Technologies

54 pages, 23072 KiB

Open AccessArticle

Advancing Image Compression Through Clustering Techniques: A Comprehensive Analysis

by Mohammed Omari, Mohammed Kaddi, Khouloud Salameh and Ali Alnoman

Technologies 2025, 13(3), 123; https://doi.org/10.3390/technologies13030123 - 19 Mar 2025

Viewed by 514

Image compression is a critical area of research aimed at optimizing data storage and transmission while maintaining image quality. This paper explores the application of clustering techniques as a means to achieve efficient and high-quality image compression. We systematically analyze nine clustering methods: [...] Read more.

Image compression is a critical area of research aimed at optimizing data storage and transmission while maintaining image quality. This paper explores the application of clustering techniques as a means to achieve efficient and high-quality image compression. We systematically analyze nine clustering methods: K-Means, BIRCH, Divisive Clustering, DBSCAN, OPTICS, Mean Shift, GMM, BGMM, and CLIQUE. Each technique is evaluated across a variety of parameters, including block size, number of clusters, and other method-specific attributes, to assess their impact on compression ratio and structural similarity index. The experimental results reveal significant differences in performance among the techniques. K-Means, Divisive Clustering, and CLIQUE emerge as reliable methods, balancing high compression ratios and excellent image quality. In contrast, techniques like Mean Shift, DBSCAN, and OPTICS demonstrate limitations, particularly in compression efficiency. Experimental validation using benchmark images from the CID22 dataset confirms the robustness and applicability of the proposed methods in diverse scenarios. Full article

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

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28 pages, 1343 KiB

Open AccessSystematic Review

Indoor Environmental Monitoring and Chronic Respiratory Diseases: A Systematic Review

by Patricia Camacho-Magriñán, Diego Sales-Lerida, Antonio León-Jiménez and Daniel Sanchez-Morillo

Technologies 2025, 13(3), 122; https://doi.org/10.3390/technologies13030122 - 18 Mar 2025

Viewed by 600

Abstract

Chronic respiratory diseases (CRD), which include Chronic Obstructive Pulmonary Disease (COPD) and asthma, are significant global health issues, with air quality playing a vital role in exacerbating these conditions. This systematic review explores how monitoring indoor air quality (IAQ) can help manage and [...] Read more.

Chronic respiratory diseases (CRD), which include Chronic Obstructive Pulmonary Disease (COPD) and asthma, are significant global health issues, with air quality playing a vital role in exacerbating these conditions. This systematic review explores how monitoring indoor air quality (IAQ) can help manage and reduce respiratory exacerbations in CRD patients. A search of the Web of Science database, yielding 301 articles, was conducted following PRISMA guidelines. Of these, 60 met the inclusion criteria, and after screening, 21 articles were analyzed. The review identified substantial gaps in current research: the lack of standardization in IAQ monitoring; the need for considering geographic variability and for long-term longitudinal studies; and the importance of linking monitored air quality data with respiratory health indicators. It also stressed the importance of considering the heterogeneity of patients in the methodological study design, as well as the convenience of introducing recommendation systems to assess the true impact of corrective measures on indoor air quality in the homes of chronic respiratory patients. The integration of home-based IAQ monitoring with machine learning techniques to enhance our understanding of the relationship between IAQ and respiratory health is emerging as a key area for future research. Addressing all these challenges has the potential to mitigate the impact of CRD and improve the quality of life for patients. Full article

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

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38 pages, 3173 KiB

Open AccessReview

SDN-Enabled IoT Security Frameworks—A Review of Existing Challenges

by Sandipan Rakeshkumar Mishra, Bharanidharan Shanmugam, Kheng Cher Yeo and Suresh Thennadil

Technologies 2025, 13(3), 121; https://doi.org/10.3390/technologies13030121 - 18 Mar 2025

Viewed by 1342

Abstract

This comprehensive systematic review examines the integration of software-defined networking (SDN) with IoT security frameworks, analyzing recent advancements in encryption, authentication, access control techniques, and intrusion detection systems. Our analysis reveals that while SDN demonstrates promising capabilities in enhancing IoT security through centralized [...] Read more.

This comprehensive systematic review examines the integration of software-defined networking (SDN) with IoT security frameworks, analyzing recent advancements in encryption, authentication, access control techniques, and intrusion detection systems. Our analysis reveals that while SDN demonstrates promising capabilities in enhancing IoT security through centralized control and dynamic policy enforcement, several critical limitations persist, particularly in scalability and real-world validation. As intrusion detection represents an integral security requirement for robust IoT frameworks, we conduct an in-depth evaluation of Machine Learning (ML) and Deep Learning (DL) techniques that have emerged as predominant approaches for threat detection in SDN-enabled IoT environments. The review categorizes and analyzes these ML/DL implementations across various architectural paradigms, identifying patterns in their effectiveness for different security contexts. Furthermore, recognizing that the performance of these ML/DL models critically depends on training data quality, we evaluate existing IoT security datasets, identifying significant gaps in representing contemporary attack vectors and realistic IoT environments. A key finding indicates that hybrid architectures integrating cloud–edge–fog computing demonstrate superior performance in distributing security workloads compared to single-tier implementations. Based on this systematic analysis, we propose key future research directions, including adaptive zero-trust architectures, federated machine learning for distributed security, and comprehensive dataset creation methodologies, that address current limitations in IoT security research. Full article

(This article belongs to the Special Issue IoT-Enabling Technologies and Applications)

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18 pages, 5092 KiB

Open AccessArticle

Predicting the Temperature of a Permanent Magnet Synchronous Motor: A Comparative Study of Artificial Neural Network Algorithms

by Nabil El Bazi, Nasr Guennouni, Mohcin Mekhfioui, Adil Goudzi, Ahmed Chebak and Mustapha Mabrouki

Technologies 2025, 13(3), 120; https://doi.org/10.3390/technologies13030120 - 17 Mar 2025

Cited by 1 | Viewed by 492

Abstract

The accurate prediction of temperature in Permanent Magnet Synchronous Motors (PMSMs) has always been essential for monitoring performance and enabling predictive maintenance in the industrial sector. This study examines the efficiency of a set of artificial neural network (ANN) models, namely Multilayer Perceptron [...] Read more.

The accurate prediction of temperature in Permanent Magnet Synchronous Motors (PMSMs) has always been essential for monitoring performance and enabling predictive maintenance in the industrial sector. This study examines the efficiency of a set of artificial neural network (ANN) models, namely Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), in predicting the Permanent Magnet Temperature. A comparative evaluation study is conducted using common performance indicators, including root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²), to assess the predictive accuracy of each model. The intent is to identify the most favorable model that balances high accuracy with low computational cost. Full article

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

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19 pages, 3501 KiB

Open AccessArticle

Cross-Encoder-Based Semantic Evaluation of Extractive and Generative Question Answering in Low-Resourced African Languages

by Funebi Francis Ijebu, Yuanchao Liu, Chengjie Sun, Nobert Jere, Ibomoiye Domor Mienye and Udoinyang Godwin Inyang

Technologies 2025, 13(3), 119; https://doi.org/10.3390/technologies13030119 - 16 Mar 2025

Viewed by 493

Abstract

Efficient language analysis techniques and models are crucial in the artificial intelligence age for enhancing cross-lingual question answering. Transfer learning with state-of-the-art models has been beneficial in this regard, but the performance of low-resource African languages with morphologically rich grammatical structures and unique [...] Read more.

Efficient language analysis techniques and models are crucial in the artificial intelligence age for enhancing cross-lingual question answering. Transfer learning with state-of-the-art models has been beneficial in this regard, but the performance of low-resource African languages with morphologically rich grammatical structures and unique typologies has shown deficiencies linkable to evaluation techniques and scarce training data. To enhance the former, this paper proposes an evaluation pipeline leveraging the semantic answer similarity method enhanced with automatic answer annotation. The pipeline uses the Language-agnostic BERT Sentence Embedding model integrated with an adapted vector measure to perform cross-lingual text analysis after answer prediction. Experimental results from the multilingual-T5 and AfroXLMR models on nine languages of the AfriQA dataset surpassed existing benchmarks deploying string-based methods for question answer evaluation. The results are also superior to the F1-score-based GPT4 and Llama-2 performances on the same downstream task. The automatic answer annotation technique effectively reduced the labelling time while maintaining a high performance. Thus, the proposed pipeline is more efficient than the prevailing string-based F1 and Exact Match metrics in mixed answer type question–answer evaluations, and it is a more natural performance estimator for models targeting real-world deployment. Full article

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28 pages, 3148 KiB

Open AccessArticle

Comparative Analysis of Different Display Technologies for Defect Detection in 3D Objects

by Vasil Kozov, Ekaterin Minev, Magdalena Andreeva, Tzvetomir Vassilev and Rumen Rusev

Technologies 2025, 13(3), 118; https://doi.org/10.3390/technologies13030118 - 14 Mar 2025

Viewed by 581

Abstract

This paper starts with an overview of current methods of displaying 3D objects. Two different technologies are compared—a glasses-free 3D laptop that uses stereoscopy, and one that uses front projection on a silver impregnated fabric screen that diffracts light to achieve a holographic [...] Read more.

This paper starts with an overview of current methods of displaying 3D objects. Two different technologies are compared—a glasses-free 3D laptop that uses stereoscopy, and one that uses front projection on a silver impregnated fabric screen that diffracts light to achieve a holographic effect. The research question is defined—which one is suitable for use by specialists. A methodology for an experiment is designed. A scenario for finding the solution to the problem during the experiment is created. An experiment environment with different workstations for each technology has been set up. An additional reference workstation with a standard screen has been created. Three-dimensional CAD models from the field of mechanical engineering were chosen. Different categories of defects were introduced to make the models usable for the scenario—finding the defects in each of the different workstations. A survey for participant feedback, using several categories of questions, was created, improved, and used during the experiment. The experiment was completed, short discussions were held with each participant, and their feedback was analyzed. The categories of the participants were discussed. The results from the experiment were discussed and analyzed. Statistical analysis was performed on the survey results. The applicability of the experiment in other fields was discussed. Conclusions were made, and the comparative advantages and specifics of each technology were discussed based on the analysis results and the experience gained during the experiment. Full article

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

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26 pages, 5752 KiB

Open AccessReview

Towards a Holistic Approach for UAV-Based Large-Scale Photovoltaic Inspection: A Review on Deep Learning and Image Processing Techniques

by Zoubir Barraz, Imane Sebari, Kenza Ait El Kadi and Ibtihal Ait Abdelmoula

Technologies 2025, 13(3), 117; https://doi.org/10.3390/technologies13030117 - 14 Mar 2025

Cited by 1 | Viewed by 909

Abstract

This paper provides an in-depth literature review on image processing techniques, focusing on deep learning approaches for anomaly detection and classification in photovoltaics. It examines key components of UAV-based PV inspection, including data acquisition protocols, panel segmentation and geolocation, anomaly classification, and optimizations [...] Read more.

This paper provides an in-depth literature review on image processing techniques, focusing on deep learning approaches for anomaly detection and classification in photovoltaics. It examines key components of UAV-based PV inspection, including data acquisition protocols, panel segmentation and geolocation, anomaly classification, and optimizations for model generalization. Furthermore, challenges related to domain adaptation, dataset limitations, and multimodal fusion of RGB and thermal data are also discussed. Finally, research gaps and opportunities are analyzed to create a holistic, scalable, and real-time inspection workflow for large-scale installation. This review serves as a reference for researchers and industry professionals to advance UAV-based PV inspection. Full article

(This article belongs to the Section Environmental Technology)

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22 pages, 8363 KiB

Open AccessArticle

Implementation of Machine Vision Methods for Cattle Detection and Activity Monitoring

by Roman Bumbálek, Tomáš Zoubek, Jean de Dieu Marcel Ufitikirezi, Sandra Nicole Umurungi, Radim Stehlík, Zbyněk Havelka, Radim Kuneš and Petr Bartoš

Technologies 2025, 13(3), 116; https://doi.org/10.3390/technologies13030116 - 12 Mar 2025

Viewed by 831

Abstract

The goal of this research was to implement machine vision algorithms in a cattle stable to detect cattle in stalls and determine their activities. It also focused on finding the optimal hyperparameter settings for training the model, as balancing prediction accuracy, training time, [...] Read more.

The goal of this research was to implement machine vision algorithms in a cattle stable to detect cattle in stalls and determine their activities. It also focused on finding the optimal hyperparameter settings for training the model, as balancing prediction accuracy, training time, and computational demands is crucial for real-world implementation. The investigation of suitable parameters was carried out on the YOLOv5 convolutional neural network (CNN). The types of the YOLOv5 network (v5x, v5l, v5m, v5s, and v5n), the effect of the learning rate (0.1, 0.01, and 0.001), the batch size (4, 8, 16, and 32), and the effect of the optimizer used (SGD and Adam) were compared in a step-by-step process. The main focus was on mAP 0.5 and mAP 0.5:0.95 metrics and total training time, and we came to the following conclusions: In terms of optimization between time and accuracy, the YOLOv5m performed the best, with a mAP 0.5:0.95 of 0.8969 (compared to 0.9070 for YOLOv5x). The training time for YOLOv5m was 7:48:19, while YOLOv5x took 16:53:27. When comparing learning rates, the variations in accuracy and training time were minimal. The highest accuracy (0.9028) occurred with a learning rate of 0.001, and the lowest (0.8897) with a learning rate of 0.1. For training time, the fastest was 7:47:17, with a difference of 1:02:00 between the fastest and slowest times. When comparing the effect of batch size, model accuracy showed only minimal differences (in tenths of a percentage), but there were significant time savings. When using a batch size of 8, the training time was 12:50:48, while increasing the batch size to 32 reduced the training time to 6:07:13, thus speeding up the training process by 6:43:35. The last parameter compared was the optimizer. SGD and Adam optimizers were compared. The choice of optimizer had a minimal impact on the training time, with differences only in seconds. However, the accuracy of the trained model was 6 per cent higher (0.8969) when using the SGD optimizer. Full article

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

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18 pages, 2668 KiB

Open AccessArticle

Optimizing Last-Mile Deliveries: Addressing Customer Absence Through Genetic Algorithm

by Javier Sánchez-Soriano, Guillermo Verdín-Urgal and Natalia Gordo-Herrera

Technologies 2025, 13(3), 115; https://doi.org/10.3390/technologies13030115 - 12 Mar 2025

Viewed by 941

Abstract

Last-mile delivery logistics face significant challenges, particularly regarding customer absences during scheduled delivery times. This issue not only frustrates customers but also imposes substantial economic costs on delivery companies, estimated at up to 15 euros per failed delivery. This research aims to address [...] Read more.

Last-mile delivery logistics face significant challenges, particularly regarding customer absences during scheduled delivery times. This issue not only frustrates customers but also imposes substantial economic costs on delivery companies, estimated at up to 15 euros per failed delivery. This research aims to address this problem by optimizing last-mile delivery processes using a genetic algorithm (GA) designed to minimize rerouting costs while respecting customer time preferences. The study compares the performance of the proposed GA with a Simulated Annealing (SA) algorithm, assessing their efficiency in route optimization. Through detailed simulations, GA reduces operational costs by over 35,000 euros annually by considering customer preferences. It significantly outperforms the SA algorithm in scenarios with high customer variability, highlighting its potential for cost-efficient last-mile delivery solutions. Additionally, the GA consistently respected 4–7 more customer preferences per route compared to traditional methods, leading to enhanced customer satisfaction. This work contributes to the field by providing a robust methodology for balancing cost efficiency and user satisfaction in last-mile deliveries, offering actionable insights for logistics optimization. Full article

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

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21 pages, 15452 KiB

Open AccessArticle

Portable DNA Probe Detector and a New Dry-QCM Approach for SARS-CoV-2 Detection

by Dhanunjaya Munthala, Thita Sonklin, Narong Chanlek, Ashish Mathur, Souradeep Roy, Devash Kumar Avasthi, Sanong Suksaweang and Soodkhet Pojprapai

Technologies 2025, 13(3), 114; https://doi.org/10.3390/technologies13030114 - 12 Mar 2025

Viewed by 745

Abstract

This work demonstrates the preliminary results of rapid and direct detection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) using the quartz crystal microbalance (QCM) method. Coronavirus Disease 2019 (COVID-19)-specific RNA-dependent RNA polymerase (RdRP) gene-dependent probe DNA was used as a selective agent toward [...] Read more.

This work demonstrates the preliminary results of rapid and direct detection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) using the quartz crystal microbalance (QCM) method. Coronavirus Disease 2019 (COVID-19)-specific RNA-dependent RNA polymerase (RdRP) gene-dependent probe DNA was used as a selective agent toward target DNA, the inactivated SARS-CoV-2 virus, and RNAs extracted from clinical samples. This study developed and utilised a unique dry-QCM approach with a mitigated experimental procedure. Contact angle measurements, Atomic Force Microscopy (AFM) and X-ray Photoelectron Spectroscopy (XPS) measurements were employed to investigate the surface during probe immobilisation and target hybridisation. This study also investigates the effect of temperature on probe immobilisation and target hybridisation. The estimated probe density was 0.51 × 10¹² probes/cm², which is below the critical limit. The estimated hybridisation efficiency was about 58.9%. The linear detection range with a Limit of Detection (LoD) was about ~1.22 nM with high selectivity toward SARS-CoV-2 target DNA. The sensor shelf-life was found to be extended to 25 days. The novelty of using a new dry-QCM approach for SARS-CoV-2 detection was proven with the results. Full article

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29 pages, 4181 KiB

Open AccessReview

Using Serious Games and Digital Games to Improve Students’ Computational Thinking and Programming Skills in K-12 Education: A Systematic Literature Review

by Sindre Wennevold Gundersen and Georgios Lampropoulos

Technologies 2025, 13(3), 113; https://doi.org/10.3390/technologies13030113 - 11 Mar 2025

Cited by 1 | Viewed by 1947

Abstract

Computational thinking and problem-solving skills have become vital for students to develop. Digital games and serious games are increasingly being used in educational settings and present great potential to aid students’ learning. This study aims to explore the role and impact of serious [...] Read more.

Computational thinking and problem-solving skills have become vital for students to develop. Digital games and serious games are increasingly being used in educational settings and present great potential to aid students’ learning. This study aims to explore the role and impact of serious games and digital games on students’ computational thinking and programming skills in primary, secondary, and K-12 education through a systematic review of the existing literature. Four research questions were set to be examined. Following the PRISMA framework, 78 studies deriving from IEEE, Scopus, and Web of Science over the period of 2011–2024 are examined. The studies are categorized into Theoretical and Review studies, Proposal and Showcase studies, and Experimental and Case studies. Based on the results, serious games and digital games arose as meaningful educational tools that are positively viewed by education stakeholders and that can effectively support and improve K-12 education students’ computational thinking and programming skills. Among the benefits identified, it was revealed that serious games offer enjoyable and interactive learning experiences that can improve students’ learning performance, engagement, and motivation, enhance students’ confidence and focus, and promote self-regulated learning and personalized learning. Additionally, serious games emerged as an educational means that can effectively support social learning and provide real-time feedback. The challenges identified were related to the selection of games and the game-related design elements, decisions, and approaches. Hence, the study highlights the significance of the design of serious games and the need to cultivate students’ computational thinking, problem-solving, and social skills from a young age. Finally, the study reveals key design principles and aspects to consider when developing serious games and digital games and highlights the need to involve education stakeholders throughout the design and development process. Full article

(This article belongs to the Collection Review Papers Collection for Advanced Technologies)

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25 pages, 26700 KiB

Open AccessArticle

Power Tracking and Performance Analysis of Hybrid Perturb–Observe, Particle Swarm Optimization, and Fuzzy Logic-Based Improved MPPT Control for Standalone PV System

by Ali Abbas, Muhammad Farhan, Muhammad Shahzad, Rehan Liaqat and Umer Ijaz

Technologies 2025, 13(3), 112; https://doi.org/10.3390/technologies13030112 - 8 Mar 2025

Viewed by 1113

Abstract

The increasing energy demand and initiatives to lower carbon emissions have elevated the significance of renewable energy sources. Photovoltaic (PV) systems are pivotal in converting solar energy into electricity and have a significant role in sustainable energy production. Therefore, it is critical to [...] Read more.

The increasing energy demand and initiatives to lower carbon emissions have elevated the significance of renewable energy sources. Photovoltaic (PV) systems are pivotal in converting solar energy into electricity and have a significant role in sustainable energy production. Therefore, it is critical to implement maximum power point tracking (MPPT) controllers to optimize the efficiency of PV systems by extracting accessible maximum power. This research investigates the performance and comparison of various MPPT control algorithms for a standalone PV system. Several cases involving individual MPPT controllers, as well as hybrid combinations using two and three controllers, have been simulated in MATLAB/SIMULINK. The sensed parameters, i.e., output power, voltage, and current, specify that though individual controllers effectively track the maximum power point, hybrid controllers achieve superior performance by utilizing the combined strengths of each algorithm. The results indicate that individual MPPT controllers, such as perturb and observe (P&O), particle swarm optimization (PSO), and fuzzy logic (FL), achieved tracking efficiencies of 97.6%, 90.3%, and 90.1%, respectively. In contrast, hybrid dual controllers such as P&O-PSO, PSO-FL, and P&O-FL demonstrated improved performance, with tracking efficiencies of 96.8%, 96.4%, and 96.5%, respectively. This research also proposes a new hybrid triple-MPPT controller combining P&O-PSO-FL, which surpassed both individual and dual-hybrid controllers, achieving an impressive efficiency of 99.5%. Finally, a comparison of all seven cases of MPPT control algorithms is presented, highlighting the advantages and disadvantages of individual as well as hybrid approaches. Full article

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19 pages, 13798 KiB

Open AccessArticle

RANFIS-Based Sensor System with Low-Cost Multi-Sensors for Reliable Measurement of VOCs

by Keunyoung Kim and Woosung Yang

Technologies 2025, 13(3), 111; https://doi.org/10.3390/technologies13030111 - 7 Mar 2025

Viewed by 843

Abstract

This study describes a sensor system for continuous monitoring of volatile organic compounds (VOCs) emitted from small industrial facilities in urban centers, such as automobile paint facilities and printing facilities. Previously, intermittent measurements were made using expensive flame ionization detector (FID)-type instruments that [...] Read more.

This study describes a sensor system for continuous monitoring of volatile organic compounds (VOCs) emitted from small industrial facilities in urban centers, such as automobile paint facilities and printing facilities. Previously, intermittent measurements were made using expensive flame ionization detector (FID)-type instruments that were impossible to install, resulting in a lack of continuous management. This paper develops a low-cost sensor system for full-time management and consists of multi-sensor systems to increase the spatial resolution in the pipe. To improve the accuracy and reliability of this system, a new reinforced adaptive neuro fuzzy inference system (RANFIS) model with enhanced preprocessing based on the adaptive neuro fuzzy inference system (ANFIS) model is proposed. For this purpose, a smart sensor module consisting of low-cost metal oxide semiconductors (MOSs) and photo-ionization detectors (PIDs) is fabricated, and an operating controller is configured for real-time data acquisition, analysis, and evaluation. In the front part of the RANFIS, interquartile range (IQR) is used to remove outliers, and gradient analysis is used to detect and correct data with abnormal change rates to solve nonlinearities and outliers in sensor data. In the latter stage, the complex nonlinear relationship of the data was modeled using the ANFIS to reliably handle data uncertainty and noise. For practical verification, a toluene evaporation chamber with a sensor system for monitoring was built, and the results of real-time data sensing after training based on real data were compared and evaluated. As a result of applying the RANFIS model, the RMSE of the MQ135, MQ138, and PID-A15 sensors were 3.578, 11.594, and 4.837, respectively, which improved the performance by 87.1%, 25.9%, and 35.8% compared to the existing ANFIS. Therefore, the precision within 5% of the measurement results of the two experimentally verified sensors shows that the proposed RANFIS-based sensor system can be sufficiently applied in the field. Full article

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

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19 pages, 30651 KiB

Open AccessArticle

Comparative Evaluation of Commercial, Freely Available, and Open-Source Tools for Single-Cell Analysis Within Freehand-Defined Histological Brightfield Image Regions of Interest

by Filippo Piccinini, Marcella Tazzari, Maria Maddalena Tumedei, Nicola Normanno, Gastone Castellani and Antonella Carbonaro

Technologies 2025, 13(3), 110; https://doi.org/10.3390/technologies13030110 - 7 Mar 2025

Viewed by 903

Abstract

In the field of histological analysis, one of the typical issues is the analysis of single cells contained in regions of interest (i.e., ROIs). Today, several commercial, freely available, and open-source software options are accessible for this task. However, the literature lacks recent [...] Read more.

In the field of histological analysis, one of the typical issues is the analysis of single cells contained in regions of interest (i.e., ROIs). Today, several commercial, freely available, and open-source software options are accessible for this task. However, the literature lacks recent extensive reviews that summarise the functionalities of the opportunities currently available and provide guidance on selecting the most suitable option for analysing specific cases, for instance, irregular freehand-defined ROIs on brightfield images. In this work, we reviewed and compared 14 software tools tailored for single-cell analysis within a 2D histological freehand-defined image ROI. Precisely, six open-source tools (i.e., CellProfiler, Cytomine, Digital Slide Archive, Icy, ImageJ/Fiji, QuPath), four freely available tools (i.e., Aperio ImageScope, NIS Elements Viewer, Sedeen, SlideViewer), and four commercial tools (i.e., Amira, Arivis, HALO, Imaris) were considered. We focused on three key aspects: (a) the capacity to handle large file formats such as SVS, DICOM, and TIFF, ensuring compatibility with diverse datasets; (b) the flexibility in defining irregular ROIs, whether through automated extraction or manual delineation, encompassing square, circular, polygonal, and freehand shapes to accommodate varied research needs; and (c) the capability to classify single cells within selected ROIs on brightfield images, ranging from fully automated to semi-automated or manual approaches, requiring different levels of user involvement. Thanks to this work, a deeper understanding of the strengths and limitations of different software platforms emerges, facilitating informed decision making for researchers looking for a tool to analyse histological brightfield images. Full article

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

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12 pages, 932 KiB

Open AccessArticle

Enhancing Computational Efficiency of Network Reliability with a New Prime Shortest Path Algorithm

by Wei-Chang Yeh, Yunzhi Jiang and Chia-Ling Huang

Technologies 2025, 13(3), 109; https://doi.org/10.3390/technologies13030109 - 7 Mar 2025

Viewed by 694

Abstract

To address the increasing demands of modern networks, evaluating computational efficiency of modified network reliability is essential, with minimal paths (MPs) serving as a critical factor. However, traditional approaches to assessing computational efficiency of network reliability often struggle with challenges such as duplicate [...] Read more.

To address the increasing demands of modern networks, evaluating computational efficiency of modified network reliability is essential, with minimal paths (MPs) serving as a critical factor. However, traditional approaches to assessing computational efficiency of network reliability often struggle with challenges such as duplicate MPs and sub-path identification, resulting in exponential computational time. In this study, we present a novel algorithm based on the Prime Shortest Path (PSP) approach, which efficiently resolves these challenges by self-detecting and eliminating duplication in polynomial time. This marks a significant improvement over existing methods. The algorithm’s correctness is rigorously validated, and its superior performance is confirmed through a detailed time complexity analysis and comparisons with the leading state-of-the-art algorithms. Full article

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

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20 pages, 6134 KiB

Open AccessArticle

A Hardware-in-the-Loop Simulation Platform for a High-Speed Maglev Positioning and Speed Measurement System

by Linzi Yin, Cong Luo, Ling Liu, Junfeng Cui, Zhiming Liu and Guoying Sun

Technologies 2025, 13(3), 108; https://doi.org/10.3390/technologies13030108 - 6 Mar 2025

Viewed by 723

Abstract

In order to solve the testing and verification problems at the early development stage of a high-speed Maglev positioning and speed measurement system (MPSS), a hardware-in-the-loop (HIL) simulation platform is presented, which includes induction loops, transmitting antennas, a power driver unit, a simulator [...] Read more.

In order to solve the testing and verification problems at the early development stage of a high-speed Maglev positioning and speed measurement system (MPSS), a hardware-in-the-loop (HIL) simulation platform is presented, which includes induction loops, transmitting antennas, a power driver unit, a simulator based on a field-programmable gate array (FPGA), a host computer, etc. This HIL simulation platform simulates the operation of a high-speed Maglev train and generates the related loop-induced signals to test the performance of a real ground signal processing unit (GSPU). Furthermore, an absolute position detection method based on Gray-coded loops is proposed to identify which Gray-coded period the train is in. A relative position detection method based on height compensation is also proposed to calculate the exact position of the train in a Gray-coded period. The experimental results show that the positioning error is only 2.58 mm, and the speed error is 6.34 km/h even in the 600 km/h condition. The proposed HIL platform also effectively simulates the three kinds of operation modes of high-speed Maglev trains, which verifies the effectiveness and practicality of the HIL simulation strategy. This provides favorable conditions for the development and early validation of high-speed MPSS. Full article

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

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15 pages, 340 KiB

Open AccessArticle

Harnessing Metacognition for Safe and Responsible AI

by Peter B. Walker, Jonathan J. Haase, Melissa L. Mehalick, Christopher T. Steele, Dale W. Russell and Ian N. Davidson

Technologies 2025, 13(3), 107; https://doi.org/10.3390/technologies13030107 - 6 Mar 2025

Cited by 2 | Viewed by 1397

Abstract

The rapid advancement of artificial intelligence (AI) technologies has transformed various sectors, significantly enhancing processes and augmenting human capabilities. However, these advancements have also introduced critical concerns related to the safety, ethics, and responsibility of AI systems. To address these challenges, the principles [...] Read more.

The rapid advancement of artificial intelligence (AI) technologies has transformed various sectors, significantly enhancing processes and augmenting human capabilities. However, these advancements have also introduced critical concerns related to the safety, ethics, and responsibility of AI systems. To address these challenges, the principles of the robustness, interpretability, controllability, and ethical alignment framework are essential. This paper explores the integration of metacognition—defined as “thinking about thinking”—into AI systems as a promising approach to meeting these requirements. Metacognition enables AI systems to monitor, control, and regulate the system’s cognitive processes, thereby enhancing their ability to self-assess, correct errors, and adapt to changing environments. By embedding metacognitive processes within AI, this paper proposes a framework that enhances the transparency, accountability, and adaptability of AI systems, fostering trust and mitigating risks associated with autonomous decision-making. Additionally, the paper examines the current state of AI safety and responsibility, discusses the applicability of metacognition to AI, and outlines a mathematical framework for incorporating metacognitive strategies into active learning processes. The findings aim to contribute to the development of safe, responsible, and ethically aligned AI systems. Full article

(This article belongs to the Special Issue Artificial Intelligence and Smart Information Systems: Trends and Innovations)

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24 pages, 20905 KiB

Open AccessArticle

A Realistic Breast Phantom for Investigating the Features of the Microwave Radiometry Method Using Mathematical and Physical Modelling

by Maxim V. Polyakov and Danila S. Sirotin

Technologies 2025, 13(3), 106; https://doi.org/10.3390/technologies13030106 - 6 Mar 2025

Cited by 1 | Viewed by 1014

Abstract

This article presents the development of an anatomical breast phantom for investigating the capabilities of microwave radiometry in assessing thermal processes in biological tissues. The phantom accounts for the heterogeneous tissue structure and haemodynamics, enabling realistic heat transfer modelling. Numerical simulation software was [...] Read more.

This article presents the development of an anatomical breast phantom for investigating the capabilities of microwave radiometry in assessing thermal processes in biological tissues. The phantom accounts for the heterogeneous tissue structure and haemodynamics, enabling realistic heat transfer modelling. Numerical simulation software was developed, accurately reproducing experimental results and allowing the study of thermal anomalies. Experimental validation demonstrated that the temperature in the subcutaneous layer differed on average by 0.3 °C from deeper tissues, confirming the method’s effectiveness. The presence of a tumour in the model resulted in a local temperature increase of up to 0.77 °C, highlighting the sensitivity of microwave radiometry to tumour-induced thermal anomalies. These findings contribute to enhancing non-invasive techniques for early breast disease detection. Full article

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34 pages, 10596 KiB

Open AccessArticle

Scalable Container-Based Time Synchronization for Smart Grid Data Center Networks

by Kennedy Chinedu Okafor, Wisdom Onyema Okafor, Omowunmi Mary Longe, Ikechukwu Ignatius Ayogu, Kelvin Anoh and Bamidele Adebisi

Technologies 2025, 13(3), 105; https://doi.org/10.3390/technologies13030105 - 5 Mar 2025

Cited by 1 | Viewed by 1308

Abstract

The integration of edge-to-cloud infrastructures in smart grid (SG) data center networks requires scalable, efficient, and secure architecture. Traditional server-based SG data center architectures face high computational loads and delays. To address this problem, a lightweight data center network (DCN) with low-cost, and fast-converging [...] Read more.

The integration of edge-to-cloud infrastructures in smart grid (SG) data center networks requires scalable, efficient, and secure architecture. Traditional server-based SG data center architectures face high computational loads and delays. To address this problem, a lightweight data center network (DCN) with low-cost, and fast-converging optimization is required. This paper introduces a container-based time synchronization model (CTSM) within a spine–leaf virtual private cloud (SL-VPC), deployed via AWS CloudFormation stack as a practical use case. The CTSM optimizes resource utilization, security, and traffic management while reducing computational overhead. The model was benchmarked against five DCN topologies—DCell, Mesh, Skywalk, Dahu, and Ficonn—using Mininet simulations and a software-defined CloudFormation stack on an Amazon EC2 HPC testbed under realistic SG traffic patterns. The results show that CTSM achieved near-100% reliability, with the highest received energy data (29.87%), lowest packetization delay (13.11%), and highest traffic availability (70.85%). Stateless container engines improved resource allocation, reducing administrative overhead and enhancing grid stability. Software-defined Network (SDN)-driven adaptive routing and load balancing further optimized performance under dynamic demand conditions. These findings position CTSM-SL-VPC as a secure, scalable, and efficient solution for next-generation smart grid automation. Full article

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20 pages, 903 KiB

Open AccessArticle

A Hybrid Solar–Thermoelectric System Incorporating Molten Salt for Sustainable Energy Storage Solutions

by Mahmoud Z. Mistarihi, Ghazi M. Magableh and Saba M. Abu Dalu

Technologies 2025, 13(3), 104; https://doi.org/10.3390/technologies13030104 - 5 Mar 2025

Viewed by 936

Abstract

Green sustainable energy, especially renewable energy, is gaining huge popularity and is considered a vital energy in addressing energy conservation and global climate change. One of the most significant renewable energy sources in the UAE is solar energy, due to the country’s high [...] Read more.

Green sustainable energy, especially renewable energy, is gaining huge popularity and is considered a vital energy in addressing energy conservation and global climate change. One of the most significant renewable energy sources in the UAE is solar energy, due to the country’s high solar radiation levels. This paper focuses on advanced technology that integrates parabolic trough mirrors, molten salt storage, and thermoelectric generators (TEGs) to provide a reliable and effective solar system in the UAE. Furthermore, the new system can be manufactured in different sizes suitable for consumption whether in ordinary houses or commercial establishments and businesses. The proposed design theoretically achieves the target electrical energy of 2.067 kWh/day with 90% thermal efficiency, 90.2% optical efficiency, and 8% TEG efficiency that can be elevated to higher values reaching 149% using the liquid-saturated porous medium, ensuring the operation of the system throughout the day. This makes it a suitable solar system in off-grid areas. Moreover, this system is a cost-effective, carbon-free, and day-and-night energy source that can be dispatched on the electric grid like any fossil fuel plant under the proposed method, with less maintenance, thus contributing to the UAE’s renewable energy strategy. Full article

(This article belongs to the Section Environmental Technology)

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40 pages, 3792 KiB

Open AccessReview

Recent Development of Corrosion Inhibitors: Types, Mechanisms, Electrochemical Behavior, Efficiency, and Environmental Impact

by Denisa-Ioana (Gheorghe) Răuță, Ecaterina Matei and Sorin-Marius Avramescu

Technologies 2025, 13(3), 103; https://doi.org/10.3390/technologies13030103 - 5 Mar 2025

Cited by 2 | Viewed by 3294

Abstract

This review examines recent advances in corrosion inhibitor technologies, with a focus on sustainable and environmentally friendly solutions that address both industrial efficiency and environmental safety. Corrosion is a ubiquitous problem, contributing to massive economic losses globally, with costs estimated between 1 and [...] Read more.

This review examines recent advances in corrosion inhibitor technologies, with a focus on sustainable and environmentally friendly solutions that address both industrial efficiency and environmental safety. Corrosion is a ubiquitous problem, contributing to massive economic losses globally, with costs estimated between 1 and 5% of GDP in different countries. Traditional inorganic corrosion inhibitors, while effective, are often based on toxic compounds, necessitating the development of more environmentally friendly and non-toxic alternatives. The present work highlights innovative eco-friendly corrosion inhibitors derived from natural sources, including plant extracts and oils, biopolymers, etc., being biodegradable substances that provide effective corrosion resistance with minimal environmental impact. In addition, this review explores organic–inorganic hybrid inhibitors and nanotechnology-enhanced coatings that demonstrate improved efficiency, durability, and adaptability across industries. Key considerations, such as application techniques, mechanisms of action, and the impact of environmental factors on inhibitor performance, are discussed. This comprehensive presentation aims to contribute to updating the data on the development of advanced corrosion inhibitors capable of meeting the requirements of modern industries while promoting sustainable and safe practices in corrosion management. Full article

(This article belongs to the Section Environmental Technology)

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76 pages, 8958 KiB

Open AccessArticle

Robust Intrusion Detection System Using an Improved Hybrid Deep Learning Model for Binary and Multi-Class Classification in IoT Networks

by Hesham Kamal and Maggie Mashaly

Technologies 2025, 13(3), 102; https://doi.org/10.3390/technologies13030102 - 4 Mar 2025

Viewed by 1396

Abstract

The rapid expansion of internet of things (IoT) applications has significantly boosted productivity and streamlined daily activities. However, this widespread adoption has also introduced considerable security challenges, making IoT environments vulnerable to large-scale botnet attacks. These attacks have often succeeded in achieving their [...] Read more.

The rapid expansion of internet of things (IoT) applications has significantly boosted productivity and streamlined daily activities. However, this widespread adoption has also introduced considerable security challenges, making IoT environments vulnerable to large-scale botnet attacks. These attacks have often succeeded in achieving their malicious goals, highlighting the urgent need for robust detection strategies to secure IoT networks. To overcome these obstacles, this research presents an innovative anomaly-driven intrusion detection approach specifically tailored for IoT networks. The proposed model employs an advanced hybrid architecture that seamlessly integrates convolutional neural networks (CNN) with multilayer perceptron (MLP), enabling precise detection and classification of both binary and multi-class IoT network traffic. The CNN component is responsible for extracting and enhancing features from network traffic data and preparing these features for effective classification by the MLP, which handles the final classification task. To further manage class imbalance, the model incorporates the enhanced hybrid adaptive synthetic sampling-synthetic minority oversampling technique (ADASYN-SMOTE) for binary classification, advanced ADASYN for multiclass classification, and employs edited nearest neighbors (ENN) alongside class weights. The CNN-MLP architecture is meticulously crafted to minimize erroneous classifications, enhance instantaneous threat detection, and precisely recognize previously unseen cyber intrusions. The model’s effectiveness was rigorously tested using the IoT-23 and NF-BoT-IoT-v2 datasets. On the IoT-23 dataset, the model achieved 99.94% accuracy in two-stage binary classification, 99.99% accuracy in multiclass classification excluding the normal class, and 99.91% accuracy in single-phase multiclass classification including the normal class. Utilizing the NF-BoT-IoT-v2 dataset, the model attained an exceptional 99.96% accuracy in the dual-phase binary classification paradigm, 98.02% accuracy in multiclass classification excluding the normal class, and 98.11% accuracy in single-phase multiclass classification including the normal class. The results demonstrate that our model consistently delivers high levels of accuracy, precision, recall, and F1 score across both binary and multiclass classifications, establishing it as a robust solution for securing IoT networks. Full article

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20 pages, 2485 KiB

Open AccessArticle

Hand Dexterity Evaluation Grounded on Cursor Trajectory Investigation in Children with ADHD Using a Mouse and a Joystick

by Alexandros Pino, Nikolaos Papatheodorou, Georgios Kouroupetroglou, Panagiotis-Alexios Giannopoulos, Gerasimos Makris and Charalambos Papageorgiou

Technologies 2025, 13(3), 99; https://doi.org/10.3390/technologies13030099 - 3 Mar 2025

Viewed by 857

Abstract

This study investigates disparities in upper limb motor skills between children with and without Attention Deficit Hyperactivity Disorder (ADHD), employing one-dimensional (1D) and two-dimensional (2D) point-and-click experiments using a mouse and a joystick and introducing one new metric for mouse cursor trajectory analysis. [...] Read more.

This study investigates disparities in upper limb motor skills between children with and without Attention Deficit Hyperactivity Disorder (ADHD), employing one-dimensional (1D) and two-dimensional (2D) point-and-click experiments using a mouse and a joystick and introducing one new metric for mouse cursor trajectory analysis. The participant pool comprised 46 children with combined type ADHD and an equivalent number of children without ADHD. The Input Device Evaluation Application (IDEA) system monitored the mouse pointer’s trajectory. Ten trajectory parameters were computed, including Index of Difficulty, Movement Time, Throughput, Missed Clicks, Target Re-Entry, Task Axis Crossing, Movement Direction Change, Movement Variability, Movement Error, Movement Offset, and Sample Entropy. The 2D joystick experiment trajectory parameters analysis conducted using a hierarchical logistic regression model achieved a 78% success rate in identifying children with ADHD. This research sheds light on the motor skill differences associated with ADHD in the context of computer-based tasks, providing valuable insights into potential diagnostic applications and intervention strategies and introducing one new metric makes for a deeper cursor trajectory analysis. Full article

(This article belongs to the Section Assistive Technologies)

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28 pages, 7320 KiB

Open AccessArticle

Technology for Improving the Accuracy of Predicting the Position and Speed of Human Movement Based on Machine Learning Models

by Artem Obukhov, Denis Dedov, Andrey Volkov and Maksim Rybachok

Technologies 2025, 13(3), 101; https://doi.org/10.3390/technologies13030101 - 3 Mar 2025

Viewed by 969

Abstract

The solution to the problem of insufficient accuracy in determining the position and speed of human movement during interaction with a treadmill-based training complex is considered. Control command generation based on the training complex user’s actions may be performed with a delay, may [...] Read more.

The solution to the problem of insufficient accuracy in determining the position and speed of human movement during interaction with a treadmill-based training complex is considered. Control command generation based on the training complex user’s actions may be performed with a delay, may not take into account the specificity of movements, or be inaccurate due to the error of the initial data. The article introduces a technology for improving the accuracy of predicting a person’s position and speed on a running platform using machine learning and computer vision methods. The proposed technology includes analysing and processing data from the tracking system, developing machine learning models to improve the quality of the raw data, predicting the position and speed of human movement, and implementing and integrating neural network methods into the running platform control system. Experimental results demonstrate that the decision tree (DT) model provides better accuracy and performance in solving the problem of positioning key points of a human model in complex conditions with overlapping limbs. For speed prediction, the linear regression (LR) model showed the best results when the analysed window length was 10 frames. Prediction of the person’s position (based on 10 previous frames) is performed using the DT model, which is optimal in terms of accuracy and computation time relative to other options. The comparison of the control methods of the running platform based on machine learning models showed the advantage of the combined method (linear control function combined with the speed prediction model), which provides an average absolute error value of 0.116 m/s. The results of the research confirmed the achievement of the primary objective (increasing the accuracy of human position and speed prediction), making the proposed technology promising for application in human-machine systems. Full article

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

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20 pages, 2678 KiB

Open AccessArticle

Low-Temperature Slow Pyrolysis: Exploring Biomass-Specific Biochar Characteristics and Potential for Soil Applications

by Matheus Antonio da Silva, Adibe Luiz Abdalla Filho, Ruan Carnier, Juliana de Oliveira Santos Marcatto, Marcelo Saldanha, Aline Renee Coscione, Thaís Alves de Carvalho, Gabriel Rodrigo Merlotto and Cristiano Alberto de Andrade

Technologies 2025, 13(3), 100; https://doi.org/10.3390/technologies13030100 - 3 Mar 2025

Viewed by 1263

Abstract

The pyrolysis process of residues has emerged as a sustainable method for managing organic waste, producing biochars that offer significant benefits for agriculture and the environment. These benefits depend on the properties of the raw biomass and the pyrolysis conditions, such as washing [...] Read more.

The pyrolysis process of residues has emerged as a sustainable method for managing organic waste, producing biochars that offer significant benefits for agriculture and the environment. These benefits depend on the properties of the raw biomass and the pyrolysis conditions, such as washing and drying. This study investigated biochar production through slow pyrolysis at 300 °C, using eight biomass types, four being plant residues (PBR)—sugarcane bagasse, filter cake, sawdust, and stranded algae—and four non-plant-based residues (NPBR)—poultry litter, sheep manure, layer chicken manure, and sewage sludge. The physicochemical properties assessed included yield, carbon (C) and nitrogen (N) content, electrical conductivity, pH, macro- and micronutrients, and potentially toxic metals. Pyrolysis generally increased pH and concentrated C, N, phosphorus (P), and other nutrients while reducing electrical conductivity, C/N ratio, potassium (K), and sulfur (S) contents. The increases in the pH of the biochars in relation to the respective biomasses were between 0.3 and 1.9, with the greatest differences observed for the NPBR biochars. Biochars from sugarcane bagasse and sawdust exhibited high C content (74.57–77.67%), highlighting their potential use for C sequestration. Filter cake biochar excelled in P (14.28 g kg⁻¹) and micronutrients, while algae biochar showed elevated N, calcium (Ca), and boron (B) levels. NPBR biochars were rich in N (2.28–3.67%) and P (20.7–43.4 g kg⁻¹), making them ideal fertilizers. Although sewage sludge biochar contained higher levels of potentially toxic metals, these remained within regulatory limits. This research highlights variations in the composition of biochars depending on the characteristics of the original biomass and the pyrolysis process, to contribute to the production of customized biochars for the purposes of their application in the soil. Biochars derived from exclusively plant biomasses showed important aspects related to the recovery of carbon from biomass and can be preferred as biochar used to sequester carbon in the soil. On the other hand, biochars obtained from residues with some animal contributions are more enriched in nutrients and should be directed to the management of soil fertility. Full article

(This article belongs to the Special Issue Recent Advances in Applied Activated Carbon Research)

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25 pages, 42227 KiB

Open AccessArticle

“The Foot Can Do It”: Controlling the “Persistence” Prosthetic Arm Using the “Infinity-2” Foot Controller

by Peter L. Bishay, Gerbert Funes Alfaro, Ian Sherrill, Isaiah Reoyo, Elihu McMahon, Camron Carter, Cristian Valdez, Naweeth M. Riyaz, Sara Ali, Adrian Lima, Abel Nieto and Jared Tirone

Technologies 2025, 13(3), 98; https://doi.org/10.3390/technologies13030098 - 1 Mar 2025

Viewed by 1326

Abstract

The “Infinity” foot controller for controlling prosthetic arms has been improved in this paper in several ways, including a foot sleeve that enables barefoot use, an improved sensor-controller unit design, and a more intuitive control scheme that allows gradual control of finger actuation. [...] Read more.

The “Infinity” foot controller for controlling prosthetic arms has been improved in this paper in several ways, including a foot sleeve that enables barefoot use, an improved sensor-controller unit design, and a more intuitive control scheme that allows gradual control of finger actuation. Furthermore, the “Persistence Arm”, a novel transradial prosthetic arm prototype, is introduced. This below-the-elbow arm has a direct-drive wrist actuation system, a thumb design with two degrees of freedom, and carbon fiber tendons for actuating the four forefingers. The manufactured prototype arm and foot controller underwent various tests to verify their efficacy. Wireless transmission speed tests showed that the maximum time delay is less than 165 ms, giving almost instantaneous response from the arm to any user’s foot control signal. Gripping tests quantified the grip and pulling forces of the arm prototype as 2.8 and 12.7 kg, respectively. The arm successfully gripped various household items of different shapes, weights, and sizes. These results highlight the potential of foot control as an alternative prosthetic arm control method and the possibility of new 3D-printed prosthetic arm designs to replace costly prostheses in the market, which could potentially reduce the high rejection rates of upper limb prostheses. Full article

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

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21 pages, 12426 KiB

Open AccessArticle

Scientific Molding and Adaptive Process Quality Control with External Sensors for Injection Molding Process

by Chen-Hsiang Chang, Chien-Hung Wen, Ren-Ho Tseng, Chieh-Hsun Tsai, Yu-Hao Chen, Sheng-Jye Hwang and Hsin-Shu Peng

Technologies 2025, 13(3), 97; https://doi.org/10.3390/technologies13030097 - 1 Mar 2025

Viewed by 1086

Abstract

This study established a real-time measurement system to monitor the melt quality in an injection molding process using a pressure sensor installed on the nozzle and a strain gauge installed on the tie bar. Based on the sensing curves from these two external [...] Read more.

This study established a real-time measurement system to monitor the melt quality in an injection molding process using a pressure sensor installed on the nozzle and a strain gauge installed on the tie bar. Based on the sensing curves from these two external sensors, the characteristic values of nozzle pressure and clamping force were used to optimize parameters. This study defined product weight as a quality indicator and developed a scientific molding parameter setup process. The optimization sequence of parameters is injection speed, V/P switchover point, packing pressure, packing time, and clamping force. Finally, an adaptive process control system was established based on the online quality characteristic values to maintain product quality consistency. Continuous production experiments were conducted at two sites to verify the system’s effectiveness. The results revealed that the optimized process parameters can ensure product weight stability during long-term production. Furthermore, using the adaptive process control system further enhanced product weight stability at both sites, reducing the standard deviation of product weight to 0.0289 g and 0.0148 g, and the coefficient of variation to 0.065% and 0.035%, respectively. Full article

(This article belongs to the Section Manufacturing Technology)

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12 pages, 375 KiB

Open AccessProtocol

Training Cognitive Functions Using DUAL-REHAB, a New Dual-Task Application in MCI and SMC: A Study Protocol of a Randomized Control Trial

by Elisa Pedroli, Francesca Bruni, Valentina Mancuso, Silvia Cavedoni, Francesco Bigotto, Jonathan Panigada, Monica Rossi, Lorenzo Boilini, Karine Goulene, Marco Stramba-Badiale and Silvia Serino

Technologies 2025, 13(3), 96; https://doi.org/10.3390/technologies13030096 - 1 Mar 2025

Viewed by 1019

Abstract

Background: Current research on Alzheimer’s Disease has progressively focused on Mild Cognitive Impairment (MCI) as a pre-dementia state, as well as on Subjective Memory Complaint (SMC), as a potential early indicator of cognitive change. Consequently, timely interventions to prevent cognitive decline are essential [...] Read more.

Background: Current research on Alzheimer’s Disease has progressively focused on Mild Cognitive Impairment (MCI) as a pre-dementia state, as well as on Subjective Memory Complaint (SMC), as a potential early indicator of cognitive change. Consequently, timely interventions to prevent cognitive decline are essential and are most effective when combined with motor training. Nevertheless, motor-cognitive dual-task training often employs non-ecological tasks and is confined to clinical contexts lacking generalizability to daily life. The integration of 360° media could overcome these limitations. Therefore, the aim of the current work is twofold: (a) to present a dual-task training using 360° technology for its interactivity, versatility, and ecological validity, and (b) to propose a protocol to test its efficacy through a randomized clinical trial. Methods: This study will recruit 90 older adults (MCI and SMC). Participants will follow two phases of training: in-hospital rehabilitation and at-home rehabilitation. The experimental design will follow a 2 × 3 × 2 structure with 3 factors: type of treatment (360° training vs. traditional rehabilitation), time (baseline, post in-hospital training, and post at-home training), and group (SMC vs. MCI). Results: The expected outcome is an improvement in cognitive and motor functioning after the experimental training. Conclusion: This study will advance the literature on non-pharmacological interventions and innovative technological tools for cognitive trainings in the early stages of cognitive decline. Full article

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

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18 pages, 11708 KiB

Open AccessArticle

The Transition to an Eco-Friendly City as a First Step Toward Climate Neutrality with Green Hydrogen

by Lăzăroiu Gheorghe, Mihăescu Lucian, Stoica Dorel and Năstasă (Băcăran) Florentina-Cătălina

Technologies 2025, 13(3), 95; https://doi.org/10.3390/technologies13030095 - 1 Mar 2025

Viewed by 958

Abstract

A city of the future will need to be eco-friendly while meeting general social and economic requirements. Hydrogen-based technologies provide solutions for initially limiting CO₂ emissions, with prospects indicating complete decarbonization in the future. Cities will need to adopt and integrate these [...] Read more.

A city of the future will need to be eco-friendly while meeting general social and economic requirements. Hydrogen-based technologies provide solutions for initially limiting CO₂ emissions, with prospects indicating complete decarbonization in the future. Cities will need to adopt and integrate these technologies to avoid a gap between the development of hydrogen production and its urban application. Achievable results are analyzed by injecting hydrogen into the urban methane gas network, initially in small proportions, but gradually increasing over time. This paper also presents a numerical application pertaining to the city of Bucharest, Romania—a metropolis with a population of 2.1 million inhabitants. Although the use of fuel cells is less advantageous for urban transport compared to electric battery-based solutions, the heat generated by hydrogen-based technologies, such as fuel cells, can be efficiently utilized for residential heating. However, storage solutions are required for residential consumption, separate from that of urban transport, along with advancements in electric transport using existing batteries, which necessitate a detailed economic assessment. For electricity generation, including cogeneration, gas turbines have proven to be the most suitable solution. Based on the analyzed data, the paper synthesizes the opportunities offered by hydrogen-based technologies for a city of the future. Full article

(This article belongs to the Section Environmental Technology)

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45 pages, 5675 KiB

Open AccessReview

A Comprehensive Review of Quality Control and Reliability Research in Micro–Nano Technology

by Nowshin Sharmile, Risat Rimi Chowdhury and Salil Desai

Technologies 2025, 13(3), 94; https://doi.org/10.3390/technologies13030094 - 1 Mar 2025

Viewed by 3379

Abstract

This paper presents a comprehensive review of quality control (QC) and reliability research in micro–nano technology, which is vital for advancing microelectronics, biomedical engineering, and manufacturing. Micro- and nanotechnologies operate at different scales, yet both require precise control to ensure the performance and [...] Read more.

This paper presents a comprehensive review of quality control (QC) and reliability research in micro–nano technology, which is vital for advancing microelectronics, biomedical engineering, and manufacturing. Micro- and nanotechnologies operate at different scales, yet both require precise control to ensure the performance and durability of small-scale systems. This review synthesizes key quality control methodologies, including statistical quality control methods, machine learning and AI-driven methods, and advanced techniques emphasizing their relevance to nanotechnology applications. The paper also discusses the application of micro/nanotechnology in quality control in other technological areas. The discussion extends to the unique reliability challenges posed by micro–nano systems, such as failure modes related to stiction, material fatigue, and environmental factors. Advanced reliability testing and modeling approaches are highlighted for their effectiveness in predicting performance and mitigating risks. Additionally, the paper explores the integration of emerging technologies to enhance and improve reliability in micro–nano manufacturing. By examining both established and novel techniques, this review underscores the evolving nature of quality control and reliability research in the field. It identifies key areas for future investigation, particularly in the adaptation of these methods to the increasing complexity of micro–nano systems. The paper concludes by proposing research directions that can further optimize quality control and reliability to ensure the continued advancement and industrial application of micro–nano technologies. Full article

(This article belongs to the Section Innovations in Materials Processing)

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