In this paper, we report highly surface-functionalized mesoporous silica adsorbents developed for ethane/ethylene separation. Several porous adsorbents with selective ethane binding have been reported with promising results. These reverse selective adsorbents are anticipated to be selectively up-taking ethane due to their enhanced van der Waals interactions with the ethane molecules. Ethane selective ionic liquid 3-aminopropylammonium oleate was impregnated on SBA-15 to enhance its paraffin affinity. Ionic liquid-modified SBA-15 was developed for selective uptake of ethane and adsorption studies with mixture of gases have shown promising results.
Owing to high theoretical power density and storage capacity, non-aqueous Li-air battery have gained much attention among academia. However, its commercialization is halted by many obstacles, particularly low discharge capacity which is mainly due to the improper cathode design. Based on this, we have proposed novel hierarchical cathode architectures with distributed initial porosity and tortuosity. Among various tortuosity-based cathode designs, hierarchical cathodes with gradient tortuosity can improve discharge capacity approximately 20.9 %. In porosity-based cathode designs, hierarchical cathode structures with exponential distribution in initial porosity have shown maximum increase in discharge capacity, nearly 56 %. The underlying reason of improved capacity in hierarchical cathodes is due to enhanced effective oxygen transport, impregnation of electrolyte, alignment of pores, and formation of permeable and low crystalline aggregates of Li2O2. Hence, our design strategies could be handy for the fabrication of advance non-aqueous Li-air batteries with enhanced power density and capacity.
For many years, this technology has been utilizing aqueous amine solutions as absorbents for the capture of CO2. However, aqueous amine solutions exhibit several drawbacks such as, solvent degradation, and negative environmental impacts. Consequently, a new class of green solvents termed as deep eutectic solvents (DESs) has emerged in recent years to replace aqueous amines. Nonetheless, most of DESs have very high viscosities, which impede their use in many applications. Therefore, this study investigates the effect of the addition of water, as a low viscous ternary component, to amine based deep eutectic solvents during the preparation step, on the physicochemical properties, thermal stability and CO2 absorption capacity. The results revealed that the addition of small quantities of water such as 10% by weight, are capable of decreasing the viscosity of the resulting DESs by approximately 25% at room temperature, while maintaining high CO2 absorption capacity and high thermal stability.
In this contribution, the polar soft-SAFT equation of state, based on Statistical Thermodynamics, was used to model the chemical absorption of carbon dioxide (CO2) in monoethanolamine (MEA)-based solvents formulated from the combination with either water, or polar solvents such as N-methyl-2-pyrrolidone, and sulfolane. The chemisorption of CO2 in MEA-based solvents was described in terms of the formation of CO2 amine aggregates physically bounded by strong intermolecular association interactions. A maximum of two adjustable parameters (for each solvent) were sufficient to obtain an accurate representation of the CO2 solubility in the examined solvents. The developed models can be used in a predictive manner to compare solubility of the different solvents at representative conditions. These results demonstrate the reliability of the model as a solvent screening tool for CO2 capture, owing to its high level of accuracy, transferability and predictive capabilities.
Wireless reprogramming is a critical issue in the Internet of Things (IoT). Current approaches designed for wireless sensor networks (WSN) are inadequate for IoT scenarios due to serious security vulnerabilities. To address this problem, we present Software Defined Function (SDF), a secure and wireless reprogramming architecture for IoT. The key is to implement a secure communication interface between the control layer and infrastructure layer. Four security protocols ensuring authentication and confidentiality are designed. In addition, the design takes into consideration the IoT devices' constrained capabilities in computation. We test the performance of SDF through a set of experiments and present a use case on a photovoltaic energy system. The evaluation results show that the proposed protocols can be implemented in real-world IoT applications.
Personal health records (PHRs) are valuable assets to individuals because they enable them to integrate and manage their medical data. Giving patients control over their medical data offers an advantageous realignment of the doctor-patient dynamic. However, today's PHR management systems fall short of giving reliable, traceable, trustful, and secure patients control over their medical data. In this paper, we propose blockchain-based smart contracts to give patients control over their data in a manner that is decentralized, immutable, transparent, traceable, trustful, and secure. The proposed system employs decentralized storage of interplanetary file systems (IPFS) and trusted reputation-based re-encryption oracles to securely fetch, store, and share patients' medical data.
This paper presents an improved version of RKEDA, one of the evolutionary algorithms for a spares part allocation problem in the telecom industry. The aim is to improve on previous approach and have the right spare parts at the right time at the right place. This will make a big difference to the quality of the service offered by a telecom organization by maximizing its utilization and revenues while minimizing the total cost. The improvement presented in this paper relates to ordering the way the initial sites are represented in the permutation in RKEDA algorithm. We describe proposed improvement, perform detail experimental analysis, and compare the performance of the new RKEDA to that of the original RKEDA and GA.
In this paper, we propose a deep-learning approach for human gender classification on RGB-D images. Unlike most of the existing methods, which use hand-crafted features from the human face, we exploit local information from the head and global information from the whole body to classify people's gender. A head detector is fine-tuned on YOLO to detect the head regions on the images automatically. Two gender classifiers are trained using head images and whole-body images separately. The final prediction is made by fusing the two classifiers' results. The presented method outperforms the state-of-art with an improvement in the accuracy of 2.6%, 7.6%, and 8.4% on three different test data of a challenging gender dataset which includes human standing, walking, and interacting scenarios.
A data-driven angle of arrival (AoA) estimation framework is proposed in this paper. A convolution neural network (CNN), which is a part of Deep Learning (DL), is employed to learn a mapping between the eigenvectors of the spatial covariance matrix of the received signals and the angles of arrival. This paper discusses the CNN architecture and provides a detailed description of the hyper-parameters. Simulation results show that the proposed approach outperforms traditional methods in a multipath environment with less execution time.
The growing adoption of microservice architecture in academia and industry has led to an increase in the amount of literature addressing microservices challenges, approaches, and practices in the last few years. Most of the published works did not consider the efficient allocation of resources of the proposed solutions. In addition, several aspects are still vague and scattered in the literature with regards to Microservices Architecture (MSA) and efficiency models. In this paper, we survey recent microservice frameworks for efficient resource utilization, and classify them based on their efficiency type. We further highlight current MSA trends and identify resource utilization and sharing amongst multiple microservices as important areas for future research.
