Search Results (16,180)

This article analyzes the governance of religious diversity in public employment through the study of Quebec’s Bill 21. It examines how the State uses neutrality to manage religious symbols, focusing on implications for pluralism and fundamental rights within democratic governance frameworks and diversity regulation in plural societies. It situates Bill 21 within Quebec’s longer legal and political trajectory, marked by failed legislative attempts, recourse to the “notwithstanding clause,” and deep social polarisation around the construction of a francophone, secular identity. Methodologically, the study combines doctrinal analysis of Canadian constitutional law with a detailed examination of European Court of Human Rights and Court of Justice of the European Union case law, as well as a critical discussion of the Bouchard–Taylor Commission’s model of “open secularism” and later reinterpretations by Bouchard, Taylor and Maclure. The article finds that Quebec’s lawmakers selectively invoke European jurisprudence and the language of neutrality to justify far-reaching restrictions on visible religious symbols, especially for officials with coercive powers such as judges, police and prison staff, in ways that go beyond typical European practice. It argues that equating impartiality with an appearance of strict neutrality reflects the cultural assumptions of the majority and produces discriminatory effects on religious minorities, limiting both freedom of religion and equal access to public employment. The conclusion contends that neutrality should be assessed primarily through officials’ conduct rather than their appearance and that more inclusive models of secularism—grounded in open secularism and reasonable accommodation—offer better tools for reconciling State neutrality, pluralism and fundamental rights. Full article

This study investigates the interplay between entrepreneurial leadership and innovation performance in Jordanian IT firms, with a specific focus on the strategic role of Artificial Intelligence (AI). Grounded in a quantitative methodology, data were collected via a structured questionnaire from 162 professionals within the Jordanian IT sector. The research model positions AI not merely as a tool but as a potential catalytic factor, examining its direct and moderating effects on the leadership–innovation dynamic. Entrepreneurial leadership was assessed through the dimensions of innovative thinking, pro-activeness, and risk-taking, while innovation performance was measured across product, process, and organizational domains. The findings demonstrate that entrepreneurial leadership exerts a significant positive influence on innovation performance. Beyond the primary leadership effect, our data also reveal a significant, direct benefit from AI adoption on innovation outcomes. However, contrary to the proposed hypothesis, the analysis indicates that AI does not function as a statistically significant moderator in the relationship between entrepreneurial leadership and innovation. This suggests that, within this context, AI operates as a parallel driver of innovation rather than an enhancer of the leadership’s effectiveness. The study provides a critical contribution to the literature by challenging the assumed interactive role of AI in leadership models within emerging economies. It offers actionable insights for leaders in technology firms, emphasizing the imperative of developing strong entrepreneurial leadership capabilities and pursuing strategic AI adoption as complementary, yet independent, pathways to achieving superior innovation. Full article

This study develops a comprehensive workflow for the analytical seismic vulnerability and structural performance assessment of a special-importance steel building located in a region of elevated seismic hazard in southern Spain. The work addresses the need for reliable analytical methodologies for facilities that must remain operational during earthquakes. The proposed framework integrates a probabilistic seismic hazard assessment, including uniform hazard spectra and hazard disaggregation to identify control earthquakes. Additionally, an analytical vulnerability assessment under the Spanish seismic design code, NCSE-02, is performed. Operational modal analysis and nonlinear analysis are combined to retrofit the numerical model of the building and capture the building’s realistic seismic response. The resulting demand spectra are derived from site-specific ground-motion scenarios for Los Barrios (Cádiz, Spain). Retrofitting strategies are designed and assessed to ensure compliance with the code-defined performance requirements. Results indicate that the retrofitted model reproduces the building’s dynamic behaviour with improved reliability, and that the strengthening interventions enhance seismic performance while still allowing moderate damage in specific components. These findings highlight the importance of analytical vulnerability approaches and code-oriented retrofitting when evaluating the seismic performance and vulnerability of essential facilities. The study demonstrates that rigorous analytical methods provide a robust basis for defining seismic vulnerability in special-importance buildings and support improved decision-making for structural safety and resilience. Full article

Air–ground joint jamming enables three-dimensional, distributed jamming configurations, making it effective against air–ground communication networks with complex, dynamically adjustable links. Once the jamming layout is fixed, dynamic jamming power scheduling becomes essential to conserve energy and prolong jamming duration. However, existing methods suffer from poor applicability in such scenarios, primarily due to their sparse deployment and adversarial nature. To address this limitation, this paper develops a set of mathematical models and a dedicated algorithm for air–ground communication countermeasures. Specifically, we (1) randomly select communication nodes to determine the jammer operation sequence; (2) schedule the number of active jammers by sorting transmission path losses in ascending order; and (3) estimate jamming effects using electromagnetic wave propagation characteristics to adjust jamming power dynamically. This approach formally converts the original dynamic, stochastic jamming resource scheduling problem into a static, deterministic one via cognitive certainty of dynamic parameters and deterministic modeling of stochastic factors—enabling rapid adaptation to unknown, dynamic communication power strategies and resolving the coordination challenge in air–ground joint jamming. Experimental results demonstrate that the proposed Transmission Loss Ordering Algorithm (TLOA) extends the system operating duration by up to 41.6% compared to benchmark methods (e.g., genetic algorithm). Full article

Against the backdrop of China’s dual-carbon goals and the global push toward net-zero emissions, Macao faces not only an innovation deficit but also the urgent need to reconfigure its economic structure toward green and low-carbon development. This study investigates collaborative innovation mechanisms within Macao’s technological ecosystem through the lens of new qualitative productivity, a paradigm emphasizing structural optimization and systemic innovation capacity. As a micro-jurisdiction within the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), Macao faces challenges due to its tourism-dependent economy and spatial constraints. Employing a qualitative methodology grounded in collaborative governance theory, the research combines theoretical framework construction with empirical case studies of technology enterprises, notably Enterprise B, to analyze stakeholder interactions, resource integration, and institutional dynamics. This study examines how collaborative technological innovation governance in a micro-jurisdiction can underpin net-zero and green supply chain transitions by mobilizing cross-border resources and institutional synergies. Key findings reveal a polycentric governance model involving government, enterprises, academic institutions, and civil society organizations. This model leverages cross-border synergies, platformization, and adaptive recalibration to overcome structural limitations. Results highlight tripartite drivers—policy incentives, market forces, and corporate strategies—that enhance innovation throughput. Despite advancements in institutional coordination, challenges persist, including low enterprise absorption of government funding, talent attrition, and fragmented academic–industrial linkages. The study proposes strategic recalibrations, such as refining policy architectures, strengthening industry–academia–research symbiosis, and optimizing transnational collaboration through Macao’s Lusophone networks. The findings provide governance insights for micro-jurisdictions seeking to align new qualitative productivity with decarbonization, renewable energy integration, and participation in regional green supply chains. Full article

To enhance the assessment accuracy of tunnel face instability risks of active collapse during shield tunneling, this study establishes a novel unified analytical framework that couples the effects of unsaturated transient seepage induced by excavation drainage with soil stratification and heterogeneity. Grounded in unsaturated effective stress theory, the framework explicitly incorporates matric suction into the Mohr–Coulomb failure criterion via suction stress and apparent cohesion. By employing a horizontal two-layer nonhomogeneous soil model and solving the one-dimensional vertical Richards’ equation, an analytical solution for the face drainage boundary is derived to quantify the spatiotemporal evolution of suction stress and apparent cohesion. Subsequently, the critical support pressure is evaluated using the upper bound theorem of limit analysis, incorporating a horizontal layer-discretized rotational failure mechanism and the power balance equation. The validity of the proposed framework is confirmed through comparative analyses. Parametric studies reveal that in the upper hard and lower soft strata, the critical support pressure decreases and converges over time, indicating that unsaturated transient seepage exerts a significant influence in the short term that stabilizes over the long term. Additionally, sand–silt stratum exhibits lower overall stability and higher sensitivity to groundwater levels and temporal factors compared to silt–clay stratum. Conversely, silt–clay stratum displays a non-monotonic evolution with increasing cover-to-diameter ratios (C/D), reaching a minimum critical support pressure at approximately C/D = 1.1. Regarding heterogeneity, the internal friction angle of the lower layer exerts dominant control over the critical support pressure compared to seepage velocity, while the influence of other strength parameters remains secondary. These findings provide a theoretical basis for the time-dependent design of tunnel face support pressure under excavation drainage conditions. Full article

A principled, model-agnostic framework for structural feature extraction in time series is presented, grounded in topological data analysis (TDA). The motivation stems from two gaps identified in the literature: First, compact and interpretable representations that summarise the global geometric organisation of trajectories across scales remain scarce. Second, a unified, task-agnostic protocol for evaluating structure preservation against established non-topological families is still missing. To address these gaps, time-delay embeddings are employed to reconstruct phase space, sliding windows are used to generate local point clouds, and Vietoris–Rips persistent homology (up to dimension two) is computed. The resulting persistence diagrams are summarised with three transparent descriptors—persistence entropy, maximum persistence amplitude, and feature counts—and concatenated across delays and window sizes to yield a multiscale representation designed to complement temporal and spectral features while remaining computationally tractable. A unified experimental design is specified in which heterogeneous, regularly sampled financial series are preprocessed on native calendars and contrasted with competitive baselines spanning lagged, calendar-driven, difference/change, STL-based, delay-embedding PCA, price-based statistical, signature (FRUITS), and network-derived (NetF) features. Structure preservation is assessed through complementary criteria that probe spectral similarity, variance-scaled reconstruction fidelity, and the conservation of distributional shape (location, scale, asymmetry, tails). The study is positioned as an evaluation of representations, rather than a forecasting benchmark, emphasising interpretability, comparability, and methodological transparency while outlining avenues for adaptive hyperparameter selection and alternative filtrations. Full article

Livestock health knowledge graphs are essential for decision-making and reasoning in animal husbandry, yet existing knowledge is scattered across unstructured literature and encoded in narrowly scoped, species-specific models, resulting in semantic fragmentation and limited reusability. To address these issues, we proposed FARM (Four-dimensional Automated-Reasoning Multi-agent), a zero-shot multi-agent framework used for constructing multi-species livestock health knowledge graphs. FARM is grounded in a Four-Dimension Livestock Health Framework encompassing Rearing Environment, Physiological Status, Feed & Water Inputs, and Production Performance, and employs a unified ontology strategy that integrates cross-species general labels with species-specific constraints to achieve semantic alignment. The framework orchestrates five specialized agents—Coordination, Entity Extraction, Ontology Normalization, Relation Extraction, and Knowledge Fusion—to automate the construction process. Experiments on 2478 expertly annotated text samples demonstrate that FARM achieves an entity-level F1 score of 0.8070 (IoU ≥ 0.5), surpassing the strongest baseline by 0.1627. Moreover, it attains a corrected entity label accuracy of 90.44% and an F1 score of 0.9277 in relation existence identification, outperforming the baseline by 0.1114. Validation on 500 image samples further confirms its capability in multimodal evidence fusion. The resulting knowledge graph contains 29,064 entities and 26,662 triples, providing a reusable foundation for zero-shot extraction and unified cross-species semantic modeling. Full article

Localization and mapping remain critical challenges for Unmanned Ground Vehicles (UGVs) operating in unstructured natural environments, such as forests and agricultural fields. While Visual SLAM (VSLAM) and Visual–Inertial SLAM (VI-SLAM) have matured significantly in structured and urban scenarios, their extension to outdoor natural domains introduces severe challenges, including dynamic vegetation, illumination variations, a lack of distinctive features, and degraded GNSS availability. Recent advances in Deep Learning have brought promising developments to VSLAM- and VI-SLAM-based pipelines, ranging from learned feature extraction and matching to self-supervised monocular depth prediction and differentiable end-to-end SLAM frameworks. Furthermore, emerging methods for adaptive sensor fusion, leveraging attention mechanisms and reinforcement learning, open new opportunities to improve robustness by dynamically weighting the contributions of camera and IMU measurements. This review provides a comprehensive overview of Visual and Visual–Inertial SLAM for UGVs in unstructured environments, highlighting the challenges posed by natural contexts and the limitations of current pipelines. Classic VI-SLAM frameworks and recent Deep-Learning-based approaches were systematically reviewed. Special attention is given to field robotics applications in agriculture and forestry, where low-cost sensors and robustness against environmental variability are essential. Finally, open research directions are discussed, including self-supervised representation learning, adaptive sensor confidence models, and scalable low-cost alternatives. By identifying key gaps and opportunities, this work aims to guide future research toward resilient, adaptive, and economically viable VSLAM and VI-SLAM pipelines, tailored for UGV navigation in unstructured natural environments. Full article

High-quality commit messages are essential software artifacts because they succinctly communicate the intent and scope of code changes, yet large language models (LLMs) often fail to reflect project-specific writing conventions when used in a zero-shot setting without contextual signals. This study investigates not whether retrieval helps, but how the same retrieved example, when represented differently in the prompt, quantitatively changes generation outcomes. We implement a retrieve-then-generate framework where the target commit’s diff is used as a query for BM25 (Best Matching 25)-based sparse retrieval over a commit-level database, and the top-1 similar commit is optionally injected as an example context. We compare a no-context condition (K = 0) against a minimal-context condition (K = 1) under three context representations: Diff-only, Message-only, and Diff+Message pair. Using Qwen-7B on 8000 evaluation samples with a fixed prompt skeleton, deterministic decoding, and identical post-processing across conditions, we observe negligible differences at K = 0 (BLEU-4 1.14, ROUGE-L 7.47–7.48, METEOR 4.88–4.91), establishing a stable baseline. At K = 1, the same top-1 retrieved case yields systematically different metric responses depending on how it is represented (Diff-only, Message-only, or Diff+Message), even under an identical prompt skeleton, deterministic decoding, and identical post-processing. This indicates that “context representation” is not a cosmetic formatting choice but a first-class prompt-design variable in retrieval-augmented in-context learning for commit message generation. Accordingly, practitioners should select the representation based on the intended objective (e.g., lexical/style alignment vs. change-intent grounding), rather than assuming a universally optimal format. Full article

Winter road safety is critically influenced by microclimatic factors that determine where frost and ice persist on pavement surfaces. Among these, shadow duration plays a decisive yet often under quantified role in mountainous regions, where complex topography and variable solar exposure create localized cold zones. This study presents a GIS-based methodology for detecting and characterizing shadow-prone areas along high-altitude roads, extending previous national-scale models of winter risk toward local, geometry-driven analysis. Using high-resolution Digital Terrain Models (DTM02) and solar radiation simulations, four representative mountain roads (CL-505, AV-501, and CA-820) were analyzed to evaluate how orientation, slope, and surrounding relief control solar incidence. The resulting shadow maps were validated through UAV-derived thermal orthophotos and ground-based temperature measurements, confirming strong correspondence between simulated low-irradiance areas and observed cold surfaces. The integration of geometric and radiometric data demonstrates that topographic shading is a reliable predictor of frost persistence and can be incorporated into winter maintenance planning. By combining high-resolution terrain analysis with empirical thermal validation, this approach not only enhances predictive accuracy but also provides actionable insights for prioritizing road sections at greatest risk. Ultimately, it offers a scalable, data-driven framework for improving infrastructure resilience, optimizing maintenance operations, and mitigating winter hazards in cold-climate mountainous environments, supporting both safety and cost-effectiveness in road management strategies. Full article

Cascade dams describe an arrangement of several dam structures built along a flow path. Failure of one upstream dam in the cascade system can trigger catastrophic consequences to the downstream dams, as evidenced recently in the Edenville Dam and Sanford Dam. Previous research has mainly focused on rainfall-induced dam failures, although recent failures have demonstrated a combination of floods and earthquakes. Moreover, limited studies have analyzed the sensitivity of dam breach parameters, such as dam breach height and width in dams arranged in a cascade system for seismic events. Most hydraulic simulations that model seismic-induced dam failures assume the complete collapse of dams to analyze the downstream consequences. Hence, this study presents a novel analysis in simulating earthquake-induced failures in a cascade dam system, considering the sensitivity of dam breach parameters. In addition, dam breach parameters have been derived from the structural analysis of dams employing Finite Element Models (FEMs) to a critical Peak Ground Acceleration (PGA) of 0.3 g. Two-dimensional hydrodynamic simulations, along with the full dynamic wave equations, are undertaken in the study to model the earthquake-induced cascade dam failures. The results further elaborate on the significance of modeling cascade dam failures in terms of the consecutive arrival of floods and total flow compared to individual dam failures. Sensitivity analysis of dam breach parameters shows that the breach height is more significant than the breach width and breach slope. However, its significance decreases as the dam breach flood flow path increases in distance. The study further confirms the novel utilization of structural analysis to derive dam breach parameters for seismic-induced dam failures of concrete arch dams and rockfill dams, which will guide the optimization of disaster mitigation strategies and the operational resilience of the dams. Full article

As a vital component of urban tourism, urban theme parks increasingly face experience homogenization and intensifying competition. Accordingly, the implementation of refined digital marketing and operational strategies based on visitor digital behavior has become increasingly essential. In this context, tourists’ social media sharing has become a crucial link between destination marketing and visitors’ experience construction. Within the SOBC (Stimulus–Organism–Behavior–Consequence) framework, this study examines how theme park servicescapes (S) shape sharing motivations (O), which, in turn, influence multimodal sharing intentions (B—text, image + text, video) and subsequently contribute to memorable theme park experience (C). A two-stage, mixed-method design was employed, and the study considered visitors to Beijing Universal Studios and Shanghai Disney Resort. Semi-structured interviews and grounded analysis identified five motivations: altruism, self-presentation, affective expression, hedonic motivation, and community identification. Testing was performed using a survey (N = 604), along with structural equation modeling. The findings indicate that the staff-related social environment exerts significant positive effects on all five motivations, whereas the effects of the physical environment are more selective. Motivations differentially predict modal intentions: text aligns with altruism and affective expression; image + text aligns with altruism, community identification, and self-presentation; and video aligns with self-presentation, hedonism, community identification, and affective expression. All three intentions positively affect memorable theme park experience. These results clarify how motivations map onto content forms and validate a support SOBC framework from servicescapes to memorable experience, offering actionable implications for experience design and digital marketing. Full article

This research develops a decision support system for prioritizing Battery Energy Storage System (BESS) installations at existing closed-circuit television (CCTV) camera locations experiencing power interruptions in Latkrabang subdistrict. The methodology integrates nine validated features: outage frequency, downtime duration, maximum outage duration, Net Present Value (NPV), combined ROI, outage impact score, annual BESS cost, combined risk score, and UPS installation cost, derived from historical power outage records (2020–2023) and engineering economics calculations. An unsupervised K-means clustering algorithm, validated through silhouette analysis and the elbow method, categorizes installations into five risk levels, namely critical, very high, high, medium, and low, addressing the absence of predefined ground truth labels. Subsequently, Support Vector Machine (SVM) with hyperparameter optimization classifies priority installations using stratified train-test splitting (80:20). The model was initially developed and validated using 82 CCTV cameras from Lamphla Tiew subdistrict (the pilot area). The validated model was then successfully applied to 101 CCTV cameras in Latkrabang subdistrict (the target area), identifying 27 critical installation points requiring immediate BESS deployment. The weighted recommendation system balances data-driven clustering with scoring: NPV (35%), outage impact (25%), combined ROI (20%), maximum outage duration (10%), and BESS cost efficiency (10%). Full article

Forest biometrics has emerged as one of the fastest-growing scientific disciplines within environmental sciences. Machine learning (ML), an increasingly essential approach that uses effective algorithms, has proven to be an accurate and cost-efficient solution to forest-related problems. Recently, ML methods have evolved, from traditional machine learning (TML) algorithms to more sophisticated approaches, such as deep learning (DL) and ensemble (ENS) methods. To uncover these developments, a structured review and analysis of 150 peer-reviewed studies was conducted, following a standardized workflow. The analysis reveals clear shifts in methodological adoption. During the most recent five-year period (2021–2025), DL and shallow neural network (SNN) methods dominated the literature, accounting for 37.5% of published studies, followed by ENS and TML methods, contributing 29.2% and 27.1%, respectively, presenting a marked increase in the utilization of artificial neural networks (ANNs) and related algorithms across the domains of forest biometrics. Nevertheless, overall trends indicate that the benefits of TML methods still need further exploration for ground-based received data. Advances in remote sensing and satellite data have brought large-scale remotely sensed data into environmental research, further boosting ML utilization. However, each field could be strengthened by implementing standardized evaluation metrics and broader geographic representation. In this way, robust and widely transferable modeling frameworks for forest ecosystems can be developed. At the same time, further research on algorithms and their applicability to natural resources proves a key component for comprehensive and sustainable forest management. Full article