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A non-isolated fast charger is proposed for electric vehicle (EV) battery system. The leakage current can be reduced by the novel charging system without bulky transformer. A zero sequence voltage control method is developed to stabilize the common mode voltage thus reduce the leakage current. The proposed fast charger includes two energy conversion stages: DC/DC converter for battery side control; DC/AC converter for grid interface and common mode voltage control. The parameters of the system with LCL filter are analyzed and optimized for a better performance of grid interface. Grid service functions are designed for the fast charger to provide grid voltage/frequency compensations. High power (22kW) and high efficiency 99% are achieved with low leakage current 20mA. The experiments are implemented to verify the proposed fast charger system.

High frequency and high efficiency inductor design method is proposed for the application of critical soft switching with large current ripple to improve the power density and efficiency of electrified energy conversion systems. Firstly for the theoretical design section, the core/coil size, number of turns, airgap, inductance are optimized to minimize the inductor losses. Secondly for the structural design section of coil, a 3D routing method of litz PCB winding is developed to reduce the AC copper losses. The strand number, trace width, thickness and layout of litz PCB are analyzed in detail. Different types of coil, including solid/litz types of PCB/wire windings, are compared and analyzed based on the high frequency copper loss reduction and space utilization. For the structural design section of core, E and I cores are optimized to reduce the volume and core losses. Specifically, E-E, E-I, I-I and E-Air types of core structures are designed and compared considering the core loss, volume reduction and airgap limitation for the stack of turns. Ten derived prototypes are built to benchmark with the commercial inductors. Power losses, temperature rise and cost are reduced by a factor of 10, 2.5 and 3 with the proposed core and coil structures.

We propose TPE, a hardware-based framework to perform workload execution forensics in microprocessors. Specifically, TPE leverages custom hardware instrumentation to capture the operational profile of the Translation Lookaside Buffer (TLB), as well as process these information off-line through multiple machine learning and/or deep learning approaches, in order to identify the executed processes and reconstruct the workload. Unlike software-based forensics methods implemented at the operating system (OS) or hypervisor level, whose data logging and monitoring mechanisms may be compromised through software attacks, TPE is implemented directly in hardware and, therefore, provides innate immunity to software tampering. A prototype of TPE is demonstrated in Linux on two representative architectures, i.e., 32-bit $\times 86$ and 64-bit RISC-V, implemented in the Simics and Spike simulation environment respectively. Experimental results using the Mibench workload benchmark suite reveal favorable process identification accuracy at low logging rate, which corroborates the effectiveness and the generalizability of TPE.

OBJECTIVE The island sign of non-contrast computed tomography (NCCT) is a risk factor for hematoma expansion (HE) after spontaneous intracerebral hemorrhage (sICH), but has inconsistent conclusions. A meta-analysis was performed to investigate the predictive accuracy of island sign for HE. METHODS A systematic review of published literature on island sign and hematoma expansion was conducted. The pooled sensitivity, specificity and summary receiver operating characteristics curve (SROC) were generated. The publication bias was assessed by Deeks’ funnel plot asymmetry test. RESULTS Nine studies with a total of 2,939 patients were included in the present study. The pooled sensitivity and specificity of island sign for predicting hematoma expansion was 0.50 and 0.89, respectively. The area under the curve (AUC) was 0.73 in the SROC curve. There was no significant publication bias. CONCLUSION This meta-analysis suggests that island sign of non-contrast CT has a good predictive accuracy for hematoma enlargement in intracerebral hemorrhage.

This article presents a methodology for finding hardware security bugs, using Lint tools that use static analysis techniques, along with best practices for code reviews and developing test cases.

Beneath the potential benefits of the rapidly growing Internet of Things (IoT) technology lurk security risks. In this article, we propose a hardware-based generic framework for IoT workload forensics, an infrastructural technique to securely monitor and ensure delivered IoT services in accordance with specifications and regulatory compliance. In particular, this technique identifies digital workloads being executed in real time through dynamic program behavior modeling based on architecture-level data, fulfilled by dedicated machine learning hardware, without the intervention of high-level software, e.g., the OS and/or the hypervisor. In contrast to the conventional software-based solutions, whose effectiveness may be undermined by software attacks, and which introduce significant runtime overhead, a hardware-based framework enables a secure, prompt and non-intrusive solution. The proposed framework was evaluated on Zedboard, a Zynq-7000 FPGA embedding an ARM Cortex-A9 core. Experimental results using Mibench workload benchmark reveal an average workload identification accuracy of 96.37 percent with insignificant area/power overhead.

Workload forensics is a key component of software security that collects and analyzes information to identify suspicious behavior. Researchers from UT Dallas explore a hardware alternative since it is immune from software attacks and does not rely on data collected from the OS or software applications.

This paper derives a variable-frequency critical soft-switching control method for nonisolated DC/DC converters using wide-bandgap devices. The critical soft switching control technique under maximum frequency trajectory is introduced to maintain zero voltage switching over a wide range of modulation ratios according to the load variation. The concept prevents turn-on losses that are typically much larger than the turn-off losses in SiC and GaN FETs and the latter can be further reduced by adding external drain-source capacitors. We have derived the boundary conditions for critical soft switching operation. For the reduction of inductor value and volume, a maximum available switching frequency is applied to the converter within the constraints of device requirement and soft switching boundary conditions. We demonstrate experimentally that the proposed concept reduces the power losses in the wide-bandgap devices by a factor of approximately 3, enables an increase of the switching frequency by a factor of about 5, and a decrease of the main inductance by a factor of about 10. Then variable frequency critical soft switching control method is proposed with the constraints to maintain the maximum frequency within soft switching operation. Since our test bench uses off-the-shelf inductors, the inductors are subject to significant high frequency losses. Despite this, the converter efficiency increases by 1%.

Background: Ferroptosis plays an important role in glioma and significantly affects the prognosis, but the specific mechanism has not yet been elucidated. Recent studies suggest that autophagy regulates the process of ferroptosis. This study aimed to find potential autophagy-ferroptosis genes and explore the prognostic significance in glioma.Methods: Ferroptosis and autophagy genes were obtained from two online databases (zhounan.org/ferrdb and autophagy.lu/). The RNAseq data and clinical information were obtained from the Chinese Glioma Genome Atlas (CGGA) database (http://www.cgga.org.cn/). Univariate, multivariate, lasso and Cox regression analysis screened out prognosis-related genes, and a risk model was constructed. Receiver operating characteristic (ROC) curve analysis evaluated the predictive efficiency of the model. Finally, a nomogram was constructed to more accurately predict the prognosis of glioma.Results: We developed a Venn diagram showing 23 autophagy-ferroptosis genes. A total of 660 cases (including RNA sequences and complete clinical information) from two different cohorts (training group n = 413, verification group n = 247) of the CGGA database was acquired. Cohorts were screened to include five prognosis-related genes (MTOR, BID, HSPA5, CDKN2A, GABARAPLA2). Kaplan-Meier curves showed that the risk model was a good prognostic indicator (p < 0.001). ROC analysis showed good efficacy of the risk model. Multivariate Cox analysis also revealed that the risk model was suitable for clinical factors related to prognosis, including type of disease (primary, recurrence), grade (III-IV), age, temozolomide treatment, and 1p19q state. Using the five prognosis-related genes and the risk score, we constructed a nomogram assessed by C-index (0.7205) and a calibration plot that could more accurately predict glioma prognosis.Conclusion: Using a current database of autophagy and ferroptosis genes, we confirmed the prognostic significance of autophagy-ferroptosis genes in glioma, and we constructed a prognostic model to help guide treatment for high grade glioma in the future.

Inductor design can be a process of repetitive core searching and iterative turn and airgap calculation, where every decision to be made involves a trade-off in terms of power loss, cost and power density. This paper deeply discusses the inductor design for a transformerless DC EV battery charger where inductance and operating DC bias are required to be high ( $\mathrm{500}\ \ \mu \mathrm{H}$ and 32 A). The design of the charger's filter is discussed to provide the values of the passive components and to motivate the inductor design. Two commonly used but seldom compared winding options, Litz wire and printed circuit board, are both designed and examined with respect to power loss and difficulty of the fabrication. Through tuning of the trace width and copper weight, the PCB design can provide similar performance to the Litz wire configuration, at higher cost and increased manufacturing complexity. In order to verify the text-colorblacktheoretical calculations, high-fidelity 3D finite element analysis is performed for both inductor types. After comparison, the Litz wire implementation was chosen for its reduction in power losses, cost and manufacturing complexity. The Litz wire inductor is assembled and tested on a transformerless DC charger platform with ≥ 99% efficiency at 11 kW and ≥98% efficiency at up to 22 kW.

The transformer is one of the most bulky, expensive and lowest efficiency components in a typical electric vehicle charging system. Its removal is conducive to enabling greater proliferation of charging infrastructure and the adoption of electrified vehicles. However, simply removing it can lead to common mode voltage and leakage current issues that present a safety hazard. To address this and to be compliant with international standards, a transformerless electric vehicle charging system enabled by control is proposed without need for additional components. This is achieved by connecting the three-phase filter capacitor neutral point to the DC negative, which allows the zero sequence voltage to be controlled, thereby mitigating leakage currents. Simulations show that the leakage current of the proposed transformerless charger can be made standard compliant with the topology while experiments provide validation through zero sequence voltage and active and reactive power steps. The results demonstrate that the transformer can be removed from electric vehicle chargers.

This paper focuses on the transformerless fast charging solution of plug-in electric vehicles (PEV). In a PEV system, the chargers can be classified into three levels: ac level 1 ( 19.2 kW) [1]. For the high-power level 3 fast charging system, it is not viable to place an on-board charger due to the volume and weight. We propose two topologies for off-board non-isolated DC charger to provide fast DC charging with high-power (up to 22 kW). The proposed topology includes two stages, AC/DC rectifier and DC/DC converter. According to the grid requirement, the AC/DC rectifier topologies can be divided into single-phase and three-phase applications. For the three-phase grid interface, the design of the DC charger uses the same hardware for both the grid interface and the DC/DC stage. The development and manufacturing costs are reduced due to the symmetric design of the two stages of circuit. The leakage current issue of the non-isolated system is solved based on the proposed topologies and the volume and weight are largely decreased because of the transformerless structure. The design procedures and a typical three-phase prototype are shown in this paper to verify the proposed structures.

Background Although chronic periodontitis has previously been reported to be linked with Alzheimer's disease (AD), the pathogenesis between the two is unclear. The purpose of this study is to analyze and screen the relevant and promising molecular markers between chronic periodontitis and Alzheimer's disease (AD). Methods In this paper, we analyzed three AD expression datasets and extracted differentially expressed genes (DEGs), then intersected them with chronic periodontitis genes obtained from text mining, and finally obtained integrated DEGs. We followed that by enriching the matching the matching cell signal cascade through DAVID analysis. Moreover, the MCODE of Cytoscape software was employed to uncover the protein–protein interaction (PPI) network and the matching hub gene. Finally, we verified our data using a different independent AD cohort. Results The chronic periodontitis gene set acquired from text abstracting was intersected with the previously obtained three AD groups, and 12 common genes were obtained. Functional enrichment assessment uncovered 12 cross-genes, which were mainly linked to cell morphogenesis involved in neuron differentiation, leading edge membrane, and receptor ligand activity. After PPI network creation, the ten hub genes linked to AD were retrieved, consisting of SPP1, THY1, CD44, ITGB1, HSPB3, CREB1, SST, UCHL1, CCL5 and BMP7. Finally, the function terms in the new independent dataset were used to verify the previous dataset, and we found 22 GO terms and one pathway, "ECM-receptor interaction pathways", in the overlapping functional terms. Conclusions The establishment of the above-mentioned candidate key genes, as well as the enriched signaling cascades, provides promising molecular markers for chronic periodontitis-related AD, which may help the diagnosis and treatment of AD patients in the future.

Background In May 2017, 17 dogs in a German Shepherd breeding kennel in northern China developed respiratory clinical signs. The owner treated the dogs with an intravenous injection of Shuang-Huang-lian, a traditional Chinese medicine, and azithromycin. The respiratory signs improved 3 days post-treatment, however, cysts were observed in the necks of eight dogs, and three of them died in the following 2 days. Case presentation Quantitative real-time PCR was used to detect canine influenza virus (CIV). All of the dogs in this kennel were positive and the remaining 14 dogs had seroconverted. Two of the dogs were taken to the China Agricultural University Veterinary Teaching Hospital for further examination. Two strains of influenza virus (A/canine/Beijing/0512–133/2017 and A/canine/Beijing/0512–137/2017) isolated from the nasal swabs of these dogs were sequenced and identified as avian-origin H3N2 CIV. For the two dogs admitted to the hospital, hematology showed mild inflammation and radiograph results indicated pneumonia. Cyst fluid was plated for bacterial culture and bacterial 16 s rRNA gene PCR was performed, followed by Sanger sequencing. The results indicated an Enterococcus faecalis infection. Antimicrobial susceptibility tests were performed and dogs were treated with enrofloxacin. All 14 remaining dogs recovered within 16 days. Conclusions Coinfection of H3N2 CIV and Enterococcus faecalis was detected in dogs, which has not been reported previously. Our results highlight that CIV infection might promote the secondary infection of opportunistic bacteria and cause more severe and complicated clinical outcomes.

Hypoglycemia is a serious and potentially fatal complication experienced by people with insulin-dependent diabetes. The complication is usually caused by insulin overdose, skipping meals, and/or excessive physical activities. In type 1 diabetes (T1D), on top of impaired pancreatic α-cells, excessive levels of somatostatin from δ-cells further inhibit glucagon secretion to counteract overdosed insulin. Herein, we aimed to develop a microneedle (MN) patch for transdermal delivery of a peptide (PRL-2903) that antagonizes somatostatin receptor type 2 (SSTR2) in α-cells. First, we investigated the efficacy of subcutaneously administered PRL-2903 and identified the optimal dose (i.e., the minimum effective dose) and treatment scheduling (i.e., the best administration time for hypoglycemia prevention) in a T1D rat model. We then designed an MN patch using a hyaluronic acid (HA)-based polymer. The possible effect of the polymer on stabilizing the native structure of PRL-2903 was studied by molecular dynamics (MD) simulations. The results showed that the HA-based polymer could stabilize the PRL-2903 structure by restricting water molecules, promoting intra-molecular H-bonding, and constraining torsional angles of important bonds. In vivo studies with an overdose insulin challenge revealed that the PRL-2903-loaded MN patch effectively increased the plasma glucagon level, restored the counter-regulation of blood glucose concentration, and prevented hypoglycemia. The proposed MN patch is the first demonstration of a transdermal microneedle patch designed to deliver an SSTR2 antagonist for the prevention of hypoglycemia. This counter-regulatory peptide delivery system may be applied alongside with insulin delivery systems to provide a more effective and safer treatment for people with insulin-dependent diabetes.

This study aims to identify the causes of coordination breakdowns among control crews and to understand their coordination-behaviour patterns during emergencies in nuclear power plants (NPPs). On the basis of in-depth interviews with 18 control-crew operators, we identified 25 causes of coordination breakdown related to work processes, personnel, and situation and organisation. In addition, we observed 12 control-crew training sessions that dealt with emergencies and conducted lag-sequential analysis. The levels of coordination effectiveness were evaluated using the proportion of coordination breakdowns and the anticipation ratio. We found that higher-performing teams exhibited more non-random coordination behavioural patterns than did lower-performing teams. Coordination-behaviour patterns specific to the higher-performing teams included adaptive workload management (from senior operators) and proactive seeking performance monitoring (from junior operators). The findings of the study enrich our understanding of the critical factors and processes that influence coordination effectiveness of NPP control crews.

Base on computational imaging, a new imaging system is proposed by making full use of global optimization, system reconstruction and control mechanism in practical operation. The theory and engineering method of space-time adaptive imaging are established, which overcome the shortcomings of complex traditional system structure and control factors, and large amount of computation and so on. In the design and implementation of the core optical system, the scientific nature of the standard group, the running speed and the maneuverability of the technology are effectively considered. It promotes a new type of large field of view, super resolution imaging technology, especially wide area applications. The physical and mathematical models are used, as well as the reconstruction algorithm is improved. The experimental system is established, and the simulation results support our theoretical prediction. This technology has potential value in brand fields.

Space-time adaptive methods, and their expansion of the aspects of application and operation platform, are extending from the traditional space-time system design to the structured devices, augmented reality, virtual reality, artificial intelligence and open Internet and so on,based on the holographic beam tracing technology, the core technology system on real scene was built, which can not only provide realistic reappearance effect, but also confirmed a practical application.

Transformer-less grid-connected photovoltaic (PV) systems face the challenge of keeping low leakage current due to their nonisolation configurations. To better attenuate the leakage current in the single-phase systems, a few neutral-point-clamped (NPC) topologies have been proposed. This paper further proposes an active NPC method by only employing one additional switch to connect the midpoint dc link and the freewheeling circuit of traditional single-phase PV inverters. After exploring the operational principles of freewheeling circuits, the construction method and the implementation of active NPC circuits in transformer-less single-phase inverters are elaborated in detail. Since only the leakage current flows through the additional NPC switch, the presented single-phase NPC inverters could still maintain the high operational efficiency with the further attenuated leakage current. The experimental results verified the theoretical findings.

The common-mode leakage current should be carefully considered when designing a transformer-less photovoltaic (PV) inverter since the leakage current can cause the output current distortion and increase the operational risk. The unipolar sinusoidal pulse width modulation of the traditional H-bridge inverter can produce the superior output performance but will cause a high-frequency fluctuated common-mode voltage and consequently the nonnegligible leakage current. To attenuate the fluctuation phenomena of common-mode voltage, few neutral-point-clamped (NPC) circuits have been designed to clamp the neutral-point voltage and maintain the common-mode voltage constant. This paper analyzes the equivalent common-mode circuit of single-phase inverters and proposes a generalized design principle of multiterminal NPC circuits, whose unidirectional and bidirectional variations are fully analyzed. Subsequently, two types of single-phase PV inverters with the NPC circuits are proposed. Also, the operational losses and component counts are compared between the proposed topologies and the traditional NPC inverters. The experimental results verified the theoretical findings.

We propose a hardware-based solution for performing real-time workload forensics that enables identification of a process while it is being executed. More specifically, we divide execution flow of a process into consecutive frames and we extract descriptive features related to the Translation Lookaside Buffer (TLB) utilization profile for each such frame. These features are then processed through trained machine learning models to analyze program behavior and identify workload at the granularity of a process. Unlike previous research on workload forensics that performs ex post facto analysis based on the complete process execution profile, this method continuously analyzes the segmented workload execution flow; thus, it does not require knowledge of process creation, switch, and termination timestamps. Furthermore, as compared with software-based workload forensics solutions, whose data logging mechanism may be compromised by software attacks, the proposed hardware-based logging mechanism does not rely on services from the operating system (OS) or high-level applications and is, therefore, inherently immune to software tampering. The proposed workload forensics method was evaluated using a Linux OS loaded on Spike, an open-source RISC-V simulator. Experimental results using the Mibench benchmark suite indicate an overall identification accuracy of 98.9% with practicable logging overhead.

The double-layer vanadium dioxide (VO2) thin film patterned by SiO2 spheres has been fabricated to enhance luminous transmittance (Tlum) without severely expense of solar modulation ability (ΔTsol) for the thermochromic smart windows. The first layer of vanadium thin film was deposited on the substrate with self-assembly SiO2 spheres and then the SiO2 spheres was removed by ultrasonic oscillation. The orderly perforated vanadium thin film with regular pores formed. Afterwards, the second layer of vanadium thin film was deposited on the perforated vanadium thin film. The semi-perforated double layer of vanadium thin film formed and was rapid thermal annealed forming the semi-perforated double VO2 thin films. Compared with continuous VO2 thin film(Tlum, 8.8%, ΔTsol, 5.9%), the semi-perforated double VO2 layer possesses orderly-patterned crater, which facilitate Tlum (42.6%) and only slightly attenuate the ΔTsol (5.5%). The phase transition temperatures for continuous VO2 thin film and the optimized semi-perforated VO2 double layer thin film were 58 °C and 52 °C, respectively, far below 68 °C of bulky VO2. This structure should be very meaningful for the real application of VO2-based smart window in the future.

Recent advances in deep learning, especially the long short-term memory (LSTM) networks, provide some useful insights on how to tackle time series prediction problems, not to mention the development of a time series model itself for prediction. Runoff forecasting is a time series prediction problem with a series of past runoff data (water level and discharge series data) as inputs and a fixed-length series of future runoff as output. Most previous work paid attention to the sufficiency of input data and the structural complexity of deep learning, while less effort has been put into the consideration of data quantity or the processing of original input data—such as time series decomposition, which can better capture the trend of runoff—or unleashing the effective potential of deep learning. Mutual information and seasonal trend decomposition are two useful time series methods in handling data quantity analysis and original data processing. Based on a former study, we proposed a deep learning model combined with time series analysis methods for daily runoff prediction in the middle Yangtze River and analyzed its feasibility and usability with frequently used counterpart models. Furthermore, this research also explored the data quality that affect the performance of the deep learning model. With the application of the time series method, we can effectively get some information about the data quality and data amount that we adopted in the deep learning model. The comparison experiment resulted in two different sites, implying that the proposed model improved the precision of runoff prediction and is much easier and more effective for practical application. In short, time series analysis methods can exert great potential of deep learning in daily runoff prediction and may unleash great potential of artificial intelligence in hydrology research.

Fault diagnosis is a key process in ensuring complex engineering system safety. It often requires collaborative and multidisciplinary efforts. This study seeks to understand the process of multidisciplinary fault diagnosis in complex engineering systems and the key human factors issues that impair this process. Data were collected from multidisciplinary diagnostic activities conducted in the commissioning phase of nuclear power plants (NPPs). In the first phase, we proposed a process model based on a combination of literature review, specialist interviews, and field observations. In phase two, the influencing issues identified in the first phase were assessed through a survey with 117 NPP commissioning specialists. Five factors influencing multidisciplinary fault diagnosis were identified: cognitive artifacts, diagnosis biases, preparation for multidisciplinary diagnosis, information sharing and collaborative reasoning, and collaborative decision-making. The significances of each factor were compared. The results provide guidance for the development of improvement measures to enhance the performance of multidisciplinary fault diagnosis. Practitioner summary The processes and influencing issues of multidisciplinary fault diagnosis during the commissioning phase of nuclear power plants were studied with field observations, interviews of 28 specialists, and a survey of 117 specialists. Five major influencing factors were identified, and their influences were compared.

Organic upconversion is a unique process in which low-energy light (usually NIR light) is converted to high-energy light through either the two-photon absorption (TPA) mechanism or the triplet-triplet annihilation (TTA) mechanism. Both TPA upconversion (TPA-UC) and TTA upconversion (TTA-UC) have been actively investigated in recent years due to their intriguing applications in optics, biophotonics, and solar energy utilization. Although they show some similarity (i.e., belonging to the nonlinear two-quantum process and needing focused excitation light), TPA-UC and TTA-UC are very different, such as in mechanism, characteristics involved, molecular design and potential applications. Here, we collectively reviewed these two kinds of upconversion processes and compared their respective characteristics and merits. We also present recent advances made in the areas of TPA- and TTA-UC, the remaining challenges and opportunities, with particular emphasis on molecular engineering of these two active upconversion materials.

A method of continuous zoom multiple configuration, with a micro lens array and core optics is attempt to establish model by novel principle on resonance energy transfer and high accuracy localization, by which the system resolution can be improved with a level of a few nanometers. A comparative study on traditional vs modern methods can demonstrate.

Electric vehicles (EV) are developing fast and EV ownerships are increasing rapidly. In the EV system, the electric traction inverter and the charging converter are two important components that will largely determine the performance of the EV. The research targets transformer-less integrated charging systems that have the potential to reduce the cost, volume, and increase efficiency. The proposed concept does not require a charging rectifier or isolating transformer and admits bidirectional operation that is the basis for providing V2G services to the grid. The omission of galvanic isolation permits common mode currents in the charging system during charging mode since both grid and vehicle chassis are grounded during charging. These currents are well controlled using a secondary converter that acts as an active filter. Furthermore, the omission of the isolating transformer increases the efficiency of the vehicle charger similar. The proposed topology is verified by simulation and on an experimental test bench.

The rates of restenosis after percutaneous transluminal angioplasty are higher in diabetic than nondiabetic patients. Insulin is protective against restenosis, but these effects are diminished in the case of insulin resistance, which develops with high fat diet (HFD). Therefore, it makes sense to use an insulin-sensitizing compound which also has vasculoprotective effects such as resveratrol. Resveratrol is a phenolic compound found in plant tissues such as the skin of grapes, and has been hypothesized to account for the lower rates of cardiovascular disease among countries with high wine consumption. We have shown that resveratrol decreases neointimal formation in both mouse and rat models of arterial injury, and also that these effects are exerted through eNOS (endothelial nitric oxide synthase). We hypothesized that resveratrol acts though Sirt1, a NAD+-dependent deacetylase, to activate eNOS and decrease neointimal formation, but did not see any difference between neointimal growth of control and Sirt1-/- mice given HFD. AMPK (AMP-activated protein kinase) is an energy sensor which also protects against neointimal hyperplasia in the vasculature via directly activating eNOS and inhibiting the migration of vascular smooth muscle cells. Knocking out the α2 subunit of AMPK in particular has been shown to increase neointimal growth in mouse carotid artery in response to injury. We investigated the effect of resveratrol on neointimal growth after femoral arterial injury of wild type and AMPKα2 knock-out mice given HFD. Resveratrol was given at 0.0375% in the diet two days after wire injury. After 28 days, arteries were harvested and histomorphometric analysis was performed on the cross-sections. We found that neointimal growth was significantly higher in AMPKα2 knock-out mice given resveratrol compared to wild type mice given resveratrol (p Disclosure L. Zhou: None. J. Guo: None. H. Zhang: None. S. Heximer: None. A. Giacca: None.

In this study, the VO2/Au/VO2 sandwiched structure was fabricated by rapid thermal annealing of sputtered V/Au/V sandwiched structure to modify the intrinsic unpleasant brown–yellow color of VO2 films based on the localized surface plasmon resonance (LSPR) for smart windows. The morphology, crystalline structures, and optical transmittance spectra of the sandwich structure were characterized by scanning electron microscopy, X-ray diffraction and UV-VIS-NIR spectrophotometry, respectively. The obtained results indicate that the VO2/Au/VO2 sandwich structure with different Au nanoparticles sizes shows different color from brown–yellow to blue–green due to the appearance and redshift of LSPR peak. Moreover, the phase-transition temperature for the optimized VO2/Au/VO2 sandwich structure was only 42.5 °C, far below 68 °C for bulky VO2. The optimized VO2/Au/VO2 thin films could simultaneously meet the requirements of comfortable color and a reduced phase-transition temperature, and these excellent features would benefit the practical application of VO2 thin film-based smart windows.

Kernel rootkits generally attempt to maliciously tamper kernel objects and surreptitiously distort program execution flow. Herein, we introduce a hardware-assisted hierarchical on-line system which detects such kernel rootkits by identifying deviation of dynamic intra-process execution profiles based on architecture-level semantics captured directly in hardware. The underlying key insight is that, in order to take effect, malicious manipulation of kernel objects must distort the execution flow of benign processes, thereby leaving abnormal traces in architecture-level semantics. While traditional detection methods rely on software modules to collect such traces, their implementations are susceptible to being compromised through software attacks. In contrast, our detection system maintains immunity to software attacks by resorting to hardware for trace collection. The proposed method is demonstrated on a Linux-based operating system running on a 32-bit x86 architecture, implemented in Simics. Experimental results, using real-world kernel rootkits, corroborate the effectiveness of this method, while a predictive 45nm PDK is used to evaluate hardware overhead.

China has a national requirement that children's vaccination status is to be checked upon entry to kindergarten and school; children who have missed vaccine doses are to be referred to vaccination clinics to receive the necessary vaccinations. Most provinces have Immunization Information Systems (IIS) that contain and manage vaccination records of children served by immunization clinics. We conducted an evaluation in Hubei and Ningxia provinces using IIS data to determine the effect of their school entry immunization record checking strategy (SECS) on vaccination coverage and rate of catch-up for selected vaccines. We selected two counties with well-functioning IISs from each province. Names and demographic information of children enrolled in 185 kindergartens and 125 primary school in 2016 were used to identify children in the IIS and obtain their vaccination records. We calculated vaccination coverage before and after implementation of the SECS and determined catch-up rates for 5 vaccines, of which 3 are given before 2 years of age and 2 are given after 2 years of age. Among the 20,215 newly-enrolled children, 75% were able to be matched with IIS records. Few children who had missed one or more doses of the 3 vaccines recommended in the first two years of life received catch-up doses after SECS. For vaccines scheduled after 2 years of age, there was a statistically significant (p 0.05) coverage increase in both provinces following the school-entry record check. Among children who were age-eligible for the selected vaccines before SCES, the catch-up rate was50%. SECS provides opportunities to identify incompletely vaccinated children, improve coverage, and remind families of school-age children about up-coming vaccinations; SECS has potential to improve completeness of IIS data and enrollment of children in immunization clinics. Evidence-based policy with implementation guidance and routine monitoring are necessary to improve China's school entry checking strategy.

We demonstrated a visible and near-infrared light tunable photonic nanostructure, which is composed of vanadium dioxide (VO2) thin film and silicon dioxide (SiO2) ordered nanosphere arrays. The vanadium films were sputtered on two-dimensional (2D) SiO2 sphere arrays. VO2 thin films were prepared by rapid thermal annealing (RTA) method with different oxygen flow rates. The close-packed VO2 shell formed a continuous surface, the composition of VO2 films in the structure changed when the oxygen flow rates increased. The 2D VO2/SiO2 composite photonic crystal structure exhibited transmittance trough tunability and near-infrared (NIR) transmittance modulation. When the oxygen flow rate increased from 3 slpm to 4 slpm, the largest transmittance trough can be regulated from 904 to 929 nm at low temperature, the transmittance troughs also appear blue shift when the VO2 phase changes from insulator to metal. The composite nanostructure based on VO2 films showed visible transmittance tunability, which would provide insights into the glass color changing in smart windows.

For aim to find a simple multiple configuration solution and achieve higher refractive efficiency, and based on to reduce the situation disturbed by FOV change, especially in a two-dimensional spatial expansion. Coded aperture system is designed by these special structure, which includes an objective，a coded component，a prism reflex system components, a compensatory plate and an imaging lens．Correlative algorithms and perfect imaging methods are available to ensure this system can be corrected and optimized adequately. Simulation results show that the system can meet the application requirements in MTF, REA, RMS and other related criteria. Compared with the conventional design, the system has reduced in volume and weight significantly. Therefore, the determining factors are the prototype selection and the system configuration.

For aim to find an effective method to structure to enhance these adaptive system with some complex function and look forward to establish an universally applicable solution in prototype and optimization. As the most attractive component in adaptive system, wave front corrector is constrained by some conventional technique and components, such as polarization dependence and narrow working waveband. Advanced configuration based on a polarized beam split can optimized energy splitting method used to overcome these problems effective. With the global algorithm, the bandwidth has been amplified by more than five times as compared with that of traditional ones. Simulation results show that the system can meet the application requirements in MTF and other related criteria. Compared with the conventional design, the system has reduced in volume and weight significantly. Therefore, the determining factors are the prototype selection and the system configuration, Results show their effectiveness.

A system method of constructing stereo vision by using neural network is proposed, and the operation and control mechanism in actual operation are proposed. This method makes effective use of the neural network in learning and memory function, by after training with samples. Moreover, the neural network can learn the nonlinear relationship in the stereoscopic vision system and the internal and external orientation elements. These considerations are Worthy of attention, which includes limited constraints, the scientific of critical group, the operating speed and the operability in technical aspects. The results support our theoretical forecast.

Safe and stable operation of power transformers is the basis for normal supply of electricity and thereby normal social production and life. Timely fault identification plays a very important role in improving technical maintenance of transformers. Traditional diagnostic methods such as three-ratio method, diagnose faults based on threshold values. In consideration of their limitations due to incomplete codes and too absolute code range definition, this paper proposes a new fault diagnosis method based on grey relational analysis and integrated weight determination. The grey relational analysis model is introduced and its discrimination coefficient is adjusted. Further, to overcome the shortcomings of the grey relational model, the weight of each indicator is determined by integrating analytic hierarchy process and entropy methods. Making use of case data from other studies, it has been demonstrated that our enhanced model can effectively improve the accuracy of fault diagnosis.

Although traditional fault diagnosis methods can qualitatively identify the failure modes for power equipment, it is difficult to evaluate the failure probability quantitatively. In this paper, a failure probability calculation method for power equipment based on multi-characteristic parameters is proposed. After collecting the historical data of different fault characteristic parameters, the distribution functions and the cumulative distribution functions of each parameter, which are applied to dispersing the parameters and calculating the differential warning values, are calculated by using the two-parameter Weibull model. To calculate the membership functions of parameters for each failure mode, the Apriori algorithm is chosen to mine the association rules between parameters and failure modes. After that, the failure probability of each failure mode is obtained by integrating the membership functions of different parameters by a weighted method, and the important weight of each parameter is calculated by the differential warning values. According to the failure probability calculation result, the series model is established to estimate the failure probability of the equipment. Finally, an application example for two 220 kV transformers is presented to show the detailed process of the method. Compared with traditional fault diagnosis methods, the calculation results not only identify the failure modes correctly, but also reflect the failure probability changing trend of the equipment accurately.

We introduce a hardware-based methodology for performing on-line intrusion detection in microprocessors. The proposed method extracts fingerprints from the basic blocks of the routine executed in response to a system call and examines their validity using a Bloom filter. Implementation in hardware renders spoofing attacks, to which operating system or hypervisor-level intrusion detection methods are vulnerable, ineffective. The proposed method is evaluated using kernel rootkits which covertly modify the system call service routines of a Linux operating system running on a 32-bit x86 architecture, implemented in the Simics simulation environment, while hardware overhead is evaluated using a predictive 45nm PDK.

The Influenza Research Database (IRD) is a U.S. National Institute of Allergy and Infectious Diseases (NIAID)-sponsored Bioinformatics Resource Center dedicated to providing bioinformatics support for influenza virus research. IRD facilitates the research and development of vaccines, diagnostics and therapeutics against influenza virus by providing a comprehensive collection of influenza-related data integrated from various sources, a growing suite of analysis and visualization tools for data mining and hypothesis generation, personal workbench spaces for data storage and sharing, and active user community support. Here, we describe the recent improvements in IRD including the use of cloud and high performance computing resources, analysis and visualization of user-provided sequence data with associated metadata, predictions of novel variant proteins, annotations of phenotype-associated sequence markers and their predicted phenotypic effects, hemagglutinin (HA) clade classifications, an automated tool for HA subtype numbering conversion, linkouts to disease event data and the addition of host factor and antiviral drug components. All data and tools are freely available without restriction from the IRD website at https://www.fludb.org.

We investigate the possibility of performing workload forensics and/or malware detection in microprocessors through exclusively hardware-based methodologies. Specifically, we first introduce a general architecture which a hardware-based forensics or malware detection method would need to follow, as well as the various processor-level information which could potentially be harnessed to ensure system security and/or integrity. In contrast to traditional forensics and/or malware detection methods implemented at the operating system (OS) and/or the hypervisor level, whose data logging and monitoring systems are vulnerable to spoofing attacks at the same level, moving implementation to hardware ensures immunity to such attacks. This work focuses on two recent incarnations of this general concept, illustrating the effectiveness of hardware-based forensics and/or malware detection. Several other recent methods related to this topic are also discussed. Experimental results corroborate that even a low-cost hardware implementation can facilitate highly successful forensics analysis and/or malware detection, while taking advantage of its innate immunity to software-based attacks.

Freeform system theory and its influence on aberration and control, is the basis to develop such a system design, which has application value in engineering aspects. The principle approach, modeling, and error analysis are analyzed, and the system configuration based on freeform is advanced in algorithm analysis.

We present our experiences in implementing hardware-based attacks to subvert the results of an election system. The election system was outlined by the Cyber Security Awareness Week (CSAW) Embedded Security Challenge (ESC) competition in 2015, held at the New York University (NYU). The system had multiple layers of security and primarily used homomorphic encryption. The competition presented a challenge to hack the election system such that a preferred candidate wins the election. We cryptanalyzed the given election system to evaluate the effectiveness of various theoretical and practical attacks, and used a custom designed embedded system to demonstrate our attacks. The embedded system was implemented on a Nexys 4 DDR Artix-7 FPGA board. Our work, which earned the first place in the competition, demonstrates that low-cost hardware-based attacks can indeed lead to catastrophic consequences.