An integrated design concept evaluation model based on interval valued picture fuzzy set and improved GRP method.

The objective of this research is to enhance the precision and efficiency of design concept assessments during the initial stages of new product creation. Design concept evaluation, which occurs at the end of the conceptual design phase, is a critical step in product development. The outcome of this evaluation significantly impacts the product's eventual success, as flawed design concepts are difficult to remedy in later stages. However, the evaluation of new product concepts is a procedure that encompasses elements of subjectivity and ambiguity. In order to deal with the problem, a novel decision-making method for choosing more logical new product concepts is introduced. Basically, the evaluation process is outlined in three main phases: the construction of evaluation index system for design concept alternatives, the calculation of weights for evaluation criteria and decision-makers, the selection of the best design concept alternatives. These stages are composed of a hybrid method based on kano model, multiplicative analytic hierarchy process (AHP) method, the entropy of IVPFS and improved grey relational projection (GRP) under interval-valued picture fuzzy set (IVPFS). The novel approach integrates the strength of interval-valued picture fuzzy number in handling vagueness, the advantage of multiplicative AHP and the merit of improved GRP method in modelling multi-criteria decision-making. In final, the effectiveness of the proposed model is validated through comparisons with other models. The potential applications of this study include but are not limited to product development, industrial design, and innovation management, providing decision-makers with a more accurate and comprehensive design concept evaluation tool.

Transferring U-Net between low-dose CT denoising tasks: a validation study with varied spatial resolutions.

Background: Recently, deep learning techniques have been widely used in low-dose computed tomography (LDCT) imaging applications for quickly generating high quality computed tomography (CT) images at lower radiation dose levels. The purpose of this study is to validate the reproducibility of the denoising performance of a given network that has been trained in advance across varied LDCT image datasets that are acquired from different imaging systems with different spatial resolutions. Methods: Specifically, LDCT images with comparable noise levels but having different spatial resolutions were prepared to train the U-Net. The number of CT images used for the network training, validation and test was 2,400, 300 and 300, respectively. Afterwards, self- and cross-validations among six selected spatial resolutions (62.5, 125, 250, 375, 500, 625 µm) were studied and compared side by side. The residual variance, peak signal to noise ratio (PSNR), normalized root mean square error (NRMSE) and structural similarity (SSIM) were measured and compared. In addition, network retraining on a small number of image set was performed to fine tune the performance of transfer learning among LDCT tasks with varied spatial resolutions. Results: Results demonstrated that the U-Net trained upon LDCT images having a certain spatial resolution can effectively reduce the noise of the other LDCT images having different spatial resolutions. Regardless, results showed that image artifacts would be generated during the above cross validations. For instance, noticeable residual artifacts were presented at the margin and central areas of the object as the resolution inconsistency increased. The retraining results showed that the artifacts caused by the resolution mismatch can be greatly reduced by utilizing about only 20% of the original training data size. This quantitative improvement led to a reduction in the NRMSE from 0.1898 to 0.1263 and an increase in the SSIM from 0.7558 to 0.8036. Conclusions: In conclusion, artifacts would be generated when transferring the U-Net to a LDCT denoising task with different spatial resolution. To maintain the denoising performance, it is recommended to retrain the U-Net with a small amount of datasets having the same target spatial resolution.

Super Resolution Dual-Energy Cone-Beam CT Imaging With Dual-Layer Flat-Panel Detector.

In flat-panel detector (FPD) based cone-beam computed tomography (CBCT) imaging, the native receptor array is usually binned into a smaller matrix size. By doing so, the signal readout speed could be increased by 4-9 times at the expense of a spatial resolution loss of 50%-67%. Clearly, such manipulation poses a key bottleneck in generating high spatial and high temporal resolution CBCT images at the same time. In addition, the conventional FPD is also difficult in generating dual-energy CBCT images. In this paper, we propose an innovative super resolution dual-energy CBCT imaging method, named as suRi, based on dual-layer FPD (DL-FPD) to overcome these aforementioned difficulties at once. With suRi, specifically, a 1D or 2D sub-pixel (half pixel in this study) shifted binning is applied instead of the conventionally aligned binning to double the spatial sampling rate during the dual-energy data acquisition. As a result, the suRi approach provides a new strategy to enable high spatial resolution CBCT imaging while at high readout speed. Moreover, a penalized likelihood material decomposition algorithm is developed to directly reconstruct the high resolution bases from these dual-energy CBCT projections containing sub-pixel shifts. Numerical and physical experiments are performed to validate this newly developed suRi method with phantoms and biological specimen. Results demonstrate that suRi can significantly improve the spatial resolution of the CBCT image. We believe this developed suRi method would greatly enhance the imaging performance of the DL-FPD based dual-energy CBCT systems in future.

Super-resolution dual-layer CBCT imaging with model-guided deep learning.

Objective.This study aims at investigating a novel super resolution CBCT imaging approach with a dual-layer flat panel detector (DL-FPD).Approach.With DL-FPD, the low-energy and high-energy projections acquired from the top and bottom detector layers contain over-sampled spatial information, from which super-resolution CT images can be reconstructed. A simple mathematical model is proposed to explain the signal formation procedure in DL-FPD, and a dedicated recurrent neural network, named suRi-Net, is developed based upon the above imaging model to nonlinearly retrieve the high-resolution dual-energy information. Physical benchtop experiments are conducted to validate the performance of this newly developed super-resolution CBCT imaging method.Main Results.The results demonstrate that the proposed suRi-Net can accurately retrieve high spatial resolution information from the low-energy and high-energy projections of low spatial resolution. Quantitatively, the spatial resolution of the reconstructed CBCT images from the top and bottom detector layers is increased by about 45% and 54%, respectively.Significance.In the future, suRi-Net will provide a new approach to perform high spatial resolution dual-energy imaging in DL-FPD-based CBCT systems.

A novel MCDM approach for design concept evaluation based on interval-valued picture fuzzy sets.

The assessment of design concepts presents an efficient and effective strategy for businesses to strengthen their competitive edge and introduce market-worthy products. The widely accepted viewpoint acknowledges this as a intricate multi-criteria decision-making (MCDM) approach, involving a multitude of evaluative criteria and a significant amount of data that is frequently ambiguously defined and subjectively influenced. In order to tackle the problems of uncertainty and fuzziness in design concept evaluation, our research creatively combines interval-valued picture fuzzy set (IVPFS) with an MCDM process of design concept evaluation. Firstly, this study draws on the existing relevant literature and the experience of decision makers to identify some important criteria and corresponding sub-criteria and form a scientific evaluation indicator system. We then introduce the essential operational concepts of interval-valued picture fuzzy numbers (IVPFNs) and the interval-valued picture fuzzy ordered weighted interactive averaging (IVPFOWIA) operator. Thirdly, an entropy weighting method based on IVPFS is proposed in this research to calculate the weights of criteria and sub-criteria, and based on this, an integrated IVPF decision matrix is further constructed based on the presented IVPFOWIA operator. Finally, the best design concept alternative is selected by applying the extended TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) approach with IVPFS. The IVPFS combined with improved MCDM method have been proven to be superior in complex and uncertain decision-making situations through experiments and comparative assessments. The information ambiguity in the evaluation of design concept is well characterized by our augmentation based on IVPFS.

A comprehensive mortise and tenon structure selection method based on Pugh's controlled convergence and rough Z-number MABAC method.

Mortise and tenon joints are widely used in the building and furniture industries because of their excellent mechanical and eco-friendly properties. In real-life cases, there are usually many available alternative structures for a joint area, it is a challenge to select a proper structure from massively available alternatives. This paper aims to select a proper multiple attribute decision-making method based on massive alternatives and unreliable, uncertain and subjective information. Pugh's controlled convergence, rough number, Z-number, consistency theory and Shannon entropy are integrated and proposed an improved rough Z-number Multi-Attributive Border Approximation Area Comparison (MABAC) method. Firstly, Pugh's controlled convergence is a selection method, simple and rapid, presented in the first phase to eliminate most of the alternatives. In the second phase, an integrated method is proposed. The consistency theory, distance measurement and the Z-number are initially aggregated to calculate the expert weight. The entropy method is then presented to determine the criteria weight. The alternatives are then ranked and the optimal mortise and tenon joint is selected based on the rough Z-number MABAC method. A real-life case is presented, and the proposed method is implemented in the joint of a bucket cabinet. Finally, the efficiency and effectiveness of the proposed method are proved by the case, sensitivity analysis and related comparisons.

Preparation of Stereo-Divergent Compounds from the Natural Product Drupacine Based on Complexity-to-Diversity Strategy.

The Complexity-to-Diversity (CtD) strategy was applied to synthesize a 23-member compound collection from the natural product drupacine, including 21 novel compounds. An unusual benzo [d] cyclopenta [b] azepin skeleton was constructed by Von Braun reaction to cleave C-N bond of drupacine. Moreover, compound 10 has potential cytotoxicity to human colon cancer cells with low toxicity to the normal human colon mucosal epithelial cell lines.

Withanolides from Physalin angulata and Their Inhibitory Effects on Nitric Oxide Production.

Six new withanolides, angulasteroidins A-F (1-6), along with twelve known analogs (7-18) were isolated from the whole plants of Physalis angulata. Their structures were elucidated by analysis of 1D and 2D NMR, ECD and IR spectra, HR-ESI-MS data, and ECD calculation. Compounds 1 and 6 were rare 1-10 seco withanolides. Compounds 2-4, 7-9, and 15 exhibited significant inhibitory activity on the production of nitric oxide in the LPS-activated RAW 264.7 mouse macrophage cell lines with IC50 values ranging from 0.23 to 9.06 µM.

Attention-based dual-branch deep network for sparse-view computed tomography image reconstruction.

Background: The widespread application of X-ray computed tomography (CT) imaging in medical screening makes radiation safety a major concern for public health. Sparse-view CT is a promising solution to reduce the radiation dose. However, the reconstructed CT images obtained using sparse-view CT may suffer severe streaking artifacts and structural information loss. Methods: In this study, a novel attention-based dual-branch network (ADB-Net) is proposed to solve the ill-posed problem of sparse-view CT image reconstruction. In this network, downsampled sinogram input is processed through 2 parallel branches (CT branch and signogram branch) of the ADB-Net to independently extract the distinct, high-level feature maps. These feature maps are fused in a specified attention module from 3 perspectives (channel, plane, and spatial) to allow complementary optimizations that can mitigate the streaking artifacts and the structure loss in sparse-view CT imaging. Results: Numerical simulations, an anthropomorphic thorax phantom, and in vivo preclinical experiments were conducted to verify the sparse-view CT imaging performance of the ADB-Net. The proposed network achieved a root-mean-square error (RMSE) of 20.6160, a structural similarity (SSIM) of 0.9257, and a peak signal-to-noise ratio (PSNR) of 38.8246 on numerical data. The visualization results demonstrate that this newly developed network can consistently remove the streaking artifacts while maintaining the fine structures. Conclusions: The proposed attention-based dual-branch deep network, ADB-Net, provides a promising alternative to reconstruct high-quality sparse-view CT images for low-dose CT imaging.

Electroacupuncture ameliorates cerebrovascular impairment in Alzheimer's disease mice via melatonin signaling.

AIMS: Cerebrovascular impairment contributes to the pathogenesis of Alzheimer's disease (AD). However, it still lacks effective intervention in clinical practice. Here, we investigated the efficacy of electroacupuncture (EA) in cerebrovascular repair in 3xTg-AD mice and its mechanism. METHODS: 3xTg-AD mice were employed to evaluate the protective effect of EA at ST36 acupoint (EAST36). Behavioral tests were performed to assess neurological disorders. Laser speckle contrast imaging, immunostaining, and Western blot were applied to determine EAST36-boosted cerebrovascular repair. The mechanism was explored in 3xTg mice and endothelial cell cultures by melatonin signaling modulation. RESULTS: EAST36 at 20/100 Hz effectively alleviated the olfactory impairment and anxiety behavior and boosted cerebrovascular repair in AD mice. EAST36 attenuated cerebral microvascular degeneration in AD mice by modulating endothelial cell viability and injury. Consequently, the Aß deposits and neural damage in AD mice were reversed after EAST36. Mechanistically, we revealed that EAST36 restored melatonin levels in AD mice. Melatonin supplement mimicked the EAST36 effect on cerebrovascular protection in AD mice and endothelial cell cultures. Importantly, blockage of melatonin signaling by antagonist blunted EAST36-induced cerebrovascular recovery and subsequent neurological improvement. CONCLUSIONS: These findings provided strong evidence to support EAST36 as a potential nonpharmacological therapy against cerebrovascular impairment in AD. Further study is necessary to better understand how EAST36 treatment drives melatonin signaling.

Energy resolving dark-field imaging with dual phase grating interferometer.

X-ray dark-filed imaging is a powerful approach to quantify the dimension of micro-structures of the object. Often, a series of dark-filed signals have to be measured under various correlation lengths. For instance, this is often achieved by adjusting the sample positions by multiple times in Talbot-Lau interferometer. Moreover, such multiple measurements can also be collected via adjustments of the inter-space between the phase gratings in dual phase grating interferometer. In this study, the energy resolving capability of the dual phase grating interferometer is explored with the aim to accelerate the data acquisition speed of dark-filed imaging. To do so, both theoretical analyses and numerical simulations are investigated. Specifically, the responses of the dual phase grating interferometer at varied X-ray beam energies are studied. Compared with the mechanical position translation approach, the combination of such energy resolving capability helps to greatly shorten the total dark-field imaging time in dual phase grating interferometer.

[Effect of electroacupuncture on renal vascular microcirculation in diabetic mice based on in vivo two-photon microscopy imaging].

OBJECTIVE: To investigate the protective effect of electroacupuncture (EA) at "Zusanli"(ST36)and "Weiwanxiashu"(EX-B3) on capillary function around the renal tubule and renal tubule structure in diabetic mice based on two-photon microscopy (TPM) imaging, so as to providing visualizable evidence for the regulatory effect of EA on diabetic renal vascular microcirculation. METHODS: Spontaneous type Ⅱ diabetes mellitus mice (db/db) were employed for this study. Twenty db/db mice were randomly divided into model group (n=10) and EA group (n=10), and 10 db/m mice used as the control group. EA was applied to bilateral ST36 and EX-B3 for 20 min/time, 6 times a week for 6 weeks. The body weight was recorded and the fasting blood glucose measured before and after the intervention. The urine production and water consumption of mice in each cage were recorded after EA. The renal in vivo imaging method based on TPM was established to display the morphological structure of renal tubules, and the mouse renal blood flow velocity was detected by injecting 500 kDa dextran-fluorescein into femoral vein after the intervention. RESULTS: Compared with the control group, the proportion of mice with decreased body mass in the model group was increased, accounting for 40%, while that in the control group was 0%; and fasting blood glucose, urine production and water consumption were significantly increased in the model group (P<0.001, P<0.000 1). A renal in vivo imaging method based on TPM was successfully established, which can be applied to quantitatively analyze the renal blood flow and renal tubular diameter of mice. Based on this method, the results showed that compared with the control group, the blood flow velocity of peritubular capillary in the model group was significantly decreased (P<0.000 1, P<0.001), renal tubular cells were slightly exfoliated and the diameter of renal tubular was significantly increased (P<0.000 1). Compared with the model group, EA reduced the body weight loss ratio from 40% to 0%, and significantly decreased the fasting blood glucose, urine production and water consumption (P<0.01, P<0.000 1, P<0.001), and the blood flow velocity of peritubular capillary in the EA group was significantly increased (P<0.001, P<0.05) and tubule dilatation significantly alleviated (P<0.0 1). CONCLUSION: EA at ST36 and EX-B3 can ameliorate renal vascular microcirculation disorder to relieve the renal structure damage and improve renal function in diabetes mice.

High-fidelity imaging of amyloid-beta deposits with an ultrasensitive fluorescent probe facilitates the early diagnosis and treatment of Alzheimer's Disease.

Background: Imaging amyloid-beta (Aß) deposits with high fidelity in naturally aging brains is crucial for the early diagnosis of Alzheimer's disease (AD). However, this is impeded by the lack of highly sensitive probes. Methods: By conducting computational modelling to quantitatively fine-tune the twisted intramolecular charge transfer (TICT) tendency of Thioflavin T (ThT) analogues, we developed an ultrasensitive probe AH-2. AH-2 retained the binding affinity and binding mode of ThT towards Aß deposits, and exhibited ca 10-fold less background fluorescence and 5-10 folds of improved signal-to-background contrast upon binding Aß deposits. These desirable features endowed AH-2 the sensitivity to detect Aß deposition in naturally aging wild-type mice. Results: AH-2 imaging revealed that Aß puncta signals appeared near the nuclei in young mice and spread through the intracellular and extracellular compartments in older mice. Moreover, Aß deposits were observed to emerge earlier in mice cerebral cortex than in the hippocampus region. Given this desirable sensitivity and good spatiotemporal resolution, AH-2 was successfully applied in the preclinical evaluation of Aß-targeted treatment by melatonin. Conclusions: We expect that AH-2 is promising for early diagnosis of AD and will serve as a sensitive tool for studying Aß-related AD pathology.

In Situ Imaging of Formaldehyde in Live Mice with High Spatiotemporal Resolution Reveals Aldehyde Dehydrogenase-2 as a Potential Target for Alzheimer's Disease Treatment.

Alterations in formaldehyde (FA) homeostasis are associated with the pathology of Alzheimer's disease (AD). In vivo tracking of FA flux is important for understanding the underlying molecular mechanisms, but is challenging due to the lack of sensitive probes favoring a selective, rapid, and reversible response toward FA. In this study, we re-engineered the promiscuous and irreversible phenylhydrazines to make them selective and reversible toward FA by tuning their nucleophilicity. This effort resulted in PFM309, a selective (selectivity coefficient KFA,methylglyoxal = 0.06), rapid (t1/2 = 32 s at [FA] = 200 µM), and reversible fluorogenic probe (K = 6.24 mM-1) that tracks the FA flux in both live cells and live mice. In vivo tracking of the FA flux was realized by PFM309 imaging, which revealed the gradual accumulation of FA in the live mice brain during normal aging and its further increase in AD mice. We further identified the age-dependent loss of catabolism enzymes ALDH2 and ADH5 as the primary mechanism responsible for formaldehyde excess. Activating ALDH2 with the small molecular activator Alda1 significantly protected neurovascular cells from formaldehyde overload and consequently from impairment during AD progress both in vitro and in vivo. These findings revealed PFM309 as a robust tool to study AD pathology and highlight ALDH2 as a potential target for AD drug development.

Timing Performance Simulation for 3D 4H-SiC Detector.

To meet the high radiation challenge for detectors in future high-energy physics, a novel 3D 4H-SiC detector was investigated. Three-dimensional 4H-SiC detectors could potentially operate in a harsh radiation and room-temperature environment because of its high thermal conductivity and high atomic displacement threshold energy. Its 3D structure, which decouples the thickness and the distance between electrodes, further improves the timing performance and the radiation hardness of the detector. We developed a simulation software-RASER (RAdiation SEmiconductoR)-to simulate the time resolution of planar and 3D 4H-SiC detectors with different parameters and structures, and the reliability of the software was verified by comparing the simulated and measured time-resolution results of the same detector. The rough time resolution of the 3D 4H-SiC detector was estimated, and the simulation parameters could be used as guideline to 3D 4H-SiC detector design and optimization.