Miracle-dz/UniFAR-FTUs huggingface dataset:
URBANIZATION AND DYNAMICS OF ECONOMIC GROWTH (THE CASE OF NUTS 2 REGIONS OF EUROPEAN UNION)

Regional development policies based on local potential triggers a shift in the economic structure of territories. Exogenous and endogenous factors determine potential of regional development and it is necessary to use different indicators and methods to its evaluation. For the paper purpose, it is required to define metropolitan and peripheral functions as well as urban areas in the form of geographic models, depicting their spatial distribution in the European Union (EU). Nowadays, regions are increasingly becoming the drivers of the economy. All regions possess development opportunities – however, use these options enough, and hence the competitiveness of regions must be efficient enough. The paper focuses on dividing the EU NUTS 2 regions based on geographic models of the European economy into efficient and inefficient ones and identifying an optimal benchmark for inefficient regions as a strategy for enhancing their economic structure to measure regional efforts and progress.
A Bayesian Model of AFLP Marker Evolution and Phylogenetic Inference

Amplified Fragment Length Polymorphism (AFLP) markers are formed by selective amplification of DNA fragments from digested total genomic DNA. The technique is popular because it is a relatively inexpensive way to produce large numbers of reproducible genetic markers. In this paper, we describe a Bayesian approach to modeling AFLP marker evolution by nucleotide substitution and an MCMC approach to estimate phylogeny from AFLP marker data. We demonstrate the method on species in Carex section Ovales, a group of sedges common in North America. We compare the results of our analysis with a clustering method based on Nei and Li's restriction-site distance and a two-state Bayesian analysis using MrBayes.
RCAnalyzer: visual analytics of rare categories in dynamic networks

A dynamic network refers to a graph structure whose nodes and/or links dynamically change over time. Existing visualization and analysis techniques focus mainly on summarizing and revealing the primary evolution patterns of the network structure. Little work focuses on detecting anomalous changing patterns in the dynamic network, the rare occurrence of which could damage the development of the entire structure. In this study, we introduce the first visual analysis system RCAnalyzer designed for detecting rare changes of sub-structures in a dynamic network. The proposed system employs a rare category detection algorithm to identify anomalous changing structures and visualize them in the context to help oracles examine the analysis results and label the data. In particular, a novel visualization is introduced, which represents the snapshots of a dynamic network in a series of connected triangular matrices. Hierarchical clustering and optimal tree cut are performed on each matrix to illustrate the detected rare change of nodes and links in the context of their surrounding structures. We evaluate our technique via a case study and a user study. The evaluation results verify the effectiveness of our system.
Construction of a new Lyapunov function for a dissipative gyroscopic system using the residual energy function

In a dissipative gyroscopic system with four degrees of freedom and tensorial variables in contravariant (right upper index) and covariant (right lower index) forms, a Lagrangian-dissipative model, i.e., { L, D }-model, is obtained using second-order linear differential equations. The generalized elements are determined using the { L, D }-model of the system. When the prerequisite of a Legendre transform is fulfilled, the Hamiltonian is found. The Lyapunov function is obtained as a residual energy function (REF). The REF consists of the sum of Hamiltonian and losses or dissipative energies (which are negative), and can be used for stability by Lyapunov's second method. Stability conditions are mathematically proven.
Proportion of living biomass in the total dry mass of belowground organs of various plant communities.

Proportion of living belowground plant biomass estimated for various types of meadows, grass stands of clear-cut areas, sedge stands of wetlands and for Cuban savannas and forests is reviewed . Analysis was done using a staining technique and laborious visual separation of living belowground biomass from soil cores and blocks. The differences in the amount of living belowground plant biomass are mostly associated with the type of plant community. The highest amounts ofliving belowground biomass ( 1100 to 2300 g·m -) were recorded most frequently in the unmown moist meadow stands (percentage of living belowground biomass was over 60 %) and in mountain grass stands of clear-cut sites (66-95%). A lower percentage and amount of living biomass was found in the driest habitat (23 %, 860 g·m-) and in several wetlands ( 1013 %, about 500 g·m or less). Mown meadows were characterized by a lower percentage of living belowground biomass and lower dry mass of total and living belowground plant parts. Living belowground biomass of 433 and 5 I 7 g·m2 (34 and 50 %) was recorded in natural savannas, while 745 (74 %), and 512 to I 122 g·m (39-65 %) was recorded, respectively, in the anthropogenic savanna stands dominated by Axonopus compressus and Paspalum notatum. The percentage of living fine roots in the total dry mass of fine roots of Cuban forests varies considerably: 41and47 % (554758 g ·m-) in mangrove forests, 30 and 56 % (64-90 g·m-) in evergreen broad-leaved mountain forests and 23 and 49 % (87200 g·m-) in scmideciduous lowland forests . Both the proportion of living plant organs in total belowground dry mass and the amount ofbelowground plant necromass vaty greatly, which may reflect differences in root mortality and the decomposition rate of dead belowground plant parts associated with various habitat conditions.
Modified Multi-Level Boost Inverter For Hybrid Energy Systems

Applications of Renewable Energy Sources increased rapidly on the limited availability of fossil fuels and the environmental awareness over the decades. Hybrid System uses two or more input sources for the single output and this is normally used over the limited availability of renewable energy sources. Wind Energy and solar Energy are normally used as sources in hybrid energy system whereas fuel cells are also used in some limited applications. This paper proposes a multilevel Inverter system for hybrid energy sources with unequal voltage values. The proposed converter is explained with and without Boost operation. This paper also proposes a suitable PWM technique to control the voltage and frequency of the system and also to make the system ready for grid connection. The MATLAB/ Simulink simulation with the results are presented to validate the performance of proposed multilevel inverter system.
Evaluation Factors Influencing the Use of Go-Pay with the Unified Theory of Acceptance and Use of Technology 2 Model

The purpose of this study is to evaluate and collect information by identifying factors that can influence Behavioral Intention and Use Behavior for the community to use Go-Pay as a payment instrument. This study described the condition of the use of Go-Pay by Generation Z based on the variables contained in the UTAUT 2 research model. The UTAUT 2 model was formed on seven independent variables, namely Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonism Motivation, Price Value, and Habit. These variables are tested to construct the dependent variable, namely Behavioral Intention, and Use behavior. The results of the test identified that Performance Expectancy, Effort Expectancy, Hedonism Motivation, and Habit influenced Behavioral intention. While behavioral intention and habits influence user behavior. Other findings from this study state that of all variables, habit is a variable that has the most significant impact that affects both behavioral intention and use behavior.
Evaluating aggressiveness and host range of Alternaria dauci in a controlled environment

The aggressiveness of Alternaria dauci isolates was investigated in greenhouse conditions. Twenty-seven isolates were pre-selected from a large collection to represent high diversity according to geographic or host origins and intergenic spacer (IGS) polymorphism. IGS sequence analysis revealed that isolates were grouped within three different clusters. Eleven isolates were selected and inoculated on a susceptible carrot cultivar. Three criteria (mean lesion number, mean necrotic leaf area and mean disease index) were used to assess the aggressiveness of isolates. Continuous variation in aggressiveness was shown and no clear division into isolate classes was evident. For the host range study, two isolates were inoculated under greenhouse conditions onto nine cultivated Apiaceae species, two wild Daucus species and six cultivated non-Apiaceae species representing six botanical families. Lesions varying in severity were observed on all dicot species (Apiaceae and non-Apiaceae), but no symptoms developed on the two monocots studied (leek and sweetcorn). Plant species were also differentiated on the basis of expanding lesions (cultivated and wild carrot, dill and fennel) or non-expanding lesions (other dicot species). Typical A. dauci conidia were observed after in vitro incubation of leaves with symptoms. Fungal structures were isolated from lesions and A. dauci was confirmed on the basis of conidial morphology and specific conventional PCR results. Genotyping of individual isolates performed with microsatellite markers confirmed the presence of the inoculated isolate. The results clearly showed that, in controlled conditions, the host range of A. dauci is not restricted to carrot.
People Re-identification in Non-overlapping Field-of-views using Cumulative Brightness Transform Function and Body Segments in Different Color Spaces

Non-overlapping field-of-view (FOV) cameras are used in surveillance system to cover a wider area. Tracking in such systems is generally performed in two distinct steps. In the first step, people are identified and tracked in the FOV of a single camera. In the second step, re-identification of the people is carried out to track them in the whole area under surveillance. Various conventional features such as clothes and appearance of person have been used to identify peoples in the cameras. However, similarity between appearance and clothes of people causes unreliable results. Thus, much reliable features are still required to increase the ability of tracking system. The aim of this paper is to propose a novel algorithm to identify people in a network of cameras with disjoints views. In our proposed methodology, according to relative size of various parts, human body are divided into three portions of head, middle section and lower section. Considering histograms of these portions, matching process was done aiming to distinguish persons in FOV of cameras. The other main challenge in such multi-camera system is to deal with illumination changes and varying appearance of people at different cameras view. The cumulative brightness transform function (CBTF) was employed to alleviate the difficulty of illumination changes. Experiments were conducted in RGB, YCbCr and HSV color spaces and it was found that YCbCr color space gives a better performance result compared to other color spaces used in this work.
Contribuição ao Conhecimento da Ictiofauna da Bacia do Alto Rio Iguaçu, Paraná, Brasil

Com base no material depositdo no Museu de HIstoria Natural Capao da Imbuia e apresentando um levantamento atualizado da ictiofauna da Bacia do Alto Rio Iguacu. O presente estudo detectou a presenca de 41 especies, das quais oito sao endemicas e dez apresentam status taxonomico ainda nao definido. Tres especies sao apresentadas pela priemira vez para a Bacia do Rio Iguacu (Astyanax sp. n., Misgurnus anguillicaudatus e Clarias gariepinus), sendo que M. anguillicaudatus e especie exotica muito comum em aquarismo e C. gariepinus e uma especie exotica utilizada na piscicultura regional. Astyanax altiparanae e citada pela primeira vez para o Alto Iguacu. Durante o presente levantamento, a especie considerada rara foi encontrada. Uma chave para a identificacao das especies do Alto Iguacu e apresentada.
A Comprehensive Review of Ethical Frameworks in Natural Language Processing

An abundance of variation can be applied to the method of experimentation and problem solving that is carried out in the ﬁeld of Natural Language Processing (NLP). This can be seen through an evaluation of the current literature. An intrinsic characteristic to any method of experimentation, within the ﬁeld of NLP and without, is to adhere to a morally and legally appropriate framework embedded in ethics. Depending on the approaches seen in the literature, it is often possible to determine individual characteristics within those frameworks that adhere to the mandates of ethical principles. By evaluating the ethical considerations as seen within the literature of NLP, and providing an overview of ethical theories, I attempt to categorise the literature into ethical frameworks whose methods correspond to those theories. I conclude that the vast majority of literatures appear to employ Consequentialist frameworks that are primarily concerned with providing an ideal environment in which NLP can progress.
Automatic Recognition of Arabic Poetry Meter from Speech Signal using Long Short-term Memory and Support Vector Machine

The recognition of the poetry meter in spoken lines is a natural language processing application that aims to identify a stressed and unstressed syllabic pattern in a line of a poem. Stateof-the-art studies include few works on the automatic recognition of Arud meters, all of which are text-based models, and none is voice based. Poetry meter recognition is not easy for an ordinary reader, it is very difficult for the listener and it is usually performed manually by experts. This paper proposes a model to detect the poetry meter from a single spoken line (“Bayt”) of an Arabic poem. Data of 230 samples collected from 10 poems of Arabic poetry, including three meters read by two speakers, are used in this work. The work adopts the extraction of linear prediction cepstrum coefficient and Mel frequency cepstral coefficient (MFCC) features, as a time series input to the proposed long short-term memory (LSTM) classifier, in addition to a global feature set that is computed using some statistics of the features across all of the frames to feed the support vector machine (SVM) classifier. The results show that the SVM model achieves the highest accuracy in the speakerdependent approach. It improves results by 3%, as compared to the state-of-the-art studies, whereas for the speaker-independent approach, the MFCC feature using LSTM exceeds the other proposed models.
Effect of Networking on Performance of Small and Medium Scale Enterprises in Benue State, Nigeria

This study was carried out to examine the effect of networking on the performance of Small and Medium Scale Enterprises (SMEs). The study focused on SMEs in Makurdi metropolis. The study specifically examined the effect of network structure, network governance and network content on the performance of SMEs in Makurdi, Benue State. The study adopted the survey research design due to its ease of application and the speed with which its results are ready. The target population for this study consists of 708 owners/senior management staff of SMEs in Makurdi metropolis in Benue State, Nigeria. These consisted of different sectors like food processing (24), livestock rearing (32), schools (86) trading (164), art/craft (42), baking (34), table water (17), fashion and designing/ hair dressing (97), ICT (92), health (65) and transportation (55). 256 respondents were sampled. The questionnaire was used as the research tool for data collection. The research tool consists of two 4-point Likert questionnaires. The 256 questionnaires administered met a 0.83 response rate and 213 questionnaires were used for statistical analysis. With the aid of SPSS version 21, multiple regression analysis was used to test the strength of the effect of network dimensions on performance of SMEs. Findings of the study revealed that network structure has significant effect on the performance of SMEs in Benue State and network governance has significant effect on the performance of SMEs in Benue State. Also, the result indicated that network content has significant effect on the performance of SMEs in Benue State. The study concluded that the structure, governance and content of networks maintained by SMEs have significant effect on their performances. Recommendations were made among others that, management of SMEs should be mindful of the content of their networks since the network content has significant effect on the performance of SMEs.
Land Surface Model influence on the simulated climatologies of temperature and precipitation extremes in the WRF v.3.9 model over North America

Abstract. The representation and projection of extreme temperature and precipitation events in regional and global climate models are of major importance for the study of climate change impacts. However, state-of-the-art global and regional climate model simulations yield a broad inter-model range of intensity, duration and frequency of these extremes. Here, we present a modeling experiment using the Weather Research and Forecasting (WRF) model to determine the influence of the land surface model (LSM) component on uncertainties associated with extreme events. First, we evaluate land-atmosphere interactions within four simulations performed by the WRF model using three different LSMs from 1980 to 2012 over North America. Results show LSM-dependent differences at regional scales in the frequency of occurrence of events when surface conditions are altered by atmospheric forcing or land processes. The inter-model range of extreme statistics across the WRF simulations is large, particularly for indices related to the intensity and duration of temperature and precipitation extremes. Areas showing large uncertainty in WRF simulated extreme events are also identified in a model ensemble from three different Regional Climate Model (RCM) simulations participating in the Coordinated Regional Climate Downscaling Experiment (CORDEX) project, revealing the implications of these results for other model ensembles. This study illustrates the importance of the LSM choice in climate simulations, supporting the development of new modeling studies using different LSM components to understand inter-model differences in simulating temperature and precipitation extreme events, which in turn will help to reduce uncertainties in climate model projections.
Modeling of the potential distribution of Eichhornia crassipes on a global scale: risks and threats to water ecosystems

O aguape e listado entre as 100 piores plantas invasoras, alem de ter sido classificado como a 11a pior especie invasora da Europa dado seu impacto na biodiversidade aquatica e utilizacao de recursos hidricos pelas populacoes humanas. Prever a distribuicao e o primeiro passo para entender a adequabilidade do nicho e prever o impacto da invasao pela especie. Nesta pesquisa, um modelo de distribuicao potencial do aguape foi elaborado para avaliar o risco global de invasao, por meio da sobreposicao deste modelo a areas de biodiversidade e consumo de agua. O algoritmo MaxEnt - Maximum Entropy foi utilizado na construcao do modelo e incluiu cinco camadas bioclimaticas e uma de areas urbanizadas. A camada de areas urbanas foi a que mais contribuiu individualmente para o modelo e destacou a importância das cidades como fonte ou mecanismo de dispersao do aguape. Os hotspots globais de biodiversidade estao predominantemente situados em regioes de alta adequabilidade para a especie. Os sitios de Ramsar e as unidades de conservacao globais estao em um nivel de risco mais baixo do que os hotspots. No entanto, cenarios futuros de mudancas climaticas e o crescimento urbano podem colocar essas areas em maior risco de invasao. Ameacas provocadas pelo aguape sao possivelmente mais agudas nas regioes que sofrem com a seca cronica. Os resultados sugerem que modelos de distribuicao potencial que nao incluem variaveis antropicas podem estar significativamente subestimando a distribuicao potencial de especies invasoras. Alem disso, a plasticidade ecologica dessa especie e sua associacao com centros urbanos aumentam a preocupacao com os impactos do aguape na biodiversidade e sobre os recursos hidricos.
Reproductive dynamics of broadbill swordfish, Xiphias gladius, in the domestic longline fishery off eastern Australia

The reproductive ecology of broadbill swordfish, Xiphias gladius, was evaluated based on 1437 fish (size range 50–300 cm orbital fork length [OFL]) caught in the domestic longline fisheries off eastern Australia and New Zealand between May 1999 and March 2001. Reproductive activity was assessed using histology, a gonadosomatic index, and maximum oocyte size. Males were significantly smaller than females and represented less than one third of the sampled fish. Sex ratio differed significantly with respect to fish size and time of year. Females began maturing at 150-cm OFL and spawned from September to March, with the greatest activity from December to February. Males matured at 90-cm OFL; ripe males were found from January to March, but also in May and October, suggesting an extended reproductive period. During the spawning period the proportion of spawning to inactive mature sized females was significantly higher in waters west of longitude 158°E than in waters to the east. Further to the east, samples taken from the New Zealand fishery showed no actively spawning fish during the main spawning period. Females were increasingly reproductively active as water temperature increased beyond 24°C and sea surface chlorophyll a decreased below 0.2 μg L–1. Batch fecundity was linearly related to fish length with a mean batch fecundity of 1.66 million oocytes for females ranging in size from 173- to 232-cm OFL. The presence of hydrated oocytes and post-ovulatory follicles (POFs) in the same ovaries indicated multiple spawnings. Depending on the time taken for POFs to degrade, these may have been daily at the height of the spawning season.
An Innovative Mkahe And A Novel Qfwc Of Crop Images In Agriculture Field

: In the field of agriculture, the images of crops are captured for checking the quality and growth of the crops. The images are transmitted to the different places based on the concept of Internet of Things (IoT). At the time of transferring the images, the images are in the state of high (i.e.) can be corrupted or hacked by the unauthorized persons. Hence this research paper is proposed for providing security to the crop images encryption, decryption, compression and decompression are performed. In this paper, an innovative Multikey Adaptive Homomorphic Encryption (MKAHE) algorithm is utilized for performing the encryption process. And in addition to this another novel algorithm of Quantized Fraction Wavelet Compression (QFWC) is employed for carrying out the compression process. The performance analysis is carried out by calculating the values of various performance metrics like PSNR, compression ratio and encoding time for the proposed work. Then these performance measures are compared with the performances of the existing and classic method. Then the compressed image is decompressed followed by the process of decryption. The values of PSNR, compression ratio are increased and similarly the value of the encoding time gets reduced.