Emergent Applications of Organ-on-a-Chip (OOAC) Technologies With Artificial Vascular Networks in the 21st Century

Advances in healthcare information systems and administration book series, Journal Year: 2024, Volume and Issue: unknown, P. 198 - 219

Published: Feb. 14, 2024

The organ-on-a-chip (OOAC) technology stands at the forefront of emergent technologies, representing a biomimetic configuration functional organs on microfluidic chip. This synergizes biomedical engineering, cell biology, and biomaterial to mimic microenvironment specific organs. It effectively replicates biomechanical biological soft tissue interfaces, enabling simulation organ functionality responses various stimuli, including drug reactions environmental effects. OOAC has vast implications for precision medicine defense strategies. In this chapter, authors delve into principles OOAC, exploring its role in creating physiological models discussing advantages, current challenges, prospects. examination is significant as it highlights transformative potential technologies 21st century contributes deeper understanding OOAC's applications advancing medical research.

Language: Английский

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Effects of Genetic Counseling on Reducing Prenatal Stress and Autism Rates in the Asia-Pacific Region

Advances in healthcare information systems and administration book series, Journal Year: 2024, Volume and Issue: unknown, P. 341 - 363

Published: Feb. 14, 2024

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties in social interaction, repetitive behaviors, and narrow interests. People with ASD often experience additional mental health issues such as depression anxiety. While genetics have long been considered significant factor the development of ASD, recent research indicates that interplay between genes environment crucial understanding its underlying causes. This chapter aims to discuss relationship prenatal stress characteristics countries within Asia-Pacific region. The findings indicate connection traits China, South Korea, Japan. Further investigation required fully comprehend specific mechanisms involved this relationship. Genetic consultation can provide insights into potential risk factors, genetic counseling, guidance on personalized interventions.

Language: Английский

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Digital Health in the Context of Healthcare Workers' Education and Training

Advances in healthcare information systems and administration book series, Journal Year: 2024, Volume and Issue: unknown, P. 66 - 85

Published: Feb. 14, 2024

Digital technologies are reshaping healthcare practices, influencing patient information-seeking behavior, and impacting ethical considerations. The emergence of eHealth, mHealth, advanced like artificial intelligence, machine learning, robotics hold promise for improving quality. However, in Portugal, digital health literacy is underexplored, particularly education. This chapter scrutinizes curricula at higher education schools, revealing that while integrated, often confined to specific modules. Portuguese institutions must reconsider equip professionals with essential skills. significance this lies its critical analysis recommendations reform. It underscores the urgent need comprehensive integration education, highlighting gap current advocating a more approach. Recommendations include implementing ongoing training enhance literacy.

Language: Английский

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An In-Depth Exploration of Nonwoven Materials in the Healthcare and Medical Sector

Advances in healthcare information systems and administration book series, Journal Year: 2024, Volume and Issue: unknown, P. 315 - 340

Published: Feb. 14, 2024

This chapter provides an overview of nonwoven materials in the healthcare industry, discussing their various uses, characteristics, advantages, challenges, recent developments, and potential future prospects. The essential nature nonwovens lies barrier efficiency, breathability, comfort, making them indispensable for surgical gowns, face masks, sterile packaging, wound dressings, hygiene products. emphasizes cost-effectiveness, disposability, infection control offered by materials, while also environmental impact compliance with regulations. dynamic advancement these is demonstrated through integration nanotechnology development smart nonwovens. Looking ahead, availability biodegradable alternatives customized solutions expected, driven sustainability, technology, emerging trends. implications sector include enhanced patient safety, improved operational increased sustainability.

Language: Английский

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Optimizing Parkinson’s Disease Detection: Hybrid S-transform-EEG Feature Reduction Through Trajectory Analysis

SN Computer Science, Journal Year: 2025, Volume and Issue: 6(2)

Published: Feb. 3, 2025

Language: Английский

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Optimization Techniques on Quantum and Classical Systems: A Comprehensive Comparative Study

E3S Web of Conferences, Journal Year: 2025, Volume and Issue: 616, P. 02024 - 02024

Published: Jan. 1, 2025

Quantum optimization is a promising field revolutionizing problem-solving across domains. This study compares Particle Swarm Optimization (PSO), Moth Flame (MFO), and Genetic Algorithm (GA) on three platforms : local computer, computer with quantum integration, an IBM machine. Results indicate PSO’s consistent performance all setups, the machine having longer elapsed time. For MFO, optimal solution found using machine, despite its execution Similarly, GA achieves best results These findings suggest that while computers excel in solving complex problems, their time for simpler tasks remains higher than classical setups. Future research should address challenges like noise, limited qubits, high material costs to improve computers’ efficiency availability.

Language: Английский

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Advanced Deep Learning Models for Improved IoT Network Monitoring Using Hybrid Optimization and MCDM Techniques

Symmetry, Journal Year: 2025, Volume and Issue: 17(3), P. 388 - 388

Published: March 4, 2025

This study addresses the challenge of optimizing deep learning models for IoT network monitoring, focusing on achieving a symmetrical balance between scalability and computational efficiency, which is essential real-time anomaly detection in dynamic networks. We propose two novel hybrid optimization methods—Hybrid Grey Wolf Optimization with Particle Swarm (HGWOPSO) Hybrid World Cup Harris Hawks (HWCOAHHO)—designed to symmetrically global exploration local exploitation, thereby enhancing model training adaptation environments. These methods leverage complementary search behaviors, where symmetry processes enhances convergence speed accuracy. The proposed approaches are validated using real-world datasets, demonstrating significant improvements accuracy, scalability, adaptability compared state-of-the-art techniques. Specifically, HGWOPSO combines hierarchy-driven leadership Wolves velocity updates Optimization, while HWCOAHHO synergizes strategies competition-driven algorithm, ensuring balanced decision-making processes. Performance evaluation benchmark functions data highlights superior precision, recall, F1 score traditional methods. To further enhance decision-making, Multi-Criteria Decision-Making (MCDM) framework incorporating Analytic Hierarchy Process (AHP) TOPSIS employed evaluate rank Results indicate that achieves most optimal accuracy followed closely by HGWOPSO, like FFNNs MLPs show lower effectiveness detection. symmetry-driven approach these algorithms ensures robust, adaptive, scalable monitoring solutions networks characterized traffic patterns evolving anomalies, thus stability integrity. findings have substantial implications smart cities, industrial automation, healthcare applications, performance efficiency crucial reliable monitoring. work lays groundwork research techniques learning, emphasizing role resilience systems.

Language: Английский

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Design of an Improved Robust Fractional‐Order PID Controller for Buck–Boost Converter using Snake Optimization Algorithm

IET Control Theory and Applications, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 1, 2025

ABSTRACT With the increasing complexity of modern power systems, effective control DC–DC converters has become crucial to ensure stability and efficiency. This paper focuses on optimizing parameters a known fractional‐order proportional–integral–derivative (FOPID) controller for buck–boost converter. The converter is achieved using aFOPID approach. gains this technique have been enhanced utilizing snake optimization (SO) algorithm. exhibits unfavourable behaviour due its non‐minimum structure, necessitating well‐regulated guarantee stability. fractional concept suggested here enhance dynamics classical PID controller, leveraging simplicity minimizing computational load in real‐time applications. idea an advantageous method that offers several benefits, such as reduced overshoot settling time, frequency response, non‐integer order dynamics, and, more importantly, higher robustness noise parametric variation. Despite advantages reported by technique, proper gain tuning needed dynamical performance decrease sensitivity error. Thus, algorithm SO tunes values affect efficiency method. novel strategy with numerous merits compared others, bi‐directional search elite opposition‐based learning strategies. variants offer promising alternative solving problems, combining efficiency, adaptability, competitive performance. contribution work lies FOPID enabling faster convergence improved under varying operating conditions. proposed approach validated through both simulation hardware‐in‐loop experiments, demonstrating superior conventional methods.

Language: Английский

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Parameter Estimation of Low-Cost Ultrasound and Laser Range Sensors to be Used for Mobile Robot Applications

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(2)

Published: April 3, 2025

In this study parameters of sensor models are estimated for low-cost ultrasound and laser range sensors. Sensor that best suited to simultaneous localization mapping (SLAM) tasks mobile robotics applications used. Mathematical functions with relevant be determined explained. Particle swarm optimization (PSO) algorithm is used find the explain experimental measurements optimally. Experiments conducted various sizes obstacles at distances results reported detailly in corresponding section. Finally, discussed future works built on proposed.

Language: Английский

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Software cost estimation predication using a convolutional neural network and particle swarm optimization algorithm

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: June 7, 2024

Abstract Over the past decades, software industry has expanded to include all industries. Since stakeholders tend use it get their work done, houses seek estimate cost of software, which includes calculating effort, time, and resources required. Although many researchers have worked it, prediction accuracy results are still inaccurate unstable. Estimating requires a lot effort. Therefore, there is an urgent need for modern techniques that contribute estimation. This paper seeks present model based on deep learning machine by combining convolutional neural networks (CNN) particle swarm algorithm (PSO) in context time series forecasting, enables feature extraction automatic tuning hyperparameters, reduces manual effort selecting parameters contributes fine-tuning. The PSO also enhances robustness generalization ability CNN its iterative nature allows efficient discovery hyperparameter similarity. was trained tested 13 different benchmark datasets evaluated through six metrics: mean absolute error (MAE), square (MSE), magnitude relative (MMRE), root (RMSE), median (MdMRE), (PRED). Comparative reveal performance proposed better than other methods evaluation criteria. were very promising predicting

Language: Английский

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