A novel potentiometric membrane sensor for determination of Co based on 5-amino-3-methylisothiazole

A new poly(vinylchloride) (PVC) membrane electrode for trace level determination of Co²⁺ ions has been developed based on 5-amino-3-methylisothiazole as an ionophore, o-nitrophenyloctylether as a plasticizer and oleic acid (OA) as a good lipophilic additive. The electrode exhibits a Nernstian slope of 29.5 ± 0.2 mV/decade in a linear range of 1.0 × 10⁻¹ to 6.3 × 10⁻⁷ M for Co²⁺ ions. The detection limit of this electrode is 3.9 × 10⁻⁷ M. It has a fast response time of 12 s and can be used for a period of 4 months without any divergence in potentials. The proposed electrode reveals a good selectivity for Co (II) over a wide variety of other tested cations and could be used in the pH range 3.3–9.0. The electrode was successfully applied as an indicator electrode for the potentiometric titration of cobalt ions with EDTA as well as for the direct determination of Co (II) in real samples.     

A survey on resource allocation in high performance distributed computing systems

An efficient resource allocation is a fundamental requirement in high performance computing (HPC) systems. Many projects are dedicated to large-scale distributed computing systems that have designed and developed resource allocation mechanisms with a variety of architectures and services. In our study, through analysis, a comprehensive survey for describing resource allocation in various HPCs is reported. The aim of the work is to aggregate under a joint framework, the existing solutions for HPC to provide a thorough analysis and characteristics of the resource management and allocation strategies. Resource allocation mechanisms and strategies play a vital role towards the performance improvement of all the HPCs classifications. Therefore, a comprehensive discussion of widely used resource allocation strategies deployed in HPC environment is required, which is one of the motivations of this survey. Moreover, we have classified the HPC systems into three broad categories, namely: (a) cluster, (b) grid, and (c) cloud systems and define the characteristics of each class by extracting sets of common attributes. All of the aforementioned systems are cataloged into pure software and hybrid/hardware solutions. The system classification is used to identify approaches followed by the implementation of existing resource allocation strategies that are widely presented in the literature.     

High-dimensional MRI data analysis using a large-scale manifold learning approach

A novel manifold learning approach is presented to efficiently identify low-dimensional structures embedded in high-dimensional MRI data sets. These low-dimensional structures, known as manifolds, are used in this study for predicting brain tumor progression. The data sets consist of a series of high-dimensional MRI scans for four patients with tumor and progressed regions identified. We attempt to classify tumor, progressed and normal tissues in low-dimensional space. We also attempt to verify if a progression manifold exists—the bridge between tumor and normal manifolds. By identifying and mapping the bridge manifold back to MRI image space, this method has the potential to predict tumor progression. This could be greatly beneficial for patient management. Preliminary results have supported our hypothesis: normal and tumor manifolds are well separated in a low-dimensional space. Also, the progressed manifold is found to lie roughly between the normal and tumor manifolds.     

Feature selection for medical diagnosis : Evaluation for cardiovascular diseases

Machine learning has emerged as an effective medical diagnostic support system. In a medical diagnosis problem, a set of features that are representative of all the variations of the disease are necessary. The objective of our work is to predict more accurately the presence of cardiovascular disease with reduced number of attributes. We investigate intelligent system to generate feature subset with improvement in diagnostic performance. Features ranked with distance measure are searched through forward inclusion, forward selection and backward elimination search techniques to find subset that gives improved classification result. We propose hybrid forward selection technique for cardiovascular disease diagnosis. Our experiment demonstrates that this approach finds smaller subsets and increases the accuracy of diagnosis compared to forward inclusion and back-elimination techniques.     

Microstructure and abrasive wear study of (Ti,W)C-reinforced high-manganese austenitic steel matrix composite

A high-manganese austenitic steel matrix composite (Fe-1.42%C-16.02%Mn-0.447%Si-0.288%Al-0.34%Cr-0.028%S-0.25%P-3.02%Ti-3.0%W, all in wt.%) reinforced with in-situ (Ti,W)C was synthesized by conventional melting and casting route. It has been found that the concentration of Ti decreases whereas the concentration of tungsten increases from core to the periphery of the (Ti,W)C particulates. The abrasive wear resistance of as-cast (Ti,W)C-reinforced composite is better than that of the as-cast high-manganese austenitic steel matrix material.     

Peak Load Management in Electrolytic Process Industries

Electrolytic process, employed for manufacturing basic chemicals like caustic soda and chlorine, is highly energy intensive. Due to escalating costs of fossil fuels and capacity addition, the electricity cost has been increasing for the last few decades. Electricity intensive industries find it very difficult to cope up with higher electricity charges particularly with time-of-use (TOU) tariffs implemented by the utilities with the objective of flattening the load curve. Load management programs focusing on reduced electricity use at the time of utility's peak demand, by strategic load shifting, is a viable option for industries to reduce their electricity cost. This paper presents an optimization model and formulation for load management for electrolytic process industries. The formulation utilizes mixed integer nonlinear programming (MINLP) technique for minimizing the electricity cost and reducing the peak demand, by rescheduling the loads, satisfying the industry constraints. The case study of a typical caustic-chlorine plant shows that a reduction of about 19% in the peak demand with a corresponding saving of about 3.9% in the electricity cost is possible with the optimal load scheduling under TOU tariff.     

Intelligent Interactive Tutoring Systemfor Engineering Mechanics

Engineering education has traditionally been imparted through the lecture-tutorial-laboratory paradigm. Education technology in the last few years has tried to make teaching more effective by supplementing the chalkboard teaching with audiovisual aids like overhead∕slide projectors and videos. These are, however, passive teaching tools. Recent advancement in computer multimedia has brought in a new teaching medium. Multimedia education has several advantages; most noticeably, the instructions can be interactive and self-paced to meet the needs of each individual student. The writers have developed an Intelligent Interactive Tutoring System for engineering mechanics for the first-year engineering students at the Nanyang Technological University, Singapore. The main objective of this system was to supplement classroom teaching via intelligent interaction with the computer-based tutoring system. This system is designed to be a self-paced virtual tutor with intelligent, user-friendly interactions. It is intended to be different from the many commercially available computerized textbooks. The prototype system was tried out with about 200 students who had earlier failed the course in the first semester and repeated it in the second semester of academic year 1997–1998. The system received good response from the students. The full version was implemented on the Nanyang Technological University Intranet and the Internet for use by the regular class of more than 1,600 students in both academic years 1998–1999 and 1999–2000.     

Temperature-induced control over phase selection among hydroxides of nickel

While at low (4°C) temperatures, addition of ammonia to aqueous metal nitrate solutions induces the precipitation of α-nickel hydroxide and at high (25–65°C) temperatures, β-Ni(OH)₂ is formed. The crystallinity of the product improves at higher temperatures of precipitation and the product obtained at 65°C is devoid of various disorders such as stacking faults, turbostraticity and interstratification. This provides a simple and efficient alternative to the hydrothermal synthesis of crystalline β-Ni(OH)₂. The temperature induced control over phase selection provides direct experimental evidence for the metastability of α-nickel hydroxide. Crystalline β-Ni(OH)₂ is, however, a poor electrode material for alkaline secondary cells and exhibits a capacity of only 75 ± 10 mAh/g, against the theoretically expected 289 mAh/g.