No abstract

Rapid depletion in fossil fuels, inflation in petroleum prices, and rising energy demand have forced towards alternative transport fuels. Among these alternative fuels, diesel-ethanol and diesel-biodiesel blends gain the most attention due to their quality characteristics and environmentally friendly nature. The viscosity and density of these biodiesel blends are slightly higher than diesel, which is a significant barrier to the commercialization of biodiesel. In this study, the density and viscosity of 30 different ternary biodiesel blends was investigated at 15 °С and 40 °С, respectively. Different density and viscosity models were developed and tested on biodiesel blends soured from different feedstock’s including palm, coconut, soybean, mustard, and calophyllum oils. The prognostic ability and precisions of these developed models was assessed statistically using Absolute Percentage Error (APE) and Mean Absolute Percentage Error (MAPE). The MAPE of 0.045% and 0.085% for density model and 1.85%, 1.41%, 3.48% and 2.27%, 1.85%, 3.50% for viscosity models were obtained on % volume and % mass basis. These developed correlations are useful for ternary biodiesel blends where alcohols are the part of biodiesel blends. The modeled values of densities and viscosities of ternary blends were significantly comparable with the measured densities and viscosities, which are feasible to avoid the harm of vehicles’ operability.

Change of land use and land cover (LULC) has been a key issue of natural resource conservation policies and environmental monitoring. In this study, we used multi-temporal remote sensing data and spatial analysis to assess the land cover changes in Fateh Jhang, Attock District, Pakistan. Landsat 7 (ETM+) for the years 2000, 2005 and 2010 and Landsat 8 (OLI/TIRS) for the year 2015 were classified using the maximum likelihood algorithms into built-up area, barren land, vegetation and water area. Post-classification methods of change detection were then used to assess the variation that took place over the study period. It was found that the area of vegetation has decreased by about 176.19 sq. km from 2000 to 2015 as it was converted to other land cover types. The built-up area has increased by 5.75%. The Overall Accuracy and Kappa coefficient were estimated at 0.92 and 0.77, 0.92 and 0.78, 0.90 and 0.76, 0.92 and 0.74, for the years 2000, 2005, 2010 and 2015, respectively. It turned out that economic development, climate change and population growth are the main driving forces behind the change. Future research will examine the effects of changing land use types on Land Surface Temperature (LST) over a given time period.

No abstract

No abstract

No abstract

Summary The Software plagiarism, which arises the problem of software piracy is a growing major concern nowadays. It is a serious risk to the software industry that gives huge economic damages every year. The customers may develop a modified version of the original software in other types of programming languages. Furthermore, the plagiarism detection in different types of source codes is a challenging task because each source code may have specific syntax rules. In this paper, we proposed a methodology for software plagiarism detection in multiprogramming languages based on machine learning approaches. The Principal Component Analysis (PCA) is applied for features extraction from source codes without losing the actual information. It extracts features by factor analysis and converts the dataset into normalized linear principal components which are further useful for predictions analysis. Then, the multinomial logistic regression model (MLR) is applied to these components to classify the source codes documents based on predictions. It gives the generalization of logistic regression to handle multiclass problems. Further, the predictors' performance in MLR is evaluated by 2 tailed z test. To apply the experiment, the dataset is collected in five different and popular languages, ie, C, C++, Java, C#, and Python. Each programming language taken in two different case studies, ie, binary search and Stack.

Students' interaction and collaboration using Internet of Things (IoT) based infrastructure is a convenient way. Measuring student attention is an essential part of educational assessment. As new learning styles develop, new tools and assessment methods are also needed. The focus in this paper is to develop IoT based interaction framework and analysis of the student experience of electronic learning (eLearning). The learning behaviors of students attending remote video lectures are assessed by logging their behavior and analyzing the resulting multimedia data using machine learning algorithms. An attention-scoring algorithm, its workflow, and the mathematical formulation for the smart assessment of the student learning experience are established. This setup has a data collection module, which can be reproduced by implementing the algorithm in any modern programming language. Number of faces, eyes, and status of eyes are extracted from video stream taken from a webcam using this module. The extracted information is saved in a dataset for further analysis. The analysis of the dataset produces interesting results for student learning assessments. Modern learning management systems can integrate the developed tool to take student learning behaviors into account when assessing electronic learning strategies.