Status of biodiesel research and development in Pakistan

Performance of biodiesel in engines is well established and biodiesel is currently adjudged as a low carbon fuel with the most potential of replacing fossil fuels. The fossil fuel sources are dwindling in Pakistan resulting in importation of about 8.1 million tonnes at approximately US$ 9.4 billion per annum. In the ambit of this justification, augmenting the energy scarce resources in Pakistan through intense harnessing of the varied biodiesel sources can adequately address the deficiency and can ensure energy security. Towards this end, the progress attained in biodiesel related researches in Pakistan are evaluated and presented with the view of highlighting ways of achieving the target set by the Government. A feedstock that drew less attention is spent triglycerides, and the little work reported by some organizations appeared promising. Now the onus is upon organizations such as the Alternative Energy Development Board and Pakistan State Oil to harness the research results from several indigenous Universities and develop a full-scale biodiesel economy in Pakistan.     

Thermophysical Properties of Platinum-Copper Alloys

Platinum and copper along with their alloys have been used in a broad range of applications including jewelry, coinage, electrical and electronic devices, and many others. Their thermophysical properties play an important role in casting processes and are required as input data for casting simulation. The focus of this work was to investigate these properties by different methods. Platinum, copper, and four platinum-copper alloys, namely, Pt96Cu04, Pt68Cu32, Pt50Cu50, and Pt25Cu75, were investigated within this work. The melting range and thermal expansion were measured at fem by differential scanning calorimetry and dilatometry, respectively. At TU Graz, wire-shaped samples were investigated by an ohmic pulse heating technique. This technique delivers thermophysical properties of electrically conducting materials far into the liquid phase. These measurements allow the calculation of specific heat capacity and the temperature dependencies of electrical resistivity, enthalpy, and density of these alloys in the solid and liquid phases. Thermal conductivity and thermal diffusivity as a function of temperature are estimated from resistivity data using the Wiedemann?CFranz law at the end of the solid phase and at the beginning of the liquid phase. The results are compared with the available literature values.     

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification technique.     

Early Detection of Diabetic Retinopathy Using Machine Intelligence through Deep Transfer and Representational Learning

Diabetic retinopathy (DR) is a retinal disease that causes irreversible blindness. DR occurs due to the high blood sugar level of the patient, and it is clumsy to be detected at an early stage as no early symptoms appear at the initial level. To prevent blindness, early detection and regular treatment are needed. Automated detection based on machine intelligence may assist the ophthalmologist in examining the patients’ condition more accurately and efficiently. The purpose of this study is to produce an automated screening system for recognition and grading of diabetic retinopathy using machine learning through deep transfer and representational learning. The artificial intelligence technique used is transfer learning on the deep neural network, Inception-v4. Two configuration variants of transfer learning are applied on Inception-v4: Fine-tune mode and fixed feature extractor mode. Both configuration modes have achieved decent accuracy values, but the fine-tuning method outperforms the fixed feature extractor configuration mode. Fine-tune configuration mode has gained 96.6% accuracy in early detection of DR and 97.7% accuracy in grading the disease and has outperformed the state of the art methods in the relevant literature.     

Synthesis and modification of silica-based epoxy nanocomposites with different sol–gel process enhanced thermal and mechanical properties

This research article describes the results of nano-silica composites filled with different epoxy contents containing nano-SiO₂ particles from (5–25 wt%). Reinforcing hybrid composites enhance thermal and mechanical properties to achieve vital and sustainable products. Silica-based nanocomposites with high purity were prepared and used for the surface modification of nanosized silica particles. The surface structure's composition and physical properties of modified nano-SiO₂ particles were characterized through Fourier transferred infrared spectrometer, X-ray photoelectron spectroscopy, thermogravimetric analyzer, and scanning electron microscopic. Silica-based nanocomposites were prepared by incorporating of modified nano-SiO₂ as an enhancing filler. The morphology of fracture surface and dynamic mechanical properties were investigated. Results showed that the silica-based epoxy nanocomposites are bearing a long chain structure that could improve the compatibility of silica nanocomposites with epoxy resin and contribute to a better dispersion state in the matrix, which enhanced the overall performance of epoxy-cured products.     

Gelatin nanocomposite films incorporated with magnetic iron oxide nanoparticles for shelf life extension of grapes

Nanocomposite films were obtained by solution casting method from aqueous solution of bovine gelatin with addition of various concentrations of magnetic iron oxide (MIO) nanoparticles (NPs) (5, 10, 15, and 20% w/w of dry gelatin). The incorporation of MIO NPs improved the mechanical and physical properties of the nanocomposites. The increase in concentration of NPs up to 10% improved barrier and mechanical properties which slightly decreased after increasing the concentration beyond that limit due to particle agglomeration. The scanning electron microscopy and X-ray diffraction (XRD) were used to evaluate the morphology and crystalline structure of gelatin nanocomposite films, respectively. Gelatin nanocomposites with 20% w/w NPs exhibited the highest antimicrobial activity against Escherichia coli (7.1 ± 0.085 mm) and Staphylococcus aureus (8.22 ± 1.04 mm). Finally, the potential of gelatin/MIO nanocomposites as packaging material was evaluated to extend the shelf life of grapes. The gelatin/MIO nanocomposites can be used as a replacement to non-biodegradable packaging.     

Optimisation of food grade mixed surfactant‐based l‐ascorbic acid nanoemulsions using response surface methodology

Co‐surfactant free l‐ascorbic acid (LAA) nanoemulsions were prepared using mixed surfactants (Soya lecithin and Tween 80). Response surface methodology (RSM) was used to optimise the emulsifying conditions for LAA nanoemulsions. The effects of water proportion (6%–14% w/w), homogenisation pressure (80–160 MPa), surfactant concentrations (4%–12% w/w) and laa concentration (0.5–1.3 w/w) on responses (size of droplets and nanoemulsion stability) were investigated. RSM results showed that the values of responses can be successfully predicted through second‐order polynomial model. The coefficients of determinations for droplet size and nanoemulsion stability were 0.9375 and 0.9027, respectively. The optimum preparation conditions for l‐LAA nanoemulsion were 9.04% water proportion, 114.48 MPa homogenisation pressure, 7.36% surfactant concentration and 1.09% LAA concentration. At the end of one month storage study, the retention of LAA in optimised nanoemulsions stored at 4°C and 25°C were 74.4% and 66.7%, respectively. These results may provide valuable contributions for food and pharmaceutical industry to develop delivery system for food additives and nutraceutical components.     

The use of an enzymatic extraction procedure for the enhancement of highland barley (Hordeum vulgare L.) phenolic and antioxidant compounds

A multistep enzymatic extraction method was compared with a conventional chemical extraction process to evaluate the phenolic content and antioxidant activity of highland barley (Z2, Zangqing 25; CHQK, Changheiqingke). The main phenolic compound extracted was (+)‐catechin, followed by ferulic acid, p‐coumaric acid, caffeic acid and chlorogenic acid. The multi‐enzymatic digestion yielded a higher retrieval of (+)‐catechin compared to the conventional chemical extraction procedure (P < 0.05). Compounds obtained from the multi‐enzymatic digestion process exhibited significantly higher (P < 0.05) antioxidant activities determined by oxygen radical absorbance capacity (ORAC) and cellular antioxidant activity (CAA) when compared to chemical extracts. These results suggest that highland barley subjected to in vitro multi‐enzymatic digestion exhibits a higher phenolic content and antioxidant activity than the chemical extraction, and this multi‐enzymatic digestion coupled with the CAA assay may be a valuable tool to evaluate the antioxidant potential of wholegrains and fruits, as well as vegetables.