Search Results (242)

Convective updrafts are one of the main characteristics of convective clouds, responsible for the convective mass flux and the redistribution of energy and condensate in the atmosphere. During the early stages of their lifecycle, convective clouds experience rapid cloud-top ascent manifested by a decrease in the geostationary IR brightness temperature ($T{B}_{IR}$). Under the assumption that the convective cloud top behaves like a black body, the ascent rate of the convective cloud top can be estimated as (${\displaystyle \frac{\partial T{B}_{IR}}{\partial t}}$), and it can be used to infer the near cloud-top convective updraft. The temporal resolution of the geostationary IR measurements and non-uniform beam-filling effects can influence the convective updraft estimation. However, the main shortcoming until today was the lack of independent verification of the strength of the convective updraft. Here, Doppler radar observations from the ESCAPE and TRACER field experiments provide independent estimates of the convective updraft velocity at higher spatiotemporal resolution throughout the convective core column and can be used to evaluate the updraft velocity estimates from the IR cooling rate for limited samples. Isolated convective cells were tracked with dedicated radar (RHIs and PPIs) scans throughout their lifecycle. Radial Doppler velocity measurements near the convective cloud top are used to provide estimates of convective updrafts. These data are compared with the geostationary IR and VIS channels (from the GOES satellite) to characterize the convection evolution and lifecycle based on cloud-top cooling rates. Full article

Background: Alzheimer’s disease (AD) is a neurological disorder that affects the brain in the elderly, resulting in memory loss, mental deterioration, and loss of the ability to think and act, while being a cause of death, with its rates increasing dramatically. A popular method to detect AD is electroencephalography (EEG) signal analysis thanks to its ability to reflect neural activity, which helps to identify abnormalities associated with the disorder. Originating from its multivariate nature, EEG signals are generally handled as multidimensional time series, and the related methodology is employed. Methods: This study proposes a new transformation strategy that generates a graph representation with time resolution, which handles EEG recordings as relatively small time windows and converts these segments into a similarity graph based on signal coherence between available channels. The retrieved adjacency matrices are further flattened to form a 1-pixel image column, which represents the coherence activity from the available electrodes within the given time window. These pixel columns are concatenated horizontally for all available sliding time windows with 50% overlap, resulting in a grayscale image representation that can be input to well-known deep learning architectures specialized for images. We name this representation Connectogram-COH, a coherence-based version of the previously proposed time graph representation, Connectogram. Results: The experimental results demonstrate that the proposed Connectogram-COH representation effectively captures the coherence dynamics of multichannel EEG data and achieves high accuracy in detecting Alzheimer’s disease. The time graph images serve as robust input for deep learning classifiers, outperforming traditional EEG representations in terms of classification performance. Conclusions: Connectogram-COH offers a powerful and interpretable approach for transforming EEG signals into image representations that are well suited for deep learning. The method not only improves the detection of AD but also shows promise for broader applications in EEG-based and general time series classification tasks. Full article

The continuous change in the entrance cross-section of a parallel-plate flow channel generally affects the mass and heat transfer on the walls of the channel. In this paper, an electrochemical parallel-plate flow channel equipped with a selenoid pulse generator has been developed to enhance the convective mass transfer on the walls of a mass transfer flow system such as an electrodeposition cell, absorption column, flow reactor, etc. A number of experimental studies have been conducted to determine the distribution of the mass transfer coefficients on the bottom wall of a parallel-plate channel for the flow conditions with/without a pulse in the research. Here, the distribution of the convective mass transfer coefficients has been determined by the electrochemical limiting diffusion current technique (ELDCT) using nickel local cathodes arranged on the bottom surface of the flow channel. The experimental results show the effects of the parameters used, which are the flow Reynolds number, opened/closed (OP/CL) ratio, and pulse number, on the distribution of mass transfer coefficients. The results have revealed that the pulse generator altered the flow structure and increased the turbulent intensity at Re < 2860 flow conditions. Within the range of Reynolds number 950 < Re < 2860, the mass transfer correlation was given as $Sh=67.02{Re}^{0.897}{\left({\displaystyle \frac{Op}{Cl}}\right)}^{-0.059}S{c}^{1/3}$. According to the research findings, the highest k_M values were obtained at Re = 2860 with an (OP/CL) ratio of 1/2. If a parallel-plate flow reactor with a pulse generator is designed using these flow conditions, it will yield a reactor that is both more efficient and more compact than a reactor without a pulse generator. Full article

In mining operations, the rock mass located between the water-conducting fault fracture zone and the waterproof protective coal column is highly susceptible to damage, which may result in sudden water inrush disasters. This paper first employs indoor experiments and on-site rock sample analysis to determine the macroscopic mechanical parameters of rocks and rock masses, as well as the microscopic mechanical parameters of block contacts. The fracture and seepage evolution mechanisms in the mining-induced rock mass adjacent to major faults were analyzed utilizing the discrete element-fluid coupling theory in Universal Distinct Element Code (UDEC). The results identified three primary pathways for water hazards caused by mining: the calculated stress field and seepage field indicated that the formation of the water-inrush channels was determined by the parameters of coal seam mining. Different waterproof protective coal columns were set up for the three geological conditions under study. Additionally, a “claw-shaped” detection and flow monitoring method has been proposed for small water-conducting faults. These findings are important and provide valuable guidance for understanding and managing water inrush hazards in mining operations near major faults. Full article

This study investigates the buckling performance of built-up cold-formed steel (CFS) columns, with a focus on how different thermal exposures and cooling strategies influence their susceptibility to various failure mechanisms. Addressing the gap in the literature on the fire behavior of mild steel (MS)-based CFS columns, the research aims to provide new insights. Compression tests were conducted on MS-based CFS column specimens after they were exposed to fire, to assess their post-fire buckling strength. The columns were subjected to controlled fire conditions following standardized protocols and then allowed to cool to room temperature. The study examined axial load-bearing capacity and deformation characteristics under elevated temperatures. To improve fire resistance, protective coatings—gypsum, perlite, and vermiculite—were applied to certain specimens before testing, and their performance was compared to that of uncoated specimens. A comprehensive finite element analysis (FEA) was also performed to model the structural response under different thermal and cooling scenarios, providing a detailed comparison of the coating effectiveness, which was validated against experimental results. The findings revealed significant variations in axial strength and failure mechanisms based on the type of fire-resistant coating used, as well as the heating and cooling durations. Among the coated specimens, those treated with perlite showed the best performance. For example, the air-cooled perlite-coated column (MBC2AC) retained a load capacity of 277.9 kN after 60 min of heating, a reduction of only 6.0% compared to the unheated reference section (MBREF). This performance was superior to that of the gypsum-coated (MBC1AC) and vermiculite-coated (MBC3AC) specimens, which showed reductions of 3.6% and 7.9% more, respectively. These results highlight the potential of perlite coatings to enhance the fire resistance of CFS columns, offering valuable insights for structural fire design. Full article

We investigate the retrieval of ozone (O₃) profiles, with a particular focus on tropospheric O₃, from backscattered ultraviolet radiances measured by the TROPOspheric Monitoring Instrument (TROPOMI), using the UV2 (300–332 nm) and UV3 (305–400 nm) channels independently. An optimal estimation retrieval algorithm, originally developed for the Ozone Monitoring Instrument (OMI), was extended as a preliminary step toward integrating multiple satellite ozone profile datasets. The UV2 and UV3 channels exhibit distinct radiometric and wavelength calibration uncertainties, leading to inconsistencies in retrieval accuracy and convergence stability. A yearly “soft” calibration mitigates overestimation and cross-track-dependent biases (“stripes”) in tropospheric ozone retrievals, enhancing retrieval consistency between UV2 and UV3. Convergence stability is ensured by optimizing the measurement error constraints for each channel. It is shown that our research product outperforms the standard product (UV1 and UV2 combined) in capturing the seasonal and long-term variabilities of tropospheric ozone. An agreement between the retrieved tropospheric ozone and ozonesonde measurements is observed within 0–3 DU ± 5.5 DU (R = 0.75), which is better than that of the standard product by a factor of two. Despite lacking Hartley ozone information in UV2 and UV3, the retrieved stratospheric ozone columns have good agreement with ozonesondes (R = 0.96). Full article

Aerosols significantly impact the brightness temperature (BT) in thermal infrared (IR) channels, and ignoring their effects can lead to relatively large observation-minus-background (OMB) bias in radiance calculations. The accuracy of aerosol datasets is essential for BT simulations and bias reduction. This study incorporated aerosol reanalysis datasets from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS) into the Advanced Radiative Transfer Modeling System (ARMS) to compare their impacts on BT simulations from the Geostationary Interferometric Infrared Sounder (GIIRS) and their effectiveness in reducing OMB biases. The results showed that, for a sandstorm event on 10 April 2023, incorporating total aerosol data from the MERRA-2 improved the BT simulations by 0.56 K on average, surpassing CAMS’s 0.11 K improvement. Dust aerosols notably impacted the BT, with the MERRA-2 showing a 0.17 K improvement versus CAMS’s 0.06 K due to variations in the peak aerosol level, thickness, and column mass density. Improvements for sea salt and carbonaceous aerosols were concentrated in the South China Sea and Bay of Bengal, where the MERRA-2 outperformed CAMS. For sulfate aerosols, the MERRA-2 excelled in the Bohai Sea and southern Bay of Bengal, while CAMS was better in the northern Bay of Bengal. These findings provide guidance for aerosol assimilation and retrieval, emphasizing the importance of quality control and bias correction in data assimilation systems. Full article

When chips perform numerous computational tasks or process complex instructions, they generate substantial heat, potentially affecting their long-term reliability and performance. Thus, accurate and effective temperature measurement and management are crucial to ensuring chip performance and lifespan. This paper presents a multi-channel temperature measurement system based on a diode temperature-sensitive array thermal test chip (TTC). The thermal test chip accurately emulates the heat power and thermal distribution of the target chip, providing signal output through row and column address selection. The multi-channel temperature measurement system centers around a microcontroller and includes voltage signal acquisition circuits and host computer software. It enables temperature acquisition, storage, and real-time monitoring of 16 channels in a 4 × 4 array thermal test chip. During experiments, the system uses a constant current source to drive temperature-sensitive diodes, collects diode output voltage through multiplexers and high-precision amplification circuits, and converts analog signals to digital signals via a high-speed ADC. Data transmission occurs via the USB 2.0 protocol, with the host computer software handling data processing and real-time display. The test results indicate that the system accurately monitors chip temperature changes in both steady-state and transient thermal response tests, closely matching measurements from a semiconductor device analyzer, with an error of about 0.67%. Therefore, this multi-channel temperature measurement system demonstrates excellent accuracy and real-time monitoring capability, providing an effective solution for the thermal design and evaluation of high power density integrated circuits. Full article

This study was carried out to appraise the groundwater fluoride removal effectiveness of Al-Fe oxide-infused diatomaceous earth (DE) in a continuous-flow fixed-bed column. The adsorbent was optimally synthesized and then characterized. A glass column designed for the experiment was packed with the test adsorbent at specific doses. The effects of flow rate, influent fluoride concentration and bed height (adsorbent dose) on fluoride removal were evaluated by fixing the value of a parameter while varying the others. The breakthrough volume was the volume of treated water obtained until the concentration of fluoride in the treated water reached 1.5 mg/L, which is the World Health Organization’s maximum limit of fluoride in drinking water. The maximum breakthrough volume obtained in this study was 118.2 mL under the optimum conditions of influent F⁻ concentration = 5 mg/L, 1 g of adsorbent with an initial bed height = 7.5 cm and a flow rate = 1.97 mL/min. Channeling and the presence of $\mathrm{P}{\mathrm{O}}_4^{3-}$ as a co-existing anion were limiting factors for the attainment of the breakthrough volume for groundwater defluoridation. Further work is encouraged to investigate a suitable binder that can hold the adsorbent particles firmly together, is not water-soluble, but remains water-permeable when dry. The resulting solid mass could then be pulverized into granules whose weight and rigidity would make them less susceptible to the channeling effect in the column. Full article

Fluorescent labeling of membrane proteins is essential for exploring their functions, signaling pathways, interaction partners, and structural dynamics. Organic fluorophores are commonly used for this purpose due to their favorable photophysical properties and photostability. However, a persistent challenge is the inaccessibility of the surface-exposed cysteine residues required for site-specific labeling, as these residues often become sequestered within detergent micelles during protein extraction. To address this limitation, we developed an approach based on polymer-encapsulated nanodiscs that preserves the protein’s native-like lipid-bilayer environment while ensuring the accessibility of surface-exposed cysteine residues. In this method, His-tagged proteins embedded in native nanodiscs are retained on a nickel affinity column, allowing for simultaneous purification and labeling by adding fluorescent dyes. This versatile technique was demonstrated with two challenging-to-label membrane proteins, the potassium channel KvAP and the urea channel HpUreI, for which detergent-based labeling had failed. This opens new possibilities for studying a wide range of fluorescently labeled membrane proteins in near-native states, advancing applications in biophysics, structural biology, and drug discovery. Full article

Automatic Modulation Recognition (AMR) technology, as a key component of intelligent wireless communication, has significant military and civilian value, and there is an urgent need to research relevant algorithms to quickly and effectively identify the modulation type of signals. However, existing models often suffer from issues such as neglecting the correlation between IQ components of signals, poor feature extraction capability, and difficulty in achieving an effective balance between detection performance and computational resource utilization. To address these issues, this article proposes an automatic modulation classification method based on convolutional neural networks (CNNs)—OD_SERCNET. To prevent feature loss or useful features from being compressed, a reversible column network (REVCOL) is used as the backbone network to ensure that the overall information remains unchanged when features are decoupled. At the same time, a novel IQ channel fusion network is designed to preprocess the input signal, fully exploring the correlation between IQ components of the same signal and improving the network’s feature extraction ability. In addition, to improve the network’s ability to capture global information, we have improved the original reversible fusion module by introducing an effective attention mechanism. Finally, the effectiveness of this method is validated using various datasets, and the simulation results show that the average accuracy of OD_SRCNET improves by 1–10% compared to other SOTA models, and we explore the optimal number of subnetworks, achieving a better balance between accuracy and computational resource utilization. Full article

By leveraging the high correlation between multi-ping echo data, low-rank and sparse decomposition methods are applied for reverberation suppression. Previous methods typically perform decomposition on the vectorized multi-ping echograph, which is obtained by stacking beamforming outputs from all directions in the same column. However, when the multi-ping correlation of beamforming outputs from different directions varies significantly due to the time-varying nature of the underwater acoustic channel, it becomes challenging to precisely capture the variations of the reverberation background. As a result, the performance of reverberation suppression is degraded. To alleviate this issue, we attempt to decompose the matrix formed by multi-ping beamforming outputs in different directions individually. The accelerated alternating projections method is used to estimate the steady reverberation for moving target detection. By exploiting the differences in spatio-temporal dimensions between moving targets and reverberation fluctuations, a weighted spatio-temporal density method with adaptive thresholding is used to further extract the target echoes. Field data were utilized to validate the effectiveness of the proposed method, and the experimental results demonstrated its superior robustness in an unstable reverberation-limited environment, maintaining an accurate estimation of steady reverberation. Full article

A high-performance data acquisition and processing system within a spectrometer provides a powerful guarantee for obtaining high-precision data in ground-based Fourier-transform infrared greenhouse-gas spectroscopy. Addressing the challenge of accurate interferogram sampling in Fourier-transform spectroscopy, a dual-channel interferogram acquisition method was designed. Dual-channel analog-to-digital converters, acquiring interferograms at different gains, enable high dynamic range and high-resolution acquisition of infrared interferometric signals; the analog-to-digital converter channel of low-gain interferograms mainly captures data near the zero-optical-range-difference spike, and the analog-to-digital converter channel of high-gain interferograms mainly acquires the weak signals from the two flanks. The simulation results, circuit design, and correction method between the two channels of the method are given. Finally, in the ground-based Fourier-transform infrared greenhouse-gas spectrometer for experimental applications, the experiment shows that under the same measurement conditions, the carbon dioxide column concentration-measurement accuracy is improved by 2.096 times, and the dual-channel interferometric data acquisition method can significantly enhance the data retrieval accuracy. Full article

Background/Objectives: Epilepsy is a prevalent neurological disorder characterized by seizures that significantly impact individuals and their social environments. Given the unpredictable nature of epileptic seizures, developing automated epilepsy diagnosis systems is increasingly important. Epilepsy diagnosis traditionally relies on analyzing EEG signals, with recent deep learning methods gaining prominence due to their ability to bypass manual feature extraction. Methods: This study proposes a continuous wavelet transform-based depthwise convolutional neural network (DCNN) for epilepsy diagnosis. The 35-channel EEG signals were transformed into 35-channel images using continuous wavelet transform. These images were then concatenated horizontally and vertically into a single image (seven rows by five columns) using Python’s PIL library, which served as input for training the DCNN model. Results: The proposed model achieved impressive performance metrics on unseen test data: 95.99% accuracy, 94.27% sensitivity, 97.29% specificity, and 96.34% precision. Comparative analyses with previous studies and state-of-the-art models demonstrated the superior performance of the DCNN model and image concatenation technique. Conclusions: Unlike earlier works, this approach did not employ additional classifiers or feature selection algorithms. The developed model and image concatenation method offer a novel methodology for epilepsy diagnosis that can be extended to different datasets, potentially providing a valuable tool to support neurologists globally. Full article

The present work investigates the influence of rectangular deflectors on the performance of a Savonius turbine mounted in an L-shaped channel, which represents a geometry like that found in one oscillating water column (OWC) device. It also performs a geometric investigation of the entrance region of the channel. More precisely, it investigates the effect of the height/length ratio (H₁/L₁) of the entering region of the channel on the system performance for three different configurations: (1) without the use of deflectors, (2) with just one deflector upstream the turbine, and (3) with one deflector upstream and another downstream the turbine. The geometric investigation is performed based on the constructal design method, and the entering channel area (A₁) is the problem constraint. The performance indicators are the mechanical power in the Savonius turbine and the available power in the device. For all cases, it is considered turbulent airflow in the domain, being solved by the unsteady Reynolds Averaged Navier–Stokes mass and momentum equations. The numerical solution was obtained with the finite-volume method using the Ansys FLUENT software (version 2021 R1). The k-ω shear stress transport turbulence closure model is used. The results demonstrated that the mechanical and available powers depend on the H₁/L₁ ratio, regardless of the usage of deflectors. For instance, differences of up to 16.35% in mechanical power and 7.25% in available power were observed between the best and worst performance configurations in the case without deflectors. The use of deflectors resulted in increases of two and three times in available and mechanical powers, respectively, when the cases with one and two deflectors are compared with those without deflectors. This demonstrates that the enclosed domain and the insertion of the deflectors can enhance the performance of the Savonius turbine. Full article