Search Results (21,351)

The continued migration of physicians from independent practice to affiliation with larger entities has garnered significant scrutiny. These affiliation models include hospitals and health systems, payers and corporate entities, and management services organizations, which may or may not be private equity (PE)-backed. Data on the impact of different physician affiliation models on cost of care is limited. We examined the relationship between provider affiliation model, site of care (SOC), and cost of care for certain high-volume procedures in procedure-intensive specialties for both Medicare and commercial insurance. We found that hospital-affiliated physicians are least likely—and PE-affiliated physicians are most likely—to provide care in lower-cost settings. For both Medicare and commercial insurance, SOC contributes meaningfully to procedure unit price, which is consistently greater in hospital-based settings. These findings suggest that the physician affiliation model and associated SOC cost differentials contribute materially to healthcare expenditures. As the Medicare cost differentials are set by statute and regulations, strategies such as site-neutral payments are needed to mitigate the monetary impact of historical and future physician practice migration. Full article

Background and Objectives: Peptide receptor radionuclide therapy (PRRT) using radiopharmaceuticals labelled with Lutetium-177 is currently a therapeutic option for patients with advanced neuroendocrine neoplasms overexpressing somatostatin receptors (SSTRs). One promising option that has gained interest for PRRT is using alpha-emitting radioisotopes such as Actinium-225. The aim of this study was to perform a systematic review and meta-analysis on the efficacy and safety of radioligand therapy with Actinium-225 DOTATATE in advanced, metastatic or inoperable neuroendocrine neoplasms. Materials and Methods: A comprehensive literature search of studies on radioligand therapy with Actinium-225 DOTATATE in neuroendocrine neoplasms was carried out. Three different bibliographic databases (Cochrane Library, Embase, and PubMed/MEDLINE) were screened up to May 2025. Eligible articles were selected, relevant data were extracted, and the main findings on efficacy and safety are summarized through a systematic review. Furthermore, proportional meta-analyses on the disease response rate and disease control rate were performed. Results: Five studies (153 patients) published from 2020 were included in the systematic review. The pooled disease response rate and disease control rate of radioligand therapy using Actinium-225 DOTATATE were 51.6% and 88%, respectively. This treatment was well-tolerated in most patients with advanced, metastatic or inoperable neuroendocrine neoplasms. Conclusions: Radioligand therapy with Actinium-225 DOTATATE in advanced, metastatic or inoperable neuroendocrine neoplasms is effective with an acceptable toxicity profile and potential advantages compared with SSTR-ligands labelled with Lutetium-177. Currently, the number of published studies on this treatment is still limited, and results from multicenter randomized controlled trials are needed to translate this therapeutic option into clinical practice. Full article

Coal miners’ unsafe behavior is the primary reason for accidents. This research aims to examine the effect of male coal miners’ shared work crew identity on their safety behavior. A 2-2-1 multilevel mediation model is established based on social identity theory and safety climate theory. To validate the model, a paper-and-pencil survey with male coal miners was carried out in Henan Province, China. A total of 212 valid responses from male coal miners nested in 53 work crews were secured, and Mplus was used to analyze the data. Results show that work crew safety climate fully mediates the effect of male coal miners’ shared work crew identity on their safety behavior. In theory, the findings support that social identity brings a safety climate. In practice, the findings highlight that making safety part of work crew norms improves male coal miners’ safety behavior. Limitations and future research are also discussed. Full article

Three-dimensional imaging technologies are increasingly used in breast reconstructive and plastic surgery due to their potential for efficient and accurate preoperative assessment and planning. This study systematically evaluates the accuracy and consistency of six commercially available 3D scanning applications (apps)—Structure Sensor, 3D Scanner App, Heges, Polycam, SureScan, and Kiri—in reconstructing the female torso. To avoid variability introduced by human subjects, a silicone breast mannequin model was scanned, with fiducial markers placed at known anatomical landmarks. Manual distance measurements were obtained using calipers by two independent evaluators and compared to digital measurements extracted from 3D reconstructions in Blender software. Each scan was repeated six times per application to ensure reliability. SureScan demonstrated the lowest mean error (2.9 mm), followed by Structure Sensor (3.0 mm), Heges (3.6 mm), 3D Scanner App (4.4 mm), Kiri (5.0 mm), and Polycam (21.4 mm), which showed the highest error and variability. Even the app using an external depth sensor (Structure Sensor) showed no statistically significant accuracy advantage over those using only the iPad’s built-in camera (except for Polycam), underscoring that software is the primary driver of performance, not hardware (alone). This work provides practical insights for selecting mobile 3D scanning tools in clinical workflows and highlights key limitations, such as scaling errors and alignment artifacts. Future work should include patient-based validation and explore deep learning to enhance reconstruction quality. Ultimately, this study lays the foundation for more accessible and cost-effective 3D imaging in surgical practice, showing that smartphone-based tools can produce clinically useful scans. Full article

In alignment with global educational trends, Australia has adopted a pluralistic approach to initial teacher education (ITE), encompassing traditional university-based programs, employment-integrated models and vocational training routes. This diversification of pathways has emerged as a strategic response to persistent workforce challenges, including chronic shortages, uneven distribution of qualified educators, and limited demographic diversity within the profession. Rather than supplanting conventional ITE models, these alternative pathways serve as complementary options, broadening access and enhancing system responsiveness to evolving societal and educational needs. The rise in non-traditional routes represents a deliberate response to the well-documented global teacher shortage, frequently examined in comparative educational research. Central to their design is a restructuring of traditional program elements, particularly duration and delivery methods, to facilitate more flexible and context-sensitive forms of teacher preparation. Such approaches often create opportunities for individuals who may be excluded from conventional pathways due to socioeconomic constraints, geographic isolation, or non-linear career trajectories. Significantly, the diversity introduced by alternative entry candidates has the potential to enrich school learning environments. These educators often bring a wide range of prior experiences, disciplinary knowledge, and cultural perspectives, contributing to more inclusive and representative teaching practices. The implications for student learning are substantial, particularly in disadvantaged communities where culturally and professionally diverse teachers may enhance engagement and academic outcomes. From a policy perspective, the development of flexible, multifaceted teacher education pathways constitutes a critical component of a sustainable workforce strategy. As demand for qualified teachers intensifies, especially in STEM disciplines and in rural, regional and remote areas, the role of alternative pathways is likely to become increasingly pivotal in achieving broader goals of equity, quality and innovation in teacher preparation. Full article

With the growing number and diversity of network attacks, traditional security measures such as firewalls and data encryption are no longer sufficient to ensure robust network protection. As a result, intrusion detection systems (IDSs) have become a vital component in defending against evolving cyber threats. Although many modern IDS solutions employ machine learning techniques, they often suffer from low detection rates and depend heavily on manual feature engineering. Furthermore, most IDS models are designed to identify only a limited set of attack types, which restricts their effectiveness in practical scenarios where a network may be exposed to a wide array of threats. To overcome these limitations, we propose a novel approach to IDSs by implementing a combined dataset framework based on an enhanced hybrid principal component analysis–Transformer (PCA–Transformer) model, capable of detecting 21 unique classes, comprising 1 benign class and 20 distinct attack types across multiple datasets. The proposed architecture incorporates enhanced preprocessing and feature engineering, followed by the vertical concatenation of the CSE-CIC-IDS2018 and CICIDS2017 datasets. In this design, the PCA component is responsible for feature extraction and dimensionality reduction, while the Transformer component handles the classification task. Class imbalance was addressed using class weights, adaptive synthetic sampling (ADASYN), and edited nearest neighbors (ENN). Experimental results show that the model achieves 99.80% accuracy for binary classification and 99.28% for multi-class classification on the combined dataset (CSE-CIC-IDS2018 and CICIDS2017), 99.66% accuracy for binary classification and 99.59% for multi-class classification on the CSE-CIC-IDS2018 dataset, 99.75% accuracy for binary classification and 99.51% for multi-class classification on the CICIDS2017 dataset, and 99.98% accuracy for binary classification and 98.01% for multi-class classification on the NF-BoT-IoT-v2 dataset, significantly outperforming existing approaches by distinguishing a wide range of classes, including benign and various attack types, within a unified detection framework. Full article

The growing adoption of IoT applications has led to increased use of low-power microcontroller units (MCUs) for energy-efficient, local data processing. However, deploying deep neural networks (DNNs) on these constrained devices is challenging due to limitations in memory, computational power, and energy. Traditional methods like cloud-based inference and model compression often incur bandwidth, privacy, and accuracy trade-offs. This paper introduces a novel Decentralized Distributed Sequential Neural Network (DDSNN) designed for low-power MCUs in Tiny Machine Learning (TinyML) applications. Unlike the existing methods that rely on centralized cluster-based approaches, DDSNN partitions a pre-trained LeNet across multiple MCUs, enabling fully decentralized inference in wireless sensor networks (WSNs). We validate DDSNN in a real-world predictive maintenance scenario, where vibration data from an industrial pump is analyzed in real-time. The experimental results demonstrate that DDSNN achieves 99.01% accuracy, explicitly maintaining the accuracy of the non-distributed baseline model and reducing inference latency by approximately 50%, highlighting its significant enhancement over traditional, non-distributed approaches, demonstrating its practical feasibility under realistic operating conditions. Full article

The hydrological response of a watershed is strongly influenced by the spatiotemporal dynamics of rainfall. Rainfall events of similar magnitude can produce markedly different flood processes due to variations in the spatiotemporal patterns of rainfall, posing significant challenges for flood forecasting under complex rainfall scenarios. Traditional methods typically rely on high-resolution or synthetic rainfall data to characterize the scale, direction and velocity of rainstorms, in order to analyze their impact on the flood process. These studies have shown that storms traveling along the main river channel tend to exert the greatest impact on flood processes. Therefore, tracking the movement of the rainfall center along the flow direction, especially when only rain gauge data are available, can reduce model complexity while maintaining forecast accuracy and improving model applicability. This study proposes a machine learning-based time-variable instantaneous unit hydrograph that integrates rainfall spatiotemporal dynamics using quantitative spatial indicators. To overcome limitations of traditional variable unit hydrograph methods, a pre-training and fine-tuning strategy is employed to link the unit hydrograph S-curve with rainfall spatial distribution. First, synthetic pre-training data were used to enable the machine learning model to learn the shape of the S-curve and its general pattern of variation with rainfall spatial distribution. Then, real flood data were employed to learn the actual runoff routing characteristics of the study area. The improved model allows the unit hydrograph to adapt dynamically to rainfall evolution during the flood event, effectively capturing hydrological responses under varying spatiotemporal patterns. The case study shows that the improved model exhibits superior performance across all runoff routing metrics under spatiotemporal rainfall variability. The improved model increased the simulation qualified rate for historical flood events, with significant rainfall center movement during the event from 63% to 90%. This study deepens the understanding of how rainfall dynamics influence watershed response and enhances hourly-scale flood forecasting, providing support for disaster early warning with strong theoretical and practical significance. Full article

This study investigates the application of the Analytic Hierarchy Process (AHP) as a decision-support mechanism for managing complex sustainability issues in industrial settings, specifically within the framework of circular economy principles. Focusing on a case from the brewery sector, developed under the EU ECOFACT initiative, this research evaluates ten distinct configurations for the must cooling process. These alternatives are assessed using environmental, economic, and technical criteria, drawing on data from life cycle assessment (LCA) and life cycle costing (LCC) methodologies. The findings indicate that selecting an optimal scenario involves balancing trade-offs among electricity and water consumption, operational efficiency, and overall environmental impacts. Notably, Scenario 3 emerges as the most balanced option, consistently demonstrating superior performance across the primary evaluation criteria. The use of AHP in this context proves valuable by introducing structure and transparency to a multifaceted decision-making process where quantitative metrics and sustainability objectives intersect. By integrating empirical industrial data with an established multi-criteria decision approach, this study highlights both the practical utility and existing limitations of conventional AHP, particularly its diminished ability to discriminate between alternatives when their scores are closely aligned. These insights suggest that hybrid or advanced AHP methodologies may be necessary to facilitate more nuanced decision-making for circular economy transitions in industrial environments. Full article

Background: Gum chewing is a common habit among young adults, often promoted for its oral health and psychological benefits. However, as a repetitive and non-functional activity, it is also considered a potential risk factor for temporomandibular disorder (TMD), particularly when practiced chronically. The aim of this study was to evaluate whether excessive gum chewing is associated with a higher prevalence of TMD among young adults presumed to be under elevated academic stress based on their demographic characteristics. Methods: Participants were examined in Krakow, Poland, using the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) protocol. Participants completed a structured questionnaire assessing gum-chewing frequency, duration, and chronicity. Associations between chewing behaviors and TMD presence were analyzed using univariate logistic regression (α = 0.05). Results: This study included young adults 66 participants aged 19–30. TMD was diagnosed in 55 participants (83.3%), including muscular disorders (n = 9; 16.4%), articular disorders (n = 10; 18.2%), and combined muscular–articular disorders (n = 38; 57.6%). More than 70% of participants reported chewing gum for over five years. No statistically significant associations were found between TMD occurrence and the frequency, duration, or chronicity of gum chewing (p > 0.05). Conclusions: These findings suggest that, in the absence of other contributing factors, gum chewing may not independently contribute to TMD development. The elevated TMD prevalence may reflect confounding variables such as high academic stress, narrow age distribution, or female predominance. However, the limited sample size limits statistical power, particularly for detecting subtle effects potentially distorted by other variables. Additionally, the cross-sectional nature of this study precludes causal interpretation. Further studies in larger and more heterogeneous populations are recommended. Full article

To address the challenges posed by weak early fault signal features, strong noise interference, low diagnostic accuracy, poor reliability when using single information sources, and the limited availability of high-quality samples in practical applications for permanent magnet synchronous motor (PMSM) bearings, this paper proposes an early bearing fault diagnosis method based on Hippopotamus Optimization Variational Mode Decomposition (HO-VMD) and weighted evidence fusion of current–vibration signals. The HO algorithm is employed to optimize the parameters of VMD for adaptive modal decomposition of current and vibration signals, resulting in the generation of intrinsic mode functions (IMFs). These IMFs are then selected and reconstructed based on their kurtosis to suppress noise and harmonic interference. Subsequently, the reconstructed signals are demodulated using the Teager–Kaiser Energy Operator (TKEO), and both time-domain and energy spectrum features are extracted. The reliability of these features is utilized to adaptively weight the basic probability assignment (BPA) functions. Finally, a weighted modified Dempster–Shafer evidence theory (WMDST) is applied to fuse multi-source feature information, enabling an accurate assessment of the PMSM bearing health status. The experimental results demonstrate that the proposed method significantly enhances the signal-to-noise ratio (SNR) and enables precise diagnosis of early bearing faults even in scenarios with limited sample sizes. Full article

Africa’s rapid urbanization often exceeds the capacity of governments to provide essential services and infrastructure, exacerbating structural inequalities and exposing vulnerable populations to serious health risks. This paper examines the case of Douala, Cameroon, to demonstrate that health inequities in African cities are not simply the result of urban growth but are shaped by spatial inequities, historical legacies, and systemic exclusion. Disadvantaged neighborhoods are particularly impacted, becoming epicenters of health crises. Using a mixed-methods approach combining spatial analysis, household surveys and interviews, the study identifies three key findings: (1) Healthcare services in Douala are unevenly distributed and dominated by private providers, which limits access for low-income residents. (2) Inadequate infrastructure and environmental risks in informal settlements lead to a higher disease burden and an overflow of demand into better-equipped districts, which overwhelms public health centers across the city. (3) This structural mismatch fuels widespread reliance on informal and unregulated care practices. This study positions Douala as a microcosm of broader public health challenges in rapidly urbanizing African cities. It highlights the need for integrated urban planning and health system reforms that address spatial inequalities, strengthen public health infrastructure, and prioritize equity—key principles for achieving the third Sustainable Development Goal (ensuring good health and well-being for all residents) in sub-Saharan Africa. Full article

ABG analysis is the gold standard for assessing acid–base balance, oxygenation, and ventilation in critically ill patients, but it is invasive and associated with patient discomfort and potential complications. Venous blood gas (VBG) analysis offers a less invasive alternative, although its clinical utility remains debated. This review evaluates the current evidence on VBG analysis, exploring its correlation with ABG, clinical applications, and limitations. Studies show a strong correlation between ABG and VBG for pH and a good correlation for bicarbonate and base excess in most cases, while the correlation for pCO₂ remains controversial. Predictably, pO₂ values differ significantly due to oxygen consumption gradients between the arterial and venous blood. VBG analysis is especially valuable for initial assessments, monitoring therapeutic responses, and guiding resuscitation in intensive care settings. It is not merely an alternative to ABG but a complementary tool that can provide unique insights, such as mixed venous oxygen saturation (SvO₂) or indices that require combined ABG and VBG data, like the pCO₂ gap. This review highlights the diagnostic equivalence of VBG in appropriate contexts and advocates for its use when arterial sampling is unnecessary or impractical. Furthermore, VBG analysis could enhance patient care by enabling the timely, less invasive assessment of hemodynamic and metabolic conditions. Future research should focus on refining interpretation algorithms and expanding the clinical applications of VBG to fully realize its potential in critical care practice. Full article

Hydrogen is widely recognized as a key enabler of the clean energy transition, but the lack of safe, efficient, and scalable storage technologies continues to hinder its broad deployment. Conventional hydrogen storage approaches, such as compressed hydrogen storage, cryo-compressed hydrogen storage, and liquid hydrogen storage, face limitations, including high energy consumption, elevated cost, weight, and safety concerns. In contrast, solid-state hydrogen storage using carbon-based adsorbents has gained growing attention due to their chemical tunability, low cost, and potential for modular integration into energy systems. This review provides a comprehensive evaluation of hydrogen storage using carbon-based materials, covering fundamental adsorption mechanisms, classical materials, emerging architectures, and recent advances in computationally AI-guided material design. We first discuss the physicochemical principles driving hydrogen physisorption, chemisorption, Kubas interaction, and spillover effects on carbon surfaces. Classical adsorbents, such as activated carbon, carbon nanotubes, graphene, carbon dots, and biochar, are evaluated in terms of pore structure, dopant effects, and uptake capacity. The review then highlights recent progress in advanced carbon architectures, such as MXenes, three-dimensional architectures, and 3D-printed carbon platforms, with emphasis on their gravimetric and volumetric performance under practical conditions. Importantly, this review introduces a forward-looking perspective on the application of artificial intelligence and machine learning tools for data-driven sorbent design. These methods enable high-throughput screening of materials, prediction of performance metrics, and identification of structure–property relationships. By combining experimental insights with computational advances, carbon-based hydrogen storage platforms are expected to play a pivotal role in the next generation of energy storage systems. The paper concludes with a discussion on remaining challenges, utilization scenarios, and the need for interdisciplinary efforts to realize practical applications. Full article

Plant growth monitoring is a complex and challenging task, which depends on a variety of environmental variables, such as temperature, humidity, nutrient availability, and solar radiation. Advances in optical sensors have significantly enhanced data collection on plant growth. These developments enable the optimization of agricultural practices and crop management through the integration of artificial vision techniques. Despite advances in the application of these technologies, limitations and challenges persist. This review aims to analyze the current state-of-the-art methodologies for using artificial vision and optical sensors in plant growth assessment. The systematic review was conducted following the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Relevant studies were analyzed from the Scopus and Web of Science databases. The main findings indicate that data collection in agricultural environments is challenging. This is due to the variability of climatic conditions, the heterogeneity of crops, and the difficulty in obtaining accurately and homogeneously labeled datasets. Additionally, the integration of artificial vision models and advanced sensors would enable the assessment of plant responses to these environmental factors. The advantages and limitations were examined, as well as proposed research areas to further contribute to the improvement and expansion of these emerging technologies for plant growth assessment. Finally, a relevant research line focuses on evaluating AI-based models on low-power embedded platforms to develop accessible and efficient decision-making solutions in both agricultural and urban environments. This systematic review was registered in the Open Science Framework (OSF). Full article