Metal surface defect detection using SLF-YOLO enhanced YOLOv8 model by Yuan Liu, Yilong Liu, Xiaoyan Guo, Xi Ling, Qingyi Geng

“…Abstract This paper addresses the industrial demand for precision and efficiency in metal surface defect detection by proposing SLF-YOLO, a lightweight object detection model designed for resource-constrained environments. …”
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A Novel Chiral Molecularly Imprinted Electrochemical Sensor Based on β-CD Functionalized Graphene Quantum Dots for Enantioselective Detection of D-Carnitine by Feng Yang, Xin Qi, Yan Chen, Kai Tang, Mengyang Fang, Yanwei Song, Jiufen Liu, Lianming Zhang

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Phenotypic variation of Thenus spp. (Decapoda, Scyllaridae) in the waters of southern Thailand and Malaysia using multivariate morphometric analysis by Ihsan Hani Radzi, Cheng-Ann Chen, Sukree Hajisamae, Kay Khine Soe

“… Thenus spp. are slipper lobsters which are commercially significant as a food source with good aquaculture potential. This study focuses on collecting population information on Thenus orientalis and Thenus indicus from selected sites in southern Thailand and Malaysia to inform sustainable fisheries management about the resources. …”
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YOLOGX: an improved forest fire detection algorithm based on YOLOv8 by Caixiong Li, Yue Du, Xing Zhang, Peng Wu

“…To tackle issues, including environmental sensitivity, inadequate fire source recognition, and inefficient feature extraction in existing forest fire detection algorithms, we developed a high-precision algorithm, YOLOGX. …”
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Rationale for obtaining protein concentrate of industrial hemp seeds of Cannabis sativa L. of modern selection based on mathematical modeling and its application in food production by A. V. Petrenko, E. S. Tarasov, O. N. Kaminir, E. N. Guba, E. A. Kalinyuk

“…In general, the studies have shown the feasibility of using protein concentrates from industrial hemp seeds of Cannabis Sativa L of modern selection in food products as a functional and technological ingredient.…”
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GLIMPSE: An Ultrafaint ≃105 M⊙ Pop III Galaxy Candidate and First Constraints on the Pop III UV Luminosity Function at z ≃ 6–7 by Seiji Fujimoto, Rohan P. Naidu, John Chisholm, Hakim Atek, Ryan Endsley, Vasily Kokorev, Lukas J. Furtak, Richard Pan, Boyuan Liu, Volker Bromm, Alessandra Venditti, Eli Visbal, Richard Sarmento, Andrea Weibel, Pascal A. Oesch, Gabriel Brammer, Daniel Schaerer, Angela Adamo, Danielle A. Berg, Rachel Bezanson, Rychard Bouwens, Iryna Chemerynska, Adélaïde Claeyssens, Miroslava Dessauges-Zavadsky, Anna Frebel, Damien Korber, Ivo Labbe, Rui Marques-Chaves, Jorryt Matthee, Kristen B. W. McQuinn, Julian B. Muñoz, Priyamvada Natarajan, Alberto Saldana-Lopez, Katherine A. Suess, Marta Volonteri, Adi Zitrin

“…These properties indicate the presence of a nascent, metal-deficient young stellar population (<5 Myr) with a stellar mass of ≃10 ^5 M _⊙ . Intriguingly, this source deviates significantly from the extrapolated UV–metallicity relation derived from recent JWST observations at z = 4–10, consistent with UV enhancement by a top-heavy Pop III initial mass function or the presence of an extremely metal-poor active galactic nucleus. …”
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Sources of Life Strengths Appraisal Scale: A Multidimensional Approach to Assessing Older Adults’ Perceived Sources of Life Strengths by Prem S. Fry, Dominique L. Debats

“…A 24-month followup of a randomly selected sample confirmed that the nine-scale appraisal measure (SLSAS) is a promising instrument for appraising older adults’ sources of life strengths in dealing with stresses of daily life’s functioning and also a robust measure for predicting outcomes of resilience, autonomy, and well-being for this age group. …”
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Bayesian Q-learning in multi-objective reward model for homophobic and transphobic text classification in low-resource languages: A hypothesis testing framework in multi-objective... by Vivek Suresh Raj, Ruba Priyadharshini, Saranya Rajiakodi, Bharathi Raja Chakravarthi

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CdZnTe- and TlBr-detectors response simulation for registration of the mixed beta- and gamma-radiation by A. I. Skrypnyk

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Loss Function Optimization Method and Unsupervised Extraction Approach D-DBSCAN for Improving the Moving Target Perception of 3D Imaging Sonar by Jingfeng Yu, Aigen Huang, Zhongju Sun, Rui Huang, Gao Huang, Qianchuan Zhao

“…Compared to 2D sonar images, 3D sonar images offer superior spatial positioning capabilities, although the data acquisition cost is higher and lacks open source references for data annotation, target detection, and semantic segmentation. …”
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Spatial behavior of mesocarnivores living in seasonal ecosystems: A case study in arid landscapes in northern-central Chile by Darío Moreira-Arce, Pablo M. Vergara, Alex Oporto, Alberto J. Alaniz, Claudia Hidalgo-Corrotea, Alfredo H. Zúñiga, Alejo Gutiérrez, Sebastián Moreno, Daniela Araya, Simone Ciuti

“…Home ranges and Resource Selection Functions were fitted to the GPS data of seven foxes tracked year-round and related to ecological landscape and site-level attributes derived from remote sensing. …”
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An Optimized FPGA-Based FDIR System for Sensor Fault Detection in Satellite Attitude Estimation by Xianliang Chen, Zhicheng Xie, Jiashu Wu, Xiaofeng Wu

“…To solve this problem, a Fault Detection, Isolation, and Recovery (FDIR) was proposed, which integrates an adaptive unscented Kalman filter (AUKF), a radial basis function (RBF) neural network for fault detection, and a QUEST-based estimator. …”
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Lysozyme functionalized zinc-oxide nanoparticles: Synthesis, characterization, and antibacterial assessment against Staphylococcus aureus with futuristic application in wastewater... by Suneeti Singh, Pavni Rekhi, Jewel Mary Saju

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Nanotechnologies associated to floral resources in agri-food sector by Ahmad S Ammar

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ContDataQC: An R package and Shiny app for quality control of continuous water quality sensor data by Michael J. Pennino, Jen Stamp, Erik W. Leppo, David A. Gibbs, Britta G. Bierwagen

“…ContDataQC helps users speed up and standardize the QC process, minimize undetected data errors, and make full use of their sensor data. It has three main functions: generate QC reports to detect anomalies and erroneous data values, merge QC'd data files from different time periods, and generate time series plots and basic summary statistics. …”
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YOLOv11-GSF: an optimized deep learning model for strawberry ripeness detection in agriculture by Haoran Ma, Qian Zhao, Runqing Zhang, Chunxu Hao, Wenhui Dong, Xiaoying Zhang, Fuzhong Li, Xiaoqin Xue, Gongqing Sun

“…To overcome these limitations, this paper introduces YOLOv11-GSF, a real-time strawberry ripeness detection algorithm based on YOLOv11, which incorporates several innovative features: a Ghost Convolution (GhostConv) convolution method for generating rich feature maps through lightweight linear transformations, thereby reducing computational overhead and enhancing resource utilization; a C3K2-SG module that combines self-moving point convolution (SMPConv) and convolutional gated linear units (CGLU) to better capture the local features of strawberry ripeness; and a F-PIoUv2 loss function inspired by Focaler IoU and PIoUv2, utilizing adaptive penalty factors and interval mapping to expedite model convergence and optimize ripeness classification. …”
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Western spotted skunk spatial ecology in the temperate rainforests of the Pacific Northwest by Marie I. Tosa, Damon B. Lesmeister, Taal Levi

“…Using these home ranges, we fitted a resource selection function using environmental covariates that we assigned to various hypotheses such as resources, predator avoidance, thermal tolerance, and disturbance. …”
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MTAHG and MTBHG: Modified Approaches for Interpreting Gravity Data by Hazel Deniz Toktay, Hanbing Ai, Ahmad Alvandi, Kejia Su, Jinlei Li

“…This paper proposes two effective edge detection tools: one combining the balanced total horizontal gradient (BHG), and the hyperbolic tangent function, abbreviated as “MTBHG”; and the other combining the tilt angle of the total horizontal gradient (TAHG) and the hyperbolic tangent function, abbreviated as “MTAHG.” …”
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Neural network for step anomaly detection in head motion during fMRI using meta-learning adaptation by N.S. Davydov, V.V. Evdokimova, P.G. Serafimovich, V.I. Protsenko, A.G. Khramov, A.V. Nikonorov

“…Quality assessment and artifact detection in functional magnetic resonance imaging (fMRI) data is essential for clinical applications and brain research. …”
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Fine-grained building function recognition with street-view images and GIS map data via geometry-aware semi-supervised learning by Weijia Li, Jinhua Yu, Dairong Chen, Yi Lin, Runmin Dong, Xiang Zhang, Conghui He, Haohuan Fu

“…In this work, we propose a geometry-aware semi-supervised method for fine-grained building function recognition, which effectively uses multi-source geoinformation data to achieve accurate function recognition in both single-city and cross-city scenarios. …”
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