Search Results (3,073)

Multiple input multiple output (MIMO) antennas have recently received attention for improving wireless communication data rates in rich scattering environments. Despite this, the challenge of isolation persists prominently in compact MIMO-based electronics. Various techniques have recently emerged to address the isolation issues, among which the defected ground structure (DGS) stands out as a cost-effective solution. Additionally, selecting the appropriate feed mechanism is crucial for enhancing the key performance indicators of MIMO antennas. However, there has been minimal focus on how different feed methods impact the operation of MIMO antennas integrated with DGS. This paper begins with a comprehensive review of diverse antenna design, feeding strategies, and DGS architectures. Subsequently, the causal relationships between various feed networks and DGSs has been established through modeling, simulation, fabrication, and measurement of MIMO antennas operating within the sub-6 GHz spectrum. Particularly, dual elements of MIMO antennas grounded by a slotted complementary split ring resonator (SCSRR)-based DGS were excited using four standard feed methods: coaxial probe, microstrip line, proximity coupled, and aperture coupled feed. The influence of each feed network on the performance of MIMO antennas integrated with SCSRR-based DGSs has been thoroughly investigated and compared, leading to guidelines for feed network selection. The coaxial probe feed network provided improved isolation performance, ranging from 16.5 dB to 46 dB in experiments.The aperture and proximity-coupled feed network provided improvements in bandwidth of 38.7% and 15.6%, respectively. Furthermore, reasonable values for envelope correlation coefficient (ECC), diversity gain (DG), channel capacity loss (CCL), and mean effective gain (MEG) have been ascertained. Full article

This study presents a numerical investigation of the dynamic behavior of graphene platelet (GPL)-reinforced ethylene tetrafluoroethylene (ETFE) tensile membrane structures subjected to harmonic excitation. Modal and harmonic response analyses were performed to assess both the natural frequencies and the dynamic responses of the ETFE membrane. GPLs were employed as the reinforcements to enhance the mechanical properties of the membrane materials, whose Young’s modulus was predicted through the effective medium theory (EMT). Parametric studies were conducted to examine the impact of pre-strain and the attributes of the GPL reinforcements, including weight fraction and aspect ratio, on the natural frequencies and amplitude–frequency response curves of the membrane structure. The first natural frequency substantially increased from 5.46 Hz without initial strain to 31.0 Hz with the application of 0.1% initial strain, resulting in a frequency shift that moved the natural frequency out of the range of typical wind-induced pulsations. Embedding GPL fillers into ETFE membrane was another potential solution to enhance the dynamic stability of the membrane structure, with a 1% addition of GPLs resulting in a 48.6% increase in the natural frequency and a 45.1% reduction in resonance amplitude. GPLs with larger aspect ratios provided better reinforcement, offering a means to fine-tune the membrane’s dynamic response. These results underscore that by strategically adjusting both pre-strain levels and GPL characteristics, the membrane’s dynamic behavior can be optimized, offering a promising approach for improving the stability of structures subjected to wind-induced loads. Full article

The phosphors with persistent luminescence in the NIR (near-infrared) region and the NIR-to-NIR Stokes luminescence properties have received considerable attention owing to their inclusive application prospects in the in vivo imaging field. In this paper, Nd³⁺ doped SrSnO₃ phosphors with remarkable NIR emission performance were prepared using a high temperature solid state reaction method; the phase structure, morphology, and luminescence properties were discussed systematically. The SrSnO₃ host exhibits broadband NIR emission (800–1300 nm) with absorptions in the near ultraviolet region. Nd³⁺ ions emerge excellent NIR-to-NIR Stokes luminescence under 808 nm laser excitation, with maximum emission at around ~1068 nm. The concentration-dependent luminescence properties, temperature dependent emission, and the luminescence decay curves of Nd³⁺ in the SrSnO₃ host were also studied. The Nd³⁺ doped SrSnO₃ phosphors exhibit exceptional thermal stability; the integrated emission intensity can retain approximately 66% at 423 K compared to room temperature. Most importantly, NIR persistent luminescence also can be observed for the SrSnO₃:Nd³⁺ samples, which is in the first and second biological windows. A possible mechanism was proposed for the persistent NIR luminescence of Nd³⁺ based on the thermo-luminescence spectra. Consequently, the exciting results indicate that multifunctional NIR luminescence has been successfully realized in the SrSnO₃:Nd³⁺ phosphors. Full article

Hyperspectral image (HSI) classification is a crucial technique that assigns each pixel in an image to a specific land cover category by leveraging both spectral and spatial information. In recent years, HSI classification methods based on convolutional neural networks (CNNs) and Transformers have significantly improved performance due to their strong feature extraction capabilities. However, these improvements often come with increased model complexity, leading to higher computational costs. To address this, we propose a compact and efficient spectral-spatial feature extraction and attention-based neural network (SSFAN) for HSI classification. The SSFAN model consists of three core modules: the Parallel Spectral-Spatial Feature Extraction Block (PSSB), the Scan Block, and the Squeeze-and-Excitation MLP Block (SEMB). After preprocessing the HSI data, it is fed into the PSSB module, which contains two parallel streams, each comprising a 3D convolutional layer and a 2D convolutional layer. The 3D convolutional layer extracts spectral and spatial features from the input hyperspectral data, while the 2D convolutional layer further enhances the spatial feature representation. Next, the Scan Block module employs a layered scanning strategy to extract spatial information at different scales from the central pixel outward, enabling the model to capture both local and global spatial relationships. The SEMB module combines the Spectral-Spatial Recurrent Block (SSRB) and the MLP Block. The SSRB, with its adaptive weight assignment mechanism in the SToken Module, flexibly handles time steps and feature dimensions, performing deep spectral and spatial feature extraction through multiple state updates. Finally, the MLP Block processes the input features through a series of linear transformations, GELU activation functions, and Dropout layers, capturing complex patterns and relationships within the data, and concludes with an argmax layer for classification. Experimental results show that the proposed SSFAN model delivers superior classification performance, outperforming the second-best method by $1.72\%$, $5.19\%$, and $1.94\%$ in OA, AA, and Kappa coefficient, respectively, on the Indian Pines dataset. Additionally, it requires less training and testing time compared to other state-of-the-art deep learning methods. Full article

Dishevelled (DVL) proteins precisely control Wnt signaling pathways with many effectors. While substantial research has advanced our understanding of DVL’s role in Wnt pathways, key questions regarding its regulatory mechanisms and interactions remain unresolved. Herein, we present the recent advances and perspectives on how DVL regulates signaling. The experimentally determined conserved domain structures of DVL in conjunction with AlphaFold-predicted structures are used to understand the DVL’s role in Wnt signaling regulation. We also summarize the role of DVL in various diseases and provide insights into further directions for research on the DVL-mediated signaling mechanisms. These findings underscore the importance of DVL as a pharmaceutical target or biological marker in diseases, offering exciting potential for future biomedical applications. Full article

Northern corn leaf blight (NCLB) is caused by a fungus and can be susceptible to the disease throughout the growing period of corn, posing a significant impact on corn yield. Aiming at the problems of under-segmentation, over-segmentation, and low segmentation accuracy in the traditional segmentation model of northern corn leaf blight, this study proposes a segmentation method based on an improved U-Net network model. By introducing a convolutional layer and maximum pooling layer to a VGG19 network, the channel attention module and spatial attention module (CBAM) are fused, and the squeeze excitation (SE) attention mechanism is combined. This enhances image feature decoding, integrates feature maps of each layer, strengthens the feature extraction process, expands the sensory fields and aggregates context information, and reduces the loss of location and dense semantic information caused by the pooling operation. Findings from the study show that the proposed NCLB-Net has significantly improved the MIoU and PA indexes, reaching 92.43% and 94.71%, respectively. Compared with the traditional methods, U-Net, SETR, DAnet, OCnet, PSPNet, etc., the MIoU is improved by 20.81%, 16.10%, 9.79%, 5.27%, and 11.06%, and the PA is improved by 11.49%, 8.18%, 9.54%, 13.11%, and 6.26%, respectively. Full article

Background: The utility of single-pulse TMS (transcranial magnetic stimulation)-evoked EEG (electroencephalograph) potentials (TEPs) has been extensively studied in the past three decades. TEPs have been shown to provide insights into features of cortical excitability and connectivity, reflecting mechanisms of excitatory/inhibitory balance, in various neurological and psychiatric conditions. In the present study, we sought to review and summarize the most studied neurological and psychiatric clinical indications utilizing single-pulse TEP and describe its promise as an informative novel tool for the evaluation of brain physiology. Methods: A thorough search of PubMed, Embase, and Google Scholar for original research utilizing single-pulse TMS-EEG and the measurement of TEP was conducted. Our review focused on the indications and outcomes most clinically relevant, commonly studied, and well-supported scientifically. Results: We included a total of 55 publications and summarized them by clinical application. We categorized these publications into seven sub-sections: healthy aging, Alzheimer’s disease (AD), disorders of consciousness (DOCs), stroke rehabilitation and recovery, major depressive disorder (MDD), Parkinson’s disease (PD), as well as prediction and monitoring of treatment response. Conclusions: TEP is a useful measurement of mechanisms underlying neuronal networks. It may be utilized in several clinical applications. Its most prominent uses include monitoring of consciousness levels in DOCs, monitoring and prediction of treatment response in MDD, and diagnosis of AD. Additional applications including the monitoring of stroke rehabilitation and recovery, as well as a diagnostic aid for PD, have also shown encouraging results but require further evidence from randomized controlled trials (RCTs). Full article

Resurrection plants present an attractive model for studying the mechanisms of desiccation tolerance. In addition to drought, the presence of light during desiccation is extremely dangerous. In the present study, we investigated the effect of light during the desiccation of shade and sun Haberlea rhodopensis from two different habitats by measuring the changes in electrolyte leakage, malondialdehyde and proline content, and photosynthetic and antioxidant activities. Moreover, the plasticity and acclimation ability of plants to changing light intensities were studied by desiccating shade plants under high light and sun plants under low light. The most significant differences between shade and sun plants were observed under moderate dehydration. Regardless of some decline in PSII activity in sun plants, it was much higher compared to shade plants. The lower PSII efficiency in the latter was accompanied by a higher extent of excitation pressure and consequently significant enhancement in non-photochemical quenching, Y(NPQ), and especially in the fraction of energy that is passively dissipated as heat and fluorescence, Y(NO). The activity of antioxidant enzymes remained high during the desiccation of H. rhodopensis, being higher in the sun compared to shade plants in an air-dried state. In addition, shade and sun plants showed high acclimation capacity when desiccated at opposite light intensities. Full article

In this paper, an improved MobileNetV3-Small algorithm model is proposed for the problem of poor real-time wildfire identification based on convolutional neural networks (CNNs). Firstly, a wildfire dataset is constructed and subsequently expanded through image enhancement techniques. Secondly, an efficient channel attention mechanism (ECA) is utilised instead of the Squeeze-and-Excitation (SE) module within the MobileNetV3-Small model to enhance the model’s identification speed. Lastly, a support vector machine (SVM) is employed to replace the classification layer of the MobileNetV3-Small model, with principal component analysis (PCA) applied before the SVM to reduce the dimensionality of the features, thereby enhancing the SVM’s identification efficiency. The experimental results demonstrate that the improved model achieves an accuracy of 98.75% and an average frame rate of 93. Compared to the initial model, the mean frame rate has been elevated by 7.23. The wildfire identification model designed in this paper improves the speed of identification while maintaining accuracy, thereby advancing the development and application of CNNs in the field of wildfire monitoring. Full article

In this work, the seismic response of a multi-story, multi-bay special truss moment frame (STMF) with Ni-Ti shape memory alloys (SMAs) incorporated in the form of X-diagonal braces in the special segment is investigated. The diameter of the SMAs per diagonal in each floor was initially determined, considering the expected ultimate strength of the special segment, developed when the frame reaches its target drift and the desirable collapse mechanism, i.e., the formation of plastic hinges, according to the performance-based plastic design procedure. To further investigate the response of the structure with the SMAs incorporated, half the calculated SMA diameters were introduced. Continuing, three more cases were investigated: the mean value of the SMA diameter was introduced at each floor (case DC1), half the SMA diameter of case DC1 (case DC2), and twice the SMA diameter of case DC1 (case CD3). Dynamic time history analyses under seven benchmark earthquakes were conducted using commercial nonlinear Finite Element software (SeismoStruct 2024). Results were presented in the form of top-displacement time histories, the SMAs force–displacement curves, and maximum inter-story drifts, calculating also maximum SMA displacements. The analysis outcomes highlight the potential of the SMAs to be considered as a novel material in the seismic retrofit of steel structures. Both design approaches presented exhibit a certain amount of effectiveness, depending on the distribution, with the placement of the SMA bars and the seismic excitation considered. Further research is suggested to fully understand the capabilities of the use of SMAs as dissipation devices in steel structures. Full article

Shear Thickening Fluid (STF) is a specialized high-concentration particle suspension capable of rapidly and reversibly altering its viscosity when exposed to sudden impacts. Consequently, STF-based dampers deliver a self-adaptive damping force and demonstrate significant potential for applications in structural vibration control. This study presents both a modeling and experimental investigation of a novel double-rod structured STF damper. Initially, a compound STF is formulated using silica particles as the dispersed phase and polyethylene glycol solution as the dispersing medium. The rheological properties of the STF are then experimentally evaluated. The STF’s constitutive rheological behavior is described using the G-R model. Following this, the flow behavior of the STF within the damper’s annular gap is explored, leading to the development of a two-dimensional axisymmetric fluid simulation model for the damper. Based on this model, the dynamic mechanism of the proposed STF damper is analyzed. Subsequently, the STF damper is optimally designed and subjected to experimental investigation using a dynamic testing platform under different working conditions. The experimental results reveal that the proposed STF damper, whose equivalent stiffness can achieve a nearly threefold change with excitation frequency and amplitude, exhibits good self-adaptive capabilities. By dividing the damper force into two parts: the frictional damping pressure drop, and the osmotic pressure drop generated by the “Jamming effect”. A fitting model is proposed, and it aligns closely with the nonlinear performance of the STF damper. Full article

Gynura formosana Kitam possesses beneficial properties such as heat-clearing, detoxification, and cough suppression, making it a highly nutritious plant with significant economic value. During its growth, the plant’s leaves are prone to infections that can impair stomatal function and hinder growth. Effective identification of stomatal openings and timely application of appropriate chemicals or hormones or indirect environmental adjustments (such as light, temperature, and humidity) to regulate stomatal openings are essential for maintaining the plant’s healthy growth. Currently, manual observation is the predominant method for monitoring stomatal openings of Gynura formosana Kitam, which is complex, labor-intensive, and unsuitable for automated detection. To address this, the study improves upon YOLOv8s by proposing a real-time, high-precision stomatal detection model, Refined GIoU. This model substitutes the original IoU evaluation methods in YOLOv8s with GIoU, DIoU, and EIoU while incorporating the SE (Squeeze-and-Excitation) and SA (Self-Attention) attention mechanisms to enhance understanding of feature representation and spatial relationships. Additionally, enhancements to the P2 layer improve the feature extraction and scale adaptation. The effectiveness of the Refined GIoU is demonstrated through training and validation on a dataset of 1500 images of Gynura formosana Kitam stomata. The results show that the Refined GIoU achieved an average precision (mAP) of 0.935, a recall of 0.98, and an F1-score of 0.88, reflecting an excellent overall performance. The GIoU loss function is better suited to detecting stomatal openings of Gynura formosana Kitam, significantly enhancing the detection accuracy. This model facilitates the automated, real-time monitoring of stomatal openings, allowing for timely control measures and improved economic benefits of Gynura formosana Kitam cultivation. Full article

Quantum information scrambling refers to the spread of the initially stored information over many degrees of freedom of a quantum many-body system. Information scrambling is intimately linked to the thermalization of isolated quantum many-body systems, and has been typically studied in a sudden quench scenario. Here, we extend the notion of quantum information scrambling to critical quantum many-body systems undergoing an adiabatic evolution. In particular, we analyze how the symmetry-breaking information of an initial state is scrambled in adiabatically driven integrable systems, such as the Lipkin–Meshkov–Glick and quantum Rabi models. Following a time-dependent protocol that drives the system from symmetry-breaking to a normal phase, we show how the initial information is scrambled, even for perfect adiabatic evolutions, as indicated by the expectation value of a suitable observable. We detail the underlying mechanism for quantum information scrambling, its relation to ground- and excited-state quantum phase transitions, and quantify the degree of scrambling in terms of the number of eigenstates that participate in the encoding of the initial symmetry-breaking information. While the energy of the final state remains unaltered in an adiabatic protocol, the relative phases among eigenstates are scrambled, and so is the symmetry-breaking information. We show that a potential information retrieval, following a time-reversed protocol, is hindered by small perturbations, as indicated by a vanishingly small Loschmidt echo and out-of-time-ordered correlators. The reported phenomenon is amenable for its experimental verification, and may help in the understanding of information scrambling in critical quantum many-body systems. Full article

Boundary layer ingestion propulsion systems have attracted much attention due to their significant potential to reduce the fuel consumption of future commercial aircraft. However, the aeroelastic stability of the fan blade is affected by the continuous non-uniform incoming flow induced by the ingestion of the boundary layer. When the fan blades rotate in the junction area between the distorted area and the clean area, blade pressure fluctuations occur. This phenomenon triggers a dynamic response process in the blade. Previous numerical investigations explored the influence of the distorted inflow on the blade vibration amplitude, and found that there are two sources of low-order excitation to the blades: the distorted inflow and the dynamic response of the blade. The results show that the low-order excitation existing in the distorted inflow varies sinusoidally with the distortion extent. However, as a new source of excitation, the key influence mechanism of dynamic response is still unclear. To explore this issue, calculations and analyses were conducted for different distorted inflow intensities. The results show that the blade vibration amplitude increases with the rise in distortion intensity. The total pressure at the leading and trailing edge of the rotor blade was extracted for analysis. It was found that when the blade enters or leaves the distorted area, there is a consistent lag in the change in total pressure at the trailing edge compared to the leading edge. This lag leads to an abrupt variation in the total pressure ratio, which constitutes the dynamic response process of the rotor blade. This periodic change generates a second-order excitation that causes the blade to vibrate. Full article

Transcranial magnetic stimulation (TMS) methods have become exciting techniques for altering brain activity and improving synaptic plasticity, earning recognition as valuable non-medicine treatments for a wide range of neurological disorders. Among these methods, repetitive TMS (rTMS) and theta-burst stimulation (TBS) show significant promise in improving outcomes for adults with complex neurological and neurodegenerative conditions, such as Alzheimer’s disease, stroke, Parkinson’s disease, etc. However, optimizing their effects remains a challenge due to variability in how patients respond and a limited understanding of how these techniques interact with crucial neurotransmitter systems. This narrative review explores the mechanisms of rTMS and TBS, which enhance neuroplasticity and functional improvement. We specifically focus on their effects on GABAergic and glutamatergic pathways and how they interact with key receptors like N-Methyl-D-Aspartate (NMDA) and AMPA receptors, which play essential roles in processes like long-term potentiation (LTP) and long-term depression (LTD). Additionally, we investigate how rTMS and TBS impact neuroplasticity and functional connectivity, particularly concerning brain-derived neurotrophic factor (BDNF) and tropomyosin-related kinase receptor type B (TrkB). Here, we highlight the significant potential of this research to expand our understanding of neuroplasticity and better treatment outcomes for patients. Through clarifying the neurobiology mechanisms behind rTMS and TBS with neuroimaging findings, we aim to develop more effective, personalized treatment plans that effectively address the challenges posed by neurological disorders and ultimately enhance the quality of neurorehabilitation services and provide future directions for patients’ care. Full article