FEATURES OF DIAGNOSTIC AND THERAPEUTIC TACTICS FOR BLUNT ABDOMINAL TRAUMA WITH DAMAGE TO THE PANCREAS by A. B. Singayevsky, S. G. Scherbak, B. V. Sigua, N. M. Vrublevsky, A. V. Nikiforenko, A. A. Kurkov, A. K. Dyukov

“…Injuries of pancreas in the closed abdominal trauma remain the one of most challenging issues in diagnosis and choice of optimal therapy.Objectives. …”
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Synergistic Integration of LQR Control and PSO Optimization for Advanced Active Suspension Systems Utilizing Electro-Hydraulic Actuators and Electro-Servo Valves by Trong Tu

“…This research remarks the fundamentals of the experimental validation and further refinement of these control algorithms to adapt to various driving conditions and vehicle models, ultimately aiming to transition these optimized controllers from theoretical frameworks to practical, real-world applications. …”
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Stochastic Variance Reduced Primal–Dual Hybrid Gradient Methods for Saddle-Point Problems by Weixin An, Yuanyuan Liu, Fanhua Shang, Hongying Liu

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Weak Fault Detection for Rolling Bearings in Varying Working Conditions through the Second-Order Stochastic Resonance Method with Barrier Height Optimization by Huaitao Shi, Yangyang Li, Peng Zhou, Shenghao Tong, Liang Guo, Baicheng Li

“…In this paper, an underdamped second-order adaptive general variable-scale stochastic resonance (USAGVSR) method with potential well parameters’ optimization is put forward. For input signals with different fault frequencies, the potential well parameters related to the barrier height are figured out and optimized through the ant colony algorithm. …”
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Optimal Allocation Strategy for Power Quality Control Devices Based on Harmonic and Three-Phase Unbalance Comprehensive Evaluation Indices for Distribution Network by Fang ZHUO, Zebin YANG, Hao YI, Guangyu YANG, Meng WANG, Xiaoqing YIN, Chengzhi ZHU

“…Secondly, taking the global configuration effects, the total number and capacity of control devices as the optimization goals, and regarding the harmonic distortions and unbalance degrees of the nodes satisfying the standard as the constraint condition, the optimal configuration node and capacity of each device is determined by multi-objective particle swarm algorithm. …”
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Surface Reconstruction Planning with High-Quality Satellite Stereo Pairs Searching by Jinwen Li, Guangli Ren, Youmei Pan, Jing Sun, Peng Wang, Fanjiang Xu, Zhaohui Liu

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A Biased–Randomized Iterated Local Search with Round-Robin for the Periodic Vehicle Routing Problem by Juan F. Gomez, Antonio R. Uguina, Javier Panadero, Angel A. Juan

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A deep dive into artificial intelligence with enhanced optimization-based security breach detection in internet of health things enabled smart city environment by S Jayanthi, Sodagudi Suhasini, N. Sharmili, E. Laxmi Lydia, V. Shwetha, Bibhuti Bhusan Dash, Mrinal Bachute

“…For the selection of the feature process, the proposed SADDBN-AMOA model designs a slime mould optimization (SMO) model to select the most related features from the data. …”
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Spatial Downscaling of TRMM Precipitation Data Using an Optimal Subset Regression Model with NDVI and Terrain Factors in the Yarlung Zangbo River Basin, China by Jinping Liu, Wanchang Zhang, Ning Nie

“…The objective of this study was to develop a reliable statistical downscaling algorithm to produce high quality, high spatial resolution precipitation products from Tropical Rainfall Monitoring Mission (TRMM) 3B43 data over the Yarlung Zangbo River Basin using an optimal subset regression (OSR) model combined with multiple topographical factors, the Normalized Difference Vegetation Index (NDVI), and observational data from rain gauge stations. …”
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Multi-user joint task offloading and resource allocation based on mobile edge computing in mining scenarios by Siqi Li, Weidong Li, Wanbo Zheng, Yunni Xia, Kunyin Guo, Qinglan Peng, Xu Li, Jiaxin Ren

“…To evaluate the effectiveness of the proposed method, we compare it with five baseline algorithms: the improved grey wolf optimizer metaheuristic algorithm, the traditional genetic algorithm, the binary offloading decision mechanism, the partial non-cooperative mechanism, and the fully local execution mechanism. …”
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Secure THz Communication in 6G: A Two-Stage DRL Approach for IRS-Assisted NOMA by Muhammad Shahwar, Manzoor Ahmed, Touseef Hussain, Muhammad Sheraz, Wali Ullah Khan, Teong Chee Chuah

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Short‐term electric power and energy balance optimization scheduling based on low‐carbon bilateral demand response mechanism from multiple perspectives by Juan Li, Yonggang Li, Huazhi Liu

“…The enhanced decision tree classifier (EDTC) algorithm is used to predict the electricity consumption behavior of transferable load (TL) users, and an improved particle swarm optimization (PSO) algorithm with “ε‐greedy” strategy is proposed to solve this model. …”
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The Impact of the Natural Grass-Growing Model on the Development of Korla Fragrant Pear Fruit, as Well as Its Influence on Post-Harvest Sugar Metabolism and the Expression of Key E... by Mingyang Yu, Lanfei Wang, Yan Chen, Weifan Fan, Hao Wang, Kailu Guo, Shutian Tao, Xin Gong, Jianping Bao

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A spherical fuzzy WASPAS approach to prioritize the factors enhancing the effectiveness of physical education classroom teaching by Yuhan Gao, Ali Yu

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Hyperspectral Anomaly Detection by Spatial–Spectral Fusion Based on Extreme Value-Entropy Band Selection and Cauchy Graph Distance Optimization by Song Zhao, Yali Lv, Wen Zhang, Lijun Wang, Zhiru Yang, Gaofeng Ren, Bin Wang, Xiaobin Zhao, Tongwei Lu, Jiayao Wang, Wei Li

“…This algorithm combines spectral extremum detection with information entropy filtering to select the most representative bands by considering multidimensional information. …”
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Accelerating Grover Adaptive Search: Qubit and Gate Count Reduction Strategies With Higher Order Formulations by Yuki Sano, Kosuke Mitarai, Naoki Yamamoto, Naoki Ishikawa

“…Grover adaptive search (GAS) is a quantum exhaustive search algorithm designed to solve binary optimization problems. …”
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Estimating forest aboveground carbon sink based on landsat time series and its response to climate change by Kun Yang, Kai Luo, Jialong Zhang, Bo Qiu, Feiping Wang, Qinglin Xiao, Jun Cao, Yunrun He, Jian Yang

“…We found that (1) GA can effectively improve the estimation accuracy of RF, the R 2 can be improved by up to 34.8%, and the optimal GA-RF model R 2 is 0.83. (2) The CSI of Pinus densata in Shangri-La was 0.45–0.72 t C·hm− 2 from 1987 to 2017. (3) Precipitation has the most significant effect on CSI. …”
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Sustainable Self-Training Pig Detection System with Augmented Single Labeled Target Data for Solving Domain Shift Problem by Junhee Lee, Heechan Chae, Seungwook Son, Jongwoong Seo, Yooil Suh, Jonguk Lee, Yongwha Chung, Daihee Park

“…Overcoming this limitation through large-scale labeling presents considerable burdens in terms of time and cost. To address the degradation issue, this study proposes a self-training-based domain adaptation method that utilizes a single label on target (SLOT) sample from the target domain, a genetic algorithm (GA)-based data augmentation search (DAS) designed explicitly for SLOT data to optimize the augmentation parameters, and a super-low-threshold strategy to include low-confidence-scored pseudo-labels during self-training. …”
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A Multi-Objective Decision-Making Method for Optimal Scheduling Operating Points in Integrated Main-Distribution Networks with Static Security Region Constraints by Kang Xu, Zhaopeng Liu, Shuaihu Li

“…Subsequently, a scheduling optimization model is formulated to minimize both the system generation costs and the comprehensive risk, where the adaptive grid density-improved multi-objective particle swarm optimization (AG-MOPSO) algorithm is employed to efficiently generate Pareto-optimal operating point solutions. …”
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Reconstruction of Highway Vehicle Paths Using a Two-Stage Model by Weifeng Yin, Junyong Zhai, Yongbo Yu

“…In the first stage, a Gaussian Mixture Model (GMM) is integrated into a path choice model to estimate the mean and standard deviation of travel times for each road segment, utilizing an improved Expectation Maximization (EM) algorithm. In the second stage, based on the estimated time parameters, path choice prior probabilities and observed data are combined using maximum likelihood estimation to infer the most probable paths among candidate routes. …”
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