Search

In dynamic environment, the suddenly appeared human or other moving obstacles can affect the safety of the bridge crane. For such dangerous situation, the bridge crane must predict potential collisions between the payload and the obstacle, keep safe distance while the swing of the payload must be considered in the mean time. Therefore, the safe distance is not a constant value, which must be adaptive to the relative speed of the bridge crane. However, as far as we know, the mathematical model between the safe distance and the relative speed of the bridge crane has never been fully discussed. In this paper, we propose a safe distance prediction method using model prediction control (MPC), which can make sure that the crane can stop before the obstacle, and avoid possible collisions, while the relative speed and anti-swing are both considered. The experimental results prove the effectiveness of our idea.

An unprecedented Household Solid Waste (HSW) separation program was launched in 46 cities and some pilot rural areas in China in 2019. This study examines the antecedents of waste separation behavior using the extended theory of planned behavior (TPB) to identify beliefs that are useful targets for interventions, and compares the urban-rural differences. Based on interviews with rural and urban residents, we identified residents&rsquo, beliefs influencing waste separation behaviors, including four behavioral, seven normative, and eight control beliefs. Then, we tested the conceptual model with a two-stage questionnaire survey using a sample of 604 residents from urban and rural areas. Evidence from structural equation modeling supports the extended TPB in that it predicts waste separation intention and behavior on the whole. Moral norm and self-identity were found to independently predict intention and may prove a useful addition to the TPB, however, the path from attitude to intention is not supported in both urban and rural models. Moreover, the specific difference of belief between urban and rural residents was examined. The behavioral beliefs associated to money, including earning, free waste bin or bag, and fine, are significant only in the rural group. The normative beliefs of family, Chinese Communist Party (CCP) members, and government can affect residents&rsquo, subjective norm in both urban and rural, while the beliefs of relatives, friends, and neighbors are significant only in the rural group. Implications for managers of operating waste separation programs were discussed. Further investigation into the potential of the model to intervene in waste separation behavior is required.

Privacy-preserving machine learning (PPML) is driven by the emerging adoption of Machine Learning as a Service (MLaaS). In a typical MLaaS system, the end-user sends his personal data to the service provider and receives the corresponding prediction output. However, such interaction raises severe privacy concerns about both the user’s proprietary data and the server’s ML model. PPML integrates cryptographic primitives such as Multi-Party Computation (MPC) and/or Homomorphic Encryption (HE) into ML services to resolve the privacy issue. However, existing PPML solutions have not been widely deployed in practice since: (i) Privacy protection comes at the cost of additional computation and/or communication overhead; (ii) Adapting PPML to different front-end frameworks and back-end hardware incurs prohibitive engineering cost.We propose AHEC, the first automated, end-to-end HE compiler for efficient PPML inference. Leveraging the capability of Domain Specific Languages (DSLs), AHEC enables automated generation and optimization of HE kernels across diverse types of hardware platforms and ML frameworks. We perform extensive experiments to investigate the performance of AHEC from different abstraction levels: HE operations, HE-based ML kernels, and neural network layers. Empirical results corroborate that AHEC achieves superior runtime reduction compared to the state-of-the-art solutions built from static HE libraries.

Advances in customers' data privacy laws create pressures and pain points across the entire lifecycle of AI products. Working figures such as data scientists and data engineers need to account for the correct use of privacy-enhancing technologies such as homomorphic encryption, secure multi-party computation, and trusted execution environment when they develop, test and deploy products embedding AI models while providing data protection guarantees. In this work, we share the lessons learned during the development of frameworks to aid data scientists and data engineers to map their optimized workloads onto privacy-enhancing technologies seamlessly and correctly.

In dynamic environment, the suddenly appeared human or other moving obstacles can affect the safety of the bridge crane. For such dangerous situation, the bridge crane must predict potential collisions between the payload and the obstacle, keep safe distance while the swing of the payload must be considered in the mean time. Therefore, the safe distance is not a constant value, which must be adaptive to the relative speed of the bridge crane. However, as far as we know, the mathematical model between the safe distance and the relative speed of the bridge crane has never been fully discussed. In this paper, we propose a safe distance prediction method using model prediction control (MPC), which can make sure that the crane can stop before the obstacle, and avoid possible collisions, while the relative speed and anti-swing are both considered. The experimental results prove the effectiveness of our idea.

Background The outbreak of novel coronavirus pneumonia is very serious, and no effective antiviral treatment has been confirmed. The fresh drug research and development cycle is too long to meet clinical emergency needs, and "old drugs and brand new applications" have a huge therapeutic potential. During our previous treatment, we found that the lopinavir/ritonavir treatment recommended in the Fifth edition of the treatment plan had little effect. Earlier studies have shown that chloroquine can inhibit coronavirus replication through multiple mechanisms. Our previous use of chloroquine to treat patients with SARS-CoV-2(novel coronavirus)-infected pneumonia has a higher negative rate of nucleic acid in throat swabs within 5 days after administration than that using lopinavir/ritonavir. However, the half-life and side effects of chloroquine vary greatly among individuals. Methods/design We plan to conduct a prospective, open-label, multicenter randomized controlled, comprehensive treatment clinical study. The study consisted of three phases: a screening period of 1-110 days, a treatment period of no more than 28 days, and a follow-up period of 1 month. Participants will be assessed at baseline and on days 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 21, and 28 after the intervention begins. In this study, chloroquine and lopinavir/ritonavir tablets were used to treat patients with eligible novel coronavirus pneumonia diagnosed at various centers between February 12, 2020 and May 31, 2020. The efficacy and safety of chloroquine and lopinavir/ritonavir are to be evaluated. At the same time, explore the correlation between patient genetic polymorphisms and chloroquine steady-state concentration, therapeutic effects and adverse reactions in the body. It is an anti-virus for pneumonitis caused by novel coronavirus. The optimization and update of the antiviral treatment plan provides evidence-based evidence. Disscussion Our study is a prospective, open-label, multicenter randomized controlled, comprehensive treatment clinical study to evaluate the efficacy and safety of chloroquine phosphate and lopinavir/ritonavir in patients with mild/general COVID-2019. The results of this study will provide valuable clinical evidence for the treatment of novel coronavirus pneumonia.

BackgroundEffective therapies are urgently needed for the SARS-CoV-2 pandemic. Chloroquine has been proved to have antiviral effect against coronavirus in vitro. In this study, we aimed to assess the efficacy and safety of chloroquine with different doses in COVID-19.MethodIn this multicenter prospective observational study, we enrolled patients older than 18 years old with confirmed SARS-CoV-2 infection excluding critical cases from 12 hospitals in Guangdong and Hubei Provinces. Eligible patients received chloroquine phosphate 500mg, orally, once (half dose) or twice (full dose) daily. Patients treated with non-chloroquine therapy were included as historical controls. The primary endpoint is the time to undetectable viral RNA. Secondary outcomes include the proportion of patients with undetectable viral RNA by day 10 and 14, hospitalization time, duration of fever, and adverse events.ResultsA total of 197 patients completed chloroquine treatment, and 176 patients were included as historical controls. The median time to achieve an undetectable viral RNA was shorter in chloroquine than in non-chloroquine (absolute difference in medians −6.0 days; 95% CI −6.0 to −4.0). The duration of fever is shorter in chloroquine (geometric mean ratio 0.6; 95% CI 0.5 to 0.8). No serious adverse events were observed in the chloroquine group. Patients treated with half dose experienced lower rate of adverse events than with full dose.ConclusionsAlthough randomised trials are needed for further evaluation, this study provides evidence for safety and efficacy of chloroquine in COVID-19 and suggests that chloroquine can be a cost-effective therapy for combating 102 the COVID-19 pandemic.

Glacial lake outburst floods (GLOFs) are one of the major natural hazards in certain populated mountainous areas, e.g. the Himalayan region, which may lead to catastrophic consequences including fatalities. Evaluating the potential socio-economic impact of GLOFs is essential for mitigating the risk of GLOFs and enhancing community resilience. Yet in most of the cases, the impact evaluation of potential GLOFs is confronted with limited data availability and inaccessibility to most of the glacial lakes in the high-altitude areas. This study aims to exploit recent advances in Earth Observation (EO), open-source data from different sources, and high-performance hydrodynamic modelling to innovate an approach for GLOF risk and impact assessment. GLOF scenarios of different glacier dam breach width and depth are designed according to high-resolution aerial imagery and terrain data acquired from unmanned aerial vehicle surveying. High-performance hydrodynamic model supported by open-source multi-resolution data from the latest EO technologies is used to simulate the flood hydrodynamics to provide spatial and temporal flood characteristics. Detailed information on communities and infrastructure systems is collected and processed from multiple sources including OpenStreetMap, Google Earth, and global data products to support impact analysis. The evaluation framework is applied to Tsho Rolpa glacial lake in Nepal, which has been identified as one of the potentially dangerous glacial lakes that may create GLOFs to threaten the downstream communities and infrastructure. According to the simulation results, the worst GLOF scenario can potentially inundate 27 villages, 583 buildings and 20.8 km2 of agricultural areas, and pose high risk to 1 airport, 1 hydro power plant, 3 bus stations, and 21 bridges. Additionally, the spatial and temporal flood simulation results, including water depth, flow velocity and flood arrival time may help identify impacted sites and objects, which would be valuable for the development of evacuation plans and early warning systems.

Cisplatin, carboplatin, and oxaliplatin are the common platinum-based anticancer drugs widely used in the chemotherapeutic treatment of solid tumors in clinic. However, the comprehensive pharmacokinetics of platinum-based anticancer drugs has not been fully understood yet. This leads to many limitations for the further studies on their pharmacology and toxicology. In this study, we conduct a systemic evaluation on the pharmacokinetics of three platinum analogues at animal and cell levels, with quantification of both total platinum and intact drugs. A detailed animal study to address and compare the different pharmacokinetic behaviors of three platinum analogues has been conducted in three biological matrices: blood, plasma, and ultrafiltrate plasma. Carboplatin showed an obviously different pharmacokinetic characteristic from cisplatin and oxaliplatin. On the one hand, carboplatin has the highest proportion of Pt distribution in ultrafiltrate plasma. On the other hand, carboplatin has the highest intact drug exposure and longest intact drug elimination time in blood, plasma, and ultrafiltrate plasma, which may explain its high hematotoxicity. Additionally, the cellular and subcellular pharmacokinetics of oxaliplatin in two colon cancer HCT-116/LOVO cell lines has been elucidated for the first time. The biotransformation of intact oxaliplatin in cells was rapid with a fast elimination, however, the generated platinum-containing metabolites still exist within cells. The distribution of total platinum in the cytosol is higher than in the mitochondria, followed by the nucleus. Enrichment of platinum in mitochondria may affect the respiratory chain or energy metabolism, and further lead to cell apoptosis, which may indicate mitochondria as another potential target for efficacy and toxicity of oxaliplatin.

Additional file 2. Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2013 Checklist: recommended items to address in a clinical trial protocol and related documents.

Background: Globe pandemic of COVID-19 endangers the frontline healthcare workers in 2020 To our knowledge, no studies about the HCWs in a field hospital speci

Additional file 1: Figure S1. OM and BSE images of single-layered paint cross sections. Perceived surface colors of samples: CS2 ∼ 4 blue, CS6 ∼ 8 green, CS9 red, CS12 red, CS14 gold, CS15 black.

© 2020 Elsevier Ltd Pedagogical agents are typically designed to take on a single role: either as a tutor who guides and instructs the student, or as a tutee that learns from the student to reinforce what he/she knows. While both agent-role paradigms have been shown to promote student learning, we hypothesize that there will be heightened benefit with respect to students’ learning and emotional engagement if the agent engages children in a more peer-like way — adaptively switching between tutor/tutee roles. In this work, we present a novel active role-switching (ARS) policy trained using reinforcement learning, in which the agent is rewarded for adapting its tutor or tutee behavior to the child's knowledge mastery level. To investigate how the three different child–agent interaction paradigms (tutee, tutor, and peer agents) impact children's learning and affective engagement, we designed a randomized controlled between-subject experiment. Fifty-nine children aged 5–7 years old from a local public school participated in a collaborative word-learning activity with one of the three agent-role paradigms. Our analysis revealed that children's vocabulary acquisition benefited from the robot tutor's instruction and knowledge demonstration, whereas children exhibited slightly greater affect on their faces when the robot behaves as a tutee of the child. This synergistic effect between tutor and tutee roles suggests why our adaptive peer-like agent brought the most benefit to children's vocabulary learning and affective engagement, as compared to an agent that interacts only as a tutor or tutee for the child. This work sheds light on how fixed role (tutor/tutee) and adaptive role (peer) agents support children's cognitive and emotional needs as they play and learn. It also contributes to an important new dimension of designing educational agents — actively adapting roles based on the student's engagement and learning needs.

Prior work in affect-aware educational robots has often relied on a common belief that the relationship between student affect and learning is independent of agent behaviors (child’s/robot’s) or unidirectional (positive/negative but not both) throughout the entire student-robot interaction. We argue that the student affect-learning relationship should be interpreted in two contexts: (1) social learning paradigm and (2) sub-events within child-robot interaction. In our paper, we examine two different social learning paradigms where children interact with a robot that acts either as a tutor or a tutee. Sub-events within child-robot interaction are defined as task-related events occurring in specific phases of an interaction (e.g., when the child/robot gets a wrong answer). We examine sub-events at a macro level (entire interaction) and a micro level (within specific sub-events). In this paper, we provide an in-depth correlation analysis of children’s facial affect and vocabulary learning. We found that children’s affective displays became more predictive of their vocabulary learning when children interacted with a tutee robot who did not scaffold their learning. Additionally, children’s affect displayed during micro-level events was more predictive of their learning than during macro-level events. Last, we found that the affect-learning relationship is not unidirectional, but rather is modulated by context, i.e., several affective states facilitated student learning when displayed in some sub-events but inhibited learning when displayed in others. These findings indicate that both social learning paradigm and sub-events within interaction modulate student affect-learning relationship. ACM Reference Format: Huili Chen, Hae Won Park, Xiajie Zhang, and Cynthia Breazeal. 2020. Impact of Interaction Context on the Student Affect-Learning Relationship in Child-Robot Interaction. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’20), March 23-26, 2020, Cambridge, United Kingdom. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3319502.3374822

This article examines how the corporate governance boundary affects corporate investment efficiency. The empirical results based on Chinese listed companies suggest that the expansion of the corpor...

This paper is the first to adopt longitudinal data analysis methods, such as the Kaplan-Meier survival analysis model, hierarchical linear model and Cox proportional hazards regression model, to investigate the relationship between large shareholders’ shareholding and earnings persistence. We find that large shareholders’ shareholding has a negative effect on corporate longitudinal persistence. The large shareholders’ shareholding will increase the risk of earnings decline and strengthen the declining trend of earnings. The effects of large shareholders’ shareholding on earnings’ longitudinal persistence for SOEs are different from those for non-SOEs, the effects in the pre-crisis period are also different from those in the post-crisis period. These findings contribute to the literature by adopting longitudinal data analysis methods and present new empirical evidence on the economic consequences of large shareholders’ shareholding.

Carbon deposition on a Ni-based anode is troublesome for the direct power generation from methane-based fuels using solid oxide fuel cell. In this paper, a redox-stable double-perovskite SrMoFeO (SMFO) is applied as an independent on-cell reforming catalyst over a Ni-YSZ anode to improve coking resistance. The morphology, catalytic activity and electrochemical performance for wet methane/coal-bed gas (CBG) are investigated. A Ni-YSZ anode supported cell with SMFO generates a high power output of 1.77 W·cm and exhibits favorable stability operated on wet CH at 800°C. Post-mortem micro-structural analyses of cells indicate the cell operated on CBG shows coking probably due to the heavy carbon compounds in CBG.

Clear utilization of low-concentration coal-bed gas (LC-CBG) would save energy, mitigate greenhouse gas emissions, and reduce explosion accidents during a coal-mining process. In this paper, a novel catalyst Ni/BaO/CeO (NBC) is developed for CH partial oxidation and is used as an independent catalyst layer of a Ni cermet anode. LC-CBG with composition of 30 % methane and 70 % air is directly fed to the catalyst-modified SOFC to generate electricity. Operated on 30 % LC-CBG, the NBC-modified cell shows better performance and discharge stability than the conventional cell without the catalyst layer. Gas chromatography (GC) analysis on effluent gases extracted from the operating catalyst-modified cells also indicates a higher CH conversion when the cell is fed with 30 % LC-CBG than when fed with 3 % HO-humidified methane. Post-mortem microstructures of cell surface were analyzed. The NBC-modified cell has a potential application for fuels containing low concentration methane and air.

Digital Elevation Model (DEM) is one of the most important controlling factors determining the simulation accuracy of hydraulic models. However, the currently available global topographic data is confronted with limitations for application in 2-D hydraulic modeling, mainly due to the existence of vegetation bias, random errors and insufficient spatial resolution. A hydraulic correction method (HCM) for the SRTM DEM is proposed in this study to improve modeling accuracy. Firstly, we employ the global vegetation corrected DEM (i.e. Bare-Earth DEM), developed from the SRTM DEM to include both vegetation height and SRTM vegetation signal. Then, a newly released DEM, removing both vegetation bias and random errors (i.e. Multi-Error Removed DEM), is employed to overcome the limitation of height errors. Last, an approach to correct the Multi-Error Removed DEM is presented to account for the insufficiency of spatial resolution, ensuring flow connectivity of the river networks. The approach involves: (a) extracting river networks from the Multi-Error Removed DEM using an automated algorithm in ArcGIS; (b) correcting the location and layout of extracted streams with the aid of Google Earth platform and Remote Sensing imagery; and (c) removing the positive biases of the raised segment in the river networks based on bed slope to generate the hydraulically corrected DEM. The proposed HCM utilizes easily available data and tools to improve the flow connectivity of river networks without manual adjustment. To demonstrate the advantages of HCM, an extreme flood event in Huifa River Basin (China) is simulated on the original DEM, Bare-Earth DEM, Multi-Error removed DEM, and hydraulically corrected DEM using an integrated hydrologic-hydraulic model. A comparative analysis is subsequently performed to assess the simulation accuracy and performance of four different DEMs and favorable results have been obtained on the corrected DEM.

China is an agricultural country with the largest population in the world. However, intensification of droughts and floods has substantial impacts on agricultural production. For effective agricultural disaster management, it is significant to understand and quantify the influence of droughts and floods on crop production. Compared with droughts, the influence of floods on crop production and a comprehensive evaluation of effects of droughts and floods are given relatively less attention. The impact of droughts and floods on crop production is therefore investigated in this study, considering spatial heterogeneity with disaster and yield datasets for 1949–2015 in China mainland. The empirical relationships between drought and flood intensity and yield fluctuation for grain, rice, wheat, maize and soybean are identified using a Bayesian hierarchical model. They are then used to explore what social-economic factors influenced the grain sensitivity to droughts and floods by the Pearson’s coefficient and locally weighted regression (LOSEE) plots. The modeling results indicate that: (a) droughts significantly reduce grain yields in 28 of 31 provinces and obvious spatial variability in drought sensitivity exists, with Loess Plateau having highest probability of crop failure caused by droughts; (b) floods significantly reduce grain yield in 20 provinces, while show positive effect in the northwestern and southwestern China; (c) the spatial patterns of influence direction of droughts and floods on rice, maize and soybean are consistent with the grain’s results; and (d) promoting capital investments and improving access to technical inputs (fertilizer, pesticide, and irrigation) can help effectively buffer grain yield lose from droughts.

Determination of the limiting nutrient of phytoplankton is critical to the lake eutrophication management. The average value of total nitrogen/total phosphorus (TN/TP) ratio is widely used to determine the limiting nutrient; while it suffers from the risk of the incorrect description of data and neglecting dynamics of the nutrient limitation. A probabilistic method was thereby proposed in this study to explore dynamics of nutrient limitation, including (a) indicator definition as the probability of TN/TP ratio failing in Redfield ratio line (PFR), indicating the possibility of TN limitation, to improve a probabilistic measure for the nutrient limitation; (b) Bayesian ANOVA analysis for posterior distributions of different treatments; and (c) dynamics determination as PFRs to show dynamics of nutrient limitation. Lake Xingyun in Southwestern China was taken as a case to explore the interannual and seasonal dynamics of the nutrient limitation. According to modeling results, we deducted that (a) for the interannual dynamics, the limiting nutrient shifted from TP to TN; and (b) for the seasonal dynamics, TN and TP were co-limiting. Deductions were further confirmed by the observed data. With the proposed probabilistic method, the co-limitation of TN and TP was identified for the seasonal dynamics; while using the average ratio solely denied the possibility of co-limitation. The current study also revealed that, due to neglecting the interannual and seasonal dynamics of nutrient limitation, the average ratio might mislead the eutrophication management strategy by recommending reducing TN and TP concentration together. The proposed probabilistic method demonstrated that TN was the limiting nutrient during the growing season of the phytoplankton in recent years and actions should focus on the TN concentration reduction.

The distribution of archaeal community and the associated environmental variables in constructed wetland (CW), especially in free water surface flow CW (FWSF-CW), remain poorly understood. The present study explored the spatial and temporal dynamics of archaeal community in an FWSF-CW used for surface water treatment and evaluated the driving environmental variables. The archaeal density varied considerably among sites and seasons, ranging from 3.37×108 to 3.59×109 16S rRNA gene copies per gram dry sediment/soil. The archaeal population density was adversely affected by high temperatures and tended to be lower during summer than during spring and winter. Moreover, considerable spatio-temporal variations of archaeal richness, diversity and community structure also occurred in the FWSF-CW. Higher nutrient contents correlated with a lower archaeal richness and diversity. Nitrate and carbon/nitrogen ratio were found to play important roles in shaping the overall archaeal community structure. Euryarchaeota and Bathyarchaeota were the dominant archaeal phyla in wetland sediments, while Thaumarchaeota tended to be dominant in wetland soils. In addition, the wetland archaeal community was related to vegetation type.

Using a difference-in-differences approach, we find that the cash holdings of firms increase significantly after announcements of irregularity-related restatements. The increase is larger for firms with a higher demand for precautionary savings and is smaller for firms with less pronounced increase in shareholder control after the restatements. Investments and repurchases of irregularity firms become more sensitive to excess cash after the restatements. In addition, we find that the market value of cash holdings increases after restatements. Overall, the evidence suggests that strengthened shareholder control reduces cash holdings, but this effect is weaker than the increase in cash holdings due to exacerbated precautionary savings concerns. Our study contributes to the literature on the effect of financial reporting credibility on real corporate decisions.

The projected frequent occurrences of extreme flood events will cause significant losses to crops and will threaten food security. To reduce the potential risk and provide support for agricultural flood management, prevention, and mitigation, it is important to account for flood damage to crop production and to understand the relationship between flood characteristics and crop losses. A quantitative and effective evaluation tool is therefore essential to explore what and how flood characteristics will affect the associated crop loss, based on accurately understanding the spatiotemporal dynamics of flood evolution and crop growth. Current evaluation methods are generally integrally or qualitatively based on statistic data or ex-post survey with less diagnosis into the process and dynamics of historical flood events. Therefore, a quantitative and spatial evaluation framework is presented in this study that integrates remote sensing imagery and hydraulic model simulation to facilitate the identification of historical flood characteristics that influence crop losses. Remote sensing imagery can capture the spatial variation of crop yields and yield losses from floods on a grid scale over large areas; however, it is incapable of providing spatial information regarding flood progress. Two-dimensional hydraulic model can simulate the dynamics of surface runoff and accomplish spatial and temporal quantification of flood characteristics on a grid scale over watersheds, i.e., flow velocity and flood duration. The methodological framework developed herein includes the following: (a) Vegetation indices for the critical period of crop growth from mid-high temporal and spatial remote sensing imagery in association with agricultural statistics data were used to develop empirical models to monitor the crop yield and evaluate yield losses from flood; (b) The two-dimensional hydraulic model coupled with the SCS-CN hydrologic model was employed to simulate the flood evolution process, with the SCS-CN model as a rainfall-runoff generator and the two-dimensional hydraulic model implementing the routing scheme for surface runoff; and (c) The spatial combination between crop yield losses and flood dynamics on a grid scale can be used to investigate the relationship between the intensity of flood characteristics and associated loss extent. The modeling framework was applied for a 50-year return period flood that occurred in Jilin province, Northeast China, which caused large agricultural losses in August 2013. The modeling results indicated that (a) the flow velocity was the most influential factor that caused spring corn, rice and soybean yield losses from extreme storm event in the mountainous regions; (b) the power function archived the best results that fit the velocity-loss relationship for mountainous areas; and (c) integrated remote sensing imagery and two-dimensional hydraulic modeling approach are helpful for evaluating the influence of historical flood event on crop production and investigating the relationship between flood characteristics and crop yield losses.

The purpose of this study was to develop a nanocarrier system for codelivery of paclitaxel (PTX) and piperlongumine (PL) and investigate the therapeutic potential of improving efficacy and reducing toxicity. PTX and PL were formulated into poly lactic-co-glycolic acid and D-α-tocopheryl polyethylene glycol succinate via organic solvent evaporation method. The average diameter was 117.1 ± 1.9 nm, and the zeta potential was −43.25 ± 2.76 mV. PL facilitated the cellular uptake of PTX, and the increased cytotoxicity was similarly displayed. The formulation with the PTX/PL concentration ratio at 1:200 showed the best antitumor activity, the IC 50 of PTX were 5.10 ± 0.08 nM in HepG2 cells, and 3.79 ± 1.01 nM in Michigan Cancer Foundation–7 cells. Correspondingly, the combination index was 0.79 and 0.76. Furthermore, intracellular uptake of PTX toward HepG2 cells in coencapsulated nanoparticles was significantly more than free solution. In addition, the antitumor effect of PTX/PL-PTNPs in the HepG2 xenograft tumor model suggested that the nanoparticles showed a higher antitumor efficacy with reduced toxicity to other tissues compared with free PTX. In summary, the results indicated that PTX/PL-PTNPs processed well characteristics and enhanced its therapeutic efficacy; thus, this delivery system could be clinically effective for treatment of cancers.

Background Extracellular vesicles (EVs) are membrane-contained vesicles shed from cells. EVs contain proteins, lipids, and nucleotides, all of which play important roles in intercellular communication. The release of EVs is known to increase during neuroinflammation. Glutaminase, a mitochondrial enzyme that converts glutamine to glutamate, has been implicated in the biogenesis of EVs. We have previously demonstrated that TNF-α promotes glutaminase expression in neurons. However, the expression and the functionality of glutaminase in astrocytes during neuroinflammation remain unknown. We posit that TNF-α can promote the release of EVs in astrocytes through upregulation of glutaminase expression. Results Release of EVs, which was demonstrated by electron microscopy, nanoparticle tracking analysis (NTA), and Western Blot, increased in mouse astrocytes when treated with TNF-α. Furthermore, TNF-α treatment significantly upregulated protein levels of glutaminase and increased the production of glutamate, suggesting that glutaminase activity is increased after TNF-α treatment. Interestingly, pretreatment with a glutaminase inhibitor blocked TNF-α-mediated generation of reactive oxygen species in astrocytes, which indicates that glutaminase activity contributes to stress in astrocytes during neuroinflammation. TNF-α-mediated increased release of EVs can be blocked by either the glutaminase inhibitor, antioxidant N-acetyl-l-cysteine, or genetic knockout of glutaminase, suggesting that glutaminase plays an important role in astrocyte EV release during neuroinflammation. Conclusions These findings suggest that glutaminase is an important metabolic factor controlling EV release from astrocytes during neuroinflammation. Electronic supplementary material The online version of this article (doi:10.1186/s12974-017-0853-2) contains supplementary material, which is available to authorized users.

In this study, a proton conducting solid oxide fuel cell (layered H+-SOFC) is prepared by introducing a La2NiO4perovskite oxide with a Ruddlesden-Popper structure as a catalyst layer onto a conventional NiO + BaZr0.4Ce0.4Y0.2O3-δ (NiO + BZCY4) anode for in situ CO2 dry reforming of methane. The roles of the La2NiO4 catalyst layer on the reforming activity, coking tolerance, electrocatalytic activity and operational stability of the anodes are systematically studied. The La2NiO4 catalyst layer exhibits greater catalytic performance than the NiO + BZCY4 anode during the CO2 dry reforming of methane. An outstanding coking resistance capability is also demonstrated. The layered H+-SOFC consumes H2 produced in situ at the anode and delivers a much higher power output than the conventional cell with the NiO + BZCY4 anode. The improved coking resistance of the layered H+-SOFC results in a steady output voltage of ∼0.6 V under a constant current density of 200 mA cm−2. In summary, the H+-SOFC with La2NiO4 perovskite oxide is a potential energy conversion device for CO2 conversion and utilization with co-generation of electricity and syngas.

Oroxylin A, obtained from the root of Scutellaria baicalensis Georgi, is a flavonoid with antitumor and other pharmacological activities. Our previous studies showed for the first time that it is mainly metabolized to oroxylin A sodium sulfonate by sulfotransferase enzymes in beagle dogs. In this study, rapid, universal, selective, and robust ultra-high-performance liquid chromatography-tandem mass spectrometry methods were established and fully validated to quantitatively detect oroxylin A, oroxylin A 7-O-glucuronide, and oroxylin A sodium sulfonate in beagle dog plasma. The quantitative analysis for oroxylin A sodium sulfonate was reported for the first time. Plasma samples were processed with acetonitrile, a universal protein precipitant. Gradient elution was performed to resolve carryover effects and to achieve separation efficiency and sufficient chromatographic retention. The linear relationships of oroxylin A, oroxylin A 7-O-glucuronide, and oroxylin A sodium sulfonate in plasma were in the range of 2.0-500.0, 5.0-500.0, and 1.881-940.5 ng/mL, respectively. The assay method was successfully applied to pharmacokinetic study. This is the first paper that reveals the pharmacokinetic profile of oroxylin A, oroxylin A 7-O-glucuronide, and oroxylin A sodium sulfonate after single-dose intravenous and oral administration of Oroxylin A in beagle dogs.

Oroxylin A (OA), as a natural flavonoid extracted from the root of Scutellaria baicalensis Georgi, is a candidate drug with multiple pharmacological activities. However, pharmacokinetic studies of OA have rarely been reported up to now. The present study aim to conduct a systemic evaluation on the pharmacokinetics, tissue distribution and excretion of OA in rats, with quantification of both OA and its two metabolites, Oroxylin A 7-O-glucuronide (OG) and Oroxylin A sodium sulfonate (OS) by the sensitive and rapid UPLC-MS/MS methods. The results show that OA was rapidly eliminated in vivo after a single–dose (2 mg/kg) i.v. administration of OA. The relative bioavailability of OA in all three i.g. administration groups (40, 120, and 360 mg/kg) were

Machine Learning as a Service (MLaaS) is becoming a popular practice where Service Consumers, e.g., end-users, send their data to a ML Service and receive the prediction outputs. However, the emerging usage of MLaaS has raised severe privacy concerns about users' proprietary data. PrivacyPreserving Machine Learning (PPML) techniques aim to incorporate cryptographic primitives such as Homomorphic Encryption (HE) and Multi-Party Computation (MPC) into ML services to address privacy concerns from a technology standpoint. Existing PPML solutions have not been widely adopted in practice due to their assumed high overhead and integration difficulty within various ML front-end frameworks as well as hardware backends. In this work, we propose PlaidML-HE, the first end-toend HE compiler for PPML inference. Leveraging the capability of Domain-Specific Languages, PlaidML-HE enables automated generation of HE kernels across diverse types of devices. We evaluate the performance of PlaidML-HE on different ML kernels and demonstrate that PlaidML-HE greatly reduces the overhead of the HE primitive compared to the existing implementations.

Logic locking inserts additional key gates to the original circuit for protecting the intellectual property (IP) of modern integrated circuits (ICs). Prior works have identified the vulnerability of logic locking to satisfiability (SAT)-based attacks. However, SAT attacks are ineffective on circuits with SAT-hard structures. In this paper, we propose GenUnlock, the first genetic algorithm-based logic unlocking attack framework addressing the above limitation of SAT attacks. GenUnlock formulates logic unlocking (i.e., identifying the correct keys) as a combinatorial optimization problem and tackles it using genetic algorithms (GAs). Multiple key sequences form the individuals in the population and undergo the following main operations: circuit fitness evaluation, population selection, crossover, and mutation. The key sequences with high fitness scores ‘survive’ the selection and are transformed into the offspring. GenUnlock's evolutionary process of key searching features high scalability, exploration efficiency, and parallelizable fitness evaluation. We take an Algorithm/Software/Hardware co-design approach to optimize GenUnlock's runtime overhead. More specifically, GenUnlock (i) Pipelines each computation stage by automatically constructing auxiliary circuitry for constraints checking, sorting, crossover, and mutation; (ii) Employs hardware emulation on programmable hardware for accelerating circuit fitness evaluation. We perform a comprehensive evaluation of GenUnlock's performance on various benchmarks and demonstrate that GenUnlock achieves up to 1014.1× speedup and is 3974.3× higher energy efficiency compared to the state-of-the-art SAT attacks for logic unlocking.

Esophageal squamous cell carcinoma (ESCC) is a leading malignancy in China with both high incidence and mortality. Towards improving outcomes, clinically-relevant biomarkers are urgently needed for use as prognostic and treatment targets. Herein we applied RNA-seq for deep sequencing of ten matched pairs of ESCC and adjacent non-cancerous tissues (NT) from Chinese patients. Transcriptomic data mapped to approximately 64% of all annotated genes with 2,047 and 708 unigenes being differentially up-regulated and down-regulated, respectively, between ESCCs and NT samples (p

Deep Neural Networks (DNNs) are vulnerable to Neural Trojan (NT) attacks where the adversary injects malicious behaviors during DNN training. This type of ‘backdoor’ attack is activated when the input is stamped with the trigger pattern specified by the attacker, resulting in an incorrect prediction of the model. Due to the wide application of DNNs in various critical fields, it is indispensable to inspect whether the pre-trained DNN has been trojaned before employing a model. Our goal in this paper is to address the security concern on unknown DNN to NT attacks and ensure safe model deployment. We propose DeepInspect, the first black-box Trojan detection solution with minimal prior knowledge of the model. DeepInspect learns the probability distribution of potential triggers from the queried model using a conditional generative model, thus retrieves the footprint of backdoor insertion. In addition to NT detection, we show that DeepInspect’s trigger generator enables effective Trojan mitigation by model patching. We corroborate the effectiveness, efficiency, and scalability of DeepInspect against the state-of-the-art NT attacks across various benchmarks. Extensive experiments show that DeepInspect offers superior detection performance and lower runtime overhead than the prior work.

Emerging hardware architectures for Deep Neural Networks (DNNs) are being commercialized and considered as the hardware- level Intellectual Property (IP) of the device providers. However, these intelligent devices might be abused and such vulnerability has not been identified. The unregulated usage of intelligent platforms and the lack of hardware-bounded IP protection impair the commercial advantage of the device provider and prohibit reliable technology transfer. Our goal is to design a systematic methodology that provides hardware-level IP protection and usage control for DNN applications on various platforms. To address the IP concern, we present DeepAttest, the first on-device DNN attestation method that certifies the legitimacy of the DNN program mapped to the device. DeepAttest works by designing a device-specific fingerprint which is encoded in the weights of the DNN deployed on the target platform. The embedded fingerprint (FP) is later extracted with the support of the Trusted Execution Environment (TEE). The existence of the pre-defined FP is used as the attestation criterion to determine whether the queried DNN is authenticated. Our attestation framework ensures that only authorized DNN programs yield the matching FP and are allowed for inference on the target device. DeepAttest provisions the device provider with a practical solution to limit the application usage of her manufactured hardware and prevents unauthorized or tampered DNNs from execution. We take an Algorithm/Software/Hardware co-design approach to optimize DeepAttest's overhead in terms of latency and energy consumption. To facilitate the deployment, we provide a high-level API of DeepAttest that can be seamlessly integrated into existing deep learning frameworks and TEEs for hardware-level IP protection and usage control. Extensive experiments corroborate the fidelity, reliability, security, and efficiency of DeepAttest on various DNN benchmarks and TEE-supported platforms.

Land use/cover change (LUCC) is one of the crucial factors influencing the hydrological process, thus the flood characteristics in time and space. Therefore the evaluation of the change of flood characteristics implies an integrated analysis of LUCC and hydraulic simulation. In this study, the effect of LUCC on flood is examined based on an approach composed of three parts: (1) reproduction of spatially explicit LUCC; (2) application of a 2D hydraulic modelling for flood simulation; (3) demonstration of results for Beijing. The approach is applied to a flood-prone area in Beijing. The results show that 8% and 21% of the study area experienced LUCC during 1991–2001 and 2001–2011, respectively, and these changes greatly influenced the characteristics of the 20-year flood, i.e.: (1) the flood zone is doubled during 1991–2001 and about four-fold during 2001–2011; (2) the water depth is increased for most of the study area; and (3) the flow velocity becomes faster. It indicates that flooding still exists within Beijing and is even more dangerous than 40 years ago and suggests that actual land use pattern and existing flood protection works should be re-evaluated regarding the flood characteristics change due to LUCC.

Novel pomegranate-like hollow carbon nanoparticles (PLHCNs) were prepared by a dual-template method based on nickel nanoparticles (Ni-NPs) as an active hard template and sodium dodecyl benzene sulfonate (SDBS) as a soft template and pore former. The synergistic effect of Ni-NPs and SDBS imparted PLHCNs with appropriate pore structure, moderate specific surface area (617 m2 g−1), ultrasmall nanocavities (∼6.88 nm), and interconnected and defective few-layer (n

Aim To test changes in the phylogenetic relatedness, niche breadth, and life-history strategies of nematodes along a latitudinal gradient. Location Sixteen wetland locations along the Pacific coast of China, from 20°N to 40°N. Methods Linear regression was used to relate nematode phylogenetic relatedness (average taxonomic distinctness (AvTD) and average phylogenetic diversity [AvPD]), life-history group (based on “c-p” colonizer–persister group classification), and dietary specificity (based on guild classification of feeding selectivity) to latitude. Results Wetland nematode taxonomic diversity (richness and Shannon diversity indices) decreased with increasing latitude along the Chinese coast. Phylogenetic diversity indices (AvTD and AvPD) significantly increased with increasing latitude. This indicates that at lower latitudes, species within the nematode community were more closely related. With increasing latitude, the nematode relative richness and abundance decreased for selective deposit feeders but increased for nonselective deposit feeders. The proportion of general opportunists decreased with increasing latitude, but persisters showed the opposite trend. The annual temperature range and the pH of sediments were more important than vegetation type in structuring nematode communities. Main conclusion Nematode niche breadth was narrower at lower latitudes with respect to dietary specificity. Higher latitudes with a more variable climate favor r over K life-history strategists. Nematode communities at lower latitudes contained more closely related species.

For commercialization-oriented solid oxide fuel cells, the state-of-the-art nickel cermet anodes are still the preferable choice because of their several favorable features, such as high electrical conductivity, good thermo-mechano compatibility with other cell components, and favorable electrocatalytic activity for hydrogen oxidation. One big drawback of such anodes is their susceptibility to sulfur poisoning, which may cause catastrophic damage to cell performance even at ppm concentration level in fuel gas, while practical fuels usually contain a certain amount of sulfur impurity with concentration usually higher than ppm level. In an attempt to make them applicable for operation on practical carbonaceous fuels, materials/morphology/cell operation mode modification has been intensively tried to alleviate the sulfur poisoning problem. Herein, recent progress in understanding the sulfur poisoning effect on the performance of SOFCs with Ni-based cermet anodes operating on sulfur-containing methane and CO fuels, and related strategies for improving the sulfur tolerance were reviewed. The application status of SOFCs operating with sulfur-containing fuels was also referred. The purpose of this review is to provide some useful guidelines for further modifications of Ni-based cermet anodes with enhanced sulfur tolerance when operating on practical sulfur-containing carbonaceous fuels.