Intelligent generative design: a transfer learning-enhanced bidirectional mapping framework for customised parametric structural design by Jiajie Hu, Yayun Wang, Hua Tian, Peng Zheng, Liang Chi, Lulu Xue, Qian Su, Qing Jiang, Yong He, Lan Li, Liya Zhu

“…We employ a genetic algorithm to optimise a back-propagation neural network (GA-BPNN) for forward prediction of elastic modulus from structural parameters (porosity, pore size). …”
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Effect of Social Sustainability on Supply Chain Resilience Before, During, and After the COVID-19 Pandemic in Mexico: A Partial Least Squares Structural Equation Modeling and Evolu... by Miguel Reyna-Castillo, Alejandro Santiago, Ana Xóchitl Barrios-del-Ángel, Francisco Manuel García-Reyes, Fausto Balderas, José Ignacio Anchondo-Pérez

“…This work starts from an epistemological premise, in which non-parametric statistical methodologies and mathematical analytics are complementary perspectives to comprehensively understand the same social phenomenon. Second-generation predictive statistics, such as the PLS-SEM algorithm, have demonstrated robustness in treating multivariate social information, making it feasible to prepare data for knowledge transfer with mathematical techniques specialized for fuzzy data. …”
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Informative Path Planning Using Physics-Informed Gaussian Processes for Aerial Mapping of 5G Networks by Jonas F. Gruner, Jan Graßhoff, Carlos Castelar Wembers, Kilian Schweppe, Georg Schildbach, Philipp Rostalski

“…Physics-informed mean functions, including a detailed ray-tracing simulation, are integrated into the GP models to enhance the extrapolation performance of the GP prediction. As a central element of the GP prediction, a quantitative evaluation of different mean functions is conducted. …”
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Investigation on the Temperature Distribution of Asphalt Overlay on the Existing Cement Concrete Pavement in Hot-Humid Climate in Southern China by Zuzhong Li, Yayun Zhang, Chunguang Fa, Xiaoming Zou, Haiwei Xie, Huaxin Chen, Rui He

“…This research is to analyze the impact of meteorological factors on temperature at 2 cm depth in asphalt overlay by ReliefF algorithm and also obtain the temperature prediction model using MATLAB. …”
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FocusDet: an efficient object detector for small object by Yanli Shi, Yi Jia, Xianhe Zhang

“…SIOU multi-dimension accurately locates the prediction box, and SoftNMS uses the Gaussian algorithm to remove redundant prediction boxes. …”
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Impacts of Spatial Expansion of Urban and Rural Construction on Typhoon-Directed Economic Losses: Should Land Use Data Be Included in the Assessment? by Siyi Zhou, Zikai Zhao, Jiayue Hu, Fengbao Liu, Kunyuan Zheng

“…Results demonstrate three key findings: (1) By introducing prototype learning, a meta-learning approach, to guide model updates, we achieved precise assessments with small training samples, attaining an MAE of 1.02, representing 58.5–76.1% error reduction compared to conventional machine learning algorithms. …”
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Comprehensive Transcriptome Sequencing and Analysis of <i>Euspira gilva</i>: Insights into Aquaculture and Conservation by Zhixing Su, Jiayuan Xu, Xiaokang Lv, Xuefeng Song, Yanming Sui, Benjian Wang, Xiaoshan Wang, Bianbian Zhang, Baojun Tang, Liguo Yang

“…A total of 7929 simple sequence repeat (SSR) loci were identified, with single nucleotide repeats predominating at 85.0%. Predictive analysis of coding DNA sequences (CDS) resulted in 1340 BLAST comparisons, while ESTScan predicted 840. …”
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Machine learning aids in the discovery of efficient corrosion inhibitor molecules by Haiyan GONG, Lingwei MA, Dawei ZHANG

“…Specifically, ML models can extract key information and construct predictive models through feature extraction and pattern recognition using existing data. …”
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Metabolic dysfunction-associated steatotic liver disease (MASLD) biomarkers and progression of lower limb arterial calcification in patients with type 2 diabetes: a prospective coh... by Damien Denimal, Maharajah Ponnaiah, Franck Phan, Anne-Caroline Jeannin, Alban Redheuil, Joe-Elie Salem, Samia Boussouar, Pauline Paulstephenraj, Suzanne Laroche, Chloé Amouyal, Agnès Hartemann, Fabienne Foufelle, Olivier Bourron

“…We also measured the serum biomarkers included in the FibroMax® panels (SteatoTest®, FibroTest®, NashTest®, ActiTest®). The predictive ability of these biomarkers of MASLD on LLACS progression was assessed through univariate and multivariate linear regression models, principal component regression analysis, as well as machine learning algorithms. …”
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Investigating potential biomarkers associated with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis using Mendelian randomization and transcriptomic analysis by Yujia Wang, Zhimin Chen, Kaiqi Huang, Keng Ye, Shiwei He, Yanfang Xu, Hong Chen

“…The ANN created based on biomarkers exhibited a robust predictive capacity for assessing the risk of AAV. Furthermore, co-enrichment of PDK4 and PPARGC1A was observed in ‘butanoate metabolism’, and ‘fatty acid metabolism’. …”
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CTAB modified SnO₂ PEDOT PSS heterojunction humidity sensor with enhanced sensitivity stability and machine learning evaluation by Poundoss Chellamuthu, Kirubaveni Savarimuthu, M Gulam Nabi Alsath, R. Krishnamoorthy, Yuvaraj T, Feras Alnaimat, Mohammad Shabaz

“…Furthermore, to validate real-time application feasibility, machine learning (ML) algorithms were implemented to model and predict sensor behavior. …”
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Radiomic Features of Mesorectal Fat as Indicators of Response in Rectal Cancer Patients Undergoing Neoadjuvant Therapy by Francesca Treballi, Ginevra Danti, Sofia Boccioli, Sebastiano Paolucci, Simone Busoni, Linda Calistri, Vittorio Miele

“…The aim was to assess the potential presence of predictive factors for favorable or unfavorable responses to neoadjuvant chemoradiotherapy, thereby optimizing treatment management and improving personalized clinical decision-making. …”
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Using electronic health records to enhance surveillance of diabetes in children, adolescents and young adults: a study protocol for the DiCAYA Network by Hui Zhou, Manmohan Kamboj, Yi Guo, Angela D Liese, Rebecca Anthopolos, Lu Zhang, John Chang, Anna Roberts, Tessa Crume, Brian E Dixon, Hui Shao, David C Lee, Lorna E Thorpe, Dimitri Christakis, Eneida A Mendonca, Katie Allen, Dana Dabelea, Giuseppina Imperatore, Mark Weiner, Meredith Akerman, Rong Wei, Kristi Reynolds, Annemarie G Hirsch, Jasmin Divers, Tianchen Lyu, Alex Ewing, Shaun Grannis, Yuan Luo, Bo Cai, Anthony Wong, Brian S Schwartz, Meda Pavkov, Meredith Lewis, Sarah Conderino, Jiang Bian, Yonghui Wu, Jihad S Obeid, Harold P Lehmann, Charles Bailey, Theresa Anderson, Elizabeth A Shenkman, Elizabeth Nauman, Christopher Forrest, Mattia Prosperi, Seho Park, Cara M Nordberg, Tessa L Crume, Anna Bellatorre, Stefanie Bendik, Marc Rosenman, Levon Utidjian, Mitch Maltenfort, Amy Shah, G Todd Alonso, Sara Deakyne-Davies, Tim Bunnell, Anne Kazak, Melody Kitzmiller, Daksha Ranade, Joseph J DeWalle, H Lester Kirchner, Dione G Mercer, Amy Poissant, Nimish Valvi, Jeff Warvel, Ashley Wiensch, Tamara Hannon, Eva Lustigova, Don McCarthy, Matthew T Mefford, George Lales, Allison Zelinski, Pedro Rivera, Thomas Carton, Victor W Zhong, Andrew Fair, Jessica Guillaume, Shahidul Islam, Alan Jacobson, Chinyere Okpara, Anand Rajan, Andrea Titus, Rebecca Conway, Toan Ong, Jack Pattee, Shawna Burgett, Bethlehem Shiferaw, Sarah J Bost, William T Donahoo, William R Hogan, Piaopiao Li, Lisa Knight, Caroline Rudisill, Jessica Stucker, Deborah Bowlby, Elaine Apperson, Deborah B Rolka

“…The network will assess the validity of computable phenotype definitions to determine diabetes status and type based on sensitivity, specificity, positive predictive value and negative predictive value of the phenotypes against the gold standard of manually abstracted medical charts. …”
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Deep MALDI-MS spatial omics guided by quantum cascade laser mid-infrared imaging microscopy by Lars Gruber, Stefan Schmidt, Thomas Enzlein, Huong Giang Vo, Tobias Bausbacher, James Lucas Cairns, Yasemin Ucal, Florian Keller, Martina Kerndl, Denis Abu Sammour, Omar Sharif, Gernot Schabbauer, Rüdiger Rudolf, Matthias Eckhardt, Stefania Alexandra Iakab, Laura Bindila, Carsten Hopf

“…QCL-MIR imaging-guided MSI allowed for unequivocal on-tissue elucidation of 157 sulfatides selectively accumulating in kidneys of arylsulfatase A-deficient mice used as ground truth concept and provided chemical rationales for improvements to ion mobility prediction algorithms. Using this workflow, we characterized sclerotic spinal cord lesions in mice with experimental autoimmune encephalomyelitis (EAE), a model of multiple sclerosis, and identified upregulation of inflammation-related ceramide-1-phosphate and ceramide phosphatidylethanolamine as markers of white matter lipid remodeling. …”
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Multimodal marvels of deep learning in medical diagnosis using image, speech, and text: A comprehensive review of COVID-19 detection by Md Shofiqul Islam, Khondokar Fida Hasan, Hasibul Hossain Shajeeb, Humayan Kabir Rana, Md. Saifur Rahman, Md. Munirul Hasan, AKM Azad, Ibrahim Abdullah, Mohammad Ali Moni

“…Motivated by the success of artificial intelligence applications during the COVID-19 pandemic, this research aims to uncover the capabilities of DL in disease screening, prediction, and classification, and to derive insights that enhance the resilience, sustainability, and inclusiveness of science, technology, and innovation systems. …”
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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

“…Deep learning (DL) has emerged as a promising tool, offering significant improvements in accuracy and efficiency for tasks such as time series prediction, spatial data analysis, and inverse modeling. …”
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Halogenated 2,4-diphenyl indeno[1,2-B]pyridinol derivatives as potential inhibitors of the androgen receptor (PDB ID: 58TE): A study of QSAR modeling, molecular docking, and pharma... by Auwal Salisu Isa, Adamu Uzairu, Umar Mele Umar, Muhammad Tukur Ibrahim, David Ebuka Arthur, Samuel Ndaghiya Adawara

“…The present research was centered on the development of a predictive model aimed at assessing the responses of prostate tumors to a range of compounds within the scope of the pIC50 project. …”
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Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan by Xiaoyi Zhang, Muhammad Usman, Ateeq ur Rehman Irshad, Mudassar Rashid, Amira Khattak

“…The use of machine learning algorithms, specifically XGBoost and Random Forest, highlighted the significant role played by the average value in the neighbourhood in predicting childhood stunting in nearby districts, confirming that the spatial spillover effect is not bounded by geographical boundaries. …”
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Admission testing for higher education: A multi-cohort study on the validity of high-fidelity curriculum-sampling tests. by A Susan M Niessen, Rob R Meijer, Jorge N Tendeiro

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Computer-guided design of novel nitrogen-based heterocyclic sphingosine-1-phosphate (S1P) activators as osteoanabolic agents by Rattanawan Tangporncharoen, Chuleeporn Phanus-Umporn, Supaluk Prachayasittikul, Chanin Nantasenamat, Veda Prachayasittikul, Aungkura Supokawej

“…QSAR modeling was performed using multiple linear regression (MLR) algorithm to successfully obtain two models with good predictive performance. …”
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