Search Results (118)

The rapid proliferation of electronic waste, driven by the short lifecycle of smartphones and planned obsolescence strategies, presents escalating global environmental challenges. To address these issues from a systems perspective, this study develops an agent-based modeling (ABM) framework that simulates consumer decisions and stakeholder interactions within the smartphone reuse and recycling ecosystem. The model incorporates key behavioral drivers—privacy concerns, moral norms, and financial incentives—to examine how social and economic factors shape consumer behavior. Four primary agent types—consumers, manufacturers, recyclers, and second-hand retailers—are modeled to capture complex feedback and market dynamics. Calibrated using empirical data from Jiangsu Province, China, the simulation reveals a dominant consumer tendency to store obsolete smartphones rather than engage in reuse or formal recycling. However, the introduction of government subsidies significantly shifts behavior, doubling participation in second-hand markets and markedly improving recycling rates. These results highlight the value of integrating behavioral insights into environmental modeling to inform circular economy strategies. By offering a flexible and behaviorally grounded simulation tool, this study supports the design of more effective policies for promoting responsible smartphone disposal and lifecycle extension. Full article

Urban transportation systems face increasing pressure to reduce car dependency and greenhouse gas emissions while supporting sustainable growth. This study addresses the lack of integrated modeling approaches that capture both spatial and temporal dynamics in transport planning. It develops a novel framework combining System Dynamics (SD) and Geographic Information Systems (GIS) to assess the sustainability of Transit-Oriented Development (TOD) strategies and parking policies in two brownfield redevelopment sites in Montreal. The framework embeds spatial metrics, such as proximity to transit, parking availability, and active transportation infrastructure into dynamic feedback loops. Using scenario analysis, the study compares a baseline reflecting current norms with an intervention scenario emphasizing higher density near transit, reduced parking ratios, and improved walkability and bike infrastructure. The results suggest that aligning TOD principles with targeted parking limits and investments in active mobility can substantially reduce car ownership and emissions. While primarily conceptual, the model provides a foundation for location-sensitive, feedback-driven planning tools that support sustainable urban mobility. Full article

This study addresses the sentiment classification of short texts in teaching evaluations. To mitigate concerns regarding data security in cloud-based sentiment analysis and to overcome the limited feature extraction capacity of traditional deep-learning methods, we propose a distributed symmetric approximate homomorphic hybrid sentiment classification model, denoted BHE+ALBERT-Mixplus. To enable homomorphic encryption of non-polynomial functions within the ALBERT-Mixplus architecture—a mixing-and-enhancement variant of ALBERT—we introduce the BHE (BERT-based Homomorphic Encryption) algorithm. The BHE establishes a distributed symmetric approximation workflow, constructing a cloud–user symmetric encryption framework. Within this framework, simplified computations and mathematical approximations are applied to handle non-polynomial operations (e.g., GELU, Softmax, and LayerNorm) under the CKKS homomorphic-encryption scheme. Consequently, the ALBERT-Mixplus model can securely perform classification on encrypted data without compromising utility. To improve feature extraction and enhance prediction accuracy in sentiment classification, ALBERT-Mixplus incorporates two core components: 1. A meta-information extraction layer, employing a lightweight pre-trained ALBERT model to capture extensive general semantic knowledge and thereby bolster robustness to noise. 2. A hybrid feature-extraction layer, which fuses a bidirectional gated recurrent unit (BiGRU) with a multi-scale convolutional neural network (MCNN) to capture both global contextual dependencies and fine-grained local semantic features across multiple scales. Together, these layers enrich the model’s deep feature representations. Experimental results on the TAD-2023 and SST-2 datasets demonstrate that BHE+ALBERT-Mixplus achieves competitive improvements in key evaluation metrics compared to mainstream models, despite a slight increase in computational overhead. The proposed framework enables secure analysis of diverse student feedback while preserving data privacy. This allows marginalized student groups to benefit equally from AI-driven insights, thereby embodying the principles of educational equity and inclusive education. Moreover, through its innovative distributed encryption workflow, the model enhances computational efficiency while promoting environmental sustainability by reducing energy consumption and optimizing resource allocation. Full article

We investigate the convergence properties of policy iteration and value iteration algorithms in reinforcement learning by leveraging fixed-point theory, with a focus on mappings that exhibit weak contractive behavior. Unlike traditional studies that rely on strong contraction properties, such as those defined by the Banach contraction principle, we consider a more general class of mappings that includes weak contractions. Employing Zamfirscu’s fixed-point theorem, we establish sufficient conditions for norm convergence in infinite-dimensional policy spaces under broad assumptions. Our approach extends the applicability of these algorithms to feedback control problems in reinforcement learning, where standard contraction conditions may not hold. Through illustrative examples, we demonstrate that this framework encompasses a wider range of operators, offering new insights into the robustness and flexibility of iterative methods in dynamic programming. Full article

In this paper, we study the stability of the stochastically heterogeneous traffic flow involving connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Taking the stochasticity of vehicle arrivals and behaviors into account, a general robust $H_{\infty }$ platoon controller is proposed to address the communication delay and unexpected disturbances such as prediction or perception errors on HDV motions. To simplify the problem complexity from a stochastically heterogeneous traffic flow to multiple long vehicle control problems, three types of sub-platoons are identified according to the CAV arrivals, and each sub-platoon can be treated as a long vehicle. The car-following behaviors of HDVs and CAVs are simulated using the optimal velocity model (OVM) and the cooperative adaptive cruise control (CACC) system, respectively. Later, the robust $H_{\infty }$ platoon controller is designed for a pair of a CAV long vehicle and an HDV long vehicle. The time-lagged system and the closed-loop system are formulated and the $H_{\infty }$ state feedback controller is designed. The robust stability and string stability of the heterogeneous platoon system are analyzed using the $H_{\infty }$ norm of the closed-loop transfer function and the time-lagged bounded real lemma, respectively. Simulation experiments are conducted considering various settings of platoon sizes, communication delays, disturbances, and CAV penetration rates. The results show that the proposed $H_{\infty }$ controller is robust and effective in stabilizing disturbances in the stochastically heterogeneous traffic flow and is scalable to arbitrary sub-platoons in various CAV penetration rates in the heterogeneous traffic flow of road vehicles. The advantages of the proposed method in stabilizing heterogeneous traffic flow are verified in comparison with a typical car-following model and the linear quadratic regulator. Full article

Background: Patient satisfaction is a key indicator for improving healthcare delivery, yet the influence of gender preferences on healthcare providers remains underexplored. Cultural norms and gender perceptions often shape the patient preferences, affecting access to care, patient–provider relationships, and overall satisfaction. Thus, this study investigates the patients’ gender preferences and their impact on satisfaction in Tanzania. Methods: The study utilized a cross-sectional design, collecting data from five health centres: Mikongeni, Konga, Mzumbe, Tangeni, and Mlali. A total of 240 randomly selected respondents participated in the study. Gender preferences were categorized as male, female, and both, and determinants were analyzed using a multivariate probit model (MPM), while satisfaction was analyzed using an ordered logit model (OLM). Results: Results reveal that female providers were preferred for empathy (58.30%), intimate care (50.00%), and receptionist roles (50.00%), while males were favored for surgery (50.00%), professionalism (0.86), and IT roles (41.70%). Professionalism (0.75) and communication (0.70) had the strongest positive effects on very high satisfaction. Male provider preference was strongly linked to higher satisfaction (0.84), while female preference showed a mild effect (0.23). Insurance (0.32) and care at Tangeni Health Centre (0.70) boosted satisfaction, while consultation fees (−0.26) reduced it. Conclusions: The study recommends that healthcare systems address gender stereotypes by equipping all providers with both technical and relational care skills, regardless of gender. It also highlights the need for culturally and religiously sensitive care practices that acknowledge how societal norms shape patient preferences and satisfaction. To enhance patient-centered care, policies should promote affordability, broaden insurance coverage, and integrate patient feedback on gender preferences into healthcare delivery models. Full article

Artificial intelligence (AI) is transforming how news is produced, curated, and consumed, challenging traditional gatekeeping theories rooted in human editorial control. We develop a robust theoretical framework to reconceptualize gatekeeping in the AI era. We integrate classic media theories—gatekeeping, agenda-setting, and framing—with contemporary insights from algorithmic news recommender systems, large language model (LLM)–based news writing, and platform studies. Our review reveals that AI-driven content curation systems (e.g., social media feeds, news aggregators) increasingly mediate what news is visible, sometimes reinforcing mainstream agendas, according to Nechushtai & Lewis, while, at other times, introducing new biases or echo chambers. Simultaneously, automated news generation via LLMs raises questions about how training data and optimization goals (engagement vs. diversity) act as new “gatekeepers” in story selection and framing. We found pervasive Simon’s theory that reliance on third-party AI platforms transfers authority from newsrooms, creating power dependencies that may undercut journalistic autonomy. Moreover, adaptive algorithms learn from user behavior, creating feedback loops that dynamically shape news diversity and bias over time. Drawing on communication studies, science & technology studies (STS), and AI ethics, we propose an updated theoretical framework of “algorithmic gatekeeping” that accounts for the hybrid human–AI processes governing news flow. We outline key research gaps—including opaque algorithmic decision-making and normative questions of accountability—and suggest directions for future theory-building to ensure journalism’s core values survive in the age of AI-driven news. Full article

As programming education becomes increasingly complex, grading student code has become a challenging task. Traditional methods, such as dynamic and static analysis, offer foundational approaches but often fail to provide granular insights, leading to inconsistencies in grading and feedback. This study addresses the limitations of these methods by integrating contrastive learning with explainable AI techniques to assess SQL code submissions. We employed contrastive learning to differentiate between student and correct SQL solutions, projecting them into a high-dimensional latent space, and used the Frobenius norm to measure the distance between these representations. This distance was used to predict the percentage of points deducted from each student’s solution. To enhance interpretability, we implemented feature ablation and integrated gradients, which provide insights into the specific tokens in student code that impact the grading outcomes. Our findings indicate that this approach improves the accuracy, consistency, and transparency of automated grading, aligning more closely with human grading standards. The results suggest that this framework could be a valuable tool for automated programming assessment systems, offering clear, actionable feedback and making machine learning models in educational contexts more interpretable and effective. Full article

The interplay among classroom environment, grit, and enjoyment in shaping the feedback-seeking behavior (FSB) of Chinese English as a Foreign Language (EFL) learners remains underexplored. This study investigates how the classroom psychological environment and L2 grit influence FSB, categorized as feedback monitoring (FM, the passive observation of feedback) and feedback inquiry (FI, proactive requests for clarification), in the context of L2 writing. This study also focuses on the mediating role of foreign language enjoyment (FLE) in this process. A mixed-methods design was utilized to study 612 Chinese junior secondary students aged 13–15 with over five years of formal English instruction. Structural equation modeling (SEM) revealed that perseverance of effort (POE) and consistency of interest (COI), as two dimensions of L2 grit, directly predicted FM (β = 0.19 and 0.27, respectively) but not FI. The classroom environment indirectly enhanced both FM (β = 0.05) and FI (β = 0.09) through FLE. Qualitative interviews highlighted cultural constraints: 83.3% of participants prioritized FM over FI due to face-saving norms, despite high grit levels (M = 3.61 on a 5-point scale), underscoring cultural barriers to proactive feedback-seeking in Chinese collectivist classrooms. These findings validate the tripartite framework of positive psychology in L2 learning and propose strategies to balance institutional support, grit cultivation, and cultural sensitivity in fostering adaptive FSB. Full article

The consumption of ultra-processed foods (UPFs) has become a central topic in discussions surrounding public health, nutrition, and consumer behaviour. This study aimed to investigate the key factors shaping customer perceptions and attitudes towards UPFs and explore their impact on purchase decisions. A total of 290 completed questionnaires from an online survey were analysed to identify the drivers influencing consumer actions and habits. Users’ opinions were systematised based on their attitudes towards UPFs, considering factors such as health consciousness, knowledge, subjective norms, and environmental concerns. Participants were then categorised using both traditional and advanced data analysis methods. Structural equation modelling (SEM), machine learning (ML), and multi-criteria decision-making (MCDM) techniques were applied to identify hidden dependencies between variables from the perspective of UPF consumers. The developed models reveal the underlying relationships that influence acceptance or rejection mechanisms for UPFs. The results provide specific recommendations for stakeholders across the food production and marketing value chain. Public health authorities can use these insights the findings to design targeted interventions that promote healthier food choices. Manufacturers and marketers can leverage the findings to optimise product offerings and communication strategies with a focus on less harmful options, aligning more closely with consumer expectations and health considerations. Consumers benefit from enhanced product transparency and tailored information that reflects their preferences and concerns, fostering informed and balanced decision-making. As attitudes toward UPFs evolve alongside changing nutrition and consumption patterns, stakeholders should regularly assess consumer feedback to mitigate the impact of these harmful foods on public health. Full article

This study aims to examine the impact of social media on interpersonal communication patterns in Jordan and determine whether there are gender differences. Through adopting a mixed-methods approach, quantitative data were collected using a structured questionnaire from a sample of 50 Facebook users in Jordan chosen based on a self-selection method, comprising 24 men and 26 women, and two semi-structured focus group discussions were conducted with randomly selected 10 men and 10 women. The quantitative analysis showed that there were statistically significant differences between genders in terms of nonverbal communication and communication roles. However, no significant differences were found in verbal communication, listening, feedback, context, communication channels, and conflict resolution. The qualitative data provided further insight into the findings, demonstrating how cultural and societal norms, particularly those related to gender roles, influence interactions on social media. The participants expressed a range of perspectives on how social media impacts their communication, with many noting changes in communication dynamics due to increased exposure to global influences. In line with Genderlect Theory this study highlights the role of gender, demonstrating that while traditional gender-based communication styles endure, they are progressively shaped by the dynamic and evolving nature of digital interactions. Full article

Two-year college students represent 35% of U.S. undergraduates, yet substance use among them is understudied. Grounded in Social Norms Theory, the present study examined alcohol and cannabis use prevalence and associations between perceived peer use (descriptive norms), approval of use (injunctive norms), and personal use among 2-year students. We also explored whether identification with the reference group or age moderated associations. Data were collected from May through August of 2020 from 1037 2-year college students in Washington State (screening sample) aged 18–29. Of these, 246 participants who reported recent, moderate alcohol and/or cannabis use completed a follow-up survey. Screening survey participants reported past-month alcohol and cannabis use and demographics, while follow-up participants provided data on perceived peer descriptive and injunctive norms and group identification. Screening participants reported drinking an average of 3.32 (SD = 7.76) drinks weekly and being high for 8.18 h (SD = 20.95). Follow-up participants overestimated peer alcohol and cannabis use. Regression analyses showed perceived descriptive alcohol and cannabis norms were positively associated with personal use, and perceived injunctive alcohol norms were positively related to alcohol-related consequences. Differences by student age were also observed. Findings suggest perceived peer norms are risk factors for substance use behaviors among 2-year college students. Tailored normative feedback interventions may reduce high-risk use in this underserved population. Full article

Achieving sustainable development in agricultural catchments requires well-designed policy measures. This study examines the intricate interactions between social dynamics and hydrological processes within agricultural systems to propose targeted policy interventions. By employing socio-hydrology models that integrate socio-economic and hydrological data, the research provides valuable insights into the feedback loops and interdependencies that influence catchment sustainability. In this study, we find that policies on population management should aim to balance natural growth rates with the carrying capacity of the basin. Strategies such as education, healthcare access, and family planning can help manage demographic pressures. Migration policies should consider the economic and environmental impacts of population influx and support balanced regional development to distribute the demographic pressures more evenly. Wage growth should be aligned with economic productivity to prevent unemployment and inequality. Policies that promote equitable wage structures and enhance labor mobility between sectors can mitigate disparities. The findings emphasize the necessity of adaptive policies that address both environmental and societal factors, advocating for interdisciplinary approaches in water resource management and agricultural policy development. This study also highlights the pivotal role of technological innovations and the societal values and norms that shape sustainability and resilience in agricultural catchments. Full article

This work reviews the key challenges surrounding teaching Science, Technology, Engineering, and Mathematics subjects known as STEM. The research has uncovered a significant gap between traditional teaching styles and the need to develop and adapt to new remote-learning modalities. The work describes the technological, pedagogical, social, and institutional challenges, finally identifying the importance of their joint interaction. Since the COVID-19 pandemic, it has become evident that STEM educators must increase their awareness and knowledge of instructional models focused on using digital platforms. The current trend is centred on developing remote-learning tools, which will likely become the predominant learning norm as the economy’s viability increases. However, these remote-learning approaches must maintain interaction with the physical world, as understanding real-world phenomena is crucial for improving learning processes. STEM learning through e-learning will have a greater chance of success if academic institutions collaborate with other sectors of society, such as the business sector, to receive feedback for the continuous improvement of the proposed teaching methods. Full article

Objectives: In China, primary healthcare (PHC) facilities have high antibiotic prescribing rates for upper respiratory tract infections (URTIs), which are primarily viral and self-limited. This study aimed to identify the main factors influencing PHC physicians’ antibiotic decisions for URITs based on the theory of planned behavior. Methods: A convergent mixed-methods study was conducted at 30 PHC facilities across Shaanxi Province, China. A total of 108 PHC physicians completed a five-point Likert Scale questionnaire focused on behavioral components of antibiotic prescribing, including attitudes, subjective norms, perceived behavioral control, belief in past experiences, and prescribing intentions. Twenty-two physicians participated in semi-structured interviews. Results: Respondents had a good awareness of AMR (Mean = 4.49) and a weak belief regarding the benefit of antibiotics (Mean = 2.34). The mean score for subjective norms was 3.36, and respondents had good control over their prescribing behavior (Mean = 4.00). A reliance on past prescribing experiences was observed (Mean = 3.34), and physicians’ antibiotic prescribing intention was 3.40 on average. Multiple linear regression revealed that physicians showing a more favorable attitude towards antibiotics (p = 0.042) and relying more on their past experiences (p = 0.039) had a higher antibiotic prescribing intention. Qualitative interviews indicated that most physicians would consider prescribing antibiotics when facing diagnostic uncertainty. Low utilization of diagnostic tests, limited effectiveness of training programs, inadequate knowledge of guidelines, and lack of feedback on antibiotic prescriptions all contributed to antibiotic overprescribing. Conclusions: PHC physicians in China demonstrated strong intentions to prescribe antibiotics for URTIs when facing diagnostic uncertainty. Beliefs about antibiotics and previous prescribing behavior were significantly linked to prescribing intentions. Multifaceted interventions that focus on facilitating diagnostic tests, improving the quality of training, effectively implementing clinical guidelines, and providing practical feedback on antibiotic prescriptions may help reduce antibiotic overprescribing in China’s PHC facilities. Full article