Leveraging Digital Twin for operational resilience in the oil and gas industry by Giovanni Mazzuto, Ilaria Pietrangeli, Marco Ortenzi, Filippo Emanuele Ciarapica, Maurizio Bevilacqua

“…Future work: Future improvements will focus on refining the air flow modelling and expanding the dataset to improve predictive performance in extreme conditions providing an efficient anomaly detection system based on the DT prediction.…”
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Context-Aware Markov Sensors and Finite Mixture Models for Adaptive Stochastic Dynamics Analysis of Tourist Behavior by Xiaolong Chen, Hongfeng Zhang, Cora Un In Wong, Zhengchun Song

“…The proposed method addresses this limitation by introducing virtual sensors that dynamically detect contextual anomalies and trigger regime switches in real-time. …”
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Evaluating pedestrian crossing safety: Implementing and evaluating a convolutional neural network model trained on paired aerial and subjective perspective images by Dylan Russon, Antoine Guennec, Juan Naredo-Turrado, Binbin Xu, Cédric Boussuge, Valérie Battaglia, Benoit Hiron, Emmanuel Lagarde

“…With pedestrian crossings implicated in a significant proportion of vehicle-pedestrian accidents and the French government's initiatives to improve pedestrian safety, there is a pressing need for efficient, large-scale evaluation of pedestrian crossings. …”
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Edge–Cloud Intelligence for Sustainable Wind Turbine Blade Transportation: Machine-Vision-Driven Safety Monitoring in Renewable Energy Systems by Yajun Wang, Xiaodan Wang, Yihai Wang, Shibiao Fang

“…These risks arise from collision hazards, complex terrain, and the difficulty of real-time monitoring under adverse environmental conditions. …”
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Reliability Assessment and Condition Monitoring of Wind Energy Conversion Systems Using Bayesian Networks: Recent Advances and Key Insights by Fatemeh Salboukh, Yashar Mousavi, Ibrahim Beklan Kucukdemiral, Afef Fekih, Umit Cali

“…Wind energy conversion systems (WECSs) play a vital role in the transition to sustainable energy, requiring continuous advancements in reliability, efficiency, and predictive maintenance. This paper provides a comprehensive review of Bayesian Networks (BNs) as a robust probabilistic framework for enhancing fault detection, risk assessment, and condition monitoring in WECSs. …”
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Advanced Diagnosis of Cardiac and Respiratory Diseases from Chest X-Ray Imagery Using Deep Learning Ensembles by Hemal Nakrani, Essa Q. Shahra, Shadi Basurra, Rasheed Mohammad, Edlira Vakaj, Waheb A. Jabbar

“…Despite the strengths of individual models, the ensemble meta-model achieved the best overall performance, with a Hamming Loss of 0.1408 and consistently higher ROC-AUC values across multiple diseases, demonstrating its superior capability to handle complex classification tasks. This robust ensemble learning framework underscores its potential for reliable and precise disease detection, offering significant improvements over traditional methods. …”
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Crayfish optimization based pixel selection using block scrambling based encryption for secure cloud computing environment by Vikas K. Soman, V. Natarajan

“…Traditional encryption algorithms are often inadequate in protecting confidential data, especially digital images, from complex cyberattacks. The increasing reliance on cloud storage and transmission of digital images has made it essential to develop strong security measures to stop unauthorized access and guarantee the integrity of sensitive information. …”
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Analisis Perbandingan Model Bert Dan Xlnet Untuk Klasifikasi Tweet Bully Pada Twitter by Teuku Radillah, Okta Veza, Sarjon Defit

“…These findings can help platforms like Twitter identify and prevent bullying content, thereby creating a safer online environment for users, and can be used as a basis for the development of more sophisticated and efficient bullying detection systems in the future. …”
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Heptad green metrics and quality by design tool evaluated chromatography method for content determination of Bexagliflozin in formulation product by Satyajit Raj Nethekar, Phani Raja Kanuparthy, Leela Prasad Kowtharapu, Vishnuvardhana Kishore Polisetti, Pankaj Kumar Lahoti, Sreekantha B Jonnalagadda, Naresh Kumar Katari

“…The study created an eco-friendly, rapid, sensitive, cost-efficient, stability-assessing method for analyzing Bexagliflozin in pharmaceuticals using a RP-UPLC approach. …”
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Structural and morphological studies of non-covalent functionalization carbon nanotubes wrapped poly(3-hexylthiophene-2,5-diyl) nanocomposites by N. Abdullah, N.M. Nurazzi, I.P. Silverwood, S.K. Matam, S.Z.N. Demon, N.S.N. Sa'aya, N.A. Halim, K.W. Baharin

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The pig pose recognition method based on pig body semantic segmentation by Nuonan Huang, Deli Zhu, Liang He, Yi Li

“…Pose recognition in pigs faces several challenges in practical applications, primarily due to high annotation costs, the labor-intensive nature of annotation processes, and the difficulty of achieving accurate target detection in complex environments. To address these challenges, this paper proposes a novel pig pose recognition method based on the semantic segmentation of pig body parts. …”
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Non-Iterative Reconstruction and Selection Network-Assisted Channel Estimation for mmWave MIMO Communications by Jing Yang, Yabo Guo, Xinying Guo, Pengpeng Wang

“…Beyond communication capabilities, these systems offer inherent advantages for integrated sensing applications, particularly in scenarios requiring precise object detection and localization. The sparse mmWave channel in the beamspace domain allows fewer radio-frequency (RF) chains by selecting dominant beams, boosting both communication efficiency and sensing resolution. …”
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Classification and prediction of rock mass drillability for a tunnel boring machine based on operational data mining by Mingshe Sun, Song Chen, Song Chen, Huafei He, Wenzheng Wang, Kezhi Song, Xuebing Lin

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DMoC-UNet: A Dynamic Mixture-of-Convolution Network for Enhanced Pathological Image Segmentation by Jingwei Zhu, Lining Qin, Zixin Teng, Xiaomin Li, Kuiwu Li, Haoran Chu

“…Pathological image segmentation is a cornerstone in medical image analysis and is crucial for tumor detection, tissue classification, and pathological diagnosis. …”
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Trajectory Optimization with Dynamic Drivable Corridor-Based Collision Avoidance by Weijie Wang, Tantan Zhang, Zihan Song, Haipeng Liu

“…Trajectory planning for autonomous vehicles is essential for ensuring driving safety, passenger comfort, and operational efficiency. Collision avoidance constraints introduce significant computational complexity due to their inherent non-convex and nonlinear characteristics. …”
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Research on a Lightweight Arrhythmia Classification Model Based on Knowledge Distillation for Wearable Single-Lead ECG Monitoring Systems by Xiang An, Shiwen Shi, Qian Wang, Yansuo Yu, Qiang Liu

“…Deep learning models, trained on ECG signals across twelve or more leads, are the predominant approach for automated arrhythmia detection in the AI-assisted medical field. While these multi-lead ECG-based models perform well in automatic arrhythmia detection, their complexity often restricts their use on resource-constrained devices. …”
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Research on Method for Intelligent Recognition of Deep-Sea Biological Images Based on PSVG-YOLOv8n by Dali Chen, Xianpeng Shi, Jichao Yang, Xiang Gao, Yugang Ren

“…Nonetheless, the inherent complexities of the deep-sea environment, the scarcity of available deep-sea organism samples, and the significant refraction and scattering effects of underwater light collectively impose formidable challenges on the current detection algorithms. …”
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Recent advancements in nanomaterial-based biosensors for diagnosis of breast cancer: a comprehensive review by Yalda Yazdani, Fereshtehsadat Jalali, Habib Tahmasbi, Mitra Akbari, Neda Talebi, Seyed Abbas Shahrtash, Ahmad Mobed, Mahsa Alem, Farhood Ghazi, Mehdi Dadashpour

“…However, these methods are limited by cost, analysis time, and complexity. Therefore, it is necessary to develop an ultrasensitive, fast, low-cost, simple method for BRCA1 detection. …”
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Melanoma Skin Cancer Recognition with a Convolutional Neural Network and Feature Dimensions Reduction with Aquila Optimizer by Jalaleddin Mohamed, Necmi Serkan Tezel, Javad Rahebi, Raheleh Ghadami

“…Additionally, the AO reduced computational complexity by up to 37.5%. <b>Conclusions:</b> The deep learning-Aquila Optimizer (DL-AO) framework offers a highly efficient and accurate approach for melanoma detection, making it suitable for deployment in resource-constrained environments such as mobile and edge computing platforms. …”
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A growth posture identification method of immature peaches in natural environments by Jidong Lv, Liangliang Niu, Ying Han, Xiaoqin Sun, Lingyun Wang, Hailong Rong, Ling Zou

“…The network models employed in single detection algorithms exhibit complexity and pose challenges for deployment on mobile terminals as they heavily rely on the availability of labeled samples. …”
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