Miracle-dz/UniFAR-FTUs huggingface dataset:
Taxonomic study of cyanoprokaryotes from medicinal plants bed with emphasis on phylogeny of complex taxa using 16S rRNA marker

Cyanoprokaryotes are simple photosynthetic microorganisms which have an important role in the soil carbon and nitrogen cycle. The current study aimed to investigate the flora of cyanoprokaryotes from medicinal plants bed. Also phylogenetic analysis based on 16S rRNA marker was performed to investigate the phylogenetic relationships between different cyanoprokaryotic taxa and evaluate the efficiency of this marker in separation of taxonomic boundaries between taxa especially in the case of complex taxa, which their relations are not well-defined. For this purpose, after collection of soil, isolation and purification of strains were performed. The cyanoprokaryotic taxa were identified morphologically and 16S rRNA marker was used to approve the identifications. Phylogenetic analysis performed using Maximum Likelihood, Maximum Parsimony and Bayesian Inference. Totally, 42 cyanoprokaryotic taxa were identified and Nostoc was an abundant genus in the soil of medicinal plants bed. The phylogenetic tree revealed Nostocales as a monophyletic group. Also, Wollea together with Anabaena, and Nostoc together with Desmonostoc created monophyletic groups. Results revealed that, 16S rRNA is an effective phylogenetic marker in high classification rankings such as order, family and genus. However, 16S rRNA could not be an effective marker in separation of close genera such as Nostoc and Desmonostoc.
Post Filtering Mechanism in Social Networking

Online social networks sites have provided platform for online social groups where individuals can collaborate and shuffle their thoughts. In this social space, content will be huge. For specific interest of user, we need mechanism which filter social media posts based on user’s interest. This can be done by an automated filtering method; required to categorize members based on their pattern of response. The posts are clustered based on stylistic, thematic, emotional, sentimental and psycholinguistic methods. After members are categorized based on their response to the posts belonging to different clustering methods. The categorization affords recommending the users, posts which might be useful to the interest of the members. The categorization posts show increased performance in case of large number of candidate members by performing clustering based on linguistic features. The contribution work is to implement location-aware personalized posts recommendation using both the users' personal interests and their geographical contexts. The system has been tested on Twitter API group data; where it offers a significant solution to an unaddressed problem associated with social networking groups.
A quantitative assessment of the diel influence on the cryptobenthic fish assemblage of the shallow Mediterranean infralittoral zone

Cryptobenthic fishes are an underestimated and probably important component of coastal marine ecosystems that are usually overlooked by standard methods for collecting and studying benthic fishes. Studies focusing on cryptobenthic fishes have been rare and all have been based on samples taken during daytime. The present study tested the difference in epibenthic and cryptobenthic fish composition, diel differences in cryptobenthic fish assemblage and diel shifts of infralittoral fish species between hidden and open bottom spaces. It also looked for the significant habitat variables structuring the cryptobenthic fish assemblage. The daylight, sunset and night samples of epibenthic and cryptobenthic fish assemblages were collected from 78 squares of 1 m 2 shallow water plots (0.5 to 3 m) on Brac island in the eastern Adriatic. The study recorded 27 species, among which the family Gobiidae dominated fish diversity with 14 species. Cryptobenthic specimens highly outnumbered epibenthic specimens, with a ratio of 7.5 to 1. Species composition of cryptobenthic and epibenthic fish assemblages differed significantly. No diel variation in composition, species richness or abundance of the cryptobenthic fish assemblage was detected. Occurrence frequencies in hidden and open bottom spaces of ambivalent species did not change significantly between times of day, so no dial switches between open and hidden places were apparent. In combination, these results suggest that the cryptobenthic fish assemblage has diel stability and is mostly composed of permanent inhabitants of hidden spaces with domination of miniature gobies. Significant habitat variables for species occurrence were the presence of multiple layers, bottom inclination and the presence of cobbles, while depth also had a large but not significant effect.
Consistency in Multiplayer Online Game in Continuous Domain

(Mauve, Vogel, Hilt, & Effelsberg, 2004). As a result, discrete domain algorithms to maintain consistency are not sufficient enough for continuous domain to maintain consistency; however those algorithms in discrete domain to maintain consistency are the foundation into continuous domain for introducing relevant algorithms for continuous domain. Further, the continuous domain consistency criteria can thus be regarded as a specialization of the use of the discrete domain consistency criteria. Abstract– This paper describes the ways to maintain the consistency in Multiplayer Online Games (MPOGs) in continuous domain. The involvement of computer networking, computer graphics, multimedia, artificial intelligence makes the MPOG environment more complex. As a result, consistency, responsiveness, scalability, fairness and avoiding cheating are examples for available issues in MPOG environment. The consistency is well discussed in the discrete domain, however the attention on consistency of MPOGs in continuous domain is poor and data centric approaches are not adequate for MPOGs due to the game state changes with passing time. Further, the consistency and responsiveness are two important considerations in the MPOG environment, however both cannot be achieved at once. As a result, the different architectures are available to implement game environment, and those are supported by latency handling techniques; time delay, local lag, time wrap, progressive slow down, dead reckoning, gossiping, PREMUB for example. The examples are discussed under each architecture for a better understanding of criticising the architecture. This paper mainly delivers the importance of consistency in MPOG, however it sacrifices the responsiveness in MPOG, as a result both aspects must be considered equally important for Quality of Experience (QoE) of the player.
Application of Customer Behavior in Using Fintech as Business Media Based on the Unified Theory of Acceptance and Use of Technology Model

— This study aims to examine how customer behavior is adopted as a business media by using the UTAUT model developed by Venkatesh. This model illustrates the factors that influence one's acceptance of information technology. There are four constructs / variables, which are significant direct determinants of technological behavior. These four variables are performance expectancy, social influence, effort expectancy, and facilitating conditions. The research method is verification, through an explanatory survey. The population of this study is 121 Bank Jabar Banten (BJB)’s customers who use Fintech as business media. The data analysis technique implemented is partial square-path (PLS-PM) modeling by using Smart 3 software. The results show that \performance expectancy, social influence, effort expectancy, and facilitating conditions have a positive effect on intention behavior and have implications for user behavior based on UTAUT model. This means that the implementation of customer behavior in using Fintech for business needs to consider performance developments.
Alternative Treatment for Infectious Arthritis in a Complicated Diabetes Mellitus Patient with Ankle Joint Instability

Treatment of diabetic foot infections requires attention to both local and systemic issues, as diabetes mellitus is accompanied by a high incidence of complications that delay wound healing and restrict treatment options. We present a patient with diabetes who had infectious arthritis of the ankle joint with a soft tissue defect involving the joint capsule. The comorbidities of the patient limited the options for treatment in reconstruction of the ankle joint defect. Therefore, nonabsorbable sutures bridging the periosteum of the fibula and calcaneus bones were applied to mimic the calcaneofibular ligament and secure the joint capsule. In addition, negative pressure wound therapy (NPWT) was administered to protect the joint space, absorb extra fluid, and promote wound healing. Acellular dermal matrix was used in combination with NPWT to enhance wound healing and tissue regeneration. Although methicillin-resistant Staphylococcus aureus was cultured from the wound several times, there were no signs that this treatment aggravated the infection. After 7 months of inpatient treatment, stable soft tissue coverage and ankle joint stabilization were achieved, allowing fair standing balance, gait and plantar flexion.
Bots in Wikipedia: Unfolding their duties

The success of crowdsourcing systems such as Wikipedia relies on people participating in these systems. However, in this research we reveal to what extent human and machine intelligence is combined to carry out semi-automatic workﬂows of complex tasks. In Wikipedia, bots are used to realize such combination of human-machine intelligence. We provide an extensive overview on various edit types bots carry out in this regard through the analysis of 1,639 approved task requests. We classify existing tasks by an action-object-pair structure and reveal existing diﬀerences in their probability of occurrence depending on the investigated work context. In the context of community services, bots mainly create reports, whereas in the area of guidelines or policies bots are mostly responsible for adding templates to pages. Moreover, the analysis of existing bot tasks revealed insights that suggest general reasons, why Wikipedia’s editor community uses bots as well as approaches, how they organize machine tasks to provide a sustainable service. We conclude by discussing how these insights can prepare the foundation for further research.
Modelling Chicken P Autoregressive Integrated Moving Average Time

This research work modeled chicken production in Nigeria using the least square approach as well as the univariate Box-Jenkins Autoregressive Moving Average (ARMA) model method. The objective is to investigate the productio make logical forecast for poultry farmers to meet up with any future challenges that may arise due to the increase in demand for poultry meat. The maximum likelihood method of estimation was used to obtain the parameters of the fitted Autoregre Integrated Moving Average (ARIMA) model. Yearly chicken production data for the period of 1961 to 2017 obtained from Food and Agricultural Organisation (FAO) was used to investigate the performance of the model. Augmented Dickey Fuller (ADF) test carried out to check for stationarity of the series shows that the original data was nonstationary and stationarity was attained after the first differencing. 1, 2) emerged as the best among other fitted models with Akaike Information Criterion (AIC) value of 1168.42 and log likelihood 579.21. The optimum model is given by: 1 1 2 2 1 1 2 2 ˆ t t t t t X X X e X             with estimated parameters 1  = 0.3169, 2  = 0.3948, = 0.9377. Diagnostic checks via standardised residuals, ACF of residuals and p-values revealed that the model captures the data well enough. Forecast values for the year 2018 to 2023 were then obtained which show a considerable variation over the years. The Autoregressive Integrated Moving Average (ARIMA) model clearly performed excellently in studying the behavior of chicken production data and forecasting its future values. KeywordsChicken products, Autoregressive, Moving average, Least square, Akaike Information Criterion (AIC)
NEUROPATHY ASSOCIATED WITH HYPERTRIGLYCERIDEMIA IN PATIENTS WITH METABOLIC SYNDROME.

Context With more studies investigating effects of high serum lipid levels, new findings are emerging regarding the damage these biomolecules may cause. Aim In this study we aimed to find a relation between neuropathy and hypertriglyceridemia in patients with metabolic syndrome (MS). Material and methods One hundred and twenty subjects (Ninety subjects with metabolic syndrome and 30 healthy controls) were included in the study. Subjects with MS were divided into three groups. HbA1C levels of the subjects were < 5.7% in group A, ≥ 5.7% - < 6.5% in group B, and ≥ 6.5% - < 8.0% in group C. Pin-Prick test and Semmes- Weinstein Monofilament were used for neurological examination. Electromyography was performed to patients with neuropathy to support the diagnosis. Results Neuropathy prevalence was found to be higher in the subjects with metabolic syndrome compared to control group. (9.9 %; 16.65 %; 23.31 % vs. 3.3%; in group A, group B, group C vs. healthy control group respectively) (p=0.003 for group A, p=0.0002 for group B, p=0.0002 for group C). There was an association between triglyceride levels and neuropathy in group C. Conclusion Patients with MS may have more neuropathy risk than we estimate.
Assessment of Physical School Environment of Public Sector High Schools in Pakistan and World Health Organization’s Guidelines

The physical school environment influences the health and educational performance of students and staff. World Health Organization (WHO) has developed guidelines for developing countries to evaluate the Physical school environment. Hence this study analyzed the physical school environment of high schools to ascertain the extent of implementation of these guidelines in Multan. T en core indicators water quality, water quantity , water facilities , hygiene promotion practices , toilets and handwashing facilities, control of vector-borne diseases, cleaning and waste disposal system, school building conditions, school safety and supportive classroom conditions were assessed by three research tools. The responses of 10 core indicators were scored and described. The study concluded that the 10 core indicators at the district level were not meeting the WHO desirable score and the district score (1.01) was below the WHO desirable score (1.5). The development of the Physical School Environment is in the positive trend of
OPTIMAL REACTIVE POWER PLANNING USING GENETIC ALGORITHM

: This paper presents a new approach to solve the optimal reactive power planning (RPP) problem based on a genetic algorithm. The reactive power planning problem involves optimal placement and sizing of capacitors in a network such that the power loss cost and investment costs of new VAR sources are minimum. The genetic algorithm is a kind of search algorithm based on natural selection and genetics. This algorithm can search for global solution. The RPP problem is solved in two stages. The first stage determines the optimal placement of capacitors. The second stage determines the optimal sizing of such capacitors. The proposed approach incorporates detailed hourly loading conditions at each bus and achieves a fairly accurate estim k of the benefits from capacitor placement. The proposed method is applied to the IEEE 14-bus system and the IEEE 30-bus system and its results are compared with the results of a conventional method. Although this method is not as fast as sophisticated traditional methods, the applied concept is quite adequate for planning applications.
Particle Swarm Optimization Method in Optimization of Grid Shell Structures

In general, there are two stages in designing a grid shell; creating a form and optimizing the form. Several different approaches such as evolutionary methods or gradient- based techniques have been used for optimization stage. Among evolutionary methods, researchers mostly have used the genetic algorithms (GA) whereas the particle swarm op- timization (PSO) has been shown to be more efficient than the GA in discrete problems. Hence, here, we use PSO method for improving the regularity of grid shell structures. It is illustrated how PSO method can be used in optimizing the grid shell structures. The tech- nique is explained step by step, and therefore there is no need for any previous knowledge of PSO.
Changes in soil organic carbon fractions in response to sugarcane planting in the central-south region of Brazil

Sugarcane planting area in Brazil has considerably increased during recent years by occupying areas used for pasture and grain crops production. Land use change (LUC) processes related to sugarcane expansion can affect the soil organic carbon (SOC) stocks, and the quality of the soil organic matter. Preliminary studies have shown that the land-use transition into sugarcane leadto a decrease in the SOC, however no studies have accessed the impact of LUC on the SOC distribution within soil particle-size classes. The study investigated the modifications on total SOC, particulate organic carbon (POM) and mineral-associated C (MOC) in response to conversion from native vegetation (NV), pastures and annual cropland (AC) to sugarcane crop (SCN). Soil samples were collected at 34 field-sites to 20 cm depth and POM fraction separated through 53-mm sieve after soil dispersion. The study results indicated that LUC affects both the labile as well as the more stable SOM fraction, with a mean C content decreasing of 40 and 30% in POM and MOC respectively, following the transition from NV to SCN. The replacement of pastures caused C depletion by 33% at POM and 30% at MOC; meanwhile C accumulation at MOC (5%) was detected for the conversion from AC to SCN. The impact of LUC on POM for AC-SCN transition could not be totally clarified, however the result could be an indication that the POM response to LUC varies as function of previous management in AC. Key words: Soil organic carbon, C fractionation, particulate organic matter, mineral-associated C, land use change.
FDI Inflows, Market Fragmentation and Their
Impact on Industrial Environmental Efficiency:
An Empirical Study in China

Sustainable development of industry, especially in developing countries, is an effective way to solve the conflict between industrial economic growth and the ecological environment. Industrial environmental efficiency is selected to measure the sustainable development of industry. We used the super-SBM method to evaluate the industrial environmental efficiency of 29 provinces during the period of 2007-2015. Our study analyzes the effect of FDI and market fragmentation on industrial environmental efficiency based on the system GMM approach. The result indicates that the impact of FDI on industrial environmental efficiency is significantly negative, which suggests that FDI significantly hinders industrial environmental efficiency. Market fragmentation significantly inhibits industrial environmental efficiency. We also further investigate the effect of the interaction between FDI and market fragmentation on industrial environmental efficiency. The result indicates that the negative effect of FDI on industrial environmental efficiency is stronger in provinces with more severe market fragmentation. Based on the findings, the government should promote the integration of regional market, optimize the structure of FDI and improve environmental quality.
Interactive comment on “ AtChem , an open source box-model for the Master Chemical Mechanism ”

This paper documents a boxmodeling system for the Master Chemical Mechanism (MCM). The MCM is a very large, near explicit mechanism describing the degradation of 143 primary compounds into CO2 and H2O. As such, the MCM is too computationally expensive to be run in global chemistry models or even air quality models. However the MCM is often used to benchmark smaller ‘lumped’ chemistry schemes by comparison using a zero-dimensional boxmodel, or by comparison with results from controlled chamber experiments. Much of the boxmodeling work in the past used expensive commercial software, and this new open source system was developed in part to avoid these costs, but also to provide potential additional capability beyond commercial ‘black-box’ systems.
Reevaluating the Mutation Classification in Genetic Studies of Bradycardia Using ACMG/AMP Variant Classification Framework

Purpose Next-generation sequencing (NGS) has become more accessible, leading to an increasing number of genetic studies of familial bradycardia being reported. However, most of the variants lack full evaluation. The relationship between genetic factors and bradycardia should be summarized and reevaluated. Methods We summarized genetic studies published in the PubMed database from 2008/1/1 to 2019/9/1 and used the ACMG/AMP classification framework to analyze related sequence variants. Results We identified 88 articles, 99 sequence variants, and 34 genes after searching the PubMed database and classified ABCC9, ACTN2, CACNA1C, DES, HCN4, KCNQ1, KCNH2, LMNA, MECP2, LAMP2, NPPA, SCN5A, and TRPM4 as high-priority genes causing familial bradycardia. Most mutated genes have been reported as having multiple clinical manifestations. Conclusions For patients with familial CCD, 13 high-priority genes are recommended for evaluation. For genetic studies, variants should be carefully evaluated using the ACMG/AMP variant classification framework before publication.
Duration : 30 months D 3 . 1 : State-ofthe-art Context Elicitation , Context Modeling and User Modeling

This report describes the state of the art of context awareness and user modeling, including the elicitation, processing and modeling of context. Specific attention is given to main issues in context awareness such as privacy, incompleteness and ambiguity. We discuss how context information relevant for software maintenance can be collected. We also discuss how context in general and users in particular can be modeled based on semantic technologies and machine learning and compare both approaches. Finally, we present first uses of context in the domain of remote software maintenance. This document has been produced in the context of the FastFix Project. The FastFix project is part of the European Community’s Seventh Framework Program for research and development and is as such funded by the European Commission. All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability. For the avoidance of all doubts, the European Commission has no liability is respect of this document, which is merely representing the authors view.
Design of a Silicon-based Optical Neural Network

— In this paper, a silicon-based fully connected optical neural network (ONN) is designed, which can be use to image classification and recognition with accuracy greater than 97%. A fully connected neural network is constructed. One layer model has been used in chip design. Chip simulation shows accuracy could not be impacted as photons large enough. This structure could be used in deep learning.