Search Results (96)

This paper develops a quantitative framework for climate–financial risk measurement that combines a spatially explicit jump–diffusion asset–loss model with prudentially aligned risk metrics. The approach connects regional physical hazards and transition variables derived from climate-consistent pathways to asset returns and credit parameters through the use of climate-adjusted volatilities and jump intensities. Fat tails and geographic heterogeneity are captured by it, which conventional diffusion-based or purely narrative stress tests fail to reflect. The framework delivers portfolio-level Spatial Climate Value-at-Risk (SCVaR) and Expected Shortfall (ES) across scenario–horizon matrices and incorporates an explicit robustness layer (block bootstrap confidence intervals, unconditional/conditional coverage backtests, and structural-stability tests). All ES measures are understood as Conditional Expected Shortfall (CES), i.e., tail expectations evaluated conditional on climate stress scenarios. Applications to bank loan books, pension portfolios, and sovereign exposures show how climate shocks reprice assets, alter default and recovery dynamics, and amplify tail losses in a region- and sector-dependent manner. The resulting, statistically validated outputs are designed to be decision-useful for Internal Capital Adequacy Assessment Process (ICAAP) and Pillar 2: climate-adjusted capital buffers, scenario-based stress calibration, and disclosure bridges that complement alignment metrics such as the Green Asset Ratio (GAR). Overall, the framework operationalises a move from exposure tallies to forward-looking, risk-sensitive, and auditable measures suitable for supervisory dialogue and internal risk appetite. Full article

Green fintech merges sustainable finance with data-intensive innovation, but national translations of EU rules can create regulatory risk. This study examines how such risk manifests in Central Europe and which policy tools mitigate it. We develop a three-dimension framework—regulatory clarity and scope, supervisory consistency, and innovation facilitation—and apply a comparative qualitative design to Hungary, Slovakia, Czechia, and Poland. Using a common EU baseline, we compile coded national snapshots from primary legal texts, supervisory documents, and recent scholarship. Results show material cross-country variation in labelling practice, soft-law use, and testing infrastructure: Hungary combines central-bank green programmes with an innovation hub/sandbox; Slovakia aligns with ESMA and runs hub/sandbox, though the green-fintech pipeline is nascent; Czechia applies a principles-based safe harbour and lacks a national sandbox; and Poland relies on a virtual sandbox and binding interpretations with limited soft law. These choices shape approval timelines, retail penetration, and cross-border portability of green-labelled products. We conclude with a policy toolkit: labelling convergence or explicit safe harbours, a cross-border sandbox federation, ESRS/ESAP-ready proportionate disclosures, consolidation of recurring interpretations into soft law, investment in suptech for green-claims analytics, and inclusion metrics in sandbox selection. Full article

Efficient monitoring of large-scale poultry farms requires the timely identification of dead broilers, as delays can accelerate disease transmission, leading to significant economic loss. Nevertheless, manual inspection remains the dominant practice, resulting in a labor-intensive, inconsistent, and poorly scalable workflow. Although recent advances in computer vision have introduced automated alternatives, most existing approaches face difficulties in crowded settings where live and dead broilers share similar visual patterns, and occlusions frequently occur. To address these problems, we propose a transformer-based multi-task segmentation framework designed to operate reliably in visually complex farm environments. The model constructs a unified feature representation that supports precise segmentation of dead broilers, while an auxiliary dead broiler counting task contributes additional supervisory features that enhance segmentation performance across diverse scene configurations. Experimental evaluations indicate that the proposed method yields accurate and stable segmentation results under various farm conditions, including densely populated and visually intricate scenes. Moreover, its overall segmentation accuracy consistently surpasses that of existing approaches, demonstrating the effectiveness of integrating transformer-based global modeling with the auxiliary regression objective. Full article

This study introduces a dynamic, multi-output stacking framework for real-time forecasting of HVAC cooling-coil behavior in air-handling units. The dynamic model encodes short-horizon system memory with input/target lags and rolling psychrometric features and enforces leakage-free, time-aware validation. Four base learners—Random Forest, Bagging (DT), XGBoost, and ANN—are each optimized with an Optuna hyperparameter tuner that systematically explores architecture and regularization to identify data-specific, near-optimal configurations. Their out-of-fold predictions are combined through a Ridge-based stacker, yielding state-of-the-art accuracy for supply-air temperature and chilled water leaving temperature (R² up to 0.9995, NRMSE as low as 0.0105), consistently surpassing individual models. Novelty lies in the explicit dynamics encoding aligned with coil heat and mass-transfer behavior, physics-consistent feature prioritization, and a robust multi-target stacking design tailored for HVAC transients. The findings indicate that this hyperparameter-tuned dynamic framework can serve as a high-fidelity surrogate for cooling-coil performance, supporting set-point optimization, supervisory control, and future extensions to virtual sensing or fault-diagnostics workflows in industrial AHUs. Full article

Doctoral student agency is increasingly regarded as a key construct in doctoral education. Yet, existing research on this topic focuses on qualitative approaches, and there remains a lack of psychometrically validated instruments, particularly in the Chinese context, where supervisory authority and institutional structures strongly shape student experiences. This study aimed to develop and validate the Doctoral Student Agency Scale (DSAS) to provide a comprehensive measure of doctoral students’ agency during the process of professional socialization. A sequential mixed-methods design was adopted. First, a conceptual model was inductively constructed from semi-structured interviews with 27 doctoral students, followed by three-level qualitative coding to generate an initial pool of items. These were refined through expert review, and 436 valid responses were subjected to exploratory and confirmatory factor analyses. The final DSAS consists of 27 items organized into 7 first-order factors, which load onto 3 second-order dimensions: self-agency, academic agency, and resource agency. Moreover, DSAS scores significantly correlated with academic ability and research role identity, two critical outcomes of doctoral student professional socialization, thus confirming the criterion validity. These findings indicate that the DSAS is a valid and reliable instrument. Theoretically, it contributes to refining the multidimensional conceptualization of doctoral agency, while practically, it provides supervisors and institutions with a diagnostic tool to design targeted interventions and foster doctoral development in context-sensitive ways. Full article

This study examines whether strengthened prudential supervision reduces the marginal influence of internal governance mechanisms on the performance of Saudi banks during the Vision 2030 reform period. Using a panel of ten listed Saudi banks from 2018 to 2024, governance measures are hand collected to align with Saudi Central Bank definitions, focusing on insider ownership and board independence. To address endogeneity arising from performance persistence and reverse causality, two-step system generalized method of moments with collapsed lagged internal instruments and Windmeijer-corrected standard errors are employed. The results reveal that insider ownership and board independence are statistically and economically insignificant for accounting performance and market valuation, whereas lagged performance remains the dominant predictor. Hansen J and Arellano–Bond AR(2) diagnostics support instrument validity, and robustness checks using alternative estimators and variable specifications produce consistent findings. The results suggest that in contexts where prudential oversight is comprehensive and consistently enforced, internal governance mechanisms may provide limited incremental monitoring value. However, they do not imply that boards or insiders are irrelevant during crises or when enforcement is uneven. Therefore, refining supervisory tools and disclosure practices should be prioritized over imposing additional structural mandates on boards or ownership configurations. Full article

The construction industry continues to face declining productivity due to its heavy reliance on labor and the repetitive, non-value-adding nature of supervision tasks. This study provides an exploratory, practitioner-based evaluation of how selected digital technologies, PDF-based documentation systems, object recognition algorithms, and 3D vision technology may contribute to productivity improvements in construction supervision. A total of 82 valid responses from field engineers were collected to examine perceived task substitution effects across major construction work types and management functions. The findings indicate that higher work-adoption rates of digital technologies are generally associated with improved supervisory productivity, with the strongest perceived benefits observed for PDF-based documentation in reinforced concrete and formwork tasks. However, other expected relationships, particularly those involving work responsibility, did not appear consistently in the practitioner data, suggesting that such perceptions may be influenced by task context and adaptation burden. This study offers a practical and context-specific framework for understanding how digital tools may support productivity enhancement in supervision work. While the results reflect tendencies based on a limited sample, they provide field-grounded insights that can inform the phased and targeted application of digital technologies in construction supervision and guide future empirical model development. Full article

Background: Clinical Learning Environments (CLEs) are essential to nursing education as a platform for students to develop professional identity; consolidate knowledge with clinical practice; and to gain cognitive, communication, and psychomotor skills. Experience in CLEs significantly impacts nursing students’ satisfaction with education and graduate career preferences. The Clinical Learning Environment, Supervision and Nurse Teacher scale (CLES+T) is widely used to measure the quality of professional experience placements (PEPs), but it has limited evidence of psychometric performance in rural and regional Australian contexts. Aim: To assess the psychometric properties of the CLES+T scale in the Australian context of rural and regional undergraduate nursing PEPs. Methods: A cross-sectional observational study of a convenience sample of 165 undergraduate nursing students from regional Victoria, Australia, who undertook PEPs between January and June 2020. Participants completed the CLES+T scale post-PEP. Statistical analyses included a test of survey tool reliability using Cronbach’s alpha and exploratory factor analysis to investigate instrument dimensionality and validity. Results: The CLES+T scale displayed adequate validity and reliability levels and demonstrated internal consistency similar to previous studies. The most important factor in the CLE was revealed as “pedagogy atmosphere and the content of supervisory relationship” followed by “role of the nurse educator”. Conclusions: The CLES+T shows adequate psychometric properties as a valid tool for use with undergraduate nursing students undertaking PEPs in Australian regional, rural, and remote settings. Full article

Background: The Minnesota Satisfaction Questionnaire is a widely recognized and used self-reporting instrument designed to measure a person’s satisfaction with various aspects of their job, as well as to provide comparative values regarding general satisfaction and its components. Objective: This study first aimed to test and validate the psychometric properties of the Minnesota Satisfaction Questionnaire–Short Form (MSQ-SF). Its second objective was to assess the job satisfaction levels of employees working within the organization and the factors influencing job satisfaction. Methods: This descriptive cross-sectional study analyzed the responses of 435 hospital staff members using the Romanian version of the MSQ-20 scale. Results: Exploratory Factor Analysis identified a three-factor structure: Task Enrichment, Autonomy Satisfaction, and Supervisory Relationships. The three-factor model with eight MSQ items discarded provided an excellent statistical fit. The MSQ-SF with a 20-item questionnaire has excellent Internal Consistency, with a Cronbach alpha of 0.935, 95% CI (0.926–0.944). Conclusions: The Romanian version of the MSQ-20 has excellent construct validity and consistency, and it provides reliable and comparable data on the health of the workforce. Full article

Standard monitoring for photovoltaic (PV) systems, often based on IEC 61724-1, the standard published by the International Electrotechnical Commission (IEC) titled “Photovoltaic system performance—Part 1: Monitoring”, is frequently slow to detect critical operational anomalies, particularly those related to energy self-consumption where conventional generation-centric metrics may appear normal. This work presents a validated industrial SCADA (i.e., Supervisory Control and Data Acquisition) framework designed for the accelerated fault diagnosis of such systems. The proposed methodology leverages high-resolution, real-time visualization of specific energy-flow indicators, including the Self-Consumption Ratio (SCR) and Self-Sufficiency Ratio (SSR), to provide immediate operational intelligence. The novelty of this approach lies not in the individual parameters themselves, but in their synergistic integration into a validated, high-speed SCADA system design and real-time diagnostic methodology. The framework’s diagnostic superiority was validated on two distinct, real-world case studies in Jaén, Spain (a 2.97 kW residential and a 58.5 kW commercial system), with primary research results confirming: (1) a simulated comparative benchmarking study demonstrated a significant reduction in Mean-Time-to-Detection (MTTD), achieving a consistent diagnostic speed improvement of over 80% for critical anomalies, and (2) a 10,000 h probabilistic simulation confirmed the statistical robustness of the proposed indicators across a wide range of operating conditions. By demonstrating the practical implementation of these principles within a scalable industrial platform, this work provides a validated and reproducible technical methodology that enhances PV system diagnostics, translating performance metrics into a tangible, high-speed tool for improving operational reliability. Full article

Automated violence detection in video surveillance is critical for public safety; however, existing methods frequently suffer notable performance degradation across diverse real-world scenarios due to domain shift. Substantial distributional discrepancies between source training data and target environments severely hinder model generalization, limiting practical deployment. To overcome this, we propose CoMT-VD, a new contrastive Mean Teacher-based violence detection model, engineered for enhanced adaptability in unseen target domains. CoMT-VD innovatively integrates a Mean Teacher architecture to adequately leverage unlabeled target domain data, fostering stable, domain-invariant feature representations by enforcing consistency regularization between student and teacher networks, crucial for bridging the domain gap. Furthermore, to mitigate supervisory noise from pseudo-labels and refine the feature space, CoMT-VD incorporates a dual-strategy contrastive learning module. DCL systematically refines features through intra-sample consistency, minimizing latent space distances for compact representations, and inter-sample consistency, maximizing feature dissimilarity across distinct categories to sharpen decision boundaries. This dual regularization purifies the learned feature space, boosting discriminativeness while mitigating noisy pseudo-labels. Broad evaluations on five benchmark datasets unequivocally demonstrate that CoMT-VD achieves the superior generalization performance (in the four integrated scenarios from five benchmark datasets, the improvements were 5.0∼12.0%, 6.0∼12.5%, 5.0∼11.2%, 5.0∼11.2%, and 6.3∼12.3%, respectively), marking a notable advancement towards robust and reliable real-world violence detection systems. Full article

Wastewater treatment plants (WWTPs) are critical infrastructures that account for a significant share of global electricity, with aeration alone often responsible for over half of the total demand. Reducing the energy intensity of blower operation is, therefore, essential for sustainable and resilient WWTP management. This study presents a modeling and simulation framework for optimizing parallel blower operation in grit chamber aeration system. The framework integrates a modular structure with a blower model, a distribution network model, and an optimization layer that work together to capture equipment performance, simulate hydraulic interactions, and determine energy-optimal operating strategies under process and safety constraints. Two optimization strategies are compared: a heuristic grid search and a Safe Bayesian Optimization (SBO) method. Both algorithms enforce vendor surge and overheat limits, network pressure constraints, and process requirements. Simulation campaigns under representative demand scenarios show that both approaches achieve feasible operating points, while SBO consistently demonstrates higher energy savings and substantially faster runtime. Overall, the findings highlight the potential of data-driven optimization for achieving efficient and safe blower control, with reduced computation time making progress for real-time supervisory optimization in WWTPs. Full article

Facing the challenges of scarce annotations in forward-looking sonar image segmentation, this paper proposes a teacher–student network for unsupervised domain adaptation. The proposed model undergoes supervised learning with optical image data to endow the student model with basic segmentation capabilities, using the segment anything model (SAM) as the teacher to generate pseudo-labels in the sonar image domain, thus achieving knowledge transfer without relying on annotated sonar images. An adaptive weighting approach is proposed, which generates a consistency map using predictive consistency in the source domain and the target domain to assess the quality of pseudo-labels. This method dynamically adjusts supervisory strength, preventing incorrect fitting caused by noisy pseudo-labels. In addition, a multi-scale attention module is designed to refine bottleneck features of the U-Net. The effectiveness of the proposed method is validated on a self-built public forward-looking sonar image dataset, achieving a mean intersection over union (IoU) of 40.8% and a mean average precision (mAP) of 70.3%, demonstrating significant improvements over existing typical UDA methods. Full article

In recent years, advancements in technologies related to hydrogen have facilitated the exploitation of this energy carrier in conjunction with renewable energies to meet the energy demands of diverse applications. This paper describes a pilot plant within the framework of a research and development (R&D) project aimed at utilizing hydrogen in both industrial and domestic sectors. To this end, this facility comprises six subsystems. Initially, a photovoltaic (PV) generator consisting of 48 panels is employed to generate electrical current from solar radiation. This PV array powers a proton exchange membrane (PEM) electrolyzer, which is responsible for producing green hydrogen by means of water electrolysis. The produced hydrogen is subsequently stored in a bottling storage system for later use in a PEM fuel cell that reconverts it into electrical energy. Finally, a programmable electronic load is utilized to simulate the electrical consumption patterns of various profiles. These physical devices exchange operational data with an open source supervisory system integrated by a set of Industry 4.0 (I4.0) and Internet of Things (IoT)-framed environments. Initially, Node-RED acts as middleware, handling communications, and collecting and processing data from the pilot plant equipment. Subsequently, this information is stored in MariaDB, a structured relational database, enabling efficient querying and data management. Ultimately, the Grafana environment serves as a monitoring platform, displaying the stored data by means of graphical dashboards. The system deployed with such I4.0/IoT applications places a strong emphasis on the continuous monitoring of the power inverter that serves as the backbone of the pilot plant, both from an energy flow and communication standpoint. This device ensures the synchronization, conversion, and distribution of electrical energy while simultaneously standing as a primary data source for the supervisory system. The results presented in this article describe the design of the system and provide evidence of its successful implementation. Full article

This study determines the role of integrated reporting (Int_Re) in affecting firm value and investigates how CEO integrity (CEOI) moderates this effect among firms listed on the Indian stock exchange. The sample consists of 150 firms listed on the Indian stock exchange who published an integrated report between the years of 2018–19 and 2022–23. This study relies on secondary data from company websites. The outcome of the multiple regression analysis reveals that there is a significant positive influence of Int_Re on firm value. The analysis also found that CEOI strengthens the relationship between Int_Re and firm value, attributable to ethical leadership exhibited by the CEO. There are some future practical implications that the study proposes: Indian firms need to engage in Int_Re practices to a greater extent; firms need to encourage ethical leadership at the executive level consistent with Int_Re; supervisory boards are limited by an obligation to monitor Int_Re adoption and CEO performance to keep the organization in line with its commitments to transparency, character, and sustainable value creation in the changing corporate governance landscape in India. Full article