Numerical Investigation on Influential Factors for Quality of Smooth Blasting in Rock Tunnels by Baoping Zou, Zhipeng Xu, Jianxiu Wang, Zhanyou Luo, Lisheng Hu

“…As prerequisite for the evaluation of the blasting quality, effective identification of the influential factors affecting smooth blasting usually plays a significant role in the improvement of smooth blasting design. Compared to the expensive and time-consuming experiments including physical modelling and field tests, numerical modelling, as a cost-efficient approach, offers an attractive alternative to investigate the influential factors in terms of weight, which might be more applicable and reliable for the optimization of smooth blasting parameters. …”
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Estimation of Optimum Dilution in the GMAW Process Using Integrated ANN-GA by P. Sreeraj, T. Kannan, Subhashis Maji

“…In this study, artificial neural network (ANN) and genetic algorithm (GA) techniques were integrated and labeled as integrated ANN-GA to estimate optimal process parameters in GMAW to get optimum dilution.…”
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Heuristic Binary Search for Modulated Predictive Control by Rafael FIgueiredo, Igor Oliani, Angelo Lunardi, Alfeu J. Sguarezi Filho

“…Experimental results comparing three variants of the Predictive Torque Control (one vector, three vector and modulated) show improvements in torque and flux ripple and improvements of current THD up to 30% over classic Modulated Predictive Torque Control implementation with reduced or similar computational cost. …”
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Two-stage resilience enhancement method for integrated electricity-gas systems through linepack and mobile compressors by Chao Qin, Yongxue Wang, Shuaihu Ye

“…The progressive hedging algorithm is employed to further improve the solution efficiency. …”
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A lightweight lattice-based group signcryption authentication scheme for Internet of things by XU Chuan, AI Xinghao, WANG Shanshan, ZHAO Guofeng, HAN Zhenzhen

“…In the key generation stage, the improved trapdoor diagonal matrix was designed to optimize the original image sampling algorithm required for key generation and reduce the overall time required for generating a large number of keys. …”
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Robust fuzzy dynamic integrated environmental-economic-social scheduling considering demand response and user’s satisfaction with electricity under multiple uncertainties by Hong Zhang, Qianwei Xi, Lei Chen, Yong Min, Xiongxiong Fan, Wenjin Fang, Nan Tian, Fei Xu

“…Taking the lowest comprehensive operation cost as the economic objective, the smallest emissions of CO2 and atmospheric pollutants as environmental objective and the largest user’s comprehensive satisfaction with electricity as the social objective, based on the robust fuzzy theory, the multi-objective uncertainty optimal scheduling model is constructed, which is transformed into deterministic model and then solved by intelligent optimization algorithm. …”
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Comprehensive Evaluation and Trade‐Off of Top‐Level Requirements for BWB UAVs by Xinshi Suo, Zhouwei Fan, Yundong Guo, Tengzhou Xu

“…The optimal design improves the competitiveness of the top‐level requirements by 46.8% compared to the baseline design, validating the feasibility of the proposed method.…”
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A Full-Profile Measurement Method for an Inner Wall with Narrow-Aperture and Large-Cavity Parts Based on Line-Structured Light Rotary Scanning by Zhengwen Li, Changshuai Fang, Xiaodong Zhang

“…Considering the structural constraints in the measurement of narrow-aperture and large-cavity parts, a structural optimization algorithm is designed to enable the sensor to achieve a high theoretical measurement resolution while satisfying the geometric constraints of the measured parts. …”
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Fingerprint Classification Based on Multilayer Extreme Learning Machines by Axel Quinteros, David Zabala-Blanco

“…In this study, we introduce, for the first time, the use of a multilayer extreme learning machine (M-ELM) for fingerprint classification, aiming to improve training efficiency. A comparative analysis is conducted with CNNs and unbalanced extreme learning machines (W-ELMs), as these represent the most influential methodologies in the literature. …”
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Multimodal Control by Variable-Structure Neural Network Modeling for Coagulant Dosing in Water Purification Process by Jun Zhang, Da-Yong Luo

“…In this paper, combined with rule base, through the PCA method, an improved multimodal variable-structure random-vector neural network algorithm (MM-P-VSRVNN) is proposed for coagulant dosing, which is a key production process in water purification process. …”
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Prediction of COD Degradation in Fenton Oxidation Treatment of Kitchen Anaerobic Wastewater Based on IPSO-BP Neural Network by Tianpeng Zhang, Pengfei Ji, Dayong Tian, Rui Xu

“…The Fenton oxidation process is used to treat kitchen anaerobic wastewater, and the effects of H2O2 dosage, Fe2+ dosage, reaction time and pH value on chemical oxygen demand (COD) degradation efficiency are explored. The improved particle swarm optimization (IPSO) algorithm is used to optimize the back propagation (BP) neural network, and a prediction model of COD degradation is established based on IPSO-BP neural network. …”
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An echo state network based on enhanced intersecting cortical model for discrete chaotic system prediction by Xubin Wang, Pei Ma, Jing Lian, Jizhao Liu, Yide Ma

“…The model incorporates a neuron model with internal dynamics, including adaptive thresholds and inter-neuron feedback, into the reservoir structure. A Bayesian Optimization algorithm was employed for the selection of hyperparameters. …”
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Network-coding-based two-way relay cooperation with energy harvesting by Shunwai Zhang, Rongfang Song, Tao Hong

“…Furthermore, we analyze the outage probability and bit error rate of the system when the optimal antenna selection algorithm is adopted at the relay to transmit data. …”
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Energy Storage Regulation Method of Base Stations in 5G Integrated Distribution Network Based on Energy Sharing and Trading Coordination by Yanru WANG, Xiyang YIN, Qinghai OU, Wenjie MA, Hui LIU, Zhigang DU

“…To this end, this paper analyzes the regulation potential of 5G base stations based on their energy consumption characteristics and constructs the energy sharing model among base stations and the energy trading model between base stations and distribution network by combining the dynamic changes of energy consumption and photovoltaic output of 5G base stations. Then, with the optimization objectives of promoting energy sharing of 5G base stations, improving operation efficiency of base stations, and participating in peak shaving and valley filling, the optimization problem of energy storage regulation of base stations in a 5G integrated distribution network is constructed, and an energy storage regulation algorithm of 5G base stations based on energy sharing and trading coordination is proposed. …”
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Maximum likelihood self-calibration for direction-dependent gain-phase errors with carry-on instrumental sensors:case of deterministic signal model by WANG Ding1, PAN Miao2, WU Ying1

“…Aim at the self-calibration of direction-dependent gain-phase errors in case of deterministic signal model,the maximum likelihood method（MLM） for calibrating the direction-dependent gain-phase errors with carry-on instrumental sensors was presented.In order to maximize the high-dimensional nonlinear cost function appearing in the MLM,an improved alternative projection iteration algorithm,which could optimize the azimuths and direction-dependent gain-phase errors was proposed.The closed-form expressions of the Cramér-Rao bound（CRB） for azimuths and gain-phase errors were derived.Simulation experiments show the effectiveness and advantage of the novel method.…”
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Statistics and behavior of clinically significant extra-pulmonary vein atrial fibrillation sources: machine-learning-enhanced electrographic flow mapping in persistent atrial fibri... by Peter Ruppersberg, Steven Castellano, Philip Haeusser, Kostiantyn Ahapov, Melissa H. Kong, Stefan G. Spitzer, Stefan G. Spitzer, Georg Nölker, Andreas Rillig, Tamas Szili-Torok

“…These findings, supported by the FLOW-AF trial, underscore the usefulness of clinical outcome-based machine learning to improve the efficacy of algorithm based medical diagnostics.…”
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Local Search-Based Metaheuristic Methods for the Solid Waste Collection Problem by Haneen Algethami

“…Local search methods, notably GLS, have significantly improved the route construction process. The nearest neighbour algorithm has often outperformed the Clarke and Wright's methods. …”
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Research on Behavior Recognition and Online Monitoring System for Liaoning Cashmere Goats Based on Deep Learning by Geng Chen, Zhiyu Yuan, Xinhui Luo, Jinxin Liang, Chunxin Wang

“…YOLOv8n demonstrated superior performance, converging within 50 epochs with an average accuracy of 95.31%, making it a baseline for further improvements. We improved YOLOv8n through dataset expansion, algorithm lightweighting, attention mechanism integration, and loss function optimization. …”
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Comparison of Spatial Predictability Differences in Truck Activity Patterns: An Empirical Study Based on Truck Tracking Dataset of China by Lianghua Li, Peng Du, Guohua Jiao, Xin Fu

“…Existing research on truck location prediction focuses on direct trajectory prediction and ignores the link between activity patterns and predictability, whereas the mode of operation is an important factor in the difference between activity trajectories, and analyzing the mode of operation can help to develop the next-location prediction algorithms to improve the efficiency of matching truckloads and to reduce costs. …”
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Multi-Level Spectral Attention Network for Hyperspectral BRDF Reconstruction from Multi-Angle Multi-Spectral Images by Liyao Song, Haiwei Li

“…For the multi-level Transformer spectral dimension enhancement algorithm, we added spectral response loss constraints to improve BRDF accuracy. …”
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