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.     

Shallow Trench Isolation for the 45-nm CMOS Node and Geometry Dependence of STI Stress on CMOS Device Performance

In the present work, a high aspect ratio process (HARP) using a new O₃/TEOS based sub atmospheric chemical vapor deposition process was implemented as STI gapfill in sub-65-nm CMOS. Good gapfill performance up to aspect ratios greater than 10:1 was demonstrated. Since the HARP process does not attack the STI liner as compared to HDP, a variety of different STI liners can be implemented. By comparing HARP with HDP, the geometry dependence of nand p-FET performance due to STI stress is discussed     

Impedance studies of polycrystalline tin oxide

Polycrystalline SnO₂ samples prepared in the laboratory were irradiated with 2 MeV He ions having doses of 3.62 × 10¹⁵ ion/cm². The ac small-signal electrical data acquired for the polycrystalline SnO₂ in the frequency (f) range 100 Hz ≤ f ≤ 1 MHz and temperature (T) range 26°C ≤ T ≤ 100°C revealed one semicircular relaxation in the impedance plane for the He implanted sample. However, two semicircular relaxations were obtained in the same plane for the samples without He implantation. The He implantation indicated enhancement in the donor density in the polycrystalline SnO₂ as depicted via terminal conductance (or resistance). An erratum to this article can be found at     

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.