Determining optimal strategies for primary prevention of cardiovascular disease: a synopsis of an evidence synthesis study by Olalekan A Uthman, Lena Al-Khudairy, Chidozie Nduka, Rachel Court, Jodie Enderby, Seun Anjorin, Hema Mistry, G J Melendez-Torres, Sian Taylor-Phillips, Aileen Clarke

“…What we did We did five detailed analyses to evaluate how well different heart disease prevention methods work and if they are worth the cost: Big picture review: we summarised findings from 95 reviews on medications and lifestyle changes. …”
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Machine learning-based risk prediction model for pertussis in children: a multicenter retrospective study by Juan Xie, Run-wei Ma, Yu-jing Feng, Yuan Qiao, Hong-yan Zhu, Xing-ping Tao, Wen-juan Chen, Cong-yun Liu, Tan Li, Kai Liu, Li-ming Cheng

“…Objective The aim of this study was to develop a pertussis risk prediction model that is both efficient and has good generalization ability, applicable to different datasets. The model was constructed using machine learning techniques based on multicenter data and screened for key features. …”
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How Small Can S-boxes Be? by Chenhao Jia, Tingting Cui, Qing Ling, Yan He, Kai Hu, Yu Sun, Meiqin Wang

“…This paper aims to determine the exact minimum area of optimal 4-bit S-boxes (whose differential uniform and linearity are both 4) under certain standard cell library. Firstly, we evaluate the upper and lower bounds upon the minimum area of S-boxes, by proposing a Prim-like greedy algorithm and utilizing properties of balanced Boolean functions to construct bijective S-boxes. …”
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Effectiveness of myocarditis therapy depending on the diagnosis approach (with or without myocardial biopsy) by O. V. Blagova, A. V. Nedostup, V. P. Sedov, A. Yu. Zaitsev, V. M. Novosadov, E. A. Kogan

“…Aim. To evaluate the effectiveness of myocarditis therapy depending on the diagnosis approach (with or without myocardial biopsy).Material and methods. …”
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The effect of Pressurised Intraperitoneal Aerosol Chemotherapy on the quality of life of patients with primary diagnosed ovarian cancer with peritoneal carcinomatosis during combin... by A. S. Dzasokhov, A. A. Kostin, V. L. Astashov, A. V. Turiev, A. D. Uskov

“…To identify a significant difference between the indicators at different stages of the study, the Page rank criterion for an ordered alternative and an algorithm based on the Friedman rank sum test were used. …”
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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

“…Secondly, the random forest algorithm was applied to evaluate the importance of the factors influencing hospitalization expense reimbursement. …”
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Cotton Fiber Micronaire and Relations to Fiber HVI and AFIS Qualities Between Deltapine^® and PhytoGen Upland Varieties by Yongliang Liu, Doug J. Hinchliffe

“…There were noticeable differences among HVI and AFIS qualities from Deltapine^® fiber samples and PhytoGen samples, with significant differences om HVI strength and elongation. …”
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Detection of litchi fruit maturity states based on unmanned aerial vehicle remote sensing and improved YOLOv8 model by Changjiang Liang, Changjiang Liang, Dandan Liu, Dandan Liu, Weiyi Ge, Weiyi Ge, Wenzhong Huang, Wenzhong Huang, Yubin Lan, Yubin Lan, Yubin Lan, Yongbing Long, Yongbing Long, Yongbing Long, Yongbing Long

“…However, existing studies are largely limited to the binary classification of immature and mature fruits, lacking dynamic evaluation and precise prediction of maturity states. …”
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Quantitative Analysis of Structural Parameters Importance of Helical Temperature Microfiber Sensor by Artificial Neural Network by Juan Liu, Minghui Chen, Hang Yu, Jinjin Han, Hongyi Jia, Zhili Lin, Zhijun Wu, Jixiong Pu, Xining Zhang, Hao Dai

“…With the assistance of the evaluation algorithms based on the well-performed backpropagation neural network (BPNN), we quantitatively analyze the importance of the structural parameters of the supported helical microfiber (HMF) temperature sensor. …”
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Incorporating Deep Learning Into Hydrogeological Modeling: Advancements, Challenges, and Future Directions by Zhenxue Dai, Chuanjun Zhan, Huichao Yin, Junjun Chen, Lulu Xu, Yuzhou Xia, Songlin Yang, Wei Chen, Mingxu Cao, Zhengyang Du, Xiaoying Zhang, Bicheng Yan, Yue Ma, Hao Wang, Farzad Moeini, Mohamad Reza Soltanian, Hung Vo Thanh, Kenneth C. Carroll

“…Furthermore, the lack of standardized evaluation benchmarks makes it difficult to compare the performance of different DL models in hydrogeological contexts. …”
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Fuzzy Model Predictive Control for Unmanned Helicopter by Łukasz Kiciński, Sebastian Topczewski

“…The proposed solution involves the use of the Model Predictive Control framework enhanced with the Takagi–Sugeno inference algorithm. The designed system uses a Parallel Distributed Compensation architecture and utilizes multiple linear dynamics models to precisely model the helicopter’s response in transitioning from hovering to forward flight. …”
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Ground truth clustering is not the optimum clustering by Lucia Absalom Bautista, Timotej Hrga, Janez Povh, Shudian Zhao

“…The results reveal that the optimum clusterings often differ significantly from the ground truth clusterings. …”
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Time synchronization for long-chain-type wireless sensor networks by Zhenping Chen, Yourui Huang, Zheng Wang, Feng Tao

“…Moreover, the sink node evaluates the network periodically and adjusts the synchronizing cycle based on the difference between the network synchronization error and the given synchronization accuracy. …”
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Regionalization of water quality parameters based on the landscape characteristics of small ungauged basins by Danilo Garofalo, Marcos Ferreira

“…The non-parametric k-nearest neighbour regression (K-NNR) algorithm was used to estimate the WQP values for the small sub-basins that lacked data. …”
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Ensuring information security in the production network of an industrial enterprise by A. R. Aydynan, D. G. Kirsanov

“…The analysis presented in the article identifies differences between automated process control systems and classical information systems, based on the NIST SP 800-82 standard, and evaluates their unique requirements and vulnerabilities. …”
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MLST reveals a clonal population structure for Cryptococcus neoformans molecular type VNI isolates from clinical sources in Amazonas, Northern-Brazil. by Diego Fernando Silva Rocha, Katia Santana Cruz, Carla Silvana da Silva Santos, Lizandra Stephanny Fernandes Menescal, João Ricardo da Silva Neto, Silviane Bezerra Pinheiro, Lucyane Mendes Silva, Luciana Trilles, João Vicente Braga de Souza

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THANTD: Triplet Hybrid Attention Network for Hyperspectral Target Detection by Ju Liu, Haoqian Wang, Xiangai Cheng, Zhongyang Xing, Zhongjie Xu

“…Extensive experiments on five datasets verify the effectiveness and superiority of the proposed THANTD across various evaluation metrics.…”
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Spatiotemporal inhomogeneity of accuracy degradation in AI weather forecast foundation models: A GNSS perspective by Junsheng Ding, Wu Chen, Junping Chen, Jungang Wang, Yize Zhang, Lei Bai, Yuyan Wang, Xiaolong Mi, Tong Liu, Duojie Weng

“…Using these metrics, we analyzed the spatiotemporal inhomogeneity in the accuracy degradation of foundation models, represented by Huawei Cloud Pangu-Weather, Google DeepMind GraphCast, and Shanghai AI Lab FengWu. We evaluated how this inhomogeneity changes with forecast time and identified the best-performing models across different regions and forecast durations. …”
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Comprehensive analysis reveals cholesterol metabolism-related signature for predicting prognosis and guiding individualized treatment of glioma by Dengfeng Lu, Fei Wang, Yayi Yang, Aojie Duan, Yubo Ren, Yun Feng, Haiying Teng, Zhouqing Chen, Xiaoou Sun, Zhong Wang

“…Finally, the drug sensitivity of gliomas in different risk groups was evaluated using the oncoPredict algorithm, and potentially sensitive chemotherapeutic and molecular-targeted drugs were identified. …”
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Anxiety-depressive syndrome in patients with arterial hypertension and chronic obstructive pulmonary disease with frequent exaccations by L. A. Shpagina, I. S. Shpagin, L. D. Khidirova, A. A. Vasilenko

“…Computer analysis of the results of this study was carried out using the SAS, USA statistical software package using standard variational algorithms. statistics. When analyzing intergroup differences in indicators measured on an interval scale, the values of Student's t-test were calculated using the corresponding formulas.Results. …”
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