A review of defect formation, detection, and effect on mechanical properties of three-dimensional braided composites by Guo Zeteng, Kong Xiangxia, Zhai Junjun, Zhang Ningxin, Yan Shi, Guo Runjia

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MXene-Based Gas Sensors for NH₃ Detection: Recent Developments and Applications by Yiyang Xu, Yinglin Wang, Zhaohui Lei, Chen Wang, Xiangli Meng, Pengfei Cheng

“…This study reviews the latest progress in the use of MXene and its composites for the low-temperature detection of ammonia gas. …”
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Few-shot network intrusion detection method based on multi-domain fusion and cross-attention. by Congyuan Xu, Donghui Li, Zihao Liu, Jun Yang, Qinfeng Shen, Ningbing Tong

“…Deep learning methods have achieved remarkable progress in network intrusion detection. However, their performance often deteriorates significantly in real-world scenarios characterized by limited attack samples and substantial domain shifts. …”
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MFCANet: Multiscale Feature Context Aggregation Network for Oriented Object Detection in Remote-Sensing Images by Honghui Jiang, Tingting Luo, Hu Peng, Guozheng Zhang

“…Rotated object detection in remote sensing images presents a highly challenging task due to the extensive fields of view and complex backgrounds. …”
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Robust Small-Object Detection in Aerial Surveillance via Integrated Multi-Scale Probabilistic Framework by Youyou Li, Yuxiang Fang, Shixiong Zhou, Yicheng Zhang, Nuno Antunes Ribeiro

“…Although recent deep learning approaches have achieved notable progress, significant challenges persist, including severe object occlusion, extreme scale variation, dense panoramic clutter, and the detection of very small targets. …”
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Assessing the Generalization Capacity of Convolutional Neural Networks and Vision Transformers for Deforestation Detection in Tropical Biomes by P. J. Soto Vega, D. Lobo Torres, G. X. Andrade-Miranda, G. A. O. P. da Costa, R. Q. Feitosa

“…Deep Learning (DL) models, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have become popular for change detection tasks, including the deforestation mapping application. …”
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V-STAR: A Cloud-Based Tool for Satellite Detection and Mapping of Volcanic Thermal Anomalies by Simona Cariello, Arianna Beatrice Malaguti, Claudia Corradino, Ciro Del Negro

“…In recent years, numerous satellite-based systems have been developed to monitor and study volcanic activity from space. This progress reflects the growing demand for accurate and timely monitoring to reduce volcanic risk. …”
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A Critical Review of the CRISPR-Cas Technology in the Detection of SARS-CoV-2 Variants by Jie Zhang, Juezhuo Li, Jiawei Zhou, Jiaye Zhong, Yue Xu, Xiaolei Mao, Minghui Xu, Shuyin Luo, Yi Yang, Ruiyao Hu, Dong-Ang Liu, Shiyu Chen, Yuting Qiu, Keyi Chen, Jinghua Yuan, Xinling Zhang, Xiaoping Li

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Advances in subsurface defect detection techniques for fused silica optical components: A literature review by Hongbing Cao, Xing Peng, Feng Shi, Ye Tian, Lingbao Kong, Menglu Chen, Qun Hao

“…Then, the commonly used destructive and non-destructive detection methods are reviewed, and the principles and latest progress of each technique are discussed. …”
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Deep Learning Frontiers in 3D Object Detection: A Comprehensive Review for Autonomous Driving by Ambati Pravallika, Mohammad Farukh Hashmi, Aditya Gupta

“…The rise of self-driving cars has driven remarkable progress in 3D object detection technologies, crucial in safe and efficient autonomous driving. …”
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Detection of Early Onset Nephropathy in Children with Sickle Cell Anaemia in Calabar, Nigeria Using Microalbuminuria by Uzomba CI, Nsa EI, Brown ES, Enyuma CO, Ekpe LE, Ineji EO, Etuk IS, Asindi AA

“… Background: Asymptomatic nephropathy in children with sickle cell anaemia starts in childhood and may progress to overt renal dysfunction in adult life. …”
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Leveraging deep learning for plant disease and pest detection: a comprehensive review and future directions by Muhammad Shoaib, Abolghasem Sadeghi-Niaraki, Farman Ali, Irfan Hussain, Shah Khalid

“…Plant diseases and pests pose significant threats to crop yield and quality, prompting the exploration of digital image processing techniques for their detection. Recent advancements in deep learning models have shown remarkable progress in this domain, outperforming traditional methods across various fronts including classification, detection, and segmentation networks. …”
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Research progress,key scientific issues,and prospects of segmented fracturing and pressure relief gas drainage technology for coal seam roof horizontal wells by Ke YANG, Zhainan ZHANG, Xinzhu HUA, Wenjie LIU, Xin LYU, Xiaolou CHI, Changcheng WANG, Caiqing LI

“…The segmented fracturing and pressure relief gas drainage technology in the horizontal well of the coal seam roof is a key means to guide the efficient gas extraction in deep, high gas, soft and low permeability coal seams.Based on this, a full lifecycle development concept for the segmented fracturing, pressure relief,and gas drainage engineering of the coal seam roof horizontal well is proposed, including three stages: early scientific planning, mid-term engineering construction, and later safety management.This paper summarizes the research progress on the full life cycle development of segmented fracturing and pressure relief gas drainage in coal seam roof horizontal wells, constructs an overall research framework for key scientific issues related to segmented fracturing and pressure relief gas drainage in coal seam roof horizontal wells, and looks forward to its future development direction.The results show that the expansion of segmented fracturing cracks in the horizontal well of the coal seam roof is extremely complex due to geological factors, construction parameters, and physical properties.It is urgent to comprehensively evaluate the primary and secondary relationships under the influence of multiple factors in crack expansion, and reveal the mechanism of coal seam roof fracturing crack expansion under the influence of multiple factors.Exploring the critical relationship between the coupling of stress, water, temperature, and coal factors on the promotion and inhibition of coalbed methane adsorption, desorption, and migration, and establishing an optimal model for coalbed methane adsorption and desorption under multiple critical indicators, is the key to achieving efficient coalbed methane extraction. …”
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Harnessing optimization with deep learning approach on intelligent transportation system for anomaly detection in pedestrian walkways by Sultan Refa Alotaibi, Fatma S. Alrayes, Mashael Maashi, Mohammed Maray, Mohammed A. Alliheedi, Donia Badawood, Moneerah Alotaibi

“…By applying innovative computer vision (CV) and machine learning (ML) methods, this approach always examines the pedestrian area to detect potential anomalies and attacks. Deep learning (DL) aided AD in pedestrian walkways, which displays a new and very effective method to progress safety and security in urban environments. …”
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Integrating the landscape scale supports SAR-based detection and assessment of the phenological development at the field level by Johannes Löw, Steven Hill, Insa Otte, Christoph Friedrich, Michael Thiel, Tobias Ullmann, Christopher Conrad

“…The framework quantifies uncertainties inherent in both remote sensing and ground observations, and evaluates trackable progress (phenological stage detectability) and tracking range (GDD variance around stages) to assess accuracy under variable acquisition geometries, weather and smoothing parameters. …”
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Is the urinary kidney injury molecule an optimum biomarker for early detection of obstructive nephropathy? An experimental study by Ahmed S El-Hefnawy, Mona A El-Hussiny, Ahmed M. A Ibrahim, Khadiga M Ali, Mohammed A Atwa, Nashwa Barakat, Mohamed Alhefnawy, Ahmed A Shokeir

“…To evaluate the urinary kidney injury molecule-1 (KIM-1) as a predictor for early detection of acute kidney injury in cases with obstructive nephropathy in an animal model and to correlate urinary KIM-1 with the progress of obstructive nephropathy on a histopathological basis. …”
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State-of-the-Art Methodologies for Self-Fault Detection, Diagnosis and Evaluation (FDDE) in Residential Heat Pumps by Francesco Pelella, Adelso Flaviano Passarelli, Belén Llopis-Mengual, Luca Viscito, Emilio Navarro-Peris, Alfonso William Mauro

“…The complexity and added value of these tools grow as they progress from simple fault detection to quantitative fault evaluation, enabling more accurate and timely maintenance strategies. …”
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Large Pretrained Foundation Model for Key Performance Indicator Multivariate Time Series Anomaly Detection by Xu Wang, Qisheng Xu, Kele Xu, Ting Yu, Bo Ding, Dawei Feng, Yong Dou

“…Yet, the development of effective deep learning models is hindered by several challenges: scarce and complex labeled data, noise interference from data handling, the necessity to capture temporal dependencies in time series KPI data, and the complexity of multivariate data analysis. Despite recent progress in large models that show potential for handling complex, multidimensional tasks, the lack of extensive, high-quality datasets presents a significant barrier for directly training these models in KPI anomaly detection. …”
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Integrated pixel-level crack detection and quantification using an ensemble of advanced U-Net architectures by Rakshitha R, Srinath S, N Vinay Kumar, Rashmi S, Poornima B V

“…It addresses critical challenges in accuracy, adaptability, and reliability under diverse operational conditions, marking significant progress in crack detection technology.…”
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Automated Pipeline for Robust Cat Activity Detection Based on Deep Learning and Wearable Sensor Data by Md Ariful Islam Mozumder, Tagne Poupi Theodore Armand, Rashadul Islam Sumon, Shah Muhammad Imtiyaj Uddin, Hee-Cheol Kim

“…Although previous studies have made progress, several key questions still need addressing: What types of data are best suited for accurately detecting activity patterns? …”
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