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An Approach for Designing 3D-Printed Assembled Rotational Joints and Assemblies for Mechanisms and Robot Models
Advancing Synthetic R&D Through Scenarios: Integrating Science, Technology, and Stakeholder Needs
Multilayer Plasmonic Nanodisk Arrays for Enhanced Optical Hydrogen Sensing
Application of Foundation Models for Colorectal Cancer Tissue Classification in Mass Spectrometry Imaging
Non-Intrusive Infant Body Position Detection for Sudden Infant Death Syndrome Prevention Using Pressure Mats

Journal Description

Technologies

Technologies is an international, peer-reviewed, open access journal singularly focusing on emerging scientific and technological trends and is published monthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within ESCI (Web of Science), Scopus, Inspec, Ei Compendex, INSPIRE, and other databases.
Journal Rank: JCR - Q1 (Engineering, Multidisciplinary) / CiteScore - Q1 (Computer Science (miscellaneous))
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 21.8 days after submission; acceptance to publication is undertaken in 3.9 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Journal Cluster of Mechanical Manufacturing and Automation Control: Aerospace, Automation, Drones, Journal of Manufacturing and Materials Processing, Machines, Robotics and Technologies.

Impact Factor: 3.6 (2024); 5-Year Impact Factor: 4.2 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2227-7080

Latest Articles

21 pages, 2374 KB

Open AccessArticle

Spectral Ripples in Normal and Electric Hearing Models

by Savine S. M. Martens, Jeroen J. Briaire and Johan H. M. Frijns

Technologies 2025, 13(11), 505; https://doi.org/10.3390/technologies13110505 - 5 Nov 2025

Devising a psychophysical test to assess spectral resolution has not been easy. Two tests that have been used previously are the spectral ripple test and the spectral-temporally modulated ripple test (SMRT). Over time, questions have been raised about the validity of these tests. [...] Read more.

Devising a psychophysical test to assess spectral resolution has not been easy. Two tests that have been used previously are the spectral ripple test and the spectral-temporally modulated ripple test (SMRT). Over time, questions have been raised about the validity of these tests. We introduce a new computational electric hearing (with a cochlear implant, CI) model that can simulate how sound is transferred through a speech processor and is received by the cochlear nerve fibers. With this electric hearing model and a normal hearing model, we investigated whether the known limitations of these tests can be detected. For the spectral ripple test, we could show the limitations in the output of the CI, the information conveyed to the cochlear nerve, to estimate the threshold, and show the benefit of current steering. In addition, we reproduced the aliasing effect with normal hearing in the SMRT, as well as the reduced ripple resolution in CI users. Our computational modeling framework can serve as a first-step assessment of the validity of new psychophysical tests. Moreover, it could be used to test new speech coding strategies. Full article

(This article belongs to the Special Issue The Challenges and Prospects in Cochlear Implantation)

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36 pages, 1841 KB

Open AccessArticle

IoT-Enabled Digital Nudge Architecture for Sustainable Energy Behavior: An SEM-PLS Approach

by Feisal Hadi Masmali, Syed Md Faisal Ali Khan and Tahir Hakim

Technologies 2025, 13(11), 504; https://doi.org/10.3390/technologies13110504 - 1 Nov 2025

The growing need for sustainable energy practices necessitates technology-driven interventions that can effectively bridge the disparity between consumer intentions and actual behavior. This paper formulates and empirically substantiates an IoT-enabled digital nudge architecture designed to promote sustainable energy behavior. The architecture provides goal-setting, [...] Read more.

The growing need for sustainable energy practices necessitates technology-driven interventions that can effectively bridge the disparity between consumer intentions and actual behavior. This paper formulates and empirically substantiates an IoT-enabled digital nudge architecture designed to promote sustainable energy behavior. The architecture provides goal-setting, social comparison, feedback, and informational nudges across multiple digital channels, utilizing linked devices, data processing layers, and a rule-based nudge engine. An 815-responder survey was analyzed using structural equation modeling with partial least squares (SEM-PLS) to identify the drivers of sustainable energy behavior and explore technology readiness as a moderating factor. The results show that nudges utilizing the Internet of Things (IoT) significantly enhance the alignment between intention and behavior. Goal-setting and feedback mechanisms have the highest effects. The findings also demonstrate that being ready for new technology improves nudge response, highlighting the importance of user-centered system design. This paper presents a scalable infrastructure for integrating IoT into sustainability projects, as well as theoretical contributions to technology adoption and behavioral intervention research. The study enhances the dialogue on environmental technology by illustrating the implementation of digital nudges through IoT infrastructures to expedite progress toward the Sustainable Development Goals (SDGs). Full article

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

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19 pages, 1738 KB

Open AccessArticle

Design and Implementation of a Smart Parking System with Real-Time Slot Detection and Automated Gate Access

by Mohammad Ali Sahraei

Technologies 2025, 13(11), 503; https://doi.org/10.3390/technologies13110503 - 1 Nov 2025

By increasing the number of vehicles, an intelligent parking system can help drivers in finding parking slots by providing real-time information. To address this issue, this study developed an Arduino-based automated parking system integrating sensors to assist drivers in quickly discovering available parking [...] Read more.

By increasing the number of vehicles, an intelligent parking system can help drivers in finding parking slots by providing real-time information. To address this issue, this study developed an Arduino-based automated parking system integrating sensors to assist drivers in quickly discovering available parking slots with real-time space detection and dynamic access control. This system consists of ultrasonic sensors, NodeMCU, an LCD screen, a servo motor, and an Arduino Uno. Each ultrasonic sensor is assigned a specific number corresponding to its slot number, which helps to identify the locations. These sensors were connected to the NodeMCU to collect, process, and transfer data to the Arduino board. If the ultrasonic sensor cannot detect the vehicle in the parking space, the LCD screen will show the number of specific slots. The Arduino will use the servo motor to open the entrance gate if a vehicle is detected by another ultrasonic sensor next to it. Otherwise, the system prevents any vehicle from entering the parking area when all of the available spaces are occupied. The system prototype is constructed and empirically evaluated to verify its performance and efficiency. The results indicate that the system successfully monitors parking spot occupancy and validates its capacity for real-time information updates. Full article

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31 pages, 2232 KB

Open AccessArticle

How Does DSS Work Between LTE and NR Systems?—Requirements, Techniques, and Lessons Learned

by Rony Kumer Saha

Technologies 2025, 13(11), 502; https://doi.org/10.3390/technologies13110502 - 1 Nov 2025

Dynamic Spectrum Sharing (DSS) enables spectrum sharing between Long-Term Evolution (LTE) and New Radio (NR) systems, addressing spectrum scarcity in NR. To avoid interference when supporting NR traffic within LTE spectrum, key factors must be compatible. Effective DSS techniques are essential for coexistence. [...] Read more.

Dynamic Spectrum Sharing (DSS) enables spectrum sharing between Long-Term Evolution (LTE) and New Radio (NR) systems, addressing spectrum scarcity in NR. To avoid interference when supporting NR traffic within LTE spectrum, key factors must be compatible. Effective DSS techniques are essential for coexistence. This paper discusses these issues in two parts. Part I covers LTE and NR coexistence using DSS, introducing resource grids, control signals, and channels, and explores DSS approaches for NR data traffic, including NR Synchronization Signal/Physical Broadcast Channels (SSB) transmission via LTE Multicast-Broadcast Single-Frequency Network (MBSFN) and non-MBSFN subframes with associated challenges and standardization efforts for DSS improvement. Part II presents a DSS technique using MBSFN subframes in a heterogeneous network with a macrocell and picocells running on LTE, and in-building small cells running on NR, sharing LTE spectrum via DSS. An optimization problem is formulated to manage traffic through MBSFN allocation, determining the optimal number of MBSFN subframes per LTE frame. System simulations indicate DSS improves Spectral and Energy Efficiency in small cells. The paper concludes with key lessons for LTE and NR coexistence. Full article

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

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20 pages, 3686 KB

Open AccessArticle

Decoding Temporally Encoded 3D Objects from Low-Cost Wearable Electroencephalography

by John LaRocco, Qudsia Tahmina, Saideh Zia, Shahil Merchant, Jason Forrester, Eason He and Ye Lin

Technologies 2025, 13(11), 501; https://doi.org/10.3390/technologies13110501 - 1 Nov 2025

Decoding visual content from neural activity remains a central challenge at the intersections of engineering, neuroscience, and computational modeling. Prior work has primarily leveraged electroencephalography (EEG) with generative models to recover static images. In this study, we advance EEG-based decoding by introducing a [...] Read more.

Decoding visual content from neural activity remains a central challenge at the intersections of engineering, neuroscience, and computational modeling. Prior work has primarily leveraged electroencephalography (EEG) with generative models to recover static images. In this study, we advance EEG-based decoding by introducing a temporal encoding framework that approximates dynamic object transformations across time. EEG recordings from healthy participants (n = 20) were used to model neural representations of objects presented in “initial” and “later” states. Individualized classifiers trained on time-specific EEG signatures achieved high discriminability, with Random Forest models reaching a mean accuracy and standard deviation of 92 ± 2% and a mean AUC-ROC and standard deviation of 0.87 ± 0.10, driven largely by gamma- and beta-band activity at the frontal electrodes. These results confirm and extend evidence of strong interindividual variability, showing that subject-specific models outperform intersubject approaches in decoding temporally varying object representations. Beyond classification, we demonstrate that pairwise temporal encodings can be integrated into a generative pipeline to produce approximated reconstructions of short video sequences and 3D object renderings. Our findings establish that temporal EEG features, captured using low-cost open-source hardware, are sufficient to support the decoding of visual content across discrete time points, providing a versatile platform for potential applications in neural decoding, immersive media, and human–computer interaction. Full article

(This article belongs to the Special Issue Advances in Biomedical Engineering and Artificial Intelligence for Neurological Health)

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21 pages, 3338 KB

Open AccessArticle

Enhancing Migraine Classification Through Machine Learning: A Comparative Study of Ensemble Methods

by Raniya R. Sarra, Ayad E. Korial, Ivan Isho Gorial and Amjad J. Humaidi

Technologies 2025, 13(11), 500; https://doi.org/10.3390/technologies13110500 - 1 Nov 2025

A migraine is a common and complex neurological disorder affecting more than 90% of people globally. Traditional migraine diagnostic and classification methods are time-intensive and prone to error. In today’s world, where health and technology are closely connected, there is an urgent need [...] Read more.

A migraine is a common and complex neurological disorder affecting more than 90% of people globally. Traditional migraine diagnostic and classification methods are time-intensive and prone to error. In today’s world, where health and technology are closely connected, there is an urgent need for more advanced tools to accurately predict and classify migraine types. Machine learning (ML) has shown promise in automating migraine diagnoses and classification. However, individual ML classifiers may not always work well, which means that they need to be improved. In this paper, we used three ML classifiers that include decision tree, naïve Bayes, and k-nearest neighbor to classify seven different types of migraines. We also investigated ensemble classifiers like bagging, boosting, stacking, and majority voting to obtain better results. All classifiers were trained on a migraine dataset of 400 patients with 24 features. Before training the classifiers, we pre-processed the data by balancing the classes, removing useless features, and checking for correlations. After evaluating the performance, the results showed that majority voting achieved the highest accuracy improvement (7.59%), followed by boosting (6.55%), bagging (5.86%), and stacking (5.52%). These results indicate that the ensemble methods are effective in improving the classification accuracy of individual ML classifiers when it comes to classifying migraines. Full article

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

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22 pages, 12315 KB

Open AccessArticle

An Open-Source Neonatal Phototherapy Device

by Joshua Givans, Augustine Waswa, Janiffer Nyambura, Gidraf Njoroge, Gordon Macharia, June Madete and Joshua M. Pearce

Technologies 2025, 13(11), 499; https://doi.org/10.3390/technologies13110499 - 31 Oct 2025

Severe neonatal hyperbilirubinemia (SNH) (jaundice) is responsible for over 114,000 preventable neonatal deaths annually, as the technology that can treat the condition is cost-prohibitive for low- and middle-income countries. In this study an open-source neonatal phototherapy device (NPTD) to treat SNH was designed, [...] Read more.

Severe neonatal hyperbilirubinemia (SNH) (jaundice) is responsible for over 114,000 preventable neonatal deaths annually, as the technology that can treat the condition is cost-prohibitive for low- and middle-income countries. In this study an open-source neonatal phototherapy device (NPTD) to treat SNH was designed, built, and validated against the phototherapy technical specifications set by the American Academy of Pediatrics and UNICEF. The open-source device can be built for a tenth of the cost of the least expensive proprietary one on the market, with treatment metrics equivalent to or exceeding commercial devices available in developed nations. This device, whose material costs are USD 93.00, was shown to deliver an irradiance up to 80 µW/cm²/nm, within the acceptable wavelength range of 420–500 nm. It was further demonstrated that the unit could deliver a uniform distribution of irradiance (34.5 ± 4.3 µW/cm²/nm) over a surface area exceeding 3200 cm². These findings show that the open-source NPTD is capable of delivering accurate, consistent, and reliable irradiances for the management of SNH. By releasing full documentation in an open-source manner, the device may be broadly used to ensure affordable and consistent low-cost means of improving the quality of care for SNH. Full article

(This article belongs to the Special Issue Breakthroughs in Bioinformatics and Biomedical Engineering)

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17 pages, 4267 KB

Open AccessArticle

Process Parameter Optimization and Removal Depth Prediction for Robotic Adaptive Hydraulically Controlled Grinding of Aircraft Skin Primer

by Dequan Shi, Xuhui Wang, Wenbo Yu and Huajun Zhang

Technologies 2025, 13(11), 498; https://doi.org/10.3390/technologies13110498 - 30 Oct 2025

The removal of primer from aircraft skin epoxy resin primer plates was investigated by using an adaptive hydraulically controlled polishing tool in conjunction with an industrial robot. This study examined the effects of several key process parameters—grinding force, rotational speed, feed speed, and [...] Read more.

The removal of primer from aircraft skin epoxy resin primer plates was investigated by using an adaptive hydraulically controlled polishing tool in conjunction with an industrial robot. This study examined the effects of several key process parameters—grinding force, rotational speed, feed speed, and abrasive grit size—on primer removal depth and surface roughness. Through both single-factor analysis and response surface methodology (RSM), the variation patterns of removal depth and surface roughness with respect to these parameters were elucidated. RSM was employed to develop regression models for the primer removal depth and removal rate. The relative errors of these regression models were found to be within 8%, while the maximum relative error of the backpropagation neural network prediction model for surface roughness Ra is 9.5%. These models exhibit high accuracy in predicting the material removal depth, material removal depth rate, and surface roughness of the primer plates. The optimal parameters for the adaptive hydraulically controlled polishing tool were identified as flows: a polishing force of 20 N, a feed speed of 40 mm·s⁻¹, a rotational speed of 2000 rpm, and 80-grit sandpaper. Under these conditions, the maximum removal depth reaches 27.5 µm, the highest removal rate is 5.501 µm·s⁻¹, and the surface roughness Ra is 1.897 µm. Full article

(This article belongs to the Section Manufacturing Technology)

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18 pages, 579 KB

Open AccessArticle

TinyML Implementation of CNN-Based Gait Analysis for Low-Cost Motorized Prosthetics: A Proof-of-Concept

by João Vitor Y. B. Yamashita, João Paulo R. R. Leite and Jeremias B. Machado

Technologies 2025, 13(11), 497; https://doi.org/10.3390/technologies13110497 - 30 Oct 2025

Real-time gait analysis is essential for the development of responsive and reliable motorized prosthetics. Deploying advanced deep learning models on resource-constrained embedded systems, however, remains a major challenge. This proof-of-concept study presents a TinyML-based approach for knee joint angle prediction using convolutional neural [...] Read more.

Real-time gait analysis is essential for the development of responsive and reliable motorized prosthetics. Deploying advanced deep learning models on resource-constrained embedded systems, however, remains a major challenge. This proof-of-concept study presents a TinyML-based approach for knee joint angle prediction using convolutional neural networks (CNNs) trained on inertial measurement unit (IMU) signals. Gait data were acquired from four healthy participants performing multiple stride types, and data augmentation strategies were applied to enhance model robustness. Multi-objective optimization was employed to balance accuracy and computational efficiency, yielding specialized CNN architectures tailored for short, natural, and long strides. A lightweight classifier enabled real-time selection of the appropriate specialized model. The proposed framework achieved an average RMSE of 2.05°, representing a performance gain of more than 35% compared to a generalist baseline, while maintaining reduced inference latency (16.8 ms) on a $40 embedded platform (Sipeed MaixBit with Kendryte K210). These findings demonstrate the feasibility of deploying compact and specialized deep learning models on low-cost hardware, enabling affordable prosthetic solutions with real-time responsiveness. This work contributes to advancing intelligent assistive technologies by combining efficient model design, hardware-aware optimization, and clinically relevant gait prediction performance. Full article

(This article belongs to the Section Assistive Technologies)

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35 pages, 6769 KB

Open AccessArticle

Non-Invasive Multimodal and Multiscale Bioelectrical Sensor System for Proactive Holistic Plant Assessment

by Jonnel Alejandrino, Elmer Dadios, Ryan Rhay Vicerra, Argel Bandala, Edwin Sybingco, Laurence Gan Lim, Raouf Naguib and Ronnie Concepcion II

Technologies 2025, 13(11), 496; https://doi.org/10.3390/technologies13110496 - 30 Oct 2025

Global crop losses of 20–40% continue because traditional plant assessment methods are either invasive, damaging plant tissues, or reactive, detecting stress only after visible symptoms. Recent developments have remained fragmented, focusing on single modalities, individual organs, or limited frequency ranges. This study developed [...] Read more.

Global crop losses of 20–40% continue because traditional plant assessment methods are either invasive, damaging plant tissues, or reactive, detecting stress only after visible symptoms. Recent developments have remained fragmented, focusing on single modalities, individual organs, or limited frequency ranges. This study developed a unified bioelectrical sensor system capable of non-invasive, multimodal, multiscale, and integrative assessment by integrating capabilities that existing methods address only separately. The system combines spectroscopy and tomography within a single platform, enabling simultaneous evaluation of multiple organs. Unlike approaches confined to narrow frequencies, it captures complete physiological responses across scales. Validation on strawberry (Fragaria × ananassa ‘Sweet Charlie’) demonstrated comprehensive multi-organ assessment: 98.3% accuracy for fruit categorization, 95.8% for leaf water status, and 88.2% for stem productivity. Tomographic performance reached 2.6–2.8 mm resolution for 3D root mapping and 2.8–3.0 mm for 2D postharvest fruit sorting. Correlations with reference metrics were used exclusively for validation, confirming that the extracted features reflect genuine physiological variations. Importantly, the system detects stress before visible symptoms, enabling intervention within the reversible window. By unifying spectroscopy and tomography with complete frequency coverage and multi-organ capability, this platform overcomes existing fragmentation and establishes a foundation for proactive, comprehensive plant monitoring essential for sustainable agriculture. Full article

(This article belongs to the Special Issue New Technologies for Sensors)

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27 pages, 3513 KB

Open AccessArticle

Hybrid VAR–XGBoost Modeling for Data-Driven Forecasting of Electricity Tariffs in Energy Systems Under Macroeconomic Uncertainty

by Sebastian López-Estrada, Orlando Joaqui-Barandica and Oscar Walduin Orozco-Cerón

Technologies 2025, 13(11), 495; https://doi.org/10.3390/technologies13110495 - 30 Oct 2025

Electricity tariffs in emerging economies are often influenced by macroeconomic volatility and regulatory design, affecting both affordability and system stability. Understanding these interactions is crucial for anticipating price fluctuations and ensuring sustainable energy policy. This paper examines the influence of macroeconomic conditions on [...] Read more.

Electricity tariffs in emerging economies are often influenced by macroeconomic volatility and regulatory design, affecting both affordability and system stability. Understanding these interactions is crucial for anticipating price fluctuations and ensuring sustainable energy policy. This paper examines the influence of macroeconomic conditions on electricity tariff dynamics in Colombia by integrating econometric and machine learning approaches. Using monthly data from 2009 to 2024 and a set of 153 macroeconomic indicators condensed via principal component analysis (PCA), we assess the predictive performance of vector autoregressive (VAR), SARIMAX, and XGBoost models, as well as a hybrid VAR–XGBoost specification. Impulse-response analysis reveals that tariff components exhibit limited sensitivity to macroeconomic shocks, underscoring the buffering role of regulation and sector-specific drivers. However, forecasting exercises demonstrate that accuracy is highly component-specific: SARIMAX performs best for transmission and restrictions, and VAR dominates for distribution and losses, while the hybrid model outperforms for generation and commercialization. These findings highlight that although macroeconomic pass-through into tariffs is weak, hybrid approaches that combine structural econometric dynamics with nonlinear learning can deliver tangible forecasting gains. The study contributes to the literature on electricity pricing in emerging economies and offers practical insights for regulators and policymakers concerned with tariff predictability and energy affordability. Full article

(This article belongs to the Section Environmental Technology)

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18 pages, 2898 KB

Open AccessReview

Modernization of GNSS, RNSS, and SBAS

by Krunoslav Špoljar, Mladen Zrinjski, Antonio Tupek and Karlo Stipetić

Technologies 2025, 13(11), 494; https://doi.org/10.3390/technologies13110494 - 29 Oct 2025

This review provides an extensive overview of the modernization of Global Navigation Satellite System (GNSS), Regional Navigation Satellite System (RNSS), and Satellite-Based Augmentation System (SBAS) up to 28 September 2025. It covers a description of the individual systems, the new launched satellites, and [...] Read more.

This review provides an extensive overview of the modernization of Global Navigation Satellite System (GNSS), Regional Navigation Satellite System (RNSS), and Satellite-Based Augmentation System (SBAS) up to 28 September 2025. It covers a description of the individual systems, the new launched satellites, and the new civil signals. GNSS includes the United States’ GPS, Russia’s GLONASS, the European Union’s Galileo, and China’s BDS. In the last several years there has been an intensive development of RNSS, which primarily encompass the Japan’s QZSS, India’s IRNSS, and the South Korea’s KPS. The focus of this research is analyzing the possibilities of advancing Position, Navigation, and Timing (PNT) service as well as future perspective and trends in the field of satellite positioning and navigation. SBAS supports satellite systems by providing civil aviation users with corrections and integrity information. Additional to WAAS and EGNOS, this paper examines several more SBAS currently under development. Furthermore, it provides a concise overview and discussion of the above systems, as well as innovations in space technology in the Republic of Croatia. In addition, it emphasizes the influence of political events on the development of satellite systems as well as the role of aerospace companies. Full article

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20 pages, 2758 KB

Open AccessArticle

Prediction of Battery Electric Vehicle Energy Consumption via Pre-Trained Model Under Inconsistent Feature Spaces

by Yizhou Wang, Haichao Huang, Ruimin Hao, Liangying Luo and Hong-Di He

Technologies 2025, 13(11), 493; https://doi.org/10.3390/technologies13110493 - 29 Oct 2025

Accurately predicting the trip-level energy consumption of battery electric vehicles (BEVs) can alleviate range anxiety of drivers and improve intelligent route planning. However, although data-driven methods excel in predicting with multi-feature inputs, each vehicle often requires a dedicated model due to potential inconsistencies [...] Read more.

Accurately predicting the trip-level energy consumption of battery electric vehicles (BEVs) can alleviate range anxiety of drivers and improve intelligent route planning. However, although data-driven methods excel in predicting with multi-feature inputs, each vehicle often requires a dedicated model due to potential inconsistencies in feature spaces of collected data. Consequently, the necessity of sufficient trip data challenges newly registered vehicles. To address the challenges, this study proposed a transformer-based pre-trained model for BEV energy consumption prediction adapting to inconsistent feature spaces, referred to as IFS-Former. By innovatively introducing trainable missing-feature embeddings and placeholder masks, the IFS-Former can tolerate new or missing features of downstream tasks after pre-training. The IFS-Former was pre-trained on a dataset comprising 837 vehicles from 8 different cities, containing 492 thousand trips, and validated on 13 vehicles with inconsistent feature spaces. After applying transfer learning to the 13 vehicles, the pre-trained IFS-Former attains high prediction accuracy (R² = 0.97, mean absolute error (MAE) = 1.19). Even under extremely inconsistent feature spaces, the IFS-Former maintains robust performance (R² = 0.96, MAE = 1.31) leveraging its pre-trained knowledge. Furthermore, the IFS-Former is well-suited for on-board deployment with a size of only 32 MB. This study facilitates on-board artificial intelligence for accurate and practical energy consumption prediction. Full article

(This article belongs to the Topic Dynamics, Control and Simulation of Electric Vehicles)

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18 pages, 723 KB

Open AccessArticle

The Compact Model Synthesis for the RADFET Device

by Vadim Kuznetsov, Dmitrii Andreev, Vladimir Andreev, Sergei Piskunov and Anatoli I. Popov

Technologies 2025, 13(11), 492; https://doi.org/10.3390/technologies13110492 - 29 Oct 2025

This article proposes a compact model for the radiation-sensitive field-effect transistor (RADFET). The model represents the basic I–V characteristics of the MOSFET device and includes the effects of threshold voltage shift as a function of absorbed dose and gate bias, gate tunneling current, [...] Read more.

This article proposes a compact model for the radiation-sensitive field-effect transistor (RADFET). The model represents the basic I–V characteristics of the MOSFET device and includes the effects of threshold voltage shift as a function of absorbed dose and gate bias, gate tunneling current, gate radiation current, dose accumulation, and fading. It accurately represents high-field effects in the gate dielectric of the RADFET device. Both the Fowler–Nordheim gate tunneling current and the radiation-induced gate current are incorporated into the model. The model enables determination of the radiation-induced charge during RADFET operation under high-field injection conditions, thereby improving the precision of accumulated dose readout. The proposed model is fully compatible with SPICE-based circuit simulators and can represent any type of RADFET device, including those with high-k gate dielectrics. Furthermore, the model was developed entirely using open-source circuit simulation tools. Full article

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

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21 pages, 6174 KB

Open AccessArticle

Modification of Al₂O₃-Based Membranes with Carbon Black for Enhanced Hydrogen Permeation

by Montree Hankoy, Mana Rodchom, Supawan Vichaphund, Duangduen Atong, Jianfeng Zhang, Mettaya Kitiwan and Phacharaphon Tunthawiroon

Technologies 2025, 13(11), 491; https://doi.org/10.3390/technologies13110491 - 28 Oct 2025

This work presents the development and characterization of alumina–carbon black (ACB) composite membranes for enhanced hydrogen separation performance. A series of membranes containing 0–3.0 wt.% carbon black was fabricated via high-temperature sintering and systematically investigated with respect to their structural, morphological, mechanical, and [...] Read more.

This work presents the development and characterization of alumina–carbon black (ACB) composite membranes for enhanced hydrogen separation performance. A series of membranes containing 0–3.0 wt.% carbon black was fabricated via high-temperature sintering and systematically investigated with respect to their structural, morphological, mechanical, and gas separation properties. The addition of carbon black significantly influenced membrane microstructure, promoting pore network formation, increasing specific surface area, and enhancing gas transport. Gas permeation tests using H₂ and N₂ revealed that all ACB membranes exhibited higher hydrogen permeance than the pure Al₂O₃ membrane. Notably, the ACB3.0 specimen demonstrated the highest H₂ permeance of 508 × 10⁻⁶ mol m⁻² s⁻¹ Pa⁻¹ at 303 K, which is nearly four times greater than the unmodified membrane. At an elevated temperature (773 K), H₂/N₂ selectivity improved with increasing carbon black content, with ACB3.0 achieving a maximum selectivity of 3.82, exceeding the theoretical Knudsen value, suggesting a synergistic contribution of Knudsen diffusion and surface diffusion. These results demonstrate that carbon black is a cost-effective and versatile additive for modifying ceramic membranes, offering a promising route for advancing hydrogen purification technologies in industrial applications. Full article

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

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25 pages, 4283 KB

Open AccessArticle

Optimization Method Based on the Minimum Action Principle for Trajectory Length of Articulated Manipulators

by Cozmin Adrian Cristoiu, Marius-Valentin Dragoi, Andrei Mario Ivan, Roxana-Mariana Nechita, Iuliana Grecu, Roxana-Adriana Puiu, Gabriel Petrea and Popescu Emilia

Technologies 2025, 13(11), 490; https://doi.org/10.3390/technologies13110490 - 28 Oct 2025

In addition to the performance parameters of a mechanical manipulator—such as precision, repeatability, payload and maximum speed—path optimization can bring significant improvements in terms of cycle time and energy consumption. In this paper, a method is proposed for post-processing trajectories initially generated by [...] Read more.

In addition to the performance parameters of a mechanical manipulator—such as precision, repeatability, payload and maximum speed—path optimization can bring significant improvements in terms of cycle time and energy consumption. In this paper, a method is proposed for post-processing trajectories initially generated by spline interpolation in joint space (cubic or quintic interpolation), so that the distances traveled are shorter. The principle of least action is used as a theoretical foundation trying to find the best cost function in terms of trajectory lengths using. In the pursuit of minimizing this cost function, an iterative method is applied. Initial trajectories are split into multiple internal nodes that are displaced little by little from their initial positions, recomposing trajectories that pass through these displaced nodes at every iteration. The purpose of this paper is to demonstrate that by post-processing trajectories initially generated by the usual spline interpolation in joint space, alternative, shorter variants can be obtained. Full article

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Graphical abstract

22 pages, 6611 KB

Open AccessArticle

Analysis of the Radio Coverage for a Mobile Private Network Implemented Using Software Defined Radio Platforms

by Vlad-Stefan Hociung, Marius-George Gheorghe, Ciprian Zamfirescu, Marius-Constantin Vochin, Radu-Ovidiu Preda and Alexandru Martian

Technologies 2025, 13(11), 489; https://doi.org/10.3390/technologies13110489 - 28 Oct 2025

The emergence of mobile private networks (MPNs) has enabled tailored communication solutions for industries, enterprises, and specialized applications, fostering improved control, security, and flexibility. With the rapid advancements in software-defined radio (SDR) platforms, implementing MPNs using cost-effective and versatile hardware has become increasingly [...] Read more.

The emergence of mobile private networks (MPNs) has enabled tailored communication solutions for industries, enterprises, and specialized applications, fostering improved control, security, and flexibility. With the rapid advancements in software-defined radio (SDR) platforms, implementing MPNs using cost-effective and versatile hardware has become increasingly feasible. Analyzing the radio coverage of such networks is critical for optimizing performance, ensuring reliable connectivity, and addressing site-specific challenges in deployment. This paper investigates the radio coverage of a 4G MPN implemented using as radio front-end an SDR platform from the Universal Software Radio Peripheral (USRP) family and the srsRAN-4G open-source software suite. Using the HTZ Communication software as simulation tool and field-test measurements performed using an off-the-shelf mobile phone as user equipment (UE), an analysis is made to evaluate the accuracy of various propagation models in predicting network coverage, in several different frequency bands. The results provide valuable insights into the design and deployment of MPNs, highlighting the importance of accurate coverage estimation in achieving robust and efficient network operation. Full article

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A Review on Risk-Averse Bidding Strategies for Virtual Power Plants with Uncertainties: Resources, Technologies, and Future Pathways

by Dongliang Xiao

Technologies 2025, 13(11), 488; https://doi.org/10.3390/technologies13110488 - 28 Oct 2025

The global energy transition, characterized by the proliferation of intermittent renewables and the evolution of electricity markets, has positioned virtual power plants (VPPs) as crucial aggregators of distributed energy resources. However, their participation in competitive markets is fraught with multifaceted uncertainties stemming from [...] Read more.

The global energy transition, characterized by the proliferation of intermittent renewables and the evolution of electricity markets, has positioned virtual power plants (VPPs) as crucial aggregators of distributed energy resources. However, their participation in competitive markets is fraught with multifaceted uncertainties stemming from price volatility, renewable generation intermittency, and unpredictable prosumer behavior, which necessitate sophisticated, risk-averse bidding strategies to ensure financial viability. This review provides a comprehensive analysis of the state-of-the-art in risk-averse bidding for VPPs. It first establishes a resource-centric taxonomy, categorizing VPPs into four primary archetypes: DER-driven, demand response-oriented, electric vehicle-integrated, and multi-energy systems. The paper then delivers a comparative assessment of different optimization techniques—from stochastic programming with conditional value-at-risk and robust optimization to emerging paradigms such as distributionally robust optimization, game theory, and artificial intelligence. It critically evaluates their application contexts and effectiveness in mitigating specific risks across diverse market types. Finally, the review synthesizes these insights to identify persistent challenges—including computational bottlenecks, data privacy, and a lack of standardization—and outlines a forward-looking research agenda. This agenda emphasizes the development of hybrid AI–physical models, interoperability standards, multi-domain risk modeling, and collaborative VPP ecosystems to advance the field towards a resilient and decarbonized energy future. Full article

(This article belongs to the Section Environmental Technology)

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Open AccessArticle

Construction of Pattern Optimization Model Driven by Fabric Parameters in 3D Garment Development Using Artificial Neural Networks

by Jiazhen Chen, Ziyi Guo, Tao Li, Yue Sun, Joanne Yip, Kit-lun Yick and Fengyuan Zou

Technologies 2025, 13(11), 487; https://doi.org/10.3390/technologies13110487 - 28 Oct 2025

Fabric properties significantly influence the accuracy of pattern dimensions derived from 3D scanned garment samples. To enhance the generated pattern accuracy, a novel predictive model was proposed to estimate the pattern dimension change ratio by integrating fabric parameters using an artificial neural network [...] Read more.

Fabric properties significantly influence the accuracy of pattern dimensions derived from 3D scanned garment samples. To enhance the generated pattern accuracy, a novel predictive model was proposed to estimate the pattern dimension change ratio by integrating fabric parameters using an artificial neural network (ANN). Thirty fabrics were tested for making flared skirts. The pattern generation involves 3D scanned garment samples, the Bowyer–Watson algorithm for surface reconstruction, and an energy model for surface development. After the pattern’s dimension change ratio was obtained, principal component analysis (PCA) was applied to reduce dimensionality before correlation analysis. Results indicated that thickness, bending rigidity, drapability, and shear performance were the primary fabric parameters influencing dimensional accuracy. Backpropagation (BP) neural networks were constructed to predict the pattern size change ratio using both full fabric parameters or a PCA-reduced set, including a 9-parameter input layer, four hidden layers, and a 12-parameter output layer. The BP ANN models outperformed the radial basis function (RBF) ANN models, achieving accuracies of 96.67% and 96.02% for the full-factor and dimension-reduced models, respectively. After parameter optimization, the dimension-reduced BP ANN model enhanced pattern accuracy by 5.11%, achieving a final 97.73% accuracy. Results validate utilizing fabric parameters and BP neural networks as a sophisticated pattern optimization method. Full article

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Open AccessArticle

Generative AI and Cognitive Challenges in Research: Balancing Cognitive Load, Fatigue, and Human Resilience

by Syed Md Faisal Ali Khan and Salem Suhluli

Technologies 2025, 13(11), 486; https://doi.org/10.3390/technologies13110486 - 28 Oct 2025

This study examines the interaction between cognitive demands and generative artificial intelligence (GenAI) technologies in shaping the quality and influence of academic research. While GenAI tools such as ChatGPT and Elicit are increasingly adopted to ease information processing and automate repetitive tasks, their [...] Read more.

This study examines the interaction between cognitive demands and generative artificial intelligence (GenAI) technologies in shaping the quality and influence of academic research. While GenAI tools such as ChatGPT and Elicit are increasingly adopted to ease information processing and automate repetitive tasks, their broader impact on researchers’ cognitive performance remains underexplored. Using data from 998 researchers and applying structural equation modeling (SEM-PLS), we examined the effects of cognitive load, task fatigue, and resilience on research outcomes, with GenAI immersion as a higher-order moderator. Results reveal that both cognitive load and fatigue negatively affect research quality, while engagement and resilience offer partial protection. Unexpectedly, high immersion in GenAI intensified the negative impact of cognitive strain, suggesting that over-reliance on AI can amplify mental burden rather than reduce it. These results enhance the design and responsible integration of AI technologies in academic environments by demonstrating that sustainable adoption necessitates a balance between efficiency and human creativity and resilience. The study provides evidence-based insights for researchers, institutions, and policymakers seeking to optimize AI-supported workflows without compromising research integrity or well-being. Full article

(This article belongs to the Special Issue Human–AI Collaboration: Emerging Technologies and Applications)

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