Application of non-invasive monitoring technology in intensive sheep farming: A review by Jinxin Liang, Zhiyu Yuan, Xinhui Luo, Jianrui Qu, Yu Qi, Chunxin Wang

“…Under the intensive sheep farming model, traditional monitoring and management methods face issues such as outdated equipment and low efficiency, which cannot fully meet the requirements for refined and intelligent management in breeding. …”
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BIM-GIS-Based Approach for Quality Management Aligned with ISO 9001 by Pablo Araya-Santelices, Pedro Moraga, Edison Atencio, Fidel Lozano-Galant, José Antonio Lozano-Galant

“…The digital workflow also facilitated the identification of nonconformities and streamlined communication between field inspectors and model managers. …”
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Framework for timely perception of spatiotemporal crowd congestion risk in public spaces based on video pedestrian tracking and geographic mapping by Shaojun Liu, Ling Zhang, Junlian Ge, Weitao Li, Yi Long

“…We introduced a framework for crowd activity perception and scene-adaptive congestion risk assessment based on a multi-object tracking model. This framework established a mutual mapping between the image and geographic spaces, facilitating precise spatiotemporal identification and grading of congestion. …”
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Feasibility of Mental Health Triage Call Priority Prediction Using Machine Learning by Rajib Rana, Niall Higgins, Kazi Nazmul Haque, Kylie Burke, Kathryn Turner, Terry Stedman

“…A total of 459 call records from a mental health helpline were investigated. The final ML model achieved a balanced accuracy of 92% correct identification of both positive and negative instances of call priority. …”
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Innovative Flood Impact Monitoring and Harvest Analysis in Oil Palm Plantations Utilizing Geographic Information Systems and Deep Learning by Supattra Puttinaovarat, Supaporn Chai-Arayalert, Wanida Saetang, Kanit Khaimook, Sasikarn Plaiklang, Paramate Horkaew

“…These findings indicate that the proposed model effectively and efficiently monitors flood-affected areas. …”
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A Three-Granularity Pose Estimation Framework for Multi-Type High-Voltage Transmission Towers Using Part Affinity Fields (PAFs) by Yaoran Huo, Xu Dai, Zhenyu Tang, Yuhao Xiao, Yupeng Zhang, Xia Fang

“…In various types of electrical tower detection, the average keypoint identification AF of the proposed model exceeds 96% and the average skeleton connection AF exceeds 93% at all granularities, which demonstrates good results on the test set and shows some degree of generalization to electricity towers not included in the dataset.…”
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Supply–Demand Spatial Patterns of Cultural Services in Urban Green Spaces: A Case Study of Nanjing, China by Qinghai Zhang, Ruijie Jiang, Xin Jiang, Yongjun Li, Xin Cong, Xing Xiong

“…However, the spatial distribution of CES supply and demand within urban green spaces remains significantly unbalanced, necessitating precise identification and quantification of CES supply–demand patterns to enhance ecosystem service efficiency. …”
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Improving fluoroprobe sensor performance through machine learning by D. Lafer, A. Sukenik, T. Zohary, O. Tal

“…Here, we use machine learning models to enhance assessment and identification of the major prevailing taxa by FP implementation in the well-studied Lake Kinneret (Sea of Galilee), Israel. …”
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Identifying and Predicting the Lagrangian Coherence of Eddies in the Gulf of Mexico Using Machine Learning and Satellite Observations by Luna Hiron, Olmo Zavala‐Romero, Eric P. Chassignet, Philippe Miron, Bulusu Subrahmanyam

“…This study also investigates the contributions of Chl‐a to identifying and forecasting LCEs' and LCFEs' Lagrangian coherence, aiming to assess the advantages of integrating this data set into data‐assimilative Gulf ocean models, in addition to ADT and SST. The machine learning model trained with ADT successfully identifies and predicts the lifetimes of eddies, achieving accuracy rates of 90% for LCE identification and 93% for lifetime prediction, along with 71% and 61% for LCFEs, respectively. …”
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A self-powered millifluidic device driven by triboelectric generator for the label-free and real-time detection of DNA hybridization by Tara Ghafouri, Sheida Bagherzadeh‑Nobari, Negin Manavizadeh

“…Numerical simulations are carried out using a two-dimensional model in COMSOL Multiphysics software. In line with the heterogeneous genoassay modeling, flow variables are tuned to attain the optimal hybridization efficiency equal to 92%. …”
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Synergizing human insight and machine learning: A dual-lens approach to uncovering healthcare research and innovation outcomes by Stijn Horck, Sanne Steens, Jermain Kaminski

“…By using narrative interviews to inform and pre-train a text embedding model, we demonstrate the potential to create a proxy for human judgement, allowing for a more natural identification of contextual patterns in project documentation. …”
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ESTIMATING THE PROPERTIES OF TECHNOLOGICAL SYSTEMS BASED ON FUZZY SETS by Vladimir Beskorovainyi, Heorhii Berezovskyi

“…Common scientific methods are used, such as: decision making, utility theory, fuzzy sets and identification. The following results are obtained. The article presents the model of preferences of the decision-maker developed by the authors for evaluating individual properties of technological systems using the membership function of fuzzy sets, which allows to realize both linear and nonlinear (convex, concave, S-shaped and Z-shaped) criteria. …”
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Production and Internal Logistics Flow Improvements through the Application of Total Flow Management by Diana Filipe, Carina Pimentel

“…Therefore, the Total Flow Management (TFM) model was implemented, and several methods and tools were used, such as Value Stream Mapping (VSM), work measurement and 5S’s. …”
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Search for single vector-like B quark production in hadronic final states at the LHC by Bingfang Yang, Zejun Li, Xinglong Jia, Stefano Moretti, Liangliang Shang

“…We use cut-and-count combined with Extreme Gradient Boosting (XGBoost) methods to classify the signal and background events in order to improve the efficiency of signal identification and background rejection. …”
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Application of Machine Learning Techniques for Bearing Fault Diagnosis by Sarra Eddai, Nabih Feki, Ahmed Ghorbel, Abdelkhalak El Hami, Mohamed Haddar

“…The methodology employed encompasses a systematic approach, including data preprocessing, feature extraction, and model development. This research employs advanced machine learning techniques, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and Naive Bayes algorithms, in conjunction with time-domain and frequency-domain feature extraction methods. …”
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Optimal thresholds and key parameters for predicting influenza A virus transmission events in ferrets by Troy J. Kieran, Xiangjie Sun, Taronna R. Maines, Jessica A. Belser

“…Higher viral loads in nasal wash (NW) specimens were strongly associated with higher transmission frequencies in DCT, but not RDT models. However, viruses that reached peak titers in NW specimens early (day 1 p.i.) were strongly associated with higher transmission in both models. …”
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Rapid diagnosis of power battery faults in new energy vehicles based on improved boosting algorithm and big data by Jiali Wang, Jia Chen

“…The experimental results indicate that the LightGBM model effectively detects anomalies in battery big data. …”
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Phenomenology of photons-enriched semi-visible jets by Cesare Cazzaniga, Alessandro Russo, Emre Sitti, Annapaola de Cosa

“…We show that, by removing any selection on the neutral electromagnetic fraction from the jet identification criteria, higher signal efficiency can be reached. …”
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Analysis of the effectiveness of natural treatments for preserving apricots and the YOLOv7 application for early damage detection by Mustafa A. J. Al-Sammarraie, Zeki Gokalp, Samsuzana Abd Aziz

“…Therefore, this study is expected to reduce the workload in post-harvest fruit processing and help in the rapid identification and detection of damaged fruits based on smart detection algorithms, thus improving sorting efficiency and reducing both waste and economic losses, which enhances smart agriculture technologies.…”
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Research on the integrated "cloud-edge-end" intelligent and precise management and control technology system for coal mine disasters by LIU Yubing, LI Yiteng, LI Zhonghui, YIN Shan, JING Chao, LI Kai, LI Zhenxing, ZHAO Shenglei, LIU Chengfei

“…On the edge side, a coal mine major disaster data fusion analysis model based on the deep learning AdaTT model was developed. …”
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