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Propensity score matching analysis of perioperative outcomes during Hub&Spoke training program in hepato-biliary surgery
Nutritional value, antibacterial activity, ACE and DPP IV inhibitory of red pomegranate seeds protein and peptides
Machine learning based intratumor heterogeneity related signature for prognosis and drug sensitivity in breast cancer
Saccades influence functional modularity in the human cortical vision network
Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems
Abstract This paper presents a machine learning (ML) model designed to track the maximum power point of standalone Photovoltaic (PV) systems. Due to the nonlinear nature of power generation in PV systems, influenced by fluctuating weather conditions, managing this nonlinear data effectively remains a challenge. As a result, the use of ML techniques to optimize PV systems at their MPP is highly beneficial. To achieve this, the research explores various ML algorithms, such as Linear Regression (LR), Ridge Regression (RR), Lasso Regression (Lasso R), Bayesian Regression (BR), Decision Tree Regression (DTR), Gradient Boosting Regression (GBR), and Artificial Neural Networks (ANN), to predict the MPP of PV systems. The model utilizes data from the PV unit’s technical specifications, allowing the algorithms to forecast maximum power, current, and voltage based on given irradiance and temperature inputs. Predicted data is also used to determine the boost converter’s duty cycle. The simulation was conducted on a 100 kW solar panel with an open-circuit voltage of 64.2 V and a short-circuit current of 5.96 A. Model performance was evaluated using metrics such as Root Mean Square Error (RMSE), Coefficient of Determination (R2), and Mean Absolute Error (MAE). Additionally, the study assessed the correlation and feature importance to evaluate model compatibility and the factors impacting the predictive accuracy of the ML models. Results showed that the DTR algorithm outperformed others like LR, RR, Lasso R, BR, GBR, and ANN in predicting the maximum current (Im), voltage (Vm), and power (Pm) of the PV system. The DTR model achieved RMSE, MAE, and R2 values of 0.006, 0.004, and 0.99999 for Im, 0.015, 0.0036, and 0.99999 for Vm, and 2.36, 0.871, and 0.99999 for Pm. Factors such as the size of the training dataset, operating conditions of the PV system, model type, and data preprocessing were found to significantly influence prediction accuracy.
Effects of prehospital advanced airway management on cardiac arrest patients who underwent extracorporeal cardiopulmonary resuscitation
CT imaging features and diagnostic algorithm for hepatic cystic echinococcosis
Abstract To systematically analyze CT imaging features of hepatic cystic echinococcosis (CE), explore radiological-pathological correlations, and develop a diagnostic algorithm for accurate disease classification. This retrospective study included 48 pathologically confirmed cases of hepatic CE from two medical centers. CT imaging features were analyzed by two experienced radiologists, evaluating lesion characteristics including location, morphology, wall features, and calcification patterns. Imaging findings were correlated with pathological results. A diagnostic algorithm was developed and validated, with inter-observer agreement assessed using Fleiss kappa coefficient. Seven distinct CT imaging patterns were identified, corresponding to different pathological stages: unilocular cystic (25.0%), multivesicular (8.3%), collapsed inner wall (10.4%), partially solidified (10.4%), solidified (16.7%), and calcified (25.0%) types, with complicated cases (4.2%) showing additional features. The proposed diagnostic algorithm achieved 94.0% accuracy (451/480 classifications) in validation testing by ten junior radiologists, with excellent inter-observer agreement (quadratic-weighted Fleiss kappa coefficient = 0.740 [95% CI 0.577–0.902], Gwet’s AC2 coefficient = 0.768). Primary diagnostic challenges involved differentiating between CE2 and CE3b lesions, and between CE3b and CE4 lesions. This study explores the correlation between CT imaging patterns and pathological stages of hepatic CE, proposing a validated diagnostic algorithm. The findings provide valuable insights for CE classification, particularly in regions where the disease is emerging or underrecognized.
The influence of inclusive leadership styles on the safety behaviors of new generation high altitude railway construction workers
Investigation on coaxial pseudo-fault characteristics induced by the outer ring defect in the single-sided axle-box bearing of wheelset in urban rail vehicles
Similarity based city data transfer framework in urban digitization
Overweight and obesity trends and association with household wealth index among children aged 5 to 19 years in Ethiopia a multilevel analysis of 2016 EDHS data
Therapeutic treatment of hepatitis E virus infection in pigs with a neutralizing monoclonal antibody
Abstract Hepatitis E virus (HEV) poses a significant risk to human health. In Europe, the majority of HEV infection are caused by the zoonotic genotype 3 (HEV-3), which can cause chronic hepatitis E in immunocompromised patients and those with pre-existing liver disease, and may eventually develop into fatal liver cirrhosis. In this study, we examined the effectiveness of a monoclonal antibody (MAb) treatment strategy using a well established HEV-3 pig model with intravenous infection. For this purpose, nine MAbs raised against the viral capsid protein were generated and the neutralizing activities were compared using in vitro assays. The antibody with the highest neutralizing activity, MAb 5F6A1, was selected for an in vivo study in pigs infected with HEV-3. Following the initial infection of pigs with HEV-3, MAb 5F6A1 was administered intravenously one and seven days post-infection. The results suggest MAb 5F6A1 significantly reduced viremia and virus shedding in pigs infected with HEV-3. This study provides significant insight into the dynamics of HEV infection in pigs and highlights the efficacy of MAb based therapy as an option for treating HEV in porcine hosts and, potentially, humans.
Development of co-doped ZnS-CdS quantum dots based composite sensor for the detection of cefixime (CXM) and tetracycline (TET), and application in real samples from local dairies
The coupling relationship and driving mechanism between ecological environment and high-quality economic development in the Middle Yellow River Basin
Abstract Promoting the dynamic balance between economic development and ecological environment is key to achieving the “dual carbon” goals and sustainable development. The Middle Yellow River Basin, characterized by severe soil erosion and intensive resource utilization, serve as a critical area for advancing ecological protection and high-quality development in the Yellow River Basin. This study examines the spatial–temporal differentiation and coupling coordination characteristics of the ecological environment (EE) and high-quality economic development (HQED) across 226 counties in the Middle Yellow River Basin from 2010 to 2020. Utilizing the Random Forest model and the Geographical and Temporal Weighted Regression model, the study investigates the driving mechanism of high-quality economic development on ecological environment. The zoning management strategy is proposed based on the types of coupling coordination and the dominant driving factors, with the aim of providing theoretical support for sustainable development in the river basins. The results show that: (1) During the study period, the level of ecological environment initially declined and then improved, while high-quality economic development consistently increased. The EE exhibited a spatial pattern of "southeast low, northwest high," while the distribution pattern of HQED was the reverse. (2) The coupling coordination degree considerably increased after 2015, displaying the spatial pattern characterized by higher levels in the southeast and northwest and lower levels in the central region, with the strong spatial positive correlation. (3) Forest cover rate, PM2.5 concentration, agricultural fertilizer application intensity, and market activity make high contributions to the ecological environment, making them key drivers. Forest cover rate is the strongest positive driver, while PM2.5 concentration is the strongest negative driver. There are evident spatial distribution differences among the various driving factors. Ultimately, the study area is divided into six types of zones, and corresponding development strategies is proposed.
Comparative analysis of dehazing algorithms on real-world hazy images
Abstract Images captured in adverse weather conditions (haze, fog, smog, mist, etc.) often suffer significant degradation. Due to the scattering and absorption of these particles, various negative effects, such as reduced visibility, low contrast, and colour distortion are introduced into the image. These degraded images are unsuitable for many computer vision applications, including smart transportation, video surveillance, weather forecasting, and remote sensing. To ensure the reliable operation of such applications, a high-quality haze-free input image is essential, which is supplied by image dehazing techniques. This review categorises recent dehazing methods, highlighting popular approaches within each group. In recent years, deep learning methods and restoration-based techniques using priors have garnered attention, particularly for addressing challenges such as dense and non-homogeneous haze. In this paper, their typical candidates are compared by using real-world hazy images because most data-driven and neural augmentation methods are trained by using synthetic hazy images. Experimental results conducted on real-world hazy images reveal that physics-driven single-image dehazing algorithms exhibit a lack of robustness, while data-driven approaches perform well on thin hazy images but struggle in dense haze conditions. Neural augmentation algorithms, however, effectively combine the strengths of both approaches, offering a better overall solution. By identifying existing gaps in recent methods, this paper provides a valuable resource for both novice and experienced researchers, while pointing towards future directions in this rapidly advancing field.
S-scheme heterojunction of MoO3 nanobelts and MoS2 nanoflowers for photocatalytic degradation
Bioinformatic analysis of glycolysis and lactate metabolism genes in head and neck squamous cell carcinoma
Influence of bearing platform size on bearing capacity of NT-CEP pile group foundation under compound force
Pertinence of contact duration as edge feature for epidemic spread analysis
Abstract Identifying superspreading nodes has attracted greater attention because of its wide practical significance in various applications. Existing studies consider the edges mostly equally while designing the algorithms for the unweighted contact networks, where each connection explicitly shows whether the individuals are in contact or not. It will not consider other relevant information in the context of epidemiology study or infectious disease spread, such as proximity or total time spent between the contact nodes. The recent studies focused on the weighted network, where most of the methods have computed the edge weights by utilizing degree and k-shell measure, which captures the topological structure of the network but not the interaction duration between pair of contacts. In this study, we mainly aim to generate weighted networks to model the pathogen spread by optimal calculation of the edge weight in terms of contact duration (time spent) between individual contacts. Leveraging this interaction duration as the edge weight, we further design a novel technique, namely Real Weighted Influence (RWInf), for identifying the superspreading nodes during an epidemic outbreak. The empirical study revealed that the proposed approach outperforms with an improvement of 0.146–0.473 kendall’s score in comparison with baseline approaches.