Insurance: is it better together? by E. A. Rusetskaya, D. A. Zakharova, A. A. Krechetova, A. I. Shchetinskaya, Ya. D. Gundar, K. S. Frolov

“…The study intends to identify the potential reasons for the relatively insignificant share of MIS in the insurance market and to assess the attractiveness of the latter in increasing the efficiency of the insurance mechanism in the system of ensuring the financial security of citizens and economic entities.Materials and methods. …”
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Quantum-inspired robust optimization for coordinated scheduling of PV-hydrogen microgrids under multi-dimensional uncertainties by Yunxiao Bai, Yu Sui, Xiaoyu Deng, Xiangbing Wang

“…These include fluctuations in PV output, stochastic hydrogen demand, and volatile market prices. Traditional deterministic or static robust methods often fall short in handling such dynamic, multi-dimensional uncertainties. …”
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A Methodology for Turbine-Level Possible Power Prediction and Uncertainty Estimations Using Farm-Wide Autoregressive Information on High-Frequency Data by Francisco Javier Jara Ávila, Timothy Verstraeten, Pieter Jan Daems, Ann Nowé, Jan Helsen

“…The results demonstrate that the proposed method improves predictive accuracy over the manufacturer’s power curve, achieving a reduction in error measurements of around 1%. …”
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CISMN: A Chaos-Integrated Synaptic-Memory Network with Multi-Compartment Chaotic Dynamics for Robust Nonlinear Regression by Yaser Shahbazi, Mohsen Mokhtari Kashavar, Abbas Ghaffari, Mohammad Fotouhi, Siamak Pedrammehr

“…Ablations reveal a scalability–efficiency trade-off: lightweight variants train in <10 s with >95% peak accuracy, while deeper configurations yield marginal gains. …”
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A novel hybrid adaptive strategy for real-time dispatch and scheduling in renewable-integrated EV charging stations by Guo Hao, Qiu Pengfei, Zhang Jiahui, Bai Xiang, Yan Laiqing, Zia Ullah

“…Unlike existing studies that treat power dispatch and EV scheduling as separate tasks or rely on heuristic methods, the proposed HADS model employs a Mixed-Integer Linear Programming (MILP) approach to co-optimally dispatch real-time energy and charge EVs within a unified decision-making framework. …”
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On the Synergy of IoMT Devices and Ceiling-Mounted Systems for Advanced Medical Data Analytics by Andreas Andreou, Constandinos X. Mavromoustakis, Evangelos K. Markakis, Athina Bourdena, George Mastorakis

“…Deep Reinforcement Learning (DRL) methods solve the resource allocation challenge under realistic constraints. …”
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Cooperative sensing, communication and computation resource allocation in mobile edge computing-enabled vehicular networks by Zhenyu Li, Yuchuan Fu, Mengqiu Tian, Changle Li

“…To solve the RA problem, a twin delayed deep deterministic policy gradient (TD3) algorithm is adopted to explore and obtain the effective solution of the RA problem. …”
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Using Transformers and Reinforcement Learning for the Team Orienteering Problem Under Dynamic Conditions by Antoni Guerrero, Marc Escoto, Majsa Ammouriova, Yangchongyi Men, Angel A. Juan

“…These findings suggest that learning-based methods can offer efficient, scalable, and adaptable solutions for routing problems in dynamic and uncertain environments.…”
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Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review by Umile Giuseppe Longo, Alberto Lalli, Guido Nicodemi, Matteo Giuseppe Pisani, Alessandro De Sire, Pieter D'Hooghe, Ara Nazarian, Jacob F. Oeding, Balint Zsidai, Kristian Samuelsson

“…The aim of this systematic review is to evaluate the effectiveness and reliability of AI applications in orthopaedic imaging, focusing on their impact on diagnostic accuracy, image segmentation and operational efficiency across various imaging modalities. Methods Based on the PRISMA guidelines, a comprehensive literature search of PubMed, Cochrane and Scopus databases was performed, using combinations of keywords and MeSH descriptors ('AI', 'ML', 'deep learning', 'orthopaedic surgery' and 'imaging') from inception to March 2024. …”
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An Integrated Framework for Infectious Disease Control Using Mathematical Modeling and Deep Learning by Mohammed Salman, Pradeep Kumar Das, Sanjay Kumar Mohanty

“…Precise modeling and prediction methods are essential to develop effective strategies for disease control. …”
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Decentralizing Coordination in Open Vehicle Fleets for Scalable and Dynamic Task Allocation by Marin Lujak, Stefano Giordani, Andrea Omicini, Sascha Ossowski

“…Self-concerned individually rational vehicle drivers have both local and global objectives, which require coordination using some fair and efficient task allocation method. In this paper, we review the literature on scalable and dynamic task allocation focusing on deterministic and dynamic two-dimensional linear assignment problems. …”
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PAPR Reduction in DCO-OFDM Visible Light Communication Systems Using Optimized Odd and Even Sequences Combination by Wei-Wen Hu

“…Even and odd sequences are obtained by adopting an inverse fast Fourier transform on the even and odd parts of a Hermitian symmetry encoded signal, respectively. Furthermore, a deterministic and efficient selection method of cyclic shift and phase rotation is derived to improve PAPR reduction performance and ensure that each candidate is unique, so that the receiver can recover the transmitted candidate signal without the use of SI. …”
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Geological modeling of carbonate fracture-cavity reservoir: case study of Shunbei fault zone No. 5 by Bolin Li, Yunfeng He, Wen Chen, Haojie Shang, Lixin Wang

“…First, deterministic modeling methods were employed to establish fault-controlled body contour models through seismic attribute fusion (structure tensor, energy gradient, and variance attributes) calibrated with well-log data. …”
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Development and Use of Biotechnological System Models in Applied Scientific Research by M. A. Kerimov

“…(Materials and methods) The paper explores the conditions governing the operation of biotechnical systems characterized by inherent randomness (in the probabilistic-statistical sense). …”
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Reinforcement Learning-Based Control for Robotic Flexible Element Disassembly by Benjamín Tapia Sal Paz, Gorka Sorrosal, Aitziber Mancisidor, Carlos Calleja, Itziar Cabanes

“…Traditional control systems struggle to handle these tasks efficiently, requiring adaptable solutions that can operate in unstructured environments that provide online adaptation. …”
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A Deep Reinforcement Learning Framework for Adaptive Resiliency Enhancement in Smart Power Grids by Boyu Wang, Aliasghar Baziar, Mohammad Reza Askari

“…To overcome these limitations, we propose a deep reinforcement learning framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm, optimized using the Root Mean Square Propagation (RMSprop) method for stable and efficient training. …”
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Sustainable Energy and Exergy Analysis in Offshore Wind Farms Using Machine Learning: A Systematic Review by Hamid Reza Soltani Motlagh, Seyed Behbood Issa-Zadeh, Abdul Hameed Kalifullah, Arife Tugsan Isiacik Colak, Md Redzuan Zoolfakar

“…Offshore wind energy represents a cornerstone in the global transition to low-carbon economies due to its scalability and superior energy yields; however, its complex operational environment, characterized by harsh marine conditions and logistical constraints, necessitates innovative analytical tools. Traditional deterministic methods often fail to capture the dynamic interactions within wind farms, thereby underscoring the need for ML-integrated approaches that enhance precision in energy forecasting, fault detection, and exergy analysis. …”
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Research on Ship Heave Motion Compensation Control Under Complex Sea State Environment Based on Improved Reinforcement Learning by ZHANG Qin, ZHOU Jingyi, WANG Xingyue, HU Xiong

“…Therefore, this study presents a compensation control method for ship heave under complex sea conditions using an improved reinforcement learning approach.MethodsThis novel method imparts fresh insights into addressing heavy compensation in offshore operations and heralds a new trajectory for the evolution of future offshore operation technologies. …”
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Optimizing Accuracy, Recall, Specificity, and Precision Using ILP by Arash Marioriyad, Pouria Ramazi

“…To obtain these measures, the probabilities generated by classifiers must be converted into deterministic labels using a threshold. Exhaustive search methods can be computationally expensive, prompting the need for a more efficient solution. …”
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Optimization of distribution networks using quantum annealing for loss reduction and voltage improvement in electrical vehicle parking management by Naser Rashnu, Babak Mozafari, Reza Sharifi

“…The results demonstrate the method’s scalability and effectiveness in handling real-time EV integration while reducing operational costs and supporting voltage stability. …”
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