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The ECG classification processor is a key component in wearable intelligent ECG monitoring devices which monitor the ECG signals in real time and detect the abnormality automatically. The state-of-the-art ECG classification processors for wearable intelligent ECG monitoring devices are faced with two challenges, including ultra-low energy consumption demand and high classification accuracy demand against patient-to-patient variability. To address the above two challenges, in this work, an ultra-energy-efficient ECG classification processor with high classification accuracy is proposed. Several design techniques have been proposed, including a reconfigurable SNN/ANN inference architecture for reducing energy consumption while maintaining classification accuracy, a reconfigurable on-chip learning architecture for improving the classification accuracy against patent-to-patient variability, and a dual-purpose binary encoding scheme of ECG heartbeats for further reducing the energy consumption. Fabricated with a 28nm CMOS technology, the proposed design consumes extremely low classification energy (0.3μJ) while achieving high classification accuracy (97.36%) against patient-to-patient variability, outperforming several state-of-the-art designs.

Misgurnus anguillicaudatus (loach) is a widely distributed benthic fish in Asia. In this study, the alkaline protease was used to hydrolyze loach, and the hydrolysate products of different molecular weights were obtained by membrane separation. In vitro antioxidant assays showed that the

The impedance flow sensors have usually been considered as the competitive components in the multi-flow measurement, primarily due to their advantages of fast response, high dynamic range and sensitivity, non-invasive and/or non-intrusive measurements. In this review, the impedance sensors have been categorized into two categories, i.e., the probes for local/average parameter measurement and sensor arrays for distributed parameter measurement. Maxwell’s equations and boundary conditions have been referred as a starting point to understand the measuring principle of impedance sensors, from which the sensitivity distribution can be attained. The sensitivity distribution can be utilized to characterize the sensor and employed as an objective function in optimizing the sensor geometry. The EMA models usually offer a way to relate the impedance measurements to individual phase fraction, which have been critically reviewed. In addition, the application range, spatial and temporal resolutions, advantages and disadvantages of the sensor arrays, i.e., the wire mesh sensor, field focusing sensor, electrical resistance and capacitance tomography sensors have also been provided. Finally, the future trends of impedance sensor development have been briefly discussed.

Due to the impact of the COVID-19 epidemic, the global economy has been affected to some extent in all aspects, with the food industry bearing the brunt. However, the specific research on the stock market segmentation industry is relatively lacking. This article aims to analyze the food industry's current status and development prospects by discussing the Fama-French three-factor model and five-factor model before and after the epidemic in the food industry and put forward constructive opinions on this. The analysis will use the method of coefficient comparison and effectiveness comparison to analyze the food industry's coefficients before and after the epidemic in the same model and model differences and combine the background of the industry to get the reasons for these differences.

Rapid identification and continuous surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are critical for guiding the response to the COVID-19 pandemic. Whole-genome sequencing (WGS) is a preferred tool for this aim, but many laboratories suffer from a lack of resources to support population-level sequencing. Here, we describe two PCR strategies targeting spike protein mutations to identify the Alpha, Delta, and Omicron variants. Signature mutations were selected using a dedicated bioinformatic program. The selected mutations in Alpha and Delta variants were detected using multicolor melting curve analysis (MMCA). Thirty-two mutations of the Omicron variant were targeted using the MeltArray approach in one reaction, which was able to detect the Omicron subvariants BA.1, BA.2, BA.3, and BA.4/5. The limits of detection varied from five to 50 copies of RNA templates/reactions. No cross-reactivity was observed with nine other respiratory viruses, including other coronaviruses. We validated the MMCA and MeltArray assays using 309 SARS-CoV-2 positive samples collected at different time points. These assays exhibited 98.3% to 100% sensitivity and 100% specificity compared with WGS. Multiplexed real-time PCR strategies represent an alternative tool capable of identifying current SARS-CoV-2 VOCs, adaptable for emerging variants and accessible for laboratories using existing equipment and personnel.

Eddy current (EC) testing has become one of the most common techniques for measuring metallic planar structures in various industrial scenarios such as infrastructures, automotive, manufacturing, and chemical engineering. There has been significant progress in measuring the geometry, electromagnetic properties, and defects of metallic planar structures based on electromagnetic principles. In this review, we summarize recent developments in EC computational models, systems, algorithms, and measurement approaches for planar structures. First, the computational models including analytical models, numerical methods, and plate property estimation algorithms are introduced. Subsequently, the impedance measurement system and probes are presented. In plate measurements, sensor signals are sensitive to probe lift-off, and various algorithms for reducing the lift-off effect are reviewed. These approaches can be used for measureing thickness and electromagnetic properties. Furthermore, defect detection for metallic plates is also discussed.

Purpose Radiation therapy (RT) is widely used in the treatment of cancer. Unfortunately, RT alone is insufficient to control the disease in most cases, as regrowth after irradiation still occur. Thus, it would be meaningful to explore the underlying mechanism of tumor regrowth after irradiation. Myeloid-derived suppressor cells (MDSCs) contribute to the immunosuppressive tumor microenvironment and hinder the therapeutic efficacy of RT. However, it is unclear whether MDSCs-mediated immune suppression contributes to local relapse after irradiation. In this article, we tried to figure out how MDSCs sabotage the therapeutic effect of RT, and tried to determine the potential synergistic effect of combination between targeting MDSCs and RT. Methods and Materials A syngeneic murine model of Lewis lung cancer was used. The abundance of tumor infiltrating MDSCs and tumor growth after irradiation was assessed. The percentage and functional state of CD8+ T cells were measured by flow cytometry, with or without polymorphonuclear (PMN)-MDSCs depletion. Arginase 1 (ARG1) expression and activity of MDSCs were examined by hematoxylin and eosin staining and flow cytometry. ARG1 inhibitor and phosphodiesterase 5 inhibitor sildenafil were administered after RT to figure out the underlying mechanism of MDSCs-mediated immunosuppression. Results We demonstrated that irradiation recruited MDSCs, especially the polymorphonuclear subset, into the tumor microenvironment. PMN-MDSCs inhibited the CD8+ T cell response by elevating ARG1 expression. Selective depletion of PMN-MDSCs or inhibition on ARG1 promoted the infiltration and activation of intratumoral CD8+ T cells, and delayed tumor regrowth after irradiation. We showed that sildenafil reduced the accumulation and ARG1 expression of PMN-MDSCs after irradiation, thus abrogating the MDSCs-mediated immunosuppression. Conclusions Our results have suggested that PMN-MDSCs participate in the irradiation-induced immune suppression through ARG1 activation. We have also found that sildenafil has the potential to facilitate antitumor immunity, which provides a new alternative to delay tumor recurrence after RT.

Electrical resistance tomography (ERT) is considered a novel sensing technique for monitoring conductivity distribution. Image reconstruction of ERT is an ill-posed inverse problem. In this paper, an improved regularization reconstruction method is presented to solve this issue. We adopted homotopic mapping to choose the regularization parameter of the iterative Tikhonov algorithm. The standard normal distribution function was used to continuously adjust the regularization parameter. Subsequently, the resultant image vector was deployed as the initial value of the iterative Tikhonov algorithm to improve the image quality. Finally, the improved method was combined with a projection algorithm based on the Krylov subspace, which was also effective in reducing the computational time. Both simulation and experimental results indicated that the new algorithm could improve the real-time performance and imaging quality.

Deep convolutional neural networks (DCNNs) have achieved state-of-the-art performance in classification, natural language processing (NLP), and regression tasks. However, there is still a great gap between DCNNs and the human brain in terms of computation efficiency. Inspired by neural synaptic plasticity and stochastic computing (SC), we propose neural synaptic plasticity-inspired computing (NSPC) to simulate the human brain’s neural network activity for inference tasks with simple logic gates. The multiplication and accumulation (MAC) is transformed by the wire connectivity in NSPC, which only requires bundles of wires and small width adders. To this end, the NSPC imitates the structure of neural synaptic plasticity from a circuit wires connection perspective. Furthermore, from the principle of NSPC, we use a data mapping method to convert the convolution operations to matrix multiplications. Based on the methodology of NSPC, fully-pipelined and low latency architecture is designed. The proposed NSPC accelerator exhibits high hardware efficiency while maintaining a comparable network accuracy level. The NSPC based DCNN accelerator (NSPC-CNN) processes DCNN at $1.5625M$ images/ $s$ with a power dissipation of $15.42~W$ and an area of $36.4~mm^{2}$ . The NSPC based deep neural network (DNN) accelerator (NSPC-DNN) that implements three fully connected layers DNN consumes only $6.6~mm^{2}$ area and $2.93~W$ power, and achieves a throughput of $400M$ images/ $s$ . Compared with conventional fixed-point implementations, the NSPC-CNN achieves $2.77 \times $ area efficiency, $2.25 \times $ power efficiency; the proposed NSPC-DNN exhibits $2.31 \times $ area efficiency and $2.09 \times $ power efficiency.

Image reconstruction for electrical resistance tomography (ERT) is an ill-posed inverse problem. L1 regularization is used to solve the inverse problem. An effective method of Barzilai-Borwein gradient projection for sparse reconstruction (GPSR-BB) can resolve the inverse problem into bound-constrained quadratic programming and achieve a gradient projection with line search. However, it is computationally expensive to solve the problem when the data dimension is substantial. Hence, a projection method is employed and combined with the GPSR-BB algorithm to improve the real-time performance. The problem can be mainly solved in the Krylov subspace. For comparison, another L1 regularization GPSR-BB method based on the truncated singular value decomposition is also conducted. Both simulation (with 3D modeling) and experimental results demonstrate the new method’s effectiveness in reducing the computational time and improving the image quality.

Extracting classical information from quantum systems is an essential step of many quantum algorithms. However, this information could be corrupted as the systems are prone to quantum noises, and its distortion under quantum dynamics has not been adequately investigated. In this work, we introduce a systematic framework to study how well we can retrieve information from noisy quantum states. Given a noisy quantum channel, we fully characterize the range of recoverable classical information. This condition allows a natural measure quantifying the information recoverability of a channel. Moreover, we resolve the minimum information retrieving cost, which, along with the corresponding optimal protocol, is efficiently computable by semidefinite programming. As applications, we establish the limits on the information retrieving cost for practical quantum noises and employ the corresponding protocols to mitigate errors in ground state energy estimation. Our work gives the first full characterization of information recoverability of noisy quantum states from the recoverable range to the recovering cost, revealing the ultimate limit of probabilistic error cancellation., 23 pages including appendix, v3 accepted by Quantum

The electrical capacitance tomography (ECT) technique has been extensively studied for real-time tomographic imaging of various industrial processes. Due to the ‘soft-field’ and under-determined problems in the image reconstruction, the tomographic images of ECT are usually low in the spatial resolution when compared with its radioactive counterparts, i.e. $\gamma $ -ray tomography and computed tomography. This paper presents an ECT sensor that consists of 8 external electrodes and 8 internal electrodes, with enhanced image quality at the center of imaging region and better accuracy on phase fraction measurement. The internal electrodes are placed on cross planes inside the investigated vessel. In this way, the signal-to-noise ratio of measurement channels as well as image quality are improved. Numerical simulations show that the proposed sensor can achieve better reconstructed images than a conventional 12-electrode sensor. In addition, the phase fraction calculation using ECT data can also benefit from the internal electrodes and corresponding measurements, which is an important parameter for multiphase flow processes.

With the increasing popularity of deep neural networks (DNNs), a large amount of research effort has been devoted to the hardware acceleration of DNNs to achieve efficient processing. Nevertheless, few works have explored the similarities between the biological essence of DNNs and arithmetic circuits. Moreover, stochastic computing (SC), which implements complex arithmetic operations with simple logic gates, has been applied to the acceleration of DNNs. However, traditional SC suffers from high latency and large hardware cost of pseudo-random number generators (PRNGs). Inspired by neural synaptic plasticity and SC, in this work, we present the reconfigurable neural synaptic plasticity-based computing (RNSP) to mimic the biological neuron behaviors and exploit the parallelism of SC to the full extent while maintaining a small hardware footprint compared to fixed-point counterparts. RNSP converts fixed-point numbers to parallel bits without logic resources, which are then synthesized by bit-wise multiplications and some full adders. In addition, we propose the arithmetic unit based on RNSP and use re-training to mitigate the accuracy degradation. Finally, a convolution engine (CE) built on RNSP with high memory bandwidth efficiency is designed. According to the implementation results on FPGA, the proposed RNSP-based CE outperforms the fixed-point counterpart in terms of power consumption and area.

In this article we present the three-body fragmentation dynamics of ${\mathrm{C}}_{2}{{\mathrm{H}}_{4}}^{3+}$ caused by $18\text{\ensuremath{-}}\mathrm{keV}/\mathrm{u}\phantom{\rule{0.16em}{0ex}}{\mathrm{Ne}}^{8+}$ ion impact. Utilizing the cold target recoil ion momentum spectroscopy, the complete kinematical information of three fragmentation channels, i.e., ${\mathrm{C}}_{2}{{\mathrm{H}}_{4}}^{3+}\ensuremath{\rightarrow}{\mathrm{H}}^{+}+{\mathrm{H}}^{+}+{\mathrm{C}}_{2}{{\mathrm{H}}_{2}}^{+}, {\mathrm{H}}^{+}+\mathrm{C}{\mathrm{H}}^{+}+\mathrm{C}{{\mathrm{H}}_{2}}^{+}$, and ${\mathrm{H}}^{+}+{{\mathrm{H}}_{2}}^{+}+{\mathrm{C}}_{2}{\mathrm{H}}^{+}$ is obtained. For each channel, both concerted and sequential fragmentation mechanisms are observed and differentiated through the momentum correlation of resultant fragments visualized by the Dalitz plot and the Newton diagram. In particular, for the channel ${\mathrm{C}}_{2}{{\mathrm{H}}_{4}}^{3+}\ensuremath{\rightarrow}{\mathrm{H}}^{+}+{\mathrm{H}}^{+}+{\mathrm{C}}_{2}{{\mathrm{H}}_{2}}^{+}$, it is found in addition to the sequential pathway that the two protons can be emitted concertedly in two ways, i.e., from the same carbon atom or from the two carbon atoms. The present results show a more diverse molecular fragmentation triggered by ion collisions in contrast to a previous study using intense laser field ionization.

Electrical resistance tomography (ERT) is an efficient tomographic technique for the online imaging of industrial processes, primarily due to its advantages of non-radiation, realtime visualization and specific capability in performing measurements on opaque fluids. In the case of industrial applications, it requires that both the ERT hardware and software should be as reliable and flexible as possible. However, most ERT systems still employ the integrated platform, i.e., the measurement system and image reconstruction computer being placed in a short-distance, usually via a serial communication. It is usually not a good choice to employ the integrated platform, wherever the intrinsic safety is required. Therefore, an ERT platform that can realize remote data perception, dynamic data transmission and real-time data analysis is presented, which features Internet of Things (IoT) capability. The combination of the ERT system and the IoT provides a convenient and standardized interface between the on-site ERT hardware and remote monitoring devices. The measurements are made by the dedicated ERT hardware, which can be sent via Ethernet to and further processed by a highperformance workstation/server. The server performs the image reconstruction algorithms and data post-processing tasks. The HTTP protocol is also deployed in the server, from which the tomographic images and numerical data can be synchronously queried and displayed on the remote clients. Experimental results showed that the average signal-to-noise ratio of the system can achieve 66.9 dB at the applied frequency of 128 kHz, and the acquisition rate can reach 60 frames per second. The Web can be updated in every 0.1 second from a smart device. In the long term test, the proposed platform has also demonstrated high reliability and performance.

The phase velocity measurement of multi-phase flow is of great interest for many industrial processes, regarding the process efficiency and safety. In flow measurement, the electromagnetic flowmeter (EMF) has usually been considered as a high standard velocity measurement tool for its advantages of high reliability, strong corrosion-resistance, high measurement accuracy, and non-intrusive characteristic. In EMF, the boundary voltage is closely related to the velocity profile across its sensing region. Conventionally, it employs a bipolar square/sinusoidal waveform current in the excitation coils to produce an alternating electromagnetic field. When EMF is used to measure the asymmetric velocity field distribution, there are serious systematic errors. The research on EMF with multi-electrode provides a solution to this problem. However, multi-electrode leads to the decrease of the equivalent output impedance of the sensor and the serious interference signal, making it more challenging to accurately obtain the signal of velocity. In this paper, a phase sensitive detector has been developed for measuring the multielectrode EMF signals. The EMF signals usually suffer from significant interferences, especially the quadrature interference. To reduce the quadrature interference signal, a synchronous differential technique is employed. Experimental results showed that the circuit achieved a flow velocity resolution of 0.01 m/s, and a relative error $\varepsilon$ less than 0.6%.

The angiotensin II type I receptor (AGTR1) has a strong influence on tumor growth, angiogenesis, inflammation and immunity. However, the role of AGTR1 on lymph node metastasis (LNM) in breast cancer, which correlates with tumor progression and patient survival, has not been examined. AGTR1 was highly expressed in lymph node-positive tumor tissues, which was confirmed by the Oncomine database. Next, inhibition of AGTR1 reduced tumor growth and LNM in orthotopic xenografts by bioluminescence imaging (BLI). Losartan, an AGTR1-specific inhibitor, decreased the chemokine pair CXCR4/SDF-1α levels in vivo and inhibited AGTR1-induced cell migration and invasion in vitro. Finally, the molecular mechanism of AGTR1-induced cell migration and LNM was assessed by knocking down AGTR1 in normal cells or CXCR4 in AGTR1high cells. AGTR1-silenced cells treated with losartan showed lower CXCR4 expression. AGTR1 overexpression caused the upregulation of FAK/RhoA signaling molecules, while knocking down CXCR4 in AGTR1high cells downregulated these molecules. Collectively, AGTR1 promotes LNM by increasing the chemokine pair CXCR4/SDF-1α and tumor cell migration and invasion. The potential mechanism of AGTR1-mediated cell movement relies on activating the FAK/RhoA pathway. Our study indicated that inhibiting AGTR1 may be a potential therapeutic target for LNM in early-stage breast cancer.

In the electrical capacitance tomography (ECT) systems, the electrodes, and cables may fail to function properly, which will cause several measurements missing. In these cases, image reconstruction can only use the remaining effective measurements. In order to make the reconstructed images close to the image results of the complete measurement set, it is necessary to use the incomplete measurements reasonably. The measurement/data recovery method and image reconstruction can be conducted to obtain the results, which meet the imaging needs under these circumstances. The measurement/data recovery method by using the sensitivity matrix and the regression model of least square support vector machine (LS-SVM) are proposed. The image recovery result is reconstructed by the method of total variation (TV) minimization. The simulations and experiments of gas-solids two-phase measurement are conducted to validate the method.

Artificial neural networks (ANNs) have gained state-of-the-art results in classification and regression tasks. However, there is still great gap between ANNs and human brain in terms of computation efficiency. In this work, we proposed the neural synaptic plasticity-like computing (NSPC) to simulate the neural network activity for inference task with ultra-simple logic gates. The multiplication of weight in traditional ANNs is transformed by the wire connectivity in NSPC, which requires only bundle of wires without any logics. To this end, the NSPC imitates the structure of neural synaptic plasticity from a circuit wires connection perspective. The proposed NSPC exhibits comparable inference accuracy with low hardware cost. According to the implementation results, the NSPC requires only 28% logic gate resources of conventional ANNs scheme, 114% throughput improvement and 8.454 times better hardware efficiency on the average.

A high-temperature proton exchange membrane with high proton conductivity over a wide humidity range still remains a challenge. PBI dendrimer containing triazine rings (TPBI) was synthesized to approach this aim considering its high content of hygroscopic terminal groups and of larger free volume. A novel proton conductor previously synthesized (zirconium 3-sulfopropyl phosphonate, ZrSP) was doped due to its good proton conductivity over a wide humidity range. TPBI was post-crosslinked with a tetrafunctional epoxy resin (N,N,N′,N′-tetraglycidyl-4,4′-diaminodiphenylmethane, TGDDM) to enhance the mechanical stability at low cross-linking degrees, which allowed high doping levels of ZrSP, and thus, high conductivity. The prepared membranes (TPBI-TGDDM/ZrSP) showed good thermal stability, high proton conductivity over wide humidity range, and good dimensional stability. At suitable degrees of branching, TPBI-TGDDM/ZrSP exhibited superior mechanical property, oxidative stability, methanol barrier property, and membrane selectivity than its linear analog (mPBI-TGDDM/ZrSP). As ZrSP instead of PA was applied as the proton conductor, TPBI-TGDDM/ZrSP showed good durability of proton conductivity, especially in comparison with TPBI-TGDDM/PA, which highly retarded decline in conductivity caused by PA leaking. The proton conductivity at 180 °C of TPBI(20)-TGDDM(10)/ZrSP(50) achieved 142, 84.2 and 23.6 mS cm−1 at 100%, 50%, and 0 RH, respectively.

Background The COVID-19 pandemic is a significant worldwide health crisis. Breast cancer patients with COVID-19 are fragile and require particular clinical care. This study aimed to identify the clinical characteristics of breast cancer patients with COVID-19 and the risks associated with anti-cancer treatment. Methods The medical records of breast cancer patients with laboratory-confirmed COVID-19 were collected among 9559 COVID-19 patients from seven designated hospitals from 13th January to 18th March 2020 in Hubei, China. Univariate and multivariate analyses were performed to assess risk factors for COVID-19 severity. Results Of the 45 breast cancer patients with COVID-19, 33 (73.3%) developed non-severe COVID-19, while 12 (26.7%) developed severe COVID-19, of which 3 (6.7%) patients died. The median age was 62 years, and 3 (6.7%) patients had stage IV breast cancer. Univariate analysis showed that age over 75 and the Eastern Cooperative Oncology Group (ECOG) score were associated with COVID-19 disease severity (P, Highlights • This multicenter retrospective trial focused on COVID-19 in breast cancer patients. • COVID-19 in breast cancer patients seems less severe than those with other tumors. • COVID-19 disease severity could be strongly associated with chemotherapy.

Background: The COVID-19 pandemic is a significant worldwide health crisis. Patients with malignancy are considered at substantially increased risk of infection and poor outcomes. Breast cancer patients with COVID-19 represent an urgent clinical need. This study aimed to identify clinical characteristics of breast cancer patients with COVID-19 and risks associated with anti-cancer treatment.Methods: This multicenter retrospective cohort study included 45 breast cancer patients with laboratory-confirmed COVID-19 at seven designated hospitals in Hubei, China. The medical records of breast cancer patients were collected from the records of 9559 COVID-19 patients from 13th January, 2020 to 18th March, 2020. Univariate and multivariate analyses were performed to assess risk factors for COVID-19 severity.Results: Of 45 breast cancer patients with COVID-19, 33 (73.3%) developed non-severe COVID-19, while 12 (26.7%) developed severe COVID-19, of which 3 (6.7%) patients died. The median age was 62 years, and 3 (6.7%) patients had stage IV breast cancer. Most patients developed fever (37, 82.2%), and most had bilateral lung involvement on chest CT (36, 80.0%). Univariate analysis showed the age over 75 and Eastern Cooperative Oncology Group (ECOG) score were associated with COVID-19 disease severity (PP=0.049; Cox proportional hazards model: HR=13.909, 95% CI 1.086-178.150, P=0.043), with more pronounced neutropenia and higher LDH, CRP and procalcitonin levels than patients else (PConclusions: The severity of COVID-19 in breast cancer patients was associated with baseline factors of the age over 75 and ECOG score, but not with tumor characteristics. Chemotherapy within 7 days before symptom onset was a risk factor for severe COVID-19, reflected by neutropenia and elevated LDH, CRP and procalcitonin levels.

Progression to severe disease is a difficult problem in treating coronavirus disease 2019 (COVID‐19). The purpose of this study is to explore changes in markers of severe disease in COVID‐19 patients. Sixty‐nine severe COVID‐19 patients were included. Patients with severe disease showed significant lymphocytopenia. Elevated level of lactate dehydrogenase (LDH), C‐reactive protein (CRP), ferritin, and D‐dimer was found in most severe cases. Baseline interleukin‐6 (IL‐6) was found to be associated with COVID‐19 severity. Indeed, the significant increase of baseline IL‐6 was positively correlated with the maximal body temperature during hospitalization and with the increased baseline of CRP, LDH, ferritin, and D‐dimer. High baseline IL‐6 was also associated with more progressed chest computed tomography (CT) findings. Significant decrease in IL‐6 and improved CT assessment was found in patients during recovery, while IL‐6 was further increased in exacerbated patients. Collectively, our results suggest that the dynamic change in IL‐6 can be used as a marker for disease monitoring in patients with severe COVID‐19., Sixty‐nine severe COVID‐19 patients were analyzed for disease monitoring. Elevated baseline interleukin (IL)‐6 was found positively correlated to maximal body temperature, CRP, LDH, ferritin and D‐dimer during disease progression. IL‐6 increased during disease exacerbation and decreased during recovery.

In this article, the modification effects on Al&ndash, Mg2Si before and after heat treatment were investigated with Ca, Sb, and (Ca + Sb). In comparison with single Ca or Sb, the samples with composition modifiers (Ca + Sb) had the optimal microstructure. The sample with a molar ratio for Ca-to-Sb of 1:1 obtained relatively higher properties, for which the Brinell hardness values before and after heat treatment were remarkably increased by 31.74% and 28.93% in comparison with bare alloy. According to differential scanning calorimetry analysis (DSC), it was found that the nucleation behavior of the primary Mg2Si phase could be significantly improved by using chemical modifiers. Some white particles were found to be embedded in the center of Mg2Si phases, which were deduced to be Ca5Sb3 through X-ray diffraction (XRD) and field-emission scanning electron microscope (FESEM) analyses. Furthermore, Ca5Sb3 articles possess a rather low mismatch degree with Mg2Si particles based on Phase Transformation Crystallography Lab software (PTCLab) calculation, meaning that the efficient nucleation capability of Ca5Sb3 for Mg2Si particles could be estimated.

The phase fraction in the industrial multi-phase flows is a key factor affecting the process efficiency and safety. The capacitance and other impedance based sensors can be employed to estimate this parameter by resorting to the effective medium approximation (EMA) method, which describes the macroscopic properties of composite materials with analytical or theoretical modeling. The EMAs have been paid increasingly attentions in determining the phase fraction in the multi-phase flow. There exist several different EMA models that can be utilized for phase fraction measurement, i.e., Maxwell-Garnett, Bruggeman and Bottcher models. It is essential to evaluate feasibility of these EMA models in gas-solid two-phase flows with different flow regimes, i.e., the homogenous and laminar flows. A four-electrode capacitance sensor is evaluated to validate multiple EMA models for estimating the solid fraction by numerical simulations and static experiments. The numerical simulation work focuses on comparing the solid fraction results obtained from different EMA models by Monte Carlo method.

The electrical tomography techniques are suitable for monitoring the multi-phase flow behaviors, primarily due to its advantages of fast response, non-invasive and real-time imaging capability. The dual-modality tomography technique that combines the capability of two or more tomographic modalities can provide the complementary information for better understanding of investigated multi-phase flows. However, the dualmodality technique requires some effective fusion methods to make better use of the additional information that obtained from the different modalities. In this paper, the multi-class LS- SVM has been proposed for implementing the dual-modality fusion between the two tomography modalities, i.e., the electrical capacitance tomography and electromagnetic tomography. In consideration that the multi-phase flow is extreme complex in phase distribution, consequently, the studies are conducted by combining the computational fluid dynamic (CFD) and finite element method (FEM) simulation. The phase distributions that approximate to the real situation can be collected by performing CFD simulation. By feeding the distributions into the electromagnetic field simulation, one can obtained the interelectrode capacitances and mutual inductances between coils, i.e., the ECT and EMT measurements, by FEM simulation. Subsequently, the corresponding tomographic images and fusion images can be achieved. Furthermore, the phase volume fractions and distributions can be quantitatively evaluated to verify the feasibility of proposed method. Simulation results show that the fusion method can distinguish the measured three phases with sharp boundaries and obtain the three-phase distributions in several flow regimes.

This paper describes a combined conductance ring and electrical resistance tomography (ERT) sensor for measuring the fraction of non-conductive phases, solid and gas phases, in the gas-liquid-solid circulating fluidized bed (GLSCFB). The conductivities of liquid and packed solid phases can be determined by the conductance ring sensor, which provides the information on the conductivity range of liquid-solid mixture. Therefore, the ERT sensor can be calibrated with both the high and low conductivity phases of known conductivities. Consequently, the pixel values of ERT reconstructed images can be normalized by the low and high conductivities, thus providing better estimation to the local phase conductivity and fraction. The dynamic experiments have been performed to validate the proposed sensor and method, in which the tap water and Al 2 O 3 particles are employed as the liquid and solid phases, respectively. Experimental results show that the proposed sensor and method can be applied to investigate the volume fraction of solid and liquid phases in a GLSCFB.

BackgroundThe outbreak of coronavirus disease 2019 (COVID-19) in Wuhan City, China has spreads rapidly since December, 2019. Most patients show mild symptoms, but some of them develop into severe disease. There is currently no specific medication. The purpose of this study is to explore changes of markers in peripheral blood of severe COVID-19 patients, which may be of value in disease monitoring.MethodsClinical data of patients with nonsevere and severe type COVID-19 diagnosed by laboratory test in our institution were collected. The relationship between peripheral blood cells and cytokines, clinical manifestation and outcome was analyzed.ResultsA total of 69 severe type COVID-19 patients were included. On admission, the median age of severe cases was 56-year old, with 52.17% female patient. The most common symptoms were fever (79.72%), cough (63.77%), shortness of breath (57.97%) and fatigue (50.72%). Diarrhea is less common. The most common comorbidity is hypertension. Upon admission, the proportion of bilateral pulmonary involvement and interstitial abnormalities evidenced by chest computed tomography (CT) imaging in severe cases was 60.87% and 27.54%, respectively. Compared with patients with nonsevere disease, those with severe disease showed obvious lymphocytopenia. Elevated level of lactate dehydrogenase (LDH), C-reactive protein (CRP), ferritin and D-dimer was found in most cases. Two patients (2.9%) needed transfer to the intensive care unit. Baseline immunological parameters and most of the inflammatory parameters were basically within the normal range. However, baseline interleukin-6 (IL-6) was significantly increased in severe type, which was closely related to the maximal body temperature during hospitalization and to CT findings. Baseline IL-6 was also significantly related to the increase of baseline level of CRP, LDH, ferritin and D-dimer. The increase of baseline IL-6 level suggests that it may positively correlate with the severity of COVID-19. Among the 30 severe type patients whose IL-6 was assessed before and after treatment, significant decrease in IL-6 and improved CT assessment was found in 25 patients after treatment. Whereas the IL-6 level was further increased in 3 cases, which was closely related to disease progression. It is suggested that IL-6 may be used as a marker for disease monitoring in severe COVID-19 patients.ConclusionsOn admission, the baseline level of IL-6, CRP, LDH and ferritin was closely related to the severity of COVID-19, and the elevated IL-6 was significantly related to the clinical manifestation of severe type patients. The decrease of IL-6 was closely related to treatment effectiveness, while the increase of IL-6 indicated disease exacerbation. Collectively, the dynamic change of IL-6 level can be used as a marker for disease monitoring in patients with severe COVID-19.

In the industrial fluidization processes, it is essential to perform the online monitoring of the dynamic phase distributions, in order to understand its internal behaviors. The electromagnetic tomography (EMT), electrical capacitance tomography (ECT) and electrical resistance tomography(ERT) have been introduced to investigate the complex multi-phase flows in various industries. The principle and differences of these tomographic modalities have been summarized. Furthermore, the dual-modality electrical tomography that consists of two or more different measuring modalities has more potentials in the two-phase and three-phase flow measurement. The representative dual-modality fusion methods that implemented in the recent researches have been analyzed and the future trend of dual-modality fusion method has also been discussed. pic: FocusSession. SC5: Electromagnetic Devices, Sensing, Imaging and Applications.

Borophenes, which are two-dimensional boron counterparts made of the three synthetic polymorphs T, β12 and χ3, have been considered as potential anode materials in Li-ion batteries with extremely high capacities. However, Li hopping on β12 and χ3 borophenes is quite slow with high energy barriers (around 0.6 eV), thus preventing the application of these borophenes in the fast charging realm. Here, we have used halogen functionalization in an attempt to boost the sluggish Li-ion diffusion dynamics in the prototype χ3 borophene system. Halogens bind strongly to χ3 borophene with substantial electron transfer from the latter to the former, thereby leading to local electron deficiency in the χ3 borophene. The synergy of electron extraction from χ3 borophene and the electrostatic attraction between halogens and Li results in an enhanced affinity between χ3 borophene and Li as well as a reduction in the Li-ion hopping barrier. Iodine is the preferred dopant, for which most diffusion paths exhibit energy barriers typically smaller than 0.2 eV. Our results suggest that halogen incorporation could facilitate intercalation and de-intercalation of Li-ions in borophene-based anode materials.

In many multi-phase flows, the online measurement and monitoring of phase fractions as well as distributions play a vital role in determining the process efficiency and safety. The dual-modality electrical capacitance tomography (ECT) and electromagnetic tomography (EMT) technique provides an efficient measure to estimate the distribution of electromagnetic property in rapidly changing multi-phase flows. A generative adversarial network (GAN) is designed to solve the fusion problem of ECT and EMT in the gas–liquid–solid (G–L-S) three-phase flow measurement. The fusion model incorporates the features of dual-modality measurements and images to generate the electromagnetic property in high precision, by which the accurate phase volume can be derived. Furthermore, a simulation approach is proposed to provide the sufficient measurement samples that approximate the real three-phase flow measurement. In the numerical study, the fluidization process of a G–L–S fluidized bed (GLSFB) reactor is simulated and measured by the models of ECT and EMT. The simulation validation on samples from GLSFB and experiments on the three-phase flow setup demonstrate the high accuracy of electromagnetic property reconstruction and generalization ability of fusion model that suitable for various flow regimes. The errors of calculated phase fraction are less than 0.15 in both simulations and experiments.

Borophenes, which are two-dimensional boron counterparts made of the three synthetic polymorphs T, β