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Social needs and hospital readmission in persons living with HIV
3D geological model reconstruction and collapse risk evaluation of old goaf in Dunqiu iron mine
A synergistic approach to enhanced oil recovery by combining in-situ surfactant production and wettability alteration in carbonate reservoirs
A lipid metabolism related gene signature predicts postoperative recurrence in pancreatic cancer through multicenter cohort validation
Network performance assessment for public health emergency response: multi-case study of SARS, H1N1 and COVID-19 in China
Author Correction: New results in stereopsis and Listing’s law
Utilizing parthenocarpic gynoecious beit alpha cucumber inbreds for their heterotic potential under different poly-net house environments
Evaluation of crop phenology using remote sensing and decision support system for agrotechnology transfer
Efficacy of slow daily home hemodialysis with internal convection on removal of uremic toxins using the Physidia S3 monitor
Design and validation of a semi-quantitative microneutralization assay for human Metapneumovirus A1 and B1 subtypes
Abstract Since 2001, human Metapneumovirus has been a significant cause of human respiratory disease worldwide, and no vaccine or preventive treatment is currently available. The ELISA-based live virus microneutralization assay is a method to detect neutralizing antibodies against a target pathogen. The aim of this study was to demonstrate the suitability of this approach to quantifying neutralizing antibodies against A1 and B1 virus subtypes in human serum samples. To standardize and validate this microneutralization assay, we carried out analytical procedures according to the International Council of Harmonization guidelines; these procedures are described in detail. In addition, we compared the validated method with the indirect ELISA, and confirmed that the ELISA-based microneutralization assay provides reliable, accurate and reproducible results. The use of this high-throughput method for large-scale serological studies could effectively support the evaluation of the immunogenicity of new vaccines, thereby improving therapeutical strategies against human Metapneumovirus.
Influence of pallet height on energy consumption and cooling effectiveness in an apple cold storage
Abstract This study investigates the influence of pallet height on energy consumption and cooling effectiveness using a validated cold storage model based on CFD simulations. The model was validated against experimental temperature data, with a maximum normalized root mean square error (NRMSE) of 4.57%, indicating good agreement. Four pallet height configurations namely No pallet (0.0 m), 0.3 m, 0.6 m, and 0.9 m were assessed over a 40-hour cooling period. Temperature distribution within apple-filled crates was used to identify the locations of hot and cold spots, and performance metrics such as compressor energy consumption, specific energy consumption, mean crate temperature, thermal heterogeneity, and cooling effectiveness were analyzed. The results indicate that the No-pallet configuration disrupts airflow resulting in insufficient cooling, as evidenced by lower cooling effectiveness, while larger pallet heights (0.6 m and 0.9 m) introduce excessive air spacing resulting in higher thermal heterogeneity. The 0.3 m pallet height exhibited best performance such as lowest mean crate temperature of 4.7 °C, (8.7% lower), lowest thermal heterogeneity of 3.29 (14.42% lower), lowest compressor energy consumption per kelvin of 1.474 kWh/K (0.5% lower) and highest cooling effectiveness of 0.4 (5.37% higher). These findings provide practical insights for optimizing pallet configurations in cold storage, aiding energy-efficient operations in commercial refrigerated warehouses and post-harvest supply chains.
Parallel boosting neural network with mutual information for day-ahead solar irradiance forecasting
Abstract The transition to sustainable energy has become imperative due to the depletion of fossil fuels. Solar energy presents a viable alternative owing to its abundance and environmental benefits. However, the intermittent nature of solar energy requires accurate forecasting of solar irradiance (SI) for reliable operation of photovoltaics (PVs) integrated systems. Traditional deep learning (DL) models and decision tree (DT)-based algorithms have been widely employed for this purpose. However, DL models often demand substantial computational resources and large datasets, while DT algorithms lack generalizability. To address these limitations, this study proposes a novel parallel boosting neural network (PBNN) framework that integrates boosting algorithms with a feedforward neural network (FFNN). The proposed framework leverages three boosting DT algorithms, Extreme Gradient Boosting (XgBoost), Categorical Boosting (CatBoost), and Random Forest (RF) regressors as base learners, operating in parallel. The intermediary forecasts from these base learners are concatenated and input into the FFNN, which assigns optimal weights to generate the final prediction. The proposed PBNN is trained and evaluated on two geographical datasets and compared with state-of-the-art techniques. The mutual information (MI) algorithm is implemented as a feature selection technique to identify the most important features for forecasting. Results demonstrate that when trained with the selected features, the mean absolute percentage error (MAPE) of PBNN is improved by $$46.9\%$$ , and $$73.9\%$$ for Islamabad and San Diego city datasets, respectively. Furthermore, a literature comparison of the PBNN is also performed for robustness analysis. Source code and datasets are available at https://github.com/Ubaid014/Parallel-Boosting-Neural-Network/tree/main
Reversible data hiding and authentication scheme for encrypted image based on prediction error compression
Abstract In the most existing reversible data hiding schemes for encrypted images, cover images can be reversibly recovered, but the integrity of image content cannot be guaranteed. This paper proposes a reversible data hiding and authentication scheme for encrypted images to implement reversible recovery and content authentication of cover images and secret data. The content owner employs a new predictor ISGAP to generate more accurate predictions and smaller errors. The errors are then compressed with adaptive Huffman coding to enlarge the embedding space, and the plaintext authentication information is embedded in it. The data hider embeds secret data into the encrypted image along with ciphertext authentication information. The receiver performs ciphertext authentication first and then implements cover recovery and plaintext authentication according to different keys. Experiments were carried out with 100 images selected from each dataset of BOSSbase and BOWS-2, and the results show that the scheme has higher embedding capacity and can effectively implement image content authentication while ensuring high security and reversible recovery.