Miracle-dz/UniFAR-FTUs huggingface dataset:
Theory of Reasoned Action and Organic Food Buying in India

: The purpose of this paper is to find out a relationship between attitude towards organic food, subjective norms and buying intention of organic food. This study explores the impact of attitude and subjective norms on buying intentions and the impact of subjective norms on attitude towards organic food. Data were collected with the help of a structured questionnaire; questionnaires were e-mailed to the respondents from different major cities of India, covering North, West and South India. Structural equation modeling was employed to test the proposed model fit. This research shows that attitude towards organic food and subjective norms have a positive impact on the buying intention of organic food. Subjective norms have a positive impact on attitude towards buying organic food.Components of the theory of reasoned action, attitude and subjective norms are being used to find out their impact on buying intention of organic food. It is the first of its kind of study in the Indian environment as per the buying of organic food is concerned. No good study previously applied the theory of reasoned action to buying intention of organic food in India
An Emprirical Analysis of the Effect of Monetary Policy on Economic Growth in Nigeria

This study investigated the impact monetary policy and economic growth in Nigeria using quarterly time series data from 1981 to 2020. The paper used growth rate of gross domestic product (GRGDP) as the dependent variable while broad money supply (MS2), monetary policy rate (MPR), Inflation (INFL), liquidity ratio (LDQR) and exchange rate (EXCH) were the independent variables as well as proxies for monetary policy. Data were obtained from Central Bank of Nigeria’s Statistical Bulletin of various years and World Bank National Account Data. The study used descriptive statistics, performed unit root test using Augmented Dickey Fuller, and used the Autoregressive Distributed Lag (ARDL) model based on the E-views 10.0 software as methods of data analysis. The empirical findings indicated that Broad Money supply has a negative but insignificant effect of economic growth, the same result was observed for Inflation and exchange rate in the long run. While an insignificant positive relationship was found to exist between Monetary Policy rate and economic growth, liquidity ratio also had a positive nexus with economic growth in the long run. Consequent upon the findings, the study recommends that Monetary Policy authorities should give Monetary Policy Rate priority attention to enable it drive economic growth, persuade lenders not to lend at extremely high interest rate, ensure good coordination with the fiscal policy authorities and strive to enhance financial inclusion. Monetary Policy Rate (MPR), Inflation (INFL), Liquidity Ratio (LDQR)and Exchange Rate (EXCH) while economic growth was proxied by Growth Rate of Gross Domestic Product (GRGDP) and was the dependent variable. Annual reports of Central Bank of Nigeria (CBN) Statistical Bulletins and World Bank Annual Account Data were sources for the quarterly Time series. The study employed descriptive statistics and unit root test. The result of the Unit root showed that GRGDP, MPR, LDR were stationary at levels while MS2, INFL and EXCH were stationary at first difference. The study further employed Autoregressive Distributive Lag (ARDL) bound test , Error Correction model and some diagnostic tests were performed using Eviews 10. gross Srithilat which CBN positive impact finding tandem with findings of Ibrahim (2019). Etale et al. (2019) Ufoeze et al (2019). For Inflation relationship was negative, to though Impact of liquidity ratio of economic was positive but not the positive association liquidity ratio economic conforms Etale et al. Exchange revealed negative nexus with economic growth.
SURFACE CHARACTERISTICS OF SCOTS PINE WOOD HEATED AT HIGH TEMPERATURES AFTER WEATHERING

This study was conducted to investigate some surface properties of wood specimens of heat treated Scots pine ( Pinus sylvestris L.) after weathering such as surface hardness, surface roughness, gloss, and color changes. Heat treatment of Scots pine wood was carried out by hot air in an oven for 1, 2, and 3 hours at 210, 220, and 230°C. The results showed that generally surface hardness losses of heat treated Scots pine wood were lower than that of un-heated Scots pine wood after weathering. Heat treated Scots pine wood gave smooth surface after weathering. Except for heat treatment at 230°C for 1 and 2 hours, heat treatment resulted in better glossiness compared to un-heated Scots pine wood after weathering. According to the test results, while heat treated Scots pine wood become lighter after weathering, un-heated wood become darken after weathering. In general, heat treated wood surface to become reddish and yellowish after weathering.
EFFECT OF PREPROCESSING IN HUMAN EMOTION ANALYSIS USING SOCIAL MEDIA STATUS DATASET

Emotion analysis using social media text is the emerging research area now a day. It helps the researcher to recognize the emotional state of the users and identify mental health-relevant problems like depression or anxiety, which may lead to suicide if not cured. The social media platforms like WhatsApp, Facebook, Instagram, etc. are widely used as these applications provide an affordable and reliable medium for transferring data, sharing thoughts, and even for routine informal communication. Social media status is normally analyzed to recognize the mood, emotion, thought process, or mental state of the individual as people generally share status for what they feel. On the other hand, pre-processing is the crucial step for any kind of text data analysis. In this paper, the social media status dataset is first pre-processed using various methods, given for feature extraction and classification purpose. For the machine learning approach, we have used count vectors and TF-IDF techniques for extracting the different features of the data. Using count vector feature extraction accuracy achieved by pre-processed data is 68.90%, 69.33%, 70.59%, 64.95%, 69.33% for naïve Bayes, LDA, Random forest, SGD and MLP respectively. Similarly, using TF-IDF feature extraction accuracy achieved by pre-processed data is 65.76%, 69.96%, 68.49%, 65.96%, 70.80% for naïve Bayes, LDA, Random forest, SGD and MLP respectively. The experimental results show that pre-processing helps to improve the accuracy of the classifier and CNN outperforms the traditional approach and achieves 79% accuracy.
Non-pharmacological methods to relieve Dysmenorrhea among Students of health colleges in Saudi Arabia

: Dysmenorrhea is the most common gynecological problem that affects females, defined as painful menstruation. Effects of dysmenorrhea on female behaviors, attitude and daily life are highly reported, therefore a pharmacological and non-pharmacological treatment in managing dysmenorrhea has been reported. There is insufficient information about the use of non-pharmacological methods among students of health colleges in Saudi Arabia. The purpose of this study was to investigate the use of non-pharmacological methods to relieve dysmenorrhea among students of health colleges at King Abdul-Aziz University. Method: A quantitative descriptive cross-sectional was conducted among 215 female students from health colleges, the study questionnaires were self-administered developed by researchers from the literature used to obtain relevant data, data analyzed by SPPS version 16. Results: The mean age of the student was 21, 38.6% had menarche between 12-14 years, 77.7% had experience intermittent pain and 47.9% suprapubic pain, also 63.3% had moderate pain, the most common symptoms were fatigue 31.16%. In managing dysmenorrhea there were 46.5% used pharmacological drugs, 53% used herbal remedies, 52.6% always take rest, 42.8% used hot compresses, a significant association between absenteeism and severity of pain p=0.01, bad dietary habits and severity p= 0.05, use of herbal remedies and severity p=0.05. Conclusion: The students were used a non-pharmacological method to relieve dysmenorrhea, but still there are some methods were not applicable, therefore more information is needed regarding these methods. Recommendation: Promotion of woman health and raising the awareness of healthy lifestyle and bad dietary is recommended. pain (p= 0.05). The finding reflects Agreements of bad dietary habits probably due to insufficient intake of vitamins and minerals, unhealthy lifestyle, inadequate meals, and snacks.
Condition Index Analysis of Mussel Mytilus Galloprovincialis (Lamarck, 1819) from the Romanian Black Sea Coast

The condition index of bivalve molluscs reflects the nutritional state and metabolic response to different environmental pressures. The development of bivalves in natural environments is mediated by the interaction between environmental factors. The aim of this study is to determine the condition index of the marine bivalve Mytilus galloprovincialis and to establish the relationship between environmental parameters and the index values. This study was carried out between November and December 2017, at four sampling sites. The mean values of condition index varied between 4.96 g (at 2 Mai) and 9.88 g (at Midia N ă vodari Port), the highest index values being recorded in port areas. From all the analyzed parameters, the condition index showed a clear relationship only with seawater temperature, chlorophyll a concentration and total suspended solids.
Bidirectional data transformation by calculation

ion S to %% > T from dd , such that from ◦ ⊆ R ⊆ to. Conversely to refinements, to and from must now be surjective and injective functions, respectively. Whenever the relation forms both a refinement and an abstraction, it determines a unique isomorphism: Corollary 2. The relation R is a bijection if and only if there exists a total isomorphism
KBC: THE KNOWLEDGE BUILD COMPLEXITY

A knowledge-based complexity measure is introduced for assessing the complexity of a given entity. At its core, the measure relies on reducing the entity to a sum of human knowledge inputs. A knowledge baseline is ﬁrst established, below which the knowledge levels are considered negligible. A complexity tree is then derived, with complexity nodes deﬁned by an ‘is a component’-type relationship, and each branch terminating when it arrives at the knowledge baseline. Diﬀerent trees may be partially compared by summing the complexity along all branches, or, more fully, by considering the tree structure and individual complexities at each node. The paper concludes with an example in practical use, a routine which receives an entity name and performs a basic recursive knowledge search with a specialized web crawler on the Wikipedia corpus. To help determine relevance for recursion, the NLP BERT machine learning model is used for relation extraction.
DATA-ENABLED CRYPTOCURRENCY MARKET ANALYSIS AND VISUALIZATION PLATFORM

The cryptocurrency industry has evolved rapidly in recent years, and it is increasingly popular as a convenient tool to complement the traditional stock and futures exchanges. Accurate market research enables traders to make more informed decisions and benefit from their investments. Our objective is to introduce a web platform for aggregating various types of cryptocurrency data, both on-and off-chain. Its novelty lies in offering a visual representation of market data analysis, which is driven by multi-modal data fusion and representation techniques, as well as artificial intelligence. We propose a full-stack framework that consists of a front-end web application for user interaction and visualization, and a backend server for data fetching, preprocessing, and analysis. In our implementation, we used data from the cryptocurrency market, on-chain statistics, and textual data from social media, to create a deep-learning-based market trend model. For market prediction, our data analysis module processed high-frequency vocabulary extracted from social media, sentiment analysis of social media content, historical price trend, and historical hash rates. Investors and market analysts can benefit from our platform by directly observing the dynamic of multi-modal cryptocurrency data and easily exploring market trends, generated by our market prediction model delivered by a front-end application. The complete implementation can be found in our publicly available GitHub link upon request.
Community Management of Grazing Lands and Impact on Environmental degradation in the Ethiopian Highlands

Communal grazing lands are important sources of livestock feed in developing countries, although unrestricted access to such resources can result in overexploitation and degradation of the resource. Collective action, through restrictions and regulating use by the community, can play a significant role in sustainable grazing land management. Restricting access and use can reduce degradation of the resource by eliminating overexploitation and, therefore, improve availability and quality of forage in the long run. On the other hand, by restricting grazing in certain areas, there is tendency to shift pressure to other unrestricted grazing areas, which can rapidly increase degradation of those resources. Using data from 98 villages in the highlands of Amhara region, this paper first examines the determinants of collective action and its effectiveness in communal grazing lands management and the effect of restricting access and use in certain grazing areas on the condition of other communal grazing resources. More than one-half of the communities had at least one such restricted grazing area, with the total area in each of those communities averaging twenty-two hectares. About 70 percent of the restricted grazing lands were managed at the village level, while the remaining was managed at a higher kebele level, usually consisting of three to five villages. The results show collective action is more likely to be successful in communities that have large areas, are far from markets, and where wealth (oxen ownership) is more equally distributed. Where there are more alternative sources of feed, as in irrigated areas, collective action is not likely to succeed. The results do not show much effect of restricting grazing access in certain areas on degradation of other unrestricted grazing areas. However, increasing the proportion of restricted grazing land had a robust negative impact on the quality of other unrestricted grazing resources, although managing the restricted grazing land at the lower village level had a robust positive impact. Population growth had the most negative effect across board: reducing availability and quality and increasing erosion of grazing lands. This is consistent with a neo-Malthusian notion regarding the negative impacts of population growth. We also found that severity of erosion of grazing lands was lower in areas with higher rainfall areas and better access to credit and extension programs offered by NGO’s. Overall, these results suggest that community grazing land management can contribute to sustainable use of grazing lands and alleviation of feed shortage problems, as in the highlands of northern Ethiopia. However, collective action for grazing land management may be more beneficial and more effective in communities with large areas, that are far from markets, where wealth is more equally distributed, and where population pressure is low.
Manufacturing Output and Economic Growth in India:An ARDL Bound Testing Approach and Granger Causality

Considering the manufacturing sector of India and its limited growth, this study analyzes the impact of manufacturing output on economic growth from 1981-2014. Along with manufacturing output, we have taken gross capital formation and population growth as control variables. For short and long run estimates, we have considered the ARDL bound testing approach. For analyzing the directional causality between variables, we have considered granger causality test. According to error correction model, our findings exhibit the significantly positive long-run relationship of manufacturing output, gross capital formation and population growth with economic growth in India. However, granger causality presents only unidirectional causality of population causing manufacturing output and capital formation. Whereas, no causality has been found between manufacturing output and economic growth. The study implies that with human resource abundance, policy makers should emphasize on the capital growth to enhance the capacities of manufacturing sector in India. Though manufacturing sector has established a positive relationship with economic growth but needs dire attention with sectoral reforms that may increase manufacturing productivity causing economic growth in the country.
Deep Learning in Traffic Flow Control and Prediction for Traffic Management

A major issue in urban settings is the heavy traffic and vehicle congestion due to poor traffic control or overpopulation. Nevertheless, this issue emphasizes the significance of proper traffic flow control and prediction to improve traffic management. This study provides a systematic literature review of novel twenty (20) papers that used the deep learning approach to solve the complex traffic management problem. The several studies gathered showed that long short-term memory (LSTM) models are effective for urban traffic flow prediction. The related studies shows that additional datasets, and a hybrid model, lead to better prediction accuracies. Based from the result of systematic literature, after criteria was se, this study proposed an AT-Conv-LSTM model that uses environmental data and data from social media.
Monte Carlo Simulation for Understanding Risk in Project Management

This article develops an algorithm for assessing the risk in project management. The main risk considered here is the risk of a project not finishing on time. This algorithm can be considered as an extension of the classical methods like Critical Path Method (CPM) and Project Evaluation and Review Technique (PERT). This extension can provide a richer understanding of risk inherent in any project planning. Specifically, the present article discusses the classical project management tools such as CPM and PERT by pointing out to their applications as well as shortcomings in the context of risk management. It then shows the steps to conduct a Monte Carlo simulation of CPM using EXCEL spreadsheet. To conduct the simulation, first CPM is recast as a couple of optimization (linear programming) problems. And then the scripts written in Visual Basic for Applications (VBA) in Excel is used to run the CPM for one hundred times. The simulation gives a range of project completion times as well as the probability for each activity becoming a part of critical path.
Feedback based Root-Cause Identification Model with User Profiler for Effective Content Correlation

Massive amounts of data available online has led to a lot of models leveraging the available knowledge for decision making. However, identifying the appropriate data for analysis seems to be a daunting task. This paper proposes a framework that can be used to effectively identify and retrieve appropriate content from large repositories. The proposed model builds the user’s profile based on context, sentiment pairs and frequency of usage of information pertaining to the pair. This leads to the building up of an effective user profile that can be utilized to retrieve content appropriate to the user when a query is presented to the system. Further, incorporation of the final feedback helps in continuous improvement of the user’s profile such that frequent usages will lead to better predictions. Comparisons with the existing sentiment analysis models indicate high efficiency in the prediction levels of the proposed feedback based root cause analysis model in terms of Accuracy, F-Measure, True and False Prediction levels.
A Comparative Analysis of Network Overhead Produced By Popular Software-Defined Networking Controllers

Due to the ever-growing needs of modern networks, software-defined networking (SDN) is becoming increasingly prevalent. SDN offers an advantage over traditional networks by separating the control plane from the data plane and abstracting network complexity away into a single controller, which facilitates communication between network devices and applications. Mininet, a rapid SDN prototyping tool, is a popular choice for teaching, research, testing and development due to its simplicity, portability, rich features, and transferability to hardware. There are numerous choices when it comes to selecting a controller. Previous studies have used Mininet to compare controllers, but experimental evaluations have been lacking. In this paper, we use Mininet to compare the default Stanford reference, OVS, and POX controllers. We generate realistic flows using iPerf and the Distributed Internet Traffic Generator (D-ITG), evaluate controller performance in terms of network overhead, and use multiple interleaved trials (MIT) to obtain fair and repeatable comparisons. Through statistical analysis, we found that POX generally outperformed the other controllers under the iPerf workloads, and OVS and POX both outperformed the default controller under D-ITG workloads. We also discovered variability in results between workload generators, underscoring the effect of the generator itself and the importance of considering this effect when designing SDN experiments. While such consideration is missing from existing work, our findings should inform future studies.
Nonlinear phenomena in photonic nanostructures : modulational instabilities and solitons

This thesis discusses nonlinear effects, such as modulation instability and solitons in nano-structured waveguides. The nanoscale optical waveguides have extremely small transverse dimensions, which can provide tight confinement of light. Therefore, by changing the waveguide geometry, the waveguide dispersion can be strongly altered. On the other hand, the confinement also enhances the nonlinear dispersion, allowing for nonlinear optical phenomena supported by dispersion of nonlinearity. The new models governing evolution of the amplitudes of components of the optical waves interacting in the waveguides are derived for continuous wave and pulse wave using perturbation expansion method. The new modulation instability condition is found, as we take into account the dispersion of nonlinearity which is enhanced through a strong variation of the modal profile with the wavelength of light in sub-wavelength waveguides. We demonstrate that this dispersion of nonlinearity can lead to the modulation instability in the regime of normal group velocity dispersion through the mechanism independent from higher order dispersions of linear waves for continuous wave. We address that the new mechanism highly associated with dispersion of nonlinearity in sub-wavelength semiconductor waveguide induces the modulation instability in picsecond regime together with the cascaded generation of higher-order sidebands. The impact of the dispersion of nonlinearity on spectral broadening of short pulses in a silicon waveguide also is considered. We study the temporal evolutions of fundamental and one-ring solitary waves with phase dislocation in dielectric-metal-dielectric waveguides with PT-symmetry and numerically analyze the properties of these nonlinear localized modes and, In particular, reveal different scenarios of their instability.
Cloud Based Learning Environment

— E-learning changes the way of teaching and learning. There are different application packages which function as a tool to organize and manage different courses of study in a planned manner. This platform teaches students with the aid of the modern educational technologies. Invention and application of cloud computing in the field of computer and networking has really increases the speed, easiness, accuracy and changes the way of social communications, activities and interactions. It reduces the chances of data loss and total financial involvements.