Motivation to learn: an international multilevel study on student autonomy and teacher emphasis on content usefulness / Motivación para aprender: un estudio internacional multinivel sobre la autonomía de los estudiantes y el énfasis de los docentes en la utilidad del contenido

En la educación superior, pocos estudios relacionan factores contextuales en la clase, como el énfasis del profesor en la utilidad del contenido y las características motivacionales de los estudiantes. El objetivo fue probar un modelo multinivel sobre la relación entre el énfasis del docente en la utilidad del contenido durante la clase, la autonomía de los estudiantes y, a su vez, la motivación para aprender. Participaron 3033 estudiantes universitarios matriculados de 1º a 4º grado de Ciencias de la Actividad Física y del Deporte, de universidades de España (N = 602), Portugal (N = 469), México (N = 1177), Chile (N = 372), y Brasil (N = 413). Se realizó un modelo de ecuaciones estructurales multinivel, en el que los participantes respondieron preguntas sobre el énfasis del profesor en la utilidad del contenido de la clase, la autonomía y la motivación para aprender. Se hipotetizó que el énfasis del profesor en la utilidad del contenido predecía la autonomía del estudiante que, por su vez, predecía la motivación para aprender. Los resultados, a nivel grupal e individual, indican que el énfasis del docente en la utilidad del contenido predijo la autonomía del estudiante, y la autonomía predijo la motivación para aprender.(AU)

Teacher autonomy support is related to improved student learn-ing. In higher education, few studies relate classroom contextual factors, such as teacher emphasis on content usefulness, and students' motivational characteristics. The aim was to test a multilevel model about the relation between the extent of teachers’ emphasis on the usefulness of class con-tent with student autonomy, and, in turn, on motivation to learn. The par-ticipants were 3033 university students enrolled from 1st to 4th grade of Sciences of the Physical Activity and Sport, from universities in Spain (N = 602), Portugal (N = 469), Mexico (N = 1177), Chile (N = 372), and Brazil (N = 413). A multilevel structural equation model was performed, in which participants answered questions about the teacher's emphasis on the use-fulness of class content, basic psychological need for autonomy, and moti-vation to learn. At the group and individual levels, the hypothesis is that the teacher's emphasis on the usefulness of class content predict the stu-dent autonomy, in turn, student autonomy predicts student motivation to learn. Results found at the group level and at the individual level the strength of teacher emphasis on class content predicted student autonomy; student autonomy predicted student motivation to learn.(AU)

Self-determined motivational health coaching ('SAMI') during outpatient treatment to promote physical activity of people with serious mental illness: a pilot controlled trial.

BACKGROUND: Brief motivational coaching, integrated into health care; seems promising to address physical inactivity of people with serious mental illness (SMI). AIMS: To test the impact of a self-determined health coaching approach (the "SAMI" intervention) during outpatient mental health treatment on moderate-to-vigorous physical activity (MVPA) of people with SMI. METHODS: Adults (mean age = 41.9, SD = 10.9) with an ICD-10 diagnosis of mental illness were semi-randomized to the SAMI-intervention group (IG) or control group (CG). The IG received 30 minutes of health coaching based on the self-determination theory (SDT). MVPA and sedentary time (ST) were measured with the International Physical Activity Questionnaire - short form (IPAQ-SF) and symptoms of mental illness with the Brief Symptom Inventory (BSI-18), each at baseline and follow-up (3-4 months). Differences in primary (MVPA) and secondary (ST, BSI-18) outcomes were evaluated using negative binomial regressions and general linear models. RESULTS: In the IG (n = 30), MVPA increased from 278 (interquartile range [IQR] = 175-551) to 435 (IQR = 161-675) min/week compared to a decrease from 250 (IQR = 180-518) to 155 (IQR = 0-383) min/week in the CG (n = 26; adjusted relative difference at follow-up: Incidence Rate Ratio [IRR] = 2.14, 95% CI: 1.17-3.93, p = 0.014). There were no statistically significant differences in ST and BSI-18. CONCLUSIONS: Brief self-determined health coaching during outpatient treatment could increase post-treatment MVPA in people with SMI, potentially up to a clinically relevant level. However, great uncertainty (for all outcomes) weakens the assessment of clinical relevance.

Stereodivergent Total Synthesis of Tacaman Alkaloids.

This paper describes a concise, asymmetric and stereodivergent total synthesis of tacaman alkaloids. A key step in this synthesis is the biocatalytic Baeyer-Villiger oxidation of cyclohexanone, which was developed to produce seven-membered lactones and establish the required stereochemistry at the C14 position (92% yield, 99% ee, 500 mg scale). Cis- and trans-tetracyclic indoloquinolizidine scaffolds were rapidly synthesized through an acid-triggered, tunable acyl-Pictet-Spengler type cyclization cascade, serving as the pivotal reaction for building the alkaloid skeleton. Computational results revealed that hydrogen bonding was crucial in stabilizing intermediates and inducing different addition reactions during the acyl-Pictet-Spengler cyclization cascade. By strategically using these two reactions and the late-stage diversification of the functionalized indoloquinolizidine core, the asymmetric total syntheses of eight tacaman alkaloids were achieved. This study may potentially advance research related to the medicinal chemistry of tacaman alkaloids.

Instabilities and self-organization in spatiotemporal epidemic dynamics driven by nonlinearity and noise.

Theoretical analysis of epidemic dynamics has attracted significant attention in the aftermath of the COVID-19 pandemic. In this article, we study dynamic instabilities in a spatiotemporal compartmental epidemic model represented by a stochastic system of coupled partial differential equations (SPDE). Saturation effects in infection spread-anchored in physical considerations-lead to strong nonlinearities in the SPDE. Our goal is to study the onset of dynamic, Turing-type instabilities, and the concomitant emergence of steady-state patterns under the interplay between three critical model parameters-the saturation parameter, the noise intensity, and the transmission rate. Employing a second-order perturbation analysis to investigate stability, we uncover both diffusion-driven and noise-induced instabilities and corresponding self-organized distinct patterns of infection spread in the steady state. We also analyze the effects of the saturation parameter and the transmission rate on the instabilities and the pattern formation. In summary, our results indicate that the nuanced interplay between the three parameters considered has a profound effect on the emergence of dynamical instabilities and therefore on pattern formation in the steady state. Moreover, due to the central role played by the Turing phenomenon in pattern formation in a variety of biological dynamic systems, the results are expected to have broader significance beyond epidemic dynamics.

Solving Maxwell's Equations Using Polarimetry Alone.

Maxwell's equations are solved when the amplitude and phase of the electromagnetic field are determined at all points in space. Generally, the Stokes parameters can only capture the amplitude and polarization state of the electromagnetic field in the radiation (far) zone. Therefore, the measurement of the Stokes parameters is, in general, insufficient to solve Maxwell's equations. In this Letter, we solve Maxwell's equations for a set of objects widely used in Nanophotonics using the Stokes parameters alone. These objects are lossless, axially symmetric, and well described by a single multipolar order. Our method for solving Maxwell's equations endows the Stokes parameters an even more fundamental role in the electromagnetic scattering theory.

Altered executive control network and default model network topology are linked to acute electronic cigarette use: A resting-state fNIRS study.

In recent years, electronic cigarettes (e-cigs) have gained popularity as stylish, safe, and effective smoking cessation aids, leading to widespread consumer acceptance. Although previous research has explored the acute effects of combustible cigarettes or nicotine replacement therapy on brain functional activities, studies on e-cigs have been limited. Using fNIRS, we conducted graph theory analysis on the resting-state functional connectivity of 61 male abstinent smokers both before and after vaping e-cigs. And we performed Pearson correlation analysis to investigate the relationship between alterations in network metrics and changes in craving. E-cig use resulted in increased degree centrality, nodal efficiency, and local efficiency within the executive control network (ECN), while causing a decrease in these properties within the default model network (DMN). These alterations were found to be correlated with reductions in craving, indicating a relationship between differing network topologies in the ECN and DMN and decreased craving. These findings suggest that the impact of e-cig usage on network topologies observed in male smokers resembles the effects observed with traditional cigarettes and other forms of nicotine delivery, providing valuable insights into their addictive potential and effectiveness as aids for smoking cessation.

Response inhibition in premotor cortex corresponds to a complex reshuffle of the mesoscopic information network.

Recent studies have explored functional and effective neural networks in animal models; however, the dynamics of information propagation among functional modules under cognitive control remain largely unknown. Here, we addressed the issue using transfer entropy and graph theory methods on mesoscopic neural activities recorded in the dorsal premotor cortex of rhesus monkeys. We focused our study on the decision time of a Stop-signal task, looking for patterns in the network configuration that could influence motor plan maturation when the Stop signal is provided. When comparing trials with successful inhibition to those with generated movement, the nodes of the network resulted organized into four clusters, hierarchically arranged, and distinctly involved in information transfer. Interestingly, the hierarchies and the strength of information transmission between clusters varied throughout the task, distinguishing between generated movements and canceled ones and corresponding to measurable levels of network complexity. Our results suggest a putative mechanism for motor inhibition in premotor cortex: a topological reshuffle of the information exchanged among ensembles of neurons.

In this study, we investigated the dynamics of information transfer among functionally identified neural modules during cognitive motor control. Our focus was on mesoscopic neural activities in the dorsal premotor cortex of rhesus monkeys engaged in a Stop-signal task. Leveraging multivariate transfer entropy and graph theory, we uncovered insights on how behavioral control shapes the topology of information transmission in a local brain network. Task phases modulated the strength and hierarchy of information exchange between modules, revealing the nuanced interplay between neural populations during generated and canceled movements. Notably, during successful inhibition, the network displayed a distinctive configuration, unveiling a novel mechanism for motor inhibition in the premotor cortex: a topological reshuffle of information among neuronal ensembles.

Effect of empowering leadership on employees' workplace loneliness: a moderated mediation model.

Background: Workplace loneliness has become a prevalent experience among employees in organizations; however, there is limited empirical research on how leaders can address and mitigate this issue. Drawing upon self-determination theory and empowering leadership theory, this study examines the impact of empowering leadership on workplace loneliness by exploring the mediator of role breadth self-efficacy and the moderator of leader-member conversational quality. Methods: A time-lagged research design was used, collecting data through a two-wave online survey involving 531 employees in Chinese public sectors. The participants consisted of 321 males and 210 females, with an average age of 35 years (SD = 7.36). Results: Our findings indicate that empowering leadership positively influences employees' role breadth self-efficacy, reducing their workplace loneliness. Moreover, leader-member conversational quality strengthens this indirect effect, suggesting that empowering leadership is more effective in reducing workplace loneliness when leader-member conversational quality is high. Conclusion: This study expands and enriches research on the antecedents of workplace loneliness from the leadership approach, providing valuable insights for organizations to implement interventions that effectively alleviate employees' workplace loneliness.

Perspective: the evolution of hormones and person perception-a quantitative genetic framework.

Evolutionary biology provides a unifying theory for testing hypotheses about the relationship between hormones and person perception. Person perception usually receives attention from the perspective of sexual selection. However, because person perception is one trait in a suite regulated by hormones, univariate approaches are insufficient. In this Perspectives article, quantitative genetics is presented as an important but underutilized framework for testing evolutionary hypotheses within this literature. We note tacit assumptions within the current literature on psychiatric genetics, which imperil the interpretation of findings thus far. As regulators of a diverse manifold of traits, hormones mediate tradeoffs among an array of functions. Hormonal pleiotropy also provides the basis of correlational selection, a process whereby selection on one trait in a hormone-mediated suite generates selection on the others. This architecture provides the basis for conflicts between sexual and natural selection within hormone-mediated suites. Due to its role in person perception, psychiatric disorders, and reproductive physiology, the sex hormone estrogen is highlighted as an exemplar here. The implications of this framework for the evolution of person perception are discussed. Empirical quantification of selection on traits within hormone-mediated suites remains an important gap in this literature with great potential to illuminate the fundamental nature of psychiatric disorders.

High-Concentrated Binary-Salt Ether Electrolytes for High-Voltage Lithium Metal Batteries with Ni-Rich Cathode.

The incompatibility of ether electrolytes with a cathode dramatically limits its application in high-voltage Li metal batteries. Herein, we report a new highly concentrated binary salt ether-based electrolyte (HCBE, 1.25 M LiTFSI + 2.5 M LiFSI in DME) that enables stable cycling of high-voltage lithium metal batteries with the Ni-rich (NCM83, LiNi0.83Co0.12Mn0.05O2) cathode. Experimental characterizations and density functional theory (DFT) calculations reveal the special solvation structure in HCBE. A solvation structure rich in aggregates (AGGs) can effectively broaden the electrochemical window of the ether electrolyte. The anions in HCBE preferentially decompose under high voltage, forming a CEI film rich in inorganic components to protect the electrolyte from degradation. Thus, the high-energy-density Li||NCM83 cell has a capacity retention of ≈95% after 150 cycles. Significantly, the cells in HCBE have a high and stable average Coulombic efficiency of over 99.9%, much larger than that of 1 M LiPF6 + EC + EMC + DMC (99%). The result emphasizes that the anionic-driven formation of a cathode electrolyte interface (CEI) can reduce the number of interface side reactions and effectively protect the cathode. Furthermore, the Coulombic efficiency of Li||Cu using the HCBE is 98.5%, underscoring the advantages of using ether-based electrolytes. This work offers novel insights and approaches for the design of high-performance electrolytes for lithium metal batteries.

Preferential adsorption of medium molecular weight proteins in extracellular polymeric substance alleviates toxicity of small-sized microplastics to Skeletonema costatum.

Extracellular polymeric substances (EPS) secreted by organisms tend to encapsulate microplastics (MPs), forming an EPS-corona that affects the fate of MPs in marine ecosystems. However, the impact of the EPS-corona on the biotoxicity of MPs to marine organisms remains poorly understood. Herein, the effect of the EPS-corona on the toxicity of polystyrene (PS) MPs of different sizes (0.1 and 1 µm) to Skeletonema costatum (S. costatum) was investigated. The preferential adsorption of medium molecule weight (∼55 kDa) proteins onto PS MPs mainly contributed to the EPS-corona formation, decreasing the surface charge negativity of small-sized PS MPs (0.1 µm) by 72.4 %. Nitrogen (N) and oxygen (O) moieties in polysaccharides and proteins were identified as the preferential adsorption sites in the EPS-PS MPs interaction. Density functional theory (DFT) calculations confirmed the nuclear magnetic resonance spectroscopy (NMR) results, revealing that the binding mode between EPS and PS MPs was mainly hydrogen bonding. In addition, EPS-corona increased the cell density of S. costatum by 35.5-36.0 % when exposed to small-sized PS MPs (0.1 µm, 25-50 mg/L). These findings provide new insights into how EPS-corona affects the environmental fate and ecological risks associated with micro- and nano-sized plastics in marine ecosystems.

Core-bishell NiFe@NC@MoS2 for boosting electrocatalytic activity towards ultra-efficient oxygen evolution reaction.

Designing and developing suitable oxygen evolution reaction (OER) catalysts with high activity and stability remain challenging in electrolytic water splitting. Hence, NiFe@NC@MoS2 core-bishell composites wrapped by molybdenum disulphide (MoS2) and nitrogen-doped graphene (NC) were prepared using hydrothermal synthesis in this research. NiFe@NC@MoS2 composite exhibits excellent performance with an overpotential of 288 mV and a Tafel slope of 53.2 mV·dec-1 at a current density of 10 mA·cm-2 in 1 M KOH solution, which is superior to commercial RuO2. NC and MoS2 bishells create profuse edge active sites that enhance the adsorption ability of OOH* while lowering the overall overpotential of the product and improving its oxygen precipitation performance. The density function theory(DFT) analysis confirms that the layered MoS2 in NiFe@NC@MoS2 provides additional edge active sites and enhances electron transfer, thus increasing the intrinsic catalytic activity. This research paves a novel way for developing OER electrocatalysts with excellent catalytic performance.

Chaotic RIME optimization algorithm with adaptive mutualism for feature selection problems.

The RIME optimization algorithm is a newly developed physics-based optimization algorithm used for solving optimization problems. The RIME algorithm proved high-performing in various fields and domains, providing a high-performance solution. Nevertheless, like many swarm-based optimization algorithms, RIME suffers from many limitations, including the exploration-exploitation balance not being well balanced. In addition, the likelihood of falling into local optimal solutions is high, and the convergence speed still needs some work. Hence, there is room for enhancement in the search mechanism so that various search agents can discover new solutions. The authors suggest an adaptive chaotic version of the RIME algorithm named ACRIME, which incorporates four main improvements, including an intelligent population initialization using chaotic maps, a novel adaptive modified Symbiotic Organism Search (SOS) mutualism phase, a novel mixed mutation strategy, and the utilization of restart strategy. The main goal of these improvements is to improve the variety of the population, achieve a better balance between exploration and exploitation, and improve RIME's local and global search abilities. The study assesses the effectiveness of ACRIME by using the standard benchmark functions of the CEC2005 and CEC2019 benchmarks. The proposed ACRIME is also applied as a feature selection to fourteen various datasets to test its applicability to real-world problems. Besides, the ACRIME algorithm is applied to the COVID-19 classification real problem to test its applicability and performance further. The suggested algorithm is compared to other sophisticated classical and advanced metaheuristics, and its performance is assessed using statistical tests such as Wilcoxon rank-sum and Friedman rank tests. The study demonstrates that ACRIME exhibits a high level of competitiveness and often outperforms competing algorithms. It discovers the optimal subset of features, enhancing the accuracy of classification and minimizing the number of features employed. This study primarily focuses on enhancing the equilibrium between exploration and exploitation, extending the scope of local search.

Decision making for people living with HIV/AIDS using the theory of innovation diffusion model at the Western Provident Association Turen Foundation.

OBJECTIVES: To describe how people living with HIV/AIDS (PLWHA) make decisions using the diffusion of innovation theory model. Decisions occur when individual decision makers engage in activities that guide choices to adopt or reject a particular innovation. METHODS: This is a descriptive analysis research using a survey method. Data collection was carried out using a decision making questionnaire. The subjects in this research were HIV/AIDS sufferers (PLWHA) who lived in Turen, Indonesia. The number of research subjects was 36 respondents obtained using the purposive sampling technique on January 2023. RESULTS: The research design used the correlation method with a cross sectional approach and the Spearman correlation coefficient statistical test. The research results show significance (2-tailed) of 0.934 (p>0.05). The correlation coefficient results are negative. Where the direction of the correlation produces showed a very weak relationship with a value of 0.014 and the results of the analysis between variables are not the same. This is because the persuasion stage was not tested in the analysis. Research shows that 8 (22.2%) patients rejected the decision making of PLWHA using the diffusion of innovation theory model at the Western Provident Association Turen Foundation, Turen, Indonesia, while 28 (77.7%) patients accepted it. It can be concluded that the majority of decisions made by HIV/AIDS patients at the ADIS Turen Peduli Warga Foundation are accepted. CONCLUSION: Knowledge of HIV/AIDS sufferers is at a good level, their decision making is mostly accepted and there is a meaningful relationship between knowledge and decision making in using a chatbot innovation. The suggestion from the research is that this chatbot innovation can be a source of further research and help provide education for PLWHA patients in everyday life.

Understanding the challenges of identifying, supporting, and signposting patients with alcohol use disorder in secondary care hospitals, post COVID-19: a qualitative analysis from the North East and North Cumbria, England.

BACKGROUND: Alcohol-related mortality and morbidity increased during the COVID-19 pandemic in England, with people from lower-socioeconomic groups disproportionately affected. The North East and North Cumbria (NENC) region has high levels of deprivation and the highest rates of alcohol-related harm in England. Consequently, there is an urgent need for the implementation of evidence-based preventative approaches such as identifying people at risk of alcohol harm and providing them with appropriate support. Non-alcohol specialist secondary care clinicians could play a key role in delivering these interventions, but current implementation remains limited. In this study we aimed to explore current practices and challenges around identifying, supporting, and signposting patients with Alcohol Use Disorder (AUD) in secondary care hospitals in the NENC through the accounts of staff in the post COVID-19 context. METHODS: Semi-structured qualitative interviews were conducted with 30 non-alcohol specialist staff (10 doctors, 20 nurses) in eight secondary care hospitals across the NENC between June and October 2021. Data were analysed inductively and deductively to identify key codes and themes, with Normalisation Process Theory (NPT) then used to structure the findings. RESULTS: Findings were grouped using the NPT domains 'implementation contexts' and 'implementation mechanisms'. The following implementation contexts were identified as key factors limiting the implementation of alcohol prevention work: poverty which has been exacerbated by COVID-19 and the prioritisation of acute presentations (negotiating capacity); structural stigma (strategic intentions); and relational stigma (reframing organisational logics). Implementation mechanisms identified as barriers were: workforce knowledge and skills (cognitive participation); the perception that other departments and roles were better placed to deliver this preventative work than their own (collective action); and the perceived futility and negative feedback cycle (reflexive monitoring). CONCLUSIONS: COVID-19, has generated additional challenges to identifying, supporting, and signposting patients with AUD in secondary care hospitals in the NENC. Our interpretation suggests that implementation contexts, in particular structural stigma and growing economic disparity, are the greatest barriers to implementation of evidence-based care in this area. Thus, while some implementation mechanisms can be addressed at a local policy and practice level via improved training and support, system-wide action is needed to enable sustained delivery of preventative alcohol work in these settings.

Influential individuals can promote prosocial practices in heterogeneous societies: a mathematical and agent-based model.

In this paper, we examine how different governance types impact prosocial behaviors in a heterogenous society. We construct a general theoretical framework to examine a game-theoretic model to assess the ease of achieving a cooperative outcome. We then build a dynamic agent-based model to examine three distinct governance types in a heterogenous population: monitoring one's neighbors, despotic leadership, and influencing one's neighbors to adapt strategies that lead to better fitness. In our research, we find that while despotic leadership may lead towards high prosociality and high returns it does not exceed the effects of a local individual who can exert positive influence in the community. This may suggest that greater individual gains can be had by cooperating and that global hierarchical leadership may not be essential as long as influential individuals exert their influence for public good and not for public ill.

Identifying discriminative features of brain network for prediction of Alzheimer's disease using graph theory and machine learning.

Alzheimer's disease (AD) is a challenging neurodegenerative condition, necessitating early diagnosis and intervention. This research leverages machine learning (ML) and graph theory metrics, derived from resting-state functional magnetic resonance imaging (rs-fMRI) data to predict AD. Using Southwest University Adult Lifespan Dataset (SALD, age 21-76 years) and the Open Access Series of Imaging Studies (OASIS, age 64-95 years) dataset, containing 112 participants, various ML models were developed for the purpose of AD prediction. The study identifies key features for a comprehensive understanding of brain network topology and functional connectivity in AD. Through a 5-fold cross-validation, all models demonstrate substantial predictive capabilities (accuracy in 82-92% range), with the support vector machine model standing out as the best having an accuracy of 92%. Present study suggests that top 13 regions, identified based on most important discriminating features, have lost significant connections with thalamus. The functional connection strengths were consistently declined for substantia nigra, pars reticulata, substantia nigra, pars compacta, and nucleus accumbens among AD subjects as compared to healthy adults and aging individuals. The present finding corroborate with the earlier studies, employing various neuroimagining techniques. This research signifies the translational potential of a comprehensive approach integrating ML, graph theory and rs-fMRI analysis in AD prediction, offering potential biomarker for more accurate diagnostics and early prediction of AD.

The physical environment matters: room effects on online purchase decisions.

Introduction: People as individual consumers are regularly targeted in sustainability campaigns or communications with the hope of enhancing sustainable behavior at an individual level, with subsequent sustainability transformation at a larger societal scale. However, psychological motivation is complex and campaigns need to be based on an understanding for what individual, and contextual, factors support or hinder sustainable behavioral choices. Methods: In a discrete choice experiment, participants made hypothetical online purchases in each of three rooms designed to evoke associations to hedonic, gain, and normative goal frames. Participants were shown a campaign message intended to prime sustainable textile consumption prior to the purchase. For each product (t-shirt or bananas) hedonic (comfort/look), gain (price), and normative (organic/ fairtrade) attributes were varied in an online choice experiment. Results: Preferences for the normative attribute of t-shirts increased in the normative room compared to the room with gain associations. No effect of the rooms with hedonic or gain priming was observed on the choice. Discussion: The study supports the hypothesis that the physical room can enhance goal frame activation and behavioral choice but concludes that such priming effect is sensitive to specificity of the prime.

Exploring the recycled water acceptance based on the technological perspective of UTAUT2: a hybrid analytical approach.

Introduction: The development of advanced sewage technologies empowers the industry to produce high-quality recycled water, which greatly influences human's life and health. Thus, this study investigates the mechanism of individuals' adoption of recycled water from the technology adoption perspective. Methods: Employing the mixed method of structural equation modeling and artificial neural network analysis, we examined a research model developed from the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) framework. To examine the research model, this study employs a leading web-survey company (Sojump) to collect 308 valid samples from the residents in mainland China. Results: The structural equation modeling results verified the associations between the six predictors (performance expectancy, effort expectancy, social influence, facilitating conditions, environmental motivation, and price value), individuals' cognitive and emotional attitudes, and acceptance intention. The artificial neural network analysis validates and complements the structural equation modeling results by unveiling the importance rank of the significant determinants of the acceptance decisions. Discussion: The study provides theoretical implications for recycled water research and useful insights for practitioners and policymakers to reduce the environmental hazards of water scarcity.

Steric Effect and Intrinsic Electrophilicity and Nucleophilicity from a Conceptual Density Functional Theory and Information-Theoretic Approach as Quantitative Probes of Chemical Reactions.

Appreciating reactivity in terms of physicochemical effects for chemical processes is one of the most important undertakings in chemistry. While transition state (TS) theory provides the framework enabling the reliable calculation of the barrier height for a given elementary step, analytical tools are necessary to gain insight into key factors governing the different processes during chemical reactions. In this contribution, we partition the potential energy surface of an elementary step along the intrinsic coordinate into three segments, the so-called Pre-TS, TS, and Post-TS regions, and then determine the most important factors dictating each segment. This analysis is based on the use of both reactivity descriptors from conceptual density functional theory and concepts from the information-theoretic approach in density functional theory. We found that in both Pre-TS and Post-TS regions, steric effects are the dominant factors, whereas in the TS region, it is the intrinsic electrophilic and nucleophilic propensity of the transition state structure that governs the reactivity. The wide applicability of our approach is shown by a validation for a total of 37 organic and inorganic reactions. This work thus, in our view, provides a novel perspective on how chemical reactivity can be quantified at different stages of chemical reactions.