Using Quantitative Trait Locus Mapping and Genomic Resources to Improve Breeding Precision in Peaches: Current Insights and Future Prospects by Umar Hayat, Cao Ke, Lirong Wang, Gengrui Zhu, Weichao Fang, Xinwei Wang, Changwen Chen, Yong Li, Jinlong Wu

“…This work shows how combining genome-wide association studies and machine learning can improve the synthesis of multi-omics data and result in faster breeding cycles while preserving genetic diversity. …”
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A User-Centered Design Approach for a Screening App for People With Cognitive Impairment (digiDEM-SCREEN): Development and Usability Study by Michael Zeiler, Nikolas Dietzel, Fabian Haug, Julian Haug, Klaus Kammerer, Rüdiger Pryss, Peter Heuschmann, Elmar Graessel, Peter L Kolominsky-Rabas, Hans-Ulrich Prokosch

“…The test was administered using different randomization options to minimize learning effects. digiDEM-SCREEN was developed as a tablet and smartphone app. …”
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Emotion regulation-based internet-delivered cognitive behavioural therapy for premenstrual dysphoric disorder: study protocol for a randomised controlled trial in Sweden by Filipa Sampaio, Alkistis Skalkidou, Gerhard Andersson, Cornelia Weise, Johanna M. Hoppe, Maria Kleinstaeuber, Johan Vegelius, Erika Comasco, Maria Gröndal, Elisavet Kaltsouni, Felicia Sundström, Monica Buhrman

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Model Evaluasi Usability Menggunakan Confirmatory Factor Analysis pada KRS Online by Endah Ratna Arumi, Pristi Sukmasetya, Agus Setiawan

“…Usability evaluation is the focus of the assessment by users of the system to find out how easy to learn and use the system. This research aims to find valid models from several usability models that are analyzed using Confirmatory Factor Analysis (CFA). …”
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TPDTNet: Two-Phase Distillation Training for Visible-to-Infrared Unsupervised Domain Adaptive Object Detection by Siyu Wang, Xiaogang Yang, Ruitao Lu, Shuang Su, Bin Tang, Tao Zhang, Zhengjie Zhu

“…Specifically, in the first phase, we incorporate a contrastive learning framework to maximize the mutual information between the source and target domains. …”
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Research Progress in Monitoring Technology of Cold Chain Logistics for Meat Products by Bin HAN, Dongmei LENG, Yuqian XU, Jianyang SHEN, Xin LI, Xiaochun ZHENG, Wei WANG, Dequan ZHANG, Chengli HOU

“…To analyze the future development of meat cold chain logistics monitoring technology combined with sensors, narrowband internet of things, machine learning, and blockchain, and to provide theoretical support for the research and application of China's cold chain logistics monitoring technology.…”
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Ophthalmologists’ and patients’ perspectives on treatments for diabetic retinopathy and maculopathy in Vietnam: a descriptive qualitative study by Tunde Peto, Nathan Congdon, Lynne Lohfeld, Hoang Tran, Katie Curran, Van Thu Nguyen, Tung Thanh Hoang, Hue Thi Nguyen, Quan Nhu Nguyen, Catherine Dardis, Hoang Huy Tran, Anh Tuan Vu, Mai Quoc Tung

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New bitongling regulates gut microbiota to predict angiogenesis in rheumatoid arthritis via the gut-joint axis: a deep neural network approach by Yin Guan, Xiaoqian Zhao, Yun Lu, Yue Zhang, Yan Lu, Yue Wang

“…The study employed 16S ribosomal DNA (16S rDNA) sequencing to analyze gut microbiota composition, machine learning techniques to identify characteristic microbial taxa, and transcriptomic analysis (GSVA) to assess the impact on the VEGF signaling pathway. …”
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PENGEMBANGAN KIT PRAKTIKUM ELEKTRONIKA DASAR II BERBASIS SIMULATOR PROTEUS UNTUK MENINGKATKAN KEMAMPUAN MAHASISWA DALAM PEMECAHAN MASALAH by Suwardi Suwardi, Erik Ayatullah, Haidul Haidul

“…The feasibility of the practicum kit as a learning media in a laboratory based on expert validation is 82.50% (very feasible) and based on the user is 82, 64% (very feasible).   …”
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Mobile application adjunct to the WHO basic emergency care course: a mixed methods study by Juma A Mfinanga, Hendry R Sawe, Anya L Greenberg, Andrea G Tenner, Alexandra Friedman, Paulina Nicholaus, Christian C Rose, Newton Addo, Catherine Reuben Shari, Upendo N George, Michael J Losak

“…Objectives The WHO developed a 5-day basic emergency care (BEC) course using the traditional lecture format. However, adult learning theory suggests that lecture-based courses alone may not promote long-term knowledge retention. …”
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Cross-sectional design and protocol for Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI) by Gerald McGwin, Linda M Zangwill, Nicholas Evans, Shannon McWeeney, Cecilia S Lee, Bhavesh Patel, Jeffrey C Edberg, Cynthia Owsley, Aaron Lee, Cecilia Lee, Sally L Baxter, Michael Snyder, Samantha Hurst, Nicole Ehrhardt, Christopher Chute, Dawn S Matthies, Julia P Owen, Amir Bahmani, Sally Baxter, Edward Boyko, Aaron Cohen, Jorge Contreras, Garrison Cottrell, Virginia de Sa, Jeffrey Edberg, Irl Hirsch, Michelle Hribar, T.Y. Alvin Liu, Bonnie Maldenado, Sara Singer, Bradley Voytek, Joseph Yracheta, Linda Zangwill

“…Introduction Artificial Intelligence Ready and Equitable for Diabetes Insights (AI-READI) is a data collection project on type 2 diabetes mellitus (T2DM) to facilitate the widespread use of artificial intelligence and machine learning (AI/ML) approaches to study salutogenesis (transitioning from T2DM to health resilience). …”
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Human Resource Practices and Employee Retention in Local Governments in Uganda: A Case of Kabale District Local Government. by Baingana, Alex

“…It was also recommended that KDLG reassess its approach to employee development through benchmarking and hiring professional trainers to ensure equitable access to training opportunities, establishing regular and comprehensive training programs, and fostering a culture that prioritizes continuous learning and professional growth. Failure to address these deficiencies will continue to impede efforts to retain talent and hinder the district’s effectiveness. …”
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Human Resource Practices and Employee Retention in Local Governments in Uganda: A Case of Kabale District Local Government. by Baingana, Alex

“…It was also recommended that KDLG reassess its approach to employee development through benchmarking and hiring professional trainers to ensure equitable access to training opportunities, establishing regular and comprehensive training programs, and fostering a culture that prioritizes continuous learning and professional growth. Failure to address these deficiencies will continue to impede efforts to retain talent and hinder the district’s effectiveness. …”
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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots by Sukhpal Singh Gill, Minxian Xu, Panos Patros, Huaming Wu, Rupinder Kaur, Kamalpreet Kaur, Stephanie Fuller, Manmeet Singh, Priyansh Arora, Ajith Kumar Parlikad, Vlado Stankovski, Ajith Abraham, Soumya K. Ghosh, Hanan Lutfiyya, Salil S. Kanhere, Rami Bahsoon, Omer Rana, Schahram Dustdar, Rizos Sakellariou, Steve Uhlig, Rajkumar Buyya

“…To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial.…”
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Vision-based manipulation of transparent plastic bags in industrial setups by F. Adetunji, F. Adetunji, A. Karukayil, A. Karukayil, P. Samant, P. Samant, S. Shabana, S. Shabana, F. Varghese, F. Varghese, U. Upadhyay, U. Upadhyay, R. A. Yadav, R. A. Yadav, A. Partridge, E. Pendleton, R. Plant, Y. R. Petillot, Y. R. Petillot, M. Koskinopoulou, M. Koskinopoulou

“…Integrating autonomous systems, including collaborative robots (cobots), into industrial workflows is crucial for improving efficiency and safety.MethodsThe proposed system employs advanced Machine Learning algorithms, particularly Convolutional Neural Networks (CNNs), for identifying transparent plastic bags under diverse lighting and background conditions. …”
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Remote-sensing-based forest canopy height mapping: some models are useful, but might they provide us with even more insights when combined? by N. Besic, N. Picard, C. Vega, J.-D. Bontemps, L. Hertzog, J.-P. Renaud, J.-P. Renaud, F. Fogel, M. Schwartz, A. Pellissier-Tanon, G. Destouet, F. Mortier, F. Mortier, M. Planells-Rodriguez, P. Ciais

“…<p>The development of high-resolution mapping models for forest attributes based on remote sensing data combined with machine or deep learning techniques has become a prominent topic in the field of forest observation and monitoring. …”
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Fiber-optic system for monitoring stability of quarry slopes by P. Sh. Madi, А. D. Аlkina, A. V. Yurchenko, A. D. Mekhtiyev, R. Zh. Aimagambetova

“…A hardware-software control complex has also been developed with a wide range of elements that allows you to adjust sensitivity and has machine learning elements.…”
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Exploring the assessment of post-cardiac valve surgery pulmonary complication risks through the integration of wearable continuous physiological and clinical data by Lixuan Li, Yuekong Hu, Zhicheng Yang, Zeruxin Luo, Jiachen Wang, Wenqing Wang, Xiaoli Liu, Yuqiang Wang, Yong Fan, Pengming Yu, Zhengbo Zhang

“…This study leverages wearable technology and machine learning algorithms to preoperatively identify high-risk individuals, thereby enhancing clinical decision-making for the mitigation of PPCs. …”
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Targeting early tau pathology: probiotic diet enhances cognitive function and reduces inflammation in a preclinical Alzheimer’s model by Cassandra M. Flynn, Tamunotonye Omoluabi, Alyssa M. Janes, Emma J. Rodgers, Sarah E. Torraville, Brenda L. Negandhi, Timothy E. Nobel, Shyamchand Mayengbam, Qi Yuan

“…The enhancement in gut microbiomes was associated with enhanced spatial learning (p < 0.05), reduced inflammation indexed by Iba-1 (F 1,25 = 5.284, p = 0.030) and CD-68 (F 1,26 = 8.441, p = 0.007) expression, and inhibited GSK-3β in female rats (p < 0.01 compared to control females). …”
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Personalized prediction of glycemic responses to food in women with diet-treated gestational diabetes: the role of the gut microbiota by Polina V. Popova, Artem O. Isakov, Anastasiia N. Rusanova, Stanislav I. Sitkin, Anna D. Anopova, Elena A. Vasukova, Alexandra S. Tkachuk, Irina S. Nemikina, Elizaveta A. Stepanova, Angelina I. Eriskovskaya, Ekaterina A. Stepanova, Evgenii A. Pustozerov, Maria A. Kokina, Elena Y. Vasilieva, Lyudmila B. Vasilyeva, Soha Zgairy, Elad Rubin, Carmel Even, Sondra Turjeman, Tatiana M. Pervunina, Elena N. Grineva, Omry Koren, Evgeny V. Shlyakhto

“…The study involved 105 pregnant women (77 with GDM, 28 healthy), who underwent continuous glucose monitoring (CGM) for 7 days, provided food diaries, and gave stool samples for microbiome analysis. Machine learning models were created using CGM data, meal content, lifestyle factors, biochemical parameters, and microbiota data (16S rRNA gene sequence analysis). …”
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