Search Results (1,291)

As a key market-based instrument for low-carbon transition, China’s national carbon emission trading market facilitates emission abatement and reshapes labor allocation in regulated firms. Based on the task-based framework, a task embedding hypothesis is proposed. Restricted by organizational adjustment costs, firms tend to integrate green compliance tasks into existing roles instead of massively establishing specialized green positions. Using 13 million job postings and matched financial data of Chinese A-share listed firms from 2016 to 2024, this study regards the launch of the national carbon market as a quasi-natural experiment and adopts a difference-in-differences (DID) approach. The results indicate that the carbon market raises the green job share of regulated enterprises by 4.7 percentage points. Such growth is not driven by newly built environmental departments. Management compliance posts decrease markedly, technical posts remain stable, while task-embedding positions combining traditional and green tasks dominate the growth effect. Heterogeneously, private enterprises and eastern China dominate the transformation, while state-owned enterprises lag behind and central and western regions witness a green job share decline. This study enriches task-based theory application in environmental regulation and provides empirical implications for sustainable green talent development and just transition policy design. Full article

Purpose-aware AI systems are increasingly deployed in safety-critical, multi-agent, and human-facing environments, where they must transform heterogeneous data into timely, explainable, and goal-aligned decisions under uncertainty. Existing architectures often couple perception, reasoning, communication, and security only at the pipeline level. This creates a research gap in unified semantic transformation, purpose-oriented judgment, bounded imperfection handling, and semantic self-protection. To address this gap, this paper proposes a DIKWP+BUG semantic–cognitive reference architecture for artificial-consciousness-oriented computing, without claiming definitive artificial consciousness. The architecture represents cognition through the Data–Information–Knowledge–Wisdom–Purpose (DIKWP) model and uses BUG theory to model bounded approximation, incomplete evidence, and confidence miscalibration in cross-dimensional reasoning. The model is mapped to an Artificial Consciousness Processing Unit (ACPU) reference substrate, an Artificial Consciousness Operating System (ACOS), a DIKWP semantic communication subsystem, and a concept–semantic fused security subsystem. The components are implemented through runtime emulation and evaluated in smart-city governance, autonomous-driving, and medical-triage simulations. Compared with selected baselines, the prototype increased cognitive throughput from $4.5\mathrm{k}$ to $7.8\mathrm{k}$ logged events, reduced perception–action latency from $340\mathrm{ms}$ to $120\mathrm{ms}$, reduced CPU utilization from $95\%$ to $68\%$, lowered smart-city congestion duration by $30\%$, improved emergency response time by approximately $40\%$, achieved 0 collisions versus approximately $2/10$ baseline IoV runs, and improved medical-triage accuracy from $85\%$ to $92\%$. These online-runtime results provide initial feasibility evidence under controlled simulation conditions; they do not include offline model-preparation costs and therefore should not be interpreted as end-to-end lifecycle speedups. Matched-compute ablation, statistical benchmarking, hardware prototyping, and real-world validation remain future work. 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

Corporate balance sheets report aggregate equity and liability totals but conceal the internal allocation of financing sources across asset categories—an identification problem that conventional econometric methods cannot resolve without additional parametric assumptions. This paper develops a maximum entropy (ME) panel estimator to recover two latent scalar parameters: x ∈ (0,1), the share of equity capital directed toward long-term asset financing, and y ∈ (0,1), the corresponding debt allocation share. Grounded in maximum entropy principle, the estimator selects the unique parameter vector that satisfies the mean-level balance-sheet constraint while maximising joint Shannon entropy—the least-biassed solution consistent with observable data. The closed-form logistic representation yields a scalar Lagrange multiplier λ*, interpreted as a financing pressure index, recoverable via bisection in at most 21 iterations at tolerance ε = 10⁻⁵. Building on the ME estimates, we introduce a continuous matching alignment index M* = x* − y* that measures the degree of compliance with the financial matching principle along a continuous spectrum rather than as a binary categorisation. Applied to a ten-firm, cross-sectoral panel spanning Technology, Finance, Energy, and Automotive sectors over an observation window spanning 2001 to 2025 (with firm-specific subperiods reflecting differences in IPO dates and data availability), the framework reveals substantial heterogeneity in latent financing flows: equity allocation shares range from 30.1% (NVIDIA) to 75.1% (ExxonMobil), while debt allocation shares span 37.1% to 77.5%. Across the panel, only Meta exhibits substantial positive matching alignment, while Microsoft, ExxonMobil, Apple, and Tesla show only very slight differences that fall within the neutral band, and the remaining firms show varying degrees of structural departure from the matching benchmark; the thresholds used to summarise these descriptive labels are interpretive aids rather than re-imposed binary criteria, and the substantive ranking of firms along M* does not depend on the specific threshold values adopted. The ME solution’s entropy H(x*, y*) and the normalised diversification index D(x*, y*) describe allocation balance under the estimator’s information–theoretic criterion rather than independently observed firm complexity; in the present sample, the cross-firm ordering of these values is not recovered by firm size, leverage, or sector classification alone. These findings, based on a ten-firm case-study panel with time-invariant allocation parameters, should be interpreted as descriptive patterns of the present sample rather than statistically validated regularities. They provide a theoretically rigorous and computationally tractable identification of unobservable corporate financing flows, with potential implications for capital structure theory, financial risk assessment, and balance sheet analysis that would benefit from validation on larger and more representative samples in future work. Full article

Lymphoid neoplasms such as multiple myeloma (MM), indolent non-Hodgkin lymphoma, and chronic lymphocytic leukemia are increasingly managed as chronic, relapsing conditions characterized by prolonged surveillance, repeated treatment transitions, and cumulative self-management demands. These trajectories expose patients and caregivers to persistent illness uncertainty, fluctuating fear of progression, symptom and comorbidity burden, communication challenges, and treatment-related workload. This theory-informed framework development paper uses an overview of selected psycho-oncological, hematological, nursing, theoretical, and patient-reported outcome literature to propose the LUMINA framework: Longitudinal illness trajectory, Uncertainty fields, Multidimensional symptom and comorbidity load, Information and interaction context, Navigation work and self-management load, and Adaptive outcomes and alignment. LUMINA is intended as a hypothesis-generating conceptual structure to organize clinically relevant domains, clarify potential relationships among uncertainty, symptom burden, communication, navigation work, and adaptive outcomes, and guide future assessment, validation, and intervention research in chronic lymphoid neoplasms. The framework builds on prior theories of illness uncertainty, treatment burden, workload–capacity balance, fear of recurrence/progression, and lymphoma-specific qualitative work on uncertainty management and psychosocial adaptation. Potential research applications include structured assessment, shared decision-making research, and domain-matched supportive-care concepts; however, these applications remain theoretical and require empirical testing. Future studies should evaluate feasibility, acceptability, construct validity, domain overlap, predictive validity beyond quality of life, and the clinical utility of LUMINA-informed research profiles. Until such validation is available, LUMINA should be interpreted as a conceptual model rather than a validated clinical tool or care pathway. Full article

Person–Environment Fit Theory explains organizational match in beliefs and values influences employee satisfaction and motivation in the workplace. Automated systems [e.g., artificial intelligence (AI)] and advanced technology have been integrated into business operations to compete in the digital era. However, how employee technology orientation and individual differences influence workplace preferences is underexplored. This study advances how organizations can strategically attract talent aligned with their technological infrastructure and work design. Parallel mediation path analysis was conducted on a surveyed U.S. convenience sample (SPSS PROCESS Model 4; N = 912). Technology adaptability was positively associated with preference for a job role highlighting working with automated systems relative to emphasizing supportive coworkers. Technology adaptability related to a greater need to belong and job satisfaction (as parallel mediators) and thereby less preference for a role working with automated systems (i.e., preference for a supportive coworkers job ad). The findings reveal that job ads promoting automated systems do not unilaterally attract tech-adaptive employees. Belonging needs and job satisfaction can function as psychological factors that redirect tech-savvy workers towards socially enriched roles. Proactively advertising social belonging and job satisfaction cues alongside advanced technology use could more comprehensively appeal to tech-adaptive job seekers. This can signal a better value congruence between an organization and these job seekers. Full article

Camera and LiDAR provide highly complementary information, and effective fusion of both modalities is desirable for 3D object detection. However, existing decision-level fusion methods mainly rely on the confidence of objects while neglecting the object uncertainty. To address this, we propose UDF-3D, an uncertainty-driven camera–LiDAR decision-level fusion method based on Dempster–Shafer evidence theory. First, object uncertainty is quantified by introducing the theory of subjective logic, where subjective opinions incorporate category belief masses and an uncertainty mass. Second, a cost matrix is designed for object matching, where each element is a weighted combination of geometric and semantic information from both sensors, and the weights are determined by the uncertainty parameters. Third, we construct a view-frustum constraint to re-evaluate unmatched objects, thereby reducing the false-negative rate. Finally, we design a novel evidence discounting factor within the Dempster–Shafer framework for matched objects, thereby mitigating cross-modal object conflicts during fusion and improving detection accuracy. Experiments on the KITTI dataset demonstrate that the proposed method outperforms existing decision-level fusion approaches, yielding improved detection accuracy. Full article

To address the limited design flexibility in meshing equations, complex modeling processes, and a lack of systematic research on bidirectional transmission in traditional parallel-axis line gears, this paper proposes a rapid design method for bidirectional transmission parallel-axis external line gears (BTPELG). Firstly, a multi-coordinate system is established, and homogeneous transformation matrices are derived. Secondly, the meshing equation is extended with an angular parameter to obtain a widely applicable conjugate condition. Then, two pairs of conjugate contact curves and center guide curves are constructed, and the offset vector for a circular-arc tooth profile is derived. Subsequently, taking the equidistant cylindrical helix as the driving contact curve, a 3D solid model is built via the sweeping method. Finally, kinematic simulations and experiments on 3D-printed prototypes verify the transmission ratios under forward and reverse conditions. Results show stable bidirectional transmission with average ratios matching theory and small fluctuations, confirming the method’s feasibility and providing a reference for rapid design and engineering applications. Full article

We examine the association between Environmental, Social, and Governance (ESG) initiatives and corporate performance using a sample of 360 S&P 500 firms from 2010 to 2018. Employing MSCI ESG ratings and controlling for industry and time effects, we find that environmental initiatives positively associate with current profitability (ROA), while gender diversity correlates with long-term growth prospects (Tobin’s Q). This study moves beyond aggregated ESG metrics by providing a disaggregated analysis, revealing that different ESG dimensions affect performance through distinct financial mechanisms. To address common endogeneity concerns, we implement a rigorous empirical identification strategy, including propensity score matching, Heckman selection models, and instrumental variable approaches using industry-average instruments. Our results quantify the economic magnitude of these effects, demonstrating that a one-standard-deviation increase in environmental performance corresponds to a 0.92 percentage point increase in ROA, representing approximately $176 million in additional annual net income for the median firm. These findings provide theoretical advancement for the resource-based view and stakeholder theory by showing that specific ESG capabilities serve as valuable, inimitable resources. Ultimately, the study contributes standardized, high-resolution evidence on how specific ESG dimensions drive superior corporate performance. Through mechanism analysis, we show that environmental effects operate primarily via operational cost reduction and risk mitigation, while gender diversity creates value through enhanced innovation findings, which has direct implications for corporate ESG strategy. Full article

Background/Objectives: Free riding in healthcare occurs when actors benefit from health-related public goods, risk-pooling arrangements, common resources, or cooperative institutions while contributing less than is socially optimal. This review clarifies how free-rider dynamics differ across vaccination, health insurance and universal health coverage, antimicrobial resistance, organ donation and transplant allocation, and global health cooperation. Methods: A narrative review with conceptual synthesis was conducted. Searches of PubMed and Scopus were complemented by citation tracking and targeted inclusion of foundational economics, game theory, public-health ethics, and market-design sources. Sources were mapped by domain, actors, strategies, payoff structure, information conditions, time horizon, enforcement mechanism and policy relevance. Results: Across domains, free riding arises when private payoffs diverge from collective welfare, but the underlying game differs: threshold public-good and coordination games in vaccination, adverse-selection and participation games in insurance, common-pool-resource dilemmas in antimicrobial use, donor-registration and matching-market problems in transplantation, and repeated public-goods games in global health. The review identifies three policy functions: altering payoffs, altering information and beliefs, and changing the structure, repetition, or enforceability of the game. Conclusions: Game theory is most useful as a mechanism-based framework rather than a stand-alone policy prescription. Its policy value depends on empirical calibration, institutional context, ethical legitimacy, and attention to equity, incomplete information, behavioral responses, and enforcement capacity. The synthesis also emphasizes boundary conditions: game-theoretic prescriptions can fail when political economy, asymmetric power, implementation capacity, access barriers, or trust-related drivers are ignored. Full article

Background: Socio-cognitive deficits constitute a core and persistent feature of adolescent-onset schizophrenia, significantly impairing functional outcomes. However, the interplay between genetic metabolic markers such as CYP2D6 and specific socio-cognitive phenotypes remains poorly understood. Methods: This cross-sectional study included 73 adolescents with schizophrenia and 58 matched healthy controls. Theory of Mind (ToM) was evaluated using the Reading the Mind in the Eyes Test (RMET), while empathy was assessed with the Cambridge Empathy Quotient. Symptom severity was measured via the Positive and Negative Syndrome Scale (PANSS). CYP2D6 polymorphisms were genotyped using RT-PCR, classifying participants as Normal or Reduced (Intermediate) metabolizers. Hierarchical multiple regression analyses were performed, controlling for sex, IQ, and psychosocial factors. Results: Patients demonstrated significantly lower RMET and empathy scores compared to controls. Reduced CYP2D6 metabolizers exhibited poorer ToM performance and more severe negative symptoms. The final RMET model accounted for 88.8% of variance (p < 0.001), with CYP2D6 status emerging as a significant independent predictor (β = 0.178, p = 0.005), alongside IQ and negative symptoms. In contrast, the empathy model explained 49.0% of variance, with CYP2D6 effects fully mediated by negative symptom severity. Conclusion: Adolescents with reduced CYP2D6 metabolic activity exhibit greater negative symptom burden and impaired social-cognitive functioning. Our findings reveal a double dissociation: ToM functions as a stable, biologically anchored trait, while empathy serves as a state-dependent construct primarily driven by the negative syndrome. These insights advocate for the integration of pharmacogenetic stratification in the treatment of early-onset schizophrenia. Full article

Why are essential workers (also known as key workers) priced out of the urban areas where essential services are concentrated? This paper addresses that question by linking residential sorting to the governance of land and housing markets in Auckland, New Zealand. Drawing on bid rent theory and motivated by Crane’s theoretical framework, this study examines how households trade off urban accessibility against housing costs with varying degrees of job location uncertainties and time pressure. The analysis uses the micro-level household data from Statistics New Zealand (Stats NZ)’s Integrated Data Infrastructure (IDI) to examine how key-worker households position themselves within the city’s rental market relative to other working households. The results show a clear urban land rent gradient: rents fall with distance from the city centre. However, access to the central location is not evenly distributed across workers. Key workers, whose jobs are typically tied to more fixed workplaces, are more inclined to live farther from the city centre to lower housing costs. By contrast, workers facing tighter time constraints, especially those working longer hours, show a stronger preference for living near the CBD to improve work proximity and reduce commuting burdens. This pattern remains evident among private vehicle commuters, suggesting that time pressure, rather than transport mode alone, is an important factor shaping residential location choice. The paper argues that this is not simply a housing market outcome but also a land-governance problem. When central land values rise without corresponding housing options for key workers, cities risk pushing socially necessary labour towards peripheral areas. The findings highlight the need for land-use and housing interventions that improve the spatial match between where key workers live and where urban services are most needed. Full article

The optoelectronic information industry is a strategic high-tech industry with wide applications. Compared with developed countries, China’s optoelectronic information industry presents a situation of “strong application and weak technology”. Evaluating the innovation capability of the optoelectronic information industry is the foundation for making scientific development plans. This study provides a methodology for evaluating the provincial innovation capability of the optoelectronic information industry to guide its high-quality development. This article applies multi-attribute utility theory to study the evaluation index system and comprehensive evaluation of the innovation capacity of China’s provincial optoelectronic information industry. Through extensive data collection and matching relationship analysis, an evaluation index system with both sequential decomposition and hierarchical interleaved structure was established, which includes four dimensions and 20 underlying indicators. To better reflect the gap in innovation capability across different provinces, a scientific piecewise non-zero nonlinear utility function model was established. According to the matching relationship between the subsystems and the underlying indicators of innovation capability, a weighted arithmetic mean comprehensive evaluation index model of innovation capability was developed. An empirical study of the optoelectronic information industry’s innovation capability in typical Chinese provinces was conducted using this comprehensive evaluation index model. The results show that Guangdong Province, Beijing Municipality, Jiangsu Province, Zhejiang Province, and Shandong Province ranked in the top five. The innovation capability of China’s optoelectronic information industry needs to be enhanced by strengthening the development of the investment mechanism, optimizing product development and promotion, improving the efficiency improvement mechanism, and solidifying the environmental support system. Full article

Although existing studies have shown that AI recommendation systems have potential in enhancing consumers’ purchase intention, there remains a lack of systematic research. This study aims to explore how the interaction between information presentation formats and AI role types influences consumers’ purchase intention. Based on metacognitive experience theory, two experiments are designed to analyze processing fluency as a mediator and consumers’ AI knowledge as a moderator. The results reveal that the interaction between information presentation format and AI role type significantly affects consumers’ purchase intention, while their separate effects are insignificant. Different from existing studies on separate factors, this study demonstrates that AI interactive marketing performance hinges on the matching of design and role positioning. This study extends the application of the theory of metacognitive experiences in the field of human–AI interaction and provides targeted recommendations for the interface design of AI recommendation systems. Full article

Nitrogen control in basic oxygen furnace (BOF) steelmaking is critical, as dissolved nitrogen concentrations exceeding 30–40 ppm detrimentally affect the mechanical properties and formability of low-carbon steel products; however, no prior study has applied a physics-informed machine learning model to nitrogen prediction at this process stage. A NitroPINN model was developed incorporating a multiplicative prediction structure that embeds Sievert’s law equilibrium, Wagner interaction coefficients, and Byrne–Belton surface blockage theory directly into the model. The model was trained and evaluated on 66 matched industrial heats from a top-blown 170-ton BOF converter, characterized by 16 physics-informed features, and benchmarked against ridge regression and a pure multilayer perceptron (MLP) under five-fold cross-validation. The NitroPINN achieved the lowest mean absolute error (MAE = 5.60 ppm) and mean absolute percentage error (MAPE = 27.2%) among the three models, whilst the learned equilibrium attainment factor η averaged 0.456 ± 0.028, consistent with sub-equilibrium nitrogen conditions imposed by intense CO flushing during oxygen blowing. All three models exhibited comparable overall accuracy, confirming that dataset size constitutes the principal performance bottleneck. The primary advantage of the NitroPINN lies in its physical interpretability, constraining predictions to metallurgically plausible ranges and providing a transparent decomposition into thermodynamic and kinetic contributions. Full article