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Selectively approaching chemicals with one composition-tunable catalyst is attractive for practical manufacturing. Bimetallic copper-indium (Cu-In) catalysts have been synthesized by using a coprecipitation method and found to be among the best reported In-based catalysts for electrochemical CO2 reduction to CO or HCOOH. By varying the metal ratio, the catalyst can be tuned from a core-shell structure that selectively produces CO to a well-mixed structure that prefers HCOOH production. The distinct selectivities depend on the structure-sensitive binding strength of key reactive intermediates. These findings can benefit the development of a broader range of selectivity-tunable catalysts.

As more and more deep-buried metro stations appear, it’s becoming more and more difficult for people in the station hall and on the platform to evacuate in an emergency in these deep-buried stations. If people in the station hall choose to evacuate to the outside when fire happens in a underground station hall, the spread direction of the smoke is in coincident with the evacuation direction, which is unfavorable for the evacuation as well as the rescue from outside. Is it possible to use the platform as a shelter when the station hall catch fire? In this paper, the numerical method was used to demonstrate the feasibility of downward evacuation under the condition that fire happens in the station hall. At first, safety evacuation criteria were made to judge the feasibility of evacuation. Then, numerical models were established by FDS software, the smoke movement, the temperature, the concentration of carbon monoxide, and the visibility were taken into consideration to demonstrate the feasibility of downward evacuation. Different locations, different heat release rate of fires in the station hall were also calculated and analyzed. Results show that the downward evacuation to the platform is feasible during a fire in the station hall under the condition that the smoke exhausting system in the station hall can work properly and has enough efficiency.

Electrochemical reduction of CO2 is promising to utilize the intermittent renewable electricity and transform CO2 into value-added products, simultaneously. Herein, we designed a cobalt phthalocyanine-based catalyst supported on pyridine-functionalized carbon nanotubes (CoPc-py-CNT). This novel hybrid catalyst exhibited a high activity (TOFCO: 34.5 s−1 at −0.63 V vs. RHE) and selectivity (FECO > 98%) for electrochemical CO2 reduction. To the best of our knowledge, it is the best one among all reported molecular based electrocatalysts for CO2-to-CO conversion. Furthermore, structure characterizations (such as Raman and X-rays photoelectron spectroscopy), loading-dependent electrochemical analysis and mechanistic studies revealed that pyridine groups, through axial coordination with Co, not only functioned as physical promoters to improve the dispersion of cobalt phthalocyanine but also tuned the electronic structure of Co sites to increase the intrinsic turnover frequency.

Molecular complexes with inexpensive transition-metal centers have drawn extensive attention, as they show a high selectivity in the electrochemical conversion of CO2 to CO. In this work, we propose a new strategy to covalently graft cobalt porphyrin onto the surface of a carbon nanotube by a substitution reaction at the metal center. Material characterization and electrochemical studies reveal that the porphyrin molecules are well dispersed at a high loading of 10 wt. %. As a result, the turnover frequency for CO formation is improved by a factor of three compared to traditional physically-mixed catalysts with the same cobalt content. This leads to an outstanding overall current density of 25.1 mA cm-2 and a Faradaic efficiency of 98.3 % at 490 mV overpotential with excellent long-term stability. This work provides an effective pathway for the improvement of the performance of electrocatalysts that could inspire rational design of molecular catalysts in the future.

Electroreduction of CO2 into CO can be catalyzed by cobalt porphyrins, and the performance can be improved by peripheral functionalization or heterogenization. Herein, we report a facile approach t...

Polymerized cobalt phthalocyanines supported on carbon nanotubes were synthesized via a one-pot polycondensation reaction using water as a green solvent. This environmentally friendly strategy allows facile tuning of the morphology and chemical structure of the catalysts by changing the loading, substrate and monomers. The optimized catalyst produces CO with near perfect faradaic efficiencies (>90%) and outstanding specific activities (124 mA mg−1) at a low overpotential of 490 mV.

High-valence metal-doped multimetal (oxy)hydroxides outperform noble metal electrocatalysts for the oxygen evolution reaction (OER) owing to the modified energetics between 3d metals and high-valence dopants. However, the rational design of sufficient and subtle modulators is still challenging. With a multimetal layered double hydroxide (LDH) as the OER catalyst, this study introduces a series of operando high-valence dopants (Cr, Ru, Ce, and V), which can restrict the 3+ valence states in the LDH template to prevent phase separation and operando transfer to the3+ valence states for sufficient electronic interaction during the OER process. Through density functional theory simulations, ultrathin Cr-doped NiFe (NiFeCr) LDH is synthesized with strong electronic interaction between Cr dopants and NiFe bimetallic sites, evidenced by X-ray absorption spectroscopy. The resulting NiFeCr-LDH catalyzes the OER with ultralow overpotentials of 189 and 284 mV, obtaining current densities of 10 and 1000 mA cm

BackgroundCervical extension and flexion are generally thought to be harmful to CSM patients. Contrary to previous notions, a proportion of CSM patients presented DSSEP improvement upon extension and/or flexion. This study aims to determine the prediction criteria for DSSEP improvement upon extension and flexion with clinical and neutral-position imaging.MethodsCSM patients between 2015 and 2019 were retrospectively evaluated. The recorded outcomes were DSSEP changes upon extension and flexion, disease duration, modified Japanese Orthopedic Association (mJOA) score, gait impairment presence, weakness of upper limb muscles, and positive Hoffmann sign designation. The collection of MRI data included the compression ratio, the number of stenotic levels, the Mühle stenosis grade and disc degeneration stage of the most severe segment, and the presence of ligamentum flavum hypertrophy and intramedullary T2WI hyperintensity. Cervical alignment types were evaluated on plain cervical lateral radiographs.ResultsForty-nine patients were finally enrolled. Nine (18.4%) and 11 (22.4%) patients showed DSSEP improvement upon extension and flexion, respectively. Logistic regression analysis showed that an involved segment number ≤ 2 (P=0.018) and a straight/sigmoid cervical alignment (P=0.033) were significant criteria for predicting DSSEP improvement upon extension (probability: 85.7%). Mühle grade 3 (P=0.022) and disease duration ≤ 6 months (P=0.04) were significant criteria for predicting improvement upon flexion (probability: 85.7%).ConclusionsCervical extension and flexion might not necessarily cause deterioration and can even improve some CSM patients' neurological function at DSSEP examination. The involved segment number and cervical alignment were related to extension DSSEP improvement, while the stenosis grade and disease durations were related to flexion improvement. Our work suggests potential significance of educating CSM patients on maintaining individualized neck positions based on their clinical and radiographic findings.BaTrial registrationThe trial was retrospectively registered on April 30th, 2020 and the registration number is "[2020]151".

Objective: This study summarized the best evidence of early active movements in mechanically ventilated patients in the ICU and applied it in the intensive care unit of our hospital to evaluate the practical effects. Methods: The best evidence for early activity in patients with mechanical ventilation in the ICU was summarized by using an evidence-based nursing method, and the results were clinically applied in the ICU. Patients who were mechanically ventilated in the ICU from January to March 2020 were selected as the control-group, and their counterparts from April to June 2020 were enrolled as the practice-group. The control-group-patients received conventional early active mobilities, and the practice-group-patients performed the best evidence-based early active mobilities. The Barthel index, muscle strength, duration of mechanical ventilation and length of ICU stay between the two groups were compared. Results: The scores of Barthel index and muscle strength of the practice group were remarkably higher than those of the control group, and the duration of mechanical ventilation and length of ICU stay were obviously shorter than those of the control group, and the difference was statistically significant (P

An amendment to this paper has been published and can be accessed via the original article.

Hepatocellular carcinoma (HCC) is a common high-mortality cancer, mainly due to diagnostic difficulties during its early clinical stages. In this study, we aimed to identify genes that are important for HCC diagnosis and treatment, and we investigated the underlying mechanism of prognostic differences. Differentially expressed genes (DEGs) were identified by using the limma package, and receiver operating characteristic curve analysis was performed to identify diagnostic markers for HCC. Bioinformatics and clinical specimens were used to assess epithelial cell transforming 2 (ECT2) in terms of expression, prognostic value, pathways, and immune correlations. In vitro experiments were used to investigate the underlying mechanism and function of ECT2, and the results were confirmed through in vivo experiments. The integrated analysis revealed 53 upregulated DEGs, and one candidate biomarker for diagnosis (ECT2) was detected. High expression of ECT2 was found to be an independent prognostic risk factor for HCC. ECT2 expression showed a strong correlation with tumor-associated macrophages. We found that ECT2 overexpression increased the migration and proliferation of HCC cells. It also promoted the expression of PLK1, which subsequently interacted with PTEN and interfered with its nuclear translocation, ultimately enhancing aerobic glycolysis and promoting M2 macrophage polarization. M2 macrophages suppress the functions of NK cells and T cells, and this was confirmed in the in vivo experiments. Overall, ECT2 may promote the polarization of M2 macrophages by enhancing aerobic glycolysis and suppressing the functions of immune cells. ECT2 could serve as a candidate diagnostic and prognostic biomarker for HCC.

International audience; Myoblast fusion is essential for formations of myofibers, the basic cellular and functional units of skeletal muscles. Recent genetic studies in mice identified two long-sought membrane proteins, Myomaker and Myomixer, which cooperatively drive myoblast fusion. It is unknown whether and how human muscles, with myofibers of tremendously larger size, use this mechanism to achieve multinucleations. Here, we report an interesting fusion model of human myoblasts where Myomaker is sufficient to induce low-grade fusion, while Myomixer boosts its efficiency to generate giant myotubes. By CRISPR mutagenesis and biochemical assays, we identified MyoD as the key molecular switch of fusion that is required and sufficient to initiate Myomixer and Myomaker expression. Mechanistically, we defined the E-box motifs on promoters of Myomixer and Myomaker by which MyoD induces their expression for multinucleations of human muscle cells. Together, our study uncovered the key molecular apparatus and the transcriptional control mechanism underlying human myoblast fusion.

Background Nowadays, a positive HBV carrier status is common among pregnant women, especially in endemic areas (such as China), little is known about the impact of maternal HBV infection on the risk of adverse pregnancy outcomes. Pregnant women with HBV infection often develop obstetric complications, such as pregnancy-induced hypertension (PIH) syndrome, postpartum hemorrhage, and gestational diabetes mellitus (GDM), and their infants often exhibit neonatal complications. Methods This study undertook a retrospective cohort analysis to explore the association of HBV carrier status with adverse pregnancy outcomes. A cohort of 85,190 women including 9699 HBsAg-positive and 73,076 HBsAg-negative pregnancies was retrospectively analyzed. Results It’s found that HBsAg-positive pregnancies may result in higher risk of various maternal outcomes such as ICP (OR 3.4,95%CI 2.80 to 4.13), postpartum hemorrhage (OR 1.16,95%CI 1.00 to 1.34). Interestingly, there was a decreased risk of Preeclampsia (OR 0.91,95%CI 0.87 to 0.96), premature rupture of membrane (OR 0.91,95%CI 0.87 to 0.96) and gestational hypertension (OR 0.828,95%CI 0.701 to 0.978). And in vaginal delivery subgroup analysis, It’s found that the HBsAg-positive group had a higher risk of placental abruption (OR, 1.44; 95% CI, 1.16–1.79). Conclusions The present results suggest that compared with HBV positive pregnancies were more likely to be ICP and postpartum hemorrhage. HBV-positive pregnant women underwent vaginal delivery were more likely to have placental abruption and premature birth compared with HBV-negative women. Obstetricians should be aware of ICP, postpartum hemorrhage, placental abruption and premature birth in HBV-positive pregnant women.

Few-shot semantic segmentation aims to segment novel-class objects in a given query image with only a few labeled support images. Most advanced solutions exploit a metric learning framework that performs segmentation through matching each query feature to a learned class-specific prototype. However, this framework suffers from biased classification due to incomplete feature comparisons. To address this issue, we present an adaptive prototype representation by introducing class-specific and class-agnostic prototypes and thus construct complete sample pairs for learning semantic alignment with query features. The complementary features learning manner effectively enriches feature comparison and helps yield an unbiased segmentation model in the few-shot setting. It is implemented with a two-branch end-to-end network (i.e., a class-specific branch and a class-agnostic branch), which generates prototypes and then combines query features to perform comparisons. In addition, the proposed class-agnostic branch is simple yet effective. In practice, it can adaptively generate multiple class-agnostic prototypes for query images and learn feature alignment in a self-contrastive manner. Extensive experiments on PASCAL-5$^i$ and COCO-20$^i$ demonstrate the superiority of our method. At no expense of inference efficiency, our model achieves state-of-the-art results in both 1-shot and 5-shot settings for semantic segmentation., Comment: 12 pages, 7 figures, Accepted to IEEE Trans. on Multimedia. arXiv admin note: substantial text overlap with arXiv:2104.09216

Background Dynamic somatosensory evoked potentials (DSSEP) can be used to disclose abnormalities of ascending sensory pathways at dynamic positions and diagnose cervical spondylotic myelopathy (CSM). However, radiographic tests including magnetic resonance imaging (MRI) and dynamic X-ray are used much more widely in the management of CSM. Our study aims to clarify the correlations between several radiographic parameters and the DSSEP results, and further determine their reliability with clinical data. Methods We retrospectively enrolled 38 CSM patients with surgical intervention. DSSEP tests were performed before surgery. Amplitude ratios of DSSEP N13 and N20 waves at extension and flexion were calculated and recorded as N13_E, N20_E, N13_F, N20_F, respectively. Baseline severity was evaluated with the modified Japanese Orthopedic Association (mJOA) score and the Nurick grades. Prognosis was evaluated based on the 2-year recovery rate. Sagittal diameter and transverse areas of the cord and canal were measured and the the compressive ratios at the compressed site (Compression_Ratio), central (Central_Ratio), and 1/4-lateral points (1/4-Lateral_Compression_Ratio), and spinal cord/Canal Area Ratio were calculated. The intramedullary T2 hyperintensity patterns (Ax-CCM types) were also collected from MRI axial images. Dynamic X-rays were used to test for segmental instability of the cervical spine. The correlations between radiologic findings, DSSEP data, and clinical assessments were investigated. Results We found that DSSEP N13_E and N13_F correlated with the Compression_Ratio, Central_Ratio, 1/4-Lateral_Compression_Ratio (Pearson, p p p Conclusions We found that the preoperative Compression_Ratio, Central_Ratio and 1/4-Lateral_Compression_Ratio in MRI and cervical segmental instability in dynamic X-ray could reflect the dynamic neural dysfunction of the spinal cord. Different Ax-CCM types corresponded to different DSSEP results at extension and flexion, suggesting divergent pathophysiology. These radiographic parameters could help evaluate disease severity and predict postoperative prognosis.

The prognoses of patients with pancreatic adenocarcinoma (PAAD) remain poor due to the lack of biomarkers for early diagnosis and effective prognosis prediction. RNA sequencing, single nucleotide polymorphism, and copy number variation data were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to identify prognosis-related genes. GISTIC 2.0 was used to identify significantly amplified or deleted genes, and Mutsig 2.0 was used to analyze the mutation data. The Lasso method was used to construct a risk prediction model. The Rms package was used to evaluate the overall predictive performance of the signature. Finally, Western blot and polymerase chain reaction were performed to evaluate gene expression. A total of 54 candidate genes were obtained after integrating the genomic mutated genes and prognosis-related genes. The Lasso method was used to ascertain 9 characteristic genes, including UNC13B, TSPYL4, MICAL1, KLHDC7B, KLHL32, AIM1, ARHGAP18, DCBLD1, and CACNA2D4. The 9-gene signature model was able to help stratify samples at risk in the training and external validation cohorts. In addition, the overall predictive performance of our model was found to be superior to that of other models. KLHDC7B, AIM1, DCBLD1, TSPYL4, and MICAL1 were significantly highly expressed in tumor tissues compared to normal tissues. ARHGAP18 and CACNA2D4 had no difference in expression between tumor and normal tissues. UNC13B and KLHL32 expression in the normal group was higher than in the tumor group. The 9-gene signature constructed in this study can be used as a novel prognostic marker to predict the survival of patients with pancreatic adenocarcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related morbidity and mortality; it has been reported that immune cell infiltration is a prognosis factor. Here we identified genes that associated with tumor immune cell infiltrate; the underlying mechanism was verified by in vivo and in vitro experiment. In this study, Weighted correlation network analysis (WGCNA) and CIBERSORT tool were used to identify MTIF2 as the hub tumor immune infiltrating gene in HCC. To investigate the underlying role played by MTIF2, MTIF2 was knocked down by transfection of shRNA targeting MTIF2, CCK8, and EdU incorporation assay was used to evaluate the effect of MTIF2 on proliferation, wound heal assay and transwell assay was used to confirm its effect on cell migration. Ecto-calreticulin on the cell surface was evaluated by flow cytometry, ATP, and HMGB1 secretion were tested to the investigated effect of MTIF2 on the immunogenic cell death (ICD) process. We found that down-regulation of MTIF2 impaired proliferation and migration capacity of HCC cells, chemoresistance to 5-Fluorouracil (5-FU) weakened after MTIF2 was knocked down. Reduced release of damage-associated molecular patterns (DAMP) was observed after MTIF2 was overexpressed, which subsequently impaired dendritic cell (DC) maturation and proliferation of CD8 + T cells. Mechanically, the co-IP experiment confirmed that MTIF2 could interact with AIFM1, prevents AIFM1 induced transcription of caspase3, and finally suppress apoptosis. In vivo experiment also used to confirm our previously conclusion, our result indicated that MTIF2 overexpression suppresses tumor apoptosis and immune cell activity in the 5-FU therapy in vivo model, by suppression maturation of tumor-infiltrated DC. Collectively, our study confirmed that MTIF2 impair drug-induced immunogenic cell death in hepatocellular carcinoma cells.

Background: The prognosis of patients with pancreatic cancer remains poor due to the lack of biomarkers for early diagnosis and effective prognosis. Methods: RNA-Seq, SNP, CNV data were downloaded from The Cancer Genome Atlas(TCGA); Univariant cox regression was used to identify prognosis-related genes; GISTIC 2.0 was used to identify significantly amplified or deleted genes; Lasso method was used to construct risk prognosis model, which was then validated in GEO and ICGC cohorts. rms package was used to evaluate the overall predictive performance of the model by calculating and comparing the C-index values with other models. Experiments of western blot were performed to evaluate the expression of genes. Results: 54 candidate genes were obtained after integrating the genomic mutated genes and prognosis-related genes. The Lasso method was conducted to finally ascertain nine characteristic genes, including UNC13B, TSPYL4, MICAL1, KLHDC7B, KLHL32, AIM1, ARHGAP18, DCBLD1, and CACNA2D4. The 9-gene signature model can help with stratifying samples at risk in the training cohort and external validation cohort. In addition, the overall predictive performance of our model is superior to the others. We found that AIM1 and DCBLD1 were highly expressed in tumor tissues, while ARHGAP18, CACNA2D4, and TSPYL4 were lowly expressed in tumor tissues at the protein and transcription levels. Conclusion: The nine-gene signatures constructed in this study can be used as the novel prognostic markers to predict the survival of PAAD patients.

Learning to follow instructions is of fundamental importance to autonomous agents for vision-and-language navigation (VLN). In this paper, we study how an agent can navigate long paths when learning from a corpus that consists of shorter ones. We show that existing state-of-the-art agents do not generalize well. To this end, we propose BabyWalk, a new VLN agent that is learned to navigate by decomposing long instructions into shorter ones (BabySteps) and completing them sequentially. A special design memory buffer is used by the agent to turn its past experiences into contexts for future steps. The learning process is composed of two phases. In the first phase, the agent uses imitation learning from demonstration to accomplish BabySteps. In the second phase, the agent uses curriculum-based reinforcement learning to maximize rewards on navigation tasks with increasingly longer instructions. We create two new benchmark datasets (of long navigation tasks) and use them in conjunction with existing ones to examine BabyWalk's generalization ability. Empirical results show that BabyWalk achieves state-of-the-art results on several metrics, in particular, is able to follow long instructions better. The codes and the datasets are released on our project page https://github.com/Sha-Lab/babywalk., Accepted by ACL 2020

Enteromorpha prolifera (EP) biomass collected from a lake in China was employed for biochar production. The EP biochar was magnetically modified by loading γ-Fe2O3 particles on the surface, and Cr(VI) adsorption behavior and mechanism were evaluated. The magnetic biochar had higher surface polarity, specific surface area and exhibited a higher Cr(VI) adsorption capacity of 95.23 mg/g biochar compared with pristine EP biochar. The pronounced electron spin resonance (ESR) signals showed that the environmental persistent free radicals (EPFRs) preferred to form at lower pyrolysis temperature and lower transition metal concentration. The g factors of BC400, BC800 and BCF400 were 1.8959, 1.7955 and 1.7954, respectively, indicating that the EPFRs mainly used carbon atom as center. In addition, biological toxicity of magnetic EP biochar was tested using the microalga Scenedesmus obliquus. Exposure of S. obliquus cells to magnetic biochar led to the accumulation of reactive oxygen species (ROS) and oxidative stress. The leaching solution toxicity of BCF400 was stronger than BCF800. Thus, the magnetic EP biochar prepared at higher temperature (such as BCF800) provide better Cr (VI) performance with low biologic toxicity. And the EP biomass could be a promising low-cost feedstock for biochar production.

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In view of the small scale of private electric vehicles in relatively backward cities in China at present, in order to avoid the problems of low utilization rate and insufficient demand of charging stations, this paper synthesizes the idea of promoting charging stations in these cities at the present stage and constructs a planning and evaluation system of charging stations for social service vehicles in relatively backward cities in China. Based on the Prospect Theory, this paper puts forward a kind of charging station for social service vehicles. The index quantification method of grey targetprospect theory evaluates the level of station construction in all aspects of candidate points, and uses VIKOR, a multi-criteria decision-making method, to determine the final decision-making scheme, so as to avoid the problem that traditional linear weighting method uses superior index to make up for inferior index. Finally, a relatively backward charging station planning project is taken as an example to illustrate the feasibility and effectiveness of the proposed method.

The relationship between consciousness and feature binding has long been a topic of debate. Although they may be associated, there is growing evidence supporting the notion that feature binding and visual consciousness can be separated to some degree. In our study, we manipulated the visibility of priming stimuli, which consisted of color-motion features, and then measured the effects of those manipulations on reporting of cued targets' direction of rotation. A priming effect was observed even when the priming stimuli were invisible, implying that the color-motion features of the prime could bind unconsciously. These findings support the notion that, at least under certain conditions, feature binding and visual consciousness are separable psychological processes wherein feature binding is not sufficient to establish visual awareness.

Electrodeposited Bi, Sn and In catalysts are employed as model catalysts to investigate the nature of active sites and the influence of surface hydroxyls on the formation of formate during CO2 electroreduction (CO2RR). With SEM, XPS, in situ XRD, electrochemical measurements and DFT calculations, the active sites are Bi0, SnO and In2O3, with their affinities to hydroxyls following the trend of Bi0

Herein, we report a facile direct solid-state reaction strategy using black phosphorus coupled black titania to prepare uniform BP-BT materials with enhanced wide-spectrum sunlight absorption properties. BP-BT possesses a typical crystalline core-amorphous shell (TiO2@TiO2−x) structure with numerous oxygen vacancies and electron-rich P dosage in the layer and exhibits excellent solar-to-chemical conversion for efficient photocatalytic CO2 reduction to CH4. Notably, the optimized BP-BT shows superior activity of CH4 formation of 16.8 μmol g−1 h−1, a selectivity of 80.1 %, and excellent stability for CO2 transformation under solar light irradiation. The kinetic isotope effects and in situ diffuse reflectance infrared Fourier transform spectroscopy analysis reveal that the adsorption and chemical activation of inert CO2 molecule over the surface-defective and surface basic-site-modulated BP-BT are kinetically-relevant steps. The existence of crucial intermediate of CO2− species from single-electron CO2 reduction route and the rate-determining step of C O bond cleavage in photocatalytic CH4 generation were also confirmed.

Internet of vehicles (IoV) is an emerging paradigm for accommodating the requirements of future intelligent transportation systems (ITSs) with the overwhelming trend of equipping vehicles with versatile sensors and communications modules, and facilitating drivers and passengers with a variety of innovative ITS applications. However, the implementation of IoV still faces many challenges, such as flexible and efficient connections, quality of service guarantee, and multiple concurrent support requests. To this end, in this paper we introduce the software-defined IoV (SD-IoV), which is able to tackle the above-mentioned issues by adopting the software-defined networking framework. We first present the architecture of SD-IoV and develop a centralized vehicular connection management approach. Then, we aim to allocate dedicated communications resources and underlying vehicular nodes to satisfy each service. We formulate the dynamic vehicular connection as an overlay vehicular network creation (OVNC) problem. A comprehensive utility function is also designed to serve as the optimization objective of OVNC. Finally, we solve the OVNC problem by developing a graph-based genetic algorithm and a heuristic algorithm, respectively. Extensive simulation results are provided to demonstrate the effectiveness of our proposed solution of dynamic vehicular connection management.

Dual-atom catalysts have the potential to outperform the well-established single-atom catalysts for the electrochemical conversion of CO2 . However, the lack of understanding regarding the mechanism of this enhanced catalytic process prevents the rational design of high-performance catalysts. Herein, an obvious synergistic effect in atomically dispersed Ni-Zn bimetal sites is observed. In situ characterization combined with density functional theory (DFT) calculations reveals that heteronuclear coordination modifies the d-states of the metal atom, narrowing the gap between the d-band centre (ed ) of the Ni (3d) orbitals and the Fermi energy level (EF ) to strengthen the electronic interaction at the reaction interface, resulting in a lower free energy barrier (ΔG) in the thermodynamic pathway and a reduced activation energy (Ea ) as well as fortified metal-C bonding in the kinetic pathway. Consequently, a CO faradaic efficiency of >90% is obtained across a broad potential window from -0.5 to -1.0 V (vs RHE), reaching a maximum of 99% at -0.8 V, superior to that of the Ni/Zn single-metal sites.

A software-defined wireless local area network (SDWLAN) has gained significant interest recently from both the academic and industrial communities and initiated a paradigmatic reconsideration on the stereotyped management and control of current WLAN due to its flexibility and programmability. However, with the proliferation of mobile devices, efficient client association with quality of service (QoS) guarantees in the high-density SDWLAN is a very challenging issue. In this paper, we study the client association problem in the SDWLAN with new features, including centralized association, global network state awareness, seamless handoff, and flow-level association. We formulate the client association in a high-density scenario as an optimization problem aiming to minimize the interpacket delay of individual flows, based on an unsaturated and heterogeneous Markovian analytical model. Furthermore, we interpret the optimization problem as an NP-hard supermodular set function minimization problem, which is solved by two low-complexity heuristic methods, namely the greedy algorithm and the bounded local search algorithm. Through simulations, we demonstrate the effectiveness of our proposed client association solution.

In delay and disruption tolerant networks, the contacts among nodes are intermittent. Because of the importance of data access, providing efficient data access is the ultimate aim of analyzing and exploiting disruption tolerant networks. Caching is widely proved to be able to improve data access performance. In this paper, we consider caching schemes for broadcasting and multicasting to improve the performance of data access. First, we propose a caching algorithm for broadcasting, which selects the community central nodes as relays from both network structure perspective and social network perspective. Then, we accommodate the caching algorithm for multicasting by considering the data query pattern. Extensive trace-driven simulations are conducted to investigate the essential difference between the caching algorithms for broadcasting and multicasting and evaluate the performance of these algorithms. Copyright © 2016 John Wiley & Sons, Ltd.

Highlights • Reaction time and event-related potentials of inhibitory control were compared in badminton experts and nonathletes. • Badminton experts showed enhanced inhibitory control and more efficient neural mechanisms. • Badminton experts performed better inhibitory control processing in re-engagement. • The re-engagement processing better demonstrated altered brain activity in badminton experts., Purpose The purpose of present study was to investigate the impact of sport experience on response inhibition and response re-engagement in expert badminton athletes during the stop-signal task and change-signal task. Methods A total of 19 badminton athletes and 20 nonathletes performed both the stop-signal task and change-signal task. Reaction times (RTs) and event-related potentials were recorded and analyzed. Results Behavioral results indicated that badminton athletes responded faster than nonathletes to go stimuli and to change signals, with faster change RTs and change-signal RTs, which take into consideration the variable stimulus onset time mean. During successful change trials in the change-signal task, the amplitudes of the event-related potential components N2 and P3 were smaller for badminton athletes than for nonathletes. Moreover, change-signal RTs and N2 amplitudes as well as change RTs and P3 amplitudes were significantly correlated in badminton athletes. A significant correlation was also found between the amplitude of the event-related potential component N1 and response accuracy to change signals in badminton athletes. Conclusion Moderation of brain cortical activity in badminton athletes was more associated with their ability to rapidly inhibit a planned movement and re-engage with a new movement compared with nonathletes. The superior inhibitory control and more efficient neural mechanisms in badminton athletes compared with nonathletes might be a result of badminton athletes’ professional training experience., Graphical abstract Image, graphical abstract

This article mainly introduces the application of a reconnaissance robot’s mechanical design, motion control algorithm and image processing program. In the mechanical structure, it adopts a motor system that drives a Mecanum wheel with double wishbone independent suspension to ensure that the robot has high speed and high mobility. The robot is equipped with a two-degree-of-freedom pan/tilt for installing a wireless video camera, which can acquire images with free viewing angles to facilitate remote control of operators and obtain on-site intelligence. The image processing program uses the machine learning method of the HOG feature and the SVM classifier to identify target objects such as enemy forces or certain types of explosive objects in disaster scenarios to reduce the pressure on operators and increase the reaction speed.

According to the CNN news, until the first day of year 2021, the total number of COVID-19 infections in the U.S. has exceeded 20 million and resulted in 350, 000 deaths. A review of the literature shows that COVID-19 has created a huge crisis in various industries such as offline department stores, tourism, airlines, and restaurants, but also contributes to the online service industry, medical and biopharmaceuticals. The quantitative assessment of the social impact of COVID-19 is based on various types of data. In this paper, stock prices of listed companies are used as indicators to explore the impact of the epidemic on stock prices, which further reflects the impact on different industries. Since the infection information and stock price data of listed companies are easily accessible, this article combines these data and conduct two analyses: correlation analysis and performance analysis, taking 468 listed companies in the U.S. stock market. In the correlation analysis, it is confirmed that the impact of COVID-19 on different industries or companies is different. In the performance analysis, this article predicts the performance of company stock prices before and after the outbreak by using different companies' basic information and find that the XGBoost model works best in the 2-classes case and the random forest model works best in the 5-classes case. © 2021 The Authors, published by EDP Sciences.

Urban rail systems are developing rapidly, and due to the limited space of the metro tunnel, once a fire occurs will result in disastrous consequences. Full-scale fire experiment is an effective testing method for fire prevention of metro systems. Aiming at the characteristics of a long continuous downhill distance and large lifting height in the sloped metro tunnel, a full-scale fire experiment under vehicle operations was carried out on a metro line. By changing the operated speed and direction of the vehicle, the airflow velocity, ceiling temperature distribution, and smoke layer characteristics in the tunnel were studied. The results show that when a fire occurs in a sloped tunnel, the running vehicle will produce piston effect, and the airflow will be affected by the stack effect and the piston effect simultaneously, which will change the flow field in this area, and then affect the flame shape, ceiling temperature and smoke distribution. Through the dimensionless treatment of the ceiling temperature, a longitudinal attenuation distribution model of the ceiling temperature was established, which reveals the law of fire smoke migration and diffusion under different vehicle operating conditions. The conclusions of this study could technically support the smoke control design in such metro tunnel fire scenarios.

Gastric cancer (GC) is one of the most malignant tumors with a poor prognosis. Alterations in metabolic pathways are inextricably linked to GC progression. However, the underlying molecular mechanisms remain elusive. We performed NMR-based metabolomic analysis of sera derived from a rat model of gastric carcinogenesis, revealed significantly altered metabolic pathways correlated with the progression of gastric carcinogenesis. Rats were histologically classified into four pathological groups (gastritis, GS; low-grade gastric dysplasia, LGD; high-grade gastric dysplasia, HGD; GC) and the normal control group (CON). The metabolic profiles of the five groups were clearly distinguished from each other. Furthermore, significant inter-metabolite correlations were extracted and used to reconstruct perturbed metabolic networks associated with the four pathological stages compared with the normal stage. Then, significantly altered metabolic pathways were identified by pathway analysis. Our results showed that oxidative stress-related metabolic pathways, choline phosphorylation and fatty acid degradation were continually disturbed during gastric carcinogenesis. Moreover, amino acid metabolism was perturbed dramatically in gastric dysplasia and GC. The GC stage showed more changed metabolite levels and more altered metabolic pathways. Two activated pathways (glycolysis; glycine, serine and threonine metabolism) substantially contributed to the metabolic alterations in GC. These results lay the basis for addressing the molecular mechanisms underlying gastric carcinogenesis and extend our understanding of GC progression.

The influence of basic oxide promoters on copper-chromium-iron oxide catalysts was investigated to determine the nature of surface oxygen species and structure-activity relationship for the reverse water-gas shift reaction. The catalysts were characterized with in situ XRD, in situ Raman, in situ XPS, in situ HS-LEIS and H2-TPR. Two surface oxygen sites with different reduction characteristics were found to be present. The overall CO2 activation rate was found to correlate with both the number and reducibility of the more active oxygen species that were likely associated with the Cu-FeOx interfacial regions for enhanced hydrogen spillover. While addition of K2O somewhat preserved the interfacial regions and facilitated the reduction kinetics of surface oxygen, both Na2O and CaO significantly suppressed the availability of metallic Cu as well as the Cu-FeOx interfaces, leading to decreased reactivity. These findings provide a direction to promote the copper-iron catalysts by creating more metal-metal oxide interfacial sites.

Research and applications of unmanned aerial vehicles (UAVs) are becoming increasingly prosperous in these years due to the maturity of the aircraft technology and regulations. A large amount of UAVs are to be deployed in cities to undertake tasks such as environment monitoring and security surveillance. For those computation-intensive tasks, on-board execution can lead to inefficiency and unsustainability due to the limited battery life and computing resources of UAVs. To this end, this paper adopts cooperative mobile edge computing such that energy consumption and task execution latency can both be reduced. The computation offloading for UAVs aims to optimize the energy and latency jointly with the help of cooperative edge servers. We obtain the most energy efficient offloading data rate by convex optimization and obtain the optimal data allocation scheme to meet the latency constraint by simulated annealing based particle swarm optimization (SAPSO). Simulation results validate the efficiency of the proposed UAV computation offloading strategy.

The past decade has witnessed significant breakthroughs in the world of computer vision. Recent deep learning-based computer vision algorithms exhibit strong performance on recognition, detection, and segmentation. While the development of vision algorithms elicits promising applications, it also presents immense computational challenge to the underlying hardware due to its complex nature, especially when attempting to process the data at line-rate.To this end we develop a highly scalable computer vision processing framework, which leverages advanced technologies such as Spark Streaming and OpenCV to achieve line-rate video data processing. To ensure the greatest flexibility, our framework is agnostic in terms of computer vision model, and can utilize environments with heterogeneous processing devices. To evaluate this framework, we deploy it in a production cloud computing environment, and perform a thorough analysis on the system's performance. We utilize existing real-world live video streams from Simon Fraser University to measure the number of cars entering our university campus. Further, the data collected from our experiments is being used for real-time predictions of traffic conditions on campus.

Sensorimotor synchronization is the coordination of rhythmic movement with an external beat. Dancers often synchronize each beat of their motion with an external rhythm. Compared with social dancing, competitive ballroom dancing requires a higher level of sensorimotor ability. Although previous studies have found that dance experience may facilitate sensorimotor synchronization, they did not examine this in competitive ballroom dancers. Thus, the present study compared sensorimotor synchronization in 41 nondancers and 41 skilled, competitive ballroom dancers as they performed a simple beat synchronization finger-tapping task. All participants finger-tapped freely at their preferred tempo before the formal experiments. Participants were then required to synchronize their finger-tapping with auditory, visual, or combined audiovisual signals in separate experiments and at varying tempos. To assess sensorimotor plasticity, the participants then repeated the free-tapping task after completing all three finger-tapping experiments. Compared with nondancers, dancers showed more accurate and stable beat synchronization. Dancers tapped before onset of all three types of sensorimotor stimulation, indicating a significant negative mean asynchrony and had a tendency to anticipate (predict) the stimuli. Dancers tended to auditory stimulation for beat sensorimotor synchronization, whereas nondancers tended to visual stimuli. Dancers had a faster tempo preference in the initial free-tapping task; however, the preferred tapping tempo increased in all participants in the second free-tapping task, suggesting that beat induction is affected by practice. Together these findings suggest that dance experience enhances sensorimotor synchronization and sensorimotor plasticity, with ballroom dancers tending to auditory stimulation for beat induction.

Cellular networks are now facing severe traffic overload problems due to the explosive growth of mobile data traffic. One of the promising solutions is to offload part of the traffic through WiFi. In this paper, we investigate an oligopoly offloading market, where several mobile virtual network operators (MVNOs) compete to serve end users using network infrastructures leased from the host mobile network operator (MNO). First, we study the competitive interaction among multiple MVNOs considering the overload problems of the offloading market. Then, we formulate the interaction as a noncooperative inventory game, where each MVNO determines the amount of cellular traffic provided to end users (named as the traffic inventory). Particularly, we investigate two different behavior patterns of the MVNOs known as Cournot and Stackelberg models. Then, we analyze and derive the existence and uniqueness of the equilibrium in each inventory game. Furthermore, algorithms are designed to achieve the equilibriums. Based on these analyses, we find the optimal inventory strategy for these competing MVNOs. Finally, simulation results demonstrate the interactions among the MNO, MVNOs, and end users in the offloading market.

Distributed caching-empowered wireless networks can greatly improve the efficiency of data storage and transmission and thereby the users' quality of experience QoE. However, how this technology can alleviate the network access pressure while ensuring the consistency of content delivery is still an open question, especially in the case where the users are in fast motion. Therefore, in this paper, we investigate the caching issue emerging from a forthcoming scenario where vehicular video streaming is performed under cellular networks. Specifically, a QoE centric distributed caching approach is proposed to fulfill as many users' requests as possible, considering the limited caching space of base stations and basic user experience guarantee. Firstly, a QoE evaluation model is established using verified empirical data. Also, the mathematic relationship between the streaming bit rate and actual storage space is developed. Then, the distributed caching management for vehicular video streaming is formulated as a constrained optimization problem and solved with the generalized-reduced gradient method. Simulation results indicate that our approach can improve the users' satisfaction ratio by up to 40%. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, we identify the potential of the television bands to be used for providing vehicular infotainment service. Specifically, content delivery from the roadside unit to vehicular users is conducted by exploiting the spectrum holes, which is also referred to as spatial spectrum reuse. In this manner, the spectrum utilization efficiency is improved and the digital television service is not interfered. We first give a thorough description on such vehicular content delivery network and indicate the major design issues. After that, we respectively develop user and resource models tailored for our scenario by considering their characteristics and formulate the resource allocation as a system throughput maximization problem based on these models. For practical implementation, we devise a vehicular content delivery protocol incorporating a dynamic spectrum access algorithm and a maximal frame size design method, both of which aim at throughput and spectrum efficiency enhancement. Extensive simulations are conducted and the numerical results show the system performance is improved with our approaches.

Objective Several members of protocadherins (PCDHs) have been identified as tumor suppressor genes in human carcinogenesis, but little is known about PCDH19. The aim of the present study was to assess the expression and methylation of PCDH19 in hepatocellular carcinoma (HCC). Methods The RNA-seq data from The Cancer Genome Atlas Database were downloaded and used for analyzing PCDH19 expression in HCC patients and normal liver tissues. We collected 63 paired tumor and nontumor liver tissues from hepatitis B virus-related HCC patients. The expression of PCDH19 was detected by real-time quantitative RT-PCR assay. The methylation of PCDH19 gene was analyzed by DNA methylation-sensitive endonuclease digestion and the sequential quantitative PCR. The prognostic value of PCDH19 gene methylation was evaluated by Kaplan-Meier analyses. Results PCDH19 expression was downregulated in HCC tissues and seven HCC cell lines compared to nontumor tissues. PCHD19 promoter was frequently hypermethylated in three (SMMC7721, Hep3B and SNU387) of seven HCC cell lines and 5-aza-dC treatment could significantly increased the PCDH19 expression in these methylated cells. In addition, HCC tumor tissues exhibited significantly increased PCDH19 hypermethylation both in frequency (30.15% vs 9.52%, P = 0.003) and in intensity (P = 0.002) compared to that in nontumor tissues. Kaplan-Meier survival analysis revealed that PCDH19 hypermethylation was correlated with the poor overall survival of HCC patients. Conclusion PCDH19 expression was downregulated in HCC, which was mediated at least in part by promoter hypermethylation. PCDH19 hypermethylation might present a potential prognostic marker in HCC patients.

Recently, with the advent of Internet of things, mobile devices are expected to carry on more and more computation-intensive applications, and mobile cloud computing also comes in to help mitigate the burden of mobile devices. However, due to the rapidly growing density of mobile devices, the current single cloud environment will still encounter resource depletion, which will lead to application offloading failure and user dissatisfaction. In order to enhance the scalability and reliability of application offloading service in mobile cloud computing, we study the heterogeneous mobile cloud, which integrates mobile cloudlet with infrastructure-based cloudlet. In such a scenario, when the infrastructure-based cloudlet becomes unavailable due to insufficient resource, mobile cloudlet can help to provide computation offloading service. We develop a centralized task scheduling algorithm to select reliable worker nodes in mobile cloudlet for the offloaded tasks. Simulation results show that heterogeneity of cloud resource can enhance scalability of offloading service efficiently. Also, the proposed scheduling algorithm can extend the network lifetime and keep balance of energy consumption in the mobile cloudlet.

Machine-to-machine (M2M) communications is regarded as the keystone of Internet of things (IoT) since it enables ubiquitous data exchange among devices without any artificial intervention. Cellular system is expected to play a significant role in successfully implementing the M2M communications for its ready-to-use infrastructure. However, there still exist many challenges, such as co-channel interferences to existing mobile users, congestions and limited battery capacity of M2M devices. In this paper, in order to realize M2M communication over cellular system, we propose a power- and spectrum-efficient massive-code-space-multiplex (massive-CSM) access scheme, in which quasi-orthogonal massive MIMO channels are utilized as resource blocks in the framework of SCMA. In the proposed scheme, group headers gather data from M2M devices by D2D links and communicate with eNB. Additionally, D2D-link power consumption optimization and transmit layers allocation algorithm are provided to minimize power consumption of codewords distribution among group headers. By applying random matrix theory, we also drive an uplink asymptotic equivalent (AE) of signal to interference plus noise ratio (SINR) of the system as the number of eNB antennas approach infinity. Simulation results show that the proposed multiplex access scheme improves the uplink capacity several times than traditional scheme and our algorithm achieves better results with less time complexity.