Techno-economic analysis and energy management strategies for micro gas turbines: A state-of-the-art review by A.H. Samitha Weerakoon, Mohsen Assadi

“…Additionally, the optimization of Micro Gas Turbine systems using advanced algorithms enhances energy efficiency by 10–15%, highlighting the importance of smart control systems and optimal component sizing. …”
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Advanced Artificial Intelligence Technologies Transforming Contemporary Pharmaceutical Research by Parveen Kumar, Benu Chaudhary, Preeti Arya, Rupali Chauhan, Sushma Devi, Punit B. Parejiya, Madan Mohan Gupta

“…This technique includes data collection, effective data usage system development, conclusion illustration, and arrangements. Analysis algorithms that are learning to mimic human cognitive activities are the most widespread application of AI. …”
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Racial disparities in continuous glucose monitoring-based 60-min glucose predictions among people with type 1 diabetes. by Helene Bei Thomsen, Livie Yumeng Li, Anders Aasted Isaksen, Benjamin Lebiecka-Johansen, Charline Bour, Guy Fagherazzi, William P T M van Doorn, Tibor V Varga, Adam Hulman

“…Linear mixed-effect models were used to investigate the association between racial composition and RMSE while accounting for age, sex, and training data size. …”
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Optimizing Tumor Detection in Brain MRI with One-Class SVM and Convolutional Neural Network-Based Feature Extraction by Azeddine Mjahad, Alfredo Rosado-Muñoz

“…However, medical imaging datasets frequently exhibit class imbalance, posing significant challenges for traditional classification algorithms that rely on balanced data distributions. …”
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Diagnosis and treatment of sepsis-induced cardiomyopathy using hemosorption therapy: a case report by S. V. Dil, V. V. Kirilin, N. M. Ivanov, V. V. Ryabov

“…The prevalence of sepsis-induced cardiomyopathy (SICM) in patients with sepsis ranges from 10 to 70%, while there are no special sections on SICM in the known resuscitation manuals. …”
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How Cybersecurity Leadership Became the New Critical Managerial Competency in Healthcare Administration by Ebone McCoy

“…Ethical implications of increased digitalization in healthcare are addressed, including patient privacy concerns, data ownership issues, and potential algorithmic bias in AI-driven solutions. The study emphasizes the need for a balanced approach that leverages technological advancements while maintaining the human-centric nature of healthcare delivery. …”
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Data-driven non-intrusive reduced order modelling of selective laser melting additive manufacturing process using proper orthogonal decomposition and convolutional autoencoder by Shubham Chaudhry, Azzedine Abdedou, Azzeddine Soulaïmani

“…The findings highlight the potential of integrating reduced-order modeling techniques with machine learning algorithms to enhance the analysis of complex AM processes. …”
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Stabilization of diastolic calcium signal via calcium pump regulation of complex local calcium releases and transient decay in a computational model of cardiac pacemaker cell with... by Alexander V Maltsev, Victor A Maltsev, Michael D Stern

“…Here we developed new terminology, classification, and computer algorithms for automatic detection of numerically simulated LCRs and examined LCR regulation by SR Ca pumping rate (Pup) that provides a major contribution to fight-or-flight response. …”
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Health inequities in medical crowdfunding: a systematic review by Yingying Cai, Syafila Kamarudin, Xiaoyu Jiang, Baiyu Zhou

“…These inequities were shaped and reinforced by platform algorithms and design features that privileged users with preexisting advantages. …”
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IoT Based Health Monitoring with Diet, Exercise and Calories recommendation Using Machine Learning by Muhammad Hassaan Naveed, Omar Bin Samin, Muhammad Bilal, Mustehsum Waseem

“…Additionally, machine learning algorithms recommend tailored exercise, diet type, Basal Metabolic Rate (BMR), and daily caloric intake based on individual member data. …”
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Prediction of Soil Field Capacity and Permanent Wilting Point Using Accessible Parameters by Machine Learning by Liwei Liu, Xingmao Ma

“…The normalized root mean square error (NRMSE) evaluation indicated that the ANN model performed best for PWP prediction (NRMSE of 19.9%), while the GEP model was superior for FC prediction (NRMSE of 29.9%). …”
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Comprehensive Analysis of the Influence of Technical and Biological Variations on De Novo Assembly of RNA-Seq Datasets by Gonzalez Sergio Alberto, Rivarola Maximo, Ribone Andres, Lew Sergio, Paniego Norma

“…De novo assembly of transcriptomes from species without reference genome remains a common problem in functional genomics. While methods and algorithms for transcriptome assembly are continually being developed and published, the quality of de novo assemblies using short reads depends on the complexity of the transcriptome and is limited by several types of errors. …”
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Survival analysis using machine learning in transplantation: a practical introduction by Andrea Garcia-Lopez, Maritza Jiménez-Gómez, Andrea Gomez-Montero, Juan Camilo Gonzalez-Sierra, Santiago Cabas, Fernando Giron-Luque

“…This study aims to provide an introduction to the application of the RSF model in survival analysis in kidney transplantation alongside a practical guide to develop and evaluate predictive algorithms. Methods We employed a RSF model to analyze a simulated dataset of kidney transplant recipients. …”
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The effective landscape design parameters with high reflective hardscapes: guidelines for optimizing human thermal comfort in outdoor spaces by design -a case on hot arid climate w... by Reham A. Abdelwahab, Ahmed A. Fekry, Reham El-Dessuky Hamed

“…Through generative design algorithms, an optimized framework was developed to identify effective strategies for urban cooling. …”
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Privacy-Preserving Detection of Tampered Radio-Frequency Transmissions Utilizing Federated Learning in LoRa Networks by Nurettin Selcuk Senol, Mohamed Baza, Amar Rasheed, Maazen Alsabaan

“…Leveraging Federated Learning (FL), our approach enables the detection of tampered RF transmissions while safeguarding sensitive IoT data, as FL allows model training on distributed devices without sharing raw data. …”
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Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities by Sonam Dukda, Manoharan Kumar, Andrew Calcino, Ulf Schmitz, Matt A. Field

“…This review explores the latest technological and computational options being used to increase genetic diagnosis rates in precision medicine programs. While interpreting genetic variation via standards such as ACMG guidelines is increasingly being recognized as a gold standard approach, the underlying datasets and algorithms recommended are often slow to incorporate additional data types and methodologies. …”
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Numerical modeling and neural network optimization for advanced solar panel efficiency by Udit Mamodiya, Indra Kishor, Mohammed Amin Almaiah, Monia Hamdi, Rami Shehab, Tayseer Alkhdour

“…Conventionally, such optimization techniques—MPPT (Maximum Power Point Tracking) along with heuristic algorithms—suffer significantly from slow adaptability and track sub optimality under dynamic environments. …”
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Enhancing the Performance of YOLOv9t Through a Knowledge Distillation Approach for Real-Time Detection of Bloomed Damask Roses in the Field by Farhad Fatehi, Hossein Bagherpour, Jafar Amiri Parian

“…To carry out agricultural operation, particularly for picking fully bloomed Damask roses, using harvesting robots offers significant potential to reduce labor costs while simultaneously improving crop quality. Recent developments in deep learning algorithms, especially in convolutional models, have shown significant promise for object detection, highlighting strong possibilities for improving the efficiency of this process. …”
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Ethical Challenges and Opportunities of AI in End-of-Life Palliative Care: Integrative Review by Abel García Abejas, David Geraldes Santos, Fabio Leite Costa, Aida Cordero Botejara, Helder Mota-Filipe, Àngels Salvador Vergés

“…Key risks include algorithmic bias, cultural insensitivity, and the potential for reduced patient autonomy. …”
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Versatile waste sorting in small batch and flexible manufacturing industries using deep learning techniques by Arso M. Vukicevic, Milos Petrovic, Nebojsa Jurisevic, Marko Djapan, Nikola Knezevic, Aleksandar Novakovic, Kosta Jovanovic

“…The proposed approach has a high potential to facilitate deployment, increase productivity, lower expenses, and minimize errors in robotic waste sorting while enhancing overall recycling and material utilization in the manufacturing industry.…”
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