A novel machine learning models for meteorological drought forecasting in the semi-arid climate  region by Chaitanya Baliram Pande, Dinesh Kumar Vishwakarma, Aman Srivastava, Kanak N. Moharir, Fahad Alshehri, Norashidah Md Din, Lariyah Mohd Sidek, Bojan Đurin, Abebe Debele Tolche

“…The study aims to assess the accuracy of the developed models in predicting future drought events and gain insights into meteorological droughts in tropical climates using the Standardized Precipitation Index (SPI)-SPI-3 and SPI-6. …”
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Chemometrics-Assisted Calibration of a Handheld LIBS Device for the Quantitative Determination of Major and Minor Elements in Artifacts from the Archaeological Park of Tindari (Ita... by Gabriele Lando, Francesco Caridi, Domenico Majolino, Giuseppe Paladini, Giuseppe Sabatino, Valentina Venuti, Paola Cardiano

“…The limits of detection reached are satisfying, being, for example, 0.24%, 0.41%, 0.43%, 1.5%, and 1.7% for Na, Al, Ca, Si, and Fe, respectively, among major elements, and 189 ppm, 165 ppm, 203 ppm, and 1 ppm for Ba, Cu, Mn, and Rb, respectively, among minor elements. Uncertainties in prediction of the element concentrations were compared with data from the literature, and the effect of another baseline pretreatment algorithm, airPLS (adaptive iteratively reweighted PLS), was also tested. …”
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System architecture and key technologies of intelligent, safe and efficient mining for rockburst coal seams by Zhigang DENG, Yunpeng LI, Shankun ZHAO, Yin WANG, Shaogang LI, Linghai KONG, Junjun JIANG, Zhenguo SU, Yizhe LI, Kai QIN

“…At the same time, the real-time data of various monitoring systems in the well and underground are combined with the dynamic update and calculation of the mine digital model, which can not only review and analyze the historical engineering effects, but also predict and deduce the impact of future engineering activities. …”
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Daily time-use compositions of physical behaviours and its association with evaluative and experienced wellbeing: a multilevel compositional analysis by Anantha Narayanan, Scott Duncan, Conal Smith, Flora Le, Lisa Mackay, Julia McPhee, Basile Chaix, Tom Stewart

“…We investigated these associations by reallocating time among behaviours and assessing the predicted impact on wellbeing outcomes. Methods Time-use behaviours were obtained from 211 adults who wore Axivity AX3 accelerometers on their wrists for seven days. …”
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Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study by Zhen Lu, Binhua Dong, Hongning Cai, Tian Tian, Junfeng Wang, Leiwen Fu, Bingyi Wang, Weijie Zhang, Shaomei Lin, Xunyuan Tuo, Juntao Wang, Tianjie Yang, Xinxin Huang, Zheng Zheng, Huifeng Xue, Shuxia Xu, Siyang Liu, Pengming Sun, Huachun Zou

“…ObjectiveThis study aimed to identify subgroups with differential cervical precancer or cancer risks using machine learning, validate subgroup predictions across datasets, and propose a computational phenomapping strategy to enhance global CCP efforts. …”
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The Prognostic Significance of Sleep and Circadian Rhythm for Myocardial Infarction Outcomes: Case-Control Study by Wei-Chih Chin, Pao-Hsien Chu, Lung-Sheng Wu, Kuang-Tso Lee, Chen Lin, Chien-Te Ho, Wei-Sheng Yang, I-Hang Chung, Yu-Shu Huang

“…The use of actigraphy with different algorithms to evaluate sleep and circadian rhythm after acute MI has the potential for predicting outcomes and preventing future disease progression. …”
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Prospective Applications of Artificial Intelligence In Fetal Medicine: A Scoping Review of Recent Updates by Miskeen E, Alfaifi J, Alhuian DM, Alghamdi M, Alharthi MH, Alshahrani NA, Alosaimi G, Alshomrani RA, Hajlaa AM, Khair NM, Almuawi AM, Al-Jaber KH, Elrasheed FE, Elhassan K, Abbas M

“…The selected works were analyzed for common themes, AI methodologies applied, and the scope of AI’s integration into fetal medicine practice.Results: The review identified several key areas where AI applications are making strides in fetal medicine, including prenatal screening, diagnosis of congenital anomalies, and predicting pregnancy complications. AI-driven algorithms have been developed to analyze complex fetal ultrasound data, enhancing image quality and interpretative accuracy. …”
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The role of artificial intelligence in promoting health and developing preventive strategies for diabetes by Ameneh Marzban

“…Dear Editor Diabetes remains a significant public health challenge, and the integration of artificial intelligence (AI) presents remarkable opportunities to enhance early diagnosis, personalized treatment, and effective prevention strategies.1 AI algorithms, including supervised learning and convolutional neural networks, can efficiently analyze large datasets to identify patterns and risk factors associated with diabetes, surpassing the capabilities of traditional methods.2 This advanced analysis enables healthcare providers to predict the likelihood of diabetes in individuals and populations, facilitating timely interventions and customized prevention strategies. …”
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GLN2 as a key biomarker and therapeutic target: evidence from a comprehensive pan-cancer study using molecular, functional, and bioinformatic analyses by Shuang Gao, Lei Zhang, Guoping Sun

“…The potential of GNL2-based cancer therapies is emphasized, assisting in predicting the response to chemotherapy.…”
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Assessing the causal effect of inflammation‐related genes on myocarditis: A Mendelian randomization study by Huazhen Xiao, Hongkui Chen, Wenjia Liang, Yucheng Liu, Kaiyang Lin, Yansong Guo

“…The results of PPI analysis showed that 20 genes were predicted to be associated with IRF7 function, such as response to type I interferon, pattern recognition receptor signalling pathway, and toll‐like receptor signalling pathway. …”
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Identification and Validation of Ferritinophagy-Related Biomarkers in Periodontitis by Yi-Ming Li, Chen‑Xi Li, Reyila Jureti, Gulinuer Awuti

“…In addition, biomarker-associated immune cells and functional pathways were analysed to predict the upstream regulatory molecules. Results: In total, 18 candidate FRGs showed significant differences between the periodontitis and control groups, and from the protein-protein interaction network, eight hub genes were identified among the 175 differentially expressed FRGs by analysing 1096 differentially expressed genes and 4479 key modular genes. …”
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Current and Future Trends of the Automotive Industry by Dieter Hermann Schramm

“…For this purpose extensive field experiments are conducted along with activities using simulation models to predict these impacts reliably for the future. It has to be made possible to successfully derive development strategies for Europe and other regions as well (Schüller et al., 2015).…”
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Recognition of pivotal immune genes NR1H4 and IL4R as diagnostic biomarkers in distinguishing ovarian clear cell cancer from high-grade serous cancer by Yumin Ke, Meili Liang, Zhimei Zhou, Yajing Xie, Li Huang, Liying Sheng, Yueli Wang, Xinyan Zhou, Zhuna Wu

“…A nomogram was constructed to predict OCCC. We further validated key DIRGs’ diagnostic ability via a validation set and immunohistochemistry (IHC). …”
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A framework of crop water productivity estimation from UAV observations: A case study of summer maize by Minghan Cheng, Ni Song, Josep Penuelas, Matthew F. McCabe, Xiyun Jiao, Yuping Lv, Chengming Sun, Xiuliang Jin

“…To address this challenge, our research develops an innovative UAV-based monitoring framework through systematic integration of long-term multispectral/thermal infrared observations with multi-model fusion: (1) Surface Energy Balance Algorithm for Land (SEBAL) and FAO-56 Penman-Monteith models for evapotranspiration (ET) estimation; (2) Random Forest algorithm incorporating four phenotypical growth indicators for yield estimation, ultimately enabling CWP quantification. …”
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Mapping Mountain Permafrost via GPR-Augmented Machine Learning in the Northeastern Qinghai–Tibet Plateau by Yao Xiao, Guangyue Liu, Guojie Hu, Defu Zou, Ren Li, Erji Du, Tonghua Wu, Xiaodong Wu, Guohui Zhao, Yonghua Zhao, Lin Zhao

“…Elevation-based spatial comparisons revealed that LightGBM and CatBoost produced more terrain-adaptive predictions at high altitudes and slope transitions. …”
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Model Optimization for High-Yield Biocrude in Co-Hydrothermal Liquefaction of Municipal Sludge by Botian HAO, Yunfei DIAO, Ya WEI, Donghai XU

“…High sludge ratios significantly suppress yield, primarily because the high ash content (59.1%) dilutes the organic biomass concentration. Finally, a genetic algorithm combined with an ANN was used to predict the optimal process conditions for the co-HTL of MS and microalgae, achieving a maximum biocrude yield of 32.2%. …”
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Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens by Paul English, Muir J. Morrison, Blaine Mathison, Elizabeth Enrico, Ryan Shean, Brendan O'Fallon, Deven Rupp, Katie Knight, Alexandra Rangel, Jeffrey Gilivary, Amanda Vance, Haleina Hatch, Leo Lin, David P. Ng, Salika M. Shakir

“…A machine learning computer vision model was trained using 11,411 annotated organisms across 109 WSI. Model predictions were correlated with final culture-confirmed results. …”
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A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture by Hussain Mobarak Albarakati, Muhammad Attique Khan, Ameer Hamza, Faheem Khan, Naoufel Kraiem, Leila Jamel, Latifah Almuqren, Roobaea Alroobaea

“…Numerous advantages for sustainable agricultural applications, including yield prediction, crop monitoring, and climate change adaptation, can be obtained from RS and artificial intelligence. …”
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Numerical model of surface morphology and solid-liquid contact angle in wire electrical discharge machining by Zhi Chen, Yifei Zhou, Zefeng Yang, Zhizhong Zhang, Jian Li, Guojun Zhang, Fenglin Han

“…The established numerical model has been experimentally verified to have high prediction accuracy (the relative error less than 4%). …”
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Identification of subcellular localization of Arabidopsis AGO2 by ZHAO Li, XIA Wenqiang, CAI Xinzhong

“…Analysis using Predict Protein software showed that AtAGO1, AtAGO2, AtAGO3, AtAGO4 and AtAGO9 contained nuclear localization signals (NLS), although differing in sequence, demonstrating that they might be localized in cell nucleus. …”
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