Search Results (49,322)

Financial fraud has expanded rapidly with the growth of the digital economy, evolving from conventional transactional misconduct to more complex and data-intensive forms. Traditional rule-based detection methods are increasingly inadequate for addressing the scale, heterogeneity, and dynamic behavior of modern fraud. In this context, artificial intelligence (AI) has become a core tool in financial fraud detection research. This review systematically surveys AI-based financial fraud detection studies published between 2015 and 2025. It summarizes representative machine learning and deep learning approaches, including tree-based models, neural networks, and graph-based methods, and examines their applications in major fraud scenarios such as credit card fraud, loan fraud, and anti-money laundering. In addition, emerging research on cryptocurrency- and blockchain-related fraud is reviewed, highlighting the distinct challenges posed by decentralized transaction environments. Through a comparative analysis of methods, datasets, and evaluation practices, this review identifies persistent issues in the literature, including severe class imbalance, concept drift, limited access to labeled data, and trade-offs between detection performance and interpretability. Based on these findings, the paper discusses practical considerations for applied fraud detection systems and outlines future research directions from a data-centric and application-oriented perspective. This review aims to provide a structured reference for researchers and practitioners working on real-world financial fraud detection problems. Full article

Background/Objectives: Cutaneous leishmaniasis (CL) remains a major public health and economic challenge in Morocco, where its transmission dynamics are increasingly influenced by climatic variability. This study aimed to evaluate the impact of meteorological factors on CL incidence in the province of Essaouira, a high-incidence region, to identify the environmental drivers behind recent epidemic trends. Methods: Epidemiological data (N = 834 cases) were collected from the Hygiene and Health Laboratory of Essaouira for the period between January 2014 and December 2023. Climatic variables were obtained from the Moroccan Directorate of National Meteorology. Data were analyzed at annual, seasonal, and monthly scales using the Spearman rank correlation in R 4.5.0 software to account for non-normal distributions and non-linear associations. Results: CL incidence remained stable from 2014 to 2021 before an unprecedented surge in cases during 2022–2023. Annual analysis indicated that warm and dry years pose a higher risk, with incidence positively correlated with temperatures and negatively associated with humidity and precipitation. Monthly results identified a biphasic regulatory mechanism: a winter hydric constraint phase with strong negative correlations with January rainfall and humidity (p < 0.05), followed by a summer thermal promotion phase where minimum temperature (T_min) emerged as the dominant driver (rho = 0.53), peaking in September (rho = 0.59). Conclusions: Our findings confirm the significant influence of climatic factors on CL incidence through complex seasonal dynamics. These results highlight the necessity of integrating high-resolution meteorological monitoring and predictive modeling into public health surveillance to anticipate future outbreaks in the context of increasing Mediterranean aridification. Full article

This study investigates, with a particular focus on understanding how digital change shapes firm performance in an emerging economy context, first, the impact of digital disruptive innovation, conceptualized as an external condition characterized by technological, market, and competitive turbulence on firm performance within tech-intensive service sector companies, and second, the mediating influence of management skills, proxied by dynamic core managerial competence, and the moderating influence of modern management practices, proxied by innovative work practices, on this relationship. It also examines the moderating effect of innovative work practices on the relationship between digital disruptive innovation and dynamic core management competence, and the impact of COVID-19 on the link between dynamic core management competence and firm performance. This study applies structural equation modelling (SEM) (AMOS 26.0 software) to explore several hypotheses testing for target relationships. The sample was collected via a Qualtrics online survey from 730 senior executives working in digital telecom and banking firms in Pakistan. The study findings show that digital disruptive innovation has a negative effect on service sector performance, and this negative impact is also mediated by dynamic core management competence, as heightened digital disruption tends to weaken managerial competence, which subsequently affects firm performance. While innovative work practices exhibit a positive association with performance, they also positively moderate the negative effect of digital disruptive innovation on performance and mitigate the negative impact of dynamic core management competence on performance. The analysis also reveals that the COVID-19 pandemic positively moderates the effect of dynamic core management competence on performance, indicating that managerial adaptability becomes particularly important when firms operate under crisis conditions. Overall, this study highlights the significance of these phenomena on firm performance in an emerging economy context and provides practical insights for managers and policymakers operating in digitally disrupted service sectors. Full article

Frequent accidents involving heavy trucks and the inadequacy of existing dynamic monitoring technologies pose significant challenges to accurate early warning risk and safety management. To address these issues, this study proposes a multi-dimensional risk measurement and real-time early warning method for heavy truck driving behavior based on trajectory data. By extracting multi-dimensional trajectory features such as lateral position, speed, and acceleration, quantitative indicators for driving stability and car-following risk were constructed. Integrated with the CRITIC objective weighting method and the K-means++ clustering algorithm, a comprehensive risk measurement model was established to systematically characterize the dynamic evolution of driving behavior, overcoming the limitations of single-dimensional risk analysis. Experimental results based on the CQSkyEyeX trajectory dataset demonstrate that the proposed method categorizes driving behavior into six risk levels. Low-risk behavior accounted for 66.70%, while medium- to high-risk behaviors mainly included serpentine driving (26.69%) and close following (4.18%). High-risk behavior constituted only 0.03%. A multi-strategy real-time warning mechanism was further developed, achieving a warning accuracy of 98.36% with the final-value method, significantly outperforming the mode method (83.62%). The outcomes of this study demonstrate the effectiveness and practical utility of the proposed model for risk identification and early warning. On a practical level, the developed risk classification framework and management strategy establish a quantitative basis for differentiated supervision, enabling a closed-loop management process of “identification–intervention–optimization”. Future work will focus on three key directions: integrating multi-source data, extending the model to other typical operational scenarios, and incorporating advanced machine learning techniques to further enhance its generalization capability and warning accuracy. Overall, this research provides a feasible technical pathway for the precise quantification, dynamic monitoring, and tiered intervention of driving behavior in heavy-duty trucks, thereby contributing to enhanced safety in road freight transportation. Full article

To address critical limitations in conventional forward collision warning (FCW) systems including inadequate road condition detection accuracy, significant warning area prediction errors, and poor environmental adaptability on wet/snow-covered roads, this study develops an adaptive collision warning framework based on real-time road slippery states recognition. An enhanced ED-ResNet50 model is proposed, incorporating grouped convolutions within the backbone network and embedding ECA attention mechanisms after the second/third residual blocks alongside DDS-DA modules after the fourth block, significantly improving discriminative capability for pavement texture analysis under adverse conditions. This vision-based recognition system synchronizes with YOLOv8 for preceding vehicle detection, enabling the construction of a friction-sensitive safety distance and the time-to-collision model that dynamically calibrates warning thresholds according to instantaneous vehicle velocity and road adhesion coefficients. Real-vehicle validation demonstrates an 8.76% improvement in overall warning accuracy and 7.29% reduction in lateral and early false alarm rates compared to static-threshold systems, confirming practical efficacy for safety assurance in inclement weather. Full article

Romerillo (Retrophyllum rospigliosii), a vulnerable conifer native to the cloud forests of Cajamarca, Peru, persists in small remnants at high altitudes in San Ignacio province, where its integration into agroforestry systems may support both conservation and sustainable production. This study aimed to model the growth of R. rospigliosii associated with coffee (Coffea arabica L.) using diameter and height as indicators. Field data were collected over 18 months in two experimental plots and the study analyzed 329 individuals selected from 600 initially planted, with monthly monitoring to evaluate early growth and survival dynamics. The data were analyzed with nonlinear mathematical models, including Schumacher, Chapman–Richards, and Weibull, with model selection based on goodness-of-fit and prediction statistics such as R², AIC, and BIC. Results showed that Schumacher provided the best performance for height (R² = 0.98, AIC = 27,978.54), while Weibull (R² = 0.80, AIC = 27,204.63) and Chapman–Richards (R² = 0.80, AIC = 27,207.97) also yielded consistent estimates. For diameter, Schumacher was the most accurate (R² = 0.92, AIC = 2627.87). Survival analysis revealed significant differences between plots (p = 0.011), with higher survival at 1820 m (87.8% at 18 months) compared to 1540 m (77.3%). These findings indicate that the Schumacher model is most suitable for growth estimation, while altitude plays a critical role in survival, underscoring its importance in establishing R. rospigliosii within coffee-based agroforestry systems. Full article

Eye tracking can provide quantitative indices of visual exploration and cognitive processing during radiographic image interpretation. This study examined eye-movement patterns and pupil dynamics and their associations with task performance while fifth-year dental students interpreted a single mixed-dentition panoramic radiograph under free-viewing conditions. Task performance was defined as the number of correctly identified pre-specified items (three radiographic findings plus two interpretive items: dental age estimation and the presence/absence of congenital anomalies). Eye-movement patterns were classified into four groups: clockwise (R, 29.6%), counterclockwise (L, 44.4%), saccadic (S, 16.7%), and concentrated (C, 9.3%). Clockwise scan paths were associated with higher task scores and more globally distributed fixations than other patterns (p < 0.001). Linear mixed-effects modeling suggested that task scores increased up to 120 s of viewing time, whereas longer viewing times were not associated with further improvements. Furthermore, ordinal logistic regression analysis revealed that higher task scores were significantly associated with a smaller mean pupil area across the entire viewing time, combined with a larger pupil area specifically during fixations, suggesting more selective allocation of cognitive resources. These findings indicate associations between global scan structure, time allocation, pupil dynamics, and task performance in this single-image setting. Generalization to overall diagnostic competence or other radiographs requires replication using multiple panoramic images and a broader range of verified findings. Full article

This paper addresses the stringent requirements of high-precision equipment for broadband, contactless active vibration isolation by tackling three key research gaps: the lack of an integrated design deeply coupling vertical and lateral subsystems, the absence of explicit characterization of the base-to-load vibration transmission chain in dynamic models, and the disconnect between theory and application due to spatial sensor–actuator mismatch. To bridge these gaps, a novel five-degree-of-freedom active magnetic levitation vibration isolation system is proposed. Its core contributions are threefold. First, an electromagnetic-structure co-design method based on the equal magnetic reluctance principle is introduced, enabling a globally optimized, integrated actuator layout that maximizes force density within spatial constraints. Second, a dynamic model incorporating explicit base kinematic excitation is established, clearly revealing the complete physical mechanism of vibration transmission through the suspension gap and providing an accurate foundation for model-based control. Third, a coordinate reconstruction control model is constructed, which transforms the ideal center-of-mass-based dynamics into a design model using only measurable gap signals via systematic coordinate transformations, thereby fundamentally eliminating control deviations from physical spatial mismatch. This work provides a comprehensive theoretical framework and solution for next-generation high-performance active vibration isolation platforms, encompassing integrated design, precise modeling, and engineering implementation. Full article

Protein–protein interactions (PPIs) are fundamental regulators of cellular function and disease. Systematic mapping of the interactome is essential for identifying therapeutic targets and advancing drug design, a pursuit that has driven significant innovation to capture the spatiotemporal regulation of PPIs in vivo. This review summarizes this methodological revolution. We outline foundational, first-generation techniques—yeast two-hybrid and co-immunoprecipitation—which established frameworks for binary interaction mapping and static network generation, especially when integrated with mass spectrometry. The discussion then pivots to second-generation methods, including proximity-dependent labeling and advanced imaging, which enable the capture of PPIs within their native, dynamic cellular contexts. We provide a comparative analysis of these techniques, detailing their principles, strengths, and limitations. The review concludes with a practical framework for method selection and a perspective on emerging frontiers—such as spatial proteomics and single-cell interactomics—that are poised to further decode the evolving interactome. This concise overview serves as a strategic guide for specialists adopting new techniques and a broader audience integrating network-level data into their research. Full article

The study investigates the dual transitions of digitalization and renewable energy in Thailand to see if digital expansion facilitates the transition to renewable energy sources and greenhouse gas (GHG) mitigation. The study utilized data from the World Bank between 2000 and 2023 to reconstruct models for autoregressive distributed lag (ARDL) analysis for short-run and long-run dynamics under ecological modernization and technology diffusion theories. Contrary to expected synergies, empirical results revealed that a developing economy would find an ‘investment trade-off’ instead. Digitalization showed no significant immediate impact on renewable energy production; however, it exerted a significant negative lagged effect (coefficient = −0.593), suggesting that digital and energy infrastructures compete for limited financial resources. It was found that there is a 6.3% increase in greenhouse gas emissions for every 1% increase in internet usage. Thus, these results challenge the belief that increased internet usage will help improve the environment. Absent proper supportive policies about both digitalization and green transitions, such as investing in plants and machinery towards digitalization rather than green technology, the pacing effects of digitalization will affect the goals of converting to clean energy. This requires a policy coordination approach to ensure that funds earmarked for green infrastructure are safeguarded. Full article

Driven by cleaner energy demands, environmental regulations, and technological advances, coal science is rapidly evolving, creating the need to understand its transition and transformation within the global energy research landscape. Building upon earlier national- and topic-specific bibliometric studies, this study presents a comprehensive long-term global bibliometric analysis of coal research (1975–2024), based on 272,370 Web of Science records, applying the Cross-Disciplinary Publication Index (CDPI), the Technology–Economic Linkage Model (TELM), VOSviewer, and Excel to assess research growth, structural shifts, and interdisciplinary integration. Results show that coal research is dominated by articles (74%) with publication output peaking at ~19,500 in 2024, reflecting fluctuations in global coal prices due to energy transition market dynamics. CDPI results highlight Energy & Fuels (0.83), Chemical Engineering (0.80), Environmental Sciences (0.77), Materials Science (0.74), and Geosciences (0.66), showing coal’s central role across technology, environment, and geological research domains and revealing a clear shift toward sustainability-oriented and advanced material applications. China leads output (122,130 publications), with strong contributions from the China University of Mining and Technology and the Chinese Academy of Sciences, while the USA, Australia, and Europe maintain strong international collaboration networks. The evolution of coal research can be divided into three major phases: conventional mining, coal preparation, combustion, and coalbed methane commercialization (1975–2004; ~64,000 publications); integrated gasification combined cycle (IGCC) and carbon capture and storage (CCS) technologies (2005–2014; ~58,707 publications); and a recent phase dominated by by-product valorization, carbon capture utilization and storage (CCUS), and digital technologies (AI, IoT, ML) (2015–2024; ~146,174 publications). Contemporary coal research spans three interconnected domains: energy supply (≈36% of global electricity generation and ~15 Gt CO₂ emissions), resource and geoscience applications (including large-scale fly ash utilization and critical element recovery), and environmental and health impacts related to greenhouse gas and pollutant emissions. The findings demonstrate that coal science is transitioning from a conventional fossil fuel-centered discipline toward an integrated, interdisciplinary energy research field, emphasizing emission reduction, resource efficiency, digitalization, and circular economy applications, thereby extending prior bibliometric studies through unprecedented temporal coverage, global scope, and the combined application of CDPI and TELM frameworks, providing critical insights for future energy strategies and policy development. Full article

Against the global push for “carbon peak and carbon neutrality” and Xinjiang’s role as a major arid-region agricultural base in China, balancing agricultural development with low-carbon transitions remains challenging due to its fragile ecology and resource-intensive farming. However, county-scale dynamics of cultivated land carbon emission intensity (CEI) and its drivers in Xinjiang are understudied, limiting targeted mitigation. This study analyzed Xinjiang’s cultivated land CEI (2000–2020) using the Geographically and Temporally Weighted Regression and Stochastic Impacts by Regression on Population, Affluence and Technology (GTWR-STIRPAT) model, geodetector, and spatiotemporal analysis, with counties as units. Data included 30 m-resolution land use data and socioeconomic statistics. Results showed CEI rose from 0.270 to 0.377 t/hm², with marked spatial differences: northern Xinjiang saw fluctuating growth and a 58.65 km northeastward shift of emission gravity, while southern Xinjiang had lower western CEI (ecological constraints) and higher eastern CEI (agricultural expansion). Key drivers were total sown area (TSAC), agricultural film usage (UAPF), and rural agricultural population (RAP). Factor interactions (machinery power × sown area, q = 0.844) non-linearly amplified CEI. The GTWR-STIRPAT model (R² = 0.97) outperformed OLS and captured heterogeneity—mechanization/area expansion dominated northern CEI, while film use/population mattered more in the south. Region-specific strategies are needed: northern Xinjiang should optimize machinery energy and control area expansion; southern Xinjiang, strengthen ecology and promote low-carbon inputs; eastern Xinjiang, leverage efficient oasis agriculture. This study supports precise carbon management in Xinjiang and similar arid regions globally. Full article

This study examines the evolution, heterogeneity, and short-term prospects of export performance in seven Central and Eastern European (CEE) economies—Croatia, Czech Republic, Hungary, Poland, Romania, Bulgaria, and Slovakia—over the period 1995–2024. Using annual World Bank data, exports are modeled as a share of GDP to ensure cross-country comparability and to capture differences in trade dependence. The analysis combines descriptive and inferential statistics with Augmented Dickey–Fuller tests, non-parametric comparisons, Granger causality analysis, and country-specific ARIMA models to investigate export dynamics, the role of foreign direct investment (FDI), and future export trajectories. The results reveal a common long-term upward trend in export intensity across all countries, driven by European integration and structural transformation, but with pronounced cross-country differences in export dependence and volatility. Highly open economies such as Slovakia, Hungary, and the Czech Republic exhibit strong export performance alongside greater exposure to external shocks, while larger domestic markets such as Poland and Romania display lower export intensity and greater stabilization. Granger causality tests indicate that FDI contributes to export growth in several economies, often with multi-year lags, highlighting the importance of absorptive capacity and institutional quality in translating investment inflows into export competitiveness. ARIMA-based forecasts for 2025–2027 suggest continued export expansion and relative stabilization despite recent global disruptions. This study’s primary contribution lies in integrating comparative export analysis, causality testing, and short-term forecasting within a unified econometric framework, offering policy-relevant insights into export-led growth and economic convergence in post-transition European economies. Full article

To enhance the sustainability of manufacturing, various clean cutting technologies have been developed, yet their sustainability assessment faces challenges in balancing multiple conflicting objectives and stakeholder interests. This paper proposes a game theory-based evaluation framework that treats environmental, technical, economic, and social dimensions as cooperative players. The Nash equilibrium model is employed to dynamically reconcile subjective weights from the analytic hierarchy process and objective weights from the entropy method, thus achieving optimal weight allocation. Experimental studies on Ti-6Al-4V titanium alloy milling compared dry milling, minimum quantity lubrication, and cryogenic minimum quantity lubrication (CMQL) under different parameters. Results demonstrate that the game-theoretic model effectively integrates preferences and achieves Nash equilibrium. CMQL showed superior performance, increasing tool life by approximately 40% and reducing surface roughness by about 25% compared to dry milling. Coated inserts reduced carbon emissions by nearly 30% versus end mills. The Nash equilibrium analysis demonstrates that dry milling with coated inserts attains the highest level of processing sustainability under high-speed conditions due to synergistic environmental and economic advantages, while simultaneously revealing practical trade-offs among competing objectives. This study confirms that the proposed framework enables scientific weight coordination and provides a quantifiable, interpretable decision-making system for sustainable process selection. Full article

The dynamic development of Industry 4.0 technologies, referred to as smart manufacturing technologies (SMTs), is significantly changing both production systems and quality management practices. The aim of this article is to analyse the impact of smart manufacturing technologies on the seven principles of quality management (QMP). The research is based on a narrative, semi-systematic review of the literature from the Web of Science and Scopus databases from the last seven years, using thematic analysis. Traditional interpretations of QMP principles were compared with new conditions resulting from the implementation of technologies such as the Internet of Things, big data, artificial intelligence, cloud computing, vision systems, virtual and augmented reality, and additive manufacturing. The results indicate that SMTs do not eliminate quality management principles, but significantly change the way they are implemented. There is a shift towards product personalisation, shorter product life cycles, decentralised decision-making, flexible and autonomous processes, digital surveillance, and intensive use of real-time data. The article argues that SMT and QMP are complementary approaches—technologies increase the effectiveness and efficiency of quality management, but do not replace it. The considerations presented here are a starting point for further empirical research on the new ‘Quality 4.0’ model in the intelligent manufacturing environment. Full article