Analysis of influencing factors and equity in hospitalization expense reimbursement for mobile populations based on random forest model: a cross-sectional study from China by Lisheng Shen, Xinan Lu, Yanyun Zhang, Lin Fei, Bo Dong

“…Factors contributing to hospitalization cost reimbursement probability inequity, listed in descending order of impact are education (42.3%), income (34.1%), health (12.4%), age (8.2%), and enrollment location (3.0%); factors contributing to the level of hospitalization reimbursement inequity, listed in descending order of impact are health (58.12%), mobility range (21.74%), total healthcare expenditures (9.35 %), type of healthcare coverage (9.28%), and illness (1.51%).ConclusionThere is still much room for improvement in the reimbursement rate of hospitalization expenses for the migrant population. …”
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A survey on resource allocation in backscatter communication networks by Yongjun XU, Haoke YANG, Yinghui YE, Qianbin CHEN, Guangyue LU

“…With the development of Internet of things (IoT) technology, wireless networks have the characteristics of massive user access, high power consumption, and high capacity requirements.In order to meet the transmission requirements and reduce energy consumption, backscatter communication technology was considered to be one of the most effective solutions to the above problems.In the fact of complex network scenarios, the improvement of spectrum efficiency, system capacity, and energy management has become an urgent problem of resource allocation areas in backscatter communications.For this problem, resource allocation algorithms in backscatter communications were surveyed.Firstly, the basic concept and different network architectures of backscatter communication were introduced.Then, resource allocation algorithms in backscatter communication networks were analyzed according to different network types, optimization objectives, and the number of antennas.Finally, the challenges and future research trends of resource allocation problems in backscatter communication networks were prospected.…”
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UAV-Based SAR-Imaging of Objects From Arbitrary Trajectories Using Weighted Backprojection by Alexander Grathwohl, Julian Kanz, Christina Bonfert, Christian Waldschmidt

“…Synthetic aperture radars (SARs) based on uncrewed aerial vehicles (UAVs) are advantageous in comparison to existing airborne systems. Apart from cost, their main advantage is the flexibility of their flight path. …”
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Normalised Diagnostic Contribution Index (NDCI) Integration to Multi Objective Sensor Optimisation Framework (MOSOF)—An Environmental Control System Case by Burak Suslu, Fakhre Ali, Ian K. Jennions

“…Building on previous work, the proposed approach leverages a multi-objective genetic algorithm to optimise key criteria, including performance, cost, reliability management, and compatibility. …”
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Development of a machine learning model for predicting renal damage in children with closed spinal dysraphism by Yu He, Wan-liang Guo, Ming-chang Zhang

“…The Shapley additive explanations (SHAP) algorithm and Local Interpretable Model-Agnostic Explanations (LIME) were used to interpret the optimal model. …”
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Parameterization of user functions in digital signal processing for obtaining angular superresolution by A. A. Shchukin, A. E. Pavlov

“…Objectives. One of the most important tasks in the development of goniometric systems is improving resolution in terms of angular coordinates. …”
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Proteomics mapping of cord blood identifies haptoglobin "switch-on" pattern as biomarker of early-onset neonatal sepsis in preterm newborns. by Catalin S Buhimschi, Vineet Bhandari, Antonette T Dulay, Unzila A Nayeri, Sonya S Abdel-Razeq, Christian M Pettker, Stephen Thung, Guomao Zhao, Yiping W Han, Matthew Bizzarro, Irina A Buhimschi

“…This was then subjected to 2(nd)-level validation against indicators of adverse short-term neonatal outcome. The optimal LCA algorithm combined Hp&HpRP switch pattern (most input), interleukin-6 and neonatal hematological indices yielding two non-overlapping newborn clusters with low (≤20%) versus high (≥70%) probability of IAI exposure. …”
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Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study by Rishishankar E. Suresh, M S Zobaer, Matthew J. Triano, Brian F. Saway, Parneet Grewal, Nathan C. Rowland

“…Linear discriminant analysis was the most accurate (74.6%) algorithm with the shortest training time (0.9 s). …”
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Weighted Hybrid Random Forest Model for Significant Feature prediction in Alzheimer’s Disease Stages by M. Rohini, D. Surendran

“…Thus, the proposed Weighted Hybrid Random Forest algorithm (WHBM) utilized the 63 features that comprise the whole brain volume. …”
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Reliability and Validity of the Single-Camera Markerless Motion Capture System for Measuring Shoulder Range of Motion in Healthy Individuals and Patients with Adhesive Capsulitis:... by Suji Lee, Unhyung Lee, Yohwan Kim, Seungjin Noh, Hungu Lee, Seunghoon Lee

“…Future enhancements to the algorithm and the incorporation of advanced metrics could improve its performance, facilitating broader clinical applications for diagnosing complex shoulder conditions.…”
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Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis by Fernando Pedro Silva Almeida, Mauro Castelli, Nadine Côrte-Real

“…This model achieved a Mean Squared Error of approximately 0.002-0.003, Mean Absolute Error of around 0.031-0.034, and Root Mean Squared Error of about 0.052-0.069. These findings contribute to improved building cooling load management, promoting insights into optimal energy utilization and sustainable building practices.   …”
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The Analysis of the Possibility to Conduct Orbital Manoeuvres of Nanosatellites in the Context of the Maximisation of a Specific Operational Task by Magdalena Lewinska, Michal Kedzierski

“…For example, slight adjustments to the altitude of the orbit with the use of Hohmann transfer proved to be optimal in terms of fuel costs. On the other hand, changes in inclination, although they are definitely energy-consuming, may significantly improve the coverage of the defined area. …”
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An End-to-End Solution for Large-Scale Multi-UAV Mission Path Planning by Jiazhan Gao, Liruizhi Jia, Minchi Kuang, Heng Shi, Jihong Zhu

“…Additionally, we integrate a Multi-Start Greedy Rollout Baseline to evaluate diverse trajectories via parallelized greedy searches, thereby reducing policy gradient variance and improving training stability. Experiments demonstrated significant improvements in scalability, particularly in 100-node scenarios, where our method drastically reduced inference time compared to conventional methods, while maintaining a competitive path cost efficiency. …”
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Subjective Air Traffic Complexity Analysis Based on Weak Supervised Learning by Weining ZHANG, Weijun PAN, Changqi YANG, Xinping ZHU, Jianan YIN, Jinghan DU

“…Compared with the K-means algorithm based on Euclidean distance, metric learning improves the optimal silhouette coefficient and Davidson-Boldin index by 31.80% and 12.97%, respectively. …”
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Using Wireless Sensor Networks to Achieve Intelligent Monitoring for High-Temperature Gas-Cooled Reactor by Jianghai Li, Jia Meng, Xiaojing Kang, Zhenhai Long, Xiaojin Huang

“…This technology not only reduces the cost of regular monitoring but also enables intelligent monitoring. …”
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Underwater Target 3D Reconstruction via Integrated Laser Triangulation and Multispectral Photometric Stereo by Yang Yang, Yimei Liu, Eric Rigall, Yifan Yin, Shu Zhang, Junyu Dong

“…At the same time, we propose to optimize the laser place calibration and laser line separation processes, further improving the reconstruction performance of underwater laser triangulation and multispectral photometric stereo. …”
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In vitro machine learning-based CAR T immunological synapse quality measurements correlate with patient clinical outcomes. by Alireza Naghizadeh, Wei-Chung Tsao, Jong Hyun Cho, Hongye Xu, Mohab Mohamed, Dali Li, Wei Xiong, Dimitri Metaxas, Carlos A Ramos, Dongfang Liu

“…Meanwhile, identifying the optimal CAR construct for a researcher to further develop a potential clinical application is limited by the current, time-consuming, costly, and labor-intensive conventional tools used to evaluate efficacy. …”
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Automating the Design of Scalable and Efficient IoT Architectures Using Generative Adversarial Networks and Model-Based Engineering for Industry 4.0 by William Villegas-Ch, Jaime Govea, Diego Buenano-Fernandez, Aracely Mera-Navarrete

“…Traditional approaches, such as heuristic and genetic algorithms, have proven insufficient in automating and optimizing large-scale IoT configurations, resulting in a high design and validation time cost. …”
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Alpine Meadow Fractional Vegetation Cover Estimation Using UAV-Aided Sentinel-2 Imagery by Kai Du, Yi Shao, Naixin Yao, Hongyan Yu, Shaozhong Ma, Xufeng Mao, Litao Wang, Jianjun Wang

“…Subsequently, four machine learning models were employed for an accurate FVC inversion, using the estimated FVC values and UAV-derived reference FVC as inputs, following feature importance ranking and model parameter optimization. The results showed that: (1) Machine learning algorithms based on Sentinel-2 and UAV imagery effectively improved the accuracy of FVC estimation in alpine meadows. …”
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From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications by Evgenia Gkintoni, Anthimos Aroutzidis, Hera Antonopoulou, Constantinos Halkiopoulos

“…Despite these advances, challenges remain more significant in real-time EEG processing, where a trade-off between accuracy and computational efficiency limits practical implementation. High computational cost is prohibitive to the use of deep learning models in real-world applications, therefore indicating a need for the development and application of optimization techniques. …”
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