Providing a Robust Dynamic Pricing Model and Comparing It with Static Pricing in Multi-level Supply Chains Using a Game Theory Approach by Sara Mehrjoo, Hanan Amoozad Mahdirji, Jalil Heidary Dahoei, Seyyed Hossein Razavi Haji Agha, Mahnaz Hosseinzadeh

“…Initially, the model introduces symbols: x as the vector of design variables, and y as the vector of control variables. Parameters A, B, and C are coefficient parameters, while b and e are parameter vectors. …”
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Development and multi-cohort validation of a machine learning-based simplified frailty assessment tool for clinical risk prediction by Jiahui Lai, Cailian Cheng, Tiantian Liang, Leile Tang, Xinhua Guo, Xun Liu

“…The extreme gradient boosting (XGBoost) algorithm exhibited superior performance across training (AUC 0.963, 95% CI: 0.951–0.975), internal validation (AUC 0.940, 95% CI: 0.924–0.956), and external validation (AUC 0.850, 95% CI: 0.832–0.868) datasets. …”
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An optimized ensemble ML-WQI model for reliable water quality prediction by minimizing the eclipsing and ambiguity issues by Ashifur Rahman, M. M. Mahbubul Syeed, Md. Rajaul Karim, Kaniz Fatema, Razib Hayat Khan, Mohammad Faisal Uddin

“…To evaluate performance, mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), R-squared ( $$R^2$$ R 2 ), fivefold cross-validation, and a comparative evaluation with existing ML models are carried out. …”
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Machine learning assisted estimation of total solids content of drilling fluids by B.T. Gunel, Y.D. Pak, A.Ö. Herekeli, S. Gül, B. Kulga, E. Artun

“…In the final stage, different evaluation metrics were employed to evaluate and compare the performance of different classes of machine learning models. …”
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Predicting the strengths of basalt fiber reinforced concrete mixed with fly ash using AML and Hoffman and Gardener techniques by Kennedy C. Onyelowe, Viroon Kamchoom, Shadi Hanandeh, Ahmed M. Ebid, José Luis Llamuca Llamuca, Juan Carlos Cayán Martínez, Evlin Rose, Paul Awoyera, Siva Avudaiappan

“…Further, performance evaluation indices were used to compare the models’ abilities and lastly, the Hoffman and Gardener’s technique was used to evaluate the sensitivity of the parameters on the concrete strengths. …”
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U-Net-based VGG19 model for improved facial expression recognition by Xiaohu ZHAO, Jingyi ZHANG, Mingzhi JIAO, Lixun XIE, Lanfei WANG, Weiqing SUN, Di ZHANG

“…The improved model not only boosts performance in terms of feature extraction and fusion but is also adept in solving the pressing problems of parameter size and computational efficiency. …”
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Predicting the risk of postoperative avascular necrosis in patients with talar fractures based on an interpretable machine learning model by Jian Zhang, Jian Zhang, Jian Zhang, Jihai Xu, Jihai Xu, Jiapei Yu, Jiapei Yu, Jiapei Yu, Hong Chen, Hong Chen, Xin Hong, Songou Zhang, Xin Wang, Xin Wang, Chengchun Shen, Chengchun Shen, Chengchun Shen

“…Univariate and multivariable logistic regression identified six independent risk factors including body mass index (BMI), fracture classification, concomitant ipsilateral foot and ankle fractures, smoking, quality of fracture reduction, and fracture type. Performance evaluation demonstrated that Extreme Gradient Boosting (XGBoost model) achieved high AUC values with superior specificity and sensitivity in both the training and testing sets. …”
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Advanced machine learning for regional potato yield prediction: analysis of essential drivers by Dania Tamayo-Vera, Morteza Mesbah, Yinsuo Zhang, Xiuquan Wang

“…Abstract Localized yield prediction is critical for farmers and policymakers, supporting sustainability, food security, and climate change adaptation. This research evaluates machine learning models, including Random Forest and Gradient Boosting, for predicting crop yields. …”
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Mortality Risk Prediction in Patients With Antimelanoma Differentiation–Associated, Gene 5 Antibody–Positive, Dermatomyositis–Associated Interstitial Lung Disease: Algorithm Develo... by Hui Li, Ruyi Zou, Hongxia Xin, Ping He, Bin Xi, Yaqiong Tian, Qi Zhao, Xin Yan, Xiaohua Qiu, Yujuan Gao, Yin Liu, Min Cao, Bi Chen, Qian Han, Juan Chen, Guochun Wang, Hourong Cai

“…Six ML algorithms (Extreme Gradient Boosting [XGBoost], logistic regression (LR), Light Gradient Boosting Machine [LightGBM], random forest [RF], support vector machine [SVM], and k-nearest neighbor [KNN]) were applied to construct and evaluate the model. …”
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Impact of Rice Husk Ash Properties on Concrete Strength: Experimental and Machine Learning Study by Yali Li, Huina Jia, Shikuan Li

“…Smaller rice husk ash particles lead to better performance in these strength tests. The ideal parameters identified were a calcination temperature of 650°C and a particle size of 5 µm. …”
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Optimizing the Mechanical and Microstructure Characteristics of Stir Casting and Hot-Pressed AA 7075/ZnO/ZrO2 Composites by P. Satishkumar, C. Saravana Murthi, Rohinikumar Chebolu, Yenda Srinivasa Rao, Rey Y. Capangpangan, Arnold C. Alguno, Vishnu Prasad Yadav, M. Chitra, Mahesh Gopal

“…The composite was made using the stir cast manufacturing method. Many parameters, like stirring speed, stirring time, ZrO2% reinforcement, and cast temperature, are evaluated in a Taguchi experimental design to see how they affected the composite properties. …”
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Population-based colorectal cancer risk prediction using a SHAP-enhanced LightGBM model by Guinian Du, Hui Lv, Yishan Liang, Jingyue Zhang, Qiaoling Huang, Guiming Xie, Xian Wu, Hao Zeng, Lijuan Wu, Jianbo Ye, Wentan Xie, Xia Li, Yifan Sun

“…Seven ML algorithms were systematically compared, with Light Gradient Boosting Machine (LightGBM) ultimately selected as the optimal framework. …”
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Machine Learning for Prediction of Relapses in Multiple Drug Resistant Tuberculosis Patients by A. S. Аlliluev, O. V. Filinyuk, E. E. Shnаyder, S. V. Аksenov

“…Сlinical, epidemiological, gender, sex, social, biomedical parameters and chemotherapy parameters were analyzed in 346 cured MDR TB patients. …”
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Predictive modeling of ultimate tensile strength in dissimilar friction stir welded aluminum alloys via machine learning approach by Meghavath Mothilal, Atul Kumar

“…Several investigations are carried out in linear and non-linear regression models, including Poisson Regressor, Gradient Boosting Regressor, Bayesian Ridge, k-Nearest Neighbours, Lasso, Random Forest, Elastic-Net, and Support Vector Regression, using datasets of welding parameters. …”
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Efficient Dynamic Performance Prediction of Railway Bridges Situated on Small-Radius Reverse Curves by Yumin Song, Bin Hu, Xiaoliang Meng

“…Our results demonstrate that this method circumvents the need for detailed vehicle-bridge interaction analysis, yielding an impressive 86.9% accuracy in predicting dynamic performance and significantly boosting computational efficiency. Besides, the top five design parameters that significantly influence the prediction of bridge dynamic performance are obtained. …”
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Efficient reliability analysis of unsaturated slope stability under rapid drawdown using XGBoost-based surrogate model by Wengang Zhang, Bo Ran, Xin Gu, Yanmei Zhang, Yulin Zou, Peiqing Wang

“…In this study, an efficient reliability analysis framework based on the extreme gradient boosting (XGBoost) surrogate model is employed to evaluate the failure probability of unsaturated slopes subjected to the rapid drawdown considering the depth-dependent properties of spatially varying soils. …”
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UAV Target Segmentation Based on Depse Unet++ Modeling by Zhaoqi Hou, Yiqing Gu, Zhen Zheng, Yueqiang Li, Haojie Li

“…By introducing Squeeze-and-Excitation, the model’s ability to discriminate camouflaged targets in high-similarity backgrounds is improved; by incorporating a depth-separated convolutional design, the parameters and computational requirements for embedded device applications are significantly reduced; and employing Dropout technique to prevent overfitting with limited sample sizes, thus boosting the model’s adaptability and generalization across environments. …”
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Teaser bulls response to oestrus heifers: weather influence on oestrus in barn and loose housing system by H. Haleema, A. Prasad, K.S. Anil, C. Balusami, S. Pramod, Sabin George, V.S. Athira

“…This study explores how weather parameters affect oestrus occurrence and the behavioural responses of teaser bulls to oestrus heifers in two housing systems: barn and loose house. …”
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The potential of spirulina platensis to substitute antibiotics in broiler chickens diets: influences on growth performance, serum biochemical profiles, meat quality, and gut microb... by Bassam A. ALAHMADI

“…The rise of antibiotic-resistant microbes has prompted a search for effective alternatives to antibiotics. This study evaluated the effects of Spirulina platensis extract (SPE) as a dietary supplement and a potential alternative to antibiotics for broiler chickens, focusing on growth performance, antioxidant activity, blood parameters, and cecal microbiota. …”
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Enhancement of postharvest performance in Lilium tigrinum Ker Gawl flowers with Salicylic acid: a signalling molecule and a growth regulator by Moonisah Aftab, Haris Yousuf Lone, Wajahat Waseem Tantray, Aijaz A. Wani, Mohmad Arief Zargar, Inayatullah Tahir

“… The postharvest longevity of Lilium tigrinum (Tiger lily) flowers is a critical factor influencing their commercial value, highlighting the need for effective strategies to extend their vase life (VL). This study evaluates the efficacy of salicylic acid (SA) at a concentration of 60 µM as a preservative for prolonging the postharvest life of L. tigrinum cut flowers. …”
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