ITD-YOLO: An Improved YOLO Model for Impurities in Premium Green Tea Detection by Zezhong Ding, Yanfang Li, Bin Hu, Zhiwei Chen, Houzhen Jia, Yali Shi, Xingmin Zhang, Xuesong Zhu, Wenjie Feng, Chunwang Dong

“…To solve this technical problem in the industry, this article proposes a lightweight algorithm for detecting and sorting impurities in premium green tea in order to improve sorting efficiency and reduce labor intensity. …”
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Synthetic graphs for link prediction benchmarking by Alexey Vlaskin, Eduardo G Altmann

“…Predicting missing links in complex networks requires algorithms that are able to explore statistical regularities in the existing data. …”
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A small underwater object detection model with enhanced feature extraction and fusion by Tao Li, Yijin Gang, Sumin Li, Yizi Shang

“…Advancements in deep learning have led to the development of many efficient detection techniques. However, the complexity of the underwater environment, limited information available from small objects, and constrained computational resources make small object detection challenging. …”
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Intelligent design of Fe–Cr–Ni–Al/Ti multi-principal element alloys based on machine learning by Kang Xu, Zhengming Sun, Jian Tu, Wenwang Wu, Huihui Yang

“…Multi-principal element alloys (MPEAs), distinguished by their complex compositions and exceptional mechanical properties, pose significant challenges for conventional predictive approaches in mechanical property optimization. …”
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Petrographic image classification of complex carbonate rocks from the Brazilian pre-salt using convolutional neural networks by Mateus Basso, João Paulo da Ponte Souza, Guilherme Furlan Chinelatto, Luis Augusto Antoniossi Mansini, Alexandre Campane Vidal

“…The use of ML enables the analysis of large datasets, the identification of complex patterns, and can save time and reduce costs compared to conventional approaches. …”
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Outlier detection algorithm based on fast density peak clustering outlier factor by Zhongping ZHANG, Sen LI, Weixiong LIU, Shuxia LIU

“…For the problem that peak density clustering algorithm requires human set parameters and high time complexity, an outlier detection algorithm based on fast density peak clustering outlier factor was proposed.Firstly, k nearest neighbors algorithm was used to replace the density peak of density estimate, which adopted the KD-Tree index data structure calculation of k close neighbors of data objects, and then the way of the product of density and distance was adopted to automatic selection of clustering centers.In addition, the centripetal relative distance and fast density peak clustering outliers were defined to describe the degree of outliers of data objects.Experiments on artificial data sets and real data sets were carried out to verify the algorithm, and compared with some classical and novel algorithms.The validity and time efficiency of the proposed algorithm are verified.…”
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Outlier detection algorithm based on fast density peak clustering outlier factor by Zhongping ZHANG, Sen LI, Weixiong LIU, Shuxia LIU

“…For the problem that peak density clustering algorithm requires human set parameters and high time complexity, an outlier detection algorithm based on fast density peak clustering outlier factor was proposed.Firstly, k nearest neighbors algorithm was used to replace the density peak of density estimate, which adopted the KD-Tree index data structure calculation of k close neighbors of data objects, and then the way of the product of density and distance was adopted to automatic selection of clustering centers.In addition, the centripetal relative distance and fast density peak clustering outliers were defined to describe the degree of outliers of data objects.Experiments on artificial data sets and real data sets were carried out to verify the algorithm, and compared with some classical and novel algorithms.The validity and time efficiency of the proposed algorithm are verified.…”
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SNPs and other features as they predispose to complex disease: genome-wide predictive analysis of a quantitative phenotype for hypertension. by Joong-Ho Won, Georg Ehret, Aravinda Chakravarti, Richard A Olshen

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TCR-engaging scaffolds selectively expand antigen-specific T-cells with a favorable phenotype for adoptive cell therapy by Marco Donia, Özcan Met, Julie Westerlin Kjeldsen, Sine Reker Hadrup, Inge Marie Svane, Arianna Draghi, Anders Handrup Kverneland, Christina Heeke, Siri Amanda Tvingsholm, Marcus Svensson Frej, Vibeke Mindahl Rafa, Ulla Kring Hansen, Maria Ormhøj, Alexander Tyron, Agnete W P Jensen, Mohammad Kadivar, Amalie Kai Bentzen, Kamilla K Munk, Gitte N Aasbjerg, Jeppe S H Ternander, Tripti Tamhane, Christian Schmess, Samuel A. Funt, Søren Nyboe Jakobsen

“…The resulting T-cell products were assessed for phenotypic and functional characteristics.Results We identified an optimal Ag-scaffold for expansion of T-cells for ACT, carrying pMHC and interleukin-2 (IL-2) and IL-21, with which we efficiently expanded both virus-specific and tumor-specific CD8+ T cells from peripheral blood of healthy donors and patients, respectively. …”
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Detection of Pear Quality Using Hyperspectral Imaging Technology and Machine Learning Analysis by Zishen Zhang, Hong Cheng, Meiyu Chen, Lixin Zhang, Yudou Cheng, Wenjuan Geng, Junfeng Guan

“…In summary, the combination of HSI and machine learning models enabled an efficient, rapid, and non-destructive detection of pear quality and provided a practical value for quality control and the commercial processing of pears.…”
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PCPE-YOLO with a lightweight dynamically reconfigurable backbone for small object detection by Weijia Chen, Jiaming Liu, Tong Liu, Yaoming Zhuang

“…Abstract In the domain of object detection, small object detection remains a pressing challenge, as existing approaches often suffer from limited accuracy, high model complexity, and difficulty meeting lightweight deployment requirements. …”
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An improved method of AUD-YOLO for surface damage detection of wind turbine blades by Li Zou, Anqi Chen, Xinhua Yang, Yibo Sun

“…Abstract The detection of wind turbine blades (WTBs) damage is crucial for improving power generation efficiency and extending the lifespan of turbines. …”
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HGLFNet: Hybrid Global Semantic and Local Detail Feature Network for Lane Detection by Lei Ding, Chunhui Tang, Yi Fang

“…The experimental results comprehensively demonstrate that HGLFNet surpasses the existing state-of-the-art techniques in both accuracy and efficiency, providing a novel and effective solution for lane detection in complex scenarios and showing significant potential in practical applications.…”
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YOLO-MES: An Effective Lightweight Underwater Garbage Detection Scheme for Marine Ecosystems by Chengxu Huang, Wenyuan Zhang, Beitian Zheng, Jiahao Li, Bochen Xie, Ruisi Nan, Zongming Tan, Baohua Tan, Neal N. Xiong

“…Experimental results indicate that YOLO-MES achieves 95.8% accuracy on the dataset, while reducing model size and computational complexity by 64% and 67%, respectively. Compared to existing mainstream detection algorithms, YOLO-MES offers significant advantages in lightweight design and computational efficiency, providing a practical and deployable solution for underwater target detection on mobile devices.…”
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Optimization Method of Wind Turbine Locations in Complex Terrain Areas Using a Combination of Simulation and Analytical Models by Dinh Van Thin, Le Quang Sang, Nguyen Huu Duc

“…For areas with complex terrain, wind resource characteristics depend largely on terrain features, so the selection of turbine installation locations is very important. …”
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Experimental Demonstration of 16-QAM DD-SEFDM With Cascaded BPSK Iterative Detection by Jun Huang, Qi Sui, Zhaohui Li, Fei Ji

“…To simplify the complexity of a spectrally efficient frequency-division multiplexing (SEFDM) system, cascaded binary-phase-shift-keying iterative detection (CBID) is proposed for square <inline-formula> <tex-math notation="LaTeX">$M$</tex-math></inline-formula>-ary quadrature-amplitude-modulation (M-QAM) SEFDM as the first decoding stage, in conjunction with a fixed sphere decoder (FSD). …”
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Enhanced anomaly traffic detection framework using BiGAN and contrastive learning by Haoran Yu, Wenchuan Yang, Baojiang Cui, Runqi Sui, Xuedong Wu

“…However, existing methods face many challenges when processing complex high-dimensional traffic data. Especially in dealing with redundant features, data sparsity and nonlinear features, traditional methods often suffer from high computational complexity and low detection efficiency. …”
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An Anomaly Detection Method for Industrial System Cybersecurity Based on GGL-WAVE-CNN by Bing Zou, Ke jun Zhang, Xin Ying Yu, Yu han Jin, Jun Wang, Ling yu Liu

“…Current approaches often struggle to handle complex, unknown topological time series data, thereby necessitating improved anomaly detection accuracy. …”
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Self-Supervised Drift-Resilient Classification for Time Series Industrial Anomaly Detection by Myung-Kyo Seo, Byeong Hoon Yoon, Junseung Ryu, Hyung Ju Hwang

“…In modern industrial environments, early detection of anomalies is essential to prevent unplanned downtime and maintain operational efficiency. …”
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DART-scale Impacts into a Complex Low-strength Target: Insights into Asteroid Dimorphos’s Interior from Simulations by Cem Berk Senel, Robert Luther, Özgür Karatekin, Gareth S. Collins, Steven Goderis, Philippe Claeys

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