Miracle-dz/UniFAR-FTUs huggingface dataset:
Does an International Academic Environment Promote Study Abroad?

Although many studies on international student mobility have examined the impact of individual factors (e.g., gender, age, family background) on students’ decisions to study abroad, much less attention has been devoted to the role played by the institutional climate and characteristics of their home university. Using data from an Italian survey containing information on a large number of university students, this research investigated the extent to which a more international academic environment incentivizes students to participate in study abroad programs. A logit model was developed to estimate the effect that the degree of internationalization of one’s home university has on the probability that its students will study abroad, while controlling for several student-level factors. The empirical estimates indicate that this effect is significant, suggesting that being part of an international academic environment, where domestic students can interact more frequently with international students, helps motivate them to undertake study abroad. This result stresses the importance of engaging domestic students in the internationalization process of their universities.
Relationship between Physical Activity in Children and Perceived Support: A Case Studies

The purpose of this study was to ascertain the levels of physical activity, family and peer support for its performance and the relationships between activity and support according to the schoolchildren’s gender and year. A total of 81 boys and 73 girls from 3 rd to 6 th year of Primary Education aged between 8 and 13 ( M = 9.82, SD = 1.35) participated in the study. A behavioural epidemiological case study was designed and the instruments administered were: Parent Support Scale, Peer Support Scale and Physical Activity Questionnaire for Older Children. Boys performed more physical activity ( p < .001) and perceived more support than girls, and levels were also higher among 3 rd -year pupils compared to their 6 th -year counterparts. There was a correlation between the physical activity of girls and family support ( p < .001) and between peer support and physical activity according to all the cate-gories of the independent variables ( p < .05). In conclusion, boys and 3 rd -year pupils performed more physical activity and perceived more support than girls and 6 th -year pupils, respectively, and peer support was more related to physical activity than to family support.
Energy-Efficient SWIPT-Empowered D2D Mode Selection

While mode selection has been envisioned as the most cost-effective way to address the interference issue in Device-to-Device (D2D) communications, existing works have been largely conducted without consideration of the energy depletion of devices. In this paper we investigate simultaneous wireless information and power transfer (SWIPT) empowered mode selection based on stochastic geometry. As a mean of solving it, system energy efficiency is formulated by determining the closed-form ergodic energy-harvested and ergodic capacity of D2D and cellular users in reuse, dedicated, and cellular communication modes with the time switching and power splitting architectures of SWIPT. We then leverage the derived results, along with the energy efficiency to design an energy-efficient mode selection mechanism. Our simulation results show that the developed mechanism is able to select the best mode for D2D communication with better energy efficiency, especially in an ultra-dense cellular network as compared with a state-of-the-art mode selection approach.
Transapical Access for Percutaneous Mitral Paravalvular Leak Repair

ABSTRACT Paravalvular leak (PVL) is a rare complication of mitral valve surgery that may result in serious clinical sequelae—including heart failure or hemolytic anemia. Treatment of these defects may be achieved with either percutaneous or open surgical repair, but recently, percutaneous approaches have become more popular due to their lower procedural risk. Percutaneous repair of mitral PVL can be performed from various access sites, including the femoral artery, femoral vein (via transeptal), or via transapical left ventricular access. The transapical approach provides simple, direct access to the entire mitral annulus—including areas that may not be reached from either transseptal or retrograde approaches. In addition, the short, in-line course from the apex to the mitral annulus simplifies delivery of large-caliber interventional equipment and limits procedural/fluoroscopy time. These advantages are counterbalanced by risk of complications, including left anterior descending coronary artery puncture and bleeding following sheath removal. This article reviews the literature addressing percutaneous PVL repair and the use of transapical access in the catheterization laboratory. We then describe our approach to percutaneous transapical mitral PVL repair.
Evaluation of gap-filling methods for Landsat 7 ETM+ SLC-off image for LULC classification in a heterogeneous landscape of West Africa

ABSTRACT The Landsat mission which has existed over five decades has remained at the forefront of providing consistent moderate spatial and temporal resolution optical images of the earth. The failure of the scan line corrector (SLC) on board the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) in May 2003 has permanently resulted in data gaps on each Landsat 7 scene. Due to the obvious negative impacts on the image usability, a number of methods have been developed to fill the no-data areas in the image. This study assessed the performance of four Landsat 7 ETM+ SLC-off gap-filling methods in a highly heterogeneous landscape of West Africa for two different seasons (dry and rainy). The methods considered are: (1) Weighted Linear Regression (WLR) integrated with Laplacian Prior Regularization Method (LPRM), (2) Localised Linear Histogram Matching (LLHM), (3) Neighbourhood Similar Pixel Interpolator (NSPI) and (4) Geostatistical Neighbourhood Similar Pixel Interpolator (GNSPI). All the images used were Landsat 7 ETM+ SLC-off images, temporally close and from the same season for each set of time step. Visual comparison, mean, and standard deviations of the histograms of all bands of only the filled areas were used to assess the results. Additionally, overall accuracy (OA), kappa coefficient (κ), and balanced accuracy (BA) per class were used to evaluate a land use/cover (LULC) classification based on the gap-filled images. Visually, all the four methods were able to completely fill the gaps in the Landsat 7 ETM+ SLC-off image. They all look similar and spatially continuous with no anomalies or artefacts on them. The histograms from each band for only the filled areas for all the four methods also gave similar means and standard deviations in most cases. All the four gap-filling methods provided satisfactory results (OA >96% and κ> 0.937 in all methods for images in the dry season and OA >93% and κ> 0.877 for the image in the rainy season) in the land cover classification considering the complexity of the study area. But the GNSPI was superiority in all cases with the highest OA of 97.1% and κ of 0.947 in the dry season and OA of 94.6% and κ of 0.899 in the rainy season. This implies that the GNSPI is more robust in gap filling of Landsat 7 ETM+ SLC-off images than the other three methods in a heterogeneous landscape of West Africa regardless of the season. This study suggests that gap filling of Landsat 7 ETM+ SLC-off images will help to increase the number of Landsat images needed to build time-series data for a data-scarce region such as West Africa.
Mental Health Care for Rural Veterans: A Systematic Literature Review, Descriptive Analysis, and Future Directions

This manuscript examines the state-of-the-state of mental health care for rural veterans through a systematic literature review. Descriptive analysis is presented regarding the quantity and quality of the current literature base including the number of articles by year published, definitions of rurality, rural subpopulations of focus, and treatment modalities. Current findings are discussed including delivery of intervention services via telehealth, emphasis on the study of specific mental health disorders, and inconsistency regarding reporting of rural definition. Areas for future research including suicide/suicide prevention, nonprescription opioid use, and web-based/online treatment modalities are discussed. Finally, a call to action is made for rural practitioners to collaborate with specialized veteran researchers and/or universities to increase targeted, prospective research.
Smart Road Accident Detection using IoT Application

Road accidents rates are very high nowadays, especially two wheelers. Timely medical aid can help in saving lives. This system aims to alert the nearby medical center about the accident to provide immediate medical aid. The proposed framework automatically learns feature representation from the spatiotemporal features of pixel intensity. We consider the accident of the vehicles as an unusual incident. The possibility of an accident is determined based on the artificial intelligence approach and alarm is generated with preventive measures.
E-government adoption: a systematic review in the context of developing nations

Objective of the Study: This paper reviews the studies of e-government adoption in developing nations to come up with the factors that influence e-government adoption.Methodology/Approach: Secondary data was collected from the findings of other researches. Systematically, two reputable databases; Scopus and IEEE Explore were searched to retrieve studies of e-government adoption in developing nations.Originality/Relevance: Adoption has a key role in the successful implementation of e-government initiatives in the context of developing nations. However, minimal research has been carried out to explore determinant factors of e-government services adoption among developing nations’ citizens.Main Results: The theories and models applied in previous researches were identified. Moreover, the frequency of factors investigated in these studies was identified. The results showed that there are many studies investigated trust as an important factor of e-government adoption in developing nations, while there are other critical challenges such as security and awareness got less attention.Theoretical/Methodological Contribution: This study results may open for future research based on the gap in theories applied and the factors investigated in the context of developing nations.
Conan: A Practical Real-Time APT Detection System With High Accuracy and Efficiency

Advanced Persistent Threat (APT) attacks have caused serious security threats and financial losses worldwide. Various real-time detection mechanisms that combine context information and provenance graphs have been proposed to defend against APT attacks. However, existing real-time APT detection mechanisms suffer from accuracy and efficiency issues due to inaccurate detection models and the growing size of provenance graphs. To address the accuracy issue, we propose a novel and accurate APT detection model that removes unnecessary phases and focuses on the remaining ones with improved definitions. To address the efficiency issue, we propose a state-based framework in which events are consumed as streams and each entity is represented in an FSA-like structure without storing historic data. Additionally, we reconstruct attack scenarios by storing just one in a thousand events in a database. Finally, we implement our design, called Conan, on Windows and conduct comprehensive experiments under real-world scenarios to show that Conan can accurately and efficiently detect all attacks within our evaluation. The memory usage and CPU efficiency of Conan remain constant over time (1-10 MB of memory and hundreds of times faster than data generation), making Conan a practical design for detecting both known and unknown APT attacks in real-world scenarios.
The economy of anatomy: Discovering the turbulent homogeneous isotropic functional core organisation of the human brain

Using large-scale neuroimaging data from 1003 healthy participants, we demonstrate empirically and theoretically that human brain dynamics is organised around a homogeneous isotropic functional core. More importantly, this homogeneous isotropic functional core follows a turbulentlike power scaling law for functional correlations in a broad spatial range suggestive of a cascade of information processing. The underlying anatomy of the brain is expensive in terms of material and metabolic costs and it has been suggested that the trade-offs between wiring cost and topological value change over many timescales but exactly how is not known (1). Here, we demonstrate how the economy of anatomy has evolved a homogeneous isotropic functional core by using wholebrain modelling with the exponential Markov-Kennedy distance rule of anatomical connections as the cost-of-wiring principle demonstrated in the massive retrograde tract tracing studies in nonhuman primates by Markov, Kennedy and colleagues (2). Overall, our results reveal a novel way of analysing and modelling whole-brain dynamics that establishes a fundamental basic principle of brain organisation. Introduction Over the last decades, a tremendous amount of research has demonstrated that the core of the functional organisation of the human brain is highly structured in the low dimensional manifold of the so-called resting state networks (3). These networks emerge and are shaped by the underlying anatomy (4). Recent detailed and exhaustive anatomical research in non-human primates has shown that the anatomical structural core appears to be radically simple, following a homogeneous
Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells

Microfluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may offer an efficient way to analyze a large number of images collected from microfluidic experiments. Here, we compare three deep learning architectures to classify microfluidic time-lapsed images of dividing yeast cells into categories that represent different stages in the yeast replicative aging process. We found that convolutional neural networks outperformed capsule networks in terms of accuracy, precision, and recall. The capsule networks had the most robust performance at detecting one specific category of cell images. An ensemble of three best-fitted single-architecture models achieves the highest overall accuracy, precision, and recall due to complementary performances. In addition, extending classification classes and augmentation of the training dataset can improve the predictions of the biological categories in our study. This work lays a useful framework for sophisticated deep-learning processing of microfluidics-based assays of yeast replicative aging.
Chlorophyll fluorescence as a tool to select salinity-tolerant cowpea genotypes

The use of saline water reduces the growth and productivity of crops, so the need for techniques that make possible the use of this resource such as the use of salinity tolerant genotypes and efficient selection methods are of great importance. Thus, this study aimed to evaluate the tolerance of cowpea (Vigna unguiculata L. Walp.) genotypes to salt stress, through the chlorophyll fluorescence analysis. The experiment was conducted in a protected environment at the Federal University of Campina Grande, Paraíba, Brazil, using a completely randomized design in a 2 x 10 factorial arrangement, with three replications, consisting of two levels of irrigation water salinity (0.6 and 5.1 dS m-1) and ten cowpea genotypes: (G1: MNCO1-649F-2-1, G2: MNCO3-736F-2, G3: PINGO DE OURO-1-2, G4: BRS GURGUÉIA, G5: BRS MARATAOÃ, G6: MNCO2-676F-3, G7: MNCO2-683F-1, G8: MNCO3-737F-5-4, G9: MNCO3-737F-5-9, and G10: BRS TUMUCUMAQUE). The stem length, stem diameter, SPAD index, and chlorophyll fluorescence transients were evaluated. The G2 and G4 genotypes had the lowest reductions in the growth, stem diameter, initial fluorescence, and primary and maximum photochemical efficiency of PSII, proving to be tolerant to salinity. Chlorophyll fluorescence is a tool that can be used in the selection of salinity-tolerant cowpea genotypes.
Archimedean spiral based artificial bee colony algorithm

Abstract Swarm intelligence techniques are popular meta-heuristics which are developed to solve very complex problems that are not decipherable using traditional optimization approaches. In this class, artificial bee colony is latest and efficient algorithm that is able to solve complex problems. During solution search process it perform exploration in the early iterations, while should utilize the identified area in later stage. By analyzing the ABC, it is clear that the position update process depends on the difference of two random vector multiplied by a random component. So the step size of ABC is exceedingly hinge on these arbitrary components that leads to skipping of good solutions. For that reason, in this paper, Archimedean Spiral inspired Local Search (ASLS) algorithm is united with ABC and the new algorithm is labelled as Archimedean Spiral based ABC (ASABC) algorithm. In ASABC, in the current swarm only good solutions get chance to exploit the nearby search space using ASLS local search approach in every iteration. To demonstrate the performance of ASABC, experiment performed over 10 recognized benchmarks and compared with basic ABC, Best-so-far ABC and Modified ABC. Outcomes prove that ASABC algorithm may be considered as a viable algorithm for optimization problems.
Mediating role of learning capability in the relationship between authentic leadership and business performance

The aim of the study is to analyze the Mediating Role of Learning Capability in the Relationship between Authentic Leadership and Business Performance. This study will contribute to the explanation of the question, “What are the reasons inter-firm differences for superior performance? ” and it will guide the firms on how they create sustainable competitive advantage and how they generate above-average profits. In this context, medium and large-sized enterprises involved the finance business and located the Marmara Region are included in the study. Data obtained from 252 employees in different grades have analyzed in order to test the hypotheses developed. As a result of the analysis, a significant and direct relationship was found between authentic leadership and business performance and also a significant and direct relationship was found between learning capability and firm performance. However, it was understood that learning capability has a mediating role in the relationship between authentic leadership and business performance
Development of Rail Precision Cleaning Vehicle for Modern Tramway

Supported by the Zhuhai Collaborative Innovation Project, we develop a Road-Rail Precision Cleaning Vehicle for the rail of the first modern tramway based on the Tram Wave system in China, which has been built in Zhuhai City. Development scheme of overall mechanical structure, electrical control and condition monitoring system and man-machine interface system has been realized. We also designed a target recognition algorithm based on OpenCV to control actuators like solenoid valve, electromagnetic clutch and air pressure regulating valve and so on.
Comprehending surprising sentences: sensitivity of post-N400 positivities to contextual congruity and semantic relatedness

ABSTRACT Any proposal for predictive language comprehension must address receipt of less expected information. While a relationship between the N400 and sentence predictability is well established, a clear picture is still emerging of the link between post-N400 positivities (PNPs) and processing of semantically unexpected words, as well as any relation to other not-specifically-linguistic and/or syntactic late positivities. The current study employs event-related brain potentials (ERPs) to congruent and anomalous words to assess the impacts of semantic relatedness and contextual plausibility on processing unpredictable sentences. We observe PNPs with different scalp topographies to plausible unexpected words unrelated to predictable continuations (anterior PNP) and to anomalous words, regardless of, but delayed by, relatedness (posterior PNP). We offer functional explanations that reconcile inconsistencies with reported PNP findings and place added constraints on the anterior PNP’s proposed link to inhibitory processing. We also suggest a testable general cognitive account for the posterior PNP.
Automatic Web Service Composition for SaaS Business Intelligence

: An atomic web service is not enough to meet complex user needs. This need can only be fulfilled by composing web services that perform operations automatically. The result of web service composition is a workflow. To the best knowledge of the authors, in the currently available methods existing a workflow output is not used to replace a workflow input. Also, web service backup is not provided. This can cause the transaction to stop because the web service that is being accessed cannot be invoked. Therefore, the authors propose a method that can utilize a workflow output to replace an input of another workflow and provide a web service backup. The supporting techniques used are tf-idf weighting and cosine similarity. The proposed method was applied to compose web services in a SaaS Business Intelligence application. The modules in Business intelligence are run using workflows that are composed based on the similarity between input parameters and output parameters required by the user with the web service metadata provided. The experimental results show that the proposed method can successfully produce workflows whose input can be replaced by other workflows and provide appropriate web service backup.
Preliminary Characterization of Exopolysaccharides Produced by Abortiporus biennis in Submerged Fermentation

Abortiporus biennis is a rare edible and medicinal mushroom that is valuable in traditional Chinese medicine. Exopolysaccharides (EPSs) as valuable metabolites have been harvested from fermentation broths of A. biennis. However, studies on preliminary characterisations and bioactivities of A. biennis EPSs are lacking. In this study, therefore, the production, characteristics and bioactivity of EPSs produced by A. biennis in submerged fermentation were investigated. An EPS-1 was isolated and purified from the culture broth of A. biennis using a Sepharose CL-6B column chromatography. The molecular weight, monosaccharide composition, structural characteristics, chain conformation and thermal property of EPS-1 were determined by gel-filtration chromatography, gas chromatography-mass spectroscopy, Fourier-transform infrared spectroscopy, Congo red test and thermogravimetric analysis, respectively. The in vitro antioxidant and immunoregulation activities of the EPS-1 were evaluated by adopting DPPH and hydroxyl radical-scavenging assays, and analysing NO production in macrophage RAW 264.7 cells, respectively. Results showed that the culture medium containing 30 g/L lactose and 3.0 g/L tryptone had the most suitable carbon and nitrogen sources, respectively, for EPS production in A. biennis. The maximum EPS production (18.29 g/L) was obtained after 6 days of cultivation in a 5 L stirred-tank reactor under the most suitable culture medium. After purification, EPS-1 with 85.4% yield and 96.1% carbohydrate content was obtained from the culture broth of A. biennis. EPS-1 was characterised as a neutral heteropolysaccharide with a molecular weight of 22.07 kDa and is composed of glucose, mannose and galactose at a molar ratio of 3.3:2.0:1.0. EPS-1 with a relatively high thermal stability existed as a random coil conformation in the aqueous medium and possessed prominent radical-scavenging ability and macrophage stimulation activity in vitro. Therefore, EPS-1 could be explored as a functional ingredient with potential applications in food, medical and cosmetic industries.