SOLVENT EXTRACTION OF PAKISTANI COALS (ii): EXTRACTION AND CHARACTERIZATION OF METHANOL-BENZENE EXTRACTS

Structural characterization of the solvent extracts from four different Pakistani lignitic coals has been carried out by their proximate, elemental analysis and FT-Infrared and ¹H n.m.r. spectra. The yield of each extracts was greater with 1:1 benzene-methanol mixture in comparison to the total yield obtained by separate extractions with benzene and methanol. The extracts contained significantly less amount of ash and fixed carbon along with an increase in the percentage of volatile matter. The FTIR and ¹H n.m.r. spectra indicated that basically all the extracts contained less condensed aromatic rings in comparison to the coal. The FTIR spectra showed sharp well resolved peaks which have been assigned to various functional groups.

The ¹H n.m.r. spectra were used to obtain average structural parameters for all the extracts. A detailed analysis of the FTIR and n.m.r. spectra of the coal and their extracts provided an important in-sight into the differences between various extracts and also between various coals and their corresponding extracts.     

Magnetic Properties of Mechanically Alloyed Cobalt-Zinc Ferrite Nanoparticles

In this work, the physical and the magnetic properties of cobalt-zinc ferrite nanoparticles, synthesized via high-energy ball milling (HEBM), were examined. X-Ray diffraction (XRD), field emission scanning electron microscopy (FeSEM), and scanning transmission electron microscopy (STEM) were used to study changes of the powder structure and morphology analyses. Hysteresis and permeability measurement were carried out using a BH hysteresisgraph system and an impedance analyzer, respectively. The results suggested improved magnetic properties of Co_0.5Zn_0.5Fe₂O₄ with increasing sintering temperature from 950 °C up to 1200 °C. However, the variations of the magnetic responses were consistent with the varying volume concentration of the ferrite composites. Unlike the highly crystalline pure ferrite which showed magnetic resonance within the measured frequency, the crystallineamorphous composites showed no visible resonance peak. This proved that the resonance peak shifted to higher frequency as a result of the single domain spin behavior in the absence of domain walls movement.     

Managing Complexity in Object-Oriented Analysis

The current approaches in Object-Oriented Analysis have limitations on modeling complex real world systems because they require priori knowledge about objects and their interactions before applying them. This may be practical in small systems and systems with clear domain knowledge, but not in large real world systems with unclear domain knowledge. Our approach uses a stepwise refinement technique in a top-down manner to the Object-Oriented Analysis stage with the application of use cases. This approach is especially good for new areas where we do not know all the information in advance. We present the approach with an example of its application to the B-ISDN service modeling and distributed systems.     

A relational language with deductions,functions and recursions

We propose here a .relational algebra capable of deducing tuples from a premise expressed in an extended relational form: it also supports user-defined and recursive functions in the form of parameterised views, and provides a facility for easier specification of queries. The paper shows the power of the language in dealing with problems such as ancestors, part explosions and connected tours.This language, called DEAL, is an enhanced version of the PRECI Algebraic Language (PAL) but presented here in a SQL-like syntax.     

Automated Patient Discomfort Detection Using Deep Learning

The Internet of Things (IoT) has been transformed almost all fields of life, but its impact on the healthcare sector has been notable. Various IoT-based sensors are used in the healthcare sector and offer quality and safe care to patients. This work presents a deep learning-based automated patient discomfort detection system in which patients’ discomfort is non-invasively detected. To do this, the overhead view patients’ data set has been recorded. For testing and evaluation purposes, we investigate the power of deep learning by choosing a Convolution Neural Network (CNN) based model. The model uses confidence maps and detects 18 different key points at various locations of the body of the patient. Applying association rules and part affinity fields, the detected key points are later converted into six main body organs. Furthermore, the distance of subsequent key points is measured using coordinates information. Finally, distance and the time-based threshold are used for the classification of movements associated with discomfort or normal conditions. The accuracy of the proposed system is assessed on various test sequences. The experimental outcomes reveal the worth of the proposed system’ by obtaining a True Positive Rate of 98% with a 2% False Positive Rate.     

Decoupling of CuO2 Planes and Superconductivity in (Cu0.5Tl0.5)Ba2Ca3Cu4O12-δ

Journal of Superconductivity and Novel Magnetism - (Cu0.5Tl0.5)Ba2(Ca3-xMgx)(Cu4-yZny)O12-δ (x?=?0, 2; y?=?0, 3) and (Cu0.5Tl0.5)Ba2(Ca3-xSrx)Cu4O12-δ...     

Designing Novel Nonsymmetric Ag/AgI Nanoplates for Superior Photocatalytic Activity

Precisely engineering the decoration of metal nanoparticles on the special surface of semiconductor represents a promising strategy to design efficient metal–semiconductor heterostructured photocatalysts. This study demonstrates a versatile soft‐template method to fabricate a novel nonsymmetrical heterostructured Ag/AgI nanoplate, in which only one side surface of the nanoplate is covered with uniform 2D Ag nanoweb. Compared with symmetrical heterostructure, the nonsymmetrical heterostructure may further facilitate the separation of photogenerated electron–hole pairs and shows a greatly enhanced photocatalytic activity. This study may open up a new way to improve the photocatalytic property by synthesizing nonsymmetrical metal–semiconductor composites.     

Statistical modeling and optimization of pretreatment of Bombax ceiba with KOH through Box–​Behnken design of response surface methodology

Bioethanol is considered the cleanest liquid fuel used as a substitute for depleting fossil fuels. Various technologies have been introduced to form bioethanol from lignocellulosic biomass. Seed pods of Bombax ceiba, which are produced and wasted in large amount annually, were used as a source of cellulose. In this study, response surface methodology was used to explore the effects of KOH concentrations, substrate loading, and residence time on cellulose exposure and liberation of reducing sugars (RS), total sugars (TS), and total phenolic compounds from seed pods of B. ceiba. Box–Behnken design with three variables and three levels showed maximum release of total phenolic compounds (394.04 mg/ml) and RS (50.06 mg/ml) corresponding to 3% KOH concentration, 15% substrate level with residence time of 8 h at 121°C, and maximum cellulose exposure (64%), and TS (206.65 mg/ml) liberation was observed at 5% KOH concentration and 10% substrate level at same temperature for same soaking time. While at room temperature maximum cellulose exposed (46%), TS (146.1480 mg/ml), total phenol (300.3901 mg/ml), and RS (9.0075 mg/ml) were observed at 3% KOH, 15% substrate concentration, and 8-h residence time. These results suggested that thermochemical pretreatment is more effective than chemical pretreatment alone. The second-order polynomial equation using analysis of variance was employed for analyzing the results.     

Impact of random defective rate on lot size focusing work-in-process inventory in manufacturing system

Literature has focused inventory models with intensive emphasis on imperfect production processes in recent past. However, the work-in-process-based inventory models have been ignored, relatively, in general and the impact of random defects in the form of reworkable and non-reworkable defect rate on lot size and total cost function in particular. This paper develops mathematical models for work-in-process-based inventory by incorporating the effect of random defects rate on lot size and expected total cost function. Our proposed models assume that defective products produced during the production process follow random distributions. Defective products, either in the form of reworkable or rejected production units, follow four types of distribution density functions: uniform, triangular, double triangular and beta distribution. Mathematical models are derived for optimum lot size based on minimization of expected total cost function through the analytical optimization approach. Numerical examples and detailed sensitivity analysis are carried to illustrate and compare the proposed models at different levels of distribution functions’ parameters.     

Prospects of reusable endogenous hydrolyzing enzymes in bioethanol production by simultaneous saccharification and fermentation

This study was conducted to evaluate the presence, origination and classification of various hydrolyzing enzymes from malt and their specified hydrolyzing effects on various substrates for bioethanol production and to link these characteristics with the future prospects of bioethanol production. These enzymes are categorized as cell wall, starch, protein, lipid, polyphenol and thiol hydrolyzing enzymes based on their substrate specificity. Waste from beer fermentation broth (WBFB) has been evaluated as a rich source of malt derived hydrolyzing enzymes with significant self potential for bioethanol production. However, yeast cells cannot survive at the high temperature required for the saccharification activities of hydrolyzing enzymes during simultaneous saccharification and fermentation (SSF). This dilemma might be resolved by bioethanol production at elevated temperatures via cell-free fermentation systems in the presence of malt hydrolyzing enzymes. Moreover, emerging technologies such as genetic engineering in biomass and biotransformation in cell-free enzymatic systems will likely hasten bioethanol production in the near future. The present study adds new dimensions to eco-friendly bioethanol production from renewable and waste energy resources based on the specific hydrolyzing activities of malt enzymes.