On the state of nature and social life: thinking about humans and chimpanzees by Eliane Sebeika Rapchan

“…Only since the 1960s has the cumulative knowledge on wild chimpanzee behavior informed us about their ability to produce and use tools, for social learning, and to produce unique patterns of collective behavior. …”
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The implementation of a system of CardioQVARK to personalize treatment in terms of the digital ecosystem healthcare formation by V. Isaenko, M. Rybina

“…Institutional support, strengthening the role of intangible assets, such as design, close attention by the State for production of small enterprise, lifelong learning-these are the challenges that need to be meet the requirements of the new economy.…”
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Assessing the capture of sociodemographic information in electronic medical records to inform clinical decision making. by Rawan Abulibdeh, Karen Tu, Debra A Butt, Anthony Train, Noah Crampton, Ervin Sejdić

“…The association between the completeness rates of the sociodemographic data and the various clinics, electronic medical record vendors, and physician characteristics was analyzed. Supervised machine learning models were used to determine the absence or presence of each characteristic for all adult patients over the age of 18 in the database. …”
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Hybrid LSTM-PSO optimization techniques for enhancing wind power bidding efficiency in electricity markets by Viet Anh Truong, Ngoc Sang Dinh, Thanh Long Duong, Ngoc Thien Le, Cong Dinh Truong, Linh Tung Nguyen

“…Past research has predominantly focused on utilizing meta-heuristic algorithms to optimize neural network structures, while the exploration of deep learning in optimization has remained relatively limited. …”
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Specifics of combating corruption at higher education institutions in the context of integrity policy by O. S. Bondarenko

“…Positions of domestic and foreign scholars on the essence and importance of academic integrity, the definition of “academic integrity” enshrined in legislation have been analysed, and the legally enshrined forms of academic integrity have been characterised, namely: academic plagiarism, self-plagiarism, fabrication, falsification, cheating, deception, bribery, biased assessment, providing assistance to students during the assessment of their learning outcomes or creating obstacles not provided for by the terms and/or procedures for such assessment, as well as impact in any form. …”
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Designing and Implementing a Competency-Based Training Program for Anesthesiology Residents at the University of Ottawa by Emma J. Stodel, Anna Wyand, Simone Crooks, Stéphane Moffett, Michelle Chiu, Christopher C. C. Hudson

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Modeling the gameplay actions of elite volleyball players and teams based on statistical match reports by Sergii Iermakov, Tetiana Yermakova, Krzysztof Prusik

“…The pandas library was used for data analysis and statistical operations, and 'scikit-learn' for machine learning. Results. Models are presented that best predict the results for teams and volleyball players. …”
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Risk Perception and Risk Communication for Training Women Apprentice Welders: A Challenge for Public Health Nursing by Clarice Alves Bonow, Marta Regina Cezar-Vaz, Marlise Capa Verde de Almeida, Laurelize Pereira Rocha, Anelise Miritz Borges, Diéssica Roggia Piexak, Joana Cezar Vaz

“…It was understood that the perception of women apprentices regarding a particular set of occupational risks is essential for public health nursing to develop an effective risk communication as a positive tool for teaching and learning.…”
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Quantum distance approximation for persistence diagrams by Bernardo Ameneyro, Rebekah Herrman, George Siopsis, Vasileios Maroulas

“…The space of persistence diagrams can be endowed with various metrics, which admit a statistical structure and allow to use these summaries for machine learning algorithms, e.g. the Wasserstein distance. …”
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Design and Experimental Validation of a Model-Free Controller for Beam Stabilization in Adaptive Optics Systems by Sicheng Guo, Tao Cheng, Kangjian Yang, Lingxi Kong, Chunxuan Su, Shuai Wang, Ping Yang

“…Additionally, the off-policy learning mechanism is deployed to simplify the optimization process. …”
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Experimental and analytical study on axial behaviour of square corrugated concrete filled single and double skin tube stub columns by Aya Mohsen Handousa, Mohamed Abdellatief, Fikry Abdo Salem, Nabil Mahmoud, Mohamed Ghannam

“…Furthermore, the study proposed two machine-learning models, namely Artificial Neural Network (ANN) and Gaussian Process Regression (GPR), to estimate the ultimate compressive strength of square CFDST columns. …”
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Comparative Analysis of Financial Flows in the Healthcare Systems of Germany, Austria and Czechia: Opportunities for Savings and Assessing the Tax-Like Nature of Health Insurance C... by Mini, Patrick

“…Originality/value: By dissecting the healthcare financial flows in Germany, Austria, and Czechia, this study offers a nuanced understanding of their systems' dynamics and identifies opportunities for cross-border learning to inform global healthcare policy reform. …”
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Sulfasalazine as a Corrosion Inhibitor on Carbon Steel Metal Surfaces in Acidic Media Using the Hydrogen Evolution Method: Experimental and Theoretical Studies by Hadi Thamer Obaid, Muthanna Mahmood Mutar, Safaa Hussein Ali

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TopoQual polishes circular consensus sequencing data and accurately predicts quality scores by Minindu Weerakoon, Sangjin Lee, Emily Mitchell, Haynes Heaton

“…TopoQual leverages techniques including partial order alignments (POA), topologically parallel bases, and deep learning algorithms to polish consensus sequences. …”
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A Clustering of Investors' Behavior according to Their Financial, Behavioral, and Demographic Characteristics (An Application of K-means Algorithm) by Marziyeh Nourahmadi, Hojjatollah Sadeqi

“…Clustering is one of the unsupervised learning methods and has a descriptive nature. In this method, the data are allocated based on a similarity criterion so that the data in each cluster are most similar and the least comparable to the data in other clusters.Methodology: This study identifies a group of investors with similar ability and willingness to accept risk using K-means clustering and Affinity propagation clustering. …”
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Maturity of knowledge management evaluation in organizational units using Data Envelopment Analysis by Nazila Adabavazeh, Mehrzad Navabakhsh

“…The most critical control solutions include training, organizational and individual learning, knowledge sharing, innovation, and formulating a knowledge vision and strategy to guide knowledge management initiatives. …”
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Exponential function LMS and fractional order pid based voltage power quality enhancement in distribution network by Prashant Kumar, Sabha Raj Arya

“…The proposed techniques derive the fundamental component using learning factor which is dependent on exponential error function and minimizes the error to estimate the reference load voltage. …”
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Artificial intelligence for the prediction of the physical and mechanical properties of a compressed earth reinforced by fibers by Bentegri Houcine, Rabehi Mohamed, Kherfane Samir, Boukansous Sarra

“…Using various validation criteria such as coefficient of determination (R), root mean squared error (RMSE) and mean absolute error (MAE), the ANN model was validated and compared to two machine learning (ML) Random Forest (RF) techniques and Multilayer Perceptron (MLP). …”
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Institute of higher education: the first quarter of a century by Василь Кремень

“…Thirdly, the reform of higher education should have taken into account modern civilizational changes that necessitated transformational processes in the global university environment, such as the development of student-centeredness, the widespread introduction of new digital learning technologies, deeper integration into globalization processes, etc. …”
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Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors by C. L. Bachand, C. L. Bachand, C. Wang, B. Dafflon, L. N. Thomas, L. N. Thomas, I. Shirley, S. Maebius, S. Maebius, C. M. Iversen, K. E. Bennett

“…We trained a random forest machine learning model to predict snow depth from variability in snow–ground interface temperature. …”
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