The role of machine learning in infectious disease early detection and prediction in the MENA region: A systematic review by Radwan Qasrawi, Ghada Issa, Suliman Thwib, Razan AbuGhoush, Malak Amro, Raghad Ayyad, Stephanny Vicuna, Eman Badran, Yousef Khader, Raeda Al Qutob, Faris Al Bakri, Hana Trigui, Elie Sokhn, Emmanuel Musa, Jude Dzevela Kong

“…This systematic review analyzes the implementation and effectiveness of machine learning (ML) approaches for infectious disease surveillance and prediction across the Middle East and North Africa (MENA) region. Adhering to PRISMA guidelines, studies published between 2016 and 2024 were examined to assess model structures, performance metrics, and dataset characteristics. …”
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Parametric Optimization and Assessment of Modern Heritage Shading Screen for a Mid-Rise Building in Arid Climate: Modernizing Traditional Designs by Anwar Ahmad, Lindita Bande, Waleed Ahmed, Kheira Tabet Aoul, Mukesh Jha

“…The construction domain in the Middle East region has experienced significant growth in recent years. …”
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Enhanced unsupervised domain adaptation with iterative pseudo-label refinement for inter-event oil spill segmentation in SAR images by Guangyan Cui, Jianchao Fan, Yarong Zou

“…The proposed method has been shown to outperform existing algorithms in eight comparison experiments for four real oil spill events.…”
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Unstructured Electronic Health Records of Dysphagic Patients Analyzed by Large Language Models by Luisa Neubig, Deirdre Larsen, Melda Kunduk, Andreas M. Kist

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Acoustic emission detection and defect identification method for micro-leakage in natural gas pipeline valves by Lizhen ZHANG, Wen'ao WANG

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easyspec: An Open-source Python Package for Long-slit Spectroscopy by Raniere de Menezes, Francesco Massaro, Michela Negro, Claudia M. Raiteri, Harold Peña-Herazo, Jose A. Acosta-Pulido

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Projecting Forest Fire Probability in South Korea Under Climate Change, Population, and Forest Management Scenarios Using AI & Process-Based Hybrid Model (FLAM-Net) by Hyun-Woo Jo, Myoungsoo Won, Florian Kraxner, Seong Woo Jeon, Yowhan Son, Andrey Krasovskiy, Woo-Kyun Lee

“…Enhancements included improving backpropagation for optimization and introducing algorithms for national-specific fire ignition dynamics. …”
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Comparison of different downscaling schemes for obtaining regional high-resolution soil moisture data by Yulin Shangguan, Cheng Tong, Zhou Shi, Hongquan Wang, Xiaodong Deng

“…However, due to the strong vegetation scattering effect, it showed two times larger uncertainty than the retrieval-first based SM over densely vegetated regions in the east and southeast. In addition, satisfactory TB downscaling performance could be achieved by leveraging machine learning algorithms and multiple covariables, but need to further reduce additional errors. …”
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Utilizing Artificial Intelligence (AI) for the optimal design of geothermal cogeneration systems in zero energy building by Ehsanolah Assareh, Mohammad Zoghi, Ali Zare, Hassan Bazazzadeh, Adnan Alboghobeysh, Saleh Mobayen, Nima Izadyar, Siamak Hoseinzadeh

“…Modeling was conducted using the widely recognized EES software, while system optimization employed a combination of neural networks and intelligent optimization algorithms. The optimized configuration achieved an exergy efficiency of 63.79 % and a cost rate of $57.82 per hour. …”
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A deep neural network framework for estimating coastal salinity from SMAP brightness temperature data by Yidi Wei, Qing Xu, Qing Xu, Xiaobin Yin, Xiaobin Yin, Yan Li, Yan Li, Kaiguo Fan

“…Despite advancements in satellite-based radiometry such as NASA’s Soil Moisture Active Passive (SMAP), significant challenges persist in coastal SSS retrieval due to radio frequency interference (RFI), land-sea contamination, and complex interactions of nearshore dynamic processes.MethodThis study proposes a deep neural network (DNN) framework that integrates SMAP L-band brightness temperature data with ancillary oceanographic and geographic parameters such as sea surface temperature, the shortest distance to the coastline (dis) to enhance SSS estimation accuracy in the Yellow and East China Seas. The framework leverages machine learning interpretability tools (Shapley Additive Explanations, SHAP) to optimize input feature selection and employs a grid search strategy for hyperparameter tuning.Results and discussionSystematic validation against independent in-situ measurements demonstrates that the baseline DNN model constructed for the entire region and time period outperforms conventional algorithms including K-Nearest Neighbors, Random Forest, and XGBoost and the standard SMAP SSS product, achieving a reduction of 36.0%, 33.4%, 40.1%, and 23.2%, respectively in root mean square error (RMSE). …”
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Comparison and Evaluation of Rain Gauge, CMORPH, TRMM PR and GPM DPR KuPR Precipitation Products over South China by Rui Wang, Huiping Li, Hao Huang, Liangliang Li

“…Several statistical metrics suggest that although the missing detection rates of TRMM and GPM are higher than those of CMORPH (probability of detection 10–60%), their false detection rates are spatially lower (false alert ratio 10–30%) in Middle-East China. This study aims to provide valuable insights for enhancing precipitation retrieval algorithms and improving the applicability of remote sensing precipitation products.…”
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Examining the Impact of Topography and Vegetation on Existing Forest Canopy Height Products from ICESat-2 ATLAS/GEDI Data by Yisa Li, Dengsheng Lu, Yagang Lu, Guiying Li

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Urban Greening Analysis: A Multimodal Large Language Model for Pinpointing Vegetation Areas in Adverse Weather Conditions by Hanzhang Liu, Shijie Yang, Chengwu Long, Jiateng Yuan, Qirui Yang, Jiahua Fan, Bingnan Meng, Zhibo Chen, Fu Xu, Chao Mou

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Deep reinforcement learning agents for dynamic spectrum access in television whitespace cognitive radio networks by Udeme C. Ukpong, Olabode Idowu-Bismark, Emmanuel Adetiba, Jules R. Kala, Emmanuel Owolabi, Oluwadamilola Oshin, Abdultaofeek Abayomi, Oluwatobi E. Dare

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Biologging intelligent Platform (BiP): an integrated and standardized platform for sharing, visualizing, and analyzing biologging data by Katsufumi Sato, Shinichi Watanabe, Takuji Noda, Takuya Koizumi, Ken Yoda, Yuuki Y. Watanabe, Kentaro Q. Sakamoto, Teijiro Isokawa, Makoto A. Yoshida, Kagari Aoki, Akinori Takahashi, Takashi Iwata, Hideaki Nishizawa, Takuya Maekawa, Ryo Kawabe, Yutaka Watanuki

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Estimating volumetric water salinity in a Tibetan endorheic lake using machine learning and remote sensing by Pengju Xu, Kai Liu, Yaling Lin, Xuefei Fu, Chenyu Fan, Chunqiao Song

“…First, we developed a model using machine learning algorithms, with remote sensing data and hydrological and topographical features, to estimate surface water salinity. …”
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Transient Attracting Profiles in the Great Pacific Garbage Patch by L. Kunz, A. Griesel, C. Eden, R. Duran, R. Duran, B. Sainte-Rose

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Cerebrospinal Fluid Leakage Combined with Blood Biomarkers Predicts Poor Wound Healing After Posterior Lumbar Spinal Fusion: A Machine Learning Analysis by Pang Z, Ou Y, Liang J, Huang S, Chen J, Huang S, Wei Q, Liu Y, Qin H, Chen Y

“…By combining logistic regression analysis with six machine learning algorithms, this study identified six predictors associated with PWH: subcutaneous lumbar spine index(SLSI), albumin, postoperative glucose, cerebrospinal fluid leakage(CSFL), neutrophil (NEU), and C-reactive protein(CRP). …”
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Advanced prediction of rice yield gaps under climate uncertainty using machine learning techniques in Eastern India by Satiprasad Sahoo, Chiranjit Singha, Ajit Govind

“…Finally, machine learning algorithms were used to identify rice yield gaps to achieve sustainable agricultural intensification. …”
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Machine learning identification of a novel vasculogenic mimicry-related signature and FOXM1’s role in promoting vasculogenic mimicry in clear cell renal cell carcinoma by Chao Xu, Sujing Zhang, Jingwei Lv, Yilong Cao, Yao Chen, Hao Sun, Shengtao Dai, Bowei Zhang, Meng Zhu, Yuepeng Liu, Junfei Gu

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