Search Results (906)

Understanding how individuals decide to adopt shelter dogs remains a significant challenge within animal welfare research, as existing studies identify correlates of adoption outcomes without explaining the underlying decision process. This hypothesis introduces a conceptual framework that synthesizes empirical findings from dog adoption studies with interdisciplinary theories to explain how adoption decisions emerge. Using a signal-to-noise perspective, the framework conceptualizes early bond formation between a potential adopter and a dog as a valuation signal that competes with uncertainty arising throughout the process. The functional model describes the adoption process as a lifecycle involving search, visitation, interaction, and decision phases, during which potential adopters seek information, evaluate available dogs, and form perceptions of compatibility. Interdisciplinary decision models, including Prospect Theory and the Diffusion Decision Model, are integrated to explain how information is framed, evaluated, and accumulated until a decision is reached. Empirical findings from human–dog interaction research are used to support the hypothesis that potential adopters evaluate companionship potential based on early bond formation associated with human–dog interactions. The framework offers a broad perspective on how adoption decisions may occur and establishes a theoretical foundation to guide future hypothesis development, measurement, and experimental research in companion animal adoption. Full article

In this work, sodium alginate (NaAlg) membranes were enhanced with synthesized zinc oxide (ZnO) nanoplates to enable efficient pervaporation dehydration of isopropyl alcohol (IPA). A comprehensive suite of characterisation techniques—scanning electron (SEM) and atomic force (AFM) microscopy, Fourier-transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA), contact angle and liquid uptake measurements—along with density functional theory (DFT) calculations, was employed to establish robust structure–property relationships and to elucidate filler–polymer interactions. Membranes with different ZnO contents were prepared, and membranes based on the optimal NaAlg-ZnO(5%) composite were cross-linked with CaCl₂ to improve stability in aqueous solutions, and supported membranes were developed for prospective applications by applying this composite onto the prepared porous cellulose acetate (CA) substrate. This developed cross-linked supported NaAlg-ZnO(5%)/CA membrane had a permeation flux increased by 2 times or more compared to a dense NaAlg membrane during dehydration of IPA (12–30 wt.% water) with a permeate water content above 99 wt.%. The integrated experimental–theoretical approach provides mechanistic insight into ZnO–NaAlg interactions and demonstrates the strong potential of these mixed matrix membranes for high-efficiency alcohol dehydration, offering a rational design paradigm for next-generation pervaporation membranes. Full article

Minimizing the impacts of coupling, randomness, time variation and uncertain nonlinearity to enhance real-time performance is critical for controlling complex industrial systems. This paper proposes an optimized adaptive filtering method (LAF-MTNF) for time-varying uncertain nonlinear systems with multiple-input multiple-output (MIMO) measurement noise, which integrates the multi-dimensional Taylor network (MTN) with Lyapunov stability theory (LST). Leveraging MTN’s inherent advantages—simple structure, linear parameterization, and low computational complexity—LAF-MTNF achieves efficient real-time filtering while avoiding the exponential computation burden of neural networks. The contributions of this work are threefold: (1) A novel integration of LST and MTN is proposed for MIMO filtering, in which an energy space is constructed with a unique global minimum to eliminate local optimization traps, addressing the stability deficit of traditional MTN filters using LMS/RLS algorithms. (2) Convergence performance is systematically quantified by deriving explicit expressions for the error convergence rate (regulated by a positive constant) and convergence region (a sphere centered at the origin) while modifying adaptive gain to avoid singularity, filling the gap of incomplete performance analysis in existing Lyapunov-based filters. (3) The design is disturbance-independent, relying only on input/output measurements and requiring no prior knowledge of noise statistics, thus enhancing robustness to unknown industrial disturbances. We systematically analyze the Lyapunov stability of LAF-MTNF, and simulations on a complex MIMO system verify that it outperforms existing methods in filtering precision (mean error 0.0227 vs. 0.0674 of RBFNN) and dynamic response speed, while ensuring asymptotic stability and real-time applicability. The proposed LAF-MTNF method achieves significant advantages over traditional adaptive filtering methods in filtering accuracy, convergence speed and anti-cross-coupling capability. This method has broad application prospects in high-precision industrial servo motion control, power system state monitoring and other multi-variable nonlinear industrial scenarios with complex noise environments. Full article

Due to its strong near-infrared (NIR) absorption and high thermal conductivity, graphene is considered an excellent nanophotothermal filler that can effectively improve the photothermal conversion performance of composites. In particular, graphene–polymer nanocomposites, new types of photothermal conversion materials, have broad application prospects in photothermal therapy, photothermal driving, and micro-/nanomachinery. Recent research results have shown that when the filling concentration of graphene nanosheets (GNSs) in the matrix reaches the percolation threshold, interface effects such as interface tunneling and Maxwell–Wagner–Sillars (MWS) polarization, the key factors affecting the photothermal conversion performance of such composites, will occur. Furthermore, graphene exhibits unique optical conductivity due to its strong interaction with light. To reveal how interface effects influence the photothermal conversion performance of these nanocomposites, the optical conductivity of graphene at near-infrared frequencies was introduced to modify the effective medium theory. By combining this with a photothermal conversion model, the photothermal conversion behaviors of GNS–polymer composites are discussed, taking into account the interface effects and optical conductivity characteristics of GNSs. Full article

Background: Psychoeducational interventions are increasingly implemented to promote psychological and physical health, yet evidence guiding personalized intervention selection remains limited. This systematic review and meta-analysis quantifies the effectiveness of psychoeducational interventions across five settings and identifies empirically derived moderator patterns to inform the selection of tailored interventions. Methods: Systematic searches of PubMed/MEDLINE, PsycINFO, Scopus, Web of Science, ERIC, the Cochrane Library, and Google Scholar were conducted to identify eligible studies published between January 2015 and December 2024. A two-tier analytical approach was employed: a random-effects meta-analysis of k = 53 studies reporting extractable effect-size data, and a direction-of-effect narrative synthesis of all 186 included studies (N = 50,328 verified from 124 studies reporting sample sizes), following SWiM guidelines. Results: The quantitative meta-analysis yielded a significant medium-to-large pooled effect (g = 0.66, 95% CI [0.50, 0.82], p < 0.001) with substantial heterogeneity (I² = 96.1%). Effects varied across settings: clinical/vulnerable populations showed the largest effect (g = 0.91), followed by university programs (g = 0.62), school-based (g = 0.60), mindfulness/positive psychology (g = 0.55), and community-based (g = 0.49). The broader narrative synthesis confirmed near-universal effectiveness: 131 studies (70.4%) reported significant positive effects, 51 (27.4%) reported mixed results, and none reported null effects—yielding 97.8% favorable outcomes across the full evidence base. Direction-of-effect moderator patterns indicated a stepped severity gradient (indicated 100% favorable, selective 98.6%, universal 95.6%), and that programs exceeding 8 weeks (99.0% vs. 96.6%), theory-based interventions (98.2% vs. 95.2%), and guided digital delivery were consistently associated with the most favorable outcomes. Publication bias assessment confirmed robustness (fail-safe N = 22,942; leave-one-out range: 0.61–0.67). GRADE evidence quality was rated Moderate for four of five research questions. Conclusions: This systematic review and meta-analysis provide converging quantitative and direction-of-effect evidence supporting the effectiveness of psychoeducational interventions. The near-universal favorable direction across 186 studies, combined with a medium-to-large pooled effect in the quantitative subset, provides a preliminary empirical foundation for personalized intervention matching. A preliminary four-phase implementation framework is proposed as a hypothesis-generating heuristic; prospective validation through a meta-analysis of individual participant data is needed before prescriptive application. Full article

This study illuminates fundamental questions in behavioral science through advanced machine learning methodologies applied to large-scale public opinion data. Drawing on Kahneman and Tversky’s dual-process theory and Sunstein’s nudge architecture, we employ hierarchical unsupervised clustering and supervised predictive models to detect cognitive biases—loss aversion, availability heuristic, and partisan motivated reasoning—embedded within a nationally representative survey of 5022 American respondents. Our primary methodological contribution is a hierarchical two-stage clustering framework that uncovers latent opinion structures without imposing a priori partisan categories, permitting discovery of cross-cutting cleavages invisible to conventional survey analysis. Three principal findings emerge: (1) loss aversion is empirically confirmed in prospective economic perception, with pessimists outnumbering optimists at a 1.14:1 ratio even among respondents rating current conditions positively; (2) partisan motivated reasoning produces a 13.15 percentage-point perception gap among individuals with identical financial circumstances; and (3) multi-platform digital engagement is associated with reduced partisan bias, providing evidence that challenges simple echo chamber assumptions. Crime safety perception emerges as the strongest predictor of economic bias, surpassing party affiliation, and substantiating availability heuristic dominance in political cognition. These findings carry implications for democratic accountability, platform governance, and the ethics of AI-augmented behavioral analysis in an era of affective polarization. Full article

Diabetic Bladder Disease (DBD), a common urological complication of diabetes mellitus, severely compromises the quality of life of affected patients. Mitochondria, the primary energy-producing organelles in cells, are closely correlated with the pathogenesis and progression of DBD. As an emerging therapeutic modality, mitochondrial transplantation exhibits substantial potential for the management of DBD. This paper presents a comprehensive review of mitochondrial transplantation, with a focus on its fundamental theories, application conditions, safety profiles, and mitochondrial sources. Subsequently, we explore the association between mitochondrial dysfunction and the pathological mechanisms underlying DBD, analyze the disparities between mitochondrial transplantation and conventional therapeutic approaches, and discuss the prospects of combined and personalized treatment regimens. Finally, this review summarizes the ethical controversies surrounding this therapeutic strategy and outlines future research trends, aiming to lay a theoretical foundation for the development of novel therapeutic modalities against DBD. Full article

While Generative Artificial Intelligence technology empowers content production on user-generated content platforms, it also gives rise to novel risks of disinformation dissemination. The effective governance of these risks is critical to ensuring the cybersecurity of the online ecosystem and maintaining long-term social stability. To address the collaborative governance dilemma, this study constructs a tripartite “platform-user-government” evolutionary game model based on prospect theory. It explores the evolutionarily stable strategies and stability conditions of each actor, supplemented by numerical simulations and practical case validation. The results indicate that: (1) under specific conditions, the system can converge to an ideal equilibrium {active platform governance, engaged user participation, stringent government supervision}; (2) the government’s reward–penalty mechanisms can drive the system towards this ideal equilibrium; (3) users’ digital literacy is a key variable influencing the system’s evolutionary path; (4) both the risk preference coefficient (β) and loss aversion coefficient (λ) from prospect theory have a significant moderating effect on the system’s evolution. Finally, targeted recommendations are proposed for the three aforementioned stakeholders to accelerate the improvement of China’s collaborative governance of the content ecosystem. Full article

Electric vehicles (EVs) are promoted to decarbonise road transport, yet uptake remains slow in many emerging markets. This study examines consumer resistance to EV adoption in Sri Lanka by modelling status quo bias (SQB) using a Prospect Theory lens. An online survey of urban vehicle owners and near-term buyers yielded 157 responses; after screening and removing influential outliers, 151 cases were analysed using partial least squares structural equation modelling (PLS-SEM). The model tests five Prospect Theory-aligned antecedents, namely, loss aversion, reference dependence, risk perception, framing effects, and uncertainty aversion, and evaluates environmental concern as a moderator. Results indicate that loss aversion has a significant positive effect on SQB (β = 0.216, p = 0.005) and uncertainty aversion is the strongest predictor (β = 0.453, p < 0.001), while reference dependence, risk perception, and framing effects show positive but statistically non-significant direct effects. Moderation tests show that environmental concern significantly moderates the effects of reference dependence (β = 0.181, p = 0.039) and framing effects (β = 0.179, p = 0.037) on SQB, but does not significantly moderate the loss aversion, risk perception, or uncertainty aversion paths. Overall, perceived losses and—especially—ambiguity surrounding EV ownership appear to sustain reliance on internal combustion vehicles in this developing-country context, underscoring the need for interventions that reduce uncertainty (credible infrastructure signals, stable policy, service capability) and mitigate perceived losses (warranties, resale assurances) alongside carefully framed communications. Full article

Vygotsky’s theory emphasizes the importance of the sociocultural environment for cognitive development, highlighting the importance of social interaction, cultural tools, and the Zone of Proximal Development. This paper explores these concepts concerning learning design, learning analytics, and risk assessment in technology-enhanced learning environments. Vygotskian methods and theory are synthesized with the knowledge from existing literature to propose ways of managing the risk of failure within a design approach that incorporates Learning Analytics language in a Backward Design process. The findings suggest dialectical associations that can provide a powerful semantic context for a design system based on the Human–Machine Pair Inspection technique. Addressing the risk of failure can become a design opportunity to support student cognitive development and to improve teacher design decisions. The main findings of this paper offer an interpretation of a dynamic approach to managing the risk of failure as well as the role of the teacher in the process of designing technology-enhanced learning scenarios. Future directions include research to empirically validate the proposed design approach in different educational settings, investigate its potential for predictive modeling, and explore technological tools to support adaptive systems for the teacher’s needs based on the proposed design approach. Above all, this manuscript must be considered as a prospective study aimed at establishing a coherent framework for future research, identifying key research questions, and proposing directions that will make a substantial scientific contribution. Full article

Two-dimensional materials have broad application prospects in the field of optoelectronic devices. As a next-generation power electronic device, AlN materials have obvious advantages in power processing, and their monolayer structure has excellent optoelectronic properties, which is of great significance for the study of 2D AlN monolayers. Properties such as electronic and optical properties of metal-adsorbed AlN (M-AlN) systems have been systematically investigated using density functional theory from first principles. The results of the energy bands of the M-AlN system indicate that the adsorption of Al, Li, Ag, Au, Bi, Cr, Mn, Na, Pb, Sn, Ti, and K metals makes the monolayer AlN magnetic, the incorporation of two metals, Al and Li, is the transition of the monolayer AlN from a semiconductor to a semi-metal, and the introduction of K metal makes the monolayer AlN transition from a semiconductor to a metal. The work function of the M-AlN system shows that the introduction of the metal reduces the work function of the monolayer AlN, especially for K-AlN, which is reduced by 56.12% compared to the monolayer AlN. In addition, the results of the optical absorption spectra of the M-AlN system revealed that the introduction of the metals made the monolayer AlN exhibit high absorption peaks in the visible and near-infrared regions; in particular, the intensity of the absorption peaks of the Ti-AlN system at 557.8 nm reached 7.4 × 10⁴ cm⁻¹ and the intensity of the absorption peaks of the K-AlN system at 1109.3 nm reached 1.01 × 10⁵ cm⁻¹. This indicates that the introduction of Ti and K metal atoms enhances the absorption properties of monolayer AlN in the visible and near-infrared regions. Finally, the time-domain finite difference using spherical metal nanoparticles is used to excite the localized surface plasmon resonance, and the results show a small area of strong electric field around the electric field hotspot of Cr and Li particles, and a good concentration of the electric field strength in the x and y directions. In summary, the system of metal atoms adsorbed on AlN will be favorable for the design of spintronics, field-emitting devices and solar photovoltaic devices. Full article

Open-plan workplaces are often associated with increased sensory exposure, which may present challenges for adults with Attention-Deficit/Hyperactivity Disorder (ADHD), a condition characterized by atypical arousal regulation and sensory sensitivity. Although the Prospect–Refuge Theory suggests that spatial configuration may influence perceived security and attentional states, objective neurophysiological evidence in workplace contexts remains limited. This exploratory pilot study employed a mixed design to examine whether desk orientation and office enclosure were associated with differences in neural activity among adults with ADHD (n = 6). Four desk configurations were tested within each office setting, while two office types (Open Office and Enclosed Private Office) were examined between participants. Neurophysiological data were collected using portable electroencephalography (EEG), and power spectral density (PSD) across canonical frequency bands was analyzed during standardized cognitive tasks. Results indicated context-dependent spatial effects. In the Open Office setting, configurations providing both outward visibility and visual backing were associated with lower beta and gamma power relative to orientations lacking these features. In the Enclosed Private Office, orientation-related differences were not statistically significant. These preliminary findings suggest that desk orientation may influence neural indicators of cognitive demand in open-plan environments. Given the small sample size, results should be interpreted cautiously but contribute initial physiological evidence to neurodiversity-informed workplace research. Full article

Virtual power plants (VPPs) aggregate flexible resources, such as distributed photovoltaics (PV), energy storage, and flexible loads, to provide substantial reserve capacity for grid operation. However, the combined effects of renewable energy output uncertainty, load forecast errors, and user-bounded rationality responses lead to significant errors in traditional deterministic VPP reserve assessment methods, severely affecting the balance between system supply and demand. To address this challenge, this paper proposes a credible reserve assessment method that accounts for user-bounded rationality. First, thermodynamic models with on–off constraints for air conditioning loads, energy feasible region, and power constraint models for electric vehicles (EVs) and energy storage systems (ESSs), as well as PV forecast error models are established to characterize physical reserve boundaries. Second, prospect theory is introduced to describe user-bounded rationality and a logit-based response probability model is developed. Monte Carlo sampling and kernel density estimation are employed to derive credible reserve sets under different confidence levels, achieving a probabilistic quantification of VPP reserve capacity distribution. Case studies demonstrate that the proposed method accurately characterizes the probabilistic distribution characteristics of VPP reserve provision under multiple uncertainties, providing comprehensive and reliable assessment information for power dispatching agencies. Full article

Although previous studies have examined factors influencing organizational appeal, how AI-adoption signals influence prospective applicants remains unclear. Building on signaling theory, this study explores whether, when, and how organizations’ AI-adoption signals enhance their attractiveness to potential applicants. Two experiments were conducted to test the hypothesized model. Study 1 (N = 145) employed a scenario-based design to compare organizational attractiveness between AI-adoption signal and no-signal conditions, confirming that AI-adoption signals are significantly positively associated with organizational attractiveness. Study 2 (N = 240) recruited active job seekers and validated a moderated mediation model: perceived innovation ability mediates the positive association between AI-adoption signals and organizational attractiveness, especially among job seekers with high AI self-efficacy. By conceptualizing AI adoption as an organizational signal, this research extends signaling theory to the context of technology-infused recruitment and offers practical insights for designing more effective recruitment strategies in the digital era. Full article

Background/Objectives: In China, developing childcare services is a key governmental strategy to promote child health under the low-fertility context. However, young couples have poor knowledge and low acceptance of formal childcare now, creating a significant gap between need and actual choice. To fulfil the unmet need and promote the development of childcare services, raising awareness among prospective parents is necessary. Guided by the theory of planned behavior and the theory of cultural transmission and evolution, this study evaluates whether current communication practices address the theoretically important factors pertaining to effective promotion. Methods: Taking provincial capital cities and sub-provincial cities as the study sample, we conducted a content analysis of policy documents related to communication of formal childcare and a social network analysis of practical promotional activities, respectively. Results: There were a series of problems with childcare service communication. First, governmental sectors failed to pay sufficient attention to communication of childcare services, and promotional activities were basically conducted as a campaign rather than a regular style. Second, there was little effective partnership between childcare centers and community committees. Third, promotion was mainly conducted through traditional channels, and new social media were less used. Conclusions: To improve the communication of childcare services to target parents, we recommend that governments (1) strengthen communication efforts through dedicated attention and funding; (2) establish regular communication based on public–private partnership modes; and (3) employ more efficient channels, especially social media. Full article