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

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

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

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

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

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

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

The population growth, economic development and urbanization of the city of Douala in Cameroon are facing great challenges, particularly environmental and economic problems. However, there is an evolution of the road transport sector that has led to an excessive increase in the consumption of fossil fuels and over the last decades to an increase in greenhouse gases in the road transport sector. From this observation, no real policy has yet been put in place with a view to improving the energy efficiency of the transport sector. This work aim at analyzing the driving force factors of energy consumption in road transport in the city of Douala. In this study, a decomposition analysis through the Logarithmic Mean Divisa Index (LMDI) method is used during the period 2010-2019 to quantify their respective contributions in energy consumption. We have broken down road transport energy consumption into four main factors as main determinants such as: vehicle energy intensity, vehicle intensity, Gross Domestic Product (GDP) per capita and population. The results show that vehicle energy intensity, vehicle intensity, GDP per capita and population effect all have a positive impact on energy consumption and therefore main responsible for the increase in fuel consumption energy contributing respectively to 13.06%, 31.30%, 12.85% and 42.76% of the total variation of energy consumption. It is therefore necessary to implement several energy saving strategies in order to achieve a rationalization of energy consumption for a reduction of road transport energy. Political decision-makers must take such a study into account to better integrate the notion of sustainable transport.

Desertification is a global environmental and socio-economical issue threatening humanity's survival and development. The Shiyang River Basin ecosystem is vulnerable and prone to desertification. In addition, establishing the quantitative analysis of desertification driving factors and understanding their relative contribution, separately or combined, is still an unresolved problem. The present study applied geographic information system (GIS) techniques and a geographic detector model to quantify desertification spatial extent and driving mechanisms. This research utilized Fractional Vegetation Cover (FVC) to elucidate desertification spatial heterogeneity. The 30 years Coefficient of Variation (CV) of the Normalized Difference Vegetation Index (NDVI) was a dependent variable and indicator of ecosystem terrestrial conditions; Elevation, near-surface air temperature, precipitation, wind velocity, land cover change, soil salinity, road buffers, waterway buffers, and soil types were independent variables. The results showed that 89.41% of the total area is under desertification risk, where 20.99% is extremely desertified, 34.45% is severely desertified, 12.05% is moderately, and 21.92% is slightly desertified. The results from the Geodetector model showed that Power Determinant (PD) values ranged between 0.004 and 0.270. Elevation and soil types had the highest contributing factors with PD values of 0.270 and 0.227, whereas precipitation, soil salinity, the buffer of the waterway, and wind velocity played a moderate role with PD values of 0.146, 0.117, 0.107, and 0.071. Near-surface air temperature, road buffer, and land cover dynamics exhibited lower impact with PD values of 0.028, 0.013, and 0.004. In most cases, investigating the interaction between driving factors resulted in a mutual or non-linear enhancement. There was an apparent linear and mutual enhancement between elevation and soil salinity, precipitation, and soil types with values of 0.3513, 0.3232, and 0.3204, respectively. In addition, there was a mutual enhancement between soil salinity and soil types with a value of 0.2962. On the other hand, a non-linear enhancement was observed between Elevation and near-surface air temperature (0.3116), Elevation and Land cover dynamics (0.2759), soil types and near-surface air temperature (0.2687), land cover dynamics and soil types (0.234), precipitation and near-surface air temperature (0.2248), precipitation and wind velocity (0.2248), and between land cover dynamics and precipitation (0.223). This research revealed irrefutable evidence that environmental factors might be the primary drivers of ecosystem disturbance, provided the basis for the environmental footprint of desertification mechanism, and might be a cornerstone for future policy on ecological restoration sustainability in the Shiyang River Basin.

Biodiversity loss has been identified as one of the environmental impacts where humankind has been tres-passing planetary boundaries most significantly. Going beyond the pressures causing damages (calling them ‘direct drivers’) and analysing their underlying driving forces, IPBES, the Intergovernmental Sci-ence-Policy Platform for Biodiversity and Ecosystem Services, also identified a series of indirect drivers. The Montreal-Kunming Global Biodiversity Framework GBF including its suggested monitoring approach is intended to and claims to be a policy response to such analyses. However, to assess the human impact on ecosystems as a basis for planning conservation and restoration, as foreseen in the GBF, monitoring ecosystem typologies (in the GBF with reference to the UN statistical standard SEEA ES, which in turn refers to the IUCN ecosystem classification) is not enough. It needs to be complemented with data on the severity of human impacts, and on the history of places, i.e. how and when the current ecosystem status was brought about. In this conceptual paper we suggest LUI, a deliberately simple ordinal scale index for land use intensity changes, to address these two gaps. It is based on the hemeroby concept, measuring the human impact as deviation from naturalness. This makes it an information collection and presentation tool for those working in landscape planning and management. LUI’s simple and intuitively understandable structure makes it suitable for citizens’ science applications, and thus for participative monitoring when extensive statistical data gathering is not feasible, and past data are not available. Of course is can also be used as a simple too for communicating when detailed statistical data series are available. While the aggregate index is expected to communicate well, its components are more relevant to motivate and help policy makers to prioritise their decisions according to the severity of recent anthropogenic ecosystem disturabances.

This study aimed to identify the influencing factors that drive the adoption of smart construction technologies by highway construction companies. Using expert interviews and expert scoring, interview data were collected from 25 experts in the field, and the TOSE framework was proposed based on the TOE framework, identifying four dimensions and fourteen influencing factors. The results were analyzed using the Fuzzy-DEMATEL-ISM method, and the findings were then summarized according to the evaluation criteria to determine the validity of the fourteen hypotheses and the extent to which they drive the adoption of intelligent construction technologies by motorway construction companies. The findings of this paper will be of great value to decision-makers and participants in highway construction companies, as well as to other companies in the construction industry, in their decision to adopt smart construction technologies.

Object segmentation is still considered a challenging problem in autonomous driving, particularly in consideration of real world conditions. Following this line of research, this paper approaches the problem of object segmentation using LiDAR-camera fusion and semi-supervised learning implemented in a fully-convolutional neural network. Our method is tested on real-world data acquired using our custom vehicle iseAuto shuttle. The data include all-weather scenarios, featuring night and rainy weather. In this work, it is shown that LiDAR-camera fusion with only a few annotated scenarios and semi-supervised learning, it is possible to achieve robust performance on real-world data in a multi-class object segmentation problem. The performance of our algorithm is measured in terms of intersection over union, precision, recall and area-under-the-curve average precision. Our network achieves 82% IoU in vehicle detection in day fair scenarios and 64% IoU in vehicle segmentation in night rain scenarios.

To explore the spatial network structure characteristics and driving effects of carbon emission intensity in China's construction industry, the investigation combined the modified gravity model and social network analysis method to deeply analyze the spatially associated network structure characteristics and driving effects of carbon emission intensity in China's construction industry, based on the measurement of carbon emission data of China's construction industry from 2006 to 2017. The results show that the regional differences of carbon emission of construction industry are significant, and the carbon emission intensity of construction industry show a fluctuation trend. The overall network of carbon emission intensity shows an obvious “core-edge” state, the hierarchical network structure is gradually broken. Economically developed provinces generally play a leading role in the network, and play an intermediary role to guide other provinces to develop together with them. Among the network blocks, most of the blocks play the role of “brokers”. The block with the leading economic development has a strong influence on the other blocks. The increase of network density, the decrease of network hierarchy and network efficiency will reduce the construction carbon emission intensity.

There is an accelerating requirement for ocean sensing where autonomous vehicles can play an essential role in assisting engineers, researchers, and scientists with environmental monitoring and collecting oceanographic data. This paper is performed to develop a rigid sail for the autonomous sailing drone. Our study aims to numerically analyze the aerodynamic characteristics of curvy twin sail and compare it with wing sail. Because racing regulations limit the sail shape, only the two-dimensional geometry was open for an optimization. Therefore, this study’s first objective was to identify the aerodynamic performance of such curvy twin sails. Simultaneously, a secondary objective was to estimate the effect of the sail’s spacing and shapes. A viscous Navier-Stokes flow solver is used for the numerical aerodynamic analysis. The 2D aerodynamic investigation is a preliminary evaluation. The results have shown that the curvy twin sail designs have improved lift, drag, and driving force coefficient compared to the wing sails. The spacing between the port and starboard sails of curvy twin sail is an important parameter. The spacing is 0.035L, 0.07L, and 0.14L shows the lift coefficient reduction because of dramatically stall effect, while flow separation is improved with spacing is 0.21L, 0.28L, and 0.35L. Significantly, the spacing 0.28L shows the maximum high pressure at the lower area and the small low pressure area at leading edges, so the highest lift is generated.

Safety of source water streams is an urgent environmental issue, while protections in monsoon controlled subtropical regions face difficulties because of the lack of small scaled observation and analysis in small source water catchments. Basing on continuous weekly water quality data (2014-2017) in Pingqiao River Catchment, the annual average values of TN, NO₃, NH₄ and TP are 3.36, 1.64, 0.28 and 0.02 (mg/L) respectively. During dry, normal and wet seasons, the variability of parameters is over 35%, which indicates an obvious seasonality. Multiple methods are combined in order to assess the water quality and find the driving factors during dry, normal and wet seasons. This study suggests precipitation and fertilization are the mainly seasonal factors, which can make water quality better in wet season than dry season due to the dilution effect. The mechanism between seasonality and compound of nutrients can also be traced by log(TN:TP), and log(NO₃:NH₄). Among six main land use types (forest, tea plantation, cropland (paddy), urban, bare soil and water), the former three ones are influential mostly during dry and wet season. Tea plantation has the largest nutrients discharge amount per area, which is similar to cropland in dry season. By contrary, forest has the double power in reducing nitrogen release in wet and normal seasons. When transformed into paddies, croplands can lower the phosphorus concentration. Conclusions of this study can be used in southeastern China and similar regions on source water protection and agricultural plans.

Aiming at the problem of model error and tracking dependence in the process of intelligent vehicle motion planning, an intelligent vehicle model transfer trajectory planning method based on deep reinforcement learning is proposed, which obtain an effective control action sequence directly. Firstly, an abstract model of the real environment is extracted. On this basis, Deep Deterministic Policy Gradient (DDPG) and vehicle dynamic model are adopted to jointly train a reinforcement learning model, and to decide the optimal intelligent driving maneuver. Secondly, the actual scene is transferred to equivalent virtual abstract scene by transfer model, furthermore, the control action and trajectory sequences are calculated according to trained deep reinforcement learning model. Thirdly, the optimal trajectory sequence is selected according to evaluation function in the real environment. Finally, the results demonstrate that the proposed method can deal with the problem of intelligent vehicle trajectory planning for continuous input and continuous output. The model transfer method improves the model generalization performance. Compared with the traditional trajectory planning, the proposed method output continuous rotation angle control sequence, meanwhile, the lateral control error is also reduced.

Driving factors of smart city construction are exploratively studied by grounded theory method based on text sources from journal papers in SCI and CSSCI databases. Initial scale of driving factors about smart city construction is obtained on the basis of above analyses. This paper modifies measuring items of the initial scale with a small sample pretest and reliability test, then forms final items by exploratory factor analysis. According to the above scale, questionnaires are designed to obtain empirical data, and confirmatory factor analysis is used to verify further validity and reliability of the scale. The results show that driving factors of smart city construction include three main dimensions: problem-oriented factors, business- or technology-driven factors and endogenous development requirements.

The scientific fields of complexity, artificial life (ALife), and artificial intelligence (A.I.) share several commonalities: historic, conceptual, methodological, and philosophical. It was possible to develop them only because of information technology, while their origins can be traced back to cybernetics. In this perspective, I'll revise the expectations and limitations of these fields, some of which have their roots in the limits of formal systems. I will use interactions, self-organization, emergence, and balance to compare different aspects of complexity, ALife, and A.I. The paper poses more questions than answers, but hopefully it will be useful to align efforts in these fields towards overcoming --- or accepting --- their limits.

This study addresses the critical challenge of Speech Enhancement (SE) in noisy environments, where the deployment of Deep Neural Networksolutions on microcontrollers is hindered by their extensive computational demands. Focusing on this gap, our research introduces a novel SE method optimized for MCUs, employing a 2-layer GRU model that capitalizes on perceptual speech properties and innovative training methodologies. By incorporating self-reference signals and a dual strategy of compression and recovery based on the Mel scale, we develop an efficient model tailored for low-latency applications. Our GRU-2L-128 model demonstrates a significant reduction in size and computational requirements, achieving a 14.2× decrease in model size and a 409.1× reduction in operations compared to traditional DNN methods like DCCRN, without compromising performance. This advancement offers a promising solution for enhancing speech intelligibility in resource-constrained devices, marking a pivotal step in SE research and application.

The main consequence of radiation damage on a Silicon-Photomultiplier (SiPM) is a significant increase in the dark current. If the SiPM is not adequately cooled, the power dissipation causes it to heat up, which in turn affects its performance based on the temperature. In order to investigate this heating effect, experiments were carried out using a KETEK SiPM glued on an Al2O3 substrate, as well as HPK SiPMs that were either glued onto an Al2O3 substrate or a flexible PCB. The assemblies were connected either directly to a temperature-controlled chuck on a probe station or through layers of materials with well-defined thermal resistance. A DC-mode LED was used to illuminate the SiPM, and the resulting SiPM current was measured to determine the steady-state temperature. This temperature depended on the power dissipated in the multiplication region of the SiPM and the thermal resistance. This information can be utilized to adjust the SiPMs working point, taking into account the effects of self-heating. Similarly, this approach can be applied to packaged SiPMs that have an unknown thermal contact to a heat sink.

The evolution of sentence representation learning, especially with parse tree encoders, has shown remarkable progress. Traditional approaches predominantly rely on recursive encoding of tree structures, which impedes parallel processing capabilities. Additionally, these methods often overlook the significance of dependency tree arc labels. To overcome these limitations, we introduce the Syntax-Enhanced Transformer (SET), incorporating a novel dual-attention mechanism that integrates relation-focused attention alongside traditional self-attention. This design effectively encodes both dependency and spatial positional relationships within sentence dependency trees. Our approach innovatively incorporates syntactic information into the Transformer framework without compromising its inherent parallelizability. The SET demonstrates superior or comparable performance to contemporary methods across various sentence representation tasks, significantly enhancing computational efficiency.

This article is a scoping review analyzing advances in experimental studies on bacterial self-healing of concrete. From Scopus, ScienceDirect, Web of Science and Google Scholar search engine databases, 54 studies were selected for analysis, and an increasing trend of publications since 2015 was found, with India as the country with the largest research presence. Different ma-terials are used as replacement or admixture in concrete, such as fibers, particles and recycled material from construction waste. It was found that the most commonly used bacteria are Bacillus subtilis and Bacillus sphaericus, which can improve the strength of concrete by generating calcite and sealing cracks. In addition, it was found that the presence of materials such as fibers, ashes and construction recyclates can protect bacteria in an alkaline environment. The designs usually in-clude more than two experimental groups and a control. In summary, this study highlights the importance of bacterial self-healing in concrete and provides information on advances in this field of research.

This review article delves into the intricate psychology behind galivanting, absenteeism, and self-denial among college students. By conducting a meticulous analysis of relevant books, journals, and articles, key psychological factors come to light. The review examines the influence of exploration and identity formation, social motivation, coping mechanisms, lack of motivation or engagement, time management, and self-denial on these behaviors. By addressing gaps in the literature, it provides a deeper understanding of the phenomena at hand. The implications of this study extend to the development of interventions and strategies that address self-denial tendencies, promote effective time management, and foster a supportive learning environment. By shedding light on the intricate interplay of these factors, this paper opens doors for future research and targeted interventions, ultimately aiming to enhance academic engagement, reduce absenteeism, and support the overall well-being and success of college students.

Background: Nursing services provided by nurses are based on knowledge and competence in the field of nursing science developed in accordance with Client needs, scientific developments, and the demands of globalization. These health services include nursing services that are carried out responsibly, accountably, with quality, and safely by nurses who have obtained registration and permission to practice. Nursepreneur is innovation and courage to take risks and is ready to work hard to achieve goals with optimism. Which has important components namely: 1) quality, 2) role, 3) option and success. The post-pandemic new world, especially in the health care system, continues to grapple with the demands of costs to provide patient care and safety. By using the application of the nursing theory model. Purpose :. Assessing scientific studies on the application of evidence-based intervention in wound care using nursing theory, identifying the important role of telenursing in improving nursing services in Nerspreneurship, Method: literature study Conclusion: Telenursing in nursing focuses on providing nursing care through information technology, such as telephone, computers, and the Internet that can be used to improve treatment outcomes among older people, especially those with low literacy skills and low financial status (Esmaeilpour-BandBoni et al., 2020). The statement from the results of the study refutes that Telenursing is intended for patients who have higher education and middle and upper economic states.

Background: Indonesia is an archipelago with various cultured, will bee affects all aspects of people's lives, including beliefs related to health behavior and health care. One problem that needs serious attention is the elderly group because the elderly are a vulnerable group who have a high risk of experiencing health problems, such as anemia with hypertension, so they need good care management. The research objective was to determine self-management intervention for hypertensive elderly. Methods: This research is a literature review with five databases (Scopus, CINAHL, ScienceDirect, PubMed, Proquest), the study design used is an experimental, descriptive, cross-sectional, and review systematic, in March 2021. Guidelines for using JBI to measure quality and listing check out Prisma in this review guide. A feasibility study based on title, abstract, full text, and research methodology. The data analysis used narrative analysis based on the research findings. Results: Twelve articles met the predefined review inclusion criteria. The study found eight interventions for self-management in elderly hypertension, namely: 1) Knowledge of hypertension, 2) Regulation of physical activity, 3) Limitation of alcohol and cigarette consumption, 4) Sleep rest and stress management, 5) Diet and body weight management, 6) Blood pressure control, 7) Family and social support, 8) Adherence to therapy regimens. Conclusion: The conclusion of the study results obtained eight elderly self-management interventions, this done properly will affect compliance in optimizing the health of the elderly.

Background: Since the beginning of the 20th century, the importance of creating healthy work environments and promoting the health of workers in the healthcare sector to create Healthy and Resilient Organizations has been emphasized. In this context, self-esteem is an essential construct which influences health and healthy life styles, and therefore, the general wellbeing of nurses. The objective of this study was to analyze the mediating role of reasons for exercising in the effect that self-esteem has on uncontrolled eating by nursing professionals. Methods: The sample was made up of 1094 nurses who were administered the Rosenberg General Self-esteem Scale, the Goal Content for Exercise Questionnaire and the Three-Factor Eating Questionnaire-R18. Results: Bivariate correlation analysis and multiple mediation analysis showed that self-esteem has direct and indirect effects on uncontrolled eating. More so, self-esteem predicts doing physical exercise to improve one’s image, recognition and social affiliation, although the effects on uncontrolled eating were only significant through image. Conclusions: The results have important practical implications in the framework of Positive Occupational Health Psychology (POHP) as it emphasizes self-esteem, physical exercise and eating as essential aspects of the health and wellbeing of employees in the healthcare sector, highlighting the importance of creating organizations committed to promoting the psychosocial health of their workers.

We develop an optimized force-field for poly(3-hexylthiophene) (P3HT) and demonstrate its utility for predicting thermodynamic self-assembly. In particular, we consider short oligomer chains, model electrostatics and solvent implicitly, and coarsely model solvent evaporation. We quantify the performance of our model to determine what the optimal system sizes are for exploring self-assembly at combinations of state variables. We perform molecular dynamics simulations to predict the self-assembly of P3HT at ~350 combinations of temperature and solvent quality. Our structural calculations predict that the highest degrees of order are obtained with good solvents just below the melting temperature. We find our model produces the most accurate structural predictions to date, as measured by agreement with grazing incident X-ray scattering experiments.

Partial differential representation of self-similarity feature has been derived from notion of the homogenous function in general sense. This representation allows consideration of stochastic self-similar systems. As well as the partial differential representation allows consideration of Stochastic Partial Differential Equation.

Though Cyber Physical Systems (CPS) become very popular in last the decade, dependability of CPS is still a critical issue and related survey is rare. We try to spell out the jigsaw of technologies and figure out the technical trends of dependable self-managing CPS. This survey first recalls the motivation and the similar concepts. By analyzing four generic architectures, we summarize the common characteristics and related assurance technologies, and propose a more generic environment-in-loop processing flow of CPS and a formal interaction flow between physical space and cyber space. Further, the similarity between correctness and dependability is formally analyzed and the new five research questions of dependable self-managing CPS are presented. Then we review the critical technologies and related correctness verification & validation (V&V) methods, the architectures for dependable self-managing CPS. Further, the detail dependability management and V&V technologies are surveyed, which covers the areas of running-time fault management methods and whole life cycle V&V technologies, maintenance and available tool sets. For holistic CPS development, Modeling techniques and MDE (model driven engineering) based V&V methods are analyzed in detail. Then we complete the jigsaw of technologies and figure out the missing part. Further, we propose the technical challenges and the further direction. To our best knowledge, this is the first comprehensive survey on dependable self-managing CPS development and evaluation.

Postpartum depression is a significant health issue during the postpartum period and not only affects the woman but also has important repercussions for the newborn. Group support interventions during the postpartum period have proven effective in helping women cope with depression and improving breastfeeding rates. The aim of this study was to assess the effectiveness of a midwife-led breastfeeding support group intervention on breastfeeding rates, postpartum depression and general self-efficacy. This was a multicentric cluster randomized controlled trial with control and intervention groups and was not blinded. It was conducted in Andalusia (southern Spain) from October 2021 to May 2023. A total of 382 women participated in the study. Results showed a significant difference in exclusive breastfeeding rates at 4 months postpartum between the groups (p < 0.001). Additionally, there was a lower mean score on the Edinburgh Postnatal Depression Scale in the intervention group (12.49 ± 3.6 vs 13.39 ± 4.0; p = 0.044). Similarly, higher scores of general self-efficacy were observed among breastfeeding women at 2 and 4 months postpartum (p = 0.002 and p < 0.001, respectively). In conclusion, midwife-led breastfeeding support groups enhanced self-efficacy, prolonged breastfeeding and reduced postpartum depression 4 months after giving birth.

Background and Objective: Intimate relationships hold significant importance during emerging adulthood. This study aimed to investigate the critical determinants of perceived well-being in friendship relationships during emerging adulthood.Method: Using a quantitative approach, a sample of 726 undergraduate students from the Ferdowsi University of Mashhad were selected through convenience sampling. They completed the Interpersonal Behaviors Questionnaire (IBQ), the Compassionate and self-image goals scale (CSIGS), the Basic Need Satisfaction in Relationships Scale (BNS-RS), the Interpersonal Needs Questionnaire (INQ-10), Friendship Self-Regulation Questionnaire (SRQ-F), Interpersonal Behaviors Questionnaire-Self (IBQ-self), State Level Subjective Vitality Scale (SVS-SL), the Perceived Relationship Quality Component (PRQC), and the Basic Empathy Scale (BES). Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM).Results: The results indicated that the critical determinants in predicting perceived well-being in friendship relationships were, in order, goals in friendship relationships, friendship self-regulation, perceived support of basic psychological needs by friends, satisfied basic psychological needs, frustrated psychological needs, and supporting the basic psychological needs of friends.Conclusion: The findings demonstrate that self-determination theory (SDT) provides a coherent framework for explaining perceived well-being in friendships.

The current article examined stoma self-care and health-related quality of life in patients with drainage enterostomy, describe clinical and sociodemographic variables and analyze the relations between all of them. Trained interviewers collected data using a standardized form that queried sociodemographic and clinical variables, collected from the electronic medical record, in addition to the Specific Self-Care for Ostomized Patients Questionnaire (CAESPO) and Stoma Quality of life (S-QoL) from January 2016 to January 2017. This was a multicenter, cross sectional study conducted in four hospitals of the province of Castellon (Spain) where 120 participants were studied. Significant differences were found in Self Care according to sex (p = .043); married participants show higher score (p < .01); also, significant differences according to work activity were observed (p < .01). Regarding the clinical variables, differences were observed according to the autonomy of care, the presence of complications in the stoma, the use of irrigation and the type of effluent (p < .01). We can highlight the importance of the skills related to self-care by ostomized patients has for a good level of quality of life related to health. In this learning process, the figure of the stoma therapist plays a very important role.

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

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

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

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

The connectivity level of last-generation vehicles is constantly on the rise. The combined use of Vehicle-To-Everything (V2X) connectivity and autonomous driving can provide remarkable benefits through the optimization of the route and speed trajectory. In this framework, this paper focuses on vehicle eco-driving optimization in a connected environment. The virtual test rig of a premium segment passenger car was used for generating the simulation scenarios. The benefits, in terms of energy and time savings, that the introduction of V2X communication, integrated with cloud computing, can have in a real-world scenario were assessed. The Reference Scenario is a pre-defined Real Driving Emissions (RDE) compliant route, while the simulation scenarios were generated by assuming two different penetration levels of V2X technologies. The associated energy minimization problem is formulated and solved by means of a global optimization algorithm, i.e., Dynamic Programming (DP). The optimization framework includes information coming from the surrounding environment, e.g., traffic lights state, speed limits, distance to travel, etc. The simulations show that introducing a smart infrastructure along with optimizing the vehicle speed in a real-world route can potentially reduce the required energy by 54% while shortening the travel time by 38%. Finally, a sensitivity analysis is performed on the bi-objective optimization cost function to find a set of Pareto optimal solutions, between energy and travel time minimization.

The gravel road pavement has a lower construction cost but poorer performance than the asphalt surface. It also emits dust and deforms under the impact of vehicle loads and ambient air factors. The resulting ripples and ruts are constantly deepening, increasing vehicle vibrations and fuel consumption, reducing safe driving speed and comfort. In this article, existing pavement quality evaluation indexes are analysed, and a methodology for their adaptation for roads with gravel pavement is proposed. This article reports the measured wave depth and length of the gravel pavement profile by the straightedge method of a 160 m long road section in three road exploitation stages. The measured pavement elevation was processed according to ISO 8608, and vehicle frequency response has been investigated using simulations in MATLAB/Simulink. The applied International Roughness Index (IRI) analysis showed that a speed of 30-45 km/h instead of 80 km/h provides the objective results of IRI calculation on the flexible pavement due to a decreasing velocity of vehicle's unsprung mass on a more deteriorated road pavement state. The influence of the corrugation phenomenon of gravel pavement has been explored, identifying specific driving safety and comfort cases. Finally, an increase in the Dynamic Load Coefficient (DLC) at a low speed of 30 km/h on the most deteriorated pavement and a high speed of 90 km/h on the middle-quality pavement demonstrates the demand for timely gravel pavement maintenance and the complicated prediction of a safe driving speed for drivers.

Visual-inertial integrated navigation system (VINS) has been extensively studied over the past decades to provide accurate and low-cost positioning solutions for autonomous systems. Satisfactory performance can be obtained in an ideal scenario with sufficient and static environment features. However, there are usually numerous dynamic objects in deep urban areas, and these moving objects can severely distort the feature tracking process which is fatal to the feature-based VINS. The well-known method mitigates the effects of dynamic objects is to detect the vehicles using deep neural networks and remove the features belongs to the surrounding vehicle. However, excessive exclusion of features can severely distort the geometry of feature distribution, leading to limited visual measurements. Instead of directly eliminating the features from dynamic objects, this paper proposes to adopt the visual measurement model based on the quality of feature tracking to improve the performance of VINS. Firstly, a self-tuning covariance estimation approach is proposed to model the uncertainty of each feature measurements by integrating two parts: 1) the geometry of feature distribution (GFD), 2) the quality of feature tracking. Secondly, an adaptive M-estimator is proposed to correct the measurement residual model to further mitigate the impacts of outlier measurements, such as the dynamic features. Different from the conventional M-estimator, the proposed method effectively alleviates the reliance of excessive parameterization of M-estimator. Experiments are conducted in a typical urban area of Hong Kong with numerous dynamic objects, and the results show that the proposed method could effectively mitigate the effects of dynamic objects and improved accuracy of VINS is obtained when compared with the conventional method.

In networked driving simulation, two or more human drivers participate interactively within a shared virtual environment. Thereby, typical applications of driving simulation can be extended to consider multi-driver scenarios, where a much closer approximation of reality with its unpredictability is achieved. However, the utilized network is typically prone to a considerable amount of message traffic. In addition to restricted system scalability, the resulting degradation of network performance leads to invalid simulation outcomes or unacceptable system behavior. High-level architecture (HLA) is an IEEE standard for networked simulation. Data distribution management (DDM) is one of the service groups provided by HLA standard. The aim of the DDM service is to reduce network traffic and to save effort required to process unnecessary received data. However, different approaches for current DDM implementations show major drawbacks in terms of utilization complexity, inefficiency, and yet added network overhead. This paper presents a concept of an interest manager that takes over the DDM functionality and avoids these drawbacks. Simulation data is exchanged between the participating driving simulators only when it is necessary according to the driving situations. The concept is validated by analyzing the network load of two driving maneuvers with and without the interest manager.

Background: Low-level physical activity (PA) among Chronic Low Back Pain (cLBP) is associated with various biopsychosocial factors. This research aimed to study the predictors of PA behavior among cLBP patients. Methods: In the present study 300 eligible patients with cLBP who referred to comprehensive health service centers in the Shahid Beheshti University of Medical Sciences (SBUMS) in Tehran, Iran were random selected.To diagnose the predictors of PA behavior, all the Theory of Reasoned Action (TRA) constructs were examined as risk factors to see if they influence on the probability of PA behavior occurrence and were interpreted through odds ratio (OR). SPSS version 19 was used to analyze the data. Results: Totally 280 cLBP patients with mean age of 57.07 ±13.09 years old participated in the study. This study showed that motivation to comply significant predictor the cLBP patients for subjective norm OR (%95CI): 2.095(0.116-2.792), p-value<0.001), intention was significant predictor for perform the PA behavior OR (%95CI): 1.431(0.138-1.538), p-value <0.001), behavior beliefs could predictor for attitude OR (%95CI): 1.276(0.106-1.355), p-value= 0.002). attitude, normative beliefs, subjective norm ,and evaluation outcome behavior could predictors the cLBP patients for intention to perform the PA behavior OR (%95CI): 1.188(0.032-1.312), p-value<0.001)., OR (%95CI): 1.158(0.076-2.208), p-value=0.003) ., OR (%95CI): 1.104(0.076-1.128), p-value<0.001) ., OR (%95CI): 0.814(0.301-1.440), p-value=0.007). Conclusions: This study showed that the cLBP patients who were normative beliefs and evaluation regarding PA behavior could effect on the intention to engage in greater PA than those via other constructs (attitude and subjective norm). This study showed that the cLBP patients who were normative beliefs and evaluation regarding PA behavior could effect on the intention to engage in greater PA than those via other constructs (attitude and subjective norm). This study showed that the cLBP patients who were normative beliefs and evaluation regarding PA behavior could effect on the intention to engage in greater PA than those via other constructs (attitude and subjective norm).

Background: The practice of self-medication is common worldwide and is an important component of medical self-care. However, the practice can be dangerous and is linked to various risks for the patients and the entire community. This study aimed to estimate the prevalence and factors associated with self-medication among Bangladeshi adults. Methods: A cross-sectional survey was conducted between April and June 2021 among Bangladeshi adults (aged &gt;19 years) using convenient sampling. A total of 1320 data were collected through face-to-face interviews using a standardized questionnaire. Multivariable logistic regression analysis was used to identify factors associated with self-medication practice (SMP). Results: Overall, 41% of adults in our survey reported SMP. The most common illnesses that prompted SMP were common cold/flu (66.4%), gastric problems (65%), and headache (64.4%). The most frequent reasons for SMP were to get better services (30.59%), perceive SMP without side effects (23.28%), and save time with effectiveness (14.56%). Potential risk factors included 10 years (AOR = 1.91; 95% CI: 1.04 - 3.50) and &gt;12 years of schooling (AOR = 5.03; 95% CI: 2.27 - 11.15), being a businessman (AOR = 4.64; 95% CI: 1.74 - 12.37), having ≤6 family members (AOR = 2.13; 95% CI: 1.40 - 3.24), being a member of a social group (AOR = 1.53; 95% CI: 1.10 - 2.12), a health status check after every six months (AOR = 1.52; 95% CI: 1.08 - 2.13), and current ill-health (AOR = 1.41; 95% CI: 1.06 - 1.87). Protective factors identified included ≤30 years of age (AOR = 0.40; 95% CI: 0.17 - 0.93), and practice of modern (AOR = 0.39; 95% CI: 0.22- 0.69) and herbal (AOR = 0.45; 95% CI: 0.21- 0.97) healing types. Conclusion: More than one-third of the study participants reported practicing self-medication. Increasing the community’s awareness of the extreme consequences of SMP and not just the average experience might sway individuals away from SMP, and implementing strict jurisdiction could be a way to minimize the SMP.

When examining the crucial role of civic competence in fostering sustainable societies, economies, and ecosystems, its significance for cultivating responsible and engaged citizens in our ever more interconnected global landscape must be recognized. Notably, education influences the development of civic competence throughout the learning experience at all levels of education. This research aims to explore the evolution of students’ self-assessment in terms of civic competence and its three sub-competences. The objective is to comprehend the present status of students’ self-assessments and explore their potential impact on the formation of a sustainable society. Data were collected for this study using an assessment tool elaborated in the ESF project “Development and Implementation of the Education Quality Monitoring System” for students’ generic competences. The tool, presented in a digital form using the QuestionPro platform, measures six generic competences, including civic competence. A total of 1,166 bachelor students and 354 Master’s students participated in the study, representing 22 Latvian higher education institutions. The study’s findings indicate that civic competence in the sample is considerably lower than the other generic competences measured, with a greater dispersion of results. The results also highlight specific areas within universities that require improvement during the learning process to cultivate individuals who are sustainable and socially responsible members of society.

Self-esteem, the evaluation of one's own worth or value, is a critical aspect of psychological well-being and mental health. In this paper, we propose an active inference account of self-esteem, casting it as a sociometer, or an inferential capacity to interpret one's standing within a social group. This approach allows us to explore the interaction between an individual's self-perception and the expectations of their social environment.When there's a mismatch between these perceptions and expectations, the individual needs to adjust their actions or update their self-perception to better align with their current experiences. We also consider this hypothesis in relation to recent research on affective inference, suggesting that self-esteem enables the individual to track and respond to this discrepancy through affective states such as anxiety or positive affect. By acting as an inferential sociometer, self-esteem allows individuals to navigate and adapt to their social environment, ultimately impacting their psychological well-being and mental health.

The purpose of this study is to analyze the self-regulated learning and the ability to write scientific papers of students after learning with the Project-Based Digital Science Module (DSM). This research is a mixed methods by analyzing quantitatively and qualitatively. The research instrument used to obtain data is using a product assessment sheet and a self-regulated learning questionnaire. Data analysis uses N-Gain to determine the increase in self-regulated learning scores and student scientific writing skills scores, and uses linear regression analysis to determine the effect of self-regulated learning on student scientific writing skills. The results obtained showed N-Gain score of learning independent is 0,65 and scientific writing ability is 0,94, then significant’s score from linear regression by using SPSS is 0,22 < 0,05. This result showed an increase in self-regulated learning and the ability to write scientific papers of students on each indicator after learning using the Project-Based Digital Science Module (DSM). In addition, based on the results of data analysis, it can be seen that there is an influence of self-regulated learning on the ability to write scientific papers of students after learning using the Project-Based Digital Science Module.

X-rays production through betatron radiation emission from electron bunches is a valuable resource for several research fields. The EuAPS (EuPRAXIA Advanced Photon Sources) project, within the framework of the EuPRAXIA project, aims to provide 1-10 keV photons (soft X-rays) developing a compact plasma-based system designed to exploit self-injection processes that occur in highly nonlinear laser-plasma interaction (LWFA) to drive electron betatron oscillations. Since the emitted radiation spectrum, intensity, angular divergence, and possible coherence strongly depend on the properties of the self-injected beam, accurate preliminary simulations of the process are necessary to evaluate the optimal diagnostic device specifications. Dedicated tools for these tasks are under development: electron trajectories from particle-in-cell (PIC) simulations are currently undergoing numerical analysis through the calculation of retarded fields and spectra for various plasma and laser parameter combinations. At the same time, an analytical approach is being examined to obtain an expression for the emitted spectrum, assuming linear or quadratic electron energy variation. Code structure and some trajectories analysis results are presented.

Self-selecting the intensity of aerobic training is a perspective that allows practitioners to develop autonomy (choice of pace), improve physical fitness and achieve weight loss. To compare the effect of four aerobic training protocols on cardiometabolic and psycho-affective responses in physically inactive adult men and women. The sample consisted of 11 individuals aged 56 ± 10 years and BMI 29.2 ± 2.4 kg/m2 performed four different treadmill aerobic training protocols, including: 1-individual with self-selected intensity (ISS), 2-individual with prescribed intensity (IPI) (64-76% of HR max), 3-small group with self-select. selected intensity (SGS) and 4-small group prescribed intensity (SGP) (64-76% of HR max). Heart rate (HR), lactate (LACT), perceived exertion (RPE), affective response (FS), activation scale (FAS), enjoyment (PACES) and intention to repeat the session (INT) were analyzed. Statistical analysis used ANOVA with Tukey's post hoc test (p&lt;0.05). HR, LACT and RPE analyzes did not show significant differences between protocols. The FS showed a smaller decline in the SI protocol (p&lt;0.05), and the intention to repeat the training session was greater in the SI protocol (p&lt;0.05). Conclusion: The data allow us to conclude that the SI training protocol produced superior results in affective response, pleasure and intention to repeat the session.

Background Coronavirus disease 2019 (COVID-19) almost ceased to spread across the globe, but post Covid psychological experiences are rarely documented. It is essential to understand the individual psychological needs and challenges regarding working during the covid-19 pandemic and resilience and psychological health to continuing work in the aftermath of this crisis. Objective To explore the psychological transformation of staff nurses after COVID-19 Methods A phenomenological and purposive sampling approach was employed. Registered nurses working at the largest health organization in Qatar includes 14 health facilities were invited to participate in this study. 17 nurses who were working in the facilities and were deployed during the first, second and third wave of Covid 19 participated in the interview. Data were collected through semi-structured face-to-face interviews using quarantine measures. The interview data were analyzed using inductive thematic analysis. Results The psychological transformation of participants was summarized into three themes fear zone, Learning zone and growth zone. These themes were classified into 10 sub-themes Fear and anxiety, Influence of media and news, Frequent change in policies and guidelines were the fear zone factors .Secondly Coping mechanism, teamwork, establishing compassionate care, social acceptance, personal and professional benefits helps them learn psychologically. Thirdly growth factors such as confident & resilient, Sense of purpose and Change life perception and meaning Conclusion Working during COVID-19 imposed many challenges on nurses such as workload and stress. However, the different waves of the pandemic may increase the resilience, confidence and life meaning in nurses. Their previous experiences in handling difficult situations during the pandemic may make the nurses become more confident in dealing with stressful situations and working under pressure.

The production associated with geopolymers is more environmentally friendly because it does not emit as much carbon dioxide as when OPCs are used. In addition, research has been initiated to produce a geopolymer with very good mechanical properties without the need to cure it at high temperatures. Bio-additives such as honey, cotton or molasses were found to have a good effect on the properties of the geopolymer. The purpose of this study was to develop a composition of geopolymer modified with bioaddi-tives (introduction of plant-derived materials containing sugars) in order to improve the strength para-meters of geopolymers cured at ambient temperature or to accelerate their setting process, and to identify whether it is possible to produce geopolymers with good strength parameters without the need for annealing. In this study, fly ash (FA)-based materials were made and addi-tionally modified with cane molasses and beet molasses in varying amounts. The made samples were subjected to surface morphology analysis (SEM), mechanical properties analysis - compres-sive and flexural strengths, phase composition analysis (XRD) and absorption analysis.

In response to escalating environmental concerns and the surge in eco-friendly sports activities, this study investigates the determinants of sustained intention to participate in plogging, a prac-tice that combines jogging and litter collection. The study seeks to discern the effects of autonomy, competence, and relatedness experiences, as suggested by Self-Determination Theory, on the intent to persist in plogging. Additionally, the study probes the moderating role of eco-friendly attitudes, thereby propelling the confluence of sports and environmental research. The research methodology encompassed surveying plogging participants and analyzing their responses. The validation of these relationships was executed using multi-group structural equation modeling. The results revealed that autonomy and competence did not significantly sway extrinsic motivation, but relatedness surfaced as the most influential factor. This suggests that plogging, primarily viewed as a volunteering activity, serves more as a prosocial behavior that bolsters relationships rather than a means to enhance physical competence. The study also found that the value participants derived from plogging and their intention to continue varied based on their eco-friendly attitudes. The study concludes that voluntary participation and socializing are the core values of plogging, and understanding these values can foster healthier and more sustainable behaviors. The findings offer valuable insights for academic, practical, and policy perspectives in promoting eco-friendly sports activities.

In the study, it was aimed to develop a valid and reliable scale in order to determine the self-efficacy perceptions of classroom teachers in the field of mathematics learning difficulties. In the development process of the scale, the basic resources and the scale development studies in the literature were examined. First, a pool of substances was created. Content validity was ensured by taking the opinions of 21 expert participants in the 24 question areas determined in the item pool. Content validity index was used in content validity. In this analysis, two questions were removed from the scale. Preliminary application was made with 44 participating teachers who are experts in their fields and exploratory factor analysis was started. For exploratory factor analysis, 359 people consisting of classroom teachers in Sakarya province were studied. In the explanatory factor analysis, 5 items were removed from the scale by looking at the factor load. The factor loadings for the 17 items in the scale vary between 0.508 and 0.976. Among the comparative fit indices obtained as a result of confirmatory factor analysis, RMSEA=0.067 and Cmin/DF=1.568 were calculated. The Cronbach alpha reliability coefficient of the measurements is 0.970. The data obtained were analyzed with statistical methods using SPSS 25, AMOS 24 and Microsoft Excel programs. The validity and reliability criteria of the scale developed within the scope of the study were met.

Research for novel vaccine adjuvants remains a critical need to improve the immune responses to a recombinant vaccine antigen. Emulsion adjuvants such as AS03 and MF59, particularly in the area of Influenza vaccines, have shown antigen dose sparing and allowed reduced immunizations. It was previously demonstrated that these emulsion adjuvants can be formulated using a simpler, low shear process of self-emulsification. The role of alpha tocopherol as an immunomodulator in emulsion adjuvants is evident from the success of AS03 in the on-going covid-19 pandemic. Although, it was a challenge to formulate alpha tocopherol with a low shear process to get closer to the AS03 composition, the self-emulsified version referred as self-emulsifying adjuvant systems (SE-AS) showed comparable immune responses to AS03 when co-administered with a Quadrivalent influenza virus (QIV) vaccine. In this paper, we first optimized the SE-AS with alpha tocopherol referred to as SE-AS44 to allow sterile filtration. We compared the in vitro cytokine profile with self-emulsifying adjuvant 160 (SEA160), a squalene-only self-emulsified adjuvant with composition similar to MF59. We compared SE-AS44 and SEA160 co-administered with a recombinant cytomegalovirus (CMV) pentamer antigen, which is a less immunogenic antigen, in vivo to compare the antibody and T-cell responses, in different adjuvanted groups, in C57BL/6 mice.

Self-healing is the capability of materials to repair themselves after damage has occurred, usually by interaction between molecules or chains. Physical and chemical processes are applied for the preparation of self-healing systems. There are different approaches for these systems such as heterogeneous systems, shape memory effects, hydrogen bonding or covalent-bond interaction, diffusion and flow dynamics. Self-healing mechanisms can occur in particular by heat and light exposure or by reconnection without direct effect. The applications of these systems display an increasing trend in both R&D and industry sectors. Moreover, self-healing systems and their energy storage applications are currently getting great importance. This review aims to provide general information on recent developments in self-healing materials and their energy applications in view of the critical importance of self-healing systems for lithium-ion batteries (LIBs). In the first part of the review, an introduction about self-healing mechanisms and design strategies of self-healing materials is given. Then, selected important healing materials in the literature for the anodes of LIBs are mentioned in the second part. The results and future perspectives are stated in the conclusion section.

Background: HIV self-testing (HIVST) is one of the recommended approaches for HIV testing services, particularly for helping reach populations who would not normally access facility-based HIV testing. HIVST must be tailored to different populations to ensure uptake. Objective: The main objective of this study was to develop an acceptable HIVST delivery strategy to help improve urban men’s engagement with HIV services. Methods: We invited key stakeholders for urban men’s HIV services to participate in a co-creation workshop aimed at developing HIVST delivery approaches for urban men, using eThekwini municipality as a study setting. We conducted purposive sampling to include health care users and health care providers, representing a range of views across the public sector and voluntary sector. We employed the Nominal Group Technique (NGT) method for data collection. The NGT workshop was conducted in two consecutive phases: phase one was focused on determining barriers for men’s engagement with the current/facility-based HIV testing services; phase two was aimed at determining HIVST delivery strategies. We used the results of the NGT to design a tailored HIVST strategy for urban men in eThekwini District. Results: Participants identified the following psychological factors as the most important barriers to uptake of HIV testing services by urban men: stigma, ignorance about the importance of testing and testing process as well as fear of positive test results. Key stakeholders suggested internal motivation strategies as a potentially effective approach to support HIVST delivery strategy. Guided by the NGT results, we designed a HIVST delivery strategy that is supported by a risk communication approach Conclusion: We designed an evidence-based risk communication mobile health (mHealth) strategy coupled with SARS COV-2 self-testing tailored to improve men’s uptake of HIVST. A follow-up study to evaluate the feasibility of implementing these approaches is recommended.

Our brains process information using a layered hierarchical network architecture, with abundant connections within each layer and sparse long-range connections between layers. As these long-range connections are mostly unchanged after development, each layer has to locally self-organize in response to new inputs to enable information routing between the sparse in- and output connections. Here we demonstrate that this can be achieved by a well-established model of cortical self-organization based on a well-orchestrated interplay between several plasticity processes. After this self-organization, stimuli conveyed by sparse inputs can be rapidly read out from a layer using only very few long-range connections. To achieve this information routing, the neurons that are stimulated form feed-forward projections into the unstimulated parts of the same layer and get more neurons to represent the stimulus. Hereby, the plasticity processes ensure that each neuron only receives projections from and responds to only one stimulus such that the network is partitioned into parts with different preferred stimuli. Along this line, we show that the relation between the network activity and connectivity self-organizes to a biologically plausible regime. Finally, we argue how the emerging connectivity may minimize the metabolic cost for maintaining a network structure under the above described constraints.

This proposal describes the design and development of an interoperable application that supports green open access with long-term sustainability and improved user experience of article deposit. Introduction: The lack of library resources and unfriendly repository user interface are two significant barriers that hinder green open access. Tasked to implement the open access mandate, librarians at an American research university developed a comprehensive system called Easy Deposit 2 to automate the support workflow of green open access. Implementation: Easy Deposit 2 is a web application that is able to harvest newly publications, outreach for manuscript on behalf of the library, and facilitate self-archiving to IR. It is developed and maintained by the library and integrated with the IR. Results and Discussion: The article deposit rate is about 25% with Easy Deposit 2, which increases significantly comparing to the previous period. It also serves as a local database for faculty publications with open access status. The lesson learned is that library cannot rely on a single commercial provider for publication data due to mismatched priorities. Conclusion: Recent IT developments provides new opportunities of innovation like Easy Deposit 2 in supporting open access. Academic librarians are vital in promoting "openness" in scholarly communication such as transparency and diversity in the sharing of publication data.

The study aimed to understand the experiences of adolescent smokers who participated in a smoking cessation program based on self-reflection, and to develop the theoretical basis for constructing similar programs. The participants, ten students from middle and high schools located in cities G and S, were interviewed after the program ended. Data were collected from August to November 2019 and analyzed using a phenomenological approach to understand participant experiences in depth. The analysis revealed five major themes: ‘Uniqueness of the Program,’ ‘Perception of Smoking Cessation,’ ‘Positive Reflection on Life,’ ‘Understanding Others,’ and ‘A Search for Hope and Vision in Life.’ The findings revealed that the smoking cessation program facilitated the development of self-identity and vision, which may indirectly strengthen the motivation for adolescent smokers to quit smoking. These findings suggest the need for a life-changing smoking cessation program that enhances self-concept and self-esteem. Moreover, it highlights the importance of follow-up research to ensure effectiveness, and the need to develop programs with creative content.

While some researchers have suggested that the self-employment (SE) sector is a haven during a financial Crisis, others believe that SE is not necessarily the desired outcome, but an indicator that the labor market is tightening for some groups. Few researchers have compared the SE sector before and after the occurrence of a significant financial Crisis, especially in developed countries. This paper analyzes the determinants of entry into self-employment during the 2008 Spanish Crisis. Using data from the Encuesta de Presupuesto Familiar (EPF), results show that although the rate of SE did not experience a significant change during this time the Crisis affected people differently based on gender, with being females more affected than males. Results also suggest differences between Comunidades Autonomas in how the self-employment sector behaved during the Crisis.

Due to limitation of the binding energy of a self-gravitating matter, the radius of a body is at least twice larger than the Schwarzschild radius. The total energy is adsorbed at the body surface, giving rise of a size-dependent surface tension. Since the Hawking temperature appears to be the critical one, the black holes possess zero surface tension. Microscopic neutrino stars are also introduced.

This study aimed to know the conditioning factors of the transition process to the self-care of women diagnosed with HIV/AIDS. This qualitative study was carried out from June to September 2015 with seven seropositive women, users of a specialized service in sexually transmitted diseases in the municipality of Imperatriz, Maranhão State, Brazil. For the data collection, an individual interview was used, and data analysis was performed by content analysis delineated by Hsieh and Shannon (2005). The resources that influence the self-care in the transition process of women with HIV/AIDS are represented by personal conditioning factors, such as the meaning they attribute to the living with the disease, personal attitudes and cultural beliefs, socioeconomic status, preparation and knowledge about the disease, and by conditioning factors found in the community and society. The transition theory can provide important insights about the resources present in the adaptation process of women diagnosed with HIV so that they can perform their self-care satisfactorily.

To better understand the shape memory materials and self-healing materials, a new series of liquid-crystalline shape memory polyurethane (LC-SMPU) composites, named SMPU-OOBAm, were successfully prepared by incorporating 4-octyldecyloxybenzoic acid (OOBA) into the PEG-based SMPU. The effect of OOBA on the structure, morphology and properties has been carefully investigated. The results demonstrate that SMPU-OOBAm have liquid crystalline properties, triple-shape memory properties and self-healing properties. The incorporated OOBA promotes the crystallizability of both soft and hard segments of SMPU, and the crystallization rate of the hard segment of SMPU decreases when the OOBA-content increases. Additionally, the SMPU-OOBAm forms a two-phase separated structure (SMPU phase and OOBA phase), and it shows two-step modulus changes upon heating. Therefore, the SMPU-OOBAm shows triple-shape memory behavior, and the shape recovery ratio decreases with an increase in the OOBA content. Finally, SMPU-OOBAm shows self-healing properties. The new mechanism can be ascribed to the heating-induced “bleeding” of OOBA in the liquid crystalline state and the subsequent re-crystallization upon cooling. This successful combination of liquid crystalline properties, triple-shape memory properties and self-healing properties make the SMPU-OOBAm composites with many promise applications in smart optical devices, smart electronic devices and smart sensors.

Two polyurethanes (PUs) were similarly synthesized by reacting a cycloaliphatic isocyanate with 1,4 butanediol and two polyols of different nature (polyester, polycarbonate diol) with molecular weights of 1000 Da. The PU made with polycarbonate diol polyol (YCD) was the only showing intrinsic self-healing at 20 °C. For assessing the mechanism of intrinsic self-healing of YCD, a structural characterization by molecular weights determination, infrared and X-ray photoelectronic spectroscopies, differential scanning calorimetry, X-ray diffraction, thermal gravimetric analysis and dynamic mechanical thermal analysis was carried out. It was concluded that the self-healing at 20 °C of YCD was due to non-covalent dynamic exchange interactions among the carbonate groups of the soft segments. Therefore, the chemical nature of the polyol played a key role in developing PUs with intrinsic self-healing at 20 °C.

This work is the first part of a series of papers on a comprehensive analysis of the processes of prebiotic self-organization and protophotosynthesis on the surface of semiconductor minerals and in systems of natural dispersed semiconductors. Comprehensive analysis within the framework of the "semiconductor world" concept allows integrating a variety of models on a single physical basis - from ZnS world and FeS world (based on inorganic semiconductors) to the PAH world and aromatic world (including organic semiconductors). Thus, we do not put forward a new alternative hypothesis of chemical evolution - a new "chemical world", but only integrate the evolutionary and geochemical criteria of different "chemical worlds" into a single "physical world", which gives one the opportunity to reconstruct and predict the directions of chemical evolution according to the uniform principles of physical chemistry. In the first part of this series we consider photoinduced self-organization and "photo-controlled" evolution of the early protobiological systems performing solar energy conversion on the surface of dispersed semiconductor minerals capable of (photo)catalyzing and photosensitizing prebiological processes. The latter include a transition from the elementary cycles of photocatalytic reactions on the surface of semiconductors to protophotometabolic cycles and protophotosynthesis, from photophysical processes on the surface of mineral semiconductors to photoinduced membrane potentials, from photocontrolled sorption on the surface of such minerals to the formation of photosensitive protomembranes and selection of photosynthetic structures. The evolutionary approach to the analysis of protobiological mechanisms and protobioenergetics within the framework of the "semiconductor world" concept provides a new approach to the study of the last common point of divergence of protobiological systems, where the emergence principles of different energy supply schemes (like the energy source-specific photoautotrophy and substrate-specific chemoautotrophy) merge at the physicochemical level. The proposed integrating scheme is consistent with most biological, geological, and physicochemical concepts, which ensures its complete cross-checking and internal consistency.

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

The paper presents the measurement campaign carried out on a real-world motorway stretch of Hungary with the participation of both industrial and academic partners from Austria and Hungary. The measurement included vehicle based as well as infrastructure based sensor data. The obtained results will be extremely useful for future automotive R&amp;D activities due to the available ground truth for static and dynamic content. The aim of the measurement campaign was twofold. On the one hand, road geometry was mapped with high precision in order to build Ultra High Definition (UHD) map of the test road. On the other hand, the vehicles - equipped with differential Global Navigation Satellite Systems (GNSS) for ground truth localization - carried out special test scenarios while collecting detailed data using different sensors. All test runs were recorded by both vehicles and infrastructure. As a complementary task, the available 5G network was monitored and tested. The paper also showcases application examples based on the measurement campaign data, in which the added value of having access to the ground truth labeling and the created UHD map of the motorway section becomes apparent. In order to present our work transparently, a part of the measured data have been shared openly such that interested automotive as well as academic parties may use it for their own purposes.

The study was conducted on applying machine learning and data mining methods to personalizing the treatment. This allows investigating individual patient characteristics. Personalization is built on the clustering method and associative rules. It was suggested to determine the average distance between instances for optimal performance metrics finding. The formalization of the medical data pre-processing stage for finding personalized solutions based on current standards and pharmaceutical protocols is proposed. The model of patient data is built. The paper presents the novel approach to clustering built on ensemble of cluster algorithm with better than k-means algorithm Hopkins metrics. The personalized treatment usually is based on decision tree. Such approach requires a lot of computation time and cannot be paralyzed. Therefore, it is proposed to classify persons by conditions, to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. This made it possible to create a personalized approach to treatment for each patient based on long-term monitoring. According to the results of the analysis, it becomes possible to predict the optimal conditions for a particular patient and to find the medicaments treatment according to personal characteristics.

Abstract: Changes in forest landscapes have been connected with human activity for centuries, which can be considered as one of the main driving forces of change in the global perspective. The spatial distribution of forests changes along with the geopolitical situation, demographic changes, intensification of agriculture, urbanization or changes in the land use policy. However, due to the limited availability of historical data, the driving forces of changes in forest landscapes are most often considered in relation to recent decades, without taking into account long-term analyzes. The aim of this paper is to determine the impact of natural and socio-economic factors on changes in forest landscapes within the protected area – Ślęża Landscape Park and its buffer zone in the aspect of long-term analyzes covering the period of 140 years (1883-2013). The comparison of historical and current maps, demographic data on 4 different periods as well as natural and location factors by using the ArcGIS software allowed analyzing selected driving forces of forest landscape transformations. We took into account natural factors like altitude, slope, exposure of the hillside and socio-economic drivers like population changes, distances to centers of municipalities, main roads and built-up areas.

In 2015, the Self-Action Leadership Theory—a qualitative, nomological expansion of self-leadership theory rooted in atmospheric and astronomical metaphor aimed at expanding the personal freedom of individuals, organizations, and nations by bolstering the existential growth of individuals through a series of Maslow-esque stages of holistic, personal development. This article introduces an accompanying, practitioner-based Model of Self-Action Leadership (SAL) aimed at the implicit enhancement of a holistic range of administrative processes through explicit training, mentoring, and coaching in the model’s general and universally-applicable principles and practices. The SAL model produces an original construct of personal leadership practice that builds upon the extant self-leadership academic canon, which dates back to 1983 (Manz, 1983). It also provides an analogue to four of the five core processes of Project Management by positioning a self-action leader (an individual) as the ongoing “project” at hand. The SAL Model is rooted in action research and was developed through a variety of self-oriented, action research projects in conjunction with a comprehensive, qualitative, analytical autoethnographic study of a scholar’s life experiences.

Nanometer-curved surfaces are abundant in biological systems as well as in nano-sized technologies. Properly functionalized polymer-grafted nanoparticles (PGNs) adhere to surfaces with different geometries and curvatures. This work explores some of the energetic and mechanical characteristics of the adhesion of PGNs to surfaces with positive, negative and zero curvatures using Coarse-Grained Molecular Dynamics (CGMD) simulations. Our calculated free energies of binding of the PGN to the curved and flat surfaces as a function of separation distance show that curvature of the surfaces critically impacts the adhesion strength. We find that the flat surface is the most adhesive, and the concave surface is the least adhesive surface. This somewhat counterintuitive finding suggests that while a bare nanoparticle is more likely to adhere to a positively curved surface than a flat surface, grafting polymer chains to the nanoparticle surface inverts this behavior. Moreover, we studied the rheological behavior of PGN upon separation from the flat and curved surfaces under external pulling force. The results presented herein can be exploited in drug delivery and self-assembly applications.

The goal of this work is to show that the underlying symmetries of effective field theory can be traced to the onset of self-similarity. In particular, we argue that the scale-free structure of fractal geometry lies at the heart of invariance principles in classical and Quantum Field Theory.

Background: This study aimed to examine the obesity¬ stress, weight control behavior, and self-control in overweight and obese female university students, and identify the mediating effect of self-control in the relationship between obesity stress and weight control behavior. Methods: This study employed a descriptive correlational design. A total of 109 female university students who were overweight and obese participated in the study. Data were analyzed using descriptive statistics, Pearson's correlation coefficient analysis, and Bootstrapping using Hayes' PROCESS macro for mediation. Results: Significant relationships between obesity stress and weight control behavior (r=0.25, p<0.001), obesity stress and self-control (r=-0.36, p<0.001), and weight control behavior and self-control (r=.26, p<.001) were observed. Self-control showed indirect mediating effect on the relationship between obesity stress and weight control behavior. Conclusion: Therefore, development of programs and strategies for improving self-control is necessary in order to decrease obesity among female university students.

Tungsten and its alloys are considered as a key component in high temperature environment. Self-passivating W-Si-xY alloys with different Y contents made from the powders milled for different times were prepared by mechanical alloying (MA) and spark plasma sintering (SPS). Their microstructures and oxidation resistance were investigated. It is found that the oxidation resistance of the alloys with low Y contents (0Y and 2Y) made from 20 h-milled powders is worse than that of the alloys made from 4 h-milled powders. It is because the protective WO3/SiO2 composite layer is discontinuous. While the alloys with high Y contents (4Y and 6Y) made from 4 h and 20 h-milled powders show good oxidation resistance. Even, the thickness of the oxide layer of the alloys with 4Y and 6Y made from 20 h-milled powders after oxidation at 1000 ℃ for 2 h is 84.0 μm and 70.7 μm, less than that made from 4 h-milled powders. This is due to the size and distribution of W5Si3, Y2Si2O7 aggregates and W-Y-O melt. The synergistic effects of Y contents and milling time on the microstructures and oxidation resistance of the alloys are revealed.

With the development of electricity spot markets, accurate electricity load forecasting enables power generation companies to supply the right amount of electricity, Significantly avoid power waste. As a result, time series forecasting in the field of power can bring significant benefits. Previously, the Informer model successfully introduced the Transformer into long time series forecasting(LTSF) by proposing the ProbSparse self-attention mechanism, which solved the inherent problem of high memory complexity in self-attention. Recent research has further demonstrated the potential of the self-attention for mining complex dependencies. However, the limited amount of historical data has become one of the main challenges in applying deep learning techniques to power LSTF tasks. Previous reserches often add a large number of time covariates to provide more information. In this paper, to address this issue, (i) we design a simple but effective Season-aware Block to enhance the model’s ability to mine artificial prior information in temporal covariates; (ii) we conduct experiments using the provincial power data of Zhejiang Province, China, from 2019 to 2022, and our model outperforms other models, achieving a 19 percent MSE relative improvement; (iii) we conduct ablation experiments to assess the efficacy of the Season-aware Block in extracting temporal periodic features. Furthermore, we elucidate the underlying reasons for the effectiveness of both the self-attention mechanism and the Season-aware Block through visualization experiments.

Background and Objectives: Although there has been growing interest in the association of ADHD with anxiety, depression, suicidal ideation, and self-esteem, there is a lack of consistent results and these associations remain unclear. This study investigated the differential patterns of anxiety, depression, suicidal ideation, and self-esteem with regard to the presence of ADHD. Materials and Methods: The subjects were divided into a patient group and a control group. The patient group included 49 patients diagnosed with ADHD at the department of psychiatry of one university hospital. For the control group, 3,727 students were invited to complete a questionnaire. Of the 1,717 students who responded to the questionnaire, 245 were selected for patient-controlled studies. All the subjects were instructed to complete the Beck Suicide Ideation Scale, Children's Depression Inventory, Beck’s Depression Inventory, State-Trait Anxiety Inventory, and Rosenberg Self-Esteem Scale. Results: ADHD subjects experienced more anxiety, depression, and suicidal ideation as well as lower self-esteem compared to the control group comprising participants without ADHD. Conclusions: These findings suggest that ADHD symptoms may contribute to greater suicidal ideation through experienced depression, anxiety, and low self-esteem in school-aged children. Therefore, appropriate ADHD symptom management is necessary to reduce suicidal ideation among school-aged children with ADHD symptoms.

cGAS is a cytosolic DNA sensor that activates innate immune responses through production of the second messenger 2’3’-cGAMP, which activates the adaptor STING. cGAS senses dsDNA in a length-dependent but sequence-independent manner, and it cannot discriminate self-DNA from foreign DNA. In normal physiological conditions, cellular DNA is sequestered in the nucleus by nuclear envelope and in mitochondria by mitochondrial membrane. When self-DNA leaks into the cytosol during cellular stress, or during mitosis, the cGAS can be exposed to self-DNA and be activated. Recently, many studies have investigated how cGAS keeps inactive and avoids to be aberrantly activated by self-DNA. Thus, the aim of this narrative review is to summarize the mechanisms by which cGAS avoids to sense self-DNA under normal physiological conditions.

Keywords: FTY720, self-assembled nanoparticles, intracerebral hemorrhage, neuroprotection

Inspired by nature's examples such as lotus leaves and insect wings, superhydrophobic surfaces have been extensively studied and engineered. However, achieving superoleophobic surfaces, which repel oils, has proven to be more challenging due to the oils' lower surface tension than water. This article presents a novel, cost-effective technique for fabricating superoleophobic surfaces with enhanced durability. We demonstrate the effectiveness of this technique by using Perfluorooctyltriethoxysilane (PFOTES) and silicon dioxide nanoparticles on stainless steel mesh substrate. The resulting surfaces exhibit remarkable superoleophobic properties by incorporating re-entrant structures, low surface energy, and high surface roughness, making them ideal for oil-repellency applications.

In this unprecedented era of computer-supported and distance learning, it is critical to rethink the way students are supported through vital skills like self-regulation and peer collaboration. A variety of digital media has emerged, allowing learners autonomy while requiring the ability to effectively regulate different components of the educational process for long-term academic success. We reviewed collaboration, communication, and self-regulation in digital learning environments as part of online learning. We discussed how these abilities are reflected in highly cited content. High-impact factor studies were identified using advanced software Herzing Publish or Perish to retrieve highly cited research publications metrics. The search resulted in 272 records from 2010 to 2022, 192 duplicates were identified to remove and 48 JCR articles were shortlisted for review. We examined and reflected on sufficient connections to make firm judgments about correlations of concepts. The review identified prominent themes surfaced in the high impact factor academic publication e.g., digital learning environment, interaction, engagement, feedback, self-efficacy, motivation, meta-cognition, achievement, technology, blended learning, and orientation of ICT. Many studies support different features of SRL &amp; PCL through different tools, access to educators or feedback, encouragement, and through peers. However, all of them discuss the processes that students use to self-regulate learning in these situations. It is suggested to examine whether the effectiveness of certain SRL processes varies by online learning mode.

This paper, which is one of the few in the world dealing with this topic, presented the psychologist's work based on the concepts of mentalization and internal working models (IWMs) of attachment, with an adolescent girl who was prone to nonsuicidal self-injury. Gaga, a student in the first grade of high school, had visible scars from cutting her hands on the inner sides of both forearms. Gaga told the psychologist that her mother believes that this is an attempt to draw attention to her problems, and even a suicide attempt. Together with Gaga, the psychologist made a formulation of her mental difficulties. The main aim of the psychologist's work with Gaga was to control unpleasant impulses and emotions, so as to eliminate self-injury. This aim has been achieved through three global tasks: 1) that Gaga, in the relationship with the psychologist, builds her IWMs as positive, so that she can see herself as a person who is worthy of the love and support of close people; 2) to develop a stable capacity for mentalization; 3) to apply this acquired skills in the interpretation of her affective experiences. The mentalizing approach in working with Gaga was based on an attitude of emotional warmth, acceptance and authentic interest in her inner world. The psychologist's work with Gaga led to her better self-control, which resulted in the cessation of her self-injury. In her work with a psychologist, Gaga acquired positive IWMs, which she transferred into other significant interpersonal relationships.

Consciousness is usually perceived as a state of being aware of one’s environment as well as self. Despite its omnipresence in our life, understanding this concept is challenging. This has given rise to several theories attempting to explain the nature of consciousness, as well as hard and soft problems of consciousness. In fact, the boundaries of consciousness defined by these theories are a topic of continued discussion, particularly in light of the recent advances in artificial intelligence (AI). Some of these theories consider consciousness as a simple integration of information while others purport the need for an agency in the process of integration for an entity to be considered conscious. Some theories consider consciousness as a graded entity and some equate consciousness with content of awareness. In this work, major theories of consciousness are reviewed and compared, focusing on awareness, attention, and sense of self. These findings are interpreted in relation to AI in order to ascertain what makes AI distinct from natural intelligence.

Consciousness is usually interpreted as a state of being aware of one’s environment as well as self, while awareness is understood as knowledge of something. Despite their semantic differences, in philosophy, these terms are often used interchangeably, as is the case of the hard problem of consciousness proposed by Chalmers, which in fact is the hard problem of awareness. Trilogy paradigm of consciousness (or simply “trilogy”) offers a new paradigm where consciousness is the result of a unique interaction between awareness and the decision-making process. By conferring the input of awareness to the decision-making process, a new mental function of awareness-based choice selection (ABCS) or true free will emerges. Likewise, application of the power of decision-making to the process of awareness gives rise to discretionary selection of information for awareness (DSIA) or intentional attention. The intertwined actions of ABCS and DSIA comprise “I” which is the faculty of our consciousness and is what sets natural intelligence (NI) apart from artificial intelligence (AI). Based on trilogy, mind is an unconscious compilation of all mental function excluding ABCS and DSIA that are the essence of consciousness. As humans, we are a union of “I,” our minds, and our bodies.

Peru has one of the highest informal employment rates in Latin America (73%). Previous studies have shown higher prevalence of poor self-perceived health (P-SPH) in informal workers compared to formal. The study’s aim is to analyse the role of working conditions in the association between informality and SPH in urban working population in Peru. We conducted a cross-sectional study based on 3098 workers from the working conditions survey of Peru 2017. Prevalence of P-SPH and exposure to poor working conditions were calculated separated by formal and informal employment and stratified by sex. Poisson regression models were used to assess the association between P-SPH and informal employment, with its prevalence ratios (PR) crude and adjusted for working conditions. Informal employment was 76% in women and 66% in men. Informal workers were more exposed to poor working conditions than formals and reported worse SPH. Workers in informal employment showed higher risk of P-SPH than formals: PR 1.38 [95% CI: 1.16 – 1.64] in women and PR 1.27 [95% CI: 1.08 – 1.49] in men. Adjustment by working conditions weakened the association in both sexes. In women, this association was only partially explained by worse working conditions 1.23 [95% CI: 1.04 – 1.46]. The negative effect on informal workers’ health is partially explained by poor working conditions. However, there is a part of the effect explained by informality per se.

Background: This study consists of a brief psychological intervention, which uses the Self-Regulation Therapy (SRT, procedure based on suggestion and classical conditioning), to improve coping with stress and emotionality by reproducing the positive effects of illegal drugs: cannabis, cocaine, ecstasy. Method: 15 volunteers (8 males, 7 females), with a mean age of 24.67 (SD = 4.43), underwent intervention to improve their coping with stress and emotionality using SRT. They carried out pre- and post-intervention scores for 10 days and during a 4-week fol-low-up. The employed instruments were: COPE (Coping Skills Inventory) and PNAS (Positive and Negative Affect Schedule). Results: SRT was superior to non-intervention for the 4 coping strategies (2= .829, .453, .411 and .606) and for positive (2= .371) and negative emotionality (2= .419). An improvement in scores was evidenced in the follow-up scores compared to the pre-intervention measures. Conclusions: This study shows for the first time that it is possible to use illegal drugs, considered harmful to public health, to improve young people’s coping capacity and emotionality by reproducing their positive effects with SRT.

Background & Aims: Globally, liver diseases account for 2 million deaths per year. For those with advanced liver disease the only curative approach is liver transplantation. However, less than 10% of those in need get a liver transplant due to limited organ availability. To circumvent this challenge, there has been a great focus in generating a bioengineered liver. Despite its essential role in liver functions, a functional biliary system has not yet been developed. In this framework, exploration of epithelial cell self-organogenesis and microengineering-driven geometrical cell confinement allow to envision the bioengineering of a functional biomimetic intrahepatic biliary tract. Approach: Three-dimensional (3D) bile ducts were built in vitro by restricting cell adhesion to two-dimensional (2D) patterns to guide cell self-organization. Tree shapes mimicking the configuration of the human biliary system were micropatterned on glass slides, restricting cell attachment to these areas. Different tree geometries and culture conditions were explored to stimulate self-organogenesis of normal rat cholangiocytes (NRCs) used as a biliary cell model, either alone or in coculture with human umbilical endothelial cells (HUVECs). Results: Pre-seeding the micropatterns with HUVECs promoted luminogenesis with higher efficiency to yield functional branched biliary tubes. Lumen formation, apico-basal polarity, and preservation of the cholangiocyte phenotype were confirmed. Moreover, intact and functional biliary structures were detached from the micropatterns for further manipulation. Conclusion: This study presents physiologically relevant 3D biliary duct networks built in vitro from 2D micropatterns. This opens opportunities for investigating bile duct organogenesis, physiopathology, and drug testing.

(1)Background: This report was an effective evaluation of using multimedia educational program for self-care and quality of life in patients with a stoma at postoperative and established a simple threshold for enterostomy self-care skills.(2)Methods:108 patients with enterostomy were randomly assigned to receive either the intervention multimedia education program (including information about preparation for enterostomy care, cleaning method, manual bag replacement steps and precautions). The primary outcome was self-care ability and quality of life (defined as overall enterostomy self-care ability and overall quality of life), which was assessed at 2 weeks after intervention via an interview.(3)Results: Compared with those who in the conventional stoma education program, patients received the multimedia education program significantly improved their overall self-care ability and quality of life in statistically. The threshold of the enterostomy self-care skill scale was 20 points, and its corresponding sensitivity was 77.8%, and the specificity was 75.7%.(4)Conclusions: According to our study, the multimedia education program could enhance self-care ability of home care and quality of life in patients with enterostomy.

The advancement of bioconcrete over cementitious composites has brought us to the application of microbes in the field of construction materials. Certain microbes like bacteria, algae, and fungi have been discussed in the review. The purpose of applying these microbes in the matrix is mainly to enhance the concrete’s strength and other properties such as durability, resistance, and self-healing ability. As these microbes are able to induce calcite biomineralizations, the process is also known as Microbiologically Induced Calcite Precipitation (MICP). Some known microorganisms with their mentioned ability are Bacillus subtilis and Bacillus cohnii (bacteria), Chlorella vulgaris and Spirulina platensis (algae), and Trichoderma reesei, Aspergillus niger, and Neurospora crassa (fungi). The paper provides a “state-of-the-art” review of research into the effects of bioconcrete and discusses the overall methodologies of every medium with their physiological, physicochemical and bioengineering properties in the light of recent researches done so far in the same field.

There is growing evidence on the relevance of self-efficacy for well-being and functioning among individuals with psychotic disorders, but specific self-efficacy for coping with stress has rarely been investigated. This study explored the outcomes of an intervention for the improvement of coping resources based on a training in coping skills and coping with stress self-efficacy (CSSE). Fourteen adult volunteers who were diagnosed with schizophrenia (n=12) or schizoaffective disorder (n=2) were matched in clinical and sociodemographic characteristics and randomly assigned to the study groups. The intervention group received the training along with their pharmacological therapy; the control group received their prescribed drug therapy. Participants completed self-reports on CSSE, perceived successful daily functioning based on coping skills, and clinical status (BPRS-E). Trained patients showed a significant increase in CSSE and reported greater successful functioning status, and significant improvements in their clinical status were also observed. All these enhancements remained at 3-month and 6-month follow-ups. Control participants showed no significant changes. Moreover, the intervention condition interacted with CSSE and perceived coping functioning in explaining improvements in clinical status: in the treatment group, greater CSSE translated into enhanced daily functioning, and this improvement predicted better clinical status. These findings stress the relevance of promoting coping resources in psychotic disorders and provide preliminary evidence for the potential benefits of CSSE.

Proteins and DNA exhibit key physical chemical properties that make them advantageous for building nanostructures with outstanding features. Both DNA and protein nanotechnology have growth notably and proved to be fertile disciplines. The combination of both types of nanotechnologies is helpful to overcome the individual weaknesses and limitations of each one, paving the way for the continuing diversification of the structural nanotechnologies. Recent studies have implemented a synergistic combination of both biomolecules to assemble unique and sophisticate protein-DNA nanostructures. These hybrid nanostructures are highly programmable and display remarkable features that create new opportunities to build in the nanoscale. This review focuses on the strategies deployed to create hybrid protein-DNA nanostructures. Here, we will discuss strategies such as polymerization, spatial directing and organizing, coating, rigidizing or folding DNA into particular shapes or moving parts. The enrichment of structural DNA nanotechnology by incorporating protein nanotechnology has been clearly demonstrated and still shows a large potential to create useful and advanced materials with cell-like properties or dynamic systems. It can be expected that structural protein-DNA nanotechnology will open new avenues in the fabrication of nano-assemblies with unique functional applications and enrich the toolbox of bionanotechnology.

Transition from primary to secondary school is more successful when students’ learning is consistent. Students are also more likely to enjoy the school, engage with learning, and have a high academic achievement in the secondary school when they feel motivated. This is a critical aspect especially in cases when global pandemics situations allow only the online schooling opportunity. Students that are away from school lack the traditional sources of motivation and self-regulated learning skills, thus research is needed to identify other important factors that can be developed in remote settings. The aim of this study was to find out how students perceive their experience with the transition from primary to secondary school and how such a transition influences students’ self-regulated learning (SRL) and motivation. Self-reported data were collected during the COVID-19 breakout from a total of N=80, 6th and 7th grade students aged 12-14 years old. Results showed that students had a successful transition, especially when they are supported by their parents and teachers. Next, Bivariate Pearson Correlation analysis indicated that students’ perceptions about their experience with the transition from primary to secondary school and their self-regulated learning and motivation are significantly correlated. No gender differences were found among all main study variables.

Background and Objectives: In Libya, prescription medicines can easily be dispensed without a prescription, as self-medication with the subsequent of potential misuse and unnecessary risk for patients. This systematic review and meta-analysis explored the prevalence of self-medication among citizens in Libya. Methods: We systematically searched PubMed, ISI Web of Knowledge, EMBASE, Medline, Scopus, google scholar and ResearchGate databases for studies published in Libya reporting the prevalence of non-prescription use of medicines in community pharmacies, targeting studies published from Jan 2008 until Dec 2019. A random meta-analysis was performed to analyze pooled estimates of non-prescription usage of medicines. Results: Out of 63 articles identified, a total of 13 papers from 7 cities were met the inclusion criteria and involved a total of 4741 subjects. The overall pooled proportion of self-medication of drugs was 53.6% (95% CI: 0.93% - 1.08%), with low heterogeneity and the P-value of the whole population tasted was found to be = 1.000 (df = 12). Seven out of the 13 studies reported data on self-medication for antibiotics without prescription. The records ranged from 15.3% (95% CI 0.61–1.65) in Misurata to 76.6% (95% CI 0.80–1.25) in Tripoli. Conclusion: Self-medication use of medicines among Libyan population is a common phenomenon involving a high proportion use of antibiotics. This misuse of medications could enhance the development and spread of antibiotic resistance.

The Photovoltaic modules are usually installed on the ground which exposes it to surface deposition of foreign particles. In the Middle East and North Africa region, the primary culprit is dust and sand. They form an insulating and opaque layer on the surface of the glass, which obstructs its heat transfer and optical properties, thereby reducing the overall yield efficiency of the solar panel. Cleaning of this layer is critical to the operation of the solar panel and often requires great effort and energy on a large-scale solar array. In this paper, we propose a novel self-cleaning mechanism for solar panels, with an understanding of the structural integrity of the Photovoltaic laminate and application of external mechanical vibration. By applying an external source of vibration, the solar panels vibrate, excites its fundamental frequencies and cleans by its own. The method is analyzed using finite element analysis method and tested using experiments. Our simulation results based on IEC 61215 show that the maximum principal stress and deformation in the critical layers is within limits. Our experimental results prove the proposed theory is feasible and can be extended to large scale solar arrays. Our proposed method is retrofittable and could save money, energy and effort in cleaning the solar arrays, which can replace current techniques.

Nanoparticle (NP)–surfactants formed by the self-assembly of NPs and end-functionalized polymers at the hydrophilic/hydrophobic interface have a wide range of applications in many fields. In this study, the influence of density of amino groups, NPs dimension and pH on the interaction between end-functionalized polymers and NPs were extensively investigated. Single amino-terminated polystyrene (PS-NH2, Mw ≈ 0.6k, 2.5k, 3.5k, 3.9k) and diamino-terminated polystyrene (H2N-PS-NH2, Mw ≈ 1.1k, 2.8k) were prepared using reversible addition–fragmentation chain transfer polymerization and atom transfer radical polymerization. NPs with different dimensions (zero-dimensional carbon dots with sulfonate groups, one-dimensional cellulose nanocrystals with sulfate groups and two-dimensional graphene with sulfonate groups) in the aqueous phase were added into the toluene phase containing the aminated PS. The influence of pH and the molecular weight of amino-terminated PS on the interfacial tension between two phases were investigated. The results indicate that aminated PS exhibited the strongest interfacial activity after compounding with sulfonated NPs at a pH of 3. Terminating PS with amino groups on both ends leads to better performance in in reducing the water/toluene interfacial tension than modifying the molecular structure of PS on a single end. The dimension of sulfonated NPs also contributed significantly to the reduction of the water/toluene interfacial tension. The minimal interfacial tension was 4.49 mN/m after compounding PS-NH2 with sulfonated zero-dimensional carbon dots. Molecular dynamics simulation on the evolution of the water/toluene interface in the presence of sulfonated carbon dots and H2N-PS-NH2 revealed that these opposite charged substances moved towards the interface in an extreme short time and orderly assembled in a thermodynamic equilibrium.

In this paper, first the Self-Written Waveguide (SWW) process in wet photopolymer media (liquid solutions), are examined for three examples: single-, counter-, and co-fibers exposure. Then the SWWs formed inside solid material are examined including the effects of manipulating the alignment of the fibers. In all cases high precision measurements are used to position the fiber optic cables (FOCs) before exposure using a microscope. The self-writing process is indirectly monitored by observing (imaging) the light emerging from the side of the material sample during SWW formation. In this way the optical waveguide trajectories formed in an Acrylamide/Polyvinyl Alcohol (AA/PVA) a photopolymer material (sensitized at 532 nm) are examined. First the transmission of light by this material is characterized. Then the bending and merging of the waveguides which occur are investigated. The predictions of our model are shown to qualitatively agree with the observed trajectories. The largest index changes taking place at any time during the exposure, i.e. during SWW formation, are shown to take place at the positions where the largest exposure light intensity is present. Typically, such maxima exist close to the input face and the first maximum is referred to as the location of the Primary Eye. Other local maxima also appear further along the SWW and are referred to as Secondary Eyes, i.e. deeper within the material.

The work shows a single case intervention based on the motivational coaching model the “Cantón’s Giraffe”, theoretically and experimentally proven in a 20-years-old dancesport practitioner, who considered improving her execution with her dance partner. Methodological triangulation was used [34], combining a test-retest quantitative measure (Using Ryff Psychological Wellbeing Scales; the Rosenberg Self-Esteem Questionnaire and an ad-hoc instrument for the motivational force assessment) and subjective analysis of the responses from the interviews conducted. A three-phase intervention protocol was used [33]: identification, intervention, and follow-up. The results show an improvement in reevaluation scores, an increase in the subjective evaluation, and participant satisfaction and perceived resources raised at the end of the intervention. These results are in line with previous similar studies.

Hydrogels incorporated with hydrophobic motifs have received considerable attention to recapitulate the cellular microenvironments, specifically for the bio-mineralization of a 3D matrix. Introduction of hydrophobic motifs into a hydrogel often results in irregular arrangement of the motifs, and further phase separation of hydrophobic domains, but limited efforts have been made to resolve this challenge in the hydrophobically-modified hydrogel. Therefore, this study presents an advanced integrative strategy to incorporate hydrophobic domains regularly in a hydrogel by self-assembling of polymer cross-linkers, building blocks of a hydrogel. Self-assemblies between polymer cross-linkers were examined as micro-domains to incorporate hydrophobic motifs in a hydrogel. The self-assembled structures in a pre-gelled solution were confirmed with the fluorescence analysis and the hydrophobicity of a hydrogel could be tuned by incorporating the motifs in a controlled manner. Overall, the results of this study would greatly serve to tuning performance of a wide array of hydrophobically-modified hydrogels in drug delivery, cell therapies and tissue engineering.

In recent decades, organizational research has given special attention to the mechanisms promoting the health and wellbeing of nursing professionals. In this context, self-esteem is a personal resource which influences wellbeing at work and psychological wellbeing of nurses. The purpose of this study was to analyze the mediating role of eating in the effect of sleep quality on self-esteem in nursing professionals. The sample of 1073 nurses were administered the Rosenberg General Self-Esteem Scale, the Pittsburgh Sleep Quality Index and the Three-Factor Eating Questionnaire-R18. The results show that poor sleep quality and type of eating directly and indirectly affect self-esteem. More so, poor sleep quality deteriorated self-esteem through emotional eating, and even though emotional eating facilitated disinhibited eating, this relationship had no significant effects on self-esteem. The findings of this study suggest that hospital management should implement employee health awareness programs on the importance of healthy sleep and design educational interventions for improving the quality of their diet.

Positive Occupational Health Psychology (POHP) examines the mechanisms that promote workers’ health and wellbeing, in addition to risk factors arising from work activity. The aim of this study was to analyze the mediating role of perceived stress in the effect that self-efficacy has on engagement in nurses. The sample comprised 1777 currently working nurses. We administered the Utrecht Work Engagement Scale (UWES), the Perceived Stress Questionnaire and the General Self-Efficacy Scale. Following bivariate correlational analysis, multiple linear regression analysis, and simple and multiple mediation analysis the results showed Self-efficacy to be a powerful personal resource that positively predicts employees’ engagement, although the effect diminishes when there are mediating variables of stress. We found differences in the way the different aspects of stress mediated the relationship between Self-efficacy and the engagement dimensions. “Energy–joy” was the strongest mediating variable for all of the engagement dimensions, and this, together with “harassment–social acceptance” dampened the effect of Self-efficacy on vigor and dedication, whereas “Overload” was only a mediator for dedication. Because nurses work in a stressful environment, risk factor arising from work activity, hospital management should design interventions to enhance their workers’ personal resources and improve personal and organizational wellbeing.

Theoretical studies propose an association between family violence and low self-control in adolescence, yet empirical findings of this association are inconclusive. The aim of the present research was to systematically summarize available findings on the relation between family violence and self-control across adolescence. We included 27 studies with 143 effect sizes, representing more than 25,000 participants of eight countries from early to late adolescence. Applying a multi-level meta-analyses, taking dependency between effect sizes into account while retaining statistical power, we examined the magnitude and direction of the overall effect size. Additionally, we investigated whether theoretical moderators (e.g., age, gender, country), and methodological moderators (cross-sectional/longitudinal, informant) influenced the magnitude of the association between family violence and self-control. Our results revealed that family violence and self-control have a small to moderate significant negative association (r = -.191). This association did not vary across gender, country, and informants. The strength of the association, however, decreased with age and in longitudinal studies. This finding provides evidence that researchers and clinicians may expect low self-control in the wake of family violence, especially in early adolescence. Recommendations for future research in the area are discussed.

Controlling surface-protein interaction during wastewater treatment is the key motivation for developing functionally modified membranes. A new biocatalytic thermo-responsive poly(vinylidene fluoride)(PVDF)/nylon-6,6/poly(N-isopropylacrylamide)(PNIPAAm) ultrafiltration membrane was fabricated to achieve dual functionality of protein-digestion and thermo-responsive self-cleaning. The PVDF/nylon-6,6/PNIPAAm composite membranes were constructed by integrating a hydrophobic PVDF cast layer and hydrophilic nylon-6,6/PNIPAAm nanofiber layer where trypsin enzymes were covalently immobilized. The immobilization density of enzymes on the membrane surface decreased with increasing PNIPAAm concentration, due to the decreased number of amine functional sites. Through a ultrafiltration study using a model solution containing BSA/NaCl/CaCl₂, the PNIPAAm containing biocatalytic membranes demonstrated a combined effect of enzymatic and thermo-switchable self-cleaning. The membrane without PNIPAAm revealed superior fouling resistance and self-cleaning with an R_PD of 22%, compared to membranes with 2 and 4 wt% PNIPAAm with 26% and 33% R_PD, respectively, after an intermediate temperature cleaning at 50°C, indicating that higher enzyme density offers more efficient self-cleaning than the combined effect of enzyme and PNIPAAm at low concentration. The conformational volume phase transition of PNIPAAm did not affect the stability of immobilized trypsin on membrane surface. Such novel surface engineering design offer a promising route to severe surface-protein contamination remediation in food and wastewater applications.

This paper presents a study of different types of parametric signals with application to underwater acoustic communications. In all the signals, the carrier frequency is 200 kHz, which corresponds to the resonance frequency of the transducer under study and different modulations are presented and compared. In this sense, we study modulations with parametric sine sweeps (4 to 40 kHz) that represent binary codes (zeros and ones), getting closer to the application in acoustic communications. The different properties of the transmitting signals in terms of bit rate, directivity, efficiency and power needed are discussed as well.