Maximizing Energy Efficiency in IRS-Assisted Phase Cooperative PS-SWIPT-Based Self-Sustainable IoT Network by Haleema Sadia, Ahmad Kamal Hassan, Ziaul Haq Abbas, Ghulam Abbas, Thar Baker, Nasir Saeed

“…The proposed solutions’ analysis shows low computational complexity and fast convergence, achieving near-optimal EE performance for different network settings. We conducted extensive simulations to evaluate our proposed framework’s performance and unveiled significant EE performance achieved by the IoT network consistent with the numerical findings. …”
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Vis/NIR Spectroscopy and Vis/NIR Hyperspectral Imaging for Non-Destructive Monitoring of Apricot Fruit Internal Quality with Machine Learning by Tiziana Amoriello, Roberto Ciorba, Gaia Ruggiero, Francesca Masciola, Daniela Scutaru, Roberto Ciccoritti

“…The fruit supply chain requires simple, non-destructive, and fast tools for quality evaluation both in the field and during the post-harvest phase. …”
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Inverse Dynamic Parameter Identification for Remote Sensing of Soil Moisture From SMAP Satellite Observations by Runze Zhang, Adam Watts, Mohamad Alipour

“…However, there is no consistent trend in the magnitudes of dynamic <italic>h</italic> and &#x03C9; between different algorithms. Identifying the most effective dynamic <italic>h</italic> and &#x03C9; parameters within the SMAP algorithmic framework necessitates not only selecting an appropriate parameter range but also accurately tracking the temporal evolutions of surface roughness and vegetation scattering. …”
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Reliability Analysis of Three-dimensional Soil Slopes Considering Spatial Variability of Soil Parameters by Wan Yukuai, Zhou Yuqi, Shao Linlan, Wang Yuke, Zhang Fei

“…To complement the random field modeling, an enhanced PSO algorithm is proposed for 3D slope stability analysis. …”
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Interpretable machine learning analysis of environmental characteristics on bacillary dysentery in Sichuan Province by Yao Zhang, Qiao-Lin Wang, Wei Peng, Meng-Yuan Zhang, Yao Qin, Lun Zhang, Rong-Jie Wei, Dian-Ju Kang

“…The eXtreme Gradient Boosting (XGBoost) algorithm was employed to assess the influence of key environmental features, including precipitation, temperature, PM10, potential evaporation, vegetation cover, and NDVI, on BD incidence. …”
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To accurately predict lymph node metastasis in patients with mass-forming intrahepatic cholangiocarcinoma by using CT radiomics features of tumor habitat subregions by Pengyu Chen, Zhenwei Yang, Peigang Ning, Hao Yuan, Zuochao Qi, Qingshan Li, Bo Meng, Xianzhou Zhang, Haibo Yu

“…Using information from the arterial and venous phases of multisequence CT images, tumor habitat subregions were delineated through the K-means clustering algorithm. Radiomic features were extracted and screened, and prediction models based on different subregions were constructed and compared with traditional intratumoral models. …”
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Ultrasound-based radiomic nomogram for predicting the invasive status of breast cancer: a multicenter study by Dan Yan, Jingwen Xie, Wanling Cheng, Wen Xue, Yaohong Den, JianXing Zhang

“…A total of 1125 radiomic features were extracted from the training set of CUS images, and Radiomics Scores (Rad-scores) were constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Different nomogram models were constructed using logistic regression, including a clinical–radiomics model (Clinic + Rad), a CUS–clinical model (CUS + Clinic), and a combined CUS–clinical–radiomics model (CUS + Clinic + Rad). …”
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Triboinformatic analysis and prediction of B4C and granite powder filled Al 6082 composites using machine learning regression models by Amit Aherwar, Anamika Ahirwar, Vimal Kumar Pathak

“…The developed model’s results were evaluated utilizing a number of statistical metrics to identify the most reliable algorithm for wear and COF prediction. …”
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Diagnostic Value of Glycosylated Extracellular Vesicle microRNAs in Gastric Cancer by Wang S, Ma C, Ren Z, Zhang Y, Hao K, Liu C, Xu L, He S, Zhang J

“…The signatures were screened in a discovery cohort of GC patients (n=55) and non-disease controls (n=46) using an integrated process, including high-throughput sequencing technology, screening using a complete bioinformatics algorithm, validation using RT-qPCR, and evaluation by constructing a diagnostic model. …”
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Spatial analysis of air pollutant exposure and its association with metabolic diseases using machine learning by Jingjing Liu, Chang Liu, Zhangdaihong Liu, Yibin Zhou, Xiaoguang Li, Yang Yang

“…Model performance is evaluated through 10-fold cross-validation using five different metrics. …”
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A comparative study of Mongolian medicine prescriptions in the Chinese Pharmacopoeia and Tibetan medicinal compounds in the Ministerial Standards by Ji Zang, Cai Rang-Zhou Tai, Jie-Jia Nan, Cai Rang-Nanjia

“…Different weights were applied to calculate the comprehensive similarity between the formulas. …”
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Assessing the potential of a Bayesian ranking as an alternative to consensus meetings for decision making in research funding: A case study of Marie Skłodowska-Curie actions. by Rachel Heyard, David G Pina, Ivan Buljan, Ana Marušić

“…Using data from the Marie Skłodowska-Curie Actions, we aimed at investigating the differences between an algorithmic approach to summarise the information from grant proposal individual evaluations to decisions after consensus meetings, and we present an exploratory comparative analysis. …”
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A CT-based machine learning model for using clinical-radiomics to predict malignant cerebral edema after stroke: a two-center study by Lingfeng Zhang, Gang Xie, Yue Zhang, Yue Zhang, Junlin Li, Junlin Li, Wuli Tang, Wuli Tang, Ling Yang, Ling Yang, Kang Li

“…Ultimately, the efficacy of these models was evaluated by measuring the operating characteristics of the subjects through their area under the curve (AUCs).ResultsLogistic regression (LR) was found to be the most effective machine learning algorithm, for forecasting the MCE. …”
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Accurate and rapid single nucleotide variation detection in PCSK9 gene using nanopore sequencing by Ilaria Massaiu, Vincenza Valerio, Valentina Rusconi, Valentina Rusconi, Francesca Bertolini, Donato De Giorgi, Veronika A. Myasoedova, Paolo Poggio, Paolo Poggio

“…Twelve subjects were analyzed using different sequencing flow cells, basecalling models, and SNV calling algorithms. …”
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Multi-label Classification Based on Label-Aware Variational Autoencoder by SUN Hongjian, XU Pengyu, LIU Bing, JING Liping, YU Jian

“…Experimental results obtained on datasets from four different domains show that the proposed method can effectively enhance feature and label embedding and fully capture the higher-order correlation information between labels for multi-label classification tasks, and the significant superiority of the proposed method in performance is verified through a comparative analysis with state-of-the-art algorithms in terms of multiple evaluation metrics.…”
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Research of color models in digital graphics by Hetman Oksana, Shpetna Svitlana

“…The prospects for further studies involve the development of advanced color processing algorithms and the integration of modern color models into emerging technologies such as augmented reality, virtual reality, and HDR imaging systems. …”
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Recent Advancements in Neuroimaging‐Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e‐Health: A Systematic Review by Zia‐Ur‐Rehman, Mohd Khalid Awang, Ghulam Ali, Muhammad Faheem

“…This comprehensive review intends to examine current developments in deep learning (DL) approaches with neuroimaging for AD diagnosis, where popular imaging types, reviews well‐known online accessible data sets, and describes different algorithms used in DL for the correct initial evaluation of AD are presented. …”
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Assessment of Tail-Cutting in Frozen Albacore (<i>Thunnus alalunga</i>) Through Ultrasound Inspection and Chemical Analysis by Masafumi Yagi, Akira Sakai, Suguru Yasutomi, Kanata Suzuki, Hiroki Kashikura, Keiichi Goto

“…Here, we evaluated this method by comparing it with chemical analysis and ultrasound inspection. …”
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Integrated Workflow for Drug Repurposing in Glioblastoma: Computational Prediction and Preclinical Validation of Therapeutic Candidates by Nazareno Gonzalez, Melanie Pérez Küper, Matías Garcia Fallit, Jorge A. Peña Agudelo, Alejandro Nicola Candia, Maicol Suarez Velandia, Ana Clara Romero, Guillermo Videla Richardson, Marianela Candolfi

“…Daporinad, a NAMPT inhibitor that permeates the blood-brain barrier was selected for further preclinical evaluation. This evaluation supported the in silico predictions of high potential efficacy and safety in GBM. …”
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Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

“…The algorithms (RF and STR) with the smallest Mean Absolute Error (MAE) and the highest residual error (RMSE) and the highest correlation coefficient (RP2) were selected for further parameter optimization and evaluation. …”
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