Search Results (3,449)

Static, one-time authentication mechanisms such as passwords and PINs are increasingly inadequate for protecting mobile devices throughout an active session. Behavioral biometric continuous authentication (BBCA) addresses this gap by passively monitoring user-specific interaction patterns—keystroke dynamics, touch and swipe gestures, gait, and motion—to verify identity on an ongoing basis. This systematic review synthesizes 80 studies selected via a PRISMA-compliant protocol from IEEE Xplore, ACM Digital Library, Scopus, ScienceDirect, Web of Science, and SpringerLink (2017–2025). We examine behavioral and multimodal biometric modalities, machine learning approaches ranging from classical classifiers to deep sequence and transformer architectures, and their integration with intelligent personal agents, wearable devices, and IoT/edge infrastructures. Security analyses cover spoofing, adversarial and generative attacks, mimicry, and model-level threats including membership inference and reconstruction. Privacy-preserving mechanisms—cancelable biometrics, Bloom filter encodings, zero-knowledge proof protocols, federated learning, and blockchain-based identity management—are evaluated against practical trade-offs in energy consumption and latency on resource-constrained devices. Key research gaps are identified: the absence of standardized adversarial benchmarks, lack of end-to-end pipeline evaluations under simultaneous adversarial and privacy threat models, and limited user-centered studies on consent and acceptance of privacy-preserving mechanisms under frameworks such as GDPR. Recommended future directions combine adaptive multimodal fusion, privacy-preserving cryptography, energy-aware modality selection, and interdisciplinary human-centered evaluation to advance practical, resilient continuous authentication for mobile and assistant-enriched environments. Full article

Understanding the pricing of climate risks in bond markets is relevant to financial stability. The real estate sector, characterized by geographically fixed and long-duration assets, exhibits high exposure to environmental shocks; yet, empirical matching between specific climate channels and real estate bond pricing remains sparse. This analysis examines the impact of climate risks on corporate bond credit spreads within the real estate sector by constructing three thematic indicators: transition risk (CTRI), chronic physical risk (ChroCPRI), and acute physical risk (AcuCPRI). Initial feature selection via machine learning suggests all three risk categories as predictive covariates for bond pricing. Subsequent regression estimations indicate that climate transition risk and acute physical risk expand credit spreads, whereas chronic physical risk compresses them—with these statistical patterns being more pronounced among state-owned enterprises (SOEs). Mechanism analyses yield threefold insights: first, transition risk elevates spreads by tightening financing constraints and restricting corporate asset growth, a channel concentrated in short-term tranches and low-liquidity firms; second, the counterintuitive spread-compressing effect of chronic risk is localized among firms with lower credit ratings and lower profitability, consistent with institutional climate support frameworks and strategic green adaptations; third, acute physical risk widens spreads by compressing operational cash flows and exacerbating financing friction, particularly for smaller enterprises. These channels align with the structural attributes of SOEs, which are characterized by larger asset scales, superior capital liquidity, and a higher propensity to secure state guarantees. Full article

High-speed Unmanned Aerial Vehicles (UAVs), characterized by high velocity and maneuverability, represent critical strategic threats within the aerospace security domain. Accurate trajectory prediction is a fundamental prerequisite for effective early warning and decision-making in defense. Despite increasing research in the domain, model-, data-, and hybrid-driven methods have not yet been comprehensively examined under a unified framework, limiting the understanding of their relative strengths and applicability. To address this gap, this paper systematically reviews the evolution and current technical status of these paradigms, categorized by the core logic underlying prediction methods. The principles and applicability limits of physical process modeling and state estimation algorithms are analyzed, along with emerging applications of machine learning and deep learning for trajectory feature extraction and pattern recognition. State-of-the-art architectures involving the integration of physical constraints and data-driven learning are discussed. Standard evaluation metrics are introduced to facilitate performance benchmarking of existing methods. Comparative analysis reveals that no single technical route can fully address the coupled challenges of uncertainty, accuracy, and real-time performance, underscoring that hybrid frameworks are essential for balancing these competing requirements. Lastly, key challenges are summarized, and future research directions are outlined to advance trajectory prediction methodologies. The provided insights can inform method selection and promote the development of high-accuracy prediction systems. Full article

Background/Objectives: Advancing clinical competence within contemporary healthcare systems requires innovative educational approaches that bridge professional silos. While Interprofessional Education (IPE) is globally recognized as a mechanism to enhance teamwork, its implementation within resource-constrained public health systems marked by historical and structural inequalities remains poorly understood. This study explored the perspectives and experiences of healthcare professionals and clinical educators regarding how interprofessional education (IPE) may contribute to clinical competence in selected South African public hospitals. Methods: A qualitative descriptive design was used. Semi-structured interviews were conducted with 22 purposively selected healthcare professionals and clinical educators from nursing, medicine, physiotherapy, occupational therapy, pharmacy, and social work. Interviews were conducted at selected hospitals following approval from hospital Chief Executive Officers. Data were collected by the researcher and co-researcher who is not part of this manuscript, audio-recorded, transcribed verbatim, and analyzed using thematic analysis. Ethical clearance and institutional permissions were obtained, and informed consent was secured from all participants. Trustworthiness was ensured through credibility, dependability, confirmability, and transferability. Findings: The thematic analysis yielded two primary patterns: (1) the perceived capacity of IPE to enhance clinical decision-making, communication, and collaborative practices; (2) systemic and structural constraints, including rigid professional hierarchies, severe time constraints, and a lack of formally institutionalized opportunities. Conclusions: IPE is perceived as an important contributor to collaborative clinical competence, but its sustainability depends on explicit institutional mandates. These findings imply that hospital managers and curriculum developers must transition from ad hoc interactions to structured, short-duration interdisciplinary platforms to overcome operational bottlenecks. Full article

The global energy system is undergoing a structural transformation as countries pursue decarbonization while safeguarding energy security and economic resilience. However, renewable energy transition performance remains difficult to assess because existing evidence is often fragmented across single indicators, capacity-based measures, or isolated policy dimensions, while transition outcomes are shaped by multiple environmental, economic, institutional, technological, and cost-related factors. This study addresses this gap by benchmarking renewable energy transition performance across G7 economies through an integrated multi-criteria decision-making framework. The proposed methodology combines distance-based CRITIC (D-CRITIC) for objective criteria weighting and COBRA for compromise-based ranking to translate heterogeneous transition indicators into transparent cross-country comparisons. Using a secondary dataset covering fourteen indicators and seven G7 economies, the framework first derives objective criterion weights from cross-country dispersion and distance-based inter-criteria dependence, and then ranks countries by measuring their compromise distances from positive ideal, negative ideal, and average solutions. Using fourteen indicators spanning renewable energy penetration, environmental pressure, economic conditions, governance capacity, technology cost feasibility, and innovation capability, the D-CRITIC results identify CO₂ emissions per capita as the most informative criterion, followed by the share of electricity generated by bioenergy, the share of primary energy consumption from renewable sources, and solar LCOE. The COBRA-based assessment ranks Canada first, followed by the United Kingdom, Italy, Germany, Japan, the United States, and France. Robustness and sensitivity analyses show broad consistency in ranking patterns across alternative MCDM methods, while weight perturbation tests confirm that the ranking remains unchanged under ±5% and ±10% relative changes in criterion weights. These findings indicate that stronger renewable energy transition performance is associated with balanced progress across emissions reduction, renewable energy penetration, technology cost feasibility, institutional capacity, and innovation-related conditions, rather than superiority in a single indicator. The proposed framework offers a transparent and replicable tool for renewable energy transition benchmarking and evidence-based policy learning, while the results should be interpreted as cross-sectional comparative performance under the selected dataset and criteria rather than as a causal evaluation of specific policy instruments. Full article

Existing platform-monitoring methods for sport communities commonly rely on isolated descriptive text-mining outputs or general sentiment scores; they rarely integrate interpretable ESG issue coding with class-sensitive risk detection and provide limited support for auditable, privacy-conscious analysis of safety, ethics, and institutional trust. These limitations motivate a multi-stage framework that converts heterogeneous platform discourse into complementary structural and evaluative signals. Conceptually, digital trust is treated as the focal governance outcome; ethics and safety are substantive domains of concern; ESG provides the bounded classification and response ontology; and early warning denotes a prototype, human-reviewed weak-signal triage concept rather than incident prediction or a deployed security-monitoring system. Using 377,700 cleaned Korean-language comments on judo and Brazilian Jiu-Jitsu (BJJ) collected from Naver News and YouTube between 2010 and 2025, the framework combines n-gram analysis, LDA topic modeling, CONCOR network analysis, bounded ESG discourse classification, and three-class sentiment prediction. The individual analytical algorithms are established; the methodological contribution lies in their governance-oriented orchestration through a bounded ESG/non-ESG coding gate, a study-specific index layer, and a human-reviewed pathway from aggregate discourse signals to proportionate review. The analytical workflow identifies issue salience, relational topic structures, ESG dimensions, sentiment risk, legitimacy balance, and platform-specific risk concentration while excluding personally identifiable information. Empirically, social and governance concerns dominate the corpus, and governance-related negative sentiment consistently exceeds social-risk sentiment, highlighting rule transparency, coach ethics, misinformation, platform reputation, and institutional response as central trust-risk domains. Cell-weighted sensitivity checks preserved the governance-over-social and YouTube-over-Naver risk ordering, although the pooled salience estimate remained sensitive to the rapid expansion of BJJ discourse on YouTube. The fine-tuned KLUE-BERT model achieved a Macro-F1 of 0.838 and a negative-class F1 of 0.862, outperforming the strongest baseline, Text-CNN (Macro-F1 = 0.791), by 0.047 absolute Macro-F1 points (approximately 6.0% relative improvement). These findings support the feasibility of a batch-oriented, human-reviewed prototype for prioritizing aggregate discourse patterns. They do not establish the effectiveness of a real-time security-monitoring, incident-detection, or operational early-warning system. Full article

The water supply–demand pattern directly affects the stability of ecosystems and the level of agricultural sustainable development. Based on the theoretical framework of water supply–demand ecosystem services, this study took 1990–2020 as the research period, and integrated the water yield module of the InVEST model, urban stormwater retention module, standard deviational ellipse method, and spatial interpolation to accurately calculate regional water yield, water demand, water supply–demand ratio, and gravity center shift. The aim was to reveal the spatiotemporal evolution and mechanism of water budget in the Sanjiang Plain under large-scale agricultural expansion. The results showed the following: (1) From 1990 to 2020, the annual water yield in the Sanjiang Plain fluctuated from 12.16 billion m³/yr to 23.28 billion m³/yr, with a spatial pattern of “high in the northwest and southeast, low in the central Songhua River”, and high-value areas were highly coincident with natural ecological land. (2) The multi-year average water demand was approximately 13.0 billion m³/yr, experiencing three phases, “slow growth–rapid rise–stable slowdown”, showing a distribution characteristic of “concentrated along rivers and high in urban areas”. (3) Water surplus and deficit areas maintained a rigid characteristic of “highland supply, lowland consumption” for a long time. The gravity center of deficit areas rotated counterclockwise from 132.35° E, 46.55° N to 132.58° E, 46.78° N, with an annual migration rate of 10–15 km/a. The gravity center of surplus areas rotated clockwise from 133.22° E, 47.45° N to 132.85° E, 46.92° N, with an annual migration rate of 15–20 km/a. (4) From 1990 to 2015, the regional water supply–demand pattern remained relatively stable. However, a pronounced transition occurred during 2015–2020, when surplus areas sharply decreased from 49% to 27%, while conflict zones expanded rapidly, indicating an evident deterioration of regional water security. Full article

Understanding multidimensional deprivation requires consideration not only of who experiences overlapping disadvantages but also of how those disadvantages vary geographically. This study provides a comprehensive descriptive assessment of geographic variation in multidimensional deprivation across the United States using pooled 2022–2023 American Community Survey (ACS) Public Use Microdata Sample data and a Multidimensional Deprivation Index (MDI) constructed using the Alkire–Foster methodology. The MDI incorporates eight indicators spanning four equally weighted dimensions of well-being: economic security, education, health, and housing. Variation is examined across four nested geographic scales—Census regions, Census divisions, states, and all 2487 Public Use Microdata Areas (PUMAs)—using the Headcount Ratio (H), Average Deprivation Intensity (A), and the MDI. The results reveal substantial geographic differences in multidimensional deprivation across the United States, with progressively finer geographic scales revealing increasingly localized patterns. Geographic areas with similar overall MDI values often differ substantially in the composition of deprivation, underscoring the importance of examining the composition as well as the overall level of deprivation. Across all geographic scales, differences in multidimensional deprivation primarily reflect variation in deprivation incidence rather than deprivation intensity, while the PUMA-level analysis suggests that important local variation is obscured by broader regional and state averages. The principal contribution of this study is its integrated multiscale framework. Rather than developing a new deprivation measure or evaluating formal spatial dependence, the analysis applies an established Alkire–Foster methodology to recent nationally representative ACS microdata. This framework provides a consistent descriptive assessment of multidimensional deprivation across multiple nested geographic scales. The findings establish a descriptive empirical benchmark for geographically informed policy discussions and for demonstrating that both the level and composition of multidimensional deprivation vary systematically across geographic scales and that important local variation is obscured by broader regional and state averages. Full article

The advancement of natural language processing (NLP), transformer architectures, and large language models (LLMs) has reshaped phishing detection research within business and enterprise environments. However, the structural evolution, thematic transitions, and collaboration patterns of this domain remain insufficiently mapped. This study conducts a bibliometric analysis of Scopus-indexed publications from 2020 to 2025. Using VOSviewer and Bibliometrix (RStudio), we perform performance analysis and science mapping, including co-authorship, co-citation, bibliographic coupling, and keyword co-occurrence analyses. The findings reveal a clear methodological shift from traditional machine learning toward deep learning and transformer-based architectures, particularly after 2023. Two dominant research clusters emerge: conventional feature-based phishing detection and NLP-driven AI security approaches. While large language models and multi-channel phishing detection are gaining prominence, enterprise-level implementation and interdisciplinary integration remain limited. This study identifies emerging trends, collaboration gaps, and underexplored themes, providing directions for future research and practical cybersecurity development. Full article

Sustainable urbanization requires reconciling rapid land development with ecological security, a challenge particularly acute in mountainous megacities. While land-use/cover change (LUCC) substantially reshapes regional carbon storage, conventional whole-region assessments often mask critical spatiotemporal dynamics and subregional heterogeneity. Taking Chongqing, China, as a representative case, this study integrates the Future Land-Use Simulation (FLUS) and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models to decode historical carbon-storage dynamics (2000–2020) and simulate future spatial trajectories (2035) under four multi-scenario land-use pathways: Integrated Development Priority Scenario (IDPS), Ecological Conservation Priority Scenario (ECPS), Farmland Conservation Priority Scenario (FCPS), and Economic Priority Scenario (EPS). Results show a net decline of 8.49 × 10⁶ t in total carbon storage during 2000–2020, primarily driven by the often-overlooked degradation of high-carbon-density grasslands alongside construction-land expansion. Scenario simulations reveal that the ECPS is the only pathway achieving a net carbon-storage increase (+4.26 × 10⁶ t), although the resulting land-use pattern was jointly shaped by ecological protection constraints, land suitability, and scenario-specific land-demand allocation. Crucially, subregional analysis highlights distinct spatial roles: the Northeastern Urban Agglomeration (NUA) emerges as the core for ecological restoration, the Main Urban Area (MUA) remains highly sensitive to development-driven carbon loss, and the Southeastern Urban Agglomeration (SUA) acts as a buffer requiring a delicate development balance. By linking coupled spatial modeling with subregional constraints, this framework advocates for a shift from “one-size-fits-all” land management to precise spatial governance, providing a scalable scientific reference for carbon-oriented sustainable planning in mountainous megacities worldwide. Full article

Using daily rainfall observations from 1961 to 2022 (62 years) across 15 typical sub-regions within the water-receiving area of the Zhejiang East Water Diversion Project, this study systematically examines the spatiotemporal distribution, long-term trends, multi-scale variability, and inter-regional correlations of rainfall. An integrated framework was employed, including the Mann–Kendall (MK) trend test, Sen’s slope estimator, Hurst exponent analysis, and multi-scale sliding window analysis. Spatially, the multi-year average daily rainfall ranges from 3.47 mm to 5.68 mm, following a distinct “high in the center, low in the west” pattern, with the Yuyao Plain Mazhu Midstream Area identified as the regional rainfall maximum. In the raw Mann–Kendall test all 15 sub-regions exhibit increasing trends, of which 14 remain statistically significant (p < 0.05) after trend-free pre-whitening; Sen’s slopes follow a “coastal > riverine > hilly” gradient. Hurst exponents greater than 0.5 suggest persistent behavior in the rainfall series, although attribution to external forcing or separation from low-frequency climate variability requires additional analysis. Multi-scale sliding window analysis reveals strong scale dependence: the amplitude of trend fluctuations decreases by approximately 90% from ±16 mm at the 3-month scale to ±1.7 mm at the 12-month scale, while inter-regional correlation coefficients increase from 0.83 to 0.87. Notably, the Yuyao Plain Mazhu Midstream Area displays a unique “increase-then-decrease” correlation pattern, reflecting its distinctive hydro-geographic conditions. These findings provide a scientific basis for climate-adaptive scheduling of water diversion projects, supporting water security, urban resilience, and climate action. Full article

Anthropogenic pressures increasingly threaten ecological connectivity and basin-scale ecological sustainability in peri-urban landscapes. This study proposes a Hybrid Ecological Typology framework for the Büyükçekmece Lake Basin (Istanbul, Türkiye), integrating Morphological Spatial Pattern Analysis (MSPA), Analytic Hierarchy Process (AHP), and Minimum Cumulative Resistance (MCR) analysis within a unified spatial decision-support system. The framework is applied to a 67,627.06 ha basin area, including a 25,976.99 ha terrestrial focus area. The results indicate a structurally heterogeneous landscape dominated by interior habitat zones (85.94%) distributed across 93 core patches. Despite this dominance, ecological connectivity is maintained through a highly fragmented network of 484 landscape elements, where limited bridge (0.08%) and branch (0.62%) structures highlight structural vulnerability. Edge-dominated zones (12.87%) further reflect strong anthropogenic fragmentation pressures. Connectivity analysis identifies 10 key habitat patches with dPC (Probability of Connectivity index) values exceeding 5% and 12 strategic ecological corridors supporting basin-scale ecological flows. The proposed hybrid typology delineates five functional planning categories: conservation areas (22.72%), ecological corridors (1.91%), restoration areas (1.63%), sustainable use areas (0.51%), and controlled development areas (8.11%). Although high-quality habitat cores dominate the basin, ecological connectivity remains spatially constrained, with bottleneck zones (0.89%) concentrated along transportation corridors that significantly reduce landscape permeability. Overall, the findings demonstrate that basin-scale ecological sustainability in peri-urban environments is governed not only by habitat quantity but also by the interaction between spatial configuration and resistance structures. The framework provides a transferable decision-support tool that bridges landscape ecology theory with spatial planning practice for basin management and ecological network design. Full article

Geological storage of carbon dioxide (CO₂) in deep-sea formations represents a pivotal strategy for mitigating atmospheric CO₂ levels, where storage security and efficacy are fundamentally governed by the pore-scale seepage behavior of CO₂. However, the microscopic displacement mechanisms of CO₂–water two-phase flow under the characteristic high-pressure, low-temperature conditions of the deep sea remain inadequately understood. This study employed a self-developed high-pressure microfluidic experimental platform (0–30 MPa, 4–50 °C) to systematically investigate the CO₂ displacement process in porous media. The effects of injection rate (0.001–5 mL/min) and system pressure (1, 5, and 10 MPa) on displacement patterns, front stability, and final saturation were quantified. The results demonstrate that injection rate is the primary controller of displacement stability: high rates (≥0.1 mL/min) induce viscous fingering and lower final saturation, whereas low rates (≤0.05 mL/min) promote stable, piston-like displacement. Crucially, elevated pressure exerts a profound stabilizing effect, effectively suppressing fingering instabilities and enhancing final gas saturation (up to 0.544 at 10 MPa). This work elucidates the synergistic regulatory mechanism between injection rate and confining pressure, providing essential pore-scale experimental evidence for optimizing injection parameters to achieve efficient and secure CO₂ storage in deep-sea reservoirs. Full article

Background/Objectives: Workplace violence (WPV) is a major occupational health hazard and psychosocial safety concern in psychiatric nursing, yet evidence integrating exposure, worker well-being, and workforce stability within a single theory-informed model remains limited in Saudi Arabia. This study examined associations between WPV and proximal occupational outcomes (burnout, job satisfaction, and absenteeism) and distal outcomes (quality of life [QoL] and turnover intentions) among psychiatric nurses and tested burnout as a statistical mediator of the WPV–turnover intentions association. Methods: A multicentre cross-sectional census survey was conducted across three psychiatric facilities in northern Saudi Arabia between June and August 2025 (N = 171). Participants reported WPV exposure over the preceding 12 months using Arabic versions of standardised instruments, including validated Arabic versions where available and a forward–backward-translated WVSQ. Linear, negative binomial, and logistic regression models examined outcome associations and factors associated with WPV exposure; the PROCESS macro (Model 4, 5000 bootstrap resamples) tested statistical mediation. Results: Most nurses reported at least one WPV incident in the preceding 12 months, most commonly verbal abuse. Younger age, male sex, shorter psychiatric nursing experience, lower perceived staffing adequacy, and inadequate security were associated with higher WPV exposure. WPV exposure was associated with lower QoL across all WHOQOL-BREF domains, higher burnout, lower job satisfaction, higher absenteeism, and stronger turnover intentions. Burnout showed a partial statistical mediation pattern in the WPV–turnover intentions association. Because the study was not specifically powered for the indirect effect, the mediation result should be interpreted with caution. Conclusions: In this cross-sectional study, WPV was a pervasive occupational exposure among Saudi psychiatric nurses and was associated with poorer QoL and higher turnover intentions, with burnout showing a partial statistical mediation pattern. Multilevel prevention, safer staffing, and structured post-incident psychological support are needed to support psychiatric nurses’ well-being and workforce retention. Full article

To address the complex pressure-response mechanisms and difficulties in quantitatively characterizing dynamic reservoir properties during CO₂ geological storage in tight oil reservoirs, this work develops a dual-region composite seepage model coupling reservoir heterogeneity and CO₂-induced fluid property variation. The reservoir is divided into a near-well CO₂-stimulated zone and a far-field unstimulated zone. Combining the Laplace transform and the Stehfest368 numerical inversion method, we derive the analytical solutions of the bottom-hole pressure (BHP) and its derivative, and we establish a complete transient BHP monitoring and parameter inversion framework. The pressure-derivative curves are divided into five typical flow stages: wellbore storage, skin transition, inner-region radial flow, inter-region transition and outer-region radial flow. The key parameters, including wellbore storage coefficient, skin factor, mobility ratio, storativity ratio and CO₂ swept radius, can be accurately inverted via the BHP data analysis, which quantitatively characterizes flow capacity evolution, stimulated region scale and fluid flow patterns after CO₂ injection. The field application on two production wells in H138 block verifies the reliability of the proposed method. Further numerical simulation validation, measurement error sensitivity analysis and cross-verification of reservoir parameters are supplemented to prove the robustness and the applicability of the model. This study provides solid theoretical and technical support for on-site pressure monitoring, storage performance evaluation and operation optimization of CO₂ geological storage in tight reservoirs, and it also offers a reference for long-term storage security and storage capacity assessment. Full article