Nonlinear optical excitation response and regulation of instabilities in a Y-type atomic system under complex interactions by Shang-Yu Zhai, Rui Shi, Fu-Han Xie

“…In this work, a Y-type energy level ensemble of an alkali metal Rydberg rubidium cold atomic system under complex interactions is considered. …”
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Identification of reference genes for RT-qPCR in the Antarctic moss Sanionia uncinata under abiotic stress conditions. by Mira Park, Soon Gyu Hong, Hyun Park, Byeong-Ha Lee, Hyoungseok Lee

“…Among the 17 candidates, the most stable references were POB1 and UFD2 for cold stress, POB1 and AKB for drought treatment, and UFD2 and AKB for the field samples from a different water contents in Antarctica. …”
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Oriented ice eddy detection network based on the Sentinel-1 dual-polarization data by Jinqun Wu, Jinqun Wu, Yiqin Zheng, Tingting Wang, Chunyong Ma, Chunyong Ma, Ge Chen, Ge Chen

“…The complex convergence of cold and warm ocean currents in the Nordic Seas provides suitable conditions for the formation and development of eddies. …”
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MLAS: Machine Learning-Based Approach for Predicting Abiotic Stress-Responsive Genes in Chinese Cabbage by Xiong You, Yiting Shu, Xingcheng Ni, Hengmin Lv, Jian Luo, Jianping Tao, Guanghui Bai, Shusu Feng

“…This study presents a computational model utilizing machine learning techniques to predict genes in Chinese cabbage that respond to four abiotic stresses: cold, heat, drought, and salt. To construct this model, data from relevant studies regarding responses to these abiotic stresses were compiled, and the protein sequences encoded by abiotic SRGs were converted into numerical representations for subsequent analysis. …”
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Determination of Pear Cultivars (Pyrus communis L.) Based on Colour Change Levels by Using Data Mining by Dilara Gerdan, Abdullah Beyaz, Mustafa Vatandaş

“…Also, it is a well-known condition, cold environmental conditions protect fruits from deformations negative effects, but most of the time, most of the consumers keep the fruits at room temperature in open packs during the consumption process. …”
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Using machine learning to identify key predictors of maternal success in sheep for improved lamb survival by Ebru Emsen, Bahadir Baran Odevci, Muzeyyen Kutluca Korkmaz

“…Using data from native and crossbred prolific ewes in a high-altitude, cold-climate region, we applied machine learning models to predict mothering scores based on dam characteristics, birth conditions, and lamb attributes. …”
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Progress on the world’s primate hotspots and coldspots: modeling ensemble super SDMs in cloud-computers based on digital citizen-science big data and 200+ predictors for more susta... by Moriz Steiner, Falk Huettmann

“…For the global cold/hotspot models, we obtained global distribution data from www.GBIF.org (approx. 420,000 raw occurrence records) and utilized the world’s largest Open Access environmental predictor set of 201 layers. …”
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Enhanced supervised classification of seasonal pastures on the Qinghai-Tibet Plateau (1990–2020) using Landsat optimal time window by Panfei Fang, Jia Wang, Haifeng Xu, Ruonan Li, Long Yu, Shaodong Huang, Yuying Liang, Pengfei Zheng

“…The framework leverages Landsat imagery within the optimal time window (Days of Year, DOYs 190–280), incorporates algorithms for automated sample generation and refinement, and employs the Random Forest (RF) classifier to enable fully automated seasonal pasture mapping. …”
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Comprehensive Review on the Control of Heat Pumps for Energy Flexibility in Distribution Networks by Gustavo L. Aschidamini, Mina Pavlovic, Bradley A. Reinholz, Malcolm S. Metcalfe, Taco Niet, Mariana Resener

“…Key takeaways suggest the need to validate control strategies for extreme cold-weather regions that require backup heaters, as well as develop approaches designed for demand charge schemes that integrate HPs with other controllable loads. …”
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Uncovering Hippo pathway-related biomarkers in acute myocardial infarction via scRNA-seq binding transcriptomics by Xingda Li, Xueqi He, Yu Zhang, Xinyuan Hao, Anqi Xiong, Jiayu Huang, Biying Jiang, Zaiyu Tong, Haiyan Huang, Lian Yi, Wenjia Chen

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Estimation of Moderate-Resolution Snow Depth in Xinjiang With Enhanced-Resolution Passive Microwave and Reanalysis Data by Machine Learning Methods by Yongchang Yan, Yan Qin, Yongqiang Liu, Yubao Qiu, Yang Liu

“…This approach focuses on accurately estimating the spatiotemporal distribution of SD over Xinjiang, characterized by dry-cold snow. The results indicate that First, upon incorporating auxiliary variables, the SD from the CatBoost model demonstrated superior performances over the other algorithms (<inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula>: CatBoost &gt; LightGBM &gt; XGBoost &gt; RF). …”
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Inferring Mechanical Properties of Wire Rods via Transfer Learning Using Pre-Trained Neural Networks by Adriany A. F. Eduardo, Gustavo A. S. Martinez, Ted W. Grant, Lucas B. S. Da Silva, Wei-Liang Qian

“…The primary objective of this study is to explore how machine learning techniques can be incorporated into the analysis of material deformation. Neural network algorithms are applied to the study of mechanical properties of wire rods subjected to cold plastic deformations. …”
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Comprehensive multi-omics and machine learning framework for glioma subtyping and precision therapeutics by Yi Ding, Zhaiyue Xu, Wenjing Hu, Peng Deng, Mian Ma, Jiandong Wu

“…Ten machine-learning algorithms in MIME were benchmarked, and the Lasso + SuperPC combination yielded an eight-gene GloMICS (Glioma Multi-Omics Consensus Signature) prognostic score. …”
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Marine soundscape forecasting: A deep learning-based approach by Shashidhar Siddagangaiah

“…Despite the rapid development of anomaly detection algorithms and deep-learning models for forecasting, their application to marine soundscapes remains unexplored. …”
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Unveiling purine metabolism dysregulation orchestrated immunosuppression in advanced pancreatic cancer and concentrating on the central role of NT5E by Junqian Zhang, Xiaobo Zhang, Ruixin Wu, Chang-Sheng Dong

“…We quantitatively appraised the PM traits of diverse cell subsets via scoring algorithms such as AUCell and Ucell. Moreover, cell development and cell-cell interaction analysis elucidated the alterations in TME induced by PM dysregulation. …”
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Western disturbances and climate variability: a review of recent developments by K. M. R. Hunt, K. M. R. Hunt, J.-P. Baudouin, A. G. Turner, A. G. Turner, A. P. Dimri, A. P. Dimri, G. Jeelani, Pooja, R. Chattopadhyay, R. Chattopadhyay, F. Cannon, T. Arulalan, T. Arulalan, M. S. Shekhar, T. P. Sabin, E. Palazzi

“…Use of new modelling frameworks and tracking algorithms has significantly improved knowledge of WD structure and variability, and a more frequentist approach can now be taken. …”
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Explainable machine learning model and nomogram for predicting the efficacy of Traditional Chinese Medicine in treating Long COVID: a retrospective study by Jisheng Zhang, Yang Chen, Aijun Zhang, Yi Yang, Liqian Ma, Hangqi Meng, Jintao Wu, Kean Zhu, Jiangsong Zhang, Ke Lin, Xianming Lin

“…Key features identified through SHapley Additive exPlanations (SHAP) included chest tightness, aversion to cold, age, TCMSS, Short Form (36) Health Survey (SF-36), C-reactive protein (CRP), and lymphocyte ratio. …”
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Permafrost–wildfire interactions: active layer thickness estimates for paired burned and unburned sites in northern high latitudes by A. C. Talucci, M. M. Loranty, J. E. Holloway, B. M. Rogers, H. D. Alexander, N. Baillargeon, J. L. Baltzer, L. T. Berner, A. Breen, L. Brodt, B. Buma, B. Buma, J. Dean, C. J. F. Delcourt, L. R. Diaz, C. M. Dieleman, T. A. Douglas, G. V. Frost, B. V. Gaglioti, R. E. Hewitt, T. Hollingsworth, T. Hollingsworth, M. T. Jorgenson, M. J. Lara, R. A. Loehman, M. C. Mack, K. L. Manies, C. Minions, S. M. Natali, J. A. O'Donnell, D. Olefeldt, A. K. Paulson, A. V. Rocha, L. B. Saperstein, T. A. Shestakova, T. A. Shestakova, T. A. Shestakova, S. Sistla, O. Sizov, A. Soromotin, M. R. Turetsky, S. Veraverbeke, M. A. Walvoord

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Comprehensive analysis of regulatory B Cell related genes in prognosis and therapeutic response in lung adenocarcinoma by Liangyu Zhang, Liangyu Zhang, Jianshen Zeng, Jianshen Zeng, Xun Zhang, Xun Zhang, Menglong Zhang, Menglong Zhang, Yilin Lin, Yilin Lin, Fancai Lai, Fancai Lai

“…Differentially expressed genes between the two clusters were then used to construct the BREGI using 32 algorithms, including traditional regression, machine learning, deep learning, and 274 different combinations. …”
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Artificial intelligence in vaccine research and development: an umbrella review by Rabie Adel El Arab, May Alkhunaizi, May Alkhunaizi, Yousef N. Alhashem, Alissar Al Khatib, Munirah Bubsheet, Salwa Hassanein, Salwa Hassanein

“…AI‑driven multi‑omic integration accelerated epitope mapping, shrinking discovery timelines by months, while predictive analytics optimized manufacturing workflows and supply‑chain operations (including temperature‑controlled, “cold‑chain” logistics). Sentiment analysis and conversational AI tools demonstrated promising capabilities for real‑time monitoring of public attitudes and tailored communication to address vaccine hesitancy. …”
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