Search Results (21,583)

Background: Outpatient drug provision is a critical component of primary healthcare systems and a key determinant of treatment continuity, adherence, and equity, yet the effectiveness of publicly funded outpatient drug programs often depends on how policies are implemented at the point of care. This study examined physician awareness, practical experience, and perceived barriers related to outpatient drug provision and drug cost compensation mechanisms in urban primary care settings in Kazakhstan. Methods: A descriptive cross-sectional survey was conducted between September and December 2024 among 380 physicians working in all 33 state-owned urban polyclinics in Almaty, using a structured author-developed questionnaire. Descriptive statistics and Pearson’s chi-square tests were applied to assess associations between physician characteristics and awareness levels. Results: Only 44.0% of physicians confirmed the existence of outpatient drug cost compensation mechanisms in their polyclinics, while 26.0% believed that no such mechanisms existed and 30.0% were unable to provide a definitive answer, indicating that 56.0% lacked accurate awareness. Limited medicine availability and recurrent shortages were frequently reported, with half of physicians advising patients to purchase medicines out of pocket. Physician awareness was significantly associated with professional experience and specialty (p < 0.001). Conclusions: These findings indicate a substantial physician-level implementation gap in outpatient drug provision, suggesting that organizational and informational barriers—rather than insufficient public financing—are the primary drivers, highlighting the need for strengthened governance. Full article

Rice blast, caused by Magnaporthe oryzae, is a devastating fungal disease threatening global rice production, with annual yield losses ranging from 10% to 30% in epidemic regions. Conventional detection methods rely on visual inspection and laboratory diagnosis, which are limited by subjectivity, time-consuming procedures, and the inability to detect early-stage infections. Hyperspectral imaging technology offers a highly promising method for detecting rice blast disease. It can capture the physiological and biochemical changes that occur in plant tissues before the appearance of visible symptoms. In this study, we propose a Convolutional-State Space Hybrid Network (CS-HybridNet) featuring a dual-branch deep learning architecture that synergistically combines a ConvNeXt-based spatial branch with a LiteMamba-based global spatial branch (which models long-range spatial dependencies with spectral embeddings). Principal component analysis was employed to reduce the dimensionality from 360 hyperspectral bands to 16 principal components, retaining 99.09% of the original information while significantly improving computational efficiency. An adaptive feature fusion module effectively integrates spatial texture features with spectral features, enabling complementary information utilization. Experimental results on a dataset comprising 166 hyperspectral images demonstrate that CS-HybridNet achieves 96.30% ± 1.38% accuracy, 98.33% precision, 95.16% recall, and an AUC–ROC value of 0.971 on the independent test set, outperforming traditional machine learning methods and existing deep learning models by 3.5–12.8% in accuracy. Ablation studies validate the effectiveness of each component. This research demonstrates the efficacy of spatial-spectral fusion architecture for automated plant disease detection and establishes a technical foundation for the development of intelligent crop disease monitoring systems. Full article

This paper proposes an approach to digitizing the environmental passport for areas where detachable parts of launch vehicles fall in Kazakhstan based on an interactive geographic information system platform and smart maps. An example is considered for zone U-4 (“Ulytau” district of the “Karaganda” region), which includes the fall zones of “Soyuz” launch vehicle blocks (IZ 26, 32, 34, 42, 56). The natural and climatic factors and hazards of the territory are analyzed: the total area of the zones under consideration exceeds 4.1 million hectares, annual precipitation varies between 218 and 289 mm, strong winds of 5.0–6.8 m/s are characteristic, and a high level of fire hazard can develop within 6–7 days. Data on fires for 2021 are provided. For an integrated assessment, a normalized system criterion, environmental sustainability indicator (0–1), has been introduced, aggregating four groups of criteria (chemical, mechanical, pyrogenic, biota) with a breakdown of contributions and calculation of uncertainty (σ and 95% CI). The system criterion of environmental sustainability map identifies local ‘hot spots’ with levels of around 0.8–1.0, while the uncertainty map shows maximums of up to 0.12–0.14 (with background values of ~0.02–0.08), which increases the validity of management decisions on monitoring and reclamation. Full article

Underground coal fires (UCFs) pose a persistent environmental and economic threat to both the built and natural worlds. In Colorado, 38 known coal fires are currently monitored by the Colorado Division of Reclamation, Mining, and Safety, many of which are in the immediate vicinity of communities and transportation infrastructure. The Axial underground coal mine fire in northwestern Colorado has been active for over 70 years and has a documented history of surface impacts, including wildfire ignition and UCF-induced slope instability near a major highway corridor. Subsurface investigations indicate active combustion in multiple coal seams, contributing to complex and evolving surface deformation. Unmanned Aerial System (UAS)-based optical surveys acquired between 2018 and 2025 were used to assess terrain changes and slope instability at the Axial site. Structure-from-motion photogrammetry was used to generate three-dimensional point clouds and orthomosaics, and surface deformation was quantified using the Multiscale Model-to-Model Cloud Comparison (M3C2) algorithm. Orthomosaic products were additionally evaluated to characterize the development of geomorphic features and cross-validate the interpretation of M3C2-derived deformation patterns. Repeat UAS surveys effectively identified changes in unstable and hazardous terrain caused by UCFs. Results reveal progressive subsidence, fracture development, and localized slope instability associated with ongoing subsurface combustion. The findings provide critical information for risk mitigation and illustrate both the capabilities and challenges of using UAS photogrammetry for long-term monitoring of geohazards associated with legacy coal mine fires. The study further emphasizes the importance of georeferencing strategies, including ground control points and real-time kinematic positioning, to ensure consistent and reliable multi-temporal change detection. Full article

In recent years, the trend toward spacecraft miniaturization has led to the widespread adoption of micro- and nanosatellites, driven by their reduced development costs and simplified launch logistics. Operating these platforms in coordinated fleets, or swarms, represents a promising approach to overcoming the inherent limitations of individual spacecraft by distributing sensing and processing capabilities across multiple units. For systems of this scale, decentralized guidance and control architectures based on so-called behavioral strategies offer an attractive solution. These approaches are inspired by biological swarms, which exhibit remarkable robustness and adaptability through simple local interactions, minimal information exchange, and the absence of centralized supervision, but their application to space scenarios is limited, if not negligible. This work investigates the feasibility of autonomous swarm maintenance subject to orbital forces, under the stringent actuation, sensing, and computational constraints typical of nanosatellite platforms. Each spacecraft is assumed to carry a single monocular camera aligned with the along-track direction. The proposed behavioral control framework enables decentralized formation keeping without ground intervention or centralized coordination. Since control actions rely on the relative motion of neighboring satellites, a lightweight relative navigation capability is required. The results indicate that complex vision pipelines can be replaced by simple blob-based image processing, although a (rough) reconstruction of elative parameters remains essential to avoid unnecessary control effort arising from suboptimal guidance decisions. Full article

This study presents and systematizes a high-reliability measurement and technological dataset suitable for prediction-based validation of the Spent Fuel Interim Storage Facility (SFISF) of the Paks Nuclear Power Plant. The primary objective of this dataset is not the validation of a general-purpose software tool, but to establish a reproducible experimental basis for the objective and quantitative validation of a three-dimensional, facility-scale heat transfer and buoyancy-driven flow model of the SFISF, developed using the finite difference method (FDM), in a passively cooled system where heat conduction, thermal radiation, and natural convection simultaneously occur. The applied measurement systems (SMAS, CTRS, and the in-house developed CFEPR), their spatial arrangement, accuracy characteristics, as well as data post-processing and the generation of model execution inputs are described in detail. Special emphasis is placed on the functional separation of the available data into initialization data, model execution data, and independent validation datasets, ensuring that model assessment does not rely on calibration or parameter fitting. Furthermore, the estimation of decay heat generated by the stored fuel assemblies is presented using both a standard correlation method (ANSI/ANS-5.1) and isotope inventory-based calculations, and the discrepancies between these approaches are treated as input uncertainties and sensitivity analysis factors. The spectral solar load is considered based on the ASTM G-173 reference spectrum, while during cloudy periods an effective irradiance estimation derived from on-site lux measurements is applied. The results indicate that the available measurement and technological information is sufficient for supporting reproducible, transparent, and quantitative validation studies of the three-dimensional numerical model of the SFISF, as well as for assessing the impact of dominant input uncertainties. Full article

Currently, marine and land oil resources have entered the high-water extraction stage. The remaining oil is dispersed, and the oil–water relationship is complex, making it increasingly difficult to extract. However, traditional electrical logging techniques are limited by the shielding effect of highly conductive steel casing, rendering them unsuitable for formation resistivity measurement in casing wells. Time-domain electromagnetic method overcomes the constraints of downhole push-off systems and casing conditions, enabling continuous measurement and acquisition of formation resistivity parameters. To overcome these limitations, this paper proposes an active compensation method based on differential measurements between specially configured coils, enabling the early response of the formation to be identified, the method enhances weak signal detection capabilities in casing formations. The coils offset part of the casing influence, while the casing background serves as baseline information. A time-domain electromagnetic instrument for metal casing resistivity measurement was developed, along with a ground water tank resistivity calibration device. The experimental results show that the instrument can effectively suppress casing response, obtain formation resistivity signals, and provide effective guidance methods for measuring formation resistivity of casing wells in the ocean and land. Full article

Background and Objectives: Serum albumin is a widely available and inexpensive biomarker that reflects nutritional status and physiological reserve. Hypoalbuminemia has been linked to poor postoperative outcomes in surgical oncology; however, its role in predicting early complications after cytoreductive surgery for ovarian cancer, as well as the potential contribution of systemic inflammatory indices in nutritionally preserved patients, remains incompletely understood. This study aimed to evaluate the predictive value of early postoperative serum albumin for early surgical complications and to explore whether inflammatory indices could offer additional prognostic information in patients with adequate albumin levels. Materials and Methods: We conducted a retrospective observational cohort study including patients undergoing cytoreductive surgery for ovarian cancer at two Italian tertiary referral centers between July 2023 and December 2025. Postoperative serum albumin was measured on the first postoperative day. Systemic inflammatory parameters were assessed using perioperative changes in neutrophils and composite indices. Early postoperative complications occurring within 30 days were recorded. Multivariable logistic regression analyses were performed, and subgroup analyses were conducted in patients with postoperative albumin ≥3 g/dL. Receiver operating characteristic (ROC) analysis was used to identify an optimal cutoff for significant inflammatory predictors. Results: A total of 121 patients were included, of whom 30 developed early postoperative complications. Patients with complications had significantly lower postoperative albumin levels than those without complications (median 2.75 vs. 3.09 g/dL; p < 0.001). In multivariable analysis, lower postoperative albumin independently predicted early complications (OR 0.26, 95% CI 0.06–0.86). In the subgroup of patients with preserved albumin levels (≥3 g/dL), a smaller postoperative neutrophil decline independently predicted complications (OR 1.56, 95% CI 1.12–2.70). A neutrophil drop cutoff of −1.15 × 10³/dL showed good specificity (81.5%) and high negative predictive value (95.7%). Conclusions: Early postoperative serum albumin is a strong predictor of early surgical complications after cytoreductive surgery for ovarian cancer. In patients with preserved nutritional status, dynamic neutrophil changes provide additional prognostic information. Incorporating low-cost metabolic and inflammatory biomarkers may enhance early postoperative risk stratification and support more personalized patient management. Full article

Video synthetic aperture radar (SAR) enables observation of moving targets by leveraging temporal information across successive frames. In particular, dynamic shadows in video SAR image sequences provide critical cues for detecting moving objects whose energy is smeared or Doppler-shifted. To achieve high-resolution imaging at a high frame rate for effective dynamic scene monitoring, video SAR systems typically operate at extremely high frequencies or even in the terahertz band, rather than the microwave band. However, terahertz video SAR suffers from significant signal attenuation due to atmospheric absorption. We present a deep learning framework to achieve high-frame-rate and high-resolution imaging for microwave video SAR systems. In this framework, the problem of microwave video SAR imaging is formulated as an image super-resolution reconstruction task for low-resolution yet high-frame-rate image sequences from microwave video SAR. We develop a simple yet effective image super-resolution reconstruction network that is completely built upon convolutional neural networks. The designed network takes a low-resolution image sequence and the corresponding high-resolution image with blurred shadows as input, and then produces a high-resolution image sequence where shadows are clearly visible. Furthermore, the network is trained in a self-supervised manner and thus does not require high-resolution image sequences with unblurred shadows as ground truth, which is appealing to practical applications. Processing results of real data from two different video SAR systems have shown good performance of the proposed approach with convincing generalization ability. Full article

Large language models (LLMs) are increasingly used in software development, yet their ability to generate and maintain large, multi-module systems through natural language interaction remains insufficiently characterized. This study presents an empirical analysis of developing a 7420-line Terminal User Interface (TUI) framework for the Ring programming language using a prompt-driven workflow with Claude Code (Opus 4.5), employing an iterative testing and corrective feedback. The system was produced through 107 prompts: 21 feature requests, 72 bug fix prompts, 9 prompts sharing information from Ring documentation, 4 prompts providing architectural guidance, and 1 prompt dedicated to generating documentation. Development progressed across five phases, with the Window Manager phase requiring the most interaction (35 prompts), followed by complex UI systems (25 prompts) and control expansion (20 prompts). Bug-related prompts covered redraw issues, event-handling faults, runtime errors, and layout inconsistencies, while feature requests focused primarily on new widgets, window-manager capabilities, and advanced UI components. Most prompts were brief (mean ≈ 258 characters; median = 207 characters), reflecting a highly iterative workflow in which the human role was limited to specifying requirements, validating behavior, and issuing corrective prompts—without writing any code manually. The resulting framework contains 28 classes, 334 methods and includes a windowing subsystem, event-driven architecture, interactive widgets, hierarchical menus, grid and tree components, tab controls, and a multi-window desktop environment. By combining quantitative prompt analysis with qualitative assessment of model behavior, this study provides empirical evidence that modern LLMs can preserve architectural coherence across iterations and support the construction of new libraries and tools for emerging programming languages, highlighting prompt-driven development as a viable methodology within software-engineering practice. Full article

Financial hardship affects 30–70% of cancer patients and is associated with worse quality-of-life outcomes and higher mortality. In response, many health systems have implemented financial navigation teams to mitigate financial hardship and provide financial guidance to cancer patients. Currently, there is a lack of standardization in financial navigation training. Our primary objective was to assess current training practices and gaps that may exist in critical information and tools for day-to-day operations for individuals providing financial navigation services. Our secondary objective was to supplement findings from the interviews with a web-based search for training resources that would be helpful in these roles. Semi-structured qualitative interviews were conducted over a video-based conferencing platform in the United States of America with nine individuals in varying roles related to financial navigation. Thematic analysis was conducted by investigators to identify common themes using a constant comparative method. Current financial navigation training practices were found to be less structured and comprehensive than desired, largely relying on experiential “on the job” learning. Participants expressed the need for more multi-dimensional training that covers insurance, cancer treatment and associated costs, financial resources, and an emphasis on developing soft skills to navigate the sensitive topics of cancer and cancer costs. The findings contribute to the development of more standardized trainings that incorporate dissemination of crucial financial information in a compassionate manner. A web-based search was also performed to create a compilation of available financial navigation training resources. Full article

Lymphatic filariasis (LF) is a mosquito-borne neglected tropical disease that causes substantial morbidity and social exclusion. Global efforts under the World Health Organization’s Global Programme to Eliminate Lymphatic Filariasis have markedly reduced prevalence, and several Pacific Island Countries and Territories (PICTs) have achieved elimination of the disease as a public health problem. However, post-validation surveillance (PVS), essential for detecting resurgence and enabling early response, has rarely been implemented, and barriers to its delivery remain poorly understood. We used two complementary qualitative approaches to identify systemic barriers and enablers to LF PVS in PICTs. First, we conducted a Nominal Group Technique followed by a structured expert elicitation involving program managers and technical staff. Data were analysed thematically and triangulated across sources. Participants identified 70 challenges which were consolidated into ten thematic domains. Pertinent barriers relate to limited leadership understanding of LF and surveillance options, inconsistent technical and financial support, and a lack of context-appropriate operational guidance. Additional challenges included limited field-ready diagnostics, procurement delays, the absence of formal mandates, and low community engagement. Enablers included embedding PVS within existing health services, leveraging trusted community networks, strengthening regional frameworks, and co-developing practical tools with countries. Sustaining LF elimination in the Pacific will require political commitment, regional collaboration, and integrated, programmatic approaches informed by recent PVS experience. Full article

Preventing inpatient falls remains challenging for healthcare institutions globally, including in Singapore. Integrating technological innovations into fall prevention measures may optimize inpatient care and improve health outcomes. A multiphase study was conducted from 2019 to 2022, employing a human-centred design (HCD) approach to develop a technology-based inpatient fall prevention system (IFPS). The four phases include (1) pre-design observations and focus groups, (2) feature prioritization and wireframe development, (3) prototype testing and safety assessments, and (4) post-design staff training and feedback collection. The developed IFPS integrated artificial intelligence (AI) video analytics for bed-exit prediction with communication devices and autonomous commode delivery to facilitate ward communication and reduce staff workload. This paper describes the development process and user evaluation of the IFPS to assess its operational usability and safety. Potential users of the IFPS, such as ward nurses and patients, suggested features for the IFPS during the pre-design phase and thereafter evaluated the system through focus group discussions and/or feedback surveys. Pre-design focus group participants (n = 24) emphasized durability and user-friendliness requirements, informing system design. When evaluating the system, nurse users (n = 39) perceived the IFPS as effective in reducing falls (65%), enabling them to perform other duties (85%), and allowing them to remain with patients without searching for a commode (64%). Patient users (n = 21) found pre-recorded messages effective (91%), though communication clarity varied. Engaging healthcare workers in IFPS development offered valuable context-based insights, highlighting the importance of addressing technology acceptance factors early to promote adoption of fall prevention technologies in acute care settings. Full article

Effective management of rye cover crops in cash-crop systems relies heavily on accurate biomass estimation. Low-altitude Unmanned Aerial Vehicle (UAV) imagery offers a promising high-resolution alternative, yet unlocking its full potential requires moving beyond basic estimation models to more integrative and explanatory models. This study obtains the measured height (MH), SPAD (Soil and Plant Analyzer Development) values, and measured dry biomass (MDB) and applies UAV remote sensing and machine learning to acquire the crop canopy height, vegetation indices (VIs), and vegetation fraction (VF) across growth stages. Among single-parameter biomass estimation models, the estimated height yields the best at the overall growth stage (R² = 0.935), whereas selected VIs perform the best at the non-seedling stage (R² = 0.851). For multi-parameters modeling, models combining height, VF, and VIs significantly outperform the single-parameter models, achieving better estimation results throughout each growth stage (Best R² = 0.951). Structural equation modeling clarifies the direct and indirect contributions of these parameters to biomass accumulation, revealing their synergistic effects. This study demonstrates the potential of UAV-based multi-parameter biomass estimation model to support more informed decisions in cover crop management and to advance broader precise agriculture practices. Additionally, the analytical framework developed here offers a transferable approach for high-resolution biomass monitoring in other crop systems. Full article

Background: Early detection of dental caries is essential for effective oral health management. Current diagnostic workflows rely heavily on radiographic imaging, which involves infrastructure requirements, workflow coordination, and resource considerations that may limit frequent use in high-throughput or resource-constrained settings. These contextual factors motivate exploration of adjunct screening concepts that could support front-end triage decisions within existing care pathways. This study evaluates, in simulation, whether modeled tooth-percussion response signals contain sufficient discriminative information to justify further translational and managerial investigation. Implementation costs, workflow optimization, and economic outcomes are not evaluated directly; rather, the objective is to assess whether the technical preconditions for a potentially scalable screening concept are satisfied under controlled in silico conditions. Methods: An in silico model of tooth percussion was developed in which enamel, dentin, and pulp/root structures were represented as a simplified layered mechanical system. Impulse responses generated from simulated tapping were used to compute the modeled surface-vibration response (enamel-layer displacement), which served as a proxy for a measurable percussion-related signal (e.g., contact vibration), rather than a recorded acoustic waveform. Carious conditions were simulated through depth-dependent reductions in stiffness and effective mass and increases in damping to represent enamel and dentin demineralization. A synthetic dataset of labeled simulated signals was generated under varying structural parameters and measurement-noise assumptions. Machine-learning models using Mel-frequency cepstral coefficient (MFCC) features were trained to classify healthy teeth, enamel caries, and dentin caries at a screening (triage) level. Results: Under baseline simulation conditions, the classifier achieved an overall accuracy of 0.97 with balanced macro-averaged F1-score (0.97). Misclassifications occurred primarily between healthy and enamel-caries categories, whereas dentin-caries cases were most consistently identified. When measurement noise and structural variability were increased, performance declined gradually, reaching approximately 0.90 accuracy under the most challenging simulated scenario. These results indicate that discriminative information is present within the modeled signals at a screening (triage) level, meaning that higher-risk categories can be distinguished probabilistically rather than with definitive diagnostic certainty. Sensitivity and specificity trade-offs were not optimized in this study, as the objective was to assess separability rather than to define clinical decision thresholds. Conclusions: Within the constraints of the in silico model, simulated tooth-percussion response signals demonstrated discriminative patterns between healthy, enamel caries, and dentin caries categories at a screening (triage) level. These findings establish technical plausibility under controlled simulation conditions and support further investigation of percussion-based screening as a potential adjunct to clinical assessment. From a healthcare management perspective, the present results address a prerequisite question—whether such signals contain sufficient information to justify translational research, rather than demonstrating workflow optimization, cost reduction, or system-level impact. Clinical validation, threshold optimization, and implementation studies are required before managerial or operational benefits can be evaluated. Full article