Advancing sustainability: The impact of emerging technologies in agriculture by Ashoka Gamage, Ruchira Gangahagedara, Shyamantha Subasinghe, Jeewan Gamage, Chamini Guruge, Sera Senaratne, Thevin Randika, Chamila Rathnayake, Zammil Hameed, Terrence Madhujith, Othmane Merah

“…The integration of data analytics and machine learning algorithms is transforming supply chain management and enhancing the capabilities of predictive analytics in the context of crop diseases. …”
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Correlation Between Clinical Indicators and Liver Pathology in Children with Chronic Hepatitis B by Chenyang Huang, Ying Lu, Ziwei Wang, Qiyu Jiang, Yi Dong, Lili Cao, Jianguo Yan, Zhiqiang Xu, Fuchuan Wang, Yinjie Gao, Junliang Fu, Min Zhang, Fu-Sheng Wang

“…Key clinical indicators, including age, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl transferase (GGT), were evaluated for their predictive value in determining disease severity using restricted cubic spline regression models. …”
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Development and validation of an early diagnosis model for severe mycoplasma pneumonia in children based on interpretable machine learning by Si Xie, Mo Wu, Yu Shang, Wenbin Tuo, Jun Wang, Qinzhen Cai, Chunhui Yuan, Cong Yao, Yun Xiang

“…Results After variable selection, eight machine learning models were developed using age, sex and 21 serum indicators identified as predictive factors for SMPP. A Light Gradient Boosting Machine (LightGBM) model demonstrated strong performance, achieving AUC of 0.92 for prospective validation. …”
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Deciphering the impact of intra-tumoral bacterial infiltration on multi-omics profiles in low-grade gliomas by Wenshu Li, Zixiang Zhu, Zixiang Zhu, Longyuan Li, Xin Wu, Jiaxuan Li, Yi Zhou, Lingwen Gu, Pranathi Vittal, Zhouqing Chen, Zhong Wang, Lingchuan Guo

“…Patients in this group exhibited shorter survival periods, potentially attributable to the heightened expression of negative immune checkpoints. Predictive analysis for targeted drugs indicated that certain agents might achieve a lower half maximal inhibitory concentration (IC50) in the low-risk group compared to the high-risk group. …”
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Research on Near-Infrared Non-Invasive Blood Glucose Detection Technology by Wang Jinshuo

“…In response to the complex and easily interfered characteristics of near-infrared spectral signals, an innovative BP neural network algorithm is introduced to construct a prediction model. …”
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Comprehensive Analysis Based on the Cancer Immunotherapy and Immune Activation of Gastric Cancer Patients by Feng Jiang, Qilong Ma

“…Finally, we were able to produce a 6-gene prognostic prediction model using immune-related genes. Further analysis revealed that the prognostic prediction model is closely related to the prognosis of patients with GC. …”
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Adaptive switching and routing protocol design and optimization in internet of things based on probabilistic models by Yi Yang

“…The paper explores the subject of data flow optimization using Fuzzy Assisted Cuckoo Search Optimization (FACSO), traffic flow using Gaussian Process Regression (GPR), and CH prediction using the Stochastic Optimization Algorithm (SOA). …”
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Multi-LiDAR-Based 3D Object Detection via Data-Level Fusion Method by Yu Luo, Tao Wang, Shuai Lu, Xuerui Dai, Zhi Li

“…Moreover, we add the direction accuracy by changing the heading angle prediction to interval prediction. Finally, to verify the effectiveness of our method, we propose the public dataset VANJEE Point Cloud, which is collected in the real world. …”
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Application of Neural Network Models for Analyzing the Impact of Flight Speed and Angle of Attack on Flow Parameter Non-Uniformity in a Turbofan Engine Inlet Duct by Adam Kozakiewicz, Maciej Adamczyk, Rafał Kieszek

“…The ANN was trained using the CFG algorithm, and the predictive accuracy was assessed through the determination coefficients computed by comparing ANN-based estimates with numerical simulation results. …”
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Developing a molecular diagnostic model for heatstroke-induced coagulopathy: a proteomics and metabolomics approach by Qingbo Zeng, Qingwei Lin, Longping He, Lincui Zhong, Ye Zhou, Xingping Deng, Nianqing Zhang, Qing Song, Qing Song, Jingchun Song, Jingchun Song

“…Additionally, three optimal predictive models (AUC >0.9) were developed and validated for classifying HSIC from HS individuals based on proteomic patterns and machine learning, with the logistic regression model showing the best diagnostic performance (AUC = 0.979, sensitivity = 81.8%, specificity = 96.7%), highlighting lactate dehydrogenase A chain (LDHA), neutrophil gelatinase-associated lipocalin (NGAL), prothrombin and glucan-branching enzyme (GBE) as key predictors of HSIC.ConclusionThe study uncovered critical metabolic and protein changes linked to heatstroke, highlighting the involvement of energy regulation, lipid metabolism, and carbohydrate metabolism. …”
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A model of indoor thermal condition based on traditional acehnese houses using artificial neural network by Muslimsyah, Abdul Munir, Yuwaldi Away, Abdullah, Teuku Yuliar Arif, Andri Novandri

“…The study employs an ANN (Artificial Neural Network) algorithm to predict thermal condition parameters including indoor: temperature, humidity, and wind speed. …”
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AMMap tool for additive manufacturing design, alloy discovery, and path planning by Alexander M Richter, Adam M Krajewski, Zhening Yang, Allison M Beese, Zi-Kui Liu

“…Equilibrium thermodynamic calculations and solidification simulations, such as Scheil–Gulliver, can be used to predict feasible compositions or compositional paths, acting as constraints before empirical or machine learning models are applied to predict properties of interest. …”
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A machine learning-based screening model for the early detection of prostate cancer developed using serum microRNA data from a mixed cohort of 8,741 participants by Cong Lai, Zhensheng Hu, Zhuohang Li, Zhikai Wu, Kuiqing Li, Lin Li, Hongze Liu, Juanyi Shi, Yi Zhou, Kewei Xu, Cheng Liu

“…Six machine learning algorithms were employed to develop a screening model for PCa using the training dataset. …”
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Simulating the Carbon, Nitrogen, and Phosphorus of Plant Above-Ground Parts in Alpine Grasslands of Xizang, China by Mingxue Xiang, Gang Fu, Jianghao Cheng, Tao Ma, Yunqiao Ma, Kai Zheng, Zhaoqi Wang

“…Therefore, the random forest algorithm based on climate data and/or the NDVImax demonstrated superior predictive performance in modeling these biogeochemical parameters.…”
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MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems by Abdul Jabbar, Marco Cocconcelli, Gianluca D’Elia, Davide Borghi, Luca Capelli, Jacopo Cavalaglio Camargo Molano, Matteo Strozzi, Riccardo Rubini

“…The primary objective is to advance research in machine health monitoring, predictive maintenance, and stochastic modeling by providing the first data set of its kind. …”
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Clinician Attitudes and Perceptions of Point-of-Care Information Resources and Their Integration Into Electronic Health Records: Qualitative Interview Study by Marlika Marceau, Sevan Dulgarian, Jacob Cambre, Pamela M Garabedian, Mary G Amato, Diane L Seger, Lynn A Volk, Gretchen Purcell Jackson, David W Bates, Ronen Rozenblum, Ania Syrowatka

“…Some recommended that further integration would allow us to leverage existing POCI tool features, such as chatbots and knowledge links, as well as aspects of artificial intelligence and machine learning, such as predictive algorithms and personalized alert systems, to enhance EHR functionality. …”
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Assessing habitat suitability for aoudad (Ammotragus lervia) reintroduction in southeastern morocco to promote ecotourism by Lahbib Naimi, El Mahi Bouziane, Lamya Benaddi, Abdeslam Jakimi, Mohamed Manaouch

“…Subsequently, a machine learning algorithm called Bagging was employed to develop a predictive model. …”
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Electric Power Consumption Forecasting Models and Spatio-Temporal Dynamic Analysis of China’s Mega-City Agglomerations Based on Low-Light Remote Sensing Imagery Incorporating Socia... by Cuiting Li, Dongmei Yan, Shuo Chen, Jun Yan, Wanrong Wu, Xiaowei Wang

“…Utilizing 2017–2021 NPP/VIIRS low-light remote sensing imagery to extract total nighttime light data, this study proposed an EPC prediction method based on the K-Means clustering algorithm combined with multiple indicators integrated with socio-economic factors. …”
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Construction of a prostate adenocarcinoma molecular classification: integrating spatial transcriptomics with retrospective cohort validation by Bingnan Lu, Yifan Liu, Guo Ji, Yuntao Yao, Zhao Yang, Bolin Zhu, Lei Wang, Keqin Dong, Yuanan Li, Jiaying Shi, Junzhe He, Runzhi Huang, Wang Zhou, Xinming Cui, Xiuwu Pan, Xingang Cui

“…Based on MDPGs, we constructed a malignant cell differentiation-based PRAD classification (MDPC) using the ConsensusClusterPlus algorithm. Then, we explored multi-omics correlations of MDPC, and constructed the regulation networks of MDPC as well as the prognostic prediction model. …”
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Smart Management of Energy Losses in Distribution Networks Using Deep Neural Networks by Ihor Blinov, Virginijus Radziukynas, Pavlo Shymaniuk, Artur Dyczko, Kinga Stecuła, Viktoriia Sychova, Volodymyr Miroshnyk, Roman Dychkovskyi

“…The primary objective is to enhance the accuracy of short-term forecasting for nodal loads and corresponding energy losses, enabling more efficient and intelligent grid operation. Two predictive approaches were explored: the first involves separate forecasting of nodal loads followed by loss calculations, while the second directly estimates network-wide energy losses. …”
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