Exploring undergraduate students’ experiences in dealing with post-editing of machine translation by Sri Harto, Fuad Abdul Hamied, Bachrudin Musthafa, Sri Setyarini

“…In addition, in PEMT practices, the students’ experiences were explored through the implementation of translation methods and techniques expected to build their awareness in PEMT with regard to language structure and language function. These experiences have given some empirical inputs for the lecturers to design relevant tasks and apply various translating practices for students’ more meaningful learning.…”
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Person identification using novel local triangular binary pattern-based texture descriptor by Arti Tekade, T. Vijayan, B. Karthik, Anurag Mahajan

“…The performance of the proposed descriptor is assessed using different machine learning classifiers such as K-nearest neighbor (KNN), support vector machine (SVM), radial basis function-SVM (RBF-SVM), classification tree (CT), and random forest (RF) for authentication of the 20 users based on hand radiographs. …”
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Psychological artificial intelligence: Designing algorithms to deal with the uncertainty of rework in construction by Peter E.D. Love, Jane Matthews, Weili Fang

“…In uncertainty settings, algorithms such as smart heuristics – simple task-specific decision strategies that function under specific conditions – have been shown to achieve equal and better performance in problems of inference than machine learning models. …”
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Going beyond the model: Characteristics of civic visual literacy by Ann-Sofie Jägerskog, Malin Tväråna

“…Practical implications: Entirety, expansion, and agency must function as focal points in SSE teaching when visual representations are used. …”
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Estimating Shape Parameters of Piecewise Linear-Quadratic Problems by Zheng, Peng, Ramamurthy, Karthikeyan Natesan, Aravkin, Aleksandr Y.

“…Piecewise Linear-Quadratic (PLQ) penalties are widely used to develop models in statistical inference, signal processing, and machine learning. Common examples of PLQ penalties include least squares, Huber, Vapnik, 1-norm, and their asymmetric generalizations. …”
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Structural Biology in the AlphaFold Era: How Far Is Artificial Intelligence from Deciphering the Protein Folding Code? by Nicole Balasco, Luciana Esposito, Luigi Vitagliano

“…Since the shape that proteins assume is fundamental for their function, understanding the chemical and structural bases that drive their three-dimensional structures represents the central problem for an atomic-level interpretation of biology. …”
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Multi-objective optimization models for power load balancing in distributed energy systems by Zhuo Wang, Yuchen Luo, Wei Wu, Lei Cao, Zhun Li

“…Uncertainty around renewable energy is handled by the suggested approach via the use of a probability density function (PDF). In order to address a multi-objective optimization (MOCO) issue, the model that was built relies on a MOCO method. …”
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Unsupervised Feature Selection With Ordinal Preserving Self-Representation by Jiangyan Dai, Yuqi Chen, Yugen Yi, Jining Bao, Lei Wang, Wei Zhou, Gang Lei

“…Then, the topology information is introduced into the objective function for utilizing the ordinal locality of high-dimensional data adequately. …”
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MANAGING FARM AGRO WASTE IN RURAL AREAS: EMERGING POTENTIALS AND CHALLENGES OF BIOCHAR PRODUCTION AND USE AMONG SMALLHOLDERS by Khairul Hisyam KAMARUDIN, Mohamad Fadhli RASHID, Wan Nurul Mardiah WAN MOHD RANI, Noraini OMAR CHONG

“…In return, agriculture provides employment opportunities and income, ensuring that rural areas continue to function as food producers for both the local community and the growing urban population. …”
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Vision-Language Model-Based Local Interpretable Model-Agnostic Explanations Analysis for Explainable In-Vehicle Controller Area Network Intrusion Detection by Jaeseung Lee, Jehyeok Rew

“…However, these models often function as black boxes, offering limited transparency into their decision-making processes, which hinders trust in safety-critical environments. …”
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KACFormer: A Novel Domain Generalization Model for Cross-Individual Bearing Fault Diagnosis by Shimin Shu, Muchen Xu, Peifeng Liu, Peize Yang, Tianyi Wu, Jie Yang

“…Fault diagnosis methods based on deep learning have been widely applied to bearing fault diagnosis. …”
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Applied Koopman Theory for Partial Differential Equations and Data-Driven Modeling of Spatio-Temporal Systems by J. Nathan Kutz, J. L. Proctor, S. L. Brunton

“…Further, we show that the clear goal in selecting observables is to place the DMD eigenvalues on the imaginary axis, thus giving an objective function for observable selection. Judiciously chosen observables lead to physically interpretable spatio-temporal features of the complex system under consideration and provide a connection to manifold learning methods. …”
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Variable-Parameter Impedance Control of Manipulator Based on RBFNN and Gradient Descent by Linshen Li, Fan Wang, Huilin Tang, Yanbing Liang

“…To address the computationally intensive nature of current hybrid force/position control methods, a variable-parameter impedance control method for manipulators, utilizing a gradient descent method and Radial Basis Function Neural Network (RBFNN), is proposed. This method employs a position-based impedance control structure that integrates iterative learning control principles with a gradient descent method to dynamically adjust impedance parameters. …”
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The Role of Autophagy in Excitotoxicity, Synaptic Mitochondrial Stress and Neurodegeneration by Charleen T. Chu

“…Brain and nervous system functions depend upon maintaining the integrity of synaptic structures over the lifetime. …”
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Robust wireless statistic division multiplexing and its performance analysis by Zhongqiang Luo, Chengjie Li

“…Second, in terms of robust wireless statistic division multiplexing receiving mechanism based on independent component analysis principle, a novel cost function based on minimization mutual information modified by minimum bit error rate constraint is investigated to improve robust performance of source recovery. …”
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A Pandemic of Possibilities: The Spread of Potentiality-Seeking Organisations under Conditions of the COVID-19 Lockdown by Andersen Niels Åkerstrøm, Carli Giacomo, Stenner Paul, Tagliaventi Maria Rita

“…They pursue opportunities stemming from learning to ‘expect the unexpected’, while still calling on function systems to stabilize expectations.…”
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An Approach to Trustworthy Article Ranking by NLP and Multi-Layered Analysis and Optimization by Chenhao Li, Jiyin Zhang, Weilin Chen, Xiaogang Ma

“…Third, a custom ranking function combines relevance and trust indicators to score and sort the remaining articles. …”
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Multilane Spatiotemporal Trajectory Optimization Method (MSTTOM) for Connected Vehicles by Pangwei Wang, Yunfeng Wang, Hui Deng, Mingfang Zhang, Juan Zhang

“…In this MSTTOM, the dynamic characteristics of connected vehicles, the vehicular state vector, the optimized objective function, and the constraints are formulated. The method for solving the trajectory problem is optimized based on Pontryagin’s maximum principle and reinforcement learning (RL). …”
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Intensity Distribution of Partially Coherent Array Finite Airy Beams Propagating in Atmospheric Turbulence by Long Jin, Zirui Zhang, Nuo Wang, Zixin Liu, Yuwei Deng, Yanhua Fu

“…Based on the extended Huygens–Fresnel integral and the Rytov phase structure function, the closed-form expression of a partially coherent array finite Airy beams (PCAFABs) cutting through the Kolmogorov atmospheric turbulence is derived in the space domain under the paraxial approximation. …”
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Intelligent Classification Method for Rail Defects in Magnetic Flux Leakage Testing Based on Feature Selection and Parameter Optimization by Kailun Ji, Ping Wang, Yinliang Jia

“…This study addresses the critical challenge of insufficient classification accuracy for different defect signals in rail magnetic flux leakage (MFL) detection by proposing an enhanced intelligent classification framework based on particle swarm optimized radial basis function neural network (PSO-RBF). Three key innovations drive this research: (1) A dynamic PSO algorithm incorporating adaptive learning factors and nonlinear inertia weight for precise RBF parameter optimization; (2) A hierarchical feature processing strategy combining mutual information selection with correlation-based dimensionality reduction; (3) Adaptive model architecture adjustment for small-sample scenarios. …”
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