Search Results (1,626)

This paper investigates engineering decision optimization in uncertain environments. Subject to constraints on cost and expected returns, engineering decisions optimize material input, equipment selection and process arrangement to minimize costs and maximize economic benefits. As an efficient formal verification technique, model checking offers a new approach to addressing this problem. Traditional model checking focuses on qualitative verification, while quantitative approaches, including probabilistic and possibilistic model checking, have been gradually developed. Among them, possibilistic model checking is more applicable to systems with fuzzy uncertainty. However, existing possibilistic model-checking techniques have notable limitations: they are only designed for closed systems and ignore interactions between the system and external environments; their simplistic information aggregation leads to information asynchrony and loss; and they cannot model and verify systems with incomplete information. Model checking based on possibilistic decision processes enables the selection of uncertain actions and initially resolves the modeling and verification of open systems. In our previous work, we introduced quality constraints into possibilistic temporal logic to mitigate information asynchrony and loss. We also established the theories of intuitionistic fuzzy Kripke structure (IFKS) and Intuitionistic Fuzzy Computation Tree Logic (IFCTL), which support the modeling and verification of systems with incomplete information. To improve the practicality and accuracy of engineering decisions, this study adopts the ideas of uncertain decision-making behavior selection, quality constraints and incomplete information modeling. It extends IFKS to the Weighted Intuitionistic Fuzzy Kripke Structure (WIFKS) and evolves IFCTL into the intuitionistic fuzzy decision tree logic (IFDTL). We further propose an IFDTL model-checking algorithm and a multi-attribute engineering decision algorithm based on the proposed method, along with corresponding correctness proofs and complexity analysis. A case study on Qinling health-preserving tourism planning verifies the rationality and effectiveness of the presented approach. This research provides a novel formal solution for engineering decision-making under uncertainty. Full article

Urban green spaces (UGSs) are considered crucial for enhancing older adults’ subjective well-being. However, limited studies have explored the synergistic effects of UGS quality and quantity on satisfaction across green spaces, residential environments, and life domains, making it challenging to uncover the multifaceted sustainable benefits of UGSs on older adults’ subjective well-being. This study drew on multi-source data and place attachment theory to depict neighborhood-accessible UGS quantity (provision, accessibility, and visibility) and quality (cognition, behavior, and affect). Through the geographical visualization of bivariate SHapley Additive exPlanations (SHAP) interaction values extracted from the trained eXtreme Gradient Boosting (XGBoost) model, and the comparison of bivariate SHAP maps with univariate SHAP maps, the study revealed the nonlinear geographic associations between UGS quantity and quality and three types of satisfaction. The results showed that when UGS quantity and quality coexisted, variations in the impact of quantity on older adults’ satisfaction were associated with quality differences. The gain effect of quality on quantity was more significant in areas with limited green space within a 500 m buffer zone. UGSs made a direct contribution to green space satisfaction, while their indirect association with life satisfaction surpassed that of residential satisfaction due to their provision of emotional qualities. This study calls for neighborhood green planning aimed at improving older adults’ subjective well-being, which should shift focus from quantity to quality and balance the relationship between quantity and quality based on regional characteristics. Full article

With the rapid expansion of the pet economy, owner–pet matching outfits have grown increasingly popular among pet owners. Grounded in the extended theory of planned behavior, this study investigates the key determinants of pet owners’ purchase intentions and actual purchase behaviors toward owner–pet matching outfits, and explores the moderating effect of aesthetic risk on the intention–behavior transition. Questionnaire survey data from 222 pet owners were collected for empirical analysis, and regression analysis was adopted to verify the proposed research hypotheses. The empirical results reveal that subjective norms exert a direct promotional effect on consumer purchase behavior and indirectly boost such behavior through the partial mediating role of purchase intention. By contrast, behavioral attitude is positively associated with purchase intention and further stimulates purchase behavior via a full mediating pathway of purchase intention. Perceived behavioral control displays a significant positive direct impact on purchase behavior yet yields no significant effect on purchase intention. Furthermore, purchase intention serves as a robust positive predictor of purchase behavior, whereas aesthetic risk significantly weakens the association between purchase intention and purchase behavior. Brands are suggested to foster consumers’ favorable behavioral attitudes by optimizing product design, enriching practical functions, and minimizing potential risks to pets in owner–pet matching outfits. Meanwhile, enterprises should actively shape supportive subjective norms to popularize the owner–pet matching outfit wearing lifestyle. Additionally, brands need to enhance consumption accessibility through diversified sales channels, reasonable pricing strategies and abundant product style options. This study pioneers the application of the extended theory of planned behavior to the emerging field of owner–pet matching outfits, empirically verifying the positive effects of behavioral attitude, subjective norms, and perceived behavioral control on consumers’ purchase intention and purchase behavior. Full article

AI technologies are changing the world of work in ways that are hard to predict, and this uncertainty is felt particularly strongly by young people who are just beginning to think about their futures. This study explores how high school students in Turkey perceive AI’s potential impact on their career choices, using Social Cognitive Career Theory (SCCT) and Uncertainty Management Theory (UMT) as interpretive lenses rather than formally tested models. SCCT helps frame AI as an environmental force that shapes how students think about their career options, while UMT helps explain how students emotionally and cognitively respond to uncertainty that cannot easily be resolved. Using a cross-sectional survey of 354 12th-grade students, we developed and validated the AI-Related Career Perception Questionnaire (AICP-Q), which yielded four factors: AI Anxiety and Career Precarity, AI Literacy and Technological Awareness, Proactive Career Adaptation, and Socio-Technical Uncertainty. Students showed moderate AI awareness but relatively high levels of socio-technical uncertainty. Academic track emerged as an exploratory statistical correlate of AI Anxiety, a descriptive association suggesting that students’ sense of threat from AI may relate more to the specific skill demands of their chosen field than to the prestige of their school, though no causal inference can be drawn from these cross-sectional data. A key finding is “the planning gap”: students recognized the potential career disruptions associated with AI but did not consistently respond with adaptive behaviors. Drawing on UMT, we advance the tentative hypothesis, to be tested in future research, that this pattern may relate to a lack of the appraisal resources needed to translate awareness into action; because these constructs were not directly measured, this remains an interpretive suggestion rather than an empirical finding. Full article

Path planning for autonomous robots is a key problem area, particularly when faced with complicated, dynamic, or uncertain environments. Even though traditional techniques (grid-based, graph-based, sampling, and optimization-based) have already been developed to solve this problem, there are notable limitations to scalability, adaptability, and responsiveness with these methods. In this paper, we explore an alternative approach based on chaotic dynamical systems, specifically chaotic attractors like those produced by the Lorenz and Rössler systems. Chaotic systems are defined by several properties that could be leveraged: non-linearity, sensitivity to initial conditions, and dense coverage of the state space are three notable properties that could be used to generate trajectories that are organized, yet ultimately unpredictable. By applying numerical integration (Runge–Kutta) directly to robot motion through MATLAB R2025b simulations, chaotic states support more effective exploration, better obstacle avoidance, and more robust navigation in dynamic or adversarial environments. The paper also examines whether chaotic path planning can be applied in multi-robot systems through state coupled robots that emerge coordinated behavior while maintaining autonomous movement. This paper is a framework for chaos theory supporting adaptable, robust navigating behaviors for purposes such as autonomous vehicles, swarm robotics, and search and rescue and surveillance applications. Full article

Opioid misuse continues to be a major public health issue in the United States. Older adults (≥65) are at particular risk of harm from opioids due to changes in opioid pharmacokinetics with age; however, healthcare professionals lack training and confidence in addressing opioid harm reduction strategies in this population. Therefore, the purpose of this study was to improve healthcare professional knowledge and beliefs regarding opioid harm reduction strategies amongst older adults. An 8 h interprofessional conference was conducted 1 May 2025 to educate healthcare providers about opioid misuse prevention strategies for older adults. This study utilized a quasi-experimental one-group pretest–posttest design to assess changes in healthcare professional knowledge and beliefs before and after the conference. Healthcare professionals in the U.S. were recruited to participate in the conference via email listservs with national reach, predominantly concentrated in Alabama. Data were collected at pre- and post-conference via an anonymous online survey informed by the Theory of Planned Behavior and Health Belief Model. Primary outcome measures included: (1) knowledge of opioid use and misuse in older adults (5 items); (2) prescribing and dispensing attitudes surrounding opioids and medications for opioid use disorder (MOUD) (5 items); (3) perceived susceptibility to harm from opioids (4 items); and (4) perceived barriers to opioid harm reduction in older adults (17-items). Constructs were measured using multiple-choice questions (knowledge) and Likert-type scales (1 = strongly disagree, 5 = strongly agree). Secondarily, intention to join a Microsoft Teams working group for ongoing collaboration was assessed through a single categorical (Yes/No/Unsure) multiple-choice question at post-conference. Data were analyzed using descriptive statistics, and differences in mean knowledge, attitudes, susceptibility, and barriers scale scores from pre- to post-conference were analyzed using Wilcoxon signed-rank tests (alpha = 0.05). Of N = 75 survey respondents, the majority were White (86.7%), female (74.7%), 50 years of age on average, and employed as pharmacists (68%). Overall, mean (SD) knowledge (83.73% [19.92] versus 90.67% [12.45]; p = 0.011) and perceived susceptibility (3.82 [0.63] versus 4.03 [0.63]; p = 0.002) increased from pre- to post-conference, while perceived barriers decreased (2.71 [0.54] versus 2.54 [0.58]; p = 0.001). Despite an upward trend, there was no statistically significant change in the mean prescribing and dispensing attitudes from baseline to post-conference. Additionally, 34.7% intended to join the Microsoft Teams working group at post-conference. Findings support the utility of interprofessional educational interventions to increase healthcare provider knowledge and beliefs regarding opioid harm reduction strategies amongst older adults. Full article

The adoption of healthier dietary behaviors has become a critical public health concern, particularly among young populations facing structural and economic constraints. Within this context, organic food consumption can be understood not only as a market choice but as a form of health-related behavior influenced by psychological factors. Drawing on the Theory of Planned Behavior, this study examines how health consciousness and core cognitive determinants shape dietary health behavior through their influence on behavioral intention and self-reported consumption patterns. A cross-sectional quantitative design was employed using data from 384 young consumers in an emerging market context (Ambato, Ecuador). The proposed model was tested using covariance-based structural equation modeling (CB-SEM). The findings indicate that perceived behavioral control is the strongest predictor of intention to engage in organic food consumption, followed by attitude and subjective norms. Health consciousness is positively associated with attitude and indirectly influences behavioral intention through this pathway. No significant relationship was found between perceived behavioral control and attitude. Behavioral intention shows a strong association with self-reported consumption behavior. These results highlight the central role of perceived feasibility in shaping health-related dietary behaviors in constrained contexts, where structural barriers may limit the translation of positive attitudes into action. The study contributes to the health psychology literature by providing context-sensitive evidence on how cognitive and motivational factors interact within the TPB framework to influence dietary behavior. Implications for promoting healthier consumption patterns emphasize the need to address both psychological drivers and structural constraints. Full article

This study aims to explore and evaluate the factors influencing Gen Z’s intention to choose green tourism destinations in Hanoi, Vietnam. The paper proposes a comprehensive analytical framework by integrating the Stimulus-Organism-Response (S-O-R) model and the Theory of Planned Behavior (TPB). A mixed-method approach was employed, in which quantitative data were collected from 269 Gen Z respondents in Hanoi and analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique through SmartPLS. The findings reveal that external environmental stimuli, including green destination image (GDI) and social media influence (SMI), positively affect individuals’ internal psychological states, namely environmental awareness (EA), attitude toward green tourism (ATT), and subjective norms (SM). These psychological states, in turn, exert positive effects and strongly promote Gen Z’s intention to choose green tourism destinations in Hanoi. This study not only contributes to filling the theoretical gap in sustainable tourism consumption behavior in the digital era but also provides practical managerial implications for policymakers and tourism businesses in developing communication strategies and tourism products that align with the preferences and expectations of younger generations. Full article

The shift to circular agrosystems necessitates using new ideas like sustainable biochar, which provides many eco-beneficial attributes like enhancing soil fertility, storing atmospheric carbon dioxide, and retaining soil moisture. However, there is still a small number of farmers worldwide (particularly those located in low-income countries) adopting biochar. Accordingly, this research is focused primarily on determining how factors affecting behavior will influence the decision of wheat producers in Marvdasht County, in Iran’s Fars Province, to use biochar as a circular technology for farming. The study will focus on addressing issues related to environmental challenges (e.g., degradation of soil and drought) through the implementation of resource-efficient, sustainable agricultural technologies. The intent of this paper was to research the behavioral characteristics associated with wheat farmers who choose to use biochar in the city of Marvdasht, Fars Region, Iran, using a new Theory of Planned Behavior (TPB). The model is theoretically enriched through the inclusion of personal norms and connectedness to the land, allowing for a more comprehensive understanding of pro-environmental decision-making. Data was collected from a total of 386 wheat farmers through the use of a structured survey. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the software Smart-PLS 3.0. The results reveal that attitude (β = 0.342, p < 0.001) and personal norms (β = 0.278, p < 0.001) are the strongest predictors of behavioral intention, while perceived behavioral control showed a weaker but significant effect (β = 0.178, p = 0.049). Subjective norms do not have a significant direct effect (β = 0.115, p = 0.199) but significantly influence intention indirectly through personal norms (β = 0.100, p < 0.001). Furthermore, connectedness to the land strongly affects personal norms (β = 0.420, p < 0.001) and exerts a significant indirect effect on intention (β = 0.117, p < 0.001), highlighting the importance of emotional attachment to land. The findings are significant because they demonstrated that farmers’ biochar adoption decisions are shaped not only by rational evaluations but also by moral obligations and emotional relationships with land. This study makes significant theoretical contributions by extending TPB with moral and relational constructs and empirically demonstrating their mediating roles in agricultural innovation adoption. The novelty of this study lies in integrating personal norms and connectedness to the land into the TPB framework to explain biochar adoption behavior within the context of circular agriculture in a developing country. Practically, the findings provide evidence-based insights for designing policies that integrate cognitive, ethical, and emotional drivers to promote biochar adoption and advance circular agriculture. Specifically, policymakers and extension agencies should prioritize behavioral-level strategies such as awareness campaigns, farmer training programs, and community-based initiatives that strengthen positive attitudes, environmental responsibility, and farmers’ emotional connection to land in order to enhance biochar adoption. Full article

Effectively modeling software failure behavior is crucial for reliability assessment and planning of releases. However, many current software reliability growth models assume that failures are independent and fault detection mechanisms are simplified. However, these assumptions may not accurately represent real-world testing environments. This study introduces a novel Nonhomogeneous Poisson Process (NHPP)-based Software Reliability Growth Model (SRGM) that includes dependent failure behavior and exponentially decaying fault detection rates to better reflect the software debugging process. The proposed model was validated using real failure datasets and compared with 17 existing models. The performance of the model was assessed using various goodness-of-fit criteria, such as errors, prediction accuracy, and metrics based on information theory. To provide a more thorough evaluation, a multi-criteria decision-making approach was used to rank the competing models based on their overall performance. Furthermore, a one-at-a-time sensitivity analysis was conducted to examine how the initial values of the parameters affected the model’s behavior. These findings indicate that the sensitivity of the model to this parameter varies depending on the dataset used. The results indicate that the proposed model achieved superior performance across multiple evaluation criteria and consistently obtained the best overall ranking under the integrated multi-criteria framework. In Dataset 1, the proposed model achieved the best performance in most goodness-of-fit criteria, whereas in Dataset 2 it produced the best results across all twelve evaluation criteria. The results show that the proposed model offers improved or competitive performance compared to existing models and provides greater flexibility in capturing complex failure processes within software systems. Full article

Household food waste (HFW) is a major contributor to global food loss, making its reduction a priority for policymakers, businesses, and organizations. Accurate waste estimates and an understanding of their drivers are essential, yet current data are inconsistent due to different quantification methods. This study introduces the open-source, Integrated Food Waste (IFWASTE) agent-based model. IFWASTE is the first agent-based model to quantify household food waste by both weight and composition, incorporating behavioral factors such as the Theory of Planned Behavior and socioeconomic variables. Simulations of 10,000 households over 100 days show substantial variability in food waste, with an average of 0.23 to 0.33 kg per capita per day, depending on the number of children. This estimate aligns with previously reported empirical data. The IFWASTE model also analyzes both individual HFW behavior over time and broader neighborhood-level patterns, supporting evidence-based reduction strategies. Full article

The voluntary carbon market (VCM) has emerged as a promising mechanism for climate mitigation; however, farmer participation in developing countries remains limited. This study combines the Theory of Planned Behavior (TPB) and the Capability–Opportunity–Motivation–Behavior (COM-B) framework to investigate factors associated with Thai farmers’ intention and self-reported stage of participation in VCM. Data were collected through face-to-face interviews with 240 farmers across multiple crop systems in Thailand and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The model explained substantial variance in intention and behavior (R² = 0.610 and 0.555, respectively), although PLS-Predict indicated limited predictive performance. Perceived behavioral control (PBC) showed the strongest positive association with reported participation behavior (β = 0.493, p < 0.001), followed by intention (β = 0.343, p < 0.001). Access to extension and technical support (AES) was positively associated with intention (β = 0.624, p < 0.001) and PBC (β = 0.338, p < 0.001). Knowledge was positively associated with PBC (β = 0.324, p < 0.001) but negatively associated with intention (β = −0.106, p = 0.045). No significant association was observed between attitude and intention; however, subjective norms were negatively associated with intention (β = −0.336, p < 0.001). Indirect associations through intention and PBC were also observed. Overall, the findings suggest that capability-, opportunity-, and trust-related factors are associated with farmers’ reported participation in VCM and may inform the design of future policies and support programs. Although the model demonstrated useful explanatory capability, its predictive performance was limited, indicating that the findings should be interpreted primarily as explanatory rather than predictive. Full article

Food loss and waste are a major global problem as reflected in the United Nations Sustainable Development Goal 12. Households constitute the primary source of food waste worldwide. The development of effective solutions depends on a comprehensive understanding of consumer attitudes and behaviors. This cross-sectional study used the Value–Attitude–Behavior (VAB) hierarchy to examine consumers’ food waste reduction intentions. It was conducted on individuals in Turkiye via an online survey. The results showed that consumers’ hedonic value and attitudes were positively associated with food waste reduction intentions. The strongest associations with intentions were observed for anticipated guilt, attitude toward reducing food waste, and hedonic value. Furthermore, education level and household size showed significant effects on food waste reduction intentions. In conclusion, these findings based on the VAB model showed the central role of anticipated guilt in shaping food waste reduction intentions, suggesting that emotionally driven intervention strategies may be more effective than approaches focusing solely on attitudes. Full article

Game-based learning (GBL) has gained widespread attention as an innovative pedagogical approach, yet its potential to enhance students’ metacognitive learning remains underexplored. Guided by self-regulated learning (SRL) theory, the review investigates how GBL design features, such as goal-setting, real-time feedback, progress visualization, and reflection tools, scaffold students’ planning, monitoring, and evaluation strategies. A systematic search across Web of Science, Scopus, and ProQuest identified the studies, which included data from physical classrooms, online learning environments, and mixed settings. This scoping review synthesizes evidence from 11 peer-reviewed studies conducted between 2015 and 2025 to evaluate the impact of GBL on metacognitive learning in primary and junior middle school contexts. Findings reveal that GBL effectively supports metacognitive learning through real-time feedback and progress indicators, though planning and evaluation scaffolds are less comprehensively addressed. Furthermore, digital trace data and behavioral logs are emerging as robust tools for assessing metacognitive processes, offering deeper insights than self-reports alone. However, the review identifies critical gaps, including insufficient focus on junior middle school students, limited representation of non-STEM disciplines, and uneven theoretical grounding across studies. The findings underscore the need for theory-driven design and balanced scaffolding to maximize GBL’s potential in fostering metacognitive competence. This study also provides practical insights for educators to foster students’ metacognitive learning by effectively integrating games into educational practices. Full article

Reliable prediction of post-initiation run-out distance of rainfall-induced landslides is essential for hazard assessment, evacuation planning, and disaster-risk mitigation. However, most existing data-driven approaches formulate run-out prediction as a deterministic regression problem and therefore provide limited information on predictive uncertainty, rare long-runout events, and explainable decision support. To address these limitations, this study proposes CQR-EVT-XAI, a trustworthy AI framework that integrates Quantile LightGBM, Conformalized Quantile Regression (CQR), Extreme Value Theory (EVT), and Explainable Artificial Intelligence (XAI) for uncertainty-aware and explainable landslide run-out risk prediction. Based on 10,158 rainfall-induced landslide samples, physics-informed features are constructed from elevation difference H, source area A, source volume V, and mean slope angle $\theta$. The proposed framework generates calibrated prediction intervals, threshold-based exceedance probabilities, upper-tail risk indicators, and interpretable risk levels. The CQR-LightGBM median model achieves high point-prediction accuracy, with $R^2$ = 0.939, RMSE = 18.03 m, and MAE = 6.55 m. Conformal calibration improves the empirical coverage of the nominal 90% and 95% prediction intervals from 0.813 to 0.903 and from 0.876 to 0.953, respectively. Tail-risk analysis shows that the upper prediction bound ${\widehat{L}}_{95}$ effectively identifies extreme long-runout events, achieving recall values of 0.974 and 0.900 for L > 300 m and L > 500 m, respectively. SHAP analysis reveals that elevation difference H, source volume V, and energy-related derived features dominate both median run-out prediction and upper-tail risk behavior, while slope-related variables mainly influence predictive uncertainty and exceedance-risk levels. These results demonstrate that the proposed CQR-EVT-XAI framework provides a practical workflow for calibrated uncertainty quantification, tail-risk identification, and explainable decision support in rainfall-induced landslide run-out risk assessment. Full article