Composite mixed matrix membranes incorporating microporous carbon molecular sieve as filler in polyethersulfone for CO2/CH4 separation

Membrane technology has been considered a key factor for sustainable growth in high-efficiency gas separation. Current mixed matrix membranes (MMMs) technology is rising, but these membranes in the dense structure are having difficulties in operating at high pressures and scale up for commercialization. The purpose of this research is to synthesize composite MMMs (CMMMs) consisting of polyethersulfone (PES), carbon molecular sieve (CMS 1–5 wt %), and Novatex 2471 nonwoven fabric (support layer). The membranes' physical, chemical, and thermal properties were evaluated by different analytical equipment. The morphology of both PES and PES-CMS composite membranes had a porous and asymmetric structure, in which CMS was uniformly distributed in the polymer matrix. The thermal properties showed that the membranes were stable up to 350 °C with a single glass transition temperature. The functional groups in the membrane were confirmed by spectral analysis. The gas performance results showed that carbon dioxide permeance increased with increased CMS concentration and methane permeance decreased due to the hindering effect of CMS under similar operating conditions. The highest selectivity achieved was 12.774 using CMMM of 5 wt % of CMS at 10 bar, which on average was 137.80%, improved selectivity compared to pure PES membrane. The support layer was able to withstand high operating pressures and showed the ability to scale up. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48476.     

Predicting catastrophic temperature changes based on past events via a CNN-LSTM regression mechanism

Neural Computing and Applications - The modelling and prediction of extreme temperature changes in enclosed compartments is a domain with applications ranging from residential fire alarms,...     

Influence of milling time on the structural and exchange bias of CoO and Co–Co oxide nanocomposite systems

This study aimed to investigate the effect of milling time on the structural and exchange bias properties of two exchange-coupled nanocomposite systems that were prepared by mechanical milling. The first system consists of an antiferromagnet (CoO), named CoO-series and the second one consists of a mixture of a ferromagnet with an antiferromagnet (10 wt% Co + CoO), called MIX-series and both systems were milled for various times. Upon field cooling, going through low temperature (5 K), hysteresis loop shifts, coercivity enhancements, and saturation magnetization reductions were observed as milling time increases. Noticeable enhancements of exchange bias (270 Oe) and coercivity (1039 Oe) of the CoO-series after 10 h of milling, in comparison with those of unmilled ones (16 Oe) and (136 Oe) respectively, showed that the main structural changes occurred at the first milling hours. Introduction of large structural defects, the formation of cobalt and crystallite size reduction, ensuing from milling, can be the causes of an increase in exchange bias and coercivity. On the other hand, ferromagnet-antiferromagnet exchange coupling induces by milling is another origin for exchange bias. The results show that ferromagnet-antiferromagnet ratio and/or crystallite sizes play key roles in exchange bias enhancement. So that milled CoO-series with lower Co content in comparison with that of MIX-series and finer crystallites have considerable higher exchange bias.     

Platinum‐promoted fibrous silica Y zeolite with enhanced mass transfer as a highly selective catalyst for n‐dodecane hydroisomerization

A fibrous silica zeolite Y (HY@KCC‐1) catalyst with a high surface area of 568 m²/g and unique core‐shell morphology was successfully synthesized via a modified KCC‐1 synthesis method. Characterization of the catalysts was achieved with X‐ray powder diffraction (XRD), field emission scanning microscope (FESEM), N₂ adsorption/desorption, and 2,6‐dimethylpyridine adsorbed Fourier‐transform infrared spectroscopy (FTIR). The Pt/HY@KCC‐1 has displayed complete n‐dodecane conversion coupled with an incredibly enhanced isomer yield of 72% at 350°C, nearly two‐fold higher than that of unmodified Pt/HY catalyst. Remarkably, Pt/HY@KCC‐1 had an internal effectiveness factor (η) of unity and negligible internal diffusion limitation, thus suggesting its potential application in hydroisomerization of higher hydrocarbons for enhancing fuel properties.     

Developing an innovative soft computing scheme for prediction of air overpressure resulting from mine blasting using GMDH optimized by GA

Engineering with Computers - Air overpressure (AOp) is one of the most important undesirable effects induced by blasting operations in the mining or tunneling projects. Hence, the present precise...     

Factors that determine residents’ acceptance of smart city technologies

ABSTRACT

While some cities attempt to determine their residents’ demand for smart-city technologies, others simply move forward with smart-related strategies and projects. This study is among the first to empirically determine which factors most affect residents’ and public servants’ intention to use smart-city services. A Smart Cities Stakeholders Adoption Model (SSA), based on Unified Theory of Acceptance and Use of Technology (UTAUT2), is developed and tested on a mid-size U.S. city as a case study. A questionnaire was administered in order to determine the influence of seven factors – effort expectancy, self-efficacy, perceived privacy, perceived security, trust in technology, price value and trust in government – on behaviour intention, specifically the decision to adopt smart-city technologies. Results show that each of these factors significantly influenced citizen intention to use smart-city services. They also reveal perceived security and perceived privacy to be strong determinants of trust in technology, and price value a determinant of trust in government. In turn, both types of trust are shown to increase user intention to both adopt and use smart-city services. These findings offer city officials an approach to gauging residential intention to use smart-city services, as well as identify those factors critical to developing a successful smart-city strategy.     

Viscoelastic and thermal properties of natural rubber low-density polyethylene composites with boric acid and borax

The influence of boric acid (BA) and borax (BO) neutron-absorbing fillers on thermal stability and viscoelastic behavior of natural rubber (NR) low-density polyethylene composites has been studied. The thermal degradation and dynamic mechanical properties of the composites have been analyzed as a function of temperature. The results revealed the enhancement of thermal stability of the composites by the addition of BA and BO fillers. The flame resistance of the material was improved by the addition of both the fillers. The storage modulus was found to be dependent upon the temperature and nature of the filler. The amount of NR chains immobilized by filler particles has been quantified from dynamic mechanical analysis and secondary filler/filler interactions have been verified by the Payne effect analysis. Finally, the experimental results have been compared with theoretical predictions.     

Load management as a smart grid concept for sizing and designing of hybrid renewable energy systems

Optimal sizing of hybrid renewable energy systems (HRES) to satisfy load requirements with the highest reliability and lowest cost is a crucial step in building HRESs to supply electricity to remote areas. Applying smart grid concepts such as load management can reduce the size of HRES components and reduce the cost of generated energy considerably. In this article, sizing of HRES is carried out by dividing the load into high- and low-priority parts. The proposed system is formed by a photovoltaic array, wind turbines, batteries, fuel cells and a diesel generator as a back-up energy source. A smart particle swarm optimization (PSO) algorithm using MATLAB is introduced to determine the optimal size of the HRES. The simulation was carried out with and without division of the load to compare these concepts. HOMER software was also used to simulate the proposed system without dividing the loads to verify the results obtained from the proposed PSO algorithm. The results show that the percentage of division of the load is inversely proportional to the cost of the generated energy.     

QoS Aware Multicast Routing Protocol for Video Transmission in Smart Cities

In recent years, Software Defined Networking (SDN) has become an important candidate for communication infrastructure in smart cities. It produces a drastic increase in the need for delivery of video services that are of high resolution, multiview, and large-scale in nature. However, this entity gets easily influenced by heterogeneous behaviour of the user's wireless link features that might reduce the quality of video stream for few or all clients. The development of SDN allows the emergence of new possibilities for complicated controlling of video conferences. Besides, multicast routing protocol with multiple constraints in terms of Quality of Service (QoS) is a Nondeterministic Polynomial time (NP) hard problem which can be solved only with the help of metaheuristic optimization algorithms. With this motivation, the current research paper presents a new Improved Black Widow Optimization with Levy Distribution model (IBWO-LD)-based multicast routing protocol for smart cities. The presented IBWO-LD model aims at minimizing the energy consumption and bandwidth utilization while at the same time accomplish improved quality of video streams that the clients receive. Besides, a priority-based scheduling and classifier model is designed to allocate multicast request based on the type of applications and deadline constraints. A detailed experimental analysis was carried out to ensure the outcomes improved under different aspects. The results from comprehensive comparative analysis highlighted the superiority of the proposed IBWO-LD model over other compared methods.     

Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s

In recent years, the number of Gun-related incidents has crossed over 250,000 per year and over 85% of the existing 1 billion firearms are in civilian hands, manual monitoring has not proven effective in detecting firearms. which is why an automated weapon detection system is needed. Various automated convolutional neural networks (CNN) weapon detection systems have been proposed in the past to generate good results. However, These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system. These models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance videos. This research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key parameter. The proposed framework is based on You Only Look Once (YOLO) and Area of Interest (AOI). Initially, the models take pre-processed frames where the background is removed by the use of the Gaussian blur algorithm. The proposed architecture will be assessed through various performance parameters such as False Negative, False Positive, precision, recall rate, and F1 score. The results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are achieved. Speed reached 0.010 s per frame compared to the 0.17 s of the Faster R-CNN. It is promising to be used in the field of security and weapon detection.