Prediction and Correction of Software Defects in Message-Passing Interfaces Using a Static Analysis Tool and Machine Learning by Norah Abdullah Al-Johany, Fathy Elbouraey Eassa, Sanaa Abdullah Sharaf, Amin Y. Noaman, Asaad Ahmed

“…The Software Defect Prediction (SDP) method forecasts the occurrence of defects at the beginning of the software development process. …”
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An AI-Based Approach for the Assessment of the Lost Efficiencies of a PVT Plant by Stefano Aneli, Giuseppe M. Tina, Bekkay Hajji, Safae Morgoum, Antonio Gagliano

“…Artificial Intelligence (AI)-based methods have become prominent due to their capability of producing accurate predictions against the uncertainty and nonlinearity phenomena. …”
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Automated analysis and textual summarization of time-varying references in advanced greenhouse climate control by Ramesh Arvind Naagarajan, Kiran Kumar Sathyanarayanan, Nadja Bauer, Stefan Streif, Stefan Streif

“…The system segments time-series data using the change point detection method to identify significant changes. The identified trend information is converted into detailed textual descriptions using the natural language generation technique. …”
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A literature review of economic evaluations for a neglected tropical disease: human African trypanosomiasis ("sleeping sickness"). by C Simone Sutherland, Joshua Yukich, Ron Goeree, Fabrizio Tediosi

“…Modelling was a common method to forecast long-term results, and publications focused on interventions by category, such as case detection, diagnostics, drug treatments, and vector control. …”
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Automated methane emission monitoring systems based on satellite data: Radiation transfer model analysis by K. Aghayeva, G. Krauklit

“…The seasonal analysis demonstrated an increase in concentrations in winter (3 ppm) and summer (2.7 ppm) due to a decrease in the dispersion rate and intensification of mining. Machine learning methods (XGBoost, Random Forest) improved the forecasting accuracy (R2=0.91 for XGBoost) by identifying key factors: wind speed, temperature, mining intensity and humidity. …”
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An Evaluation of the Safety, Immunogenicity, and Protective Efficacy of a Combined Diphtheria–Tetanus–Acellular Pertussis, <i>Haemophilus influenzae</i> Type b, and ACYW135 Meningo... by Xiuwen Sui, Zhujun Shao, Yuanyuan Ji, Hairui Wang, Qingfu Xu, Bochao Wei, Zhuojun Duan, Chang Wang, Ying Yang, Jiayu Zhao, Tao Zhu

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Fuzzy control system of multioperational machine status by Andrey K. Tugengold, Andrey I. Izyumov, Roman N. Voloshin, Mikhail Y. Solomykin

“…The work objective is to develop a system for monitoring the condition, detecting dimensional wear, and determining the period of tool life on the basis of the fuzzy logic methods. …”
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Designing an Interactive Visual Analytics System for Precipitation Data Analysis by Dong Hyun Jeong, Pradeep Behera, Bong Keun Jeong, Carlos David Luna Sangama, Bryan Higgs, Soo-Yeon Ji

“…To enhance understanding of precipitation data and analysis results, researchers often use graphical representation methods to show the data in visual formats. Although existing precipitation analysis and basic visual representations are helpful, it is critical to have a comprehensive analysis and visualization system to detect significant patterns and anomalies in high-resolution temporal precipitation data more effectively. …”
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Estimation of Flood Inundation Area Using Soil Moisture Active Passive Fractional Water Data with an LSTM Model by Rekzi D. Febrian, Wanyub Kim, Yangwon Lee, Jinsoo Kim, Minha Choi

“…Flood estimation using satellite observations with deep learning algorithms is effective in detecting flood patterns and environmental relationships that may be overlooked by conventional methods. …”
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Assessing Seasonal Variations of Vegetation Cover Using NDVI in Context Climate Change in Wasit by Mohanad I. Khalbas, Jasim H. Kadhum

“…Using the ARIMA model for forecasting, NDVI is expected to continue a slight upward trend, reaching 0.442 in 2024 and 0.463 in 2025. …”
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Assessing Seasonal Variations of Vegetation Cover Using NDVI in Context Climate Change in Wasit by Mohanad I. Khalbas, Jasim H. Kadhum

“…Using the ARIMA model for forecasting, NDVI is expected to continue a slight upward trend, reaching 0.442 in 2024 and 0.463 in 2025. …”
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Hydrogen Safety in Solid Oxide Fuel Cells: an LSTM-Based Model for Predicting Temperature Anomalies and Change Points by Tomaso Vairo, Davide Clematis, Maria Paola Carpanese, Bruno Fabiano

“…The results indicate that the LSTM-based model significantly outperforms traditional methods in both prediction accuracy and early anomaly detection. …”
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Fumarprotocetraric acid and geraniin were identified as novel inhibitors of human respiratory syncytial virus infection in vitro by Chao Wang, Chao Wang, Yi-Man Huang, Yi-Man Huang, Jun Zhao, Jun Zhao, Yi-Ming Bai, Yi-Ming Bai, Cai-Qin Yan, Cai-Qin Yan, Guan-Hua Du, Guan-Hua Du, Li-Shu Zheng, Li-Shu Zheng, Ai-Lin Liu, Ai-Lin Liu

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Creation and verification of a predictive nomogram model for the incidence of social isolation among China’s older population by Mei You, Yuan Ding, Zixuan Wei, Nannan Han, Annuo Liu

“…ObjectivesTo explore the risk factors associated with social isolation among the older adult in China, develop a nomogram model to forecast the risk, and evaluate its predictive accuracy.MethodsAn investigation was conducted into the demographic, socioeconomic, health, and health behavior aspects of the older adult population. …”
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Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree‐Based Machine Learning Model by Md. Siddikur Rahman, Miftahuzzannat Amrin, Md. Abu Bokkor Shiddik

“…To address this, we propose an interpretable tree‐based machine learning (ML) model for dengue early warning systems and outbreak prediction in Bangladesh based on climatic, sociodemographic, and landscape factors. Methods A framework for forecasting DF risk was developed by using high‐performance ML algorithms, namely Random Forests, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), based on sociodemographic, climate, landscape, and dengue surveillance epidemiological data (January 2000 to December 2021). …”
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Attribution of the Increased Intensity of Upper‐Level Hadley Circulation Under Climate Change by Bo Sun, Yi Zheng, Wanling Li, Siyu Zhou, Jiarui Cai, Huixin Li, Shengping He, Huijun Wang

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Current situation in relation to parasites and measures of struggle against them in Russia and cis countries (based on materials of coordinating reports) by A. V. USPENSKY, E. I. MALAHOVA, T. A. YERSHOVA

“…Furthermore for the first time metacercariae opisthorchis, mesocercariae alaria and larvae of trematodes using a method of peptolysis are released. Techniques for axonomic identifying of genotype of northern isolates of Trichinella spp. on the territory of RF are devel- oped that allows to complete the system characteristic and to detect phylogenetic relationship inside different taxonomic groups. …”
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An explainable predictive model for anxiety symptoms risk among Chinese older adults with abdominal obesity using a machine learning and SHapley Additive exPlanations approach by Tengfei Niu, Shiwei Cao, Jingyu Cheng, Yu Zhang, Zitong Zhang, Ruiling Xue, Jingxi Ma, Qian Ran, Xiaobing Xian, Xiaobing Xian

“…The objective of this research is to formulate and validate a predictive model that forecasts the probability of developing anxiety symptoms in elderly Chinese individuals with abdominal obesity.MethodThis research’s model development and internal validation encompassed 2,427 participants from the 2017-2018 Study of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). …”
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How to trace the origins of short-lived atmospheric species: an Arctic example by A. Da Silva, L. Marelle, J.-C. Raut, Y. Gramlich, K. Siegel, K. Siegel, S. L. Haslett, S. L. Haslett, C. Mohr, C. Mohr, J. L. Thomas

“…However, the accuracy of these methods is not well quantified. This study provides an evaluation of these analysis protocols by combining backward trajectories from the FLEXible PARTicle dispersion model (FLEXPART) with simulations of tracers from the Weather Research and Forecast model including Chemistry (WRF-Chem). …”
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Integration of Machine Learning and Wavelet Algorithms for Processing Probing Signals: An Example of Oil Wells by Zukhra Abdiakhmetova, Zhanerke Temirbekova

“…A key innovation of this study is the development of an algorithm that processes low-amplitude high-frequency signals, which are often difficult to detect with conventional methods. Additionally, a neural network-based forecasting algorithm utilizing the Daubechies wavelet transform is introduced to improve the predictive accuracy of subsurface characteristics. …”
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