Minjiang University

Photocatalytic reduction of CO2 into energy-rich carbon compounds has attracted increasing attention. However, it is still a challenge to selectively and effectively convert CO2 to a desirable reaction product. Herein, we report a design of a synergistic photocatalyst for selective reduction of CO2 to CO by using a covalent organic framework bearing single Ni sites (Ni-TpBpy), in which electrons transfer from photosensitizer to Ni sites for CO production by the activated CO2 reduction under visible-light irradiation. Ni-TpBpy exhibits an excellent activity, giving a 4057 μmol g–1 of CO in a 5 h reaction with a 96% selectivity over H2 evolution. More importantly, when the CO2 partial pressure was reduced to 0.1 atm, 76% selectivity for CO production is still obtained. Theoretical calculations and experimental results suggest that the promising catalytic activity and selectivity are ascribed to synergistic effects of single Ni catalytic sites and TpBpy, in which the TpBpy not only serves as a host for CO2 molecules and Ni catalytic sites but also facilitates the activation of CO2 and inhibits the competitive H2 evolution.

The outbreak of novel coronavirus-caused pneumonia (COVID-19) in Wuhan has attracted worldwide attention. Here, we propose a generalized SEIR model to analyze this epidemic. Based on the public data of National Health Commission of China from Jan. 20th to Feb. 9th, 2020, we reliably estimate key epidemic parameters and make predictions on the inflection point and possible ending time for 5 different regions. According to optimistic estimation, the epidemics in Beijing and Shanghai will end soon within two weeks, while for most part of China, including the majority of cities in Hubei province, the success of anti-epidemic will be no later than the middle of March. The situation in Wuhan is still very severe, at least based on public data until Feb. 15th. We expect it will end up at the beginning of April. Moreover, by inverse inference, we find the outbreak of COVID-19 in Mainland, Hubei province and Wuhan all can be dated back to the end of December 2019, and the doubling time is around two days at the early stage.

In this article, we propose an infrared and visible image fusion network based on the salient target detection, termed STDFusionNet, which can preserve the thermal targets in infrared images and the texture structures in visible images. First, a salient target mask is dedicated to annotating regions of the infrared image that humans or machines pay more attention to, so as to provide spatial guidance for the integration of different information. Second, we combine this salient target mask to design a specific loss function to guide the extraction and reconstruction of features. Specifically, the feature extraction network can selectively extract salient target features from infrared images and background texture features from visible images, while the feature reconstruction network can effectively fuse these features and reconstruct the desired results. It is worth noting that the salient target mask is only required in the training phase, which enables the proposed STDFusionNet to be an end-to-end model. In other words, our STDFusionNet can fulfill salient target detection and key information fusion in an implicit manner. Extensive qualitative and quantitative experiments demonstrate the superiority of our fusion algorithm over the current state of the arts, where our algorithm is much faster and the fusion results look like high-quality visible images with clear highlighted infrared targets. Moreover, the experimental results on the public datasets reveal that our algorithm can improve the entropy (EN), mutual information (MI), visual information fidelity (VIF), and spatial frequency (SF) metrics with about 1.25%, 22.65%, 4.3%, and 0.89% gains, respectively. Our code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jiayi-ma/STDFusionNet</uri> .

According to the resource-based view (RBV), an organization can be viewed as a collection of human, physical and organizational resources. These resources are valuable and inimitable, and are the main source of sustainable competitive advantage and sustained higher performance. Green human resource management (GHRM) practices help organizations to obtaina competitive advantage and align business strategies with the environment. In the same way, increases in environmental awareness and strict implementation of international environmental regulations havea greater impact on business sustainability. Environmentalism and sustainability are becoming more of a concern for organizations. For this reason, green human resource managementpractices and green intellectual capital are the main elements of business sustainability. Based on the resource-based view and intellectual capital-based view theory, this study investigated the impact of GHRM practices and green intellectual capital on sustainability, using cross-sectional data. The results show that the two dimensions of GHRM practices (green recruitment and selection, and green rewards) and green intellectual capital (green human capital, green structural capital and green relational capital) have a positive effect on a firm’s sustainability. GHRM practices and green intellectual capital have a positive role in this model. Practitioners, scholars and academics all may take benefits from the findings of this study.Limited variables andemerging and developingeconomies were the scope of this study. Future studies could investigate and explore the impact of green HRM practices and the role of management and stakeholder pressureonnew areas of sustainability.

Fog computing has been proposed as an extension of cloud computing to provide computation, storage, and network services in network edge. For smart manufacturing, fog computing can provide a wealth of computational and storage services, such as fault detection and state analysis of devices in assembly lines, if the middle layer between the industrial cloud and the terminal device is considered. However, limited resources and low-delay services hinder the application of new virtualization technologies in the task scheduling and resource management of fog computing. Thus, we build a new task-scheduling model by considering the role of containers. Then, we construct a task-scheduling algorithm to ensure that the tasks are completed on time and the number of concurrent tasks for the fog node is optimized. Finally, we propose a reallocation mechanism to reduce task delays in accordance with the characteristics of the containers. The results showed that our proposed task-scheduling algorithm and reallocation scheme can effectively reduce task delays and improve the concurrency number of the tasks in fog nodes.

Abstract Herein, an effective tandem catalysis strategy is developed to improve the selectivity of the CO 2 RR towards C 2 H 4 by multiple distinct catalytic sites in local vicinity. An earth‐abundant elements‐based tandem electrocatalyst PTF(Ni)/Cu is constructed by uniformly dispersing Cu nanoparticles (NPs) on the porphyrinic triazine framework anchored with atomically isolated nickel–nitrogen sites (PTF(Ni)) for the enhanced CO 2 RR to produce C 2 H 4 . The Faradaic efficiency of C 2 H 4 reaches 57.3 % at −1.1 V versus the reversible hydrogen electrode (RHE), which is about 6 times higher than the non‐tandem catalyst PTF/Cu, which produces CH 4 as the major carbon product. The operando infrared spectroscopy and theoretic density functional theory (DFT) calculations reveal that the local high concentration of CO generated by PTF(Ni) sites can facilitate the C−C coupling to form C 2 H 4 on the nearby Cu NP sites. The work offers an effective avenue to design electrocatalysts for the highly selective CO 2 RR to produce multicarbon products via a tandem route.

) air pollution worldwide. Observations during winter haze pollution episodes in urban China show that most of this SOA originates from fossil-fuel combustion but the chemical mechanisms involved are unclear. Here we report field observations in a Beijing winter haze event that reveal fast aqueous-phase conversion of fossil-fuel primary organic aerosol (POA) to SOA at high relative humidity. Analyses of aerosol mass spectra and elemental ratios indicate that ring-breaking oxidation of POA aromatic species, leading to functionalization as carbonyls and carboxylic acids, may serve as the dominant mechanism for this SOA formation. A POA origin for SOA could explain why SOA has been decreasing over the 2013-2018 period in response to POA emission controls even as emissions of volatile organic compounds (VOCs) have remained flat.

Epileptic seizure detection is commonly implemented by expert clinicians with visual observation of electroencephalography (EEG) signals, which tends to be time consuming and sensitive to bias. The epileptic detection in most previous research suffers from low power and unsuitability for processing large datasets. Therefore, a computerized epileptic seizure detection method is highly required to eradicate the aforementioned problems, expedite epilepsy research and aid medical professionals. In this work, we propose an automatic epilepsy diagnosis framework based on the combination of multi-domain feature extraction and nonlinear analysis of EEG signals. Firstly, EEG signals are pre-processed by using the wavelet threshold method to remove the artifacts. We then extract representative features in the time domain, frequency domain, time-frequency domain and nonlinear analysis features based on the information theory. These features are further extracted in five frequency sub-bands based on the clinical interest, and the dimension of the original feature space is then reduced by using both a principal component analysis and an analysis of variance. Furthermore, the optimal combination of the extracted features is identified and evaluated via different classifiers for the epileptic seizure detection of EEG signals. Finally, the performance of the proposed method is investigated by using a public EEG database at the University Hospital Bonn, Germany. Experimental results demonstrate that the proposed epileptic seizure detection method can achieve a high average accuracy of 99.25%, indicating a powerful method in the detection and classification of epileptic seizures. The proposed seizure detection scheme is thus hoped to eliminate the burden of expert clinicians when they are processing a large number of data by visual observation and to speed-up the epilepsy diagnosis.

DNA methylation promotes transcription DNA methylation generally represses transcription, but in some instances, it has also been implicated in transcription activation. Harris et al. identified a protein complex in Arabidopsis that is recruited to chromatin by DNA methylation. This complex specifically activated the transcription of genes that are already mildly transcribed but had no effect on transcriptionally silent genes such as transposable elements. The complex thereby counteracts the repression effect caused by transposon insertion in neighboring genes while leaving transposons silent. Thus, by balancing both repressive and activating transcriptional effects, DNA methylation can act to fine-tune gene expression. Science , this issue p. 1182

The manufacturing sector is one of the major sources contributing towards economies as well as environmental pollution. Contributing to the theory of ability motivation opportunity theory, the aim of the current study was to investigate the mediating role of organizational citizenship behavior towards the environment on the relationship between green human resources management (green recruitment and selection, green training, green rewards, and green performance evaluation), corporate social responsibility, and sustainable performance (economic, social, and environmental performance). The quantitative survey research design was used in the current study and structured questionnaires were distributed for the collection of data. The cross-sectional data were collected from 150 firms. Sample of the study was consisted of HRM managers, directors. Total 200 questionnaires were distributed but 150 completed responses were received and analyzed. A structured questionnaire was used. For data analysis, smart partial least square structural equation modeling (PLS-SEM) was used. The measurement model and the structural model were developed and tested in the study. The measurement model aim was to investigate and establish reliabilities and validities of the instrument while to test hypotheses structural model was formulated/developed. Results revealed that the instrument was found reliable and valid; the instrument has met all standard criteria for average variance extracted, composite/construct reliability factor loadings, and alpha values. While structural models illustrated that all hypotheses are accepted. It is concluded from the results that organizational citizenship behavior towards the environment significantly mediated the relationship between corporate social responsibility and green human resource management practices. This implies that organizational citizenship behavior towards environment significantly effects sustainable performance. The originality of the current study lies in highlighting corporate social responsibility, green human resources management practices to enhance sustainable performance through organizational citizenship behavior towards environment.

This paper presents a novel facial expression recognition network, called Distract your Attention Network (DAN). Our method is based on two key observations in biological visual perception. Firstly, multiple facial expression classes share inherently similar underlying facial appearance, and their differences could be subtle. Secondly, facial expressions simultaneously exhibit themselves through multiple facial regions, and for recognition, a holistic approach by encoding high-order interactions among local features is required. To address these issues, this work proposes DAN with three key components: Feature Clustering Network (FCN), Multi-head Attention Network (MAN), and Attention Fusion Network (AFN). Specifically, FCN extracts robust features by adopting a large-margin learning objective to maximize class separability. In addition, MAN instantiates a number of attention heads to simultaneously attend to multiple facial areas and build attention maps on these regions. Further, AFN distracts these attentions to multiple locations before fusing the feature maps to a comprehensive one. Extensive experiments on three public datasets (including AffectNet, RAF-DB, and SFEW 2.0) verified that the proposed method consistently achieves state-of-the-art facial expression recognition performance. The DAN code is publicly available.

This study aimed to examine the role of environmental commitment, environmental consciousness, green lifestyle, and green self-efficacy in influencing pro-environmental behaviour. Data were obtained through a survey of 72 students at one of the training centers in Malaysia. The hypothesized relationships were tested using partial least squares (PLS) methodology. Results showed that environmental commitment, environmental consciousness, green lifestyle, and green self-efficacy positively influenced pro-environmental behaviour, thereby providing new insights to existing literature on environmental sustainability. The results may be used by educational institutions, the government, and private agencies to strengthen students’ knowledge, attitude, and behaviour that support environment-based education. The scope of the study was limited to students at a training center, so the results may not be generalizable to different settings. Another limitation was that the study used limited contextual elements. The novelty of this study is that it examined the role of environmental commitment, environmental consciousness, green lifestyle, and green self-efficacy as determinants of pro-environmental behaviour among students in an educational setting in Malaysia.

Engineered exosomes have become popular drug delivery carriers for cancer treatment. This is partially due to the interesting property, i.e. exosome organotropism, which plays an important role in organ distribution post systemic administration. Here, we demonstrated that breast cancer (MDA-MB-231) cell-derived exosomes (231-Exo) could be specifically internalized by non-small cell lung cancer cells via a specific interaction between overexpressed integrin β4 (on exosomes) and surfactant protein C (SPC) on the cancer cells. We showed that 231-Exo was capable of recognizing A549 cells in blood and effectively escaping from the immune surveillance system in vitro. Once loaded with microRNA molecules in the exosome carriers, the resulting, miRNA-126 loaded 231-Exo (miRNA-231-Exo) strongly suppressed A549 lung cancer cell proliferation and migration through the interruption of the PTEN/PI3K/AKT signaling pathway. Intravenous administration of the miRNA-126 laden exosomes led to an effective lung homing effect in mice. When tested in a lung metastasis model, miRNA-231-Exo resulted in an efficacious effect in inhibiting the formulation of lung metastasis in vivo. Collectively, our data demonstrated the possibility of using the organotropism feature of exosomes in exosome carrier design, generating a potent anti-metastasis effect in a mouse model.

Cobalt is a transition metal located in the fourth row of the periodic table and is a neighbor of iron and nickel. It has been considered an essential element for prokaryotes, human beings, and other mammals, but its essentiality for plants remains obscure. In this article, we proposed that cobalt (Co) is a potentially essential micronutrient of plants. Co is essential for the growth of many lower plants, such as marine algal species including diatoms, chrysophytes, and dinoflagellates, as well as for higher plants in the family Fabaceae or Leguminosae . The essentiality to leguminous plants is attributed to its role in nitrogen (N) fixation by symbiotic microbes, primarily rhizobia. Co is an integral component of cobalamin or vitamin B 12 , which is required by several enzymes involved in N 2 fixation. In addition to symbiosis, a group of N 2 fixing bacteria known as diazotrophs is able to situate in plant tissue as endophytes or closely associated with roots of plants including economically important crops, such as barley, corn, rice, sugarcane, and wheat. Their action in N 2 fixation provides crops with the macronutrient of N. Co is a component of several enzymes and proteins, participating in plant metabolism. Plants may exhibit Co deficiency if there is a severe limitation in Co supply. Conversely, Co is toxic to plants at higher concentrations. High levels of Co result in pale-colored leaves, discolored veins, and the loss of leaves and can also cause iron deficiency in plants. It is anticipated that with the advance of omics, Co as a constitute of enzymes and proteins and its specific role in plant metabolism will be exclusively revealed. The confirmation of Co as an essential micronutrient will enrich our understanding of plant mineral nutrition and improve our practice in crop production.

Abstract The visible‐light‐driven photocatalytic CO 2 reduction is one appealing approach to simultaneously mitigate the energy crisis and environmental issues. It is highly desirable but challenging to selectively and efficiently convert CO 2 into desirable products. Herein, a covalent organic framework hosting metalloporphyrin‐based carbon dots (M‐PCD@TD‐COF, M = Ni, Co, and Fe) is first presented, which serves as heterogeneous catalysts for CO 2 photoreduction. M‐PCD@TD‐COF not only enriches available COF‐based catalytic materials, but also provides suitable environment for CO 2 adsorption and activation on metalloporphyrin‐based carbon dots. The advantages of the host environment in COFs are highlighted by the satisfactory catalytic activity and remarkable selectivity of CO 2 ‐to‐CO conversion over H 2 generation up to 98%. The photocatalytic system is effective for both pure CO 2 and the simulated flue gas. This work provides new protocols for the rational design of COF‐based heterogeneous catalysts for selective CO 2 photoreduction.

Molybdenum disulfide has been one of the most studied hydrogen evolution catalyst materials in recent years, but its disadvantages, such as poor conductivity, hinder its further development. Here, we employ the common hydrothermal method, followed by an additional solvothermal method to construct an uncommon molybdenum disulfide with two crystal forms of 1T and 2H to improve catalytic properties. The low overpotential (180 mV) and small Tafel slope (88 mV/dec) all indicated that molybdenum disulfide had favorable catalytic performance for hydrogen evolution. Further conjunctions revealed that the improvement of performance was probably related to the structural changes brought about by the 1T phase and the resulting sulfur vacancies, which could be used as a reference for the further application of MoS2.

Though community empowerment and sustainable tourism development (STD) have been discussed in the existing literature, little research has focused on the elaborate mechanisms between these two variables. Therefore, the present study examines the relationship between community empowerment and STD, along with the mediating role played by community support for tourism. Using social exchange theory, this research establishes theoretical relationships between vital variables for STD. A survey of empirical study was undertaken, and data were collected from 353 local residents in the northern area of Pakistan. The results for data analyses demonstrated a significant relationship between community empowerment and STD initiatives, and community support for tourism was shown to partially mediate the relationship between the two variables. The findings imply that high community empowerment enables the community to establish successful sustainable tourism development through local people’s support for tourism. This study contributes theoretically to identifying the idea that community members’ support for tourism has a crucial function bridging the link from community empowerment to sustain tourism in a local area.

Speckle noise in optical coherence tomography (OCT) impairs both the visual quality and the performance of automatic analysis. Edge preservation is an important issue for speckle reduction. In this paper, we propose an end-to-end framework for simultaneous speckle reduction and contrast enhancement for retinal OCT images based on the conditional generative adversarial network (cGAN). The edge loss function is added to the final objective so that the model is sensitive to the edge-related details. We also propose a novel method for obtaining clean images for training from outputs of commercial OCT scanners. The results show that the overall denoising performance of the proposed method is better than other traditional methods and deep learning methods. The proposed model also has good generalization ability and is capable of despeckling different types of retinal OCT images.

Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and social spammer detection. However, most existing methods neglect the complex cross-modality interactions between network structure and node attribute. In this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between network structure and node attribute for high-quality embeddings. Specifically, Anoma-lyDAE consists of a structure autoencoder and an attribute autoencoder to learn both node embedding and attribute embedding jointly in latent space. Moreover, attention mechanism is employed in structure encoder to learn the importance between a node and its neighbors for an effective capturing of structure pattern, which is important to anomaly detection. Besides, by taking both the node embedding and attribute embedding as inputs of attribute decoder, the cross-modality interactions between network structure and node attribute are learned during the reconstruction of node attribute. Finally, anomalies can be detected by measuring the reconstruction errors of nodes from both the structure and attribute perspectives. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed method.

Imine-linked covalent organic frameworks (COFs) are popular candidates for photocatalytic CO2 reduction, but high polarization of the imine bond is less efficient for π-electron delocalization between the linked building units, leading to low intramolecular electron transfer and poor photocatalytic efficiency. Herein, we present a structural and electronic engineering strategy through integrating the imine-linked COF consisting of Zn–porphyrin and Co–bipyridyl units with cadmium sulfide (CdS) nanowires to form a CdS@COF core–shell structure. The experimental and theoretical results have validated that CdS serves as the electron transfer channel through the interfacial electron effects, which induces photoelectron transfer from Zn–porphyrin to CdS and subsequent injection into Co–bipyridyl units for CO2 reduction. The as-prepared CdS@COF generates 4057 μmol g–1 CO in 8 h under visible-light irradiation, which is considerably higher than those of its neat CdS and imine-linked COF counterparts. This work provides protocols to tackle intramolecular charge transfer across polar linkages between photosensitizers and active sites for solar-to-chemical energy conversion.