Technologies

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 (registering DOI) - 1 Mar 2025

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)

► Show Figures

Graphical abstract

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 (registering DOI) - 1 Mar 2025

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)

► Show Figures

Figure 1

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 (registering DOI) - 1 Mar 2025

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)

► Show Figures

Figure 1

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 (registering DOI) - 1 Mar 2025

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)

► Show Figures

Figure 1

31 pages, 4379 KiB

Open AccessSystematic Review

A Systematic Literature Review of the Latest Advancements in XAI

by Zaid M. Altukhi, Sojen Pradhan and Nasser Aljohani

Technologies 2025, 13(3), 93; https://doi.org/10.3390/technologies13030093 (registering DOI) - 1 Mar 2025

Abstract

This systematic review details recent advancements in the field of Explainable Artificial Intelligence (XAI) from 2014 to 2024. XAI utilises a wide range of frameworks, techniques, and methods used to interpret machine learning (ML) black-box models. We aim to understand the technical advancements [...] Read more.

This systematic review details recent advancements in the field of Explainable Artificial Intelligence (XAI) from 2014 to 2024. XAI utilises a wide range of frameworks, techniques, and methods used to interpret machine learning (ML) black-box models. We aim to understand the technical advancements in the field and future directions. We followed the PRISMA methodology and selected 30 relevant publications from three main databases: IEEE Xplore, ACM, and ScienceDirect. Through comprehensive thematic analysis, we categorised the research into three main topics: ‘model developments’, ‘evaluation metrics and methods’, and ‘user-centred and XAI system design’. Our results uncover ‘What’, ‘How’, and ‘Why’ these advancements were developed. We found that 13 papers focused on model developments, 8 studies focused on the XAI evaluation metrics, and 12 papers focused on user-centred and XAI system design. Moreover, it was found that these advancements aimed to bridge the gap between technical model outputs and user understanding. Full article

► Show Figures

Figure 1

26 pages, 3719 KiB

Open AccessArticle

Design of Multi-Sourced MIMO Multiband Hybrid Wireless RF-Perovskite Photovoltaic Energy Harvesting Subsystems for Iots Applications in Smart Cities

by Fanuel Elias, Sunday Ekpo, Stephen Alabi, Mfonobong Uko, Sunday Enahoro, Muhammad Ijaz, Helen Ji, Rahul Unnikrishnan and Nurudeen Olasunkanmi

Technologies 2025, 13(3), 92; https://doi.org/10.3390/technologies13030092 (registering DOI) - 1 Mar 2025

Abstract

Energy harvesting technology allows Internet of Things (IoT) devices to be powered continuously without needing battery charging or replacement. In addressing existing and emerging massive IoT energy supply challenges, this paper presents the design of multi-sourced multiple input and multiple output (MIMO) multiband [...] Read more.

Energy harvesting technology allows Internet of Things (IoT) devices to be powered continuously without needing battery charging or replacement. In addressing existing and emerging massive IoT energy supply challenges, this paper presents the design of multi-sourced multiple input and multiple output (MIMO) multiband hybrid wireless RF-perovskite photovoltaic energy harvesting subsystems for IoT application. The research findings evaluate the efficiency and power output of different RF configurations (1 to 16 antennas) within MIMO RF subsystems. A Delon quadruple rectifier in the RF energy harvesting system demonstrates a system-level power conversion efficiency of 51%. The research also explores the I-V and P-V characteristics of the adopted perovskite tandem cell. This results in an impressive array capable of producing 6.4 V and generating a maximum power of 650 mW. For the first time, the combined mathematical modelling of the system architecture is presented. The achieved efficiency of the combined system is 90% (for 8 MIMO) and 98% (for 16 MIMO) at 0 dBm input RF power. This novel study holds great promise for next-generation 5G/6G smart IoT passive electronics. Additionally, it establishes the hybrid RF-perovskite energy harvester as a promising, compact, and eco-friendly solution for efficiently powering IoT devices in smart cities. This work contributes to the development of sustainable, scalable, and smart energy solutions for IoT integration into smart city infrastructures. Full article

► Show Figures

Graphical abstract

18 pages, 5532 KiB

Open AccessArticle

Field Data Retrieval for Electric Vehicles and Estimating Equivalent Circuit Model Parameters via Particle Swarm Optimization

by Syed Adil Sardar, Shahzad Iqbal, Jeongju Park, Sekyung Han and Woo Young Kim

Technologies 2025, 13(3), 91; https://doi.org/10.3390/technologies13030091 (registering DOI) - 1 Mar 2025

Abstract

Data retrieval techniques are crucial for developing an effective battery management system for an electric vehicle to accurately assess the battery’s health and performance by monitoring operating conditions such as voltage, current, time, temperature, and state of charge. This paper proposes an efficient [...] Read more.

Data retrieval techniques are crucial for developing an effective battery management system for an electric vehicle to accurately assess the battery’s health and performance by monitoring operating conditions such as voltage, current, time, temperature, and state of charge. This paper proposes an efficient approach to retrieve real-world field data (voltage, current, and time) under running vehicle conditions. In the first step, noise is removed from the field data using a moving-average filter. Then, first- and second-order derivations are applied to the filtered data to determine specific data set conditions. After that, a new approach based on zero-crossing is implemented to retrieve the field data. A second-order Randle circuit (SORC) is utilized in this study to analyze the selected field data. Further, a particle swarm optimization algorithm is adapted to estimate the parameters of the SORC. Our experiments indicate that the relative errors of the equivalent circuit model (ECM) are less than 2% compared to the model voltage and real voltage, which is consistent with the stable parameters of ECM. Full article

► Show Figures

Graphical abstract

16 pages, 4441 KiB

Open AccessArticle

Electrospinning of Chitosan–Halloysite Nanotube Biohybrid Mats for Clobetasol Propionate Delivery

by Natallia V. Dubashynskaya, Valentina A. Petrova, Igor V. Kudryavtsev, Andrey S. Trulioff, Artem A. Rubinstein, Alexey S. Golovkin, Alexander I. Mishanin, Anton A. Murav’ev, Iosif V. Gofman, Daria N. Poshina and Yury A. Skorik

Technologies 2025, 13(3), 90; https://doi.org/10.3390/technologies13030090 - 21 Feb 2025

Abstract

The application of electrospinning technologies for the preparation of mats based on mucoadhesive polysaccharides, such as chitosan (CS), is an attractive strategy for the development of biopolymeric delivery systems for topical corticosteroids. In this work, an electrospinning technique is described for the preparation [...] Read more.

The application of electrospinning technologies for the preparation of mats based on mucoadhesive polysaccharides, such as chitosan (CS), is an attractive strategy for the development of biopolymeric delivery systems for topical corticosteroids. In this work, an electrospinning technique is described for the preparation of CS-based mats doped with halloysite nanotubes (HNTs) with modified release of clobetasol propionate (CP). The optimized composition of the electrospinning solution was determined: 2.4% solution of CS in 46% acetic acid with addition of PEO (10% of CS mass) and HNTs (5% of CS mass); CP was introduced as an ethanol solution at the rate of 2 mg CP per 1 g of the obtained nonwoven material. The process parameters (the electrospinning voltage of 50–65 kV, the rotation speed of the spinning electrode of 10 min⁻¹, and the distance between the electrodes of 24 cm) were also optimized. The developed technology allowed us to obtain homogeneous nanofiber mats with excellent mechanical properties and biphasic drug release patterns (66% of CP released within 0.5 h and 88% of CP released within 6 h). The obtained nanofiber mats maintained the anti-inflammatory activity of corticosteroid at the level of free CP and showed no cytotoxicity. Full article

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

► Show Figures

Figure 1

20 pages, 4114 KiB

Open AccessArticle

Effect of Pore Characteristics of Biomass-Derived Activated Carbon for Automobile Canisters via Chemical Stabilization Method on Butane Adsorption Characteristics

by Dong-Sin Jo, Ju-Hwan Kim, Byung-Joo Kim and Hye-Min Lee

Technologies 2025, 13(3), 89; https://doi.org/10.3390/technologies13030089 - 21 Feb 2025

Abstract

In this study, kenaf-derived activated carbons (AK-AC) was prepared for automobile canisters via chemical stabilization and physical activation methods. The thermogravimetric analysis and differential thermogravimetry revealed a crystallite change in the kenaf with chemical stabilization. The AK-AC texture properties were studied using the [...] Read more.

In this study, kenaf-derived activated carbons (AK-AC) was prepared for automobile canisters via chemical stabilization and physical activation methods. The thermogravimetric analysis and differential thermogravimetry revealed a crystallite change in the kenaf with chemical stabilization. The AK-AC texture properties were studied using the Brunauer–Emmett–Teller, Dubinin–Radushkevitch, and non-local density functional theory equations, with N₂/77K isotherm adsorption–desorption curves. The AK-AC nanocrystallite characteristics were observed through X-ray diffraction and Raman spectroscopy. The AK-AC butane adsorption characteristics were analyzed via breakthrough curves and compared with those of commercial coconut-derived activated carbon (Coconut AC). As the activation time increased, the specific surface area and mesopore volume ratio of the AK-AC increased to 1080–1940 m²/g and 10.6–50.0%, respectively. The AK-AC also exhibited better mesoporous pore characteristics than the Coconut AC. The AK-AC butane adsorption capacity increased from 0.31 to 0.79 g/g. In particular, the AK-AC had an approximately 50% improved butane adsorption capacity compared to the Coconut AC. In addition, the butane adsorption characteristics of the AK-AC were determined using the mesopore volume, with a diameter of 3.0–4.0 nm. The results suggest that AK-AC may be proposed as an adsorbent to improve evaporative emissions from automotive canisters in the future. Full article

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

► Show Figures

Figure 1

40 pages, 4296 KiB

Open AccessArticle

Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Under Varying Imbalance Levels

by Mehdi Imani, Ali Beikmohammadi and Hamid Reza Arabnia

Technologies 2025, 13(3), 88; https://doi.org/10.3390/technologies13030088 - 20 Feb 2025

Abstract

This study examines the efficacy of Random Forest and XGBoost classifiers in conjunction with three upsampling techniques—SMOTE, ADASYN, and Gaussian noise upsampling (GNUS)—across datasets with varying class imbalance levels, ranging from moderate to extreme (15% to 1% churn rate). Employing metrics such as [...] Read more.

This study examines the efficacy of Random Forest and XGBoost classifiers in conjunction with three upsampling techniques—SMOTE, ADASYN, and Gaussian noise upsampling (GNUS)—across datasets with varying class imbalance levels, ranging from moderate to extreme (15% to 1% churn rate). Employing metrics such as F1 score, ROC AUC, PR AUC, Matthews Correlation Coefficient (MCC), and Cohen’s Kappa, this research provides a comprehensive evaluation of classifier performance under different imbalance scenarios, focusing on applications in the telecommunications domain. The findings highlight that tuned XGBoost paired with SMOTE (Tuned_XGB_SMOTE) consistently achieves the highest F1 score and robust performance across all imbalance levels. SMOTE emerged as the most effective upsampling method, particularly when used with XGBoost, whereas Random Forest performed poorly under severe imbalance. ADASYN showed moderate effectiveness with XGBoost but underperformed with Random Forest, and GNUS produced inconsistent results. This study underscores the impact of data imbalance, with MCC, Kappa, and F1 scores fluctuating significantly, whereas ROC AUC and PR AUC remained relatively stable. Moreover, rigorous statistical analyses employing the Friedman test and Nemenyi post hoc comparisons confirmed that the observed improvements in F1 score, PR-AUC, Kappa, and MCC were statistically significant (p < 0.05), with Tuned_XGB_SMOTE significantly outperforming Tuned_RF_GNUS. While differences in ROC-AUC were not significant, the consistency of these results across multiple performance metrics underscores the reliability of our framework, offering a statistically validated and attractive solution for model selection in imbalanced classification scenarios. Full article

(This article belongs to the Special Issue Data Science and Big Data in Biology, Physical Science and Engineering II)

► Show Figures

Figure 1

17 pages, 973 KiB

Open AccessArticle

Using AI and NLP for Tacit Knowledge Conversion in Knowledge Management Systems: A Comparative Analysis

by Ouissale Zaoui Seghroucheni, Mohamed Lazaar and Mohammed Al Achhab

Technologies 2025, 13(2), 87; https://doi.org/10.3390/technologies13020087 - 19 Feb 2025

Abstract

Tacit knowledge, often implicit and deeply embedded within individuals and organizational practices, is critical for fostering innovation and decision-making in knowledge management systems (KMS). Converting tacit knowledge into explicit forms enhances organizational effectiveness by making this knowledge accessible and reusable. This paper presents [...] Read more.

Tacit knowledge, often implicit and deeply embedded within individuals and organizational practices, is critical for fostering innovation and decision-making in knowledge management systems (KMS). Converting tacit knowledge into explicit forms enhances organizational effectiveness by making this knowledge accessible and reusable. This paper presents a comparative analysis of natural language processing (NLP) algorithms used for document and report mining to facilitate tacit knowledge conversion. This study focuses on algorithms that extract insights from semi-structured and document-based natural language representations, commonly found in organizational knowledge artifacts. Key NLP strategies, including text mining, information extraction, sentiment analysis, clustering, classification, recommendation systems, and affective computing, are evaluated for their effectiveness in identifying and externalizing tacit knowledge. The findings highlight the relative strengths and limitations of these techniques, offering practical guidance for selecting suitable algorithms based on organizational needs. Additionally, this paper identifies challenges and emerging opportunities for advancing NLP-driven tacit knowledge conversion, providing actionable insights for researchers and practitioners aiming to enhance KMS capabilities. Full article

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

► Show Figures

Figure 1

13 pages, 3281 KiB

Open AccessArticle

Compliant Parallel Asymmetrical Gripper System

by Andrea Deaconescu and Tudor Deaconescu

Technologies 2025, 13(2), 86; https://doi.org/10.3390/technologies13020086 - 19 Feb 2025

Abstract

The paper presents an innovative soft gripper system designed for automated assembling operations. The novel robotic soft gripper utilizes a linear pneumatic muscle as its motor, due to its inherently compliant behavior. This renders redundant the deployment of sensors or complex controllers, due [...] Read more.

The paper presents an innovative soft gripper system designed for automated assembling operations. The novel robotic soft gripper utilizes a linear pneumatic muscle as its motor, due to its inherently compliant behavior. This renders redundant the deployment of sensors or complex controllers, due to its mechanical system that ensures the desired adaptive behavior. Adaptivity is attained by adjusting the air pressure in the pneumatic muscle, monitored and controlled in a closed loop by means of a proportional pressure regulator. The kinematic diagram and the functional and constructive models of the gripper system are presented. The developed forces were measured followed by the calculation of stiffness and compliance. The paper concludes with recommendations for the operation of the gripper. Full article

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

► Show Figures

Graphical abstract

19 pages, 7109 KiB

Open AccessArticle

Exploration and Deconstruction of Correlation Cycles in Multidimensional Datasets

by Adam Dudáš, Emil Kršák and Miroslav Kvaššay

Technologies 2025, 13(2), 85; https://doi.org/10.3390/technologies13020085 - 18 Feb 2025

Abstract

Correlation analysis is one of the most prolific statistical methods used in data analysis problems, mining of knowledge focused on relationships of attributes in large datasets, and in various predictive tasks utilizing statistical, machine learning, and deep learning models. This approach to the [...] Read more.

Correlation analysis is one of the most prolific statistical methods used in data analysis problems, mining of knowledge focused on relationships of attributes in large datasets, and in various predictive tasks utilizing statistical, machine learning, and deep learning models. This approach to the analysis of functional relationships in multidimensional datasets is commonly used in conjunction with visual analysis approaches, which offer novel context for the relationships in data and clarify the results presented in large correlation matrices. One of such visualization methods uses graphical models called correlation graphs and chains, which visualize individual direct and indirect relationships between pairs of attributes in a dataset of interest as a graph structure, where vertices of the graph represent attributes of the dataset and edges between vertices represent the correlation of these attributes. This work focuses on the definition, identification, and exploration of so-called correlation cycles, which can be—through their deconstruction—used as an approach to lower error values in regression tasks. After the implementation of the correlation cycle identification and deconstruction, the proposed concept is evaluated on predictive analysis tasks in the context of three benchmarking datasets from the engineering field—the Sensor dataset, Superconductivity dataset, and Energy Farm dataset. The results obtained in this study show that when using simple, explainable regressors, the method utilizing deconstructed correlation cycles reaches a lower error rate in 83.3% of regression cases compared to the same regression models without the cycle incorporation. Full article

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

► Show Figures

Figure 1

15 pages, 2779 KiB

Open AccessArticle

Systematic Generation and Evaluation of Synthetic Production Data for Industry 5.0 Optimization

by Solomiia Liaskovska, Sviatoslav Tyskyi, Yevgen Martyn, Andy T. Augousti and Volodymyr Kulyk

Technologies 2025, 13(2), 84; https://doi.org/10.3390/technologies13020084 - 18 Feb 2025

Abstract

Our research focused on analyzing and advancing information technologies to identify ecological parameters in production. The primary goals were to enhance efficiency, reduce waste, and minimize the environmental impact of manufacturing processes. By incorporating the results of the study, we observed and systematized [...] Read more.

Our research focused on analyzing and advancing information technologies to identify ecological parameters in production. The primary goals were to enhance efficiency, reduce waste, and minimize the environmental impact of manufacturing processes. By incorporating the results of the study, we observed and systematized changes occurring in the transition from Industry 4.0 to Industry 5.0. Special attention was given to studying processes and technologies related to the generation of synthetic data and analyzing the implementation of cutting-edge technologies. The research object includes new parameters introduced within the framework of Industry 5.0, encompassing automation and cognitive technologies. Our scientific interests also extended to synthetic data used in modeling various production processes, including optimizing device performance in manufacturing and forecasting abnormal situations in industrial equipment operations. The subject of the research involves algorithms for generating synthetic data and methods for validating them to ensure their statistical similarity to real-world data. During the study, we also analyzed the impact of artificial intelligence implementation on improving the efficiency and adaptability of manufacturing systems. Full article

► Show Figures

Graphical abstract

18 pages, 2925 KiB

Open AccessArticle

Instrumentation and Evaluation of a Sensing System with Signal Conditioning Using Fuzzy Logic for a Rotary Dryer

by Juan Manuel Tabares-Martinez, Adriana Guzmán-López, Micael Gerardo Bravo-Sánchez, Alejandro Israel Barranco-Gutierrez, Juan José Martínez-Nolasco and Francisco Villaseñor-Ortega

Technologies 2025, 13(2), 83; https://doi.org/10.3390/technologies13020083 - 18 Feb 2025

Abstract

The growing demand for innovative solutions to accurately measure variables in dewatering processes has driven the development of advanced technologies. This study focuses on the evaluation of a measurement system in a rotary dryer used to dehydrate carrots at an operating temperature of [...] Read more.

The growing demand for innovative solutions to accurately measure variables in dewatering processes has driven the development of advanced technologies. This study focuses on the evaluation of a measurement system in a rotary dryer used to dehydrate carrots at an operating temperature of 70 °C. The system uses the Arduino platform, strain gauges, and LM35 temperature sensors. Experimental tests were designed to evaluate the performance of the dryer, using initial quantities of carrots of 1.5 kg, 1.0 kg, and 0.5 kg. The novelty of this study lies in the application of fuzzy logic for signal conditioning in real time, in order to improve the precision of measurements, designed in MATLAB (version 9.5) and programmed in Arduino. The dryer reduces the water content of the product to a final average of 10%. The research offers a novel solution for the integration of an intelligent measurement system that optimizes dewatering efficiency. The manuscript is organized as follows: in the methodology section, the design of the measurement system is described; subsequently, the experimental results and the analysis of the dryer efficiency are presented, and finally, in the conclusions, the implications of the system and its possible applications in other processes are discussed. Full article

(This article belongs to the Section Assistive Technologies)

► Show Figures

Figure 1

18 pages, 9690 KiB

Open AccessArticle

Reducing Energy Consumption in Embedded Systems Applications

by Ioannis Sofianidis, Vasileios Konstantakos and Spyridon Nikolaidis

Technologies 2025, 13(2), 82; https://doi.org/10.3390/technologies13020082 - 16 Feb 2025

Abstract

One of the most important challenges in modern digital systems, especially regarding autonomous embedded systems, is energy efficiency. This work studies an energy consumption optimization approach on a microcontroller that implements IoT-like applications, featuring Dynamic Voltage and Frequency Scaling (DVFS) capabilities, by dynamically [...] Read more.

One of the most important challenges in modern digital systems, especially regarding autonomous embedded systems, is energy efficiency. This work studies an energy consumption optimization approach on a microcontroller that implements IoT-like applications, featuring Dynamic Voltage and Frequency Scaling (DVFS) capabilities, by dynamically changing the supply voltage and clock frequency. The proposed approach categorizes tasks according to their demands on timing requirements and analyzes speed–energy efficiency trade-offs. Results strongly indicate that energy performance is improved due to the proper adjustment of configurations towards required tasks. The findings are verified within a set of scenarios that highlight the potential balance between energy economy and operational demands for specialized IoT contexts. Full article

(This article belongs to the Collection Selected Papers from the MOCAST Conference Series)

► Show Figures

Figure 1

22 pages, 9768 KiB

Open AccessArticle

Research on Circuit Partitioning Algorithm Based on Partition Connectivity Clustering and Tabu Search

by Linzi Yin, Hao Hu and Changgeng Li

Technologies 2025, 13(2), 81; https://doi.org/10.3390/technologies13020081 - 14 Feb 2025

Abstract

In this paper, a circuit-partitioning method is proposed based on partition connectivity clustering and tabu search. It includes four phases: coarsening, initial partitioning, uncoarsening, and refinement. In the initial partitioning phase, the concept of partition connectivity is introduced to optimize the vertex-clustering process, [...] Read more.

In this paper, a circuit-partitioning method is proposed based on partition connectivity clustering and tabu search. It includes four phases: coarsening, initial partitioning, uncoarsening, and refinement. In the initial partitioning phase, the concept of partition connectivity is introduced to optimize the vertex-clustering process, which clusters vertices with high connectivity in advance to provide an optimal initial solution. In the refinement phase, an improved tabu search algorithm is proposed, which combines two flexible neighborhood search rules and a candidate solution-selection strategy based on vertex-exchange frequency to further optimize load balancing. Additionally, a random perturbation method is suggested to increase the diversity of the search space and improve both the depth and breadth of global search. The experimental results based on the ISCAS-89 and ISCAS-85 benchmark circuits show that the average cut size of the proposed circuit-partitioning method is 0.91 times that of METIS and 0.86 times that of the KL algorithm, with better performance for medium- and small-scale circuits. Full article

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

► Show Figures

Figure 1

20 pages, 12626 KiB

Open AccessArticle

Nanostructured TiNi Wires for Textile Implants: Optimization of Drawing Process by Means of Mechano-Chemical Treatment

by Nadezhda V. Artyukhova, Anastasiia V. Shabalina, Sergey G. Anikeev, Helmut-Takahiro Uchida and Sergei A. Kulinich

Technologies 2025, 13(2), 80; https://doi.org/10.3390/technologies13020080 - 13 Feb 2025

Abstract

TiNi-based alloys are widely utilized in various engineering and medical applications. This study presents a newly developed and optimized technology for producing TiNi wires with a diameter of 40 μm utilizing a combined mechano-chemical treatment and drawing process. The resulting thin wires were [...] Read more.

TiNi-based alloys are widely utilized in various engineering and medical applications. This study presents a newly developed and optimized technology for producing TiNi wires with a diameter of 40 μm utilizing a combined mechano-chemical treatment and drawing process. The resulting thin wires were tested and characterized using multiple methods to determine their structural, phase, and mechanical properties. The structure of the TiNi wires, designed for use as textile implants in reconstructive medicine, features a TiNi metal matrix (B2 and B19′ phases) at the core and a surface oxide layer. A key structural characteristic of these wires is the presence of fine nanograins averaging 15–17 nm in size. No texturizing of the metallic material was observed during repeated plastic deformations throughout the drawing process. The applied mechano-chemical treatment aimed to modify the structure of the wires’ surface oxide layer. Specifically, reducing the thickness and roughness of this layer decreased the friction coefficient of the alloy during drawing, thus significantly reducing the number of breaks during production. At the same time, the cryogenic treatment of the final product was found to stabilize the martensitic phase B19′, which reduces the Young’s modulus by 10 GPa. Consequently, this newly developed methodology enhances the material’s quality and reduces labor costs during production. Full article

(This article belongs to the Section Manufacturing Technology)

► Show Figures

Figure 1

28 pages, 12512 KiB

Open AccessArticle

The Design, Simulation, and Construction of an O₂, C₃H₈, and CO₂ Gas Detection System Based on the Electrical Response of MgSb₂O₆ Oxide

by José Trinidad Guillen Bonilla, Maricela Jiménez Rodríguez, Héctor Guillen Bonilla, Alex Guillen Bonilla, Emilio Huízar Padilla, María Eugenia Sánchez Morales, Ariadna Berenice Flores Jiménez and Juan Carlos Estrada Gutiérrez

Technologies 2025, 13(2), 79; https://doi.org/10.3390/technologies13020079 - 13 Feb 2025

Abstract

In this paper, the prototype of a gas detector based on the electrical response of MgSb₂O₆ oxide at 400 °C and with a concentration of 560 ppm was designed, simulated, and fabricated. This design considers a PIC18F4550 microcontroller and a [...] Read more.

In this paper, the prototype of a gas detector based on the electrical response of MgSb₂O₆ oxide at 400 °C and with a concentration of 560 ppm was designed, simulated, and fabricated. This design considers a PIC18F4550 microcontroller and a response time of 3 s for the sensor. It is worth noting that the response system can be reduced in concordance with the mathematical model of the sensor’s electrical response. The proposed device is capable of detecting one to three gases: O₂, C₃H₈, and CO₂. The configuration is achieved through three switches. In programming the prototype, factors such as the gas sensor signals, device configuration, corrective gas signals, and indicator signals were carefully considered. The characteristic of the gas detector is an operational temperature of 400 °C, which is ideal for industrial processing. This can be configured to detect a single gas or all three of them

(O_{2}, C_{3} H_{8}, a n d {C O}_{2})

. Each gas type has its corresponding corrective signal and an indicator-led diode. The operation concentration is 560 ppm, the device is scalable, and its programming can be extended to cover industrial networks. Full article

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

► Show Figures

Figure 1

Journal Description

Technologies

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI