Search Results (88,757)

Sediment yield remains a major challenge in erosion-prone watersheds because it affects reservoir capacity, water quality, hydraulic infrastructure, and ecological stability. Although numerous studies have examined sediment yield estimation and sediment control, these topics are often treated separately, limiting the development of integrated watershed management strategies. Unlike many existing sediment yield review papers that focus primarily on predictive models, erosion processes, or management measures in isolation, this study provides an integrated synthesis of sediment yield estimation methods and sediment control strategies within a single watershed management framework for erosion-prone environments. The review covers empirical models, traditional sampling, physically based models, and emerging data-driven tools such as artificial intelligence, machine learning, remote sensing, and sensor-based monitoring, alongside structural, vegetative, and adaptive sediment control measures. The reviewed literature indicates three major trends: increasing integration of GIS and remote sensing with conventional models, wider use of process-based models for scenario analysis, and rapid growth of AI-based methods for real-time and nonlinear prediction. The findings further show that no single estimation or control strategy is universally applicable; performance depends strongly on watershed scale, sediment connectivity, land use, climatic regime, and data availability. Overall, the review highlights the need for integrated, adaptive, and site-specific sediment management frameworks that combine predictive modeling, monitoring technologies, and practical control interventions to improve long-term watershed resilience. Full article

This study analyzes how sustainability practices and digitalization jointly influence the financial performance of European industrial firms, emphasizing differences between Western and Eastern Europe. The empirical analysis relies on a large multi-country panel dataset and employs fixed effects regression models with robust standard errors to account for unobserved firm-specific heterogeneity and common time shocks. Environmental sustainability is captured by the environmental component of ESG scores, digitalization is measured by digital investment intensity, and financial performance is proxied by return on equity (ROE). The findings indicate that stronger environmental practices are positively associated with profitability across the full sample. Digital investment intensity also has a positive and statistically significant effect on ROE. Importantly, the interaction term between environmental performance and digitalization is positive and significant for Western European firms but not for the full sample, suggesting that the relationship between environmental practices and financial performance may vary with the level of digital investment under specific regional conditions. However, the results reveal substantial regional heterogeneity. The positive effects of environmental practices, digitalization, and their interaction are primarily driven by firms in Western Europe, whereas the relationships are weaker and statistically insignificant in Eastern Europe. These findings underline the complementary role of digital transformation and the importance of institutional and technological readiness. Full article

This study presents the experimental characterization of the volumetric and transport properties of pseudo-binary mixtures of commercial diesel and residual chicken fat methyl ester biodiesel over the temperature range of 293.15–353.15 K at 0.078 MPa. Density measurements were performed using a U-shaped vibrating-tube densimeter; kinematic viscosities were obtained using Cannon–Fenske capillary viscometers. The results show that density decreased with increasing temperature and diesel content. The excess molar volume (V^E) was negative for all mixtures; the strongest volumetric contraction took place at around x₁ ≈ 0.4–0.6. The Redlich–Kister equation and the Prigogine–Flory–Patterson (PFP) model were applied to represent excess molar volumes, with an absolute average deviation (AAD) lower than 14.92%. The thermal expansion coefficient (${\alpha }_P$) and its excess property (${\alpha }_P^E$) further confirmed the existence of non-ideal mixing driven by polar–apolar interactions. The kinematic viscosity ($\nu$) was confirmed to be temperature-dependent and increased with the amount of FAMEs; this effect can be associated with the greater polarity and structural rigidity of esters. The McAllister model also adequately reproduced the dynamic viscosity ($\eta$) with an AAD < 4.2%. Furthermore, an increase in the activation enthalpy (${\Delta H}^{\ne }$) was observed at higher FAME fractions, indicating a high energy demand is required to overcome the internal energy barrier for the initial displacement of the molecules. Full article

Railway traffic monitoring requires robust detection technologies capable of operating reliably under real-world vibration and environmental conditions. In this work, we present the design and validation of an optical vibration sensor based on a Single-mode–Multimode–Single-mode (SMS) fiber structure for Light Rail Vehicle (LRV) detection. The sensing mechanism relies on multimodal interference in the multimode fiber (MMF), where rail-induced vibrations modify the guided mode distribution and, consequently, the transmitted optical intensity. The optical signal is converted to voltage and processed through an embedded acquisition system. Additionally, we conducted tests with freight trains and maintenance trains in order to evaluate the applicability of the sensor in other types of trains besides the LRV. We conducted laboratory experiments to assess mechanical stability, sensibility, and packaging strategies, followed by supervised field tests on an operational LRV line. The recorded time-domain signal exhibited clear modulation during train passage, and first-derivative and sliding-window variance analyses were applied to reliably identify vibration events, even in the presence of slow baseline drift. In addition, frequency-domain analysis was performed by applying the Fast Fourier Transform (FFT) to the measured signal, enabling the identification of characteristic low-frequency spectral components induced by train passage. A quantitative sensitivity assessment was further carried out by correlating the integrated spectral energy (0–12 Hz) with vehicle weight, yielding a linear response with a sensitivity of 0.0017 a.u./t and coefficient of determination $R^2=0.933$. The proposed solution demonstrated stable operation using commercially available low-cost components, confirming the feasibility of SMS-based optical sensing for railway monitoring. These results indicate strong potential for future deployment in traffic safety systems and distributed sensing networks. Full article

Background: Active surveillance of small renal masses is challenged by cumulative radiation exposure from repeated CT imaging, raising long-term health concerns. Low-dose CT protocols offer a strategy to mitigate this risk but are limited by uncertainty regarding measurement accuracy and potential effects on clinical decision-making. Methods: We propose an uncertainty-aware analytical framework using a multi-observer dataset of 40 paired CT cases (low-dose vs. standard-dose). The methodology combines statistical agreement assessment (concordance correlation coefficient, intraclass correlation coefficient), multi-algorithm machine learning prediction (linear regression, random forest, gradient boosting, and SVR), and integrated uncertainty quantification to evaluate equivalence across imaging protocols. Results: Comparative analysis demonstrates near-perfect concordance between protocols (concordance correlation coefficient = 0.9930). Linear regression achieved the highest predictive performance (R² = 0.9933, MAE = 0.4239 mm, MAPE = 2.07%), outperforming more complex ensemble models, highlighting that interpretable models can achieve superior accuracy without compromising reliability. Conclusions: Clinically, the framework supports the safe adoption of low-dose CT for longitudinal tumor assessment, preserving measurement fidelity and diagnostic confidence essential for timely intervention or continued surveillance. Radiologically, it ensures robust lesion characterization across protocols while minimizing cumulative radiation exposure, particularly in younger patients. By integrating uncertainty quantification, this approach enhances transparency, informs clinical decision-making, and facilitates personalized, evidence-based surveillance strategies, promoting safer, dose-optimized imaging in the management of small renal masses. Full article

Ongoing health system reforms in Kazakhstan have transformed the working environment of primary health care (PHC) staff and may increase workload and psychosocial stress. This study aimed to assess perceived stress among PHC workers in Almaty and its associations with socio-demographic characteristics and health-related behaviors. A cross-sectional survey was conducted in October–November 2023 across all 36 state-funded PHC facilities in Almaty. General practitioners (GPs) and family nurses employed in these facilities were invited to participate. In total, 1484 respondents completed a standardized questionnaire in Kazakh or Russian administered electronically via Google Forms. Perceived stress was assessed using PSS-10, physical activity using IPAQ-SF, alcohol consumption using AUDIT-C, and tobacco use through items aligned with STEPS/GATS. Statistical analyses were performed using SAS. Associations between variables were evaluated using χ² and Fisher’s exact tests, and multivariable logistic regression models were applied. Statistical significance was set at p < 0.05. Higher stress levels were more common among GPs than nurses (OR = 2.58; p < 0.0001) and less common in younger workers (18–29 vs. 50–59: OR = 0.504; p = 0.017) and alcohol abstainers (OR = 0.587; p = 0.0004). Kazakh ethnicity showed a borderline protective association (OR = 0.472; p = 0.057), while physical activity was not a significant predictor. Perceived stress is highly prevalent in Almaty PHC and disproportionately affects GPs; younger age and alcohol abstinence are protective. The findings support prioritizing organizational measures to reduce role-related burden and maladaptive coping behaviors. Full article

This study maps European countries’ resilience to cognitive warfare by developing a cross-national composite measure. The framework integrates three pillars: information ecology, institutional-digital capacity, and socioeconomic context—drawing on a systemic perspective linking social structures to societal functions. Publicly available secondary indicators are compiled from online sources for EU (European Union) and EEA (European Economics Area) states. The dataset is examined through descriptive analysis, association testing, multivariate modelling, dimensionality reduction to derive a composite resilience score, and unsupervised clustering to produce a country typology. Indicators capture governance effectiveness, e-government maturity, public-sector AI (Artificial Intelligence) readiness, digital connectivity and infrastructure, media freedom and broader media-ecosystem quality, academic freedom, and socioeconomic vulnerabilities such as youth labour market exclusion. Results show that resilience aligns most strongly with institutional capacity and governance performance; a healthy ecology acts as a reinforcing layer. Digital infrastructure appears necessary but insufficient without capable, credible institutions and coherent public policy. Socioeconomic vulnerabilities tend to erode resilience and heighten susceptibility to hostile cognitive influence. The study concludes that policy efforts should prioritise governance integrity and effectiveness, end-to-end digital government, responsible public-sector AI capability, and safeguards for media and academic autonomy, alongside measures that improve youth inclusion. Full article

Nowadays information logistics and its integration with information systems is a competitive advantage for a company. The focus is on theoretical knowledge gained from e-maintenance environments, security measures, and objectives. In companies, it is important to conduct a risk analysis and subsequently to specify security measures. Risk analysis focuses on the creation of a Quality Function Deployment (QFD) matrix, taking into account customer requirements, with the outcome being the determination of the importance of these requirements. The result of the regression and correlation analyses confirm the research hypothesis, demonstrating a strong positive relationship (r = 0.849) between flexibility in problem solving and the implementation of security measures. The Mann–Kendall test was used to verify the trend of specified solved problems. When performed on the current data set, the test provided a variance of S = 31 and a standardized test statistic of Z_s = 2.0669. The outcomes of this article may guide organizations in refining their security strategies using customer-driven methodologies such as QFD. The field of information logistics and its integration with information systems can be beneficial for companies. Full article

Accurate and fast junction-temperature estimation in Silicon Carbide (SiC) power modules is crucial for reliable operation, health monitoring and predictive control of power electronic converters in different applications. However, direct temperature measurement inside the module is difficult and high-fidelity thermal models are often very computationally expensive for real-time implementation. This paper proposes a digital twin development approach for fast and accurate temperature estimation in all three dimensions of a SiC MOSFET power module by a combination of finite element method (FEM) modelling and neural networks. The work is especially relevant in thermal monitoring and managing power electronics converters such as renewable energy systems, energy storage systems, Electric Vehicles (EV), etc. The model incorporates a neural network trained on data generated from an FEM model built in COMSOL Multiphysics. The developed digital twin can estimate the temperature distribution, including the ten junction temperatures of the Wolfspeed EAB450M12XM3 module, with an average estimation time of 0.063 s, enabling predictive control. In order to improve practical applicability and model synchronization with the physical system, NTC-based feedback techniques are discussed (single-Temperature Coefficient (NTC) and double-NTC approaches). The proposed framework is investigated in terms of prediction accuracy and computational performance related to the FEM-generated reference data. The approach improves model reliability by adjusting the parameters of the critical digital and physical modules. The combination of FEM-based modelling and machine learning can provide a foundation for accurate, real-time thermal monitoring in power electronic modules. Full article

We present computed tomography angiography images of a rare pseudoaneurysm of the left brachial artery, a complication of idiopathic injury to the artery caused by an osteochondral exostosis of the left humerus. A 22-year-old Caucasian man with no significant medical history was admitted to the emergency department due to sudden, intense pain in his left arm, numbness, and pallor of his left forearm and hand. The patient’s consulting vascular surgeon referred him to the computed tomography (CT) laboratory for a computed tomography angiography (CTA) of the arteries of his left upper limb. In the CTA examination, at the level of the proximal segment of the left brachial artery, an excess of contrast was visualized, measuring up to approximately 1.5 × 1.2 cm in cross-sections and up to approximately 0.7 cm in the craniocaudal dimension. The CTA image was suggestive of a pseudoaneurysm of the left brachial artery. Laterally, the pseudoaneurysm was adjacent to the apex of the imaged osteochondral exostosis on the medial surface of the proximal shaft of the left humerus. A surgical procedure was performed to repair the pseudoaneurysm of the left brachial artery, including removal of the bony exostosis of the left humerus. In summary, relatively common, benign bone lesions can occasionally result in serious vascular complications. CTA is the gold standard for diagnosing these complications. Full article

Robust and transferable approaches for evaluating research capacity—whose measurable expression is reflected in research output—are essential for evidence-based science policy and strategic research management. This study develops an integrated framework to assess global scholarly capacity and regional disparities by combining semantic-similarity-based literature filtering, bibliometric mapping, dynamic performance assessment, and spatial analytical techniques into a coherent and replicable model. A Sentence-BERT model ensures thematic precision and dataset consistency, while CiteSpace 6.1.R3 is used tomap publication trajectories, thematic evolution, and influential contributors. A dynamically weighted TOPSIS model incorporates temporal variation to quantify national research capacity, and spatial analyses—including gravity center analysis, Theil index decomposition, spatial autocorrelation, gray relational analysis, and the Geographical Detector Model—identify disparity patterns and their explanatory associations. Applied to urban ecosystem security research (2001–2023), an emerging interdisciplinary field within sustainability science, the framework shows that China and the United States dominate research output, whereas European journals exert strong academic influence. The field has advanced through three stages, with increasing emphasis on ecosystem services and sustainable development. GDP, environmental pressure, and urbanization rate show the strongest explanatory associations with research capacity, and interactive effects—especially those involving GDP—exceed single-factor explanatory strength. Ecological baseline conditions such as NDVI and climate exhibit only limited associations, functioning mainly as contextual factors. Policy implications highlight four priorities: strengthening interdisciplinary and cross-regional collaboration in developing regions; promoting equity-oriented research agendas in developed regions; establishing unified definitions and validated evaluation frameworks; and advancing dynamic, systems-based approaches to ecosystem security analysis. By shifting attention from ecological status assessment to the dynamics of scientific knowledge production and research capacity, this study advances methodological foundations for research evaluation and enriches analytical approaches in urban ecosystem security, offering a generalizable framework for identifying capacity differences and supporting evidence-informed policy design. Full article

Accurate mapping of near-surface air temperature ($T_{\mathrm{air}}$) at the fine spatial resolution is required for city-scale monitoring and remains a critical challenge in Earth Observation (EO). Reliance on ground-based measurements is constrained by their sparse spatial coverage and high operational costs. We present a novel, scalable machine learning framework designed to overcome this limitation. Our method utilizes interpretable Convolutional Neural Networks (CNNs) to fuse high-resolution Landsat data, integrating both thermal and reflective spectral bands, with contextual spatiotemporal metadata. This approach allows for inference, at 30 m resolution, of $T_{\mathrm{air}}$ fields without relying on dense, localized ground monitoring networks. Our hybrid CNN architecture is optimized for spatial generalization, maintaining strong and transferable performance (station-wise $R^2\approx 0.88$) across diverse environments from humid coasts ($R^2\approx 0.89$) to arid interiors ($R^2\approx 0.84$). Although focused on a specific geographical region, our results suggest a robust and reproducible pathway for generating spatially consistent temperature fields from globally available EO archives, directly supporting urban heat island mitigation, climate policy development, and high-resolution public health assessment worldwide. Full article

Background/Objectives: Recent guidelines have emphasized the importance of early mobilization and rehabilitation of patients following cardiac surgery. However, studies on the optimal targets and prescription methods for phase I cardiac rehabilitation (CR) are lacking. This study aimed to evaluate the feasibility and utility of an early phase 1 submaximal cardiopulmonary exercise test (CPET) using a recumbent ergometer in patients who have undergone cardiac surgery. Methods: Twenty ambulatory patients who underwent cardiac surgery between December 2021 and February 2023 were referred to the CR department on the fifth postoperative day, and a CR program was initiated. The program was conducted five times a week, with hour-long sessions consisting of warm-up exercises, resistance training, aerobic exercises, and a cool-down period. A recumbent ergometer-based submaximal CPET was performed approximately nine days after the surgery, prior to discharge. Participants initiated the test at 0 W, and the workload was increased by 20 W after 2 min. During the test, researchers evaluated parameters including submaximal peak values of oxygen consumption (VO₂), metabolic equivalents of task, respiratory exchange ratio (RER), blood pressure, heart rate (HR), and rating of perceived exertion (RPE). The grip strength test, 6 min walk test (6MWT), Korean Activity Scale/Index (KASI), EuroQol-5 dimension (EQ-5D), and short-form 36-item health survey (SF-36) values were also measured prior to discharge. Results: Twenty patients (75% male, average age 62.50 ± 1.99 years) underwent CPET at a median of 9.0 (8.0; 12.5) days postoperative. The average exercise duration of the CPET was 411.75 ± 168.25 s. During the test, their submaximal peak VO₂ was 12.32 ± 0.75 mL/kg/min (corresponding to 46.65 ± 2.08% of VO₂ max). The submaximal peak RER was 1.01 (0.98–1.12), and the submaximal peak RPE was 15.00 ± 0.51. Furthermore, the submaximal peak HR was 111.8 ± 3.76 beats/min (equivalent to 70.95 ± 2.09% of age-predicted maximal HR). After adjustment for age and sex, statistically significant positive correlations were observed between the submaximal peak VO₂ and 6MWT, squat endurance test, KASI, EQ-5D, and the physical component summary (PCS) of the SF-36 questionnaire. The 6MWT, squat endurance test, KASI, and PCS of SF-36 showed a correlation coefficient (r) of 0.522 (p = 0.026), 0.628 (p = 0.005), 0.586 (p = 0.011), and 0.546 (p = 0.019), respectively. No significant cardiac events, such as ST elevation/depression or hemodynamic instability, were observed during the test. Conclusions: Our findings suggest that performing recumbent ergometer-based CPET during early phase 1 CR is safe and feasible. These results highlight the potential of recumbent ergometer-based CPET as a valuable tool for guiding the appropriate prescription of early CR programs following hospital discharge in patients undergoing cardiac surgery. Full article

Background: Main risk factors associated with the development of sarcopenia (coexistence of muscle mass loss and dysfunction) are a sedentary lifestyle coupled with obesity. Associated mitochondrial dysfunction leads to energy deficits and perturbations in the balance between protein synthesis and degradation, thereby triggering muscle dysfunction or atrophy. Aside from exercise, which is challenging to implement and maintain, particularly in women, treatments for diminishing sarcopenia are scarce. The objective of the present study was to evaluate the effect of the flavanol (−)-epicatechin (EC) in a hypercaloric diet-induced obese female rat model. Muscle strength and endurance, as well as relative mitochondrial DNA content in skeletal muscle, were assessed. Methods: Female rats were fed a hypercaloric diet to induce obesity, as evidenced by increases in body weight, Lee index, and lipid profile alterations, and by abdominal fat accumulation, and to promote a sarcopenic phenotype. Functional tests of grip strength and mobility (treadmill) were performed. Mitochondrial relative content was evaluated by measuring the ratio of mtDNA/nuclear DNA, and the expression of genes related to mitochondrial biogenesis (Pgc1-α, Tfam), fusion (Mfn1 and Opa1), fission (Drp1 and Fis1), and mitophagy (Pink1 and Pkn), and function; citrate synthase and Ucp3 were also evaluated. Results: A significant decrease in mobility and strength was observed in obese female rats, accompanied by reduced mitochondrial numbers, activity, and dynamics, but not by changes in muscle size or weight. Treatment with EC induced mitochondrial biogenesis and positive changes in mitochondrial dynamics (fission and fusion) and activity, as measured indirectly by changes in citrate synthase and Ucp3 expression. Discussion: Results reinforce the potential of EC as a modulator of mitochondrial function in dysfunctional conditions associated with obesity, thereby attenuating the mechanisms underlying sarcopenia. Full article

Reliable video transmission over error-prone channels remains a significant challenge due to the inherent trade-off between compression efficiency and noise resilience in conventional systems. To address these issues, this paper introduces a novel quantum Fourier transform (QFT)-based framework that integrates video compression and transmission within a unified quantum frequency-domain representation. The framework converts video data into a classical bitstream and maps it onto multi-qubit quantum states with variable encoding sizes (n), enabling flexible control over compression levels. Through the application of the QFT, these states are transformed into the frequency domain, where only selected coefficients are transmitted to reduce bandwidth requirements. At the receiver, the transmitted components are used to reconstruct the full representation, followed by inverse transformation and decoding to recover the video sequence. The performance of the proposed framework is evaluated using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and video multi-method assessment fusion (VMAF). The results demonstrate that increasing the number of qubits enables exponential compression, achieving ratios up to $2^n:1$, while maintaining high reconstruction quality under ideal transmission conditions. However, higher-qubit configurations exhibit increased sensitivity to channel noise, leading to a more rapid degradation as the signal-to-noise ratio decreases. In contrast, lower-qubit configurations provide improved robustness, maintaining more stable reconstruction quality under noisy conditions, albeit with reduced compression efficiency. Among the evaluated configurations, the two-qubit system achieves an effective trade-off, providing a compression ratio of $4:1$ while maintaining strong visual and structural fidelity along with enhanced resilience to channel impairments. Full article