Acute Supplementation of Soluble Mango Leaf Extract (Zynamite^® S) Improves Mental Performance and Mood: A Randomized, Double-Blind, Placebo-Controlled Crossover Study by Yolanda Castellote-Caballero, Ana Beltrán-Arranz, Agustín Aibar-Almazán, María del Carmen Carcelén-Fraile, Yulieth Rivas-Campo, Laura López-Ríos, Tanausú Vega-Morales, Ana María González-Martín

“…<b>Background/Objectives:</b> A mango (<i>Mangifera indica</i>) leaf extract (Zynamite^®), rich in the polyphenol mangiferin, has been demonstrated to modulate brain activity, boost cognitive function, and reduce mental fatigue. …”
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Modeling the impact of temperature level in the zone of active combustion in the contents of nitrogen oxides and benz(a)pyrene in the combustion products of boiler plants heating s... by M. S. Ivanitskiy

“…The article presents the results of numerical simulation of the influence of the temperature level in the zone of active combustion in the contents of nitrogen oxides and benz(a)pyrene in the combustion products of cracking of fuel oil for the boiler BKZ-420-140 NGM operating under boost. Obtained important characteristics of the combustion process for regulation of the specific release of toxic substances in flue gases. …”
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Rapid synthesis of carbon quantum dot-integrated metal–organic framework nanosheets via electron beam irradiation for selective 5-hydroxymethylfurfural electrooxidation by Qianjia Ni, Mingwan Zhang, Bijun Tang, Weidong Hou, Kang Wang, Huazhang Guo, Jiye Zhang, Tao Han, Minghong Wu, Liang Wang

“…These findings underscore the critical role of structural optimization and adsorption balance in catalytic performance enhancement and offer valuable insights for designing high-efficiency catalysts, advancing sustainable catalytic processes.…”
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Securing fruit trees future: AI-driven early warning and predictive systems for abiotic stress in changing climate by Muhammad Ahtasham Mushtaq, Muhammad Ateeq, Muhammad Ikram, Shariq Mahmood Alam, Muhammad Mohsin Kaleem, Muhammad Atiq Ashraf, Muhammad Asim, Khalid F. Almutairi, Mahmoud F. Seleiman, Fareeha Shireen

“…Emerging abiotic stresses—drought, salinity, temperature extremes, and waterlogging—threaten crop productivity by disrupting key physiological processes, challenging sustainable agriculture under climate change. …”
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On Combining Deep Neural Network Classifiers for Source Device Identification by Ioannis Tsingalis, Constantine Kotropoulos

“…The proposed combination scheme enhances the accuracy of each classifier, which, in turn, boosts the overall combined accuracy during a post-processing step. …”
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Hyperspectral imaging as a non-destructive technique for estimating the nutritional value of food by Juan-Jesús Marín-Méndez, Paula Luri Esplandiú, Miriam Alonso-Santamaría, Berta Remirez-Moreno, Leyre Urtasun Del Castillo, Jaione Echavarri Dublán, Eva Almiron-Roig, María-José Sáiz-Abajo

“…This study shows that it is possible to predict the energy and nutrient values of processed complex foods, using hyperspectral imaging systems combined with supervised machine learning methods.…”
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Genetic and molecular insights into tiller development and approaches for crop yield improvement by Zaid Chachar, Xiaoming Xue, Junteng Fang, Ming Chen, Weiwei Chen, Xuhui Li, Nazir Ahmed, Sadaruddin Chachar, Aamir Ali, Zhong liang Chen, Lina Fan, Ruiqiang Lai, Yongwen Qi

“…Tiller development is a critical factor in boosting agricultural productivity and securing global food security. …”
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Multitier ensemble classifiers for malicious network traffic detection by Jie WANG, Lili YANG, Min YANG

“…A malicious network traffic detection method based on multi-level distributed ensemble classifier was proposed for the problem that the attack model was not trained accurately due to the lack of some samples of attack steps for detecting attack in the current network big data environment,as well as the deficiency of the existing ensemble classifier in the construction of multilevel classifier.The dataset was first preprocessed and aggregated into different clusters,then noise processing on each cluster was performed,and then a multi-level distributed ensemble classifier,MLDE,was built to detect network malicious traffic.In the MLDE ensemble framework the base classifier was used at the bottom,while the non-bottom different ensemble classifiers were used.The framework was simple to be built.In the framework,big data sets were concurrently processed,and the size of ensemble classifier was adjusted according to the size of data sets.The experimental results show that the AUC value can reach 0.999 when MLDE base users random forest was used in the first layer，bagging was used in the second layer and AdaBoost classifier was used in the third layer.…”
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Global Research Trends in Circular Economy: A Bibliometric Analysis in E-Commerce by Anh Viet Tran, Bui Thanh Khoa

“…This study delivers important knowledge that researchers and practitioners can use to formulate future investigations that will boost e-commerce in sustainable development.…”
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Research and Analysis of Facial Recognition Based on FaceNet, DeepFace, and OpenFace by Li Minghan

“…However, the study also identifies ongoing issues, including the need for efficient processing and reliance on large, annotated datasets. …”
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Multitier ensemble classifiers for malicious network traffic detection by Jie WANG, Lili YANG, Min YANG

“…A malicious network traffic detection method based on multi-level distributed ensemble classifier was proposed for the problem that the attack model was not trained accurately due to the lack of some samples of attack steps for detecting attack in the current network big data environment,as well as the deficiency of the existing ensemble classifier in the construction of multilevel classifier.The dataset was first preprocessed and aggregated into different clusters,then noise processing on each cluster was performed,and then a multi-level distributed ensemble classifier,MLDE,was built to detect network malicious traffic.In the MLDE ensemble framework the base classifier was used at the bottom,while the non-bottom different ensemble classifiers were used.The framework was simple to be built.In the framework,big data sets were concurrently processed,and the size of ensemble classifier was adjusted according to the size of data sets.The experimental results show that the AUC value can reach 0.999 when MLDE base users random forest was used in the first layer，bagging was used in the second layer and AdaBoost classifier was used in the third layer.…”
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Performance evaluations of AI-based obfuscated and encrypted malicious script detection with feature optimization by Kookjin Kim, Jisoo Shin, Jong-Geun Park, Jung-Tae Kim

“…The LGBM outperformed other artificial intelligence models by achiev-ing 97% accuracy and the minimum processing time in the decoded, obfus-cated, and encrypted dataset cases.…”
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Prediction of implant failure risk due to periprosthetic femoral fracture after primary elective total hip arthroplasty: a simplified and validated model based on 154,519 total hip... by M. A. Alagha, Justin Cobb, Alexander D. Liddle, Henrik Malchau, Ola Rolfson, Maziar Mohaddes

“…If the risk of fracture can be forecasted, it would aid the shared decision-making process related to cementless stems. Our study aimed to develop and validate predictive models of periprosthetic femoral fracture (PPFF) necessitating revision and reoperation after elective total hip arthroplasty (THA). …”
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Research on screening key proteins related to the pathogenesis of sepsis associated encephalopathy based on DIA proteomics technology by Wu Shuhui, Hu Hongjie, Lu Yuru, Zhu Wei

“…Key differential proteins were further screened using machine learning models such as Extreme Gradient Boosting （XGBoost） and Least Absolute Shrinkage and Selection Operator （LASSO） regression to construct a nomogram and evaluate its diagnostic performance. …”
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Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis by Yi Xu, Ying Mao, Xiaoting Hua, Yan Jiang, Yi Zou, Zhichao Wang, Zubi Liu, Hongrui Zhang, Lingling Lu, Yunsong Yu

“…Quantifying the important SNPs’ contribution to model decisions makes the ML algorithmic process more transparent, interpretable enabling and enables clinical practice.…”
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Clinical characteristics, outcomes, and predictive modeling of patients diagnosed with immune checkpoint inhibitor therapy-related pneumonitis by Antonious Hazim, Irene Riestra Guiance, Jacob Shreve, Gordon Ruan, Damian McGlothlin, Allison LeMahieu, Robert Haemmerle, Keith Mcconn, Richard C. Godby, Lisa Kottschade, Anna Schwecke, Casey Fazer-Posorske, Tobias Peikert, Eric Edell, Konstantinos Leventakos, Ashley Egan

“…Predictive modeling was performed using gradient boosting machine learning technology, XGBoost (Chen in 1(4):1, 2015), to conduct binary classification and model reverse engineering using Shapley statistics (Lundberg and Lee in Adv Neural Inf Process Syst 30, 2017). …”
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Enhancing liver disease diagnosis with hybrid SMOTE-ENN balanced machine learning models—an empirical analysis of Indian patient liver disease datasets by Ritu Rani, Garima Jaiswal, Nancy, Lipika, Shashi Bhushan, Fasee Ullah, Prabhishek Singh, Manoj Diwakar, Manoj Diwakar

“…IntroductionThe liver is one of the vital organs of human body that performs some of the most crucial biological processes such as protein and biochemical synthesis, which is required for digestion and cleansing. …”
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Graphene-Based Nanomaterials in Photodynamic Therapy: Synthesis Strategies, Functional Roles, and Clinical Translation for Tumor Treatment by Liang J, Wu Y, Zhang C, Yi R, Zheng J, Zhao R, Shan D, Wang B

“…To facilitate the translation from laboratory research to clinical application, strategies for scaling up production are discussed, emphasizing the need to simplify synthesis processes and improve efficiency for broader biomedical use. …”
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Enhancing Tomato (<i>Solanum lycopersicum</i> L.) Resistance Against Bacterial Canker Disease (<i>Clavibacter michiganensis</i> ssp. <i>michiganensis</i>) via Seed Priming with β-A... by Nazlı Özkurt, Harun Bektas, Yasemin Bektas

“…Seed priming is the process of boosting germination and seedling development by treating seeds with particular pre-treatments before germination. …”
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Improving Affective Associations With Physical Activity via a Message-Based mHealth Intervention (WalkToJoy): Proof-of-Concept Study by Soo Ji Serisse Choi, Pei-Yao Hung, Mengyun Liu, Walter Dempsey, Mark W Newman, Predrag Klasnja

“… BackgroundTraditional mobile health interventions for physical activity (PA) primarily rely on reflective self-regulatory processes, often neglecting the role of affective associations in sustaining long-term engagement. …”
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