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While most network embedding techniques model the proximity between nodes in a network, recently there has been significant interest in structural embeddings that are based on node equivalences , a notion rooted in sociology: equivalences or positions are collections of nodes that have similar roles—i.e., similar functions, ties or interactions with nodes in other positions—irrespective of their distance or reachability in the network. Unlike the proximity-based methods that are rigorously evaluated in the literature, the evaluation of structural embeddings is less mature. It relies on small synthetic or real networks with labels that are not perfectly defined, and its connection to sociological equivalences has hitherto been vague and tenuous. With new node embedding methods being developed at a breakneck pace, proper evaluation, and systematic characterization of existing approaches will be essential to progress. To fill in this gap, we set out to understand what types of equivalences structural embeddings capture. We are the first to contribute rigorous intrinsic and extrinsic evaluation methodology for structural embeddings, along with carefully-designed, diverse datasets of varying sizes. We observe a number of different evaluation variables that can lead to different results (e.g., choice of similarity measure, classifier, and label definitions). We find that degree distributions within nodes’ local neighborhoods can lead to simple yet effective baselines in their own right and guide the future development of structural embedding. We hope that our findings can influence the design of further node embedding methods and also pave the way for more comprehensive and fair evaluation of structural embedding methods.

The present study examined the perception, reaction (i.e., possible measures), and future development from the perspectives of hotel and tourism practitioners and experts to investigate the influence of coronavirus disease 2019 (i.e., COVID-19) on the hospitality and tourism industry in China. After conducting 58 in-depth interviews among hotel and tourism practitioners and experts, feasible and practical measures were proposed to reduce such influence and predict the future development of China’s hospitality and tourism industry. Findings indicate that the influence of COVID-19 on the industry is perceived mainly through the pandemic’s economic and social effects. Possible measures that can be adopted for the recovery of China’s hospitality and tourism industry include the following aspects: government financial support, employee relationship management and electronic (e)-training, business marketing management, and industry co-operation network. A Perception-Reaction-Predication (PRP) crisis model is also proposed.

Literature on Shared Mental Models (SMMs) has been burgeoning in recent years and this has provided increasingly detailed insight and evidence into the importance of SMMs within specific contexts. However, because past research predominantly focused on SMM structure as measured by diverse, context-dependent measures, a consolidated multi-dimensional measure of perceived SMMs that can be used across diverse team contexts is currently lacking. Furthermore, different conceptualizations of the dimensionality of SMMs exist, which further impedes the comparison between studies. These key limitations might hinder future development in the SMM literature. We argue that the field of SMMs has now matured enough that it is possible to take a deductive approach and evaluate the prior studies in order to refine the key SMMs dimensions, operationalizations, and measurement. Hence, we take a three-stage approach to consolidate existing literature scale-based measures of SMMs, using four samples. Ultimately, this leads to a 20-item five-dimensional scale (i.e., equipment, execution, interaction, composition, and temporal SMMs) – the Five Factor Perceived Shared Mental Model Scale (5-PSMMS). Our scale provides scholars with a tool which enables the measurement, and comparison, of SMMs across diverse team contexts. It offers practitioners the option to more straightforwardly assess perceived SMMs in their teams, allowing the identification of challenges in their teams and facilitating the design of appropriate interventions.

AbstractTo eliminate the negative effect of traditional metal-working fluids and achieve sustainable manufacturing, the usage of nano-enhanced biolubricant (NEBL) is widely researched in minimum quantify lubrication (MQL) machining. It’s improved tool wear and surface integrity have been preliminarily verified by experimental studies. The previous review papers also concluded the major influencing factors of processability including nano-enhancer and lubricant types, NEBL concentration, micro droplet size, and so on. Nevertheless, the complex action of NEBL, from preparation, atomization, infiltration to heat transfer and anti-friction, is indistinct which limits preparation of process specifications and popularity in factories. Especially in the complex machining process, in-depth understanding is difficult and meaningful. To fill this gap, this paper concentrates on the comprehensive quantitative assessment of processability based on tribological, thermal, and machined surface quality aspects for NEBL application in turning, milling, and grinding. Then it attempts to answer mechanisms systematically considering multi-factor influence of molecular structure, physicochemical properties, concentration, and dispersion. Firstly, this paper reveals advanced lubrication and heat transfer mechanisms of NEBL by quantitative comparison with biolubricant-based MQL machining. Secondly, the distinctive filmformation, atomization, and infiltration mechanisms of NEBL, as distinguished from metal-working fluid, are clarified combining with its unique molecular structure and physical properties. Furtherly, the process optimization strategy is concluded based on the synergistic relationship analysis among process variables, physicochemical properties, machining mechanisms, and performance of NEBL. Finally, the future development directions are put forward aiming at current performance limitations of NEBL, which requires improvement on preparation and jet methods respects. This paper will help scientists deeply understand effective mechanism, formulate process specifications, and find future development trend of this technology.

Plants produce numerous structurally and functionally diverse signaling metabolites, yet only relatively small fractions of which have been discovered. Multi-omics has greatly expedited the discovery as evidenced by increasing recent works reporting new plant signaling molecules and relevant functions via integrated multi-omics techniques. The effective application of multi-omics tools is the key to uncovering unknown plant signaling molecules. This review covers the features of multi-omics in the context of plant signaling metabolite discovery, highlighting how multi-omics addresses relevant aspects of the challenges as follows: (a) unknown functions of known metabolites; (b) unknown metabolites with known functions; (c) unknown metabolites and unknown functions. Based on the problem-oriented overview of the theoretical and application aspects of multi-omics, current limitations and future development of multi-omics in discovering plant signaling metabolites are also discussed.

Photodetectors converting optical signals into electrical signals have been widely utilized and have received more and more attention in scientific research and industrial fields including optical interconnection, optical communication, and environmental monitoring. Herein, we summarize the latest development of photodetectors with different micro-nano structures and different materials and the performance indicators of photodetectors. Several photodetectors, such as flexible, ultraviolet two-dimensional (2D) microscale, and dual-band photodetectors, are listed in this minireview. Meanwhile, the current bottleneck and future development prospects of the photodetector are discussed.

Compared with traditional alloys with one principal component up to 40–90%, multi-principal element alloys (MPEAs) were born in the complicated intermingling of traditional and non-traditional physical metallurgy, and brings us a great amount of excellent performances. Here, we would briefly summarize the potential applications in some key areas, which is helpful for latecomers to quickly and comprehensively understand this new alloy system. Especially, the applications of MPEAs in aerospace, industrial equipment, national defense, energy, navigation and so on are discussed roughly. Subsequently, several emerging areas have also been compared. Finally, some suggestions are given for the future development trend.

Traditional face-to-face teaching in medical schools has been suspended during the global COVID-19 pandemic, and remote online learning has consequently been implemented as an emergency measure. This study aims to share our experiences in exploring online teaching of human parasitology and to discuss the possible advantages, challenges and perspectives that we observed during Wuhan’s lockdown due to the pandemic. The application of online education is likely to be an indispensable component of post-COVID-19 interactive online parasitology courses. Our experience might provide an example for the future development of interactive online medical courses.

ObjectiveRecent studies have found that the levels of plasma amino acids, such as branched-chain amino acids and aromatic amino acids, were associated with visceral obesity, insulin resistance, future development of diabetes and cardiovascular diseases. However, few studies have involved a Chinese Han population. This study aimed to examine the association between amino acid profile and metabolic syndrome (MetS) and its components in the Chinese Han population.MethodsThis is a cross-sectional study, which enrolled a cohort of 473 participants from a community. We employed the isotope internal standard method to determine the plasma concentrations of 28 amino acids using high-performance liquid chromatography-tandem mass spectrometry (LC/MS). Participants were divided into MetS (n = 72) and non-MetS groups (n = 401) to analyze the association between amino acids and MetS and its components.ResultsThe prevalence of MetS was 15.2% according to the criteria. Plasma concentrations of isoleucine (Ile), leucine (Leu), valine (Val), tyrosine (Tyr), tryptophan (Trp), phenylalanine (Phe), glutamic acid (Glu), aspartic acid (Asp), alanine (Ala), histidine (His), methionine (Met), asparagine (Asn), and proline (Pro) were significantly higher in the MetS group than those in the non-MetS group (P < 0.05), but taurine (Tau) was significantly lower (P < 0.05). When MetS components were increased, the concentrations of these 13 amino acids significantly increased (P < 0.05), but Tau concentration was significantly decreased (P < 0.05). We extracted the amino acid profile by principal component analysis (PCA), PC1 and PC2, which extracted from the 14 amino acids, were significantly associated with MetS (odds ratio, 95% confidence interval: 1.723, 1.325–2.085 and 1.325, 1.043–1.684, respectively). A total of 260 non-MetS participants were followed up effectively, and 42 participants developed new-onset MetS within 5 years. We found that the amino acid profile of PC1 was linked to the occurrence of future MetS. Decreased Tau was correlated with the future development of MetS.ConclusionParticipants with MetS exhibit an abnormal amino acid profile, and its components gradually increase when these amino acids are altered. Amino acid PCA profile can be employed for assessing and monitoring MetS risk. Finally, decreased Tau may be linked to the future development of MetS.