On the prediction of solubility of alkane in carbon dioxide using the LSSVM algorithm

The increasing global energy demand and declination of oil reservoir in recent years cause the researchers attention focus on the enhancement of oil recovery approaches. One of the extensive applicable methods for enhancement of oil recovery, which has great efficiency and environmental benefits, is carbon dioxide injection. The CO₂ injection has various effects on the reservoir fluid, which causes enhancement of recovery. One of these effects is extraction of lighter components of crude oil, which straightly depends on solubility of hydrocarbons in carbon dioxide. In order to better understand of this parameter, in this study, Least squares support vector machine (LSSVM) algorithm was developed as a novel predictive tool to estimate solubility of alkane in CO₂ as function of carbon number of alkane, carbon dioxide density, pressure, and temperature. The predicting model outputs were compared with the extracted experimental solubility from literature statistically and graphically. The comparison showed the great ability and high accuracy of developed model in prediction of solubility.     

DEA window analysis and Malmquist index to assess energy efficiency and productivity in Jordanian industrial sector

This research evaluates the energy efficiency and productivity growth in the industrial sector over the period of 1999 till 2013 using data envelopment analysis (DEA). Two cases are analyzed; in the first case (GVA), the output is the gross value added, whereas two outputs are considered in the second case (GCO), CO₂ emission and GVA. Five key input factors are considered in both cases. From DEA window analysis, the technical inefficiency (TIE) values are zeros in windows (2001–2005) till (2003–2007), (2007–2011), and (2008–2012), whereas the pure technical inefficiency (PTIE) values are zeros in windows (1999–2003) till (2003–2007). Finally, the scale inefficiency (SIE) values are zeros in windows (2001–2005) till (2003–2007). These results help policy planners on how to better utilize resources and management efficiency over time and guide operational managers when to increase or decrease the scale. Moreover, the averages of inefficiency values in the GVA case are smaller than their corresponding in the GCO case, which indicates the negative effect of CO₂ emission on efficiency. Further, Malmquist index is estimated for three 5-year energy plans. The productivity index is found less than one for the third plan (2009–2013), which indicates a decrease productivity growth. In conclusions, research results provide valuable support when assessing the progress of energy efficiency and productivity in industrial sector.     

Analyzing and automatically labelling the types of user issues that are raised in mobile app reviews

Mobile app reviews by users contain a wealth of information on the issues that users are experiencing. For example, a review might contain a feature request, a bug report, and/or a privacy complaint. Developers, users and app store owners (e.g. Apple, Blackberry, Google, Microsoft) can benefit from a better understanding of these issues – developers can better understand users’ concerns, app store owners can spot anomalous apps, and users can compare similar apps to decide which ones to download or purchase. However, user reviews are not labelled, e.g. we do not know which types of issues are raised in a review. Hence, one must sift through potentially thousands of reviews with slang and abbreviations to understand the various types of issues. Moreover, the unstructured and informal nature of reviews complicates the automated labelling of such reviews. In this paper, we study the multi-labelled nature of reviews from 20 mobile apps in the Google Play Store and Apple App Store. We find that up to 30 % of the reviews raise various types of issues in a single review (e.g. a review might contain a feature request and a bug report). We then propose an approach that can automatically assign multiple labels to reviews based on the raised issues with a precision of 66 % and recall of 65 %. Finally, we apply our approach to address three proof-of-concept analytics use case scenarios: (i) we compare competing apps to assist developers and users, (ii) we provide an overview of 601,221 reviews from 12,000 apps in the Google Play Store to assist app store owners and developers and (iii) we detect anomalous apps in the Google Play Store to assist app store owners and users.     

Synthesis of Ion Imprinted Polymers by Copolymerization of Zn(II) and Al(III)8-hydroxy Quinolone Complexes with Divinylbenzene and Methacryclic Acid

In this research work, Zinc(II) and Aluminum(III)-IIP's were synthesized by optimizing the amount of methacrylic acid as monomer, divinylbenzene as cross-linker. The IIP's were functionalized with 8-hydroxy quinolone complexes of the two metal ions under thermal conditions by copolymerization with monomer and cross-linker. The IIP's and Non-IIP's were characterized using FT-IR, TGA, and SEM analysis. A quite remarkable difference in the size was observed of the polymers (Zn(II) 1.0 µm and Al(III) 0.1 µm). A stronger affinity was observed with IIP in comparison with Non-IIP at pH 3.1 and 4.5 for Zn(II) and Al(III) ions on their respective polymers.     

Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks

To optimize mitigation, preparedness, response, and recovery procedures for infrastructure systems, it is essential to use accurate and efficient means to evaluate system reliability against probabilistic events. The predominant approach to quantify the impact of natural disasters on infrastructure systems is the Monte Carlo approach, which still suffers from high computational cost, especially when applied to large systems. This article presents a deep learning framework for accelerating seismic reliability analysis, on a transportation network case study. Two distinct deep neural network surrogates are constructed and studied: (1) a classifier surrogate that speeds up the connectivity determination of networks and (2) an end‐to‐end surrogate that replaces modules such as roadway status realization, connectivity determination, and connectivity averaging. Numerical results from k‐terminal connectivity analysis of a California transportation network subject to a probabilistic earthquake event demonstrate the effectiveness of the proposed surrogates in accelerating reliability analysis while achieving accuracies of at least 99%.     

Exploring the best sequence LSTM modeling architecture for flood prediction

Neural Computing and Applications - Accurate and efficient models for rainfall–runoff (RR) simulations are crucial for flood risk management. Recently, the success of the recurrent neural...     

A green approach for modification and functionalization of wool fabric using bio- and nano-technologies

In the present work, we propose a green and sustainable strategy for eco-friendly surface modification of wool structure using biosynthesized kerationlytic proteases, from C4-ITA-EGY, Streptomyces harbinensis S11-ITA-EGY and Streptomyces carpaticus S33-ITA-EGY, followed by subsequent environmentally sound functionalization of the bio-treated substrates using ZnONPs, ZrO₂NPs, ascorbic acid and vanillin, individually, to provide durable antibacterial as well as UV-protection properties. Both surface modification changes and the extent of functionalization of the final products were characterized by SEM, EDX, antibacterial efficacy, UV-blocking ability, loss in weight, nitrogen content and durability to washing analysis. The obtained data reveal that the developed green wool fabrics exhibit outstanding durable antibacterial activity and UV-blocking ability for fabricating multi-functional textile products that can be utilized in a wide range of sustainable protective textiles, irrespective of the used post-finishing formulation ingredients. The results also show that both modification and functionalization processes are governed by the type of enzyme and kind of active material respectively. Moreover, the biosynthesized kerationlytic proteases could be accessibly used to remove protein-based stains like blood and egg.

Graphical abstract