Search Results (29,181)

Background and Objectives: Health systems increasingly require workforce governance approaches that move beyond formal qualifications and make physician skills more visible for planning, education, quality management, and resource allocation. However, evidence remains limited on how physician skill information is organisationally documented and used within hospitals, particularly in small health systems. This study examined how information on physician skills is organisationally documented, interpreted, and used for workforce governance across all Latvian university hospitals, and identified organisational, cultural, and digital barriers to systematic skills monitoring. Materials and Methods: A qualitative, exploratory, multi-institutional design was adopted. Semi-structured interviews were conducted with 12 stakeholders from human resources, education, quality, and finance across all three Latvian university hospitals. Data were analysed using reflexive thematic analysis, with reporting guided by COREQ and SRQR. Results: Eight interrelated themes were identified across three domains. Current practices were fragmented and primarily focused on formal qualifications rather than verifiable practical skills. Internally delivered education was partially documented, whereas externally acquired skills remained largely outside organisational systems. Skill gaps were usually identified reactively through incidents, complaints, or managerial observation. Training needs were determined through decentralised channels, and skill assessment depended heavily on local leadership judgement. Participants also highlighted financial blind spots, weak digital infrastructure, governance ambiguity, cultural resistance, and limited staffing capacity as barriers to implementation. At the same time, respondents consistently reported that improved skill visibility could support strategic workforce planning, targeted education, patient safety, better financial justification, and greater organisational transparency. Conclusions: Latvian university hospitals appear to recognise the organisational value of physician skill visibility but currently lack integrated systems for capturing and using such information systematically. The findings support the need for a context-sensitive, phased approach to skills-based workforce governance and inform a data-informed conceptual framework (Three-Layer Skills Governance Model) for aligning regulatory expectations, organisational processes, and individual skill records in small health systems. Full article

Background: Whether financial deepening and trade integration support or hinder environmental sustainability in hydrocarbon-dependent economies remains contested. Methods: This study examines the relationships among financial inclusion, energy intensity, trade openness, and CO₂ emissions per capita in Saudi Arabia for 1980–2020. The empirical strategy combines ARDL bounds testing, FMOLS, DOLS, CCR robustness, Toda–Yamamoto causality, and a battery of structural-break tests comprising Zivot–Andrews unit-root tests, Bai–Perron sup-F tests, and Chow tests. To address the mechanical correlation between carbon productivity and GDP, the per capita emissions specification (LNCP) is used as the primary outcome; carbon productivity (LNES) is reported for robustness. The small-sample sub-period results are stress-tested using ridge regression, residual-bootstrap confidence intervals, a GDP-augmented (scale-control) specification, and a break-date sensitivity analysis. Results: Cointegration is established. The Chow test identifies a significant break in the cointegrating relationship at 2001 (F = 7.36, p < 0.001 for LNCP), supported by the Zivot–Andrews endogenous-break dates for the financial-inclusion series (2000) and trade-openness series (2005), and by the Bai–Perron sup-F test (sup-F = 26.37 at 1990, exceeding the 1% Andrews critical value). Sub-sample re-estimation around 2001 shows that energy intensity, urbanisation, and trade openness are robust drivers of per capita emissions only after the break, while financial inclusion is statistically insignificant in both regimes once the GDP–carbon-productivity mechanical relationship is removed. Conclusions: The Saudi finance–environment relationship is structurally unstable, and policy assessments based on full-sample averages can be misleading. The evidence is best read as describing regime-dependent, conditional long-run associations rather than as identifying structural causal effects. By exposing the interactions, synergies, and trade-offs among financial deepening (SDG 8), energy efficiency (SDG 7), sustainable consumption and production (SDG 12), and climate action (SDG 13), the study shows how this descriptive quantitative evidence can inform—rather than directly identify—an instrument-level policy discussion. The findings are consistent with a Vision 2030 mix that prioritises energy efficiency and green-finance reform, with implications for SDG Targets 7.3, 8.10, 12.2, and 13.2 across oil-exporting economies. Full article

Air pollution from particulate matter (PM2.5 and PM10) poses a serious public health risk in urban environments, particularly in areas with heavy vehicular traffic. Against this backdrop, the present study proposes an Internet of Things (IoT)-based system designed to support air quality monitoring and evidence-based decision-making regarding PM2.5 and PM10 concentrations, integrating low-cost sensors with a machine learning prediction module. The study follows an experimental-applied design with a quantitative–comparative approach. Its scientific contribution is organized around an integrated IoT-ML framework addressing a concrete gap in the literature: the lack of local empirical evidence regarding which family of machine learning algorithms delivers the greatest accuracy, stability, and computational efficiency for particulate matter forecasting in mid-altitude urban environments using low-cost sensors. On one hand, the framework proposes and deploys a four-node IoT network for continuous PM2.5 and PM10 monitoring in high-traffic urban microenvironments—representing one of the first sustained deployments with low-cost, high-temporal-resolution sensors (10-minute intervals) in Arequipa, Peru. On the other hand, the study presents the most extensive benchmarking reported in the local literature: a systematic evaluation of 44 machine learning algorithms under homogeneous experimental conditions, covering classical statistical models, traditional machine learning techniques, deep learning architectures, and hybrid approaches, along with an analysis of ensemble learning strategies using Ridge stacking and K-Fold cross-validation. This unified comparative analysis—applying consistent metrics (MAE, RMSE, R², and MAPE), the same prediction horizon, and a shared dataset—provides replicable empirical evidence that had not previously been reported for the urban context of Arequipa. The results show that traditional statistical models perform poorly overall, while tree-based and boosting algorithms consistently achieve R² values above 0.90 for both pollutants. Ensemble models, particularly stacking with Ridge regression and cross-validation, yielded the strongest overall performance, demonstrating greater robustness and prediction stability. Explainability criteria were also incorporated, enabling an assessment of each base model’s individual contribution and identifying the variables most relevant to the prediction process. The methodological contribution provides future researchers with a rigorous reference framework for algorithm selection in environmental IoT systems. Taken together, the findings demonstrate that combining low-cost IoT networks with advanced machine learning and ensemble learning techniques constitutes an effective, scalable, and cost-efficient alternative for air quality monitoring, predictive analysis, and the support of informed mitigation strategies in urban environments. Full article

This study investigates the effectiveness of common thermal, climate, and envelope features in predicting annual site energy use intensity (site EUI) for different types of residential buildings in the USA. A proposed multi-level data approach that consists of regression algorithms and feature analysis has been implemented to derive models from different sets of features related to thermal, envelope, and climate, respectively. Feature set analysis is conducted using correlation analysis methods besides chi-square testing (CHI) and gain ratio (GR) methods to offer interpretable global features rankings. Models were developed using regression-based algorithms (linear, lasso, and ridge) under a 10-fold cross-validation on different distinct sets of features besides permutation feature importance (PFI) analyses to validate the models in terms of root mean squared error (RMSE). The novelty of this study lies in the comparison of feature groups and the evaluation of their individual and incremental contributions to site EUI prediction. Results against the WiDS Datathon 2022 building energy dataset demonstrate consistently ranked climate and thermal indicators (accumulated annual heating degree days (AAH) and accumulated annual cooling degree days (AAC), and heating dominance (HD), cooling dominance (CD), snowfall, and extreme temperature days) as the most informative predictors among the evaluated feature groups. The model with the best performance has an RMSE value of about 38.68; however, from the low Coefficient of determination (R²) values, it can be noted that yearly climatic conditions and building envelope characteristics cannot be only used to account for the variation in site EUIs on their own, thus showing the need to consider other factors. Full article

Fungal communities associated with the phyllosphere can influence plant health, stress responses, and disease dynamics in managed crop systems. However, limited information is available on fungal microbiome structure across phyllosphere compartments of Eucalyptus cinerea cultivated for cut foliage production. In this study, fungal communities (including epiphytic and endophytic fungi) associated with leaf, stem, and bark tissues of intensively managed E. cinerea grown in Ireland were characterised using ITS amplicon sequencing. Samples were collected from five trees, with tissues pooled by compartment to generate 15 biological samples. Following quality control and denoising, 405 fungal amplicon sequence variants (ASVs) were retained for analysis. Observed richness, Shannon and Simpson indices, and Faith’s phylogenetic diversity differed among compartments, with bark exhibiting higher values than leaf and stem tissues (p < 0.05). PERMANOVA analysis indicated that both compartment (R² = 0.239, p = 0.002) and tree identity (R² = 0.451, p = 0.002) significantly influenced fungal community composition. Bark communities were dominated by Diaporthe (52.9%), Peniophora (12.8%), and Talaromyces (10.4%), whereas leaf and stem communities were characterised primarily by Vishniacozyma and Sporobolomyces. Differential abundance analysis identified 26 and 23 differentially abundant ASVs between bark and leaf, and bark and stem tissues, respectively, whereas no significant differences were detected between leaf and stem communities. Weighted UniFrac analyses further revealed separation of bark-associated communities from photosynthetic tissues. These findings demonstrate compartment-associated variation in fungal community structure within the phyllosphere of managed E. cinerea and highlight the importance of considering both host-level and tissue-level effects in plant microbiome studies. This study provides a baseline assessment of fungal assemblages associated with commercially managed Eucalyptus under Irish growing conditions and supports future investigations into the functional significance of these microbial communities for plant health and resilience. Full article

Despite the broad scientific consensus on anthropogenic climate change, the rejection or contestation of this consensus remains socially relevant. This study adopts a qualitative approach to understand climate denial as a process by analyzing individual trajectories of adherence to climate misinformation. Twelve semi-structured interviews were conducted with individuals who expressed positions of denial, minimization, or contestation of the dominant scientific explanation of climate change. The findings indicate that questioning of the climate consensus most often emerges from informal exposure to digital content, particularly on social media and online platforms. These initial exposures are subsequently reinforced by closed informational ecosystems, often organized around private groups, where alternative criteria of credibility are consolidated, and dissenting figures are valued as legitimate authorities. A systematic delegitimization of institutional science was also observed, frequently associated with perceived political or economic agendas. A central finding of the study is the emergence of selective trust in science, in which scientific knowledge is accepted in domains such as medicine, biology, and technology but rejected in climatology. Additionally, the analyzed discourses reveal strong resistance to refutation and the absence of clear criteria for revising previously held positions. The study contributes to research in Environmental Education and Climate Literacy by demonstrating that climate denial should be understood as a socio-epistemic phenomenon rather than merely as a deficit of scientific knowledge. Full article

Sustainable agricultural development in natural heritage sites poses a challenge, requiring food security without compromising the conservation of ecosystems and their outstanding universal values (OUV). The Galapagos Islands, recognized as a Natural World Heritage, have problems of scarce water and arable land, compounded by anthropogenic pressures such as high population and tourism growth and dependence on food imports. The objective of this research is to evaluate the environmental impacts of implementing agricultural greenhouses in the Galapagos by applying a traditional environmental matrix alongside a UNESCO World Heritage approach, integrated with a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, to formulate strategies for strengthening local agriculture without compromising ecosystems. This study employed a semi-quantitative methodological approach, integrating three key aspects: (i) a baseline of agricultural information and water availability on the islands; (ii) an integrated Environmental Impact Assessment (EIA) approach to greenhouse implementation; and (iii) sustainable agricultural development and environmental impact mitigation strategies. The results of the traditional EIA and the UNESCO approach through the OUV showed negative impacts classified as insignificant to moderately significant. For the evaluated design, these impacts can be managed through the active participation of academia, the community, and government entities. However, their scalability depends on a more in-depth analysis of the potential long-term risks associated with the availability of natural resources, microplastic pollution, and the use of agrochemicals. Among the proposed strategies, the importance of monitoring water and soil quality and of agricultural and environmental education campaigns in the community was highlighted. This study presents agricultural greenhouses as well-known alternatives for food self-sufficiency, adapted to the realities of the island territory and the objectives of ecosystem conservation. The proposed methodological approach can be applied in protected areas to promote conservation and sustainable agricultural production. Full article

Fixed-bottom or floating offshore structures are the foundations, platforms, and associated infrastructure that allow for oil and gas production systems, offshore wind turbines, and cabling. The remote nature of these structures and the harsh environment with high variability in wind, waves, currents, and weather make construction activity very difficult and unpredictable; the cost of variation in the schedule can lead to high construction vessel and personnel costs. The adoption of artificial intelligence using trends observed in historical data can help achieve more accurate construction costs and schedule predictions, reducing the capital expenditure cost of installation. A resource-significant activity, sometimes called a resource-critical activity or high-resource-demand activity, is an activity on a construction or project schedule that consumes a disproportionately large share of one or more resources compared with others. Plant Design Modelling (PDM) is a digital process that creates and manages a detailed 3D model of a building’s physical and functional characteristics and semantic information, such as cost and schedule. PDM serves as a single source of truth for multidisciplinary activities and, therefore, serves as a rich data source for various construction applications, including project scheduling and cost estimation. Neural networks (NNs), a subset of machine learning algorithms inspired by the human brain, excel at identifying patterns in complex datasets and making predictions, such as forecasting costs based on non-linear relationships and historical trends. Data from an offshore structure modification project were extracted from Aveva’s Everything PDM, focusing on installation activities to create a dataset for machine learning model training. The structured data extracted exhibit non-linear patterns; therefore, linear, regularised linear, robust linear, and the ensemble (tree-based) models and supervised neural network models with varied architecture and hyperparameter values were evaluated and compared. The best performance was obtained using the deep-optimised ANN model. The result obtained is consistent with previous studies. The neural network models show a superior ability to predict the non-linear nature of offshore construction activities’ time. Full article

PM₁₀ filters provide short-term quantitative information on particle-bound potentially toxic elements (PTEs), whereas mosses integrate deposition and accumulation over longer periods but do not provide air-volume-normalised concentrations. Their combined use may therefore provide a more complete assessment of atmospheric PTE deposition. The study aimed to assess whether active moss biomonitoring and filter-based PM₁₀ monitoring provide complementary information on atmospheric deposition of PTEs under comparable exposure conditions. During the six-month campaign in Opole, PM₁₀ was collected during repeated 24 h sampling events, while three moss species: Pleurozium schreberi, Sphagnum fallax, and Dicranum polysetum were exposed cumulatively. PTE concentrations were determined by ICP-MS; particle-size descriptors, including Q₁₀, Q₅₀, and Q₉₀, were analysed for a subset of filters, whereas net concentration change and RAF were calculated relative to identically processed unexposed moss controls. Spearman correlation, PCA, and Bray–Curtis dissimilarity were used for data analysis. The material retained on the PM₁₀ filters was dominated by Fe, Zn, and Pb, whilst elevated peak values for Cd, Zn, and Pb indicated episodic enrichment in some samples. In mosses, Pb and Co showed the most consistent relative enrichment, while mean RAF exceeded 1.0 for five elements in P. schreberi and two elements each in D. polysetum and S. fallax. PCA separated PM₁₀ from moss profiles, with the first two components explaining 80.4% of the variance, while PM₁₀-moss Bray–Curtis distances ranged from 0.75 to 0.81. The results indicate that PM₁₀ filters and mosses record different but complementary aspects of the atmospheric PTE signal. The simultaneous use of both methods allows the atmospheric PTE signal to be interpreted at two levels: the short-term composition of PM₁₀ material retained on the filters, and the long-term retention and accumulation of elements within the moss matrix. Full article

Global Navigation Satellites Systems (GNSSs) have become a predominant feature in the digital life of citizens, and they provide positioning services in various applications including pedestrian and vehicular navigation. In urban environments with the presence of buildings and another obstacles, GNSS positioning may be unreliable because of non-line-of-sight (NLOS) conditions, and the classification of observed satellite visibility between LOS and NLOS may improve GNSS receivers to improve their performance to provide the positioning services. In this context, machine learning algorithms using features like signal noise ratio, pseudorange, elevation angle, and others have been applied to this problem both in supervised and unsupervised mode. Because the ground truth information on LOS/NLOS conditions may not always be available, unclustering algorithms have been applied for unsupervised classification, but the classification performance is still limited. This paper proposes an ensemble approach where different clustering algorithms, both historical and recently introduced in the literature, are combined to improve the LOS/NLOS classification accuracy. Even if the ensemble approach manages to achieve a significant improvement, a novel and more sophisticated approach is proposed in this paper, where the contributions of each clustering algorithm are weighted. The optimal values of the weights are estimated using various Meta-Heuristics Algorithms (MHA) on a subset of GNSS data where the ground-truth information is available (i.e., labeled data set). In a subsequent step, the performance of the optimal weighted clustering ensemble is evaluated. The approach is applied to a recent public data set with 57 satellites, where it is shown to outperform the specific clustering approaches by a large margin (more than 7%). The Meta Heuristics Algorithm (MHA)s have similar performance, with the Dynamic Opposition Grey Wolf Optimization (DOLGWO) having a minor advantage against the other MHAs. Full article

Clostridioides difficile infection (CDI) is a leading healthcare-associated infection worldwide, causing significant morbidity, mortality, healthcare burden, and costs. Clinical manifestations range from mild, self-limiting diarrhea to severe, life-threatening complications such as toxic megacolon and septic shock. Early identification of patients at high risk of severe disease is essential to guide clinical decision-making and optimize therapy. This narrative review summarizes recent epidemiological data, current trends, and known risk factors as clinical context for severity prediction and then examines the utility and limitations of biomarkers that may predict CDI severity, including inflammatory, hematological, fecal, renal, and immune-response biomarkers. While some markers are already used in guideline-based assessment or routine clinical practice (e.g., C-reactive protein, white blood cell count, serum creatinine), they have limited specificity. Other markers emerging from CDI research, including procalcitonin, interleukins, and presepsin, may provide complementary prognostic information. The key challenge is not simply to identify additional biomarkers but to determine which biomarkers are clinically useful, at which stage of CDI progression, and in which patients they add value beyond conventional severity criteria. Validated predictive models integrating combinations of these biomarkers with clinical and microbiological data are needed to support early risk stratification and therapeutic decision-making at the time of diagnosis. Full article

Background/Objectives: Over the past decade, an academic hospital in Montréal has progressively integrated patient partnership into quality improvement committees and peer support. In January 2024, this approach was extended by appointing two patient partners to the Steering Committee, a strategic governance body. This study aimed to describe their integration, examine perceived effects and limitations from patient partners’ and executives’ perspectives, and formulate recommendations for similar initiatives. Methods: An in-depth qualitative case study was conducted between August 2024 and April 2025. Semi-structured interviews were carried out with Steering Committee members, including the two patient partners, and with the former Chief Executive Officer. Data were analyzed using thematic content analysis to identify themes related to implementation, participation, perceived contributions, and organizational conditions. Results: Integrating patient partners into the Steering Committee was unanimously perceived as relevant and value-adding. Their presence reintroduced the patient perspective, grounded deliberations in lived experience, reinforced the hospital’s mission, supported shared understanding, and encouraged simplification of complex issues. Challenges constrained more active participation, including insufficient clarity regarding roles and objectives; variable access to information due to confidentiality; technical language and acronyms; meeting formats that did not systematically create space for patient partners’ input; and incomplete institutional recognition. Variation across departments also emerged. Conclusions: Integrating patient partners into a Steering Committee is a promising governance innovation, but deliberate organizational adjustments are required. Co-constructed expectations and roles, strengthened onboarding and ongoing support, formalized information-access modalities, improved facilitation and plain-language practices, and stronger symbolic and practical recognition are needed to sustain meaningful participation. Full article

MicroRNA (miRNA) dysregulation is a crucial pathogenic factor that extensively participates in the occurrence and progression of various human diseases, especially cancers. Identifying unknown miRNA–disease connections is essential for understanding disease pathogenesis and improving clinical treatment strategies. Traditional biological experiments are often expensive and technically restricted, so computational prediction has become a widely used auxiliary research tool. In this study, we develop a novel predictive model called Attribute-Aware Message Passing Graph Convolutional Network (AAMPGCN) to identify potential miRNA–disease associations. The advantage of AAMPGCN lies in integrating miRNA and disease attribute information into the message-passing process: it partitions the miRNA–disease heterogeneous graph that incorporates miRNA functional similarity, disease semantic similarity, and Gaussian interaction kernel similarity into attribute-homogeneous subgraphs, while restricting high-order message propagation within each subgraph. This mechanism effectively filters cross-attribute noise, preserves the discriminability of miRNA and disease embeddings during deep convolution, and is thus well-adapted to miRNA–disease heterogeneous networks. The AAMPGCN prioritizes miRNA and disease attributes, aggregating messages specifically among nodes with similar attribute characteristics that are relevant to miRNA–disease interactions. Experimental results show that the AAMPGCN model achieves AUC and AUPR values of 94.06 and 93.52 on the HMDD2.0 dataset, which outperforms existing methods. The proposed AAMPGCN provides a new and effective method for miRNA–disease association prediction, and also offers theoretical support for the research on disease molecular mechanisms and the screening of clinical therapeutic targets. Full article

Background and Objectives: Knee osteoarthritis (OA) is a prevalent musculoskeletal disorder. This study aims to assess the hip-knee-ankle anatomical alignment in patients with OA at different stages. Materials and Methods: Radiological images of 200 OA patients were analyzed to measure parameters such as femoral and tibial lengths (both anatomical and mechanical), abductor length, hip center length, femoral offset, collum femoris length, and shaft angle using ImageJ software (version 1.53k; National Institutes of Health, Bethesda, MD, USA). Results: The female-to-male ratio was 2.17:1. Male participants exhibited greater values for femoral and tibial lengths, abductor length, hip center length, femoral offset, collum femoris length, and collum femoris shaft angle, whereas females showed higher Q angle, hip–knee–ankle angle, mechanical lateral distal femoral angle, mechanical medial proximal tibial angle, and femoral angle measurements. Significant differences in both linear and angular parameters were observed among age groups. Q angle, hip–knee–ankle angle, femoral mechanical axis shaft angle, plateau angle, and ankle tilt angle values were significantly higher in individuals aged 62–75 years. According to Kellgren–Lawrence staging, significant increases were observed in Q angle, hip–knee–ankle angle, femoral mechanical axis shaft angle, plateau angle, and ankle tilt angle in advanced-stage disease (Stage 4) (p < 0.05). Although mechanical lateral distal femoral angle, femoral Q angle, tibial Q angle, condylar plateau angle, and tibiotalar angle showed numerical differences across disease stages, these findings did not reach statistical significance. Conclusions: Comprehensive assessment of lower-extremity alignment may provide complementary information regarding biomechanical changes associated with knee osteoarthritis progression and could support individualized clinical follow-up and treatment planning. Full article

This paper proposes an advanced metering infrastructure (AMI)-informed hierarchical energy management framework for coordinated operation of electric vehicles (EVs), photovoltaic (PV) systems, and battery energy storage systems (BESS) in campus microgrids. The proposed two-layer architecture integrates a soft actor–critic (SAC) deep reinforcement learning (DRL) agent in the upper layer with a receding horizon model predictive control (MPC) optimizer in the lower layer. The key novelty is an AMI-to-control pipeline that transforms historical 15 min smart-meter measurements into operational flexibility features and embeds them into a hierarchical SAC–MPC architecture, where the DRL layer provides adaptive coordination and the MPC layer enforces grid, storage, and EV-service constraints. The proposed framework using the real-world Pecan Street data (15 min resolution) of 73 homes across Austin, Texas and California (2014–2019) achieves a 53.1% cost reduction and a 25.7% peak demand reduction when compared with uncontrolled charging, and the proposed framework outperforms MPC-only (50.9%), DRL-only (−5.2%), and rule-based (5.1%) baselines. The statistically significant contributions of network-aware constraints, demand-response activation, and predictive look-ahead horizon are statistically significant (n = 10 independent runs) contributions (p = 0.001). The state representation informed by AMI offers directional cost improvement (+8.4%, p = 0.055) with 11% faster convergence of training. The zero network constraint violation is observed in all evaluation scenarios and the average MPC solve time is around 150 ms, which is much less than the 15 min sampling period. Sensitivity analyses show that the hierarchical DRL–MPC architecture remains computationally feasible across EV penetration, seasonal, and forecast-uncertainty scenarios. However, BESS provided no net economic benefit under the evaluated energy-only TOU tariff, increasing weekly cost by $15.25 and peak grid demand by 14.2 kW. Break-even analysis indicates that demand charges of approximately $9.9/kW per month are required for BESS to become cost-effective in the proxy system, highlighting that storage value depends strongly on tariff design and peak-demand objective formulation. Full article