Self-Supervised Learning for Precise Individual Tree Segmentation in Airborne LiDAR Point Clouds by Lama Shaheen, Bader Rasheed, Manuel Mazzara

“…In this study, we propose a novel self-supervised learning framework that eliminates the need for manual labeling by integrating transformation-invariant feature extraction, an energy-based segmentation loss, and soft clustering. The framework operates in two stages: a pretext task applies geometric transformations—rotation (from –45° to +45°), translation (between –1 and 1 units), and scaling (between 0.5 and 2.0)—to learn robust features, while an unsupervised segmentation step leverages an energy function that combines height, density, and slope attributes to cluster points corresponding to individual trees. …”
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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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THE CHOICE OF RELAXATION IN DESCRIBING MECHANICAL CHARACTERISTICS OF HIGH TEMPERATURE AREA OF DISSIPATIVE LOSS IN THE SPECTRUM OF INTERNAL FRICTION by A. A. Valishin, E. M. Kartashov, A. A. Kukhtenkova, V. A. Lomovskoy

“…Different analytical functions, which are often used in describing the inelasticity phenomenon, were studied as relaxation cores for describing the inelastic response of such subsystem in the area of increasing relaxation background of dissipative loss. …”
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Evaluation of additional points in the 24-2C visual field test in pre-perimetric glaucoma by Gökhan Çelik, Tuncay Karaçocuk, Furkan Çiftci

“…Conclusion: Although no glaucomatous change was observed in the VF in patients with PPG, these patients showed reduced sensitivity of the additional points. The reduced sensitivity of the additional points in the 24-2C VF test may reflect early macular functional alterations that correlate with structural damage during the pre-perimetric period.…”
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Large live biomass carbon losses from droughts in the northern temperate ecosystems during 2016-2022 by Xiaojun Li, Philippe Ciais, Rasmus Fensholt, Jérome Chave, Stephen Sitch, Josep G. Canadell, Martin Brandt, Lei Fan, Xiangming Xiao, Shengli Tao, Huan Wang, Clément Albergel, Hui Yang, Frédéric Frappart, Mengjia Wang, Ana Bastos, Philippe Maisongrande, Yuanwei Qin, Zanpin Xing, Tianxiang Cui, Ling Yu, Lei He, Yi Zheng, Xiangzhuo Liu, Yuqing Liu, Aurelien De Truchis, Jean-Pierre Wigneron

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Association Between Vertical Bone Defects and Interdental Papilla Loss in Periodontitis: A Cross-Sectional Analysis by Hristina Maynalovska, Kamen Kotsilkov

“…<b>Background:</b> The interdental papilla plays a critical role in maintaining both the esthetic and functional integrity of the periodontium. Although the relationship between the papilla presence and the contact point–bone crest distance is well established, the impact of vertical bone defect morphology—common in periodontitis—remains largely unexplored. …”
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A Simple Estimation of Coupling Loss Factors for Two Flexible Subsystems Connected via Discrete Interfaces by Jun Zhang, Lin Ji, Zhenyu Huang, Pingping Zhang, Wei Wang

“…If ensemble- and frequency-averaged, the intermodal coupling stiffness terms can simply reduce to a function of the characteristic dynamic properties of each subsystem and the subsystem mass, as well as the number of interface points. …”
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Speech Recognition in Noise: Analyzing Phoneme, Syllable, and Word-Based Scoring Methods and Their Interaction with Hearing Loss by Saransh Jain, Vijaya Kumar Narne, Bharani, Hema Valayutham, Thejaswini Madan, Sunil Kumar Ravi, Chandni Jain

“…All procedures were designed to reflect standard diagnostic protocols in clinical audiology. Discriminant function analysis examined how these scoring methods differentiate the degree of hearing loss. …”
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Reduction of Active Power Loss at 110/220 kV Node Substations Using Fuzzy Controller by Krysanov V.N., Burkovsky V.L., Danilov A.D.

“…Herewith, the most accurate accounting of consumer load, the maximum use of equipment at the substation for voltage regulation at the points of release and a significant reduction of active power losses at the tie substa-tions 110/220 kV (up to 12,12%) compared to already known solutions were observed.…”
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THz-Enabled UAV Communications Under Pointing Errors: Tractable Statistical Channel Modeling and Security Analysis by Mohammad Javad Saber, Mazen Hasna, Osamah S. Badarneh

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Capacity analysis for MISO-UWOC systems over GGD weak turbulence with zero boresight pointing error by Yueheng LI, Yining XU, Meiyan JU, Ping HUANG

“…Generalized Gamma distribution (GGD) was chosen to describe the weak oceanic turbulence, and a new hybrid fading channel model that integrated the GGD weak turbulence, the zero boresight pointing error, and the implicit path loss plus multipath propagation characterized by the fading free impulse response (FFIR) was proposed.Subsequently, mathematical expressions for the ergodic capacity and outage capacity of the multiple-input single-output underwater wireless optical communication (MISO-UWOC) systems were derived through the Meijer-G function under a selective transmission (ST) diversity scheme especially while inter-symbol interference (ISI) effects were considered or not.Finally, the correctness of the theoretical formulas derived above was verified by some numerical results.The simulation results show that with the introduction of the ST diversity, the ergodic capacity of the MISO-UWOC systems, taking the transmission ports N=2 as an example, is at least 1.3 times better than that of the conventional point-to-point (P2P) transmission under the same channel condition and system parameters, while the maximum outage capacity decreases is also more than 60% compared with the conventional P2P one.However, the introduction of the ISI will severely reduce this performance improvement.…”
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A Virus Propagation Model and Optimal Control Strategy in the Point-to-Group Network to Information Security Investment by Liping Feng, Ruifeng Han, Hongbin Wang, Qingshan Zhao, Chengli Fu, Qi Han

“…Second, the optimal control measure is formulated by making a tradeoff between control cost and network loss caused by virus intrusion. Third, optimal control strategies are theoretically investigated by Pontryagin’s maximum principle and the Hamiltonian function. …”
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Capacity analysis for MISO-UWOC systems over GGD weak turbulence with zero boresight pointing error by Yueheng LI, Yining XU, Meiyan JU, Ping HUANG

“…Generalized Gamma distribution (GGD) was chosen to describe the weak oceanic turbulence, and a new hybrid fading channel model that integrated the GGD weak turbulence, the zero boresight pointing error, and the implicit path loss plus multipath propagation characterized by the fading free impulse response (FFIR) was proposed.Subsequently, mathematical expressions for the ergodic capacity and outage capacity of the multiple-input single-output underwater wireless optical communication (MISO-UWOC) systems were derived through the Meijer-G function under a selective transmission (ST) diversity scheme especially while inter-symbol interference (ISI) effects were considered or not.Finally, the correctness of the theoretical formulas derived above was verified by some numerical results.The simulation results show that with the introduction of the ST diversity, the ergodic capacity of the MISO-UWOC systems, taking the transmission ports N=2 as an example, is at least 1.3 times better than that of the conventional point-to-point (P2P) transmission under the same channel condition and system parameters, while the maximum outage capacity decreases is also more than 60% compared with the conventional P2P one.However, the introduction of the ISI will severely reduce this performance improvement.…”
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PLFF-SLAM: A Point and Line Feature Fused Visual SLAM Algorithm for Dynamic Illumination Environments by Shucheng Huang, Wenhan Ren, Mingxing Li

“…Firstly, we designed a point feature extraction method based on the GCNV2 network, which improves the extraction performance of the point feature network by modifying the encoding layer structure and reconstructing the loss function. …”
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A Collaborative Multi-Agent Reinforcement Learning Approach for Non-Stationary Environments with Unknown Change Points by Suyu Wang, Quan Yue, Zhenlei Xu, Peihong Qiao, Zhentao Lyu, Feng Gao

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A noncoding point mutation of Zeb1 causes multiple developmental malformations and obesity in Twirler mice. by Kiyoto Kurima, Ronna Hertzano, Oksana Gavrilova, Kelly Monahan, Karl B Shpargel, Garani Nadaraja, Yoshiyuki Kawashima, Kyu Yup Lee, Taku Ito, Yujiro Higashi, David J Eisenman, Scott E Strome, Andrew J Griffith

“…We conclude that a noncoding point mutation of Zeb1 acts via a gain-of-function to disrupt regulation of Zeb1(Tw) expression, epithelial-mesenchymal cell fate or interactions, and structural development of the inner ear in Twirler mice. …”
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Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network by Shengwei Qin, Yao Jin, Hailong Hu

“…These loss functions enable efficient network training, leading to superior upsampling results. …”
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YOLORM: An Advanced Key Point Detection Method for Accurate and Efficient Rotameter Reading in Low Flow Environments by Huang Yong, Xia Xing, Xiao Shengwang

“…Several innovations were introduced to the YOLOv8n framework: (1) An ALKBlock-based C2f module incorporating DACov structure to enhance spatial perception; (2) An EffQA-FPN feature pyramid network inspired by QARepVGG to mitigate precision loss during quantization and reparameterization; (3) A DynamicHead attention mechanism with multi dimensional perception capabilities; and (4) EIoU and Adaptive Wing Loss functions to optimize bounding box and key point regression. …”
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An Assessment of the Impacts of Private Sector Investments on Kashan’s Historic Districts from Local Communities’ Point of View by Naimeh Rezaei, Gelareh Alborzi, leila alilou

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Enhancing Landmark Point Detection in <i>Eriocheir Sinensis</i> Carapace with Differentiable End-to-End Networks by Chong Wu, Shuxian Wang, Shengmao Zhang, Hanfeng Zheng, Wei Wang, Shenglong Yang

“…Three types of convolutional neural network models were used to compare detection accuracy, generalization ability, and model power consumption, with different loss functions compared. The results showed that the Convolutional Neural Network (CNN) model based on the Differentiable Spatial to Numerical Transform (DSNT) module had the highest R² value of 0.9906 on the test set, followed by the CNN model based on the Gaussian heatmap at 0.9846. …”
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