SUPERCRITICAL AND CATALYTIC FLUID EXTRACTIONS OF TEA WASTES

Abstract

Air dried and ground tea waste was subjected to supercritical and catalytic fluid extraction by using water or acetone as solvent at different temperatures. The most important reactions variables were temperature and ratio of catalyst to the solid sample. The yields of the catalytic fluid reaction have been increased from 70.3 % to 92.4 % as the temperature increased from 230 °C to 340 °C by using water as solvent. The yield of extract was obtained from non catalytic supercritical water extraction was about 50.0 % at380°C.     

Model Based IMC Controller for Processes with Dead Time

Abstract

A system with varying transportation lags has been experimentally studied for modeling. Modeling is performed using a step test. The tracer is sodium chloride solution whose conductivity is measured using an online conductivity analyzer. Based on the step response, the model parameters are determined and the lag processes are represented by a first order plus dead time (FOPDT) model. For the models developed, an internal model control (IMC) scheme is designed. Performance comparison, based on rise time, settling time, and overshoot, is done among the designed IMC controllers, conventional PID controllers, and Smith Predictor controllers. The present study depicts that IMC controllers outperform PID and Smith Predictor controllers.     

Microfluidic fabrication of cationic curcumin nanoparticles as an anti-cancer agent

Curcumin nanoparticles of less than 50 nm in diameter are accessible using a continuous flow microfluidic rotating tube processor (RTP) under scalable conditions, at room temperature. A mixture of DDAB and Pluronic F127 renders higher stability of the curcumin nanoparticles in physiological pH 7.4 for up to eight hours. The nanoparticles have enhanced cytotoxicity in estrogens receptor negative and positive breast cancer cell lines compared with free curcumin.     

Hyperbranched polysiloxane‐based diglycidyl ether of bisphenol a epoxy composite for low k dielectric application

A new type of diglycidyl ether of bisphenol A (DGEBA) epoxy‐based hybrids has been developed by the incorporation of varying percentages of glycidyl‐terminated hyperbranched polysiloxane (HPSiE) into DGEBA resin and are characterized for their physicochemical, thermal, mechanical, and dielectric behaviors by modern analytical techniques. Data resulted from different studies indicate that the incorporation of HPSiE into DGEBA epoxy resin significantly improved the impact strength, thermal, and dielectric properties with an increase in the HPSiE loading. The contact angle [water and diiodomethane (DI)] increase with increases according to the weight percentages of HPSiE, which indicates the HPSiE‐modified DGEBA shows hydrophobic in nature. The resulting epoxy‐based hybrid composites can be used effectively for different industrial and engineering applications for better performance with improved longevity. POLYM. COMPOS., 34:904–911, 2013. © 2013 Society of Plastics Engineers     

A bibliography of neural network business applications research: 1988–September 1994

Abstract: The purpose of this research is to present a comprehensive bibliography of neural network application research in business. One hundred and twenty-seven neural network application papers and reports are identified through exhaustive literature searches. A classification of these articles by application area reveals that neural networks are being used for a diverse range of corporate functional activities, particularly in the areas of production/operations and finance. Information on the neural network development language/tool, the learning paradigm and the computer operating environment as reported in each article is included. Those journals which have published the most neural network business applications are also presented.     

Constrained Optimal Hybrid Control of a Flow Shop System

We consider an optimal control problem for the hybrid model of a deterministic flow shop system, in which the jobs are processed in the order they arrive at the system. The problem is decomposed into a higher-level discrete-event system control problem of determining the optimal service times, and a set of lower-level classical control problems of determining the optimal control inputs for given service times. We focus on the higher-level problem which is nonconvex and nondifferentiable. The arrival times are known and the decision variables are the service times that are controllable within constraints. We present an equivalent convex optimization problem with linear constraints. Under some cost assumptions, we show that no waiting is observed on the optimal sample path. This property allows us to simplify the convex optimization problem by eliminating variables and constraints. We also prove, under an additional strict convexity assumption, the uniqueness of the optimal solution and propose two algorithms to decompose the simplified convex optimization problem into a set of smaller convex optimization problems. The effects of the simplification and the decomposition on the solution times are shown on an example problem.     

A Grid resource brokering strategy based on resource and network performance in Grid

To achieve high performance distributed data access and computing in Grid environment, monitoring of resource and network performance is vital. Our proposed Grid network monitoring architecture is modeled by the Grid scheduler. The proposed Grid network monitoring retrieves network metrics using sensors as network monitoring tools. The mobile agents are migrated to start the sensors to measure the network metrics in all Grid Resources from the Resource Broker. The raw data provided by the monitoring tools is used to produce a high level view of the Grid through the set of internal cost functions. The network cost function is formed by combining various network metrics such as bandwidth, Round Trip Time, jitter and packet loss to measure the network performance. This paper presents the Grid Resource Brokering strategy which analyzes the network metrics along with the resource metrics for the selection of the Grid resource to submit the job and the proposed approach is integrated with CARE Resource Broker (CRB) for job submission. The experimental results are evident for the minimization of job completion time for the submitted job. The simulation results also prove that the more number of jobs are completed with the proposed strategy which influences the better utilization of the Grid resources.     

SA-MSVM: Hybrid Heuristic Algorithm-based Feature Selection for Sentiment Analysis in Twitter

One of the drastically growing and emerging research areas used in most information technology industries is Bigdata analytics. Bigdata is created from social websites like Facebook, WhatsApp, Twitter, etc. Opinions about products, persons, initiatives, political issues, research achievements, and entertainment are discussed on social websites. The unique data analytics method cannot be applied to various social websites since the data formats are different. Several approaches, techniques, and tools have been used for big data analytics, opinion mining, or sentiment analysis, but the accuracy is yet to be improved. The proposed work is motivated to do sentiment analysis on Twitter data for cloth products using Simulated Annealing incorporated with the Multiclass Support Vector Machine (SA-MSVM) approach. SA-MSVM is a hybrid heuristic approach for selecting and classifying text-based sentimental words following the Natural Language Processing (NLP) process applied on tweets extracted from the Twitter dataset. A simulated annealing algorithm searches for relevant features and selects and identifies sentimental terms that customers criticize. SA-MSVM is implemented, experimented with MATLAB, and the results are verified. The results concluded that SA-MSVM has more potential in sentiment analysis and classification than the existing Support Vector Machine (SVM) approach. SA-MSVM has obtained 96.34% accuracy in classifying the product review compared with the existing systems.     

Smart Prediction and Monitoring of Waste Disposal System Using IoT and Cloud for IoT Based Smart Cities

One of the prominent applications of Internet of Things (IoT) in this digital era is the development of smart cities. In IoT based smart cities, the smart objects (devices) are connected with each other via internet as a backbone. The sensed data by the smart objects are transmitted to the sink for further processing using multi hop communication. The smart cities use the analyzed data to improve their infrastructure, public utilities and they enhance their services by using the IoT technology for the betterment of livelihood of the common people. For IoT based smart cities, waste collection is a prominent issue for municipalities that aim to achieve a clean environment. With a boom in population in urban areas, an increasing amount of waste is generated. A major issue of waste management system is the poor process used in waste collection and segregation. Public bins begin to overflow for a long period before the process of cleaning starts, which is resulting in an accumulation of bacteria causing bad odors and spreading of diseases. In order to overcome this issue, in this paper an IoT based smart predication and monitoring of waste disposal system is proposed which utilizes off-the-shelf components that can be mounted to a bin of any size and measure fill levels. An Arduino microcontroller is employed in the proposed model to interface the infrared (IR), ultraviolet (UV), weight sensors, and a Global Positioning System (GPS) module is used to monitor the status of bins at predetermined intervals. The proposed system transmits the data using the cluster network to the master module which is connected to the backend via Wi-Fi. As data is collected, an intelligent neural network algorithm namely Long Short-Term Memory (LSTM) is used which will intelligently learn and predict the upcoming wastage from waste generation patterns. Moreover, the proposed system uses Firebase Cloud Messaging to notify the appropriate people when the bins were full and needed to be emptied. The Firebase Cloud Messaging (FCM) JavaScript Application Programming Interface (API) is used to send notification messages in web apps in browsers that provide service work support. Hence, the proposed system is useful to the society by providing facilities to the governments for enforcing stricter regulations for waste disposal. Additional features such as automated calibration of bin height, a dynamic web data dashboard as well as collation of data into a distributed real-time firebase database are also provided in the proposed system.

     

DAG Scheduling in Heterogeneous Computing and Grid Environments Using Variable Neighborhood Search Algorithm

DAG scheduling is a process that plans and supervises the execution of interdependent tasks on heterogeneous computing resources. Efficient task scheduling is one of the important factors to improve the performance of heterogeneous computing systems. In this paper, an investigation on implementing Variable Neighborhood Search (VNS) algorithm for scheduling dependent jobs on heterogeneous computing and grid environments is carried out. Hybrid Two PHase VNS (HTPHVNS) DAG scheduling algorithm has been proposed. The performance of the VNS and HTPHVNS algorithm has been evaluated with Genetic Algorithm and Heterogeneous Earliest Finish Time algorithm. Simulation results show that VNS and HTPHVNS algorithm generally perform better than other meta-heuristics methods.