Investigation of parallel joint density thresholds for granite tunnel failure based on physical model experiments and multiscale monitoring techniques by Huai-Sheng Xu, Shao-Jun Li, Ding-Ping Xu, Quan Jang, Liu Liu, Min-Zong Zheng, Xu-Feng Liu

“…Their failure mode was primarily joint-controlled, featuring macro-block structural failure as the dominant pattern and extensive stress-weakened zones due to parallel joint network effects. …”
Get full text

Integration of Machine Learning and Wavelet Algorithms for Processing Probing Signals: An Example of Oil Wells by Zukhra Abdiakhmetova, Zhanerke Temirbekova

“…Furthermore, machine learning models, including standard regression algorithms and neural networks, are leveraged to enhance signal interpretation and predictive capabilities. …”
Get full text

Change Detection for Forest Ecosystems Using Remote Sensing Images with Siamese Attention U-Net by Ashen Iranga Hewarathna, Luke Hamlin, Joseph Charles, Palanisamy Vigneshwaran, Romiyal George, Selvarajah Thuseethan, Chathrie Wimalasooriya, Bharanidharan Shanmugam

“…To address these challenges, we propose a novel pipeline by refining the U-Net design, including employing two different schemata of early fusion networks and a Siam network architecture capable of processing RGB images specifically designed to identify high-risk areas in forest ecosystems through change detection across different time frames in the same location. …”
Get full text

Machine Learning and Deep Learning for Crop Disease Diagnosis: Performance Analysis and Review by Habiba Njeri Ngugi, Andronicus A. Akinyelu, Absalom E. Ezugwu

“…In terms of comparative performance, DL architectures like ResNet50, VGG16, and convolutional neural network demonstrated robust accuracy (95–99%) across diverse datasets, underscoring their effectiveness in managing complex image data. …”
Get full text

Multi-Objective Scheduling for Green Flexible Assembly Job-Shop System via Multi-Agent Deep Reinforcement Learning With Game Theory by Xiao Wang, Zhongyuan Liang, Peisi Zhong, Dongmin Li, Hongqi Li, Mei Liu

“…The processing state feature data that uses a deep convolutional neural network to fit the value function is extracted from three matrices including the processing time matrix, task assignment Boolean matrix, and an adjacency matrix. …”
Get full text

Sea Surface Height Inversion Model Based on Multimodal Deep Learning for the Fusion of Heterogeneous FY-3E GNSS-R Data by Yun Zhang, Ganyao Qin, Shuhu Yang, Yanling Han, Zhonghua Hong

“…The global sea surface dataset Danmarks Tekniske Universitet 2018 is used as ground truth for model training and evaluation. Data from 1 to 31 July 2022 are used to train PIMFA-Net, while data from August to October 2022 are used to evaluate the general ability of PIMFA-Net. …”
Get full text

Predicting indoor temperature of solar green house by machine learning algorithms: A comparative analysis and a practical approach by Wenhe Liu, Tao Han, Cong Wang, Feng Zhang, Zhanyang Xu

“…The innovative aspect of this study lied in its systematic evaluation of temperature predictions across various time steps. …”
Get full text

An ECoG-Based Binary Classification of BCI Using Optimized Extreme Learning Machine by Xinman Zhang, Qi Xiong, Yixuan Dai, Xuebin Xu, Guokun Song

Get full text

Deep learning-based classification of lymphedema and other lower limb edema diseases using clinical images by Thanat Lewsirirat, Taravichet Titijaroonroj, Sirin Apichonbancha, Ason Uthatham, Veera Suwanruangsri, Nirut Suwan, Surakiat Bokerd, Tossapol Prapassaro, Wanchai Chinchalongporn, Nutcha Yodrabum

“…Grad-CAM analyses enhanced model interpretability, highlighting clinically relevant features such as swelling and hyperpigmentation. The AI system consistently outperformed human evaluators, whose diagnostic accuracy plateaued at 62.7%. …”
Get full text

Modeling of Battery Storage of Photovoltaic Power Plants Using Machine Learning Methods by Rad Stanev, Tanyo Tanev, Venizelos Efthymiou, Chrysanthos Charalambous

“…This study compares the results of feedforward neural networks (FNNs), a homogeneous ensemble of FNNs, multiple linear regression, multiple linear regression with polynomial features, decision-tree-based models like XGBoost, CatBoost, and LightGBM, and heterogeneous ensembles of decision tree modes in the day-ahead forecasting of an existing real-life BESS in a PV power plant. …”
Get full text

Sustainable Clothing Buying Behavior of Generations X and Y

“…The aim of the paper is to analyse the main features of sustainable clothing buying behaviour within Generations X and Y and to suggest some marketing activities for fashion companies. …”
Get full text

Comparative analysis of deep learning and traditional methods for IoT botnet detection using a multi-model framework across diverse datasets by Saeed Ullah, Junsheng Wu, Zhijun Lin, Mian Muhammad Kamal, Hala Mostafa, Muhammad Sheraz, Teong Chee Chuah

“…Abstract The proliferation of Internet of Things (IoT) devices has created unprecedented cybersecurity vulnerabilities, with botnets emerging as a critical threat to network infrastructure. This study focuses on traditional machine learning and deep learning approaches, proposes a novel ensemble framework to address these issues, integrating Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), Random Forest (RF), and Logistic Regression (LR) via a weighted soft-voting mechanism. …”
Get full text

Strategies for Construction and Optimization of the Ecological Security Pattern for Human Settlements in the Wuding River Basin by Xiaomeng WANG, Anrong DANG, Biao TONG, Xinyi LIU

“…These corridors serve to link ecological source areas, thereby creating a networked spatial structure that follows a distinct pattern: Denser in the north and sparser in the south. …”
Get full text

Green Health by Marcel Cardinali

“…PRIGSHARE offers a 21-item checklist that aids in the systematic evaluation and comparison of studies, focusing on aspects like objectives, scope, types of green space assessments and context variables. …”
Get full text

En face swept-source optical coherence tomography (SS-OCT) and SS-OCT angiography findings of retinal astrocytic hamartomas in patients with tuberous sclerosis complex by Chen-Xi Zhang, Chen-Xi Zhang, Chen-Xi Zhang, Kai-Feng Xu, Qin Long, Qin Long, Qin Long, Xiao Zhang, Xiao Zhang, Xiao Zhang, Zhi-Kun Yang, Zhi-Kun Yang, Zhi-Kun Yang, Rong-Ping Dai, Rong-Ping Dai, Rong-Ping Dai, Zhi-Qiao Zhang, Zhi-Qiao Zhang, Zhi-Qiao Zhang

“…IntroductionTuberous sclerosis complex (TSC) is an autosomal dominant disorder characterized by multisystem hamartomas, including retinal astrocytic hamartomas (RAHs), which are a key diagnostic criterion. This study evaluates the en face swept-source optical coherence tomography (SS-OCT) and SS-OCT angiography (SS-OCTA) features of TSC-associated RAHs.MethodsA retrospective analysis of 10 patients with TSC-associated RAHs was conducted using en face SS-OCT, SS-OCTA, and fundus photography. …”
Get full text

Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry by PNV Srinivasa Rao, PVY Jayasree

“…The selected data set is pre-processed and feature selection for the optimization for the improvement in accuracy, and automation decision making the framework of the convolution neural network along with the ensemble boosted tree classifier developed is optimized using the jellyfish optimization and Recurrent Neural Network and Long Short-Term Memory (RNN-LSTM) model for the recognition of patterns and numerical vectors in the real-world data after processing of output then it is sent back as the input for the recurrent network to make the decision in the shipbuilding process. …”
Get full text

Bioinformatic identification of signature miRNAs associated with fetoplacental vascular dysfunction in gestational diabetes mellitus by Yulan Lu, Chunhong Liu, Xiaoxia Pang, Xinghong Chen, Chunfang Wang, Huatuo Huang

“…The receiver operator characteristic curve (ROC), the nomogram diagram, gene set enrichment analysis (GSEA), and signature miRNAs-target genes interaction network were implemented further to explore the features and functions of signature genes. …”
Get full text

Novel and legacy per- and polyfluoroalkyl substances in humans: Long-term temporal variability and metabolic perturbations by Che-Jung Chang, Anna S. Young, Alexander Keil, Catherine E. Mullins, Donghai Liang, Shanshan Zhao, Dean P. Jones, Xin Hu, Douglas I. Walker, Alexandra J. White

“…Temporal variability was assessed using Spearman correlations and intraclass correlation coefficients. Network analysis, metabolome-wide association studies, and pathway analysis were used to evaluate the impacts of PFAS mixtures on the human metabolome. …”
Get full text

Design and Validation of a Multi-Epitope mRNA Vaccine Construct Against Human Monkeypox Virus (hMPXV) by Annotating Protein of Intracellular Mature Virus (IMV) Form of hMPXV by Mohammad Asrar Izhari, Siraj B. Alharthi, Raed A. Alharbi, Ahmad H. A. Almontasheri, Wael A. Alghamdi, Abdulmajeed Abdulghani A. Sindi, Ahmad Abdulmajed Salem, Ali Mahzari, Fahad Alghamdi, Ahmed R. A. Gosady

“…Epitope prediction for B-cells (lymphocytes), cytotoxic T-cells or cytotoxic T-lymphocytes (CTLs), and helper T-cells (HTLs) was executed using ABCpred, IEDB’s ANNs 4.0, and an artificial neural network-based alignment tool (NN-align 2.3)/ML-based tool (NetMHCII 2.3). …”
Get full text

AΚtransU-Net: Transformer-Equipped U-Net Model for Improved Actinic Keratosis Detection in Clinical Photography by Panagiotis Derekas, Charalampos Theodoridis, Aristidis Likas, Ioannis Bassukas, Georgios Gaitanis, Athanasia Zampeta, Despina Exadaktylou, Panagiota Spyridonos

“…This configuration allows the network to maintain semantic coherence and spatial continuity in AK detection. …”
Get full text