy output fed into the grid each month on the cloudiest day varies between16.1 MWh in August and 192.8 MWh in February. Also, the percentage variation in energy produced between the sunniest and cloudiest days within a month ranges from 16.9% (December) to 94.1% (August). The reference and system yield analysis showed that the PV plant has a high conversion efficiency of 91.3%; however, only the sunniest and overcast days had an efficiency of 38% and 92%, respectively. The BPA plant's performance can be enhanced by using this analysis to identify erratic power generation on sunny days and schedule timely maintenance to keep the plant's performance from deteriorating. Optimizing a solar PV system's design, installation, and operation can significantly improve its AC energy output, performance ratio, and capacity factor on sunny and cloudy days.

Using the example of a generator model with dedicated inertia, a theoretical study of two coupled self-oscillators with capacitive coupling, their sequential single-frequency synchronization, chaos and two-frequency synchronization with an adiabatic change in the magnitude of the coupling between partial self-oscillators was carried out. The parameters of self-oscillators and the values of the coupling coefficient at which the specified operating modes of coupled self-oscillators exist are determined. The results of numerical studies, illustrating the conditions for excitation of single-frequency, chaotic and dual-frequency oscillations in a system of coupled self-oscillators are presented.

Switzerland, a wealthy country, has a cutting-edge healthcare system, yet per capita, it emits over one ton of CO2, ranking among the world's most polluting healthcare systems. To estimate the carbon footprint of the healthcare system of Geneva’s canton, we collected raw data on the activities of its stakeholders. Our analysis shows that when excluding medicines and medical devices hospitals are the main greenhouse gas emitter by far, accounting for 48% of the healthcare system’s emission, followed by nursing homes (20%), private practice (18%), analysis laboratories (7%), dispensing pharmacies (4%), the homecare institution (3%) and the ambulance services (

The study of the tribological behavior of the braking system in auto vehicles requires knowing the characteristics of the material in contact and, in the work process, for the friction pair, brake disc-pads. Materials structural analysis is necessary because the wear process depends on the friction pair chemical composition, and brake disk-pads, respectively on the work process parameters (pressing force, rotational speed, traffic conditions, etc.). The material of the brake discs is generally the same gray cast iron, but the brake pads can be semi-metallic (particles of steel, copper, brass, and graphite, all united with a special resin), organic materials (particles of rubber, glass, and kevlar all joined with the help of a resin), composite materials that contain different constituents and ceramic materials (rarely have small copper particles). Therefore, the purpose of the paper is to know the crystalline structure and the mechanical properties of the brake pad materials, through specific studies and analyses, with important implications on the friction and wear behavior.

Session-based recommendations which aim to predict subsequent user-item interaction based on historical user behavior during anonymous sessions can be challenging tasks. Two main challenges need to be addressed and improved: (1) How to analyze these sessions to accurately and completely capture users’ preferences, and (2) how to identify and eliminate any interference caused by noisy behavior. Existing methods have not adequately addressed these issues since they either neglected the valuable insight that can be gained from analyzing consecutive groups of items or failed to take these noisy data in sessions seriously and handled them properly, which can jointly impede recommendation systems from capturing users’ real intentions. To address these two problems, we designed a multi-order semantic denoising (MSD) model for session-based recommendations. Specifically, we grouped items of different lengths as varying multi-order semantic units to mine the user’s primary intention from multiple dimensions. Meanwhile, a novel denoising network was designed to alleviate the interference of noisy behavior and provide a more precise session representation. The result of extensive experiments on three real-world datasets demonstrated that the proposed MSD model exhibited improved performance compared with existing state-of-the-art methods in session-based recommendation.