Ricinoleic acid inPhyllanthus niruri seed oil

Seed oil ofPhyllanthus niruri (Euphorbiaceae) contains 1.2% ricinoleic (12-hydroxy-cis-9-octadecenoic) acid, previously unknown in the genusPhyllanthus. Identification is based on thin layer and gas liquid chromatography, infrared, nuclear magnetic resonance, and mass spectrometry analysis as well as chemical methods. Other major components of the oil are linoleic acid (21%) and linolenic acid (51.4%). Presented at the ISF/AOCS World Congress, April 1980, New York City.     

Quantitative determination of castor oil in edible and heat-abused oils by13C nuclear magnetic resonance spectroscopy

Application of¹³C nuclear magnetic resonance (NMR) spectroscopy for detection of castor oil (CO) in various edible oils, such as coconut oil, palm oil, groundnut oil and mustard oil, is described. Characteristic signals observed at δ 132.4, δ 125.6, δ 71.3, δ 36.8 and δ 35.4 ppm, due to C10, C9, C12, C13 and C11 carbons of ricinoleic acid (RA) in CO, were selected for distinguishing it from edible oils. Quantitative¹³C NMR spectra of oils were recorded in CDCl₃ with a gated decoupling technique. The minimum detection limits for qualitative and quantitative analyses were 2.0 and 3.0%, respectively. The proposed method is simple, nondestructive and requires no sample pretreatment. Its application to heat-abused oils has also been demonstrated successfully without any of the interferences observed in most other methods.     

Abutilon indicum Seed Oil — Characterisation of HBr-Reactive Acids

The seed oil of Abutilon indicum (Malvaceae) contains three HBr-reactive fatty acids. These are shown to be cis-12,13-epoxyoleic (vernolic) acid, 1.6% 9,10-methylene-octadec-9-enoic (sterculic) acid, 0.9%; as well as 8,9-methylene-heptadec-8-enoic (malvalic) acid, 2.3%. Quantitative results are obtained by combining informations about the HBr-titration, the preparative thin layer separation of oxygenated and non-oxygenated acids, and gas liquid chromatographic analysis.     

Role of situational method engineering to improve visual information systems in agile distributed environment

Software product prone to continuous evolution due to increase in the use of technology. Therefore, more stakeholders are involved in software evolution increases the cost and complexity. This required optimization of resources and cost to handle evolution with Global Software Development (GSD) to utilize time zones efficiently. The significance challenge of GSD reports: time zone difference, geographical location, communication delays, knowledge sharing, control among stakeholders and development team. Because of these challenges, the requirements for development in GSD environment are also challenge as compared to on site development. Different requirement engineering methods have been used to improve the requirements analysis to deal with ambiguities and inconsistency in large set of requirements. The customization and tailoring of requirements according to changing project’s situations required to improve project development with reusing existing agile methods during requirement engineering phase. Moreover, complex information systems where heterogeneity is inevitable that implies the involvement of divergent stakeholders and necessitate a comprehensive framework to capture multidimensional viewpoints and fulfill aforementioned issues. Therefore, a situational multi-dimensional agile requirement engineering method has been proposed to support team and stakeholders’ viewpoints. The schema of the proposed method is based on challenges recognized by performing Literature Review. Then proposed method has been evaluated via experimental approach and statistical analysis conducted to validated reliability of data collected. This result is significant approved both practically and statistically that the proposed approach ease to use, implement, trained and increased productivity and performance. Hence, the experimental study for the evaluation of the proposed approach results concluded that, proposed approach is the important multimedia tool for supporting organization and distributed development team for information sharing, collaboration, product development.

     

Influence of nickel and molybdenum on the phase stability and mechanical properties of maraging steels

The effect of nickel and molybdenum concentrations on the phase transformation and mechanical properties of conventional 18Ni(350) maraging steel has been investigated. Both of these elements act as strong austenite stabilizers. When the concentration of molybdenum or nickel is greater than 7.5 or 24 wt %, respectively, the austenite phase remains stable up to room temperature. In both molybdenum- and nickel-alloyed steels, the austenite phase could be transformed to martensite by either dipping the material in liquid nitrogen or subjecting it to cold working. When 7.5 wt% Mo and 24 wt% Ni were added in combination, however, the austenite phase obtained at room temperature did not transform to martensite when liquid-nitrogen quenched or even when cold rolled to greater than 95% reduction. The aging response of these materials has also been investigated using optical, scanning electron, and scanning transmission electron microscopy.     

Modulation Based Combination of High Level Features Generated from SCCF & Contourlet Transforms for CBIR Applications

The need for suitable and cost-effective technologies rise with the growth of the internet of things (IoT) applications. These aim at handling voluminous data transmission in addition to minimum energy and latency cost constraints. LoRa networks are recommended for applications in confined spaces, long ranges, and less battery consumption requirements. However, the end devices in these networks communicate to all gateways in their ranges, thereby expediting energy unproductively in redundant transmissions. In our article, we explore the possibilities of whether LoRa networks could employ the advantages of clustering and propose two algorithms, path-based and data-centric, for such networks. We suggest that LoRaWAN technology with clustering can be apt for long-range, low power consumption IoT applications in the future. We study the impact of network density, node range, and cluster range on the energy consumption in data transmissions. The algorithms are compared with the inherent star-based communication of LoRa networks based on energy consumed, and our results show that, for dense deployments, clustering becomes advantageous.

     

The Double Edge Sword Based Distributed Executor Service

Scalability is one of the most important quality attribute of software-intensive systems, because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload. In order to achieve scalability, thread pool system (TPS) (which is also known as executor service) has been used extensively as a middleware service in software-intensive systems. TPS optimization is a challenging problem that determines the optimal size of thread pool dynamically on runtime. In case of distributed-TPS (DTPS), another issue is the load balancing b/w available set of TPSs running at backend servers. Existing DTPSs are overloaded either due to an inappropriate TPS optimization strategy at backend servers or improper load balancing scheme that cannot quickly recover an overload. Consequently, the performance of software-intensive system is suffered. Thus, in this paper, we propose a new DTPS that follows the collaborative round robin load balancing that has the effect of a double-edge sword. On the one hand, it effectively performs the load balancing (in case of overload situation) among available TPSs by a fast overload recovery procedure that decelerates the load on the overloaded TPSs up to their capacities and shifts the remaining load towards other gracefully running TPSs. And on the other hand, its robust load deceleration technique which is applied to an overloaded TPS sets an appropriate upper bound of thread pool size, because the pool size in each TPS is kept equal to the request rate on it, hence dynamically optimizes TPS. We evaluated the results of the proposed system against state of the art DTPSs by a client-server based simulator and found that our system outperformed by sustaining smaller response times.     

End-to-End 2D Convolutional Neural Network Architecture for Lung Nodule Identification and Abnormal Detection in Cloud

The extent of the peril associated with cancer can be perceived from the lack of treatment, ineffective early diagnosis techniques, and most importantly its fatality rate. Globally, cancer is the second leading cause of death and among over a hundred types of cancer; lung cancer is the second most common type of cancer as well as the leading cause of cancer-related deaths. Anyhow, an accurate lung cancer diagnosis in a timely manner can elevate the likelihood of survival by a noticeable margin and medical imaging is a prevalent manner of cancer diagnosis since it is easily accessible to people around the globe. Nonetheless, this is not eminently efficacious considering human inspection of medical images can yield a high false positive rate. Ineffective and inefficient diagnosis is a crucial reason for such a high mortality rate for this malady. However, the conspicuous advancements in deep learning and artificial intelligence have stimulated the development of exceedingly precise diagnosis systems. The development and performance of these systems rely prominently on the data that is used to train these systems. A standard problem witnessed in publicly available medical image datasets is the severe imbalance of data between different classes. This grave imbalance of data can make a deep learning model biased towards the dominant class and unable to generalize. This study aims to present an end-to-end convolutional neural network that can accurately differentiate lung nodules from non-nodules and reduce the false positive rate to a bare minimum. To tackle the problem of data imbalance, we oversampled the data by transforming available images in the minority class. The average false positive rate in the proposed method is a mere 1.5 percent. However, the average false negative rate is 31.76 percent. The proposed neural network has 68.66 percent sensitivity and 98.42 percent specificity.     

Switched reluctance motor modeling with on-line parameteridentification

A nonlinear model with on-line parameter estimation using recursive identification for switched reluctance motors (SRMs) is presented. The model is robust toward parameter variations in the motor or any system disturbances. The parameters of the model are adjusted to account for errors in rotor position, which allows the use of crude inexpensive position sensors. The proposed modeling approach allows self-tuning of SRMs in a production unit. The simulations and experiments performed to test the model demonstrate the accuracy of estimation of the model     

Identification of pole-like structures from mobile lidar data of complex road environment

Pole-like structures (PLSs) located in road environment are important roadway assets. They play a vital role in road safety inspection and road planning. The use of light detection and ranging (lidar) based mobile mapping technology for mapping of PLSs is an important area of research as it holds the potential for automation. Point cloud data of rural, peri-urban, and urban road environment are used in this study, which pose special challenge in view of the complexity of terrain, unlike well-planned roads, which have been the subject of interest in existing literature for identification of PLSs. A new five-step method is proposed in this article. The first two steps, i.e. ground filtering and voxelization of filtered non-ground points, are used for data size reduction. Next three steps are used to extract PLSs from reduced data. The proposed method was tested on point cloud data of three test sites having different levels of complexities. PLSs including partially occluded pole, tilted pole, pole situated very close to other objects, and vertical pole attached to tilted pole were accurately identified. Average correctness and completeness, respectively of 92.6% and 94.9%, were achieved in three different complex test sites, i.e. urban, peri-urban, and rural sites, respectively. Computation complexity shows that our proposed method delivers fast and computationally efficient solution for identifying the PLSs from volumetric mobile lidar point cloud. Impact of PLSs on road safety and road planning is also addressed for these selected test sites.