Abstract: Plastics cause severe environmental and societal issues when post-consumer plastics are discarded in the environment without proper management. Pyrolysis of plastic waste is a promising method for plastic waste management due to its valuable by-products such as fuel oil. The objective of the present study was to pyrolysis waste polypropylene (PP) and waste low-density polyethylene (LDPE) plastics into fuel oil in a low-cost, lab-scale apparatus at the maximum temperatures of 330°C and 355°C, respectively, and in turn to assess the physical properties, gross calorific value, density, kinematic viscosity, flash point, ash content, and sulfur content, of resultant fuel oil. Commercially available, recycled grades of LDPE and PP pellets, 70.0 g of each, were obtained, and pyrolysis was done in a lab-scale, low-cost batch reactor. The experiment was triplicated, and the resultant fuel oil was analyzed for physical properties by ASTM standard methods. The properties of resultant fuel oil were also compared with diesel grade criteria. Results revealed that the highest yield of fuel oil was obtained from waste PP at 79.57 wt.% while the waste LDPE resulted at 74.06 wt.% of fuel oil. No wax formation was observed in the waste PP pyrolysis process, unlike in the waste LDPE pyrolysis. The gross calorific values of waste PP and waste LDPE were 12 016 kcal/kg and 11 961 kcal/kg, respectively. It was higher than the standard value of commercial diesel. The distillation results indicated that short-chain hydrocarbon content is higher in the waste PP fuel oil sample than in the waste LDPE fuel oil sample. The results indicated that the resultant fuel oil samples were nearly closer to the diesel grade criteria. Therefore, the fuel oil produced from waste plastics is a viable alternative to traditional fossil fuels, with potential applications in power generation and transportation. Published on 2023-04-20 00:00:00
Abstract: The changes in the quality of coconut milk in industrial storage conditions have received scarce attention despite its widespread use. The study was carried out to evaluate the quality of industrially pasteurized coconut milk based on physicochemical parameters; pH, total solid content, acidity, total microbial count, and the changes in the fatty acid profile during storage. The study revealed that pH of the coconut milk samples varied in the range of 5.2-7.2 and acidity drastically increased during storage. The total microbial count increased with an increasing number of days in all samples. The highest total microbial count was observed on day 5 in 3 samples indicating the unfavourable microbial spoilage. Total solids in all samples were higher than 35% throughout the study. The ANOVA confirmed a significant increase (p>0.05) in the acidity and total microbial count from day 1 to 5, illustrating the crucial roles which these variables play in determining the quality of industrially pasteurized coconut milk during storage. The presence and changes of fatty acids in industrially pasteurized coconut milk were quantitatively analyzed by GC/MS and a total of 8 fatty acids were identified. The fatty acid contents changed significantly after industrial pasteurization and storage. Published on 2023-04-20 00:00:00
Abstract: Cellulose is one of the most widely used natural polymers developed in eco-friendly methods, which has been used in various industrial processes and products since ancient times. The sources of cellulose materials are plant and wood fibers. Cellulosic materials are converted into cellulose nanocrystals (CNCs) using mechanical or chemical methods. In this study, the CNCs were obtained from cotton balls by acid hydrolysis method using sulfuric acid. The sulfuric acid hydrolysis method was performed with 64% (w/w) sulfuric acid and combined using a liquor ratio of 1:20 with cotton balls while being subjected to vigorous stirring at 500C for 60 minutes. The cellulose nanocrystals were characterized by Transmission Electron Microscopy (TEM), Fourier Transform Infrared (FTIR) spectroscopy analysis and X-ray Diffraction (XRD) techniques. The extracted cellulose nanocrystals had needle-shaped particles with a 6.35 nm average diameter and a length of 108.8 nm on average. The functional groups of the extracted cellulose nanocrystals were shown to have been evaluated through analysis of the FTIR spectra. Therefore, it was confirmed that the cellulose nanocrystals were successfully extracted from cotton balls using sulfuric acid hydrolysis. The distinctive crystalline cellulose phase of artificial cellulose nanocrystals was recognized using the XRD spectrum. Published on 2023-04-20 00:00:00
Abstract: Performance enhancement of a computer program is an important aspect of today's world. Developers produce programs, and there is a lack of accurate methods for predicting the execution time of a computer program prior to its execution in an executable environment. Predicting the execution time of a particular program before execution would be great for developing the program with the highest performance efficiency and with the lowest execution latency. This research introduces a Machine Learning based solution to predict an execution-time-based label for a given computer program. There are three main types of parameters in a computer program that affect the execution time, such as Static Code Features, HTTP Calls, and the Hardware Performance of the execution environment. In this research, the Machine Learning model was trained for the parameters of the above types (Programs with Static Programs & HTTP calls) by executing them on a fixed hardware infrastructure execution condition. We analyzed the number of if conditions, methods, breaks, switches, loops, nested-loop-depth, frequencies, and the behaviour of HTTP calls, kind of features of a computer program in order to generate an accurate execution time complexity prediction label of a computer program. Further, in the collected data set, the most prominent feature which affects the complexity among the features that we considered is the number of HTTP calls and nested loop depth, followed by loops. Accuracy Score, Precision, Recall, and F1 Score values of the ML model were generated for the traditional classification algorithms such as Decision Tree Classifier, K Nearest Neighbor Classifier, Random Forest Classifier, Naive Bayes Classifier, Support Vector Classifier, and MLP Classifiers in order to verify the effectiveness of the model. The best accuracy score was achieved with an overall 88% by using the approach of Random Forest. The findings of this research can be optimized for implementing an IDE plugin or a developer tool that can predict the exact execution time of a given computer program live by integrating the specifications of the execution device. It will help developers in terms of optimizing a particular computer program and developing it for a minimum execution latency and enhancing the performance of the program. Published on 2023-04-20 00:00:00
Abstract: The appropriate operation of a water distribution network (WDN) of any water supply scheme is vital to supply sufficient potable water to consumers at sufficient pressure. However, the performance of the WDN may vary from the original design in the long run. In this study, a WDN network model was built using WaterGEMS and WaterCAD computer simulators, and hydraulic analyses were conducted to obtain an optimal WDN for a community water supply scheme of a village in Sri Lanka. A series of steps such as; selection of models, network representation, simulation of network, problem identification, network configuration finalization, and results analysis were carried out in developing the WDN simulation model. The hydraulic parameters such as pressure, flow velocity, and flow rate were analyzed under extended period simulation. The result indicated that the nodal pressure head in the junctions (100%) is above the required pressure level of 10 meters H2O, which is adequate for the effective performance of the water distribution system (WDS) during peak and off-peak demand hours. The elevated water tower was optimized with a 10 m height to supply water at satisfactory pressure. Nodal pressure is negatively correlated with ground elevation. The flow velocity was observed within the range of 0.1-0.4 m/s in 67% of the pipe network, while 17% of the pipe network velocity was below 0.1 m/s. The low daily water demand of the small community could be the reason for the low-velocity scenario, which shall lead to silt deposition in the pipelines; hence frequent line washout to eliminate the silt deposition in the system is recommended. The WDN was designed for optimized pipe sizes with availability in the market. Published on 2023-04-20 00:00:00
Abstract: Exchange rates serve as a medium for currency trading in the financial market. The variations and the uncertainty movements in exchange rates have a potential effect on the performance of a country. The objective of this study is to forecast daily exchange rates in Sri Lanka using Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) with Autoregressive Conditional Heteroscedasticity (ARCH)/ Generalized ARCH (GARCH) models. The study collected a few daily exchange rates from the Yahoo finance website in terms of LKR from 1st January 2008 to 28th February 2022. The DSARIMA and SARIMA models were incorporated with the ARCH/ GARCH specifications of normal, skew-normal, student-t and skew-t due to the accurate specification of the proper error distribution led to an increase in the accuracy of the fitted model. The model comparisons were carried out considering different performance measures. The overall results from the actual and fitted graphs and lower error values of the fitted models suggested a SARIMA model for CHF/LKR, a SARIMA model with ARCH/GARCH for USD, EURO, JPY, GBP and AUD against LKR and a DSARIMA model with ARCH/GARCH for CAD and SGD against LKR were suitable to forecast the respective exchange rate. Overall, the results from this study will support government, investors, corporate, financial and managerial sectors in their future decisions to accomplish their objectives. The originality of this study concerns the application of DSARIMA models in exchange rates due to the availability of double seasonality in data. Published on 2022-12-23 03:43:13
Abstract: This study intends to fill an essential knowledge gap in the field of environmental information in Sri Lanka, by providing a reliable data bank supporting the information of existing chemical species of two important components, well water and soil. Further, this study would provide reference information for future work and aid in explaining the changes that would occur due to changes in the chemical composition of the environment. Thalawathuhenpita North Grama Niladhari division was selected as the initial site of study and sampling was done from 23 sites, where two representative samples of each soil and well water were collected from each site. This study investigates the well water quality parameters such as pH determined by a pH meter, hardness and Calcium content using complexometric titrations, nitrate content using a spectrometric analysis, Iron content using a colorimetric analysis, and soil quality parameters such as active pH using a pH meter, water-soluble and exchangeable cations(Na+,Ca2+,K+)using a flame photometer and complexometric titration, organic matter content using a redox titration, iron content and nitrate content as mentioned above. The readings were duplicated and reported as means ± standard deviation, and contour maps were produced using Surfer ® (Golden Software, LLC). Maps depict the scope of variation of the determined parameters within the selected area and highlight the point that it is crucial to monitor the environmental resources chemically in a regular manner to address the environmental problems that may occur in the future. Published on 2022-12-12 03:43:43
Abstract: The COVID-19 pandemic has had a disastrous impact on organizations and changed most employees' working methods. Since the beginning of the pandemic, researchers have been researching and publishing their studies on the impact on both remote-working and non-remote-working employees. The current study was conducted more than one and a half years after the pandemic initially started, focusing on the long-term impact of working from home (WFH). The study uses a qualitative thematic analysis approach to identify factors that impact WFH employees in Sri Lanka during COVID-19. Ten in-depth interviews have been conducted with employees in the Information Technology sector who have worked from home since the beginning of the COVID-19 pandemic. The paper provides insight into different factors affecting employees who worked from home during the pandemic. The study findings will aid the decision-makers of organizations in taking the necessary actions to make working from home more comfortable. Published on 2022-12-12 00:00:00
Abstract: During the COVID-19 pandemic, instructors quickly transitioned to online teaching to protect themselves and their students. Due to this abrupt transition and lack of communication between instructor and student, instructors had little time to create online teaching methods that successfully adapt and teach the material to best meet the student's needs. Some students did well with online teaching methods, while others seemed to struggle to engage with the material in classes. Without immediate face-to-face feedback from students, we believe that background information such as learning styles, goals, motivations, and expectations of the potential students- will help teachers improve and increase classroom engagement and comprehension. We surveyed and interviewed STEM introductory-level courses online to collect data to create personas in STEM students. Personas are "life-like models whose characteristics are driven by the various goals and motivations of real or potential users." We used persona methodology to survey the diverse learning group in a STEM classroom to create four personas found in STEM classrooms. These personas represent real-life students enrolling in introductory STEM classrooms, allowing instructors to use these personas as a novel tool to design online courses that better engage undergraduate students across STEM disciplines. Published on 2022-11-09 00:00:00
Abstract: The sand fly species Phlebotomus argentipes is reported to be the suspected primary vector of leishmaniasis in Sri Lanka transmitting Leishmania donovani which causes visceral leishmaniasis. Thus, studies about some aspects of the reproductive biology of P. argentipes and the performances of immature stages are highly important for adopting control measures. Accordingly, sand flies were captured from the wild using five CDC light traps set in pre-identified places in the Kurunegala district. Gravid and fully engorged females were sorted out and they were directly placed for oviposition in plaster of Paris lined pots. The unengorged ones were allowed to feed on a mouse and placed for oviposition in similar pots. The rate of oviposition, egg hatching, and adult emergence was determined by making daily observations. Moreover, the duration of eggs, each larval instar, pupae, and adult longevity were recorded. According to the observations the life cycle of P. argentipes lasted about 3 to 4 months. The eggs were laid 10-13 days after the blood meal, and they hatched after 12-16 days. Similarly, each second instar, third instar, fourth instar and pupae spent 12-16 days for the emergence. However, the pupal duration was 8-10 days. Furthermore, the rate of oviposition was 15.8 per female and egg hatching and adult emergence was 62.52% and 52.32% respectively. The longevity of the emerged adults was 8-15 days. The information presented on P. argentipes in this study is highly important to initiate and maintain a sand fly colony under laboratory conditions for the implementation of successful control measures. Published on 2022-10-24 00:00:00
Open Access journal
