Search Results (911)

This study systematically investigates the effects of trap concentration on self-heating and terahertz (THz) responses in GaN HEMTs using Sentaurus TCAD. Traps, inherently unavoidable in semiconductors, can be strategically introduced to engineer specific energy levels that establish competitive dynamics between the electron momentum relaxation time and the carrier lifetime. A simulation-based exploration of this mechanism provides significant scientific value for enhancing device performance through self-heating mitigation and THz response optimization. An AlGaN/GaN heterojunction HEMT model was established, with trap concentrations ranging from 0 to $5\times {10}^{17} {\mathrm{c}\mathrm{m}}^{-3}$. The analysis reveals that traps significantly enhance channel current (achieving 3× gain at $1\times 10^{17} {\mathrm{c}\mathrm{m}}^{-3}$) via new energy levels that prolong carrier lifetime. However, elevated trap concentrations (>$1\times {10}^{16} {\mathrm{c}\mathrm{m}}^{-3}$) exacerbate self-heating-induced current collapse, reducing the min-to-max current ratio to 0.9158. In THz response characterization, devices exhibit a distinct DC component (${\mathrm{U}}_{\mathrm{d}\mathrm{c}}$) under non-resonant detection ($\mathsf{\omega }\mathsf{\tau }\ll 1$). At a trap concentration of $1\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$, ${\mathrm{U}}_{\mathrm{d}\mathrm{c}}$ peaks at $0.12 \mathrm{V}$ when ${\mathrm{V}}_{\mathrm{g}}\mathrm{D}\mathrm{C}=-7.8 \mathrm{V}$. Compared to trap-free devices, a maximum response attenuation of 64.89% occurs at ${\mathrm{V}}_{\mathrm{g}}\mathrm{D}\mathrm{C}=-4.9 \mathrm{V}$. Furthermore, ${\mathrm{U}}_{\mathrm{d}\mathrm{c}}$ demonstrates non-monotonic behavior with concentration, showing local maxima at $4\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$ and $7\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$, attributed to plasma wave damping and temperature-gradient-induced electric field variations. This research establishes trap engineering guidelines for GaN HEMTs: a concentration of $4\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$ optimally enhances conductivity while minimizing adverse impacts on both self-heating and the THz response, making it particularly suitable for high-sensitivity terahertz detectors. Full article

Seven lager beers and seven non-alcoholic counterparts, marketed by the same producers, were analyzed for their total phenolic content (TPC), radical scavenging activity (RSA) towards the DPPH radical and ThioBarbituric Index (TBI). All beers were also subjected to spin trapping experiments at 60 °C in the presence of PBN. To our knowledge, this is the first time that non-alcoholic beers (NABs) have been subjected to spin trapping experiments coupled with Electron Spin Resonance (ESR) spectroscopy. The evolution of the intensity of the PBN radical adducts during the first 150 min was represented graphically and the intensity at 150 min (I₁₅₀) and the area under the curve (AUC), were measured. The I₁₅₀ and the AUC of lagers and NABs are significantly different, whereas the TPC, the EC₅₀ of the DPPH assay, and the TBI of the two groups are superimposed. A relationship, previously proposed by us, to correlate ESR spectroscopy parameters with others obtained from UV-Vis spectrophotometry, was also applied, demonstrating its practicability. Multivariate analysis shows that clustering in two separate groups occurs only if I₁₅₀ and AUC are included in the model. Based on these results, ESR spectroscopy can be applied to study the oxidative stability of NABs. Full article

Driven by the urgent demand for low-frequency vibration and noise control in engineering scenarios such as automobiles, acoustic metamaterials (AMs), as a new class of functional materials, have demonstrated significant application potential. This paper proposes a low-frequency band gap optimization design method for local resonance acoustic metamaterials (LRAMs) based on a multi-objective genetic algorithm. Within a COMSOL Multiphysics 6.2 with MATLAB R2024b co-simulation framework, a parameterized unit cell model of the metamaterial is constructed. The optimization process targets two objectives: minimizing the band gap’s deviation from the target and reducing the structural mass. A multi-objective fitness function is formulated by incorporating the band gap deviation and structural mass constraints, and non-dominated sorting genetic algorithm II (NSGA-II) is employed to perform a global search over the geometric parameters of the resonant unit. The resulting Pareto-optimal solution set achieves a unit cell mass as low as 26.49 g under the constraint that the band gap deviation does not exceed 2 Hz. The results of experimental validation show that the optimized metamaterial configuration reduces the peak of the low-frequency frequency response function (FRF) at 63 Hz by up to 75% in a car door structure. Furthermore, the simulation predictions exhibit good agreement with the experimental measurements, confirming the effectiveness and reliability of the proposed method in engineering applications. The proposed multi-objective optimization framework is highly general and extensible and capable of effectively balancing between the acoustic performance and structural mass, thus providing an efficient engineering solution for low-frequency noise control problems. Full article

Templestay programs in South Korea represent a unique convergence of Buddhist ritual, cultural immersion, and wellness tourism. While often treated as niche cultural experiences, their broader significance within sustainable wellness tourism remains underexplored. This study examines participant reflections from the Beomeosa Templestay program through thematic analysis of over 600 reviews sourced from TripAdvisor, Google Reviews, and handwritten guestbooks. Using a triangulated framework combining Grounded Theory, Symbolic Interactionism, and the Wellness Tourism Model, the research identifies four recurring experiential themes: spiritual development, emotional healing, cultural immersion, and conscious consumption. Findings reveal cross-cultural variations: non-Korean participants emphasized spiritual exploration and cultural learning, while Korean participants prioritized emotional renewal and reconnection with heritage. Yet, across all groups, participants reported transformative outcomes, including heightened clarity, inner calm, and enhanced self-awareness. These results suggest that Templestays serve as accessible, culturally grounded wellness retreats that align with rising global demand for intentional, mindful travel. This study contributes to sustainable tourism scholarship by framing Templestays as low-impact, spiritually resonant alternatives to commercialized wellness retreats. Practical recommendations are offered to expand participation while maintaining program authenticity and safeguarding the spiritual and cultural integrity of monastic hosts in an increasingly globalized wellness landscape. Full article

Colorectal carcinoma remains one of the primary contributors to cancer deaths; however, it is also considered a preventable type of cancer, because the prognosis of the disease is directly dependent on its timely detection. Developing accurate risk prediction models for colorectal cancer is crucial for identifying individuals at both low and high risk, as risk stratification determines the need for additional interventions, which carry their own risks. The development of new non-invasive diagnostic methods based on biomaterial analysis, alongside standard diagnostic techniques such as colonoscopy with biopsy, computed tomography scanning, and magnetic resonance imaging, can address multiple objectives: improving screening accuracy, providing a comprehensive assessment of minimal residual disease, identifying patients at a high risk of colorectal cancer, and evaluating the effectiveness of ongoing treatments. The lack of sensitive diagnostic methods drives contemporary research toward the discovery of new tools for detecting tumor cells, particularly through the examination of biological materials, including blood, exhaled air, and tumor tissue itself. In this article, we analyze current studies regarding biomarkers in colorectal cancer and prognostic significance. Full article

The resonant frequency of an SAW oscillator can be modulated by varying the signal amplitude, due to non-linear acoustic interactions within the chemoselective layer. In this study, we developed an explicit model to describe the amplitude–frequency behavior of a tunable SAW oscillator. A polymeric layer of variable thickness was deposited in a circular area (radius 1.1 mm) at the center of the piezoactive surface. Increasing the oscillator loop attenuation resulted in a continuous increase in the resonant frequency by up to 1.8 MHz. The layer was modeled as a succession of non-interacting sub-layers of varying thicknesses. As a result, the function model consists of a superposition of terms, each corresponding to a layer region of distinct length and thickness. The maximum difference between the experimental data and function model (also known as residual of the fit) was below 1% (13.02 kHz) of the resonant frequency variation, thus supporting the validity of our approach. While our model proved successful, the results suggest that some interactions are unaccounted for, as evidenced by the periodicity of the residuals of fit and unrealistically large variation in acoustic wave velocity. Full article

The risk of developing symptomatic knee osteoarthritis (KOA) during a lifetime, i.e., pain, aching, or stiffness in a joint associated with radiographic KOA, was estimated in 2008 to be around 40% in men and 47% in women. The clinical and scientific communities lack an efficient diagnostic method to effectively monitor, evaluate, and predict the evolution of KOA before and during the therapeutic protocol. In this review, we summarize the main methods that are used or seem promising for the diagnosis of osteoarthritis, with a specific focus on non- or low-invasive methods. As standard diagnostic tools, arthroscopy, magnetic resonance imaging (MRI), and X-ray radiography provide spatial and direct visualization of the joint. However, discrepancies between findings and patient feelings often occur, indicating a lack of correlation between current imaging methods and clinical symptoms. Alternative strategies are in development, including the analysis of biochemical markers or acoustic emission recordings. These methods have undergone deep development and propose, with non- or minimally invasive procedures, to obtain data on tissue condition. However, they present some drawbacks, such as possible interference or the lack of direct visualization of the tissue. Other original methods show strong potential in the field of KOA monitoring, such as electrical bioimpedance or near-infrared spectrometry. These methods could permit us to obtain cheap, portable, and non-invasive data on joint tissue health, while they still need strong implementation to be validated. Also, the use of Artificial Intelligence (AI) in the diagnosis seems essential to effectively develop and validate predictive models for KOA evolution, provided that a large and robust database is available. This would offer a powerful tool for researchers and clinicians to improve therapeutic strategies while permitting an anticipated adaptation of the clinical protocols, moving toward reliable and personalized medicine. Full article

This study presents an analytical and experimental approach to enhance cantilever-based piezoelectric energy harvesters by optimizing thickness distribution. Using a gradient projection algorithm within a state-space framework, the unimorph beam’s geometry is tailored while constraining the first natural frequency. The objective is to amplify axial strain within the piezoelectric layers, thereby increasing electric polarization and maximizing the conversion efficiency of mechanical vibrations into electrical energy. The steady-state response under harmonic base excitation at resonance was modeled to evaluate the harvester’s dynamic behavior against uniform-thickness counterparts. Results show that the optimized beam achieves significantly higher output voltage and energy harvesting efficiency. Simulations reveal effective strain concentration in regions of high piezoelectric sensitivity, enhancing power generation under resonant conditions. Two independent experimental setups were employed for empirical validation: a non-contact laser vibrometry system (Polytec 3D) and a first resonant base excitation setup. Eigenfrequencies matched within 5% using a Polytec multipath interferometry system, and constant excitation tests showed approximately 30% higher in optimal shapes electrical potential value generation. The outcome of this study highlights the efficacy of geometric tailoring—specifically, non-linear thickness shaping—as a key strategy in achieving enhanced energy output from piezoelectric harvesters operating at their fundamental frequency. This work establishes a practical route for optimizing unimorph structures in real-world applications requiring efficient energy capture from low-frequency ambient vibrations. Full article

Compliant mechanisms are often utilized in precise positioning systems but have not been thoroughly examined in vibration-aided fine CNC machining. This study aims to develop a new 02-DOF flexure stage for vibration-aided fine CNC milling. A hybrid displacement amplifier, featuring a two-lever mechanism, two Scott–Russell mechanisms, and a parallel leading mechanism, was integrated into a symmetric perpendicular series configuration to create an innovative design. The pseudo-rigid body model (PRBM), Lagrangian approach, finite element analysis (FEA), and Firefly optimization algorithm were employed to develop, verify, and optimize the quality response of the new positioner. The PRBM and Lagrangian methods were used to construct an analytical model, while finite element analysis was used to validate the theoretical solution. The primary natural frequency results from theoretical and FEM methods were 318.16 Hz and 308.79 Hz, respectively. The difference between these techniques was 3.04%, demonstrating a reliable modelling strategy. The Firefly optimization approach applied mathematical equations to enhance the key design factors of the mechanism. The prototype was then built, revealing an error of 7.23% between the experimental and simulated frequencies of 331.116 Hz and 308.79 Hz, respectively. The specimen was subsequently mounted on the fabricated optimization positioner, and vibration-assisted fine CNC milling was performed at 100–1000 Hz. At 400 Hz, the specimen achieved ideal surface roughness with a Ra value of 0.187 µm. The developed design is a potential structure that generates non-resonant frequency power for vibration-aided fine CNC milling. Full article

Modifying the potential profiles in low-dimensional semiconductor heterostructures changes the confinement of particles, impacting the electronic transport properties. In this work, we study the electronic transport properties of a modified Pöschl-Teller double-barrier potential heterostructure of GaAs/AlGaAs, and for a similar double-barrier system including a Pöschl-Teller well between the barriers. For these two configurations, we calculated the current density–bias voltage characteristics, varying barrier and well half-width, the separation between barriers, and the depth of the central well. Additionally, the application of a non-resonant intense laser field. Our results show a redshift in the electronic transmission with increasing barrier separation, and a decrease in the area under the electronic transmission curve with the increase in the half-width of the barriers for both models. The characteristic current density-bias voltage curves in both models exhibit negative differential resistance, with tunable peaks that can be varied through changes in structural parameters and the external laser field. Full article

Determining the technological quality of fresh meat pieces is essential in the meat industry to ensure the production of high-quality products. For this purpose, nuclear magnetic resonance (NMR) is a non-destructive and non-invasive technique that appears as an alternative to traditional methodologies. The objective of this work is to determine the potential of magnetic resonance imaging (MRI) and time-domain (TD-NMR) relaxometry for determining the physicochemical characterization of fresh hams with different industrial destinations (both fresh and cured products, such as dry-cured ham). For this study, the biceps femoris, semimembranosus, and semitendinosus muscles of 20 fresh hind legs from white pigs, classified into four categories according to their fat content, were analyzed. The semitendinosus muscle was selected as a model, and positive and negative correlations were obtained between different physicochemical parameters and the longitudinal (T1) and transverse (T2) relaxation times obtained by MRI and TD-NMR. Regression models using T1 and T2 were also developed to predict the muscle water-holding capacity (WHC) and drip loss, using high, medium, and low magnetic field NMR (R² > 0.80). Therefore, MRI and TD-NMR could be considered as highly suitable and accurate non-destructive techniques for the WHC determination in the meat industry. Full article

In this study, we discuss a series of eight energy scales, some of which are our own speculations, and fit the logarithms of these energies as a straight line versus a quantity related to the dimensionalities of action terms in a way to be defined in the article. These terms in the action are related to the energy scales in question. So, for example, the dimensionality of the Einstein–Hilbert action coefficient is one related to the Planck scale. In fact, we suppose that, in the cases described with quantum field theory, there is, for each of our energy scales, a pair of associated terms in the Lagrangian density, one “kinetic” and one “mass or current” term. To plot the energy scales, we use the ratio of the dimensionality of, say, the “non-kinetic” term to the dimensionality of the “kinetic” one. For an explanation of our phenomenological finding that the logarithm of the energies depends, as a straight line, on the dimensionality defined integer q, we give an ontological—i.e., it really exists in nature in our model—“fluctuating lattice” with a very broad distribution of, say, the link size a. We take the Gaussian in the logarithm, $ln\left(a\right)$. A fluctuating lattice is very natural in a theory with general relativity, since it corresponds to fluctuations in the gauge depth of the field of general relativity. The lowest on our energy scales are intriguing, as they are not described by quantum field theory like the others but by actions for a single particle or single string, respectively. The string scale fits well with hadronic strings, and the particle scale is presumably the mass scale of Standard Model group monopoles, the bound state of a couple of which might be the dimuon resonance (or statistical fluctuation) found in LHC with a mass of 28 GeV. Full article

This study presents the successful fabrication of lead zirconate titanate (PZT) thin films on silicon (Si) substrates using a hybrid deposition method combining spin-coating and RF sputtering techniques. Initially, a PZT layer was deposited through four successive spin-coating cycles, followed by an additional layer formed via RF sputtering. The resulting multilayer structure was annealed at ${700 }^{°}$C for 2 h to improve crystallinity. Comprehensive material characterization was conducted using XRD, SEM, cross-sectional SEM, EDX, and UV–VIS absorbance spectroscopy. The analyses confirmed the formation of a well-crystallized perovskite phase, a uniform surface morphology, and an optical band gap of approximately 3.55 eV, supporting its suitability for sensing applications. Building upon these findings, a multilayer PZT-based touch sensor was fabricated and electrically characterized. Low-frequency I–V measurements demonstrated consistent and repeatable polarization behavior under cyclic loading conditions. In addition, $\left|Z\right|$–f measurements were performed to assess the sensor’s dynamic electrical behavior. Although expected dielectric responses were observed, the absence of distinct anti-resonance peaks suggested non-idealities linked to ${\mathrm{Ag}}^{+}$ ion diffusion from the electrode layers. To account for these effects, the classical Butterworth–Van Dyke (BVD) equivalent circuit model was extended with additional inductive and resistive components representing parasitic pathways. This modified model provided excellent agreement with the measured impedance and phase data, offering deeper insight into the interplay between material degradation and electrical performance. Overall, the developed sensor structure exhibits strong potential for use in piezoelectric sensing applications, particularly for tactile and pressure-based interfaces. Full article

We study the entanglement dynamics of a pair of non-identical interacting atoms (qubits) coupled off-resonance to a single-mode cavity radiation field and exposed to dephasing environments. The qubits are studied starting from various initial states that are disentangled from an initially coherent field. The system models the basic building units of quantum information processing (QIP) platforms under the realistic considerations of asymmetry and external environmental influences. We investigate how introducing a radiation field alters the system’s entanglement dynamics in the presence of dephasing environments, and how it impacts the effects of the dephasing environments themselves. The work examines the problem under various settings of inter-qubit interactions, which are now experimentally controllable in some of the newly engineered artificial qubit systems. We illustrate that only upon introducing the radiation field, the system suffers a terminal disentanglement (followed by no revivals) in a finite time. This behavior is exacerbated when the atoms’ interaction with the field is stronger. Moreover, the effects of the field’s intensity and the atoms’ detunings are vastly sensitive to the choice of the initial state. We also demonstrate that the closer the atoms’ transition frequencies are to resonance with the field, the more pronounced are the effects of strengthening the independent dephasing environments corresponding to some initial states. Those states also suffered a greater reduction in entanglement content when the qubits with stronger atom–field interaction strength were influenced by a stronger independent dephasing environment. In addition, we examined the ability of the correlated dephasing environment to induce a noise-enhanced efficiency in the presence of an external radiation field. We showed that the radiation field could play a decisive role in enabling or restricting noise-enhanced efficiency, but one that is also highly sensitive to the system’s initial state. Full article

Significant challenges persist in diagnosing non-ischemic cardiomyopathies (NICMs) owing to early morphological overlap and subtle functional changes. While cardiac magnetic resonance (CMR) offers gold-standard structural assessment, current morphology-based AI models frequently overlook key biomechanical dysfunctions like diastolic/systolic abnormalities. To address this, we propose a dual-path hybrid deep learning framework based on CNN-LSTM and MLP, integrating anatomical features from cine CMR with biomechanical markers derived from intraventricular pressure gradients (IVPGs), significantly enhancing NICM subtype classification by capturing subtle biomechanical dysfunctions overlooked by traditional morphological models. Our dual-path architecture combines a CNN-LSTM encoder for cine CMR analysis and an MLP encoder for IVPG time-series data, followed by feature fusion and dense classification layers. Trained on a multicenter dataset of 1196 patients and externally validated on 137 patients from a distinct institution, the model achieved a superior performance (internal AUC: 0.974; external AUC: 0.962), outperforming ResNet50, VGG16, and radiomics-based SVM. Ablation studies confirmed IVPGs’ significant contribution, while gradient saliency and gradient-weighted class activation mapping (Grad-CAM) visualizations proved the model pays attention to physiologically relevant cardiac regions and phases. The framework maintained robust generalizability across imaging protocols and institutions with minimal performance degradation. By synergizing biomechanical insights with deep learning, our approach offers an interpretable, data-efficient solution for early NICM detection and subtype differentiation, holding strong translational potential for clinical practice. Full article