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Fluorinated organic compounds are of great importance to modern industries, while their release to the environment is inevitable, causing extreme environmental pollution and the subsequent hazardou...

Purpose The purpose of this paper is to investigate the correlation and mechanism between international students and inbound tourism. Design/methodology/approach Taking 269 prefecture-level cities in China as a sample, bringing international students and inbound tourism into the same analytical framework, the paper applies the fixed-effects model, instrumental variables model and mediating effects model to investigate the impact of international students on inbound tourism. Findings International students significantly contribute to the increase in inbound tourists and tourism revenue in China, which remain valid after a series of robustness tests. When the relocation data of Chinese urban universities and faculties in the 1950s are used as an instrumental variable for the potential endogenous problems of international students, the results are still valid. Heterogeneity research shows that the impact of international students on inbound tourism has heterogeneous effects in different regions. In addition, the mechanism analysis shows that international students promote China’s inbound tourism mainly through two channels: the economic growth effect and the human capital effect. Research limitations/implications This paper provides evidence of the correlation between international students and inbound tourism in developing countries. However, as the data for the study are all from China, caution should be taken when applying the findings of this paper to other regions. Practical implications This paper provides evidence on the causal relationship between international students and inbound tourism in developing countries. However, as the data for the study are all from China, caution should be taken when applying the findings of this paper to other regions. Social implications This paper creatively finds out the instrumental variables of international students with academic education to deal with the potential endogenous problems, and then identifies the causal relationship between international students and inbound tourism. Originality/value This paper creatively finds out the instrumental variables of international students to deal with the potential endogenous problems and then identifies the correlation between international students and inbound tourism.

Cloud computing offers an efficient, scalable, and flexible platform; through techniques and methods proven by it, many complex projects were successfully carried out. It provides consumers the opportunity to interact with previously unknown ease to measure sources and connections to IT-managed services. Online shopping has been considered as one of the commonly used channels for easy shopping in the Internet world and becomes a convenient way to search for products from various places and to make the operation quicker. The digital economy is powered by high-speed Internet connectivity launched by IT as a practical platform for interaction between online shopping and organizations. The platforms for e-commerce have led to extensive international access by local consumers to goods and services. E-commerce has improved productivity in enterprises and has added to the economies in the countries of the business industry. However, e-commerce security is the primary concern to protect details from electronic theft such as credit card or debit card fraud, retailer, or bank leakage of details while a card is not used for purchases online. In this paper, Cloud Computing-Based Multilevel Decision Support System (CCMDSS) has been proposed for secure fund transfer in the e-commerce platform. Furthermore, this study utilizes cryptography techniques and multiple networks to safeguard the security of information and decrease the systematic risks. The experimental results show that the proposed method enhances the efficiency and feasibility and secures the information while transferring the money. Compared with other models, Prediction Ratio validation has a significant improvement, and the error rate is greatly reduced, only 5.99%. Multiple data prove that the comprehensive performance of CCMDSS model ranks among the top among existing models.

Exploring platinum group metal-free electrocatalysts with superior catalytic performance and favorable durability for oxygen reduction reaction is a remaining bottleneck in process of developing sustainable techniques in energy storage and conversion. Herein, a hierarchical porous single atomic Fe electrocatalyst (Fe/Z8-E-C) is rationally designed and synthesized via acid etching, calcination, adsorption of Fe precursor and recalcination processes. This unique electrocatalyst Fe/Z8-E-C shows excellent oxygen reduction performance with a half-wave potential of 0.89 V in 0.1 mol/L KOH, 30 mV superior to that of commercial Pt/C (0.86 V), which is also significantly higher than that of typical Fe-doped ZIF-8 derived carbon nanoparticles (Fe/Z8-C) with a half-wave potential of 0.84 V. Furthermore, Fe/Z8-E-C-based Zn-air battery exhibits greatly enhanced peak power density and specific capacity than those of original Fe/Z8-C, verifying the remarkable performance and practicability of this specially designed hierarchical structure due to its efficient utilization of the active sites and rapid mass transfer. This present work proposes a new method to rationally synthesize single atom electrocatalysts loaded on hierarchical porous frame materials for catalysis and energy conversion.

As economic globalization intensifies, the recent increase in agricultural products and travelers from abroad has led to an increase in the probability of invasive alien species. A major pathway for invasive alien species is agricultural quarantine materials (AQMs) in travelers' baggage. Thus, it is meaningful to develop efficient methods for early detection and prompt action against AQMs. In this study, a method based on the combination of odor detection of AQMs using ion mobility spectroscopy (IMS) and convolutional neural network (CNN) analysis for the identification of AQM species in luggage was developed. Two different ways were investigated to feed the IMS data of AQMs into the CNN, either as one-dimensional data (1D) (as a spectrum) or as two-dimensional data (2D) (as an IMS topographic map). The performances of CNN models were also compared to those of the commonly used classification algorithms: partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA). By doing gradient-weighted class activation mapping (Grad-CAM), the essential IMS feature regions from the CNN models to predict different AQM species were also identified. The results of this research demonstrated that the application of the CNN to the IMS data of AQMs yielded superior classification performance compared to PLS-DA and SIMCA. Especially, the CNN-2D model which utilized the IMS topographic map as input achieved the best classification accuracy both on the calibration and validation sets. In addition, the Grad-CAM method had an ability to detect critical discriminating spectral regions for different types of AQM samples, and could provide explanation for the CNNs' decision-making. Despite the inherent limitations of the present analytical protocol, the results showed that the method of IMS in combination with a CNN has great potential to be a complement for sniffer dogs and X-ray imaging techniques to detect AQMs.

A W-doping-induced phase transition strategy was reported to construct heterojunctions. W doping can not only regulate the morphology of the catalysts but also induce to form W-Ni3S2/Ni7S6 heterojunction which is a promising OER catalyst.

An unprecedented zirconium metal–organic framework featuring a T-shaped benzimidazole strut was constructed and employed as a sponge-like material for selective absorption of macrocyclic guests. The neutral benzimidazole domain of the as-synthesized framework can be readily protonated and fully converted to benzimidazolium. Mechanical threading of [24]crown-8 ether wheels onto recognition sites to form pseudorotaxanes was evidenced by solution nuclear magnetic resonance, solid-state fluorescence, and infrared spectroscopy. Selective absorption of [24]crown-8 ether rather than its dibenzo counterpart was also observed. Further study reveals that this binding process is reversible and acid–base switchable. The success of docking macrocyclic guests in crystals via host–guest interactions provides an alternative route to complex functional materials with interpenetrated structures.

A well-defined nickel metal-organic framework (Ni-MOF) is obtained via solvothermal synthesis from nickel and 3,3′,5,5′-azobenzene tetracarboxylic acid ligands (H4abtc). Ni-MOF exhibits remarkable performance for photocatalytic CO2-to-CO conversion.

Although the selective hydrogenation of α,β-unsaturated aldehyde to unsaturated alcohol (UOL) is an extremely important transformation, it is still a great challenge to achieve high selectivity to UOL due to thermodynamic favoring of the C C hydrogenation over the C O hydrogenation. Herein, we report that iridium nanoclusters (Ir NCs) confined within hollow MIL-101(Fe) expresses satisfied reaction activity (93.9%) and high selectivity (96.2%) for the hydrogenation of cinnamaldehyde (CAL) to cinnamyl alcohol (COL) under 1 bar H2 atmosphere and room temperature. The unique hollow structure of MIL-101(Fe) benefits for the fast transport of reactant, ensuring the comparable reaction activity and better recyclability of Ir@MIL-101(Fe) than the counterparts which Ir NCs were on the surface of MIL-101(Fe). Furthermore, The X-ray photoelectron spectroscopy data indicates the electropositive Ir NCs, owing to the electron transfer from Ir to MIL-101(Fe), can interact with oxygen lone pairs, and Fourier transform infrared spectrum shows the Lewis acid sites in MIL-101(Fe) can strongly interact with C O bond, which contributes to a high selectivity for COL. This work suggests the considerable potential of synergetic effect between hollow MOFs and metal nanoclusters for selective hydrogenation reactions.

Background: Forest musk deer (FMD) is a valuable species distributed throughout southwest China, but there is a lack of comprehensive and reliable information on its growth and development. Methods: We sequenced the hair follicle transcriptome of FMD from 13 individuals by RNA-seq. Vital biological processes or pathways of differentially expressed genes (DEGs) in FMD were examined by GO and KEGG enrichment analyses and constructed a network of DEGs protein-protein interactions based on the STRING protein interaction database. Results: The assembly yielded 71167 transcripts with a maximum length of 101907 bp and N50 value of 4942 bp. A total of 219 differentially expressed genes were identified in the study, of which 10 up-regulated and 209 down-regulated genes were at the anagen stage of hair follicles compared to their catagen stage. We identified ESR1, CAV1, FSTL1, TLR2, REM1, and FGFR1 as the hub genes. The result of the KEGG pathway enrichment analysis showed that 31 KEGG pathways were significantly enriched in these two hair follicle groups. Including glycosaminoglycan binding proteins, endocrine system, cellular community-eukaryotes, etc. Conclusion: An analysis of both GO and KEGG enrichment revealed that up-regulated genes were mostly associated with cell development and regulated signaling pathway. On the other hand, down-regulated genes were closely associated with diseases and the immune system. In conclusion, our findings provide insight into the mechanism of molecular regulation in hair follicle growth and will lay the groundwork for future studies on diseases caused by captive FMD.

Selective hydrogenation of α,β-unsaturated aldehydes to obtain a high yield of unsaturated alcohols is important in industrial production. This is still a great challenge because it is thermally more favorable for the hydrogenation of CC than for the CO bond. Various strategies have been developed to optimize the catalysts for improving selectivity but are usually accompanied by the sacrifice of catalytic activity. Herein, we adopt the inert metal inducement strategy to synthesize a series of Ir-M alloy nanoparticle catalysts. The optimal catalyst IrCd

A novel metal–organic framework (NiCoBDC-Fc) was synthesized via a multiscale structural regulation strategy. The introduction of Co and Fe regulated both the morphology and electronic structure of the catalyst, which enhanced the OER performance.

To study the impact of the agricultural information system based on the Internet of Things (IoT) on the income of agricultural products, an agricultural information system was constructed based on the agricultural IoT technology, and its impact on the income of agricultural products was discussed through the deep belief network. First, the relevant theories of agricultural IoT were introduced. Then, an agricultural information system based on agricultural IoT technology was constructed, and a deep belief network model was proposed. The vegetable prices and influencing factors were collected. The data were distributed in the range of 0–1 after normalization. The collinearity between the data was eliminated through principal component analysis. Then, the principal component analysis of vegetable prices and influencing factors from 2015 to 2019 was performed. A total of 96 sample data of calibration set and 24 sample data of test machine were collected. The optimal number of hidden layers of the deep belief network model and the number of nodes contained in the hidden layer were obtained through experiments. The results show that the first, second, and third hidden layers have 8, 6, and 10 nodes, respectively; the prediction accuracy of the deep belief network model is more accurate than that of the BP neural network and wavelet neural network. Besides, the absolute value of the prediction error of the deep belief model is within 0.1, which has good prediction accuracy. In short, the deep belief model has a good development prospect in agricultural product price forecasting, and it can provide relevant reference for the establishment and research of other agricultural product price forecasting models.

Transport infrastructure connectivity (TIC) has strong endogeneity issues, making it difficult to directly assess its impact on local conflict resolution. This study presents new evidence of the effects of TIC on conflict resolution by conducting a natural experiment and applying machine learning methods to overcome the endogeneity issue. Based on global conflict data from 2010 to 2017, the empirical results show the following: (1) TIC can significantly improve countries’ global ranking for conflict resolution; in particular, the marginal benefit of developed countries is greater than that of developing countries. (2) The mechanism behind this effect is the promotion of trade facilitation, a more balanced employment ratio across genders, and improved income levels through TIC, which further enhances the conflict governance capacity of countries. In light of the findings, policy-makers should consider the opportunity to combine TIC with greater security for the realization of economic and social benefits, taking into account the significant opportunities for developing countries and the importance of balance across genders and income levels.

Exploring efficient and stable electrocatalysts for oxygen evolution reaction is a critical and challenging task for sustainable-energy supplies. In this issue of Chem Catalysis, Huang and co-workers constructed a unique spontaneously oxidized interface on noble metal sub-nanoclusters that facilitated oxygen evolution.

The construction of a heterojunction is an important strategy to develop efficient electrocatalysts. However, the precise design and preparation of the heterojunction with desirable catalytic perfo...