Search Results (443)

Introduction: The anonymisation of Personal Data (PD) and its release as Open Data (OD) hold considerable potential for innovation across health, research, public administration, and the economy. However, practical experiences regarding data anonymisation and OD publication remain underexplored in Germany. This study empirically investigates the current state of anonymised data practices, the barriers to implementation, and the desired support mechanisms for publishing formerly PD as OD. Methods: Embedded in a mixed-methods approach, this cross-sectional study examines research interest in the collection, processing, and use of anonymised data, as well as potential barriers and support services for the anonymisation and publication of former PD. A nationwide online survey was conducted in October–November 2024 via LimeSurvey. A total of 215 responses were included in the descriptive analysis. Results: The findings indicate limited experience with PD anonymisation and OD publication across industries. The potential added value of these processes was often not fully recognised, and data-handling responsibilities were rarely standardised. Data collectors, data protection officers, and IT departments were identified as the most frequently involved parties in these processes. Technical and educational support were the most desired forms of assistance. Discussion: To foster broader OD utilisation, stakeholders require comprehensive support. According to the sample, specific training and further education on the anonymisation and publishing process, as well as the desired software, are most important. Developing standardised process descriptions that integrate ethical and legal considerations, supported by national networks or governmental institutions, could significantly enhance the responsible and effective use of anonymised OD in Germany. Full article

Background/Objectives: Hypoxic–ischemic encephalopathy (HIE) is a common neurological outcome of perinatal asphyxia, with cerebral palsy (CP) being the most severe lasting effect. Perinatal brain injury activates the immune system and induces the release of inflammatory mediators. Matrix Metalloproteinases (MMPs) play a crucial role in neuroinflammation and neurodegeneration. This study explored the potential link between MMP2 promoter polymorphisms and the development of CP in children with a history of perinatal asphyxia. Methods: We enrolled 212 patients (130 males and 82 females) with documented perinatal asphyxia, who underwent a comprehensive neurological assessment and neuroimaging, including ultrasound and magnetic resonance imaging (MRI). We genotyped the MMP2 promoter polymorphisms rs243866, rs243865, and rs243864 using real-time polymerase chain reaction. Haplotype frequencies were calculated using Haploview software. Results: As expected, patients with HIE are more likely to develop CP (p = 0.000). In a study of 104 patients who developed CP, the frequencies of the A (rs243866), T (rs243865), and G alleles (rs243864) were nearly twice as high compared to those without CP (p = 0.008, p = 0.019, and p = 0.008, respectively). Haplotype analysis supported these findings, showing that the ATG haplotype was significantly more common among patients who developed CP (p = 0.004). Additionally, in patients with MRI-confirmed brain damage, the ATG haplotype was more frequently observed (p = 0.019). Conclusions: The ATG haplotype of the MMP2 promoter may indicate a risk factor for developing cerebral palsy (CP) in patients who experience perinatal asphyxia and could serve as a potential diagnostic predictor of CP. Full article

Background/Objectives: Hope plays an important role in coping with difficulties and is predictive of resilience, improving the skills necessary to sustain life plans and well-being, and overcoming stressful situations in adulthood and especially in older age adults. We hypothesised that the dimensions of hope, including spirituality, are connected to personal, educational, and contextual conditions, and play a mediating role in fostering resilience and well-being after stressful events; this mediating role might differ in adulthood and among the elderly. Methods: The sample consisted of 100 adults without severe pathologies and living in their own homes, aged between 50 and 86 years (M = 66.08; SD = 8.48). They completed an online survey that included the Stress Event scale, the Comprehensive State Hope Scale, the Resilience Scale for Adults, and the Well-being Profile. The data were analysed using SPSS and JAMOVI software, applying the following statistical tests: t-test, ANOVA and mediational model. Results: Stressful events influence resilience and well-being differently in adulthood and old age, with non-significant differences due to gender and living conditions. Hope mediates between stress resulting from negative life events and resilience and well-being, but this mediation involves different hope components for adults (trust) and older adults (self-realisation and perception of social support in interpersonal relationships). Spirituality is a non-significant mediator in both age stages. Conclusions: Appropriate psychological and psychosocial supports are needed to enhance the mediating potential of hope between stressful events and resilience. The results of our study help clarify which components of hope specifically promote resilience in different conditions of normal old age, differentiating them from those more beneficial for adults. Full article

The main objective of the paper is to design a data collection and processing system for a holonic system as part of an intelligent manufacturing system. The purpose was to design and experimentally verify a communication interface capable of collecting and evaluating data and communicating it with other holons. These holons represented information systems and process-level control systems. The defined methodology for the design of data collection from a holonic manufacturing system was verified on selected parts of the manufacturing holonic system. The design of a manufacturing data collection system contains a comprehensive sequence of steps and a list of necessary supporting equipment. Partial objectives include an analysis of the methods of obtaining production data from centralized and distributed control systems, and identification of differences in the methods of collection and control of centralized and distributed control systems. The core of the paper is devoted to the identification of key functional parts of the systems, the design of requirements for information manufacturing systems, and the design of holons and their information protocols in intelligent distributed manufacturing systems and in decentralized control systems. The main output is the design of an algorithm for collecting and evaluating data from holons, which also includes a unified communication protocol for a holonic production system and the technical and software equipment for solving the task. Full article

Background: Pregnancy induces substantial cardiovascular remodeling, yet whether maternal cardiac adaptation differs across ethnic groups remains unclear. Body size, ventricular geometry, and thoracoabdominal configuration may modulate key functional indices such as left ventricular ejection fraction (LVEF) and global longitudinal strain (LV-GLS). This systematic review compared echocardiographic characteristics between Asian and Western healthy pregnant women in late gestation and explored physiological mechanisms underlying observed differences. Methods: A comprehensive search of PubMed, Scopus, and EMBASE identified studies reporting transthoracic echocardiography in healthy singleton third-trimester pregnancies across Asian and Western populations. Extracted variables included anthropometry, ventricular dimensions and volumes, LVEF, and LV-GLS. Pooled estimates were calculated using inverse-variance weighting, with heterogeneity quantified using the I² statistic. Study quality was assessed with the NIH Case–Control Quality Assessment Tool. Comparative forest plots visualized population differences. Results: Twenty studies involving 1431 participants (578 Asian and 853 Western women) met inclusion criteria. Asian women consistently exhibited smaller ventricular chambers, higher LVEF, and more favorable LV-GLS. Importantly, these differences persisted after indexing LV-GLS to BSA, indicating that body-size normalization attenuates—but does not eliminate—population differences in myocardial deformation. Western women demonstrated slightly attenuated GLS despite preserved LVEF, plausibly attributable to larger cardiac size, higher wall stress, greater diaphragmatic elevation, and increased extrinsic thoracic compression. Between-study heterogeneity was substantial (I² > 95%) due to variation in imaging platforms, strain software, and population characteristics. Methodological quality was fair, with frequent lack of sample-size justification and incomplete confounder adjustment. Conclusions: Healthy Asian pregnant women display a hyperdynamic systolic phenotype, whereas Western women show a physiologically appropriate, load-related attenuation of LV-GLS with preserved LVEF. These findings highlight the need for ethnicity-associated and anatomy-aware echocardiographic reference values and support incorporating thoracic geometric indices, such as the modified Haller Index, into strain interpretation during pregnancy. Full article

Subway stations are enclosed spaces with high passenger density and complex evacuation conditions. Fires in such environments can escalate rapidly and cause severe consequences. This study proposes a dynamic risk assessment model grounded in dual symmetries. The first symmetry is a balanced “Human–Machine–Environment–Management” analytical structure. The second is a coherent model transformation from a Fault Tree (FT) to a Bayesian Network (BN). Shuanggang Station on Nanchang Metro Line 1 serves as a case study. This work establishes a comprehensive evaluation system based on 4 first-level indicators of man–machine–environment–management, 9 secondary indicators, and 27 tertiary indicators. FT analysis identified 117 minimal cuts and 14 minimal paths, pinpointing core risk nodes such as flammable materials and oxidizers, electrical equipment overheating, and fire management deficiencies. The model was then symmetrically converted into a BN using GeNle Academic 4.1 software to support dynamic probability inference. The results show that prevention measures at Shuanggang Station reduce the fire occurrence probability from 0.000249 to 0.00007 (a 71.9% reduction). The probability importance of rescue escape routes is 0.00223. This indicates that the accessibility of rescue routes constitutes a highly sensitive hazard. The symmetric framework and modeling approach offer a scientific basis for targeted fire prevention, control, and evacuation management in the Nanchang Metro and similar stations. The findings support improvements in the safety and resilience of metro operations. Full article

This study examines the readiness of the accounting and auditing profession to adopt disruptive innovations, with a particular focus on sustainable digital transformation. It investigates the factors influencing auditors’ and accountants’ intention to adopt blockchain technology (BT) as a sustainable digital infrastructure that enhances transparency, accountability, traceability, and operational efficiency. The research integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) with the third iteration of the Technology Acceptance Model (TAM3), providing a comprehensive framework for understanding the sustainable adoption of emerging technologies. A quantitative research design was employed through an online questionnaire, collecting empirical data from 394 prospective and existing blockchain users within the accounting and auditing professions in Saudi Arabia. Data validation and hypothesis testing were conducted using Structural Equation Modeling (SEM) with Smart-PLS software (version 4.1.0.8). The results reveal a strong and significant positive influence of performance expectancy (PE), effort expectancy (EE), and social influence (SI) on intention to use (IU). Additionally, PE is positively and significantly associated with job relevance (JR) and output quality (OQ). Conversely, computer self-efficacy (CSE) shows no significant impact on EE, while compatibility (CO) positively influences EE but not IU. Moreover, EE has a substantial effect on PE. These findings contribute to the growing discourse on how disruptive ICTs are reshaping the accounting and auditing profession while supporting sustainable digital transformation. The study provides practical insights for policymakers, regulators, corporate leaders, and blockchain providers seeking to leverage BT not only for technological efficiency but also to advance long-term organizational sustainability and responsible governance. Full article

This paper presents METAS VNA Tools Version 2.9.0, a metrology software suite designed to support the digital traceability chain in vector network analyzer measurements. Built on the METAS UncLib Version 2.9.0 uncertainty engine, the software enables rigorous modeling of the entire measurement process and comprehensive uncertainty evaluation. By encapsulating values, dependencies, and sensitivities in structured uncertainty objects, the software ensures that traceability and correlation information are preserved and propagated throughout complex calibration chains. This approach allows for seamless, modular uncertainty evaluation and supports the generation of digitally signed calibration certificates with embedded calibration data. The methodology enhances transparency, reproducibility, and interoperability, aligning with the goals of digital transformation in metrology. VNA Tools thus provides a robust foundation for implementing traceable, data-driven workflows across all levels of the metrological infrastructure. Full article

This study revisits a recently proposed member of the truncated positive family of distributions, referred to as the positively truncated Student’s-t distribution. The distribution retains the structure of the classical Student’s-t distribution while explicitly incorporating a kurtosis parameter, yielding a flexible three-parameter formulation that governs location, scale, and tail behavior. A closed-form quantile function is derived, allowing a novel reparameterization based on the pth quantile and thereby facilitating integration into quantile regression models. The analytical tractability of the quantile function also enables efficient random number generation via the inverse transform method, which supports a comprehensive simulation study demonstrating the strong performance of the proposed estimators, particularly for the degrees-of-freedom parameter. The entire methodology is implemented in the tpn package for the R software. Finally, two real-data applications involving PM2.5 measurements—one without covariates and another with covariates—highlight the model’s robustness and its ability to capture heavy-tailed behavior. Full article

Purpose: Remote work continues to transform both organizational structures and individual work experiences, introducing new dynamics that can simultaneously enhance or undermine employees’ mental health and overall well-being. These evolving conditions influence levels of stress, resilience and burnout within increasingly digital and flexible work environments. The present study aimed to provide a comprehensive understanding of how remote work affects occupational stress, mental health, burnout and employee well-being through a bibliometric analysis of Scopus-indexed literature complemented by a narrative synthesis of emerging research themes. Methods: A total of 185 peer-reviewed documents were retrieved from the Scopus database and analyzed using the VOSviewer software, version 1.6.20 (0) (Leiden University, Leiden, The Netherlands). The study employed performance analysis techniques to assess annual publication trends, identify the most influential authors and sources and evaluate citation impact. Furthermore, science mapping methods, including co-authorship, bibliographic coupling, co-citation, and co-occurrence analyses, were used to visualize the intellectual structure and thematic evolution of the field. Results: The co-occurrence analysis identified three major thematic clusters. We first explored how the abrupt shift to teleworking redefined employee well-being, the second examined the psychological consequences of evolving work environments on mental health and occupational stress and the third investigated the prevalence, determinants and contextual factors of occupational burnout across professional domains. Conclusions: The findings demonstrated complex interrelations among remote work, occupational stress, mental health, and burnout, highlighting that the accelerated adoption of remote work—especially during and after the COVID-19 pandemic—has substantially reshaped employees’ psychological health and work experiences. Thus, remote work emerges as a double-edged phenomenon that can either foster resilience and well-being or heighten stress and burnout, depending on the availability of supportive organizational structures, psychosocial resources, and adaptive coping mechanisms. Full article

The rapid adoption of electric vehicles (EVs) has increased the need for sustainable charging infrastructure supported by renewable energy. This study presents a comprehensive techno-economic and environmental analysis of private EV charging stations in Kuwait powered by grid-connected solar and wind systems using the HOMER Pro 3.18.4 optimization software. Four configurations—grid-only, grid–solar, grid–wind, and grid–solar–wind—were modelled and evaluated in terms of energy output, cost performance, and carbon emission reduction under Kuwait’s climatic conditions. HOMER simulated 484 systems, of which 244 were technically feasible. The optimal configuration, combining grid, 5 kW photovoltaic (PV) (BEIJIAYI 600 W panels), and a 5.1 kW AWS wind turbine, achieved a renewable fraction of 78%, reducing grid dependency by 78.1% and annual CO₂ emissions by approximately 7027 kg. Although the hybrid system required a higher initial investment (USD 7662) than the grid-only setup (USD 1765), it achieved the lowest Levelized Cost of Energy (LCOE = USD 0.017/kWh) and long-term cost competitiveness through reduced operating expenses. Sensitivity analysis confirmed the hybrid system’s robustness against ±15% variations in wind speed and ±10% changes in solar irradiance. The results highlight that hybrid solar–wind systems can effectively mitigate intermittency through diurnal complementarity, where daytime solar generation and nighttime wind activity ensure continuous supply. The findings demonstrate that integrating renewables into Kuwait’s EV charging infrastructure enhances economic viability, energy security, and environmental sustainability. The study provides practical insights to guide renewable policy development, pilot deployment, and smart grid integration under Kuwait Vision 2030’s clean-energy framework. Full article

For many historic engineering structures, including early 20th-century truss bridges, no comprehensive technical documentation has survived, making them highly vulnerable to irreversible loss. This study addresses this challenge by developing and testing a non-invasive, UAV-based photogrammetric methodology for the comprehensive documentation of the Niestępowo railway viaduct in Northern Poland. A dense geodetic control network was established using GNSS and total station measurements, providing a metrically verified reference framework for 3D reconstruction. Two photogrammetric software platforms—Bentley ContextCapture and Agisoft Metashape—were employed and comparatively evaluated in terms of processing workflow, accuracy, and model fidelity. To ensure methodological robustness, both tools were used for cross-validation of the generated 3D models and for the comparative assessment of their dimensional consistency against archival documentation. The results confirm that both platforms can produce highly accurate, photorealistic 3D models suitable for engineering inventory and heritage preservation, with Agisoft Metashape yielding slightly higher geometric precision, while Bentley ContextCapture ensured superior automation for large datasets. The generated 3D models reproduced details such as rivets, cracks, and corrosion marks with millimeter-level accuracy. The presented workflow demonstrates the potential of UAV photogrammetry as a reliable and scalable method for safeguarding cultural and technical heritage. By enabling the creation of metrically precise digital archives of historic bridges, the methodology supports future conservation, monitoring, and restoration efforts—preserving not only physical form but also the historical and engineering legacy of these structures. Full article

Background: Asset management has become crucial to organizational performance over the past three decades. Implementing an Asset Management System (AMS) can be pivotal in managing the operation, sustainability, and efficiency of both tangible and intangible organizational assets. However, many organizations still underappreciate AMSs, particularly in healthcare, where poor organization, unclear processes, and a lack of control contribute to long patient waiting times, financial losses, regulatory non-compliance, and diminished credibility. Methods: This study provides a comprehensive review of the existing body of research on AMSs, discusses AMSs in the context of healthcare, and identifies the specific healthcare areas that have most frequently been the focus of AMS research. This study applies bibliometric analysis of 16,667 documents on AMSs, complemented by a focused bibliometric analysis of a subset of 248 publications specifically addressing AMSs in healthcare. All documents, published up to the end of 2024 and indexed in the Scopus database, were analyzed to investigate the evolution of AMS research, with a particular emphasis on its applications within healthcare. The research employs several bibliometric approaches, utilizing the Python and VOSviewer software. Results: The findings highlight the rapid growth of AMS research, evolving from a niche topic into a strategic discipline that enhances predictive maintenance, efficiency, and sustainability. In healthcare, the adoption of AMSs has grown substantially, supported by the integration of artificial intelligence (AI) and the Internet of Things (IoT). Conclusions: The incorporation of these technologies has enabled more effective monitoring of medical equipment, improved oversight of critical infrastructure, and optimized the operational performance of healthcare providers. Nevertheless, significant research gaps remain concerning the direct impact of AMSs on the quality of patient care, provider coordination, and strategic decision-making. Addressing these gaps is essential not only for advancing academic knowledge but also for leveraging the full potential of AMSs to enhance healthcare delivery, improve outcomes, and support the evidence-based management of healthcare systems. Full article

CerMapp is a multi-platform and system application designed to address a critical gap in veterinary public health: the lack of a standardized, national-scale geodatabase for wildlife diseases. This gap has long hindered the effective application of GIS and remote sensing in spatial epidemiology. Currently deployed at the prototype level in Aosta Valley, NW Italy, the application’s core innovation is its ability to generate a structured, analysis-ready data repository, which serves as a foundational resource for One Health initiatives. Developed by the National Reference Center for Wildlife Diseases on the ESRI ArcGIS Survey123 platform v.3.24, CerMapp enables veterinarians, foresters, and wildlife professionals to easily collect and georeference field data, including species, health status, and photographic evidence using flexible methods such as Global Navigation Satellite System or manual map entry. Data collected via CerMapp are stored in a centralized geodatabase, facilitating risk analyses and detailed geospatial studies. This data can be integrated with remote sensing information processed on cloud platforms like Google Earth Engine or within traditional GIS software, contributing to a comprehensive and novel wildlife health registry. By promoting the rational and standardized collection of essential geospatial data, CerMapp data may support predictive disease modeling, risk assessment, and habitat suitability mapping for wildlife diseases, zoonoses, and vector-borne pathogens. Its scalable, user-friendly design ensures alignment with existing national systems like the Italian Animal Disease Information System (SIMAN), making advanced geospatial analysis accessible without requiring specialized digital skills from field operators or complex IT maintenance from institutions. Full article

Program dependence analysis plays a fundamental role in program comprehension, software maintenance, and defect detection. However, existing static approaches—such as those based on Program Dependence Graphs or Abstract Syntax Trees—struggle to model fine-grained syntactic changes and fail to capture how dependencies evolve as code changes over time. To address these limitations, this study proposes DyTSSAM, a dynamic dependency analysis model built upon the Dynamic Abstract Syntax Tree (DAST). DyTSSAM decomposes DAST into temporally ordered change subtrees to capture the minimal syntactic units of code evolution, and incorporates local–global dependency analysis to enrich node representations with heterogeneous dependency information. The model further integrates a dynamic structural-syntax layer and a temporal-semantic layer, which jointly learn dynamic syntactic structures and temporal dependency patterns through a dynamic graph neural network. Experiments conducted on real-world datasets compare DyTSSAM with seven state-of-the-art dynamic graph neural networks. Results demonstrate that DyTSSAM achieves significantly higher AUC and AP scores, improves fine-grained modeling of node- and subtree-level dependencies, and exhibits greater sensitivity in capturing dependency evolution throughout code changes. To support reproducibility and enable future research, the complete datasets, preprocessing code, and model implementation are publicly available on GitHub. These findings suggest that DyTSSAM provides an effective and scalable framework for dynamic program dependence analysis. Full article