Search Results (18,310)

This study examines the psychological mechanisms through which anthropomorphic artificial intelligence (AI) relates to consumer adoption intentions in fragile, low-trust economies. Integrating the Stimulus–Organism–Response framework with the Computers Are Social Actors paradigm, Institutional Trust Theory, and Privacy Calculus Theory, we investigate how human-like AI design shapes cognitive and affective responses within Sierra Leone’s banking sector. Using survey data from 277 banking customers and partial least squares structural equation modeling, we find that AI anthropomorphism exhibits no direct association with adoption intention (β = −0.013, p = 0.760). Instead, its influence is entirely indirect—transmitted in parallel through perceived social presence (β = 0.144, 95% CI [0.062, 0.226]) and trust in the AI system (β = 0.139, 95% CI [0.068, 0.210]). Critically, customer skepticism—shaped by institutional fragility—functions as a boundary condition that substantially attenuates both pathways: among highly skeptical users (+1 SD), anthropomorphism’s conditional effect on social presence becomes non-significant (β = 0.098, p = 0.124) compared to low-skepticism users (β = 0.412, p < 0.001), while its effect on trust is reduced by more than half (β = 0.118 vs. 0.284). These findings identify a critical boundary condition on human-like AI design: in low-trust environments, anthropomorphism operates not as a standalone adoption driver but as a relational amplifier whose efficacy depends on foundational trust and is substantially weakened when skepticism is high. The study challenges universalist assumptions in human–AI interaction research and underscores the need for institutionally sensitive design approaches in fragile economies. Full article

The rapid integration of artificial intelligence (AI) into digital platforms has raised critical questions about how AI’s ethical declarations influence this sector. This study adopts a mixed-methods approach. First, a descriptive content analysis examined 54 declarations, including 45 national declarations across Africa, Asia, Europe, and the Americas, and 9 from major global actors (MGAs) such as the OECD, G7, and the EU. Ethical principle frequency was examined, and a benchmarking index was developed to compare “dominant principles” cited in over 50% of regional declarations with those cited in over 50% of MGA declarations. The analysis reveals universal adoption of societal well-being, fairness, accountability, and privacy (100%), while transparency and security show regional variation (75%). Second, a semi-systematic literature review following PRISMA guidelines identified four opportunities (e.g., global participation) and seven limitations (e.g., lack of standard frameworks, definitional ambiguities, implementation challenges, and legal enforcement difficulties). The implications of these limitations for digital platforms are then examined, leading to the identification of two dimensions for responsible platform governance: assessment mechanisms (e.g., UNESCO’s Ethical Impact Assessment) and governance implementation structures. The study further distinguishes three tiers of enforceability: declarative, procedural, and institutionalized ethics, bridging normative declarations and operational practice in platform governance. Full article

With the increasing demand for earth observation and communication missions, semi-rigid airships have emerged as critical aerial platforms due to their long endurance and high payload capacity. However, high-precision dynamic modeling and robust autonomous flight control remain challenging because of large hull volume and strong aerodynamic nonlinearities. This study proposes an integrated framework combining computational fluid dynamics (CFD) aerodynamic modeling with full-envelope gain scheduling control. First, nonlinear aerodynamic characteristics over wide ranges of angles of attack and sideslip are identified via CFD simulation, and a six-degree-of-freedom (6-DOF) nonlinear dynamic model incorporating added-mass effects is established. Subsequently, a gain scheduling linear quadratic regulator (LQR) controller is then designed using airspeed, climb rate, and yaw rate as scheduling variables, enabling coordinated control allocation between low-speed thrust vectoring and high-speed aerodynamic surfaces. Simulation results demonstrate improved three-dimensional (3D) path following performance and smooth flight mode transitions. The mean absolute errors (MAEs) in altitude, airspeed, and heading are limited to 0.711 m, 0.028 m/s, and 2.377°, respectively. Furthermore, the system’s robustness is validated under composite wind disturbances, confirming effectiveness of the proposed approach across the full flight envelope. Full article

The Dibaya Granitic and Migmatitic Complex (DGMC), located in the Kanyiki–Kapangu sector of the Kasaï Shield (Congo–Kasaï Craton, Democratic Republic of the Congo), represents a key exposure of Neoarchean continental crust in Central Africa. Despite its geological importance, information on its petrology, geochronology, geochemistry, and structural evolution remains dispersed across historical studies. This contribution presents a structured geological synthesis based exclusively on previously published cartographic, petrographic, structural, and isotopic data. No new analytical data are introduced; rather, existing datasets are systematically compiled, critically reassessed, and integrated into a coherent tectono-thermal framework. Published Rb–Sr and U–Pb ages indicate high-grade metamorphism and widespread migmatitization at ca. 2.72 Ga, followed by granitoid emplacement at ca. 2.65 Ga. Documented mineral assemblages (garnet–biotite–plagioclase–quartz ± K-feldspar ± amphibole) and the absence of reported high-pressure index minerals support high-temperature, moderate-pressure metamorphism consistent with intracrustal reworking. Reported regional geochemical characteristics suggest high-K calc-alkaline, weakly to moderately peraluminous granitoids derived predominantly from reworking of older TTG-type crust. Structural relationships, particularly along the Malafudi corridor, demonstrate strong coupling between deformation, anatexis, and magma emplacement. Collectively, this synthesis formalizes a Neoarchean intracrustal reworking model and provides a structured analytical basis for future high-resolution petrochronological and geochemical investigations. Although no new quantitative datasets are presented, this study provides the first systematic integration of dispersed geological and isotopic information for the Dibaya Complex, establishing a transparent analytical framework for future high-resolution investigations. Full article

The 2025 approval of the selective NaV1.8 blocker suzetrigine for acute pain marked a pivotal advance in analgesic drug development. Yet the subsequent failure of Vertex’s next-generation NaV1.8 inhibitor VX993 to demonstrate clinical analgesia underscores enduring challenges in translating mechanistic promise into patient benefit. This review examines why promising targets and compounds, spanning NaV and TRP channels, often falter and outlines a path toward more reliable target selection and validation. I first summarize the pain pathway, from nociceptor transduction through spinal processing to cortical perception, emphasizing how inflammation and peripheral sensitization reshape excitability. Historically serendipitous, pain drug discovery now prioritizes molecular precision. Most approved chronic pain therapies act in the CNS and are limited by modest efficacy and adverse effects. Nociceptor-enriched targets (NaV1.7/1.8/1.9; TRP channels) remain attractive, yet redundancy among NaV subtypes and the necessity of blocking targets at the correct anatomical sites complicate translation. Human genetics and multi-omics provide a powerful, unbiased engine for target discovery. Rare high-impact variants offer strong causal hypotheses, while common polygenic contributions illuminate broader susceptibility. Large biobanks increasingly reveal a mismatch between legacy pain targets and genetically supported candidates across neuronal and non-neuronal cells. Human DRG transcriptomics highlight NaV channel redundancy. Human in vitro electrophysiology and PK/PD analyses show suzetrigine achieves ~90–95% NaV1.8 engagement, yet neurons can still fire unless additional channels are blocked. Species differences and drug distribution (including BBB/PNS penetration and P-gp efflux) critically influence efficacy; centrally accessible blockade (e.g., for NaV1.7 or TRPA1) may be necessary to achieve robust analgesia, challenging peripherally restricted strategies. Osteoarthritis illustrates how obesity-driven metabolic inflammation, synovial immune activation, subchondral bone remodeling, and specific nociceptor subtypes converge to drive mechanical pain. Multi-omic integration across diseased human tissues can pinpoint causal processes and cell types, enabling more selective and safer target choices. I propose a practical framework for target validation that integrates: (i) rigorous human genetic support; (ii) cell-type and site-of-action mapping; (iii) human-relevant electrophysiology and PK/PD with verified target engagement; (iv) species-appropriate models; (v) consideration of modality (small molecule, biologic, RNA, targeted protein degradation). Advancing genetically and anatomically aligned targets, tested at the right sites and exposures, offers the best path to genuinely effective, better-tolerated pain therapeutics. Full article

The rapid growth of Digital Financial Services (DFSs), including what is occurring in Indonesia, necessitates evaluation methods that are capable of objectively and systematically handling multiple assessment criteria. Therefore, this study aimed to propose a hybrid FSS–CRITIC–TOPSIS framework for selecting optimal DFSs. Fuzzy soft sets (FSSs) were used to model uncertainty and subjectivity in criterion assessments. The Criteria Importance Through Inter-criteria Correlation (CRITIC) method determined the weights objectively based on the degree of contrast and inter-criteria correlation. Subsequently, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was used to rank the alternatives based on the closeness to the ideal solution. The incorporation led to a formally defined decision operator, τ, which mapped FSS to complete preference orderings while ensuring provable stability and strong discriminative properties. The framework was applied to five major Indonesian digital wallets, namely ShopeePay, GoPay, OVO, LinkAja, and DANA, as well as being evaluated across five criteria. This framework identified DANA as the optimal alternative, with a score of 0.9282, followed by ShopeePay (0.8354) and GoPay (0.6958). Comparative analysis with other methods showed a near-perfect ranking correlation (ρ = 0.9−1) with a more proportional score distribution and ranking results that reflected actual conditions. Sensitivity analysis also confirmed robustness, with ranking changes remaining logically consistent underweight variations. In conclusion, the FSS-CRITIC-TOPSIS framework provided an effective, mathematically rigorous method for multi-criteria decision-making (MCDM) under uncertainty, which applied to digital wallet selection as well as potential extension to broader evaluation contexts supporting SDGs 8, 9, and 10. Full article

The growing scale and complexity of industrial infrastructure make efficient and reliable inspections a critical challenge. Inspection task demands often vary dynamically, requiring efficient and demand-responsive inspection strategies to ensure stable operation. However, existing UAV inspection approaches typically deploy UAV base stations (UAV-BSs) based on fixed inspection frequencies, which are inadequate for adapting to such dynamic demands and may reduce inspection efficiency. Moreover, these approaches often rely on historical inspection data, whose empirical distributions may deviate from the true distributions, thereby compromising solution robustness. To address these issues, this paper proposes a two-stage distributionally robust optimization (TDRO) framework for joint UAV-BS deployment and inspection routing in dynamic environments. The framework accounts for uncertainties in both inspection frequency and distributional perturbations. Uncertainty sets constructed based on probability metrics are employed to capture deviations between empirical and true distributions, forming the foundation of the two-stage distributionally robust optimization model. The resulting model is solved using column-and-constraint generation (C&CG) integrated with column generation (CG), yielding robust deployment decisions and an effective trade-off between total system cost and inspection efficiency. Simulation results show that the framework effectively addresses inspection frequency uncertainty, reducing the total objective by 5.50% on average, with a further 2.16% reduction when distributional perturbations are considered. Full article

Aiming at the problems of difficult hard-constraint enforcement and weak environmental generalization ability in the safe trajectory planning of manipulators in complex environments, a Policy-Guided Model Predictive Path Integral (PG-MPPI) planning framework is proposed. This framework integrates the advantages of reinforcement learning and model predictive control to construct a global prior guidance, local real-time optimization and hard-constraint safety assurance: a Constraint-Discounted Soft Actor–Critic (CD-SAC) offline learning policy is designed, which incorporates the configuration-space distance field as a safety guidance term to realize the learning of obstacle avoidance behavior; the offline policy is used to guide the online sampling and optimization of MPPI, improving sampling efficiency and planning quality; and a Control Barrier Function (CBF) safety filter is introduced to revise control commands in real time, ensuring the strict satisfaction of constraints. Taking the SIASUN T12B manipulator as the research object, simulation comparison experiments are carried out in multi-obstacle scenarios. The results show that the PG-MPPI algorithm outperforms the comparison algorithms in the success rate of collision-free target reaching, ensures the smoothness and feasibility of the trajectory, and has a good adaptive capacity to complex environments with unknown obstacle configurations, thus providing an efficient solution for the autonomous and safe operation of manipulators. Full article

Unmanned Aerial Vehicle (UAV) swarms operating in contested environments face a critical “semantic gap” between raw, high-velocity network traffic and high-level mission security constraints, compounded by the risk of privacy leakage during collaborative learning. Existing deep learning (DL)-based Network Intrusion Detection Systems (NIDSs) suffer from opacity, prohibitive resource consumption, and vulnerability to gradient leakage attacks in federated settings, while traditional rule-based systems fail to handle encrypted payloads and evolving attack patterns. To bridge this gap, we present NeSySwarm-IDS (Neuro-Symbolic Swarm Intrusion Detection System), an end-to-end differentiable neuro-symbolic framework that simultaneously achieves high accuracy, strong privacy guarantees, and built-in interpretability under resource constraints. NeSySwarm-IDS integrates an extremely lightweight 1D convolutional neural network with a differentiable ukasiewicz fuzzy logic reasoner incorporating attack-specific rules. By aggregating only low-dimensional logic rule weights with calibrated differential privacy noise, we drastically reduce communication overhead while providing $(ϵ,\delta )$-DP guarantees with negligible utility loss. Extensive experiments on the UAV-NIDD dataset and our self-collected dataset demonstrate that NeSySwarm-IDS achieves near-perfect detection accuracy, significantly outperforming traditional machine learning baselines despite using limited training data. A detailed case study on GPS spoofing confirms the interpretability of our approach, providing axiomatic explanations suitable for autonomous mission verification. These results establish that end-to-end neuro-symbolic learning can effectively bridge the semantic gap in UAV swarm security while ensuring privacy and interpretability, offering a practical pathway for deploying trustworthy AI in contested environments. Full article

With the intensification of human activities and climate variability, balancing ecosystem service (ES) supply and demand is critical for regional sustainable development. Existing studies predominantly focus on linear driving effects and lack integrated quantitative frameworks that link the spatiotemporal dynamics of ES supply–demand relationships (ESSDRs) with their nonlinear driving mechanisms, and few have systematically quantified the critical thresholds of driving factors and their interactive effects. To address these research gaps, this study quantified the supply, demand, and supply–demand ratios of four key ESs (food production [FP], carbon sequestration [CS], water yield [WY], and soil retention [SR]) in the Yellow River Basin of Henan Province (2000–2020) using the InVEST model and multi-source data. An analytical framework integrating the Extreme Gradient Boosting (XGBoost) model and Shapley Additive Explanations (SHAP) was established to identify dominant drivers, reveal nonlinear response patterns, and quantify critical thresholds. The results showed that FP and CS supply increased continuously, while WY and SR supply slightly declined; CS and WY demand grew faster than supply, leading to expanding deficits, whereas FP and SR maintained relative balance. Spatially, FP/CS surpluses concentrated in eastern plains and southwestern forests, WY deficits occurred in the northwest, and SR balance prevailed in most regions. Dominant drivers differed by ES type—arable land proportion (FP), population density (CS), precipitation (WY), and slope (SR)—all exhibiting distinct threshold effects (e.g., arable land proportion >0.6, slope >3°). These findings provide novel insights into ESSDR spatial heterogeneity and threshold-based regulation, offering a scientific basis for differentiated ecological management and sustainable spatial planning in the Yellow River Basin and similar ecologically vulnerable regions. Full article

The illegal wildlife trade (IWT) poses a significant global challenge that threatens biodiversity and ecosystem balance. This study addresses these complexities by proposing the Integrated Ecological Intervention Optimization Model (IEIOM). The model integrates three core metrics—habitat area, crime rate, and quantity of IWT—while incorporating multidimensional analysis and predictive modeling across ecological, social, and economic dimensions. To enhance predictive accuracy, we employed nonlinear regression, grey prediction, and autoregressive models. These predictive insights, combined with empirical data, were integrated into a multi-index intervention optimization framework using a sum-of-sines function. A simulated annealing algorithm was subsequently applied to achieve global optimization. Results indicate that the proposed IEIOM outperforms the traditional entropy weight method by providing a more dynamic, data-driven weight allocation. The optimal weights prioritized crime suppression (50%), habitat protection (28%), and trade regulation (22%), underscoring the critical roles of law enforcement and environmental preservation. Sensitivity analysis further demonstrated that technological innovation, community collaboration, and public awareness are pivotal to successful interventions. Overall, the IEIOM provides a robust decision-support tool for policymakers, enabling effective resource allocation to combat IWT and contributing to long-term sustainable development. Full article

Under high renewable energy penetration, wind power forecasting faces pronounced challenges due to strong randomness and uncertainty, making conventional point-forecast-centric paradigms insufficient for risk-aware and reliable power system scheduling. An uncertainty-aware temporal transformer framework for wind power forecasting is presented, integrating probabilistic modeling with deep temporal representation learning to jointly optimize prediction accuracy and uncertainty characterization. Crucially, rather than treating uncertainty quantification merely as a post-processing step, the central conceptual contribution lies in modularizing uncertainty directly within the attention mechanism. A probability-driven temporal attention mechanism is incorporated at the encoding stage to emphasize high-variability and high-risk time slices during feature aggregation, while a multi-quantile output and interval modeling strategy is adopted at the prediction stage to directly learn the conditional distribution of wind power, enabling simultaneous point and interval forecasts with statistical confidence. Extensive experiments on multiple public wind power datasets demonstrate that the proposed method consistently outperforms traditional statistical models, deep temporal models, and deterministic transformers, as validated by formal statistical significance testing. Specifically, the method achieves an MAE of $0.089$, an RMSE of $0.132$, and a MAPE of $10.84\%$ on the test set, corresponding to reductions of approximately $8\%$–$10\%$ relative to the deterministic transformer. In uncertainty evaluation, a PICP of $0.91$ is attained while compressing the MPIW to $0.221$ and reducing the CWC to $0.241$, indicating a favorable balance between coverage reliability and interval compactness. Compared with mainstream probabilistic forecasting methods, the model further reduces RMSE while maintaining coverage levels close to the 90% target, effectively mitigating excessive interval conservatism. Moreover, by adaptively generating heteroscedastic intervals that widen during high-volatility events and narrow under stable conditions, the model achieves a highly focused and effective capture of critical uncertainty information. Full article

Social media is an integral part of everyday life for women from migrant and refugee backgrounds and is sometimes recognised as a ‘critical lifeline’ enabling access to essential support during settlement. Despite this, Culturally and Linguistically Diverse (CALD) women in Australia often have limited and uneven access to critical social media literacy education opportunities, and there remains a lack of in-depth research exploring what CALD women want to learn and how they wish to participate in such educational interventions. Adopting an education justice approach, this article advances a framework for social media literacy education developed with women from refugee backgrounds. The study employed semi-structured interviews and co-design workshops with women from refugee backgrounds, alongside staff from local public libraries and refugee support organisations. The study demonstrates that women from refugee backgrounds primarily use social media for communication and connection, but are also interested in learning how to use these platforms to navigate sexism, racism, and other systemic barriers to settlement. The proposed framework—which can be adopted by public libraries and other grassroots organisations—responds directly to women’s calls for peer-led, value-driven, context-specific, and culturally responsive social media literacy interventions. Full article

Background. Smart warehousing increasingly relies on digital twin technologies to enhance operational efficiency, real-time visibility, and decision-making in logistics systems. However, existing research primarily focuses on technological capabilities while paying limited attention to the organizational practices that shape successful implementation. Methods. This study aims to identify and prioritize the critical success factors (CSFs) for integrating digital twins into smart warehousing using the Practice-Based View (PBV) as the theoretical lens. Based on insights from prior research and expert validation, nine CSFs were identified and evaluated using the Best–Worst Method (BWM). Empirical input was obtained from six industry experts with experience in digital transformation, warehousing, and supply chain management. Results. The results indicate that collaborative learning, contextual training, and gamification elements emerge as the most influential critical success factors, highlighting the importance of organizational practices in supporting digital twin adoption in smart warehousing. Conclusions. By linking technological capabilities with organizational routines, the proposed framework provides both theoretical insights and practical guidance for implementing digital twins in smart warehouse environments. Full article

Generative Artificial Intelligence (GAI) is rapidly transforming higher education by introducing new mechanisms for supporting the development of advanced cognitive processes and research-related capabilities. This study examines how postgraduate students employ GAI to develop their cognitive and research talent, conceptualized here as higher-order academic skills such as analysis, synthesis, and critical reasoning, across six domains: literature review, theoretical development, research design, data analysis, academic writing, ethical use, and challenges encountered—signaled explicitly rather than listed line by line. We administered a validated multidimensional scale to 214 postgraduate students, and the results indicate a moderate overall use of GAI, with notably high involvement in practices that emphasize ethics and responsibility. Students reported clear cognitive benefits in tasks involving information processing, linguistic refinement, and conceptual clarification while showing caution toward delegating higher-order analytical or theoretical reasoning to AI systems. Key challenges included limited institutional training, concerns about data privacy and academic integrity, and difficulties evaluating the originality and reliability of AI-generated content. Inferential analyses indicated significant differences based on gender, academic level, and general technology proficiency, whereas no differences emerged across age groups, departments, or specializations. Overall, this study demonstrates how GAI can contribute to the development of higher-level cognitive skills and research competencies, with “moderate use” operationalized as consistent but selective engagement across domains, while underscoring the need for structured training, clear guidelines, and teaching approaches that foster the responsible and effective incorporation of AI within postgraduate research. The results highlight practical implications for higher education, including the importance of institutional training programs, governance frameworks for responsible AI use, and pedagogical models that foster critical engagement with GAI. Full article