In Silico Analysis of Coding/Noncoding SNPs of Human RETN Gene and Characterization of Their Impact on Resistin Stability and Structure by Lamiae Elkhattabi, Imane Morjane, Hicham Charoute, Soumaya Amghar, Hind Bouafi, Zouhair Elkarhat, Rachid Saile, Hassan Rouba, Abdelhamid Barakat

“…Stability analysis predicted 9 nsSNPs (I32S, C51Y, G58E, G58R, C78S, G79C, W98C, C103G, and C104Y) which can decrease protein stability with at least three out of the four algorithms used in this study. …”
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Accelerating Wound Healing Through Deep Reinforcement Learning: A Data-Driven Approach to Optimal Treatment by Fan Lu, Ksenia Zlobina, Prabhat Baniya, Houpu Li, Nicholas Rondoni, Narges Asefifeyzabadi, Wan Shen Hee, Maryam Tebyani, Kaelan Schorger, Celeste Franco, Michelle Bagood, Mircea Teodorescu, Marco Rolandi, Rivkah Isseroff, Marcella Gomez

“…However, due to the complexities of human–drug interactions and a lack of predictive models, it is challenging to determine how one should adjust drug dosage to achieve the desired biological response. …”
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Evaluation of AI-Powered Routine Screening of Clinically Acquired cMRIs for Incidental Intracranial Aneurysms by Christina Carina Schmidt, Robert Stahl, Franziska Mueller, Thomas David Fischer, Robert Forbrig, Christian Brem, Hakan Isik, Klaus Seelos, Niklas Thon, Sophia Stoecklein, Thomas Liebig, Johannes Rueckel

“…<b>Results</b>: The algorithm demonstrates high sensitivities (100% for findings >4 mm in diameter), a 17.8% MRA alert rate and positive predictive values of 11.5–43.8% (depending on whether inconclusive findings are considered or not). …”
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The Extended CG Method for Non-Quadratic Models by Abbas Al-Bayati, Basim Hassan, Sawsan Ismael

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L2R-MLP: a multilabel classification scheme for the detection of DNS tunneling by Emmanuel Oluwatobi Asani, Mojiire Oluwaseun Ayoola, Emmanuel Tunbosun Aderemi, Victoria Oluwaseyi Adedayo-Ajayi, Joyce A. Ayoola, Oluwatobi Noah Akande, Jide Kehinde Adeniyi, Oluwambo Tolulope Olowe

“…This highlights the effectiveness of L2 regularization in improving predictive capabilities and model generalization for unseen instances.…”
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Probabilistic daily runoff forecasting in high-altitude cold regions using a hybrid model combining DBO and transformer variants by Qiying Yu, Wenzhong Li, Yungang Bai, Zhenlin Lu, Yingying Xu, Chengshuai Liu, Lu Tian, Chen Shi, Biao Cao, Tianning Xie, Jianghui Zhang, Caihong Hu

“…Across various forecast periods, the model’s NSE values are 6.9–26.9 % higher than those of the TCN and Transformer models, offering more reliable short-term and long-term predictions. Furthermore, the Bootstrap algorithm’s probabilistic approach provides valuable insights into forecast uncertainty, a crucial feature for managing water resources and mitigating flood risks in high-altitude cold regions with complex hydrological dynamics.…”
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Label efficient phenotyping for Long COVID using electronic health records by Chuan Hong, Jun Wen, Harrison G. Zhang, Vidul Ayakulangara Panickan, Doris Y. Yang, Alicia W. Chen, Xin Xiong, Xuan Wang, Michele Morris, Sara Morini, Rahul Sangar, Andrew Dey, Malarkodi J. Samayamuthu, Katherine Liao, Clara-Lea Bonzel, Vidisha Tanukonda, Monika Maripuri, Jacqueline Honerlaw, Yuk-Lam Ho, Shyam Visweswaran, Isaac S. Kohane, Kelly Cho, Gabriel Brat, Zongqi Xia, Tianxi Cai

“…Developing efficient phenotyping algorithms is crucial to enabling risk prediction and effective management of Long COVID. …”
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A Novel Forest Dynamic Growth Visualization Method by Incorporating Spatial Structural Parameters Based on Convolutional Neural Network by Linlong Wang, Huaiqing Zhang, Kexin Lei, Tingdong Yang, Jing Zhang, Zeyu Cui, Rurao Fu, Hongyan Yu, Baowei Zhao, Xianyin Wang

“…The results show that: first, spatial structural parameters C and U have a certain contribution to the forest growth, and C and U can explain 21.5&#x0025;, 15.2&#x0025;, and 9.3&#x0025; of the variance in DBH, H, and CW growth models, respectively; second, CNN model outperformed machine learning algorithms SVR, MARS, Cubist, RF, and XGBoost in terms of prediction performance; third, based on FDGVM-CNN-SSP, we simulated Chinese fir plantations at individual tree level and stand level from 2018 to 2022 and found that DBH and H&#x0027;s fitting performance in measured and predicted data was highly consistent with <italic>R</italic>² and root-mean-square error (RMSE) of 86.8&#x0025;, 2.06 cm in DBH and 79.2&#x0025;, 1.11 m in H, but CW&#x0027;s <italic>R</italic>² and RMSE of 72.2&#x0025;, 0.65 m caused crowding (C) inconsistency.…”
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Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids by Xin-Ru Wen, Jia-Wei Tang, Jie Chen, Hui-Min Chen, Muhammad Usman, Quan Yuan, Yu-Rong Tang, Yu-Dong Zhang, Hui-Jin Chen, Liang Wang

“…Multiple ML models were constructed and optimized, with the convolutional neural network (CNN) model achieving the highest prediction accuracy at 99%. Gradient-weighted class activation mapping (Grad-CAM) was used to highlight important regions in the images for prediction. …”
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VGGBM-Net: A Novel Pixel-Based Transfer Features Engineering for Automated Coffee Bean Diseases Classification by Muhammad Shadab Alam Hashmi, Azam Mehmood Qadri, Ali Raza, Saleem Ullah, Aseel Smerat, Changgyun Kim, Muhammad Syafrudin, Norma Latif Fitriyani

“…A novel transformation of the VGG-19 model for feature engineering based on transfer learning is introduced, where spatial features extracted from coffee bean images are transformed into class prediction probabilities using LGBM. These enhanced features are then used as inputs for advanced machine-learning algorithms. …”
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Stratified allocation method for water injection based on machine learning: A case study of the Bohai A oil and gas field by Changlong Liu, Pingli Liu, Qiang Wang, Lu Zhang, Zechao Huang, Yuande Xu, Shaojiu Jiang, Le Zhang, Changxiao Cao

“…Second, the training and prediction effects of three machine learning prediction models—support vector machine, BP neural network, and random forest—were compared, and the BP neural network was selected as the machine learning mathematical model for injection allocation optimization. …”
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Early Diagnosis of Knee Osteoarthritis With a Natural Language Processing–Driven Approach Based on Clinician Notes: Development and Validation Study by Narathip Thanyakunsajja, Kulsawasd Jitkajornwanich, Shan Xu, Donghee Shin, Pattama Charoenporn

“…The findings indicate the feasibility of using text data (symptom descriptions reported by patients and recorded by doctors) to predict knee OA. Medical notes of symptom reports can be considered a valuable data source for predicting whether a particular knee is likely to experience OA progression.…”
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Quantifying Uncertainties in Solar Wind Forecasting due to Incomplete Solar Magnetic Field Information by Stephan G. Heinemann, Jens Pomoell, Ronald M. Caplan, Mathew J. Owens, Shaela Jones, Lisa Upton, Bibhuti Kumar Jha, Charles N. Arge

“…Solar wind forecasting plays a crucial role in space weather prediction, yet significant uncertainties persist duet to incomplete magnetic field observations of the Sun. …”
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Detection of litchi fruit maturity states based on unmanned aerial vehicle remote sensing and improved YOLOv8 model by Changjiang Liang, Changjiang Liang, Dandan Liu, Dandan Liu, Weiyi Ge, Weiyi Ge, Wenzhong Huang, Wenzhong Huang, Yubin Lan, Yubin Lan, Yubin Lan, Yongbing Long, Yongbing Long, Yongbing Long, Yongbing Long

“…In addition, YOLOv8-FPDW was more competitive than mainstream object detection algorithms. The study predicted the optimal harvest period for litchis, providing scientific support for orchard batch harvesting and fine management.…”
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Exploration of ductility for refractory high entropy alloys via interpretive machine learning by Shaolong Zheng, Lingwei Yang, Liyang Fang, Chenran Xu, Guanglong Xu, Yifang Ouyang, Xiaoma Tao

“…This study constructs an ML model for accurate ductility prediction from sparse compositional data, accelerating the design of ductile RHEAs within infinite compositional space. …”
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A Tile with Nested Chain Abacus by Eman F. Mohammed

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Performance of the Oncuria-Detect bladder cancer test for evaluating patients presenting with haematuria: results from a real-world clinical setting by Ian Pagano, Zhen Zhang, Michael Luu, Sergei Tikhonenkov, Florence Le Calvez-Kelm, Steve Goodison, Toru Sakatani, Kaoru Murakami, Takashi Kobayashi, Patrice Avogbe, Howard Kim, Riko Lee, Arnaud Manel, Emmanuel Vian, Charles J. Rosser, Hideki Furuya

“…In the test set, the Oncuria-Detect assay correctly identified bladder cancer in 62 of 73 cases resulting in a sensitivity of 85%, a specificity of 72%, and a negative predictive value (NPV) of 95%. The performance of Oncuria was similar for both low-grade/low-stage and high-grade/high-stage. …”
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Unmanned Aerial Vehicles Applicability to Mapping Soil Properties Under Homogeneous Steppe Vegetation by Azamat Suleymanov, Mikhail Komissarov, Mikhail Aivazyan, Ruslan Suleymanov, Ilnur Bikbaev, Arseniy Garipov, Raphak Giniyatullin, Olesia Ishkinina, Iren Tuktarova, Larisa Belan

“…In this study, we aimed to predict soil organic carbon, soil texture at several depths, as well as the thickness of the AB soil horizon and penetration resistance using a machine learning algorithm in combination with UAV images. …”
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Reliability evaluation and multi-objective optimization of combustion chamber’s key components of marine engine by Lei Hu, Wentong Wang, Xu Wang, Jianguo Yang, Yonghua Yu, Chunyang Mei

“…Constrained multi-objective optimization of reliability is conducted through contrastive analysis of different optimization algorithms. The research shows that the multi-objective particle swarm optimization algorithm achieves the best performance, the maximum temperatures of the piston, cylinder head, and liner decrease by 3.90 %, 5.66 %, and 6.52 %, the maximum thermo-mechanical coupling stresses reduced by 9.41 %, 7.83 %, and 4.97 % respectively, and creep-fatigue life enhancements reach 3.84 % and 12.67 % for the piston and cylinder head. …”
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