Design of OFDM-IM system based on IRS-assisted by Mengmeng ZHAO, Liming HE, Fangfang LIU

“…Index modulation (IM) and intelligent reflecting surface (IRS) are emerging mobile communication technologies.In order to improve the reliability of traditional orthogonal frequency division multiplexing (OFDM) system, an orthogonal frequency division multiplexing with index modulation (OFDM-IM) system based on IRS-assisted was designed.Firstly, the OFDM-IM system was designed by using spatial modulation and frequency domain modulation to increase the Euclidean distance between subcarriers.Then, by establishing an equivalent circuit model, a practical IRS model was obtained.Finally, an alternating optimization algorithm was used to optimize the active transmission power of the access point (AP) and passive beamforming of the IRS jointly.The simulation results show that compared to the benchmark scheme, the symbol error rate (SER) or bit error rate (BER) of the OFDM-IM system based on IRS-assisted can be reduced by 60%～90%.Especially in the case of high signal-to-noise ratio, the SER or BER of the system can reach 1.0×10^-6, which indicates that the introduction of IM and IRS technologies has optimized the link transmission quality of end-to-end communication system.In addition, based on the IRS-assisted OFDM-IM system as the standard, simulations are conducted to demonstrate the impact of various parameters from the IRS model and IM.It concludes that the parameters in the system should be selected reasonably according to channel state information (CSI).…”
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Dynamic Reporting Nodes Selection Method for Network Awareness Based on Active–Passive Integrated Network Telemetry in LEO Satellite Networks by Hang Di, Tao Dong, Zhihui Liu, Shuotong Wei, Qiwei Zhang, Dingyun Zhang

“…Specifically, an APINT model is built which can dynamically select the optimal reporting node and reduce awareness time through the bidirectional aggregation of telemetry packets along the service path. …”
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Satellite-Derived Bathymetry Combined With Sentinel-2 and ICESat-2 Datasets Using Deep Learning by Weidong Zhu, Yanying Huang, Tiantian Cao, Xiaoshan Zhang, Qidi Xie, Kuifeng Luan, Wei Shen, Ziya Zou

“…The model employs BOA to optimize the key hyperparameters of the CNN-BILSTM architecture, thereby improving inversion performance. …”
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An Efficient Anomalous Sound Detection System for Microcontrollers by Yi-Cheng Lo, Tsung-Lin Tsai, Chieh-Wen Yang, An-Yeu Wu

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Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning–Based Prediction Models in a Retrospective S... by Chun-Chi Lai, Cheng-Yu Chen, Tzu-Hao Chang

“…The application of logistic regression with recursive feature elimination with cross-validation was found to demonstrate the optimal performance among the various algorithms that were evaluated in this study. …”
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The authors would like to thank the staff of the Organisation of Transportation and Transport Management Department by B. S. Trofimov, I. S. Trofimova

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Vehicle detection and classification for traffic management and autonomous systems using YOLOv10 by Anning Ji, Xintao Ma

“…The main purpose of this study is to address these challenges and improve vehicle detection in ITS environments. We propose a novel detection framework, YDFNet, that integrates the YOLOv10 algorithm for fast feature extraction, the BiFPN (Bidirectional Feature Pyramid Network) for multi-scale feature fusion, and the DETR (Detection Transformer) architecture for global feature modeling. …”
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SC-Route: A Scalable Cross-Layer Secure Routing Method for Multi-Hop Inter-Domain Wireless Networks by Yanbing Li, Yang Zhu, Shangpeng Wang

“…To address these limitations, we propose a new routing method—the Secure Cross-Layer Route—designed for multi-hop inter-domain wireless networks to achieve unified optimization of security, delay, and throughput. First, we construct a multi-objective optimization model that integrates authentication delay, link load, and resource states, enabling balanced trade-offs between security and transmission performance in dynamic conditions. …”
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Studying the influence of correction codes on coherent reception of M-PSK signals in the presence of noise and harmonic interference by V. D. Nguyen

“…The methods of statistical radio engineering, optimal signal reception theory and computer simulation modeling were used.Results. …”
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Data-Driven ANN-Based Predictive Modeling of Mechanical Properties of 5Cr-0.5Mo Steel: Impact of Composition and Service Temperature by Muhammad Ishtiaq, Saurabh Tiwari, Molakatala Nagamani, Sung-Gyu Kang, Nagireddy Gari Subba Reddy

“…The ANN model’s findings offer valuable insights for researchers and designers, aiding in developing steel components with optimized properties. …”
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Low-cost and scalable machine learning model for identifying children and adolescents with poor oral health using survey data: An empirical study in Portugal. by Susana Lavado, Eduardo Costa, Niclas F Sturm, Johannes S Tafferner, Octávio Rodrigues, Pedro Pita Barros, Leid Zejnilovic

“…Such a model could enable scalable and cost-effective screening and targeted interventions, optimizing limited resources to improve oral health outcomes. …”
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Enhancing cotton irrigation with distributional actor–critic reinforcement learning by Yi Chen, Meiwei Lin, Zhuo Yu, Weihong Sun, Weiguo Fu, Liang He

“…This study introduces a novel intelligent irrigation approach based on distributional reinforcement learning, ensuring that the algorithm simultaneously considers weather, soil, and crop conditions to make optimal irrigation decisions for long-term benefits. …”
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A Dual-Variable Selection Framework for Enhancing Forest Aboveground Biomass Estimation via Multi-Source Remote Sensing by Dapeng Chen, Hongbin Luo, Zhi Liu, Jie Pan, Yong Wu, Er Wang, Chi Lu, Lei Wang, Weibin Wang, Guanglong Ou

“…A dual-variable selection strategy based on SHapley Additive exPlanations (SHAP) was developed, and a genetic algorithm (GA) was used to optimize the parameters of five machine learning models—elastic net (EN), least absolute shrinkage and selection operator (Lasso), support vector regression (SVR), Random Forest (RF), and Categorical Boosting (CatBoost)—to estimate the AGB of <i>Pinus kesiya</i> var. …”
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Enhancing grid-connected PV-EV charging station performance through a real-time dynamic power management using model predictive control by Aziz Watil, Hamid Chojaa

“…This paper presents a novel station manager algorithm for grid-connected PV-EV charging stations, designed to address key challenges in current systems. …”
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Risk prediction and analysis of gallbladder polyps with deep neural network by Kerong Yuan, Xiaofeng Zhang, Qian Yang, Xuesong Deng, Zhe Deng, Xiangyun Liao, Weixin Si

“…This predictive model can be employed to differentiate the nature of polyps before surgery, enhancing diagnostic accuracy. …”
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Robust Sandstorm Image Restoration via Adaptive Color Correction and Saturation Line Prior-Based Dust Removal by Shan Zhou, Fei Shi, Zhenhong Jia, Guoqiang Wang, Jian Huang

“…Finally, the atmospheric scattering model is employed for comprehensive image restoration, ensuring that color correction and dust removal are optimized. …”
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Nodal Carbon Emission Factor Prediction for Power Systems Based on MDBO-CNN-LSTM by Lihua Zhong, Feng Pan, Yuyao Yang, Lei Feng, Haiming Shao, Jiafu Wang

“…In this study, we address these limitations by improving population initialization, search mechanisms, and iteration strategies and developing a hybrid strategy Modified Dung Beetle Optimization (MDBO) algorithm. …”
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Bridging the Gap: A Review of Machine Learning in Water Quality Control by Herlina Abdul Rahim, Nur Athirah Syafiqah Noramli, Indrabayu

“…Emerging solutions like federated learning (35% accuracy improvement in pan-African E. coli prediction) and explainable AI (SHAP-guided nitrate models) address these barriers. …”
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Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods by Yihao Yu, Yuqi Yang, Qian Li, Jing Yuan, Yan Zha

“…The random forest (RF) model performed best in discriminative ability among 6 ML models, and the optimal 10-feature RF model was finally chosen. …”
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Multi-strategy fusion binary SHO guided by Pearson correlation coefficient for feature selection with cancer gene expression data by Yu-Cai Wang, Hao-Ming Song, Jie-Sheng Wang, Xin-Ru Ma, Yu-Wei Song, Yu-Liang Qi

“…Firstly, the CEC-2022 test functions were used to test the performance of the multi-strategy fusion SHO, from which the best variant TanASSHO was selected, and then compared with other nine swarm intelligent optimization algorithms. Performance tests of various algorithm variants on 18 UCI datasets show that V1PTASSHO is the most effective binary version. …”
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