A Classification-Based Blood–Brain Barrier Model: A Comparative Approach by Ralph Saber, Sandy Rihana

“…<b>Methods</b>: In this study, we compare the effectiveness of sequential feature selection (SFS) and genetic algorithms (GAs) in optimizing descriptor selection for BBB permeability prediction. …”
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Multi-Modal Federated Learning Over Cell-Free Massive MIMO Systems for Activity Recognition by Seyed Mohammad Sheikholeslami, Pai Chet Ng, Jamshid Abouei, Konstantinos N. Plataniotis

“…We propose a prioritized device-modality selection scheme that allows flexible participation of devices. …”
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Tomato hybrids recommended for growing in photoculture conditions by V. G. Korol

“…The most important influence on the yield, the timing of growing vegetables in greenhouses and the cost of the resulting crop, in addition to the arrival of solar radiation, is exerted by the presence of a system of artificial lighting in greenhouses and its capacity.It is no exaggeration to say that today artificial lighting in greenhouses is more effective than natural lighting. …”
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MDFN: Enhancing Power Grid Image Quality Assessment via Multi-Dimension Distortion Feature by Zhenyu Chen, Jianguang Du, Jiwei Li, Hongwei Lv

“…Additionally, previous methods only use the CLS token for predicting the quality score of the image. …”
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An Artificial Intelligence-Based Approach With Photoplethysmogram and Heart Rate Variability for Sleep Bruxism Diagnosis by Omer Eris, Mehmet Recep Bozkurt, Seval Bulut Eris, Cahit Bilgin

“…We used Principal Component Analysis (PCA) to reduce the data size and Fisher&#x2019;s feature selection algorithm to identify the most important features. …”
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Hybrid classical and quantum computing for enhanced glioma tumor classification using TCGA data by Emine Akpinar, Murat Oduncuoglu

“…These classifiers utilize selected features to differentiate LGGs from HGGs. …”
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Multi-objective combinatorial methodology for nuclear reactor site assessment: A case study for the United States by Omer Erdem, Kevin Daley, Gabrielle Hoelzle, Majdi I. Radaideh

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A Near-Real-Time Imaging Algorithm for Focusing Spaceborne SAR Data in Multiple Modes Based on an Embedded GPU by Yunju Zhang, Mingyang Shang, Yini Lv, Xiaolan Qiu

“…This article analyzes the computational complexity of the two algorithms and provides a criterion called the Method Choice Indicator (MCI) for selecting the appropriate method. Additionally, the mathematical expressions for time–frequency transformation are derived, providing the theoretical basis for calculating the equivalent PRF and the azimuth width represented by a single pixel. …”
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Data driven design of ultra high performance concrete prospects and application by Bryan K. Aylas-Paredes, Taihao Han, Advaith Neithalath, Jie Huang, Ashutosh Goel, Aditya Kumar, Narayanan Neithalath

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A Network Analysis Approach to Detect and Differentiate Usher Syndrome Types Using miRNA Expression Profiles: A Pilot Study by Rama Krishna Thelagathoti, Wesley A. Tom, Chao Jiang, Dinesh S. Chandel, Gary Krzyzanowski, Appolinaire Olou, Rohan M. Fernando

“…<b>Methods:</b> We collected microarray miRNA-expression data from 17 samples, representing four patient-derived USH cell lines and a non-USH control. Supervised feature selection was utilized to identify key miRNAs that differentiate USH cell lines from the non-USH control. …”
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Evaluation of genetic diversity and population structure of wheat core collection using SNP and KASP markers for major agronomic traits by Xiaohan Wang, Sukyeung Lee, Yu-Mi Choi, JungYoon Yi, Kebede Taye Desta, Myoung-Jae Shin, Gi-An Lee, Young-Ah Jeon, Eunae Yoo

“…A total of 397 global wheat core collections were selected from 19,698 accessions, with associated traits accumulated from 1984 to 2017. …”
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Maintenance of flower color dimorphism in Ophiorrhiza japonica (Rubiaceae): responses to fluctuating temperatures in a dolomite Karst region by Xiao-Yue Wang, Xiao-Yue Wang, Han-Qing Tang, Han-Qing Tang, Yun-Jing Liu, Yun-Jing Liu, Meng-Da Xiang, Meng-Da Xiang, Ren-Xiu Yao, Ren-Xiu Yao, Bai-Zhu Li, Yu Li, Yu Li, Yin Yi, Yin Yi, Zhi-Rui Wen, Zhi-Rui Wen, Ming Tang, Ming Tang, Xiao-Xin Tang, Xiao-Xin Tang

“…IntroductionFlower color polymorphism is often attributed to selection pressures from Q9 pollinators or other non-pollinator stress factors. …”
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A hybrid fuzzy decision-making approach for evaluating the suppliers based on industry 5.0 and viable supply chain dimensions: a case study in medical devices manufacturing by Zeinab Asadi, Hassanali Aghajani, Mohammad Valipourkhatir, Erfan Babaee Tirkolaee

“…Also, the results show that in addition to the general criteria, such as cost and quality, other criteria, such as reliability, technical capability, pollution control, risk reduction, and service, also play a significant role in the process of selecting suppliers. …”
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Software Defects Identification: Results Using Machine Learning and Explainable Artificial Intelligence Techniques by Momotaz Begum, Mehedi Hasan Shuvo, Imran Ashraf, Abdullah Al Mamun, Jia Uddin, Md Abdus Samad

“…We also used Explainable Artificial Intelligence (XAI), Local Interpretable Model (LIME), and SHapley Additive exPlanations (SHAP) to determine software fault features. …”
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Fluid volume status detection model for patients with heart failure based on machine learning methods by Haozhe Huang, Jing Guan, Chao Feng, Jinping Feng, Ying Ao, Chen Lu

“…Data from 186 heart failure patients collected between January 2022 and July 2022 were employed as an external validation set to investigate the effects of model training. SHapley Additive exPlanations (SHAP) were used to interpret the ML models Results: Thirty features were selected for model development, and the area under the ROC curve AUC (95 % CI) for the four machine learning models in the testing set was 0.75 (0.73–0.77), 0.77 (0.74–0.79), 0.70 (0.67–0.73), and 0.76 (0.73–0.78), and the AUC (95 % CI) in the external validation set was 0.74 (0.71–0.76), 0.70 (0.67–0.73), 0.64 (0.59–0.68), and 0.67 (0.63–0.71). …”
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A deep neural network framework for estimating coastal salinity from SMAP brightness temperature data by Yidi Wei, Qing Xu, Qing Xu, Xiaobin Yin, Xiaobin Yin, Yan Li, Yan Li, Kaiguo Fan

“…The framework leverages machine learning interpretability tools (Shapley Additive Explanations, SHAP) to optimize input feature selection and employs a grid search strategy for hyperparameter tuning.Results and discussionSystematic validation against independent in-situ measurements demonstrates that the baseline DNN model constructed for the entire region and time period outperforms conventional algorithms including K-Nearest Neighbors, Random Forest, and XGBoost and the standard SMAP SSS product, achieving a reduction of 36.0%, 33.4%, 40.1%, and 23.2%, respectively in root mean square error (RMSE). …”
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Whole-genome resequencing to investigate the genetic diversity and mechanisms of plateau adaptation in Tibetan sheep by Xue Li, Buying Han, Dehui Liu, Song Wang, Lei Wang, Quanbang Pei, Zian Zhang, Jincai Zhao, Bin Huang, Fuqiang Zhang, Kai Zhao, Dehong Tian

“…Abstract Introduction Tibetan sheep, economically important animals on the Qinghai–Tibet Plateau, have diversified into numerous local breeds with unique characteristics through prolonged environmental adaptation and selective breeding. However, most current research focuses on one or two breeds, and lacks a comprehensive representation of the genetic diversity across multiple Tibetan sheep breeds. …”
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A comparative framework to develop transferable species distribution models for animal telemetry data by Joshua A. Cullen, Camila Domit, Margaret M. Lamont, Christopher D. Marshall, Armando J. B. Santos, Christopher R. Sasso, Mehsin Al Ansi, Kristen M. Hart, Mariana M. P. B. Fuentes

“…A correlative SDM using a hierarchical Gaussian process regression (HGPR) algorithm exhibited greater transferability than a hybrid SDM using HGPR, as well as correlative and hybrid forms of hierarchical generalized linear models. Additionally, models that evaluated habitat selection at the finest spatial scale and that did not account for life stage proved to be the most transferable in this study. …”
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Explaining the Earnings Management Prediction Model Using the Hybrid of Machine Learning Methods by Hassan Hassani, Esfandiar Malekian Kallehbasti, Yahya Kamyabi

“…However, the results did not show the superiority of the relief-based feature selection model over the principal component analysis-based feature selection model in predicting real earnings management. …”
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Advances in 3D and 4D Printing of Soft Robotics and Their Applications by Hao Liu, Changchun Wu, Senyuan Lin, James Lam, Ning Xi, Yonghua Chen

“…At the same time, advancements in 3D printing technology empower contemporary 3D printers with higher resolution, faster printing speed, and a broader selection of materials. The progression of 3D printing technologies offers additional avenues for fabricating soft robots, facilitating their practical utilization, and commercialization. …”
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