Broadband wavelength designable achromatic grating based on a cholesteric liquid crystal template.

Liquid crystal (LC) gratings have played important roles in light field control due to the advantages of being lightweight, low cost, having no moving parts, and low power consumption. However, the chromatic aberration limits the bandwidth of the LC device and affects the efficiency of the grating. To solve the chromatic aberration issue, a broadband wavelength designable achromatic grating is proposed. Different grating structures are integrated into a single-layer templated cholesteric liquid crystal (CLC) device, and the achromatic diffraction wavelength of the grating can be freely designed from the visible spectral region to the infrared range within the Bragg reflection band of the CLCs. The diffraction intensity of different orders can be changed with the electric field applied to meet the need for dynamic modulation. This grating shows suitable potential applications in optical communication and displays.

Accelerated Fatigue Test for Electric Vehicle Reducer Based on the SVR-FDS Method.

The reducer serves as a pivotal component within the power transmission system of electric vehicles. On one hand, it bears the torque load within the power transmission system. On the other hand, it also endures the vibration load transmitted from other vehicle components. Over extended periods, these dynamic loads can cause fatigue damage to the reducer. Therefore, the reliability and durability of the reducer during use are very important for electric vehicles. In order to save time and economic costs, the durability of the reducer is often evaluated through accelerated fatigue testing. However, traditional approaches to accelerated fatigue tests typically only consider the time-domain characteristics of the load, which limits precision and reliability. In this study, an accelerated fatigue test method for electric vehicle reducers based on the SVR-FDS method is proposed to enhance the testing process and ensure the reliability of the results. By utilizing the support vector regression (SVR) model in conjunction with the fatigue damage spectrum (FDS) approach, this method offers a more accurate and efficient way to evaluate the durability of reducers. It has been proved that this method significantly reduces the testing period while maintaining the necessary level of test reliability. The accelerated fatigue test based on the SVR-FDS method represents a valuable approach for assessing the durability of electric vehicle reducers and offering insights into their long-term performance.

Live Imaging of 3D Hanging Drop Arrays through Manipulation of Light-Responsive Pyroelectric Slippery Surface and Chip Adhesion.

Three-dimensional (3D) hanging drop cell culture is widely used in organoid culture because of its lack of selection pressure and rapid cell aggregation. However, current hanging drop technology has limitations, such as a dependence on complex microfluidic transport channels or specific capillary force templates for drop formation, which leads to unchangeable drop features. These methods also hinder live imaging because of space and complexity constraints. Here, we have developed a hanging drop construction method and created a flexible 3D hanging drop construction platform composed of a manipulation module and an adhesion module. Their harmonious operation allows for the easy construction of hanging drops of varying sizes, types, and patterns. Our platform produces a cell hanging drop chip with small sizes and clear fields of view, thereby making it compatible with live imaging. This platform has great potential for personalized medicine, cancer and drug discovery, tissue engineering, and stem cell research.

On-chip construction of a fully structured scaffold-free vascularized renal tubule.

Renal tubule chips have emerged as a promising platform for drug nephrotoxicity testing. However, the reported renal tubule chips hardly replicate the unique structure of renal tubules with thick proximal and distal tubules and a thin loop of Henle. In this study, we developed a fully structured scaffold-free vascularized renal tubule on a microfluidic chip. On the chip, the renal epithelial cell-laden hollow calcium-polymerized alginate tube with thick segments at both ends and a thin middle segment was U-shaped embedded in collagen hydrogel, parallel to the endothelial cell-laden hollow calcium-polymerized alginate tube with uniform tube diameter. After the alginate tubes were on-chip degraded, the renal epithelial cells and endothelial cells automatically attached to the collagen hydrogel and proliferated to form the renal tubule with proximal tubule, loop of Henle and distal tubule as well as peritubular blood vessel. We evaluated the viability of cells on the hollow alginate tubes, characterized the distribution and morphology of cells before and after the degradation of the alginate tube, and confirmed the proliferation of cells and the metabolic function of cells in terms of ATP synthesis, fibronectin secretion and VEGFR2 expression on the chip. The enhanced metabolic functions of renal epithelial cells and endothelial cells were preliminarily demonstrated. This study provides new insights into designing a more biomimetic renal tubule on a microfluidic chip.

Evaluation of allylestrenol for clinical pregnancies in patients treated with assisted reproductive techniques: a retrospective, propensity score matched, observational study.

BACKGROUND: Allylestrenol is an oral progestogen being increasingly used for luteal phase support in assisted reproductive techniques. However, evidence of the clinical efficacy of allylestrenol in luteal phase support is lacking. Dydrogesterone is a representative drug used for luteal phase support, the efficacy of which has been clinically confirmed. As such, we aimed to compare the effects of allylestrenol with the standard dydrogesterone on clinical pregnancy rates and pregnancy outcomes. METHODS: This retrospective study included 3375 assisted reproductive technique cycles using either allylestrenol or dydrogesterone between January 2015 and March 2020. Patients using either allylestrenol or dydrogesterone were matched in a 1:1 ratio using propensity scores. The primary outcomes were clinical pregnancy rate and pregnancy outcomes. RESULTS: No significant difference was found in the clinical pregnancy rate (53.5% vs. 53.2%, P = 0.928) and pregnancy outcomes (all P > 0.05) between allylestrenol and dydrogesterone. Compared with dydrogesterone, the use of allylestrenol significantly reduced the rate of biochemical pregnancies (6.4% vs. 11.8%, P < 0.001) and multiple gestation rate (16.8% vs. 26.3%, P = 0.001). Moreover, endometrial thickness, morphology, and blood flow were significantly improved by allylestrenol treatment (all P < 0.05). CONCLUSIONS: Allylestrenol exhibited similar effects on clinical pregnancy rates and pregnancy outcomes as dydrogesterone. Moreover, allylestrenol can significantly reduce the biochemical pregnancy rate and improve the endometrial receptivity.

From Corrective to Predictive Maintenance-A Review of Maintenance Approaches for the Power Industry.

Appropriate maintenance of industrial equipment keeps production systems in good health and ensures the stability of production processes. In specific production sectors, such as the electrical power industry, equipment failures are rare but may lead to high costs and substantial economic losses not only for the power plant but for consumers and the larger society. Therefore, the power production industry relies on a variety of approaches to maintenance tasks, ranging from traditional solutions and engineering know-how to smart, AI-based analytics to avoid potential downtimes. This review shows the evolution of maintenance approaches to support maintenance planning, equipment monitoring and supervision. We present older techniques traditionally used in maintenance tasks and those that rely on IT analytics to automate tasks and perform the inference process for failure detection. We analyze prognostics and health-management techniques in detail, including their requirements, advantages and limitations. The review focuses on the power-generation sector. However, some of the issues addressed are common to other industries. The article also presents concepts and solutions that utilize emerging technologies related to Industry 4.0, touching on prescriptive analysis, Big Data and the Internet of Things. The primary motivation and purpose of the article are to present the existing practices and classic methods used by engineers, as well as modern approaches drawing from Artificial Intelligence and the concept of Industry 4.0. The summary of existing practices and the state of the art in the area of predictive maintenance provides two benefits. On the one hand, it leads to improving processes by matching existing tools and methods. On the other hand, it shows researchers potential directions for further analysis and new developments.

Accelerating Neurite Growth and Directing Neuronal Connections Constrained by 3D Porous Microtubes.

Investigation of neural growth and connection is crucial in the field of neural tissue engineering. Here, using a femtosecond laser direct writing (fs-DLW) technique, we propose a directionally aligned porous microtube array as a culture system for accelerating the growth of neurons and directing the connection of neurites. These microtubes exhibited an unprecedented guidance effect toward the outgrowth of primary embryonic rat hippocampal neurons, with a wrap resembling the myelin sheaths of neurons. The speed of neurite growth inside these microtubes was significantly faster than that outside these microtubes. We also achieved selective/directing connection of neural networks inside the magnetic microtubes via precise microtube delivery to a gap between two neural clusters. This work not only proposes a powerful microtube platform for accelerated growth of neurons but also offers a new idea for constructing biological neural circuits by arranging the size, location, and pattern of microtubes.

Risk assessment approach for analyzing risk factors to overcome pandemic using interval-valued q-rung orthopair fuzzy decision making method.

The process of developing and implementing sustainable strategies to prevent spread of COVID-19 for society typically requires integrating all social, technological, economic, governmental aspects in a systematic way. Since the clear understanding of risk factors contribute to the success of the strategies applied against COVID-19, a risk assessment procedure is applied in this study to properly evaluate risk factors cause to spread of pandemic as a multi-complex decision problem. Therefore, due to the evaluation of risk factors, which often involves uncertain information, the model is constructed based on interval-valued q-rung orthopair fuzzy-COmplex PRoportional ASsessment (IVq-ROF-COPRAS) method. While the developed framework is efficient to enhance the quality of decisions by implementing more realistic, precise, and effective application procedure under uncertain environment, it has capability to help governments for developing comprehensive strategies and responses. According to the results of the proposed risk analysis model, the top three risk factors are "The Approach that Prioritizes the Economy in Policies", "Insufficient Process Control in Normalization" and "Lack of Epidemic Management Culture in Individuals and Businesses". Lastly, to show applicability and efficiency of the model sensitivity and comparative analysis were conducted at the end of the study.

MIC-Net: A deep network for cross-site segmentation of COVID-19 infection in the fog-assisted IoMT.

The automatic segmentation of COVID-19 pneumonia from a computerized tomography (CT) scan has become a major interest for scholars in developing a powerful diagnostic framework in the Internet of Medical Things (IoMT). Federated deep learning (FDL) is considered a promising approach for efficient and cooperative training from multi-institutional image data. However, the nonindependent and identically distributed (Non-IID) data from health care remain a remarkable challenge, limiting the applicability of FDL in the real world. The variability in features incurred by different scanning protocols, scanners, or acquisition parameters produces the learning drift phenomena during the training, which impairs both the training speed and segmentation performance of the model. This paper proposes a novel FDL approach for reliable and efficient multi-institutional COVID-19 segmentation, called MIC-Net. MIC-Net consists of three main building modules: the down-sampler, context enrichment (CE) module, and up-sampler. The down-sampler was designed to effectively learn both local and global representations from input CT scans by combining the advantages of lightweight convolutional and attention modules. The contextual enrichment (CE) module is introduced to enable the network to capture the contextual representation that can be later exploited to enrich the semantic knowledge of the up-sampler through skip connections. To further tackle the inter-site heterogeneity within the model, the approach uses an adaptive and switchable normalization (ASN) to adaptively choose the best normalization strategy according to the underlying data. A novel federated periodic selection protocol (FED-PCS) is proposed to fairly select the training participants according to their resource state, data quality, and loss of a local model. The results of an experimental evaluation of MIC-Net on three publicly available data sets show its robust performance, with an average dice score of 88.90% and an average surface dice of 87.53%.

Accelerating Ethanol Complete Electrooxidation via Introducing Ethylene as the Precursor for the C-C Bond Splitting.

The crucial issue restricting the application of direct ethanol fuel cells (DEFCs) is the incomplete and sluggish electrooxidation of ethanol due to the chemically stable C-C bond thereof. Herein, a unique ethylene-mediated pathway with a 100 % C1-selectivity for ethanol oxidation reaction (EOR) is proposed for the first time based on a well-structured Pt/Al2 O3 @TiAl catalyst with cascade active sites. The electrochemical in situ Fourier transform infrared spectroscopy (FTIR) and differential electrochemical mass spectrometry (DEMS) analysis disclose that ethanol is primarily dehydrated on the surface of Al2 O3 @TiAl and the derived ethylene is further oxidized completely on nanostructured Pt. X-ray absorption and density functional theory (DFT) studies disclose the Al component doped in Pt nanocrystals can promote the EOR kinetics by lowering the reaction energy barriers and eliminating the poisonous species. Strikingly, Pt/Al2 O3 @TiAl exhibits a specific activity of 3.83 mA cm-2 Pt , 7.4 times higher than that of commercial Pt/C and superior long-term durability.

Unveiling the Surface Structure of ZnO Nanorods and H2 Activation Mechanisms with 17O NMR Spectroscopy.

ZnO plays a very important role in many catalytic processes involving H2, yet the details on their interactions and H2 activation mechanism are still missing, owing to the lack of a characterization method that provides resolution at the atomic scale and follows the fate of oxide surface species. Here, we apply 17O solid-state NMR spectroscopy in combination with DFT calculations to unravel the surface structure of ZnO nanorods and explore the H2 activation process. We show that six different types of oxygen ions in the surface and subsurface of ZnO can be distinguished. H2 undergoes heterolytic dissociation on three-coordinated surface zinc and oxygen ions, while the formed hydride species migrate to nearby oxygen species, generating a second hydroxyl site. When oxygen vacancies are present, homolytic dissociation of H2 occurs and zinc hydride species form from the vacancies. Reaction mechanisms on oxide surfaces can be explored in a similar manner.

Safety of inactivated SARS-CoV-2 vaccines in patients with allergic diseases.

BACKGROUND: Considering the considerable prevalence of allergic disease in the general population, an urgent need exists for inactivated SARS-CoV-2 vaccines that can be safely administered to those subjects. METHODS: This retrospective cohort study including 1926 participants who received inactivated SARS-CoV-2 vaccines, compared their local and systemic reactions in 7 days after each dose of inactivated SARS-CoV-2 vaccine, and anti-SARS-CoV-2 IgG after vaccination in all participants. RESULTS: Pain at the injection site within seven days after the first injection was the most commonly reported local reaction, occurring in 31.0% of the patients with allergic disease and 18.9% in the control group, respectively (P < 0.001). After the first dose, systemic events were more frequently reported in patients with allergic disease than control group (30.2% vs. 22.9%, P < 0.001). After the second dose, systemic events occurred less often, affecting 17.1% of the patients with allergic disease and 11.1% of the control group (P < 0.002). The occurrence of fatigue, vertigo, diarrhea, skin rash, sore throat were the most frequent systemic reactions. Overall, a lower incidence of local and systemic reactive events was observed after the second dose than the first dose in patients with allergic disease and control group. Nearly all participants had positive IgG antibodies, and participants with allergic disease had higher frequencies compared with control group (100.0 vs.99.4%). CONCLUSIONS: Although local and systemic reactions were more frequently reported in patients with allergic disease than control group, administration of the inactivated SARS-CoV-2 vaccine was safe and well tolerated by all participants; no participants experienced a serious adverse event, and none were hospitalized. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR2100048549. Registered Jul 10, 2021.

Nanosheets of Ni-SAPO-34 Molecular Sieve for Selective Oxidation of Cyclohexanone to Adipic Acid.

Nanosheets of nickel doped SAPO-34 molecular sieves in thickness of ∼10 nm (denoted as NS-Ni-SAPO-34) has been successfully prepared through a morphology-reserved method of synthesis. A special aluminum phosphate in two-dimensional layered structure is used as precursor and converts to crystallized SAPO-34 molecular sieve, in nanosheet morphology reserved from the aluminum phosphate precursor, under hydrothermal conditions with tetraethyl orthosilicate and templates of mixed amines added. It is found that adequate amount of nickel, ∼5 wt %, added to the synthetic system is a key factor for the morphology-reserved synthesis. By characterization, the nickel is proved to be doped in the framework of the molecular sieve, which more likely helps to balance the high surface energy of nanosheet products. The NS-Ni-SAPO-34 shows excellent catalytic performance for oxidation of cyclohexanone to adipic acid by gaseous oxygen.

An Unsupervised Fuzzy Clustering Approach for Early Screening of COVID-19 From Radiological Images.

A global pandemic scenario is witnessed worldwide owing to the menace of the rapid outbreak of the deadly COVID-19 virus. To save mankind from this apocalyptic onslaught, it is essential to curb the fast spreading of this dreadful virus. Moreover, the absence of specialized drugs has made the scenario even more badly and thus an early-stage adoption of necessary precautionary measures would provide requisite supportive treatment for its prevention. The prime objective of this article is to use radiological images as a tool to help in early diagnosis. The interval type 2 fuzzy clustering is blended with the concept of superpixels, and metaheuristics to efficiently segment the radiological images. Despite noise sensitivity of watershed-based approach, it is adopted for superpixel computation owing to its simplicity where the noise problem is handled by the important edge information of the gradient image is preserved with the help of morphological opening and closing based reconstruction operations. The traditional objective function of the fuzzy c-means clustering algorithm is modified to incorporate the spatial information from the neighboring superpixel-based local window. The computational overhead associated with the processing of a huge amount of spatial information is reduced by incorporating the concept of superpixels and the optimal clusters are determined by a modified version of the flower pollination algorithm. Although the proposed approach performs well but should not be considered as an alternative to gold standard detection tests of COVID-19. Experimental results are found to be promising enough to deploy this approach for real-life applications.

Generalized Susceptible-Exposed-Infectious-Recovered model and its contributing factors for analysing the death and recovery rates of the COVID-19 pandemic.

COVID-19 is a highly contagious disease that has infected over 136 million people worldwide with over 2.9 million deaths as of 11 April 2021. In March 2020, the WHO declared COVID-19 as a pandemic and countries began to implement measures to control the spread of the virus. The spread and the death rates of the virus displayed dramatic differences among countries globally, showing that there are several factors affecting its spread and mortality. By utilizing the cumulative number of cases from John Hopkins University, the recovery rate, death rate, and the number of active, recovered, and death cases were simulated to analyse the trends and patterns within the chosen countries. 10 countries from 3 different case severity categories (high cases, medium cases, and low cases) and 5 continents (Asia, North America, South America, Europe, and Oceania) were studied. A generalized SEIR model which considers control measures such as isolation, and preventive measures such as vaccination is applied in this study. This model is able to capture not only the dynamics between the states, but also the time evolution of the states by using the fourth-order-Runge-Kutta process. This study found no significant patterns in the countries under the same case severity category, suggesting that there are other factors contributing to the pattern in these countries. One of the factors influencing the pattern in each country is the population's age. COVID-19 related deaths were found to be notably higher among older people, indicating that countries comprising of a larger proportion of older age groups have an increased risk of experiencing higher death rates. Tighter governmental control measures led to fewer infections and eventually reduced the number of death cases, while increasing the recovery rate, and early implementations were found to be far more effective in controlling the spread of the virus and produced better outcomes.

Robust weakly supervised learning for COVID-19 recognition using multi-center CT images.

The world is currently experiencing an ongoing pandemic of an infectious disease named coronavirus disease 2019 (i.e., COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computed Tomography (CT) plays an important role in assessing the severity of the infection and can also be used to identify those symptomatic and asymptomatic COVID-19 carriers. With a surge of the cumulative number of COVID-19 patients, radiologists are increasingly stressed to examine the CT scans manually. Therefore, an automated 3D CT scan recognition tool is highly in demand since the manual analysis is time-consuming for radiologists and their fatigue can cause possible misjudgment. However, due to various technical specifications of CT scanners located in different hospitals, the appearance of CT images can be significantly different leading to the failure of many automated image recognition approaches. The multi-domain shift problem for the multi-center and multi-scanner studies is therefore nontrivial that is also crucial for a dependable recognition and critical for reproducible and objective diagnosis and prognosis. In this paper, we proposed a COVID-19 CT scan recognition model namely coronavirus information fusion and diagnosis network (CIFD-Net) that can efficiently handle the multi-domain shift problem via a new robust weakly supervised learning paradigm. Our model can resolve the problem of different appearance in CT scan images reliably and efficiently while attaining higher accuracy compared to other state-of-the-art methods.

Edge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view information.

In clinical medicine, magnetic resonance imaging (MRI) is one of the most important tools for diagnosis, triage, prognosis, and treatment planning. However, MRI suffers from an inherent slow data acquisition process because data is collected sequentially in k-space. In recent years, most MRI reconstruction methods proposed in the literature focus on holistic image reconstruction rather than enhancing the edge information. This work steps aside this general trend by elaborating on the enhancement of edge information. Specifically, we introduce a novel parallel imaging coupled dual discriminator generative adversarial network (PIDD-GAN) for fast multi-channel MRI reconstruction by incorporating multi-view information. The dual discriminator design aims to improve the edge information in MRI reconstruction. One discriminator is used for holistic image reconstruction, whereas the other one is responsible for enhancing edge information. An improved U-Net with local and global residual learning is proposed for the generator. Frequency channel attention blocks (FCA Blocks) are embedded in the generator for incorporating attention mechanisms. Content loss is introduced to train the generator for better reconstruction quality. We performed comprehensive experiments on Calgary-Campinas public brain MR dataset and compared our method with state-of-the-art MRI reconstruction methods. Ablation studies of residual learning were conducted on the MICCAI13 dataset to validate the proposed modules. Results show that our PIDD-GAN provides high-quality reconstructed MR images, with well-preserved edge information. The time of single-image reconstruction is below 5ms, which meets the demand of faster processing.

Experimental demonstration and investigation of vortex circular Pearcey beams in a dynamically shaped fashion.

Optical vortex, typically characterized by a helical phase front, results in a possession of orbital angular momentum. In recent years, teleportation of the vortex mode using novel beams with peculiar features has gained great interest. Here, we experimentally demonstrate the propagation dynamics for a new class of the auto-focusing vortex circular Pearcey beam (VCPB), which is theoretically described by delivering the coaxial or off-axial spiral phases into the circular Pearcey beam (CPB), forming the crescent or bottle-like focal structure with self-rotation. Notably, such a hybrid beam with various types is experimentally obtained through a digital micromirror device (DMD) with the binary amplitude holography, and this DMD-based modulation scheme combined with controllable vortex modes enables dynamic switching among the VCPBs. We also measure the topological phase by interferometry and we explain the beam property on the basis of Poynting vector, showing a good agreement with the simulations. Further, the number, location and mode of embedded vortices could offer multiple dimensions of flexibility for target beam modulation, thus the experimentally controllable VCPBs will bring potential to high-speed optical communications and particle manipulations that require dynamic shaping.

Fusion of intelligent learning for COVID-19: A state-of-the-art review and analysis on real medical data.

The unprecedented surge of a novel coronavirus in the month of December 2019, named as COVID-19 by the World Health organization has caused a serious impact on the health and socioeconomic activities of the public all over the world. Since its origin, the number of infected and deceased cases has been growing exponentially in almost all the affected countries of the world. The rapid spread of the novel coronavirus across the world results in the scarcity of medical resources and overburdened hospitals. As a result, the researchers and technocrats are continuously working across the world for the inculcation of efficient strategies which may assist the government and healthcare system in controlling and managing the spread of the COVID-19 pandemic. Therefore, this study provides an extensive review of the ongoing strategies such as diagnosis, prediction, drug and vaccine development and preventive measures used in combating the COVID-19 along with technologies used and limitations. Moreover, this review also provides a comparative analysis of the distinct type of data, emerging technologies, approaches used in diagnosis and prediction of COVID-19, statistics of contact tracing apps, vaccine production platforms used in the COVID-19 pandemic. Finally, the study highlights some challenges and pitfalls observed in the systematic review which may assist the researchers to develop more efficient strategies used in controlling and managing the spread of COVID-19.

RCTE: A reliable and consistent temporal-ensembling framework for semi-supervised segmentation of COVID-19 lesions.

The segmentation of COVID-19 lesions from computed tomography (CT) scans is crucial to develop an efficient automated diagnosis system. Deep learning (DL) has shown success in different segmentation tasks. However, an efficient DL approach requires a large amount of accurately annotated data, which is difficult to aggregate owing to the urgent situation of COVID-19. Inaccurate annotation can easily occur without experts, and segmentation performance is substantially worsened by noisy annotations. Therefore, this study presents a reliable and consistent temporal-ensembling (RCTE) framework for semi-supervised lesion segmentation. A segmentation network is integrated into a teacher-student architecture to segment infection regions from a limited number of annotated CT scans and a large number of unannotated CT scans. The network generates reliable and unreliable targets, and to evenly handle these targets potentially degrades performance. To address this, a reliable teacher-student architecture is introduced, where a reliable teacher network is the exponential moving average (EMA) of a reliable student network that is reliably renovated by restraining the student involvement to EMA when its loss is larger. We also present a noise-aware loss based on improvements to generalized cross-entropy loss to lead the segmentation performance toward noisy annotations. Comprehensive analysis validates the robustness of RCTE over recent cutting-edge semi-supervised segmentation techniques, with a 65.87% Dice score.