Research on intelligent energy management strategies for connected range-extended electric vehicles based on multi-source information by Xuewen Zhai, Hanwu Liu, Wencai Sun, Zihang Su

“…The Euclidean distance between consecutive traffic scenario matrices is used as a basis for similarity to optimize speed and predict future vehicle speeds. Moreover, a multi-objective intelligent EMS based on deep reinforcement learning (DRL) is employed, utilizing the Deep Deterministic Policy Gradient (DDPG) algorithm to comprehensively consider vehicle dynamics, energy consumption economy, and the degradation of batteries. …”
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Reconstruction for Scanning LiDAR with Array GM-APD on Mobile Platform by Di Liu, Jianfeng Sun, Wei Lu, Sining Li, Xin Zhou

“…This method avoids the need for field-of-view registration, improves data utilization, and reduces the complexity of the algorithm while eliminating the effect of LiDAR motion. …”
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A Method of Locating and Measuring Train Wheel Tread Defects Based on YOLOv3-tiny by JIN Kairong, WANG Junping, CHEN Shenglan

“…In order to solve the key problems that the detection frame is too large and too small, traditional image algorithms such as Fourier transform, band-stop filter, and threshold segmentation are used to construct a defect size measurement model, contours of roughly located defects are extracted and detection frame size is optimized, and finally location and size of defect are accurately calculated. …”
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Federated Synergy: Hierarchical Multi-Agent Learning for Sustainable Edge Computing in IIoT by S. Benila, K. Devi

“…It employs multi-agent reinforcement learning to optimize resource allocation and task offloading by leveraging local Actor-Critic models on IoT edge devices. …”
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Simulation and test study of transmission characteristics of V-shaped rubber belt CVT by TIAN Zhewen, TIAN Hao, CHEN Shuoyan

“…ObjectiveThe dynamic matching problem of continuously variable transmission （CVT） with the engine and Baja racing car was subjected to research analysis.MethodsThe parameters of key components of CVT were first optimized by genetic algorithm in Matlab software, then the dynamic modeling and simulation of CVT and the whole vehicle were carried out in Matlab/Simulink and RecurDyn, and finally the vehicle test was carried out on the chassis dynamometer.ResultsThe results of modeling simulation and vehicle test show that the dynamic index of Baja racing car is significantly improved after the optimization of the parameters of key components of CVT. …”
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Systemic therapy for laryngeal carcinoma by Thorsten Fuereder, Florian Kocher, Jan Baptist Vermorken, Jan Baptist Vermorken

“…Consequently, multiple recent efforts have been made to integrate novel immunotherapies into the current treatment algorithm for LSCC. In particular, perioperative immunotherapy regimens appear to be the most promising approach for preserving laryngeal function and optimizing event-free and overall survival rates in the locally advanced setting. …”
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Deep Learning–Based Prediction of Freezing of Gait in Parkinson's Disease With the Ensemble Channel Selection Approach by Sara Abbasi, Khosro Rezaee

“…Method To address this, we developed a novel algorithm for detecting FoG events based on movement signals. …”
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A geometric approach for accelerating neural networks designed for classification problems by Mohsen Saffar, Ahmad Kalhor, Ali Habibnia

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An interpretable integrated machine learning framework for genomic selection by Jinbu Wang, Jia Zhang, Wenjie Hao, Wencheng Zong, Mang Liang, Fuping Zhao, Longchao Zhang, Lixian Wang, Huijiang Gao, Ligang Wang

“…Moreover, we introduced the NuSVR model, which achieved the highest accuracy among nine compared algorithms. …”
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MRI-based intratumoral and peritumoral radiomics for assessing deep myometrial invasion in patients with early-stage endometrioid adenocarcinoma by Jing Yang, Yang Liu, Xiaolong Liu, Yaoxin Wang, Xianhong Wang, Conghui Ai, Qiu Bi, Ying Zhao

“…Integrated models were constructed by ensemble and stacking algorithms based on the above radiomics models. …”
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Uncertainty quantification with graph neural networks for efficient molecular design by Lung-Yi Chen, Yi-Pei Li

“…Using benchmarks from the Tartarus and GuacaMol platforms, our results show that UQ integration via probabilistic improvement optimization (PIO) enhances optimization success in most cases, supporting more reliable exploration of chemically diverse regions. …”
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Vehicle Reidentification via Multifeature Hypergraph Fusion by Wang Li, Zhang Yong, Yuan Wei, Shi Hongxing

“…To merge multiple feature descriptors, an efficient and appropriate representation is particularly necessary, and a hypergraph is naturally suitable for modeling high-order relationships. In addition, the spatiotemporal correlation of traffic status between cameras is the constraint beyond the image, which can greatly improve the re-ID accuracy of different vehicles with similar appearances. …”
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Hybrid machine learning techniques for knowledge extraction in communication networks and advanced decision based complex q-Rung orthopair fuzzy frameworks by Hushuang Zeng, Yonghua Zhang

“…Abstract Hybrid machine learning techniques for knowledge extraction in communication networks combine the strengths of multiple learning algorithms to analyze complex, high-dimensional, and dynamic network data effectively. …”
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Deep learning-based technique for investigating the behavior of MEMS systems with multiwalled carbon nanotubes and electrically actuated microbeams by Muhammad Amir, Jamshaid Ul Rahman, Ali Hasan Ali, Ali Raza, Zaid Ameen Abduljabbar, Husam A. Neamah

“…Numerical simulations and graphical demonstrations are presented to verify the accuracy and efficiency of the algorithm. • The study develops a novel DNN-based model to solve non-linear systems in MEMS, particularly for oscillators with MWCNTs. • Deep learning optimizers are applied to improve the accuracy and efficiency of predicting MEMS behavior. • Numerical simulations confirm the effectiveness of the proposed methodology.…”
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Unraveling C-to-U RNA editing events from direct RNA sequencing by Adriano Fonzino, Caterina Manzari, Paola Spadavecchia, Uday Munagala, Serena Torrini, Silvestro Conticello, Graziano Pesole, Ernesto Picardi

“…Using in vitro synthesized and human ONT reads, our model optimizes the signal-to-noise ratio improving the detection of C-to-U editing sites with high accuracy, over 90% in all samples tested. …”
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Exploring Monte Carlo Negotiation Search with Nontrivial Agreements by Elijah Alden Malaby, John Licato

“…We demonstrate significant improvement of our algorithm over random agreement sampling, although further work is required to more consistently produce optimal agreements.…”
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Predicting Employee Turnover Using Machine Learning Techniques by Adil Benabou, Fatima Touhami, My Abdelouahed Sabri

“…This study aims to identify the most effective machine learning model for predicting employee attrition, thereby providing organizations with a reliable tool to anticipate turnover and implement proactive retention strategies.Objective: This study aims to address the challenge of employee attrition by applying machine learning techniques to provide predictive insights that can improve retention strategies.Methods: Nine machine learning algorithms are applied to a dataset of 1,470 employee records. …”
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Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques by Antonietta Eliana Barrasso, Claudio Perone, Roberto Romaniello

“…The transition to efficient production requires innovative water control techniques to maximize irrigation efficiency and minimize waste. Analyzing and optimizing irrigation practices is essential to improve water use and reduce environmental impact. …”
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Detecting Simulated Nosocomial Disease Outbreaks with Sequential Monte Carlo Methods by Dr Conor Rosato

“…Conclusion: By detecting nosocomial disease outbreaks early, healthcare facilities can implement timely interventions to mitigate the spread of infections, protect vulnerable patients and optimize resource allocation. Our methodology offers a valuable tool for improving infection control practices and enhancing patient safety in healthcare settings.…”
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Machine learning and transfer learning techniques for accurate brain tumor classification by Seyed Matin Malakouti, Mohammad Bagher Menhaj, Amir Abolfazl Suratgar

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