Miracle-dz/UniFAR-FTUs huggingface dataset:
Breaking the silence of travel agency employees: The moderating role of gender

This study aims to examine the associations between employee voice, job satisfaction and intention to leave. In addition, it investigates the moderating role of employee gender on the link between job satisfaction and intention to leave. Using structural equation modelling, and based on a sample of 551 travel agency employees in Egypt, the results show that employee voice is a key predictor of employees’ intention to leave. Furthermore, it has been found that employee voice has a higher association with job satisfaction for male employees, while the association between job satisfaction and intention to leave is almost identical between males and females. The findings hold significant implications for theory and practice. Theoretically, the study makes a valuable contribution to the body of knowledge focusing on employee voice in the tourism industry. Practically, the findings help travel agency managers improve employee voice practices, which, given the tourism industry’s reputation for high turnover and poor job security, is critical in achieving greater job stability and employee retention. Among other recommendations, one of the most important proffered by this study is for tourism businesses to improve job satisfaction by increasing the communication flow between managers and employees.
Accounting student burnout and engagement: The role of major satisfaction in mitigating or enforcing functional and dysfunctional behavior

This study begins by establishing the nature of the debatable relationship between student burnout and engagement in an accounting context and investigates the impact of student major satisfaction as an antecedent factor for accounting student burnout and engagement. Hence, a survey of 280 students was conducted using Maslash Burnout inventory-student survey, Utrecht Work Engagement Scale and the Academic Major Satisfaction Scale for Students. The results partially support the argument that student engagement is independent and is a distinct concept from burnout. Furthermore, student major satisfaction was found to significantly impact both concepts. The results can be important for an appropriate university intervention in mitigating or enforcing these behaviors.
Research of Recommendation System Based on Deep Interest Network

With the development of e-commerce, it provides users with many choices. But how to quickly and accurately recommend goods to users is an important topic in this field. Matrix factorization recommendation model based on scoring data is widely used, but data sparsity affects the recommendation quality of the model. In the paper, a product recommendation algorithm based on deep interest network is proposed. First, the user’s purchased goods and the user’s search are embedded coded, and the sparse features are transformed into low-dimensional dense features.Then, the feature vectors of the purchased goods and the search text vectors are joined together to input the deep interest network as the feature to predict the user’s interest.Finally, the effectiveness of the model is validated by using record data. The validity of the model is verified by comparing with other models.
Photocatalytic degradation of organic pollutants through conjugated poly(azomethine) networks based on terthiophene–naphthalimide assemblies

A conjugated poly(azomethine) network based on ambipolar terthiophene–naphthalimide assemblies has been synthesized and its electrochemical and UV-vis absorption properties have been investigated. The network has been found to be a promising candidate for the photocatalytic degradation of organic pollutants in aqueous media.
Forests can help diminish the number of dengue cases in Costa Rica

Approximately 3.9 billion people are at risk of infection with dengue fever, a group of viruses transmitted by mosquitos.1,2 In 2019 Central America has suffered a strong dengue epidemic.3 Costa Rica has almost doubled the number of dengue cases between in the first 24 epidemilogical weeks of 2019 regarding the same period in the previous year.4 In the Americas, forest cover is thought to diminish anthropogenic habitats for mosquito larvae, as well as increase the presence of their predators.5,6 Here we estimate the marginal effects of increasing forest cover on dengue prevalence, using econometric models to relate hospital admission records and forest cover maps from 2001 and 2011. We find that increasing the percentage of forest cover significantly decreased both the number of hospital admissions for dengue and the probability of an outbreak. Using the same models, we predict that increasing forest cover by one percentage point would have avoided between 85 to 103 dengue hospital admissions per year. This represents savings between USD 21,500 to 295,000 per year, depending on the severity of dengue cases. Our study shows that forest conservation can be a public health investment that increases welfare both by avoiding sickness and by reducing associated health care expenditures. Understanding the contribution of nature to diminish the risk of disease outbreaks turn even more urgent and important under the COVID-19 global pandemic the world has faced in 2020.7,8
The commonality of core biological groups across freshwater food webs

Assessing how organisms interact is fundamental to understand biodiversity patterns. Like many ecosystems, most freshwater environments are susceptible to spatial and temporal variations, especially floodplains. In spite of the expected variability, the structure of interactions in food webs is a paramount aspect to uncover processes related to ecosystem functioning. Here, we surveyed four large floodplains to identify the biological groups that are protagonists across different freshwater food webs. We used limnological data on physical and biochemical variables, combined with biological information on 10 communities grouped into 41 trait‐based categories. Despite the expected differences among floodplains and between hydrologic periods (dry and flood), we identified that complex freshwater food webs shared core network compartments. Using the eigenvector centrality as a measure of importance to the whole food web, we found that both primary producers (i.e., phytoplankton) and potential top predators (i.e., fish) were the core groups in freshwater trophic networks. These biological groups were consistently among the most linked compartments in all reconstructed food webs. Considering variations among food webs, we found that most differences were dictated by how compartments interact, rather than by their co‐occurrences. Perhaps equally important is that omnivory was important for the reconstructed food webs, drawing attention to this interaction type as a fundamental aspect to unveil ecosystem processes. Although more empirical research is required to fully understand the importance of biotic interactions in freshwater food webs, our findings underline that ecosystems are more likely to collapse when disturbances affect the boundaries of the energy paths.
Making AI available for everyone at anywhere: A Survey about Edge Intelligence

With the rapid development of the 5thgeneration of wireless network communication technology, a high-speed data rate, broader bandwidth, and low-latency wireless network has already come to the truth. Meanwhile, Edge Computing which put data processing at the edge of the network has already been well developed, and it has changed the computing mode tremendously. And also, Artificial Intelligence (AI) has made a great breakthrough from Computer Vision, Unmanned Vehicle to Natural Language Processing. Nevertheless, with the increasing model size and model depth, the training data set and communication delay has become the bottleneck of AI popularization. As zillions of bytes of data are being generated at the network edge, and also lots of AI applications are being utilized by enormous customers lied at the edge, the combination of Edge Computing and AI is imperative. This paper has done a deep investigation into the new field about the confluence of Edge Computing and AI, aiming of discussing the concept, architecture, and research ideas on Edge Intelligence. To the end, this paper provides the road-map for future researching work.
Recognition System by Using Machine Vision Tools and Machine Learning Techniques for Mobile Robots

The systems based on image recognition play an important role in many cases where inspection methods are critical for industrial processes. Machine vision is required in industry for monitoring and detecting objects in real-time applications. Industrial robots are increasingly used in a variety of industries and applications such as manipulators and mobile robots. These devices are necessary for dangerous work conditions and tasks that humans cannot do; however, these industrial robots can also do some human activities such as transport material, select, and identify objects. In order to substitute human capabilities is no easy task. Nevertheless, the use of artificial intelligence has been replacing some human activities for increased productivity. By supporting machine learning technologies, this chapter presents a k nearest neighbors' algorithm for image classification of mobile robots to detect and recognize objects.
Individualised Risk Assessments for Recurrent Venous Thromboembolism: New Frontiers in the Era of Direct Oral Anticoagulants

Venous thromboembolism (VTE) is a leading cause of morbidity and mortality and is associated with high recurrence rates. The introduction of direct oral anticoagulants (DOACs) in the 2010s has changed the landscape of VTE management. DOACs have become the preferred anticoagulant therapy for their ease of use, predictable pharmacokinetics, and improved safety profile. Increasingly, guidelines have recommended long term anticoagulation for some indications such as following first unprovoked major VTE, although an objective individualised risk assessment for VTE recurrence remains elusive. The balance of preventing VTE recurrence needs to be weighed against the not insignificant bleeding risk, which is cumulative with prolonged use. Hence, there is a need for an individualised, targeted approach for assessing the risk of VTE recurrence, especially in those patients in whom the balance between benefit and risk of long-term anticoagulation is not clear. Clinical factors alone do not provide the level of discrimination required on an individual level. Laboratory data from global coagulation assays and biomarkers may provide enhanced risk assessment ability and are an active area of research. A review of the prediction models and biomarkers for assessing VTE recurrence risk is provided, with an emphasis on contemporary developments in the era of DOACs and global coagulation assays.
Gas Adsorption Studies on Vertically Aligned Carbon Nanotubes

Carbon based adsorbents, especially activated carbons are one of the most widely used adsorbents. This is attributed to its excellent adsorption capacity as well as its better stability in the presence of moisture and corrosive gases. However the pores in these activated carbons in general, are often ill-defined, irregular and irreproducible. Modeling the actual pore system in activated carbon is far too complex and this seriously hinders a deeper understanding of the adsorption phenomenon in these materials. Carbon nanotubes (CNTs), on the other hand, have a defined, uniform and a reproducible pore structure. Compared with randomly oriented CNTs, a better well-defined structure is obtained by aligning the carbon nanotubes in a vertical manner. The vertically alignment of carbon nanotubes leads to the creation of multiple well-defined adsorption sites and results in an enhanced adsorption capacity. Vertically aligned carbon nanotubes (VACNTs) can thus serve as an ideal model structure for understanding gas adsorption in carbon materials. In this work VACNTs are synthesized by water assisted chemical vapor deposition technique. The as-prepared CNTs are usually double to multi-walled with an average internal diameter of 8 nm and have a specific surface area of 500 m2/g. To assess the adsorption characteristics of these VACNTs, two different experimental adsorption setups are constructed. A commercially available TG setup is modified to measure the adsorption capacity at ambient pressures. To determine the adsorption characteristics over a wider temperature and pressure range, a high pressure volumetric setup is designed, built, calibrated and installed as a fully operable high pressure device which can handle smaller sample quantities (< 100 mg) and can determine adsorption up to a pressure of 100 bar in the temperature range 10–50 ˚C. In addition, the volumetric setup is designed to measure adsorption isotherms of gas mixtures as well. Using these two techniques, the CO2 adsorption characteristics of the as-prepared VACNTs are determined and the influence of various other parameters like diameter, intertube distance etc. are explored systematically. Functionalization of VACNTs is carried out by preserving the alignment and the effect of addition of various functional groups on gas adsorption characteristics are investigated. Finally the adsorption characteristics of VACNTs are compared with other carbon based adsorbents. The adsorption capacity of VACNTs is found to increase linearly with pressure and at high pressure, the adsorption characteristics of modified VACNTs is found to be superior to that of other standard adsorbents.
Penetration of Different Liquids in Wood-Based Composites: The Effect of Adsorption Energy

The penetration properties of three different liquids on the surface of medium-density fiberboard (MDF) and particleboard panels were studied. Water, as a polar liquid, was compared to two other less polar liquids (namely, ethanol and kerosene) with significantly larger molecules. Measurement of penetration time and wetted area demonstrated significantly higher values for water in comparison with the other two liquids, in both composite types. Calculation of adsorption energies, as well as adsorption distances, of the three liquid molecules on hemicellulose showed higher potentiality of water molecules in forming bonds on hemicellulose. However, comparison of the adsorption energies of cellulose with hemicellulose indicated a higher impact of the formation of bonds between hydroxyl groups in water and cellulose in hindering the penetration of water molecules into the composite textures. It was concluded that the formation of strong and stable bonds between the hydroxyl groups in water and cellulose resulted in a significant increase in penetration time and wetted area.
Creating large-scale maps for agriculture using remote sensing

Reliable information on the real state of agricultural lands will be required to the development of appropriate measures for the rational use of agricultural lands. To obtain such information, it is necessary to keep permanent and systematic records and inventories of land resources. Large-scale special plans and maps will be required for accounting, inventory and classification of agricultural land. Currently in Uzbekistan such cartographic materials are being created on the scale 1: 10 000 and 1: 25 000 by administrative and territorial units, farms or individual land plots. The article considers the issues of creation of special maps of agricultural land in scale 1:10000 on the example of Sharof Rashidov district of Jizzakh region using remote sensing data with very high spatial resolution KOMPSAT-3.
Automated 3D Road Boundary Extraction and Vectorization Using MLS Point Clouds

To meet the urgent demands in a wide range of geospatial applications, such as road management, intelligent transportation systems, road safety evaluation, and traffic accident analysis, automatic and accurate extraction of 3D roads and associated geometric parameters from point clouds is receiving wide attention. In this paper, we propose an accurate 3D road boundary extraction and vectorization method to bridge the gap from unstructured mobile laser scanning (MLS) point clouds to the vector-based representation of road boundary. Firstly, we propose a supervoxel generation method to extract candidate curbs with fine border preservation and high computation efficiencies. Then the candidate curb supervoxels are recognized and clustered to produce continuous road boundary segments with a contracted distance clustering strategy. Finally, the vectorized road boundary is represented by fitting, tracking, and completion from the extracted road boundary segments, resulting in road geometric parameters including boundary location, road widths, turning radius, and slopes. The performance of the proposed method was evaluated on two large-scale datasets collected in urban and industrial areas. Comprehensive experiments reveal that the proposed method is robust to various road shapes and point densities, in terms of precision of 95.0% and recall of 91.0%, respectively.
A Clarke-Wavelet-Based Time-Domain Power Transformer Differential Protection

Phasor-based differential protection is widely used as the main protection function of the power transformer due to its reliability and ability to discriminate internal from external faults and inrush currents. However, these methods present some delays due to phasor convergence during the fault occurrence and can fail during challenging situations, such as transformer energizations with low second-harmonic content and turn-to-turn and turn-to-ground internal faults. This paper proposes a novel time-domain power transformer differential protection based on Clarke and wavelet transforms with only one differential unit and with automatic setting to be used in any power transformer. Considering both actual and simulation data, the performance validation reveals that the proposed method is efficient, ultra-fast, simple, and independent of the fundamental and harmonic components of the differential current. The method was also implemented in a real-time digital simulator to demonstrate its practical feasibility.
Longitudinal bidirectional relationships between maternal depressive/anxious symptoms and children’s tics

Background: Previous studies have revealed an association between maternal depressive/anxious symptoms and children’s tics. However, the longitudinal relationships between these symptoms remain unclear. We examined the longitudinal relationships between maternal depressive/anxious symptoms and children’s tics across early adolescence in a population-based sample.Methods: The participants consisted of 3,171 children and their mothers from the Tokyo Teen Cohort (TTC) study, a population-representative longitudinal study that was launched in Tokyo in 2012. Maternal depressive/anxious symptoms and children’s tics were examined using self-report questionnaires at the ages of 10 (time 1, T1) and 12 (time 2, T2). A cross-lagged model was used to explore the relationships between maternal depressive/anxious symptoms and children’s tics.Results: Higher levels of maternal depressive/anxious symptoms at T1 were related to an increased likelihood of children’s tics at T2 (β = .06, p = .001). Furthermore, the presence of children’s tics at T1 was positively related to maternal depressive/anxious symptoms at T2 (β = .06, p = .001). Conclusions: These findings suggest a longitudinal bidirectional relationship between maternal depressive/anxious symptoms and children’s tics during early adolescence that may exacerbate each other over time and possibly create a vicious cycle. When an early adolescent has tics, it might be important to identify and treat related maternal depressive/anxious symptoms.
MOLECULAR DOCKING STUDIES ON THE THERAPEUTIC TARGETS OF ALZHEIMER'S DISEASE (AChE AND BChE) USING NATURAL BIOACTIVE ALKALOIDS

Objective: Alzheimer’s disease (AD), a progressive neurodegenerative disorder with many cognitive and neuropsychiatric symptoms, is biochemically characterized by a significant decrease in the brain neurotransmitter Acetylcholine (ACh). Methods: In the present insilico study, six plant bioactive compounds namely Harmol, Vasicine, Harmaline, Harmine, Harmane and Harmalol (from P. Nigellastrum Bunge ) were analyzed for their inhibitory role on AChE (Acetylcholinesterase) and BChE (Butyrylcholinesterase) activity by applying the molecular docking studies. Other parameters viz. determination of molecular interaction-based binding affinity values, protein-ligand interactions, Lipinski rule of five, functional properties and biological activities for the above compounds were also calculated by employing the appropriate bioinformatics tools. Results: The results of docking analysis clearly showed that Harmalol has highest binding affinity with AChE (-8.6 kcal/mole) and BChE (-8.0 kcal/mole) but it does not qualified the enzyme inhibitory activity, since it was exerted, and also has least percentage activity on AD and neurodegenerative disease. Whereas, the Harmine has been second qualified binding affinity (-8.4 kcal/mol) and first in other parameters when compared with Harmalol. Conclusion: Based on docking results and other parameters conducted, we are concluding that Harmine is the best compound for further studies to treat AD. Keywords: Alzheimer's disease (AD), Acetylcholinesterase , Butyrylcholinesterase, Lead Molecules
Power, Politics, and Education: Canadian Universities and International Education in an Era of New Geopolitics

This paper focuses on the recent political spars between Canada and Saudi Arabia as well as China and their impact on Canadian universities. It asks three questions: (1) What key issues did Canada’s political strains with Saudi Arabia and China raise for Canadian universities’ international education (IE) initiatives and what issues were absent? (2) What do these key issues suggest about Canada’s approaches to IE in an era of new geopolitics? and (3) What implications can be drawn from these cases about Canadian university-government relations in the context of new geopolitics? Given the powerful role media plays in education policy, a systematic study was conducted across three main media sources to identify 74 articles and news releases between August 2018 and November 2019. Three dominant themes are identified and analyzed, each vividly illustrating the close ties between global politics, government foreign policy and IE within Canadian Universities. On the one hand, the narratives speak to concerns about IE as a risk to national security and, on the other, as a vehicle for Canada’s economic prosperity. However, what the media has not achieved is a broader discussion on how Canada needs to revisit its IE objectives and approaches in light of broader geopolitical shifts. Using the theoretical framework of soft power, the paper speaks to the limitations and short-sightedness of Canada’s approach to IE as soft power in this era of new geopolitics and concludes with three recommendations for Canada.