Search Results (13,182)

In this work, we present a neural network-based study capable of predicting faults in oil-insulated power transformers through the analysis of dissolved gases. The advantage of this study lies in using data already collected by electric power companies, which gather it to comply with international or regional standards; however, they sometimes act only after the equipment is already in a faulty condition. Therefore, the challenge in this work was data regularization, as collections typically occur at long intervals of 6 to 12 months. Furthermore, samples are often irregular, as data collection depends on factors such as weather and the availability of maintenance teams. As a result of this work, Multilayer Perceptron (MLP), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) were used to predict failures with advanced forecasts ranging from 1 to 6 months, achieving accuracies of 97.5% and 85%, respectively. Thus, these models prove to be important tools for maintenance planning, enabling adequate predictability for organizing equipment shutdowns without the need for high investments in installing tools to capture this information online and adapting substations to send data to control rooms or other analysis centers. Full article

Background/Objectives: Foot and ankle presentations during Hajj occur in a dense mass-gathering environment where prolonged walking, heat exposure, crowding, variable footwear, and limited self-care can interact with chronic disease and wound vulnerability. Previous Hajj studies have described foot injuries and diabetes-related complications, but less is known about whether simple high-risk foot documentation flags identify presentation records with higher care-pathway intensity. The primary objective was to estimate the presentation-level burden of core high-risk foot profiles among pilgrims presenting with foot and ankle conditions during Hajj 2025. Secondary objectives were to evaluate associations with a surgery-coded care-intensity indicator, hospital referral, and component heterogeneity. Methods: This observational presentation-level analysis included 3957 foot and ankle presentation records. The unit of analysis was the presentation/case record, not a unique individual pilgrim. A core high-risk foot profile was defined as diabetes, neuropathy, diabetic foot ulcer, foot ulcer, complications of open wound, or osteomyelitis. The primary outcome was a surgery-coded care-intensity indicator, defined solely from treatment documentation containing “Surgery” and interpreted as a care-pathway proxy rather than confirmed operating-room surgery. Logistic regression estimated crude and adjusted odds ratios (ORs); exploratory risk-category analyses assessed heterogeneity within the composite profile. Results: Core high-risk foot profiles were identified in 1793/3957 presentations (45.3%). The primary outcome occurred in 239/1793 high-risk presentations (13.3%) and 201/2164 non-high-risk presentations (9.3%), an absolute difference of 4.0 percentage points. The crude OR was 1.50 (95% CI 1.23–1.83; p < 0.001). The association persisted in the primary adjusted model (adjusted OR 1.47; 95% CI 1.20–1.79; p < 0.001) and in the extended clinical sensitivity model (adjusted OR 1.47; 95% CI 1.20–1.80; p < 0.001). Care pathways and secondary outcomes are summarized was also more frequent in high-risk presentations (12.2% vs. 9.8%; crude OR 1.28; 95% CI 1.05–1.57; p = 0.017). Exploratory category analysis showed that chronic-risk-only presentations had a primary outcome rate similar to non-high-risk presentations (9.0% vs. 9.3%), whereas ulcer/wound/deep-infection presentations had a higher rate (17.3%; crude OR 2.04; 95% CI 1.63–2.55; p < 0.001). Model discrimination was modest (C-statistics 0.55–0.64). Conclusions: Core high-risk foot flags were common among Hajj foot and ankle presentation records and were associated with surgery-coded care-intensity and referral documentation. However, the composite was clinically heterogeneous, the outcome was not a validated surgery endpoint, and the models were not prediction tools. These findings support cautious use of high-risk foot flags as operational prompts for assessment and pathway planning rather than as standalone clinical risk estimates. Full article

This study reconsiders the meaning of the term “miqweh” in early rabbinic literature and challenges its common rendering as “immersion pool” or “ritual bath.” Surveying biblical and Second Temple texts, it demonstrates that “miqweh” originally indicated a general “gathering of water” and was not associated with purificatory bathing. Only in early rabbinic sources did the term acquire a specialized, legal-technical sense, referring to pooled water within the narrow context of ritual purification through immersion in water. This semantic shift likely derives from rabbinic interpretation of Leviticus 11:36 and reflects broader patterns of legal abstraction in rabbinic discourse. Crucially, the study shows that “miqweh” never referred to the physical installation or structure containing the water; instead, terms such as “mǝʿārâ” (“cave”) and “bêt haṭṭǝbîlâ” (“place of immersion”) were used for such spaces. These two terms, the study tentatively suggests, likely emerged before the more abstract “miqweh” was coined. Full article

The primary aim of this study was to validate a self-assessment instrument about the organisation of the Early Childhood Education and Care (ECEC) environment and to investigate how early childhood educators perceive their educational environments, including quality, intentionality (specifically, their anticipatory considerations in planning the ECEC environment) and pedagogical dynamics. The EduIn&Out Organisation of ECEC Environments Questionnaire was completed by 802 Portuguese ECEC educators (children’s ages 3–6) and explored educators’ perceptions of various aspects of the ECEC environment, including the quality of the organisation of the space, materials and equipment (both indoor and outdoor), time management and daily routines, family and child participation, coordination with the educational team and with the centre’s leadership. It also gathers educators’ characterisation of their pedagogical dynamics (routine flow, children’s agency, and the use of indoor and outdoor contexts), associated with the quality of the educational environment, and educators’ intentionality while considering different needs and interests when organising the educational environment. The tool demonstrated good psychometric characteristics. Educators reported higher quality in time and routine organisation, but lower quality in outdoor spaces, family and child participation and coordination with the centre’s leadership. Enhanced quality was associated with more stimulating, child-centred routines that balanced indoor and outdoor activities. Overall, the characteristics of the instrument highlight its potential for supporting educators’ reflection. Full article

The increasing adoption of renewable energy sources introduces significant variability in power generation, requiring effective strategies to ensure maintain grid stability. Incentive-based demand response programs provide a practical solution for balancing supply and demand, however disputes may arise over energy data integrity. The existing solutions frequently rely on centralized authorities, exposing a single point of failure, or high costs and privacy limitation of recording granular data on-chain. To address this challenge, we propose a decentralized framework that separates cloud storage from integrity certification. This system employs a community aggregator to collect high-frequency energy measurements, store the raw data in the cloud, while anchors unique cryptographic hashes for batch of raw data to a public blockchain. This process creates an auditable and tamper-evident record of data. By recording only hashes on chain, our approach achieves privacy and scalability. Evaluation using a real-world Australian dataset confirms that the system enables transparent dispute resolution, with blockchain transaction costs consistently representing less than 0.10% of the total incentives awarded to participants. Full article

Renovations aimed at improving energy conservation in older urban residential areas are essential for sustainable urban development; however, they encounter obstacles such as energy inefficiency and issues in sustaining long-term sustainability following renovation. Based on resource-based theory and collaborative governance theory, this study investigates how organisational resilience affects the efficacy of energy-saving renovations and confirms the mediating role of resource allocation efficiency. A mixed-methods approach was used in this investigation. Grounded theory was first used to establish the components of organisational resilience. A questionnaire survey was then used to gather information from those participating in energy-efficient renovation of old urban residential complexes. System dynamics (SD) was applied for empirical validation and simulation analysis across many intervention scenarios after structural equation modelling (SEM) was used to develop and evaluate study hypotheses. The results show that rather than the support of any particular strategy, the crucial elements in improving the efficacy of energy-saving renovations are efficient interdepartmental coordination and rational budget allocation. Notably, all energy-saving renovation outcome measures in this study are based primarily on stakeholder perceptions and survey responses rather than objectively measured energy consumption data. Full article

Untreated dental caries in permanent teeth is the most frequent disease of all 371 diseases and traumas assessed by the Global Burden of Disease Study in 2021, and there are reported to be 2.24 billion cases worldwide. Demineralization is a disintegration process of minerals and apatite crystals in hard tissue, provoked by biofilm activities, dietary factors, and the micro-oral environment—the three main mechanisms of dental caries. Restoration of mineral ions in the crystal structure is defined as remineralization. Remineralization enables the deposition of new minerals within the crystal structure of demineralized enamel, aiming to increase mineral production. Environments suitable for remineralization and inhibiting demineralization could be created by using a caries prevention agent. Objectives: Providing scientific evidence regarding Aloe vera as an alternative agent for caries prevention. Materials and Method: The method used in this study is a scoping review, utilizing the PRISMA-ScR as a guideline to conduct article screening and further analysis, following a thematic analysis approach. Database searches were conducted in PubMed, EBSCOhost, and ScienceDirect, based on the keywords generated. Results: A total of 13 articles were gathered for further analysis. Conclusions: Aloe vera shows promising preliminary potential, but further standardized in vivo and randomized clinical studies are necessary to confirm its remineralizing efficacy and clarify its mechanisms of action as a cavity prevention agent. Clinical Relevance: Using Aloe vera as an alternative caries prevention agent. Full article

Introduction: AQUArestore is a three-year project focused on promoting adaptive ecological restoration strategies for river ecosystems in the vulnerable cross-border region of Portugal. The project responds to pressing environmental challenges across the territory, including severe habitat degradation, climate vulnerability, declining water security, and biodiversity loss, with particular concern for freshwater fish communities, making river restoration essential to preserve native species and freshwater ecosystem services. Objective: The project aims to develop a replicable framework for restoration of Mediterranean transboundary riverine habitats, supporting the objectives of the EU Nature Restoration Law (NRL, Regulation 2024/1991). The consortium AQUArestore will develop (1) robust restoration indicators, (2) implement living labs for restoration experimentation, and (3) establish capacity-building and training programs for technicians and citizens. Methodology: The project kick-off meeting was used to operationalize project tasks, detail the implementation calendar and milestones, and clarify responsibilities of each project member and partner institutions within the different work tasks. The meeting gathered consortium members from the coordinating institution CEF-ISA (researchers at the Instituto Superior de Agronomia) and partners WWF Portugal (an environmental NGO) and Mushmore Cooperative, each one contributing according to their respective expertise and institutional objectives. Results: The AQUArestore project kick-off meeting took place in January 2026 at ISA, Lisbon, and included a presentation of the NRL and a detailed discussion of project task development. In detail, the activities will begin with the compilation of information on previously restored sites (Task 1). This will support the development and validation of environmental and biodiversity indicators of restoration outcomes, including those linked to freshwater fish assemblages and riparian vegetation (Task 2). The project will then establish two living labs as platforms to test nature-based solutions in collaboration with stakeholders and local communities (Task 3). In parallel, AQUArestore will strengthen technical capacity through training for practitioners and public authorities (Task 4). Finally, dissemination will be supported through citizen science, communication activities, and stakeholder engagement, fostering a broader impact (Task 5). Together, these tasks provide an integrated, science-based, and participatory framework aiming to support adaptive river restoration under climate and environmental changes. Conclusions: By integrating ecological restoration, biodiversity and environmental monitoring, and stakeholder engagement, AQUArestore is expected to contribute to the recovery of Mediterranean freshwater ecosystems and improve habitat quality and connectivity for native fish communities, enhancing resilience to climate change and other anthropogenic pressures. Full article

Background/Objectives: Exercise is increasingly recognized as a modulator of host–microbiome interactions, yet its role in irritable bowel syndrome (IBS) remains poorly characterized. Methods: In this prospective, single-arm, before-and-after interventional study, we used an integrated multi-omics approach based on metataxonomics and metabolomics to assess the effects of a structured 12-week moderate aerobic exercise program in 80 patients with mild-to-moderate IBS, stratified by Global Physical Capacity Score (GPCS). Biochemical and inflammatory markers have been gathered. Results: Exercise did not alter overall microbial diversity but selectively enriched short-chain fatty acid (SCFA)-producing taxa and remodeled the volatile organic compound (VOC) profile toward a more efficient metabolic state. Notably, conventional biochemical and inflammatory markers failed to distinguish response subgroups, whereas GPCS stratification revealed distinct microbial and metabolomic trajectories. Individuals with higher baseline physical capacity had higher acetate levels and lower levels of VOCs associated with dysbiosis and oxidative stress. Conclusions: Our results suggest that baseline physical capacity is a primary determinant of the microbiome’s responsiveness to exercise, challenging the reliance on static biochemical profiling. Despite the lack of a control group and the exploratory nature of some metabolomic signals, this study provides a framework for precision exercise interventions in IBS. Our work identifies GPCS as a clinically relevant stratification tool. The full trial protocol is registered on ClinicalTrials.gov under the identifier NCT05453084. Full article

A recent generalized Bilal (G-Bilal) model demonstrates remarkable flexibility in capturing a wide spectrum of failure behaviors, including monotonic and non-monotonic (upside-down bathtub-shaped) hazard patterns, outperforming several existing models such as the Weibull, gamma, and exponential families. This paper develops several inferential frameworks for different G-Bilal parameters of life using samples gathered by improved Type-II adaptive progressive censoring. This enhanced design ensures optimal control of test duration while maintaining high inferential precision. Expressions for the model parameters, reliability, and hazard rate functions are derived, followed by the development of maximum likelihood (ML) and maximum product of spacing (MPS) estimators with their asymptotic confidence intervals using the observed Fisher information with the delta approach. Furthermore, Bayesian estimators and two associated credible intervals are proposed under independent gamma priors and computed through Markov iterations, with both ML and MPS posteriors considered. Extensive Monte Carlo experiments confirm the consistency, robustness, and precision of the proposed estimators, with Bayesian spacing-based methods exhibiting superior accuracy and coverage. The model’s practical potential is further verified through two real applications: one involving mechanical system lifetimes and another analyzing health insurance premium data, representing physical and actuarial domains, respectively. Using the introduced censoring, the proposed G-Bilal model outperforms all competing models in terms of goodness-of-fit and reliability estimates in both cases. The results underscore the G-Bilal model’s adaptability, computational stability, and empirical superiority, establishing it as a powerful tool for modern reliability and actuarial risk assessments. Full article

Monitoring urban biodiversity is essential for designing resilient and sustainable cities. Urban trees provide a wide range of ecosystem services (ESs), including air pollution reduction, urban heat island mitigation, and psychological benefits for citizens. Accurate and updated tree inventories are therefore essential tools for urban environmental monitoring. However, existing urban tree inventories are often incomplete or outdated, especially in private areas, limiting accurate ES assessment and urban planning. Earth observation satellite missions, particularly very-high-resolution multispectral (VHR-MS) imagery, offer a valuable alternative to field surveys for gathering information on urban environments. This work proposes a deep learning (DL) framework based on VHR-MS satellite imagery for the automatic generation of accurate urban tree inventories. DL models reduce human effort and save operational time by automatically learning complex representations and patterns from satellite imagery. The proposed encoder–decoder architecture extends prior point-based detection approaches by integrating a ResNet-50 backbone and a percentile-based threshold calibration procedure. Given the lack of suitable training data covering heterogeneous and densely vegetated urban environments, a dedicated dataset was constructed from VHR-MS satellite imagery acquired over the Lombardy region (Italy). The dataset encompasses a wide range of land uses and land covers, including residential and industrial zones, public parks, private gardens, and agricultural areas. Through the photointerpretation of more than 2800 images, precise coordinates for more than 50,000 manually annotated trees were obtained. The DL model is trained with point-level annotations, enabling precise localization of individual trees while reducing annotation ambiguity in dense urban contexts. On the Lombardy dataset at 30 cm/px resolution, the proposed framework achieves 86.72% Precision, 66.92% Recall, an F1-score of 75.54%, and a localization error of 1.473 m. Full article

Surface seismic data often suffer from limited bandwidth and uneven illumination, which degrade PSDM (prestack depth migration) in deep and structurally complex settings. VSP (vertical seismic profiling), particularly DAS-VSP, provides a higher signal-to-noise ratio and richer high-frequency content near the wellbore but has a limited lateral imaging aperture. To exploit the complementary strengths of these two observation systems, we propose an OVT domain (offset vector tile) joint Kirchhoff prestack depth migration workflow that integrates surface seismic and VSP data within a unified depth domain framework. The workflow includes wavelet (amplitude–phase) matching, consistent datuming, joint well–surface tomographic velocity model building using both surface CIG (common image gather) residual moveout and VSP first-arrival constraints, efficient travel time table construction based on 3D eikonal solvers, OVT domain joint migration, azimuth-dependent CIG depth correction for anisotropy, and ray-based illumination compensation for amplitude balancing. Synthetic tests demonstrate that the proposed method improves reflector continuity and increases the effective bandwidth of the joint image relative to surface-only PSDM. A field application in the northwest Sichuan Basin further shows that the joint imaging better matches well synthetics in the target interval, increasing the correlation coefficient from 0.753 (surface-only) and 0.738 (VSP-only) to 0.787 (joint) while reducing inter-azimuth CIG depth residuals to within 3 m after anisotropy correction. These results indicate that OVT domain joint imaging can enhance thin-bed resolution and near-well structural delineation, providing a practical multi-source data fusion solution for high-fidelity depth imaging in complex reservoirs. Full article

Under the national context of energy transition and energy conservation, accurate prediction of thermal load in oil and gas gathering and transportation stations is crucial for ensuring operational safety and reducing energy consumption. To address the limitations of traditional forecasting methods in handling the nonlinear, non-stationary, and long-term temporal dependencies of thermal load data, this paper proposes a hybrid deep learning model that integrates convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and an attention mechanism, namely the CNN–LSTM–Attention model. First, key influencing factors such as ambient temperature, return water temperature, and the previous hour’s thermal load were selected as model inputs through correlation analysis. Subsequently, a CNN was employed to extract spatial features from multi-source data, LSTM to capture temporal dependencies, and an attention mechanism to dynamically focus on critical operational nodes, thereby enhancing the model’s ability to perceive important features. The experimental results show that the proposed model performs excellently in heat load prediction, achieving a root mean square error of 5.98, a mean absolute error of 4.66, and a mean absolute percentage error of 9.66%, with an R-squared (R²) value of 0.9568. Its prediction accuracy and stability are significantly superior to those of the standalone CNN and standalone LSTM models. This study provides an effective algorithmic solution for precise thermal load forecasting in oil and gas gathering and transportation stations and offers insights for optimizing the applicability of deep learning models in industrial scenarios. Full article

This article examines the medium to long-term enrollment patterns of an AI-based platform designed to support children in learning robotics and participating in a national robotics competition in Romania. Drawing on registration and participation data covering students and teachers across urban and rural schools between 2020 and 2025, the study documents a consistent pattern: an initial period of high enrollment and rapid adoption followed by a steady decline over time. A key feature of the initiative is that hardware, platform access, and learning resources were provided entirely free of charge, allowing cost-related explanations for the decline to be set aside and structural and human factors to be examined directly. The paper makes two primary contributions. First, it proposes a System Dynamics framework grounded in innovation diffusion theory as a first-generation calibration model for understanding AI-enabled educational robotics adoption in a resource-constrained national context. The model is designed to be progressively tested and refined as anonymized aggregate data accumulates, and it relies exclusively on anonymized aggregated public data in accordance with GDPR requirements. Second, it advances the hypothesis that an AI-based educational platform, even one from which all financial barriers have been removed, will experience sustained enrollment decline in the absence of adequate human teacher involvement. The empirical trajectory and model outputs are consistent with this hypothesis and motivate further investigation. This represents a hypothesis-generating and framework-building paper. The framework reveals pronounced urban-rural disparities and differential outcomes by age of entry. All findings are presented as model-generated hypotheses rather than empirically demonstrated conclusions. The paper invites researchers gathering comparable data from similar initiatives in other countries to collaborate in testing and refining the model. The central conclusion is cautiously optimistic: AI may support robotics education adoption, but it is not a substitute for dedicated teachers, and without sustained investment in human capital, even a financially accessible platform is insufficient to maintain long-term enrollments. Full article

The Maule Region in Chile possesses a rich cultural heritage associated with petroglyphs created by ancient hunter-gatherer inhabitants. This rock art has suffered damage over time due to natural and anthropic causes. Fostering positive attitudes toward petroglyphs may influence behavioral intentions related to their preservation. This study evaluates an augmented reality card game developed to promote positive attitudes toward the rock art heritage of the Maule Region, examining its usability and the effects of incorporating augmented reality elements. The game achieved a System Usability Scale (SUS) score of 79.7 (SD = 14.2), corresponding to an A-grade on the Sauro-Lewis curved grading scale, indicating good usability.Participants in the game condition showed higher heritage attitudes than controls (M = 6.13, SD = 0.80, $t_{\left(24\right)}$ = −2.33, p = 0.028). Augmented reality enhanced attitudes at moderate levels of usability (B = −1.02, p = 0.043), but produced no detectable main effect in mean comparisons alone. The results indicate that the game constitutes a system with adequate usability, effective in fostering positive attitudes toward cultural heritage, and that augmented reality enhances attitudinal outcomes under conditions of moderate perceived usability. Full article