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We introduce the python software package Kernel Mixed Model (KMM), which allows users to incorporate the network structure into transcriptome-wide association studies (TWASs). Our software is based on the association algorithm KMM, which is a method that enables the incorporation of the network structure as the kernels of the linear mixed model for TWAS. The implementation of the algorithm aims to offer users simple access to the algorithm through a one-line command. Furthermore, to improve the computing efficiency in case when the interaction network is sparse, we also provide the flexibility of computing with the sparse counterpart of the matrices offered in Python, which reduces both the computation operations and the memory required.

This study aimed to explore the effect of psychological capital on the professional identity of ideological and political teachers in colleges and universities,and the current situation of psychological capital and professional identity.A total of 793 ideological and political teachers from 11 colleges and universities were surveyed with the Psychological Capital Scale and Professional Identity Scale.The results were as follows: The total score of psychological capital and the four dimensions of self-efficacy,hope,resilience and optimism of ideological and political teachers in Colleges and Universities are at high levels.The total score of professional identity and the four dimensions of role values,professional behavior. tendency,professional values,and professional sense of belonging of ideological and political teachers in Colleges and Universities are at high levels.For ideological and political teachers in colleges and universities,self-efficacy,hope,and optimism positively predict professional identity.The results provided a theoretical basis for enhancing the professional identity of ideological and political teachers in colleges and universities.

Ferroelectric HfO2-based materials hold great potential for widespread integration of ferroelectricity into modern electronics due to their robust ferroelectric properties at the nanoscale and compatibility with the existing Si technology. Earlier work indicated that the nanometer crystal grain size was crucial for stabilization of the ferroelectric phase of hafnia. This constraint caused high density of unavoidable structural defects of the HfO2-based ferroelectrics, obscuring the intrinsic ferroelectricity inherited from the crystal space group of bulk HfO2. Here, we demonstrate the intrinsic ferroelectricity in Y-doped HfO2 films of high crystallinity. Contrary to the common expectation, we show that in the 5% Y-doped HfO2 epitaxial thin films, high crystallinity enhances the spontaneous polarization up to a record-high 50 µC/cm2 value at room temperature. The high spontaneous polarization persists at reduced temperature, with polarization values consistent with our theoretical predictions, indicating the dominant contribution from the intrinsic ferroelectricity. The crystal structure of these films reveals the Pca21 orthorhombic phase with a small rhombohedral distortion, underlining the role of the anisotropic stress and strain. These results open a pathway to controlling the intrinsic ferroelectricity in the HfO2-based materials and optimizing their performance in applications.

Introduction: Prediction of high-risk events amongst patients with mental disorders is critical for personalized interventions. In our previous study, we developed a deep learning-based model, DeepBiomarker by utilizing electronic medical records (EMR) to predict the outcomes of patients with suicide-related events in post-traumatic stress disorder (PTSD) patients. Methods We improved our deep learning model to develop DeepBiomarker2 through data integration of multimodal information: lab tests, medication use, diagnosis, and social determinants of health (SDoH) parameters (both individual and neighborhood level) from EMR data for outcome prediction. We further refined our contribution analysis for identifying key factors. We applied DeepBiomarker2 to analyze EMR data of 38,807 patients from University of Pittsburgh Medical Center diagnosed with PTSD to determine their risk of developing alcohol and substance use disorder (ASUD). Results DeepBiomarker2 predicted whether a PTSD patient will have a diagnosis of ASUD within the following 3 months with a c-statistic (receiver operating characteristic AUC) of 0·93. We used contribution analysis technology to identify key lab tests, medication use and diagnosis for ASUD prediction. These identified factors imply that the regulation of the energy metabolism, blood circulation, inflammation, and microbiome is involved in shaping the pathophysiological pathways promoting ASUD risks in PTSD patients. Our study found protective medications such as oxybutynin, magnesium oxide, clindamycin, cetirizine, montelukast and venlafaxine all have a potential to reduce risk of ASUDs. Discussion DeepBiomarker2 can predict ASUD risk with high accuracy and can further identify potential risk factors along with medications with beneficial effects. We believe that our approach will help in personalized interventions of PTSD for a variety of clinical scenarios.

Few-shot semantic segmentation aims to learn to segment unseen class objects with the guidance of only a few support images. Most previous methods rely on the pixel-level label of support images. In this paper, we focus on a more challenging setting, in which only the image-level labels are available. We propose a general framework to firstly generate coarse masks with the help of the powerful vision-language model CLIP, and then iteratively and mutually refine the mask predictions of support and query images. Extensive experiments on PASCAL-5i and COCO-20i datasets demonstrate that our method not only outperforms the state-of-the-art weakly supervised approaches by a significant margin, but also achieves comparable or better results to recent supervised methods. Moreover, our method owns an excellent generalization ability for the images in the wild and uncommon classes. Code will be available at https://github.com/Whileherham/IMR-HSNet., ijcai 2022

Nowadays, SiO2 based material is one of the widest used materials in optical, microelectromechanical system, aerospace and some other industries. In practical application, SiO2 based materials are required to be joined with themselves or other heterogeneous materials to assemble products with various functions. And the joining quality directly affects the mechanical performances and functional properties of assembled products. Though many researchers studied different joining technologies, explored the microstructure of joining interface and tested after-joining properties, a review that summarizes the recent cutting-edge researches and development tendency of joining technologies for SiO2 based ceramics is still absent. Therefore, according to different applications and requirements, this review summarizes the widely used joining technologies and discusses corresponding joining mechanisms, current research progress and suitable applications. Due to the wide applications in specific industry, laser welding, anodic bonding and wafer bonding are discussed in detail. Further, brazing, the widest used method in SiO2 joining, has been elaborated deeply in its wetting mechanism, residual stress control, interfacial microstructure and mechanical performance aspects. In the end, this review proposed the future development trends of SiO2 ceramics joining, aiming at offering a full-view of potential improvements in SiO2 ceramic joining technologies.

Motivated by empirical arguments that are well known from the genome-wide association studies (GWAS) literature, we study the statistical properties of linear mixed models (LMMs) applied to GWAS. First, we study the sensitivity of LMMs to the inclusion of a candidate single nucleotide polymorphism (SNP) in the kinship matrix, which is often done in practice to speed up computations. Our results shed light on the size of the error incurred by including a candidate SNP, providing a justification to this technique to trade off velocity against veracity. Second, we investigate how mixed models can correct confounders in GWAS, which is widely accepted as an advantage of LMMs over traditional methods. We consider two sources of confounding factors—population stratification and environmental confounding factors—and study how different methods that are commonly used in practice trade off these two confounding factors differently.

To extend the application of the SiO2f/SiO2 composites, self-joining process is indispensable. Reliable self-joining of SiO2f/SiO2 composites was achieved by Bi2O3–B2O3–ZnO glass. With a low glass transition temperature, the onset temperature of residual stress is much lower than that of active brazing. In addition, bismuth glass exhibits a good wettability on the surface of SiO2f/SiO2 composites. When the joints were brazed at 750 °C/30 min, the average shear strength of the joints reached 21.2 MPa, reaching 77.8% of the shear strength of SiO2f/SiO2 composites. This technology can effectively fill the bank of self-joining of SiO2f/SiO2 composites.

Deep learning based algorithms have been widely employed to build reliable steel surface defect detection systems, which are important for manufacturing. The performance of deep learning models relies heavily on abundant annotated data. Nevertheless, the labeled image volume in industrial datasets is often limited. The scarcity of training data would lead to poor detection precision. To tackle this issue, we propose the first few-shot defect detection framework. Through pre-training models using data relevant to the target task, the proposed framework can produce well-trained networks with a few labeled images. Meanwhile, we release the first publicly available few-shot defect detection dataset, namely few-shot NEU-DET (FS-ND). This dataset will serve as a fair benchmark for contrasting various methods. Afterwards, we analyze the characteristics of steel surface defect detection. It is observed that the limited amount of training data can hardly cover the data distributions in practical applications. Given this observation, we develop two domain generalization strategies that enhance the appearance and scale diversity of extracted features. Furthermore, it is found that noise existing in industrial images could result in the collapse of models. To address this problem, we devise a noise regularization strategy that improves the robustness of trained models significantly. We have conducted extensive experiments to evaluate the effectiveness of our framework. The results indicate that our framework outperforms the contrasted baseline by around 15 mAP and achieves comparable performance with models trained using abundant data.

An Aurivillius-phase multiferroic material Bi1.25AlMnO3.25 embedded with Au NPs displays tunable functionalities at various deposition temperatures.

Session-based recommendation (SBR) aims to predict the user's next action based on short and dynamic sessions. Recently, there has been an increasing interest in utilizing various elaborately designed graph neural networks (GNNs) to capture the pair-wise relationships among items, seemingly suggesting the design of more complicated models is the panacea for improving the empirical performance. However, these models achieve relatively marginal improvements with exponential growth in model complexity. In this paper, we dissect the classical GNN-based SBR models and empirically find that some sophisticated GNN propagations are redundant, given the readout module plays a significant role in GNN-based models. Based on this observation, we intuitively propose to remove the GNN propagation part, while the readout module will take on more responsibility in the model reasoning process. To this end, we propose the Multi-Level Attention Mixture Network (Atten-Mixer), which leverages both concept-view and instance-view readouts to achieve multi-level reasoning over item transitions. As simply enumerating all possible high-level concepts is infeasible for large real-world recommender systems, we further incorporate SBR-related inductive biases, i.e., local invariance and inherent priority to prune the search space. Experiments on three benchmarks demonstrate the effectiveness and efficiency of our proposal. We also have already launched the proposed techniques to a large-scale e-commercial online service since April 2021, with significant improvements of top-tier business metrics demonstrated in the online experiments on live traffic.

Combining plasmonic and magnetic properties, namely magneto-plasmonic coupling, inspires great research interest and the search for magneto-plasmonic nanostructure becomes considerably critical. Here we designed a nanopillar-in-matrix structure with core–shell alloyed nanopillars for both BaTiO3 (BTO)-Au0.5Co0.5 (AuCo) and BTO-Au0.25Cu0.25Co0.25Ni0.25 (AuCuCoNi) hybrid systems, i.e., ferromagnetic alloy cores (e.g., Co or CoNi) with plasmonic shells (e.g., Au or Au/Cu). These core–shell alloy nanopillars are uniformly embedded into a dielectric BTO matrix to form a vertically aligned nanocomposite (VAN) structure. Both hybrid systems present excellent epitaxial quality and interesting multi-functionality, e.g., high magnetic anisotropy, magneto-optical coupling response, tailorable plasmonic resonance wavelength, tunable hyperbolic properties and strong optical anisotropy. These alloyed nanopillars-in-matrix designs provide enormous potential for complex hybrid material designs with multi-functionality and demonstrate strong interface enabled magneto-plasmonic coupling along with plasmonic and magnetic performance.

Jiefeng Wang,1 Ailian Wang,2 Lian Zhu,3 Haohan Wang1 1Business School, Ningbo University, Ningbo, Zhejiang Province, People’s Republic of China; 2Laboratory of Applied Brain and Cognitive Sciences, School of Business and Management, Shanghai International Studies University, Shanghai, People’s Republic of China; 3School of Journalism and Communication, Shanghai International Studies University, Shanghai, People’s Republic of ChinaCorrespondence: Lian Zhu; Haohan Wang Email zhulian@shisu.edu.cn; 347230846@qq.comPurpose: The normalization of epidemic prevention and control triggered a fierce scuffle in the e-commerce of fresh food, as well as for aquatic products online shopping. The main difficulty for consumers to buy fresh food online has always been information asymmetry. Previous study reported that the image is still the primary information source to address information asymmetry. Yet, few studies have focused on the image presentation of aquatic products in e-commerce. The current study aims to probe the effect of perceived movement of e-commerce pictures on purchase intention of aquatic products. Further, we examine how consumers’ cognitive conflict and emotion occur when purchasing specific aquatic products with different image dynamism.Methods: Twenty-eight subjects participated in an experiment with a 2-level product category (fresh vs frozen) × 2-level image dynamism (static vs dynamic) design. During the experiment, participants were asked to rate their purchase intention after they browse the experimental stimulus. We recorded subjects’ electroencephalograms (EEGs) throughout the experiment.Results: Behaviorally, participants’ purchase intention for the dynamic image was significantly greater than that for the static image, regardless of aquatic product categories. At the neural level, we found that dynamic image produced less cognitive conflict and aroused consumers’ positive feelings, which were reflected in decreased N2 amplitudes and latency as well as increased LPP (late positive potential) amplitude, respectively. This effect was enhanced for fresh aquatic products.Conclusion: Although picture dynamism only increases perceived movement, it can still induce positive emotions toward the product and lead to a greater purchase intention. The current study emphasized the value of the neuroscience method in revealing consumer cognition and emotion duration product evaluation.Keywords: image dynamism, ERPs, N2, LPP, aquatic product, purchase intention

Reliable Si3N4/Si3N4 joints is successfully prepared in air using pure Al filler. The interfacial microstructure, phase composition and mechanical properties of the joint are investigated in detail. The effects of joining temperature on joint structure and mechanical properties are studied. The experimental results indicate that the diffusion occurs at the interface between Si3N4 ceramics and Al filler. A maximum shear strength of ~42 MPa is obtained for joints joining at 950 °C for 1 h. The work makes an attempt on the joining of Si3N4 ceramics in air, which broadens the joining method of Si3N4 ceramics.

As a mixed-methods research in economics and psychology, this study aimed to analyze the influence from the intergenerational succession on the financialization level including asset financialization and revenue financialization, and further test the moderating effect of the heirs’ typical growing experience according to The Imprinting Theory, based on the 2009–2020 annual data of listed family enterprises of China. There were two key findings. First, the effect of Chinese family enterprises’ intergenerational succession on asset financialization was positively significant while the effect on revenue financialization was not significant, indicating that the financialization behavior has not brought about effective financial profits. Second, among the heirs’ typical growing experiences, their parents’ entrepreneurial experience during their childhood, oversea study experience, and MBA education experience had the significantly positive moderating effects on the influence from intergeneration succession to asset financialization level of Chinese family enterprises, which was an important internal mechanism for the heirs to promote the financialization process of family enterprises.

Pollination success is essential for hybrid oilseed rape (OSR

Neutron powder diffraction (NPD) and x-ray magnetic circular dichroism (XMCD) spectroscopy are employed to investigate the magnetism and spin structure in single-phase B20 Co1.043Si0.957. The magnetic contributions to the NPD data measured in zero fields are consistent with the helical order among the allowed spin structures derived from group theory. The magnitude of the magnetic moment is (0.3 ± 0.1)μB/Co according to NPD, while the surface magnetization probed by XMCD at 3 kOe is (0.18–0.31)μB/Co. Both values are substantially larger than the bulk magnetization of 0.11μB/Co determined from magnetometry at 70 kOe and 2 K. These experimental data indicate the formation of a helical spin phase and the associated conical states in high magnetic fields.

Identifying patients with high risk of suicide is critical for suicide prevention. We examined lab tests together with medication use and diagnosis from electronic medical records (EMR) data for prediction of suicide-related events (SREs; suicidal ideations, attempts and deaths) in post-traumatic stress disorder (PTSD) patients, a population with a high risk of suicide. We developed DeepBiomarker, a deep-learning model through augmenting the data, including lab tests, and integrating contribution analysis for key factor identification. We applied DeepBiomarker to analyze EMR data of 38,807 PTSD patients from the University of Pittsburgh Medical Center. Our model predicted whether a patient would have an SRE within the following 3 months with an area under curve score of 0.930. Through contribution analysis, we identified important lab tests for suicide prediction. These identified factors imply that the regulation of the immune system, respiratory system, cardiovascular system, and gut microbiome were involved in shaping the pathophysiological pathways promoting depression and suicidal risks in PTSD patients. Our results showed that abnormal lab tests combined with medication use and diagnosis could facilitate predicting SRE risk. Moreover, this may imply beneficial effects for suicide prevention by treating comorbidities associated with these biomarkers.

Materials with magneto-optic coupling properties are highly coveted for their potential applications ranging from spintronics and optical switches to sensors. In this work, a new, three-phase Au-Fe-La0.5Sr0.5FeO3 (LSFO) hybrid material grown in a vertically aligned nanocomposite (VAN) form has been demonstrated. This three-phase hybrid material combines the strong ferromagnetic properties of Fe and the strong plasmonic properties of Au and the dielectric nature of the LSFO matrix. More interestingly, the immiscible Au and Fe phases form Au-encapsulated Fe nanopillars, embedded in the LSFO matrix. Multifunctionalities including anisotropic optical dielectric properties, plasmonic properties, magnetic anisotropy, and room-temperature magneto-optic Kerr effect coupling are demonstrated. The single-step growth method to grow the immiscible two-metal nanostructures (i.e., Au and Fe) in the complex hybrid material form opens exciting new potential opportunities for future three-phase VAN systems with more versatile metal selections.

Element doping is a general and effective approach to modify the electrocatalytic performances, but the low intrinsic activity in each electroactive site still limits the further improvements. Herein, we provide an effective strategy by simultaneously introducing P doping and Se vacancies to enhance the intrinsic activities in NiSe2 nanosheet arrays (A-NiSe2|P) through Ar plasma treatment. Owing to the increased active sites and enhanced electrical conductivity, the resulted A-NiSe2|P shows the enhanced hydrogen evolution performances. Theoretical calculations reveal that introduction of Se vacancies plays a significant role in lowering the adsorption free energy of H* in Ni, Se and P sites, leading to promoted intrinsic activities in A-NiSe2|P. Further, A-NiSe2|P as bifunctional electrocatalysts only needs 1.62 V to reach 10 mA cm−2 for overall water splitting. Our study and understanding of A-NiSe2|P may highlight the importance of element doping and vacancies in enhancing the catalytic activities in overall water splitting.

Constructing high-efficiency electrocatalysts is vital towards electrocatalytic water splitting, but it remains a challenge. Although Ni-based materials have drawn extensive attention as highly active catalysts, the relatively limited electroactive sites in Ni-based catalysts still remains a great issue. In order to further boost the electrocatalytic performances, heteroatom doping and interface engineering are usually adopted for modification. Here, a new strategy is developed to construct W doped NiO/NiS2 interfaced nanosheets directly on carbon sheet, which is working as efficient and bifunctional electrocatalysts for overall water splitting. W doped NiO nanosheets are directly constructed on the carbon sheet by the hydrothermal and annealing processes. After that, W-NiO was subjected to Ar plasma assisted sulfuration treatment for forming W doped NiO/NiS2 interfaced nanosheets. Based on systematic investigations, we find that W doping can effectively induce the modified electronic structure of Ni to boost the intrinsic activities in NiO/NiS2. Further, forming NiO/NiS2 nanointerfaces can also provide rich electroactive sites and boost the charge transfer rate. Consequently, W doped NiO/NiS2 exhibits the much enhanced performances for overall water splitting. As a bifunctional electrode, W-NiO/NiS2 demonstrates a remarkable activity with a 1.614 V cell voltage at 10 mA cm−2 for overall water splitting.

Background The current understanding of the genetic basis of complex human diseases is that they are caused and affected by many common and rare genetic variants. A considerable number of the disease-associated variants have been identified by Genome Wide Association Studies, however, they can explain only a small proportion of heritability. One of the possible reasons for the missing heritability is that many undiscovered disease-causing variants are weakly associated with the disease. This can pose serious challenges to many statistical methods, which seems to be only capable of identifying disease-associated variants with relatively stronger coefficients. Results In order to help identify weaker variants, we propose a novel statistical method, Constrained Sparse multi-locus Linear Mixed Model (CS-LMM) that aims to uncover genetic variants of weaker associations by incorporating known associations as a prior knowledge in the model. Moreover, CS-LMM accounts for polygenic effects as well as corrects for complex relatednesses. Our simulation experiments show that CS-LMM outperforms other competing existing methods in various settings when the combinations of MAFs and coefficients reflect different scenarios in complex human diseases. Conclusions We also apply our method to the GWAS data of alcoholism and Alzheimer’s disease and exploratively discover several SNPs. Many of these discoveries are supported through literature survey. Furthermore, our association results strengthen the belief in genetic links between alcoholism and Alzheimer’s disease.

Owing to low-cost and 3d electronic configurations, Co3O4 material is considered as promising candidate for oxygen evolution reaction (OER) electrocatalyst, but the intrinsically low conductivity and limited active site exposure greatly limit the electrocatalytic performances, Herein, we successfully achieve modulation of Co3O4 arrays by Mn and S dual-doping for OER. Results demonstrate that Mn doping modifies the electronic structure of Co center to boost the intrinsic activity of active site in Co3O4, while inducing S in Co3O4 increases the electrical conductivity and provides ample S sites for proton adsorption. In addition, Mn and S dual-doping effectively increase the proportion of Co3+, resulting in facilitating the four-electron transfer and thus higher electrochemical activities. Consequently, the optimal Mn and S dual-doping Co3O4 presents low overpotentials of 330, 407 and 460 mV at 10, 100 and 300 mA cm−2 for OER, as well as a low Tafel slope of 68 mV dec−1 and a good durability after 20 h. Current work highlights a feasible strategy to design electrocatalysts via dual-doping and maximizing the high-valence transition metal ions.

As NLP models achieved state-of-the-art performances over benchmarks and gained wide applications, it has been increasingly important to ensure the safe deployment of these models in the real world, e.g., making sure the models are robust against unseen or challenging scenarios. Despite robustness being an increasingly studied topic, it has been separately explored in applications like vision and NLP, with various definitions, evaluation and mitigation strategies in multiple lines of research. In this paper, we aim to provide a unifying survey of how to define, measure and improve robustness in NLP. We first connect multiple definitions of robustness, then unify various lines of work on identifying robustness failures and evaluating models' robustness. Correspondingly, we present mitigation strategies that are data-driven, model-driven, and inductive-prior-based, with a more systematic view of how to effectively improve robustness in NLP models. Finally, we conclude by outlining open challenges and future directions to motivate further research in this area., Accepted by NAACL 2022 main conference (Long paper). Camera-ready version

The combustion of wall-impinging diesel spray of heavy-duty diesel engines deteriorates combustion quality under cold-start conditions, making it difficult to control soot emissions. To investigate the causes of soot increase in the combustion of wall-impinging spray at low temperature and low speed starting conditions, the effect of the starting fuel mass on the soot formation and oxidation process was analyzed using a multidimensional computational fluid dynamics (CFD) model. The results show that the diesel spray is guided by the piston wall and the limited space, the spray impinged on the wall and the vapor-phase fuel flowed in the spray interaction zone. Thus, the soot mainly accumulates in the spray interaction zone, the region near the cylinder head and the bowl wall in the combustion chamber bowl. The soot from the vapor of deposited fuel film in the piston bowl wall and near wall region accumulates continuously in the after combustion stage, becoming the main source of soot emissions at the time of exhaust valve opening (EVO). Increasing the mass of starting fuel raises the mass of the rich mixture and wall-impinging fuel, which enhances the mismatch between fuel and air, resulting in higher soot generation, while soot is more difficult to be completely oxidized by OH radicals, and ultimately soot emissions increase significantly. It can be deduced that the engine-optimized injection strategy may mitigate the increase in soot emissions at high start-up fuel injection conditions.

Ferroelectric HfO

The next-generation spintronic devices including memristors, tunneling devices, or stochastic switching exert surging demands on magnetic nanostructures with novel coupling schemes. Taking advantage of a phase decomposition mechanism, a unique Ni-NiO nanocomposite has been demonstrated using a conventional pulsed laser deposition technique. Ni nanodomains are segregated from NiO and exhibit as faceted "emerald-cut" morphologies with tunable dimensions affected by the growth temperature. The sharp interfacial transition between ferromagnetic (002) Ni and antiferromagnetic (002) NiO, as characterized by high-resolution transmission electron microscopy, introduces a strong exchange bias effect and magneto-optical coupling at room temperature. In situ heating-cooling X-ray diffraction (XRD) study confirms an irreversible phase transformation between Ni and NiO under ambient atmosphere. Synthesizing highly functional two-phase nanocomposites with a simple bottom-up self-assembly via such a phase decomposition mechanism presents advantages in terms of epitaxial quality, surface coverage, interfacial coupling, and tunable nanomagnetism, which are valuable for new spintronic device implementation.

Rational design and fabrication of advanced electrodes with tailored functionality is of great significance for high-performance asymmetric supercapacitors. Herein, we report the preparation of Fe2O3 nanotube arrays and core-branch NiO nanotube arrays directly on carbon paper by using ZnO nanorods as sacrificial templates via similar synthesis process, which works anode and cathode electrodes for the asymmetric supercapacitors. Benefiting from the free-standing nanotube nanostructures, a large number of surface active sites and fast ion diffusion paths, the Fe2O3 and NiO electrodes could achieve the high capacity up to 81.9 mAh g−1 and 119.7 mAh g−1, respectively. Further, an asymmetric supercapacitor is also assembled by using Fe2O3 as anode and NiO as cathode, which shows a high energy density of 48 Wh kg−1 at the power density of 2089 W kg−1. Current research may put a general route for constructing high-performance anode and cathode materials in energy storage devices.

Herein, a general strategy is provided to construct N-doped carbon-confined MoO2 and MnO by the hydrothermal process, polymerization process and carbonation process, which serves as negative electrode and positive electrode for hybrid supercapacitors. The N-doped carbon as shell can provide fast electron pathways and short ion diffusion paths, resulting in the improved performances at the high rates. Consequently, the obtained N-doped carbon-confined MoO2 and MnO possess the good electrochemical performances, including high specific capacitance, excellent cycling stability and good rate capability. Furthermore, the as-fabricated hybrid supercapacitor using N-doped carbon-confined MoO2 and MnO shows a high energy density up to 44.82 Wh kg−1 at a power density of 900 W kg−1, as well as good cycling performance. This work may provide a general strategy for constructing high-performance energy storage devices.

Road networks are extremely vulnerable to cascading failure caused by traffic accidents or anomalous events. Therefore, accurate identification of critical nodes, whose failure may cause a dramatic reduction in the road network transmission efficiency, is of great significance to traffic management and control schemes. However, none of the existing approaches can locate city-wide critical nodes in real road networks. In this paper, we propose a novel data-driven framework to rank node importance through mining from comprehensive vehicle trajectory data, instead of analysis solely on the topology of the road network. In this framework, we introduce a trip network modeled by a tripartite graph to characterize the dynamics of the road network. Furthermore, we present two algorithms, integrating the origin-destination entropy with flow (ODEF) algorithm and the crossroad-rank (CRRank) algorithm, to better exploit the information included in the tripartite graph and to effectively assess the node importance. ODEF absorbs the idea of the information entropy to evaluate the centrality of a node and to calculate its importance rating by integrating its centrality with the traffic flow. CRRank is a ranking algorithm based on eigenvector centrality that captures the mutual reinforcing relationships among the OD-pair, path, and intersection. In addition to the factors considered in ODEF, CRRank considers the irreplaceability of a node and the spatial relationships between neighboring nodes. We conduct a synthetic experiment and a real case study based on a real-world dataset of taxi trajectories. Experiments verify the utility of the proposed algorithms.

CF/PEEK composites were welded assisted by ultrasonic. The influence of vibration time and using of energy director (ED) to the joints were investigated in details. The joints had defect of incomplete fusion at the interface under short welding time without ED, and defects of cracks and voids appeared at the interface of carbon fiber and PEEK resin, which attributed to too much heat accumulation at the heat affected zone (HAZ). By using flat ED, joint with sound bonding was realized under vibration time of 0.9 s. The lap shear strength of joint can reach 28 MPa, and the joint fractured at the HAZ. The real-time temperature at interfaces were monitored. It is found that the heating rate can be accelerated by using flat ED, and the peak temperature was lowered. At last, the influence mechanism of processing parameter to the joint were discussed in details.

Pollination success is essential for hybrid oilseed rape (OSR, Brassica napus) seed production, and the pollination method has some influences on the OSR plant growth traits. In order to explore the roles of different pollination methods, four pollination methods of “unmanned agricultural aerial system” (UAAS), “natural wind + UAAS” (NW+UAAS), “honeybee” (HB), and “no pollinators” (NP) were set in a hybrid OSR field to investigate their effects on OSR plant traits and rapeseed yields in this study. The control check (CK) area with natural wind (NW) pollination was set as a reference for comparison. The experiments were conducted continuously for 20 days during the OSR plant early to full-bloom stage. The results based on the evaluated OSR plants showed that the growth traits and the rapeseed yields exhibited some differences under different pollination methods. The average plant height under NP pollination was maximum, which was 231.52 cm, while the average plant heights under the other pollination methods exhibited nearly no difference. Except for the HB pollination, the average first-branch heights of the evaluated plants all exceeded 100 cm under the other pollination methods. The average once branch quantity of all the evaluated plants under different pollination methods was 5–7. The average number of effective siliques per plant varied greatly. The average quantity of effective siliques in each OSR plant was about 160 under UAAS, NW+UAAS, and NW pollination, about 100 under HB pollination, and only 2.12 under NP pollination. The thousand-rapeseed weight was 7.32 g under HB pollination, which was the highest of all of the pollination areas. In terms of rapeseed yield, the average rapeseed yields per plant were all more than 10 g, except for the one under NP pollination; the yield per hectare was highest under NW+UAAS pollination, reaching 4741.28 kg, and the yield under NP pollination was lowest, which was only 360.39 kg. The research results provide technical support for supplementary pollination in hybrid OSR seed production.

Transition metal nitrides such as titanium nitride (TiN) possess exceptional mechanical-, chemical-, and thermal-stability and have been utilized in a wide variety of applications ranging from super-hard, corrosion-resistive, and decorative coatings to nanoscale diffusion barriers in semiconductor devices. Despite the ongoing interest in these robust materials, there have been limited reports focused on engineering high-aspect ratio TiN-based nanocomposites with anisotropic magnetic and optical properties. To this end, we explored TiN–Fe thin films with self-assembled vertical structures integrated on Si substrates. We showed that the key physical properties of the individual components (e.g., ferromagnetism from Fe) are preserved, that vertical nanostructures promote anisotropic behavior, and interactions between TiN and Fe enable a special magneto-optical response. This TiN–Fe nanocomposite system presents a new group of complex multifunctional hybrid materials that can be integrated on Si for future Si-based memory, optical, and biocompatible devices.

We demonstrate the highly sensitive and fast response/recovery gas sensors for detecting isopropanol (IPA), in which the Au-nanoparticles-modified ZnO (Au@ZnO) nanofilms act as the active layers. The data confirm that both the response and the response/recovery speed for the detection of IPA are significantly improved by adding Au nanoparticles on the surface of ZnO nanofilms. The gas sensor with an Optimum Au@ZnO nanofilm exhibits the highest responses of 160 and 7 to the 100 and 1 ppm IPA at 300 °C, which indicates high sensitivity and a very low detecting limit. The sensor also exhibits a very short response/recovery time of 4/15 s on the optimized Au@ZnO nanofilm, which is much shorter than that of the sensor with a pure ZnO nanofilm. The mechanisms of the performance improvement in the sensors are discussed in detail. Both the electronic sensitization and the chemical sensitization of the ZnO nanofilms are improved by the modified Au nanoparticles, which not only regulate the thickness of the depletion layer but also increase the amount of adsorbed oxygen species on the surfaces. This work proposes a strategy to develop a highly sensitive gas sensor for real-time monitoring of IPA.

The electrochemical performance of nanostructured nickel-cobalt sulfides is greatly limited by the sluggish reaction kinetics and limited electroactive sites. Herein, we design and synthesize free-standing Se doped nickel-cobalt sulfides with controllable-component directly on carbon cloth, which involves the hydrothermal process and sulfuration/selenylation reaction. Serving as free-standing electrode, as-synthesized Se doped nickel-cobalt sulfides not only favor the fast ion diffusion path and low contact resistance, but also provide rich electroactive sites with electrolyte. More importantly, proper Se doping in nickel-cobalt sulfides greatly increases the electrochemically active surface area and reduces the charge transfer resistance. Based on the X-ray photoelectron spectroscopy and transmission electron microscopy results, the reaction mechanism is convincingly revealed that Se dopants have been changed into SeOx. And electrochemical activated oxyhydroxides are mainly involved in electrochemical reactions. As a result, as-fabricated Se doped nickel-cobalt sulfides show a good electrochemical performance for supercapattery. Further, the supercapattery device is also assembled by using nickel-cobalt sulfide/selenide as positive electrode and activated carbon as negative electrode, which shows a high energy density of 39.6 Wh kg−1 at the power density of 1501 W kg−1.

Motivation: Cryo-Electron Tomography (cryo-ET) is a 3D bioimaging tool that visualizes the structural and spatial organization of macromolecules at a near-native state in single cells, which has broad applications in life science. However, the systematic structural recognition and recovery of macromolecules captured by cryo-ET are difficult due to high structural complexity and imaging limits. Deep learning based subtomogram classification have played critical roles for such tasks. As supervised approaches, however, their performance relies on sufficient and laborious annotation on a large training dataset. Results: To alleviate this major labeling burden, we proposed a Hybrid Active Learning (HAL) framework for querying subtomograms for labelling from a large unlabeled subtomogram pool. Firstly, HAL adopts uncertainty sampling to select the subtomograms that have the most uncertain predictions. Moreover, to mitigate the sampling bias caused by such strategy, a discriminator is introduced to judge if a certain subtomogram is labeled or unlabeled and subsequently the model queries the subtomogram that have higher probabilities to be unlabeled. Additionally, HAL introduces a subset sampling strategy to improve the diversity of the query set, so that the information overlap is decreased between the queried batches and the algorithmic efficiency is improved. Our experiments on subtomogram classification tasks using both simulated and real data demonstrate that we can achieve comparable testing performance (on average only 3% accuracy drop) by using less than 30% of the labeled subtomograms, which shows a very promising result for subtomogram classification task with limited labeling resources., Statement on authorship changes: Dr. Eric Xing was an academic advisor of Mr. Haohan Wang. Dr. Xing was not directly involved in this work and has no direct interaction or collaboration with any other authors on this work. Therefore, Dr. Xing is removed from the author list according to his request. Mr. Zhenxi Zhu's affiliation is updated to his current affiliation