Van der Waals heterostructures designed by assembling isolated two‐dimensional (2D) crystals have emerged as a new class of artificial materials with interesting and unusual physical properties. ...Here, the multilayer MoS2–WS2 heterostructures with different configurations are reported and their optoelectronic properties are studied. It is shown that the new heterostructured material possesses new functionalities and superior electrical and optoelectronic properties that far exceed the one for their constituents, MoS2 or WS2. The vertical transistor exhibits a novel rectifying and bipolar behavior, and can also act as photovoltaic cell and self‐driven photodetector with photo‐switching ratio exceeding 103. The planar device also exhibits high field‐effect ON/OFF ratio (>105), high electron mobility of 65 cm2/Vs, and high photoresponsivity of 1.42 A/W compared to that in isolated multilayer MoS2 or WS2 nanoflake transistors. The results suggest that formation of MoS2–WS2 heterostructures could significantly enhance the performance of optoelectronic devices, thus open up possibilities for future nanoelectronic, photovoltaic, and optoelectronic applications.
Newly designed MoS
2
–WS
2
heterostructures perform novel and enhanced optoelectronic performances. Vertical transistors possess new functionalities such as rectifying, bipolarity, photovoltaic effect, and self‐driven photodetection. Planar devices exhibit superior optoelectronic properties with high field‐effect ON/OFF ratio (>105), electron mobility of 65 cm2/Vs, and photoresponsivity of 1.42 A/W that far exceed the one for their constituents MoS2 or WS2.
Automatic registration of multimodal remote sensing data e.g., optical, light detection and ranging (LiDAR), and synthetic aperture radar (SAR) is a challenging task due to the significant nonlinear ...radiometric differences between these data. To address this problem, this paper proposes a novel feature descriptor named the histogram of orientated phase congruency (HOPC), which is based on the structural properties of images. Furthermore, a similarity metric named HOPCncc is defined, which uses the normalized correlation coefficient (NCC) of the HOPC descriptors for multimodal registration. In the definition of the proposed similarity metric, we first extend the phase congruency model to generate its orientation representation and use the extended model to build HOPCncc. Then, a fast template matching scheme for this metric is designed to detect the control points between images. The proposed HOPCncc aims to capture the structural similarity between images and has been tested with a variety of optical, LiDAR, SAR, and map data. The results show that HOPCncc is robust against complex nonlinear radiometric differences and outperforms the state-of-the-art similarities metrics (i.e., NCC and mutual information) in matching performance. Moreover, a robust registration method is also proposed in this paper based on HOPCncc, which is evaluated using six pairs of multimodal remote sensing images. The experimental results demonstrate the effectiveness of the proposed method for multimodal image registration.
Fractional derivative models, which are expressed by combining standard dashpots, fractional dashpots and elastic springs in series or parallel, are often utilized to account for the behaviors for ...viscoelastic materials. Even with the models extended to finite deformation, the precise definition of objective fractional derivative remains challenging. The proposed fractional derivative model is expressed by the combination of an elastic spring in series with two parallel fractional dashpots. We extend the fractional derivative model to finite deformation through a new approach without defining an objective fractional derivative and assuming the decomposition of the deformation rate into the elastic and inelastic parts. This proposed model can be reduced to the Maxwell model for finite deformation. Such reduction results in a model that stands in between the two existing Maxwell models in which the objective rate of the Cauchy stress is taken as the material corotational rate and the relative corotational rate respectively. The proposed model is applied to the simple shear deformation.
This study examines the relationship between proactive coping, future time orientation, and perceived work productivity during the coronavirus (COVID-19) pandemic, based on the work-from-home ...experience of employees in Taiwan and the United States (U.S.). It draws on the conservation of resources (COR) theory, which posits that proactive coping and future time orientation are crucial personal resources that affect the capacity of an individual to adapt to stressful situations. The results show that in the relationship between proactive coping and perceived work productivity, future time orientation acts as a full mediator in Taiwan and a partial mediator in the U.S. The study extends the application of the COR theory to the context of the COVID-19 pandemic and offers important insights that will enable professionals to assess the role of proactive coping and future time orientation in their productivity evaluations of working tasks and to design appropriate training sessions.
•Telecommuting experiences of two samples were examined during COVID-19.•Proactive coping is positively associated with future time orientation and productivity.•Future time orientation served as an important mediator.•Mediating effect was different in two cultural samples.•Eastern cultural sample reported stronger links between examined constructs.
Alzheimer’s disease (AD) is a progressive and irreversible brain degenerative disorder. Mild cognitive impairment (MCI) is a clinical precursor of AD. Although some treatments can delay its ...progression, no effective cures are available for AD. Accurate early-stage diagnosis of AD is vital for the prevention and intervention of the disease progression. Hippocampus is one of the first affected brain regions in AD. To help AD diagnosis, the shape and volume of the hippocampus are often measured using structural magnetic resonance imaging (MRI). However, these features encode limited information and may suffer from segmentation errors. Additionally, the extraction of these features is independent of the classification model, which could result in sub-optimal performance. In this study, we propose a multi-model deep learning framework based on convolutional neural network (CNN) for joint automatic hippocampal segmentation and AD classification using structural MRI data. Firstly, a multi-task deep CNN model is constructed for jointly learning hippocampal segmentation and disease classification. Then, we construct a 3D Densely Connected Convolutional Networks (3D DenseNet) to learn features of the 3D patches extracted based on the hippocampal segmentation results for the classification task. Finally, the learned features from the multi-task CNN and DenseNet models are combined to classify disease status. Our method is evaluated on the baseline T1-weighted structural MRI data collected from 97 AD, 233 MCI, 119 Normal Control (NC) subjects in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The proposed method achieves a dice similarity coefficient of 87.0% for hippocampal segmentation. In addition, the proposed method achieves an accuracy of 88.9% and an AUC (area under the ROC curve) of 92.5% for classifying AD vs. NC subjects, and an accuracy of 76.2% and an AUC of 77.5% for classifying MCI vs. NC subjects. Our empirical study also demonstrates that the proposed multi-model method outperforms the single-model methods and several other competing methods.
A study linking the two economic and social phenomena of rural labor force migration and energy transition can help analyze the underlying causes of rural “Energy Poverty”. However, how off-farm ...employment affects household low-carbon energy consumption and its potential mechanisms requires further research. Using 1351 sampled rural households from the “Rural Energy, Population Transfer and Well-being” survey in 2018 and 2021 to explore response mechanisms through which off-farm employment can influence low-carbon energy intensity. Utilizing the multivariate regression, Sobel test, and moderating effect test, the results demonstrate that off-farm employment, including short-term and long-term off-farm employment, significantly increases the intensity of low-carbon energy use among rural households. Specifically, long-term off-farm employment tends to have a greater positive contribution to the low-carbon energy intensity than short-term off-farm employment. Furthermore, off-farm employment can affect household low-carbon energy intensity through the total income, and effect of the surrounding people in the off-farm employment process also increases their consumption intensity. The research reveals that the rural energy revolution under the constraints of “Carbon Neutral” and “Carbon Peak” should relate to the off-farm development of rural households to achieve “Precise Energy Poverty Alleviation”.
Understanding the relationship between the millions of functional DNA elements and their protein regulators, and how they work in conjunction to manifest diverse phenotypes, is key to advancing our ...understanding of the mammalian genome. Next-generation sequencing technology is now used widely to probe these protein-DNA interactions and to profile gene expression at a genome-wide scale. As the cost of DNA sequencing continues to fall, the interpretation of the ever increasing amount of data generated represents a considerable challenge.
We have developed ngs.plot - a standalone program to visualize enrichment patterns of DNA-interacting proteins at functionally important regions based on next-generation sequencing data. We demonstrate that ngs.plot is not only efficient but also scalable. We use a few examples to demonstrate that ngs.plot is easy to use and yet very powerful to generate figures that are publication ready.
We conclude that ngs.plot is a useful tool to help fill the gap between massive datasets and genomic information in this era of big sequencing data.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
While the fifth-generation systems are being rolled out across the globe, researchers have turned their attention to the exploration of radical next-generation solutions. At this early evolutionary ...stage, we survey five main research facets of this field, namely Facet 1: next-generation architectures, spectrum, and services; Facet 2: next-generation networking; Facet 3: Internet of Things; Facet 4: wireless positioning and sensing; and Facet 5: applications of deep learning in 6G networks. In this article, we provide a critical appraisal of the literature of promising techniques ranging from the associated architectures, networking, and applications, as well as designs. We portray a plethora of heterogeneous architectures relying on cooperative hybrid networks supported by diverse access and transmission mechanisms. The vulnerabilities of these techniques are also addressed and carefully considered for highlighting the most of promising future research directions. Additionally, we list a rich suite of learning-driven optimization techniques. We conclude by observing the evolutionary paradigm shift that has taken place from pure single-component bandwidth efficiency, power efficiency, or delay optimization toward multi-component designs, as exemplified by the twin-component ultra-reliable low-latency mode of the fifth-generation system. We advocate a further evolutionary step toward multi-component Pareto optimization, which requires the exploration of the entire Pareto front of all optimal solutions, where none of the components of the objective function may be improved without degrading at least one of the other components.
Anaerobic oxidation of methane (AOM) reduces methane emissions from marine ecosystems but we know little about AOM in rivers, whose role in the global carbon cycle is increasingly recognized. We ...measured AOM potentials driven by different electron acceptors, including nitrite, nitrate, sulfate, and ferric iron, and identified microorganisms involved across contrasting riverbeds. AOM activity was confined to the more reduced, sandy riverbeds, whereas no activity was measured in the less reduced, gravel riverbeds where there were few anaerobic methanotrophs. Nitrite-dependent and nitrate-dependent AOM occurred in all sandy riverbeds, with the maximum rates of 61.0 and 20.0 nmol CO
g
(dry sediment) d
, respectively, while sulfate-dependent and ferric iron-dependent AOM occurred only where methane concentration was highest and the diversity of AOM pathways greatest. Diverse Candidatus Methylomirabilis oxyfera (M. oxyfera)-like bacteria and Candidatus Methanoperedens nitroreducens (M. nitroreducens)-like archaea were detected in the sandy riverbeds (16S rRNA gene abundance of 9.3 × 10
to 1.5 × 10
and 2.1 × 10
to 2.5 × 10
copies g
dry sediment, respectively) but no other known anaerobic methanotrophs. Further, we found M. oxyfera-like bacteria and M. nitroreducens-like archaea to be actively involved in nitrite- and nitrate/ferric iron-dependent AOM, respectively. Hence, we demonstrate multiple pathways of AOM in relation to methane, though the activities of M. oxyfera-like bacteria and M. nitroreducens-like archaea are dominant.
High-performance pervaporation membranes have potential in industrial separation applications, but overcoming the permeability-selectivity trade-off is a challenge. We report a strategy to create ...highly flexible metal-organic framework nanosheet (MOF-NS) membranes with a faveolate structure on polymer substrates for alcohol-water separation. The controlled growth followed by a surface-coating method effectively produced flexible and defect-free superhydrophobic MOF-NS membranes. The reversible deformation of the flexible MOF-NS and the vertical interlamellar pathways were captured with electron microscopy. Molecular simulations confirmed the structure and revealed transport mechanism. The ultrafast transport channels in MOF-NS exhibited an ultrahigh flux and a separation factor of 8.9 in the pervaporation of 5 weight % ethanol-water at 40°C, which can be used for biofuel recovery. MOF-NS and polydimethylsiloxane synergistically contribute to the separation performance.
Honeycomb channels enhance separations
Pervaporation membranes use a combination of permeation and evaporation for energy-efficient separations of volatile compounds from solutions. Xu
et al
. designed a strategy to fabricate defect-free superhydrophobic metal-organic framework (MOF) nanosheet membranes. Instead of dispersing the MOFs into a polydimethylsiloxane (PDMS) matrix, the authors grew a continuous and uniform layer of embedded MOF seeds on polymeric substrates that were then sealed with PDMS. This procedure results in a honeycomb-like structure with high flexibility and fast molecular transport channels, thus enhancing the separation of alcohols from water. —MSL
Flexible metal-organic framework honeycombed nanosheet membranes are applied for alcohol-water separations.
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