On the design of neural-fuzzy control system

This article presents a neural–network-based fuzzy logic control (NN–FLC) system. The NN–FLC model has the learning capabilities for constructing membership functions and extracting fuzzy rules from training examples. Both unsupervised and supervised training algorithms are used to find the membership functions of the FLC. Competitive learning algorithms are employed to evaluate fuzzy logic rules. Matlab programs using both neural and fuzzy toolboxes are developed to implement the NN–FLC model. Computer simulations of the inverted pendulum controlled by NN–FLC system were conducted to illustrate the self-learning ability of the network. © 1998 John Wiley & Sons, Inc.13: 11–26, 1998     

In vitro digestibility,structural and functional properties of starch from pigeon pea (Cajanus cajan) cultivars grown in India

Starches isolated from two pigeon pea cultivars (AL-15 and AL-201) were evaluated for their in vitro digestibility, structural and functional properties. Both the cultivars exhibited a characteristic C-type diffraction pattern with relative crystallinity values of 31.9% and 34.2%, for AL-15 and AL-201, respectively. The structural characterization obtained using high performance size exclusion column chromatography revealed that molecular weight of amylopectin and amylose was higher for AL-201 (396 and 3.92 × 10⁶ g/mol, respectively) in comparison to AL-15 starch (354 and 3.31 × 10⁶ g/mol, respectively). AL-201 exhibited higher values for transition temperatures, enthalpy of gelatinization, pasting temperature and percentage retrogradation in comparison to AL-15 starch. Slowly digestible starch, readily digestible starch and resistant starch contents of AL-15 and AL-201 cultivars were observed to be 31.0, 8.1 and 60.9% and 29.6, 5.2 and 65.2%, respectively. The hydrolysis index and RS values indicated that pigeon pea starches were highly resistant to digestion.     

Design and performance analysis of a CNFET-based TCAM cell with dual-chirality selection

The carbon nanotube field-effect transistor (CNFET) is emerging as one of the most promising alternatives to complementary metal–oxide–semiconductor (CMOS) transistors due to its one-dimensional (1-D) band structure, low off-current capability, near-ballistic transport operation, high stability, and low power consumption. This paper presents the design of a CNFET-based ternary content-addressable memory (TCAM) cell and rigorously analyzes its performance in terms of power–delay product (PDP) and static noise margin (SNM). The effect of variations of the chiral vector on the performance of the TCAM cell is also comprehensively investigated. While selecting the chirality, SNM, PDP, and search time are considered as figures of merit. In this TCAM cell design, we apply the same chirality for all CNFETs of the same type. Extensive HSPICE simulations have been performed for computation of performance parameters using the Stanford University CNFET model. Comparison of CNFET- and CMOS-based TCAM cells has been carried out at the 16-nm technology node. The results show that the CNFET-based TCAM cell exhibits significant improvements of PDP, i.e., by 38 % during write operation and 98 % during search operation, and 53 % in SNM, compared with its CMOS counterpart. It is also observed that the best chirality for the TCAM cell design is (22, 19, 0) or (10, 19, 0) from the point of view of SNM and PDP, respectively.     

A Conditionally Fluorescent Peptide Reporter of Secondary Structure Modulation

Proteins containing intrinsic disorder often form secondary structure upon interaction with a binding partner. Modulating such structures presents an approach for manipulating the resultant functional outcomes. Translational repressor protein 4E-BP1 is an example of an intrinsically disordered protein that forms an α-helix upon binding to its protein ligand, eIF4E. Current biophysical methods for analyzing binding-induced structural changes are low-throughput, require large amounts of sample, or are extremely sensitive to signal interference by the ligand itself. Herein, we describe the discovery and development of a conditionally fluorescent 4E-BP1 peptide that reports structural changes of its helix in high-throughput format. This reporter peptide is based on conditional quenching of fluorescein by thioamides. In this case, fluorescence signal increases as the peptide becomes more ordered. Conversely, destabilization of the α-helix results in decreased fluorescence signal. The low concentration and low volume of peptide required make this approach amenable for high-throughput screening to discover ligands that alter peptide secondary structure.     

Conventional and Non-conventional Edible Oils: An Indian Perspective

The Indian dietary pattern has undergone major changes over the past 40 years. Many of these changes involve modification in dietary intake of fats and oils. In developing countries like India, vegetable oils are replacing animal fats because of the cost and health concerns. A wide range of vegetable oils are available in the market but the choice of healthy cooking oil has been a controversial subject since ideas keep on changing as new evidence accumulates. One of the factors holding back the increased use of value-added healthy oils in India is that most potential consumers are genuinely not aware of their health benefits. Edible oil consumption is primarily a community-driven phenomenon in India. The purpose of this paper is to highlight the current scenario of edible oils in India, their composition, health benefits, food applications, stability issues and future aspects as buying the right oil for health has become of great importance.     

Flame retardancy of ceramic-based rigid polyurethane foam composites

In this work, ceramic fillers zirconia and alumina powder were incorporated in the rigid polyurethane foams derived from modified castor oil and their impact on the mechanical, thermal, and fire performances of composite foams have been analyzed. It was observed that the addition of ceramic filler showed improved mechanical and thermal properties and best properties were shown by 6% zirconia with compressive strength of 6.61 MPa and flexural strength of 5.72 MPa. Zirconia also demonstrated an increase in T_5% up to 260 °C. Cone calorimetry shows a decrease in peak of heat release from 118 to 84 kW m⁻² and 94 kW m⁻² by the incorporation of alumina and zirconia powder, respectively. Furthermore, total heat release (THR), smoke production rate (SPR), and total smoke release (TSR) were also found to decrease remarkably on the incorporation of ceramic fillers. So, these fillers have a great potential as an additive to incorporate good mechanical, thermal, and fire properties in bio-based rigid PU foams. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 48250.     

Centralized sliding mode frequency regulation approach for an uncertain islanded micro grid integrated with disturbance observer

This paper proposes a novel secondary frequency regulation technique for an uncertain islanded micro grid (MG). The major motivation of the work is to integrate the intrinsic robustness of the sliding mode control scheme with the disturbance observer to estimate and alleviate the unknown mismatched uncertainties caused by renewable resources and load variations. To this end, a dynamical sliding manifold is first utilized and then a control law is derived with Lyapunov's method which stabilizes the MG dynamics. Moreover, in order to ensure faster time domain responses of the closed‐loop system, we employ a power rate reaching law in our proposed control design. Thereafter, the performances of the introduced control strategy are tested on an islanded MG using MATLAB/Simulink, and robustness analysis is also carried out by considering five different case studies. Further, in contrast to the existing approaches such as robust H _∞ and robust PID control, the proposed strategy renders appealing time domain characteristics such as settling time, peak overshoot, and integral absolute frequency error.     

Comparing and evaluating the effectiveness of mobile Web adequacy evaluation tools

Universal Access in the Information Society - The number of Web sites is growing exponentially and so are the people who are accessing them on mobile devices including people with special...     

Intelligent heart disease prediction in cloud environment through ensembling

Cloud computing is the delivery of on‐demand computing resources. Cloud computing has numerous applications in fields of education, social networking, and medicine. But the benefit of cloud for medical purposes is seamless, particularly because of the enormous data generated by the health care industry. This colossal data can be managed through big data analytics, and hidden patterns can be extracted using machine learning procedures. In particular, the latest issue in the medical domain is the prediction of heart diseases, which can be resolved through culmination of machine learning and cloud computing. Hence, an attempt has been made to propose an intelligent decision support model that can aid medical experts in predicting heart disease based on the historical data of patients. Various machine learning algorithms have been implemented on the heart disease dataset to predict accuracy for heart disease. Naïve Bayes has been selected as an effective model because it provides the highest accuracy of 86.42% followed by AdaBoost and boosted tree. Further, these 3 models are being ensembled, which has increased the overall accuracy to 87.91%. The experimental results have also been evaluated using 10,082 instances that clearly validate the maximum accuracy through ensembling and minimum execution time in cloud environment.