Search Results (558)

Background/Objectives: Migraine affects approximately 3–10% of school-aged children and up to 28% of adolescents, with prevalence increasing during adolescence. For pediatric specialty providers, increased awareness of this condition may influence patient care. This study examined pediatric dentists’ education, clinical exposure, and perceived knowledge gaps related to pediatric migraine, with the goal of identifying barriers to recognition and referral, as well as informing future training to support accurate diagnosis and interdisciplinary care. Methods: A 28-item electronic questionnaire was distributed to all members of the American Academy of Pediatric Dentistry, including pediatric dentists and postgraduate pediatric dental residents, assessing knowledge, beliefs, clinical experience, and interest in further training regarding pediatric headache/migraine management. Respondents with and without previous training were compared in terms of general understanding using t-tests; a linear regression model analyzed predictors of provider awareness regarding links between oral conditions and headache/migraine. Results: Among 315 respondents, the mean self-perceived awareness score was 2.7 ± 1.3 (on a 0–5 scale). The most frequently identified contributing factors were clenching (73.7%), bruxism (72.4%), and temporomandibular disorders (65.7%). Nearly all respondents (95.2%) reported no formal education on headache/migraine prevention, yet 78.1% agreed on the importance of understanding the relationship between oral health and headache/migraine. Respondents with prior training were significantly more aware (p < 0.001) than those without prior training. Educating families (p < 0.001), frequency of patient encounters with headache (p = 0.032), coordination with healthcare providers (p = 0.002), and access to appropriate management resources (p < 0.001) were significant predictors of providers’ awareness. Conclusions: Pediatric dental providers expressed strong interest in enhancing their knowledge of headache/migraine management, highlighting the value of integrating headache/migraine-related education into training programs and promoting greater interdisciplinary collaboration. Full article

Background: Measuring frequencies of antigen-specific memory B cells (B_mem), their immunoglobulin (Ig) class and subclass usage, cross-reactivity, and affinity can provide insights into the efficacy of future antibody responses in case of antigen re-encounter. B cell ImmunoSpot^® assays can provide such information; however, like most cell-based tests, they require considerable amounts of blood to be drawn from the donor and this has hindered their inclusion in clinical trials and routine immune diagnostics. Methods: We introduce strategies for reducing the cell numbers required to 2–3 million peripheral blood mononuclear cells (PBMCs) per antigen, obtainable from 2–3 mL of blood from healthy adult donors. Results: Except when B_mem frequencies were very low, we found that testing PBMCs in singlet wells, but in serial dilution, enables as reliable B_mem frequency assessments as when testing replicate wells at a single fixed cell number. Additionally, B cell ImmunoSpot^® assays can be multiplexed for detecting four Ig classes, or IgG subclasses, simultaneously and without loss of sensitivity. The requirement for low cell numbers and the retention of B cell functionality by cryopreserved PBMCs equivalent to freshly isolated material implies that fewer than the standard 10 million PBMCs per vial can be frozen. This would reduce the number of individuals who could not be tested for B_mem due to insufficient availability of PBMCs, a common problem with such assays. Conclusions: The predictable need for and recovery of cryopreserved PBMCs facilitates planning of and optimal cell utilization in B cell ImmunoSpot^® assays and increases the practical feasibility of extensive B_mem characterization in larger cohorts. Full article

Internal leakage within the valve body constitutes a severe potential safety hazard in industrial fluid control systems, attributable to its high concealment and the resultant difficulty in detection via conventional methodologies. Acoustic emission (AE) technology, functioning as an efficient non-destructive testing approach, is capable of capturing the transient stress waves induced by leakage, thereby furnishing an effective means for the real-time monitoring and quantitative assessment of internal leakage within the valve body. This paper conducts a systematic review of the theoretical foundations, signal-processing methodologies, and the latest research advancements related to the technology for detecting internal leakage in the valve body based on acoustic emission. Firstly, grounded in Lechlier’s acoustic analogy theory, the generation mechanism of acoustic emission signals arising from valve body leakage is elucidated. Secondly, a detailed analysis is conducted on diverse signal processing techniques and their corresponding optimization strategies, encompassing parameter analysis, time–frequency analysis, nonlinear dynamics methods, and intelligent algorithms. Moreover, this paper recapitulates the current challenges encountered by this technology and delineates future research orientations, such as the fusion of multi-modal sensors, the deployment of lightweight deep learning models, and integration with the Internet of Things. This study provides a systematic reference for the engineering application and theoretical development of the acoustic emission-based technology for detecting internal leakage in valves. Full article

Rolling bearings are indispensable but also the most fault-prone components of rotating machinery, typically used in fields such as industrial aircraft, production workshops, and manufacturing. They encounter diverse mechanical stresses, such as vibration and friction during operation, which may lead to wear and fatigue cracks. From this standpoint, the present study combined a 1-D convolutional neural network (1-D CNN) with a long short-term memory (LSTM) algorithm for classifying different ball-bearing health conditions. A physics-guided method that adopts fault characteristics frequencies was used to calculate an optimal input size (sample length). Moreover, grid search was utilized to optimize (1) the number of epochs, (2) batch size, and (3) dropout ratio and further enhance the efficacy of the proposed 1-D CNN-LSTM network. Therefore, an attempt was made to reduce epistemic uncertainty that arises due to not knowing the best possible hyper-parameter configuration. Ultimately, the effectiveness of the physics-guided optimized 1-D CNN-LSTM was tested by comparing its performance with other state-of-the-art models. The findings revealed that the average accuracies could be enhanced by up to 20.717% with the help of the proposed approach after testing it on two benchmark datasets. Full article

This paper describes a compact multi-port sub-6 GHz multiple-input multiple-output (MIMO) antenna system tailored for 5G NR mobile terminals operating in the n77 (3.3–4.2 GHz), n78 (3.3–3.8 GHz), and n79 (4.4–5.0 GHz) frequency bands. The proposed design leverages a shared coupling approach that exploits the smartphone metal frame as the radiating element, facilitating efficient integration within the spatial constraints of modern mobile devices. A two-stage method is used to mitigate the mutual coupling and correlation issues typically encountered when designing compact MIMO configurations. Initially, a four-port structure is used to evaluate broadband impedance and spatial feasibility. Based on the observed limitations in terms of isolation and the envelope correlation coefficient (ECC), the final configuration was reconfigured as an optimized two-port layout with a refined coupling geometry and effective current path control. The fabricated two-port prototype exhibited a measured voltage standing wave ratio below 3:1 across the n78 band on both ports, with the isolation levels attaining –12.4 dB and ECCs below 0.12. The radiation efficiency exceeded −6 dB across the operational band, and the radiation patterns were stable at 3.3, 3.5, and 3.8 GHz, confirming that the system was appropriate for MIMO deployment. The antenna supports asymmetric per-port efficiency targets ranging from −4.5 to −10 dB. These are the realistic layout constraints of commercial smartphones. In summary, this study shows that a metal frame integrated two-port MIMO antenna enables wideband sub-6 GHz operation by meeting the key impedance and system-level performance requirements. Our method can be used to develop a scalable platform assisting future multi-band antenna integration in mass-market 5G smartphones. Full article

An efficient numerical approach utilizing a variational weak form, grounded in 2D elastic theory and variational principles, is proposed for analyzing the in-plane vibrational behavior of rectangular plates resting on elastically restrained boundaries. The differential and integral operators can be discretized into matrix representations employing the differential quadrature method (DQM) and Taylor series expansion techniques. The discretization of dynamics equations stems directly from a weak formulation that circumvents the need for any transformation or discretization of higher-order derivatives encountered in the corresponding strong equations. Utilizing the matrix elementary transformation technique, the displacements of boundary and internal nodes are segregated, subsequently leading to the derivation of the generalized eigenvalue problem pertaining to the free vibration analysis of the Functionally Graded Material (FGM) rectangular plate. Furthermore, the study examines the impact of the gradient parameter, aspect ratio, and elastic constraints on the dimensionless frequency characteristics of the FGM rectangular plate. Ultimately, the modal properties of an in-plane FGM rectangular plate are investigated. Full article

This research aims to assess the contribution of artificial intelligence (AI)-driven digital twin technology in improving the predictive planning of European smart cities, particularly in Greece. It considers the effect of specific elements including simulation accuracy, real-time data processing, artificial intelligence tools, and system readiness on the urban planning process. Structured questionnaires were administered to 301 urban professionals working in smart cities across Greece, focusing on their perceptions of the impact of digital twin features on predictive urban planning effectiveness. Respondents were asked how crucial they found the different features of digital twins in actually improving predictive urban planning. Measurement data were described using the arithmetic mean, standard deviation, and coefficient of variation, while categorical data were described using frequency distribution tables and percentages. This study revealed that the simulation fidelity, available real-time data integration, artificial intelligence analytics, and results- oriented monitoring system maturity have a positive impact on the accuracy, speed, and flexibility of urban planning. Some of the respondents noted these features as very useful for the prediction of urban conditions and decision-making purposes. Nevertheless, some drawbacks related to the computational load and data flow were also revealed. AI-driven digital twins are useful for improving the effectiveness of urban planning. However, they encounter technical issues; therefore, seeking to focus on system maturity and data integration is necessary for their successful implementation. Cities should adopt advanced digital twin technologies and enhance the compatibility of data and maintain AI transparency for better urban planning results. Full article

Phase noise constitutes a pivotal performance parameter in microwave systems, and the evolution of microwave signal sources presents new demands on phase noise analyzers (PNAs) regarding sensitivity and bandwidth. Traditional electronics-based PNAs encounter significant limitations in meeting these advanced requirements. This paper provides an overview of recent progress in photonics-based microwave PNA research. Microwave photonic (MWP) PNAs are categorized into two main types: phase-detection-based and frequency-discrimination-based architectures. MWP phase-detection-based PNAs utilize ultra-short-pulse lasers or optical–electrical oscillators as reference sources to achieve superior sensitivity. On the other hand, MWP frequency-discrimination-based PNAs are further subdivided into photonic-substitution-type PNA and MWP quadrature-frequency-discrimination-based PNA. These systems leverage innovative MWP technologies to enhance overall performance, offering broader bandwidth and higher sensitivity compared to conventional approaches. Finally, the paper addresses the current challenges faced in phase noise measurement technologies and suggests potential future research directions aimed at improving measurement capabilities. Full article

Defective phononic crystals (PnCs) have garnered significant attention for their ability to localize and amplify elastic wave energy within defect sites or to perform narrowband filtering at defect-band frequencies. The necessity for continuously tunable defect characteristics is driven by the variable excitation frequencies encountered in rotating machinery. Conventional tuning methodologies, including synthetic negative capacitors or inductors integrated with piezoelectric defects, are constrained to fixed, offline, and incremental adjustments. To address these limitations, the present study proposes an active feedback approach that facilitates online, wide-range steering of defect bands in a one-dimensional PnC. Each defect is equipped with a pair of piezoelectric sensors and actuators, governed by three independently tunable feedback gains: displacement, velocity, and acceleration. Real-time sensor signals are transmitted to a multivariable proportional controller, which dynamically modulates local electroelastic stiffness via the actuators. This results in continuous defect-band frequency shifts across the entire band gap, along with on-demand sensitivity modulation. The analytical model that incorporates these feedback gains has been demonstrated to achieve a level of agreement with COMSOL benchmarks that exceeds 99%, while concurrently reducing computation time from hours to seconds. Displacement- and acceleration-controlled gains yield predictable, monotonic up- or down-shifts in defect-band frequency, whereas the velocity-controlled gain permits sensitivity adjustment without frequency drifts. Furthermore, the combined-gain operation enables the concurrent tuning of both the center frequency and the filtering sensitivity, thereby facilitating an instantaneous remote reconfiguration of bandpass filters. This framework establishes a new class of agile, adaptive ultrasonic devices with applications in ultrasonic imaging, structural health monitoring, and prognostics and health management. Full article

In this contribution, a model order reduction (MOR) strategy for systems characterized by Warburg-type impedance behavior, frequently encountered in electrochemical applications, is addressed. In particular, the interest is focused on the time-domain approach for deriving low-order models of such a system, in contrast to the current approaches based on the frequency domain. By exploiting the peculiar structure of positive real (PR) systems, a characteristic value technique relying on the Riccati Equation Balancing strategy is introduced to approximate such models with reduced complexity. The characteristic values of the system are used to define suitable reduced-order models. A numerical case study is presented to validate the effectiveness of the proposed method. The model is also compared against experimental data from the literature, confirming its capability to capture dominant Warburg behavior. Performance indices are computed to quantitatively assess the approximation accuracy across different model orders. The results are critically compared with those obtained using conventional MOR techniques, allowing a thorough assessment of accuracy, stability, and implementation feasibility. Full article

For a highly beneficial mutation A at locus 1 spreading in a very large population, we have analyzed the scenario that at a closely linked locus 2 a second beneficial mutant B arises before A has fixed. Under the assumptions that the fitness of B is greater than that of A and that A- and B-carrying chromosomes can recombine at some rate $r$, recombinants AB may form and eventually fix. We present explicit formulas for the fixation time of AB under additive fitness of the mutants as a function of the frequency $X_2\left(0\right)$ of A at the time when B is introduced. Our analysis suggests that the effect of interference between the beneficial mutations is most pronounced for small values of $X_2\left(0\right)<0.1.$ Furthermore, we identify a threshold value for $r$, above which recombination speeds up fixation. Using published simulation data, we also describe the genomic footprint of competing beneficial mutations. At neutral sites between the two linked selected loci, an excess of intermediate-frequency variants may occur when interference is strong, i.e.,$X_2\left(0\right)$ small. Finally, we discuss under which circumstances this scenario may be encountered in real sequences from recombining genomic regions. Full article

This work—mixing an original experimental approach, as well as numerical simulations—proposes to study film boiling modes around a small nickel sphere. While dealing with a simple looking phenomenon that is found in many industrial processes and has been solved for basic quenching regimes, we focus on describing precisely how vapor formation and film thicknesses, as well as vapor bubble evacuation, affect cooling kinetics. As instrumenting small spheres may lead to experimental inaccuracies, we optically captured, using a high-speed camera, the vapor film thickness at mid height, the vapor bubble volume, and the bubble detachment frequency, along with the heat flux. More precisely, an estimation of the instant sphere temperature, in different conditions, was obtained through cooling time measurement before the end of the film boiling mode, subsequently facilitating heat flux evaluation. We encountered a nearly linear decrease in both the vapor film thickness and vapor bubble volume as the sphere temperature decreased. Notably, the detachment frequency remained constant across the whole temperature range. The estimation of the heat fluxes confirmed the prevalence of conduction as the primary heat transfer mode; a major portion of the energy was spent increasing the liquid temperature. The results were then compared to finite element simulations using an in-house multiphysics solver, including thermic phase changes (liquid to vapor) and their hydrodynamics, and we also captured the interfaces. While presenting a challenge due to the contrast in densities and viscosities between phases, the importance of the small circulations along them, which improve the heat removal in the liquid phase, was highlighted; we also assessed the suitability of the model and the numerical code for the simulation of such quenching cases when subcooling in the vicinity of a saturation temperature. Full article

A leak detection method is developed for leaks typically encountered in industrial production. Leaks of 1 mm diameter and less are considered at operating pressures up to 10 bar. The system uses two separate datasets—one for the leak noises and the other for the background noises—both are linked using a developed mixup technique and thus simulate leaks trained in background noises. A specific frequency window between 11 and 20 kHz is utilized to generate a quadratic input for image recognition. With this method, detection accuracies of over 95% with a false alarm rate under 2% can be achieved on a test dataset under the background noises of hydraulic machines in laboratory conditions. Full article

Combine harvesters, as essential equipment in agricultural engineering, frequently experience structural faults due to their complex structure and harsh working conditions, which severely affect their reliability and operational efficiency, leading to significant downtime and reduced agricultural productivity during critical harvesting periods. Therefore, developing accurate and timely Fault Detection and Diagnosis (FDD) techniques is crucial for ensuring food security. This paper provides a systematic and critical review and analysis of the latest advancements in research on data-driven FDD methods for structural faults in combine harvesters. First, it outlines the typical structural sections of combine harvesters and their common structural fault types. Subsequently, it details the core steps of data-driven methods, including the acquisition of operational data from various sensors (e.g., vibration, acoustic, strain), signal preprocessing methods, signal processing and feature extraction techniques covering time-domain, frequency-domain, time–frequency domain combination, and modal analysis among others, and the use of machine learning and artificial intelligence models for fault pattern learning and diagnosis. Furthermore, it explores the required system and technical support for implementing such data-driven FDD methods, such as the applications of on-board diagnostic units, remote monitoring platforms, and simulation modeling. It provides an in-depth analysis of the key challenges currently encountered in this field, including difficulties in data acquisition, signal complexity, and insufficient model robustness, and consequently proposes future research directions, aiming to provide insights for the development of intelligent maintenance and efficient and reliable operation of combine harvesters and other complex agricultural machinery. Full article

High demand for 6G wireless has made photonics-aided D-band (110–170 GHz) communication a research priority. Photonics-aided technology integrates optical and wireless communications to boost spectral efficiency and transmission distance. This study presents a Radio-over-Fiber (RoF) communication system utilizing photonics-aided technology for 4600 m long-distance D-band transmission. We successfully show the transmission of a 50 Gbps (25 Gbaud) QPSK signal utilizing a 128.75 GHz carrier frequency. Notwithstanding these encouraging outcomes, RoF systems encounter considerable obstacles, including pronounced nonlinear distortions and phase noise related to laser linewidth. Numerous factors can induce nonlinear impairments, including high-power amplifiers (PAs) in wireless channels, the operational mechanisms of optoelectronic devices (such as electrical amplifiers, modulators, and photodiodes), and elevated optical power levels during fiber transmission. Phase noise (PN) is generated by laser linewidth. Despite the notable advantages of classical Volterra series and deep neural network (DNN) methods in alleviating nonlinear distortion, they display considerable performance limitations in adjusting for phase noise. To address these problems, we propose a novel post-processing approach utilizing a two-dimensional convolutional neural network (2D-CNN). This methodology allows for the extraction of intricate features from data preprocessed using traditional Digital Signal Processing (DSP) techniques, enabling concurrent compensation for phase noise and nonlinear distortions. The 4600 m long-distance D-band transmission experiment demonstrated that the proposed 2D-CNN post-processing method achieved a Bit Error Rate (BER) of 5.3 × 10⁻³ at 8 dBm optical power, satisfying the soft-decision forward error correction (SD-FEC) criterion of 1.56 × 10⁻² with a 15% overhead. The 2D-CNN outperformed Volterra series and deep neural network approaches in long-haul D-band RoF systems by compensating for phase noise and nonlinear distortions via spatiotemporal feature integration, hierarchical feature extraction, and nonlinear modelling. Full article