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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.
Experimental study on grouting diffusion and reinforcement law of grouting backfilling mining in caving zone
Carbon footprint analysis and emission reduction pathways of Bogie frame manufacturing process in Urban Rail Transportation
Wild horseshoe crab image denoising based on CNN-transformer architecture
Development and validation of a clinical prognosis prediction model for malignant intestinal obstruction: A retrospective cohort study
Entanglement between microwave fields and squeezing of the optical output field in an opto-magnomechanical ring cavity
Assessment of family planning service utilization and associated factors among female students at Assosa university, Ethiopia
Publisher Correction: Dynamics and development of interhemispheric conflict solving in pigeons
The visual communication using generative artificial intelligence in the context of new media
Superconductivity in bcc-selenium under megabar pressure
Green synthesis of superhydrophobic cotton filters using Pistacia atlantica gum for efficient oil and water separation
Safety assessment of proteasome inhibitors real world adverse event analysis from the FAERS database
A fine-tuned convolutional neural network model for accurate Alzheimer’s disease classification
Abstract Alzheimer’s disease (AD) is one of the primary causes of dementia in the older population, affecting memories, cognitive levels, and the ability to accomplish simple activities gradually. Timely intervention and efficient control of the disease prove to be possible through early diagnosis. The conventional machine learning models designed for AD detection work well only up to a certain point. They usually require a lot of labeled data and do not transfer well to new datasets. Additionally, they incur long periods of retraining. Relatively powerful models of deep learning, however, also are very demanding in computational resources and data. In light of these, we put forward a new way of diagnosing AD using magnetic resonance imaging (MRI) scans and transfer learned convolutional neural networks (CNN). Transfer learning makes it easier to reduce the costs involved in training and improves performance because it allows the use of models which have been trained previously and which generalize very well even when there is very little training data available. In this research, we used three different pre-trained CNN based architectures (AlexNet, GoogleNet, and MobileNetV2) each implemented with several solvers (e.g. Adam, Stochastic Gradient Descent or SGD, and Root Mean Square Propagation or RMSprop). Our model achieved impressive classification results of 99.4% on the Kaggle MRI dataset as well as 98.2% on the Open Access Series of Imaging Studies (OASIS) database. Such results serve to demonstrate how transfer learning is an effective solution to the issues related to conventional models that limits the accuracy of diagnosis of AD, thus enabling their earlier and more accurate diagnosis. This would in turn benefit the patients by improving the treatment management and providing insights on the disease progression.
A three-stage machine learning and inference approach for educational data
Abstract A central task in educational studies is to uncover factors that drive a student’s academic performance. While existing studies have utilized meticulous regression designs, it is challenging to select appropriate controls. Machine learning, however, offers a solution whereby the entire variable set can be inspected and filtered by different optimization schemes. In that light, this paper adopts a three-stage framework to analyze and discover potentially latent causal relationships from an open dataset from UCI. In the first stage, machine learning methods are employed to select candidate variables that are closely associated with student grades, and then a “post-double-selection” process is implemented to select the set of control variables. In the final stage, three case studies are conducted to illustrate the effectiveness of the three-stage design. The model pipeline is suitable for situations where there is only minimal prior knowledge available to address a potentially causal research question.
A muscle synergy-based method to improve robot-assisted movements
An IPv6 address fast scanning method based on local domain name association
Abstract With the increase of security issues in IPv6 networks, conducting address scanning in IPv6 networks proves beneficial for identifying potential security risks and vulnerabilities. To enhance the privacy of users’ IPv6 addresses, mainstream OS (Operating System) nodes currently employ randomized interface identifiers and temporary IPv6 addresses. Additionally, since most existing IPv6 address scanning methods rely on active scanning, which makes current on-link IPv6 address scanning methods face the challenges of incomplete scan results, poor coverage across different OSs, significant impact on network performance, and the inability to promptly detect subsequently joined hosts. To this end, An IPv6 address fast scanning method based on local domain name association (FScan6), which combines active scanning and passive listening, is proposed. The active scanning module targets different OSs using distinct protocols (Browser and DNS-SD) to obtain local domain names of on-link hosts. Meanwhile, the passive listening module monitors traffic to extract local domain names of on-link hosts. Then, it employs mDNS protocol to retrieve IPv6 addresses associated with these local domain names. A typical on-link IPv6 network environment was constructed, comprising 26 versions of Windows, Apple, and Linux OSs, and FScan6 was compared with 9 IPv6 address scanning methods. The experimental results show that FScan6 outperforms existing IPv6 address scanning methods in terms of OS coverage and scanning result completeness. Specifically, regarding OS coverage, FScan6 successfully detected all IPv6 addresses across 26 different OS versions, which outperformed 9 address scanning tools and scripts by a factor of 2.89 times at most. Regarding scanning result completeness, FScan6 identified up to 54 additional IPv6 addresses at most compared to these tools and scripts. Additionally, FScan6 has a minimal impact on network performance, with the packet loss rate induced by the tool consistently remaining at 0%.
Secular trends in heat related illness and excess sun exposure rates across climatic zones in the United States from 2017 to 2022
Abstract Heat waves are a major public health challenge, yet the link between heat-related illness (HRI) and regional climate and geography is underexplored. We examined HRI and excess sun exposure incidence rates (IR) [95% confidence interval (CI) per 100,000 person-years], and their correlation with regional maximum temperatures across 9 US climatic zones 33,603,572 individuals were followed from 2017 to 2022. We observed 10,652 individuals with HRI diagnosis (median age: 49 years, 62.3% male). Seasonal peaks occurred during summer: highest overall IR (130.97 [119.93–142.75]) was recorded in July 2019, highest regional IR was reported in the South (186.04 [117.93–279.15]) during 2020. Strongest correlations between monthly maximum temperature and incidence of HRI were observed in the West (Pearson Correlation Coefficient (cor) = 0.854) and Southwest (cor = 0.832). In contrast, we observed 131,204 individuals with excess sun exposure (predominantly older adults [median age: 67 years], 52.3% female, 30% with history of cancer). Overall IR for sun exposure peaked in March 2021 (664.31 [644.84–684.21]) and lacked a consistent seasonal pattern. Sun exposure exhibited weaker correlations with regional temperatures, even in high-temperature regions like the West (cor = 0.305). These data indicate regional variations in HRI. With distinct at-risk groups for HRI and sun exposure, targeted regional interventions may be beneficial, such as heat safety protocols to reduce HRI risk and sun protection campaigns for older adults to mitigate sun exposure risk.