AI-driven FMEA: integration of large language models for faster and more accurate risk analysis by Ibtissam El Hassani, Tawfik Masrour, Nouhan Kourouma, Jože Tavčar

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Expanding and Interpreting Financial Statement Fraud Detection Using Supply Chain Knowledge Graphs by Shanshan Zhu, Tengyun Ma, Haotian Wu, Jifan Ren, Daojing He, Yubin Li, Rui Ge

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Blockchain-Based Supply Chain Management Ensuring Transparency and Traceability in Logistics Operations by Chinthamu Narender, A Swathi, Banka Venkata Ramana, S Jagadheeswaran, Yoganandh Sampathirao, Jebaseeli S Kanmani

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Agentic AI Frameworks in SMMEs: A Systematic Literature Review of Ecosystemic Interconnected Agents by Peter Adebowale Olujimi, Pius Adewale Owolawi, Refilwe Constance Mogase, Etienne Van Wyk

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An induction machine‐based drive system and supervisory control for application to smart water‐pumped storage and networks by O. S. Ebrahim, M. A. Badr

“…Under the wide umbrella of supervisory control, the following objectives are achieved: (1) Recurrent neural network (RNN) and RNN‐aided Kalman filter are proposed for wired and wireless sensor fault‐tolerant control of the plant based on Wald's likelihood ratio test. (2) Cheap fuzzy logic‐based availability assessment scheme is presented and deployed on cloud to arrange for preventive maintenance of the starter. (3) Robust energy saving mode decision‐making algorithm is developed for best energetic cost reduction. (4) Remote synchronisation method of multi‐motor starting with a mechanically switched capacitor is introduced to minimise voltage dip. …”
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Evaluation of the Maternal Death Surveillance and Response System, Sierra Leone, 2019 by Kadijatu Nabie Kamara, Adel Hussein Elduma, Joseph Sam Kanu, James Sylvester Squire, Gebrekrstos Negash Gebru

“…Only 42% (15/36) reported using MDSR data for decision-making. All respondents viewed maternal death notification as their duty, with 33% holding regular MDSR meetings. …”
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Entrepreneurship skills application among ICT Graduates of Muni University, Uganda by Ritah, Nafuna, Nicholas, Nkamwesiga, Phelix, Businge Mbabazi

“…The results show that the graduates are competent in: computer literacy and information technology (ICT applications); creativity, innovation and opportunity identification; life skills (problem solving, time management, decision making, ability to learn and change management); literacy and numeracy among others. …”
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Optimised autoencoder-based ensemble deep learning approaches for cyber-physical event classification utilizing synchrophasor PMU data by Dhinu Lal M, Ramesh Varadarajan

“…This work offers a scalable and generalisable solution for high-fidelity CPS event classification, providing timely insights for operational decision-making and fault response in Synchrophasor-enabled power systems.…”
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Predicting Cervical Lymph Node Metastasis Using Preoperative Multiparameter Data Based on Imaging, Serology, and Clinical Features in Unifocal Papillary Thyroid Carcinoma by Junlin HE, Wenjing CHEN, Lili HU, Yongsheng WANG, Jing CHEN, Ming WU

“…The constructed nomogram can assist in clinical decision-making and benefit patients with PTC.…”
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Diagnostic efficacy of large language models in the pediatric emergency department: a pilot study by Francesco Del Monte, Roberta Barolo, Maria Circhetta, Angelo Giovanni Delmonaco, Emanuele Castagno, Emanuele Pivetta, Letizia Bergamasco, Letizia Bergamasco, Matteo Franco, Gabriella Olmo, Claudia Bondone

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Multivariate analysis of water quality of Sacramento-San Joaquin Delta by Rahul Kumar, Prachi Pandey, Aditya Pandey, Ujjwal Kumar, Vikrant Singh, Patricia Gilbert, Lydia Kenison, Jeffrey Caudill, Vijay P. Singh, Pramod K. Pandey

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BrainTumNet: multi-task deep learning framework for brain tumor segmentation and classification using adaptive masked transformers by Cheng Lv, Xu-Jun Shu, Xu-Jun Shu, Quan Liang, Jun Qiu, Zi-Cheng Xiong, Jing bo Ye, Shang bo Li, Cheng Qing Liu, Jing Zhen Niu, Sheng-Bo Chen, Hong Rao

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Using Monte Carlo conformal prediction to evaluate the uncertainty of deep-learning soil spectral models by Y.-C. Huang, J. Padarian, B. Minasny, A. B. McBratney

“…<p>Uncertainty quantification is a crucial step in the practical application of soil spectral models, particularly in supporting real-world decision making and risk assessment. While machine learning has made remarkable strides in predicting various physiochemical properties of soils using spectroscopy, its practical utility in decision making remains limited without quantified uncertainty. …”
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Predicting mortality in critically ill patients with hypertension using machine learning and deep learning models by Ziyang Zhang, Jiancheng Ye

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AI-Based Deep Learning of the Water Cycle System and Its Effects on Climate Change by Hasib Khan, Wafa F. Alfwzan, Rabia Latif, Jehad Alzabut, Rajermani Thinakaran

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Evaluating the impact of autonomous material handling on the performance of production system: a simulation approach by Ratna Purwaningsih, Annisa Rahma Shintyastuti, Ary Arvianto, Chaterine Alvina Prima Hapsari, Ade Aisyah Arifna Putri, Rifki Daris Dzulfikar

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Design Principles for Reinforcement Learning in Congestion Control Environments by Lincoln Kamau Kiarie, Mahsa Derakhshani, Konstantinos G. Kyriakopoulos

“…Reinforcement Learning (RL) has emerged as a powerful tool for optimising sequential decision-making processes. Recent years have seen growth in interest in applying RL to managing Congestion Control (CC) in computer networks, with the goal of handling diverse kinds of traffic that may be challenging for human-designed algorithms. …”
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Microgrid Reliability Incorporating Uncertainty in Weather and Equipment Failure by Sakthivelnathan Nallainathan, Ali Arefi, Christopher Lund, Ali Mehrizi-Sani

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Towards Secure and Efficient Farming Using Self-Regulating Heterogeneous Federated Learning in Dynamic Network Conditions by Sai Puppala, Koushik Sinha

“…Through a comprehensive assessment of computational power, energy efficiency, and latency, our system intelligently classifies devices, significantly enhancing the overall efficiency of federated learning processes. …”
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Artificial Intelligence in Revolutionizing Kidney Care and Beyond: Kid-AI Revolution by Kounaina Khan, Farhan Zameer, Pratheek Jain, Ravi KR, Vidya Niranjan, Manoj S, Ravish H, Subrahmanya Padyana

“…Recent advancements in artificial intelligence (AI) have significantly impacted the diagnosis and treatment of kidney diseases, offering novel approaches for precise quantitative assessments of nephropathology. The collaboration between computer engineers, renal specialists, and nephropathologists has led to the development of AI- assisted technology, presenting promising avenues for renal pathology diagnoses, disease prediction, treatment effectiveness assessment, and outcome prediction. …”
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