Search Results (43,935)

PCNL treatment is often associated with complications of hemorrhagic or infectious origin, which can result in prolonged hospitalization. This study aims to develop predictive models using machine learning (ML) techniques to anticipate these outcomes. Multiple ML algorithms—including Logistic Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting—were evaluated on separate validation and test datasets. The Random Forest model achieved the highest predictive performance for hospitalization need (AUC 0.726/0.736) and infectious complications (AUC 0.799/0.735). Threshold adjustment was applied to increase sensitivity, reducing false negatives. The interpretability of the models was ensured through SHAP analysis, identifying clinically meaningful variables. Risk factors for both hospitalization and infectious complications models included nephrostomy drainage, a neutrophils percentage higher than 80, Guy’s score of grade 4, leukocytes level higher than 15 or lower than 4.5, and balloon dilation, while protective features included tubeless intervention, easy localization of a stone, negative culture, and microorganism results. However, no model achieved acceptable performance for predicting hemorrhagic complications, likely due to limited data. These results suggest that AI-based models can contribute to risk stratification after PCNL. Further experiments with larger, multi-center datasets are needed to confirm these findings and improve the generalizability of the models. Full article

(1) Background: The development of Generative Artificial Intelligence (GenAI) is transforming knowledge-intensive domains such as Education. However, Large Language Models (LLMs), which serve as the foundational components for GenAI tools, are trained on static datasets, often producing misleading, factually incorrect, or outdated responses. Our study explores the performance gains of Retrieval-Augmented LLMs over baseline LLMs while also identifying the trade-off opportunity between smaller-parameter LLMs augmented with user-specific data to larger parameter LLMs. (2) Methods: We experimented with four different LLMs, each with a different number of parameters, to generate outputs. These outputs were then evaluated across seven lexical and semantic metrics to identify performance trends in Retrieval-Augmented Generation (RAG)-Augmented LLMs and analyze the impact of parameter size on LLM performance. (3) Results and Discussions: We have synthesized 968 different combinations to identify this trend with the help of different LLM sizes/parameters: TinyLlama 1.1B, Mistral 7B, Llama 3.1 8B, and Llama 1 13 B. These studies were grouped into two themes: RAG-Augmented LLM percentage improvements to baseline LLMs and compelling trade-off possibilities of RAG-Augmented smaller-parameter LLMs to larger-parameter LLMs. Our experiments show that RAG-Augmented LLMs demonstrate high lexical and semantic scores relative to baseline LLMs. This offers RAG-Augmented LLMs as a compelling trade-off for reducing the number of parameters in LLMs and lowering overall resource demands. (4) Conclusions: The findings outline that by leveraging RAG-Augmented LLMs, smaller-parameter LLMs can perform better or equivalently to large-parameter LLMs, particularly demonstrating strong lexical improvements. They reduce the risks of hallucination and keep the output more contextualized, making them a better choice for knowledge-intensive content in academic and research sectors. Full article

Agricultural markets are increasingly exposed to global risks as climate change intensifies and macro-financial volatility becomes more prevalent. This study examines the dynamic interconnection between major agricultural commodities—soybeans, corn, wheat, rough rice, and sugar—and key uncertainty indicators, including climate policy uncertainty, global economic policy uncertainty, geopolitical risk, financial market volatility, oil price volatility, and the U.S. Dollar Index. Using a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with monthly data, we assess both internal spillovers within the commodity system and external spillovers from macro-level uncertainties. On average, the external shock from the VIX to corn reaches 12.4%, and the spillover from RGEPU to wheat exceeds 10%, while internal links like corn to wheat remain below 8%. The results show that external uncertainty consistently dominates the connectedness structure, particularly during periods of geopolitical or financial stress, while internal interactions remain relatively subdued. Unexpectedly, recent global disruptions such as the COVID-19 pandemic and the Russia–Ukraine conflict do not exhibit strong or persistent effects on the connectedness patterns, likely due to model smoothing, stockpiling policies, and supply chain adaptations. These findings highlight the importance of strengthening international macro-financial and climate policy coordination to mitigate the propagation of external shocks. By distinguishing between internal and external connectedness under climate stress, this study contributes new insights into how systemic risks affect agri-food systems and offers a methodological framework for future risk monitoring. Full article

Falls from height are a critical safety concern in the construction industry, underscoring the need for effective identification of high-risk worker behaviors near hazardous edges for proactive accident prevention. This study aimed to address this challenge by developing an improved action recognition model. We propose a novel dynamic spatio-temporal graph convolutional network (CoG-STGCN) that incorporates a center of gravity (CoG)-aware mechanism. The method computes global and local CoG using anthropometric priors and extracts four key dynamic CoG features, which a Multi-Layer Perceptron (MLP) then uses to generate modulation weights that dynamically adjust the skeleton graph’s adjacency matrix, enhancing sensitivity to stability changes. On a self-constructed dataset of eight typical edge-related hazardous behaviors, CoG-STGCN achieved a Top-1 accuracy of 95.83% (baseline ST-GCN: 93.75%) and an average accuracy of 94.17% in fivefold cross-validation (baseline ST-GCN: 92.91%), with significant improvements in recognizing actions involving rapid CoG shifts. The CoG-STGCN provides a more effective and physically informed approach for intelligent unsafe behavior recognition and early warning in built environments. Full article

During the continuous operation of trailing suction hopper dredger (TSHD), equipment workload exhibits significant time-varying characteristics. Maintaining dynamic symmetry between power generation and consumption is crucial for ensuring system stability and preventing power supply failures. Key challenges lie in dynamic perception, accurate prediction, and real-time power management to achieve this equilibrium. To address this issue, this paper proposes and constructs a “prediction-driven dynamic power management method.” Firstly, to model the complex temporal dependencies of the workload sequence, we introduce and improve a dilated convolutional long short-term memory network (Dilated-LSTM) to build a workload prediction model with strong long-term dependency awareness. This model significantly improves the accuracy of workload trend prediction. Based on the accurate prediction results, a dynamic power management strategy is developed: when the predicted total power consumption is about to exceed a preset margin threshold, the Power Management System (PMS) automatically triggers power reduction operations for adjusfigure loads, aiming to maintain grid balance without interrupting critical loads. If the power that the generator can produce is still less than the required power after the power is reduced, and there is still a risk of supply-demand imbalance, the system uses an Improved Grey Wolf Optimization (IGWO) algorithm to automatically disconnect some non-critical loads, achieving real-time dynamic symmetry matching of generation capacity and load demand. Experimental results show that this mechanism effectively prevents generator overloads or ship-wide power failures, significantly improving system stability and the reliability of power supply to critical loads. The research results provide effective technical support for intelligent energy efficiency management and safe operation of TSHDs and other vessels with complex working conditions. Full article

Background/Objectives: Maternal depression, anxiety, perceived stress, and resilience are recognized determinants of perinatal health, yet routine psychological screening is still uncommon in Romanian obstetric practice. This study examined how these four psychological factors relate to preterm birth, gestational hypertension, intra-uterine growth restriction (IUGR), and low birth weight in primiparous women. Methods: In a cross-sectional study at a tertiary maternity center in Timișoara (February 2024–February 2025), 240 women at 20–28 weeks’ gestation completed the Edinburgh Postnatal Depression Scale (EPDS), Generalized Anxiety Disorder-7 (GAD-7), Perceived Stress Scale-10 (PSS-10), and Connor–Davidson Resilience Scale-25 (CD-RISC-25). Obstetric outcomes were abstracted from medical records. Pearson correlations described bivariate associations; multivariate logistic regression assessed independent effects after mutual adjustment. Results: Preterm birth occurred in 21% of pregnancies, gestational hypertension in 17%, IUGR in 15%, and low birth weight in 21%. Higher EPDS, GAD-7, and PSS-10 scores correlated positively with each complication (r = 0.19–0.36; p < 0.02), whereas CD-RISC-25 scores showed inverse correlations (r = −0.22 to −0.29; p ≤ 0.012). In the fully adjusted model, GAD-7 remained the only independent psychological predictor of the composite obstetric outcome (β = 0.047; 95% CI 0.010–0.083; p = 0.013). Perceived stress approached significance; depression and resilience were no longer significant after adjustment. Conclusions: Generalized anxiety was the most robust psychological determinant of adverse obstetric outcomes, with perceived stress, depression, and lower resilience showing contributory roles at the unadjusted level. Incorporating brief instruments such as the GAD-7, PSS-10, and CD-RISC-25 into routine prenatal care could facilitate early identification of at-risk pregnancies and inform targeted preventive interventions. Full article

Introduction: Asthma is a chronic airway inflammatory disease characterized by variable airflow limitation and intermittent symptoms. In well-controlled asthma, auscultation and spirometry often appear normal, making diagnosis challenging. Moreover, bronchial provocation tests carry a risk of inducing acute bronchoconstriction. This study aimed to develop a non-invasive, objective, and reproducible diagnostic method using machine learning-based lung sound analysis for the early detection of asthma, even during stable periods. Methods: We designed a machine learning algorithm to classify controlled asthma patients and healthy individuals using respiratory sounds recorded with a digital stethoscope. We enrolled 120 participants (60 asthmatic, 60 healthy). Controlled asthma was defined according to Global Initiative for Asthma (GINA) criteria and was supported by normal spirometry, no pathological auscultation findings, and no exacerbations in the past three months. A total of 3600 respiratory sound segments (each 3 s long) were obtained by dividing 90 s recordings from 120 participants (60 asthmatic, 60 healthy) into non-overlapping clips. The samples were analyzed using Mel-Frequency Cepstral Coefficients (MFCCs) and Tunable Q-Factor Wavelet Transform (TQWT). Significant features selected with ReliefF were used to train Quadratic Support Vector Machine (SVM) and Narrow Neural Network (NNN) models. Results: In 120 participants, pulmonary function test (PFT) results in the asthma group showed lower FEV1 (86.9 ± 5.7%) and FEV1/FVC ratios (86.1 ± 8.8%) compared to controls, but remained within normal ranges. Quadratic SVM achieved 99.86% accuracy, correctly classifying 99.44% of controls and 99.89% of asthma cases. Narrow Neural Network achieved 99.63% accuracy. Sensitivity, specificity, and F1-scores exceeded 99%. Conclusion: This machine learning-based algorithm provides accurate asthma diagnosis, even in patients with normal spirometry and clinical findings, offering a non-invasive and efficient diagnostic tool. Full article

This study analyses the impacts of winter storms on beach response, as well as the subsequent recovery during spring and summer, at a dissipative sandy beach in Brandon Bay, Ireland. Shoreline dynamics were assessed through the integration of field data from five survey campaigns conducted between October 2021 and November 2022 with a 1D Xbeach (version 1.23) numerical model. Cross-sectional profiles were measured at seven locations, revealing pronounced erosion during winter, followed by recovery in calmer seasons, especially in the lower beach zone. The model effectively simulated short-term storm-induced morphological changes, demonstrating that rates of shoreline retreat and profile alteration are higher in the eastern bay, where wave energy is greater. Most morphological changes occurred between the low and high astronomical tide marks, characterized by upper beach erosion and lower beach accretion. Models were subsequently employed to examine future climate scenarios, including sea level rise and increased storm intensity. The projections indicated an exponential increase in erosion rates, correlated with higher storm wave heights and frequencies. These results highlight the dynamic response of dissipative beaches to extreme events and reinforce the necessity for adaptive coastal management strategies to address the escalating risks posed by climate change. Full article

Neurogenic bladder is a debilitating condition caused by neurological dysfunction that impairs urinary control, often requiring timed intermittent catheterisation. Although effective, intermittent catheterisation is invasive, uncomfortable, and associated with infection risks, reducing patients’ quality of life. SafeBladder is a low-cost wearable device developed to enable real-time, non-invasive bladder volume monitoring using near-infrared spectroscopy (NIRS) and machine learning algorithms. The prototype employs LEDs and photodetectors to measure light attenuation through abdominal tissues. Bladder filling was simulated through experimental tests using stepwise water additions to containers and tissue-mimicking phantoms, including silicone and porcine tissue. Machine learning models, including Linear Regression, Support Vector Regression, and Random Forest, were trained to predict volume from sensor data. The results showed the device is sensitive to volume changes, though ambient light interference affected accuracy, suggesting optimal use under clothing or in low-light conditions. The Random Forest model outperformed others, with a Mean Absolute Error (MAE) of 25 ± 4 mL and $R^2$ of 0.90 in phantom tests. These findings support SafeBladder as a promising, non-invasive solution for bladder monitoring, with clinical potential pending further calibration and validation in real-world settings. Full article

Understanding Farmer households’ subjective flood risk cognition is important for effectively mitigating the impacts of flood, and adequate disaster preparedness reduces the impact of floods on the sustainability of farmers’ livelihoods. The existing literature focuses on objective flood risk assessment and subjective–objective risk consistency and less systematically explores the correlation between Farmer households’ subjective flood risk cognition and disaster preparedness. Therefore, this study aims to explores the correlation between Farmer households’ subjective flood risk cognition and disaster preparedness. This study employed a random sampling method to conduct a survey among 540 households in Gaoxian County, Jiajiang County, and Yuechi County, which are flood-prone areas in Southwest China. Based on the survey results, this research framework can be used to evaluate systems of subjective flood risk cognition and farmers’ disaster preparedness. We chose the Tobit Regression Model to empirically explore the correlation between subjective flood risk cognition and farmers’ disaster preparedness. The results showed that among the 540 surveyed farmers, their overall subjective flood risk cognition was at a medium-high level (3.58), with self-efficacy more than response efficacy, more than threat, and more than probability. Further, the overall disaster preparedness of farmers was at a medium level (0.5), with physical disaster preparedness more than emergency disaster preparedness and more than knowledge and skills preparedness. The regression analysis showed that the probability of flooding and the threat in Farmer households’ subjective flood risk cognition were positively related to disaster preparedness, whereas self-efficacy, response efficacy, and overall risk cognition in Farmer households’ subjective flood risk cognition were negatively related to disaster preparedness. This study is representative of or may serve as a reference for building governance systems and disaster prevention in other flood risk areas in Southwest China. Full article

This study presents a three-dimensional numerical analysis of natural convection during the postharvest storage of corn and wheat in a galvanized steel silo with a conical roof and floor, measuring 3 m in radius and 18.7 m in height, located in the Bajío region of Mexico. Simulations were carried out specifically for December, a period characterized by cold ambient temperatures (10–20 °C) and comparatively lower solar radiation than in warmer months, yet still sufficient to induce significant heating of the silo’s metallic surfaces. The governing conservation equations of mass, momentum, energy, and species were solved using the finite volume method under the Boussinesq approximation. The model included grain–air sorption equilibrium via sorption isotherms, as well as metabolic heat generation: for wheat, a constant respiration rate was assumed due to limited biochemical data, whereas for corn, respiration heat was modeled as a function of grain temperature and moisture, thereby more realistically representing metabolic activity. The results, obtained for December storage conditions, reveal distinct thermal and hygroscopic responses between the two grains. Corn, with higher thermal diffusivity, developed a central thermal core reaching 32 °C, whereas wheat, with lower diffusivity, retained heat in the upper region, peaking at 29 °C. Radial temperature profiles showed progressive transitions: the silo core exhibited a delayed response relative to ambient temperature fluctuations, reflecting the insulating effect of grain. In contrast, grain at 1 m from the wall displayed intermediate amplitudes. In contrast, zones adjacent to the wall reached 40–41 °C during solar exposure. In comparison, shaded regions exhibited minimum temperatures close to 15 °C, confirming that wall heating is governed primarily by solar radiation and metal conductivity. Axial gradients further emphasized critical zones, as roof-adjacent grain heated rapidly to 38–40 °C during midday before cooling sharply at night. Relative humidity levels exceeded 70% along roof and wall surfaces, leading to condensation risks, while core moisture remained stable (~14.0% for corn and ~13.9% for wheat). Despite the cold ambient temperatures typical of December, neither temperature nor relative humidity remained within recommended safe storage ranges (10–15 °C; 65–75%). These findings demonstrate that external climatic conditions and solar radiation, even at reduced levels in December, dominate the thermal and hygroscopic behavior of the silo, independent of grain type. The identification of unstable zones near the roof and walls underscores the need for passive conservation strategies, such as grain redistribution and selective ventilation, to mitigate fungal proliferation and storage losses under non-aerated conditions. Full article

Background/Objectives: Cardiac sarcoidosis (CS) is a granulomatous inflammatory cardiomyopathy with heterogeneous presentations, from palpitations to heart failure and sudden cardiac arrest. Despite advances in imaging and immunosuppressive (IS) therapy, relapse patterns and long-term outcomes remain poorly defined. This study aimed to characterize relapse and identify predictors of relapse and major adverse cardiac events (MACE) in a real-world CS cohort. Methods: This retrospective single-center study included 25 adults diagnosed with CS at Geneva University Hospitals between 2016 and 2024, classified per the 2024 American Heart Association diagnostic criteria. Relapse was defined as clinical, arrhythmic, or imaging deterioration requiring treatment escalation. MACE included cardiovascular hospitalization, device therapy, left ventricular assist device, heart transplant, or death. Statistical methods included Kaplan–Meier analysis with log-rank tests and multivariable Cox regression adjusted for age and sex. Results: Relapse occurred in 13 patients (56%), frequently subclinical (61.5%) and detected incidentally on routine PET-CT during IS tapering. In the multivariate model, predictors of relapse included right ventricular FDG uptake (aHR 13.1; 95% CI 1.3–133.7; p = 0.03) and second-line immunosuppression duration ≤24 months (aHR 20.1; 95% CI 1.1–363.8; p = 0.04). Relapse-free patients were more often maintained on dual or triple IS therapy (71.4% vs. 15.4%; p = 0.02) and low-dose prednisone (<10 mg/day) (57.1% vs. 7.7%; p = 0.03). Conclusions: Relapse is common in CS, often subclinical, and associated with PET-CT findings and premature IS tapering. Maintenance therapy may reduce risk. Multimodal imaging remains critical for disease monitoring, though tracers with higher specificity are needed. Further research should refine relapse definitions and support personalized treatment strategies. Full article

The rise of financial technology (FinTech) has generated substantial research on its adoption by customers and the associated implications for traditional banks. This systematic review addresses two questions: (1) What factors enable or hinder consumer adoption of FinTech? (2) How does consumer adoption of FinTech affect the performance of traditional banks? Following the PRISMA guidelines, we screened and analyzed 109 peer-reviewed articles published between 2016 and 2024 in Scopus and Web of Science. The findings show that adoption is driven by economic incentives, digital infrastructure, personalized services, and institutional support, while barriers include limited literacy, perceived risk, and regulatory uncertainty. At the bank level, adoption enhances operational efficiency, customer loyalty, and revenue growth but also generates compliance costs, cybersecurity risks, and competition. Consumer adoption studies primarily employ the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), often extended with trust and privacy constructs. In contrast, bank performance research relies on empirical analyses with limited theoretical grounding. This review bridges behavioral and institutional perspectives by linking consumer-level drivers of adoption with organizational outcomes, offering an integrated conceptual framework. The limitations include a restriction of the retrieved literature to English publications in two databases. Future work should apply longitudinal, multi-theory models to deepen the understanding of how consumer behavior shapes bank performance. Full article

This study proposes a multi-objective optimization model for capital allocation in an energy stock portfolio. The model integrates two financial objectives (maximizing return and minimizing value-at-risk) and four environmental objectives (minimizing carbon, energy, water, and waste intensities), providing a more comprehensive representation of corporate environmental performance in the energy sector. A closed-form analytical solution is developed to enhance theoretical clarity, analytical tractability, and interpretability without relying on iterative simulations. Methodologically, the model adopts a weighted utility function approach to aggregate multiple objectives into a single unified function, and the optimal solution is derived using the Lagrange multiplier method. The proposed model is then implemented on Indonesian energy stock data selected based on the lowest aggregate scores of financial and environmental attributes. This selection yields four stocks across three different energy subsectors: oil, gas, and coal. This implementation demonstrates that the optimal portfolio solution is simply and efficiently obtained without the need for iterative numerical approaches. Additionally, this implementation also shows a clear, representative, and rational trade-off between financial aspects and environmental impacts. This study makes a theoretical contribution to the sustainable portfolio literature and has practical implications for investors seeking to balance financial and environmental objectives quantitatively. Full article

Disaster risk reduction requires efficient flood control in lowland and flood-prone areas, especially in agricultural areas like the Bang Rakam model area in Phitsanulok province, Thailand. In order to improve flood prediction and response, this study proposes the creation of a low-cost, real-time water-level monitoring integrated with spatial data analysis using Geographic Information System (GIS) technology. Ten ultrasonic sensor-equipped monitoring stations were installed thoughtfully around sub-catchment areas to provide highly accurate water-level readings. To define inundation zones and create flood depth maps, the sensors gather flood level data from each station, which is then processed using a 1-m Digital Elevation Model (DEM) and Python-based geospatial analysis. In order to create dynamic flood maps that offer information on flood extent, depth, and water volume within each sub-catchment, an automated method was created to use real-time water-level data. These results demonstrate the promise of low-cost IoT-based flood monitoring devices as an affordable and scalable remedy for communities that are at risk. This method improves knowledge of flood dynamics in the Bang Rakam model area by combining sensor technology and spatial data analysis. It also acts as a standard for flood management tactics in other lowland areas. The study emphasizes how crucial real-time data-driven flood monitoring is to enhancing early-warning systems, disaster preparedness, and water resource management. Full article