Deep learning-enhanced signal detection for communication systems. by Yang Liu, Peng Liu, Yu Shi, Xue Hao

“…At the same time, a DCNet decoder based on a convolutional neural network batch normalization mechanism is designed to suppress inter-subcarrier interference in OFDM systems effectively. …”
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CALCULATION OF OPERATION MODE OF OUTDOOR LIGHTING LINE WITH BILATERAL SUPPLY. Part 2 by V. B. Kozlovskaya, V. N. Kalechyts

“…The most important features of outdoor lighting lines are of considerable length and the use of a large number of light sources of an average power. Under such conditions, in order to determine and maintain the optimal modes of operation of outdoor lighting networks with a bilateral supply it is necessary to choose the wire section of the line. …”
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Research into prediction and influential factors of circuit breaker closing time using BFGS-NN by Longcheng Dai, Jiaying Yu, Zhihui Huang, Hui Ni, Yifan Zhang, Junting Dou

“…The BFGS algorithm trained on these data generated a closing time prediction model, achieving rapid convergence and optimal fit during learning. Results show an average prediction error within ±0.21 ms, with the maximum error tolerance meeting controlled closing requirements. …”
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Automated Recognition and Measurement of Corrugated Pipes for Precast Box Girder Based on RGB-D Camera and Deep Learning by Jiongyi Zhu, Zixin Huang, Dejiang Wang, Panpan Liu, Haili Jiang, Xiaoqing Du

“…The results show that under the optimal fitting scheme (BP neural network with circle fitting constraint), the average measurement errors for the three pipes are 2.2 mm, 1.4 mm, and 1.6 mm, with Maximum Errors of −5.8 mm, −4.2 mm, and −5.7 mm, respectively, meeting the standard requirements. …”
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Short-term Power Load Forecasting for a 33/11 KV Sub-Station by Utilizing Attention-Based Hybrid Deep Learning Architectures by Mukkamala R.

“…This evaluation included the following models: Autoregressive Integrated Moving Average (ARIMA), Multi-Layer Perceptron (MLP), Ran-dom Forest (RF), Gradient Boosting (GB), Long Short-Term Memory (LSTM) networks with Atten-tion mechanisms, Double Attention mechanisms, LSTM-Convolutional Neural Network (CNN) Attention, Recurrent Neural Networks (RNN) with Input Attention, Bidirectional LSTM (BiLSTM) with Attention, and CNN-BiLSTM Attention mechanism. …”
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Research on Mobile Advertising Users' Propagation Ability Evaluation based on the Neutrosophic Cubic Number Multiple-Attribute Decision-Making by Minming Weng, Ying Long

“…The evaluation of mobile advertising users' propagation ability assesses how effectively users can share or spread advertisements within their networks. It involves analyzing factors such as user engagement, social influence, sharing frequency, and network reach. …”
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Climate Change and Its Impacts on the Planting Regionalization of Potato in Gansu Province, China by Yulan Lu, Junying Han, Guang Li, Zhengang Yan, Lixia Dong, Zhigang Nie, Qiang Liu

“…The backpropagation neural network was employed to interpolate irregular and missing data in the time-series data. …”
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The burden of breast cancer in Poland: current status and future directions by Wojciech Sierocki, Wojciech Sierocki, Wojciech Sierocki, Ligia Kornowska, Alicja Dudek, Alicja Dudek, Małgorzata Szpakowicz, Claudia Dompe, Claudia Dompe, Magdalena Roszak

“…Breast cancer poses a significant public health challenge in Poland, with incidence rates exceeding the EU average and a concerning disparity between incidence and mortality. …”
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Novel Aerogels Based on Citric Acid Crosslinked Bacterial Cellulose for Sustained Release of Nicotine in Oral Nicotine Products by Zhou Jian, Zhang Zhan, Ma Ke, Xu Yongming, Li Peng, Wang Yixuan

“…Material characterization revealed that the CA-BC aerogel had an improved thermal stability and decreased water absorption, a 3D fiber network structure at the microscopic level with an optimal average pore size of 30 μm was used to explain this difference.…”
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Accurate and Robust Train Localization: Fusing Degeneracy-Aware LiDAR-Inertial Odometry and Visual Landmark Correction by Lin Yue, Peng Wang, Jinchao Mu, Chen Cai, Dingyi Wang, Hao Ren

“…Next, a kilometer post factor is constructed, and multi-source information is optimized within a factor graph framework. Finally, onboard experiments conducted on real railway vehicles demonstrate high-precision landmark detection at 35 FPS with 94.8% average precision. …”
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Adaptive User Pairing With Non-Orthogonal Medium Access Choices for Balanced Coexistence of Mission-Critical and eMBB Services in Cellular IoT by Farnaz Khodakhah, Aamir Mahmood, Patrik Osterberg, Mikael Gidlund

“…By deriving a signal-to-noise ratio (SNR) gap threshold between a pair of eMBB and MC users, we identify optimal access scheme&#x2014;puncturing, non-orthogonal multiple access (NOMA), or rate-splitting multiple access (RSMA)&#x2014;with respect to activation probability <inline-formula> <tex-math notation="LaTeX">$(p_{m})$ </tex-math></inline-formula> and cellular network radius. …”
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MXene-based multilayered and ultrawideband absorber for solar cell and photovoltaic applications by Jusu M. Ngobeh, Vishal Sorathiya, Abdullah Alwabli, Amar Y. Jaffar, Osama S. Faragallah

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Lightweight visible damage detection algorithm for embedded systems applied to pipeline automation equipment by Jiale Xiao, Lei Xu, Changyun Li, Ling Tang, Guogang Gao

“…In addition, the performance of the model is optimized by knowledge refinement. When tested on a customized pipeline defect dataset, CGYOLO used 25 % less memory, ran 39.5 % fewer gigaprocessors per second, had 28.5 % fewer parameters, and improved average accuracy by 4 % to 94.3 % compared to the smallest known state-of-the-art model. …”
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Design of and Experiment with a Dual-Arm Apple Harvesting Robot System by Wenlei Huang, Zhonghua Miao, Tao Wu, Zhengwei Guo, Wenkai Han, Tao Li

“…Secondly, combining a dual-vision perception system, the paper adopts a fruit recognition method based on a multi-task network model and a frustum-based fruit localization approach to identify and localize fruits. …”
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The Dependence of Water and Gas Breakthrough on the Choice of Model Parameters in Naturally Fractured Reservoirs Using Sector and Full Reservoir Models by Barham Sabir Mahmood, Mohammed Jamal Awl, Farhad Abdulrahman Khoshnaw

“…Understanding the characteristics of these fractures is vital for optimizing oil production. Additionally, extremely heterogeneous and anisotropic permeability distribution within the natural fractured reservoir is often caused by the complexity of a fracture network. …”
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Advanced dynamic ensemble framework with explainability driven insights for precision brain tumor classification across datasets by Retinderdeep Singh, Sheifali Gupta, Ashraf Osman Ibrahim, Lubna A. Gabralla, Salil Bharany, Ateeq Ur Rehman, Seada Hussen

“…An adaptive dynamic weight distribution strategy is employed during training to optimize the contribution of each networks in the framework. …”
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DMM-YOLO: A high efficiency soil fauna detection model based on an adaptive dynamic shuffle mechanism by Jiehui Ke, Renbo Luo, Guoliang Xu, Yuna Tan, Zhifeng Wu, Liufeng Xiao

“…Additionally, different efficient modules with multi-branch fusion structures, integrating DLSConv, are adopted for the Backbone and Neck to enhance feature extraction and fusion, while optimizing the feature maps fed into the detection head, thereby improving the network’s ability to extract features and detect targets. …”
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Multi-user frequency selective beam steering by reconfigurable intelligent surfaces in the Ka-band by Lukas Mueller, Alexander Wolff, Steffen Klingel, Janis Krieger, Lars Franke, Ralf Stemler, Marco Rahm

“…For such targeted multi-frequency beam steering, we implemented a customized neural network-based machine learning architecture specifically designed to optimize the bias voltage patterns of the RIS. …”
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Enhancing Online Security: A Novel Machine Learning Framework for Robust Detection of Known and Unknown Malicious URLs by Shiyun Li, Omar Dib

“…For unknown MURLs, the proposed framework utilizes CL_K-means, a modified k-means clustering algorithm, alongside two additional biased classifiers, achieving 92.54% accuracy on simulated zero-day datasets. With an average processing time of under 14 milliseconds per instance, MUDS is optimized for real-time integration into network endpoint systems. …”
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Machine Learning for Earthquake Emergency Evacuation: Site Selection and Neighborhood Navigation by Amirmasoud Amiran, Behrouz Behnam, Sanaz Seyedin

“…By employing an artificial neural network model informed by data from San Francisco&#x2014;a city with similar seismic risks, urban structure, building types, and population density&#x2014;this study achieved a 23% reduction in the average minimum distance from each parcel to the nearest emergency evacuation center compared to the current conditions. …”
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