Laparoscopic Surgeries in Pediatric Abdominal Surgical Emergencies by Elena Yu. D’yakonova, Aleksandr Yu. Razumovskii, Abdumanap B. Alkhasov, Alexander A. Baranov, Leyla S. Namazova-Baranova, Aleksandr S. Bekin, Aleksei А. Gusev, Ekaterina A. Romanova

“…The possible risks of laparoscopic access and methods for their reduction were analyzed. The advantages of this technique were specified in comparison with open surgical interventions. …”
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Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank by Jukyung Lee, Hyosung Joo, Jihwan Woo

“…In this study, we propose a machine learning-based filter bank (MLFB) noise reduction algorithm to improve the quality of rPPG signals. …”
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Design of a Lifting Robot for Repetitive Inter-Floor Material Transport with Adjustable Gravity Compensation by Byungseo Kwak, Seungbum Lim, Jungwook Suh

“…Manipulator-based robotic systems have been explored as alternatives; however, they require complex control algorithms and struggle with confined construction environments. …”
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An Improved NSGA‐III With Hybrid Crossover Operator for Multi‐Objective Optimization of Complex Combined Cooling, Heating, and Power Systems by Lejie Ma, Dexuan Zou

“…NSGAIII‐AC‐GM delivers a 20% reduction in operational costs and a 10% decrease in CO2 emissions, outperforming seven other algorithms in optimization efficiency on DTLZ and IMOP problems. …”
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Natural gas bi-level demand response strategies considering incentives and complexities under dynamic pricing by Huibin Zeng, Jie Zhou, Hongbin Dai

“…Abstract As global energy demand rises and carbon reduction targets intensify, natural gas is gaining prominence as a clean energy source. …”
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Multi energy dynamic soaring trajectory optimization method based on reinforcement learning by ZHANG Yunfei, WANG Honglun, ZHANG Menghua, GONG Yinan

“…On the foundation of the trained policies, the method utilizes the twin delayed deep deterministic policy gradient algorithm for policy enhancement. This significantly boosts the real-time inference capabilities while addressing the challenges traditional optimal control algorithms face in dynamic soaring due to varying wind fields. …”
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Asymptotic Approximation of the Stationary Solution with Internal Layer for FitzHugh–Nagumo System by A. A. Melnikova, R. L. Argun

“…Blood clotting, nerve impulse propagation, reduction of the heart muscle, the pattern-formation in nature are auto-wave processes. …”
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Dynamic Skin: A Systematic Review of Energy-Saving Design for Building Facades by Jian Wang, Shengcai Li, Peng Ye

“…Parametric modeling, computer simulation, and multi-objective algorithms are commonly used to optimize the performance of dynamic skin. …”
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APPLICATON OF MICROSEISMIC NOISE ESTIMATION IN OIL AND GAS EXPLORATION AIMED AT REDUCING ENVIRONMENTAL IMPACT by A. Yu. Tsivadze, Yu. V. Sirotinskiy, M. A. Abaturov

“…When implementing the MNE algorithms, Chebyshev spectroscopy and analysis of the noise in terms of Gaussian distribution were used.Results. …”
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NORMALISATION OF THE DRIVE PRECISION OF METAL-CUTTING MACHINES by Irina A. Klenova, Dmitry A. Rudikov, Svetlana N. Kholodova

“…The presented complex helps to reveal the role of the rounding error of the output criteria in the formation of the general relative error and provides a basis for its possible reduction. Conclusion The use of the proposed methodology expands the scientific basis for the development of algorithms and programs facilitating the selection of the optimal number of gear teeth for multiplying groups and structures and guaranteeing high accuracy of the gained series.…”
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A novel framework for optimizing residential load response planning with consideration of user satisfaction by Mohammad Hossein Erfani Majd, Gholam-Reza Kamyab, Saeed Balochian

“…The proposed method utilizes the Meerkat Optimization Algorithm (MOA), which is compared against other optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Teaching-Learning-Based Optimization (TLBO). …”
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Reducing the resources required by ADAPT-VQE using coupled exchange operators and improved subroutines by Mafalda Ramôa, Panagiotis G. Anastasiou, Luis Paulo Santos, Nicholas J. Mayhall, Edwin Barnes, Sophia E. Economou

“…We combine the key improvements along with a novel operator pool—which we term Coupled Exchange Operator (CEO) pool—to assess the cost of running state-of-the-art ADAPT-VQE on hardware in terms of measurement counts and circuit depth. We show a dramatic reduction of these quantum computational resources compared to the early versions of the algorithm: CNOT count, CNOT depth and measurement costs are reduced by up to 88%, 96% and 99.6%, respectively, for molecules represented by 12 to 14 qubits (LiH, H6 and BeH2). …”
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Modern modes of chronic hepatitis C treatment by V. T. Ivashkin, M. V. Mayevskaya, M. A. Morozova, Ye. O. Lyusina

“…In some cases it enables reduction of treatment terms, decrease of cost and increase of patient’s compliance. …”
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Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions by Cosmina-Mihaela Rosca, Adrian Stancu

“…The collected data for average heart rate (AR), minimum (mR), maximum (MR), number of steps (S), deep sleep time (DST), and superficial sleep time (SST) is processed in real-time through a health anomaly detection algorithm (HADA), based on the dimensionality reduction method using PCA. …”
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Enhanced AODV routing analysis for mobile Ad Hoc networks by Arvind Kumar, Adesh Kumar, Anurag Vijay Agrawal

“…Conventional routing algorithms such as ad-hoc on-demand distance vector (AODV) frequently struggle with hardware inefficiencies and increase overhead in large-scale networks. …”
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Non-destructive detection of internal quality of apple based on CT image by HUANG Taotao, SUN Teng, ZHANG Jingping

“…The model can be used to predict and analyze the apple quality.Generally, we had an arbitrary scale with air defined as having a CT number of —1 000 HU and water of 0 HU. …”
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Machine Learning-Based Analysis of Travel Mode Preferences: Neural and Boosting Model Comparison Using Stated Preference Data from Thailand’s Emerging High-Speed Rail Network by Chinnakrit Banyong, Natthaporn Hantanong, Supanida Nanthawong, Chamroeun Se, Panuwat Wisutwattanasak, Thanapong Champahom, Vatanavongs Ratanavaraha, Sajjakaj Jomnonkwao

“…It conducts a comparative assessment of predictive capabilities between the conventional Multinomial Logit (MNL) framework and advanced data-driven methodologies, including gradient boosting algorithms (Extreme Gradient Boosting, Light Gradient Boosting Machine, Categorical Boosting) and neural network architectures (Deep Neural Network, Convolutional Neural Network). …”
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Unsupervised Clustering of Cell Populations in Germinal Centers Using Multiplexed Immunofluorescence by Simon Burgermeister, Michail Orfanakis, Spiros Georgakis, Cloe Brenna, Helen Lindsay, Craig Fenwick, Giuseppe Pantaleo, Raphael Gottardo, Constantinos Petrovas

“…Additionally, we investigate the predictive potential of common GC markers (CD3, CD4, CD20 and BCL6) for PD-1 expression, an important immune checkpoint regulator. …”
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Machine learning-based diagnostic model of lymphatics-associated genes for new therapeutic target analysis in intervertebral disc degeneration by Maoqiang Lin, Maoqiang Lin, Shaolong Li, Yabin Wang, Yabin Wang, Guan Zheng, Guan Zheng, Fukang Hu, Fukang Hu, Qiang Zhang, Qiang Zhang, Pengjie Song, Haiyu Zhou, Haiyu Zhou

“…The nomogram and DCA further prove that the diagnosis model has good performance and predictive value. Additionally, drug regulatory networks and ceRNA networks were constructed, revealing potential therapeutic drugs and post-transcriptional regulatory mechanisms.ConclusionWe developed and validated a lymphatics-associated genes diagnostic model by machine learning algorithms that effectively identify IVDD patients. …”
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Bregman–Hausdorff Divergence: Strengthening the Connections Between Computational Geometry and Machine Learning by Tuyen Pham, Hana Dal Poz Kouřimská, Hubert Wagner

“…We also describe computational geometric algorithms that have been extended to this geometry, focusing on algorithms relevant for machine learning.…”
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