Search Results (10,285)

With the digital economy advancing at a fast pace, digital transformation plays a pivotal role in reinforcing firms’ innovation capability and promoting high-quality development. This study analyzes Chinese non-financial publicly listed firms on the A-share market over the period 2009–2023. Based on text mining of annual reports, this study constructs an index capturing digital transformation and empirically evaluate its impact on innovation output with firm and year fixed effects. The estimates suggest that digital transformation meaningfully increases firms’ innovation output; the inference is unchanged when applying instrumental-variable approaches and conducting extensive robustness checks. Mechanism analysis reveals two parallel channels: (1) the R&D investment mechanism, characterized by improvements in R&D intensity, capitalization rate, per capita efficiency, and investment growth; (2) the governance environment mechanism, reflected in enhanced internal control, improved information disclosure quality, and strengthened audit supervision. Once firms are stratified by characteristics, the estimated positive effect of digital transformation is most pronounced for firms with low financial constraints, large size, eastern locations, and state ownership. This study identifies both direct and indirect mechanisms linking digital transformation to innovation and highlights how firm- and region-specific features condition the magnitude of this effect, thereby offering empirical implications for corporate digitalization strategies and policy design. Full article

The study investigates the state-dependent dynamics of overconfidence in the Bangladesh equity market by exploring the relationship between market returns and trading volume within a nonlinear information-theoretic framework. Building up on the traditional return–volume literature, the study differentiates between total market returns and unexpected returns, with the latter representing unexpected information shocks obtained using the Market Index Model. Transfer Entropy with bootstrap inference estimates the directional and asymmetric information flows across five different market states, namely: bullish, bearish, crisis, extended crisis, and COVID-19. The evidence suggests that the overconfidence biases in aggregate market returns are small and intermittent and are reflected in poor and unstable information flow between market returns and trading volume. In comparison, unexpected market returns have a directionally significant impact on trading behavior, which supports the behavior of state-dependent overconfidence. The findings also reveal that overconfidence is higher in normal and bullish market situations but drops significantly in crisis-based situations. The asymmetric analysis indicates increased trading responses to negative returns shocks, as it is more evident that investors are more sensitive to losses and recovery expectations. The research adds to behavioral finance literature on frontier markets through an unexpected return decomposition with nonlinear causality model. The results have serious implications on market surveillance, assessment of investor behavior and design of regulatory policies. Full article

Saudi Arabia currently lacks a nationally representative, multi-day National Household Travel Survey comparable to the US, UK, or New Zealand programmes; existing official data products focus on aggregate road-transport indicators or general household statistics rather than detailed day-to-day travel diaries. This study develops a benchmark-driven framework for NHTS–KSA by comparing Saudi demographic, geographic, infrastructure, climate, and mobility indicators with those of the United States, United Kingdom, and New Zealand, and by systematically assessing 15 survey-design indicators across their national household travel surveys. Context benchmarking identifies the United States as the closest for highway-oriented interurban structure and motorisation level, New Zealand for geography and demographic structure (in particular, near-identical physiological density on limited arable land), and the United Kingdom as the most aspirationally aligned benchmark for the multimodal mobility patterns Saudi Arabia aims to develop under Vision 2030. Design benchmarking shows that the three surveys are closely matched in aggregate similarity but lead on distinct elements: New Zealand on diary length and integrated passive tracking, the US on digital tools and emerging-behaviour modules, and the UK on interviewer-led recruitment and multimodal analysis, a pattern that proves robust to plausible variation in individual scores. The resulting NHTS–KSA blueprint specifies a statistically justified, stratified multistage annual household sample, a two-day diary with rolling 12-month fieldwork, interviewer-assisted recruitment, a digital-first diary with optional GPS tracking, and modules on long-distance travel, telework, e-commerce, gendered mobility, accessibility, safety, and environmental attitudes. While preserving international comparability, the framework provides the data foundation required to steer public-transport investment, demand-management measures, and land-use policies in line with Saudi Arabia’s Vision 2030 objectives for sustainable, inclusive, and smart mobility. Full article

The aim of this paper is to give an overview of emerging technologies for the greening of aviation, how they can be applied to different classes of aircraft, and the challenges to be overcome in achieving efficiency and environmental objectives. The following steps are part of the journey towards the greening of aviation: (i) developing and maturing new technologies, including electrification and sustainable fuels; (ii) where possible, using new technologies in the current fleet to maximize short-term benefits—i.e., EU Fit for 55; (iii) when it is not possible to retrofit new technologies to current aircraft, incorporating them into new next-generation aircraft designs from 2035; and (iv) replacing existing fleets with new, cleaner aircraft to meet the ICAO Net Zero 2050 goal. These technologies of prime importance will have to be supplemented by operational, regulatory, and economic enablers to support wide deployment. There will not be one solution that meets the requirements of all aircraft classes or mission profiles, but rather a combination of electrification, hydrogen propulsion, and sustainable aviation fuels will be required. Achievement of aviation’s environmental goals will hence not solely be a function of technological progress but also certification pathways, investment in infrastructure, and integrated policy strategies. Full article

Microplastics (MPs; <5 mm) represent a growing environmental concern that increasingly challenges environmental monitoring, governance, and evidence-based decision-making. This review critically examines how current scientific understanding of microplastic sources, classification, occurrence, and environmental behavior can support environmental governance. MPs are classified as primary and secondary particles; however, persistent inconsistencies in size definitions, shape descriptors, and polymer identification limit the comparability of monitoring data and constrain the development of coherent regulatory frameworks. Evidence on the occurrence of MPs in surface waters and sediments highlights widespread contamination and pronounced spatial variability, raising challenges for risk assessment and policy harmonization across regions. Key transport pathways, including atmospheric deposition, terrestrial runoff, and riverine fluxes, are analyzed to illustrate how local emissions translate into large-scale environmental impacts. Rivers emerge as key components linking sources to receptors, offering relevant points for policy intervention and management measures. The review evaluates current policy responses to microplastic pollution, identifying significant gaps in standardized monitoring, data integration, and risk assessment approaches. It emphasizes the need for stronger alignment between scientific outputs and policy requirements, including the co-production of knowledge involving scientists, regulators, and stakeholders. By outlining pathways through which scientific evidence can inform regulatory design and environmental management, this study provides actionable insights for improving policy effectiveness. Advancing harmonized methodologies and integrating science into decision-making processes are essential steps toward mitigating microplastic pollution and supporting sustainable environmental governance. Full article

AI-enabled infrastructure systems increasingly govern access to emergency services, disaster relief, and utility restoration, yet they routinely produce inequitable outcomes even when allocation algorithms apply procedurally neutral rules. The standard explanation locates the cause inside the algorithm. This paper argues instead that inequity arises from the interaction between the algorithm and the physical environment in which it operates: network topology, resource locations, and demand distribution jointly constrain what any policy can achieve, and when those constraints are sufficiently binding, ethical infeasibility is structural rather than algorithmic. We introduce a constraint-based formulation that embeds ethical requirements into the feasible region, and a hierarchical Irreducible Infeasible Subsystem (IIS) procedure that attributes infeasibility to rule design, algorithmic choice, or physical infrastructure. We further establish the Structural Infeasibility Theorem, deriving closed-form bounds on inter-group disparity across all feasible policies. The framework was applied to zone-decomposable infrastructure allocation problems generally, with a metropolitan ambulance-dispatch system serving as a concrete instantiation. The study delivers four findings. First, the minimum-service violation may not be caused by the allocation algorithm itself; rather, it may arise from the physical layout of the infrastructure. Second, the observed efficiency–equity trade-off may not be an unavoidable feature of equitable allocation, but may instead reflect the difficulty of achieving equity within an underbuilt system. Third, before new infrastructure is added, improvements in equity may represent harm redistribution rather than harm reduction. Fourth, the IIS certificate can be translated into a concrete capital-investment requirement, showing what physical change may be needed to restore ethical feasibility. Full article

Comprehensive biological and ecological data are essential for the appropriate stock management of Solenocera alticarinata Kubo, 1949. The lack of ecological knowledge on S. alticarinata, a species of potential economic value in the East China Sea, limits the development and implementation of appropriate fishery management measures such as minimum landing size and seasonal closure. Accordingly, we employed research vessels to characterize the seasonal spatial distribution patterns of S. alticarinata within the study area (26.5–35° N, 120–127° E) in 2018–2019. Our findings indicate that S. alticarinata can survive at a depth of 50–120 m and sea bottom salinity of 33–35. The highest biomass-based CPUE and greatest abundance of S. alticarinata were found during the summer and autumn, respectively. The seasonal ranking of the total catch per unit effort in number was as follows: autumn (1438.7 ind·h⁻¹) > summer and winter (1012.1–1078.2 ind·h⁻¹) > spring (287 ind·h⁻¹). In terms of mean average individual size, the order was summer > spring > autumn and winter. Overall, our findings provide a basis for developing management policies, and offer insights for designing fishery management and conservation strategies. Full article

Life Cycle Sustainability Assessment (LCSA) has been widely used to assess the environmental, economic, and social impacts of emerging technologies. However, its practical application in decision support remains limited due to incompatibility of units of measurement among sustainability dimensions and a lack of transparent integration mechanisms. This study constructs a monetized LCSA framework to examine how battery electric vehicles (BEVs) replacing gas-powered vehicles (GVs) in cold regions covered by carbon-intensive power systems affects overall sustainability performance. The results show that over a 15-year lifespan, BEVs reduce life cycle costs by 28.74% and carbon-related environmental costs by 25.27% compared to GVs, demonstrating significant economic and environmental advantages. However, BEVs show a 4.23% decrease in standardized socially perceived performance, primarily due to consumer concerns about transparency, privacy, and end-of-life liability. These findings suggest that incorporating social dimensions can significantly alter sustainability conclusions and reveal trade-offs that traditional single-dimensional assessments cannot capture. This study provides new empirical evidence for the comprehensive application of monetized life cycle sustainability assessment and offers valuable insights for vehicle design improvements, increased social acceptance, and low-carbon transportation policies in cold and carbon-intensive regions. Full article

Small ruminant production in Kosovo is predominantly extensive, and biosecurity practices remain poorly characterized. The emergence of Peste des Petits Ruminants (PPR) in Europe (beginning in 2024) and the first confirmed case in Kosovo (July 2025) highlight the urgent need for baseline biosecurity data to inform disease control. A cross-sectional pilot study was conducted on 63 small ruminant farms (53 meat-producing, 10 dairy-producing) across seven municipalities in Kosovo between September 2025 and February 2026. Biosecurity practices were assessed using the Biocheck.UGent™ questionnaire during direct on-farm visits. External (Ext) biosecurity scores (preventing pathogen introduction) were higher (p < 0.0001) than internal (Int) scores (limiting spread within farms). For external biosecurity, the highest scores were observed for purchase and reproduction (Ext A), intermediate scores existed for feed and water (Ext C) and visitors and farm workers (Ext D), and the lowest scores were found for transport and carcass removal (Ext B) and infrastructure (Ext E). For internal biosecurity, the highest scores were observed for lamb/kid management (Int H) and dairy management (Int I), followed by the management of adult animals (Int J); work organization (Int K) and reproduction management (Int G) formed an intermediate-low cluster, whereas disease management (Int F) scored the lowest. Benchmarking against the Biocheck.UGent™ worldwide database (predominantly intensive systems, thus not directly comparable) indicated that internal biosecurity and overall biosecurity levels were lower than the benchmark, while external biosecurity was comparable for some components. Given the convenience sample (36.4% response rate), findings are exploratory and are not directly generalizable. Larger herd size was positively correlated with external (ρ = 0.54, p < 0.0001), internal (ρ = 0.35, p = 0.005), and overall (ρ = 0.57, p < 0.0001) biosecurity scores. This first empirical biosecurity assessment of small ruminant farms in Kosovo reveals critical gaps in transport hygiene, disease management, and reproductive management pathways that enable PPR spread and perpetuate endemic zoonoses. The positive association between herd size and biosecurity may indicate structural barriers and/or knowledge gaps for small farms. Current biosecurity tools, designed for intensive systems, require adaptation for extensive production systems. These findings provide a baseline for targeted interventions, policy development, and validation of context-appropriate biosecurity instruments in Kosovo and similar extensive systems globally. Full article

With the increasing complexity of unmanned aerial vehicle (UAV) missions in complex obstacle environments, cooperative hunting of maneuvering ground targets by UAV swarms has become an important problem for multi-agent autonomous decision-making. This paper focuses on a simulated three-UAV hunting scenario in a two-dimensional obstructed environment, where UAVs must search for, approach, encircle, and continuously track a target while avoiding static obstacles under local observation. To address the problem of multi-UAV cooperative hunting of dynamic targets in complex obstacle environments, this paper proposes a curriculum learning (CL)-based Multi-Agent Proximal Policy Optimization algorithm, termed CL-MAPPO. Specifically, a three-stage progressive training curriculum is designed to overcome the challenges of low exploration efficiency, slow environmental adaptation, and difficult convergence of cooperative hunting policies faced by multi-agent deep reinforcement learning in hunting tasks, thereby gradually enhancing the cooperative hunting capability of UAVs in complex environments. Curriculum I employs fixed obstacles and a stationary target position to train the UAVs’ basic obstacle avoidance and target search abilities. Curriculum II introduces randomly generated obstacles and target positions to improve the UAVs’ adaptability to varying environments. Curriculum III further incorporates a dynamic target, prompting the UAVs to learn effective hunting strategies against maneuvering targets. The simulation experiment includes ablation experiments against MAPPO without curriculum learning and comparative simulations against MADDPG and MADQN, using reward convergence curves and trajectory visualizations to evaluate the training results. The results show that, under the same training episodes in the ablation experiment, CL-MAPPO reaches a higher and more stable reward level than vanilla MAPPO, indicating improved learning efficiency without increasing model complexity. In the comparative experiment, the CL-MAPPO algorithm achieved a higher success rate in cooperative hunting. These simulation experiments verify the effectiveness and superiority of the CL-MAPPO algorithm in multi-agent cooperative hunting tasks. Full article

Background: Cardiac telemetry monitoring represents an important aspect of in-hospital patient safety in both telemetry and critical care settings. Despite technological advancements, telemetry effectiveness may be diminished due to systemic failures including operational processes, instructional policies, and human factors. Alarm fatigue, recognized by the Joint Commission as a leading contributor to serious patient harm, lies at the forefront of these failures. Objective: This narrative review utilized and synthesized sources indexed through PubMed, PubMed Central, MEDLINE, Web of Science, Google Scholar, Directory of Open Access Journals (DOAJ), and Scopus to illustrate the factors involved in hospital related monitoring failures. We purport that alarm fatigue and telemetry monitoring failures are the result of complex systemic failures comprising technological and human failures. Through this narrative, we propose an evidence-based framework known as the Safer Telemetry Architecture (STA) to pinpoint redundancies and promote closed-loop communication regarding alarm management. Conclusions: Monitored in-hospital environments represent a key area of preventable morbidity and mortality due to systemic design flaws. Our STA framework addresses such flaws via improvements in nurse-driven protocols, alarm routing, mandatory coverage standards for backup, and increased performance auditing. Systemic improvements via such a framework may represent an important institutional strategy for hospitals with cardiac monitoring, but requires further prospective validation. Managing redundancies in alerts and sounds, improving backup and nursing telemetry protocols, and promoting closed or continuous loops targeting alarm response times and telemetry utilization are key to effectively improving patient safety. Full article

Against the coordinated advancement of building a maritime power, high-quality development of marine tourism and ecological civilization construction, realizing positive interaction between marine resource conservation and tourism industrial development has emerged as a pivotal issue for high-quality growth in coastal regions. Taking 11 coastal provincial-level administrative regions in China spanning 2008 to 2024 as the research sample, this paper first establishes an evaluation indicator system covering marine resources and the tourism industry. It further adopts an integrated empirical framework encompassing the coupling coordination degree model, spatial Markov chain model, obstacle degree model, fixed-effect model and geographically and temporally weighted regression (GTWR) model to systematically unpack the spatiotemporal differentiation characteristics, internal restrictive obstacle factors and external driving determinants of the two-system coupling coordination. On this basis, a marine resource compensation mechanism for tourist destinations is formulated. Empirical results demonstrate four core findings: (1) In terms of temporal evolution, the overall coupling coordination level keeps rising and goes through three phases: initial development, rapid improvement and post-shock recovery. After a short-term decline triggered by the pandemic, the index rebounds markedly after 2023, showing that the two systems can recover and stabilize. (2) In terms of spatial layout, a persistent stratified spatial pattern featuring “higher coordination in southern coast versus lower coordination in northern coast with three-tier hierarchical differentiation” is identified; high-level neighboring regions exert prominent positive spatial spillover effects, whereas low-level adjacent areas are prone to fall into development lock-in traps. (3) For internal constraint obstacles, the marine resource subsystem is persistently restricted by resource exploitation limits and coastal spatial scarcity, while the dominant bottleneck of the tourism industrial subsystem shifts from insufficient market scale to inadequate human capital supply. (4) Regarding external driving forces, the proportion of tertiary industry and the digital infrastructure constitute core driving contributors, whereas marketization progress and opening-up degree act as primary restrictive factors, with pronounced spatial heterogeneity existing across all driving indicators. Finally, in line with the quasi-public-good attribute and ecological externality of marine resources, this study constructs a differentiated and synergistic marine resource compensation mechanism from three dimensions: stakeholder identification, compensation implementation pathways and institutional guarantee systems. The proposed framework provides theoretical references and practical policy options to facilitate high-level coupling and coordinated development between marine resource preservation and the coastal tourism industry. The marginal contribution of this research lies in integrating coupling coordination measurement, obstacle factor diagnosis, driving mechanism identification and compensation mechanism design into an integrated analytical framework, which delivers theoretical foundations and operable policy solutions for coastal marine resource protection, tourism industrial upgrading and differentiated compensation system construction. Full article

Even though the literature extensively examines aspects of the efficiency, waterway infrastructure, and competitiveness of inland waterway transport (IWT), a systemic composite index that integrates navigational, operational, and digital factors of IWT infrastructure into a single comparable evaluation system at the level of port hinterlands has not been identified. This study proposes a multi-criteria inland waterway transport infrastructure index (IWTI) designed to assess complex infrastructure conditions. The IWTI measures infrastructural readiness, physical navigational, operational, and digital prerequisites that enable the realisation of IWT potential. The index is calculated using the multi-criteria decision-making method TOPSIS, with criterion weights determined based on expert evaluation (n = 7) data. Rank stability was tested via Spearman’s rank correlation sensitivity analysis under three alternative weighting scenarios. The methodology was applied to assess seven ports in the Baltic Sea region using data from 2023. The IWTI ranges from 0.00 (Tallinn does not have an IWT connection) to 0.80 (Szczecin). The study revealed significant regional differences: the hinterland of the HaminaKotka port is characterised by a mature Saimaa canal and lake system; the hinterlands of Szczecin and Klaipėda demonstrate strong potential for the Oder/Odra and Nemunas corridors; the port of Gdańsk was identified as a medium-potential case with clearly defined priorities for infrastructure improvements. The ports of Gdynia and Riga lack functional inland waterway connections, but the infrastructural potential scenario indicates significant IWTI growth opportunities upon the implementation of planned investments. Sensitivity analysis confirmed high stability of the rank (Spearman ρ ≥ 0.964 in all scenarios). The proposed index provides a structured methodological basis for evaluating IWT potential and can serve as a decision-making tool for infrastructure planning and transport policy development, contributing to sustainable cargo carriage and the decarbonisation goals of the transport sector. Full article

Achieving long-term urban sustainability requires energy subsidy frameworks that evolve with changing technological conditions and system needs. Renewable energy subsidy regimes have played a decisive role in accelerating building-integrated solar photovoltaic deployment, but many were designed for an earlier expansion phase focused mainly on increasing generation capacity and reducing technology costs. As electricity systems move toward an integration phase characterized by higher renewable penetration, flexibility constraints, storage needs, and cross-sectoral coordination, generation-centric subsidy architectures may become increasingly misaligned with system-level requirements. This study conducts a structured comparative analysis of subsidy design in Hong Kong, Chinese Mainland, and Australia, examining legal foundations, target scope, incentive structures, and technology orientation across expansion and integration phases. Despite major differences in governance systems and market organization, the findings show a common pattern: Principal subsidy instruments remain anchored in output-based performance metrics, while storage, hydrogen, and hybrid technologies are generally supported through supplementary rather than core mechanisms. The study argues that this policy layering may limit technological inclusiveness and reduce alignment between subsidy design and evolving system needs. It therefore proposes a system-value-oriented comparative framework for subsidy redesign that recognizes flexibility, reliability, and integrated clean energy performance in the built environment. Full article

Manipulated satellite imagery threatens analytic workflows, policy decisions, and trust in geospatial intelligence. Operational systems increasingly benefit from capabilities for both manipulation detection and manipulation-family attribution to support verification, triage, and downstream analysis. We present a unified benchmark for characterizing three representative manipulation families in geospatial imagery—generative manipulations, pixel-level perturbations, and adversarial patches—using a controlled, class-balanced design and 20 modern vision architectures spanning conventional, Earth-observation-pretrained, and vision-language models. Across architectures, the dominant failure boundary is between authentic imagery and subtle pixel-level perturbations, whereas generative manipulations and adversarial patches are generally more separable under matched in-domain conditions. Additional analyses reveal important generalization limitations under unseen manipulation variants and external-domain transfer, demonstrating that strong benchmark performance does not necessarily translate to reliable operational screening. The framework also enables systematic comparison of unified multi-attack and specialized detection strategies, providing insight into their relative strengths and limitations. Rather than proposing a new defense, this work provides a reproducible methodology for characterizing manipulation artifacts, model failure modes, and deployment-relevant screening behavior in geospatial imagery, with applications to analyst triage, verification workflows, and trustworthy use of satellite data. Full article