Historical changes in the Causal Effect Networks of compound hot and dry extremes in central Europe by Yinglin Tian, Domenico Giaquinto, Giorgia Di Capua, Judith N. Claassen, Javed Ali, Hao Li, Carlo De Michele

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A machine learning approach to identifying key predictors of Peruvian school principals' job satisfaction by Luis Alberto Holgado-Apaza, Dany Dorian Isuiza-Perez, Nelly Jacqueline Ulloa-Gallardo, Yban Vilchez-Navarro, Ruth Nataly Aragon-Navarrete, Wilian Quispe Layme, Marleny Quispe-Layme, Danger David Castellon-Apaza, Remo Choquejahua-Acero, Jaime Cesar Prieto-Luna

“…Despite the significance of this issue, there is limited research on satisfaction predictors for these professionals, particularly using machine learning approaches. …”
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Artificial intelligence technology in the clinical analysis of a patient with a mental disorder (case report) by Dmitry I. Nozdrachev, Maksim P. Marachev, Pavel S. Evgenov, Natalia N. Petrova

“…The capabilities of neural networks in identifying hidden patterns make them an essential component of scientific research, and these advances are expected to be implemented in clinical practice in the near future. …”
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Prediction of knee joint pain in Tai Chi practitioners: a cross-sectional machine learning approach by Yang Chen, Xiaojie Su, Fei Yao, Yushan Liu, Hua Xing, Yubin Ju, Zhiran Kang, Wuquan Sun, Lijun Yao, Li Gong

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Implementation of Clustering and Association for Early Warning of Disasters in Bojonegoro Regency by Denny Nurdiansyah, Erna Hayati, Ika Purnamasari, Anna Apriana Hidayanti, Yuliana Fuji Rahayu

“…The research aimed to analyze the relationships between different types of disasters, assess the likelihood of disaster occurrences, and enhance knowledge and understanding of disaster patterns in Bojonegoro Regency. …”
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VIRUS CHANGE MODEL IN ORGANIZATIONAL TRANSFORMATION by A. Dolgaya

“…Virus technology organizational changes model is presented as an outcome of multidisciplinary research. The model consists of organizational virus structure, chang-es algorithm and immune instruments for process interruption. …”
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Validation Indicator Identification and Customer Ranking in Microloans: A Study at Middle East Bank in Iran by Azadeh Ahmadi Kousha, Faegh Ahmadi, Mohammad Hossein Ranjbar, Hamidreza Kordlouie

“…Naive Bayes, Meta, Attribute Selected Classifier, and j48 algorithms were implemented and WEKA software was used to classify criteria and create patterns. …”
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Planning Flexible Bus Service as an Alternative to Suspended Bicycle-Sharing Service: A Data-Driven Approach by Yujia Wang, Yuan Tian, Binyu Yang, Jian Wang, Xiaowei Hu, Shi An

“…After that, a frequent pattern mining algorithm is applied to the multiday path clusters, and frequent pattern results with spatio-temporal correlation will be merged into the final service area. …”
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The role of artificial intelligence in promoting health and developing preventive strategies for diabetes by Ameneh Marzban

“…Dear Editor Diabetes remains a significant public health challenge, and the integration of artificial intelligence (AI) presents remarkable opportunities to enhance early diagnosis, personalized treatment, and effective prevention strategies.1 AI algorithms, including supervised learning and convolutional neural networks, can efficiently analyze large datasets to identify patterns and risk factors associated with diabetes, surpassing the capabilities of traditional methods.2 This advanced analysis enables healthcare providers to predict the likelihood of diabetes in individuals and populations, facilitating timely interventions and customized prevention strategies. …”
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Locality-guided based optimization method for bounded model checker by Shun WANG, Ye DU, Zhen HAN, Jiqiang LIU

“…For software model checking,approaches that combine with different kind of verification methods are now under research.The key to improve scale and complexity of verifiable software is handling the method for abstraction widening and strengthening wisely and precisely.To archive that,using extra knowledge that extracted from programming pattern or learned through verifying procedure to help eliminate the redundant state has been proved effective.Definition of program locality was given.It took the important role in accelerating software verification,then the strategy was raised and an algorithm was implemented to take advantage of program locality.This method exploits the features of modern BMC (bounded model checker) and scales up the capability of its power in large scale and comprehensive software modules.…”
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Ripening Study Based on Multi-Structural Inversion of Cherry Tomato qMRI by Yanan Li, Jingfa Yao, Wenhui Yang, Zhao Wei, Peng Luan, Guifa Teng

“…Mono-exponential analysis reveals the patterns of changes in moisture mobility (T2) and content (A) across various structures. …”
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Optical Field Localization in the Three-Dimensional Percolating System with Gaussian Distribution Disorder by Erika Martinez Sanchez, Gennadiy Burlak, Gustavo Medina Angel, Ricardo Magallanes Rivera

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A Multi-Phase DRL-Driven SDN Migration Framework Addressing Budget, Legacy Service Compatibility, and Dynamic Traffic by Kai Yuan Tan, Saw Chin Tan, Teong Chee Chuah

“…By integrating a DRL model with a clustering algorithm, SMART determines the migration sequence to minimize link utilization and reduce the number of SDN-enabled nodes required for effective traffic load distribution under dynamic traffic patterns. …”
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An optimized domain-specific shrimp detection architecture integrating conditional GAN and weighted ensemble learning by L. Ravi Kumar, Ravi Kumar Tata, T. R. Mahesh, Endris Mohammed Ali

“…Sometimes, there is a need to improve the accuracy score by changing the fine-tuning parameters or generating the synthetic data, which leads to reducing the gap in organizing the patterns. To address this, our research introduces the synthetic data generation for “enhanced shrimp detection using integrated augmentation (ESDIA)” approach to detect shrimps. …”
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Factors associated with tuberculosis drug resistance among presumptive multidrug resistance tuberculosis patients identified in a DRTB surveillance study in western Kenya by Albert Okumu, James Orwa, Ruth Sitati, Isaiah Omondi, Ben Odhiambo, Jeremiah Ogoro, George Oballa, Benjamin Ochieng, Steve Wandiga, Collins Ouma

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Selection of suitable reference lncRNAs for gene expression analysis in Osmanthus fragrans under abiotic stresses, hormone treatments, and metal ion treatments by Yingting Zhang, Yingting Zhang, Qingyu Yan, Hui Xia, Xiangling Zeng, Xiangling Zeng, Xiangling Zeng, Jie Yang, Jie Yang, Jie Yang, Xuan Cai, Xuan Cai, Xuan Cai, Zeqing Li, Zeqing Li, Hongguo Chen, Hongguo Chen, Hongguo Chen, Jingjing Zou, Jingjing Zou, Jingjing Zou

“…Despite its importance, research on long non-coding RNAs (lncRNAs) in O. fragrans has been constrained by the absence of reliable reference genes (RGs).MethodsWe employed five distinct algorithms, i.e., delta-Ct, NormFinder, geNorm, BestKeeper, and RefFinder, to evaluate the expression stability of 17 candidate RGs across various experimental conditions.Results and discussionThe results indicated the most stable RG combinations under different conditions as follows: cold stress: lnc00249739 and lnc00042194; drought stress: lnc00042194 and lnc00174850; salt stress: lnc00239991 and lnc00042194; abiotic stress: lnc00239991, lnc00042194, lnc00067193, and lnc00265419; ABA treatment: lnc00239991 and 18S; MeJA treatment: lnc00265419 and lnc00249739; ethephon treatment: lnc00229717 and lnc00044331; hormone treatments: lnc00265419 and lnc00239991; Al3+ treatment: lnc00087780 and lnc00265419; Cu2+ treatment: lnc00067193 and 18S; Fe2+ treatment: lnc00229717 and ACT7; metal ion treatment: lnc00239991 and lnc00067193; flowering stage: lnc00229717 and RAN1; different tissues: lnc00239991, lnc00042194, lnc00067193, TUA5, UBQ4, and RAN1; and across all samples: lnc00239991, lnc00042194, lnc00265419 and UBQ4. …”
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Machine learning-based characteristic identification of MSG content in gravy foods by Rosyady Phisca Aditya, Habibah Nurina Umy, Masita, Yudhana Anton

“…This research determines the identification of MSG using the Machine Learning method Naive Bayes classifier algorithm in Python software. …”
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Construction and validation of acetylation-related gene signatures for immune landscape analysis and prognostication risk prediction in luminal breast cancer by Mengdi Zhu, Jinna Lin, Haohan Liu, Jingru Wang, Nianqiu Liu, Yudong Li, Hongna Lai, Qianfeng Shi

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Aerosols in the Mixed Layer and Mid-Troposphere from Long-Term Data of the Italian Automated Lidar-Ceilometer Network (ALICENET) and Comparison with the ERA5 and CAMS Models by Annachiara Bellini, Henri Diémoz, Gian Paolo Gobbi, Luca Di Liberto, Alessandro Bracci, Francesca Barnaba

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Real-time monitoring to predict depressive symptoms: study protocol by Yu-Rim Lee, Jong-Sun Lee

“…By collecting continuous, real-time data on physiological and behavioral patterns, the research uncovers subtle changes in heart rate, activity levels and sleep that correlate with depressive symptoms, providing a deeper understanding of the disorder. …”
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