IPS-MAC: an informative preamble sampling MAC protocol for wireless sensor networks

Radio transceivers are the main source of energy consumption in wireless sensor networks (WSNs) where the source of energy supply is non-rechargeable battery. Several MAC protocols have been proposed in order to efficiently conserve energy in the link layer via duty-cycling. Low power listening (LPL) methods have been shown to outperform other schemes in lightly loaded situations which are common in environment monitoring applications. Nonetheless, as the network becomes dense, in LPL protocols such as BMAC a large number of nodes stay awake for each transmission, resulting in high levels of energy consumption. This paper introduces the informative preamble sampling (IPS) protocol in which a transmitter implicitly embeds information about its intended receiver via the power at which the preamble is transmitted. This results in far fewer nodes staying awake for each preamble. Upon hearing the preamble, a receiver executes a decision-making algorithm to decide whether to stay awake. If the decision-making algorithm is too lax, then more nodes stay awake following the preamble. On the other hand if the algorithm is too strict, it is likely that the intended receiver misses the preamble. In this paper we derive the optimal operating points for the IPS protocol. We show analytically that the IPS protocol can achieve a gain in energy by at least a factor of 2 over BMAC. We also conduct extensive simulations to show that IPS can achieve significant energy gains compared to BMAC.     

Smart utilization of renewable energy sources in a microgrid system integrated with plug‐in hybrid electric vehicles

Mass roll‐out of plug‐in hybrid electric vehicles (PHEVs) and significant penetration of renewable energy sources in distribution system play a major role in delivering low carbon environment. However, placing and utilizing these units randomly result in overloading, increased power loss, and reduced voltage profile. This paper responds to these technical challenges by using a strategic placement method for locating the distributed generation (DG) and the charging station (CS) of PHEVs in a multi‐zone distribution system. For simultaneously scheduling of these units in each zone, the smart energy management framework is proposed in this paper. Apart from usual energy management constraints, this paper also incorporates the real‐time constraints involving the capacity of PHEV batteries, the mobility pattern, and the power level of the charging infrastructure. The simulation studies are carried out for each hour of a day. To cope with this time constraint execution, particle swarm optimization algorithm‐based approach is used. The proposed framework is tested in IEEE 33 and IEEE 69 bus radial distribution system. The obtained results imply that the presented energy management framework provides maximum profits for the vehicle owner, and meanwhile it fulfills preferences of the user in each zone simultaneously.     

Integrated decision support system for waste minimization analysis in chemical processes

The need to build and operate environmentally friendly plants has challenged the chemical industry to consider waste minimization or even elimination starting from the early stages of process development. A thorough waste minimization analysis requires specialized expertise and is laborious, time-consuming, expensive, and knowledge-intensive. This has caused a major technical barrier for implementing waste minimization programswithin the industry. Previously, we had reported a systematic methodology and a knowledge-based system, called ENVOPExpert, for identifying waste minimization opportunities in chemical processes. In this paper, we propose an integrated qualitative-quantitative methodology to identify waste minimization alternatives and assess their efficacy in terms of environmental impact and process economics. A qualitative analysis is first conducted to identify the sources of wastes and to propose alternatives for eliminating or minimizing them. Environmental impact of each alternative is then calculated by doing a quantitative pollutant balance. The capital expenditure required for implementing the alternative and the resulting plant operating costs are also calculated and used in the evaluation of the waste minimization alternatives. Through this, practical and cost-effective options can be identified. This methodology has been implemented as an integrated decision support system and tested using the hydrodealkylation process case study with satisfactory results.     

Fast Distributed Outlier Detection in Mixed-Attribute Data Sets

Efficiently detecting outliers or anomalies is an important problem in many areas of science, medicine and information technology. Applications range from data cleaning to clinical diagnosis, from detecting anomalous defects in materials to fraud and intrusion detection. Over the past decade, researchers in data mining and statistics have addressed the problem of outlier detection using both parametric and non-parametric approaches in a centralized setting. However, there are still several challenges that must be addressed. First, most approaches to date have focused on detecting outliers in a continuous attribute space. However, almost all real-world data sets contain a mixture of categorical and continuous attributes. Categorical attributes are typically ignored or incorrectly modeled by existing approaches, resulting in a significant loss of information. Second, there have not been any general-purpose distributed outlier detection algorithms. Most distributed detection algorithms are designed with a specific domain (e.g. sensor networks) in mind. Third, the data sets being analyzed may be streaming or otherwise dynamic in nature. Such data sets are prone to concept drift, and models of the data must be dynamic as well. To address these challenges, we present a tunable algorithm for distributed outlier detection in dynamic mixed-attribute data sets.     

Scalable Load and Store Processing in Latency-Tolerant Processors

New load and store processing algorithms let memory-latency-tolerant architectures sustain thousands of in-flight instructions without scaling cycle-critical fully-associative load and store queues. These algorithms rely on redoing some stores after fetching cache miss data from memory (to fix memory dependences). Doing so provides better power and area characteristics than constantly enforcing memory dependences among a several loads and stores, many of which have unknown addresses.     

Influence of dietary spices and their active principles on pancreatic digestive enzymes in albino rats

A few common spices or their active principles were examined for their possible influence on digestive enzymes of pancreas in experimental rat. Groups of animals were maintained for 8 weeks on the following spice diets: curcumin (0.5%), capsaicin (15 mg%), piperine (20 mg%), ginger (50 mg%), cumin (1.25%), fenugreek (2%), mustard (250 mg%) and asafoetida (250 mg%). Dietary curcumin, capsaicin, piperine, ginger, fenugreek and asafoetida prominently enhanced pancreatic lipase activity. Curcumin, capsaicin, piperine, ginger, cumin and asafoetida also stimulated pancreatic amylase. Trypsin was significantly stimulated by curcumin, capsaicin, piperine, ginger and cumin, while chymotrypsin was stimulated by all the spices tested except mustard. This stimulatory influence of test spices on the pancreatic digestive enzymes was however not observed when their intake was restricted to a single oral dose. The positive influences on the pancreatic digestive enzymes exerted by a good number of spices consumed in diet could be a factor contributing to the well recognised digestive stimulant action of spices.     

Effects of curtailment on an admissions model for a graduate management program.

Developed 2 admissions models to predict future academic performance of graduate management students. The 1st model was based on 40 students who were admitted and enrolled in the program and was uncorrected for curtailment. The 2nd model was developed from the total applicant population of 222 students after curtailment correction. The corrected model demonstrated higher predictive validity than the uncorrected model for 2 future classes of students. Furthermore, different predictors entered each model, affecting the beta weights, validities of predictors, and the total amount of variance explained by the models. A factor analysis and an analysis of admissions decisions offer additional support for the curtailment-corrected model in selecting students with high academic potential. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Asymmetric membrane fiber-based carbon dioxide removal devices for ion chromatography

The application of hollow and filament-filled single asymmetric membrane fibers, consisting of a thin silicone layer on a tubular microporous support, for removing CO(2) in suppressed ion chromatography effluents is described. With appropriate choice of the removal device and operating conditions, the CO(2) can be essentially quantitatively (99+%) removed. For carbonate-based eluents, the use of such devices greatly reduces or eliminates the water dip, permitting better quantitation of poorly retained anions that elute close to the dip, allows practical gradient chromatography, and improves noise levels and attainable detection limits. In hydroxide eluent chromatography, the device largely removes the response from CO(2) present in the samples; this greatly aids atmospheric trace gas analysis by IC. Device dimensions are such that the dispersion introduced by the device is small.