Study Of Behavior Research Articles

Efficient removal of environmental pollutants by green synthesized metal nanoparticles of Clitoria ternatea

Prevention and management of water pollution are becoming a great challenge in the present scenario. Different conventional methods like carbon adsorption, ion exchange, chemical precipitation, evaporation, and biological treatments remove water pollutants. Nowadays, the requirement for effective, non-toxic and safe waste management strategies is very high. Nanomaterials have been explored in various fields due to their unique characteristics. Green synthesis of nanomaterial is becoming more popular due to their safety, non-toxicity, and ease of scale-up technology. Metal nanoparticles can be synthesized using a green synthesis method using biological sources provided by eco-friendly, non-hazardous nanomaterials with superior properties to bulk metals. Hence, this study has designed a green synthesis of magnetic (cobalt oxide) and noble (gold) nanoparticles from the fresh flowers of Clitoria ternatea. The flavonoids and polyphenols in the extract decreased the energy band gap of cobalt oxide and gold nanoparticles; hence, the capping of the natural constituents in Clitoria ternatea helped form stable metal nanoparticles. The cobalt oxide and gold nanoparticles are evaluated for their potential for eliminating organic pollutants from industrial effluent. The novelty of this present work represents the application of cobalt oxide nanoparticles in the removal of organic pollutants and a comparative study of the catalytic behaviour of both metal nanoparticles. The degradation of bromophenol blue, bromocresol green, and 4–nitrophenol in the presence of gold nanoparticles was completed in 120, 45, and 20 min with rate constants of 3.7 × 10−3/min, 6.9 × 10−3/min, and 16.5 × 10−3/min, respectively. Similarly, the photocatalysis of bromophenol blue, bromocresol green, and 4–nitrophenol in the presence of cobalt oxide nanoparticles was achieved in 60, 90, and 40 min with rate constants of 2.3 × 10−3/min, 1.8 × 10−3/min, and 1.7 × 10−3/min, respectively. The coefficient of correlation (R2) values justify that the degradation of organic pollutants follows first-order kinetics. The significance of the study is to develop green nanomaterials that can be used efficiently to remove organic pollutants in wastewater using a cost-effective method with minimal toxicity to aquatic animals. It has proved to be useful in environmental pollution management.

Вплив «сільських» поведінкових стратегій населення України на якість життя в умовах війни

The purpose of the article is to analyse and understand how «rural» behavioural aspects of the Ukrainian population influence the perception and adaptation to the conditions of armed conflict. The novelty of the study lies in the analysis of those rural behavioural aspects that are common to the entire population and may impact adaptation strategies and the improvement of quality of life during military events. In the context of modern conflicts and war scenarios, the study of population behaviour and its impact on the resilience of quality of life becomes particularly significant. Military conflicts not only threaten the safety and stability of society but also sharply increase the importance of understanding population behavioural aspects in the conditions of armed conflict and their impact on the quality of life. The examination of population behaviour during an extreme period of armed conflict potentially provides answers to how effectively the Ukrainian population can adapt to economic difficulties and overcome them. For the investigation of the impact of «rural» behavioural aspects on the quality of life of the population in Ukraine, a combination of specialized and general scientific methods was employed. These include the monographic method, bibliometrics, and content analysis. Additionally, forecasting and modelling methods were utilized to predict the impact of fullscale military actions on poverty, a key indicator of quality of life. After the conducted research, it can be noted that in the conditions of a full-scale invasion by Russian forces, the «rural» behavioural characteristics of Ukrainians exhibit numerous advantages, arising from the close interaction between rural and urban existence. It is demonstrated that a potentially significant portion of the country’s population, particularly those of rural origin, possesses skills in land management. The presence of relatives in villages and small towns, who have large living spaces and substantial land plots, enabled the acceptance of many forcibly displaced individuals during the war. The study confirms that behavioural traits such as the tradition of stockpiling food supplies, maintaining habitable housing conditions in rural areas, and the existence of close familial and social ties have significantly mitigated the impact of the war on the population.

Using artificial intelligence methods to study the effectiveness of exercise in patients with ADHD.

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that significantly affects children and adults worldwide, characterized by persistent inattention, hyperactivity, and impulsivity. Current research in this field faces challenges, particularly in accurate diagnosis and effective treatment strategies. The analysis of motor information, enriched by artificial intelligence methodologies, plays a vital role in deepening our understanding and improving the management of ADHD. The integration of AI techniques, such as machine learning and data analysis, into the study of ADHD-related motor behaviors, allows for a more nuanced understanding of the disorder. This approach facilitates the identification of patterns and anomalies in motor activity that are often characteristic of ADHD, thereby contributing to more precise diagnostics and tailored treatment strategies. Our approach focuses on utilizing AI techniques to deeply analyze patients' motor information and cognitive processes, aiming to improve ADHD diagnosis and treatment strategies. On the ADHD dataset, the model significantly improved accuracy to 98.21% and recall to 93.86%, especially excelling in EEG data processing with accuracy and recall rates of 96.62 and 95.21%, respectively, demonstrating precise capturing of ADHD characteristic behaviors and physiological responses. These results not only reveal the great potential of our model in improving ADHD diagnostic accuracy and developing personalized treatment plans, but also open up new research perspectives for understanding the complex neurological logic of ADHD. In addition, our study not only suggests innovative perspectives and approaches for ADHD treatment, but also provides a solid foundation for future research exploring similar complex neurological disorders, providing valuable data and insights. This is scientifically important for improving treatment outcomes and patients' quality of life, and points the way for future-oriented medical research and clinical practice.

Pro-environmental behavior and the theory of planned behavior: a state of the art science mapping

PurposeThis science mapping analysis aims to discern current, emerging and future trends of pro-environmental behavior and the theory of planned behavior (TPB).Design/methodology/approachBibliometric analysis through bibliographic coupling and co-word analysis were used to reveal the progress of this phenomenon. Of the 1,120 documents search in Web of Science (WoS) database, 1,031 were used in this analysis after restricting to journal publications and studies after the year 2000.FindingsThe results show that four themes emerged, namely the fundamentals of TPB for pro-environmental behavior, antecedents of pro-environmental behavior, integration of TPB with the norm activation model and value belief theory and studies of pro-environmental behavior in developing countries. Environmental concern, environmental awareness, environmental knowledge and environmental education were the most commonly integrated variables.Research limitations/implicationsThis research is unique in the sense that the integration between TPB and other prominent theories of pro-environmental behavior is vital to predict individual pro-environmental behavior and understand the fundamental scientific importance of the domain. The norm activation model has been integrated with TPB in many pro-environmental behaviors. Mainstream media stakeholders should design and implement a plan for strategic communication and awareness campaigns in the community to encourage consumers to engage in many behaviors that lead to environmental sustainability.Originality/valueThis study presents a science mapping approach to uncover crucial knowledge structure related to pro-environmental behavior and the theory of planned behavior.

Enhancing stability and interpretability in the study of strength behavior for coarse-grained soils

Coarse-grained soils have been commonly employed as filling materials in engineering projects, and comprehending the mechanical properties of these soils is crucial for ensuring project safety. Currently, artificial intelligence (AI) plays a significant role in advancing these fields. However, the inherent lack of stability and interpretability of AI models constrains their potential for providing substantial assistance. To solve these shortcomings, this study investigates the mechanical properties of coarse-grained soils and proposes an approach that integrates a stable learning module with neural networks to enhance the stability of an AI model. The proposed method addresses the challenge of relaxing the strong assumption of independent and identically distributed (i.i.d.) data. In addition, it improves the stability of the prediction of a relationship between the deviatoric stress and axial strain. The proposed method is verified by experiments. The experimental results confirm that the proposed method performs well on real-world data samples that deviate from the i.i.d. assumption. Moreover, the proposed method enhances the interpretability of the AI model and facilitates the identification of crucial features for assessing the strength behavior of coarse-grained soils. This feature of interpretability is invaluable for practical engineering applications.

A Study of Consumer Behavior: Preferences for Vernacular and Modern Architecture in Hot and Dry Indian Regions

Global warming and energy shortages have become major global problems, with the building industry being a major energy consumer. Vernacular architecture, which has been developed over generations, is more adaptable to changing environments, including building orientation, form, spacing, layout orientation, and climatic features. This approach considers the home's aesthetics, physical characteristics, and social purposes, resulting in unique designs in hot, arid climate locations. Examples include Rajasthan and Gujarat, which use locally produced sandstone and lime, Jaali screens, and courtyard designs. Modern architecture in these areas integrates advanced materials, shading technologies, passive cooling techniques, and sustainable energy systems. This study aims to examine vernacularism's climatic adaptation, focusing on climatic adaptive technology and its potential in house shape, construction method, building components, and materials. The findings have implications for architects, urban planners, and policymakers seeking to create resilient and sustainable built environments that respect cultural identity while embracing innovation and local knowledge. The essay will examine 23 previous studies and the work of architects who have adapted traditional and vernacular architecture to create a new identity in architecture, showcasing the blending of traditional and vernacular architectural knowledge with contemporary technology and materials.

The Hierarchical Configurations of Policy Networks: A Study of Decision-Making Processes in Urban Transport

This study sheds light on a commonly overlooked aspect in network literature: the potential for hierarchized behavior within networks. Providing a longitudinal case study of decision-making behavior in urban transport policy networks, the study also adds to calls for longitudinal studies in the field of public administration. By analyzing documents and interviews, the study reveals the gradual shift towards hierarchical decision-making within networked structures. Furthermore, it highlights how organizational variables shape networked behavior over time, both facilitating and constraining hierarchized behavior. Consequently, this article suggests that hierarchized and collaborative dynamics co-exist and co-evolve within policy networks.

Structural and Tribological Analysis of Brake Disc–Pad Pair Material for Cars

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

Experimental Study on Erosion Modeling of Architectural Red Sandstone under the Action of the Natural Environment

When buildings are exposed to erosion from the natural environment, erosion behaviors such as surface damage and structural instability occur, which greatly affect the aesthetic value and service life of the buildings. The study of erosion behaviors and the establishment of a suitable erosion model are constructive references for the protection and restoration of buildings. In order to establish a suitable erosion model for architectural red sandstone, two types of red sandstone specimens were selected in this paper to carry out dry and wet cycle tests. Combining the theoretical analysis and the actual erosion situation, a unidirectional corrosion model is proposed to describe the erosion of buildings by the natural environment. In this model, it is assumed that only the outer surface of the building is in contact with external erosion factors for a long period of time, so this situation can be considered a unidirectional erosion process. The paper uses XRD, SEM, and ultrasonic methods to record changes in the properties of the red sandstone samples. Finally, the rationality of the unidirectional erosion model was verified numerically. The test results show that the red sandstone specimens subjected to erosion by the natural environment will be accompanied by the development of defects, such as cracks, fissures, and holes, as well as the generation of fresh material. The demarcation point of different erosion stages exists in both the in-service red sandstone specimens and the fresh red sandstone specimens, which is consistent with the results of the unidirectional erosion model. In this paper, a calculation model for the demarcation point of different erosion stages is established, and the model estimation shows that the demarcation point of different erosion stages of the in-service red sandstone sample is 1.1528 cm from the erosion surface, and the demarcation point of different erosion stages of the fresh red sandstone sample is 1.67 cm.

Stability Analysis of Cofferdam with Double-Wall Steel Sheet Piles under Wave Action from Storm Surges

Double-wall steel sheet piles (DSSPs) are widely used in large-span cofferdams for docks due to their good performance against wave action during storm surges. This paper describes a study of the dynamic behavior of a DSSP cofferdam under wave action through flume tests and a numerical simulation that combined computational fluid dynamics (CFD) and the finite element method. The influences of the water level and wave height on the DSSP cofferdam were investigated experimentally and numerically. Tall waves in shallow water broke upon and impacted the seaside pile with large dynamic wave pressure, dramatically increasing the stress and displacement of the seaside pile. The overlap of the traveling and reflected waves increased the excess pore water pressure near the seaside pile due to taller overlapped waves and higher wave frequency. The DSSP cofferdam failed under the combined actions of the dynamic wave pressure and erosion of the landside seabed. The leakage and overflow of the breaking waves resulted in significant erosion of the landside seabed and greatly weakened the support of the seabed. The dynamic wave pressure then pushed the DSSP cofferdam until it failed. The simulation with the combined methods of CFD and FEM resulted in trends that were similar to those of the test measurements. Compared to the quasi-static method and pseudo-dynamic method, the results of the simulation via the present method were much closer to the test results because the simulation included the effects of breaking waves. The reinforced measure worked well to prevent the DSSP cofferdam in a sandy seabed foundation from continuous failures of deformation–leakage–erosion–tilting. However, it failed in a clay interlayer seabed foundation due to the large settlement.

Study of soot dynamic behavior and catalytic regeneration in diesel particulate filters

Catalytic diesel particulate filters (CDPFs) are key technologies for controlling particulate matter emissions. Nevertheless, it is challenging to experimentally observe the internal working states of the CDPF, which hinders comprehensive research on the flow, capture, and regeneration characteristics. In this study, we utilized quartet structure generation set to generate two-dimensional porous walls, lattice Boltzmann method to simulate CDPF flow and diffusion, cellular automata to simulate soot motion, and global reaction kinetics to simulate soot oxidation. The accuracy of the model is validated through flow, combustion, and pressure drop experiments. We propose a new concept of local porosity, where the porosity of a partially porous wall is used to represent the filtering parameters of that part. These findings indicate that porosity dominates filtering. A higher local porosity results in increased near-wall velocity and greater deposition of soot particles. Similarly, alterations in local porosity and deposition mutually influence each other. The velocity distribution and pressure difference at the inlet and outlet are directly influenced by the upstream velocity. Compared with velocity and porosity, particle diameter has less effect on deposition location, but more effect on deposition efficiency. Active regeneration (800 K) occurs approximately 31.17 times more quickly than passive regeneration (600 K) in the absence of a catalyst. The platinum-based catalyst significantly enhanced the soot oxidation rate by oxidizing NO to NO2, resulting in a 3.62-fold increase in passive regeneration and an estimated 1.17-fold increase in active regeneration. These findings are crucial for developing efficient CDPF systems and for reducing particulate emissions.

Developmental trajectory of social reward motivation from early adolescence into adulthood in female and male Long-Evans rats.

Most studies of adolescent and adult behavior involved one age group of each, whereas the dynamic changes in brain development suggest that there may be behavioral flux in adolescence. In two studies, we investigated developmental changes in social reward motivation in female and male Long-Evans rats from prepuberty to early adulthood in a social operant conditioning task. Given the earlier onset of puberty in females than in males, we predicted the course of social reward development would differ between the sexes. Overall, the pattern of results from both studies suggests that the trajectory of social motivation across adolescence is characterized by upward and downward shifts that do not depend on the sex of the rats. During training, in both studies, the mean number of social gate openings and percentage of social gate openings was higher at P30 (prepubertal, early adolescence) and P50 (late adolescence) than at P40 (mid adolescence) and P70 (adulthood) irrespective of sex. Nevertheless, the specific age comparisons that were significant depended on the study. In both studies, P30 rats had greater levels of social motivation than did adults in accessing a social reward when increased effort was required (progressive ratio tests). In an extinction test, only P30 and P50 rats continued to show more nose-pokes at the previously social gate than at the nonsocial gate, suggesting resistance to extinction. The results highlight the importance of characterizing behavior at several timepoints in adolescence to understand the neural mechanisms, many of which show similar discontinuities as they develop across adolescence.

Downside of Helping Professions: A Comparative Study of Health Indicators and Health Behaviour among Nurses and Early Childhood Educators.

The activities of health care workers and early childhood educators have received increased attention both in lay public discourse and in scientific discourse. These professional groups play a significant role in shaping the health behaviours of those they interact with; thus, understanding the patterns they convey is of paramount importance. The aim of our study is a comparative analysis of health conditions and health behaviours of professionals working in Hungarian early childhood education and nurses working in the healthcare system (n = 1591). We carried out our quantitative, cross-sectional research using convenience sampling among healthcare professionals working in nursing job positions (n = 581) and as early childhood educators (n = 1010), in south-west Hungary. Diagnosed chronic illnesses affect early childhood educators at a significantly higher rate (p < 0.05): the prevalence of musculoskeletal disorders is particularly high among them, as a result of which they reported a significant degree of physical limitation in relation to work. In the context of mental health, comparing the professional groups, nurses' indicators were significantly (p < 0.001) more unfavourable in all examined dimensions. Moreover, the comparison in terms of educational attainment directed attention to the worse indicators of non-graduates. In this context, early childhood educators are less affected by all three dimensions of burnout (p < 0.001). As for health behaviour, the smoking habits of nurses are more unfavourable (p < 0.05). Regarding screening tests, participation in cytological testing was significantly higher among nurses, whereas early childhood educators showed increased participation in mammography (p < 0.001). Our findings draw attention to the fact that early childhood educators are primarily affected by chronic musculoskeletal disorders, while healthcare workers are more affected by problems related to mental health. Mental well-being can be further endangered by the fact that both professional groups perceive low social appreciation for the work they carry out.

Mechanisms of CO2-rich industrial waste gas enhanced shale oil recovery in kerogen slit based on adsorption behavior, gas flooding and surfactant synergy study

Enhanced oil recovery (EOR) by injection of CO2-rich industrial waste gas has been proposed as an energy-efficient and environmentally friendly method for shale oil reservoirs, in which the waste gas can be utilized and sequestered to reduce greenhouse gas emissions. In this work, the adsorption behaviors of shale oil and waste gas components in kerogen slits and the effects of gas flooding on EOR were investigated using molecular dynamics (MD) method, which can provide technical guide for shale oil development. In terms of the adsorption behaviors, an intensity decrease of oil adsorption is observed in kerogen slits after the injection of the waste gas components. Desorption of shale oil during injection of SO2 and CO2 is more pronounced in shallow reservoirs, whereas it is more evident for NO and N2 in deep reservoirs. The desorption efficiency can be 10.91 % for pure CO2 injection and 9.09 % for NO injection at a depth of 1000 m, and the relative stability ratios of CO2 and NO sequestration are 77.09 % and 76.06 %, respectively. For gas flooding, the existence of pressure difference provides favorable conditions for gas molecules to pass through the oil zone, and waste gas can be more effective than pure CO2 as driving gas. For the waste gas injection, the percentage of CO2 retained in the kerogen nanopores is much higher than that of pure CO2 injection, suggesting that more CO2 molecules can enter into kerogen matrix to take part in the displacement process. Furthermore, the effect of surfactants on oil desorption and displacement was also investigated and the interaction energy of the surfactant/oil/gas system was calculated and analyzed, finding that addition of the oil-soluble surfactants can be beneficial to shale oil displacement. This work can provide a comprehensive evaluation of the feasibility of CO2-rich industrial waste gas application for EOR, which is of great significance in the effective production of shale oil and the sequestration of waste gas in shale reservoirs.

Synergizing Zoological Research and AI in Business: Unveiling Biological Strategies for Sustainable Innovation

This paper explores the symbiotic relationship between zoological research and artificial intelligence (AI)-driven business management, elucidating how their integration can unlock profound biological insights with transformative implications for various industries. With advancements in AI technologies and an increasing emphasis on sustainability, businesses are increasingly turning to interdisciplinary approaches to optimize operations and enhance decision-making processes. Leveraging insights from zoological research, which encompasses the study of diverse animal species and their ecological interactions, presents a novel avenue for innovation and strategic development in the business landscape. The integration of zoological research with AI-driven business management offers multifaceted benefits. Firstly, it provides businesses with a deeper understanding of complex biological systems, facilitating informed decision-making regarding resource management, product development, and sustainability initiatives. Zoological studies offer insights into the intricate interdependencies within ecosystems, shedding light on ecological dynamics, species behaviors, and evolutionary patterns. By integrating these insights into AI algorithms, businesses can develop predictive models to anticipate environmental changes, species migrations, and biodiversity trends, thereby enhancing resilience and adaptive capacity. Furthermore, the synergy between zoological research and AI enables businesses to optimize processes across various domains, ranging from supply chain management to customer engagement. For instance, by analyzing animal foraging behaviors and social structures, businesses can derive inspiration for efficient routing algorithms, inventory optimization strategies, and collaborative network designs. Moreover, AI-driven data analytics can streamline market research efforts by identifying consumer preferences, market trends, and emerging opportunities, drawing parallels with animal behavior studies that elucidate patterns of resource utilization and adaptation. In addition to operational enhancements, the integration of zoological insights with AI-driven business management fosters innovation and product development. By studying biological adaptations and evolutionary processes, businesses can derive inspiration for biomimetic design principles, leading to the creation of sustainable materials, energy-efficient technologies, and biologically inspired innovations. Moreover, by leveraging AI techniques such as machine learning and natural language processing, businesses can analyze vast datasets of biological literature and ecological observations to identify novel bioactive compounds, genetic sequences, and ecological niches with commercial potential. Importantly, the incorporation of zoological research into AI-driven business management fosters a paradigm shift towards holistic and ethical business practices. By recognizing the interconnectedness of ecological systems and economic activities, businesses can develop strategies that prioritize environmental stewardship, biodiversity conservation, and social responsibility. Zoological insights inform businesses about the intrinsic value of biodiversity, highlighting the importance of preserving natural habitats, protecting endangered species, and mitigating anthropogenic impacts on ecosystems. Through AI-powered simulations and scenario analyses, businesses can assess the long-term consequences of their decisions on ecological integrity, societal well-being, and corporate reputation, fostering a culture of responsible innovation and sustainable growth. However, the integration of zoological research with AI-driven business management also poses challenges and ethical considerations. Ensuring the ethical treatment of animals in research and development processes, safeguarding data privacy and security, and addressing biases in AI algorithms are paramount concerns that require interdisciplinary collaboration and stakeholder engagement. Moreover, fostering inclusivity and diversity in both scientific research and business leadership is essential for promoting equitable outcomes and fostering innovation. In conclusion, the integration of zoological research with AI-driven business management holds immense potential for unlocking biological insights and driving sustainable innovation in diverse industries. By bridging the gap between biological sciences and business practices, this interdisciplinary approach offers a pathway towards a more resilient, adaptive, and ethically conscious future. Through collaborative efforts among scientists, entrepreneurs, policymakers, and community stakeholders, we can harness the power of nature-inspired solutions to address complex challenges and create positive societal impact.

Study of mechanical behavior of grout sleeve splicing of rebars

Study of mechanical behavior of grout sleeve splicing of rebars

Experimental study of the potential of concentrated NaCl solutions for use in pressure-retarded osmosis process

Pressure retarded osmosis is a process that enables useful work generation from the salinity difference of solutions. The literature most often considers using pressure retarded osmosis with natural sodium chloride (NaCl) solutions, such as seawater, dedicated for open systems. To explore the full potential of this process, however, optimized, highly concentrated solutions of various compounds can be used. The presented research is focused on evaluating the impact of increasing draw solution temperature and concentration on the permeate flow in the osmotic process. The permeate flow is directly related to achievable work in this process, therefore, it is important to find feed and draw solution parameters that maximize it. An experimental setup developed in this study provides full control over the process parameters. Furthermore, the performance characteristics of the membrane over process time were investigated, as it became evident during preliminary experiments that the membrane impact is significant. The studies were conducted without back-pressure, in a configuration typical of the forward osmosis process, with solution circulation on both sides of the membrane. The obtained results show a clear positive impact of both the temperature and concentration increase on the potential output of a pressure retarded osmosis system. The membrane behaviour study allowed for correct interpretation of the results, by establishing the dynamics of the membrane degradation process.

Experimental and Numerical Study of the Physico-Hygrothermal Behavior of Two Tropical Wood Species

Experimental and Numerical Study of the Physico-Hygrothermal Behavior of Two Tropical Wood Species

Study of climate literacy and pro-environmental behavior amongst students

This research examines the role of effort at the school level in ensuring climate literacy and the influence of climate literacy on the environmental behavior of children. Literature has shown that pro-environmental behavior is mediated by hope and despair amongst the children. An attempt has been made to study the same. Also, the role of climate literacy in inducing indoor generation behavior was studied. The research is based on a questionnaire developed to measure the constructs (Efforts at the school level, hope, despair, climate literacy, pro-environmental behavior, and indoor generation behavior). The questionnaires were used to survey 477 students of the middle and high-level schools in Delhi National Capital Region (DelhiNCR), India. CB -Structural Equation modeling was used to study the influence and significance of the relationships. The research revealed that climate literacy, positively and significantly influenced proenvironmental behavior, while it wasn’t positively influencing indoor generation behavior. Hope was been found to positively mediate pro-environmental behavior. This research is one of its kind and explored the psychological and behavioral implications of climate literacy amongst school students.

A comprehensive study of low-velocity impact behavior and damage evaluation of angle-ply thin woven GFRP composites under elevated temperatures

The recent climatic changes have resulted in a significant increase in temperatures over 50 °C during summer midday in certain countries. This has motivated the study of the low-velocity impact (LVI) response of angle-ply thin woven glass fiber-reinforced polymer composites under temperatures of 25 °C, 50 °C and 75 °C. The LVI performance of the laminates was assessed by eighteen parameters. A new inexpensive image processing method is developed to accurately measure the damage diameter, area, and perimeter length, which significantly aids in analyzing the damage mechanisms under various test conditions. The damage perimeter length is a new parameter that is used to interprets the change of material behaviors with temperature. The LVI results showed that the impact bending stiffness is increased with impact energy (Ei) due to increasing strain rates, which leads to decreasing the molecular mobility of polymer chains and hence, the materials tend to behave more brittle. Delamination threshold force increased with impact energy and vice versa with temperature. Soaking the specimens for 1 h at 50°C improved post curing and thus, enhanced the cross linking density, which significantly improved most of the impact properties. The penetration energy threshold of 25.5J (85 % of Ei) is observed at 30J-50 °C. Relaxation of the polymer chain within the glass transition region at 75°C leads to full fracture energy threshold at 30J, which correlated with open force-deformation curve, absorbed energy of 100 %, zero elastic energy, zero rebound energy, zero rebound velocity, and increase contact duration. The proposed multivariate regression models accurately predict the impact properties.