Technologies

14 pages, 3134 KiB

Open AccessArticle

Development of a Low-Cost Multi-Physiological Signal Simulation System for Multimodal Wearable Device Calibration

by Tumenkhuslen Delgerkhaan, Qun Wei, Jiwoo Jung, Sangwon Lee, Gangoh Na, Bongjo Kim, In-Cheol Kim and Heejoon Park

Technologies 2025, 13(6), 239; https://doi.org/10.3390/technologies13060239 (registering DOI) - 10 Jun 2025

Abstract

Using multimodal wearable devices to diagnose cardiovascular diseases early is essential for providing timely medical assistance, particularly in remote areas. This approach helps prevent risks and reduce mortality rates. However, prolonged use of medical devices can lead to measurement inaccuracies, necessitating calibration to [...] Read more.

Using multimodal wearable devices to diagnose cardiovascular diseases early is essential for providing timely medical assistance, particularly in remote areas. This approach helps prevent risks and reduce mortality rates. However, prolonged use of medical devices can lead to measurement inaccuracies, necessitating calibration to maintain precision. Unfortunately, wearable devices often lack affordable calibrators that are suitable for personal use. This study introduces a low-cost simulation system for phonocardiography (PCG) and photoplethysmography (PPG) signals designed for a multimodal smart stethoscope calibration. The proposed system was developed using a multicore microprocessor (MCU), two digital-to-analog converters (DACs), an LED light, and a speaker. It synchronizes dual signals by assigning tasks based on a multitasking function. A designed time adjustment algorithm controls the pulse transit time (PTT) to simulate various cardiovascular conditions. The simulation signals are generated from preprocessed PCG and PPG signals collected during in vivo experiments. A prototype device was manufactured to evaluate performance by measuring the generated signal using an oscilloscope and a multimodal smart stethoscope. The preprocessed signals, generated signals, and measurements by the smart stethoscope were compared and evaluated through correlation analysis. The experimental results confirm that the proposed system accurately generates the features of the physiological signals and remains in phase with the original signals. Full article

(This article belongs to the Special Issue Wearable Vital Signs and Activities Detection: Systems and Applications)

► Show Figures

Figure 1

36 pages, 667 KiB

Open AccessArticle

Transition to a Circular Bioeconomy in the Sugar Agro-Industry: Predictive Modeling to Estimate the Energy Potential of By-Products

by Yoisdel Castillo Alvarez, Reinier Jiménez Borges, Gendry Alfonso-Francia, Berlan Rodríguez Pérez, Carlos Diego Patiño Vidal, Luis Angel Iturralde Carrera and Juvenal Rodríguez-Reséndiz

Technologies 2025, 13(6), 238; https://doi.org/10.3390/technologies13060238 - 10 Jun 2025

Abstract

The linear economy model in the sugar agroindustry has generated multiple impacts due to the underutilization of by-products and reliance on fossil fuels. Through predictive modeling and anaerobic digestion, the circular bioeconomy of sugarcane biomass enables the generation of biogas and electricity in [...] Read more.

The linear economy model in the sugar agroindustry has generated multiple impacts due to the underutilization of by-products and reliance on fossil fuels. Through predictive modeling and anaerobic digestion, the circular bioeconomy of sugarcane biomass enables the generation of biogas and electricity in an environmentally sustainable manner. This theoretical-applied research proposes a predictive model to estimate the energy potential of by-products such as bagasse, vinasse, molasses, and filter cake, based on historical production data and validated technical coefficients. The model uses milled sugarcane as a baseline and projects its energy conversion under three scenarios through 2030. In its most favorable configuration, the model estimates energy production of up to 15.5 billion Nm³ of biogas in Cuba and 9.9 billion in Peru. The model’s architecture includes four residual biomass flows and bioenergy conversion factors applicable to electricity generation. It is validated using national statistical series from 2000 to 2018 and presents relative errors below 5%. Cuba, with a peak of over 13,000 GWh of electricity from bagasse, and Peru, with a stable output between 6500 and 7500 GWh, reflect the highest and lowest projected energy utilization, respectively. Bagasse accounts for over 60% of the total estimated energy contribution. This modeling tool is fundamental for advancing a transition toward a circular economy, as it helps mitigate environmental impacts, improve agroindustrial waste management, and guide sustainable policies in sugarcane-based contexts. Full article

(This article belongs to the Section Environmental Technology)

► Show Figures

Graphical abstract

29 pages, 3636 KiB

Open AccessArticle

Design, Development, and Evaluation of a Contactless Respiration Rate Measurement Device Utilizing a Self-Heating Thermistor

by Reza Saatchi, Alan Holloway, Johnathan Travis, Heather Elphick, William Daw, Ruth N. Kingshott, Ben Hughes, Derek Burke, Anthony Jones and Robert L. Evans

Technologies 2025, 13(6), 237; https://doi.org/10.3390/technologies13060237 - 9 Jun 2025

Abstract

The respiration rate (RR) is an important vital sign for early detection of health deterioration in critically unwell patients. Its current measurement has limitations, relying on visual counting of chest movements. The design of a new RR measurement device utilizing a self-heating thermistor [...] Read more.

The respiration rate (RR) is an important vital sign for early detection of health deterioration in critically unwell patients. Its current measurement has limitations, relying on visual counting of chest movements. The design of a new RR measurement device utilizing a self-heating thermistor is described. The thermistor is integrated into a hand-held air chamber with a funnel attachment to sensitively detect respiratory airflow. The exhaled respiratory airflow reduces the temperature of the thermistor that is kept at a preset temperature, and its temperature recovers during inhalation. A microcontroller provides signal processing, while its display screen shows the respiratory signal and RR. The device was evaluated on 27 healthy adult volunteers, with a mean age of 32.8 years (standard deviation of 8.6 years). The RR measurements from the device were compared with the visual counting of chest movements, and the contact method of inductance plethysmography that was implemented using a commercial device (SOMNOtouch™ RESP). Statistical analysis, e.g., correlations were performed. The RR measurements from the new device and SOMNOtouch™ RESP, averaged across the 27 participants, were 14.6 breaths per minute (bpm) and 14.0 bpm, respectively. The device has a robust operation, is easy to use, and provides an objective measure of the RR in a noncontact manner. Full article

(This article belongs to the Special Issue Innovations in Design, Development and Evaluation of Assistive Technologies)

► Show Figures

Figure 1

18 pages, 3130 KiB

Open AccessArticle

Mechatronic Test Bench Used to Simulate Wind Power Conversion to Thermal Power by Means of a Hydraulic Transmission

by Victor Constantin, Ionela Popescu and Mihai Avram

Technologies 2025, 13(6), 236; https://doi.org/10.3390/technologies13060236 - 6 Jun 2025

Viewed by 285

Abstract

The work presented in this paper discusses the steps taken to design, implement, and test a mechatronic test stand that uses historical wind power data to generate thermal power that could be used by small-to-medium consumers. The work also pertains to usage in [...] Read more.

The work presented in this paper discusses the steps taken to design, implement, and test a mechatronic test stand that uses historical wind power data to generate thermal power that could be used by small-to-medium consumers. The work also pertains to usage in areas where large wind turbines could not be installed due to space restrictions, such as highly populated areas. A rotor flux control (RFC) speed-controlled 2.2 kW AC motor was used to simulate the action of a wind turbine on a 6 cm³ hydraulic pump. The setup allows for a small form factor and a much lighter turbine to be installed. The paper describes the schematic, installation, usage, and initial results obtained using a hydraulic test stand developed by the authors. The initial work allowed us to obtain different temperatures of the hydraulic oil, up to 60 °C, over a period of 30 min, for various pressures and flow rates, thus confirming that the system is functional overall. Further work will elaborate on the effect of different wind patterns on the setup, as well as provide an in-depth study on a use case for the system. Full article

(This article belongs to the Section Environmental Technology)

► Show Figures

Figure 1

24 pages, 2229 KiB

Open AccessArticle

Mathematical Modeling of Optimal Drone Flight Trajectories for Enhanced Object Detection in Video Streams Using Kolmogorov–Arnold Networks

by Aida Issembayeva, Oleksandr Kuznetsov, Anargul Shaushenova, Ardak Nurpeisova, Gabit Shuitenov and Maral Ongarbayeva

Technologies 2025, 13(6), 235; https://doi.org/10.3390/technologies13060235 - 6 Jun 2025

Viewed by 148

Abstract

This study addresses the critical challenge of optimizing drone flight parameters for enhanced object detection in video streams. While most research focuses on improving detection algorithms, the relationship between flight parameters and detection performance remains poorly understood. We present a novel approach using [...] Read more.

This study addresses the critical challenge of optimizing drone flight parameters for enhanced object detection in video streams. While most research focuses on improving detection algorithms, the relationship between flight parameters and detection performance remains poorly understood. We present a novel approach using Kolmogorov–Arnold Networks (KANs) to model complex, non-linear relationships between altitude, pitch angle, speed, and object detection performance. Our main contributions include the following: (1) the systematic analysis of flight parameters’ effects on detection performance using the AU-AIR dataset, (2) development of a KAN-based mathematical model achieving R² = 0.99, (3) identification of optimal flight parameters through multi-start optimization, and (4) creation of a flexible implementation framework adaptable to different UAV platforms. Sensitivity analysis confirms the solution’s robustness with only 7.3% performance degradation under ±10% parameter variations. This research bridges flight operations and detection algorithms, offering practical guidelines that enhance the detection capability by optimizing image acquisition rather than modifying detection algorithms. Full article

(This article belongs to the Special Issue AI Robotics Technologies and Their Applications)

► Show Figures

Figure 1

16 pages, 1874 KiB

Open AccessArticle

Computationally Efficient Transfer Learning Pipeline for Oil Palm Fresh Fruit Bunch Defect Detection

by Yang Luo, Anwar P. P. Abdul Majeed, Zaid Omar, Saad Aslam and Yi Chen

Technologies 2025, 13(6), 234; https://doi.org/10.3390/technologies13060234 - 6 Jun 2025

Viewed by 153

Abstract

The present study addresses the inefficiencies of the manual classification of oil palm fresh fruit bunches (FFBs) by introducing a computationally efficient alternative to traditional deep learning approaches that require extensive retraining and large datasets. Using feature-based transfer learning, where pre-trained Convolutional Neural [...] Read more.

The present study addresses the inefficiencies of the manual classification of oil palm fresh fruit bunches (FFBs) by introducing a computationally efficient alternative to traditional deep learning approaches that require extensive retraining and large datasets. Using feature-based transfer learning, where pre-trained Convolutional Neural Network architectures, namely EfficientNet_B0, EfficientNet_B4, ResNet152, and VGG16, serve as fixed feature extractors coupled with the Logistic Regression classifier, this research evaluated the performance on a dataset of 466 images categorized as defective or non-defective. The results demonstrate a robust classification performance across all architectures, with the EfficientNet_B4–LR pipeline achieving an exceptional accuracy value of 96.81%, which was further enhanced through hyperparameter optimization. This confirms that feature-based transfer learning offers a reliable, resource-efficient, and practical solution for automated FFB defect detection that can significantly benefit the palm oil industry by providing a scalable alternative to subjective manual-grading methods. Full article

(This article belongs to the Section Manufacturing Technology)

► Show Figures

Figure 1

20 pages, 2342 KiB

Open AccessArticle

Comparing Strategies for Optimal Pumps as Turbines Selection in Pressurised Irrigation Networks Using Particle Swarm Optimisation: Application in Canal del Zújar Irrigation District, Spain

by Mariana Akemi Ikegawa Bernabé, Miguel Crespo Chacón, Juan Antonio Rodríguez Díaz, Pilar Montesinos and Jorge García Morillo

Technologies 2025, 13(6), 233; https://doi.org/10.3390/technologies13060233 - 5 Jun 2025

Viewed by 252

Abstract

The modernisation of irrigation networks has enhanced water use efficiency but increased energy demand and costs in agriculture. Energy recovery (ER) is possible by utilising excess pressure to generate electricity with pumps as turbines (PATs), offering a cost-effective alternative to traditional turbines. This [...] Read more.

The modernisation of irrigation networks has enhanced water use efficiency but increased energy demand and costs in agriculture. Energy recovery (ER) is possible by utilising excess pressure to generate electricity with pumps as turbines (PATs), offering a cost-effective alternative to traditional turbines. This study assesses the use of PATs in pressurised irrigation networks for recovering wasted hydraulic energy, employing the particle swarm optimisation (PSO) algorithm for PAT sizing based on two single-objective functions. The analysis focuses on minimising the payback period (MPP) and maximising energy recovery (MER) at specific excess pressure points (EPPs). A comparative analysis of values for each EPP and objective function is conducted independently in Sector II of the Canal del Zújar Irrigation District (CZID) in Extremadura, Spain. A sensitivity analysis on energy prices and installation costs is also performed to assess socioeconomic trends and volatility, examining their effects on both objective functions. The optimisation process predicts an annual ER for an average irrigation season using 2015 data ranging from 9554.86 kWh to 43,992.15 kWh per PATs from the MER function, and payback periods (PPs) from 12.92 years to 3.01 years for the MPP function. The sensitivity analysis replicated the optimisation for the years 2022 and 2023, showing potential annual ER of up to 54,963.21 kWh and PPs ranging from 0.88 to 5.96 years for the year 2022. Full article

(This article belongs to the Special Issue Sustainable Water and Environmental Technologies of Global Relevance)

► Show Figures

Figure 1

19 pages, 16547 KiB

Open AccessArticle

A New Method for Camera Auto White Balance for Portrait

by Sicong Zhou, Kaida Xiao, Changjun Li, Peihua Lai, Hong Luo and Wenjun Sun

Technologies 2025, 13(6), 232; https://doi.org/10.3390/technologies13060232 - 5 Jun 2025

Viewed by 229

Abstract

Accurate skin color reproduction under varying CCT remains a critical challenge in the graphic arts, impacting applications such as face recognition, portrait photography, and human–computer interaction. Traditional AWB methods like gray-world or max-RGB often rely on statistical assumptions, which limit their accuracy under [...] Read more.

Accurate skin color reproduction under varying CCT remains a critical challenge in the graphic arts, impacting applications such as face recognition, portrait photography, and human–computer interaction. Traditional AWB methods like gray-world or max-RGB often rely on statistical assumptions, which limit their accuracy under complex or extreme lighting. We propose SCR-AWB, a novel algorithm that leverages real skin reflectance data to estimate the scene illuminant’s SPD and CCT, enabling accurate skin tone reproduction. The method integrates prior knowledge of human skin reflectance, basis vectors, and camera sensitivity to perform pixel-wise spectral estimation. Experimental results on difficult skin color reproduction task demonstrate that SCR-AWB significantly outperforms traditional AWB algorithms. It achieves lower reproduction angle errors and more accurate CCT predictions, with deviations below 300 K in most cases. These findings validate SCR-AWB as an effective and computationally efficient solution for robust skin color correction. Full article

(This article belongs to the Special Issue Image Analysis and Processing)

► Show Figures

Figure 1

19 pages, 8696 KiB

Open AccessArticle

In Situ Ceramic Phase Reinforcement via Short-Pulsed Laser Cladding for Enhanced Tribo-Mechanical Behavior of Metal Matrix Composite FeNiCr-B₄C (5 and 7 wt.%) Coatings

by Artem Okulov, Olga Iusupova, Alexander Stepchenkov, Vladimir Zavalishin, Elena Marchenkova, Kun Liu, Jie Li, Tushar Sonar, Aleksey Makarov, Yury Korobov, Evgeny Kharanzhevskiy, Ivan Zhidkov, Yulia Korkh, Tatyana Kuznetsova, Pei Wang and Yuefei Jia

Technologies 2025, 13(6), 231; https://doi.org/10.3390/technologies13060231 - 4 Jun 2025

Viewed by 165

Abstract

This study elucidates the dynamic tribo-mechanical response of laser-cladded FeNiCr-B₄C metal matrix composite (MMC) coatings on AISI 1040 steel substrate, unraveling the intricate interplay between microstructural features and phase transformations. A multi-faceted approach, employing high-resolution scanning electron microscopy (SEM) and advanced [...] Read more.

This study elucidates the dynamic tribo-mechanical response of laser-cladded FeNiCr-B₄C metal matrix composite (MMC) coatings on AISI 1040 steel substrate, unraveling the intricate interplay between microstructural features and phase transformations. A multi-faceted approach, employing high-resolution scanning electron microscopy (SEM) and advanced X-ray diffraction/Raman spectroscopy techniques, provided a comprehensive characterization of the coatings’ behavior under mechanical and scratch testing, shedding light on the mechanisms governing their wear resistance. Specifically, microstructural analysis revealed uniform coatings with a columnar structure and controlled defect density, showcasing an average thickness of 250 ± 20 μm and a transition zone of 80 ± 10 μm. X-ray diffraction and Raman spectroscopy confirmed the presence of α-Fe (Im-3m), γ-FeNiCr (Fm-3m), Fe₂B (I-42m), and B₄C (R-3m) phases, highlighting the successful incorporation of B₄C reinforcement. The addition of 5 and 7 wt.% B₄C significantly increased microhardness, showing enhancements up to 201% compared to the B₄C-free FeNiCr coating and up to 351% relative to the AISI 1040 steel substrate, respectively. Boron carbide addition promoted a synergistic strengthening effect between the in situ formed Fe₂B and the retained B₄C phases. Furthermore, scratch test analysis clarified improved wear resistance, excellent adhesion, and a tailored hardness gradient. These findings demonstrated that optimized short-pulsed laser cladding, combined with moderate B₄C reinforcement, is a promising route for creating robust, high-strength FeNiCr-B₄C MMC coatings suitable for demanding engineering applications. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2024)

► Show Figures

Graphical abstract

21 pages, 8188 KiB

Open AccessArticle

New Approach to Dominant and Prominent Color Extraction in Images with a Wide Range of Hues

by Yurii Kynash and Mariia Semeniv

Technologies 2025, 13(6), 230; https://doi.org/10.3390/technologies13060230 - 4 Jun 2025

Viewed by 216

Abstract

Dominant colors significantly influence visual image perception and are widely used in computer vision and design. Traditional extraction methods often neglect visually salient colors that occupy small areas yet possess high aesthetic relevance. This study introduces a method for detecting both dominant and [...] Read more.

Dominant colors significantly influence visual image perception and are widely used in computer vision and design. Traditional extraction methods often neglect visually salient colors that occupy small areas yet possess high aesthetic relevance. This study introduces a method for detecting both dominant and visually prominent colors in a wide range of hues and images. We analyzed the color gamut of images in the CIE L^*a^*b^* color space and concluded that it is difficult to identify the dominant and prominent colors due to high color variability. To address these challenges, the proposed approach transforms images into the orthogonal ICaS color space, integrating the properties of RGB and CMYK models, followed by K-means clustering. A spectral residual saliency map is applied to exclude background regions and emphasize perceptually significant objects. Experimental evaluation on an image database shows that the proposed method yields color palettes with broader gamut coverage, preserved luminance, and visually balanced combinations. A comparative analysis was conducted using the ΔE₀₀ metric, which accounts not only for differences in lightness, chroma, and hue but also for the perceptual interactions between colors, based on their proximity in the color space. The results confirm that the proposed method exhibits greater color stability and aesthetic coherence than existing approaches. These findings highlight the effectiveness of the orthogonal saliency mean method for delivering a more perceptually accurate and visually consistent representation of the dominant colors in an image. This outcome validates the method’s applicability for image analysis and design. Full article

(This article belongs to the Special Issue Image Analysis and Processing)

► Show Figures

Figure 1

18 pages, 43879 KiB

Open AccessArticle

Using AI to Reconstruct and Preserve 3D Temple Art with Old Images

by Naai-Jung Shih

Technologies 2025, 13(6), 229; https://doi.org/10.3390/technologies13060229 - 3 Jun 2025

Viewed by 181

Abstract

How can AI help us connect to the past in terms of conservation? How can 17-year-old photos be helpful in renewed preservation efforts? This research aims to use AI to connect both in a seamless 3D reconstruction of heritage from images taken of [...] Read more.

How can AI help us connect to the past in terms of conservation? How can 17-year-old photos be helpful in renewed preservation efforts? This research aims to use AI to connect both in a seamless 3D reconstruction of heritage from images taken of Gongfan Palace, Yunlin, Taiwan. AI-assisted 3D modeling was used to reconstruct the details of these images across different 3D platforms of the 3DGS or NeRF models generated by Postshot^®, RODIN^®, and KIRI Engine^®. Mesh and point models created using Zephyr^® were referred to and assessed in three sets. The consistent and inconsistent reconstructed results also included AI-assisted modeling outcomes in Stable Diffusion^®- and Postshot^®-based animations, followed by a 3D assessment and section-based composition analysis. The AI-assisted environment concluded with a recursive reconstruction involving 3D models and 2D images. AI assisted the 3D modeling process in an alternative approach, producing extraordinary structural and visual details. AI-trained models can be assessed and their use extended to composition analysis by section. Evolved documentation and interpretation using AI enables new structures and the management of resources, formats, and interfaces as part of continuous preservation efforts. Full article

(This article belongs to the Section Construction Technologies)

► Show Figures

Graphical abstract

27 pages, 5926 KiB

Open AccessArticle

Evaluation of Machine Learning Models for Enhancing Sustainability in Additive Manufacturing

by Waqar Shehbaz and Qingjin Peng

Technologies 2025, 13(6), 228; https://doi.org/10.3390/technologies13060228 - 3 Jun 2025

Viewed by 272

Abstract

Additive manufacturing (AM) presents significant opportunities for advancing sustainability through optimized process control and material utilization. This research investigates the application of machine learning (ML) models to directly associate AM process parameters with sustainability metrics, which is often a challenge by experimental methods [...] Read more.

Additive manufacturing (AM) presents significant opportunities for advancing sustainability through optimized process control and material utilization. This research investigates the application of machine learning (ML) models to directly associate AM process parameters with sustainability metrics, which is often a challenge by experimental methods alone. Initially, experimental data are generated by systematically varying key AM parameters, layer height, infill density, infill pattern, build orientation, and number of shells. Subsequently, four ML models, Linear Regression, Decision Trees, Random Forest, and Gradient Boosting, are trained and evaluated. Hyperparameter tuning is conducted using the Limited-memory Broyden–Fletcher–Goldfarb–Shanno with Box constraints (L-BFGS-B) algorithm, which demonstrates the superior computational efficiency compared to traditional approaches such as grid and random search. Among the models, Random Forest yields the highest predictive accuracy and lowest mean squared error across all target sustainability indicators: energy consumption, part weight, scrap weight, and production time. The results confirm the efficacy of ML in predicting sustainability outcomes when supported by robust experimental data. This research offers a scalable and computationally efficient approach to enhancing sustainability in AM processes and contributes to data-driven decision-making in sustainable manufacturing. Full article

(This article belongs to the Special Issue Advanced Manufacturing Technologies: From Material Jetting to 3D Printing)

► Show Figures

Graphical abstract

26 pages, 8159 KiB

Open AccessArticle

A Combined Mirror–EMG Robot-Assisted Therapy System for Lower Limb Rehabilitation

by Florin Covaciu, Bogdan Gherman, Calin Vaida, Adrian Pisla, Paul Tucan, Andrei Caprariu and Doina Pisla

Technologies 2025, 13(6), 227; https://doi.org/10.3390/technologies13060227 - 3 Jun 2025

Viewed by 678

Abstract

This paper presents the development and initial evaluation of a novel protocol for robot-assisted lower limb rehabilitation. It integrates dual-modal patient interaction, employing mirror therapy and an auto-adaptive EMG-driven control system, designed to enhance lower limb rehabilitation in patients with hemiparesis impairments. The [...] Read more.

This paper presents the development and initial evaluation of a novel protocol for robot-assisted lower limb rehabilitation. It integrates dual-modal patient interaction, employing mirror therapy and an auto-adaptive EMG-driven control system, designed to enhance lower limb rehabilitation in patients with hemiparesis impairments. The system features a robotic platform specifically engineered for lower limb rehabilitation, which operates in conjunction with a virtual reality (VR) environment. This immersive environment comprises a digital twin of the robotic system alongside a human avatar representing the patient and a set of virtual targets to be reached by the patient. To implement mirror therapy, the proposed protocol utilizes a set of inertial sensors placed on the patient’s healthy limb to capture real-time motion data. The auto-adaptive protocol takes as input the EMG signals (if any) from sensors placed on the impaired limb and performs the required motions to reach the virtual targets in the VR application. By synchronizing the motions of the healthy limb with the digital twin in the VR space, the system aims to promote neuroplasticity, reduce pain perception, and encourage engagement in rehabilitation exercises. Initial laboratory trials demonstrate promising outcomes in terms of improved motor function and subject motivation. This research not only underscores the efficacy of integrating robotics and virtual reality in rehabilitation but also opens avenues for advanced personalized therapies in clinical settings. Future work will investigate the efficiency of the proposed solution using patients, thus demonstrating clinical usability, and explore the potential integration of additional feedback mechanisms to further enhance the therapeutic efficacy of the system. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare and Information Processing)

► Show Figures

Figure 1

23 pages, 2955 KiB

Open AccessArticle

Numerical Simulations of Scaling of the Chamber Dimensions of the Liquid Piston Compressor for Hydrogen Applications

by Marina Konuhova, Valerijs Bezrukovs, Vladislavs Bezrukovs, Deniss Bezrukovs, Maksym Buryi, Nikita Gorbunovs and Anatoli I. Popov

Technologies 2025, 13(6), 226; https://doi.org/10.3390/technologies13060226 - 3 Jun 2025

Viewed by 520

Abstract

Hydrogen compression is a critical process in hydrogen storage and distribution, particularly for energy infrastructure and transportation. As hydrogen technologies expand beyond limited industrial applications, they are increasingly supporting the green economy, including offshore energy systems, smart ports, and sustainable marine industries. Efficient [...] Read more.

Hydrogen compression is a critical process in hydrogen storage and distribution, particularly for energy infrastructure and transportation. As hydrogen technologies expand beyond limited industrial applications, they are increasingly supporting the green economy, including offshore energy systems, smart ports, and sustainable marine industries. Efficient compression technologies are essential for ensuring reliable hydrogen storage and distribution across these sectors. This study focuses on optimizing hydrogen compression using a Liquid Piston Hydrogen Compressor through numerical simulations and scaling analysis. The research examines the influence of compression chamber geometry, including variations in radius and height, on thermal behavior and energy efficiency. A computational model was developed using COMSOL Multiphysics^® 6.0, incorporating Computational Fluid Dynamics (CFD) and heat transfer modules to analyze thermodynamic processes. The results highlight temperature distribution in hydrogen, working fluid, and chamber walls at different initial pressures (3.0 MPa and 20.0 MPa) and compression stroke durations. Larger chamber volumes lead to higher temperature increases but reach thermal stabilization. Increasing the chamber volume allows for a significant increase in the performance of the hydraulic compression system with a moderate increase in the temperature of hydrogen. These findings provide insights into optimizing hydrogen compression for enhanced production and broader applications. Full article

(This article belongs to the Special Issue Machine Learning and Related Statistical Applications in Complex Systems)

► Show Figures

Figure 1

21 pages, 2822 KiB

Open AccessArticle

Non-Contact Platform for the Assessment of Physical Function in Older Adults: A Pilot Study

by Ana Sobrino-Santos, Pedro Anuarbe, Carlos Fernandez-Viadero, Roberto García-García, José Miguel López-Higuera, Luis Rodríguez-Cobo and Adolfo Cobo

Technologies 2025, 13(6), 225; https://doi.org/10.3390/technologies13060225 - 2 Jun 2025

Viewed by 252

Abstract

In the context of global population aging, identifying reliable, objective tools to assess physical function and postural stability in older adults is increasingly important to mitigate fall risk. This study presents a non-contact platform that uses a Microsoft Azure Kinect depth camera to [...] Read more.

In the context of global population aging, identifying reliable, objective tools to assess physical function and postural stability in older adults is increasingly important to mitigate fall risk. This study presents a non-contact platform that uses a Microsoft Azure Kinect depth camera to evaluate functional performance related to lower-limb muscular capacity and static balance through self-selected depth squats and four progressively challenging stances (feet apart, feet together, semitandem, and tandem). By applying markerless motion capture algorithms, the system provides key biomechanical parameters such as center of mass displacement, knee angles, and sway trajectories. A comparison of older and younger individuals showed that the older group tended to perform shallower squats and exhibit greater mediolateral and anteroposterior sway, aligning with age-related declines in strength and postural control. Longitudinal tracking also illustrated how performance varied following a fall, indicating potential for ongoing risk assessment. Notably, in 30 s balance trials, the first 10 s often captured meaningful differences in stability, suggesting that short-duration stance tests can reliably detect early signs of imbalance. These findings highlight the feasibility of low-cost, user-friendly depth-camera technologies to complement traditional clinical measures and guide targeted fall-prevention strategies in older populations. Full article

(This article belongs to the Special Issue Assistive Technologies in Care and Rehabilitation: Research, Developments, and International Initiatives)

► Show Figures

Graphical abstract

20 pages, 1984 KiB

Open AccessArticle

The Use of Perlite and Rhyolite in Concrete Mix Design: Influence on Physical-Mechanical and Environmental Performance

by Giovanna Concu, Marco Zucca, Flavio Stochino, Monica Valdes and Francesca Maltinti

Technologies 2025, 13(6), 224; https://doi.org/10.3390/technologies13060224 - 29 May 2025

Viewed by 337

Abstract

During the last decades, the ever-growing evolution of the construction industry has led to a significant increase in demand for increasingly high-performing construction materials both in terms of mechanical characteristics and sustainability. Focusing on concrete, several researchers have designed different mixes to improve [...] Read more.

During the last decades, the ever-growing evolution of the construction industry has led to a significant increase in demand for increasingly high-performing construction materials both in terms of mechanical characteristics and sustainability. Focusing on concrete, several researchers have designed different mixes to improve mechanical properties such as compressive strength, workability and durability, and in many of the proposed mixes, the use of industrial waste stands out both for their ability to improve the mechanical properties of concrete and for the importance of their reuse from a sustainability point of view. In this paper, the use of two waste materials, perlite and rhyolite, in concrete mix design was studied in detail, considering their influence on the compressive strength at 7 and 28 days of curing. The waste materials were introduced in the mix design as substitutes for cement in percentages of 15% and 30% in weight. In addition, perlite was micronized to two different particle sizes, 20 μm and 63 μm, respectively, according to what is already used in concrete within perlite in the mix design. The behavior of the structural concrete containing perlite and rhyolite was compared in terms of compressive strength, Young modulus and produced equivalent CO₂ with that of a standard C25/30 reference concrete, and with that of a mix design created using other waste materials, namely fly ash, metakaolin and silica fume, considering cement replacements that are always at 15% and 30% by weight. Moreover, ultrasonic testing and rebound hammer tests were run to evaluate a possible relationship between the physical-mechanical properties of the design mixes and their volumetric and surface characteristics. Full article

(This article belongs to the Section Construction Technologies)

► Show Figures

Figure 1

26 pages, 2438 KiB

Open AccessArticle

A Hybrid KAN-BiLSTM Transformer with Multi-Domain Dynamic Attention Model for Cybersecurity

by Aleksandr Chechkin, Ekaterina Pleshakova and Sergey Gataullin

Technologies 2025, 13(6), 223; https://doi.org/10.3390/technologies13060223 - 29 May 2025

Viewed by 456

Abstract

With the exponential growth of cyberbullying cases on social media, there is a growing need to develop effective mechanisms for its detection and prediction, which can create a safer and more comfortable digital environment. One of the areas with such potential is the [...] Read more.

With the exponential growth of cyberbullying cases on social media, there is a growing need to develop effective mechanisms for its detection and prediction, which can create a safer and more comfortable digital environment. One of the areas with such potential is the application of natural language processing (NLP) and artificial intelligence (AI). This study applies a novel hybrid-structure Hybrid Transformer–Enriched Attention with Multi-Domain Dynamic Attention Network (Hyb-KAN), which combines a transformer-based architecture, an attention mechanism, and BiLSTM recurrent neural networks. In this study, a multi-class classification method is used to identify comments containing cyberbullying features. For better verification, we compared the proposed method with baseline methods. The Hyb-KAN model demonstrated high results on the multi-class classification dataset, achieving an accuracy of 95.25%. The synergy of BiLSTM, Transformer, MD-DAN, and KAN components provides flexibility and accuracy of text analysis. The study used explainable visualization techniques, including SHAP and LIME, to analyze the interpretability of the Hyb-KAN model, providing a deeper understanding of the decision-making mechanisms. In the final stage of the study, the results were compared with current research data to confirm their relevance to current trends. Full article

► Show Figures

Figure 1

18 pages, 466 KiB

Open AccessArticle

A Novel Dataset for Early Cardiovascular Risk Detection in School Children Using Machine Learning

by Rafael Alejandro Olivera Solís, Emilio Francisco González Rodríguez, Roberto Castañeda Sheissa, Juan Valentín Lorenzo-Ginori and José García

Technologies 2025, 13(6), 222; https://doi.org/10.3390/technologies13060222 - 29 May 2025

Viewed by 267

Abstract

This study introduces the PROCDEC dataset, a novel collection of 1140 cases with 30 cardiovascular risk factors gathered over a 10-year period from school children in Santa Clara, Cuba. The dataset was curated with input from medical experts in pediatric cardiology, endocrinology, general [...] Read more.

This study introduces the PROCDEC dataset, a novel collection of 1140 cases with 30 cardiovascular risk factors gathered over a 10-year period from school children in Santa Clara, Cuba. The dataset was curated with input from medical experts in pediatric cardiology, endocrinology, general medicine, and clinical laboratory, ensuring its clinical relevance. We conducted a rigorous performance evaluation of 10 machine learning (ML) algorithms to classify cardiovascular risk into two categories: at risk and not at risk. The models were assessed using a stratified k-fold cross-validation approach to enhance the reliability of the findings. Among the evaluated models—Bayes Net, Naive Bayes, SMO, K-Nearest Neighbors (KNN), Logistic Regression, AdaBoost, Multilayer Perceptron (MLP), J48, Logistic Model Tree (LMT), and Random Forest (RF)—the best-performing classifiers (MLP, LMT, J48 and Logistic Regression) achieved F1-score values exceeding 0.83, indicating strong predictive capability. To improve interpretability, we employed feature selection techniques to rank the most influential risk factors. Key contributors to classification performance included hypertension, hyperreactivity, body mass index (BMI), uric acid, cholesterol, parental hypertension, and sibling dyslipidemia. These findings align with established clinical knowledge and reinforce the potential of ML models for pediatric cardiovascular risk assessment. Unlike previous studies, our research not only evaluates multiple ML techniques but also emphasizes their clinical applicability and interpretability, which are critical for real-world implementation. Future work will focus on validating these models with external datasets and integrating them into decision-support systems for early risk detection. Full article

(This article belongs to the Special Issue Data Science and Big Data in Biology, Physical Science and Engineering—2nd Edition)

► Show Figures

Figure 1

30 pages, 707 KiB

Open AccessReview

Review of Converter Circuits with Power Factor Correction

by Angel Quiroga, Jhon Bayona and Helbert Espitia

Technologies 2025, 13(6), 221; https://doi.org/10.3390/technologies13060221 - 28 May 2025

Viewed by 340

Abstract

This article reviews converter circuits with power factor correction considering issues that arise in implementing such circuits. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) procedure are employed for the review. Six topologies with power factor correction were considered including boost, [...] Read more.

This article reviews converter circuits with power factor correction considering issues that arise in implementing such circuits. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) procedure are employed for the review. Six topologies with power factor correction were considered including boost, buck, buck-boost, Cük, dual boost, and totem pole bridgeless. The main findings highlight various implementation alternatives for these converters, taking into account complexity, performance, control strategies, and applications. Additionally, the review identified studies based on simulation and hardware implementation. Several alternatives exist for research to improve energy conversion circuits using conventional techniques such as PI controllers or novel controllers using artificial intelligence techniques such as neural networks. Finally, it should be noted that converter circuits with power factor correction are crucial for developing various electrical and electronic devices in domestic and industrial applications. Full article

(This article belongs to the Collection Electrical Technologies)

► Show Figures

Graphical abstract

4 pages, 2581 KiB

Open AccessCorrection

Correction: Polymeropoulos et al. Enhancing Solar Plant Efficiency: A Review of Vision-Based Monitoring and Fault Detection Techniques. Technologies 2024, 12, 175

by Ioannis Polymeropoulos, Stavros Bezyrgiannidis, Eleni Vrochidou and George A. Papakostas

Technologies 2025, 13(6), 220; https://doi.org/10.3390/technologies13060220 - 28 May 2025

Viewed by 155

Abstract

The authors wish to make the following corrections to this paper [...] Full article

► Show Figures

Figure 4

22 pages, 2967 KiB

Open AccessArticle

A Study on Interpretable Electric Load Forecasting Model with Spatiotemporal Feature Fusion Based on Attention Mechanism

by Shuaishuai Li and Weizhen Chen

Technologies 2025, 13(6), 219; https://doi.org/10.3390/technologies13060219 - 27 May 2025

Viewed by 230

Abstract

Driven by the global “double carbon” goal, the volatility of renewable energy poses a challenge to the stability of power systems. Traditional methods have difficulty dealing with high-dimensional nonlinear data, and the single deep learning model has the limitations of spatiotemporal feature decoupling [...] Read more.

Driven by the global “double carbon” goal, the volatility of renewable energy poses a challenge to the stability of power systems. Traditional methods have difficulty dealing with high-dimensional nonlinear data, and the single deep learning model has the limitations of spatiotemporal feature decoupling and being a “black box”. Aiming at the problem of insufficient accuracy and interpretability of power load forecasting in a renewable energy grid connected scenario, this study proposes an interpretable spatiotemporal feature fusion model based on an attention mechanism. Through CNN layered extraction of multi-dimensional space–time features such as meteorology and electricity price, BiLSTM bi-directional modeling time series rely on capturing the evolution rules of load series before and after, and the improved self-attention mechanism dynamically focuses on key features. Combined with the SHAP quantitative feature contribution and feature deletion experiment, a complete chain of “feature extraction time series modeling weight allocation interpretation and verification” is constructed. The experimental results show that the determination coefficient R² of the model on the Australian electricity market data set reaches 0.9935, which is 84.6% and 59.8% higher than that of the LSTM and GRU models, respectively. The prediction error (RMSE = 105.5079) is 9.7% lower than that of TCN-LSTM model and 52.1% compared to the GNN (220.6049). Cross scenario validation shows that the generalization performance is excellent (R² ≥ 0.9849). The interpretability analysis reveals that electricity price (average absolute value of SHAP 716.7761) is the core influencing factor, and its lack leads to a 0.76% decline in R². The research breaks through the limitation of time–space decoupling and the unexplainable bottleneck of traditional models, provides a transparent basis for power dispatching, and has an important reference value for the construction of new power systems. Full article

► Show Figures

Graphical abstract

15 pages, 245 KiB

Open AccessArticle

A Checklist to Assess Technologies for the Diagnosis and Rehabilitation of Geriatric Syndromes: A Delphi Study

by Alessia Gallucci, Cosimo Tuena, Anna Vedani, Marco Stramba-Badiale, Lorena Rossi, Antonio Greco, Fabrizio Giunco and Pietro Davide Trimarchi

Technologies 2025, 13(6), 218; https://doi.org/10.3390/technologies13060218 - 27 May 2025

Viewed by 221

Abstract

Technologies for frail elderly individuals facilitate the integration of care services, support the post-discharge period, and enhance independence and quality of life while reducing isolation. However, the lack of methodological rigor in studies on technologies for diagnosing and treating geriatric syndromes limits applied [...] Read more.

Technologies for frail elderly individuals facilitate the integration of care services, support the post-discharge period, and enhance independence and quality of life while reducing isolation. However, the lack of methodological rigor in studies on technologies for diagnosing and treating geriatric syndromes limits applied research. This study aimed to develop and validate a checklist considering technical readiness, clinical needs, and context to support the use of technologies primary in clinical practice and also in research settings. To this aim, a Delphi procedure was conducted in four rounds, followed by a pilot test assessing the checklist’s practical effectiveness. Twenty-nine items were defined and discussed. Among them, no item was deleted, while a total of eight items were reformulated. At the end of the assessment steps, 73% of items showed a high median relevance rating. The pilot test showed the difficulty in finding relevant information to complete the checklist as the only critical issue. This tool represents the first validated checklist in the field of technology-based healthcare for elderly individuals and supports the application of technologies in the diagnosis and treatment of geriatric syndromes by clinicians and researchers. Full article

(This article belongs to the Section Assistive Technologies)

15 pages, 4095 KiB

Open AccessArticle

AI-Generated Mnemonic Images Improve Long-Term Retention of Coronary Artery Occlusions in STEMI: A Comparative Study

by Zahraa Alomar, Meize Guo and Tyler Bland

Technologies 2025, 13(6), 217; https://doi.org/10.3390/technologies13060217 - 26 May 2025

Viewed by 323

Abstract

Medical students face significant challenges retaining complex information, such as interpreting ECGs for coronary artery occlusions, amidst demanding curricula. While artificial intelligence (AI) is increasingly used for medical image analysis, this study explored using generative AI (DALLE-3) to create mnemonic-based images to enhance [...] Read more.

Medical students face significant challenges retaining complex information, such as interpreting ECGs for coronary artery occlusions, amidst demanding curricula. While artificial intelligence (AI) is increasingly used for medical image analysis, this study explored using generative AI (DALLE-3) to create mnemonic-based images to enhance human learning and retention of medical images, in particular, electrocardiograms (ECGs). This study is among the first to investigate generative AI as a tool not for automated diagnosis but as a human-centered educational aid designed to enhance long-term retention in complex visual tasks like ECG interpretation. We conducted a comparative study with 275 first-year medical students across six campuses; an experimental group (n = 40) received a lecture supplemented with AI-generated mnemonic ECG images, while control groups (n = 235) received standard lectures with traditional ECG diagrams. Student achievement and retention were assessed by course examinations, and student preference and engagement were measured using the Situational Interest Survey for Multimedia (SIS-M). Control groups showed a significant decline in scores on the relevant exam question over time, whereas the experimental group’s scores remained stable, indicating improved long-term retention. Experimental students also reported significantly higher situational interest in the mnemonic-based images over traditional images. AI-generated mnemonic images can effectively improve long-term retention of complex ECG interpretation skills and enhance student engagement and preference, highlighting generative AI’s potential as a valuable cognitive tool in image analysis during medical education. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Medical Image Analysis)

► Show Figures

Figure 1

31 pages, 529 KiB

Open AccessReview

Review of Virtual Power Plant Response Capability Assessment and Optimization Dispatch

by Junhui Huang, Hui Li and Zhaoyun Zhang

Technologies 2025, 13(6), 216; https://doi.org/10.3390/technologies13060216 - 26 May 2025

Viewed by 453

Abstract

Functioning as a smart aggregation entity that combines distributed energy resources, energy storage systems, and flexible loads, virtual power plants (VPPs) serve as a pivotal technology in advancing the decarbonization and flexibility enhancement of modern power systems. Initially, we summarize the developmental context, [...] Read more.

Functioning as a smart aggregation entity that combines distributed energy resources, energy storage systems, and flexible loads, virtual power plants (VPPs) serve as a pivotal technology in advancing the decarbonization and flexibility enhancement of modern power systems. Initially, we summarize the developmental context, evolutionary trajectory, and conceptual framework of VPPs. The architecture is functionally partitioned into three tiers: the aggregation layer, communication layer, and dispatch optimization layer (central layer). The dispatch optimization layer of VPPs serves as the “intelligent brain” connecting physical resources with electricity markets, whose core lies in achieving “controllable, adjustable, and optimizable” distributed resources through algorithmic and data-driven approaches, driving the energy system transition towards low-carbon, flexible, and efficient directions. Next, we critically examine core technologies in the dispatch optimization layer, particularly the response capacity assessment and optimal resource scheduling. Its content mainly focuses on the latest research on the aggregated resource response capability evaluation, virtual power plant dispatching optimization models, and dispatching strategies. Conclusively, we analyze prevailing technical bottlenecks and summarize significant advancements, concluding with prospective insights into future research frontiers and developmental priorities for VPPs. In the future energy system transition, VPPs will play an increasingly important role. It is foreseeable that the utilization efficiency of renewable energy will be significantly enhanced, and the energy market will become more diverse and vibrant. We look forward to VPPs integrating more quickly and effectively into daily life, transforming lifestyles and helping people collectively step into a low-carbon, green future. Full article

(This article belongs to the Special Issue Next-Generation Distribution System Planning, Operation, and Control)

► Show Figures

Graphical abstract

17 pages, 3108 KiB

Open AccessArticle

Optimal Transient Control Scheme for Grid-Forming Permanent Magnet Synchronous Generator-Based Wind Farms

by Pan Hu, Dan Liu, Kan Cao and Lai Wei

Technologies 2025, 13(6), 215; https://doi.org/10.3390/technologies13060215 - 26 May 2025

Viewed by 195

Abstract

In this paper, an optimal transient control (OTC) scheme is proposed to improve the transient stability of the grid-forming (GFM) wind farm (WF) based on the transient stability of the WTs. The converter’s current operating safety range is considered to quantify the maximum [...] Read more.

In this paper, an optimal transient control (OTC) scheme is proposed to improve the transient stability of the grid-forming (GFM) wind farm (WF) based on the transient stability of the WTs. The converter’s current operating safety range is considered to quantify the maximum KES capabilities of the WTs. At the WF control level, the global transient voltage control problem is solved by optimizing the output reactive power of different WTs of the WF. At the WT control level, the transient stability of WT is improved by regulating the output power and weak magnetic current. The simulation results in MATLAB/Simulink show that the proposed control scheme can more efficiently improve the transient stability of WT by suppressing the DC bus voltage fluctuations and enhancing the voltage support capability of WT compared with the traditional control schemes. Full article

(This article belongs to the Special Issue Next-Generation Distribution System Planning, Operation, and Control)

► Show Figures

Figure 1

34 pages, 11729 KiB

Open AccessArticle

Intelligent Battery Management in a Hybrid Photovoltaic Using Fuzzy Logic System

by Jo-Ann V. Magsumbol, Argel A. Bandala, Alvin B. Culaba, Edwin Sybingco, Ryan Rhay P. Vicerra, Raouf Naguib and Elmer P. Dadios

Technologies 2025, 13(6), 214; https://doi.org/10.3390/technologies13060214 - 26 May 2025

Viewed by 400

Abstract

LiFePO₄ batteries need a battery management system (BMS) to improve performance, extend their lifespan, and maintain safety by utilizing advanced monitoring, control, and optimization techniques. This paper presents the design, development, and implementation of an intelligent battery management system (i-BMS) that integrates [...] Read more.

LiFePO₄ batteries need a battery management system (BMS) to improve performance, extend their lifespan, and maintain safety by utilizing advanced monitoring, control, and optimization techniques. This paper presents the design, development, and implementation of an intelligent battery management system (i-BMS) that integrates the real-time monitoring and control of batteries. The system was extensively tested using multiple datasets, and the results show that the system was able to maintain battery temperature within the set range, balance the cell voltages, and distribute energy according to load prioritization. It uses a fuzzy logic system approach to effectively manage farm energy requirements. Additionally, the proposed method embedded a three-level load prioritization algorithm woven into the fuzzy rule set to allocate energy dynamically among essential, regular, and non-essential loads. Full article

(This article belongs to the Topic Internet of Things Architectures, Applications, and Strategies: Emerging Paradigms, Technologies, and Advancing AI Integration)

► Show Figures

Figure 1

20 pages, 2062 KiB

Open AccessArticle

Dynamic Simulation Model to Monitor Flow Growth Rivers in Rapid-Response Catchments Using Humanitarian Logistic Strategies

by Jesús Delgado-Maciel, Guillermo Cortés-Robles, José Roberto Grande-Ramírez, Luis Rolando Guarneros-Nolasco, José Ernesto Domínguez-Herrera, Roberto Alvarado-Juárez and Enrique Delgado-Alvarado

Technologies 2025, 13(6), 213; https://doi.org/10.3390/technologies13060213 - 26 May 2025

Viewed by 250

Abstract

Climate change, driven by natural factors and human activity, produces significant environmental changes worldwide. One consequence is increased rainfall, which leads to intense and increasingly frequent storms, sudden increases in river flows, and increased likelihood of emergencies linked to natural disasters. This framework [...] Read more.

Climate change, driven by natural factors and human activity, produces significant environmental changes worldwide. One consequence is increased rainfall, which leads to intense and increasingly frequent storms, sudden increases in river flows, and increased likelihood of emergencies linked to natural disasters. This framework proposes a model based on the System Dynamics (SD) approach that aims to monitor the increase in flow in rapid-response catchments (RRCs). The model evaluates humanitarian logistics strategies to manage supplies during emergency situations and it is based on dynamic simulation, whose advantages are the analysis of causal relationships between variables and their behavior over time, mathematical support during the creation of the simulation model, and the creation of a graphical interface that allows the user to carry out a visual analysis of the variables involved in the model. The results show, through a case study, the implementation of a containment plan based on early decision-making from rapid-response catchment monitoring to generate humanitarian logistics strategies preventing material and human damage. Therefore, the main contribution of this framework is the creation of a simulation model that involves the synergy between two different systems: the analysis of RRC behavior and the humanitarian logistics plan to establish provision policies (food, water and medicine) based on the number of people at risk. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2024)

► Show Figures

Figure 1

22 pages, 7738 KiB

Open AccessArticle

Application of Machine Learning Methods for Identifying Wave Aberrations from Combined Intensity Patterns Generated Using a Multi-Order Diffractive Spatial Filter

by Paval. A. Khorin, Aleksey P. Dzyuba, Aleksey V. Chernykh, Muhammad A. Butt and Svetlana N. Khonina

Technologies 2025, 13(6), 212; https://doi.org/10.3390/technologies13060212 - 26 May 2025

Viewed by 288

Abstract

A multi-order combined diffraction spatial filter, integrated with a set of Zernike phase functions (representing wavefront aberrations) and Zernike polynomials, enables the simultaneous formation of multiple aberration-transformed point spread function (PSF) patterns in a single plane. This is achieved using an optical Fourier [...] Read more.

A multi-order combined diffraction spatial filter, integrated with a set of Zernike phase functions (representing wavefront aberrations) and Zernike polynomials, enables the simultaneous formation of multiple aberration-transformed point spread function (PSF) patterns in a single plane. This is achieved using an optical Fourier correlator and provides significantly more information than a single PSF captured in focal or defocused planes—all without requiring mechanical movement. To analyze the resulting complex intensity patterns, which include 49 diffraction orders, a convolutional neural network based on the Xception architecture is employed. This model effectively identifies wavefront aberrations up to the fourth Zernike order. After 80 training epochs, the model achieved a mean absolute error (MAE) of no more than 0.0028. Additionally, a five-fold cross-validation confirmed the robustness and reliability of the approach. For the experimental validation of the proposed multi-order filter, a liquid crystal spatial light modulator was used. Optical experiments were conducted using a Fourier correlator setup, where aberration fields were generated via a digital micromirror device. The experimental results closely matched the simulation data, confirming the effectiveness of the method. New advanced aberrometers and multichannel diffractive optics technologies can be used in industry for the quality control of optical elements, assessing optical system alignment errors, and the early-stage detection of eye diseases. Full article

(This article belongs to the Section Information and Communication Technologies)

► Show Figures

Figure 1

25 pages, 3624 KiB

Open AccessArticle

Advanced Machine Learning Methods for the Prediction of the Optical Parameters of Tellurite Glasses

by Fahimeh Ahmadi, Mohsen Hajihassani, Tryfon Sivenas, Stefanos Papanikolaou and Panagiotis G. Asteris

Technologies 2025, 13(6), 211; https://doi.org/10.3390/technologies13060211 - 25 May 2025

Viewed by 261

Abstract

This study evaluates the predictive performance of advanced machine learning models, including DeepBoost, XGBoost, CatBoost, RF, and MLP, in estimating the Ω₂, Ω₄, and Ω₆ parameters based on a comprehensive set of input variables. Among the models, DeepBoost [...] Read more.

This study evaluates the predictive performance of advanced machine learning models, including DeepBoost, XGBoost, CatBoost, RF, and MLP, in estimating the Ω₂, Ω₄, and Ω₆ parameters based on a comprehensive set of input variables. Among the models, DeepBoost consistently demonstrated the best performance across the training and testing phases. For the Ω₂ prediction, DeepBoost achieved an R² of 0.974 and accuracy of 99.895% in the training phase, with corresponding values of 0.971 and 99.902% in the testing phase. In comparison, XGBoost ranked second with an R² of 0.929 and accuracy of 99.870% during testing. For Ω₄, DeepBoost achieved a training phase R² of 0.955 and accuracy of 99.846%, while the testing phase results included an R² of 0.945 and accuracy of 99.951%. Similar trends were observed for Ω₆, where DeepBoost obtained near-perfect training phase results (R² = 0.997, accuracy = 99.968%) and testing phase performance (R² = 0.994, accuracy = 99.946%). These findings are further supported by violin plots and correlation analyses, underscoring DeepBoost’s superior predictive reliability and generalization capabilities. This work highlights the importance of model selection in predictive tasks and demonstrates the potential of machine learning for capturing complex relationships in data. Full article

► Show Figures

Figure 1

Journal Menu

Journal Browser

Technologies, Volume 13, Issue 6 (June 2025) – 29 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI