Technologies

16 pages, 467 KiB

Open AccessArticle

A Socially Assistive Robot as Orchestrator of an AAL Environment for Seniors

by Carlos E. Sanchez-Torres, Ernesto A. Lozano, Irvin H. López-Nava, J. Antonio Garcia-Macias and Jesus Favela

Technologies 2025, 13(6), 260; https://doi.org/10.3390/technologies13060260 - 19 Jun 2025

Viewed by 284

Abstract

Social robots in Ambient Assisted Living (AAL) environments offer a promising alternative for enhancing senior care by providing companionship and functional support. These robots can serve as intuitive interfaces to complex smart home systems, allowing seniors and caregivers to easily control their environment [...] Read more.

Social robots in Ambient Assisted Living (AAL) environments offer a promising alternative for enhancing senior care by providing companionship and functional support. These robots can serve as intuitive interfaces to complex smart home systems, allowing seniors and caregivers to easily control their environment and access various assistance services through natural interactions. By combining the emotional engagement capabilities of social robots with the comprehensive monitoring and support features of AAL, this integrated approach can potentially improve the quality of life and independence of elderly individuals while alleviating the burden on human caregivers. This paper explores the integration of social robotics with ambient assisted living (AAL) technologies to enhance elderly care. We propose a novel framework where a social robot is the central orchestrator of an AAL environment, coordinating various smart devices and systems to provide comprehensive support for seniors. Our approach leverages the social robot’s ability to engage in natural interactions while managing the complex network of environmental and wearable sensors and actuators. In this paper, we focus on the technical aspects of our framework. A computational P2P notebook is used to customize the environment and run reactive services. Machine learning models can be included for real-time recognition of gestures, poses, and moods to support non-verbal communication. We describe scenarios to illustrate the utility and functionality of the framework and how the robot is used to orchestrate the AAL environment to contribute to the well-being and independence of elderly individuals. We also address the technical challenges and future directions for this integrated approach to elderly care. Full article

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

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16 pages, 924 KiB

Open AccessArticle

Optimal Control Strategy and Evaluation Framework for Frequency Response of Combined Wind–Storage Systems

by Jie Hao, Huiping Zheng, Xueting Cheng, Yuxiang Li, Liming Bo and Juan Wei

Technologies 2025, 13(6), 259; https://doi.org/10.3390/technologies13060259 - 19 Jun 2025

Viewed by 296

Abstract

The increasing integration of wind turbines into the power grid has reduced the system frequency stability, necessitating the integration of energy storage systems in primary frequency regulation. This paper proposes an MPC-based control method to optimize the frequency response of a combined wind–storage [...] Read more.

The increasing integration of wind turbines into the power grid has reduced the system frequency stability, necessitating the integration of energy storage systems in primary frequency regulation. This paper proposes an MPC-based control method to optimize the frequency response of a combined wind–storage system. An evaluation system is also developed to characterize frequency response stability and guide power dispatch. First, the system model and state-space equations for MPC are established. Then, the control strategy is proposed to achieve the combined objective of minimizing power variation and frequency deviation. Finally, frequency stability is assessed using the evaluation system. MATLAB/Simulink case studies confirm the effectiveness of the proposed method in enhancing frequency regulation performance. The results show that this control strategy not only accelerates the response speed of the system frequency but also reduces its fluctuations, thereby improving the frequency stability of the system. Full article

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

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21 pages, 1062 KiB

Open AccessArticle

Red-KPLS Feature Reduction with 1D-ResNet50: Deep Learning Approach for Multiclass Alzheimer’s Staging

by Syrine Neffati, Ameni Filali, Kawther Mekki and Kais Bouzrara

Technologies 2025, 13(6), 258; https://doi.org/10.3390/technologies13060258 - 19 Jun 2025

Viewed by 444

Abstract

The early detection of Alzheimer’s disease (AD) is essential for improving patient outcomes, enabling timely intervention, and slowing disease progression. However, the complexity of neuroimaging data presents significant obstacles to accurate classification. This study introduces a computationally efficient AI framework designed to enhance [...] Read more.

The early detection of Alzheimer’s disease (AD) is essential for improving patient outcomes, enabling timely intervention, and slowing disease progression. However, the complexity of neuroimaging data presents significant obstacles to accurate classification. This study introduces a computationally efficient AI framework designed to enhance AD staging using structural MRI. The proposed method integrates discrete wavelet transform (DWT) for multi-scale feature extraction, a novel reduced kernel partial least squares (Red-KPLS) algorithm for feature reduction, and ResNet-50 for classification. The proposed technique, referred to as Red-KPLS-CNN, refines MRI features into discriminative biomarkers while minimizing redundancy. As a result, the framework achieves 96.9% accuracy and an F1-score of 97.8% in the multiclass classification of AD cases using the Kaggle dataset. The dataset was strategically partitioned into 60% training, 20% validation, and 20% testing sets, preserving class balance throughout all splits. The integration of Red–KPLS enhances feature selection, reducing dimensionality without compromising diagnostic sensitivity. Compared to conventional models, our approach improves classification robustness and generalization, reinforcing its potential for scalable and interpretable AD diagnostics. These findings emphasize the importance of hybrid wavelet–kernel–deep learning architectures, offering a promising direction for advancing computer-aided diagnosis (CAD) in clinical applications. Full article

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

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19 pages, 2771 KiB

Open AccessArticle

Dynamic Hypergraph Convolutional Networks for Hand Motion Gesture Sequence Recognition

by Dong-Xing Jing, Kui Huang, Shi-Jian Liu, Zheng Zou and Chih-Yu Hsu

Technologies 2025, 13(6), 257; https://doi.org/10.3390/technologies13060257 - 19 Jun 2025

Viewed by 221

Abstract

This paper introduces a novel approach to hand motion gesture recognition by integrating the Fourier transform with hypergraph convolutional networks (HGCNs). Traditional recognition methods often struggle to capture the complex spatiotemporal dynamics of hand gestures. HGCNs, which are capable of modeling intricate relationships [...] Read more.

This paper introduces a novel approach to hand motion gesture recognition by integrating the Fourier transform with hypergraph convolutional networks (HGCNs). Traditional recognition methods often struggle to capture the complex spatiotemporal dynamics of hand gestures. HGCNs, which are capable of modeling intricate relationships among joints, are enhanced by Fourier transform to analyze gesture features in the frequency domain. A hypergraph is constructed to represent the interdependencies among hand joints, allowing for dynamic adjustments based on joint movements. Hypergraph convolution is applied to update node features, while the Fourier transform facilitates frequency-domain analysis. The T-Module, a multiscale temporal convolution module, aggregates features from multiple frames to capture gesture dynamics across different time scales. Experiments on the dynamic hypergraph (DHG14/28) and shape retrieval contest (SHREC’17) datasets demonstrate the effectiveness of the proposed method, achieving accuracies of 96.4% and 97.6%, respectively, and outperforming traditional gesture recognition algorithms. Ablation studies further validate the contributions of each component in enhancing recognition performance. Full article

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

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41 pages, 8353 KiB

Open AccessArticle

Optimizing LoRaWAN Gateway Placement in Urban Environments: A Hybrid PSO-DE Algorithm Validated via HTZ Simulations

by Kanar Alaa Al-Sammak, Sama Hussein Al-Gburi, Ion Marghescu, Ana-Maria Claudia Drăgulinescu, Cristina Marghescu, Alexandru Martian, Nayef A. M. Alduais and Nawar Alaa Hussein Al-Sammak

Technologies 2025, 13(6), 256; https://doi.org/10.3390/technologies13060256 - 17 Jun 2025

Viewed by 622

Abstract

With rapid advancements in the Internet of Things (IoT), Low-Power Wide-Area Networks (LPWANs) play a crucial role in expanding IoT’s capabilities while using minimal energy. Among the various LPWAN technologies, LoRaWAN (Long-Range Wide-Area Network) is particularly notable for its capacity to enable long-range, [...] Read more.

With rapid advancements in the Internet of Things (IoT), Low-Power Wide-Area Networks (LPWANs) play a crucial role in expanding IoT’s capabilities while using minimal energy. Among the various LPWAN technologies, LoRaWAN (Long-Range Wide-Area Network) is particularly notable for its capacity to enable long-range, low-rate communications with low power needs. This study investigates how to optimize the placement of LoRaWAN gateways by using a combination of Particle Swarm Optimization (PSO) and Differential Evolution (DE). The approach is validated through simulations driven by HTZ to evaluate network performance in urban settings. Centered around the area of the Politehnica University of Bucharest, this research examines how different gateway placements on various floors of a building affect network coverage and packet loss. The experiment employs Adeunis Field Test Devices (FTDs) and Dragino LG308-EC25 gateways, systematically testing two spreading factors, SF7 and SF12, to assess their effectiveness in terms of signal quality and reliability. An innovative optimization algorithm, GateOpt PSODE, is introduced, which combines PSO and DE to optimize gateway placements based on real-time network performance metrics, like the Received Signal Strength Indicator (RSSI), the Signal-to-Noise Ratio (SNR), and packet loss. The findings reveal that strategically positioning gateways, especially on higher floors, significantly improves communication reliability and network efficiency, providing a solid framework for deploying LoRaWAN networks in intricate urban environments. Full article

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26 pages, 2415 KiB

Open AccessArticle

RL-SCAP SigFox: A Reinforcement Learning Based Scalable Communication Protocol for Low-Power Wide-Area IoT Networks

by Raghad Albalawi, Fatma Bouabdallah, Linda Mohaisen and Shireen Saifuddin

Technologies 2025, 13(6), 255; https://doi.org/10.3390/technologies13060255 - 17 Jun 2025

Viewed by 255

Abstract

The Internet of Things (IoT) aims to wirelessly connect billions of physical things to the IT infrastructure. Although there are several radio access technologies available, few of them meet the needs of Internet of Things applications, such as long range, low cost, and [...] Read more.

The Internet of Things (IoT) aims to wirelessly connect billions of physical things to the IT infrastructure. Although there are several radio access technologies available, few of them meet the needs of Internet of Things applications, such as long range, low cost, and low energy consumption. The low data rate of low-power wide-area network (LPWAN) technologies, particularly SigFox, makes them appropriate for Internet of Things applications since the longer the radio link’s useable distance, the lower the data rate. Network reliability is the primary goal of SigFox technology, which aims to deliver data messages successfully through redundancy. This raises concerns about SigFox’s scalability and leads to one of its flaws, namely the high collision rate. In this paper, the goal is to prevent collisions by switching to time division multiple access (TDMA) from SigFox’s Aloha-based medium access protocol, utilizing only orthogonal channels, and eliminating redundancy. Consequently, during a designated time slot, each node transmits a single copy of the data message over a particular orthogonal channel. To achieve this, a multi-agent, off-policy reinforcement learning (RL) Q-Learning technique will be used on top of SigFox. In other words, the objective is to increase SigFox’s scalability through the use of Reinforcement Learning based time slot allocation (RL-SCAP). The findings show that, especially in situations with high node densities or constrained communication slots, the proposed protocol performs better than the basic SCAP (Slot and Channel Allocation Protocol) by obtaining a higher Packet Delivery Ratio (PDR) in average of 60.58%, greater throughput in average of 60.90%, and a notable decrease in collisions up to 79.37%. Full article

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19 pages, 6471 KiB

Open AccessArticle

A Miniaturized RHCP Slot Antenna for Wideband Applications Including Sub-6 GHz 5G

by Atyaf H. Mohammed, Falih M. Alnahwi, Yasir I. A. Al-Yasir and Sunday C. Ekpo

Technologies 2025, 13(6), 254; https://doi.org/10.3390/technologies13060254 - 17 Jun 2025

Viewed by 367

Abstract

The rapid development of 5G and next-generation wireless systems has increased the demand for antennas that support circular polarization (CP), wide frequency coverage, and a compact size. Achieving wideband CP performance in a low-profile and simple structure remains a key challenge for modern [...] Read more.

The rapid development of 5G and next-generation wireless systems has increased the demand for antennas that support circular polarization (CP), wide frequency coverage, and a compact size. Achieving wideband CP performance in a low-profile and simple structure remains a key challenge for modern antenna designs. In response to this, this paper presents a compact wide-slot antenna with a single feed, offering a wide operational bandwidth and circularly polarized radiation. The proposed design is excited by a 50 Ohm microstrip feedline, and it is fabricated on an (54 × 50 × 1.6 mm³) FR4 dielectric substrate. On the bottom side of the dielectric substrate, the ground plane is engraved to form a square-shaped radiating slot. The shape of the tuning stub of the antenna is modified in order to attain a wide impedance bandwidth and an axial ratio bandwidth (ARBW). The modifications include inserting a rectangular strip and thin horizontal strips into the tuning stub after tapering its upper corner. On the other hand, the radiating slot is appended by two rectangular stubs. The radiation of the resulted structure has right-hand circular polarization (RHCP). The measured results of the proposed antenna show a −10 dB impedance bandwidth equal to 78% (2.65 GHz, 2.08–4.73 GHz), whereas its broadside 3 dB ARBW is 71.6% over the frequencies (2.31 GHz, 2.07–4.38 GHz), which is compatible with various wireless communication applications. Furthermore, the peak value of the measured gain is equal to 4.68 dB, and its value is larger than 2 dBi along the operational bandwidth of the antenna. Full article

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

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34 pages, 6816 KiB

Open AccessArticle

Towards an Emotion-Aware Metaverse: A Human-Centric Shipboard Fire Drill Simulator

by Musaab H. Hamed-Ahmed, Diego Ramil-López, Paula Fraga-Lamas and Tiago M. Fernández-Caramés

Technologies 2025, 13(6), 253; https://doi.org/10.3390/technologies13060253 - 17 Jun 2025

Viewed by 369

Abstract

Traditional Extended Reality (XR) and Metaverse applications focus heavily on User Experience (UX) but often overlook the role of emotions in user interaction. This article addresses that gap by presenting an emotion-aware Metaverse application: a Virtual Reality (VR) fire drill simulator for shipboard [...] Read more.

Traditional Extended Reality (XR) and Metaverse applications focus heavily on User Experience (UX) but often overlook the role of emotions in user interaction. This article addresses that gap by presenting an emotion-aware Metaverse application: a Virtual Reality (VR) fire drill simulator for shipboard emergency training. The simulator detects emotions in real time, assessing trainees’ responses under stress to improve learning outcomes. Its architecture incorporates eye-tracking and facial expression analysis via Meta Quest Pro headsets. Two experimental phases were conducted. The first revealed issues like poor navigation and lack of visual guidance. These insights led to an improved second version with a refined User Interface (UI), a real-time task tracker and clearer visual cues. The obtained results showed that the included design improvements can reduce task completion times between 14.18% and 32.72%. Emotional feedback varied, suggesting a need for more immersive elements. Overall, this article provides useful guidelines for creating the next generation of emotion-aware Metaverse applications. Full article

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

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16 pages, 1439 KiB

Open AccessArticle

Linear Average Yield Criterion and Its Application in Failure Pressure Evaluation of Defect-Free Pipelines

by Jian-Hong Ji, Ming-Ming Sun and Jie Zhang

Technologies 2025, 13(6), 252; https://doi.org/10.3390/technologies13060252 - 13 Jun 2025

Viewed by 312

Abstract

Analysis of internal pressure failure is a crucial aspect of assessing pipeline integrity. By combining the unified yield criterion with actual burst data, the applicability of different yield criteria is elucidated. Based on the distribution law of burst data, a linear average yield [...] Read more.

Analysis of internal pressure failure is a crucial aspect of assessing pipeline integrity. By combining the unified yield criterion with actual burst data, the applicability of different yield criteria is elucidated. Based on the distribution law of burst data, a linear average yield criterion is proposed. The results indicate that the yield function of the linear average yield criterion is a linear expression, and the yield path forms an equilateral non-equiangular inscribed dodecagon within the von Mises circle. For the evaluation of failure pressure, this yield criterion exhibits the highest level of applicability, followed by the ASSY and Tresca yield theories. The linear average yield criterion limits the failure pressure prediction error, with low strain-hardening (0 ≤ n ≤ 0.06) to within 3%. Full article

(This article belongs to the Section Construction Technologies)

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43 pages, 1485 KiB

Open AccessReview

Smart Textile Design: A Systematic Review of Materials and Technologies for Textile Interaction and User Experience Evaluation Methods

by Manoella Guennes, Joana Cunha and Isabel Cabral

Technologies 2025, 13(6), 251; https://doi.org/10.3390/technologies13060251 - 13 Jun 2025

Viewed by 607

Abstract

Creating meaningful interactions using smart textiles involves both a comprehensive understanding of relevant materials and technologies (M&T) and how users engage with this type of interface. Despite its relevance to design research, user experience (UX) evaluation remains limited within the smart textile field. [...] Read more.

Creating meaningful interactions using smart textiles involves both a comprehensive understanding of relevant materials and technologies (M&T) and how users engage with this type of interface. Despite its relevance to design research, user experience (UX) evaluation remains limited within the smart textile field. This research aims to systematize information regarding the main M&T used in recent smart textile design research and the evaluation methods (EMs) employed to assess the UX. For this purpose, a systematic literature review was conducted in the Scopus database. The search covered the period from 2018 to 2025 and yielded a total of 232 results. Of these, 56 full papers in English, available on the internet, and focusing on experimental research on smart textile interaction and experience evaluation were included. This review identifies the prevalent use of electronic components and conductive materials, emphasizing the importance of selecting materials that enable sensing, actuation, communication, and processing capabilities. UX evaluation focused on the pragmatic dimension, whereas the combination with the hedonic dimension was generally regarded as future work. The study led to the proposal of four key topics to support the creation of meaningful interactions and highlights the need for further research on evaluating users’ emotional experiences with smart textiles. Full article

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

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23 pages, 4656 KiB

Open AccessArticle

A Hybrid Intelligent Model for Olympic Medal Prediction Based on Data-Intelligence Fusion

by Ning Li, Junhao Li, Hejia Fang, Jian Wang, Qiao Yu and Yafei Shi

Technologies 2025, 13(6), 250; https://doi.org/10.3390/technologies13060250 - 13 Jun 2025

Viewed by 551

Abstract

This study presents a hybrid intelligent model for predicting Olympic medal distribution at the 2028 Los Angeles Games, based on data-intelligence fusion (DIF). By integrating historical medal records, athlete performance metrics, debut medal-winning countries, and coaching resources, the model aims to provide accurate [...] Read more.

This study presents a hybrid intelligent model for predicting Olympic medal distribution at the 2028 Los Angeles Games, based on data-intelligence fusion (DIF). By integrating historical medal records, athlete performance metrics, debut medal-winning countries, and coaching resources, the model aims to provide accurate national medal forecasts. The model introduces a Performance Score (PS) system combining a Traditional Advantage Index (TAI) via K-means clustering, an Athlete Strength Index (ASI) using a backpropagation neural network, and a Host effect factor. Sub-models include an autoregressive integrated moving average model for time-series forecasting, logistic regression for predicting debut medal-winning countries, and random forest regression to quantify the “Great Coach” effect. The results project America winning 44 gold and 124 total medals, and China 44 gold and 94 total medals. The model demonstrates strong accuracy with root mean square errors of 3.21 (gold) and 4.32 (total medals), and mean-relative errors of 17.6% and 8.04%. Compared to the 2024 Paris Olympics, the model projects a notable reshuffling in 2028, with the United States expected to strengthen its overall lead as host while countries like France are predicted to experience significant declines in medal counts. Findings highlight the nonlinear impact of coaching and event expansion’s role in medal growth. This model offers a strategic tool for Olympic planning, advancing medal prediction from simple extrapolation to intelligent decision support. Full article

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

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25 pages, 3966 KiB

Open AccessArticle

Tribomechanical Analysis and Performance Optimization of Sustainable Basalt Fiber Polymer Composites for Engineering Applications

by Corina Birleanu, Razvan Udroiu, Mircea Cioaza, Paul Bere and Marius Pustan

Technologies 2025, 13(6), 249; https://doi.org/10.3390/technologies13060249 - 13 Jun 2025

Viewed by 356

Abstract

This study investigates the effect of fiber weight fraction on the tribomechanical behavior of basalt fiber-reinforced polymer (BFRP) composites under dry sliding conditions. Composite specimens with 50%, 65%, and 70% basalt fiber contents were manufactured and tested through tensile, flexural, and pin-on-disc tribological [...] Read more.

This study investigates the effect of fiber weight fraction on the tribomechanical behavior of basalt fiber-reinforced polymer (BFRP) composites under dry sliding conditions. Composite specimens with 50%, 65%, and 70% basalt fiber contents were manufactured and tested through tensile, flexural, and pin-on-disc tribological evaluations. Key tribological parameters, including the coefficient of friction (COF), specific wear rate (K), and contact temperature, were measured under various applied loads and sliding speeds. Statistical analysis was performed using a generalized linear model (GLM) to identify significant factors and their interactions. Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) analyses indicated that abrasive wear, matrix cracking, and fiber–matrix interfacial failure were the dominant wear mechanisms. The experimental results revealed that the fiber weight fraction had the most significant influence on COF (42.78%), while the sliding speed had the predominant effect on the specific wear rate (77.69%) and contact temperature (32.79%). These findings highlight the potential of BFRP composites for applications requiring enhanced wear resistance and mechanical stability under varying loading conditions. Full article

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

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19 pages, 5754 KiB

Open AccessArticle

Neck Functional Status Assessment Using Virtual Reality Simulation of Daily Activities

by José Angel Santos-Paz, Álvaro Sánchez-Picot, Elena Bocos-Corredor, Filippo Moggioli, Aitor Martin-Pintado-Zugasti, Rodrigo García-Carmona and Abraham Otero

Technologies 2025, 13(6), 248; https://doi.org/10.3390/technologies13060248 - 12 Jun 2025

Viewed by 501

Abstract

Neck pain is a significant global health concern and a leading cause of disability. Conventional clinical neck assessments often rely on maximal Cervical Range of Motion (CROM) measurements, which may not accurately reflect functional limitations experienced during activities of daily living (ADLs). This [...] Read more.

Neck pain is a significant global health concern and a leading cause of disability. Conventional clinical neck assessments often rely on maximal Cervical Range of Motion (CROM) measurements, which may not accurately reflect functional limitations experienced during activities of daily living (ADLs). This study introduces a novel approach to evaluate neck functional status by employing a virtual reality (VR) environment to simulate an apple-harvesting task. Three-dimensional head kinematics were continuously recorded in 60 participants (30 with clinically significant neck pain and 30 asymptomatic) as they performed the task. Spectral analysis of the data revealed that individuals with neck pain exhibited slower head rotation speed, particularly in the transverse and frontal planes, compared to the pain-free group, as evidenced by higher spectral power in the low-frequency band [0, 0.1] Hz and lower power in the [0.1, 0.5] Hz band. Furthermore, participants with neck pain required significantly more time to complete the apple-harvesting task. The VR system demonstrated high usability (SUS score = 84.21), and no adverse effects were reported. These findings suggest that VR-based assessment during simulated ADLs can provide valuable information about the functional impact of neck pain beyond traditional CROM measurements, potentially enabling remote evaluation and personalized telerehabilitation strategies. Full article

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

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29 pages, 3418 KiB

Open AccessArticle

Green Ground: Construction and Demolition Waste Prediction Using a Deep Learning Algorithm

by Wadha N. Alsheddi, Shahad E. Aljayan, Asma Z. Alshehri, Manar F. Alenzi, Norah M. Alnaim, Maryam M. Alshammari, Nouf K. AL-Saleem and Abdulaziz I. Almulhim

Technologies 2025, 13(6), 247; https://doi.org/10.3390/technologies13060247 - 12 Jun 2025

Viewed by 508

Abstract

The waste management and recycling industry in Saudi Arabia is facing ongoing challenges in reducing the negative impact resulting from the recycling process. Different types of waste lack an efficient and accurate method for classification, especially in cases that require the rapid processing [...] Read more.

The waste management and recycling industry in Saudi Arabia is facing ongoing challenges in reducing the negative impact resulting from the recycling process. Different types of waste lack an efficient and accurate method for classification, especially in cases that require the rapid processing of materials. A deep learning prediction model based on a convolutional neural network algorithm was developed to classify and predict the types of construction and demolition waste (CDW). The CDW image dataset used contained 9273 images, including concrete, asphalt, ceramics, and autoclaved aerated concrete. The model obtained an overall accuracy of 97.12%. The Green Ground image prediction model is extremely useful in the construction and demolition industry for automating sorting processes. The model improves recycling rates by ensuring that materials are sorted correctly, thus reducing waste sent to landfills, by accurately identifying different types of materials in CDW images. As part of Saudi Arabia’s 2030 sustainability objectives, these steps contribute to achieving a greener future, complying with environmental regulations, and promoting sustainability. Full article

(This article belongs to the Section Environmental Technology)

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24 pages, 7065 KiB

Open AccessArticle

Center of Mass Auto-Location in Space

by Lucas McLeland, Brian Erickson, Brendan Ruchlin, Eryn Daman, James Mejia, Benjamin Ho, Joshua Lewis, Bryan Mann, Connor Paw, James Ross, Christopher Reis, Scott Walter, Stefanie Coward, Thomas Post, Andrew Freeborn and Timothy Sands

Technologies 2025, 13(6), 246; https://doi.org/10.3390/technologies13060246 - 12 Jun 2025

Viewed by 314

Abstract

Maintaining a spacecraft’s center of mass at the origin of a body-fixed coordinate system is often key to precision trajectory tracking. Typically, the inertia matrix is estimated and verified with preliminary ground testing. This article presents groundbreaking preliminary results and significant findings from [...] Read more.

Maintaining a spacecraft’s center of mass at the origin of a body-fixed coordinate system is often key to precision trajectory tracking. Typically, the inertia matrix is estimated and verified with preliminary ground testing. This article presents groundbreaking preliminary results and significant findings from on-orbit space experiments validating recently proposed methods as part of a larger study over multiple years. Time-varying estimates of inertia moments and products are used to reveal time-varying estimates of the location of spacecraft center of mass using geosynchronous orbiting test satellites proposing a novel two-norm optimal projection learning method. Using the parallel axis theorem, the location of the mass center is parameterized using the cross products of inertia, and that information is extracted from spaceflight maneuver data validating modeling and simulation. Mass inertia properties are discerned, and the mass center is experimentally revealed to be over thirty centimeters away from the assumed locations in two of the three axes. Rotation about one axis is found to be very well balanced, with the center of gravity lying on that axis. Two-to-three orders of magnitude corrections to inertia identification are experimentally demonstrated. Combined-axis three-dimensional maneuvers are found to obscure identification compared with single-axis maneuvering as predicted by the sequel analytic study. Mass center location migrates 36–95% and subsequent validating experiments duplicate the results to within 0.1%. Full article

(This article belongs to the Special Issue Advanced Autonomous Systems and Artificial Intelligence Stage)

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42 pages, 9998 KiB

Open AccessReview

Routing Challenges and Enabling Technologies for 6G–Satellite Network Integration: Toward Seamless Global Connectivity

by Fatma Aktas, Ibraheem Shayea, Mustafa Ergen, Laura Aldasheva, Bilal Saoud, Akhmet Tussupov, Didar Yedilkhan and Saule Amanzholova

Technologies 2025, 13(6), 245; https://doi.org/10.3390/technologies13060245 - 12 Jun 2025

Viewed by 1402

Abstract

The capabilities of 6G networks surpass those of existing networks, aiming to enable seamless connectivity between all entities and users at any given time. A critical aspect of achieving enhanced and ubiquitous mobile broadband, as promised by 6G networks, is merging satellite networks [...] Read more.

The capabilities of 6G networks surpass those of existing networks, aiming to enable seamless connectivity between all entities and users at any given time. A critical aspect of achieving enhanced and ubiquitous mobile broadband, as promised by 6G networks, is merging satellite networks with land-based networks, which offers significant potential in terms of coverage area. Advanced routing techniques in next-generation network technologies, particularly when incorporating terrestrial and non-terrestrial networks, are essential for optimizing network efficiency and delivering promised services. However, the dynamic nature of the network, the heterogeneity and complexity of next-generation networks, and the relative distance and mobility of satellite networks all present challenges that traditional routing protocols struggle to address. This paper provides an in-depth analysis of 6G networks, addressing key enablers, technologies, commitments, satellite networks, and routing techniques in the context of 6G and satellite network integration. To ensure 6G fulfills its promises, the paper emphasizes necessary scenarios and investigates potential bottlenecks in routing techniques. Additionally, it explores satellite networks and identifies routing challenges within these systems. The paper highlights routing issues that may arise in the integration of 6G and satellite networks and offers a comprehensive examination of essential approaches, technologies, and visions required for future advancements in this area. 6G and satellite networks are associated with technical terms such as AI/ML, quantum computing, THz communication, beamforming, MIMO technology, ultra-wide band and multi-band antennas, hybrid channel models, and quantum encryption methods. These technologies will be utilized to enhance the performance, security, and sustainability of future networks. Full article

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

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26 pages, 2568 KiB

Open AccessArticle

Unified Framework for RIS-Enhanced Wireless Communication and Ambient RF Energy Harvesting: Performance and Sustainability Analysis

by Sunday Enahoro, Sunday Ekpo, Yasir Al-Yasir, Mfonobong Uko, Fanuel Elias, Rahul Unnikrishnan and Stephen Alabi

Technologies 2025, 13(6), 244; https://doi.org/10.3390/technologies13060244 - 12 Jun 2025

Viewed by 447

Abstract

The increasing demand for high-capacity, energy-efficient wireless networks poses significant challenges in maintaining spectral efficiency, minimizing interference, and ensuring sustainability. Traditional direct-link communication suffers from signal degradation due to path loss, multipath fading, and interference, limiting overall performance. To mitigate these challenges, this [...] Read more.

The increasing demand for high-capacity, energy-efficient wireless networks poses significant challenges in maintaining spectral efficiency, minimizing interference, and ensuring sustainability. Traditional direct-link communication suffers from signal degradation due to path loss, multipath fading, and interference, limiting overall performance. To mitigate these challenges, this paper proposes a unified RIS framework that integrates passive and active Reconfigurable Intelligent Surfaces (RISs) for enhanced communication and ambient RF energy harvesting. Our methodology optimizes RIS-assisted beamforming using successive convex approximation (SCA) and adaptive phase shift tuning, maximizing desired signal reception while reducing interference. Passive RIS efficiently reflects signals without external power, whereas active RIS employs amplification-assisted reflection for superior performance. Evaluations using realistic urban macrocell and mmWave channel models reveal that, compared to direct links, passive RIS boosts SNR from 3.0 dB to 7.1 dB, and throughput from 2.6 Gbps to 4.6 Gbps, while active RIS further enhances the SNR to 10.0 dB and throughput to 6.8 Gbps. Energy efficiency increases from 0.44 to 0.67 (passive) and 0.82 (active), with latency reduced from 80 ms to 35 ms. These performance metrics validate the proposed approach and highlight its potential applications in urban 5G networks, IoT systems, high-mobility scenarios, and other next-generation wireless environments. Full article

(This article belongs to the Special Issue Microwave/Millimeter-Wave Future Trends and Technologies)

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23 pages, 3557 KiB

Open AccessArticle

Analysis of Surface Roughness and Machine Learning-Based Modeling in Dry Turning of Super Duplex Stainless Steel Using Textured Tools

by Shailendra Pawanr and Kapil Gupta

Technologies 2025, 13(6), 243; https://doi.org/10.3390/technologies13060243 - 11 Jun 2025

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Abstract

One of the most critical aspects of turning, and machining in general, is the surface roughness of the finished product, which directly influences the performance, functionality, and longevity of machined components. The accurate prediction of surface roughness is vital for enhancing component quality [...] Read more.

One of the most critical aspects of turning, and machining in general, is the surface roughness of the finished product, which directly influences the performance, functionality, and longevity of machined components. The accurate prediction of surface roughness is vital for enhancing component quality and machining efficiency. This study presents a machine learning-driven framework for modeling mean roughness depth (Rz) during the dry machining of super duplex stainless steel (SDSS 2507). SDSS 2507 is known for its exceptional mechanical strength and corrosion resistance, but it poses significant challenges in machinability. To address this, this study employs flank-face textured cutting tools to enhance machining performance. Experiments were designed using the L₂₇ orthogonal array with three continuous factors, cutting speed, feed rate, and depth of cut, and one categorical factor, tool texture type (dimple, groove, and wave), along with surface roughness as an output parameter. Gaussian Data Augmentation (GDA) was employed to enrich data variability and strengthen model generalization, resulting in the improved predictive performance of the machine learning models. MATLAB R2021a was employed for preprocessing, the normalization of datasets, and model development. Two models, Least-Squares Support Vector Machine (LSSVM) and Multi-Gene Genetic Programming (MGGP), were trained and evaluated on various statistical metrics. The results showed that both LSSVM and MGGP models learned well from the training data and accurately predicted Rz on the testing data, demonstrating their reliability and strong performance. Of the two models, LSSVM demonstrated superior performance, achieving a training accuracy of 98.14%, a coefficient of determination (R²) of 0.9959, and a root mean squared error (RMSE) of 0.1528. It also maintained strong generalization on the testing data, with 94.36% accuracy and 0.9391 R² and 0.6730 RMSE values. The high predictive accuracy of the LSSVM model highlights its potential for identifying optimal machining parameters and integrating into intelligent process control systems to enhance surface quality and efficiency in the complex machining of materials like SDSS. Full article

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

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37 pages, 2828 KiB

Open AccessArticle

An Approach to Business Continuity Self-Assessment

by Nelson Russo, Henrique São Mamede and Leonilde Reis

Technologies 2025, 13(6), 242; https://doi.org/10.3390/technologies13060242 - 11 Jun 2025

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Abstract

Business Continuity Management (BCM) is critical for organizations to mitigate disruptions and maintain operations, yet many struggle with fragmented and non-standardized self-assessment tools. Existing frameworks often lack holistic integration, focusing narrowly on isolated components like cyber resilience or risk management, which limits their [...] Read more.

Business Continuity Management (BCM) is critical for organizations to mitigate disruptions and maintain operations, yet many struggle with fragmented and non-standardized self-assessment tools. Existing frameworks often lack holistic integration, focusing narrowly on isolated components like cyber resilience or risk management, which limits their ability to evaluate BCM maturity comprehensively. This research addresses this gap by proposing a structured Self-Assessment System designed to unify BCM components into an adaptable, standards-aligned methodology. Grounded in Design Science Research, the system integrates a BCM Model comprising eight components and 118 activities, each evaluated through weighted questions to quantify organizational preparedness. The methodology enables organizations to conduct rapid as-is assessments using a 0–100 scoring mechanism with visual indicators (red/yellow/green), benchmark progress over time and against peers, and align with international standards (e.g., ISO 22301, ITIL) while accommodating unique organizational constraints. Demonstrated via focus groups and semi-structured interviews with 10 organizations, the system proved effective in enhancing top management commitment, prioritizing resource allocation, and streamlining BCM implementation—particularly for SMEs with limited resources. Key contributions include a reusable self-assessment tool adaptable to any BCM framework, empirical validation of its utility in identifying weaknesses and guiding continuous improvement, and a pathway from initial assessment to advanced measurement via the Plan-Do-Check-Act cycle. By bridging the gap between theoretical standards and practical application, this research offers a scalable solution for organizations to systematically evaluate and improve BCM resilience. Full article

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

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18 pages, 605 KiB

Open AccessArticle

A Novel Framework for Co-Expansion Planning of Transmission Lines and Energy Storage Devices Considering Unit Commitment

by Edimar José de Oliveira, Lucas Santiago Nepomuceno, Leonardo Willer de Oliveira and Arthur Neves de Paula

Technologies 2025, 13(6), 241; https://doi.org/10.3390/technologies13060241 - 11 Jun 2025

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Abstract

This paper presents a methodology for the co-expansion planning of transmission lines and energy storage systems, considering unit commitment constraints and uncertainties in load demand and wind generation. The problem is formulated as a mixed-integer nonlinear program and solved using a decomposition-based approach [...] Read more.

This paper presents a methodology for the co-expansion planning of transmission lines and energy storage systems, considering unit commitment constraints and uncertainties in load demand and wind generation. The problem is formulated as a mixed-integer nonlinear program and solved using a decomposition-based approach that combines a genetic algorithm with mixed-integer linear programming. Uncertainties are modeled through representative day scenarios obtained via clustering. The methodology is validated on a modified IEEE 24-bus system. The results show that co-planning reduces total expansion costs by 14.69%, annual operating costs by 26.19%, and wind curtailment by 91.99% compared to transmission only expansion. These improvements are due to the flexibility introduced by energy storage systems, which enables more efficient thermal dispatch, reduces fuel consumption, and minimizes renewable energy curtailment. Full article

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25 pages, 2627 KiB

Open AccessArticle

Photovoltaic Power Estimation for Energy Management Systems Addressing NMOT Removal with Simplified Thermal Models

by Juan G. Marroquín-Pimentel, Manuel Madrigal-Martínez, Juan C. Olivares-Galvan and Alma L. Núñez-González

Technologies 2025, 13(6), 240; https://doi.org/10.3390/technologies13060240 - 11 Jun 2025

Viewed by 362

Abstract

For energy management systems, it is crucial to determine, in advance, the available energy from renewable sources to be dispatched in the next hours or days, in order to meet their generation and consumption goals. Predicting the photovoltaic power output strongly depends on [...] Read more.

For energy management systems, it is crucial to determine, in advance, the available energy from renewable sources to be dispatched in the next hours or days, in order to meet their generation and consumption goals. Predicting the photovoltaic power output strongly depends on accurate weather forecasting data and properly photovoltaic panel models. In this context, several traditional thermal models are expected to become obsolete due to the removal of the widely used Nominal Module Operating Temperature parameter, stated in the IEC 61215-2:2021 standard, according to reports of longer time periods in test data processing. The main contribution of the photovoltaic power estimation algorithm developed in this paper is the integration of an accurate procedure to calculate the hourly day-ahead power output of a photovoltaic plant, based on three simplified thermal models in steady state. These models are proposed and evaluated as remedial alternatives to the removal of the Nominal Module Operating Temperature parameter—a subject that has not been widely addressed in the related literature. The proposed estimation algorithm converts specific Numerical Weather Prediction data and solar module specifications into photovoltaic power output, which can be used in energy management applications to provide economic and ecological benefits. This approach focuses on rooftop-mounted mono-crystalline silicon photovoltaic panel arrays and incorporates a nonlinear translation of Standard Test Conditions parameters to real operating conditions. All necessary input data are provided for the analysis, and the accuracy of experimental results is validated using appropriate error metrics. Full article

(This article belongs to the Section Environmental Technology)

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14 pages, 3134 KiB

Open AccessArticle

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

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

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

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Abstract

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

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

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

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36 pages, 667 KiB

Open AccessArticle

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

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

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

Viewed by 577

Abstract

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

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

(This article belongs to the Section Environmental Technology)

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29 pages, 3636 KiB

Open AccessArticle

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

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

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

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Abstract

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

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

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

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18 pages, 3130 KiB

Open AccessArticle

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

by Victor Constantin, Ionela Popescu and Mihai Avram

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

Viewed by 489

Abstract

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

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

(This article belongs to the Section Environmental Technology)

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24 pages, 2229 KiB

Open AccessArticle

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

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

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

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Abstract

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

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

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

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16 pages, 1874 KiB

Open AccessArticle

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

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

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

Viewed by 382

Abstract

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

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

(This article belongs to the Section Manufacturing Technology)

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20 pages, 2342 KiB

Open AccessArticle

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

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

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

Viewed by 421

Abstract

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

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

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

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19 pages, 16547 KiB

Open AccessArticle

A New Method for Camera Auto White Balance for Portrait

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

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

Viewed by 585

Abstract

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

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

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

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18 pages, 8696 KiB

Open AccessArticle

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

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

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

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Abstract

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

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

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

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