Ergogenic Attributes of Young and Mature Coconut (Cocos nucifera L.) Water Based on Physical Properties,Sugars and Electrolytes Contents

The present work aimed to compare the main ergogenic attributes of two commercialized stages (young and mature) of coconut water (CW) obtained from four coconut varieties. The changes of electrolytes and sugars in CW upon maturation were quantified by inductively coupled plasma mass spectrophotometer and high-performance liquid chromatography, respectively. Based on the electrolyte profiling, potassium yielded the highest amount (ranging from 237.41 to 361.20 mg/100 mL) followed by sodium, magnesium, calcium, iron, manganese, copper, selenium, and zinc across all the maturity stages tested. For sugars, there were lower amounts of fructose and glucose, but a higher amount of sucrose with the maturation of the fruits. In conclusion, the amount of beneficial nutrients in the form of sugars and minerals was higher than that of young CW, and the ergogenic attributes of mature CW especially from MATAG variety (M-MATAG) were the best to be exploited further in the development of natural energy drinks.     

Structured estimation of tire forces and the ground slope using SM observers

In agricultural context, the principal cause of serious accidents for all-terrain vehicles(ATVs) is rollover. The most important parameters related to this risk is the ground slope. In this paper, we propose a structured observer to estimate the system states and the longitudinal tire forces using only wheel angular velocities measurement. The robust estimation is based on a second order sliding mode observer. This estimation is then used to build up a ground slope estimation. The algorithm is composed by two cascaded estimators. This structured estimation is then applied to the model of an agricultural vehicle G7(GregoireTM) integrated in the driving simulation environment SCANeRTM-Studio.     

A neural network bid/no bid model: the case for contractors in Syria

Despite the crucial importance of the ‘bid/no bid’ decision in the construction industry, it has been given little attention by researchers. This paper describes the development and testing of a novel bid/no bid model using the artificial neural network (ANN) technique. A back‐propagation network consisting of an input buffer with 18 input nodes, two hidden layers and one output node was developed. This model is based on the findings of a formal questionnaire through which key factors that affect the ‘bid/no bid’ decision were identified and ranked according to their importance to contractors operating in Syria. Data on 157 real‐life bidding situations in Syria were used in training. The model was tested on another 20 new projects. The model wrongly predicted the actual bid/no bid decision only in two projects (10%) of the test sample. This demonstrates a high accuracy of the proposed model and the viability of neural network as a powerful tool for modelling the bid/no bid decision‐making process. The model offers a simple and easy‐to‐use tool to help contractors consider the most influential bidding variables and to improve the consistency of the bid/no bid decision‐making process. Although the model is based on data from the Syrian construction industry, the methodology would suggest a much broader geographical applicability of the ANN technique on bid/no bid decisions.     

Synthesis of ZnO/Cu micro and nanostructures via a vapor phase transport method using different tube systems

ZnO micro and nanostructures were grown on copper coated silicon substrates using two different systems: an opened system (both ends opened tube) and a closed system (one closed end tube). The thermodynamic conditions of the systems made a significant difference in boundary layer and super-saturation between the systems. The results indicate that diffusion of the gaseous species through the boundary layers at low and high pressures controls the final formation of the morphologies. The ZnO nanostructures which have been grown in a restricted place have larger diameters and lengths. The structure of the products was analyzed by X-ray diffractometer (XRD) and it was found that the good crystalline quality of the samples was obtained in a closed system. To study the optical properties, photoluminescence (PL) and ultra violet–visible (UV–vis) spectroscopy were employed. It was observed that a decrease in the growth temperature of the opened system caused a broad and dominant visible emission covering the blue and green emission in the PL spectra.     

Microgravity Modifies the Phenotype of Fibroblast and Promotes Remodeling of the Fibroblast–Keratinocyte Interaction in a 3D Co-Culture Model

Microgravity impairs tissue organization and critical pathways involved in the cell–microenvironment interplay, where fibroblasts have a critical role. We exposed dermal fibroblasts to simulated microgravity by means of a Random Positioning Machine (RPM), a device that reproduces conditions of weightlessness. Molecular and structural changes were analyzed and compared to control samples growing in a normal gravity field. Simulated microgravity impairs fibroblast conversion into myofibroblast and inhibits their migratory properties. Consequently, the normal interplay between fibroblasts and keratinocytes were remarkably altered in 3D co-culture experiments, giving rise to several ultra-structural abnormalities. Such phenotypic changes are associated with down-regulation of α-SMA that translocate in the nucleoplasm, altogether with the concomitant modification of the actin-vinculin apparatus. Noticeably, the stress associated with weightlessness induced oxidative damage, which seemed to concur with such modifications. These findings disclose new opportunities to establish antioxidant strategies that counteract the microgravity-induced disruptive effects on fibroblasts and tissue organization.     

The glass transition temperature of nitrated polystyrene/poly(acrylic acid) blends

Low-molecular-weight polystyrene was nitrated to different levels. The nitrated polystyrene was blended with different molecular weights of poly(acrylic acid), PAA. The glass transition temperatures (T_g) for the mixtures were investigated by differential scanning calorimetry. A single T_g was observed for all blends, indicating single-phase blends. In general, it was found that the T_g increases with molecular weight of PAA. The T_g values of the blends showed a positive deviation from the linear average T_g as a result of strong hydrogen bonding between the segments of the component polymers. The observed T_g values were not adequately represented by simple predictive equations or by single-parameter fitting equations. However, two-parameter fitting equations gave a reasonable representation of the data.     

A graphical approach for confidence limits of optimal preventive maintenance cycles

Facilities management (FM) is the management of infrastructure resources and services to support and sustain the operational strategy of an organization over time. Maintenance is often the business process that has not been optimized and is considered as a liability of business operations. Therefore, extensive studies have been done to determine the optimal replacement interval for irreparable parts of repairable systems where typically the time between failures is characterized by lifetime distribution in which the parameters are estimated from failure data. As a result, the optimal preventive maintenance (PM) interval computed is exposed to sampling risk as the repair cost and failure data used for estimation are typically highly censored due to issues related to data collection and unobserved failures. In this paper, we present a graphical approach to obtain the confidence interval for the optimal PM interval that resulted from sampling variations parameter estimates. The proposed methodology is applied in the context of FM as a strategy for opportunistic replacement and for the purpose of validating the cost components in maintenance. Copyright © 2008 John Wiley & Sons, Ltd.     

Development and test of a new catalytic converter for natural gas fuelled engine

This paper presents characteristics of a new catalytic converter (catco) to be used for natural gas fuelled engine. The catco were developed based on catalyst materials consisting of metal oxides such as titanium dioxide (TiO₂) and cobalt oxide (CoO) with wire mesh substrate. Both of the catalyst materials (such as TiO₂ and CoO) are inexpensive in comparison with conventional catalysts (noble metals) such as palladium or platinum. In addition, the noble metals such as platinum group metals are now identified as human health risk due to their rapid emissions in the environment from various resources like conventional catalytic converter, jewelers and other medical usages. It can be mentioned that the TiO₂/CoO based catalytic converter and a new natural gas engine such as compressed natural gas (CNG) direct injection (DI) engine were developed under a research collaboration program. The original engine manufacture catalytic conveter (OEM catco) was tested for comparison purposes. The OEM catco was based on noble metal catalyst with honeycomb ceramic substrate. It is experimentally found that the conversion efficiencies of TiO₂/CoO based catalytic converter are 93%, 89% and 82% for NO_x, CO and HC emissions respectively. It is calculated that the TiO₂/CoO based catalytic converter reduces 24%, 41% and 40% higher NO_x, CO and HC emissions in comparison to OEM catco respectively. The objective of this paper is to develop a low-cost three way catalytic converter to be used with the newly developed CNG-DI engine. Detailed review on catalytic converter, low-cost catalytic converter development characteristics and CNGDI engine test results have been presented with discussions.     

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.