Construction and Application of Intelligent O&M System for Urban Rail Transit Signaling Systems by YU Meng, LU Huaike, LIANG Zhe, FAN Zichao, GAO Ning

“…The goal is to break through the technical and managerial bottlenecks of traditional O&M models and achieve efficient, precise, and sustainable O&M system. …”
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InBRwSANet: Self-attention based parallel inverted residual bottleneck architecture for human action recognition in smart cities. by Yasir Khan Jadoon, Muhammad Attique Khan, Yasir Noman Khalid, Jamel Baili, Nebojsa Bacanin, MinKyung Hong, Yunyoung Nam

“…The proposed architecture is trained on the selected datasets, whereas the hyperparameters are chosen using the particle swarm optimization (PSO) algorithm. The trained model is employed in the testing phase for the feature extraction from the self-attention layer and passed to the shallow wide neural network classifier for the final classification. …”
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Drying Kinetics and Quality of Whole, Halved, and Pulverized Tiger Nut Tubers (Cyperus esculentus) by Ernest Ekow Abano, Joshua Akanson, Nazir Kizzie-Hayford

“…The activation energy was 18.17 kJ/mol for the unbroken, 14.78 kJ/mol for the halved, and 26.61 kJ/mol for the pulverized tiger nut samples. The model MR=0.997 exp−0.02t1.266+0.0000056t was the most suitable thin-layer drying model among the models examined for convective hot-air drying of tiger nuts. …”
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Industrial Image Anomaly Detection via Synthetic-Anomaly Contrastive Distillation by Junxian Li, Mingxing Li, Shucheng Huang, Gang Wang, Xinjing Zhao

“…During training, <i>SACD</i> adopts a dual-branch strategy, where one branch encodes multi-scale features from defect-free images, while a Siamese anomaly branch processes synthetically corrupted counterparts. <i>FeaCali</i> modules are trained to strip out a student’s anomalous patterns in anomaly branches, enhancing the student network’s exclusive modeling of normal patterns. …”
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Green corrosion inhibition of mild steel in acidic media using Wahlenbergia riparia extract: gravimetric via CCD-RSM, electrochemical and surface analyses by Precious Peter Didam, Victor Olatunji Ajibola, Zakka Israila Yashim, Abuchi Elebo

“…The operational parameters analysed included temperature (299–333 K), WRE concentration (0.1–0.5 g/L), immersion duration (1–6 h), and acid concentration (0.5–2.5 M), all of which significantly affected the model's predictions regarding the corrosion process. …”
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EEG–fNIRS signal integration for motor imagery classification using deep learning and evidence theory by Mohammed E. Seno, Niladri Maiti, Maulik Patel, Mihirkumar M. Patel, Kalpesh B. Chaudhary, Ashish Pasaya, Babacar Toure

“…At the decision fusion stage, decision outputs from both modalities are first quantified using Dirichlet distribution parameter estimation to model uncertainty, followed by a two-layer reasoning process using Dempster-Shafer Theory (DST) to fuse evidence from basic belief assignment (BBA) methods and both modalities. …”
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Using Laser Profilometry to Investigation FDM Printing Parameters for Outer-Perimeter Analysis and Surface Quality Improvement by Olha Kalman, Jozef Husár, Peter Lazorík

“…This study explores the optimization of fused deposition modeling (FDM), a prominent 3D printing technology known for its accessibility and cost-effectiveness. …”
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A VERSATILE ADDITIVE MANUFACTURING INSTRUMENT: PHOTOPOLYMERIZATION-BASED 3D PRINTING AND CURRENT TRENDS FOR DENTISTRY AND ORTHODONTICS APPLICATIONS by Serkan SALMAZ, Çağın BOLAT

“…In this context, this article aims to draw a broad perspective on the new interdisciplinary efforts and to emphasize the great potential of layer-by-layer production in the field of dentistry.…”
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Secure Cooperative Dual-RIS-Aided V2V Communication: An Evolutionary Transformer–GRU Framework for Secrecy Rate Maximization in Vehicular Networks by Elnaz Bashir, Francisco Hernando-Gallego, Diego Martín, Farzaneh Shoushtari

“…To further enhance convergence and secrecy performance, we incorporate an improved gray wolf optimizer (IGWO) to adaptively regulate the model’s hyper-parameters and fine-tune the RIS phase shifts, resulting in a more stable and optimized learning process. …”
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Deep learning multilayer architecture for analysis of three-dimensional Eyring-Powell nanofluid flow subject to viscous dissipation and joule heating by Zahoor Shah, Muflih Alhazmi, Maryam Jawaid, Nafisa A. Albasheir, Mohammed M.A. Alma Zah, Nashwan Adnan Othman, Waqar Azeem Khan

“…Not only does it simplify the computational process, but it also boosts the model's predictive abilities. …”
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A machine learning neural network architecture for the accelerating universe based modified gravity by Zulqurnain Sabir, Basma Souayeh, Zahraa Zaiour, Alyn Nazal, Mir Waqas Alam, Huda Alfannakh

“…The designed stochastic process for solving the MAUMGM contains log-sigmoid activation function, thirty neurons in the hidden layer, dataset based explicit Runge-Kutta, and Bayesian regularization for the optimization. …”
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EfficientNet-b0-Based 3D Quantification Algorithm for Rectangular Defects in Pipelines by Di Wu, Yong Hong, Jie Wang, Shaojun Wu, Zhihao Zhang, Yizhang Liu

“…This multi-task learning model utilizes a shared feature extraction layer and numerous branching networks to create individual predictions for each task: length, width, and depth. …”
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Stock Price Pumping and Dumping Manipulation Detection Using Deep Learning Methods in Tehran Stock Exchange by meysam amiri, moslem peymany, Mohammad Ali Dehghan Dehnavi, Reza Jahanbin

“…In the following, by using the adversarial generative network, misbalancing of data has been solved, and by using multi-layer perceptron neural network models, recurrent neural network, long-short-term memory neural network, and radial basis function network, price manipulation has been detected. …”
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Research on Multi-Scale Spatio-Temporal Graph Convolutional Human Behavior Recognition Method Incorporating Multi-Granularity Features by Yulin Wang, Tao Song, Yichen Yang, Zheng Hong

“…Secondly, a sparse multi-scale adjacency matrix is introduced to solve the bias weighting problem that amplifies the multi-scale spatial domain modeling process under multi-granularity conditions. …”
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The method for calculating phase angle between exciter force of vibration exciter and roller displacement by E. A. Shishkin

“…Introduction. In the process of soil compaction it is important to have information about the current density of the layer, as it enables to quickly adjust the load on the compacted material. …”
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Analyzing the innovation ecosystem of China's major science and technology projects: From the ecological and systems science perspective by Kun Wang, Yu Feng, Siyao Liu

“…By utilizing theories and methods from ecology and systems science, we draw an analogy between the innovation ecosystem of China's national major science and technology projects and the structure of ecological systems, analyzing the interrelationships among the components of the innovation ecosystem and establishing the four-level model. The findings indicate that collaboration and policy guidance among the enterprise, research, and auxiliary innovation layers promote scientific output and innovation, while the external environmental layer provides robust support and safeguards, collectively driving the development of the innovation ecosystem. …”
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Temporal Relational Graph Convolutional Network Approach to Financial Performance Prediction by Brindha Priyadarshini Jeyaraman, Bing Tian Dai, Yuan Fang

“…A logistic regression model uses these combined features and social media embeddings for performance prediction. …”
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Estimation of thermal conductivity of snow by its density and hardness in Svalbard by V. M. Kotlyakov, A. V. Sosnovsky, N. I. Osokin

“…To verify the reliability of the approach to the determination of snow thermal conductivity, numerical experiments were performed on a mathematical model, which did show good convergence of the results. …”
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Detection of Pathogenic Microorganisms by Fusion of Recursive Feature Pyramid by HUANGZhitian, XIEYining, ZHAOJing, HEYongjun

“…Since the pathogenic microorganisms with small size will lose important features in the downsampling process , the recursive feature pyramid RFP is used in the feature fusion stage and the detection layer of the small target is added. …”
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Filament Type Recognition for Additive Manufacturing Using a Spectroscopy Sensor and Machine Learning by Gorkem Anil Al, Uriel Martinez-Hernandez

“…Data are collected using the Triad Spectroscopy module AS7265x (composed of AS72651, AS72652, AS72653 sensor units) positioned at three measurement distances (12 mm, 16 mm, 20 mm) to evaluate recognition performance under varying configurations. Machine learning models, including k-Nearest Neighbors (kNN), Logistic Regression, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP), are employed with hyperparameter tuning applied to optimise classification accuracy. …”
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