Developing CGMap: Characterizing Continuous Glucose Monitoring Data in Patients with Type 2 Diabetes by Shuzhen Bai, Chu Lin, Xiaoling Cai, Suiyuan Hu, Jing Wu, Ling Chen, Wenjia Yang, Linong Ji

“…However, glucose variability decreased with increased BMI. Meanwhile, greater glycemic variability was associated with worse islet function, higher baseline glucose level, and higher hemoglobin.…”
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EVALUATION OF A DECRESE IN WORK PRODUCTIVITY IN PATIENTS WITH RHEUMATOID ARTHRITIS by Olga Yuryevna Vakulenko, D V Goryachev, O A Krichevskaya, Sh F Erdes

“…The Spearman correlation coefficients with WPAI indicators were higher than 0.4 in all the cases. …”
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Application of Post-Occupancy Evaluation in the Design of Collegiatae Educational Space: (Case study: Faculty of Arts and Architecture, Islamic Azad University-Tehran South Branch) by Reza Naghdbishi, Zeinab Gohari

“…This analysis identified the relationship between research variables and evaluated their impact on the design. …”
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Incorporating Traffic Flow Model into A Deep Learning Method for Traffic State Estimation: A Hybrid Stepwise Modeling Framework by Yuyan Annie Pan, Jifu Guo, Yanyan Chen, Siyang Li, Wenhao Li

“…We build a hybrid cost function to adjust the weights of model-driven and data-driven proportions. …”
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Multi-Objective Optimization Methods for University Campus Planning and Design—A Case Study of Dalian University of Technology by Lin Qi, Chaoran Chen, Jun Dong

“…A five-dimensional objective system—comprising energy efficiency, spatial quality, economic cost, ecological benefits, and cultural expression—was established, alongside the identification and standardization of 29 key variables to construct mapping relationships among objective functions. …”
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Load frequency control in isolated island city microgrids using deep graph reinforcement learning considering extensive scenarios by Ping He, Xiongwei Huang, Ruobing He, Linkun Yuan

“…Demonstrated effectively in China Southern Grid’s island microgrid setup, LE-LFC emerges as an advanced solution for modern grid variability, offering superior robustness, adaptability, and learning speed, thus enabling flexible and efficient energy system management.…”
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Semantic ECG hash similarity graph by Yixian Fang, Shilin Zhang, Yuwei Ren

“…Subsequently, a lightweight linear hash function is utilized to produce a hash representation for the unseen signal. …”
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Structural strength analysis and optimization of converter lug based on Kriging model by HE Guanqiang, LIU Yongjiang, LI Hua, CHEN Liming

“…In this paper, machine learning methods were considered to approximate the functional relationships between design variables and responses. …”
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A De-Nesting Hybrid Reliability Analysis Method and Its Application in Marine Structure by Chenfeng Li, Tenglong Jin, Zequan Chen, Guanchen Wei

“…Traditional methods for hybrid reliability analysis usually require a nested optimization framework, which will lead to too many calls to the limit state function (LSF) and result in poor computational efficiency. …”
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An Optimization Model for Shell Plate Seam Landing Using Minimum Manufacturing Cost and a Solution by Genetic Algorithm by Ho Rim Pae, Min Hyok Jon, Chol Jun Pak

“…In this paper, a new optimization model and its solution method for the landing of seams and butts (for convenience, seam and butt are simply called seam) on the ship hull surface are proposed in order to improve the shipbuilding efficiency. The minimum manufacturing cost of ship hull shell plates (SHSPs) is the objective function, the rules and requirements for the seam position and the size of a single shell plate are the constraints, and the position and shape of the seam are the design variables. …”
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Strong Amplitude Modulation of Hard-band X-Ray Quasiperiodic Oscillation with Soft-band Flux in RE J1034+396 by Ruisong Xia, Hao Liu, Yongquan Xue

“…They are highly correlated, with an average cross-correlation function (CCF) peak coefficient of 0.61 and a lag of approximately 3 ks. …”
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Parameter Sensitivity Analysis and Algorithm Improvement of Optimization Scheduling Model for Cascade Pumping Stations by LIU Xiaolian, LI Zhenrong, WANG Xue-ni, ZHAI Yu, ZHANG Lei-ke, GUO Weiwei, TIAN Yu

“…Additionally, a Tent boundary mapping strategy was employed to regenerate the boundary value, resulting in improved the optimization efficiency of RUN. Six benchmark functions, including unimodal, multimodal and fixed dimension, were used to verify the performance and improvement strategies of TRUN. …”
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Research on the Combined Load-Bearing Mechanism of Deeply Buried Pressure Pipelines Under High Internal Water Pressure by LIU Yijie, ZHANG Xuhui, HUANG Bensheng, YANG Fengjie, YUAN Mingdao, XU Yunqian

“…To enhance the surrounding rock’s contribution, reinforcement measures such as grouting may be necessary.ConclusionsAiming at the calculation problem of load transfer and distribution in deep-buried pressure pipelines under high internal water pressure conditions, a power series solution method based on complex variable functions was proposed. By solving the potential functions for the steel liner and surrounding rock as well as the contact stress between the bearing bodies, the stress, deformation at any position of the bearing structure and surrounding rock sharing rate can be accurately determined. …”
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Deletion of genes linked to the C1-fixing gene cluster affects growth, by-products, and proteome of Clostridium autoethanogenum by Ugochi Jennifer Nwaokorie, Kristina Reinmets, Lorena Azevedo de Lima, Pratik Rajendra Pawar, Kurshedaktar Majibullah Shaikh, Audrey Harris, Michael Köpke, Kaspar Valgepea

“…However, our understanding of the functionalities of the genes involved in the C1-fixing gene cluster and its closely-linked genes is incomplete. …”
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Deep learning algorithm on H&E whole slide images to characterize TP53 alterations frequency and spatial distribution in breast cancer by Chiara Frascarelli, Konstantinos Venetis, Antonio Marra, Eltjona Mane, Mariia Ivanova, Giulia Cursano, Francesca Maria Porta, Alberto Concardi, Arnaud Gerard Michel Ceol, Annarosa Farina, Carmen Criscitiello, Giuseppe Curigliano, Elena Guerini-Rocco, Nicola Fusco

“…The DL model exhibited high accuracy in tissue quantification and TP53 status prediction, outperforming traditional methods in terms of precision and efficiency. DL-based approaches offer significant promise for enhancing biomarker testing and precision oncology by reducing intra- and inter-observer variability, but further validation is required to optimize their integration into real-world clinical workflows. …”
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Reconfiguration of dynamic brain networks in heart failure with preserved ejection fraction: Linking neurovascular coupling and cardiac dysfunction by Xiulin Liang, Qinghua Zhang, Chuanlong Zhang, Jingjing Liu, Pengcheng Sang, Qing Mao, Lei Wang

“…Results: (1) Static network topology properties exhibited significantly decreased local network efficiency among patients with HFpEF. The connectivity strength and information processing efficiency were diminished in the left parahippocampal gyrus, left cingulate gyrus, and right insular gyrus brain regions, and improved in the left thalamus, right fusiform gyrus, and right precuneus regions. (2) Dynamic network topology properties of patients with HFpEF showed decreased variability in critical nodes and brain subregions (e.g., the superior frontal gyrus, left amygdala, and left fusiform gyrus) and compensatory increases in the variability of specific regions (e.g., right insular gyrus, right postcentral gyrus, and right temporal gyrus). (3) In HFpEF, the static and dynamic functional brain network topology properties of the fusiform gyrus, cingulate gyrus, superior temporal gyrus, precuneus, parahippocampal gyrus, insular gyrus, and amygdala regions were significantly correlated with cardiac structural and functional indices, such as LVDd, LVMI, and E/e′ ratio. …”
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IRAQ'S AGRICULTURAL ECONOMY AND THE REALITY OF THE VARIABLRS AND THE RESULTS OBJECTIVE CURRICULUM AND QUANTITATIVE ANALYSIS FOR THE PERIOD (1990-2011) by Salim Al-Nuaimy, Mahasen. M Al-Jeboury

“…The research aims to formulate sober policies and practical actions contribute to adjust the course of the agricultural sector and to achieve its goals, which will make it easier to raise production and productivity, Variables of agricultural sector were agricultural work, capital accumulation agricultural, technical mechanism, technical chemical, agricultural loans, agricultural export, import of agricultural, national income, domestic consumption, the cultivatedareas, that have been expressed as (policies of productivity and investment, marketing and price) where its impact was deare on the growth of the agricultural sector and overall productivity during (1990-2010) and the development process and increase production and productivity in the agricultural sector associated with economic, social and institutional multiple and overlapping factors that effects the forms and varying proportions in its impact on agricultural output, which had been measured under function producing an aggregate of variables agricultural policy and its contribution to productivity growth as well as measuring productivity elements total (TFP) by using index (Malmaquist) depending on the (DEAP). …”
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Quality control of DEMs using check surfaces by Francisco Javier Ariza-López, José Rodríguez-Avi

“…Using the Kolmogorov–Smirnov statistic, the observed distribution functions of the results for different sample sizes (20, 50, 100) and different patch sizes (3 × 3, 5 × 5, 9 × 9, 11 × 11, 15 × 15, 19 × 19) and for a sample of points are compared against the true population distribution function. …”
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Neuroscience-informed nomogram model for early prediction of cognitive impairment in Parkinson's disease by Sudharshan Putha, Swaroop Reddy Gayam, Bhavani Prasad Kasaraneni, Krishna Kanth Kondapaka, Sateesh Kumar Nallamala, Praveen Thuniki

“…The least absolute shrinkage and selection operator (LASSO) algorithm was applied to identify highly correlated clinical variables influencing cognitive function. Subsequently, these variables were integrated into a visualized nomogram model to facilitate early prediction of cognitive impairment (CI) risk. …”
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Network Analysis of Key Instrumental Activities of Daily Living and Cognitive Domains for Targeted Intervention in US Older Adults Without Dementia: Cross-Sectional Study by Jiaying Li, Rendong He, Erh-Chi Hsu, Junxin Li

“…Network analysis identified central and bridge variables. Nonparametric and case-dropping bootstrap methods checked network stability. …”
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