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  1. 1321

    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

    Published 2024-01-01
    “…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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  2. 1322

    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

    Published 2024-01-01
    “…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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  3. 1323

    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

    Published 2023-05-01
    “…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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  4. 1324

    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

    Published 2025-10-01
    “…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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  5. 1325

    Machine learning-based prediction of pervaporation permeation using physicochemical properties of permeant-membrane and process conditions by Muhammad Mujiburohman, Marwen Elkamel, Farzad Hourfar, Ali Elkamel

    Published 2025-02-01
    “…Two types of inputs were evaluated: regular variables (Type I) and dimensionless groups derived from these regular variables (Type II). …”
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  6. 1326

    Peroral endoscopic myotomy for complex achalasia and the POEM difficulty score: An update by Carmen Ching Hui Yee, Michael Youssef, Matthew Woo, Robert Bechara

    Published 2025-04-01
    “…PDS correlates moderately with procedural efficiency with a correlation coefficient of 0.595 (Spearman's p < 0.001). …”
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  7. 1327

    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

    Published 2017-08-01
    “…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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  8. 1328

    Quality control of DEMs using check surfaces by Francisco Javier Ariza-López, José Rodríguez-Avi

    Published 2025-01-01
    “…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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  9. 1329

    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

    Published 2025-06-01
    “…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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  10. 1330

    Detecting and reducing heterogeneity of error in acoustic classification by Oliver C. Metcalf, Jos Barlow, Yves Bas, Erika Berenguer, Christian Devenish, Filipe França, Stuart Marsden, Charlotte Smith, Alexander C. Lees

    Published 2022-11-01
    “…We recommend assessing—and if necessary correcting for—heterogeneity of precision error when using automated classification on acoustic data to quantify species presence as a function of an environmental, spatial or temporal predictor variable.…”
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  11. 1331

    Resilience for activity engagement among frail older adults: moderated mediation effect of social support by Eunna Oh, Nayoung Kim, Lkhagvajav Gansukh, Rhayun Song

    Published 2025-05-01
    “…Resilience has been identified as a protective factor that supports physical function, cognitive health, and independence in daily activities. …”
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  12. 1332
  13. 1333

    Faktor-Faktor yang Mempengaruhi Auditor Switching dengan Financial Distress sebagai Pemoderasi by Herninda Pitaloka, Agung Guritno

    Published 2021-12-01
    “…The analysis included descriptive statistical tests, model feasibility tests, classification matrix tests, the formation of logistic regression functions, partial parameter significance tests, simultaneous parameter significance tests, coefficients of determination tests and MRA tests. …”
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  14. 1334

    Bearing Fault Diagnosis under Transient Conditions: Using Variational Mode Decomposition and the Symmetrized Dot Pattern-Based Convolutional Neural Network Model by Jide Jia, Jianmin Mei, Chuang Sun, Fengjuan Yang

    Published 2024-01-01
    “…To eliminate the noise components, VMD was used to decompose the vibration signal into different frequency bands to obtain the intrinsic mode functions (IMFs), which were further represented by SDP images, and feature images were extracted according to the noncorrelation coefficient. …”
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  15. 1335

    APPLICATION AND PERFORMANCE COMPARISON OF MULTI-OUTPUT MACHINE LEARNING FOR NUMERICAL-NUMERICAL AND NUMERICAL-CATEGORICAL OUTPUTS by Karin Joan, Robyn Irawan, Benny Yong

    Published 2025-04-01
    “…Multi-Output Machine Learning is an advancement of traditional machine learning, designed to predict multiple output variables simultaneously while considering the relationships between these output variables. …”
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  16. 1336

    Characterization and treatment monitoring of ureagenesis disorders using stable isotopes by Gabriella Allegri, Martin Poms, Nadia Zürcher, Véronique Rüfenacht, Nicole Rimann, Déborah Mathis, Beat Thöny, Matthias Gautschi, Ralf A. Husain, Daniela Karall, Karolina Orchel-Szastak, Francesco Porta, Dominique Roland, Barbara Siri, Carlo Dionisi-Vici, René Santer, Johannes Häberle

    Published 2025-05-01
    “…Thus, the method proved to be safe and efficient to monitor UCD patients of variable severity pre- and post-therapy, being suitable as physiological endpoint for development of therapies.…”
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  17. 1337
  18. 1338

    Open real-time, non-invasive fish detection and size estimation utilizing binocular camera system in a Portuguese river affected by hydropeaking by Jürgen Soom, Isabel Boavida, Renan Leite, Maria João Costa, Gert Toming, Mairo Leier, Jeffrey A. Tuhtan

    Published 2025-12-01
    “…Nonetheless, employing an automated camera system for underwater monitoring requires that the algorithms function under highly variable environmental conditions, which affect the ability to detect and assess fish size. …”
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  19. 1339

    Safety Prediction Using Vehicle Safety Evaluation Model Passing on Long-Span Bridge with Fully Connected Neural Network by Yang Yang, Lin Yang, Bo Wu, Gang Yao, Hang Li, Soltys Robert

    Published 2019-01-01
    “…Many research studies have been done to find convenience and efficiency measures. A vehicle safety evaluation model passing on a long-span bridge is presented in this paper based on fully connected neural network (FCN). …”
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  20. 1340

    Multiple metabolic requirements for size homeostasis and initiation of division in Saccharomyces cerevisiae by Shivatheja Soma, Kailu Yang, Maria I. Morales, Michael Polymenis

    Published 2014-08-01
    “…Furthermore, we show that adenylate kinase, encoded by ADK1, is a significant determinant of the efficiency of size control mechanisms. Finally, our data argue strongly that the cell size at division is not necessarily a function of the rate cells increase in size in the G1 phase of the cell cycle. …”
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