Rolling element bearing design through genetic algorithms

The design of rolling element bearings has been a challenging task in the field of mechanical engineering. While most of the real aspects of the design are never disclosed by bearing manufacturers, the common engineer is left with no other alternative than to refer to standard tables and charts containing the bearing performance characteristics. This paper presents a more viable method to solve this problem using genetic algorithms (GAs). Since the algorithm is basically a guided random search, it weakens the chances of getting trapped in local maxima or minima. The method used has yielded improved performance parameters than those catalogued in standard tables.     

Selective greedy routing: exploring the path diversity in backbone mesh networks

This paper proposes a new routing protocol for IEEE 802.11s wireless mesh network to overcome the problems associated with the conventional proactive and reactive routing approaches. The proposed ‘Selective Greedy’ (SelG) routing protocol operates in two phases. In the first phase it exploits the proactive mode of Hybrid Wireless Mesh Protocol, the standard routing protocol for IEEE 802.11s mesh, and constructs a set of potential forwarders for every mesh point, that can act as the next-hop relay. In the second phase, during the actual data communication, a candidate is selected from the set of potential forwarders based on a local optimization. The optimization procedure considers the statistical effect of local link quality fluctuation and interference over the global routing path selection. This way the SelG protocol reduces the control packet flooding in the network (a major drawback for reactive protocols). At the same time, the optimization procedure captures the network dynamics, and thus avoids the possibility of routing based on stale information (a drawback for proactive protocols). The routing properties and the correctness of the SelG protocol is established theoretically, and the performance of the protocol is analyzed through simulation results. The proposed protocol is implemented in an indoor wireless mesh testbed, and the performance is evaluated and compared with other traditional approaches.     

Data-Driven DPPM Estimation and Adaptive Fault Coverage Calibration Using MATLAB?

A manufacturing defect is a finite chip area with electrically malfunctioning circuitry caused by fabrication errors. The fraction of defective chips that escapes to the customer is called the defect level, also known as defective parts per million (DPPM, or simply PPM) when normalized to one million units. This paper demonstrates a technique used to correlate coverage goals to DPPM based on test fallout data using a MATLAB^?-based error function minimization approach. This analysis is explained using regression models for DPPM yield versus fault/defect coverage. This approach is beneficial to semiconductor companies for calibrating their fault coverage goals to meet DPPM requirements from automotive or other customers that have very aggressive (i.e., ultra-low) DPPM demands.     

Aperture Field Estimation in E Plane and H-Plane T-Junctions Using Field Theoretic Analysis

Wireless Personal Communications - This paper presents method of moment based analysis of E Plane and H-Plane T-junctions to find the field distributions on different transverse and longitudinal...     

Optimization Process of an Air Dense Medium Fluidized Bed Separator for Treating High-Ash Non-coking Indian Coal

The performance of an air dense medium fluidized bed separator, used for coal cleaning, is significantly affected by the operating process parameters. Proper estimation of these parameters is of great importance from both operational and control standpoints. The existing literature reveals that the optimum operating range for these parameters has been experimentally determined by pursuing a traditional “one variable at a time approach” which fails to consider parameter interactions and sometimes misleading, especially when the behavior of two process parameters changes as a function of a third parameter. It is realized that the interrelationship among these process parameters have a significant effect on the overall performance of this separator. In this study, a detailed investigation is carried out to examine the interdependencies among these parameters and their interactional effect on the separation performance of an air dense medium fluidized bed separator. Response surface methodology is used for this purpose and empirical models for two key response parameters, namely, product ash content and combustible recovery, are developed. These empirical models are utilized to establish the new levels of the parameters to achieve the optimum performance of the equipment and subsequently these model predictions are validated against the experimental results. Preliminary results show a strong interaction among some operating parameters.     

Topology Management Ensuring Reliability in Delay Sensitive Sensor Networks with Arbitrary Node Failures

The lifetime of a sensor network is influenced by the efficient utilization of the resource constrained sensor nodes. The tree-based data gathering offers good quality of service (QoS) for the running applications. However, data gathering at the sink reduces the network lifetime due to a fast failure of highly loaded nodes. Loss of connectivity and sensing coverage affect the performance of the applications that demand critical QoS. In this paper, a data gathering tree management scheme has been proposed to deal with arbitrary node failures in delay-sensitive sensor networks. A load-balanced distributed BFS tree construction procedure has been introduced for an efficient data gathering. Based on the initial tree construction, a tree maintenance scheme and an application message handler have been designed to ensure the reliable delivery of the application messages. The correctness of the proposed scheme has been verified both theoretically and with the help of simulation. The proposed scheme offers low overhead, enhanced network lifetime and good QoS in terms of delay and reliability of the application messages.     

Analysis of Fatty Acid and Lignan Composition of Indian Germplasm of Sesame to Evaluate Their Nutritional Merits

An attempt was made to individually analyze a germplasm collection of 54 indigenous Indian sesame cultivars for fatty acid and lignan composition of their seed oil by gas chromatography and high performance liquid chromatography, respectively. The entries varied in their fatty acid and lignan composition. The mean percentage contents of palmitic, stearic, oleic, linoleic and α‐linolenic acids ranged between 10–22, 5–10, 38–50, 18–43 and less than 1 whereas sesamol, sesamin and sesamolin scored between 3–37, 27–67, 20–59 of the total percentage of lignan, respectively. The highest percentage of α‐linolenic acid (ALA) was obtained in Var‐9 (1.3 % of the total fatty acids). Among the lignans, high sesamin content is considered to be significant, particularly in terms of long shelf life and nutraceutical value of sesame seed oil. The study has broadened our understanding related to differential biochemical composition of the rich sesame germplasms, thereby providing us with a useful groundwork for identifying potential targets and suitable cultivars for genetic engineering approaches to be undertaken in order to improve the nutritional quality of sesame oil, which in turn would be beneficial towards human health.     

A facile synthesis of a novel three‐phase nanocomposite: Single‐wall carbon nanotube/silver nanohybrid fibers embedded in sulfonated polyaniline

A three‐phase water‐soluble nanocomposite of single wall carbon nanotube/silver nanoparticle hybrid fibers embedded in sulfonated polyaniline has been synthesized by a simple chemical solution mixing process. The nanocomposite has been characterized by high resolution electron microscopy, X‐ray diffractometry, FTIR spectroscopy, Raman spectroscopy, and thermogravimetric analysis. Optical and electrical characteristics of the nanocomposite have been determined by UV–vis absorption spectroscopy, photoluminescence spectroscopy, and four‐probe electrical conductivity measurement. A surface plasmon absorption band obtained around 460 nm indicates the presence of silver nanoparticles in the composite. The optical band gap calculation for sulfonated polyaniline vis‐a‐vis the nanocomposite supported the conductivity measurement. Over 1300 times increase in DC electrical conductivity has been observed for the three‐phase nanocomposite, with a filler loading of 20 wt %, at 306 K. This observation could be explained by Mott's variable range hopping model considering a three‐dimensional conduction. Such a nanocomposite has immense potential for use as a cathode material in lithium‐ion batteries and supercapacitors. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41692.     

Prediction of in vivo drug performance using in vitro dissolution coupled with STELLA: a study with selected drug products

Prediction of the in vivo performance of the drug product from the in vitro studies is the major challenging job for the pharmaceutical industries. From the current regulatory perspective, biorelevant dissolution media should now be considered as quality control media in order to avoid the risk associated. Physiological based pharmacokinetic models (PBPK) coupled with biorelevant dissolution medium is widely used in simulation and prediction of the plasma drug concentration and in vivo drug performance. The present investigation deals with the evaluation of biorelevant dissolution media as well as in vivo drug performance by PBPK modelling using STELLA® simulation software. The PBPK model was developed using STELLA® using dissolution kinetics, solubility, standard gastrointestinal parameters and post-absorptive disposition parameters. The drug product selected for the present study includes Linezolid film-coated immediate-release tablets (Zyvox), Tacrolimus prolonged-release capsules (Advagraf), Valganciclovir tablets (Valcyte) and Mesalamine controlled-release capsules (Pentasa) each belonging to different biopharmaceutics classification system (BCS). The simulated plasma drug concentration was analyzed and pharmacokinetic parameters were calculated and compared with the reported values. The result from the present investigation indicates that STELLA® when coupled with biorelevant dissolution media can predict the in vivo performance of the drug product with prediction error less than 20% irrespective of the dosage form (immediate release versus modified release) and BCS Classification. Thus, STELLA® can be used for in vivo drug prediction which will be helpful in generic drug development.