Technologies

22 pages, 1026 KiB

Open AccessArticle

Ellipsoidal-Set Design of Robust and Secure Control Against Denial-of-Service Cyber Attacks in Electric-Vehicle Induction Motor Drives

by Ehab H. E. Bayoumi, Hisham M. Soliman and Sangkeum Lee

Technologies 2025, 13(7), 289; https://doi.org/10.3390/technologies13070289 - 7 Jul 2025

Abstract

Electric vehicles face increasing cybersecurity threats that can compromise the integrity of their electric drive systems, especially under Denial-of-Service (DoS) attacks. To precisely regulate torque and speed in electric vehicles, vector-controlled induction motor drives rely on continuous communication between controllers and sensors. This [...] Read more.

Electric vehicles face increasing cybersecurity threats that can compromise the integrity of their electric drive systems, especially under Denial-of-Service (DoS) attacks. To precisely regulate torque and speed in electric vehicles, vector-controlled induction motor drives rely on continuous communication between controllers and sensors. This flow could be broken by a DoS attack, which could result in unstable motor operation or complete drive system failure. To address this, we propose a novel ellipsoidal-set-based state feedback controller with integral action, formulated via linear matrix inequalities (LMIs). This controller improves disturbance rejection, maintains system stability under DoS-induced input disruptions, and enhances security by constraining the system response within a bounded invariant set. The proposed tracker has a faster dynamic reaction and better disturbance attenuation capabilities than the traditional H_ꚙ control method. The effectiveness of the proposed controller is validated through a series of diverse testing scenarios. Full article

(This article belongs to the Special Issue Smart Transportation and Driving)

12 pages, 1072 KiB

Open AccessArticle

Performance Evaluation of IM/DD FSO Communication System Under Dust Storm Conditions

by Maged Abdullah Esmail

Technologies 2025, 13(7), 288; https://doi.org/10.3390/technologies13070288 - 7 Jul 2025

Abstract

Free-space optical (FSO) communication is a promising high-capacity solution for future wireless networks, particularly for backhaul and fronthaul links in 5G and emerging 6G systems. However, it remains highly vulnerable to environmental impairment, especially in arid regions prone to dust storms. While prior [...] Read more.

Free-space optical (FSO) communication is a promising high-capacity solution for future wireless networks, particularly for backhaul and fronthaul links in 5G and emerging 6G systems. However, it remains highly vulnerable to environmental impairment, especially in arid regions prone to dust storms. While prior studies have addressed atmospheric effects such as fog and turbulence, the specific impact of dust on signal performance remains insufficiently explored. This work presents a probabilistic modeling framework for evaluating the performance of an intensity modulation/direct detection (IM/DD) FSO system under dust storm conditions. Using a controlled laboratory environment, we conducted measurements of the optical signal under dust-induced channel conditions using real-world dust samples collected from an actual dust storm. We identified the Beta distribution as the most accurate model for the measured signal fluctuations. Closed-form expressions were derived for average bit error rate (BER), outage probability, and channel capacity. The close agreement between the analytical, approximate, and simulated results validates the proposed model as a reliable tool for evaluating FSO system performance. The results show that the forward error correction (FEC) BER threshold of

10^{- 3}

is achieved at approximately 10.5 dB, and the outage probability drops below

10^{- 3}

at 10 dB average SNR. Full article

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

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25 pages, 4334 KiB

Open AccessArticle

Multi-Task Learning-Based Traffic Flow Prediction Through Highway Toll Stations During Holidays

by Xiaowei Liu, Yunfan Zhang, Zhongyi Han, Hao Qiu, Shuxin Zhang and Jinlei Zhang

Technologies 2025, 13(7), 287; https://doi.org/10.3390/technologies13070287 - 4 Jul 2025

Abstract

Accurate traffic flow prediction is essential for highway operations, especially during holidays when surging traffic poses significant challenges. This study focuses on holiday traffic and introduces a spatiotemporal cross-attention network (ST-Cross-Attn) that combines a bidirectional convolutional LSTM (Bi-ConvLSTM) with a cross-attention module to [...] Read more.

Accurate traffic flow prediction is essential for highway operations, especially during holidays when surging traffic poses significant challenges. This study focuses on holiday traffic and introduces a spatiotemporal cross-attention network (ST-Cross-Attn) that combines a bidirectional convolutional LSTM (Bi-ConvLSTM) with a cross-attention module to jointly predict toll station inbound flow and outbound flow. Under the multi-task learning framework, the model shares spatial–temporal features between inbound flow and outbound flow, enhancing their representations and improving multi-step prediction accuracy. Using three years of highway traffic flow data during Labor Day from Shandong, China, ST-Cross-Attn outperformed eight state-of-the-art benchmarks, achieving an average improvement of 4.34% in inbound flow prediction and 2.3% in outbound flow prediction. Extensive ablation studies further confirmed the effectiveness of the model’s components and multi-task learning framework, demonstrating its potential for reliable holiday traffic forecasting. Full article

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22 pages, 3063 KiB

Open AccessArticle

High-Temperature Methane Sensors Based on ZnGa₂O₄:Er Ceramics for Combustion Monitoring

by Aleksei V. Almaev, Zhakyp T. Karipbayev, Askhat B. Kakimov, Nikita N. Yakovlev, Olzhas I. Kukenov, Alexandr O. Korchemagin, Gulzhanat A. Akmetova-Abdik, Kuat K. Kumarbekov, Amangeldy M. Zhunusbekov, Leonid A. Mochalov, Ekaterina A. Slapovskaya, Petr M. Korusenko, Aleksandra V. Koroleva, Evgeniy V. Zhizhin and Anatoli I. Popov

Technologies 2025, 13(7), 286; https://doi.org/10.3390/technologies13070286 - 4 Jul 2025

Abstract

The use of CH₄ as an energy source is increasing every day. To increase the efficiency of CH₄ combustion and ensure that the equipment meets ecological requirements, it is necessary to measure the CH₄ concentration in the exhaust gases of [...] Read more.

The use of CH₄ as an energy source is increasing every day. To increase the efficiency of CH₄ combustion and ensure that the equipment meets ecological requirements, it is necessary to measure the CH₄ concentration in the exhaust gases of combustion systems. To this end, sensors are required that can withstand extreme operating conditions, including temperatures of at least 600 °C, as well as high pressure and gas flow rate. ZnGa₂O₄, being an ultra-wide bandgap semiconductor with high chemical and thermal stability, is a promising material for such sensors. The synthesis and investigation of the structural and CH₄ sensing properties of ceramic pellets made from pure and Er-doped ZnGa₂O₄ were conducted. Doping with Er leads to the formation of a secondary Er₃Ga₅O₁₂ phase and an increase in the active surface area. This structural change significantly enhanced the CH₄ response, demonstrating an 11.1-fold improvement at a concentration of 10⁴ ppm. At the optimal response temperature of 650 °C, the Er-doped ZnGa₂O₄ exhibited responses of 2.91 a.u. and 20.74 a.u. to 100 ppm and 10⁴ ppm of CH₄, respectively. The Er-doped material is notable for its broad dynamic range for CH₄ concentrations (from 100 to 20,000 ppm), low sensitivity to humidity variations within the 30–70% relative humidity range, and robust stability under cyclic gas exposure. In addition to CH₄, the sensitivity of Er-doped ZnGa₂O₄ to other gases at a temperature of 650 °C was investigated. The samples showed strong responses to C₂H₄, C₃H₈, C₄H₁₀, NO_2, and H₂, which, at gas concentrations of 100 ppm, were higher than the response to CH₄ by a factor of 2.41, 2.75, 3.09, 1.16, and 1.64, respectively. The study proposes a plausible mechanism explaining the sensing effect of Er-doped ZnGa₂O₄ and discusses its potential for developing high-temperature CH₄ sensors for applications such as combustion monitoring systems and determining the ideal fuel/air mixture. Full article

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

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27 pages, 374 KiB

Open AccessArticle

Computational Resources and Infrastructures for a Novel Bioinformatics Laboratory: A Case Study

by Emanuel Maldonado and Manuel C. Lemos

Technologies 2025, 13(7), 285; https://doi.org/10.3390/technologies13070285 - 4 Jul 2025

Abstract

Introduction: Bioinformatics is a relatively recent multidisciplinary research field continuously offering novel opportunities. Although many researchers are actively working in/with bioinformatics, some research centers still face difficulties in hiring bioinformaticians and establishing the appropriate (first) bioinformatics infrastructures and computational resources. In our research [...] Read more.

Introduction: Bioinformatics is a relatively recent multidisciplinary research field continuously offering novel opportunities. Although many researchers are actively working in/with bioinformatics, some research centers still face difficulties in hiring bioinformaticians and establishing the appropriate (first) bioinformatics infrastructures and computational resources. In our research center, we started from scratch and established initial bioinformatics infrastructures for common use and also for the specific case of precision/personalized medicine. Case description: Here, we report a case study reflecting our specific needs and circumstances during the implementation of a novel bioinformatics laboratory. This involved the preparation of rooms, computer networks, computational resources novel designs, and upgrades to existing designs. Moreover, this work involved people from diverse areas and institutions, such as companies, institutional projects, informatics, and technical infrastructures services. Discussion and evaluation: The work resulted in the implementation of four novel designs dedicated to genomic medicine and in the adaptation of two existing designs dedicated to common use located in the dry-lab room. This is not an accurate and objective work, as it often depends on the available computer hardware and the target bioinformatics field(s). The four novel designs offered substantial improvements when compared to the upgraded designs, additionally corroborated by performance evaluations, which resulted in an overall highest performance of the novel designs. Conclusions: We present work that was developed over two years until completion with functioning infrastructure. This project enabled us to learn many novel aspects not only related to redundant disk technologies, but also related to computer networks, hardware, storage-management operating systems, file systems, performance evaluation, and also in the management of services. Moreover, additional equipment will be important to maintain and expand the potential and reliability of the bioinformatics laboratory. We hope that this work can be helpful for other researchers seeking to design their bioinformatics equipment or laboratories. Full article

25 pages, 6019 KiB

Open AccessArticle

Intelligent Automated Monitoring and Curing System for Cracks in Concrete Elements Using Integrated Sensors and Embedded Controllers

by Papa Pio Ascona García, Guido Elar Ordoñez Carpio, Wilmer Moisés Zelada Zamora, Marco Antonio Aguirre Camacho, Wilmer Rojas Pintado, Emerson Julio Cuadros Rojas, Hipatia Merlita Mundaca Ramos and Nilthon Arce Fernández

Technologies 2025, 13(7), 284; https://doi.org/10.3390/technologies13070284 - 3 Jul 2025

Abstract

This study addresses the formation, detection, and repair of cracks in concrete elements exposed to temperatures above 25 °C, where accelerated evaporation compromises their structural strength. An automated intelligent curing system with embedded sensors (DS18B20, HD-38) and Arduino controllers was developed and applied [...] Read more.

This study addresses the formation, detection, and repair of cracks in concrete elements exposed to temperatures above 25 °C, where accelerated evaporation compromises their structural strength. An automated intelligent curing system with embedded sensors (DS18B20, HD-38) and Arduino controllers was developed and applied to solid slabs, columns, and concrete test specimens (1:2:3.5 mix ratio). The electronic design was simulated in Proteus and validated experimentally under tropical conditions. Data with normal distribution (p > 0.05) showed a significant correlation between internal and ambient temperature (r = 0.587; p = 0.001) and a low correlation in humidity (r = 0.143; p = 0.468), indicating hygrometric independence. The system healed cracks of 0.01 mm observed two hours after pouring the mixture, associated with an evaporation rate of 1.097 mL/s in 4 m². For 28 days, automated irrigation cycles were applied every 30 to 60 min, with a total of 1680 L, achieving a 20% reduction in water consumption compared to traditional methods. The system maintained stable thermal conditions in the concrete despite ambient temperatures of up to 33.85 °C. A critical evaporation range was identified between 11:00 and 16:00 (UTC-5). The results demonstrate the effectiveness of the embedded system in optimizing curing, water efficiency, and concrete durability. Full article

(This article belongs to the Section Construction Technologies)

13 pages, 1883 KiB

Open AccessArticle

A GAN-Based Method for Cognitive Covert Communication UAV Jamming-Assistance Under Fully Labeled Sample Conditions

by Wenxuan Fu, Bo Li, Haipeng Wang, Haochen Gong and Xiang Lin

Technologies 2025, 13(7), 283; https://doi.org/10.3390/technologies13070283 - 3 Jul 2025

Abstract

This paper addresses the optimization problem for mobile jamming assistance schemes in cognitive covert communication (CR-CC), where cognitive users adopt the underlying mode for spectrum access, while an unmanned aerial vehicle (UAV) transmits the same-frequency noise signals to interfere with eavesdroppers. Leveraging the [...] Read more.

This paper addresses the optimization problem for mobile jamming assistance schemes in cognitive covert communication (CR-CC), where cognitive users adopt the underlying mode for spectrum access, while an unmanned aerial vehicle (UAV) transmits the same-frequency noise signals to interfere with eavesdroppers. Leveraging the inherent dynamic game-theoretic characteristics of covert communication (CC) systems, we propose a novel covert communication optimization algorithm based on generative adversarial networks (GAN-CCs) to achieve system-wide optimization under the constraint of maximum detection error probability. In GAN-CC, the generator simulates legitimate users to generate UAV interference assistance schemes, while the discriminator simulates the optimal signal detection of eavesdroppers. Through the alternating iterative optimization of these two components, the dynamic game process in CC is simulated, ultimately achieving the Nash equilibrium. The numerical results show that, compared with the commonly used multi-objective optimization algorithm or nonlinear programming algorithm at present, this algorithm exhibits faster and more stable convergence, enabling the derivation of optimal mobile interference assistance schemes for cognitive CC systems. Full article

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

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31 pages, 4728 KiB

Open AccessArticle

A Dynamic Assessment of Digital Maturity in Industrial SMEs: An Adaptive AHP-Based Digital Maturity Model (DMM)with Customizable Weighting and Multidimensional Classification (DAMA-AHP)

by Elvis Krulčić, Sandro Doboviček, Duško Pavletić and Ivana Čabrijan

Technologies 2025, 13(7), 282; https://doi.org/10.3390/technologies13070282 - 3 Jul 2025

Abstract

The ongoing digitalization of industrial companies requires a structured, strategic integration of digital concepts into business processes. Digital transformation (DT) requires clearly defined roadmaps that align digital technologies with business objectives. Although there are many digital maturity models (DMMs), most are industry-specific and [...] Read more.

The ongoing digitalization of industrial companies requires a structured, strategic integration of digital concepts into business processes. Digital transformation (DT) requires clearly defined roadmaps that align digital technologies with business objectives. Although there are many digital maturity models (DMMs), most are industry-specific and do not address the unique characteristics of individual companies. Even SME-focused models often struggle to close the gap between current and target maturity levels, hindering effective DT implementation. This study examines the existing academic and professional literature on DMMs for SMEs and assesses digital readiness in an industrial context. From these findings, the Dynamic Adaptive Maturity Assessment Model (DAMA-AHP) was developed. It comprises 66 DT elements in six dimensions: People and Expertise, Operability, Organization, Products and Production Processes, Strategy, and Technology. DAMA-AHP incorporates the Analytic Hierarchy Process (AHP), which has been enhanced with customizable weighting at both the dimension and element levels. This enables precise alignment with the company’s priorities and the definition of customized target maturity levels that form the basis for a tailored transformation roadmap. Validation through a case study confirmed the practical value of DAMA-AHP in measuring digital maturity and defining strategic DT priorities. It provides a comprehensive, adaptable, and dynamic framework that promotes continuous improvement and sustainable competitiveness of SMEs in the evolving digital economy. Full article

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

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31 pages, 4803 KiB

Open AccessReview

Advanced HVOF-Sprayed Carbide Cermet Coatings as Environmentally Friendly Solutions for Tribological Applications: Research Progress and Current Limitations

by Basma Ben Difallah, Yamina Mebdoua, Chaker Serdani, Mohamed Kharrat and Maher Dammak

Technologies 2025, 13(7), 281; https://doi.org/10.3390/technologies13070281 - 3 Jul 2025

Abstract

Thermally sprayed carbide cermet coatings, particularly those based on tungsten carbide (WC) and chromium carbide (Cr₃C₂) and produced with the high velocity oxygen fuel (HVOF) process, are used in tribological applications as environmentally friendly alternatives to electroplated hard chrome [...] Read more.

Thermally sprayed carbide cermet coatings, particularly those based on tungsten carbide (WC) and chromium carbide (Cr₃C₂) and produced with the high velocity oxygen fuel (HVOF) process, are used in tribological applications as environmentally friendly alternatives to electroplated hard chrome coatings. These functional coatings are especially prevalent in the automotive industry, offering excellent wear resistance. However, their mechanical and tribological performances are highly dependent on factors such as feedstock powders, spray parameters, and service conditions. This review aims to gain deeper insights into the above elements. It also outlines emerging advancements in HVOF technology—including in situ powder mixing, laser treatment, artificial intelligence integration, and the use of novel materials such as rare earth elements or transition metals—which can further enhance coating performance and broaden their applications to sectors such as the aerospace and hydro-machinery industries. Finally, this literature review focuses on process optimization and sustainability, including environmental and health impacts, critical material use, and operational limitations. It uses a life cycle assessment (LCA) as a tool for evaluating ecological performance and addresses current challenges such as exposure risks, process control constraints, and the push toward safer, more sustainable alternatives to traditional WC and Cr₃C₂ cermet coatings. Full article

(This article belongs to the Topic Advances in Design, Manufacturing, and Dynamics of Complex Systems)

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15 pages, 7157 KiB

Open AccessArticle

RADAR: Reasoning AI-Generated Image Detection for Semantic Fakes

by Haochen Wang, Xuhui Liu, Ziqian Lu, Cilin Yan, Xiaolong Jiang, Runqi Wang and Efstratios Gavves

Technologies 2025, 13(7), 280; https://doi.org/10.3390/technologies13070280 - 2 Jul 2025

Abstract

As modern generative models advance rapidly, AI-generated images exhibit higher resolution and lifelike details. However, the generated images may not adhere to world knowledge and common sense, as there is no such awareness and supervision in the generative models. For instance, the generated [...] Read more.

As modern generative models advance rapidly, AI-generated images exhibit higher resolution and lifelike details. However, the generated images may not adhere to world knowledge and common sense, as there is no such awareness and supervision in the generative models. For instance, the generated images could feature a penguin walking in the desert or a man with three arms, scenarios that are highly unlikely to occur in real life. Current AI-generated image detection methods mainly focus on low-level features, such as detailed texture patterns and frequency domain inconsistency, which are specific to certain generative models, making it challenging to identify the above-mentioned general semantic fakes. In this work, (1) we propose a new task, reasoning AI-generated image detection, which focuses on identifying semantic fakes in generative images that violate world knowledge and common sense. (2) To benchmark the new task, we collect a new dataset Spot the Semantic Fake (STSF). STSF contains 358 images with clear semantic fakes generated by three different modern diffusion models and provides bounding boxes as well as text annotations to locate the fakes. (3) We propose RADAR, a reasoning AI-generated image detection assistor, to locate semantic fakes in the generative images and output corresponding text explanations. Specifically, RADAR contains a specialized multimodal LLM to process given images and detect semantic fakes. To improve the generalization ability, we further incorporate ChatGPT as an assistor to detect unrealistic components in grounded text descriptions. The experiments on the STSF dataset show that RADAR effectively detects semantic fakes in modern generative images. Full article

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

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21 pages, 2915 KiB

Open AccessArticle

Intelligent Control System for Multivariable Regulation in Aquaculture: Application to Mugil incilis

by Andrés Valle González, Carlos Robles-Algarín and Adriana Rodríguez Forero

Technologies 2025, 13(7), 279; https://doi.org/10.3390/technologies13070279 - 2 Jul 2025

Abstract

Aquaculture has emerged as a sustainable alternative to meet the growing demand for aquatic products while preserving natural ecosystems. This study presents the design, simulation, and experimental validation of an intelligent multivariable control system for aquaculture tanks aimed at cultivating Mugil incilis, [...] Read more.

Aquaculture has emerged as a sustainable alternative to meet the growing demand for aquatic products while preserving natural ecosystems. This study presents the design, simulation, and experimental validation of an intelligent multivariable control system for aquaculture tanks aimed at cultivating Mugil incilis, a native species of the Colombian Caribbean. The system integrates three control strategies: a classical Proportional-Integral-Derivative (PID) controller, a fuzzy logic–based PID controller, and a neural network predictive controller. All strategies were evaluated in simulation using a third-order transfer function model identified from real pond data. The fuzzy PID controller reduced the mean squared error (MSE) by 66.5% compared to the classical PID and showed faster settling times and lower overshoot. The neural predictive controller, although anticipatory, exhibited high computational cost and instability. Only the fuzzy PID controller was implemented and validated experimentally, demonstrating robust, accurate, and stable regulation of potential hydrogen (pH), dissolved oxygen, and salinity under dynamic environmental conditions. The system operated in real time on embedded hardware powered by a solar kit, confirming its suitability for rural or off-grid aquaculture contexts. This approach provides a viable and scalable solution for advancing intelligent, sustainable aquaculture practices, particularly for sensitive native species in tropical regions. Full article

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22 pages, 1770 KiB

Open AccessArticle

A Logarithmic Compression Method for Magnitude-Rich Data: The LPPIE Approach

by Vasileios Alevizos, Zongliang Yue, Sabrina Edralin, Clark Xu, Nikitas Gerolimos and George A. Papakostas

Technologies 2025, 13(7), 278; https://doi.org/10.3390/technologies13070278 - 1 Jul 2025

Abstract

This study introduces Logarithmic Positional Partition Interval Encoding (LPPIE), a novel lossless compression methodology employing iterative logarithmic transformations to drastically reduce data size. While conventional dictionary-based algorithms rely on repeated sequences, LPPIE translates numeric data sequences into highly compact logarithmic representations. This achieves [...] Read more.

This study introduces Logarithmic Positional Partition Interval Encoding (LPPIE), a novel lossless compression methodology employing iterative logarithmic transformations to drastically reduce data size. While conventional dictionary-based algorithms rely on repeated sequences, LPPIE translates numeric data sequences into highly compact logarithmic representations. This achieves significant reduction in data size, especially on large integer datasets. Experimental comparisons with established compression methods—such as ZIP, Brotli, and Zstandard—demonstrate LPPIE’s exceptional effectiveness, attaining compression ratios nearly 13 times superior to established methods. However, these substantial storage savings come with elevated computational overhead due to LPPIE’s complex numerical operations. The method’s robustness across diverse datasets and minimal scalability limitations underscore its potential for specialized archival scenarios where data fidelity is paramount and processing latency is tolerable. Future enhancements, such as GPU-accelerated computations and hybrid entropy encoding integration, are proposed to further optimize performance and broaden LPPIE’s applicability. Overall, LPPIE offers a compelling alternative in lossless data compression, substantially redefining efficiency boundaries in high-volume numeric data storage. Full article

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20 pages, 4257 KiB

Open AccessArticle

Photocatalytic Degradation of Toxic Dyes on Cu and Al Co-Doped ZnO Nanostructured Films: A Comparative Study

by Nadezhda D. Yakushova, Ivan A. Gubich, Andrey A. Karmanov, Alexey S. Komolov, Aleksandra V. Koroleva, Ghenadii Korotcenkov and Igor A. Pronin

Technologies 2025, 13(7), 277; https://doi.org/10.3390/technologies13070277 - 1 Jul 2025

Abstract

The article suggests a simple one-step sol–gel method for synthesizing nanostructured zinc oxide films co-doped with copper and aluminum. It shows the possibility of forming hierarchical ZnO:Al:Cu nanostructures combining branches of different sizes and ranks and quasi-spherical fractal aggregates. It demonstrates the use [...] Read more.

The article suggests a simple one-step sol–gel method for synthesizing nanostructured zinc oxide films co-doped with copper and aluminum. It shows the possibility of forming hierarchical ZnO:Al:Cu nanostructures combining branches of different sizes and ranks and quasi-spherical fractal aggregates. It demonstrates the use of the synthesized samples as highly efficient photocatalysts providing the decomposition of toxic dyes (methyl orange) under the action of both ultraviolet radiation and visible light. It establishes the contribution of the average crystallite size, the proportion of zinc atoms in the crystalline phase, their nanostructure, as well as X-ray amorphous phases of copper and aluminum to the efficiency of the photocatalysis process. Full article

(This article belongs to the Section Environmental Technology)

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21 pages, 1476 KiB

Open AccessArticle

AI-Driven Handover Management and Load Balancing Optimization in Ultra-Dense 5G/6G Cellular Networks

by Chaima Chabira, Ibraheem Shayea, Gulsaya Nurzhaubayeva, Laura Aldasheva, Didar Yedilkhan and Saule Amanzholova

Technologies 2025, 13(7), 276; https://doi.org/10.3390/technologies13070276 - 1 Jul 2025

Abstract

This paper presents a comprehensive review of handover management and load balancing optimization (LBO) in ultra-dense 5G and emerging 6G cellular networks. With the increasing deployment of small cells and the rapid growth of data traffic, these networks face significant challenges in ensuring [...] Read more.

This paper presents a comprehensive review of handover management and load balancing optimization (LBO) in ultra-dense 5G and emerging 6G cellular networks. With the increasing deployment of small cells and the rapid growth of data traffic, these networks face significant challenges in ensuring seamless mobility and efficient resource allocation. Traditional handover and load balancing techniques, primarily designed for 4G systems, are no longer sufficient to address the complexity of heterogeneous network environments that incorporate millimeter-wave communication, Internet of Things (IoT) devices, and unmanned aerial vehicles (UAVs). The review focuses on how recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), are being applied to improve predictive handover decisions and enable real-time, adaptive load distribution. AI-driven solutions can significantly reduce handover failures, latency, and network congestion, while improving overall user experience and quality of service (QoS). This paper surveys state-of-the-art research on these techniques, categorizing them according to their application domains and evaluating their performance benefits and limitations. Furthermore, the paper discusses the integration of intelligent handover and load balancing methods in smart city scenarios, where ultra-dense networks must support diverse services with high reliability and low latency. Key research gaps are also identified, including the need for standardized datasets, energy-efficient AI models, and context-aware mobility strategies. Overall, this review aims to guide future research and development in designing robust, AI-assisted mobility and resource management frameworks for next-generation wireless systems. Full article

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18 pages, 5139 KiB

Open AccessArticle

Exploring the Failures of Deep Groove Ball Bearings Under Alternating Electric Current in the Presence of Commercial Lithium Grease

by Shubrajit Bhaumik, Mohamed Yunus, Sarveshpranav Jothikumar, Gurram Hareesh, Viorel Paleu, Ashok Kumar Sharma and Shail Mavani

Technologies 2025, 13(7), 275; https://doi.org/10.3390/technologies13070275 - 1 Jul 2025

Abstract

Deep groove ball bearings are important mechanical elements in the automotive and process industries, particularly in electric motors. One of the primary reasons for their failure is lubricant degradation due to stray shaft current. Thus, the present work exhibited the failure of bearings [...] Read more.

Deep groove ball bearings are important mechanical elements in the automotive and process industries, particularly in electric motors. One of the primary reasons for their failure is lubricant degradation due to stray shaft current. Thus, the present work exhibited the failure of bearings under simulated lubricated conditions similar to those of real time bearings failing in presence of stray electric current. The test was conducted using a full bearing test rig with an applied radial load, 496 N, an alternating current, 10 A, and a rotation of 2000 rpm for 24 h. The bearings (6206 series) were greased using two commercially available ester-polyalphaolefin oil-based greases with viscosity 46–54 cSt (Grease 1) and 32–35 cSt (Grease 2, also contained aromatic oil). The optical microscopic images of the bearing raceways after the tribo test indicated the superior performance of Grease 1 compared to Grease 2, with lesser formation of white etching areas, micro-pitting, spot welds, and fluting on the surfaces of the bearings. Additionally, 80% less vibrations were recorded during the test with Grease 1, indicating a stable lubricating film of Grease 1 during the test as compared to Grease 2. Furthermore, a higher extent of Grease 2 degradation during the tribo test was also confirmed using Fourier transform infrared spectroscopy. Statistical analysis (t-test) indicated the significant variation of the vibrations produced during the test with electrified conditions. The present work indicated that the composition of the greases plays a significant role in controlling the bearing failures. Full article

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16 pages, 2546 KiB

Open AccessArticle

A Multi-Point Moment Matching Approach with Frequency-Aware ROM-Based Criteria for RLCk Model Order Reduction

by Dimitrios Garyfallou, Christos Giamouzis and Nestor Evmorfopoulos

Technologies 2025, 13(7), 274; https://doi.org/10.3390/technologies13070274 - 30 Jun 2025

Abstract

Model order reduction (MOR) is crucial for efficiently simulating large-scale RLCk models extracted from modern integrated circuits. Among MOR methods, balanced truncation offers strong theoretical error bounds but is computationally intensive and does not preserve passivity. In contrast, moment matching (MM) techniques are [...] Read more.

Model order reduction (MOR) is crucial for efficiently simulating large-scale RLCk models extracted from modern integrated circuits. Among MOR methods, balanced truncation offers strong theoretical error bounds but is computationally intensive and does not preserve passivity. In contrast, moment matching (MM) techniques are widely adopted in industrial tools due to their computational efficiency and ability to preserve passivity in RLCk models. Typically, MM approaches based on the rational Krylov subspace (RKS) are employed to produce reduced-order models (ROMs). However, the quality of the reduction is influenced by the selection of the number of moments and expansion points, which can be challenging to determine. This underlines the need for advanced strategies and reliable convergence criteria to adaptively control the reduction process and ensure accurate ROMs. This article introduces a frequency-aware multi-point MM (MPMM) method that adaptively constructs an RKS by closely monitoring the ROM transfer function. The proposed approach features automatic expansion point selection, local and global convergence criteria, and efficient implementation techniques. Compared to an established MM technique, MPMM achieves up to 16.3× smaller ROMs for the same accuracy, over 99.18% reduction in large-scale benchmarks, and up to 4× faster runtime. These advantages establish MPMM as a strong candidate for integration into industrial parasitic extraction tools. Full article

(This article belongs to the Collection Modern Circuits, System Technologies, and Selected Papers from MOCAST)

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17 pages, 1673 KiB

Open AccessArticle

Model-Driven Clock Synchronization Algorithms for Random Loss of GNSS Time Signals in V2X Communications

by Wei Hu, Jiajie Zhang and Ximing Cheng

Technologies 2025, 13(7), 273; https://doi.org/10.3390/technologies13070273 - 27 Jun 2025

Abstract

Onboard Vehicle-to-Everything (V2X) communication technology is being widely implemented in domains such as intelligent driving, vehicle–road cooperation, and smart transportation. Nevertheless, time synchronization in V2X systems suffers from instability due to the random loss of Global Navigation Satellite System (GNSS) Pulse-Per-Second (PPS) signals. [...] Read more.

Onboard Vehicle-to-Everything (V2X) communication technology is being widely implemented in domains such as intelligent driving, vehicle–road cooperation, and smart transportation. Nevertheless, time synchronization in V2X systems suffers from instability due to the random loss of Global Navigation Satellite System (GNSS) Pulse-Per-Second (PPS) signals. To address this challenge, a model-driven local clock correction approach is proposed. Leveraging probability theory and mathematical statistics, models for the randomly lost GNSS PPS signals are developed. High-order polynomials are used to model local clocks. An optimized Kalman-filter-based time compensation algorithm is then devised to compensate for time errors during PPS signal loss. A software-based task-scheduling solution for precision-time synchronization is developed. An experimental testbed was then built to measure both terminal clocks and PPS signals. The proposed algorithm was integrated into the V2X terminals. Results show that the full-value PPS signals follow an exponential distribution. The onboard clock correction algorithm operates stably across three V2X terminals and accurately predicts clock variations. Furthermore, the virtual clocks achieve an average absolute error of 1.1 μs and a standard deviation of 16 μs, meeting the time synchronization requirements for V2X communication in intelligent connected vehicles. Full article

(This article belongs to the Special Issue Smart Transportation and Driving)

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23 pages, 2455 KiB

Open AccessReview

Agent-Based Modeling of Epidemics: Approaches, Applications, and Future Directions

by Xiangyu Zhang, Jiaojiao Wang, Chunmiao Yu, Jiaqiang Fei, Tianyi Luo and Zhidong Cao

Technologies 2025, 13(7), 272; https://doi.org/10.3390/technologies13070272 - 26 Jun 2025

Abstract

The spread of infectious diseases is inherently linked to human social behavior, characterized by complexity, diversity, and openness. Intelligent agents in computer science provide a powerful framework for capturing such dynamics, enabling complex epidemic patterns to emerge from simple local rules. These agents [...] Read more.

The spread of infectious diseases is inherently linked to human social behavior, characterized by complexity, diversity, and openness. Intelligent agents in computer science provide a powerful framework for capturing such dynamics, enabling complex epidemic patterns to emerge from simple local rules. These agents exhibit self-organization, adaptability, and self-optimization, making them well suited for individual-level modeling. Agent-based models (ABMs) have shown promising results in epidemic simulation and policy evaluation. However, current implementations often suffer from simplistic behavioral assumptions and rigid interaction mechanisms, limiting their realism and flexibility. This paper first reviews the current landscape of epidemic modeling approaches. It then analyzes the underlying mechanisms of advanced intelligent agents, highlighting their modeling capabilities. The study focuses on four key advantages of intelligent agent-based modeling and elaborates on three critical roles these agents play in evaluating and optimizing intervention strategies. Full article

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

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26 pages, 3485 KiB

Open AccessArticle

Experimental Evaluation of the Effects of SRP Reinforcement on the Flexural Behavior of CLT Panels

by Giovanna Concu, Daniel Meloni and Monica Valdes

Technologies 2025, 13(7), 271; https://doi.org/10.3390/technologies13070271 - 26 Jun 2025

Abstract

Cross-laminated timber (CLT) is a high-performance engineered timber that is very widely adopted. In several conditions, such as strength improvement at vulnerable connection points, local stress concentrations, existing structure retrofitting, and others, it is desirable to enhance the mechanical performance of CLT by [...] Read more.

Cross-laminated timber (CLT) is a high-performance engineered timber that is very widely adopted. In several conditions, such as strength improvement at vulnerable connection points, local stress concentrations, existing structure retrofitting, and others, it is desirable to enhance the mechanical performance of CLT by applying additional reinforcement systems. This paper reports on an experimental campaign aimed at assessing the mechanical behavior of CLT panels reinforced with steel-reinforced polymers (SRPs). Twenty Sardinian Maritime Pine CLT panels, including unreinforced, SRP single- and double-layer reinforced panels, have been subjected to bending tests to determine the bending strength, stiffness, failure mechanism and enhancement of the reinforced panels compared to the unreinforced ones. In addition, an analytical model has been proposed to understand the mechanical behavior of SRP-reinforced CLT panels. The results show that SRP reinforcement significantly increases the strength and stiffness and influences the failure mechanism of CLT panels. The strength improvement induced by the reinforcement, however, is not proportional to its amount, since the increase due to the SRP double layer is only slightly higher than that due to the SRP single layer. The stiffness enhancement is less relevant, as expected. Attention has been paid to the possible shear failures of reinforced panels. Full article

(This article belongs to the Section Construction Technologies)

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