Search

A traceability and analysis method for measurement laboratory testing data based on the intelligent Internet of Things (IoT) and deep belief network (DBN) is proposed to address the issue of low accuracy in identifying anomalies in measurement testing data and difficulty in identifying the causes of anomalies. First, a data analysis system for the metrology laboratory is designed based on an intelligent IoT architecture of “cloud-management-edge-end.” Then, the Gaussian Bernoulli-Restricted Boltzmann machine is introduced to improve the DBN model, which is deployed on the edge side for learning the ledger data sample library to determine the anomaly detection data of the metrology device. Finally, a stacked denoising autoencoder model is used in the cloud center to extract historical electricity consumption curve features, and the cause of anomalies is determined by calculating the cosine similarity between it and the target device feature curve to complete traceability analysis. Based on the selected dataset, the proposed method is experimentally demonstrated, and the results show that its traceability accuracy and time consumption are 88.72% and 3.949 s, respectively, which can meet the detection requirements of the metrology laboratory.

For traditional Fourier transform (FTS), its integral sampling usually meets the Spectral Modulation Transfer Function (SMTF) criterion. However, for bandpass-sampling Fourier transform spectroscopy (BPS-FTS), based on our analysis, the integral sampling condition derived from the Spectral Modulation Transfer Function (SMTF) is excessively stringent. In other words, the interval of the integral sampling time that fulfills the tolerance requirements for the reconstructed spectrum is very narrow. There are numerous integration sampling time intervals outside this range that still meet the tolerance requirements for the reconstructed spectrum. In this paper, through theoretical modeling, we propose a method based on average |SMTF| as the selection criterion for the integration sampling time. Through simulation analysis, it is evident that the intervals and range of the integral sampling time obtained via this method are more accurate, ensuring the tolerance requirements of the reconstructed spectrum. Under these intervals, when conducting integral sampling on the interferogram, the spectral deviation of the reconstructed spectrum is minimal, and the Spectral Correlation Mapper (SCM) is nearly equal to one. This indicates that compared with the SMTF criterion in traditional FTS, this method is more suitable for the characteristics of BPS-FTS. The analysis in this paper can provide theoretical and simulation support for the implementation of BPS-FTS. [ABSTRACT FROM AUTHOR]

To better address and improve the issues related to DC power quality, this paper proposes an identification method tailored for DC power quality disturbances. First, it explores the underlying mechanisms and waveform characteristics of common DC power disturbances. By integrating the results of time–frequency analysis obtained through the S-transform, five distinct features are designed and extracted to serve as classification indicators. The SABO algorithm is subsequently employed to optimize the BP neural network, assisting in determining the optimal input weights and hidden layer thresholds. This optimization technique helps prevent the network from becoming stuck in local minima, thereby enhancing its robustness and generalization capabilities. This paper presents a simulation system for AC/DC power systems to conduct experimental verification. The system simulates various DC power quality issues and monitors abnormal waveforms. According to the designated classification index, the features of simulated disturbance signals are extracted. The SABO-BP classification prediction model is then used to automatically classify and identify the samples. The experimental results demonstrate high accuracy in classification and identification using the proposed method. In comparison to the BP neural network method, the SABO-BP method demonstrates an 8.207% improvement in accurately identifying disturbance signals. It is capable of accurately identifying direct current power quality signals, thereby assisting in the evaluation and control of power quality issues. [ABSTRACT FROM AUTHOR]

Scale variation presents a significant challenge in object detection. To address this, multi-level feature fusion techniques have been proposed, exemplified by methods such as the feature pyramid network (FPN) and its extensions. Nonetheless, the input features provided to these methods and the interaction among features across different levels are limited and inflexible. In order to fully leverage the features of multi-scale objects and amplify feature interaction and representation, we introduce a novel and efficient framework known as a multi-resolution and semantic-aware bidirectional adapter (MSBA). Specifically, MSBA comprises three successive components: multi-resolution cascaded fusion (MCF), a semantic-aware refinement transformer (SRT), and bidirectional fine-grained interaction (BFI). MCF adaptively extracts multi-level features to enable cascaded fusion. Subsequently, SRT enriches the long-range semantic information within high-level features. Following this, BFI facilitates ample fine-grained interaction via bidirectional guidance. Benefiting from the coarse-to-fine process, we can acquire robust multi-scale representations for a variety of objects. Each component can be individually integrated into different backbone architectures. Experimental results substantiate the superiority of our approach and validate the efficacy of each proposed module. [ABSTRACT FROM AUTHOR]

Raman spectroscopy has been widely applied in numerous fields including bioanalysis, disease diagnosis, and molecular recognition, owing to its unique advantages of being non-invasive, rapid, and highly specific. However, the acquisition of Raman spectral data is often susceptible to various noise interferences, such as shot noise from the internal detector and dark current noise from the instrument. As a result, the weak Raman signals typically become arduous to discern, which affects the localization and identification of characteristic spectral peaks. This study investigates variational mode decomposition (VMD), empirical wavelet transform (EWT), and integrated encoder-bidirectional long short-term memory (EBiLSTM) modules to propose a neural network algorithm for adaptive denoising of Raman spectra. By combining VMD and EWT, the Raman spectra are decomposed into several sub-sequences, which solves the problem of mode mixing between high-frequency signals and noise in empirical mode decomposition, and significantly reduces the complexity of the original Raman spectral lines. The correlation coefficient between each modal component and the original signal is calculated along with the zero-crossing rate index to categorize the noise and signal sequences. Leveraging the linear differences between the ideal spectral lines and the noisy spectral curves, an encoder-bidirectional long short-term memory (EBiLSTM) denoising network is constructed for hierarchical denoising to extract valid spectral feature information from the high-frequency components, realizing refined adaptive denoising of the Raman spectra. Industry standard objective evaluation metrics on the signal-to-noise ratio and root mean square error are utilized to conduct simulation experiments comparing state-of-the-art algorithms, including empirical mode decomposition, VMD, sliding window averaging, and wavelet thresholding. The experimental results demonstrate that the Raman spectral denoising algorithm combining variational mode decomposition and neural networks improves the denoising performance by 13.38% to 72%, exhibiting higher accuracy and reliability. [ABSTRACT FROM AUTHOR]

Single-lens imaging systems can capture spectral information, but they are affected by aberrations, defocusing, and other factors, leading to spectral data overlap. It is then necessary to perform restoration on overlapped spectral data. The restoration method should not only deblur image information, but also recover spectral information for each wavelength band. In other words, the restoration process needs to handle both spatial and spectral information in parallel while ensuring that neither is distorted. In this study, while considering the characteristics of capturing overlapped information in a single-lens imaging system, a spectrum restoration method based on gradient prior information optimization is proposed. This method is shown to achieve high-quality restoration of both images and spectra. The feasibility of this algorithm is demonstrated through simulation and experimental verification, which show that the restoration quality of spectra using the proposed algorithm is improved compared to that achieved using the MTV algorithm. [ABSTRACT FROM AUTHOR]

AlSi10Mg alloy modified with 4 wt.% cerium was fabricated by selective laser melting and subjected to a T6 solutionizing (2 h at 535°C) and ageing (10 h at 160°C) heat treatment. Microstructure, precipitates and hardness of as-built and heat-treated samples were analysed. Results show that as-built samples contained a refined cellular-dendritic microstructure as well as Ce-containing precipitates at heat-affected zone (HAZ). After T6 heat treatment, cellular-dendritic microstructure converted to fine microstructure containing Si agglomerates and sub-micron Ce-containing precipitates, while precipitates present in as-built HAZ underwent spheroidisation. Hardness testing showed that addition of Ce did not affect alloy hardness. However, the fine dispersion of Ce-containing precipitates and good coarsening resistance shows that Ce is a promising alloying element for the development of heat-resistant alloys. [ABSTRACT FROM AUTHOR]

We propose a new spectral super-resolution technique combined with a Fabry–Perot interferometer (FPI) and an interferometric hyperspectral imager. To overcome the limitation of the maximal optical path difference (OPD) on the spectral resolution, the object spectrum is periodically modulated based on the FPI, and an optical Fourier transform of the modulated spectrum information is performed using a double-beam interferometer to obtain an interferogram. Drawing on the concept of nonlinear structured light microscopy, the displacement of the high-frequency interference information in the interferogram after adding the FPI is analyzed to restore the high-frequency interference information and improve the spectral resolution. The optical system has a compact structure with little impact on complexity, spectral range, or luminous flux. Our simulation results show that this method can realize multicomponent joint-interference imaging to obtain spectral super-resolution information. The effects of the FPI's reflectance and interval are analyzed, and the reflectance needs to be within 20~80% and the interval must be as close as possible to the maximum optical range of the interferometer. Compared with previous, related innovations, this innovation has the advantages of higher system stability, higher data utilization, and better suitability for interferometric imaging spectrometers. [ABSTRACT FROM AUTHOR]

In this paper, phase-change material (PCM) and ceramsite were used to increase the heat resistance of the asphalt mixture. The ceramsite asphalt mixture with PCM can bring a specific cooling effect to the road surface and alleviate the rapid deterioration at high temperature. Two phase-change materials, PCM-43 and PCM-48, were compared and selected as the heat absorption material of the asphalt mixture. It is found that PCM-43 has better thermal stability, temperature regulation performance, higher enthalpy value, and a less adverse effect on the rheological properties of asphalt. According to the road performance of the asphalt mixture, it suggests that the maximum content of ceramsite is 40%. The specific heat capacity of asphalt mixtures was studied by the method of the insulation bucket test, and the thermal conductivity coefficient of asphalt mixtures was tested by a thermal conductivity instrument. The results show that the specific heat capacity and thermal conductivity of the asphalt mixture can be reduced by adding PCM and ceramsite. The effect of ceramsite asphalt concrete with PCM on the temperature field of road structure was further analyzed by finite element method. Due to the thermal resistance of ceramsite in the upper layer, the cooling range and depth in the middle and lower surface layers can be improved. Meanwhile, the heat absorption of phase-change material can alleviate the heating phenomenon of the upper layer. Therefore, ceramsite asphalt concrete with PCM is effective for decreasing the high temperatures in the asphalt pavements. [ABSTRACT FROM AUTHOR]

Aiming at the motion blur restoration of large-scale dual-channel space-variant images, this paper proposes a dual-channel image deblurring method based on the idea of block aggregation, by studying imaging principles and existing algorithms. The study first analyzed the model of dual-channel space-variant imaging, reconstructed the kernel estimation process using the side prior information from the correlation of the two-channel images, and then used a clustering algorithm to classify kernels and restore the images. In the kernel estimation process, the study proposed two kinds of regularization terms. One is based on image correlation, and the other is based on the information from another channel input. In the image restoration process, the mean-shift clustering algorithm was used to calculate the block image kernel weights and reconstruct the final restored image according to the weights. As the experimental section shows, the restoration effect of this algorithm was better than that of the other compared algorithms. [ABSTRACT FROM AUTHOR]

Various robotic rehabilitation devices have been developed for acute stroke patients to ease therapist's efforts and provide high-intensity training, which resulted in improved strength and functional recovery of patients; however, these improvements did not always transfer to the performance of activities of daily living (ADLs). This is because previous robotic training focuses on the proximal joints or training with exoskeleton-type devices, which do not reflect how humans interact with the environment. To improve the training effect of ADLs, a new robotic training paradigm is suggested with a parallel manipulator that mimics rotational ADL tasks. This study presents training of the proximal and distal joints simultaneously while performing manipulation tasks in a device named spherical parallel instrument for daily living emulation (SPINDLE). Six representative ADLs were chosen to show that both proximal and distal joints are trained when performing tasks with SPINDLE, as compared to the natural ADLs. These results show that SPINDLE can train individuals with movements similar to the ADLs while interacting with the manipulator. We envision using this compact tabletop device as a home-training device to increase the performance of ADLs by restoring the impaired motor function of stroke patients, leading to improved quality of life. [ABSTRACT FROM AUTHOR]

Adhesions are an abnormal union of membranous surfaces. They are a painful and expensive consequence of abdominal surgeries, specifically in the peritoneal cavity. This complication requires a second surgery to remove the problem, known as adhesiolysis, which we are trying to avoid. Current solutions to adhesion formation either lack efficacy or induce an inflammatory response in the peritoneum. Our focus is to develop a post-surgical adhesion prevention polyelectrolyte complex (PEC) film, composed of an optimal ratio of chitosan (Chi) and polygalacturonic acid (PgA) to prevent adhesion formation. Adhesive properties of fibroblasts and macrophages on the PEC were also studied, since both cell types play a central role in the adhesion formation process. Additionally, we examined PEC attachment methods, ultimately for in vivo application. Current in vitro studies evaluated the material properties of the film to determine the stability of the material. The results of our studies identified certain ratios of Chi-PgA films which are mechanically consistent for use as intra-peritoneal barriers. Furthermore, their potential as anti-adhesive barriers were further supported by the fact that their surfaces are non-permissive for viable fibroblast and macrophage attachment. [ABSTRACT FROM AUTHOR]

CBA reactor is the crucial equipment of fou-stage transformation cold bed adsorption technology of the BV Company. In Yinjin Branch of Chongqing Natural Gas Purification Plant General, there were many times of temporary shutdown incidents caused by the collapse of refractory liner and the leakage of catalyst at the manholes of the Claus reactors, which result in choking up of liquid sulfur pipeline. Reasons based on technology and equipment were analyzed. As a result, in the chemical technology aspects, it was pointed out that adding fuel gas mixing with the acid gas could increase the temperature of reactors under low load operation. In the aspect of equipment, improving the insulation construction, ontology structure and inside structure near the manholes were proposed. The technical renovation schemes have already made a significant difference. [ABSTRACT FROM AUTHOR]

Introduction: Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique with simultaneous (during stimulation) and cumulative effects (after repeated sessions) on blood flow and neuronal metabolism. These effects remain mostly unclear especially in multiple sclerosis (MS). This work aims to elucidate brain metabolic and hemodynamic underpinnings of tDCS and its potential therapeutic impact in MS patients using quantitative tDCS-MRI., Methods: MS participants (n = 20; age = 45.4 ± 12.3 years, 7 males) underwent 3 T MRI scans before and after 20 daily sessions of dorsolateral prefrontal cortex (DLFPC) tDCS (2.0 mA, left anodal) paired with adaptive cognitive training (aCT). During both visits, imaging measurements of cerebral blood flow (CBF), cerebral venous blood oxygenation (Yv) and calculated cerebral metabolic rate of oxygen (CMRO 2 ) were obtained at pre-tDCS, during-tDCS and post-tDCS., Results: At baseline, significant increase from pre- to during-tDCS was observed in CMRO 2 (7.6%; p  = 0.002), CBF (11.0%; p  < 0.0001) and Yv (1.9%; p  = 0.006). At follow up, we observed an increase in pre-tDCS CMRO 2 (140.59 ± 13.83 μmol/100 g/min) compared to baseline pre-tDCS levels (128.30 ± 14.00 μmol/100 g/min; p  = 0.006). Sustained elevations in CMRO 2 and CBF into post-tDCS were also observed (tDCS lingering effects). Cumulative tDCS effects were observed in the form of sustained elevations in CMRO 2 and CBF in pre-tDCS follow up, reaching the magnitudes measured at baseline during-tDCS., Discussion: TDCS induces an acute surge in metabolic activity persisting immediately after the stimulation is removed. Moreover, treatment composed of repeated tDCS-aCT paired sessions contributes to establishing long-lasting increases in neuronal activity., Competing Interests: Soterix Medical Inc. provided the tDCS equipment used for research use. The City University of New York holds patents on brain stimulation with MB as inventor. The City University of New York holds patents on brain stimulation with AD as inventor. AD is an employee of Soterix Medical Inc. AD has equity in Soterix Medical Inc. MB has equity in Soterix Medical Inc. MB consults, received grants, assigned inventions, and/or serves on the SAB of SafeToddles, Boston Scientific, GlaxoSmithKline, Biovisics, Mecta, Lumenis, Halo Neuroscience, Google-X, i-Lumen, Humm, Allergan (Abbvie), Apple. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Muccio, Pilloni, Walton Masters, He, Krupp, Datta, Bikson, Charvet and Ge.)

Delayed cerebral ischemia (DCI) could lead to poor clinical outcome(s). The aim of the present study was to establish and validate a predictive model for DCI after aneurysmal subarachnoid hemorrhage (aSAH) based on clinical data. Data from a series of 217 consecutive patients with aSAH were reviewed and analyzed. Related risk factors within 72 h after aSAH were analyzed depending on whether DCI recurred. Least absolute shrinkage and selection operator (LASSO) analysis was performed to reduce data dimensions and screen for optimal predictors. Multivariable logistic regression was used to establish a predictive model and construct a nomogram. Receiver operating characteristic (ROC) and calibration curves were generated to assess the discriminative ability and goodness of fit of the model. Decision curve analysis was applied to evaluated the clinical applicability of the predictive model. LASSO regression identified 4 independent predictors, including Subarachnoid Hemorrhage Early Brain Edema Score (i.e., "SEBES"), World Federation of Neurosurgical Societies scale score (i.e., "WFNS"), modified Fisher Scale score, and intraventricular hemorrhage (IVH), which were incorporated into logistic regression to develop a nomogram. After verification, the area under the ROC curve for the model was 0.860. The calibration curve indicated that the predictive probability of the new model was in good agreement with the actual probability, and decision curve analysis demonstrated the clinical applicability of the model within a specified range. The prediction model could precisely calculate the probability of DCI after aSAH, and may contribute to better clinical decision-making and personalized treatment to achieve better outcomes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Chen, Jiang, He, Tang and Chen.)

Our design aimed to explore the potential involvement of matrix metalloproteinase-9 (MMP-9) in the inflammatory response associated with acute ischemic stroke (AIS). We also aimed to preliminarily examine the potential impact of a disintegrin-like and metalloprotease with thrombospondin type I repeats-13 (ADAMTS13) on MMP-9 in AIS. We conducted oxygen-glucose deprivation models of microglia cells and mice models of AIS with middle cerebral artery occlusion (MCAO). We assessed the expression pattern of MMP-9 with western blotting (WB) and real-time quantitative PCR both in vivo and in vitro. MMP-9 downregulation was achieved by using ACE inhibitors such as trandolapril. For the MCAO model, we used ADAMTS13-deficient mice. We then evaluated the related neurological function scores, cerebral edema and infarct volume. The levels of inflammation-related proteins, such as COX2 and iNOS, were assessed using WB, and the expression of inflammatory cytokines was measured via enzyme-linked immuno sorbent assay in vivo. Our findings indicated that MMP-9 was up-regulated while ADAMTS13 was down-regulated in the MCAO model. Knockdown of MMP-9 reduced both inflammation and ischemic brain injury. ADAMTS13 prevented brain damage, improved neurological function and decreased the inflammation response in mice AIS models. Additionally, ADAMTS13 alleviated MMP-9-induced neuroinflammation in vivo. It showed that ADAMTS13 deficiency exacerbated ischemic brain injury through an MMP-9-dependent inflammatory mechanism. Therefore, the ADAMTS13-MMP-9 axis could have therapeutic potential for the treatment of AIS., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Spectral resolution is a key parameter of a spectrometer. Typically, the Rayleigh criterion is used to evaluate spectral resolution; however, it is not applicable to a single-lens-based spectrometer, as its principle is different from that using a prism or grating. Therefore, this work proposes that a method resolution is a key parameter to evaluate spectral resolution by exploiting the concept of focus depth. Accordingly, the spectral resolution is determined using three factors, namely, aperture factor F of the lens, pixel size p of the charge-coupled device, and the derivative of the rate of change of focal length with respect to wavelength f ' ( λ ). The proposed method is verified by simulations with the following lens parameters: a diameter of 50.8 mm, focal length of 200 mm at 587.6 nm, and F =3.94. The calculated and simulated spectral resolution values are, respectively, 1.7 nm and 1.2 nm at 480 nm. Based on an analysis of the influences of F , p , and f ' ( λ ) on the spectral resolution, increasing f ' ( λ ) or decreasing both F and p might improve the spectral resolution. Finally, the proposed method is validated via experiments for lenses with different F values as well as materials, and we determine their spectral resolutions; these results are observed to be similar to the calculated values.

In this study, a spectral acquisition method is proposed in which axial chromatic and spherical aberrations are introduced as an error function. These aberrations lead to changes in the focal lengths as the wavelengths of the incident light changes. A coefficient matrix representing the variation in the intensity distribution of each image, formed at the focal point (the detection position) corresponding to a wavelength, is obtained by calibration. The least square method is used to reconstruct the spectrum. The numerical simulation results show that the spectral correlation coefficient and the spectral mean square error between the reconstructed spectrum and the original spectrum are 0.9997 and 0.0025, and 0.9683 and 0.0204, respectively, for the polychromatic light spectrum obtained from the mercury lamp using our experimental set-up. These results confirm the feasibility and efficiency of the proposed spectral imaging method.