Analysis of the criteria selection problem in diversification models by Анна Бакурова, Алла Савранська, Еліна Терещенко, Дмитро Широкорад, Марк Шевчук

“… The digitalization of the economy reduces the cost of doing business by automating the relevant processes, but any transformation creates new risks and economic instability. …”
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Proposal of Low-Speed Sensorless Control of IPMSM Using a Two-Interval Six-Segment High-Frequency Injection Method with DC-Link Current Sensing by Daniel Konvicny, Pavol Makys, Alex Franko

“…This approach ensures that no portion of the injected voltage space vector falls into the immeasurable region of space vector modulation, which could otherwise compromise current measurements. …”
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Molecular cloning, sequencing, expression and purification of Alkhumra hemorrhagic fever virus capsid protein in Saudi Arabia by Ahmed M Hassan, Nada M Aljuaid, Magdah A Ganash, Sayed S Sohrab, Esam I Azhar

“…Methodology: We cloned, sequenced, analyzed, expressed, and purified the recombinant AHFV capsid protein (CP) using the PET-28a (+) vector. The CP gene was amplified through reverse transcriptase polymerase chain reaction (RT-PCR) and cloned into a vector. …”
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American Sign Language Recognition Model Using Complex Zernike Moments and Complex-Valued Deep Neural Networks by Selda Bayrak, Vasif Nabiyev, Celal Atalar

“…In the developed model, complex Zernike moments are used to obtain the feature vector of character images. A complex-valued deep neural network (CVDNN) capable of processing the feature vector composed of complex numbers across layers is also developed. …”
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Analysis of the criteria selection problem in diversification models by Анна Бакурова, Алла Савранська, Еліна Терещенко, Дмитро Широкорад, Марк Шевчук

“… The digitalization of the economy reduces the cost of doing business by automating the relevant processes, but any transformation creates new risks and economic instability. …”
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Using machine learning to assist decision making in the assessment of mental health patients presenting to emergency departments by Oliver Higgins, Rhonda L. Wilson, Stephan K. Chalup

“…Six different ML models were tested: Random Forest, XGBoost, CatBoost, k-Nearest Neighbours (kNN), Explainable Boosting Machine (EBM) using InterpretML, and Support Vector Machine using Support Vector Classification (SVC). …”
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A systems biology approach to the characterization of stress response in Dermacentor reticulatus tick unfed larvae. by Margarita Villar, Marina Popara, Nieves Ayllón, Isabel G Fernández de Mera, Lourdes Mateos-Hernández, Ruth C Galindo, Marina Manrique, Raquel Tobes, José de la Fuente

“…However, despite its role as a vector of emerging or re-emerging diseases, very little information is available on the genome, transcriptome and proteome of D. reticulatus. …”
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Innovative segmentation technique for aerial power lines via amplitude stretching transform by Pengfei Xu, Nor Anis Asma Sulaiman, Yafei Ding, Jiangwei Zhao

“…For this reason, this paper designs a pure amplitude stretching kernel function to form a Fourier amplitude vector field and uses this amplitude vector field to implement the stretching transformation of the amplitude field of the aerial power line image, so that the angular field after the Fourier inverse transformation can better react to the spatial domain line targets, and finally, after the Relative Total Variation (RTV) processing, the power line can be well detected. …”
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, and -like MO-1 infection in the brain of a child with seizures, mycotoxin exposure and suspected Rasmussen's encephalitis by Edward B. Breitschwerdt, Ricardo G. Maggi, Cynthia Robveille, Emily Kingston

“…Background In conjunction with more sensitive culture and molecular diagnostic testing modalities, simultaneous or sequential infection with more than 1 vector borne zoonotic pathogen is being increasingly documented in human patients. …”
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Discrimination of pressed sesame oil: A comparison study of non-targeted UV spectral fingerprints combined with different chemometric methods by Dongmin Wang, Yuxin Liu, Jiahui Qian, Yake Li, Lixia Hou, Huamin Liu

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Trends and Prospects in Educational Modernisation: A Hermeneutic Approach by A. F. Zakirova, Ye. N. Volodina

“…Due to the long and unproductive nature of the modernisation process, researchers are paying special attention to systemic factors, noting that system optimisation processes are structured primarily around organisational and technological models derived from industry. …”
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Machine-Learning-Based Integrated Mining Big Data and Multi-Dimensional Ore-Forming Prediction: A Case Study of Yanshan Iron Mine, Hebei, China by Yuhao Chen, Gongwen Wang, Nini Mou, Leilei Huang, Rong Mei, Mingyuan Zhang

“…Combined with spectral and elemental analysis, the universality of alteration features such as chloritization and carbonation, which are closely related to the mineralization process, was further verified. (3) Based on the spectral and elemental grade data of rock and mineral samples, a training model for ore grade–spectrum correlation was constructed using Random Forests, Support Vector Machines, and other algorithms, with the SMOTE algorithm applied to balance positive and negative samples. …”
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Performance Comparison of 10 State-of-the-Art Machine Learning Algorithms for Outcome Prediction Modeling of Radiation-Induced Toxicity by Ramon M. Salazar, PhD, Saurabh S. Nair, MS, Alexandra O. Leone, MBS, Ting Xu, PhD, Raymond P. Mumme, BS, Jack D. Duryea, BA, Brian De, MD, Kelsey L. Corrigan, MD, Michael K. Rooney, MD, Matthew S. Ning, MD, Prajnan Das, MD, Emma B. Holliday, MD, Zhongxing Liao, MD, Laurence E. Court, PhD, Joshua S. Niedzielski, PhD

“…The training set was used to create and tune the model, and the test set served to assess it by calculating performance metrics. This process was repeated 100 times by each algorithm for each data set. …”
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A novel mechanism of sperm midpiece epididymal maturation and the role of CCDC112 in sperm midpiece formation and establishing an optimal flagella waveform by Maddison L. Graffeo, Joseph Nguyen, Farin Yazdan Parast, Jessica E. M. Dunleavy, Denis Korneev, Hongyi Yang, Hidenobu Okuda, Anne E. O’Connor, Donald F. Conrad, Reza Nosrati, Brendan J. Houston, Moira K. O’Bryan

“…We further unveil a previously unrecognized process of epididymal mitochondrial sheath maturation. …”
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Spin-orbital conversion of the light field immediately behind an ideal spherical lens by V.V. Kotlyar, A.A. Kovalev, S.S. Stafeev, E.S. Kozlova, M.A. Telegin

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Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models by Edgar Omar Molina Molina, Victor H. Diaz-Ramirez

“…Next, a three-channel image is created using the local phase, amplitude, and orientation features of the ROI. A feature vector is constructed for the processed mammogram using the developed CNN model. …”
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Metabolomics Biomarker Discovery to Optimize Hepatocellular Carcinoma Diagnosis: Methodology Integrating AutoML and Explainable Artificial Intelligence by Fatma Hilal Yagin, Radwa El Shawi, Abdulmohsen Algarni, Cemil Colak, Fahaid Al-Hashem, Luca Paolo Ardigò

“…The TreeSHAP approach, which is a type of XAI, was used to interpret the model by assessing each metabolite’s individual contribution to the categorization process. <b>Results:</b> TPOT had superior performance in distinguishing between HCC and cirrhosis compared to other AutoML approaches AutoSKlearn and H2O AutoML, in addition to traditional machine learning models such as random forest, support vector machine, and k-nearest neighbor. …”
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Chemogenetic silencing of the subiculum blocks acute chronic temporal lobe epilepsy by Jianbang Lin, Jing Liu, Qi Zhang, Taian Liu, Zexuan Hong, Yi Lu, Cheng Zhong, Zhonghua Lu, Yuantao Li, Yu Hu

“…We then injected an adeno-associated viral (AAV) vector carrying an inhibitory chemogenetic element, hM4Di, directly into the subiculum. …”
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Application of machine learning techniques to predict the compressive strength of steel fiber reinforced concrete by Ala’a R. Al-Shamasneh, Arsalan Mahmoodzadeh, Faten Khalid Karim, Taoufik Saidani, Abdulaziz Alghamdi, Jasim Alnahas, Mohammed Sulaiman

“…Six advanced regression-based algorithms, including support vector regression (SVR), Gaussian process regression (GPR), random forest regression (RFR), extreme gradient boosting regression (XGBR), artificial neural networks (ANN), and K-nearest neighbors (KNN), were benchmarked through rigorous model validation processes including hold-out testing, K-fold cross-validation, sensitivity analysis, and external validation with unseen experimental data. …”
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Analysis of multiple faults in induction motor using machine learning techniques by Puja Pohakar, Ravi Gandhi, Surender Hans, Gulshan Sharma, Pitshou N. Bokoro

“…Traditional diagnostic methods are expert judgment-based and pre-threshold-based and, therefore, less efficient when dealing with vast industrial processes. Based on key operating parameters like voltage, current, and speed, this article describes how machine learning (ML) algorithms like Random Forest (RF), K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), Support Vector Machines (SVM), and Extreme Gradient Boosting with Feature Interaction (XGBoost + FIS) are used to detect different motor faults. …”
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