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Technologies
Volume 13
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Technologies, Volume 13, Issue 2 (February 2025) – 24 articles

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21 pages, 831 KiB  
Article
The Role of BIM 6D and 7D in Enhancing Sustainable Construction Practices: A Qualitative Study
by Hanan Al-Raqeb and Seyed Hamidreza Ghaffar
Technologies 2025, 13(2), 65; https://doi.org/10.3390/technologies13020065 (registering DOI) - 3 Feb 2025
Abstract
The construction industry in Kuwait is experiencing a transformative shift with the adoption of Building Information Modeling (BIM) technologies, particularly BIM 6D for sustainability analysis and 7D for facility management. This study investigates the integration of these dimensions to address sustainability challenges in [...] Read more.
The construction industry in Kuwait is experiencing a transformative shift with the adoption of Building Information Modeling (BIM) technologies, particularly BIM 6D for sustainability analysis and 7D for facility management. This study investigates the integration of these dimensions to address sustainability challenges in Kuwait’s construction sector, aligning practices with the United Nations’ Sustainable Development Goals (SDGs). Through qualitative interviews with 15 stakeholders—including architects, engineers, and contractors—and analysis of industry reports, policies, and case studies, the research identifies both opportunities for and barriers to BIM adoption. While BIM offers significant potential for lifecycle analysis, waste reduction, and energy efficiency, its adoption remains limited, with only 27% of construction waste recycled. Challenges include high initial costs, a shortage of skilled personnel, and resistance to change. The study highlights actionable strategies, including enhanced regulatory frameworks, university curriculum integration, and professional training programs led by the Kuwait Society of Engineers, to address these barriers. It also emphasizes the critical role of collaboration among government bodies, industry leaders, and institutions like the Kuwait Institute for Scientific Research. Drawing from successful international BIM projects, the findings offer a practical framework for improving sustainability in arid regions, positioning Kuwait’s experience as a model for other Middle Eastern and North African countries. This research underscores the transformative role of BIM technologies in advancing global sustainable construction practices and achieving a more efficient and eco-friendly future. Full article
(This article belongs to the Section Construction Technologies)
25 pages, 5932 KiB  
Review
A Review of Academic and Patent Progress on Internet of Things (IoT) Technologies for Enhanced Environmental Solutions
by Usharani Hareesh Govindarajan, Chuyi Zhang, Rakesh D. Raut, Gagan Narang and Alessandro Galdelli
Technologies 2025, 13(2), 64; https://doi.org/10.3390/technologies13020064 (registering DOI) - 3 Feb 2025
Abstract
Environmental pollution is a pressing global issue, and the Internet of Things (IoT) offers transformative potential for its management through its application in advanced real-time monitoring and analytics. However, the heterogeneous and fragmented nature of IoT technologies poses challenges to seamless integration, limiting [...] Read more.
Environmental pollution is a pressing global issue, and the Internet of Things (IoT) offers transformative potential for its management through its application in advanced real-time monitoring and analytics. However, the heterogeneous and fragmented nature of IoT technologies poses challenges to seamless integration, limiting the efficacy of these solutions in addressing environmental impacts. This paper addresses these challenges by reviewing recent developments in IoT technologies, encompassing sensor networks, computing frameworks, and application layers for enhanced pollution management. A comprehensive analysis of 74,604 academic publications and 35,000 patent documents spanning from 2008 to 2024 is conducted using a textual analysis that combines quantitative bibliometric methods along with a qualitative analysis based on both scholarly research and patent innovations. This approach allows us to identify key challenges in IoT implementation for environmental monitoring—including integration, interoperability, and scalability issues—and to highlight corresponding architectural solutions. Our findings reveal emerging technology trends that aim to overcome a few of these challenges, and we present a scalable IoT architecture as key discussions that enhances system interoperability and efficiency for pollution monitoring. This framework provides targeted solutions for specific tasks in pollution monitoring while guiding decision-makers to adopt solutions effectively. Full article
(This article belongs to the Special Issue IoT-Enabling Technologies and Applications)
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18 pages, 2819 KiB  
Article
Solid-State Kinetic Modeling and Experimental Validation of Cu-Fe Bimetallic Catalyst Synthesis and Its Application to Furfural Hydrogenation
by Bárbara Jazmín Lino Galarza, Javier Rivera De la Rosa, Eduardo Maximino Sánchez Cervantes, Carlos J. Lucio-Ortiz, Marco Antonio Garza-Navarro, Carolina Solís Maldonado, Ramón Moreno-Tost, Juan Antonio Cecilia-Buenestado and Antonia Infantes Molina
Technologies 2025, 13(2), 63; https://doi.org/10.3390/technologies13020063 (registering DOI) - 3 Feb 2025
Abstract
In this work, combined experimental and modeling techniques were used to understand the bimetallic catalyst formation of Cu and Fe. The first part of this study aims to address this gap by employing analytical techniques such as X-ray diffraction (XRD), thermal and gravimetric [...] Read more.
In this work, combined experimental and modeling techniques were used to understand the bimetallic catalyst formation of Cu and Fe. The first part of this study aims to address this gap by employing analytical techniques such as X-ray diffraction (XRD), thermal and gravimetric (TGA), thermoprogrammed oxidation and reduction. These were used to track the evolution of the different crystalline phases formed for CuFe-Bulk and CuFe/Al2O3 catalysts, as well as hydrogen thermoprogrammed reduction (H2-TPR), to evaluate the reducibility of the oxide phases. Both bulk and supported catalysts were also studied in the hydrogenation of furfural at 170 °C, and 4 MPa of H2. The research provides insights into the thermal events and structural transformations that occur during oxidation and reduction processes, revealing the formation of multiple oxide and metallic phases. The proposed reaction mechanism obtained from XRD analysis and TG-based mathematical modeling provides valuable information about the chemical reaction and the diffusion control mechanisms. Furthermore, a catalytic test using furfural, a biomass-derived molecule, was conducted. This interconnects with the initial section of the study, in which we found that active Cu4Fe sites have superior performance in the CuFe/Al2O3 catalyst in the hydrogenation batch test. Full article
(This article belongs to the Section Innovations in Materials Processing)
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15 pages, 256 KiB  
Article
Barriers to the Adoption of Augmented Reality Technologies for Education and Training in the Built Environment: A Developing Country Context
by Opeoluwa Akinradewo, Mohamed Hafez, John Aliu, Ayodeji Oke, Clinton Aigbavboa and Samuel Adekunle
Technologies 2025, 13(2), 62; https://doi.org/10.3390/technologies13020062 (registering DOI) - 3 Feb 2025
Viewed by 242
Abstract
The construction industry has been tasked to adapt to technological advancements that other industries have implemented to grow and remain relevant. One of these technological advancements is augmented reality technologies. ART combines real and virtual worlds without completely immersing the individual in a [...] Read more.
The construction industry has been tasked to adapt to technological advancements that other industries have implemented to grow and remain relevant. One of these technological advancements is augmented reality technologies. ART combines real and virtual worlds without completely immersing the individual in a virtual simulation. The use of ART can significantly improve education and training, especially in the construction industry, by analysing real-world environments while training in a controlled setting. This study, therefore, sets out to identify the factors that hinder the use of ART in the built environment. To achieve this, a quantitative research approach was adopted, and questionnaires were distributed to professionals in the built environment using South Africa as the research location. Retrieved data were analysed using both descriptive and inferential statistics. Findings revealed that investment cost is the major hindrance stakeholders face in implementing ART for education and training in the built environment. The exploratory factor analysis result clustered the identified barriers as internal organisation-related, culture-related, knowledge-related, and educator-related barriers. The study concluded that stakeholders in the built environment still have major responsibilities to ensure there is proper awareness of the benefits of adopting ART for education and training. Full article
(This article belongs to the Collection Technology Advances on IoT Learning and Teaching)
15 pages, 3011 KiB  
Article
Intent-Bert and Universal Context Encoders: A Framework for Workload and Sensor Agnostic Human Intention Prediction
by Maximillian Panoff, Joshua Acevedo, Honggang Yu, Peter Forcha, Shuo Wang and Christophe Bobda
Technologies 2025, 13(2), 61; https://doi.org/10.3390/technologies13020061 (registering DOI) - 2 Feb 2025
Viewed by 459
Abstract
Determining human intention is a challenging task. Many existing techniques seek to address it by combining many forms of data, such as images, point clouds, poses, and others, creating multi-modal models. However, these techniques still often require significant foreknowledge in the form of [...] Read more.
Determining human intention is a challenging task. Many existing techniques seek to address it by combining many forms of data, such as images, point clouds, poses, and others, creating multi-modal models. However, these techniques still often require significant foreknowledge in the form of known potential activities and objects in the environment, as well as specific types of data to collect. To address these limitations, we propose Intent-BERT and Universal Context Encoders, which combine to form workload-agnostic framework that can be used to predict the next activity that a human performs as an Open Vocabulary Problem and the time until that switch, along with the time the current activity ends. Universal Context Encoders utilize the distances between the embeddings of words to extract relationships between Human-Readable English descriptions of both the current task and the origin of various multi-modal inputs to determine how to weigh the values themselves. We examine the effectiveness of this approach by creating a multi-modal model using it and training it on the InHARD dataset. It is able to return a completely accurate description of the next Action performed by a human working alongside a robot in a manufacturing task in ∼42% of test cases and has a 95% Top-3 accuracy, all from a single time point, outperforming multi-modal gpt4o by about 50% on a token by token basis. Full article
(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing, 2nd Volume)
19 pages, 840 KiB  
Article
Backfill for Advanced Potash Ore Mining Technologies
by Evgeny Kovalsky, Cheynesh Kongar-Syuryun, Angelika Morgoeva, Roman Klyuev and Marat Khayrutdinov
Technologies 2025, 13(2), 60; https://doi.org/10.3390/technologies13020060 (registering DOI) - 2 Feb 2025
Viewed by 196
Abstract
In today’s world, advanced technologies are indispensable. In the field of mining, the use of machine-learning techniques is a reliable and productive way to solve various problems. This article touches upon the issues of increasing the recovery rate at potash mines, using the [...] Read more.
In today’s world, advanced technologies are indispensable. In the field of mining, the use of machine-learning techniques is a reliable and productive way to solve various problems. This article touches upon the issues of increasing the recovery rate at potash mines, using the technology of backfilling with hardening materials. The compositions of backfills with increased strength are developed. The results of laboratory studies are given. To reduce the labor intensity of the experimental work, as well as to develop and validate methodological approaches to machine-learning introduction in the fields of mining and geomechanical research, this paper also presents the results of the predicted calculated values of the multi-component backfill strength, obtained with the help of neural networks. Full article
21 pages, 7254 KiB  
Article
Enhancing Electricity Load Forecasting with Machine Learning and Deep Learning
by Arbër Perçuku, Daniela Minkovska and Nikolay Hinov
Technologies 2025, 13(2), 59; https://doi.org/10.3390/technologies13020059 - 1 Feb 2025
Viewed by 471
Abstract
The electricity load forecasting handles the process of determining how much electricity will be available at a given time while maintaining the balance and stability of the power grid. The accuracy of electricity load forecasting plays an important role in ensuring safe operation [...] Read more.
The electricity load forecasting handles the process of determining how much electricity will be available at a given time while maintaining the balance and stability of the power grid. The accuracy of electricity load forecasting plays an important role in ensuring safe operation and improving the reliability of power systems and is a key component in the operational planning and efficient market. For many years, a conventional method has been used by using historical data as input parameters. With swift progress and improvement in technology, which shows more potential due to its accuracy, different methods can be applied depending on the identified model. To enhance the forecast of load, this paper introduces and proposes a framework developed on graph database technology to archive large amounts of data, which collects measured data from electrical substations in Pristina, Kosovo. The data includes electrical and weather parameters collected over a four-year timeframe. The proposed framework is designed to handle short-term load forecasting. Machine learning Linear Regression and deep learning Long Short-Term Memory algorithms are applied to multiple datasets and mean absolute error and root mean square error are calculated. The results show the promising performance and effectiveness of the proposed model, with high accuracy in load forecasting. Full article
(This article belongs to the Collection Selected Papers from the MOCAST Conference Series)
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27 pages, 1115 KiB  
Article
Distributed Ledger Technology in Healthcare: Enhancing Governance and Performance in a Decentralized Ecosystem
by Juan Minango, Henry Carvajal Mora, Marcelo Zambrano, Nathaly Orozco Garzón and Francisco Pérez
Technologies 2025, 13(2), 58; https://doi.org/10.3390/technologies13020058 - 1 Feb 2025
Viewed by 502
Abstract
This paper evaluates the technical feasibility of Distributed Ledger Technology (DLT) within the healthcare ecosystem, with a focus on the use of Corda DLT to enhance governance and performance in a decentralized ecosystem, ensuring data integrity, security, and trustworthiness. Key attributes examined include [...] Read more.
This paper evaluates the technical feasibility of Distributed Ledger Technology (DLT) within the healthcare ecosystem, with a focus on the use of Corda DLT to enhance governance and performance in a decentralized ecosystem, ensuring data integrity, security, and trustworthiness. Key attributes examined include the guarantee of data integrity, ensuring that transmitted data remain unaltered; authenticity through the implementation of digital signatures and certificates; confidentiality achieved via secure peer-to-peer communication accessible only to authorized parties; and traceability and auditing mechanisms that enable tracking of information changes and accountability. To validate these features, a Corda Distributed Application (CorDapp) was developed to manage the core logic of the healthcare ecosystem. The CorDapp was deployed across nodes and executed within the Corda network. Its performance was assessed using metrics such as throughput, latency, CPU usage, and memory consumption in both local and cloud network environments. Results demonstrate the feasibility of using Corda DLT technology in healthcare, effectively addressing critical requirements such as integrity, authenticity, confidentiality, traceability, and auditing while maintaining satisfactory performance across diverse deployment scenarios. Full article
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19 pages, 5444 KiB  
Article
Portable Solar-Integrated Open-Source Chemistry Lab for Water Treatment with Electrolysis
by Giorgio Antonini, Md Motakabbir Rahman, Cameron Brooks, Domenico Santoro, Christopher Muller, Ahmed Al-Omari, Katherine Bell and Joshua M. Pearce
Technologies 2025, 13(2), 57; https://doi.org/10.3390/technologies13020057 - 1 Feb 2025
Viewed by 657
Abstract
Harnessing solar energy offers a sustainable alternative for powering electrolysis for green hydrogen production as well as wastewater treatment. The high costs and logistical challenges of electrolysis have resulted in limited widespread investigation and implementation of electrochemical technologies on an industrial scale. To [...] Read more.
Harnessing solar energy offers a sustainable alternative for powering electrolysis for green hydrogen production as well as wastewater treatment. The high costs and logistical challenges of electrolysis have resulted in limited widespread investigation and implementation of electrochemical technologies on an industrial scale. To overcome these challenges, this study designs and tests a new approach to chemical experiments and wastewater treatment research using a portable standalone open-source solar photovoltaic (PV)-powered station that can be located onsite at a wastewater treatment plant with unreliable electrical power. The experimental system is equipped with an energy monitoring data acquisition system. In addition, sensors enable real-time monitoring of gases—CO, CO2, CH4, H2, H2S, and NH3—along with temperature, humidity, and volatile organic compounds, enhancing safety during electrochemical experiments on wastewater, which may release hazardous gases. SAMA software was used to evaluate energy-sharing scenarios under different grid-connected conditions, and the system can operate off the power grid for 98% of the year in Ontario, Canada. The complete system was tested utilizing a laboratory-scale electrolyzer (electrodes of SS316L, Duplex 2205, titanium grade II and graphite) with electrolyte solutions of potassium hydroxide, sulfuric acid, and secondary wastewater effluent. The electrolytic cell specifically developed for testing electrode materials and wastewater showed a Faraday efficiency up to 95% and an energy efficiency of 55% at STP, demonstrating the potential for use of this technology in future work. Full article
(This article belongs to the Section Environmental Technology)
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36 pages, 3700 KiB  
Article
Analysis and Optimization of DC-DC Converters Through Sensitivity to Parametric Variations
by Nikolay Hinov, Plamen Stanchev and Gergana Vacheva
Technologies 2025, 13(2), 56; https://doi.org/10.3390/technologies13020056 - 1 Feb 2025
Viewed by 240
Abstract
The optimization of DC-DC converters is crucial for enhancing their performance and efficiency in various applications. This study focuses on the sensitivity analysis of DC-DC converters to parametric variations, which plays a key role in designing robust and efficient systems. The methodology involves [...] Read more.
The optimization of DC-DC converters is crucial for enhancing their performance and efficiency in various applications. This study focuses on the sensitivity analysis of DC-DC converters to parametric variations, which plays a key role in designing robust and efficient systems. The methodology involves developing a simulation model that describes the behavior of converters under different conditions and analyzing the effects of parameter variations through simulation tools. Sensitivity analysis of DC-DC converters involves understanding the sources of harmonics, modeling the converter, analyzing the harmonic content, and implementing mitigation techniques. By combining theoretical analysis with practical design modifications, engineers can optimize DC-DC converters for improved performance, efficiency, and compliance with electromagnetic compatibility standards. Examples of harmonic analysis of the main types of DC-DC converters—Buck, Boost, and Buck-Boost—are discussed in the manuscript. Based on a study of the influence of harmonics in the operating modes, ratios have been derived to be applied during design. In this respect, the research presented is useful for designers and for use in power electronics education. Full article
(This article belongs to the Collection Selected Papers from the MOCAST Conference Series)
32 pages, 4336 KiB  
Article
PictureGuard: Enhancing Software-Defined Networking–Internet of Things Security with Novel Image-Based Authentication and Artificial Intelligence-Powered Two-Stage Intrusion Detection
by Hazem (Moh’d Said) Hatamleh, As’ad Mahmoud As’ad Alnaser, Said S. Saloum, Ahmed Sharadqeh and Jawdat S. Alkasassbeh
Technologies 2025, 13(2), 55; https://doi.org/10.3390/technologies13020055 - 1 Feb 2025
Viewed by 440
Abstract
Software-defined networking (SDN) represents a transformative approach to network management, enabling the centralized and programmable control of network infrastructure. This paradigm facilitates enhanced scalability, flexibility, and security in managing complex systems. When integrated with the Internet of Things (IoT), SDN addresses critical challenges [...] Read more.
Software-defined networking (SDN) represents a transformative approach to network management, enabling the centralized and programmable control of network infrastructure. This paradigm facilitates enhanced scalability, flexibility, and security in managing complex systems. When integrated with the Internet of Things (IoT), SDN addresses critical challenges such as security and efficient network management, positioning the SDN-IoT paradigm as an emerging and impactful technology in modern networking. The rapid proliferation of IoT applications has led to a significant increase in security threats, posing challenges to the safe operation of IoT systems. Consequently, SDN-IoT-based applications and services have been widely adopted to address these issues and challenges. However, this platform faces critical limitations in ensuring scalability, optimizing energy consumption, and addressing persistent security vulnerabilities. To overcome these issues, we proposed a secure SDN-IoT environment for intrusion detection and prevention using virtual blockchain (V-Block). Initially, IoT users are registered and authenticated to the shadow blockchain nodes using a picture-based authentication mechanism. After that, authenticated user flows validation was provided by considering effective metrics utilizing the Trading-based Evolutionary Game Theory (TEGT) approach. Then, we performed a local risk assessment based on evaluated malicious flows severity and then the attack graph was constructed using an Isomorphism-based Graph Neural Network (IGNN) model. Further, multi-controllers were placed optimally using fox optimization algorithm. The generated global paths were securely stored in the virtual blockchain Finally, the two agents in the multi-controllers were responsible for validating and classifying the incoming suspicious flow packets into normal and malicious packets by considering the operative metrics using the Dueling Deep Q Network (DDQN) algorithm. The presented work was conducted by Network Simulator-3.26 and the different performance matrices were used to itemize the suggested V-Block model based on its malicious traffic, attack detection rate, link failure rate, anomaly detection rate, and scalability. Full article
(This article belongs to the Special Issue IoT-Enabling Technologies and Applications)
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28 pages, 3337 KiB  
Article
Lung and Colon Cancer Classification Using Multiscale Deep Features Integration of Compact Convolutional Neural Networks and Feature Selection
by Omneya Attallah
Technologies 2025, 13(2), 54; https://doi.org/10.3390/technologies13020054 - 1 Feb 2025
Viewed by 363
Abstract
The automated and precise classification of lung and colon cancer from histopathological photos continues to pose a significant challenge in medical diagnosis, as current computer-aided diagnosis (CAD) systems are frequently constrained by their dependence on singular deep learning architectures, elevated computational complexity, and [...] Read more.
The automated and precise classification of lung and colon cancer from histopathological photos continues to pose a significant challenge in medical diagnosis, as current computer-aided diagnosis (CAD) systems are frequently constrained by their dependence on singular deep learning architectures, elevated computational complexity, and their ineffectiveness in utilising multiscale features. To this end, the present research introduces a CAD system that integrates several lightweight convolutional neural networks (CNNs) with dual-layer feature extraction and feature selection to overcome the aforementioned constraints. Initially, it extracts deep attributes from two separate layers (pooling and fully connected) of three pre-trained CNNs (MobileNet, ResNet-18, and EfficientNetB0). Second, the system uses the benefits of canonical correlation analysis for dimensionality reduction in pooling layer attributes to reduce complexity. In addition, it integrates the dual-layer features to encapsulate both high- and low-level representations. Finally, to benefit from multiple deep network architectures while reducing classification complexity, the proposed CAD merges dual deep layer variables of the three CNNs and then applies the analysis of variance (ANOVA) and Chi-Squared for the selection of the most discriminative features from the integrated CNN architectures. The CAD is assessed on the LC25000 dataset leveraging eight distinct classifiers, encompassing various Support Vector Machine (SVM) variants, Decision Trees, Linear Discriminant Analysis, and k-nearest neighbours. The experimental results exhibited outstanding performance, attaining 99.8% classification accuracy with cubic SVM classifiers employing merely 50 ANOVA-selected features, exceeding the performance of individual CNNs while markedly diminishing computational complexity. The framework’s capacity to sustain exceptional accuracy with a limited feature set renders it especially advantageous for clinical applications where diagnostic precision and efficiency are critical. These findings confirm the efficacy of the multi-CNN, multi-layer methodology in enhancing cancer classification precision while mitigating the computational constraints of current systems. Full article
(This article belongs to the Special Issue Advancements in Medical and Assistive Technologies Using Artificial Intelligence and Deep Learning Techniques)
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28 pages, 6569 KiB  
Article
A New Efficient Hybrid Technique for Human Action Recognition Using 2D Conv-RBM and LSTM with Optimized Frame Selection
by Majid Joudaki, Mehdi Imani and Hamid R. Arabnia
Technologies 2025, 13(2), 53; https://doi.org/10.3390/technologies13020053 - 1 Feb 2025
Viewed by 396
Abstract
Recognizing human actions through video analysis has gained significant attention in applications like surveillance, sports analytics, and human–computer interaction. While deep learning models such as 3D convolutional neural networks (CNNs) and recurrent neural networks (RNNs) deliver promising results, they often struggle with computational [...] Read more.
Recognizing human actions through video analysis has gained significant attention in applications like surveillance, sports analytics, and human–computer interaction. While deep learning models such as 3D convolutional neural networks (CNNs) and recurrent neural networks (RNNs) deliver promising results, they often struggle with computational inefficiencies and inadequate spatial–temporal feature extraction, hindering scalability to larger datasets or high-resolution videos. To address these limitations, we propose a novel model combining a two-dimensional convolutional restricted Boltzmann machine (2D Conv-RBM) with a long short-term memory (LSTM) network. The 2D Conv-RBM efficiently extracts spatial features such as edges, textures, and motion patterns while preserving spatial relationships and reducing parameters via weight sharing. These features are subsequently processed by the LSTM to capture temporal dependencies across frames, enabling effective recognition of both short- and long-term action patterns. Additionally, a smart frame selection mechanism minimizes frame redundancy, significantly lowering computational costs without compromising accuracy. Evaluation on the KTH, UCF Sports, and HMDB51 datasets demonstrated superior performance, achieving accuracies of 97.3%, 94.8%, and 81.5%, respectively. Compared to traditional approaches like 2D RBM and 3D CNN, our method offers notable improvements in both accuracy and computational efficiency, presenting a scalable solution for real-time applications in surveillance, video security, and sports analytics. Full article
(This article belongs to the Special Issue Data Science and Big Data in Biology, Physical Science and Engineering II)
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23 pages, 2686 KiB  
Article
The Role of Surface {010} Facets in Improving the NOx Depolluting Activity of TiO2 and Its Application on Building Materials
by Manuel Luna, Jose L. Cruces, José M. Gatica, Alvaro Cruceira, Gustavo A. Cifredo, Hilario Vidal and María J. Mosquera
Technologies 2025, 13(2), 52; https://doi.org/10.3390/technologies13020052 - 31 Jan 2025
Viewed by 423
Abstract
Air pollution, a major health concern, necessitates innovative solutions such as TiO2-based photocatalytic building materials to combat its harmful effects. This study focuses on developing high-performance TiO2 photocatalysts for NOx removal in building applications, aiming to overcome the limitations [...] Read more.
Air pollution, a major health concern, necessitates innovative solutions such as TiO2-based photocatalytic building materials to combat its harmful effects. This study focuses on developing high-performance TiO2 photocatalysts for NOx removal in building applications, aiming to overcome the limitations of commercial TiO2. These photocatalysts were synthesized via a hydrothermal method, with parameters such as synthesis time and post-treatment investigated to optimize their properties. Hydrothermal synthesis yielded TiO2 nanoparticles with reduced aggregation and a high proportion of elongated particles with exposed {010} facets. This resulted in significantly enhanced photocatalytic activity compared to commercial P25 in methylene blue degradation and NOx depollution. Subsequently, the optimized hydrothermal TiO2 was successfully integrated into a silica sol–gel coating for application on building materials. The coated concrete demonstrated significantly higher NOx removal efficiency and lower NO2 release, achieving a 1.7-fold improvement in overall NOx removal and significantly higher depolluting effectiveness compared to its P25 counterpart. These findings highlight the potential of hydrothermally synthesized TiO2 with controlled morphology for the development of high-performance, environmentally friendly building materials with enhanced air purification capabilities. Full article
(This article belongs to the Section Environmental Technology)
56 pages, 577 KiB  
Review
From Google Gemini to OpenAI Q (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape*
by Timothy R. McIntosh, Teo Susnjak, Tong Liu, Paul Watters, Dan Xu, Dongwei Liu and Malka N. Halgamuge
Technologies 2025, 13(2), 51; https://doi.org/10.3390/technologies13020051 - 30 Jan 2025
Viewed by 482
Abstract
This comprehensive survey explored the evolving landscape of generative Artificial Intelligence (AI), with a specific focus on the recent technological breakthroughs and the gathering advancements toward possible Artificial General Intelligence (AGI). It critically examined the current state and future trajectory of generative AI, [...] Read more.
This comprehensive survey explored the evolving landscape of generative Artificial Intelligence (AI), with a specific focus on the recent technological breakthroughs and the gathering advancements toward possible Artificial General Intelligence (AGI). It critically examined the current state and future trajectory of generative AI, exploring how innovations in developing actionable and multimodal AI agents with the ability scale their “thinking” in solving complex reasoning tasks are reshaping research priorities and applications across various domains, while the survey also offers an impact analysis on the generative AI research taxonomy. This work has assessed the computational challenges, scalability, and real-world implications of these technologies while highlighting their potential in driving significant progress in fields like healthcare, finance, and education. Our study also addressed the emerging academic challenges posed by the proliferation of both AI-themed and AI-generated preprints, examining their impact on the peer-review process and scholarly communication. The study highlighted the importance of incorporating ethical and human-centric methods in AI development, ensuring alignment with societal norms and welfare, and outlined a strategy for future AI research that focuses on a balanced and conscientious use of generative AI as its capabilities continue to scale. Full article
30 pages, 13507 KiB  
Review
Solid-State Transformers: A Review—Part II: Modularity and Applications
by Dragoș-Mihail Predescu and Ștefan-George Roșu
Technologies 2025, 13(2), 50; https://doi.org/10.3390/technologies13020050 - 28 Jan 2025
Viewed by 579
Abstract
The Solid-State Transformer (SST) is a complex conversion device that intends to replace the Low-Frequency Transformers (LFTs) used in various power applications with Medium- or High-Frequency Transformers (MFTs/HFTs) that integrate modular converter structures as their input and output stages. The purpose is to [...] Read more.
The Solid-State Transformer (SST) is a complex conversion device that intends to replace the Low-Frequency Transformers (LFTs) used in various power applications with Medium- or High-Frequency Transformers (MFTs/HFTs) that integrate modular converter structures as their input and output stages. The purpose is to obtain additional capabilities, such as power factor correction, voltage control, and interconnection of distributed supplies, among others, while reducing the overall volume. Given the expansive research conducted in this area in the past years, the volume of information available is large, so the main contribution of this paper is a new method of classification based on the modular construction of the SST derived from its applications and available constructive degrees of freedom. This paper can be considered the second part of a broader review in which the first part presented the fundamental converter roles and topologies. As a continuation, this paper aims to expand the definition of modularity to the entire SST structure and analyze how the converters can be combined in order to achieve the desired SST functionality. Three areas of interest are chosen: partitioning of power, phase modularity, and port configuration. The partitioning of power analyzes the fundamental switching cells and the arrangement of the converters across stages. Phase modularity details the construction of multiphase-system SSTs. Finally, the types of input/output ports, their placements, and roles are discussed. These characteristics are presented together with the applications in which they were suggested to give a broader context. Full article
(This article belongs to the Special Issue Next-Generation Distribution System Planning, Operation, and Control)
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7 pages, 2025 KiB  
Communication
A New Bundling and Packaging Method Using Nonwoven Polylactide Based on Polymer Shrinkage by Carbon Dioxide
by Takafumi Aizawa
Technologies 2025, 13(2), 49; https://doi.org/10.3390/technologies13020049 - 28 Jan 2025
Viewed by 445
Abstract
This study proposes the exposure of nonwoven fabrics to carbon dioxide for bundling and packaging purposes. The proposed process, which utilizes the shrinking property of the nonwoven fabric during carbon dioxide exposure, is demonstrated on a polylactic acid (PLA) nonwoven fabric produced by [...] Read more.
This study proposes the exposure of nonwoven fabrics to carbon dioxide for bundling and packaging purposes. The proposed process, which utilizes the shrinking property of the nonwoven fabric during carbon dioxide exposure, is demonstrated on a polylactic acid (PLA) nonwoven fabric produced by the melt-blown method. Evaluating the shrinkage induced by carbon dioxide in PLA nonwoven fabrics with varying degrees of crystallinity, it was found that increasing the crystallinity decreases both the speed and amount of shrinkage. This process is potentially applicable as a simple, inexpensive, and environmentally friendly approach for packaging food and drug products. Full article
(This article belongs to the Section Innovations in Materials Processing)
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28 pages, 676 KiB  
Article
Challenges and Ethical Considerations in Implementing Assistive Technologies in Healthcare
by Eleni Gkiolnta, Debopriyo Roy and George F. Fragulis
Technologies 2025, 13(2), 48; https://doi.org/10.3390/technologies13020048 - 27 Jan 2025
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Abstract
Assistive technologies are becoming an increasingly important aspect of healthcare, particularly for people with physical or cognitive problems. While earlier research has investigated the ethical, legal, and societal implications of AI and assistive technologies, many studies have failed to address real-world obstacles such [...] Read more.
Assistive technologies are becoming an increasingly important aspect of healthcare, particularly for people with physical or cognitive problems. While earlier research has investigated the ethical, legal, and societal implications of AI and assistive technologies, many studies have failed to address real-world obstacles such as data privacy, algorithm bias, and regulatory issues. To further understand these issues, we conducted a thorough analysis of the current literature and analyzed real-world case studies. As AI-powered solutions become more widely used, we discovered that stronger legal frameworks and robust data security standards are required. Furthermore, privacy-preserving procedures and transparent accountability are critical for retaining patient trust and guaranteeing the effective use of these technologies in healthcare. This research provides important insights into the ethical and practical challenges that must be tackled for the successful integration of assistive technologies. Full article
(This article belongs to the Special Issue Advancements in Medical and Assistive Technologies Using Artificial Intelligence and Deep Learning Techniques)
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36 pages, 15476 KiB  
Article
Hybrid System for Fault Tolerance in Selective Compliance Assembly Robot Arm: Integration of Differential Gears and Coordination Algorithms
by Claudio Urrea, Pablo Sari and John Kern
Technologies 2025, 13(2), 47; https://doi.org/10.3390/technologies13020047 - 24 Jan 2025
Viewed by 590
Abstract
This study presents a fault-tolerant control system for Selective Compliance Assembly Robot Arm (SCARA) robots, ensuring operational continuity in cooperative tasks. It is evaluated in five scenarios: normal operation, failures without reconfiguration, and with active reconfiguration. The system employs redundant actuators, differential gears, [...] Read more.
This study presents a fault-tolerant control system for Selective Compliance Assembly Robot Arm (SCARA) robots, ensuring operational continuity in cooperative tasks. It is evaluated in five scenarios: normal operation, failures without reconfiguration, and with active reconfiguration. The system employs redundant actuators, differential gears, torque limiters, and rapid detection and reconfiguration algorithms. Simulations in MATLAB R2024a demonstrated reconfiguration times of 0.5 s and reduced trajectory errors (0.0042 m on the X-axis for Robot 1), achieving efficiency above 99%. Nonlinear Model Predictive Controllers (NLMPCs) and Adaptive Sliding Mode Control (ASMC) were compared, with NLMPC excelling in stability and ASMC in precision. The system showcased high productivity in pick-and-place tasks, even under critical failures, establishing itself as a robust solution for industrial environments requiring high reliability and advanced automation. Full article
(This article belongs to the Section Assistive Technologies)
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21 pages, 629 KiB  
Article
Deep Learning Framework Using Spatial Attention Mechanisms for Adaptable Angle Estimation Across Diverse Array Configurations
by Constantinos M. Mylonakis, Pantelis Velanas, Pavlos I. Lazaridis, Panagiotis Sarigiannidis, Sotirios K. Goudos and Zaharias D. Zaharis
Technologies 2025, 13(2), 46; https://doi.org/10.3390/technologies13020046 - 24 Jan 2025
Viewed by 455
Abstract
Rapid advancement of wireless communication systems and the increasing need for accurate, real-time signal processing have driven innovations in direction-of-arrival (DoA) estimation techniques. This paper introduces a novel convolutional neural network (CNN) architecture that combines spatial attention mechanisms with a transfer learning framework [...] Read more.
Rapid advancement of wireless communication systems and the increasing need for accurate, real-time signal processing have driven innovations in direction-of-arrival (DoA) estimation techniques. This paper introduces a novel convolutional neural network (CNN) architecture that combines spatial attention mechanisms with a transfer learning framework to enhance both accuracy and versatility in DoA estimation. The model integrates spatial attention layers to dynamically prioritize signal regions with the highest information value, allowing it to isolate relevant signals and suppress interference in noisy or crowded signal environments. In addition, we utilize a transfer learning framework that enables the model to generalize across various antenna array configurations (i.e., planar, linear, and circular arrays) with minimal additional training. Extensive simulation results benchmark the proposed model against existing state-of-the-art methods for DoA estimation, achieving improved absolute error across diverse conditions. This hybrid approach not only enhances DoA estimation precision, but also significantly reduces retraining requirements when adapting to new array configurations, positioning it as a robust, scalable tool for next-generation wireless communication systems. Full article
(This article belongs to the Collection Selected Papers from the MOCAST Conference Series)
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16 pages, 3510 KiB  
Article
An Intelligent Technique for Android Malware Identification Using Fuzzy Rank-Based Fusion
by Altyeb Taha, Ahmed Hamza Osman and Yakubu Suleiman Baguda
Technologies 2025, 13(2), 45; https://doi.org/10.3390/technologies13020045 - 23 Jan 2025
Viewed by 553
Abstract
Android’s open-source nature, combined with its large market share, has made it a primary target for malware developers. Consequently, there is a dramatic need for effective Android malware detection methods. This paper suggests a novel fuzzy rank-based fusion approach for Android malware detection [...] Read more.
Android’s open-source nature, combined with its large market share, has made it a primary target for malware developers. Consequently, there is a dramatic need for effective Android malware detection methods. This paper suggests a novel fuzzy rank-based fusion approach for Android malware detection (ANDFRF). The suggested ANDFRF primarily consists of two steps: in the first step, five machine learning algorithms, comprising K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), XGbooost (XGB) and Light Gradient Boosting Machine (LightGBM), were utilized as base classifiers for the initial identification of Android Apps either as goodware or malware apps. Second, the fuzzy rank-based fusion approach was employed to adaptively integrate the classification results obtained from the base machine learning algorithms. By leveraging rankings instead of explicit class labels, the proposed ANDFRF method reduces the impact of anomalies and noisy predictions, leading to more accurate ensemble outcomes. Furthermore, the rankings reflect the relative importance or acceptance of each class across multiple classifiers, providing deeper insights into the ensemble’s decision-making process. The proposed framework was validated on two publicly accessible datasets, CICAndMal2020 and DREBIN, with a 5-fold cross-validation technique. The proposed ensemble framework achieves a classification accuracy of 95.51% and an AUC of 95.40% on the DREBIN dataset. On the CICAndMal2020 LBC dataset, it attains an accuracy of 95.31% and an AUC of 95.30%. Experimental results demonstrate that the proposed scheme is both efficient and effective for Android malware detection. Full article
(This article belongs to the Section Information and Communication Technologies)
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33 pages, 1641 KiB  
Article
Transforming Telemedicine: Strategic Lessons and Metrics from Italy’s Telemechron Project (Telemechron Study)
by Sara Jayousi, Martina Cinelli, Roberto Bigazzi, Stefano Bianchi, Simonetta Scalvini, Gabriella Borghi, Palmira Bernocchi, Sandro Inchiostro, Alexia Giovanazzi, Marina Mastellaro, Maria Adalgisa Gentilini, Lorenzo Gios, Mauro Grigioni, Carla Daniele, Giuseppe D’Avenio, Sandra Morelli and Daniele Giansanti
Technologies 2025, 13(2), 44; https://doi.org/10.3390/technologies13020044 - 23 Jan 2025
Viewed by 485
Abstract
Background: The Telemechron project addresses critical needs in telemedicine by evaluating technology assessment frameworks and key performance indicators (KPIs), among other objectives. Effective frameworks and KPIs are crucial for assessing telemedicine services, especially in terms of their impact on patient outcomes and service [...] Read more.
Background: The Telemechron project addresses critical needs in telemedicine by evaluating technology assessment frameworks and key performance indicators (KPIs), among other objectives. Effective frameworks and KPIs are crucial for assessing telemedicine services, especially in terms of their impact on patient outcomes and service efficiency. Methods: This study adopted a dual approach to assess the contributions of the Telemechron project. Firstly, it applied a technology assessment framework to quantitatively evaluate telemedicine services, focusing on measurable improvements and systematic analysis. Secondly, it investigated a set of predefined KPIs using specific metrics and parameters to evaluate their applicability and limitations in telemedicine settings. Results and Discussion: The technology assessment framework demonstrated significant utility by providing structured, quantifiable improvements across key aspects of telemedicine services. It was effective in evaluating the alignment of services with strategic goals and their integration with existing healthcare systems. The predefined KPIs, although not developed within this study and not directly used by the different operational units (OUs)—which established their own context-specific KPIs—still provided valuable insights into telemedicine performance. However, the study revealed that these KPIs highlighted a need for further customization to enhance their relevance across various contexts. While the KPIs may offer useful performance indicators, their general applicability necessitated adjustments to better address the specific needs of telemedicine applications. The analysis model for the KPI set, in terms of metrics and parameters, is exportable and generalizable to other national and international telemedicine contexts. Conclusions: The study confirms the effectiveness of the framework in delivering measurable improvements in telemedicine services and underscores the importance of adapting KPIs for specific contexts. Future research should focus on further applying and expanding the framework and metrics, refining KPI models, integrating new technologies, and conducting cross-national comparisons to enhance the applicability and effectiveness of telemedicine evaluations. Full article
(This article belongs to the Special Issue Assistive Technologies in Care and Rehabilitation: Research, Developments, and International Initiatives)
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13 pages, 708 KiB  
Article
Enhancing Decision-Making and Data Management in Healthcare: A Hybrid Ensemble Learning and Blockchain Approach
by Geetanjali Rathee and Razi Iqbal
Technologies 2025, 13(2), 43; https://doi.org/10.3390/technologies13020043 - 23 Jan 2025
Viewed by 523
Abstract
Currently, big data is considered one of the most significant areas of research and development. The advancement in technologies along with the involvement of intelligent and automated devices in each field of development leads to huge generation, analysis, and the recording of information [...] Read more.
Currently, big data is considered one of the most significant areas of research and development. The advancement in technologies along with the involvement of intelligent and automated devices in each field of development leads to huge generation, analysis, and the recording of information in the network. Though a number of schemes have been proposed for providing accurate decision-making while analyzing the records, however, the existing methods lead to massive delays and difficulty in the management of stored information. Furthermore, the excessive delays in information processing pose a critical challenge to making accurate decisions in the context of big data. The aim of this paper is to propose an effective approach for accurate decision-making and analysis of the vast volumes of data generated by intelligent devices in the healthcare sector. The processed and managed records can be stored and accessed in a systematic and efficient manner. The proposed mechanism uses the hybrid of ensemble learning along with blockchain for fast and continuous recording and surveillance of information. The recorded information is analyzed using several existing methods focusing on various measurement outcomes. The results of the proposed technique are compared with existing techniques through various experiments that demonstrate the efficiency and accuracy of this technique. Full article
(This article belongs to the Special Issue Data Science and Big Data in Biology, Physical Science and Engineering II)
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37 pages, 7256 KiB  
Article
Time-to-Fault Prediction Framework for Automated Manufacturing in Humanoid Robotics Using Deep Learning
by Amir R. Ali and Hossam Kamal
Technologies 2025, 13(2), 42; https://doi.org/10.3390/technologies13020042 - 21 Jan 2025
Viewed by 742
Abstract
Industry 4.0 is transforming predictive failure management by utilizing deep learning to enhance maintenance strategies and automate production processes. Traditional methods often fail to predict failures in time. This research addresses this issue by developing a time-to-fault prediction framework that utilizes an enhanced [...] Read more.
Industry 4.0 is transforming predictive failure management by utilizing deep learning to enhance maintenance strategies and automate production processes. Traditional methods often fail to predict failures in time. This research addresses this issue by developing a time-to-fault prediction framework that utilizes an enhanced long short-term memory (LSTM) model to predict machine faults. The proposed method integrates real-time sensor data, including current, voltage, and temperature calibrated via ultra-sensitive optical sensing technologies based on the typical whispering gallery optical mode (WGM) to create a robust dataset. Due to the high-quality factor that these sensors exhibit, any minute change on the surrounding medium will makes a significant change on its transmission spectrum. The LSTM model trained on these data demonstrated rapid and stable convergence, outperforming other deep learning techniques with a mean absolute error (MAE) of 0.83, a root mean squared error (RMSE) of 1.62, and a coefficient of determination (R2) of 0.99. The results show the superior performance of LSTM in predicting machine failures early in real-world environments within 10 min lead time, improving productivity and reducing downtime. This framework advances smart industries by improving fault prediction in manufacturing precision robotics components, demonstrated through two humanoid robots, GUCnoid 1.0 and ARAtronica. Full article
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