Transforming waste into value: Advancements in protein recovery and functional product development from wastewater sources by Thirugnanasambandham Karchiyappan, Parameswari Ettiyagounder, Paul Sebastian Selvaraj, Krishnan Ramanujam

“…This review highlights sustainable waste management approaches and the development of value-added products through protein recovery and functional product enhancement from industrial wastewater. …”
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From Viewpoint to Reverse Metaphorization: An Analysis of Deliberate Visual Metaphors in Lifelong Learning by N. V. Mongilyova, Y. S. Talybova

“…The objective of this research is to unveil the interpretative function of the viewpoint in photographs depicting lifelong learning. …”
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Green ILC: A Novel Energy-Efficient Iterative Learning Control Approach by Yu Dou, Emmanuel Prempain

“…In this paper, we introduce Green Iterative Learning Control (Green ILC), an innovative hybrid control method that addresses the critical need for energy-efficient control in dynamic, repetitive-task environments. …”
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Refined Judgment of Urban Traffic State Based on Machine Learning and Edge Computing by Lijuan Liu

“…It is committed to real-time simulation of human learning methods, which can effectively improve learning efficiency. …”
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A Multiobjective Cooperative Driving Framework Based on Evolutionary Algorithm and Multitask Learning by Xia Jiang, Jian Zhang, Qing-yang Li, Tian-yi Chen

“…Based on a multi-objective evolutionary algorithm, CAVs are denoted as a sequence to be searched in solution space, while a multitask learning neural network with adaptive loss function is implemented for optimization target feedback to surrogate the simulation test procedure. …”
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Anomaly detection with domain specific shapelet learning for sucker rod pump system by Xiangyu Li, Zhupei Liao, Chunhua Yuan

“…The AD-DSL utilizes a mechanistic information matrix and introduces a sparsity-promoting objective function, enabling Shapelet-based features to learn from motor power time-series data interpretably. …”
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Machine learning for Parkinson’s disease: a comprehensive review of datasets, algorithms, and challenges by Sahar Shokrpour, AmirMehdi MoghadamFarid, Sepideh Bazzaz Abkenar, Mostafa Haghi Kashani, Mohammad Akbari, Mostafa Sarvizadeh

“…Abstract Parkinson’s disease (PD) is a devastating neurological ailment affecting both mobility and cognitive function, posing considerable problems to the health of the elderly across the world. …”
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LLM-FMS: A fine-grained dataset for functional movement screen action quality assessment. by Qingjun Xing, Xuyang Xing, Ping Guo, Zhenhui Tang, Yanfei Shen

“…The Functional Movement Screen (FMS) is a critical tool for assessing an individual's basic motor abilities, aiming to prevent sports injuries. …”
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The results of the analysis of the research-methodological functions of the test current control of knowledge for natural sciences in MGRI by E. S. Gaponenko, V. N. Komarov

“…An obligatory part of the quality management system of educational institutions is pedagogical control of digestion of the learning material by students, which should be considered as an important component of the diagnosis and monitoring of training. …”
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Lateral hypothalamus and eating: cell types, molecular identity, anatomy, temporal dynamics and functional roles by Deok-Hyeon Cheon, Sheejune Park, Jihyun Park, MinSeo Koo, Hyun-Hyung Kim, Seol Han, Hyung Jin Choi

“…The LH is characterized by high heterogeneity and functional complexity, with many aspects still unexplored. …”
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Moral Values in the Fable "An-Nahlah wan-Namlah" by Ridwan Syukri: An Actantial and Functional Structure Study by Muhammad Riyadi Nugraha, Muhammad Hasyim, Abdul Muntaqim Al Anshory

“…The results of this study show that the story of Al-Nahlah wa An-Namlah has a perfect actantial structure. In addition, the functional structure fulfills all the required stages according to the functional structure in general. …”
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Functional characterization of all CDKN2A missense variants and comparison to in silico models of pathogenicity by Hirokazu Kimura, Kamel Lahouel, Cristian Tomasetti, Nicholas Jason Roberts

“…We found that 17.7% of all missense variants were functionally deleterious. We also used our functional classifications to assess the performance of in silico models that predict the effect of variants, including recently reported models based on machine learning. …”
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Bearing Small Sample Fault Diagnosis based on Data Generation and Transfer Learning by Dinghui Wu, Qin Fang, Chuyi Wu

“…Finally， the Softmax function is used for fault diagnosis on the output of the fully-connected layer of the target model. …”
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Machine learning algorithms can predict emotional valence across ungulate vocalizations by Romain A. Lefèvre, Ciara C.R. Sypherd, Élodie F. Briefer

“…Summary: Vocalizations can vary as a function of their context of production and provide an immediate measure of an animal’s affective states. …”
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Deep-Learning-Based Cognitive Assistance Embedded Systems for People with Visual Impairment by Huu-Huy Ngo, Hung Linh Le, Feng-Cheng Lin

“…The user can easily select a function by using a remote control device, and the system vocally reports the results to the user. …”
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Effect of Interactive Media on Fifth Graders’ Learning Motivation at SDN 9 Ampenan by Alfarizi Alfarizi, Ida Ermiana, Aisa Nikmah Rahmatih

“… Instructional media serve a pivotal function in facilitating the achievement of learning objectives. …”
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Deep reinforcement learning for path planning of autonomous mobile robots in complicated environments by Zhijie Zhang, Hao Fu, Juan Yang, Yunhan Lin

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Reinforcement Learning-Based Robust Vehicle Control for Autonomous Vehicle Trajectory Tracking by Attila Lelkó, Balázs Németh, Péter Gáspár

“…By choosing the reward function chosen during reinforcement learning, various driving styles can be realized, e.g., lap time minimization, track tracking, and travel comfort. …”
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FAILURE PREDICTION OF BOLTED CONNECTION OF COMPOSITE MATERIALS BASED ON DEEP LEARNING (MT) by PENG Fan, ZOU SiNong, REN YiRu

“…The mean square error between the predicted value and the finite element simulation value is taken as the loss function, and the learning rate is set at 0.01. When the mean square error is the minimum, the training is stopped, and the best deep learning prediction model is obtained. …”
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Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning by Nathanial C. Stevens, Tong Shen, Joshua Martinez, Veneese J. B. Evans, Morgan C. Domanico, Elizabeth K. Neumann, Laura S. Van Winkle, Oliver Fiehn

“…Our data reveal differences in the abundance of spatially distinct lipids, support the potential role of lipid saturation in healthy lung function, and highlight sex differences in regional lung lipid distribution following ozone exposure. …”
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