Miracle-dz/UniFAR-FTUs huggingface dataset:
Entrepreneurial Marketing: Conceptual Systematization in English and Portuguese Languages

This study aims to provide a meaningful contribution to the theoretical field of entrepreneurial marketing (EM), by listing a conceptual systematization of the EM field, considering its evolution in recent decades. An extensive search has been carried out in the database Web of Science with the term "Entrepr * Marketing". The selection included the years from 1900 to 2016, in English and Portuguese and the research was conducted on 12 December 2016. It was compiled a bibliography of all publications related to the research (49 articles), in this database. The systematization of concepts presented can be used by managers in defining strategy and planning policies of EM. Our findings detail three clusters in EM research, which help in contextualising the literature review: (1) cluster 1 - entrepreneurial marketing process; (2) cluster 2 - international new venture; (3) cluster 3 - entrepreneurial orientation. This work contributes to a better understanding of the concepts, their thread in the theoretical field, its evolution in recent decades, listing a conceptual systematization. This study seeks to convey a large theoretical basis that may serve as the point of departure for future studies.
iABACUS: A Wi-Fi-Based Automatic Bus Passenger Counting System

Since the early stages of the Internet-of-Things (IoT), one of the application scenarios that have been affected the most by this new paradigm is mobility. Smart Cities have greatly benefited from the awareness of some people’s habits to develop efficient mobility services. In particular, knowing how people use public transportation services and move throughout urban infrastructure is crucial in several areas, among which the most prominent are tourism and transportation. Indeed, especially for Public Transportation Companies (PTCs), long- and short-term planning of the transit network requires having a thorough knowledge of the flows of passengers in and out vehicles. Thanks to the ubiquitous presence of Internet connections, this knowledge can be easily enabled by sensors deployed on board of public transport vehicles. In this paper, a Wi-Fi-based Automatic Bus pAssenger CoUnting System, named iABACUS, is presented. The objective of iABACUS is to observe and analyze urban mobility by tracking passengers throughout their journey on public transportation vehicles, without the need for them to take any action. Test results proves that iABACUS efficiently detects the number of devices with an active Wi-Fi interface, with an accuracy of 100% in the static case and almost 94% in the dynamic case. In the latter case, there is a random error that only appears when two bus stops are very close to each other.
Analysis on facilities and intensity of utilization of biology laboratories in four state schools in medan

This research aims for analyze the completeness of laboratory facilities and the intensity of the utilization of biology laboratories in four State schools in Medan. This type of research was descriptive with quantitative result. Samples were taken at four schools by Cluster Sampling, based on the criteria of SMA Negeri in Medan, namely MAN 1, MAN 2, SMA Negeri 3, and SMA Negeri 4 Medan and their biology teachers. Using data collection techniques consisting of observation sheets to determine the level of availability of facilities and intensity of utilization of laboratory data obtained from the number of practical done and not done as well as using descriptive analysis techniques by calculating the average data value. The results in this study are complete laboratory facilities in the biological category are in good category (77.34) and the intensity of utilization are in high category (82.14).
Simulation and organizational studies in Japan

: Since the 1990s, simulation and organizational studies have been conducted in Japan. In this paper, we review the simulation and organizational studies in Japan, including the relationships between researchers. The global trend is to cite the results of simulation studies as metaphors. By contrast, in Japan, there are unique research groups which critically examine the existing models, perform simulations, and further test them against survey data. The lessons they learned are: (a) The animation of the simulation results stir the imagination of researchers and business persons. However, (b) if the phenomena indicated by simulation and the reality of parameter values are not supported by the survey data, the implications derived from simulation are no more than a delusion.
The arthropod complex associated with Ips typograpfius (L.) (Coleoptera, Scolytidae): species composition, phenology, and impact on bark beetle productivity

The biology of forest arthropods associated with the bark beetle lps typographus and the impact of these associates on bark beetle productivity were investigated in the field. Arthropods were allowed access to spruce bolts for different lengths of time, 0, 1, 4, and 8 weeks, following onset of attack by lps typograplus, after which time the bolts were moved to a nonforested area and caged. Arthropods were collected as they emerged from the bolts in autumn and the following spring. At least 32 arthropod species were collected, of which 17 are known to feed on bark beetle brood. The longer the exposure period the more bark beetle enemies and the fewer lps typographus emerged. Natural enemies were estimated to have reduced bark beetle productivity by 83%. Arrival and emergence patterns and relative impact on bark beetle productivity of different species are discussed.
Taylor least squares reconstruction technique for material point methods

The material point method (MPM) is an effective computational tool for simulating problems involving large deformations. However, its direct mapping of the material-point data to the background grid frequently leads to severe inaccuracies. The standard function reconstruction techniques can considerably decrease these errors, but do not always guarantee the conservation of the total mass and linear momentum as the MPM algorithm does. In this paper, we introduce a novel technique, called Taylor Least Squares (TLS), which combines the Least Squares approximation with Taylor basis functions to reconstruct functions from scattered data. Within each element, the TLS technique approximates quantities of interest, such as stress and density, and when used with a suitable quadrature rule, it conserves the total mass and linear momentum after mapping the material-point information to the grid. The numerical and physical properties of the reconstruction technique are first illustrated on one- and two-dimensional functions. Then the TLS technique is tested as part of MPM, Dual Domain Material Point Method (DDMPM), and B-spline MPM (BSMPM) on a one-dimensional problem experiencing small and large deformations. The obtained results show that applying the TLS approximation significantly improves the accuracy of the considered versions of the material point method, while preserving the physical properties of the standard MPM.
Borealization of the New England – Acadian Forest: a review of the evidence

The New England – Acadian Forest (NEAF) is an ecoregion spanning 24 million hectares of the northeastern United States and eastern Canada. The region is characterized as a transitional forest naturally composed of both boreal and temperate species. The term “borealization” is sometimes used to describe various processes driving the NEAF toward a more boreal character at the expense of its temperate forest species and ecological communities. That the NEAF has undergone significant landscape-scale change in the last four centuries since European settlement is well understood. The purpose of this manuscript is to review the literature on the forest composition and dynamics of this region to investigate whether past, current, and (or) predicted future processes of change are indeed driving the forest toward a more boreal character. We examine studies on the historical forest composition and impacts of past and current land-use practices, as well as indirect anthropogenic changes that are predicted to influence future forest compositions of the NEAF. We review over 100 peer-reviewed scientific journal articles and government reports related to this issue. We find ample evidence to suggest that, at the landscape scale, there has been widespread replacement of temperate tree species by boreal species since European settlement. Five primary drivers have facilitated borealization across the NEAF: logging and high-grading, natural reforestation of abandoned farmland, industrial clearcutting, anthropogenic fire, and boreal conifer plantations. Furthermore, the borealization of the NEAF has continued to occur in direct contrast to the predicted impacts of climate change. We encourage future scholarship to tackle these aspects of borealization in the NEAF, including its social, economic, and ecological implications.
Who is a ‘Salafi’? Salafism and the Politics of Labelling in the UK

In this article, I trace the history of the label ‘Salafi’ in the UK to show that there has been a marked change in its desirability and use within some ‘Salafi’ circles. Drawing on interviews conducted with members of various streams of the Salafi movement as well as content analysis of the websites, social media pages and audio-visual content of ‘Salafi’ groups, I argue that while the oftentimes unqualified association between Salafism and terrorism in public discourse may have had a negative impact on the label’s desirability, intra-Salafi politics shed more light on why some ‘Salafis’ adopt the label while others do not.
A Spectral Data Compression (SDCOMP) Radiative Transfer Model for High-Spectral-Resolution Radiation Simulations

With the increasing use of satellite and ground-based high-spectral-resolution (HSR) measurements for weather and climate applications, accurate and efficient radiative transfer (RT) models have become essential for accurate atmospheric retrievals, for instrument calibration, and to provide benchmark RT solutions. This study develops a spectral data compression (SDCOMP) RT model to simulate HSR radiances in both solar and infrared spectral regions. The SDCOMP approach “compresses” the spectral data in the optical property and radiance domains, utilizing principal component analysis (PCA) twice to alleviate the computational burden. First, an optical-property-based PCA is performed for a given atmospheric scenario (atmospheric, trace gas, and aerosol profiles) to simulate relatively low-spectral-resolution radiances at a small number of representative wavelengths. Second, by using precalculated principal components from an accurate radiance dataset computed for a large number of atmospheric scenarios, a radiance-based PCA is carried out to extend the low-spectral-resolution results to desired HSR results at all wavelengths. This procedure ensures both that individual monochromatic RT calculations are efficiently performed and that the number of such computations is optimized. SDCOMP is approximately three orders of magnitude faster than numerically exact RT calculations. The resulting monochromatic radiance has relative errors less than 0.2% in the solar region and brightness temperature differences less than 0.1 K for over 95% of the cases in the infrared region. The efficiency and accuracy of SDCOMP not only make it useful for analysis of HSR measurements, but also hint at the potential for utilizing this model to perform RT simulations in mesoscale numerical weather and general circulation models.
An Improved Algorithm for Detecting Pneumonia Based on YOLOv3

Pneumonia is a disease that develops rapidly and seriously threatens the survival and health of human beings. At present, the computer-aided diagnosis (CAD) of pneumonia is mostly based on binary classification algorithms that cannot provide doctors with location information. To solve this problem, this study proposes an end-to-end highly efficient algorithm for the detection of pneumonia based on a convolutional neural network—Pneumonia Yolo (PYolo). This algorithm is an improved version of the Yolov3 algorithm for X-ray image data of the lungs. Dilated convolution and an attention mechanism are used to improve the detection results of pneumonia lesions. In addition, double K-means is used to generate an anchor box to improve the localization accuracy. The algorithm obtained 46.84 mean average precision (mAP) on the X-ray image dataset provided by the Radiological Society of North America (RSNA), surpassing other detection algorithms. Thus, this study proposes an improved algorithm that can provide doctors with location information on lesions for the detection of pneumonia.
Rôle du gène foraging dans l’évolution du comportement alimentaire de noctuelles foreuses de céréales

En reponse aux changements importants d’usage des terres via l’agriculture, certaines especes d’insectes phytophages se sont adaptees en exploitant les plantes cultivees, provoquant ainsi de lourds dommages agro-economiques. L’espece Sesamia nonagrioides (Lepidoptera : noctuidae), un representant important des especes ravageuses du mais, presente deux populations distinctes : une population ravageuse trouvee dans les champs de mais, et une population non-nuisible trouvee exclusivement sur herbacees sauvages. Ces preferences ecologiques specifiques suggerent l’evolution de strategies alimentaires distinctes, conferant aux insectes une fitness specifique selon la plante-hote consommee. Puisque le gene foraging (for) est un gene candidat pour l’evolution des strategies alimentaires, celui-ci pourrait etre implique dans l’adaptation a des ressources specifiques. Sur la base d’une approche integrative incluant les analyses de conservation de sequence, du polymorphisme, des frequences alleliques, de l'activite recherche alimentaire, et des traits d'histoire de vie, nous avons compare des larves issues des populations sauvages et ravageuses. L’ensemble des resultats obtenus ont demontre la conservation de la fonction du gene for chez S. Nonagrioides. L’etablissement d’un lien causal entre les genotypes et les phenotypes associes a l’ecologie des populations illustre comment un polymorphisme nucleotidique (SNP) peut moduler une fonction comportementale majeure. L'evolution du gene for pourrait ainsi etre impliquee dans l'adaptation d’un insecte phytophage aux ressources cultivees.
Insect Viruses and Pest Management

BASIC PRINCIPLES. Rationale for the Use of Microbial Pesticides. Characteristics of Insect Pathogenic Viruses. Assessment of Biological Activity. The Ecology of Baculoviruses in Insect Hosts. Control Strategies. Virus Production. Formulation. Spray Application of Baculoviruses. Conduct and Recording of Field Control Trials. Future Developments. WORLD SURVEY. A World Survey of Virus Control of Insect Pests. Western Europe. Eastern Europe and the Former Soviet Union. Indian Subcontinent. South--east Asia and the Western Pacific. Peoplea s Republic of China. Japan. Africa, the Near and Middle East. Australasia. North America. Central America and the Caribbean. South America. PRACTICAL TECHNIQUES. General Laboratory Practice. Working with the Host. Working with the Virus. Enumeration of Virus. Cell Culture. Mass Production, Product Formulation and Quality Control. Spray Application. Registration Requirements. ENVIRONMENTAL FACTORS INFLUENCING VIRAL SURVIVAL. Solar Radiation, with Emphasis on the Ultraviolet. Plant Surfaces. Glossary. Further Reading. Index.
Weakening trend of the southwest monsoon current through peninsular India from 1950 to the present

The southwest monsoon current in the form of a crossequatorial low level jetstream (LLJ) with its core at an altitude of about 1.5 km and core wind speeds of 80 – 100 km/h is a conduit carrying moisture generated over south Indian Ocean and the Arabian Sea that supports the June to September monsoon rainfall in India. During spells of active (strong) monsoon, the core of the LLJ passes through peninsular India between latitudes 12.5 and 17.5 N. In break (weak) monsoon spells, LLJ bypasses India and flows to its south through the latitude belt 2.5 to 7.5 N. The mean June to September monsoon flow through India from surface to 1.5 km altitude between latitudes 12.5 and 17.5 N, had a si gnificant decreasing trend and that between 2.5 and 7.5 N a significant increasing trend during the recent half century, i.e. 1950 to the present. The dur ation of break (weak) monsoon spells in a monsoon season has increased by about 30% during this period. These changes have resulted in the number of days during the monsoon season 1 June to 30 September with daily average rainfall less than 8 mm/day increasing and days with rainfall more than 12 mm/day decreasing through 45.4% and 78.1% respectively, during the last 53 years. These are alarming findings for a cou ntry whose food production and economy depend heavily on monsoon rainfall.
A machine learning method for selection of genetic variants to increase prediction accuracy of type 2 diabetes mellitus using sequencing data

Type 2 diabetes mellitus (T2DM) affects millions of people through its life‐altering complications. Worldwide, 3.4 million people die of diabetes annually. Studying the effect of genetic polymorphism on T2DM has been plagued by the available sample size. A 2016 Nature Reviews article summarized that the accuracy of predicting future type 2 diabetes from genetic polymorphism is very low at the population level. Innumerable associations between genes, environmental factors, and type 2 diabetes remain to be discovered. This research presents a method to identify subtle effects of genetic variants using whole genome sequencing data and improve prediction accuracy of T2DM at the population level. To achieve this, a new feature selection procedure and a classifier are proposed. The method involves (a) first applying sparse principal component analysis to genotype data to obtain orthogonal features; (b) building a new classifier using single nucleotide polymorphism (SNP)‐specific regularization parameters to reduce the false positive rate of feature selection; (c) verifying feature relevance through penalized logistic regression. After application to a dataset containing 625 597 SNPs and 23 environmental variables from each of 3326 humans, the method identified 271 genetic variants with subtle effects on T2DM prediction. These variants led to greatly improved prediction accuracy for new patients at the population level. The proposed method also has the advantage of computational efficiency, over 15 times faster than random forest and extreme gradient boosting (XGBoost) classifiers, and thus provides a promising tool for large‐scale genome‐wide association studies.
Deep-learning-based information mining from ocean remote-sensing imagery

Abstract With the continuous development of space and sensor technologies during the last 40 years, ocean remote sensing has entered into the big-data era with typical five-V (volume, variety, value, velocity and veracity) characteristics. Ocean remote-sensing data archives reach several tens of petabytes and massive satellite data are acquired worldwide daily. To precisely, efficiently and intelligently mine the useful information submerged in such ocean remote-sensing data sets is a big challenge. Deep learning—a powerful technology recently emerging in the machine-learning field—has demonstrated its more significant superiority over traditional physical- or statistical-based algorithms for image-information extraction in many industrial-field applications and starts to draw interest in ocean remote-sensing applications. In this review paper, we first systematically reviewed two deep-learning frameworks that carry out ocean remote-sensing-image classifications and then presented eight typical applications in ocean internal-wave/eddy/oil-spill/coastal-inundation/sea-ice/green-algae/ship/coral-reef mapping from different types of ocean remote-sensing imagery to show how effective these deep-learning frameworks are. Researchers can also readily modify these existing frameworks for information mining of other kinds of remote-sensing imagery.
Analgesic, anti-inflammatory and antipyretic properties of Acacia suma stem bark

Acacia suma (Fabaceae) is a medium sized erect tree found in the greater part of India. Present study was carried out for evaluation of ethanolic extract of stem bark of Acacia suma (EEAS) at 200 and 400 mg/kg, p.o. for analgesic, anti-inflammatory and antipyretic activity. EEAS was screened for analgesic activity by writhing, tail flick, tail immersion and hot plate method in mice. The anti-inflammatory activity by acute carrageenan induced paw oedema and chronic Freund’s adjuvant arthritis models in rats. The antipyretic activity was evaluated using Brewer’s yeast induced pyrexia in rabbits. Acute toxicity in mice was found to be higher than 2000 mg/kg. , p.o. Analgesic activity revealed that test dose of 400 mg/kg, p.o. , had significant activity in various tested models. Anti-inflammatory studies at 200 and 400 mg/kg. , p.o. , of extract showed significant activity (P<0.01). The extract showed significant (P<0.01) effect on yeast-induced fever in rabbits in dose dependant manner. Preliminary phytochemical tests revealed presence of carbohydrates, tannins, alkaloids, saponins and phenolic compounds in the ethanol extract of A. suma bark. The present study therefore provides scientific base for its use in the folklore remedies as an analgesic, anti-inflammatory and antipyretic properties of natural origin.