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This paper presents a two-axis AlScN-based water-immersible MEMS mirror fabricated in an 8-inch MEMS process. Compared with other studies, this device has a larger optical aperture 10 mm in diameter. The resonant frequencies of the device are 1011 Hz in air and 342 Hz in water. The scanning angle reaches ±5° and ±2° at resonant frequencies in air and water, respectively. The cavitation phenomenon is observed when the device is operating in water, which leads the device to electrical failure. To address this issue, a device with reduced resonant frequencies—246 Hz and 152 Hz in air and water—is characterized, through which the bubbles can be effectively prohibited. This MEMS mirror could potentially be used in ultrasound and photoacoustic microscopy applications. [ABSTRACT FROM AUTHOR]

Transient pulses generated by high-energy particles can cause soft errors in circuits, resulting in spacecraft malfunctions and posing serious threats to the normal operation of spacecraft. For integrated circuits used in space applications, it is necessary to first evaluate soft errors caused by transient pulses. Conventional soft-error-rate evaluation tools are designed to simulate the generation of transient pulses using many accurate models, while the propagation of transient pulses is primarily simulated by circuit-level simulation tools. Due to the limitations of simulation tools, conventional evaluation approaches are limited to the circuit scale. The simulation runtime is unbearable for large-scale integrated circuits. This paper presents an approach for evaluating the soft error rate using machine learning. A back propagation neural network is implemented in the proposed approach. It helps to determine the probability of transient pulse propagation. Compared with the conventional soft-error-rate evaluation results, the proposed approach demonstrates a strong correlation in both trend and magnitude. The average difference between the results obtained using the proposed evaluation method and the experimental results is 23.5%, which is 7.5% higher than that between the results obtained using the conventional evaluation method and the experimental results. Compared to the conventional evaluation method, the proposed approach improves the runtime by an order of magnitude. The proposed approach also benefits the locating of highly sensitive circuit nodes in large-scale integrated circuits. Circuit design and radiation hardening are both useful applications. [ABSTRACT FROM AUTHOR]

Phase transition behaviors of poly(N-isopropylacrylamide) nanogels with different compositions induced by (−)-epigallocatechin-3-gallate (EGCG) and ethyl gallate (EG) has been investigated systematically. Monodisperse poly(N-isopropylacrylamide-co-N-hydroxymethyl acrylamide) (P(NIPAM-co-NMAM)) and poly(N-isopropylacrylamide-co-2-hydroxyethyl methacrylate) (P(NIPAM-co-HEMA)) nanogels with different feeding monomer ratios were prepared by emulsion polymerization. P(NIPAM-co-NMAM) nanogels exhibit rapid isothermal phase transition behavior in EGCG solutions with low concentration (10−3 mol/L) in less than 10 minutes. The thermosensitive phase transition behaviors of nanogels are affected not only by the copolymerized monomers but also by the concentrations of EGCG and EG in aqueous solutions. Nanogels remain in a shrunken state and do not exhibit thermosensitive phase transition behaviors in EGCG solutions (≥5 mmol/L), whereas they display thermo-responsive phase transition behaviors in EG solutions. The volume phase transition temperature (VPTT) shifts to lower temperatures with increasing EG concentration. The diameters of P(NIPAM-co-NMAM) nanogels decrease with increasing EG concentration at temperatures between 29 and 33 °C. In contrast, the diameters of P(NIPAM-co-HEMA) nanogels increase with increasing EGCG concentration at temperatures between 37 and 45 °C. The results demonstrate the potential of nanogels for simple detection of EG and EGCG concentrations in aqueous solutions over a wide temperature range, and EGCG can serve as a signal for the burst-release of drugs from the P(NIPAM-co-NMAM)-based carriers at physiological temperature. [ABSTRACT FROM AUTHOR]

As environmental awareness continues to grow and government policies provide incentives, electric vehicles (EVs) are becoming more widely used in logistics distribution. Considering green power trading and carbon emissions, this paper addresses the green vehicle routing problem (GVRP) and constructs an electric vehicle path model with time windows to minimize the total cost. To solve the model, a hybrid adaptive genetic algorithm (HAGA) is proposed. An improved nearest-neighbor algorithm is adopted to improve the quality of the initial population, and the adaptive crossover and mutation operators are introduced to achieve the better solution. In addition, based on the Schneider case, HAGA is used to solve the models with and without considering green power trading separately, and the results show that considering green power trading can reduce the total cost by 3.22% and carbon emissions by 23.38 kg. Finally, the experimental simulations further prove that with the increase in case size, HAGA can effectively reduce total cost. And it is beneficial for the popularization of electric vehicles in logistics distribution. [ABSTRACT FROM AUTHOR]

Pine wilt disease (PWD) is one of the most concerning diseases in forestry and poses a considerable threat to forests. Since the deep learning approach can interpret the raw images acquired by UAVs, it provides an effective means for forest health detection. However, the fact that only PWD can be detected but not the degree of infection can be evaluated hinders forest management, so it is necessary to establish an effective method to accurately detect PWD and extract regions infected by PWD. Therefore, a Mask R-CNN-based PWD detection and extraction algorithm is proposed in this paper. Firstly, the extraction of image features is improved by using the advanced ConvNeXt network. Then, it is proposed to change the original multi-scale structure to PA-FPN and normalize it by using GN and WS methods, which effectively enhances the data exchange between the bottom and top layers under low Batch-size training. Finally, a branch is added to the Mask module to improve the ability to extract objects using fusion. In addition, a PWD region extraction module is proposed in this paper for evaluating the damage caused by PWD. The experimental results show that the improved method proposed in this paper can achieve 91.9% recognition precision, 90.2% mapping precision, and 89.3% recognition rate of the affected regions on the PWD dataset. It can effectively identify the distribution of diseased pine trees and calculate the damage proportion in a relatively accurate way to facilitate the management of forests. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) threatens public health all over the world. It is well-accepted that the immune cells in peripheral blood are widely involved in the pathological process of COVID-19. However, hematopoietic stem and progenitor cells (HSPCs), as the main source of peripheral immune cells, have not been well studied during COVID-19 infection. We comprehensively revealed the transcriptome changes of peripheral blood HSPCs after COVID-19 infection and vaccination by single-cell RNA-seq. Compared with healthy individuals, the proportion of HSPCs in COVID-19 patients significantly increased. The increase in the proportion of HSPCs might be partly attributed to the enhancement of the HSPCs proliferation upon COVID-19 infection. However, the stemness damage of HSPCs is reflected by the decrease of differentiation signal, which can be used as a potential specific indicator of the severity and duration of COVID-19 infection. Type I interferon (IFN-I) and translation signals in HSPCs were mostly activated and inhibited after COVID-19 infection, respectively. In addition, the response of COVID-19 vaccination to the body is mild, while the secondary vaccination strengthens the immune response of primary vaccination. In conclusion, our study provides new insights into understanding the immune mechanism of COVID-19 infection. [ABSTRACT FROM AUTHOR]

Dedicated equipment, which is widely used in many different types of vehicles, is the core system that determines the combat capability of special vehicles. Therefore, assuring the normal operation of dedicated equipment is crucial. With the increase in battlefield complexity, the demand for equipment functions is increasing, and the complexity of dedicated equipment is also increasing. To solve the problem of fault diagnosis of dedicated equipment, a fault diagnosis algorithm based on CNN-LSTM was proposed in this paper. CNN and LSTM are used in the model adopted by the algorithm to extract spatial and temporal features from the data. CBAM is used to enhance the model's accuracy in identifying faults for dedicated equipment. Data on dedicated equipment faults were obtained from a hardware-in-loop simulation platform to verify the model. It is demonstrated that the proposed fault diagnosis algorithm has high recognition ability for dedicated equipment by comparing it to other neural network models. [ABSTRACT FROM AUTHOR]

Grain yield prediction affects policy making in various aspects such as agricultural production planning, food security assurance, and adjustment of foreign trade. Accurately predicting grain yield is of great significance in ensuring global food security. This paper is based on the MODIS remote sensing image data products from 2010 to 2020, and adds band information such as vegetation index and temperature to form composite remote sensing data as a dataset. Aiming at the lack of models for large-scale forecasting and the need for human intervention in traditional models, this paper proposes a grain production estimation model based on deep learning. First, image cropping and yield mapping techniques are used to process the data to generate training samples. Then the channel and spatial attention mechanism (convolutional block attention module, CBAM) is added to extract spatial information in different remote sensing bands to improve the efficiency of the model. Long short-term memory (LSTM) neural networks are added to obtain feature information in the time dimension. Finally, a national-scale grain yield prediction model is constructed. After the study, it was found that the LSTM model using a combination of multi-source satellite images and an attention mechanism can effectively predict grain yield in China. Furthermore, the proposed model was tested on data from 2018 to 2020 showing an average R 2 of 0.940 and an average RMSE of 80,020 tons, indicating that it can predict Chinese grain yield better. The model proposed in this paper extracts grain yield information directly from the composite remote sensing data, and solves the problem of small-scale research and imprecise yield prediction in an end-to-end manner. [ABSTRACT FROM AUTHOR]

Oily sludge, as a critical hazardous waste, requires appropriate treatment for resource recovery and harmfulness reduction. Here, fast microwave-assisted pyrolysis (MAP) of oily sludge was conducted for oil removal and fuel production. The results indicated the priority of the fast MAP compared with the MAP under premixing mode, with the oil content in solid residues after pyrolysis reaching below 0.2%. The effects of pyrolysis temperature and time on product distribution and compositions were examined. In addition, pyrolysis kinetics can be well described using the Kissinger-Akahira-Sunose (KAS) and the Flynn-Wall-Ozawa (FWO) methods, with the activation energy being 169.7–319.1 kJ/mol in the feedstock conversional fraction range of 0.2–0.7. Subsequently, the pyrolysis residues were further treated by thermal plasma vitrification to immobilize the existing heavy metals. The amorphous phase and the glassy matrix were formed in the molten slags, resulting in bonding and, hence, immobilization of heavy metals. Operating parameters, including working current and melting time, were optimized to reduce the leaching concentrations of heavy metals, as well as to decrease their volatilization during vitrification. [ABSTRACT FROM AUTHOR]

Power MOSFETs are found to be very vulnerable to single-event burnout (SEB) in space irradiation environments, and the military components generally require that devices could operate reliably as the temperature varies from 218 K to 423 K (−55 °C to 150 °C); thus, the temperature dependence of single-event burnout (SEB) in power MOSFETs should be investigated. Our simulation results showed that the Si power MOSFETs are more tolerant to SEB at a higher temperature at the lower LET (10 MeV∙cm2/mg) due to the decrease of the impact ionization rate, which is in good agreement with the previous research. However, the state of the parasitic BJT plays a primary role in the SEB failure mechanism when the LET value is greater than 40 MeV∙cm2/mg, which exhibits a completely different temperature dependence from that of 10 MeV∙cm2/mg. Results indicate that with the temperature increasing, the lower difficulty to turn on the parasitic BJT and the increasing current gain all make it easier to build up the regenerative feedback process responsible for SEB failure. As a result, the SEB susceptibility of power MOSFETs increases as ambient temperature increases when the LET value is greater than 40 MeV∙cm2/mg. [ABSTRACT FROM AUTHOR]

Based on Doppler radar observation and reanalysis data, the statistical characteristics of mesovortices (MVs) during the first rainy season (April–June) in Guangdong, South China, from 2017 to 2019 are studied, including their spatiotemporal distributions, structural features and favorable environmental conditions. The results show that the MVs usually exhibit short lifetimes; about 70% last for less than 30 min. The intensity and horizontal scale of the MVs are proportional to their lifetime. Long-lived MVs have larger horizontal scales and stronger intensities than short-lived ones. The MVs are mainly observed over the Pearl River Delta region, followed by western Guangdong Province, but relatively fewer in both eastern and northern Guangdong Province. The uneven spatial distribution of the MVs is closely related to the differences in the topographical features and the environment conditions over South China. MVs are prone to form over flat regions. The Pearl River Delta and eastern Guangdong regions are relatively flat compared to the more mountainous western and northern Guangdong regions. Moreover, the monsoonal south-westerlies, water vapor flux, atmospheric instability and vertical wind shear over southwest Guangdong are significantly larger than those in other regions and are favorable for the formation of MVs. The occurrence frequencies of MVs in central and southern parts of Guangdong display similar diurnal variations, reaching the peak during the late afternoon and early evening while dropping to the minimum overnight. However, the situation is opposite in northern Guangdong, with the peak overnight and the minimum during the late afternoon and early evening. The regional differences in diurnal variation are likely related to the moving direction of mesoscale convective systems (MCSs) in Guangdong. [ABSTRACT FROM AUTHOR]

The deformation and fracture characterization of an Mg−2Sn−1Ca alloy were studied through uniaxial isothermal compression tests. The flow stress curves, the efficiency of power dissipation, the instability parameter and the fracture behavior of an Mg−2Sn−1Ca alloy under the condition of various hot working parameters were investigated according to the experimental data. Processing maps were established by superimposing the instability map over the power dissipation map. It was found that flow stress reduces with increases in the deformation temperature and decreases in the strain rate. The processing of Mg−2Sn−1Ca alloys should avoid the instability region in which the conditions are high strain under high temperature and low strain under low temperature. At 473 K or a high strain rate, unidirectional cracks and fish scale cracks can be produced, and cracks can be avoided under the optimum processing area of 623–723 K/0.001–0.1 s−1. [ABSTRACT FROM AUTHOR]

To achieve a wide range and high accuracy detection of the vacuum level, for example, in an encapsulated vacuum microcavity, a composite-type MEMS Pirani gauge has been designed and fabricated. The Pirani gauge consists of two gauges of different sizes connected in series, with one gauge having a larger heat-sensitive area and a larger air gap for extending the lower measurable limit of pressure (i.e., the high vacuum end) and the other gauge having a smaller heat-sensitive area and a smaller air gap for extending the upper measurable limit. The high-resistivity titanium metal was chosen as the thermistor; SiNx was chosen as the dielectric layer, considering the factors relevant to simulation and manufacturing. By simulation using COMSOL Multiphysics and NI Multisim, a range of measurement of 2 × 10−2 to 2 × 105 Pa and a sensitivity of 52.4 mV/lgPa were obtained in an N2 environment. The performance of the fabricated Pirani gauge was evaluated by using an in-house made vacuum test system. In the test, the actual points of measurement range from 6.6 × 10−2 to 1.12 × 105 Pa, and the highest sensitivity is up to 457.6 mV/lgPa. The experimental results are better in the range of measurement, sensitivity, and accuracy than the simulation results. The Pirani gauge proposed in this study is simple in structure, easy to manufacture, and suitable for integration with other MEMS devices in a microcavity to monitor the vacuum level therein. [ABSTRACT FROM AUTHOR]

In order to obtain high-quality through-silicon via (TSV) arrays for high voltage applications, we optimized the fabrication processes of the Si holes, evaluated the dielectric layers, carried out hole filling by Cu plating, and detected the final structure and electric properties of the TSVs. The Si through-hole array was fabricated in an 8-inch Si substrate as follows: First, a blind Si hole array was formed by the Si deep reactive etching (DRIE) technique using the Bosch process, but with the largest width of the top scallops reduced to 540 nm and the largest notch elimidiameternated by backside grinding, which also opens the bottom ends of the Si blind holes and forms 500-μm-deep Si through holes. Then, the sidewalls of the Si holes were further smoothed by a combination of thermal oxidation and wet etching of the thermal oxide. The insulating capability of the dielectric layers was evaluated prior to metal filling by using a test kit. The metal filling of the through holes was carried out by bottom-up Cu electroplating and followed by annealing at 300 °C for 1 h to release the electroplating stress and to prevent possible large metal thermal expansion in subsequent high-temperature processes. The TSV arrays with different hole diameters and spacing were detected: no visible defects or structure peeling was found by scanning electron microscopy (SEM) observations, and no detectable interdiffusion between Cu and the dielectric layers was detected by energy dispersive X-ray (EDX) analyses. Electric tests indicated that the leakage currents between two adjacent TSVs were as low as 6.80 × 10−10 A when a DC voltage was ramped up from 0 to 350 V, and 2.86 × 10−9 A after a DC voltage was kept at 100 V for 200 s. [ABSTRACT FROM AUTHOR]

The safe operation of dams is related to the lifeline of the national economy, the safety of the people, and social stability, and dam safety monitoring plays an essential role in scientifically controlling the safety of dams. Since the effects of environmental variables were not considered in conventional monitoring data repairing methods (such as the single time series model and spatial interpolation model), a spatial model for repairing monitoring data combining the variable importance for projection (VIP) method and cokriging was put forward in this paper. In order to improve the accuracy of the model, the influence of different combinations of covariates on it was discussed, and the VIPj value greater than 0.8 was proposed as the threshold of covariates. The engineering verification shows that the VIP-cokriging spatial model had the advantages of high precision and strong applicability compared with the inverse distance weighting (IDW) model, the ordinary kriging model, and the universal kriging model, and the overall error can be reduced by more than 60%, which could better realize the expansion of the monitoring effect variable to the whole area of the dam space. The engineering application of the PBG dam showed that the model scientifically correlated the existing monitoring points with the spatial location of the dam, and reasonably repaired the measured values of the stopping and abnormal measured points, effectively ensuring that the spatial regular of the monitoring data could truly reflect the actual safety and operational status of the dam. [ABSTRACT FROM AUTHOR]

Copper serves as a co-factor for a host of metalloenzymes, particularly cytochrome c oxidase (COX). Although it is known that impaired COX function can lead to the excessive accumulation of reactive oxygen species (ROS), the mechanisms underlying how copper depletion leads to cell damage are poorly understood. Here, we have investigated the role of copper depletion during ferroptosis. The bathocuproinedisulfonic (BCS) treatment depolarized the mitochondrial membrane potential, increased the total cellular ROS levels, stimulated oxidative stress, and reduced the glutathione levels. Moreover, the depletion of copper limited the protein expression of glutathione peroxidase 4 (GPX4), which is the only enzyme that is known to prevent lipid peroxidation. Furthermore, we found that copper depletion decreased the sensitivity of the dermal papilla cells (DPCs) to erastin (an inducer of ferroptosis), and the ferroptosis inhibitor ferrostatin-1 (Fer-1) partially prevented BCS-mediated cell death. Overall, these findings establish a direct link between copper and ferroptosis; BCS-mediated copper depletion strongly enhances ferroptosis via mitochondrial perturbation and a reduction in antioxidative mechanisms. [ABSTRACT FROM AUTHOR]

In the past decade, with the development of communication technology, indoor air quality (IAQ) monitoring technology has made significant progress, which improves the real-time performance of the monitoring system, and reduces potential impacts on health-related pollutants. To investigate and analyze the development of IAQ monitoring technology and the application of communication technology in this field, this review conducts a comprehensive search, collation, and analysis of the literature in the relevant fields over the past decade, and reviews 91 articles from the usage scenarios, monitoring parameters, and communication technology of the IAQ monitoring system. The review shows the development and main research direction of IAQ monitoring, and focuses on the selection and application of communication technology in different projects. In addition, this study also discusses the problems and challenges in the IAQ monitoring system, which provides a reference for researchers and promotes the rapid and all-around development of IAQ monitoring. [ABSTRACT FROM AUTHOR]

This paper presents AlScN piezoelectric two-axis MEMS mirrors with gimbal-less and gimbaled designs fabricated in a CMOS-compatible manner. Integrated piezoelectric sensors provided feedback signals of the actual mirror positions. The mirror with a diameter of 1.5 mm possessed adjustable optical tilt angles of up to 22.6° @ 30 V, with a high resonance frequency of about 8.2 kHz, while the 3 mm mirror reached 48.5° @ 41 V. The mirror with the gimbaled structure exhibited an excellent field of view and good mechanical decoupling. Additionally, a significant improvement in mirror scanning performance was observed in a vacuum (4 Pa), proving that the optical field of view was magnified by more than a factor of 10. [ABSTRACT FROM AUTHOR]

Optimization with edge contact avoidance and contact stress minimization is essential for gear design. Due to the complex geometry of modified helical face gear drives, it is complicated to find the optimal design parameters with the consideration of issues including loading, assembly errors, and edge contact. As the finite element method is tedious and time-consuming, an optimization model with a simplified algorithm of the loaded tooth contact analysis with errors (ELTCA) for modified helical face gears is presented, and it can programmatically optimize the contact stress with edge contact avoidance. Firstly, a simplified ETCA algorithm is introduced, which reduces the five unknowns in the traditional contact equations to three. Secondly, the LTCA is analytically implemented according to the Hertz theory. Subsequently, an optimization model with the objective function of avoiding edge contact and reducing maximum contact stress is proposed. Furthermore, the proposed model is applied to reveal the effects of design parameters and assembly errors on the optimized contact path and stress. The results show that the optimization model is accurate and efficient; the design parameters and assembly errors have great effects on the meshing of modified helical face gears. [ABSTRACT FROM AUTHOR]

Neuro-glia antigen 2/chondroitin sulfate proteoglycan 4 (NG2/CSPG4, also called MCSP, HMW-MAA, MSK16, MCSPG, MEL-CSPG, or gp240) is a large cell-surface antigen and an unusual cell membrane integral glycoprotein frequently expressed on undifferentiated precursor cells in multiple solid organ cancers, including cancers of the liver, pancreas, lungs, and kidneys. It is a valuable molecule involved in cancer cell adhesion, invasion, spreading, angiogenesis, complement inhibition, and signaling. Although the biological significance underlying NG2/CSPG4 proteoglycan involvement in cancer progression needs to be better defined, based on the current evidence, NG2/CSPG4+ cells, such as pericytes (PCs, NG2+/CD146+/PDGFR-β+) and cancer stem cells (CSCs), are closely associated with the liver malignancy, hepatocellular carcinoma (HCC), pancreatic malignancy, and pancreatic ductal adenocarcinoma (PDAC) as well as poor prognoses. Importantly, with a unique method, we successfully purified NG2/CSPG4-expressing cells from human HCC and PDAC vasculature tissue blocks (by core needle biopsy). The cells appeared to be spheres that stably expanded in cultures. As such, these cells have the potential to be used as sources of target antigens. Herein, we provide new information on the possibilities of frequently selecting NG2/CSPG4 as a solid organ cancer biomarker or exploiting expressing cells such as CSCs, or the PG/chondroitin sulfate chain of NG2/CSPG4 on the cell membrane as specific antigens for the development of antibody- and vaccine-based immunotherapeutic approaches to treat these cancers. [ABSTRACT FROM AUTHOR]

The heavy ion-induced sensitive area is an essential parameter for space application integrated circuits. Circuit Designers need it to evaluate and mitigate heavy ion-induced soft errors. However, it is hard to measure this parameter due to the lack of test structures and methods. In this paper, a test method called TAISAM was proposed to measure the heavy ion-induced sensitive area. TAISAM circuits were irradiated under the heavy ions. The measured sensitive areas are 1.75 μ m 2 and 1.00 μ m 2 with different LET values. TAISAM circuits are also used to investigate the layout structures that can affect the sensitive area. When the source region of the target transistor is floating, the heavy ion-induced sensitive area decreases by 28.5% for the target PMOS transistor while it increases by more than 28% for the target NMOS transistor. When the well contacts are added, the heavy ion-induced sensitive area decreases by more than 25% for the target PMOS transistor while it remains unchanged for the target NMOS transistor. Experimental results directly validate that the two structures significantly affect the heavy ion-induced sensitive area. [ABSTRACT FROM AUTHOR]

Accurate and efficient measurements of the piezoelectric properties of AlN and AlScN films are very important for the design and simulation of micro-electro-mechanical system (MEMS) sensors and actuator devices. In this study, a process control monitor (PCM) structure compatible with the device manufacturing process is designed to achieve accurate determination of the piezoelectric coefficients of MEMS devices. Double-beam laser interferometry (DBLI) and laser Doppler vibrometry (LDV) measurements are applied and combined with finite element method (FEM) simulations, and values of the piezoelectric parameters d33 and d31 are simultaneously extracted. The accuracy of d31 is verified directly by using a cantilever structure, and the accuracy of d33 is verified by in situ synchrotron radiation X-ray diffraction; the comparisons confirm the viability of the results obtained by the novel combination of LDV, DBLI and FEM techniques in this study. [ABSTRACT FROM AUTHOR]

Three layout-hardened Dual Interlocked Storage Cell (DICE) D Flip-Flops (DFFs) were designed and manufactured based on an advanced 28 nm planar technology. The systematic vertical and tilt heavy ion irradiations demonstrated that the DICE structure contributes to radiation tolerance. However, it is hard to achieve immunity from a Single Event Upset (SEU), even when a ~3-µm well isolation is utilized. The SEU mitigation of the hardened DFFs was affected by the data patterns and clock signals due to the imbalance in the number of upset nodes. When the clock signal equalled 0, no error was observed in 181Ta irradiation, indicating that the DICE DFFs are SEU tolerant in vertical irradiation owing to their reasonable isolation of sensitive volumes. The divergences of SEU cross-sections were enlarged by our specially designed joint change of tilt incidences for both the along-cell and cross-cell irradiation of heavy ions. The evaluations of SEU for both the vertical and tilt irradiations assist with eliminating the overestimation of SEU tolerance and guarantee the in-orbit safety of spacecraft in harsh radiation environments. [ABSTRACT FROM AUTHOR]

Protoporphyrin IX-based all-solid-state choline (Ch) ion-selective electrodes (ISEs) were fabricated and characterized. Poly (3,4-ethylene dioxythiophene) doped with poly (styrene sulfonate) (PEDOT/PSS) functioning as an ion-to-electron transducer was electropolymerized on the gold wire (0.5 mm diameter). The conductive polymer was covered with a Ch selective membrane containing protoporphyrin IX as an ionophore, which exhibited a lower detection limit of 0.49 μM with the potentiometric method. The Ch sensor performed a wide linear range from 1 μM to 1 mM, a fast response time of less than 5 s, and a decent selectivity of common inorganic and organic ions in the human body. Characteristics such as pH and temperature stability, life span, reproducibility and repeatability were also investigated to be satisfied. With the background of artificial cerebrospinal fluid, the recovery rate in 10 − 5 M of Ch solution was measured by the standard addition method, revealing the potential for biological application. [ABSTRACT FROM AUTHOR]

Crest cracking is one of the most common damage types for high earth-rockfill dams. Cracking risk of dam crest is closely related to the duration of abnormal deformation state. In this paper, a methodology for dynamic risk evaluation and early warning of crest cracking for high earth-rockfill dams is proposed and mainly consists of: (a) The discrimination of abnormal deformation state related to crest cracking, which is implemented by comparing the crest settlement inclination with the threshold value. (b) Computation of crest cracking probability and estimation of cracking time. The exponential distribution is adopted to represent the probability distribution of the duration TAS of abnormal state before crest cracking. Then the crest cracking probability in a given time can be computed by integration with respect to TAS. Inversely, the cracking time corresponding to a given probability can be estimated. (c) Determination of the values of probability adopted to early warn crest cracking, which are suggested to be selected by statistical analysis of the calculated probabilities at the observed cracking times. (d) Bayesian estimation and updating of probability distribution of the parameter λ in the PDF of TAS, according to observed durations of abnormal state before crest cracking. The methodology is illustrated and verified by the case study for an actual earth-rockfill dam, of which crest cracking and recracking events were observed during the periods of high reservoir level. According to the observed values of TAS, the probability distribution for λ is progressively updated and the dispersion of the distributions of λ gradually decreases. The crest cracking probability increases with the duration of abnormal state and the width of confidence interval of the estimated cracking probability progressively contracts with the updating of the distribution for λ. Finally, the early warning of crest cracking for the dam is investigated by estimating the lower limit of cracking time. It is shown that early warning of crest cracking can be issued from at least 20 days ahead of the occurrence of crest cracking event. The idea of using duration of abnormal state of crest settlement to evaluate crest cracking risk of the earth-rockfill dam in this paper may be applicable to other dams. [ABSTRACT FROM AUTHOR]

Concrete gravity dams are one of the most common water retention structures, some of which are located in seismically active regions. Determination of damage level after earthquakes plays an important role in assessing the safety behavior of concrete dams. Compared with the traditional performance parameters obtained from numerical simulations, such as the damage coefficient, energy dissipation, failure modes, and stress state, etc., the displacement of dams can be acquired from daily monitoring data conveniently and quickly. It is of great significance for the rapid and effective evaluation of dam properties after earthquakes. The residual displacement in the concrete gravity dam crest was adopted as the performance parameter in the paper, and the linear mapping function between the residual displacement and the damage coefficient was established based on the concrete damaged plasticity model (CDP). Based on the traditional classification method with damage coefficient, a residual displacement-based seismic damage classification method with corresponding level limits was proposed. The seismic fragility analysis of Guandi concrete gravity dam was conducted as an example to illustrate the presented methodology. The results indicate that the proposed method is reasonable, effective, and can be easily applied to different projects after slight modifications. [ABSTRACT FROM AUTHOR]

To solve the problems of poor exploration ability and convergence speed of traditional deep reinforcement learning in the navigation task of the patrol robot under indoor specified routes, an improved deep reinforcement learning algorithm based on Pan/Tilt/Zoom(PTZ) image information was proposed in this paper. The obtained symmetric image information and target position information are taken as the input of the network, the speed of the robot is taken as the output of the next action, and the circular route with boundary is taken as the test. The improved reward and punishment function is designed to improve the convergence speed of the algorithm and optimize the path so that the robot can plan a safer path while avoiding obstacles first. Compared with Deep Q Network(DQN) algorithm, the convergence speed after improvement is shortened by about 40%, and the loss function is more stable. [ABSTRACT FROM AUTHOR]

IR-drop is a fundamental constraint by almost all integrated circuits (ICs) physical designs, and many iterations of timing engineer change order (ECO), IR-drop ECO, or other ECO are needed before design signoff. However, IR-drop analysis usually takes a long time and wastes so many resources. In this work, we develop a fast dynamic IR-drop predictor based on a machine learning technique, XGBoost, and the prediction method can be applied to vector-based and vectorless IR-drop analysis simultaneously. Correlation coefficient is often used to characterize the symmetry of prediction data and golden data, and our experiments show that the prediction correlation coefficient is more than 0.96 and the average error is no more than 1.3 mV for two industry designs, which are of 2.4 million and 3.7 million instances, respectively, and that the analysis is speeded up over 4.3 times compared with the IR-drop analysis by commercial tool, Redhawk. [ABSTRACT FROM AUTHOR]

In order to carry out various detections of system indicators during the research and development phase of infrared guided missiles, the article first analyzes several main design schemes of the infrared guided missile detection simulator and finds that it has the disadvantages of difficult processing technology and low detection accuracy. The overall structure of the detection device was designed, including the design of the rotation and swing mechanism, lens mechanism, optical system and control system. The optical system error analysis is performed on the infrared guided missile detection simulator. The position of the receiving light source is obtained by analyzing the mechanism characteristics of the detection simulator and the kinematics model of the device. The phase difference analysis of the eccentricity and tilt system is obtained. The image quality was evaluated by the optical transfer function (MTF), and the system error was found to meet the requirements of imaging quality. The experiments show that the simulation of 1.7~4.9 um medium wave infrared dynamic target signals provides an accurate and reasonable experimental environment for the missile and the verification of the light source target and meets the experimental requirements. [ABSTRACT FROM AUTHOR]

In this paper, a voltage-boost-type non-voltage drop single-phase full-bridge inverter connected to a switched-capacitor structure is proposed. The output voltage of the inverter is controlled by the pulse width modulation of a DSP to control the lead and break of the active switches. The full-bridge switches work at low frequency; the other switches work at high frequency. The inverter uses two capacitor modules to charge and discharge alternately so as to overcome the problem of voltage drop on the output side of the inverter in the transition stage from series capacitor discharge to parallel charge. By analyzing the charge–discharge characteristics of the RC charge–discharge circuit, the capacitor charge–discharge cycle can be adjusted to alter the output voltage within a certain range. The results from the physical construction verify the Simulation results achieved well, which demonstrates satisfactory performance that supports the verification of the above theory. [ABSTRACT FROM AUTHOR]

The interoperations of endpoint devices are generally achieved by gateways in Internet of Things (IoT) systems. However, the gateways mainly focus on networking communication, which is lack of data logic control capabilities. The microcontrollers with embedded intelligence could work as an intermediate device to help the interconnections of the endpoint devices. Moreover, they could help control the endpoint devices. In this paper, a microcontroller architecture with intelligent and scalable characteristics is proposed. The intelligence means that the microcontroller could control the target endpoint devices by its logical circuits, and the scalability means that the microcontroller architecture could be easily extended to deal with more complex problems. Two real world industrial implementations of the proposed architecture are introduced. The implementations show that the microcontroller is important to provide the intelligent services to users in IoT systems. Furthermore, a simulation experiment based on the cloud model is designed to evaluate the proposed method. The experimental results demonstrate the effectiveness of the proposed architecture. [ABSTRACT FROM AUTHOR]

The reliability of integrated circuits under advanced process nodes is facing more severe challenges. Single-event transients (SET) are an important cause of soft errors in space applications. The SET caused by heavy ions in the 28 nm bulk silicon inverter chains was studied. A test chip with good symmetry layout design was fabricated based on the 28 nm process, and the chip was struck by using 5 kinds of heavy ions with different linear energy transfer (LET) values on heavy-ion accelerator. The research results show that in advanced technology, smaller sensitive volume makes SET cross-section measured at 28 nm smaller than 65 nm by an order of magnitude, the lower critical charge required to generate SET will increase the reliability threat of low-energy ions to the circuit, and high-energy ions are more likely to cause single-event multiple transient (SEMT), which cannot be ignored in practical circuits. The transients pulse width data can be used as a reference for SET modeling in complex circuits. [ABSTRACT FROM AUTHOR]

The degradation of methylene blue (MB) using an upgraded dielectric barrier discharge (DBD) plasma reactor was investigated in this paper. Air plasma was generated in the glass bead packed bed in the reactor, which was propagated into MB solution through a microporous diffuser plate. Microdischarge phenomenon can be observed on the interface of MB solution and the diffuser plate, where plasma active species were generated. The effects of air flow rate, initial solution concentration, initial solution pH, and initial solution conductivity on MB degradation were examined. Experimental results indicated that the proposed plasma reactor was effective for MB degradation. No obvious change in MB degradation efficiency was obtained for solution with various initial pH and conductivities, which suggested the potential of the reactor in actual wastewater treatment. The possible mechanism of the generation of plasma active species for MB degradation was proposed. In addition, the total organic carbon removal and chemical oxidation demand removal after 30 min treatment were 38.5% and 48.3%, which was higher than that obtained by ozone. The energy yield for MB degradation reached up to 9.3 g/kWh. Finally, a possible degradation pathway of MB solution was proposed. [ABSTRACT FROM AUTHOR]

The investigation of damage development is essential for the design and optimization of hybrid structures. This paper provides a reference for the structural design of brittle-ductile hybrid LVI-resistant laminates through analyzing the damage development mechanism of carbon/Kevlar fabric-reinforced composite laminates. The effects of Kevlar fabric hybrid ply and intercalation on the damage development of carbon/Kevlar composite laminates under low-velocity impact (LVI) were investigated using quasi-static indentation (QSI). It was found that an increase in the Kevlar hybrid ratio significantly reduced the peak load and stiffness of these laminates (the maximum decreases in strength and stiffness were 46.03% and 41.43%, respectively), while laminates with identical hybrid ratios but different plying configurations maintained a comparable stiffness under QSI, with differences of less than 5%. Interestingly, Kevlar fibers exhibited irregular fractures as the yarn was splitting, while carbon fibers presented neat breaks, which indicated material-specific failure modes. Notably, the introduction of Kevlar hybridization beyond pure Kevlar configurations (KKKK) resulted in a decrease in the percentage of fiber damage (CCCC, CCCK, CCKK, and KCCK accounted for 80%, 79.8%, 70%, and 60% of fiber damage, respectively), attributed to an increase in resin cracks and lower levels of Kevlar yarn breakage. The internal damage diameter of specimens was accurately predicted from the diameter of visible damage on the QSI surface. Compared with CCCC and CCKK setups, which are affected by resin cracks formed via the carbon surface on the loading side propagating along the yarn direction (including the yarn settling direction), KCCK demonstrated less delamination between the first and second ply.

Phase transition behaviors of poly( N -isopropylacrylamide) nanogels with different compositions induced by (-)-epigallocatechin-3-gallate (EGCG) and ethyl gallate (EG) has been investigated systematically. Monodisperse poly( N -isopropylacrylamide- co - N -hydroxymethyl acrylamide) (P(NIPAM- co -NMAM)) and poly( N -isopropylacrylamide- co -2-hydroxyethyl methacrylate) (P(NIPAM- co -HEMA)) nanogels with different feeding monomer ratios were prepared by emulsion polymerization. P(NIPAM- co -NMAM) nanogels exhibit rapid isothermal phase transition behavior in EGCG solutions with low concentration (10 -3 mol/L) in less than 10 minutes. The thermosensitive phase transition behaviors of nanogels are affected not only by the copolymerized monomers but also by the concentrations of EGCG and EG in aqueous solutions. Nanogels remain in a shrunken state and do not exhibit thermosensitive phase transition behaviors in EGCG solutions (≥5 mmol/L), whereas they display thermo-responsive phase transition behaviors in EG solutions. The volume phase transition temperature (VPTT) shifts to lower temperatures with increasing EG concentration. The diameters of P(NIPAM- co -NMAM) nanogels decrease with increasing EG concentration at temperatures between 29 and 33 °C. In contrast, the diameters of P(NIPAM- co -HEMA) nanogels increase with increasing EGCG concentration at temperatures between 37 and 45 °C. The results demonstrate the potential of nanogels for simple detection of EG and EGCG concentrations in aqueous solutions over a wide temperature range, and EGCG can serve as a signal for the burst-release of drugs from the P(NIPAM- co -NMAM)-based carriers at physiological temperature.

In order to obtain high-quality through-silicon via (TSV) arrays for high voltage applications, we optimized the fabrication processes of the Si holes, evaluated the dielectric layers, carried out hole filling by Cu plating, and detected the final structure and electric properties of the TSVs. The Si through-hole array was fabricated in an 8-inch Si substrate as follows: First, a blind Si hole array was formed by the Si deep reactive etching (DRIE) technique using the Bosch process, but with the largest width of the top scallops reduced to 540 nm and the largest notch elimidiameternated by backside grinding, which also opens the bottom ends of the Si blind holes and forms 500-μm-deep Si through holes. Then, the sidewalls of the Si holes were further smoothed by a combination of thermal oxidation and wet etching of the thermal oxide. The insulating capability of the dielectric layers was evaluated prior to metal filling by using a test kit. The metal filling of the through holes was carried out by bottom-up Cu electroplating and followed by annealing at 300 °C for 1 h to release the electroplating stress and to prevent possible large metal thermal expansion in subsequent high-temperature processes. The TSV arrays with different hole diameters and spacing were detected: no visible defects or structure peeling was found by scanning electron microscopy (SEM) observations, and no detectable interdiffusion between Cu and the dielectric layers was detected by energy dispersive X-ray (EDX) analyses. Electric tests indicated that the leakage currents between two adjacent TSVs were as low as 6.80 × 10 -10 A when a DC voltage was ramped up from 0 to 350 V, and 2.86 × 10 -9 A after a DC voltage was kept at 100 V for 200 s.

Predictive maintenance plays an important role in modern Cyber-Physical Systems (CPSs) and data-driven methods have been a worthwhile direction for Prognostics Health Management (PHM). However, two main challenges have significant influences on the traditional fault diagnostic models: one is that extracting hand-crafted features from multi-dimensional sensors with internal dependencies depends too much on expertise knowledge; the other is that imbalance pervasively exists among faulty and normal samples. As deep learning models have proved to be good methods for automatic feature extraction, the objective of this paper is to study an optimized deep learning model for imbalanced fault diagnosis for CPSs. Thus, this paper proposes a weighted Long Recurrent Convolutional LSTM model with sampling policy (wLRCL-D) to deal with these challenges. The model consists of 2-layer CNNs, 2-layer inner LSTMs and 2-Layer outer LSTMs, with under-sampling policy and weighted cost-sensitive loss function. Experiments are conducted on PHM 2015 challenge datasets, and the results show that wLRCL-D outperforms other baseline methods.

Web of Things (WoT) facilitates the discovery and interoperability of Internet of Things (IoT) devices in a cyber-physical system (CPS). Moreover, a uniform knowledge representation of physical resources is quite necessary for further composition, collaboration, and decision-making process in CPS. Though several efforts have integrated semantics with WoT, such as knowledge engineering methods based on semantic sensor networks (SSN), it still could not represent the complex relationships between devices when dynamic composition and collaboration occur, and it totally depends on manual construction of a knowledge base with low scalability. In this paper, to addresses these limitations, we propose the semantic Web of Things (SWoT) framework for CPS (SWoT4CPS). SWoT4CPS provides a hybrid solution with both ontological engineering methods by extending SSN and machine learning methods based on an entity linking (EL) model. To testify to the feasibility and performance, we demonstrate the framework by implementing a temperature anomaly diagnosis and automatic control use case in a building automation system. Evaluation results on the EL method show that linking domain knowledge to DBpedia has a relative high accuracy and the time complexity is at a tolerant level. Advantages and disadvantages of SWoT4CPS with future work are also discussed.