Rind of the rambutan, Nephelium lappaceum, a potential source of natural antioxidants

The rind of rambutan, which is normally discarded was found to contain extremely high antioxidant activity when assessed using several methods. Although having a yield of only 18%, the ethanolic rambutan rind extract had a total phenolic content of 762 ± 10 mg GAE/g extract, which is comparable to that of a commercial preparation of grape seed extract. Comparing the extract’s pro-oxidant capabilities with vitamin C, α-tocopherol, grape seed and green tea, the rind had the lowest pro-oxidant capacity. In addition, the extract at 100 μg/ml was seen to limit oxidant-induced cell death (DPPH at 50 μM) by apoptosis to an extent similar to that of grape seed. The extracts were not cytotoxic to normal mouse fibroblast cells or splenocytes while the powderised rind was seen to have heavy metals contents far below the permissible levels for nutraceuticals. Our study for the first time reveals the high phenolic content, low pro-oxidant capacity and strong antioxidant activity of the extract from rind of Nephelium lappaceum. This extract, either alone or in combination with other active principles, can be used in cosmetic, nutraceutical and pharmaceutical applications.     

Heat exchanger fouling model and preventive maintenance scheduling tool

The crude preheat train (CPT) in a petroleum refinery consists of a set of large heat exchangers which recovers the waste heat from product streams to preheat the crude oil. In these exchangers the overall heat transfer coefficient reduces significantly during operation due to fouling. The rate of fouling is highly dependent on the properties of the crude blends being processed as well as the operating temperature and flow conditions. The objective of this paper is to develop a predictive model using statistical methods which can a priori predict the rate of the fouling and the decrease in heat transfer efficiency in a heat exchanger. A neural network based fouling model has been developed using historical plant operating data. Root mean square error (RMSE) of the predictions in tube- and shell-side outlet temperatures of 1.83% and 0.93%, respectively, with a correlation coefficient, R², of 0.98 and correct directional change (CDC) values of more than 92% show that the model is adequately accurate. A case study illustrates the methodology by which the predictive model can be used to develop a preventive maintenance scheduling tool.     

Pyrochlore type semiconducting ceramic oxides in Ca-Ce-Ti-M-O system (M = Nb or Ta)—Structure, microstructure and electrical properties

A new series of pyrochlore type ceramic semiconducting oxides in Ca-Ce-Ti-M-O (M = Nb or Ta) system has been synthesized by the conventional ceramic route. The electrical conductivity measurements show that these oxides exhibit semiconducting behavior and the conductivity increases with the Ce content in the compound. Activation energy of the current carriers is in the range of 0.5-1.6 eV. The electrical conductivity in these oxides is due to the presence of Ce³⁺, which remains in the reduced state without being oxidized to Ce⁴⁺ by structural stabilization. The photoluminescence and X-ray photoelectron spectroscopy analysis corroborate the presence of Ce in the 3+ state. Impedance spectral analysis is carried out to evaluate the transport properties and indicates that the conduction in these compounds is mainly due to electronic contribution. The X-ray powder diffraction and Raman spectroscopy analysis establishes that these oxides belong to a cubic pyrochlore type structure.     

Performance monitoring of the EC - 1030 system by a microprocessor - based monitor

This paper presents a M6800 microprocessor based hardware monitor for measuring certain selected parameters of the EC-1030 computer system. The motivation for this work was to get an insight into the operation of the system under the present work load and use these results towards improvement and design studies. The three main classes of quantitative indices of computer system performance/productivity, responsiveness and use have been measured using hardware and software tools. This paper describes the hardware monitor design and discusses the measurements made.     

Two-Dimensional Modeling and Simulation of Mass Transport in Microfabricated Preconcentrators

The adsorption and desorption behavior of a planar microfabricated preconcentrator (PC) has been modeled and simulated using the computational fluid dynamics (CFD) package CFDRC-ACE+trade. By comparison with the results of a designed experiment, model parameters were determined. Assuming a first-order reaction for the adsorption of a light hydrocarbon chemical analyte onto the PC adsorbent and a unity-value sticking coefficient, a rate constant of 36 500 s^-1 was obtained. This compares favorably with the value of 25 300 s^-1 obtained by application of the Modified-Wheeler equation. The modeled rate constant depends on the concentration of adsorbent sites, estimated to be 6.94 ldr 10^-8 kmol/m² for the Carboxen 1000 adsorbent used. Using the integral method, desorption was found to be first order with an Arrhenius temperature dependence and an activation energy of 30.1 kj/mol. Validation of this model is reported herein, including the use of Aris-Taylor dispersion to predict the influence of fluidics surrounding the PC. A maximum in desorption peak area with flow rate, predicted from a quadratic fit to the results of the designed experiment, was not observed in the 2-D simulation. Either approximations in the simulated model or the nonphysical nature of the quadratic fit are responsible. Despite the apparent simplicity of the model, the simulation is internally self consistent and capable of predicting performance of new device designs. To apply the method to other analytes and other adsorbent materials, only a limited number of comparisons to experiment are required to obtain the necessary rate constants.     

Estimating regularity in epileptic seizure time-series data

The authors apply Ziv-Lempel (LZ) complexity and approximate entropy (ApEn) as measures to quantify the regularity in the various epochs of epileptic seizure time series data. They demonstrate the potential of complexity measures such as LZ and ApEn in quantifying the regularity at different epochs of epileptic seizure time-series data. It is clearly shown that these measures have high values at the beginning and the end of the seizure, and that they decrease during mid-seizure. In fact, the authors observe in the histogram plot that the frequency of the complexity measure in mid-seizure is quite prominent. This gives one an idea about the epoch where one can find more regular patterns. These measures can also be used as relative indices (comparing across state), rather than absolute indices, by using a larger number of subjects to obtain statistical validity in comparing across conditions. The analysis of time series obtained from complex systems, such as the brain, by the above measures provides an alternative easy way to quantify the regularity with finite-length segments (of the order of 1000 samples). The same can be inferred by calculating the correlation dimension and Lyapunov exponent, but the algorithms used to estimate these invariants are susceptible to error due to the finite sample size and are also highly sensitive to noise. The computational complexity of these algorithms is also high. The authors have also applied these measures across the various states of epilepsy     

Dependence of disperse dye diffusion on the structure and morphology of oriented heat–set polyester fibres

The structural parameters that predominantly influence dye diffusion behaviour in heat–set polyester fibres have been identified. Dye diffusion has been shown to depend on two factors: the volume of the accessible region (amorphous region) and the tortuosity of the dye diffusion path. The accessible region can be represented in terms of the amorphous volume per crystal, and the tortuosity can be expressed quantitatively by combining the orientation of the amorphous phase and the nature of coupling between the amorphous and the crystalline regions. An integrated model has been proposed by combining these parameters, and has been shown to correlate well with dye uptake in polyester fibres heat–set under slack and taut conditions.     

Asymmetric Contribution of Aromatic Interactions Stems from Spatial Positioning of the Interacting Aryl Pairs in β‐Hairpins

Isolated aromatic interactions in designed octapeptide β‐hairpin scaffolds display a near‐universal T‐shaped face‐to‐edge geometry in all positional permutations, with the exception of aryl‐Trp interactions. The heterogeneous asymmetric indole ring of Trp competes for a “shielding” face geometry, which lowers the scaffold stability in FtE aryl‐Trp pairs. Assessment of the contributions of aryl pairs (in the absence of solvent‐driven interactions) to the overall β‐hairpin structure reveals the superiority of Trp‐Phe and Trp‐Tyr contributions over the well‐established scaffold stabilization by Trp‐Trp.     

A survey of multiscale modeling: Foundations,historical milestones,current status,and future prospects

Research problems in the domains of physical, engineering, biological sciences often span multiple time and length scales, owing to the complexity of information transfer underlying mechanisms. Multiscale modeling (MSM) and high-performance computing (HPC) have emerged as indispensable tools for tackling such complex problems. We review the foundations, historical developments, and current paradigms in MSM. A paradigm shift in MSM implementations is being fueled by the rapid advances and emerging paradigms in HPC at the dawn of exascale computing. Moreover, amidst the explosion of data science, engineering, and medicine, machine learning (ML) integrated with MSM is poised to enhance the capabilities of standard MSM approaches significantly, particularly in the face of increasing problem complexity. The potential to blend MSM, HPC, and ML presents opportunities for unbound innovation and promises to represent the future of MSM and explainable ML that will likely define the fields in the 21st century.     

Review on recent advances of zinc substituted cobalt ferrite nanoparticles: Synthesis characterization and diverse applications

Researchers have taken a prodigious consideration in characterizing and synthesizing zinc substituted cobalt ferrite nanoparticles because of their substantial applications across diverse technological and industrial fields. Zinc substituted cobalt ferrite nanoparticles are a class of lenient magnetic nanomaterials, which have potentially high magnetic, optical, electrical, and dielectric properties. These properties include a high value of permeability, low power losses, permittivity, saturation magnetization, coercivity, resistivity, and other beneficial properties that make them promise candidates for applications in various fields. These ferrites are also used in biomedical areas such as MRI and cancer treatments. In electronic fields, zinc substituted cobalt ferrite nanoparticles are used to make transducers, transformers, biosensors, and sensors. Apart from these advantages, they are found in our everyday electronic and electrical appliances like LED bulb, refrigerator, mobile charger, TV, microwave oven, juicer, washing machine, mixer, iron, printer, laptop, mobile, desktop, etc. Hence, the current review reports some properties of these spinel ferrites and emphasizes the different synthesis techniques that can be used to prepare them. Afterward, the impact of dopant on the materials' properties, the characterization techniques, and the momentous application in the present era have been well discussed.