Search Results (108)

This study investigates the impact of environmental, social, and governance (ESG) performance on corporate corruption risk management (CCRM) and examines the moderating role of the risk management committee (RMC) among non-financial firms in Gulf Cooperation Council (GCC) countries for the period spanning from 2015 to 2024. Building on agency and legitimacy theories, the study argues that ESG performance strengthens governance quality and ethical accountability, which is reflected in higher quality CCRM. Additionally, RMCs are expected to play a moderating role in enhancing oversight effectiveness, which boosts such a relationship. Using panel data derived from the Refinitiv Eikon database and employing Feasible Generalized Least Squares (FGLS) regression, the results reveal that firms with higher ESG performance exhibit significantly stronger corruption risk management practices. Moreover, the interaction between ESG performance and RMC presence positively amplifies this relationship, underscoring the committee’s role in institutionalizing ethical conduct and improving governance transparency. Robustness tests using alternative ESG and CCRM measures confirm the consistency of these findings. The study provides novel empirical evidence from the GCC context, highlighting how governance structures and sustainability practices jointly enhance corporate integrity. It offers theoretical, practical, and policy implications for promoting ethical governance and sustainable development in emerging markets. Full article

Financial institutions face increased reputational, regulatory, and ethical risks as the frequency and complexity of Environmental, Social, and Governance (ESG) controversies increase. Whistleblowing mechanisms are essential in the context of institutional resilience and the mitigation of internal governance failures. This study quantifies the exposure of 364 European financial institutions to a variety of ESG controversies to assess the effectiveness of whistleblowing during the fiscal year 2024. A whistleblowing performance index that captures the relative influence of ESG-related risk factors—such as corruption allegations, environmental violations, and executive misconduct—is constructed using a hybrid Multi-Criteria Decision-Making (MCDM) framework that is based on Entropy Weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The results emphasize that the perceived efficacy of whistleblower systems is substantially influenced by the frequency of media-reported controversies and the presence of robust anti-bribery policies. The study provides a data-driven, replicable paradigm for assessing internal governance capabilities in the face of ESG risk pressure. Our findings offer actionable insights for regulators, compliance officers, and ESG analysts who are interested in evaluating and enhancing ethical accountability systems within the financial sector by connecting the domains of financial risk management, corporate ethics, and sustainability governance. Full article

Money laundering poses a serious threat to financial stability and requires effective national frameworks for prevention. This study investigates how the quality of legal and institutional frameworks affects the effectiveness of national anti-money laundering (AML) systems and their implications for financial risk management. We conducted an empirical analysis of 132 jurisdictions in 2024 using the Basel AML Index (AMLI) and the WJP Rule of Law Index (RLI). The Random Forest method was employed to model the relationship between rule-of-law indicators and AML risk levels. Findings reveal a significant inverse relationship between rule-of-law indicators and AML risk levels, with an overall classification accuracy of 69.6%. The model performed best for low-risk countries (precision 75%, recall 92.31%), moderately for medium-risk countries (precision 65.22%, recall 78.95%), but failed to identify high-risk jurisdictions, suggesting a legal institutional “threshold” necessary for effective AML functioning. Key predictors included protection of fundamental rights and mechanisms for civil oversight, with strong negative correlations between AML risk and criminal justice impartiality (−0.35), civil justice fairness (−0.35), and equality before the law (−0.41). These results show that legal factors strongly affect AML risk and can guide regulators in improving risk-based standards, enhancing regulatory certainty, and managing financial risk. Full article

Gears, as critical components of rotating machinery, are prone to wear and fracture due to their complex structural dynamics and harsh operating conditions, leading to catastrophic failures, economic losses, and safety risks. AE technology enables real-time fault diagnosis by capturing stress wave emissions from material defects with high sensitivity. However, mechanical background noise significantly corrupts AE signals, while optimal selection of gear health indicators remains challenging, critically impacting fault feature extraction accuracy. This study develops an adaptive feature extraction method for fault diagnosis using AE. Through gear fault simulation experiments, VMD analyzes mode number and penalty factor effects on signal decomposition. Correlation coefficient-based reconstruction optimization is implemented. For feature selection challenges, SVM-RFE enables adaptive parameter ranking. Finally, SVM with optimized kernel parameters achieves effective fault classification. Optimized VMD enhances signal decomposition, while SVM-RFE reduces feature dimensionality, addressing manual selection uncertainty and computational redundancy. Experimental results demonstrate superior accuracy in gear fault classification. This study proposes an AE-based adaptive feature extraction method with three innovations: (1) establishing VMD parameter–decomposition quality relationships; (2) developing an SVM-RFE feature selection framework; (3) achieving high-accuracy gear fault classification. The method provides a novel technical approach for rotating machinery diagnostics with significant engineering value. Full article

Generative adversarial networks (GANs) typically require large datasets for effective training, which poses challenges for volumetric medical imaging tasks where data are scarce. This study addresses this limitation by extending adaptive discriminator augmentation (ADA) for three-dimensional (3D) StyleGAN2 to improve generative performance on limited volumetric data. The proposed 3D StyleGAN2-ADA redefines all 2D operations for volumetric processing and incorporates the full set of original augmentation techniques. Experiments are conducted on the NoduleMNIST3D dataset of lung CT scans containing 590 voxel-based samples across two classes. Two augmentation pipelines are evaluated—one using color-based transformations and another employing a comprehensive set of 3D augmentations including geometric, filtering, and corruption augmentations. Performance is compared against the same network and dataset without any augmentations at all by assessing generation quality with Kernel Inception Distance (KID) and 3D Structural Similarity Index Measure (SSIM). Results show that volumetric ADA substantially improves training stability and reduces the risk of a mode collapse, even under severe data constraints. A strong augmentation strategy improves the realism of generated 3D samples and better preserves anatomical structures relative to those without data augmentation. These findings demonstrate that adaptive 3D augmentations effectively enable high-quality synthetic medical image generation from extremely limited volumetric datasets. The source code and the weights of the networks are available in the GitHub repository. Full article

The reliability of embedded processors in safety- and mission-critical domains is increasingly threatened by radiation-induced soft errors, particularly multiple-cell upsets (MCUs) that simultaneously corrupt adjacent cells in external SDRAM. While prior studies on the LEON3 processor have largely focused on single-event upsets (SEUs) in internal SRAM structures, they overlook MCU effects in off-chip SDRAM, a critical gap that limits fault coverage and compromises system-level reliability assessment in modern high-density embedded systems. This paper presents an SDRAM-based fault injection framework using FPGA emulation to evaluate the impact of MCUs on the LEON3 soft-core processor, with faults directly injected into the external memory subsystem where data corruptions can rapidly propagate into system-level failures. The methodology injects spatially correlated two-bit MCUs directly into SDRAM during realistic workload execution. Three architecturally diverse benchmarks were analyzed, each representing a distinct computational workload: a numerical (matrix multiplication), signal-processing (FFT), and a cryptographic (AES-128 encryption) application, chosen to capture arithmetic-intensive, iterative, and control-intensive execution profiles, respectively. The results reveal a distinct workload-dependent vulnerability profile. Matrix multiplication exhibited >99.99% fault activation, with outcomes overwhelmingly dominated by data store errors. FFT showed >97% activation in steady-state execution, following an initial phase sensitive to alignment and data access exceptions. AES displayed 88.12% non-propagating faults, primarily due to injections in inactive memory regions, but remained exposed to critical memory access violations and control-flow exceptions that enable fault-based cryptanalysis. These findings demonstrate that SEU-only models severely underestimate real-world MCU risks and underscore the necessity of selective, workload-aware fault-tolerance strategies: lightweight ECC for cryptographic data structures, alignment monitoring for signal processing, and algorithm-based fault tolerance (ABFT) for numerical kernels. This work provides actionable insights for hardening LEON3-based systems against emerging multi-bit threats in radiation-rich and adversarial environments. Full article

Corruption in Colombia remains a critical barrier to development, institutional trust, and equitable access to public services, despite legislative efforts such as the Anti-Corruption Statute. This article explores the intersection between fiscal management and artificial intelligence (AI) as integrated strategies for enhancing transparency, accountability, and risk assessment in public administration. Drawing on theoretical frameworks and empirical data from 2020 to 2022, this study analyzes the scale and impact of corruption and the effectiveness of oversight mechanisms led by the Comptroller General of the Republic (CGR). A key innovation examined is the implementation of a GPT-based scoring model that automates the evaluation of internal accounting controls in 219 public entities. By leveraging AI to support fiscal audits, Colombia demonstrates a scalable approach to modernizing anti-corruption practices. The study concludes with policy recommendations that emphasize digital transformation, institutional strengthening, citizen engagement, and capacity building to improve fiscal governance and reduce corruption. Full article

Cybersexual victimization is a growing public health concern with severe psychosocial consequences, particularly for younger populations. Despite growing awareness of its prevalence, understanding how cybersexual victimization evolves across different demographic and regional contexts remains limited. The aim was to analyze sex- and age-specific temporal trends and projections of cybersexual victimization in Spain (2011–2022), disaggregated by sex, age group, autonomous community, and offense type, to identify where disparities emerge and persist (particularly from adolescence (<18) into midlife) while also examining gender and regional inequalities to provide evidence for prevention strategies that are both gender-sensitive and tailored to different developmental stages and territorial contexts. Spanish national police-reported data on seven cybersexual offenses (sexual abuse, sexual harassment, corruption of minors, grooming, exhibitionism, child sexual abuse images, and sexual provocation) from 2011 to 2022 were analyzed. Data were disaggregated by sex, age group, and regions. Mean rates per 100,000 inhabitants were calculated, independent-sample t-tests assessed sex differences, and linear regression models projected trends to 2035 for each age-sex group. Between 2011 and 2022, cybersexual crimes in Spain increased across most offense types, with grooming, child sexual abuse images, and contact offenses showing the steepest upward trends (all p < 0.001). Women consistently presented higher mean victimization rates than men in most offense types and age groups. Among those under 18, mean grooming rates were 2.55 for females versus 0.95 per 100,000 for males (p < 0.001), with significant differences also in corruption of minors (p < 0.01). In young adulthood (18–25 years), women showed higher rates in sexual harassment (p < 0.001) and sexual abuse (p < 0.01), while, in midlife (26–40 and 41–50 years), female predominance persisted for sexual harassment, sexual abuse, and sexual provocation (all p < 0.05). Projections to 2035 indicate that sex gaps will remain or widen, particularly among females under 18 and in the 26–40 age group. The Balearic, Canary Islands, and Andalusia regions recorded the highest mean rates, whereas Galicia and Castilla-La Mancha reported the lowest. Cybersexual victimization in Spain disproportionately affects females from adolescence into midlife, with the most considerable disparities emerging before age 18 and persisting into adulthood. The combination of rapid offense growth, persistent sex-based disparities, and marked regional inequalities underscores the urgent need for gender-sensitive, developmentally targeted prevention strategies that address both early vulnerability and the reinforcement of risk in adult digital environments. Full article

This article examines the effects of climate change on the 32 million inhabitants of the Megalopolis of Central Mexico (MCM), which is threatened by chaotic urbanization, land-use changes, the deforestation of the Forest of Water by organized crime, unsustainable agriculture, and biodiversity loss. Expensive hydraulic management extracting water from deep aquifers, long pipes exploiting water from neighboring states, and sewage discharged outside the endorheic basin result in expensive pumping costs and air pollution. This mismanagement has increased water scarcity. The overexploitation of aquifers and the pollution by toxic industrial and domestic sewage mixed with rainfall has increased the ground subsidence, damaging urban infrastructure and flooding marginal neighborhoods with toxic sewage. A system approach, satellite data, and participative research methodology were used to explore potential water scarcity and weakened water security for 32 million inhabitants. An alternative nature-based approach involves recovering the Forest of Water (FW) with IWRM, including the management of Natural Protected Areas, the rainfall recharge of aquifers, and cleaning domestic sewage inside the valley where the MCM is found. This involves recovering groundwater, reducing the overexploitation of aquifers, and limiting floods. Citizen participation in treating domestic wastewater with eco-techniques, rainfall collection, and purification filters improves water availability, while the greening of urban areas limits the risk of climate disasters. The government is repairing the broken drinking water supply and drainage systems affected by multiple earthquakes. Adaptation to water scarcity and climate risks requires the recognition of unpaid female domestic activities and the role of indigenous people in protecting the Forest of Water with the involvement of three state authorities. A digital platform for water security, urban planning, citizen audits against water authority corruption, and aquifer recharge through nature-based solutions provided by the System of Natural Protected Areas, Biological and Hydrological Corridors [SAMBA] are improving livelihoods for the MCM’s inhabitants and marginal neighborhoods, with greater equity and safety. Full article

Cyber-sexual crimes have become a growing concern in the digital age, as rapid technological progress continues to create new forms of violence and victimization. These offenses affect society unevenly, striking more intensely among minors, women, and other vulnerable groups. Their prevalence is shaped by structural inequalities, educational, economic, and technological, that condition both exposure to digital risks and the capacity for protection. Although international research has connected these disparities with digital victimization, evidence from Spain remains limited. The aim was to analyze the regional distribution of cyber-sexual crimes in Spain between 2011 and 2022 and to explore how education, income, and digital access relate to their incidence. To this end, official data from the Spanish Statistical Crime Portal (PEC) were combined with structural indicators provided by the Spanish National Institute of Statistics. The analysis encompassed reported cases of sexual abuse, sexual harassment, corruption of minors, online grooming, exhibitionism, pornography, and sexual provocation, using standardized incidence rates per 100,000 inhabitants. Statistical methods included ANOVA with post hoc comparisons, correlation analyses, and K-means clustering to identify territorial patterns. Results revealed a sustained national increase in cyber-sexual crimes, with grooming and sexual harassment showing the most pronounced growth. The Balearic Islands (mean 4.9), Canary Islands (4.0), and Andalusia (3.9) registered the highest incidence rates, well above the national average (3.0). Educational disadvantages and low income were linked to sexual abuse and corruption of minors, whereas greater digital connectivity, expressed through higher mobile phone use, broadband access, and computer ownership, was strongly associated with grooming and other technology-facilitated offenses. Cluster analysis identified three distinct territorial profiles: high-incidence regions (Balearic and Canary Islands, Andalusia), intermediate (Murcia, Madrid, Navarre, Valencian Community), and low-incidence (Galicia, Catalonia, Castile and León, among others). In conclusion, the findings demonstrate that cyber-sexual crimes in Spain are unevenly distributed and closely linked to persistent structural vulnerabilities that shape digital exposure. These results underscore the need for territorially sensitive prevention strategies that reduce educational and economic inequalities, foster sexual and digital literacy, and promote safer online environments. Without addressing these underlying structural dimensions, public policies risk overlooking the conditions that sustain regional disparities and limit adequate protection against technology-driven sexual crimes. Full article

The transition from traditional power systems to smart grids demands advanced methods for detecting and classifying Power Quality Disturbances (PQDs)—variations in voltage, current, or frequency that disrupt device performance. The rise of renewable energy and nonlinear loads, such as LED lighting, has increased PQD occurrences. While deep learning models can effectively analyze data from grid sensors to detect PQD occurrences, privacy concerns often prevent operators from sharing raw data which is necessary to train the models. To address this, Federated Learning (FL) enables collaborative model training without exposing sensitive information. However, FL’s decentralized design introduces new risks, particularly data poisoning attacks, where malicious clients corrupt model updates to degrade the global model accuracy. Despite these risks, PQD classification under FL and its vulnerability to such attacks remain largely unexplored. In this work, we develop FL-based classifiers for PQD detection and compare their performance to traditionally trained, centralized models. As expected from prior FL research, we observed a slight drop in performance: the model’s accuracy decreased from 97% (centralized) to 96% (FL), while the false alarm rate increased from 0.19% to 4%. We also emulate five poisoning scenarios, including indiscriminate attacks aimed at degrading model accuracy and class-specific attacks intended to hide particular disturbance types. Our experimental results show that the attacks are very successful in reducing the accuracy of the classifier. Furthermore, we implement a detection mechanism designed to identify and isolate corrupted client updates, preventing them from influencing the global model. Experimental results reveal that our defense substantially curtails the performance degradation induced by poisoned updates, thereby preserving the robustness of the global model against adversarial influence. Full article

The estuaries of the Mississippi River Delta are among the most productive coastal ecosystems in the world and have attracted human fishing communities for centuries. Beginning in the early 20th century, the oil and gas industry also emerged as a powerful economic force in exploiting coastal fossil fuel deposits. This paper reviews the complex history of the oil and gas industry in Southeast Louisiana, including its relationships with political corruption, inequality, pollution, and environmental catastrophe; and also its role in supporting coastal fishing communities with complementary economic opportunities. In the 21st century, a series of disasters—above all Hurricane Katrina in 2005 and the B.P. oil spill in 2010—drew attention to the risks inherent to the region, as well as its crucial role in buffering the impacts of tropical storms for inland urban communities. This paper examines the evolution of fisheries regulations and their consequences of small-scale fishers, focusing especially on the banning of gill net use in 1990s. By combining historical information with ethnographic interviews and participant observation, this paper examines the complex political–economic forces involved in shifting regulatory frameworks and policies, and it shows their negative consequences for fishing communities facing an existentially threatening combination of coastal erosion, fisheries declines, and various macroeconomic headwinds. This paper argues that resilient coastal communities are crucial to combating the environmental problems facing coastal regions and that rethinking fisheries regulations may be a dynamic tool in enhancing community resilience. Full article

One of the main challenges when working with time series captured online using sensors is the appearance of noise or null values, generally caused by sensor failures or temporary disconnections. These errors compromise data reliability and can lead to incorrect decisions. Particularly in the treatment of diabetes mellitus, where medical decisions depend on continuous glucose monitoring (CGM) systems provided by modern sensors, the presence of corrupted data can pose a significant risk to patient health. This work presents an approach that encompasses online detection and imputation of anomalous data using physiological inputs (insulin and carbohydrate intake), which enables decision-making in automatic glucose monitoring systems or for glucose control purposes. Four deep neural network architectures are proposed: CNN-LSTM, GRU, 1D-CNN, and Transformer-LSTM, under a controlled fault injection protocol and compared with the ARIMA model and the Temporal Convolutional Network (TCN). The obtained performance is compared using regression (MAE, RMSE, MARD) and classification (accuracy, precision, recall, F1-score, AUC) metrics. Results show that the CNN-LSTM network is the most effective for fault detection, achieving an F1-score of 0.876 and an accuracy of 0.979. Regarding data imputation, the 1D-CNN network obtained the best performance, with an MAE of 2.96 mg/dL and an RMSE of 3.75 mg/dL. Then, validation on the OhioT1DM dataset, containing real CGM data with natural sensor disconnections, showed that the CNN–LSTM model accurately detected anomalies and reliably imputed missing glucose segments under real-world conditions. Full article

Real-time operating systems (RTOSs) are widely used in embedded systems to ensure deterministic task execution, predictable responses, and concurrent operations, which are crucial for time-sensitive applications. However, the growing complexity of embedded systems, increased network connectivity, and dynamic software updates significantly expand the attack surface, exposing RTOSs to a variety of security threats, including memory corruption, privilege escalation, and side-channel attacks. Traditional security mechanisms often impose additional overhead that can compromise real-time guarantees. In this work, we present a Risk-aware Permission Adjustment (RPA) framework, implemented on CHERIoT RTOS, which is a CHERI-based operating system. RPA aims to detect anomalous behavior in real time, quantify security risks, and dynamically adjust permissions to mitigate potential threats. RPA maintains system continuity, enforces fine-grained access control, and progressively contains the impact of violations without interrupting critical operations. The framework was evaluated through targeted fault injection experiments, including 20 real-world CVEs and 15 abstract vulnerability classes, demonstrating its ability to mitigate both known and generalized attacks. Performance measurements indicate minimal runtime overhead while significantly reducing system downtime compared to conventional CHERIoT and FreeRTOS implementations. Full article

Numerous industries have begun using cloud computing. Among other things, this presents a plethora of novel security and dependability concerns. Thoroughly verifying cloud solutions to guarantee their correctness is beneficial, just like with any other computer system that is security- and correctness-sensitive. While there has been much research on distributed system validation and verification, nobody has looked at whether verification methods used for distributed systems can be directly applied to cloud computing. To prove that cloud computing necessitates a unique verification model/architecture, this research compares and contrasts the verification needs of distributed and cloud computing. Distinct commercial, architectural, programming, and security models necessitate distinct approaches to verification in cloud and distributed systems. The importance of cloud-based Service Level Agreements (SLAs) in testing is growing. In order to ensure service integrity, users must upload their selected services and registered services to the cloud. Not only does the user fail to update the data when they should, but external issues, such as the cloud service provider’s data becoming corrupted, lost, or destroyed, also contribute to the data not becoming updated quickly enough. The data saved by the user on the cloud server must be complete and undamaged for integrity checking to be effective. Damaged data can be recovered if incomplete data is discovered after verification. A shared resource pool with network access and elastic extension is realized by optimizing resource allocation, which provides computer resources to consumers as services. The development and implementation of the cloud platform would be greatly facilitated by a verification mechanism that checks the data integrity in the cloud. This mechanism should be independent of storage services and compatible with the current basic service architecture. The user can easily see any discrepancies in the necessary data. While cloud storage does make data outsourcing easier, the security and integrity of the outsourced data are often at risk when using an untrusted cloud server. Consequently, there is a critical need to develop security measures that enable users to verify data integrity while maintaining reasonable computational and transmission overheads. A cryptography-based public data integrity verification technique is proposed in this research. In addition to protecting users’ data from harmful attacks like replay, replacement, and forgery, this approach enables third-party authorities to stand in for users while checking the integrity of outsourced data. This research proposes a Cloud Integrity Verification and Validation Model using the Double Token Key Distribution (CIVV-DTKD) model for enhancing cloud quality of service levels. The proposed model, when compared with the traditional methods, performs better in verification and validation accuracy levels. Full article