Results of a cluster randomized trial testing the Systems Analysis and Improvement Approach to increase cervical cancer screening in family planning clinics in Mombasa County, Keny... by McKenna C. Eastment, George Wanje, Barbra A. Richardson, Emily Mwaringa, Shem Patta, Kenneth Sherr, Ruanne V. Barnabas, Kishorchandra Mandaliya, Walter Jaoko, R. Scott Mcclelland

“…Conclusions The primary analysis did not show an effect on cervical cancer screening. …”
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Sustainable extraction of phytochemicals from Mentha arvensis using supramolecular eutectic solvent via microwave Irradiation: Unveiling insights with CatBoost-Driven feature analy... by Zubera Naseem, Muhammad Bilal Qadir, Abdulaziz Bentalib, Zubair Khaliq, Muhammad Zahid, Fayyaz Ahmad, Nimra Nadeem, Anum Javaid

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White Matter Imaging Phenotypes Mediate the Negative Causality of Mitochondrial DNA Copy Number on Sleep Apnea: A Bidirectional Mendelian Randomization Study and Mediation Analysis by Ying Q, Wang M, Zhao Z, Wu Y, Sun C, Huang X, Zhang X, Guo J

“…This study aimed to investigate the causality between mtDNA-CN and SA while identifying potential mediating brain imaging phenotypes (BIPs).Methods: Two-sample bidirectional Mendelian randomisation (MR) analysis was performed to estimate the causal relationship between mtDNA-CN and SA, with further validation using Bayesian framework-based MR analysis. …”
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AI-powered smart emergency services support for 9-1-1 call handlers using textual features and SVM model for digital health optimization by Afraa Attiah, Manal Kalkatawi

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ED-Stacking：A Construction Method of Few-shot Prediction Model for Beef Microbial Growth Based on Ensemble Learning by Hanqiang LI, Yi CHEN, Yufei GAO, Kun HOU, Liping SONG, Jing LI

“…Results showed that ED-Stacking achieved 0.229 and 0.147 in MAE and MSE metrics, respectively, with lower prediction errors than the five baseline models of SLN, XGBoost, GBRT, GRU, and Transformer. …”
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Design and Analysis of Modular Neural Network-Based PI-Controller Ensemble With Karush-Kuhn-Tucker Conditions for Grid-Connected Photovoltaic Systems Under Ground Fault Conditions by Burla Sridhar, Soorya Prakash Shukla, Ritesh Dash, Arpan Dwivedi, G. C. Biswal, Nivedita Singh, Mukesh Kumar Chandrakar, Kalvakurthi Jyotheeswara Reddy, Bhabasis Mohapatra, Binod Kumar Sahu

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Multimodal malware classification using proposed ensemble deep neural network framework by Sadia Nazim, Muhammad Mansoor Alam, Safdar Rizvi, Jawahir Che Mustapha, Syed Shujaa Hussain, Mazliham Mohd Su’ud

“…The cross-domain research in data fusion strives to integrate information from multiple sources to augment reliability and minimize errors in detecting sophisticated cyber threats. …”
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Predicting Endpoint Temperature of Molten Steel in VD Furnace Refining Process Using Metallurgical Mechanism and Bayesian Optimization XGBoost by Ji XU, Zicheng XIN, Mo LAN, Wenhui LIN, Bo ZHANG, Qing LIU

“…This study proposes a method that combines metallurgical mechanism analysis, data analysis, and machine learning to develop a temperature prediction model for VD furnace refining. …”
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Discussion: Comments on the Article Titled as “Bitlis Air Pollution Emission Inventory and Estimation of Health Effects by Multiple Linear Regression” (Journal of Natural Hazards a... by Edip Avşar

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ANALISIS PENGENDALIAN KUALITAS DAGING DENGAN MENGGUNAKAN SEVEN TOOLS DI THE FOODHALL PLAZA INDONESIA by Nico Trisno, Besse Arnawisuda Ningsi, Irvana Arofah

“…This study aims to determine quality control, the most dominant types of defects, the causes of defects, and determine strategies to improve the quality of meat products at The Foodhall Plaza Indonesia. The data used is secondary data in the form of an existing database of goods received from May to June 2023 sourced from the Foodhall Plaza Indonesia SAP application, which will be analyzed using statistical aids in the form of control charts and seven tools, namely checking sheets, histograms, proportion control maps, process capability, Pareto diagrams, and fishbone diagrams. …”
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Digital Assets and the Global Economy: How the Use of Statistical Models Can Help Bitcoin Price Prediction by L. P. Bakumenko, N. S. Vasileva

“…The LSTM neural network model on a similar data set showed a MAE error equal to 2.57%.Conclusion. …”
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基于CEEMDAN-LSTM模型的大伙房水库入库流量中长期预报 by WANG Chunyu, ZHANG Jing, YANG Xu, YAN Bin

“…The results show that the CEEMDAN-LSTM model can effectively improve the forecast accuracy and become the optimal model for the monthly flow forecast of Dahuofang Reservoir, when four principal components are selected by using principal component analysis to reduce the dimensionality of the forecast data after the addition of the previous average temperature and maximum temperature data, thus providing technical support for the formulation of the future medium and long-term dispatching plan of Dahuofang Reservoir.…”
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Technical efficiency of teff production in South Soddo District, Gurage Zone, Central Ethiopia Regional State, Ethiopia by Seid Mohamed Hussen, Anbes Tenaye

“…The study used the Stochastic Frontier Analysis (SFA) method, specifically the Cobb–Douglas production function, to analyze the data, allowing for the distinction between random errors and inefficiency. …”
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Enhancing State of Health Prediction Accuracy in Lithium-Ion Batteries through a Simplified Health Indicator Method by Dongxu Han, Nan Zhou, Zeyu Chen

“…Validation of the models with different datasets shows that the proposed method achieves both average relative error and root mean square error within 5%, outperforming other methods in terms of minimizing error and ensuring stability. …”
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Medium- and Long-term Forecast of Inflow of Dahuofang Reservoir Based on CEEMDAN-LSTM Model by WANG Chunyu, ZHANG Jing, YANG Xu, YAN Bin

“…According to the results, when four principal components are selected by using principal component analysis to reduce the dimensionality of the forecast factor sets after the addition of the previous average temperature and maximum temperature data, the CEEMDAN-LSTM model can effectively improve the forecast accuracy and become the optimal model for the monthly flow forecast of Dahuofang Reservoir. …”
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Increasing the sensor channels: a solution for the pressing offsets that cause the physiological parameter inaccuracy in radial artery pulse signal acquisition by Chao Chen, Zhendong Chen, Hongmiin Luo, Bo Peng, Bo Peng, Yinan Hao, Xiaohua Xie, Haiqing Xie, Xinxin Li

“…When involving single or few-channel sensors, pressing offsets have substantial impacts on obtaining inaccurate physiological parameters like tidal peak (P2).Methods: This study discovers the pressing offsets in multi-channel pulse signals and analyzes the relationship between the pressing offsets and time of P2 (T2) by qualifying the pressing offsets. First, we employ a data acquisition system to capture 3DPIs. Subsequently, the errorT2 is developed to qualify the pressing offsets.Results: The outcomes display a central low and peripheral high pattern. …”
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Mean centering is not necessary in regression analyses, and probably increases the risk of incorrectly interpreting coefficients by Lee H. Wurm, Miles Reitan

“…We also find errors in people’s beliefs about how to interpret first-order regression coefficients in moderated regression. …”
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Forecasting antimicrobial resistance in China using a hybrid ARIMA-GM(1,1) model by Feng Liu, Caixia Dang, Hengliang Lv, Ziqian Zhao, Sijin Zhu, Yang Wang, Hongbin Song, Yuanyong Xu, Hui Chen

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Technology for Improving the Accuracy of Predicting the Position and Speed of Human Movement Based on Machine Learning Models by Artem Obukhov, Denis Dedov, Andrey Volkov, Maksim Rybachok

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Height of Hydraulic Fracture Zone Based on PSO_LSSVM Model by Hebin Zhang, Tingting Wang, Bin Wu, Haijun Feng

“…To achieve its effective prediction, this study selects four main control parameters for prediction, including the proportion coefficient of hard rock in the overlying strata, the inclination distance of the working face, and the thickness and depth of mining. Grey correlation analysis is performed on the collected sample data. …”
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