Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Corruption on Tourism Sector in Nigeria: Empirical Insights by Using an Autoregressive Distributed Lag Bounds (ARDL) Testing Approach

ABSTRACT The study investigates the impact of corruption on tourism in Nigeria. The study employs quarterly frequency data spanning from 2002Q1 to 2018Q4. In examining the short- and long-run relationships between corruption and tourism in Nigeria, the study uses the autoregressive distributed lag (ARDL) technique. The findings show that the impact of corruption on tourism is negative in both the long and short-run, while individual income and foreign direct investment, as control variables, have a direct effect on tourism in the two periods. The negative relationship between corruption and tourism lends support to the “sanding the wheels” hypothesis in both the long and short-run. The study recommends that the Nigerian government should adopt corruption-reduction strategies for reducing the negative effect of corruption on tourism in Nigeria.
Characterization of Hillslope Hydrologic Events through a Self-Organizing Map

Abstract. Hydrologic events can be characterized as particular combinations of hydrological processes on a hillslope scale. To configure hydrological mechanisms, we analyzed a dataset using an unsupervised machine learning algorithm to cluster the hydrologic events based on the dissimilarity distances between the weighting components of a self-organizing map (SOM). The time series of soil moisture was measured at 30 points (in 10 locations with 3 varying depths) for 356 rainfall events on a steep, forested hillslope between 2007 and 2016. Soil moisture features for hydrologic events can be effectively represented by the antecedent soil moisture, maximum variation, and standard deviation of peak-to-peak time between rainfall and soil moisture response. Five clusters were delineated for hydrologically meaningful event classification in the SOM representation. The two-dimensional spatial weighting patterns in the SOM provided greater insight on relationships between rainfall characteristics, antecedent wetness, and soil moisture response at different locations and depths. The distinction of the classified events can be explained by several rainfall features and antecedent soil moisture conditions that resulted in different patterns made by combinations of hillslope hydrological processes, vertical flow, and lateral flow along either surface or subsurface boundaries for the upslope and downslope areas.
The Cognition of Audience to Artistic Style Transfer

Artificial Intelligence (AI) is becoming more popular in various fields, including the area of art creation. Advances in AI technology bring new opportunities and challenges in the creation, experience, and appreciation of art. The neural style transfer (NST) realizes the intelligent conversion of any artistic style using neural networks. However, the artistic style is the product of cognition that involving from visual to feel. The purpose of this paper is to study factors affecting audience cognitive difference and preference on artistic style transfer. Those factors are discussed to investigate the application of the AI generator model in art creation. Therefore, based on the artist’s encoding attributes (color, stroke, texture) and the audience’s decoding cognitive levels (technical, semantic, effectiveness), this study proposed a framework to evaluate artistic style transfer in the perspective of cognition. Thirty-one subjects with a background in art, aesthetics, and design were recruited to participate in the experiment. The experimental process consists of four style groups, including Fauvism, Expressionism, Cubism, and Renaissance. According to the finding in this study, participants can still recognize different artistic styles after transferred by neural networks. Besides, the features of texture and stroke are more impact on the perception of fitness than color. The audience may prefer the samples with high cognition in the semantic and effectiveness levels. The above indicates that through AI automated routine work, the cognition of the audience to artistic style still can be kept and transferred.
Modeling of Topside Ionosphere and Plasmasphere

We developed a new topside ionosphere and plasmasphere model using a machine learning technique using approximately five million electron density datasets from the Japanese satellites, namely, Hinotori, Akebono, and Arase. The topside ionosphere and plasmasphere model (TIP-model) can estimate electron densities at altitudes ranging from 500 km to 30,000 km in terms of latitude, longitude, universal time, season, and solar and magnetic activities with time history. The model shows the time-dependent 3D structure of the plasmasphere in response to solar and magnetic activities. The constructed TIP-model reproduces plasmapause, plasma tail/erosion of the plasmasphere, and the plasma escape near the magnetic pole. The total electron content (TEC) in the plasmasphere was also obtained through the integration of electron density from 2,000 km to 30,000 km altitudes. The TEC of the plasmasphere is approximately 5 TECU near the magnetic equator, and it depends strongly on geomagnetic latitude, longitude, local time, and solar and magnetic activities.
Synergy between Low Earth Orbit (LEO) - MODIS and Geostationary Earth Orbit (GEO) - GOES Sensors for Sargassum Monitoring in the Atlantic Ocean

Since 2011, massive stranding of the brown algae Sargassum has regularly affected the coastal waters of the West Caribbean, Brazil, and West Africa, leading to heavy environmental and socio-economic impacts. Ocean color remote sensing observations as performed by sun-synchronous satellite sensors such as MODIS (NASA), MERIS (ESA), or OLCI (ESA/Copernicus) are used to provide quantitative assessments of Sargassum coverage through the calculation of indices as the Alternative Floating Algae Index (AFAI). Sun-synchronous sensors usually provide at best one daytime observation per day of a given oceanic area. However, such a daily temporal revisit rate is not fully satisfactory to monitor the dynamics of Sargassum aggregation due to their potentially significant drift over the course of the day as a result of oceanic currents and sea surface wind stress. In addition, the sun glint and the presence of clouds limit the use of low earth orbit observations, especially in tropical zones. The high frequency sampling provided by geostationary sensors can be a relevant alternative approach in synergy with ocean color sun-synchronous sensors to increase the temporal resolution of the observations, thus allowing efficient monitoring of Sargassum dynamics. In this study, data acquired by a geostationary satellite sensor located at 36,000 km from Earth, namely GOES-16 (NASA/NOAA), which was primarily designed for meteorology applications, are analyzed to investigate the Sargassum dynamics. The results demonstrate that a GOES-16 hourly composite product is appropriate to identify Sargassum aggregations using an index commonly used for vegetation monitoring, namely NDVI (Normalized Difference Vegetation Index). It is also shown that GOES hourly observations can significantly improve the simulated drift obtained with a transport circulation model, which uses geostrophic current, wind, and waves. This study thus highlights the significant relevance of the effective synergy between sun-synchronous and geostationary satellite sensors for characterizing the Sargassum dynamics. Such a synergy could be summarized as follows: (i) A sun-synchronous sensor enables accurate Sargassum detection and quantitative estimates (e.g., fractional coverage) through AFAI Level-2 products while (ii) a geostationary sensor enables the determination of the displacement features of Sargassum aggregations (velocity, direction).
Stochastic Optimization of Microgrids With Hybrid Energy Storage Systems for Grid Flexibility Services Considering Energy Forecast Uncertainties

This paper presents a stochastic framework for the optimization of microgrids that has the functionality of providing flexibility services to System Operators (SOs) considering uncertainties in the energy forecast. The methodology is developed with the aim of being applied to complex microgrids composed of different distributed energy resources and hybrid energy storage systems (ESS). The associated optimization problem is operated in two stages: the first one performs a stochastic optimization of the microgrid in order to reserve an up/down regulation capacity with which to deal with the energy forecast uncertainties of the microgrid. The different microgrid devices are optimized by considering their operational costs in order to achieve their optimal operation in the Day-Ahead Market (DM). The second stage is used to re-schedule the initial planning according to the signal request and an economic offer from the SO. The control problem is developed using Stochastic Model Predictive Control (SMPC) techniques and Mixed-Integer Quadratic Programming (MIQP), owing to the presence of logic, integer, mixed and probabilistic variables. The simulation results show that the proposed methodology reduces the risk of undergoing up/down-penalty deviations in the Regulation Service Market (RM), also being able to provide flexibility services to the SOs, despite being subject to uncertainties in the energy forecast carried out for the microgrid.
A hybrid WT-FBPNN optimisation algorithm to identify the investment risk of wind power projects†

Wind power projects face an uncertain external environment, they are complex projects in themselves and the capabilities of the designers, erectors and operators are limited. All this makes the identification of investment risks for wind power projects extremely complicated. In this paper, we propose a method for identifying the investment risk scientifically and accurately using a back propagation (BP) neural network. Specifically, we propose a hybrid wavelet transform fuzzy BP neural network (WT-FBPNN) optimisation model based on the construction of a risk evaluating index system. This improved model can not only exploit the time frequency localisation characteristic of wavelet transforms (WT), but also enhance the fit precision and algorithm convergence speed. The simulation results show that this model is reliable, and that this method of identifying the investment risk of wind power projects is feasible.
Study on Extraction Technology of Soybean Saponins

The extraction technology of total soybean saponins in soybean was studied with ethanol as extractor. The best conditions were chosen through L9(34) orthogonal test by spectrophotometry based on oleanolic acid as standard substance. The results showed the first factor affecting extractor productivity was volume percentage of ethonal. The second and the third ones were temperature and rotio of feed liqued respectively. The best extraction conditions were to use 75% ethanols extractor withthe ratio of feed liqued of 1:30 at the temperature of 80℃ for 5h. The extraction productivity was 1.05%.
Variation in Oxalic Acid Content among Commercial Table Beet Cultivars and Related Crops

Oxalic acid (C2O4 2- ) is a compound of interest as a result of its relationship with kidney stone formation and antinutritive properties. Because table beet (Beta vulgaris ssp. vulgaris (garden beet group)) is considered a high oxalate food, breeding to decrease oxalic acid levels is an area of interest. In this study, a field trial was conducted over 2 years for 24 members of the Chenopodiaceae using two different planting dates to determine if variation exists for both total and soluble oxalic acid levels in roots and leaves. Total and soluble oxalic acid was extracted from homogenized root core and leaf tissue samples and a colorimetric enzymatic assay was used to determine total and soluble oxalic acid levels. Mean values ranged from 722 to 1909 mg/100 g leaf tissue and 553 to 1679 mg/100 g leaf tissue for total and soluble oxalate levels, respectively. Beet cultivar Forono and swiss chard (B. vulgaris ssp. vulgaris (leaf beet group)) cultivar Burpee's Fordhook Giant Chard produced the respective highest and lowest soluble and total oxalic acid leaf levels. Swiss chard cultivars produced 38% less total oxalate compared with table beet cultivars based on overall means. Root soluble oxalate values ranged from 103 to 171 mg/100 g root tissue and total values ranged from 95 to 142 mg/100 g root tissue. Significant variation for both total and soluble oxalic acid levels were detected, indicating progress could be made toward breeding for lower oxalic acid levels in table beet. However, gains in oxalic acid nutritional quality may be limited because it would take a substantial decrease in levels for table beet to be reclassified as a low oxalate food.
Mixed pixel removal in north Tamil Nadu region for accurate area measurement

In remote sensing, vegetation and water areas delineation from satellite image plays a vital role for urban and rural planning. Delineation of vegetation and water area is a challenging task due to mixed pixels and geometric distortion over the boundary region. Geometric distortion arises due to change in velocity and speed of satellite during image acquisition, and mixed pixels arises due to different surfaces in a particular area. Traditional methods apply classifier algorithms such as support vector machine, neural network, and fuzzy for vegetation and water area delineation. The traditional methods require more training dataset and consume more interpretation time for delineation. In this article, we propose transverse dyadic wavelet transform (TDyWT) to delineate vegetation and water area from Landsat 8 images. The TDyWT method enhances the boundary and curvature area of satellite image for accurate delineation. From the experimental results, the proposed TDyWT approach delineates the area of subclass for vegetation and water areas with 95% of accuracy with respect to the ground truth.
A Privacy Preserving and Efficient Randomness Routing in Adhoc Wireless Network

Now a day‟s automatic key establishment of any two devices in the network is placed an important role and generation of key is used for public key based algorithm. By using public key based algorithm we can automatically generated secret key any two devices in the network. So that by performing this process we can randomly generate secret key. In the ad hoc networks another concepts is routing from source node to destination node. The generation of routing process can be done by randomly and performing this process we can improve the efficiency in the routing. In this paper we are implementing random routing of secure data transmission protocol for generating routing and provide privacy of transferred message. By implementing this protocol we can provide random routing process for transferring message. Before transferring message the server will randomly generate routing for source node to destination node. After that the source node will send data to destination node. Before transferring message or data the source node will encrypt and send the cipher format data to destination node. The destination node will retrieve cipher format data and perform the decryption process. After completion of decryption process the destination node will get original message. By implementing those concepts we can improve the efficiency for generating routing and also provide security of transferring message.
Designing job ads to stimulate the decision to apply: a discrete choice experiment with business students

Abstract This study advances a differentiated understanding of job ad pre-hire effectiveness. It analyzes the utility values for varying levels of job ad attributes through a hierarchical Bayesian approach to conjoint analysis. This method allows not only to measure the relative importance of different attributes on the individual level but also to explore heterogeneity in evaluations influenced by educational level, international career orientation and time to graduation. The main results from the experiment show that for both undergraduate and graduate business students, salary and opportunity for advancement by far outweigh employer familiarity and the description of job-related tasks. The study contributes to the recruitment literature by assessing to what extent these four core attributes of a job ad drive job pursuit intentions. It also discusses the implications of attribute trade-offs for employer branding activities.
Tuning the Optical Properties of WO3 Films Exhibiting a Zigzag Columnar Microstructure

Tungsten oxide WO3 thin films are deposited by DC reactive magnetron sputtering. The Reactive Gas Pulsing Process (RGPP) associated with the GLancing Angle Deposition method (GLAD) are implemented to produce zigzag columnar structures. The oxygen injection time (tON time) and the pulsing period are kept constant. Three tilt angles α are used: 75, 80, and 85° and the number of zigzags N is progressively changed from N = 0.5, 1, 2, 4, 8 to 16. For each film, refractive index, extinction coefficient, and absorption coefficient are calculated from optical transmission spectra of the films measured in the visible region from wavelength values only. Absorption and extinction coefficients monotonously drop as the number of zigzags increases. Refractive indices are the lowest for the most grazing tilt angle α = 85°. The highest refractive index is nevertheless obtained for a number of zigzags close to four. This optimized optical property is directly correlated to changes of the microstructure, especially a porous architecture, which is favored for high tilt angles, and tunable as a function of the number of zigzags.
Interactions between Cryptosporidium, Enterocytozoon, Giardia and Intestinal Microbiota in Bactrian Camels on Qinghai-Tibet Plateau, China

Cryptosporidium spp. , Enterocytozoon bieneusi, and Giardia duodenalis are zoonotic pathogens commonly found in the intestinal tract of mammalian hosts including livestock and humans. The prevalence of these eukaryote microorganisms in domestic animals and their interaction with intestinal microbiota are not yet fully recognized. We analyzed the intestinal microbiota composition with metagenomics and functional characterization with Cluster of Orthologous (COG) in Bactrian camels, which were raised on Qinghai-Tibet Plateau, Northwest China. Thus, fecal samples were collected from the animals to determine the parasite infection and the profile of microbiota. Analysis of intestinal microbiota at genus level revealed important features of interaction between parasites infection and bacterial community. Coprococcus and Prevotella were more abundant while Akkermansia had lower relative abundance with E. bieneusi infection. Bacteria of Akkermansia, Lactococcus, Oxalobacter, Sphaerochaeta, Paludibacter, Fibrobacter, Anaerovibrio, Pseudomonas, Mogibacterium, Pseudoramibacter_Eubacterium, YRC22, Flexispira, SMB53, AF12, and Roseburia genera were found under-presented and Oscillospira genus over-presented when G. duodenalis infection was present. Meanwhile, Cryptosporidium spp. and E. bieneusi co-infected animals showed lower relative abundance of Allobaculum, Rikenella, Shuttleworthia, Epulopiscium, Bilophila, Dorea, Fibrobacter, and TG5. Results demonstrate important interaction between the intestinal parasites and microbiota, and provide informative link for understanding the co-evolution of zoonotic pathogens and bacteria in domestic animals.
Key Success Factors (KSFs) Underlying the Adoption of Social Media Marketing Technology

In developing world, most decisions by Micro, Small, and Medium Enterprises (MSMEs) to adopt social media marketing technology (SMMT) rely heavily on study findings from the Western World without much recourse to cross-context differences in structure and managerial capabilities. Thus, the lack of inquiries that provide complete guideline on the adoption of SMMT in developing economies hinders the development of integrated framework(s) that explains MSMEs’ successful adoption. The study used technology–organization–environment (T-O-E) framework as the theoretical basis to examine the critical factors that stimulate MSMEs adoption of SMMT in Nigeria. The adopted methodological choice was qualitative, involving interviews with 20 participants selected from the online directories via purposive and snowball sampling techniques. However, thematic analysis was the data treatment technique; and the study extended the T-O-E framework to provide an understanding into the dominant factors that specifically influence MSMEs’ adoption, without losing sight of vendors who would rely on the extended framework to get audiences continually satisfied.
The role of AI and machine learning in contemporary orthodontics

APOS Trends in Orthodontics • Volume 11 • Issue 1 • January-March 2021 | 75 is is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms. ©2021 Published by Scientific Scholar on behalf of APOS Trends in Orthodontics Review Article
Determination of antiradical activity and phenolic and flavonoid contents of extracts and fractions of jackfruit (Artocarpus heterophyllus Lamk) seeds

Jackfruit (Artocarpus heterophyllus Lamk) is a plant from the Moraceae family that is widespread in Indonesia. Empirically, jackfruit can be used to cure hypertension, diabetes, cancer, asthma, dermatosis, coughs, wounds, acne, and diarrhea. The bioactive compounds in jackfruit include phenolics and flavonoids, which function as natural antioxidants. This research investigated the antiradical activity of jackfruit seed extracts and fractions using DPPH (2-2-diphenyl-1-picrylhydrazyl) and examined the total phenolic and flavonoid contents which may be developed as functional medicines and foods. The jackfruit seed powder was extracted using the maceration method. Radical scavenging activities were measured using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) method. Total phenolic contents were determined using the Folin-Ciocalteu method. Meanwhile, the contents of flavonoids were determined using the aluminum chloride complex colorimetric method. The IC50 value of ethyl acetate fraction, methanol extract, n -hexane fraction, and water fraction was 5.435±0.064 μg/mL, 5.639±0.302 μg/mL, 7.201±0.475 μg/mL, and 9.134±0.2911 μg/mL, respectively. The phenolic and flavonoid contents of ethyl acetate fraction, methanol extract, n-hexane fraction, and water fraction were 49.597±1.589, 47.949±1.966, 41.214±4.354 and 35.504±0.913 g GAE/100 g of sample, respectively for phenolic, and 70.199±0.458, 65.228±0.615, 59.907±0.719, and 54.234±0.351 g of quercetin equivalent/100 g of sample, respectively of flavonoids, with a correlation value (R) to antiradical power of 0.4582 for phenolic and 0.5281 for flavonoids. The ethyl acetate fraction of jackfruit seeds can be further developed as an anti -radicals and functional food.