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Search Results (1,861)

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16 pages, 2015 KB  
Article
Flapless Immediate Implants: Soft Tissue Alterations Following a Trimodal Approach with or Without Modifying Osseous and Mucosal Compartments in the Esthetic Zone: A Non-Randomized Clinical Trial with Historical Control Group
by Gustavo Cabello, María Rioboo, Daniel Torres-Lagares and Javier Fábrega
Dent. J. 2025, 13(10), 478; https://doi.org/10.3390/dj13100478 - 17 Oct 2025
Abstract
Objective: This study aimed to compare two protocols for immediate implants with fixed provisional restoration, no grafting (trimodal approach = TA) versus grafting in both the osseous gap and peri-implant mucosa (a trimodal approach with modification of the bony and mucosal compartments = [...] Read more.
Objective: This study aimed to compare two protocols for immediate implants with fixed provisional restoration, no grafting (trimodal approach = TA) versus grafting in both the osseous gap and peri-implant mucosa (a trimodal approach with modification of the bony and mucosal compartments = TAOM), by measuring soft tissue changes over time. The periodontal phenotype was noted to investigate the relationship between its thickness and the clinical outcomes. Methods: Thirty-one patients met the inclusion criteria (15 in the TA group and 16 in the TAOM group). The TA group was a historical control group. Measurements were taken using a digital caliper at T0 and 3, 6, and 12 months following the procedure (T3), (T6), and (T12), respectively, from reference points marked in a dental-supported stent. The periodontal phenotype was determined using an analogical caliper. Results: T12: Vertical midfacial change was −0.17 ± 0.37 in the TAOM group and 0.54 ± 0.33 in the TA group, respectively. Statistical significance (p = 0.0001) was found. Papilla vertical change in the TAOM group was −0.16 ± 0.45 mesially and 0.00 ± 0.44 distally. In the TA group, it was 0.55 ± 0.82 mesially and 0.86 ± 0.95 distally. Statistical significance (p = 0.0001) was also found. Conclusions: There were differences in soft tissue change between the two groups, and changes were related to the periodontal phenotype. Studies with more extended follow-up periods are needed to assess the long-term evolution of both protocols. Full article
26 pages, 1351 KB  
Review
Trends and Limitations in Transformer-Based BCI Research
by Maximilian Achim Pfeffer, Johnny Kwok Wai Wong and Sai Ho Ling
Appl. Sci. 2025, 15(20), 11150; https://doi.org/10.3390/app152011150 - 17 Oct 2025
Abstract
Transformer-based models have accelerated EEG motor imagery (MI) decoding by using self-attention to capture long-range temporal structures while complementing spatial inductive biases. This systematic survey of Scopus-indexed works from 2020 to 2025 indicates that reported advances are concentrated in offline, protocol-heterogeneous settings; inconsistent [...] Read more.
Transformer-based models have accelerated EEG motor imagery (MI) decoding by using self-attention to capture long-range temporal structures while complementing spatial inductive biases. This systematic survey of Scopus-indexed works from 2020 to 2025 indicates that reported advances are concentrated in offline, protocol-heterogeneous settings; inconsistent preprocessing, non-standard data splits, and sparse efficiency frequently reporting cloud claims of generalization and real-time suitability. Under session- and subject-aware evaluation on the BCIC IV 2a/2b dataset, typical performance clusters are in the high-80% range for binary MI and the mid-70% range for multi-class tasks with gains of roughly 5–10 percentage points achieved by strong hybrids (CNN/TCN–Transformer; hierarchical attention) rather than by extreme figures often driven by leakage-prone protocols. In parallel, transformer-driven denoising—particularly diffusion–transformer hybrids—yields strong signal-level metrics but remains weakly linked to task benefit; denoise → decode validation is rarely standardized despite being the most relevant proxy when artifact-free ground truth is unavailable. Three priorities emerge for translation: protocol discipline (fixed train/test partitions, transparent preprocessing, mandatory reporting of parameters, FLOPs, per-trial latency, and acquisition-to-feedback delay); task relevance (shared denoise → decode benchmarks for MI and related paradigms); and adaptivity at scale (self-supervised pretraining on heterogeneous EEG corpora and resource-aware co-optimization of preprocessing and hybrid transformer topologies). Evidence from subject-adjusting evolutionary pipelines that jointly tune preprocessing, attention depth, and CNN–Transformer fusion demonstrates reproducible inter-subject gains over established baselines under controlled protocols. Implementing these practices positions transformer-driven BCIs to move beyond inflated offline estimates toward reliable, real-time neurointerfaces with concrete clinical and assistive relevance. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces: Development, Applications, and Challenges)
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22 pages, 4835 KB  
Article
Enhanced Voltage Balancing Algorithm and Implementation of a Single-Phase Modular Multilevel Converter for Power Electronics Applications
by Valentine Obiora, Wenzhi Zhou, Wissam Jamal, Chitta Saha, Soroush Faramehr and Petar Igic
Machines 2025, 13(10), 955; https://doi.org/10.3390/machines13100955 - 16 Oct 2025
Abstract
This paper presents an innovative primary control strategy for a modular multilevel converter aimed at enhancing reliability and dynamic performance for power electronics applications. The proposed method utilises interactive modelling tools, including MATLAB Simulink (2022b) for algorithm design and Typhoon HIL (2023.2) for [...] Read more.
This paper presents an innovative primary control strategy for a modular multilevel converter aimed at enhancing reliability and dynamic performance for power electronics applications. The proposed method utilises interactive modelling tools, including MATLAB Simulink (2022b) for algorithm design and Typhoon HIL (2023.2) for real-time validation. The circuit design and component analysis were carried out using Proteus Design Suite (v8.17) and LTSpice (v17) to optimise the hardware implementation. A power hardware-in-the-loop experimental test setup was built to demonstrate the robustness and adaptability of the control algorithm under fixed load conditions. The simulation results were compared and verified against the experimental data. Additionally, the proposed control strategy was successfully validated through experiments, demonstrating its effectiveness in simplifying control development through efficient co-simulation. Full article
(This article belongs to the Special Issue Power Converters: Topology, Control, Reliability, and Applications)
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38 pages, 9661 KB  
Article
Flight-Parameter-Based Motion Vector Prediction for Drone Video Compression
by Altuğ Şimşek, Ahmet Öncü and Günhan Dündar
Drones 2025, 9(10), 720; https://doi.org/10.3390/drones9100720 - 16 Oct 2025
Abstract
Block-based hybrid video coders typically use inter-prediction and bidirectionally coded (B) frames to improve compression efficiency. For this purpose, they employ look-ahead buffers, perform out-of-sequence frame coding, and implement similarity search-based general-purpose algorithms for motion estimation. While effective, these methods increase computational complexity [...] Read more.
Block-based hybrid video coders typically use inter-prediction and bidirectionally coded (B) frames to improve compression efficiency. For this purpose, they employ look-ahead buffers, perform out-of-sequence frame coding, and implement similarity search-based general-purpose algorithms for motion estimation. While effective, these methods increase computational complexity and may not suit delay-sensitive practical applications such as real-time drone video transmission. If future motion can be predicted from external metadata, encoding can be optimized with lower complexity. In this study, a mathematical model for predicting motion vectors in drone video using only flight parameters is proposed. A remote-controlled drone with a fixed downward-facing camera recorded 4K video at 50 fps during autonomous flights over a marked terrain. Four flight parameters were varied independently, altitude, horizontal speed, vertical speed, and rotational rate. OpenCV was used to detect ground markers and compute motion vectors for temporal distances of 5 and 25 frames. Polynomial surface fitting was applied to derive motion models for translational, rotational, and elevational motion, which were later combined. The model was validated using complex motion scenarios (e.g., circular, ramp, helix), yielding worst-case prediction errors of approximately −1 ± 3 and −6 ± 14 pixels at 5 and 25 frames, respectively. The results suggest that flight-aware modeling enables accurate and low-complexity motion vector prediction for drone video coding. Full article
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34 pages, 3860 KB  
Article
Sensor-Level Anomaly Detection in DC–DC Buck Converters with a Physics-Informed LSTM: DSP-Based Validation of Detection and a Simulation Study of CI-Guided Deception
by Jeong-Hoon Moon, Jin-Hong Kim and Jung-Hwan Lee
Appl. Sci. 2025, 15(20), 11112; https://doi.org/10.3390/app152011112 - 16 Oct 2025
Abstract
Digitally controlled DC–DC converters are vulnerable to sensor-side spoofing, motivating plant-level anomaly detection that respects the converter physics. We present a physics-informed LSTM (PI–LSTM) autoencoder for a 24→12 V buck converter. The model embeds discrete-time circuit equations as residual penalties and uses a [...] Read more.
Digitally controlled DC–DC converters are vulnerable to sensor-side spoofing, motivating plant-level anomaly detection that respects the converter physics. We present a physics-informed LSTM (PI–LSTM) autoencoder for a 24→12 V buck converter. The model embeds discrete-time circuit equations as residual penalties and uses a fixed decision rule (τ=μ+3σ, N=3 consecutive samples). We study three voltage-sensing attacks (DC bias, fixed-sample delay, and narrowband noise) in MATLAB/Simulink. We then validate the detection path on a TMS320F28379 DSP. The detector attains F1 scores of 96.12%, 91.91%, and 97.50% for bias, delay, and noise (simulation); on hardware, it achieves 2.9–4.2 ms latency with an alarm-wise FPR of ≤1.2%. We also define a unified safety box for DC rail quality and regulation. In simulations, we evaluate a confusion index (CI) policy for safety-bounded performance adjustment. A operating point yields CI0.25 while remaining within the safety limits. In hardware experiments without CI actuation, the Vr,pp and IRR stayed within the limits, whereas the ±2% regulation window was occasionally exceeded under the delay attack (up to ≈2.8%). These results indicate that physics-informed detection is deployable on resource-constrained controllers with millisecond-scale latency and a low alarm-wise FPR, while the full hardware validation of CI-guided deception (safety-bounded performance adjustment) under the complete safety box is left to future work. Full article
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38 pages, 72154 KB  
Article
Dynamic Self-Triggered Fuzzy Formation Control for UAV Swarm with Prescribed-Time Convergence
by Jianhua Lu, Zehao Yuan and Ning Wang
Drones 2025, 9(10), 715; https://doi.org/10.3390/drones9100715 - 15 Oct 2025
Viewed by 65
Abstract
This study focuses on the cooperative formation control problem of six-degree-of-freedom (6-DOF) fixed-wing unmanned aerial vehicles (UAVs) under constraints of limited communication resources and strict time requirements. The core innovation of the proposed framework lies in the deep integration of a dynamic self-triggered [...] Read more.
This study focuses on the cooperative formation control problem of six-degree-of-freedom (6-DOF) fixed-wing unmanned aerial vehicles (UAVs) under constraints of limited communication resources and strict time requirements. The core innovation of the proposed framework lies in the deep integration of a dynamic self-triggered communication mechanism (DSTCM) with a prescribed-time control strategy. Furthermore, a fuzzy control strategy is designed to effectively suppress system disturbances, enhancing the robustness of the formation. The designed DSTCM not only retains the adaptive triggering threshold characteristic of dynamic event-triggered communication, significantly reducing communication frequency, but also completely eliminates the need for continuous state monitoring required by traditional event-triggered mechanisms. As a result, both communication and onboard computational resources are effectively conserved. In parallel, a novel time-varying unilateral constrained performance function is introduced to construct a prescribed-time controller, which guarantees that the formation tracking error converges to a predefined residual set within a user-specified time. The convergence process is independent of initial conditions and strictly adheres to full-state constraints. A rigorous Lyapunov-based stability analysis demonstrates that all signals in the closed-loop UAV velocity and attitude system are semi-globally uniformly ultimately bounded (SGUUB). Furthermore, the proposed DSTCM ensures the existence of a strictly positive lower bound on the inter-event triggering intervals of the UAVs, thereby avoiding the occurrence of Zeno behavior. Numerical simulation results are provided to verify the effectiveness and superiority of the proposed control scheme. Full article
(This article belongs to the Section Artificial Intelligence in Drones (AID))
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20 pages, 2201 KB  
Article
Coffee Drying as a Catalytic Gas–Solid Dehydration Analogy: A Desiccant-Assisted Theoretical Framework
by Eduardo Duque-Dussán
ChemEngineering 2025, 9(5), 112; https://doi.org/10.3390/chemengineering9050112 - 15 Oct 2025
Viewed by 72
Abstract
Coffee drying in humid regions is frequently hindered by high rainfall and elevated relative humidity during peak harvest, prolonging drying times and risking microbial spoilage and quality deterioration. This study introduces a novel framework in which low-temperature drying is reframed as a gas–solid [...] Read more.
Coffee drying in humid regions is frequently hindered by high rainfall and elevated relative humidity during peak harvest, prolonging drying times and risking microbial spoilage and quality deterioration. This study introduces a novel framework in which low-temperature drying is reframed as a gas–solid dehydration reaction, promoted by a catalyst analog represented by regenerable desiccants integrated into the inlet air stream to lower the humidity ratio (ΔY) and intensify the evaporation driving force. Two adsorbents, silica gel type A and zeolite 13X, were evaluated using a coupled reactor model linking fixed-bed adsorption kinetics with tensorial heat–mass transport in a 70 kg batch of parchment coffee arranged in a 0.20 m thick bed. Drying simulations from 53% to 12% (wb) at 40, 45, and 50 °C showed time reductions of 35–37% with silica gel and 44–57% with zeolite, yielding kinetic promotion factors of up to 2.3× relative to the control. Breakthrough analysis supported a dual-bed alternation strategy, with regeneration at ≤130 °C for silica and moderately higher for zeolite. A nomograph was developed to scale desiccant requirements across airflow and ΔY targets. These results confirm the feasibility and scalability of desiccant-assisted drying, providing a modular intensification pathway for farm-scale coffee processing. Full article
(This article belongs to the Topic Advanced Materials in Chemical Engineering)
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22 pages, 6104 KB  
Article
Real-Time Adaptive Nanofluid-Based Lubrication in Stainless Steel Turning Using an Intelligent Auto-Tuned MQL System
by Mahip Singh, Amit Rai Dixit, Anuj Kumar Sharma, Akash Nag and Sergej Hloch
Materials 2025, 18(20), 4714; https://doi.org/10.3390/ma18204714 - 14 Oct 2025
Viewed by 110
Abstract
Achieving optimal lubrication during machining processes, particularly turning of stainless steel, remains a significant challenge due to dynamic variations in cutting conditions that affect tool life, surface quality, and environmental impact. Conventional Minimum Quantity Lubrication (MQL) systems provide fixed flow rates and often [...] Read more.
Achieving optimal lubrication during machining processes, particularly turning of stainless steel, remains a significant challenge due to dynamic variations in cutting conditions that affect tool life, surface quality, and environmental impact. Conventional Minimum Quantity Lubrication (MQL) systems provide fixed flow rates and often fail to adapt to changing process parameters, limiting their effectiveness under fluctuating thermal and mechanical loads. To address these limitations, this study proposes an ambient-aware adaptive Auto-Tuned MQL (ATM) system that intelligently controls both nanofluid concentration and lubricant flow rate in real time. The system employs embedded sensors to monitor cutting zone temperature, surface roughness, and ambient conditions, linked through a feedback-driven control algorithm designed to optimize lubrication delivery dynamically. A Taguchi L9 design was used for experimental validation on AISI 304 stainless steel turning, investigating feed rate, cutting speed, and nanofluid concentration. Results demonstrate that the ATM system substantially improves machining outcomes, reducing surface roughness by more than 50% and cutting force by approximately 20% compared to conventional MQL. Regression models achieved high predictive accuracy, with R-squared values exceeding 99%, and surface analyses confirmed reduced adhesion and wear under adaptive lubrication. The proposed system offers a robust approach to enhancing machining performance and sustainability through intelligent, real-time lubrication control. Full article
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19 pages, 1196 KB  
Article
Fixed-Time Formation Control for MAV/UAVs with Switching Threshold Event-Triggered Strategy
by Xueyan Han, Maolong Lv, Di Shen, Yuyuan Shi, Boyang Zhang and Peng Yu
Drones 2025, 9(10), 710; https://doi.org/10.3390/drones9100710 - 14 Oct 2025
Viewed by 97
Abstract
The cooperative flight of manned and unmanned aerial vehicles (MAV/UAVs) has recently become a focus in the research of civilian and humanitarian fields, in which formation control is crucial. This paper takes the improvement of convergence performance and resource conservation as the entry [...] Read more.
The cooperative flight of manned and unmanned aerial vehicles (MAV/UAVs) has recently become a focus in the research of civilian and humanitarian fields, in which formation control is crucial. This paper takes the improvement of convergence performance and resource conservation as the entry point to study control problems of cooperative formation configuration of MAV/UAVs. Following the backstepping recursive design procedures, an event-triggered fixed-time formation control strategy for MAV/UAVs operating under modeling uncertainties and external disturbances is presented. Moreover, a novel switching threshold event-triggered mechanism is introduced, which dynamically adjusts control signal updates based on system states. Compared with periodic sampling control (Controller 1), fixed threshold strategies (Controller 2) and relative threshold strategies (Controller 3), this mechanism enhances resource efficiency and prevents Zeno behavior. On the basis of Lyapunov stability theory, the closed-loop system is shown to be stable in the sense of the fixed-time concept. Numerical simulations are carried out in Simulink to validate the effectiveness of the theoretical findings. The results show that compared with the three comparison methods, the proposed control method saves 86%, 34%, and 43% of control transmission burden respectively, which significantly reduces the number of triggered events. Full article
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17 pages, 1290 KB  
Review
Virtual Reality Training for Balance in Patients with Chronic Low Back Pain: A Systematic Review and Meta-Analysis
by Abrar I. AlSadiq, Fuad A. Abdulla and Ali M. Alshami
J. Clin. Med. 2025, 14(20), 7247; https://doi.org/10.3390/jcm14207247 - 14 Oct 2025
Viewed by 152
Abstract
Background: Chronic low back pain is often associated with impaired balance and reduced functional mobility. Recent studies suggest that virtual reality-based interventions may be effective in improving balance outcomes in individuals with chronic low back pain. Objective: In this systematic review and meta-analysis, [...] Read more.
Background: Chronic low back pain is often associated with impaired balance and reduced functional mobility. Recent studies suggest that virtual reality-based interventions may be effective in improving balance outcomes in individuals with chronic low back pain. Objective: In this systematic review and meta-analysis, we aimed to investigate the impact of virtual reality training on static and dynamic balance outcomes in patients with chronic low back pain. Methods: Two independent reviewers searched English-language studies from inception to 1 July 2024, using the following databases: PubMed, Web of Science, Scopus, Dimensions, Semantic Scholar, and ProQuest. Randomized clinical trials with a PEDro score of ≥6 were included. Fixed- and random-effects meta-analyses were conducted on eligible trials. Results: Of 3172 records screened, 13 trials were eligible. Meta-analyses of six trials (n = 183) across diverse adults using 2–8 week interventions showed that virtual reality training improved dynamic balance: timed up and go (mean difference: −2.29 s; 95% confidence interval: −2.91 to −1.66; I2 = 0%; p < 0.00001) and forward reach (mean difference: 7.80 cm; 95% confidence interval: 2.08 to 13.52; I2 = 0%; p = 0.008). However, no significant effects were found for static balance, single-leg stance, center of pressure medio-lateral displacement, or center of pressure velocity, compared with controls. Conclusions: Virtual reality-based training seems to be more effective than control interventions in improving dynamic and functional balance, but not static balance, in patients with chronic low back pain. Full article
(This article belongs to the Section Orthopedics)
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25 pages, 3535 KB  
Article
Decentralized Cycle-Free Game-Theoretic Adaptive Traffic Signal Control: Model Enhancement and Testing on Isolated Signalized Intersections
by Amr K. Shafik and Hesham A. Rakha
Sensors 2025, 25(20), 6339; https://doi.org/10.3390/s25206339 - 14 Oct 2025
Viewed by 142
Abstract
This research enhances and evaluates the performance of a Decentralized Nash Bargaining (DNB) adaptive traffic signal controller that operates a flexible National Electrical Manufacturers Association (NEMA) phasing and timing scheme responding dynamically to fluctuating traffic demands. The DNB controller is enhanced to (1) [...] Read more.
This research enhances and evaluates the performance of a Decentralized Nash Bargaining (DNB) adaptive traffic signal controller that operates a flexible National Electrical Manufacturers Association (NEMA) phasing and timing scheme responding dynamically to fluctuating traffic demands. The DNB controller is enhanced to (1) use traffic density estimates instead of queues to optimize signal timings; (2) to consider the eight-phase two-ring NEMA controller configuration within the game-theoretic approach; and (3) to consider dynamically adaptable control time steps. The enhanced DNB controller is benchmarked against (1) a fixed-time traffic signal control using the state-of-practice Webster’s method and an emerging Laguna-Du-Rakha (LDR) method for computing the optimum cycle length; (2) a state-of-the-practice actuated traffic signal control; and (3) a state-of-the-art reinforcement learning (RL) traffic signal controller presented in the literature. The controller is tested on two isolated signalized intersections, demonstrating enhanced overall intersection performance compared to the baseline pretimed and actuated controllers at various demand levels, and offers better performance than a previously developed RL controller. Specifically, the DNB controller results in a decrease in the average vehicle delay and queue size by up to 54% and 63%, respectively, compared to Webster’s state-of-the-practice pretimed control. Unlike the RL controller, the DNB controller requires no pre-training while adapting to fluctuating traffic conditions, thereby providing a flexible framework for reducing traffic congestion at signalized intersections. As such, this research contributes to the development of smarter and more responsive urban traffic control systems. Full article
(This article belongs to the Special Issue Feature Papers in Smart Sensing and Intelligent Sensors 2025)
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27 pages, 4446 KB  
Article
HAPS-PPO: A Multi-Agent Reinforcement Learning Architecture for Coordinated Regional Control of Traffic Signals in Heterogeneous Road Networks
by Qiong Lu, Haoda Fang, Zhangcheng Yin and Guliang Zhu
Appl. Sci. 2025, 15(20), 10945; https://doi.org/10.3390/app152010945 - 12 Oct 2025
Viewed by 475
Abstract
The increasing complexity of urban traffic networks has highlighted the potential of Multi-Agent Reinforcement Learning (MARL) for Traffic Signal Control (TSC). However, most existing MARL methods assume homogeneous observation and action spaces among agents, ignoring the inherent heterogeneity of real-world intersections in topology [...] Read more.
The increasing complexity of urban traffic networks has highlighted the potential of Multi-Agent Reinforcement Learning (MARL) for Traffic Signal Control (TSC). However, most existing MARL methods assume homogeneous observation and action spaces among agents, ignoring the inherent heterogeneity of real-world intersections in topology and signal phasing, which limits their practical applicability. To address this gap, we propose HAPS-PPO (Heterogeneity-Aware Policy Sharing Proximal Policy Optimization), a novel MARL framework for coordinated signal control in heterogeneous road networks. HAPS-PPO integrates two key mechanisms: an Observation Padding Wrapper (OPW) that standardizes varying observation dimensions, and a Dynamic Multi-Strategy Grouping Learning (DMSGL) mechanism that trains dedicated policy heads for agent groups with distinct action spaces, enabling adequate knowledge sharing while maintaining structural correctness. Comprehensive experiments in a high-fidelity simulation environment based on a real-world road network demonstrate that HAPS-PPO significantly outperforms Fixed-time control and mainstream MARL baselines (e.g., MADQN, FMA2C), reducing average delay time by up to 44.74% and average waiting time by 59.60%. This work provides a scalable and plug-and-play solution for deploying MARL in realistic, heterogeneous traffic networks. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation and Its Applications)
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22 pages, 5131 KB  
Article
Predictive Torque Control for Induction Machine Fed by Voltage Source Inverter: Theoretical and Experimental Analysis on Acoustic Noise
by Bouyahi Henda and Adel Khedher
Acoustics 2025, 7(4), 63; https://doi.org/10.3390/acoustics7040063 - 11 Oct 2025
Viewed by 164
Abstract
Induction motors piloted by voltage source inverters constitute a major source of acoustic noise in industry. The discrete tonal bands generated by induction motor stator current spectra controlled by the fixed Pulse Width Modulation (PWM) technique have damaging effects on the electronic noise [...] Read more.
Induction motors piloted by voltage source inverters constitute a major source of acoustic noise in industry. The discrete tonal bands generated by induction motor stator current spectra controlled by the fixed Pulse Width Modulation (PWM) technique have damaging effects on the electronic noise source. Nowadays, the investigation of new advanced control techniques for variable speed drives has developed a potential investigation field. Finite state model predictive control has recently become a very popular research focus for power electronic converter control. The flexibility of this control shows that the switching times are generated using all the information on the drive status. Predictive Torque Control (PTC), space vector PWM and random PWM are investigated in this paper in terms of acoustic noise emitted by an induction machine fed by a three-phase two-level inverter. A comparative study based on electrical and mechanical magnitudes, as well as harmonic analysis of the stator current, is presented and discussed. An experimental test bench is also developed to examine the effect of the proposed PTC and PWM techniques on the acoustic noise of an induction motor fed by a three-phase two-level voltage source converter. Full article
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11 pages, 651 KB  
Article
Dietary Modification with Food Order and Divided Carbohydrate Intake Improves Glycemic Excursions in Healthy Young Women
by Yuki Higuchi, Takashi Miyawaki, Shizuo Kajiyama, Kaoru Kitta, Shintaro Kajiyama, Yoshitaka Hashimoto, Michiaki Fukui and Saeko Imai
Nutrients 2025, 17(20), 3194; https://doi.org/10.3390/nu17203194 - 10 Oct 2025
Viewed by 416
Abstract
Background/Objectives: Previous studies show that allocating carbohydrates earlier and vegetables/protein later in late-evening meals improves glycemic control in both healthy individuals and those with type 2 diabetes. However, evidence remains insufficient regarding the effects of distributing carbohydrate intake across the day by dividing [...] Read more.
Background/Objectives: Previous studies show that allocating carbohydrates earlier and vegetables/protein later in late-evening meals improves glycemic control in both healthy individuals and those with type 2 diabetes. However, evidence remains insufficient regarding the effects of distributing carbohydrate intake across the day by dividing three regular meals into five smaller meals. Methods: We conducted a randomized, controlled, crossover trial to compare the effects of two dietary patterns: (1) a conventional three-meal pattern with simultaneous intake of all food components, and (2) a five-meal pattern incorporating divided carbohydrate portions and a fixed food order—vegetables first, followed by protein, and then carbohydrates. Eighteen healthy young women consumed the same test meals under both patterns. Glucose fluctuations were monitored using an intermittently continuous glucose monitoring system. Results: The five-meal pattern with food sequencing significantly improved the mean amplitude of glycemic excursions (MAGE; 2.56 ± 0.13 vs. 3.49 ± 0.32 mmol/L, p < 0.01), glucose peak, and incremental area under the glucose curve for breakfast, lunch, and dinner, and the time above the target glucose range [>7.8 mmol/L; 1.4 ± 0.6 vs. 4.2 ± 1.0%, p < 0.01] compared to the three-meal pattern. Conclusions: These findings suggest that divided carbohydrate intake and food order ameliorates the MAGE in healthy young women. Full article
(This article belongs to the Section Clinical Nutrition)
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20 pages, 1650 KB  
Article
Power-Based Statistical Detection of Substance Accumulation in Constrained Places Using a Contact-Less Passive Magnetoelastic Sensor
by Ioannis Kalyvas and Dimitrios Dimogianopoulos
Vibration 2025, 8(4), 64; https://doi.org/10.3390/vibration8040064 - 10 Oct 2025
Viewed by 205
Abstract
A contactless passive magnetoelastic sensing setup, recently proposed for detecting pest/substance accumulation in confined spaces (labs, museum reserves), is optimized for enhanced low-frequency performance. The setup uses a short flexible polymer slab, clamped at one end. There, a short Metglas® 2826MB magnetoelastic [...] Read more.
A contactless passive magnetoelastic sensing setup, recently proposed for detecting pest/substance accumulation in confined spaces (labs, museum reserves), is optimized for enhanced low-frequency performance. The setup uses a short flexible polymer slab, clamped at one end. There, a short Metglas® 2826MB magnetoelastic ribbon is fixed upon the slab’s surface. The opposite end receives excitation by a remotely controlled module of ultra-low amplitude vibration. When vibrating (with the slab), the ribbon generates magnetic flux, which depends on (and reflects) the slab’s dynamics. This changes when loads accumulate on its surface. The flux induces voltage in a contactless manner in a low-cost pick-up coil suspended above the ribbon. Voltage monitoring allows for evaluation of the vibrating slab’s real-time dynamics and, consequently, the detection of load-induced changes. This work innovates by introducing a low-cost passive circuit for real-time voltage processing, thus achieving an accurate representation of the low-frequency dynamics of the magnetic flux. Furthermore, it introduces an algorithm, which statistically detects load-induced changes using the voltage’s low-frequency power characteristics. Both additions enable load detection at relatively low frequencies, thus addressing a principal issue of passive contactless sensing setups. Extensive testing at different occasions demonstrates promising load detection performance under various conditions, especially given its cost-efficient hardware and operation. Full article
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