Miracle-dz/UniFAR-FTUs huggingface dataset:
FAMILY FARMING AND SUSTAINABLE DEVELOPMENT OF AGRIBUSINESS IN THE METROPOLITAN REGION OF CAMPINAS

Brazil is one of the countries where agribusiness has more space and potential to develop. Thus, currently, agribusiness in the country is the main pillar of the Brazilian economy. However, the traditional system brings with it many social and, above all, environmental problems. Given this, family farming takes place in this scenario with its sustainable techniques. The main objective of this article was to verify the role of family farming in the sustainable development of agribusiness in the metropolitan region of Campinas (MRC). For this, a research of the descriptive type of qualitative nature was promoted with the participation of professionals who invest and operate in family farming, as well as executives who work in the Association of Natural Agriculture of Campinas and Region (ANC). Family farming is the main economic activity in many regions of Brazil and needs to be strengthened and studied. The potential of family farmers to generate employment and income must be observed throughout the future development of Brazilian agriculture. As a result of the research, it was perceived, in the economic dimension, the great need for credit and economic incentive by the State. In the social dimension, these state incentives would also help to create or strengthen the social value of an association or union. Finally, in the environmental dimension, it was seen and confirmed that family farmers have great environmental responsibility with their production technique.
SEMANTIC CLASSIFICATION OF SANDSTONE LANDSCAPE POINT CLOUD BASED ON NEIGHBOURHOOD FEATURES

Abstract. The technology of airborne laser scanning enables fast and accurate gathering spatial data containing also echoes from the terrain below the vegetation canopy that is beneficial for topographic mapping of wooded sandstone landscapes in Czechia, Poland, and Germany. The challengeable task is to determine the ground points in the point cloud because commonly used filtration methods do not successfully distinguish between vegetation and rock pillars and faces. In this paper, we replace filtration with classification approach using the features derived from characteristics of points within a neighbourhood of optimized sizes, such as eigenvalue-based features and echo ratio. Random Forest classifier is trained and tested on the manually labelled dataset with a density of almost 650 points/m2 from the Adrspach-Teplice Rocks. The overall accuracy reaches 87% but recall and precision of non-ground points are unsatisfactory. Misclassified non-ground points are located also within trees, thus we do not consider the result as suitable for DTM processing without further processing.
A MULTI CRITERIA DECISION URBAN DEVELOPMENT FRAMEWORK FOR LAND EXPROPRIATION IN SOUTH AFRICA: A STRATEGIC APPROACH

Abstract. The land question in South Africa has been a long-standing issue for more than 360 years. Consequent to unjust legislation such as The Natives Land Act No.27 of 1913 to this day, there is a racial imbalance in the distribution of land ownership in South Africa. Coupled with the socio-economic and spatial segregative mandates of the apartheid-government to enrich the white minority, such unjust legislation fostered mass-land dispossessions and displacements of black people relocating them to peripheral areas known as ‘Bantu stands’ where they were further ethnically grouped in remote from socio-economic opportunities. The preceding has resulted in the impoverishment of the black people as they no longer had land – their primary source of livelihood. The limited access to land by black people remains true in post-apartheid South Africa. Since the dawn of democracy, limited access to urban land has coursed challenges for housing development. Spatial transformation towards socio-economic integration has also become problematic as large areas of strategically located land remain locked in the hands of the minorities. Thus, to realise the mandates of South Africa’s democratic government – equal access to land and opportunities, this land needs to be acquired, particularly for the previously disadvantaged, poor, and landless. As cities move towards being smart, this research will demonstrate the use of Evidence Based Planning (EBP) in order to assist Local Government to foster scientific decision making methods. The use of the Multi-Criteria Decision Analysis (MCDA), Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) as a method to develop a Strategic Urban Development Decision Framework (SSUDDF) as a Planning Support System (PSS) that will be used to investigate the best suitable land for possible expropriation. Various criteria such as proximity to road connectivity, proximity to current and future economic activity, proximity to public transport routes, dolomitic land, priority areas and proximity to city centres are some of the criteria selected for the research. The Strategic Spatial Urban Development Decision Framework (SSUDDF) enabled us to stream line significant criteria and processes that where specific to strategic urban development in the Benoni town situated in the City of Ekurhuleni using critical spatial policy and strategic objectives of the city.
Mathematica Bohemica

We consider the Cahn-Hilliard equation in H(R ) with two types of critically growing nonlinearities: nonlinearities satisfying a certain limit condition as |u| → ∞ and logistic type nonlinearities. In both situations we prove the H(R )-bound on the solutions and show that the individual solutions are suitably attracted by the set of equilibria. This complements the results in the literature; see J.W. Cholewa, A.Rodriguez-Bernal (2012).
Developing correlations between the soil fines content and CPT results using neural networks

Knowledge of the fines content is necessary for all soil classification systems and an important factor in the evaluation of soil strength in liquefaction and seismic settlement analysis. This paper presents the application of cone penetration test, CPT data for estimating the soil fines content. The correlation can be used either as a first estimate of fines content (for example in the offshore environment) or to provide statistical information on the variation of fines content within a given area of interest (e.g. for a regional liquefaction study). The paper shows how field and laboratory test data were used with a neural network to correlate the CPT results and the fines content. Data from five site investigation locations across Northern Croatia were utilised. Verification of the approach is performed using field and lab test data from the Veliki vrh landslide. RÉSUMÉ: La connaissance de la teneur en fines est nécessaire pour tous les systèmes de classification des sols et constitue un facteur important dans l'évaluation de la résistance des sols lors de l'analyse de la liquéfaction et du tassement sismique. Cet article présente l'application du test de pénétration au cône et des données CPT pour l'estimation de la teneur en particules fines du sol. La corrélation peut être utilisée soit comme première estimation de la teneur en fines (par exemple dans l’environnement offshore), soit pour fournir des informations statistiques sur la variation de la teneur en fines dans une zone d’intérêt donnée (par exemple, pour une étude régionale sur la liquéfaction). Le document montre comment les données de tests sur le terrain et en laboratoire ont été utilisées avec un réseau de neurones pour corréler les résultats du CPT et le contenu en fines. Les données provenant de cinq sites de recherche sur le nord de la Croatie ont été utilisées. La vérification de l'approche est effectuée à l'aide des données de test de terrain et de laboratoire du glissement de terrain Veliki vrh.
TARGETED ENRICHMENT STRATEGIES FOR NEXT-GENERATION PLANT BIOLOGY 1

• Premise of the study: The dramatic advances offered by modem DNA sequencers continue to redefine the limits of what can be accomplished in comparative plant biology. Even with recent achievements, however, plant genomes present obstacles that can make it difficult to execute large-scale population and phylogenetic studies on next-generation sequencing platforms. Fac­ tors like large genome size, extensive variation in the proportion of organellar DNA in total DNA, polyploidy, and gene num­ ber/redundancy contribute to these challenges, and they demand flexible targeted enrichment strategies to achieve the desired goals.
Molecular and Symbiotic Efficiency Characterization of Rhizobia Nodulating Bambara Groundnut (Vigna subterranea L.) from Agricultural Soils of Daloa Localities in Côte D’ivoire

Rhizobia are soil bacteria belonging to the groups of α, ß and δ Proteobacteria (Chen et al., 2003; Garrity et al., 2003; Benhizia et al., 2004). They are truly characterized by their capacity to form nodules on the roots and sometimes the stems of host plants generally legumes and to grow on the isolation YEM medium (Vincent, 1970). Inside the nodule and in the bacteroids form and via the nitrogenase enzyme activity, they fix and reduce atmospheric nitrogen (N2) to ammonia (NH3) exploitable by the host plant (Dixon and Wheeler, 1986; Graham, 2008). Thus, these symbionts are widely used in the International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 9 Number 7 (2020) Journal homepage: http://www.ijcmas. com
Single Phase Power Factor Improvement Based Instantaneous Power P Q Theorm

This paper presents how to harmonic disturbance reduce on single phase power system. One of the most popular method is harmonic signal extraction with instantaneous power p q theorm. This algorithm implemented to harmonic cencellation by active power filter circuit be paralled with the non linier load. This filter generates of current compensation that are injected into the grid to improve power quality. This Filter is built by an inverter circuit that consists of a mosfet switch array and capasitor mounted on DC link side. Two types of loads are resitive inductive and resistive capasitive produce of current source wave form that close pure sinusoid. Simulation results show the THD index fell to the level of 2.7% in accordance with IEEE 519 standard rules which states that the harmonics content of the power system may occur below 4%, indicating that the power system can be categorized as having good quality.
Applicability of a Three-Stage Hybrid Model by
Employing a Two-Stage Signal Decomposition
Approach and a Deep Learning Methodology
for Runoff Forecasting at Swat River Catchment,
Pakistan

The optimal management of hydropower resources is highly dependent on accurate and reliable hydrological runoff forecasting. The development of a suitable runoff-forecasting model is a challenging task due to the complex and nonlinear nature of runoff. To meet the challenge, this study proposed a three-stage novel hybrid model namely IVG (ICEEMDAN-VMD-GRU), by coupling gated recurrent unit (GRU) with a two-stage signal decomposition methodology, combining improved complete ensemble empirical decomposition with additive noise (ICEEMDAN) and variational mode decomposition (VMD), to forecast the monthly runoff of SWAT river, Pakistan. ICEEMDAN decomposed the runoff time series into subcomponents, and VMD performed further decomposition of the high-frequency component obtained by ICEEMDAN decomposition. Afterward, the GRU network was employed to the decomposed subcomponents for forecasting purposes. The performance of the IVG model was compared with other hybrid models including, ICEEMDAN-VMD-SVM (support vector machine), ICEEMDANGRU, VMD-GRU, ICEEMDAN-SVM, VMD-SVM; and standalone models including GRU and SVM by utilizing statistical indices. Experimental results proved that the IVG model outperformed other models in terms of accuracy and error reduction, which indicates the feasibility of the IVG model to analyze the nonlinear features of runoff time series and for runoff forecasting with applicability for future planning and management of water resources.
Impact of Artificial Intelligence on Vietnam Commercial Bank Operations

Through secondary data, the research study about the impact of artificial intelligence (AI) on commercial bank operations. Research results show that AI is being applied in the banking industry with many different applications such as Chatbot, risk assessment, anti-money laundering, fraud detection, and algorithmic. The application of AI in the banking industry is changing day by day, but the trend of AI application of the bank focuses on three goals that affect the bank's operations. The impact of AI on banking operations including the impact on customer experience, the impact on the bank's cost and performance, the impact on risk management, and the impact on bank compliance.
Effects of Vehicle Restriction Policies on Urban Travel Demand Change from a Built Environment Perspective

License plate restriction (LPR) policy presents the most straightforward way to reduce road traffic and emissions worldwide. However, in practice, it has aroused great controversy. This policy broke the original structure of the urban transportation mode, which needed some matching strategies to adapt to this change. Investigating this travel demand change is a challenging task because it is greatly influenced by features of the local built environment. Fourteen variables from four dimensions, location, land-use diversity, distance to transit, and street design, are used to depict the built environment; moreover, the severe collinearity underlies these feature variables. To solve the multicollinearity among the variables and high-dimensional problem, this study utilizes two different penalization-based regression models, the LASSO (least absolute shrinkage and selection operator) and Elastic Net regression algorithms, to achieve the variable selection and explore the impacts of the built environment on the change of travel demand triggered by the LPR policy. Travel demand changes are assessed by the relative variation in taxi ridership in each traffic analysis zone based on the taxi GPS data. Built environment variables are measured using the transportation network data and the Baidu Map Service points of interest (POI) data. The results show that regions with a higher level of public transportation service and a higher degree of the land mix have a stronger resilience to the vehicle restriction policy. Besides, the contribution rate of public transportation is stable as a whole, while the contribution rate of richness depends on specific types of land use. The conclusions in this study can provide in-depth insights into the influence of the LPR policy and underpin traffic complementary policies to ensure the effectiveness of LPR.
Microbial Contributions on Ripening of Squid Shiokara.

To clarify the effect of microbes on the formation of amino acids during the ripening of squid shiokara, changes in the viable cell counts in 2.5% and 10% NaCl-BPG medium, 10% NaCl-PDA medium, and 10% NaCl-LBS medium, and amino acid contents etc. during ripening at 20°C were compared to shiokara containing 10% NaCl with and without antibiotics. Though the viable cell counts in the shiokara without antibiotics markedly increased during the ripening in contrast to those with antibiotics, neither the total nor the individual amounts of amino acids significantly differed between the samples, suggesting that microbes have a slight effect on the formation of amino acids. The initial bacterial flora of the shiokara without antibiotics were dominated by Staphylococcus, irrespective of the medium used for counting. The dominant flora in the later stage of ripening, however, differed depending on the media: Staphylococcus dominated on the 2.5% NaCl-BPG medium, while Micrococcus dominated and Streptococcus appeared in the deteriorating stage on the 10% NaCl-BPG medium.
IMPROVING THE CATALYTIC FEATURES OF THE LIPASE FROM Rhizomucor miehei IMMOBILIZED ON CHITOSAN-BASED HYBRID MATRICES BY ALTERING THE CHEMICAL ACTIVATION CONDITIONS

Elizabete Araújo Carneiroa, Ana Karine Pessoa Bastosb, Ulisses Marcondes Freire de Oliveirac, Leonardo José Brandão Lima de Matosd, Wellington Sabino Adrianoe, Rodolpho Ramilton de Castro Monteiroc, José Cleiton Sousa dos Santosc,f,*, and Luciana Rocha Barros Gonçalvesc Instituto Federal do Ceará, Campus Quixadá, 63900-000 Quixadá – CE, Brasil Eixo de Química e Meio Ambiente, Instituto Federal do Ceará, Campus Maracanaú, 61939-140 Maracanaú CE, Brasil Departamento de Engenharia Química, Universidade Federal do Ceará, Campus do Pici, 60455-760 Fortaleza – CE, Brasil Instituto Federal do Maranhão, Campus Caxias, 65600-992 Caxias – MA, Brasil Universidade Federal de Campina Grande, Centro de Educação e Saúde, Campus Cuité, 58175-000 Cuité – PB, Brasil Instituto de Engenharias e Desenvolvimento Sustentável, Universidade da Integração Internacional da Lusofonia Afro-Brasileira, Campus das Auroras, 62790-970 Redenção – CE, Brasil
Implications of Responsive Cultures of Knowledge Sharing on Non-Financial Performance: An Empirical Study of Family Owned Micro, Small and Medium Enterprises in Migori, Kenya

In the last decade, sharing of new knowledge has become an essential component for the survival of enterprises that are keen on maintaining their overall competitiveness. More and more studies are confirming that when a culture of sharing knowledge is prevalent in an organization, then its competitive edge is significantly enhanced. However, it is yet to be confirmed whether family firm’s non-financial performance can be greatly improved as a result of fostering of a culture of sharing of the organization’s knowledge. A culture of knowledge sharing is premised on the notion that an enterprise’s most prized asset is the knowledge of its workforce. Nevertheless, despite the tremendous attention that sharing of knowledge in organizations has received in the last decade, its effect on family owned Micro Small and Medium Enterprises’ non-financial performance has been largely ignored. The purpose of this study was therefore to investigate the effect of knowledge sharing culture on non-financial performance of family owned Micro, Small and Medium Enterprises in Migori County, Kenya. Survey research design was used for the study with a sample size of 118 respondents. Simple and stratified random sampling techniques were adopted. Findings revealed that knowledge sharing culture had a positive and statistically significant effect on non-financial performance of family owned Micro, Small and Medium Enterprises in Migori County, Kenya. The study recommends that the family owned firm management should aspire to create an environment where knowledge sharing is cultivated through effective processes, enhanced collaboration, openness and availing of rewards to enable improved non-financial performance. Policy makers, on the other hand, should create a model knowledge sharing framework for family owned firms aimed at improving nonfinancial performance.
Image malware detection using deep learning

We are currently living in an area where artificial intelligence is making out every day to day life much easier to manage. Some researchers are continuously developing the codes of artificial intelligence to utilize the benefits of the human being. And there is the process called data mining, which is used in many domains, including finance, engineering, biomedicine, and cyber security. The utilization of data mining, artificial intelligence algorithms like deep learning is so vast that we can't even name them all. This technology has almost touched every industry and cyber security is the most beneficial. The process of enhancing cyber security with the help of deep learning methods has come out of the theory books and many organizations are utilizing them rather than using a traditional piece of software to defend against online threats. Especially in the field of recognizing and classifying codes or malware. And this is essential, because, with the advent of cloud computing and the Internet of Things, expand potential malware infection sites from PCs to any electronic device. This makes our day to day life very unsafe. In this post, first, we will describe in brief how deep learning can be the most useful and promising techniques to detect malware. Besides this we will go through a deep neural network,ResNet for malware dynamic behavior classification jobs.
UNDERSTANDING OF CROP LODGING INDUCED CHANGES IN SCATTERING MECHANISMS USING RADARSAT-2 AND SENTINEL-1 DERIVED METRICS

Abstract. Crop lodging – the bending of crop stems from the vertical – is a major yield-reducing factor in cereal crops and causes deterioration in grain quality. Accurate assessment of crop lodging is important for improving estimates of crop yield losses, informing insurance loss adjusters and influencing management decisions for subsequent seasons. The role of remote sensing data, particularly synthetic aperture radar (SAR) data has been emphasized in the recent literature for crop lodging assessment. However, the effect of lodging on SAR scattering mechanisms is still unknown. Therefore, this research aims to understand the possible change in scattering mechanisms due to lodging by investigating SAR image pairs before and after lodging. We conducted the study in 26 wheat fields in the Bonifiche Ferraresi farm, located in Jolanda di Savoia, Ferrara, Italy. We measured temporal crop biophysical (e.g. crop angle) parameters and acquired multi-incidence angle RADARSAT-2 (R-2 FQ8-27° and R-2 FQ21-41°) and Sentinel-1 (S-1 40°) images corresponding to the time of field observations. We extracted metrics of SAR scattering mechanisms from RADARSAT-2 and Sentinel-1 image pairs in different zones using the unsupervised H/α decomposition algorithm and Wishart classifier. Contrasting results were obtained at different incidence angles. Bragg surface scattering increased in the case of S-1 (6.8%), R-2 FQ8 (1.8%) while at R-2 FQ21, it decreased (8%) after lodging. The change in double bounce scattering was more prominent at low incidence angle. These observations can guide future use of SAR-based information for operational crop lodging assessment in particular, and sustainable agriculture in general.
Red Cell Enzyme Variation among Chinese Residing in Japan

Abstract Six red cell enzyme genetic variations, namely, acid phosphatase (AcP), phosphoglucomutase (PGM), adenosine deaminase (ADA), 6-phosphogluconate dehydrogenase (PGD), adenylate kinase (AK) and phosphoglycerate kinase (PGK), have been studied by means of starch gel electrophoresis.Samples were taken from unrelated c.270 Chinese individuals residing in Japan.The gene frequencies and phenotypic distribution of each enzyme are presented.In the cases of AcP, PGM and PGD systems, the gene frequencies for each phenotype were within ranges observed in Japanese, while the gene frequency of ADA2 appeared to be higher than the compiled data for Japanese.The geographical heterogeneity of ADA2 frequency in China was suggested.In accordance with the data previously reported for neighbouring population including Japanese, no AK variant was detected.In PGK system, also, any variant other than common 1 type was not observed in this material.