Growing RBFNN-based soft computing approach for congestion management

In the emerging restructured power system, the congestion management (CM) has become extremely important in order to ensure the security and reliability of the system. In addition to this, lack of CM can impose a hindrance in electricity trading. This paper presents a novel, growing radial basis function neural network (GRBFNN)-based approach for CM. For achieving CM, Nodal congestion price (NCP) forecasting is performed in real time competitive power market. NCP forecasting is an effective way of price-based preventive CM as it directly indicates the presence as well as the severity of the congestion in the system. In present paper, GRBFNN has been developed for NCP forecasting dividing the whole power system into various congestion zones. An unsupervised learning vector quantization (VQ) clustering algorithm is applied as feature selection technique for the developed GRBFNN and for partitioning the power system into different congestion zones. For each congestion zone a separate neural network has been developed to ensure faster training and accurate forecasting results. The proposed approach of CM is implemented on an RTS 24-bus system. The results obtained are compared with a different constructive algorithm-based RBF network called as general regression neural network (GRNN) and two back-propagation algorithms based ANNs. Comparison results show that proposed GRBFNN is more computationally efficient with better predictive ability.     

Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture

Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield. Precision agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental concerns. Weed control has become one of the significant problems in the agricultural sector. In traditional weed control, the entire field is treated uniformly by spraying the soil, a single herbicide dose, weed, and crops in the same way. For more precise farming, robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed type. This may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable agriculture. This study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection (HHOGCN-WD) technique for Precision Agriculture. The HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision agriculture. For image pre-processing, the HHOGCN-WD model utilizes a bilateral normal filter (BNF) for noise removal. In addition, coupled convolutional neural network (CCNet) model is utilized to derive a set of feature vectors. To detect and classify weed, the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection performance. The experimental results of the HHOGCN-WD technique are investigated under the benchmark dataset. The results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches, with increased accuracy of 99.13%.     

P2P consistency support for large-scale interactive applications

Peer-to-Peer (P2P) systems have been widely used by networked interactive applications to relieve the drawback and reduce the reliance on well-provisioned servers. A core challenge is to provide consistency maintenance for a massive number of users in a P2P manner. This requires propagating updates on time by only using the uplink bandwidth from individual users instead of relying on dedicated servers. In this paper, we present a P2P system called PPAct to provide consistency maintenance for large-scale fast-interactive applications. We use massive multi-player online games as example applications to illustrate PPAct. The design can be directly applied to other interactive applications. We adopt the Area-of-Interest (AOI) filtering method, which is proposed in prior works [1], [2], to reduce bandwidth consumption of update delivery. We solve the AOI’s critical problem of bandwidth shortage in hot regions by dynamically balancing the workload of each region in a distributed way. We separate the roles of view discovery from consistency maintenance by assigning players as “region hosts” and “object holders.” A region host is responsible for tracking objects and players within a region, and an object holder is responsible for sending updates about an object to interested players. Lookup queries for view discovery are processed by region hosts, while consistency maintenance of objects is taken by object holders. Separating the roles not only alleviates the workload overflow in hot regions, but also speeds up view discovery and update delivery. Another key idea is that peers contribute spare bandwidth in a fully distributed way to forwarding updates about objects of interest. Thus popular, high-demand objects will have more peers forward updates. We also present how to select capable and reliable players for region hosts and object holders.A P2P network simulator is developed to evaluate PPAct on two major types of online games: role-playing games (RPGs) and first-person shooter (FPS) games. The results demonstrate that PPAct successfully supports 10,000 players in RPGs and 1500 players in FPS games. PPAct outperforms SimMud [2] in RPGs and Donnybrook [3] in FPS games by 40% and 30% higher successful update rates respectively.     

Comparative study of polystyrene/chemically modified wheat straw composite for green packaging application

The recalcitrant nature of native wheat straw (WS) biomass results from cellulose, lignin, hemicellulose and some amount of protein embedded together in a composite structure causing poor adhesion to a polymer matrix composite. Adequate pre-treatment can curtail the recalcitrance structure of WS and enhance the susceptible cellulose area to synthesize a green composite. This paper examined the effect of various chemical pre-treatment procedures in improving surface morphology of wheat straw. Moreover, polystyrene (PS), PS (60 wt%)/native WS (40 wt%), PS (60 wt%)/NaOH-treated WS (40 wt%), PS (60 wt%)/HCl-treated WS (40 wt%), and PS (60 wt%)/H2SO4-treated WS (40 wt%) composite films were prepared using solution casting method. The changes in crystalline structure, hydrophobicity, water vapor migration rate, and thermal and mechanical stabilities of synthesized bio-composites were examined. From the results it can be concluded that the alkali-treated WS is highly compatible with the PS and can be used to synthesize a biodegradable composite film for various industrial green packaging applications.     

Influence of Galactooligosaccharides and Modified Waxy Maize Starch on Some Attributes of Yogurt

Abstract: This study examined the influence of galactooligosaccharides (GOS) and modified waxy maize starch (MWMS) addition on the growth of starter cultures, and syneresis and firmness of low‐fat yogurt during storage for 28 d at 4 °C. The control yogurt (CY) was prepared without any prebiotics. Incorporation of 2.0% (w/v) GOS improved the growth of L. delbrueckii ssp. bulgaricus ATCC 11842 resulting in a shorter fermentation time. There was a significant (P < 0.05) increase in proteolysis in yogurt made with GOS (GOSY) as measured by absorbance value (0.728). Addition of GOS resulted in higher (P < 0.05) concentration of lactic and acetic acids in comparison with that of MWMSY and the CY up to day 14, thereafter, the product showed a decrease in lactic acid content in all 3 batches until the end of storage. The level of syneresis was the lowest (2.14%) in MWMSY as compared with that of GOSY (2.35%) and CY (2.53%). There was no statistically significant (P > 0.05) difference in the firmness among the 3 types of yogurt.     

Chemical Composition and Fatty Acid Profile of Khat (Catha edulis) Seed Oil

The proximate, physicochemical, and fatty acid compositions of seed oil extracted from khat (Catha edulis) were determined. The oil, moisture, crude protein, crude fiber, crude carbohydrate, and ash content in seeds were 35.54, 6.63, 24, 1.01, 30.4 %, and 1.32 g/100 g DW respectively. The free fatty acids, peroxide value, saponification value, and iodine value were 2.98 %, 12.65 meq O₂/kg, 190.60 mg KOH/g, and 145 g/100 g oil, respectively. Linolenic acid (C_18:3, 50.80 %) and oleic (C_18:1, 16.96 %) along with palmitic acid (C_16:0, 14.60 %) were the dominant fatty acids. The seed oil of khat can be used in industry for the preparation of liquid soaps and shampoos. Furthermore, high levels of unsaturated fatty acids make it an important source of nutrition especially as an animal product substitute for omega‐3 fatty acids owing to the high content of linolenic acid.     

Production and characterization of cellobiose dehydrogenase from <Emphasis Type="Italic">Phanerochaete chrysosporium</Emphasis> KCCM 60256 and its application for an enzymatic fuel cell

The enzyme cellobiose dehydrogenase (CDH), with high ability of electron transport, has been widely used in enzymatic fuel cells or biosensors. In this study, the cellobiose dehydrogenase gene from Phanerochaete chrysosporium KCCM 60256 was amplified and expressed in the methylotrophic yeast Pichia pastoris X-33. The recombinant enzyme (PcCDH) was purified using a metal affinity chromatography under non-denaturing conditions. The purified enzyme was analyzed by SDS-PAGE, confirming a corresponding band about 100 kDa. The enzyme activity of this purified PcCDH was determined as 1,845U/L (65mg/L protein). The enzyme showed the maximum activity at pH 4.5 and high activity in broad ranges of temperature from 30°C to 60°C. Moreover, the application of PcCDH to enzymatic fuel cell (EFC) was demonstrated. Lactose was used as the substrate in the EFC system; anode and cathode were immobilized with PcCDH and laccase, respectively. The cell’s open circuit voltage and maximum power density of the EFC system were, respectively, determined as 0.435 V and 314 μW/cm² (at 0.247 V) with 10 mM lactose.     

Reserve constrained multi-area economic dispatch employing differential evolution with time-varying mutation

For a power pool that involves several generation areas interconnected by tie-lines, the objective of economic dispatch (ED) is to determine the most economical generation dispatch strategy that could supply the area load demands without violating the tie-line capacity constraints. The objective of multi-area economic dispatch (MAED) is to determine the generation levels and the interchange power between areas which would minimize total fuel cost while satisfying power balance constraint, upper/lower generation limits, ramp rate limits, transmission constraints and other practical constraints. In reserve constrained MAED (RCMAED) problem inter-area reserve sharing can help in reducing the operational cost while ensuring that spinning reserve requirements in each area are satisfied. The tie-line limits too play a pivotal role in optimizing the cost of operation. The cost curves of modern generating units are discontinuous and non-convex which necessitates the use of powerful heuristic search based methods that are capable of locating global solutions effectively, with ease. This paper explores and compares the performance of various differential evolution (DE) strategies enhanced with time-varying mutation to solve the reserve constrained MAED (RCMAED) problem.The performance is tested on (i) two-area, four generating unit system, (ii) four area, 16-unit system and (iii) two-area, 40-unit system. The results are found to be superior compared to some recently published results.     

Growth of textured films of BiSCCO from alkali halide solutions

Films of Bi-Sr-Ca-Cu-O with lead doping using SrF₂ substrates have been grown from KCl solution. The films were highly textured withC-axis perpendicular to the substrate surface. The onset of the resistive transition was at 81 K and zero resistance at 78 K. This technique is permising in view of the possibility to grow large films of both Bi₂Sr₂CaCu₂O _x and Bi₂Sr₂Ca₂Cu₃O _y phases.     

Effect of Maintenance on the Dependability and Performance of Mulitprocessor Systems

This paper presents analytic models for dependability and performance evaluation of multiprocessor systems with both on-line and off-line maintenance. Markov models are developed to compute the system reliability and performance availability incorporating the reliability of the maintenance processor. The maintenance processor failure is considered separately in order to emphasize its effect on system performance and dependability. The reliability of the maintenance processor can not be ignored for degradable multiprocessors. Probabilistic models are presented to compute the system downtime and the service cost for three off-line maintenance policies: scheduled maintenance (SM), unscheduled maintenance (UM), and scheduled & unscheduled maintenance (SUM). The SUM policy, that combines both SM and UM, can be used to give a compromise between cost and downtime.