Restoration of Motion Blurred Images Based on Rich Edge Region Extraction Using a Gray-Level Co-Occurrence Matrix by Minghua Zhao, Xin Zhang, Zhenghao Shi, Peng Li, Bing Li

“…An image quality evaluation function and the operation time are used to evaluate the performance of the proposed method. …”
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Spatial Variability and Periodicity of Precipitation in the Middle Reaches of the Yellow River, China by Yi He, Xingmin Mu, Peng Gao, Guangju Zhao, Fei Wang, Wenyi Sun, Yuqing Zhang

“…The empirical orthogonal function (EOF), ensemble empirical mode decomposition, and Hurst exponent are used to detect spatial, multiscale characteristics of periodicity and trend for precipitation. …”
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Computerised Method of Multiparameter Optimisation of Predictive Control Algorithms for Asynchronous Electric Drives by Grygorii Diachenko, Serhii Semenov, Katarzyna Marczak, Gernot Schullerus, Ivan Laktionov

“…A computer model was designed in MATLAB and Simulink R2024a based on the gradient-based model predictive control strategy. A series of simulation experiments were carried out by varying the sampling step, number of iterations, prediction horizon, loss function parameters, and maximum linear search step to identify their impact on the control quality indicators. …”
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Analysis of Parameterized Quantum Circuits: On the Connection Between Expressibility and Types of Quantum Gates by Yu Liu, Kazuya Kaneko, Kentaro Baba, Jumpei Koyama, Koichi Kimura, Naoyuki Takeda

“…In the context of variational-quantum-algorithm-based quantum machine learning (QML), a QML model composed of a highly expressible PQC and a sufficient number of qubits is theoretically capable of approximating any arbitrary continuous function. While much research has explored the relationship between expressibility and learning performance, as well as the number of layers in PQCs, the connection between expressibility and PQC structure has received comparatively less attention. …”
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Transjugular intrahepatic portocaval shunting as a preparation phase for liver transplantation by P. G. Tarazov, D. A. Granov, A. A. Polikarpov, M. I. Generalov, I. O. Rutkin, O. A. Gerasimova, F. K. Zherebtsov

“…TIPS was successfully carried out in all the patients. The portovenous gradient was reduced by 2—3 times (to 9—12 mm Hg). …”
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Enhancing Uplink Communication in Wireless Powered Communication Networks Through Rate-Splitting Multiple Access and Joint Resource Optimization by Iqra Hameed, Mario R. Camana, Mohammad Abrar Shakil Sejan, Hyoung Kyu Song

“…To solve this non-convex joint optimization problem, we employ the simultaneous perturbation stochastic approximation (SPSA) algorithm, a gradient-free method that efficiently estimates optimal parameters with minimal function evaluations. …”
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Multiscale mechanical analysis for biomimetic implant design based on Triply Periodic Minimal Surfaces (TPMS) lattices: Application to partial replacement of femoral bone by Chrysoula Chatzigeorgiou, Boris Piotrowski, Fodil Meraghni, Yves Chemisky

“…A statistical analysis is performed and a function factor is proposed to facilitate the comparison. …”
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Friction and wear characteristics of acidic phosphate ester boundary layers analyzed by near-edge X-ray absorption fine structure by Naoko Takechi-Takahashi, Kensuke Matsushima, Noritake Isomura, Satoru Kosaka, Toshihide Ohmori, Toshinari Sano, Satoru Maegawa, Fumihiro Itoigawa

“…The maximum effective viscosity calculated from a function obtained by fitting the friction diagram was approximately 3,000 Pa·s, which was equivalent to that of common grease. …”
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Detecting deformation mechanisms of metals from acoustic emission signals through knowledge-driven unsupervised learning by Boyuan Gou, Yan Chen, Songhua Xu, Jun Sun, Turab Lookman, Ekhard K. H. Salje, Xiangdong Ding

“…The novel method leverages a family of gradient-driven supervised base learners and integrates them with a knowledge-infused aggregate loss function, effectively transforming the approach into an unsupervised learning framework. …”
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Inverse design of 3D nanophotonic devices with structural integrity using auxiliary thermal solvers by Kuster Oliver, Augenstein Yannick, Hernández Roberto Narváez, Rockstuhl Carsten, Sturges Thomas Jebb

“…Alongside the optical response, this heat metric becomes part of our objective function. We demonstrate the utility of our algorithm by designing two 3D nanophotonic devices. …”
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Machine Learning for Enhanced COPD Diagnosis: A Comparative Analysis of Classification Algorithms by Walaa H. Elashmawi, Adel Djellal, Alaa Sheta, Salim Surani, Sultan Aljahdali

“…These models were applied to a dataset comprising 1603 patients after being referred for a pulmonary function test. <b>Results</b>: The RFC has achieved superior accuracy, reaching up to 82.06% in training and 70.47% in testing. …”
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Modelling the spatial-temporal progression of the 2009 A/H1N1 influenza pandemic in Chile by Raimund Bürger, Gerardo Chowell, Pep Mulet, Luis M. Villada

“…A spatial-temporal transmission model of 2009 A/H1N1 pandemic influenza across Chile,a country that spans a large latitudinal range, is developed to characterize the spatial variation in peak timingof that pandemic as a function of local transmission rates, spatial connectivity assumptions for Chilean regions, andthe putative location of introduction of the novel virus into the country. …”
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Coordinated Reentry Guidance with A* and Deep Reinforcement Learning for Hypersonic Morphing Vehicles Under Multiple No-Fly Zones by Cunyu Bao, Xingchen Li, Weile Xu, Guojian Tang, Wen Yao

“…The A* algorithm generates heuristic trajectories circumventing no-fly zones, reducing the evaluation function by 6.2% compared to greedy methods, while DDPG optimizes sweep angles to minimize velocity loss and terminal errors (0.09 km position, 0.01 m/s velocity). …”
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Application of Machine Learning Techniques for Predicting Students’ Acoustic Evaluation in a University Library by Dadi Zhang, Kwok-Wai Mui, Massimiliano Masullo, Ling-Tim Wong

“…Using the collected personal information, room-related parameters, and sound pressure levels as input, six machine learning models (Support Vector Machine–Radial Basis Function (SVM (RBF)), Support Vector Machine–Sigmoid (SVM (Sigmoid)), Gradient Boosting Machine (GBM), Logistic Regression (LR), Random Forest (RF), and Naïve Bayes (NB)) were trained to predict students’ acoustic acceptance/satisfaction. …”
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Multi-Agent Reinforcement Learning in Games: Research and Applications by Haiyang Li, Ping Yang, Weidong Liu, Shaoqiang Yan, Xinyi Zhang, Donglin Zhu

“…Building upon stochastic game and extensive-form game-theoretic frameworks, we establish a methodological taxonomy across three dimensions: value function optimization, policy gradient learning, and online search planning, thereby clarifying the evolutionary logic and innovation trajectories of algorithmic advancements. …”
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Malaria Disease Prediction Based on Convolutional Neural Networks by Dhrgam AL Kafaf, Noor N. Thamir, Samara S. AL-Hadithy

“…In order to confront the challenge of diminishing gradient and facilitate the attainment of convergence, the activation function known as Rectified Linear Unit (ReLU) is ingeniously employed. …”
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Utilising machine learning classification models for meteorological drought monitoring and analysis by Iqra Mumtaz, Rizwan Niaz, Zamama Sajid, Abdu Qaid Alameri, Zulfiqar Ali, Khaled A. Gepreel

“…To address model-specific challenges, ridge regression was applied to mitigate multicollinearity in Logistic Regression, while SVM incorporated the Radial Basis Function (RBF) kernel and isolation forest to manage non-linearity and outliers. …”
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Mycorrhizal Fungi Influence on Mature Tree Growth: Stronger in High‐Nitrogen Soils for an EMF‐Associated Tree and in Low‐Nitrogen Soils for Two AMF‐Associated Trees by Inés Ibáñez, Morgan R. McPherson, Rima A. Upchurch, Donald R. Zak

“…ABSTRACT The plant–mycorrhizal fungi relationship can range from mutualistic to parasitic as a function of the fungal taxa involved, plant ontogeny, as well as the availability of resources. …”
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RIS-Assisted CR-MEC Systems Using Deep Reinforcement Learning Approach by Pham Duy Thanh, Hoang Thi Huong Giang, Ic-Pyo Hong

“…A Markov decision process problem is formulated and then is firstly solved by a proposed deep policy gradient scheme, in which the system directly learns the policy from gradients of actions. …”
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Study on shallow grain induced micro-region surface topography instability of bulk Sn alloys upon internal stress conditions by Bei Hu, Wenjun Zhou, Kensuke Tsuchiya

“…This work summarizes the fundamental understanding of SG behavior under varying internal stress conditions, elevating the function-oriented manufacturing process design on bulk Sn-alloy for ensuring the surface quality.…”
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