GrainNet: efficient detection and counting of wheat grains based on an improved YOLOv7 modeling by Xin Wang, Changchun Li, Chenyi Zhao, Yinghua Jiao, Hengmao Xiang, Xifang Wu, Huabin Chai

“…We propose a wheat grain detection and counting model called GrainNet, which significantly improves the counting performance and detection speed across diverse conditions and adhesion levels by incorporating lightweight and efficient feature fusion modules. …”
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Modeling and Evaluating a Cache System in ICN Routers Using a Programmable Switch and Computers by Junji Takemasa, Yuki Koizumi, Toru Hasegawa

“…Information-centric networking (ICN) is one of promising networking architectures to replace IP because its notable feature, in-network caching, is expected to reduce about a one-third of the forever increasing Internet traffic. …”
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Advanced predictive modeling for enhanced mortality prediction in ICU stroke patients using clinical data. by Armin Abdollahi, Negin Ashrafi, Xinghong Ma, Jiahao Zhang, Daijia Wu, Tongshou Wu, Zizheng Ye, Maryam Pishgar

“…Conclusion Through enhanced feature selection and data cleaning, proposed model demonstrates a 13% AUROC improvement compared to existing models while reducing feature number from 1095 in previous studies to 30.…”
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An Optical Flow- and Machine Learning-Based Fall Recognition Model for Stair Accessing Service Robots by Jun Hua Ong, Abdullah Aamir Hayat, Mohan Rajesh Elara, Kristin Lee Wood

“…A study was also performed to study the effects of moving objects on optical flow fall detection, and it showed that moving objects have minimal to no impact on sparse optical flow in an environment with otherwise sufficient features. …”
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Establishment of rat model of diabetic nephropathy induced by left nephrectomy combined with intraperitoneal injection of streptozotocin by GAO Jun-li, ZHU Yun-jie, WANG Wei-wei, ZHANG Jin-yuan

“…Objective To investigate the establishment of a rat model of diabetic nephropathy induced by intraperitoneal injection of streptozotocin and left nephrectomy.Methods Sixteen healthy male SD rats were randomly divided into 3 groups:normal control group(N group,5 rats),sham operation group(SO group,5 rats),and model group(M group,6 rats).After 3 days of sexual feeding,M group underwent left kidney resection,and a single intraperitoneal injection of 60 mg/kg streptozotocin was performed one week after surgery to establish a diabetic nephropathy rat model.Two times at 72 h after modeling,randomized blood glucose levels greater than 16.7 mmol/L were criteria for the establishment of a diabetic model.Blood,urine,and kidney specimens were collected at the 3 rd month after modeling,and blood and urine biochemical parameters were measured.HE,PAS,Masson staining,and electron microscopy were used to observe the pathological features of the kidneys.Results As compared with N group and SO group,the weight gain in M group was slowed down,the body weight showed a negative growth trend at the later stage,the drinking water amount increased significantly,and diabetic retinopathy occurred.The urine volume,urinary protein and urinary albumin quantification were significantly increased at 24 h in M group.Blood creatinine and blood urea nitrogen levels were elevated in M group.HE,PAS,and Masson staining indicated that in M group,glomerular basement membrane and vascular intima were thickened,tubular vacuolar degeneration occurred,and some rats developed kidney Interstitial fibrosis.Electron microscopy suggested that the glomerular basement membrane was thickened and some podocyte foot processes were fused or absent.Conclusions A left kidney nephrectomy combined with intraperitoneal injection of streptozotocin can establish a reliable rat model of diabetic nephropathy.…”
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Justification of the simulation model of a static arched vault in a moving mass of bulk material by B.A. Sheludchenko, O.V. Medvedsky, O.B. Pluzhnikov, V.R. Biletsky, V.M. Borovsky, V.A. Yanovsky

“…The performed adequacy check based on the phenomenological features of the proposed simulator model proves that such a model can be used to study the regularities of the dynamics of masses of bulk media with different mechanical, rheological and phenomenological properties.…”
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A Bio-Inspired Learning Dendritic Motion Detection Framework with Direction-Selective Horizontal Cells by Tianqi Chen, Yuki Todo, Zhiyu Qiu, Yuxiao Hua, Hiroki Sugiura, Zheng Tang

“…Unlike present deep learning models, which rely on extension of computation and extraction of global features, the HCdM mimics the localized processing of dendritic neurons, enabling efficient motion feature integration. …”
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Study on lithology identification using a multi-objective optimization strategy to improve integrated learning models: a case study of the Permian Lucaogou Formation in the Jimusae... by Xili Deng, Jiahong Li, Junkai Chen, Cheng Feng

“…Initially, a comprehensive sampling model—Smote-Tomek (ST) is used to introduce new feature information into the dataset while removing redundant features, effectively addressing the issue of data imbalance. …”
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Sinusoidal exponential distribution for modeling elevated creatinine kinase level as the initial presentation of Covid-19 by N. M. Kilany, A. M. Alharpy

“…Among its interesting properties, it has the feature of possessing a periodic hazard rate function. …”
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Zero-shot classification with multi-model information interaction and its application in Mars exploration scenarios by TAN Xiaomeng, XI Bobo, XUE Changbin, LI Yunsong

“…For algorithm research, a zero-shot scene classification algorithm based on multi-modal feature interaction was proposed. Then combined with knowledge distillation, the model was compressed and optimized, which greatly reduced the number of parameters and complexity of the model while ensuring the performance of zero-shot classification. …”
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Machine Learning Models for Carbonation Depth Prediction in Reinforced Concrete Structures: A Comparative Study by Rafael Aredes Couto, Igor Augusto Guimarães Campos, Elvys Dias Reis, Daniel Hasan Dalip, Flávia Spitale Jacques Poggiali, Péter Ludvig

“…This study applied machine learning (ML) techniques—Random Forest (RF), Support Vector Regression (SVR), and Artificial Neural Networks (ANNs)—to predict carbonation depth using a synthetic dataset of 20,000 instances generated from the validated Possan equation. Model performances were evaluated across multiple scenarios, with compressive strength and exposure time identified as the most influential features, while relative humidity and exposure conditions had intermediate effects. …”
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Contemporary comprehensive approaches to assessing the effectiveness of experimental model of neurodegenerative disorders with cognitive status changes by M. V. Danukalo, Yu. M. Kolesnyk, O. V. Hancheva

“…Aim of the study was to characterize the locomotor and cognitive aspects of the behavior of experimental rats under intracerebroventricular colchicine administration in the open field test and the 8-arm radial maze, as well as identify a set of behavioral features of experimental animals that emerged during the study. …”
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Prediction of the strength characteristics of basalt fibre reinforced concrete using explainable machine learning models by B. Y. Wickramasuriya, Yasitha Alahakoon, B. R. G. A. Krishantha, Janaka Alawatugoda, I. U. Ekanayake

“…Three datasets, each with 267 samples, were used for model development. Each sample included 10 features: cement, fly ash, silica ash, coarse aggregate, fine aggregate, water, superplasticiser, fibre diameter, fibre length, and fibre content. …”
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Civil Service Ethics in Germany from an International Perspective – Between a Role Model and Latecomer by Christoph Demmke

“…It demonstrates that Germany’s ostensibly old-fashioned model performs robustly in practice, while highlighting structural and informational deficits that must be addressed if ethics policies are to keep pace with technological and organisational change. …”
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Construction of a sugar and acid content estimation model for Miliang-1 kiwifruit during storage by LIU Li, YANG Tianyi, DONG Congying, SHI Caiyun, SI Peng, WEI Zhifeng, GAO Dengtao

“…These results underscore the effectiveness of the developed models and the significant role of preprocessing and feature selection in enhancing model performance. …”
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Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning by Zhe Chen, Dong Zhang, Chunxiang Liu, Hui Wang, Xinyao Jin, Fengwen Yang, Junhua Zhang

“…We assessed the performance of the model using precision, recall, and F1 scores as evaluation metrics. …”
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Machine Learning Model Coupled with Graphical User Interface for Predicting Mechanical Properties of Flax Fiber by T. Nageshkumar, Prateek Shrivastava, L. Ammayapan, Manisha Jagadale, L. K. Nayak, D. B. Shakyawar, Indran Suyambulingam, P. Senthamaraikannan, R. Kumar

“…The SHapley Additive exPlanations (SHAP) analysis was performed and found sample weight and gauge length were the most influential features for breaking load and elongation, respectively. …”
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An analytical approach to modeling conjunctival viral disease using fuzzy logic and time-delay dynamics by Muhammad Tashfeen, Hothefa Shaker Jassim, Fazal Dayan, Muhammad Azizur Rehman, Alwahab Dhulfiqar Zoltán, Husam A. Neamah

“…Simulation results confirm that the NSFD approach maintains the qualitative features of the model even under larger time steps. …”
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MRC-DETR: A High-Precision Detection Model for Electrical Equipment Protection in Power Operations by Shenwang Li, Yuyang Zhou, Minjie Wang, Li Liu, Thomas Wu

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Improved Dab-Deformable Model for Runway Foreign Object Debris Detection in Airport Optical Images by Yang Cao, Yuming Wang, Yilin Zhu, Rui Yang

“…First, this paper introduces a Lightweight Deep-Shallow Feature Fusion algorithm (LDSFF), which integrates a hotspot sensing network and a spatial mapping enhancer aimed at focusing the model on significant regions. …”
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