Food security: state Financial support Measures for sustainable Development of Agriculture in Russian Regions by A. I. Borodin, I. Yu. Vygodchikova, E. I. Dzyuba, G. I. Panaedova

“…The hierarchical procedure is based on a system of mathematical filtering of data, which is fundamentally different from existing methods for analyzing hierarchies. …”
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Small object detection in complex open-pit mine backgrounds based on improved YOLOv11 by ZHU Yongjun, CAI Guangqi, HAN Jin, MIAO Yanzi, MA Xiaoping, JIAO Wenhua

“…The improved YOLOv11 model introduced a Robust Feature Downsampling (RFD) module to replace the stride convolution downsampling module, effectively preserving the feature information of small objects. …”
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SMILES all around: structure to SMILES conversion for transition metal complexes by Maria H. Rasmussen, Magnus Strandgaard, Julius Seumer, Laura K. Hemmingsen, Angelo Frei, David Balcells, Jan H. Jensen

“…We compare these three different ways of obtaining SMILES for a subset of the CSD (tmQMg) and find >70% agreement for all three pairs. …”
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Efficient Identification and Classification of Pear Varieties Based on Leaf Appearance with YOLOv10 Model by Niman Li, Yongqing Wu, Zhengyu Jiang, Yulu Mou, Xiaohao Ji, Hongliang Huo, Xingguang Dong

“…Images were collected at different times of the day to cover changes in natural lighting and ensure model robustness. …”
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Enhancing Satellite Image Coregistration Using Mirror Array as Artificial Point Source for Multisource Image Harmonization by Muhammad Daniel Iman bin Hussain, Vaibhav Katiyar, Masahiko Nagai, Dorj Ichikawa

“…Results show notable improvements in geolocation accuracy, with root mean square error values of 3.59, 4.05, and 4.14 m in the subpixel range for three different GRUS-1 bands when compared to the Global Reference Image. …”
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Tomato ripeness detection and fruit segmentation based on instance segmentation by Jinfan Wei, Yu Sun, Yu Sun, Lan Luo, Lingyun Ni, Mengchao Chen, Minghui You, Minghui You, Ye Mu, Ye Mu, He Gong, He Gong

“…The method proposes two innovative modules: the Adaptive and Oriented Feature Refinement module (AOFRM) and the Custom Multi-scale Pooling module (CMPRD) with Residuals and Depth. By deformable convolution and multi-directional asymmetric convolution, the AOFRM module adaptively extracts the shape and direction features of tomatoes to solve the problems of occlusion and overlap. …”
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Influence of chronic prenatal hypoxia on the specialized contact apparatus of rat heart ventricles during ontogeny by N. S. Petruk

“…Pairwise comparisons between means of different groups were performed using Student’s t-test where, for each couple of normally distributed populations, the null hypothesis that the means are equal was verified. …”
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Beyond Nyquist: A Comparative Analysis of 3D Deep Learning Models Enhancing MRI Resolution by Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald, Anitha Bhat Talagini Ashoka, Mayura Gurjar Cheepinahalli Vasudeva, Shudarsan Saravanan, Venkatesh Thirugnana Sambandham, Pavan Tummala, Steffen Oeltze-Jafra, Oliver Speck, Andreas Nürnberger

“…In order to overcome these limitations, super-resolution MRI deep-learning-based techniques can be utilised. In this work, different state-of-the-art 3D convolution neural network models for super resolution (RRDB, SPSR, UNet, UNet-MSS and ShuffleUNet) were compared for the super-resolution task with the goal of finding the best model in terms of performance and robustness. …”
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CSA-Net: Complex Scenarios Adaptive Network for Building Extraction for Remote Sensing Images by Dongjie Yang, Xianjun Gao, Yuanwei Yang, Minghan Jiang, Kangliang Guo, Bo Liu, Shaohua Li, Shengyan Yu

“…The HFE obtains high-level semantic information at different levels and fuses it with low-level detailed information by skipping connections to enhance the reasoning and perception ability of building structure in complex scenes. …”
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HyDA-Net: A Hybrid Dense Attention Network for Remote Sensing Multi-Image Super-Resolution by Mohamed Ramzy Ibrahim, Robert Benavente, Daniel Ponsa, Felipe Lumbreras

“…Extensive experiments using real-captured satellite datasets, namely PROBA-V and MuS2, show that HyDA-Net outperforms state-of-the-art models in different spectral bands. Moreover, a cross-dataset experiment is conducted to further evaluate the robustness and generalizability of the proposed HyDA-Net.…”
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An improved ShuffleNetV2 method based on ensemble self-distillation for tomato leaf diseases recognition by Shuiping Ni, Yue Jia, Mingfu Zhu, Mingfu Zhu, Yizhe Zhang, Wendi Wang, Shangxin Liu, Yawei Chen

“…To address this issue, this study proposes an ensemble self-distillation method and applies it to the lightweight model ShuffleNetV2.MethodsSpecifically, based on the architecture of ShuffleNetV2, multiple shallow models at different depths are constructed to establish a distillation framework. …”
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Automated classification of mandibular canal in relation to third molar using CBCT images [version 1; peer review: 2 approved] by Yogesh Chhaparwal, Veena Mayya, Sharath S, Neil Abraham Barnes, Roopitha C H, Winniecia Dkhar

“…To accurately classify the mandibular canal in relation to the third molar, both AlexNet and ResNet50 demonstrated high accuracy, with F1 scores ranging from 0.64 to 0.92 for different classes, with accuracy of 81% and 83%, respectively, for accurately classifying the mandibular canal in relation to the third molar. …”
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Coalmine image super-resolution reconstruction via fusing multi-dimensional feature and residual attention network by Jian CHENG, Lifei MI, Hao LI, Heping LI, Guangfu WANG, Yongzhuang MA

“…First, a multi-branch network is employed to parallelly integrate dynamic convolution and channel attention mechanisms, capturing different spatial statistical characteristics through “horizontal-channel” and “vertical-channel” interactions. …”
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Combined influence of quantum iterative reconstruction level and kernel sharpness on image quality in photon counting CT angiography of the upper leg by Kristina Krompaß, Florian Andreas Goldbrunner, Viktor Hartung, Süleyman Ergün, Dominik Peter, Robin Hendel, Henner Huflage, Theresa Sophie Patzer, Jan-Lucca Hennes, Thorsten Alexander Bley, Jan-Peter Grunz, Philipp Gruschwitz

“…Abstract Aim was to evaluate the influence of different quantum iterative reconstruction (QIR) levels on the image quality of femoral photon-counting CT angiographies (PCD-CTA). …”
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Deep Fuzzy Credibility Surfaces for Integrating External Databases in the Estimation of Operational Value at Risk by Alejandro Peña, Lina M. Sepúlveda-Cano, Juan David Gonzalez-Ruiz, Nini Johana Marín-Rodríguez, Sergio Botero-Botero

“…Following the above, this paper develops and analyzes a Deep Fuzzy Credibility Surface model (DFCS), which allows the integration in a single structure of different loss event databases for the estimation of an operational value at risk (OpVar), overcoming the limitations imposed by the low frequency with which a risk event occurs within an organization (sparse data). …”
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Handwritten Urdu Characters and Digits Recognition Using Transfer Learning and Augmentation With AlexNet by Aqsa Rasheed, Nouman Ali, Bushra Zafar, Amsa Shabbir, Muhammad Sajid, Muhammad Tariq Mahmood

“…The purpose of this research is to present a classification framework for automatic recognition of handwritten Urdu character and digits with higher recognition accuracy by utilizing theory of transfer learning and pre-trained Convolution Neural Networks (CNN). The performance of transfer learning is evaluated in different ways: by using pre-trained AlexNet CNN model with Support Vector Machine (SVM) classifier, and fine-tuned AlexNet for extracting features and classification. …”
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A lightweight mechanism for vision-transformer-based object detection by Yanming Ye, Qiang Sun, Kailong Cheng, Xingfa Shen, Dongjing Wang

“…XFA simplifies the attention mechanism’s computational process and reduces complexity through L2 normalization and two one-dimensional convolutions applied in different directions. This design reduces the computational complexity from quadratic to linear while preserving spatial context awareness. …”
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A Lightweight Method for Detecting Bearing Surface Defects Based on Deep Learning and Ontological Reasoning by Xiaolin Shi, Haisong Xu, Han Zhang, Yi Li, Xinshuo Li, Fan Yang

“…Therefore, the quality control of bearings must be very strict. Aiming at the different sizes and textures of the defect types on the surface of the outer ring of the bearing, and the fact that most of the target detection algorithms relying on deep learning show low speed and low precision in the detection of defects on the surface of the bearing, this paper presents a lightweight method for detecting the defects on the surface of the bearing based on deep learning and ontological reasoning. …”
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DAU-YOLO: A Lightweight and Effective Method for Small Object Detection in UAV Images by Zeyu Wan, Yizhou Lan, Zhuodong Xu, Ke Shang, Feizhou Zhang

“…To enhance feature extraction, a Receptive-Field Attention (RFA) module is introduced in the backbone, allowing adaptive convolution kernel adjustments across different local regions, thereby addressing the challenge of dense object distributions. …”
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Design and synthesis of reversible Vedic multiplier using cadence 180 nm technology for low-power high-speed applications by Narayanan Mageshwari, Periyasamy Sakthivel, Ramasamy Seetharaman

“…In this work, a high-speed 64-bit reversible Vedic multiplier is proposed using five different adders, namely reversible ripple carry adder (RRCA), reversible carry look-ahead adder (RCLA), reversible carry save adder (RCSA), reversible carry bypass or carry skip adder (RCSKA)adder, and reversible carry select adder (RCSLA). …”
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