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by Kai-Chao Yao, Hsiu-Chu Hung, Ching-Hsin Wang, Wei-Lun Huang, Hui-Ting Liang, Tzu-Hsin Chu, Bo-Siang Chen and Wei-Sho Ho

Information 2025, 16(10), 857; https://doi.org/10.3390/info16100857 - 3 Oct 2025

Abstract

This study explores the application of generative artificial intelligence (AI) in financial risk forecasting, aiming to assess its potential in enhancing both the accuracy and interpretability of predictive models. Traditional methods often struggle with the complexity and nonlinearity of financial data, whereas generative AI—such as large language models and generative adversarial networks (GANs)—offers novel solutions to these challenges. The study begins with a comprehensive review of current research on generative AI in financial risk prediction, with a focus on its roles in data augmentation and feature extraction. It then investigates techniques such as Generative Adversarial Explanation (GAX) to evaluate their effectiveness in improving model interpretability. Case studies demonstrate the practical value of generative AI in real-world financial forecasting and quantify its contribution to predictive accuracy. Furthermore, the study identifies key challenges—including data quality, model training costs, and regulatory compliance—and proposes corresponding mitigation strategies. The findings suggest that generative AI can significantly improve the accuracy and interpretability of financial risk models, though its adoption must be carefully managed to address associated risks. This study offers insights and guidance for future research in applying generative AI to financial risk forecasting. Full article

(This article belongs to the Special Issue Modeling in the Era of Generative AI)

24 pages, 2442 KB

Open AccessArticle

Development of a Novel Weighted Maximum Likelihood-Based Parameter Estimation Technique for Improved Annual Energy Production Estimation of Wind Turbines

by Woobeom Han, Kanghee Lee, Jonghwa Kim and Seungjae Lee

Energies 2025, 18(19), 5265; https://doi.org/10.3390/en18195265 - 3 Oct 2025

Abstract

Conventional statistical models consider all wind speed ranges as equally important, causing significant prediction errors, particularly in wind speed intervals that contribute the most to wind turbine power generation. To overcome this limitation, this study proposes a novel parameter estimation method—Weighted Maximum Likelihood Estimation (WMLE)—to improve the accuracy of annual energy production (AEP) predictions for wind turbine systems. The proposed WMLE incorporates wind-speed-specific weights based on power generation contribution, along with a weighting amplification factor (β), to construct a power-oriented wind distribution model. WMLE performance was validated by comparing four offshore wind farm candidate sites in Korea—each exhibiting distinct wind characteristics. Goodness-of-fit evaluations against conventional wind statistical models demonstrated the improved distribution fitting performance of WMLE. Furthermore, WMLE consistently achieved relative AEP errors within ±2% compared to those of time-series-based methods. A sensitivity analysis identified the optimal β value, which narrowed the distribution fit around high-energy-contributing wind speeds, thereby enhancing the reliability of AEP predictions. In conclusion, WMLE provides a practical and robust statistical framework that bridges the gap between statistical distribution fitting and time-series-based methods for AEP. Moreover, the improved accuracy of AEP predictions enhances the reliability of wind farm feasibility assessments, reduces investment risk, and strengthens financial bankability. Full article

(This article belongs to the Section B: Energy and Environment)

14 pages, 732 KB

Open AccessArticle

Association of Preoperative Imaging and Surgical Delay with Hemorrhagic Mortality in Abdominal Trauma: A Retrospective Multicenter Study

by Juhong Park, Youngmin Kim, Hangjoo Cho, Gil Jae Lee and Junsik Kwon

J. Clin. Med. 2025, 14(19), 7020; https://doi.org/10.3390/jcm14197020 - 3 Oct 2025

Abstract

Background: Surgical delay in abdominal trauma with hemorrhage is a leading cause of preventable death, yet the precise time threshold for adverse outcomes remains uncertain. This study examined the association between emergency department (ED)-to-operating room (OR) time and hemorrhagic mortality and evaluated the impact of preoperative computed tomography (CT). Methods: We retrospectively analyzed patients ≥15 years old who underwent emergency laparotomy for abdominal trauma at two Level I trauma centers in South Korea (2016–2023). The primary outcome was hemorrhagic death, adjudicated by a multidisciplinary review panel. Multivariable and segmented logistic regression was used to assess the association between ED-to-OR time and mortality. The effect of preoperative CT was evaluated using inverse probability of treatment weighting (IPTW). Results: Among 414 patients, 71 (17.1%) died from hemorrhage. Each 1-min increase in ED-to-OR time was associated with 1.8% higher odds of hemorrhagic death (adjusted OR = 1.018; 95% CI, 1.007–1.030). Segmented regression identified a changepoint at 91 min (bootstrap 95% CI, 62.0–97.6), beyond which mortality risk rose sharply. Preoperative CT was performed in 27.5% of patients and was associated with a mean surgical delay of over 30 min. After IPTW adjustment, CT use was not significantly associated with hemorrhagic death (14.3% vs. 10.3%, p = 0.542). Conclusions: Longer ED-to-OR intervals were associated with increased hemorrhagic mortality, particularly beyond approximately 90 min. Although preoperative CT contributed to procedural delay, it was not independently associated with worse outcomes when selectively used in stable patients. These findings represent observational associations in current practice rather than causal effects, underscoring the importance of minimizing surgical delay while cautiously considering CT in appropriate patients. Full article

(This article belongs to the Special Issue Trauma and Acute Care Surgery Research: Innovations, Challenges, and Future Directions)

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22 pages, 13067 KB

Open AccessArticle

Numerical Modeling of Photovoltaic Cells with the Meshless Global Radial Basis Function Collocation Method

by Murat Ispir and Tayfun Tanbay

Energies 2025, 18(19), 5267; https://doi.org/10.3390/en18195267 - 3 Oct 2025

Abstract

Accurate prediction of photovoltaic performance hinges on resolving the electron density in the P-region and the hole density in the N-region. Motivated by this need, we present a comprehensive assessment of a meshless global radial basis function (RBF) collocation strategy for the steady current continuity equation, covering a one-dimensional two-region P–N junction and a two-dimensional single-region problem. The study employs Gaussian (GA) and generalized multiquadric (GMQ) bases, systematically varying shape parameter and node density, and presents a detailed performance analysis of the meshless method. Results map the accuracy–stability–computation-time landscape: GA achieves faster convergence but over a narrower stability window, whereas GMQ exhibits greater robustness to shape-parameter variation. We identify stability plateaus that preserve accuracy without severe ill-conditioning and quantify the runtime growth inherent to dense global collocation. A utopia-point multi-objective optimization balances error and computation time to yield practical node-count guidance; for the two-dimensional case with equal weighting, an optimum of 19 intervals per side emerges, largely insensitive to the RBF choice. Collectively, the results establish global RBF collocation as a meshless, accurate, and systematically optimizable alternative to conventional mesh-based solvers for high-fidelity carrier-density prediction in P-N junctions, thereby enabling more reliable performance analysis and design of photovoltaic devices. Full article

(This article belongs to the Topic Numerical Methods and Computer Simulations in Energy Analysis, 3rd Edition)

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17 pages, 2215 KB

Open AccessArticle

Fault Location of Generator Stator with Single-Phase High-Resistance Grounding Fault Based on Signal Injection

by Binghui Lei, Yifei Wang, Zongzhen Yang, Lijiang Ma, Xinzhi Yang, Yanxun Guo, Shuai Xu and Zhiping Cheng

Sensors 2025, 25(19), 6132; https://doi.org/10.3390/s25196132 - 3 Oct 2025

Abstract

This paper proposes a novel method for locating single-phase grounding faults in generator stator windings with high resistance, which are typically challenging to locate due to weak fault characteristics. The method utilizes an active voltage injection technique combined with traveling wave reflection analysis, singular value decomposition (SVD) denoising, and discrete wavelet transform (DWT). A DC voltage signal is then injected into the stator winding, and the voltage and current signals at both terminals are collected. These signals undergo denoising using SVD, followed by DWT, to identify the arrival time of the traveling waves. Fault location is determined based on the reflection and refraction of these waves within the winding. Simulation results demonstrate that this method achieves high accuracy in fault location, even with fault resistances up to 5000 Ω. The method offers a reliable and effective solution for locating high-resistance faults in generator stator windings without requiring winding parameters, demonstrating strong potential for practical applications. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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40 pages, 1781 KB

Open AccessArticle

Exponentiated Inverse Exponential Distribution Properties and Applications

by Aroosa Mushtaq, Tassaddaq Hussain, Mohammad Shakil, Mohammad Ahsanullah and Bhuiyan Mohammad Golam Kibria

Axioms 2025, 14(10), 753; https://doi.org/10.3390/axioms14100753 - 3 Oct 2025

Abstract

This paper introduces Exponentiated Inverse Exponential Distribution (EIED), a novel probability model developed within the power inverse exponential distribution framework. A distinctive feature of EIED is its highly flexible hazard rate function, which can exhibit increasing, decreasing, and reverse bathtub (upside-down bathtub) shapes, making it suitable for modeling diverse lifetime phenomena in reliability engineering, survival analysis, and risk assessment. We derived comprehensive statistical properties of the distribution, including the reliability and hazard functions, moments, characteristic and quantile functions, moment generating function, mean deviations, Lorenz and Bonferroni curves, and various entropy measures. The identifiability of the model parameters was rigorously established, and maximum likelihood estimation was employed for parameter inference. Through extensive simulation studies, we demonstrate the robustness of the estimation procedure across different parameter configurations. The practical utility of EIED was validated through applications to real-world datasets, where it showed superior performance compared to existing distributions. The proposed model offers enhanced flexibility for modeling complex lifetime data with varying hazard patterns, particularly in scenarios involving early failure periods, wear-in phases, and wear-out behaviors. Full article

(This article belongs to the Special Issue Probability, Statistics and Estimations, 2nd Edition)

23 pages, 730 KB

Open AccessArticle

She Wants Safety, He Wants Speed: A Mixed-Methods Study on Gender Differences in EV Consumer Behavior

by Qi Zhu and Qian Bao

Systems 2025, 13(10), 869; https://doi.org/10.3390/systems13100869 - 3 Oct 2025

Abstract

Against the backdrop of the rapid proliferation of electric vehicles (EVs), gender-oriented behavioral mechanisms remain underexplored, particularly the unique pathways of female users in usage experience, value assessment, and purchase decision-making. This study constructs an integrated framework based on the Stimulus–Organism–Response (SOR) model, leveraging social media big data to analyze in depth how gender differences influence EV users’ purchase intentions. By integrating natural language processing techniques, grounded theory coding, and structural equation modeling (SEM), this study models and analyzes 272,083 pieces of user-generated content (UGC) from Chinese social media platforms, identifying key functional and emotional factors shaping female users’ perceptions and attitudes. The results reveal that esthetic value, safety, and intelligent features more strongly drive emotional responses among female users’ decisions through functional cognition, with gender significantly moderating the pathways from perceived attributes to emotional resonance and cognitive evaluation. This study further confirms the dual mediating roles of functional cognition and emotional experience and identifies a masking (suppression) effect for the ‘intelligent perception’ variable. Methodologically, it develops a novel hybrid paradigm that integrates data-driven semantic mining with psychological behavioral modeling, enhancing the ecological validity of consumer behavior research. Practically, the findings provide empirical support for gender-sensitive EV product design, personalized marketing strategies, and community-based service innovations, while also discussing research limitations and proposing future directions for cross-cultural validation and multimodal analysis. Full article

15 pages, 3332 KB

Open AccessArticle

YOLOv11-XRBS: Enhanced Identification of Small and Low-Detail Explosives in X-Ray Backscatter Images

by Baolu Yang, Zhe Yang, Xin Wang, Baozhong Mu, Jie Xu and Hong Li

Sensors 2025, 25(19), 6130; https://doi.org/10.3390/s25196130 - 3 Oct 2025

Abstract

Identifying concealed explosives in X-ray backscatter (XRBS) imagery remains a critical challenge, primarily due to low image contrasts, cluttered backgrounds, small object sizes, and limited structural details. To address these limitations, we propose YOLOv11-XRBS, an enhanced detection framework tailored to the characteristics of XRBS images. A dedicated dataset (SBCXray) comprising over 10,000 annotated images of simulated explosive scenarios under varied concealment conditions was constructed to support training and evaluation. The proposed framework introduces three targeted improvements: (1) adaptive architectural refinement to enhance multi-scale feature representation and suppress background interference, (2) a Size-Aware Focal Loss (SaFL) strategy to improve the detection of small and weak-feature objects, and (3) a recomposed loss function with scale-adaptive weighting to achieve more accurate bounding box localization. The experiments demonstrated that YOLOv11-XRBS achieves better performance compared to both existing YOLO variants and classical detection models such as Faster R-CNN, SSD512, RetinaNet, DETR, and VGGNet, achieving a mean average precision (mAP) of 94.8%. These results confirm the robustness and practicality of the proposed framework, highlighting its potential deployment in XRBS-based security inspection systems. Full article

(This article belongs to the Special Issue Advanced Spectroscopy-Based Sensors and Spectral Analysis Technology)

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26 pages, 14595 KB

Open AccessArticle

Practical Application of Passive Air-Coupled Ultrasonic Acoustic Sensors for Wheel Crack Detection

by Aashish Shaju, Nikhil Kumar, Giovanni Mantovani, Steve Southward and Mehdi Ahmadian

Sensors 2025, 25(19), 6126; https://doi.org/10.3390/s25196126 - 3 Oct 2025

Abstract

Undetected cracks in railroad wheels pose significant safety and economic risks, while current inspection methods are limited by cost, coverage, or contact requirements. This study explores the use of passive, air-coupled ultrasonic acoustic (UA) sensors for detecting wheel damage on stationary or moving wheels. Two controlled datasets of wheelsets, one with clear damage and another with early, service-induced defects, were tested using hammer impacts. An automated system identified high-energy bursts and extracted features in both time and frequency domains, such as decay rate, spectral centroid, and entropy. The results demonstrate the effectiveness of UAE (ultrasonic acoustic emission) techniques through Kernel Density Estimation (KDE) visualization, hypothesis testing with effect sizes, and Receiver Operating Characteristic (ROC) analysis. The decay rate consistently proved to be the most effective discriminator, achieving near-perfect classification of severely damaged wheels and maintaining meaningful separation for early defects. Spectral features provided additional information but were less decisive. The frequency spectrum characteristics were effective across both axial and radial sensor orientations, with ultrasonic frequencies (20–80 kHz) offering higher spectral fidelity than sonic frequencies (1–20 kHz). This work establishes a validated “ground-truth” signature essential for developing a practical wayside detection system. The findings guide a targeted engineering approach to physically isolate this known signature from ambient noise and develop advanced models for reliable in-motion detection. Full article

(This article belongs to the Special Issue Sensing and Imaging for Defect Detection: 2nd Edition)

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17 pages, 2869 KB

Open AccessArticle

Romanino’s Colour Palette in the “Musicians” Fresco of the Duomo Vecchio, Brescia

by Fatemeh Taati Anbuhi, Alfonso Zoleo, Barbara Savy and Gilberto Artioli

Heritage 2025, 8(10), 416; https://doi.org/10.3390/heritage8100416 - 3 Oct 2025

Abstract

This study examines the pigments and materials used in Girolamo Romanino’s Musicians fresco (1537–1538), located in the Duomo Vecchio in Brescia, with the aim of identifying and analyzing the artist’s colour palette. Ten samples of the pictorial layer and mortar were collected from two frescoes and characterized using microscopic and spectroscopic techniques. Confocal laser scanning microscopy (CLSM) was used to define the best positions where single-point, spectroscopic techniques could be applied. Raman spectroscopy and micro-Fourier transform Infrared spectroscopy (micro-FTIR) were used to detect pigments and organic binders, respectively. X-ray powder diffraction (XRPD) provided additional insights into the mineral composition of the pigmenting layers, in combination with environmental scanning electron microscopy equipped with energy-dispersive spectroscopy (ESEM-EDS). The analysis revealed the use of traditional fresco pigments, including calcite, carbon black, ochres, and copper-based pigments. Smalt, manganese earths, and gold were also identified, reflecting Romanino’s approach to colour and material selection. Additionally, the detection of modern pigments such as titanium white and baryte points to restoration interventions, shedding light on the fresco’s conservation history. This research provides one of the most comprehensive analyses of pigments in Romanino’s works, contributing to a deeper understanding of his artistic practices and contemporary fresco techniques. Full article

(This article belongs to the Special Issue Interdisciplinary Applications of Spectroscopic Techniques for Heritage Conservation)

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37 pages, 579 KB

Open AccessReview

Assessing Regional Public Transport Evaluation Indicators for Advanced Mobility Systems: A Review

by Ran Du, Fumitaka Kurauchi, Toshiyuki Nakamura and Masahiro Kuwahara

Sustainability 2025, 17(19), 8854; https://doi.org/10.3390/su17198854 - 3 Oct 2025

Abstract

The emergence of advanced mobility systems, including Demand Responsive Transport (DRT), shared mobility, and Mobility as a Service (MaaS), has required a reassessment of the evaluation indicators for public transportation systems. Existing studies often address only limited aspects and lack a comprehensive, structured classification, while the unique impacts of advanced systems remain insufficiently captured. Moreover, little attention has been given to which indicators are suitable for simulation despite their growing role in transport planning. To fill these gaps, this study develops a structured classification of quantitative evaluation indicators from the existing literature, serving as a foundation for assessing advanced mobility systems. It highlights system-specific characteristics, identifies relevant indicators, and examines their correspondence with conventional ones. Furthermore, it explores the applicability of these indicators in simulation environments, offering guidance for selecting representative indicators in simulation setup, operational monitoring, and impact assessment. Finally, it highlights the potential of quantitative indicators to approximate qualitative ones, suggesting directions for future research in simulation-based evaluation. By integrating environmental, economic, and societal dimensions, this study contributes to a sustainability-oriented framework for evaluating advanced mobility systems, providing insights for both academic research and practical mobility planning. Full article

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18 pages, 960 KB

Open AccessArticle

Are Carbon Credits Important for Indigenous Fire Stewardship? Insights from British Columbia

by Philippe Ambeault, William Nikolakis and Russel Myers Ross

Fire 2025, 8(10), 391; https://doi.org/10.3390/fire8100391 - 3 Oct 2025

Abstract

Indigenous Fire Stewardship (IFS) has long been practiced by Indigenous Peoples to care for the land, reduce wildfire risk, and maintain ecological and cultural values. In British Columbia, Yunesit’in, a member of the Tsilhqot’in Nation, has revitalized their IFS practices following the 2017 Hanceville Fire. As climate policy increasingly supports nature-based solutions, carbon credit programs are emerging as a potential funding source for IFS. This study used grounded theory with interviews to understand Yunesit’in IFS practitioners’ and community leaders’ perspectives on carbon credits. Using the concept of “cultural signatures,” we identified core values shaping community engagement in carbon markets. While most interviewees (7/10) were initially unfamiliar with carbon credits, many saw their potential to support long-term program goals after learning more. Three cultural signatures emerged from the analysis: (1) a sense of stewardship responsibility, (2) the importance of a community-grounded program, and (3) the revitalization of Indigenous knowledge and land-based practices. Interviewees expressed concern that carbon credits might shift the program’s focus away from land and culture toward technical goals that exclude community participation. We conclude that building awareness about carbon and carbon credits among Indigenous Peoples, and supporting engagement processes that reflect cultural signatures in carbon frameworks, are both critical. Full article

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53 pages, 3279 KB

Open AccessReview

Cognitive Bias Mitigation in Executive Decision-Making: A Data-Driven Approach Integrating Big Data Analytics, AI, and Explainable Systems

by Leonidas Theodorakopoulos, Alexandra Theodoropoulou and Constantinos Halkiopoulos

Electronics 2025, 14(19), 3930; https://doi.org/10.3390/electronics14193930 - 3 Oct 2025

Abstract

Cognitive biases continue to pose significant challenges in executive decision-making, often leading to strategic inefficiencies, misallocation of resources, and flawed risk assessments. While traditional decision-making relies on intuition and experience, these methods are increasingly proving inadequate in addressing the complexity of modern business environments. Despite the growing integration of big data analytics into executive workflows, existing research lacks a comprehensive examination of how AI-driven methodologies can systematically mitigate biases while maintaining transparency and trust. This paper addresses these gaps by analyzing how big data analytics, artificial intelligence (AI), machine learning (ML), and explainable AI (XAI) contribute to reducing heuristic-driven errors in executive reasoning. Specifically, it explores the role of predictive modeling, real-time analytics, and decision intelligence systems in enhancing objectivity and decision accuracy. Furthermore, this study identifies key organizational and technical barriers—such as biases embedded in training data, model opacity, and resistance to AI adoption—that hinder the effectiveness of data-driven decision-making. By reviewing empirical findings from A/B testing, simulation experiments, and behavioral assessments, this research examines the applicability of AI-powered decision support systems in strategic management. The contributions of this paper include a detailed analysis of bias mitigation mechanisms, an evaluation of current limitations in AI-driven decision intelligence, and practical recommendations for fostering a more data-driven decision culture. By addressing these research gaps, this study advances the discourse on responsible AI adoption and provides actionable insights for organizations seeking to enhance executive decision-making through big data analytics. Full article

(This article belongs to the Special Issue Feature Papers in Artificial Intelligence)

17 pages, 275 KB

Open AccessArticle

Digital Finance Adoption in Brazil: An Exploratory Analysis on Financial Apps and Digital Financial Literacy

by Natali Morgana Cassola, Kalinca Léia Becker, Kelmara Mendes Vieira, Maria Fernanda da Silveira Feldmann, Mariana Rodrigues Chaves, Iasmin Camile Berndt and Anna Febe Machado Arruda

J. Risk Financial Manag. 2025, 18(10), 560; https://doi.org/10.3390/jrfm18100560 - 3 Oct 2025

Abstract

Digital transformation has fundamentally altered how individuals manage their finances. The expansion of financial technologies and the digitalization of banking services underscore the need for digital financial literacy, defined as the ability to safely use financial applications and make informed decisions within virtual environments. This study examined the perceptions of financial application use across age groups and their corresponding level of digital financial literacy. This exploratory study used a convenience sample of 41 semi-structured interviews conducted in 2025. The data were analyzed using content analysis and descriptive statistics. The findings indicated that most participants prioritized digital apps over traditional channels and expressed confidence in their use, although concerns about data security remained. Participants identified key advantages, including convenience, efficiency, and centralized access, yet few used apps for financial planning. Most respondents demonstrated an intermediate level of digital knowledge, with limited proficiency in executing complex financial tasks. Perceptions revealed both optimism and apprehension: while participants valued the practicality of digital tools, they also recognized risks such as fraud, exclusion of vulnerable groups, and technological dependence. The limited and non-representative sample limits generalization, suggesting the need for broader surveys. Enhanced public policies promoting digital financial education in Brazil are recommended. Full article

(This article belongs to the Special Issue The New Horizons of Global Financial Literacy)

19 pages, 36886 KB

Open AccessArticle

Topographic Inversion and Shallow Gas Risk Analysis in the Canyon Area of Southeastern Qiongdong Basin Based on Multi-Source Data Fusion

by Hua Tao, Yufei Li, Qilin Jiang, Bigui Huang, Hanqiong Zuo and Xiaolei Liu

J. Mar. Sci. Eng. 2025, 13(10), 1897; https://doi.org/10.3390/jmse13101897 - 3 Oct 2025

Abstract

The submarine topography in the canyon area of the Qiongdongnan Basin is complex, with severe risks of shallow gas hazards threatening marine engineering safety. To accurately characterize seabed morphology and assess shallow gas risks, this study employed multi-source data fusion technology, integrating 3D seismic data, shipborne multibeam bathymetry data, and high-precision AUV topographic data from key areas to construct a refined seabed terrain inversion model. For the first time, the spatial distribution characteristics of complex geomorphological features such as scarps, mounds, fissures, faults, and mass transport deposits (MTDs) were systematically delineated. Based on attribute analysis of 3D seismic data and geostatistical methods, the enrichment intensity of shallow gas was quantified, its distribution patterns were systematically identified, and risk level evaluations were conducted. The results indicate: (1) multi-source data fusion significantly improved the resolution and accuracy of terrain inversion, revealing intricate geomorphological details in deep-water regions; and (2) seismic attribute analysis effectively delineated shallow gas enrichment zones, clarifying their spatial distribution patterns and risk levels. This study provides critical technical support for deep-water drilling platform site selection, submarine pipeline route optimization, and engineering geohazard prevention, offering significant practical implications for ensuring the safety of deep-water energy development in the South China Sea. Full article

(This article belongs to the Special Issue Advances in Marine Engineering: Geological Environment and Hazards—3rd Edition)

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