Search Results (1,941)

Background and Clinical Significance: Osteonevus of Nanta is a rare histological phenomenon characterized by bone formation within a benign melanocytic nevus, most commonly in intradermal nevi of the head and neck. Although osteonevus of Nanta is rare, ossification in a cellular blue nevus is even more uncommon. To date, only one case of a cellular blue nevus with ossification has been documented. This case report adds to the limited literature and emphasizes the clinical importance of recognizing this rare phenomenon, as osteonevus of Nanta has been potentially associated with malignant melanoma. Case Presentation: A 72-year-old woman presented with an asymptomatic, pigmented scalp lesion that had recently increased in size. On clinical examination, the tumor appeared as a well-demarcated, firm, and nodular mass with dark blueish to violet pigmentation that measured 15 × 12 × 7 mm. To ensure a definitive diagnosis and rule out malignancy, the lesion was excised with narrow margins. Histological examination revealed a cellular blue nevus with prominent osseous metaplasia. Due to the absence of clear margins, a wider re-excision was performed. No residual tumor was found, and the patient remained asymptomatic with no recurrence. Conclusions: This case represents only the second published example of a cellular blue nevus with ossification. While osteonevus of Nanta is benign, its potential association with malignant melanoma, as well as its clinical resemblance to malignant entities such as nodular melanoma, malignant blue nevus, and pigmented basal cell carcinoma, underscores the need for thorough clinical and histopathologic evaluation. Full article

Workplace violence (WV) is a ubiquitous risk in healthcare settings where it has been associated with physical and mental health problems. We aimed to investigate the relationship between the violence experienced by healthcare workers (HCWs) and the presence of eating disorders (EDs). During routine health surveillance, 1215 HCWs were questioned about their experience of WV and the short version of the Eating Disorder Examination Questionnaire (EDE-QS) was used to assess their eating behaviors. Sleep quality, stress, and the presence of common mental illnesses and metabolic disorders were also evaluated. HCWs who had experienced one or more assaults in the previous year had a significantly higher EDE score than their colleagues. In a multivariate model, WV doubled the risk of EDs (odds ratio 2.33, confidence intervals 95% 1.30; 4.18, p < 0.01). A very significant association was observed between common mental disorders and EDs (OR 1.13, CI 95% 1.04; 1.23, p < 0.01), while low sleep quality almost reached a significant level (OR 1.09, CI 95% 0.99; 1.20). The higher frequency of EDs among workers subjected to violence may result from maladaptive coping mechanisms used when stress and mental health problems caused by WV lead to compensatory overeating. However, reverse causation, where WV is induced by stigmatization, cannot be ruled out. Because of the considerable impact EDs have on physical and mental health, productivity, and patient care, healthcare organizations should adopt programs designed to prevent these disorders in HCWs. Full article

(1) Background: This article aims to understand the forms and elements through which the inhabitants of the city of Medellin have configured their subjectivity in the context of the application of neoliberal policies in the last two decades. In this way, we can approach the frameworks of understanding that constitute a fundamental part of the individuation processes in which the incorporation of their subjectivities is evidenced in neoliberal contexts that, in the historical process, have been converging with authoritarian, antidemocratic and neoconservative elements. (2) Method: A qualitative approach with a hermeneutic-interpretative paradigm was used. In-depth semi-structured interviews were conducted with 41 inhabitants of Medellín who were politically identified with right-wing or center-right positions. Data analysis included thematic coding to identify patterns of thought and points of view. (3) Results: Participants associate success with individual effort and see state intervention as an obstacle to development. They reject redistributive policies, arguing that they generate dependency. In addition, they justify authoritarian models of government in the name of security and progress, from a moral superiority, which is related to a negative and stigmatizing perception of progressive sectors and a negative view of the social rule of law and public policies with social sense. (4) Conclusions: The naturalization of merit as a guiding principle, the perception of themselves as morally superior based on religious values that grant a subjective place of certainty and goodness; the criminalization of expressions of political leftism, mobilizations and redistributive reforms and support for policies that establish authoritarianism and perpetuate exclusion and structural inequalities, closes roads to a participatory democracy that enables social and economic transformations. Full article

By 2050, it is estimated that approximately 200 million people will be displaced due to the impacts of climate change. Vulnerability to climate change is shaped not only by environmental factors but fundamentally by systemic power relations and structural conditions present at both the places of origin and destination. In Latin America, climate-displaced persons predominantly settle in marginalised neighbourhoods, where widely accepted informality facilitates their rapid arrival but obstructs genuine progress and full integration as urban citizens. This paper critically examines the prevailing myths that justify the persistence of informality, revealing the socioeconomic challenges faced by climate migrants in the region. These four dominant myths are (1) Latin America’s inherently low productivity levels; (2) concessions by the ruling class enabling excluded groups to merely survive; (3) the perceived privilege of marginalised neighbourhoods to generate income outside formal legal frameworks, which supports their social capital; and (4) the limited benefits associated with formalisation. Debunking these myths is essential for developing effective public policies aimed at reducing informality and promoting inclusive urban integration, ultimately benefiting both climate migrants and host communities. Full article

Soft data, such as seismic imagery, plays a critical role in subsurface modeling by providing indirect constraints away from hard data locations. However, validating whether subsurface model realizations honor this type of data remains a challenge due to the lack of robust quantitative tools. This study introduces a machine learning-based workflow for soft data checking that uses an autoencoder (AE) to encode 2D seismic slices into a latent space. Subsurface model realizations are transformed into the same domain and projected into this latent space, enabling both visual and quantitative comparisons using principal component analysis and Euclidean distances. We demonstrate the workflow on rule-based models and their associated synthetic seismic data (soft data), showing that models with similar Markov chain parameters to the reference soft data score higher in proximity metrics. This approach provides a scalable, quantitative, and interpretable framework for evaluating the consistency between soft data and subsurface models, supporting better decision-making in reservoir characterization and other geoscience applications. Full article

This study is the first to systematically integrate supervised machine learning (decision tree) and association rule mining techniques to analyze accident data from the Taipei Metro system, conducting a large-scale data-driven investigation into both passenger injury and train malfunction events. The research demonstrates strong novelty and practical contributions. In the passenger injury analysis, a dataset of 3331 cases was examined, from which two highly explanatory rules were extracted: (i) elderly passengers (aged > 61) involved in station incidents are more likely to suffer moderate to severe injuries; and (ii) younger passengers (aged ≤ 61) involved in escalator incidents during off-peak hours are also at higher risk of severe injury. This is the first study to quantitatively reveal the interactive effect of age and time of use on injury severity. In the train malfunction analysis, 1157 incidents with delays exceeding five minutes were analyzed. The study identified high-risk condition combinations—such as those involving rolling stock, power supply, communication, and signaling systems—associated with specific seasons and time periods (e.g., a lift value of 4.0 for power system failures during clear mornings from 06:00–12:00, and 3.27 for communication failures during summer evenings from 18:00–24:00). These findings were further cross-validated with maintenance records to uncover underlying causes, including brake system failures, cable aging, and automatic train operation (ATO) module malfunctions. Targeted preventive maintenance recommendations were proposed. Additionally, the study highlighted existing gaps in the completeness and consistency of maintenance records, recommending improvements in documentation standards and data auditing mechanisms. Overall, this research presents a new paradigm for intelligent metro system maintenance and safety prediction, offering substantial potential for broader adoption and practical application. Full article

(1) Background: Sertoli–Leydig cell tumors (SLCTs) are rare ovarian neoplasms that account for less than 0.5% of all ovarian tumors. They usually affect young women and often present with androgenic symptoms. We report a unique case of a 40-year-old woman diagnosed with both SLCT and clear cell papillary renal cell carcinoma (CCP-RCC), a rare tumor association with unclear pathogenesis. (2) Methods: Both tumors were treated surgically. The diagnostic workup included hormonal testing, imaging studies, and extensive genetic testing, including DICER1 mutation analysis and multiplex ligation-dependent probe amplification (MLPA), as well as the examination of a next-generation sequencing (NGS) panel covering ~280 cancer-related genes. (3) Results: Histopathologic examination confirmed a well-differentiated SLCT and CCP-RCC. No pathogenic variants in DICER1 were identified by WES or MLPA. No clinically relevant changes were found in the extended NGS panel either, so a known hereditary predisposition could be ruled out. The synchronous occurrence of both tumors without genomic alterations could indicate a sporadic event or as yet unidentified mechanisms. (4) Conclusions: This case highlights the importance of a multidisciplinary approach in the management of rare tumor compounds. The exclusion of DICER1 mutations and the absence of genetic findings adds new evidence to the limited literature and underscores the importance of long-term surveillance and further research into potential shared oncogenic pathways. Full article

Estrogen receptor alpha (ERα) plays a vital role in the development and progression of breast cancer by regulating the expression of genes associated with cell proliferation in breast tissue. ERα inhibition is a key strategy in the prevention and treatment of breast cancer. Previous research modified chalcone compounds into pyrazoline benzenesulfonamide derivatives (Modifina) which show activity as an ERα inhibitor. This study aimed to design novel pyrazoline benzenesulfonamide derivatives (PBDs) as ERα antagonists using in silico approaches. Structure-based and ligand-based drug design approaches were used to create drug target molecules. A total of forty-five target molecules were initially designed and screened for drug likeness (Lipinski’s rule of five), cytotoxicity, pharmacokinetics and toxicity using a web-based prediction tools. Promising candidates were subjected to molecular docking using AutoDock 4.2.6 to evaluate their binding interaction with ERα, followed by molecular dynamics simulations using AMBER20 to assess complex stability. A pharmacophore model was also generated using LigandScout 4.4.3 Advanced. The molecular docking results identified PBD-17 and PBD-20 as the most promising compounds, with binding free energies (ΔG) of −11.21 kcal/mol and −11.15 kcal/mol, respectively. Both formed hydrogen bonds with key ERα residues ARG394, GLU353, and LEU387. MM-PBSA further supported these findings, with binding energies of −58.23 kJ/mol for PDB-17 and −139.46 kJ/mol for PDB-20, compared to −145.31 kJ/mol, for the reference compound, 4-OHT. Although slightly less favorable than 4-OHT, PBD-20 demonstrated a more stable interaction with ERα than PBD-17. Furthermore, pharmacophore screening showed that both PBD-17 and PBD-20 aligned well with the generated model, each achieving a match score of 45.20. These findings suggest that PBD-17 and PBD-20 are promising lead compounds for the development of a potent ERα inhibitor in breast cancer therapy. Full article

Background and Clinical Significance: Radial nerve palsy typically presents as wrist drop due to nerve compression, with conventional management often yielding prolonged recovery. We report a case where ultrasound-guided hydrodissection (HD) with 5% dextrose in water (D5W) achieved immediate functional restoration, suggesting neurapraxia as the underlying pathology. Case Presentation: A 54-year-old diabetic female presented with acute left wrist drop without trauma. Examination confirmed radial nerve palsy (MRC grade 0 wrist extension), while radiographs ruled out structural causes. Ultrasound revealed fascicular swelling at the spiral groove. Under real-time guidance, 50 mL D5W (no local anesthetic) was injected to hydrodissect the radial nerve. Immediate post-procedure assessment showed restored wrist extension (medical research council (MRC) grade 4+). At one- and three-month follow-ups, the patient maintained complete resolution of symptoms and normal function. Conclusions: This case highlights two key findings: (1) HD with D5W can serve as both a diagnostic tool (confirming reversible neurapraxia through immediate response) and therapeutic intervention, and (2) early HD may circumvent prolonged disability associated with conservative management. The absence of electrodiagnostic studies limits objective severity assessment, though ultrasound localized the lesion. While promising, these observations require validation through controlled trials comparing HD to standard care, particularly in diabetic patients with heightened compression susceptibility. Technical considerations—including optimal injectate volume and the role of adjuvant therapies—warrant further investigation. US-guided HD with D5W emerges as a minimally invasive, surgery-sparing option for acute compressive radial neuropathies, with potential to redefine treatment paradigms when applied at symptom onset. Full article

This comprehensive review maps the fast-evolving landscape in which artificial intelligence (AI) and deep-learning (DL) techniques converge with the Internet of Things (IoT) to manage energy, comfort, and sustainability across smart environments. A PRISMA-guided search of four databases retrieved 1358 records; after applying inclusion criteria, 143 peer-reviewed studies published between January 2019 and April 2025 were analyzed. This review shows that AI-driven controllers—especially deep-reinforcement-learning agents—deliver median energy savings of 18–35% for HVAC and other major loads, consistently outperforming rule-based and model-predictive baselines. The evidence further reveals a rapid diversification of methods: graph-neural-network models now capture spatial interdependencies in dense sensor grids, federated-learning pilots address data-privacy constraints, and early integrations of large language models hint at natural-language analytics and control interfaces for heterogeneous IoT devices. Yet large-scale deployment remains hindered by fragmented and proprietary datasets, unresolved privacy and cybersecurity risks associated with continuous IoT telemetry, the growing carbon and compute footprints of ever-larger models, and poor interoperability among legacy equipment and modern edge nodes. The authors of researches therefore converges on several priorities: open, high-fidelity benchmarks that marry multivariate IoT sensor data with standardized metadata and occupant feedback; energy-aware, edge-optimized architectures that lower latency and power draw; privacy-centric learning frameworks that satisfy tightening regulations; hybrid physics-informed and explainable models that shorten commissioning time; and digital-twin platforms enriched by language-model reasoning to translate raw telemetry into actionable insights for facility managers and end users. Addressing these gaps will be pivotal to transforming isolated pilots into ubiquitous, trustworthy, and human-centered IoT ecosystems capable of delivering measurable gains in efficiency, resilience, and occupant wellbeing at scale. Full article

This work was conducted within the framework of French university reforms undertaken since 2022. Regardless of learning level and target audience, project-based learning has proved its effectiveness as a teaching strategy for many years. The novelty of the present contribution lies in the gamification of this learning method. A popular game, Trivial Pursuit, was adapted to enable students to acquire knowledge in a playful manner while preparing for upcoming technical challenges. Various technical subjects were chosen to create new cards for the game. A total of 180 questions and their answers were created. The colored tokens were then used to trace manufactured products. This teaching experiment was conducted as part of a project-based learning program with third-year Bachelor students (Electrical Engineering and Industrial Computing Department). The game components associated with the challenge proposed to the students comprised six key elements: objectives, challenges, mechanics, components, rules, and environment. Within the framework of the Industry 4.0 concept, this pedagogical activity focused on the knowledge, understanding, development, and application of an RFID (Radio Frequency Identification) system demonstrating the capabilities of this technology. This contribution outlines the various stages of the work assigned to the students. An industrial partner was also involved in this work. Full article

The enforcement of the European Union Deforestation Regulation (EUDR) may reduce the supply of natural rubber to the European Union (EU), potentially leading to price increases due to the inelastic nature of rubber demand. This study assesses the potential financial implications for smallholder producers in Ghana, considering both the opportunities and risks associated with the evolving regulatory environment under EUDR and local market access conditions. A cost–benefit analysis (CBA) was conducted to evaluate the impact of different EUDR-related export decline scenarios on the net present value (NPV) of a standard 4-hectare plantation. The results suggest that even a minor 2.5% decline in global exports to the EU could increase the NPV by 17% for an independent compliant producer. However, a simulated COVID-19-like crisis in the fifth year of production leads to a 20% decline in NPV, reflecting vulnerability to external shocks. Based on these findings, the study identifies two priorities. This first is improving the coordination and harmonization of compliance efforts across the value chain to enable more producers to benefit from potential EUDR-related price increases. The recent creation of the Association of Natural Rubber Actors of Ghana (ANRAG) presents an opportunity to support such collective mechanisms. Second, minimizing losses during demand shocks requires the Tree Crops Development Authority (TCDA) to establish clear rules and transparent reporting for authorizing unprocessed rubber exports when factories reduce purchases due to low international prices—thus preserving market access for vulnerable producers. Together, these approaches would ensure that the potential benefits of the EUDR are realized inclusively, remain stable despite market downturns, and do not undermine value addition in domestic processing factories. Full article

Amid the global shift toward clean energy, wind power has emerged as a critical pillar of the modern energy matrix. To improve the reliability and maintainability of wind farms, this work proposes a novel hybrid fault detection approach that combines expert-driven diagnostic knowledge with data-driven modeling. The framework integrates autoencoder-based neural networks with Failure Mode and Symptoms Analysis, leveraging the strengths of both methodologies to enhance anomaly detection, feature selection, and fault localization. The methodology comprises five main stages: (i) the identification of failure modes and their observable symptoms using FMSA, (ii) the acquisition and preprocessing of SCADA monitoring data, (iii) the development of dedicated autoencoder models trained exclusively on healthy operational data, (iv) the implementation of an anomaly detection strategy based on the reconstruction error and a persistence-based rule to reduce false positives, and (v) evaluation using performance metrics. The approach adopts a fault-specific modeling strategy, in which each turbine and failure mode is associated with a customized autoencoder. The methodology was first validated using OpenFAST 3.5 simulated data with induced faults comprising normal conditions and a 1% mass imbalance fault on a blade, enabling the verification of its effectiveness under controlled conditions. Subsequently, the methodology was applied to a real-world SCADA data case study from wind turbines operated by EDP, employing historical operational data from turbines, including thermal measurements and operational variables such as wind speed and generated power. The proposed system achieved 99% classification accuracy on simulated data detect anomalies up to 60 days before reported failures in real operational conditions, successfully identifying degradations in components such as the transformer, gearbox, generator, and hydraulic group. The integration of FMSA improves feature selection and fault localization, enhancing both the interpretability and precision of the detection system. This hybrid approach demonstrates the potential to support predictive maintenance in complex industrial environments. Full article

Background/Objective: Frontline healthcare staff who contend diseases and mitigate their transmission were repeatedly exposed to high-risk conditions during the COVID-19 pandemic. They were at risk of mental health issues, in particular, psychological stress, depression, anxiety, financial stress, and/or burnout. This study aimed to investigate and evaluate the occupational stress of medical doctors, nurses, pharmacists, physiotherapists, and other hospital support crew during the COVID-19 pandemic in Saudi Arabia. Methods: We collected both qualitative and quantitative data from a survey given to public and private hospitals using methods like correspondence analysis, cluster analysis, and structural equation models to investigate the work-related stress (WRS) and anxiety of the staff. Since health-related factors are unclear and uncertain, a fuzzy association rule mining (FARM) method was created to address these problems and find out the levels of work-related stress (WRS) and anxiety. The statistical results and K-means clustering method were used to find the best number of fuzzy rules and the level of fuzziness in clusters to create the FARM approach and to predict the work-related stress and anxiety of healthcare staff. This innovative approach allows for a more nuanced appraisal of the factors contributing to work-related stress and anxiety, ultimately enabling healthcare organizations to implement targeted interventions. By leveraging these insights, management can foster a healthier work environment that supports staff well-being and enhances overall productivity. This study also aimed to identify the relevant health factors that are the root causes of work-related stress and anxiety to facilitate better preparation and motivation of the staff for reorganizing resources and equipment. Results: The results and findings show that when the financial burden (FIN) of healthcare staff increased, WRS and anxiety increased. Similarly, a rise in psychological stress caused an increase in WRS and anxiety. The psychological impact (PCG) ratio and financial impact (FIN) were the most influential factors for the staff’s anxiety. The FARM results and findings revealed that improving the financial situation of healthcare staff alone was not sufficient during the COVID-19 pandemic. Conclusions: This study found that while the impact of PCG was significant, its combined effect with FIN was more influential on staff’s work-related stress and anxiety. This difference was due to the mutual effects of PCG and FIN on the staff’s motivation. The findings will help healthcare managers make decisions to reduce or eliminate the WRS and anxiety experienced by healthcare staff in the future. Full article

Emerging digital technologies are transforming how consumers participate in financial markets, yet their benefits depend critically on the speed, reliability, and transparency of the underlying platforms. Online stock trading platforms must maintain high efficiency underload to ensure a good user experience. This paper presents performance analysis under various load conditions based on the containerized stock exchange system. A comprehensive data logging pipeline was implemented, capturing metrics such as API response times, database query times, and resource utilization. We analyze the collected data to identify performance patterns, using both statistical analysis and machine learning techniques. Preliminary analysis reveals correlations between application processing time and database load, as well as the impact of user behavior on system performance. Association rule mining is applied to uncover relationships among performance metrics, and multiple classification algorithms are evaluated for their ability to predict user activity class patterns from system metrics. The insights from this work can guide optimizations in similar distributed web applications to improve scalability and reliability under a heavy load. By framing performance not merely as a technical property but as a determinant of financial decision-making and well-being, the study contributes actionable insights for designers of consumer-facing fintech services seeking to meet sustainable development goals through trustworthy, resilient digital infrastructure. Full article