Novel electrospun polymer electrolytes incorporated with Keggin‐type hetero polyoxometalate fillers as solvent‐free electrolytes for lithium ion batteries

In this study, solvent‐free nanofibrous electrolytes were fabricated through an electrospinning method. Polyethylene oxide (PEO), lithium perchlorate and ethylene carbonate were used as polymer matrix, salt and plasticizer respectively in the electrolyte structures. Keggin‐type hetero polyoxometalate (Cu‐POM@Ru‐rGO, Ni‐POM@Ru‐rGO and Co‐POM@Ru‐rGO (POM, polyoxometalate; rGO, reduced graphene oxide)) nanoparticles were synthesized and inserted into the PEO‐based nanofibrous electrolytes. TEM and SEM analyses were carried out for further evaluation of the synthesized filler structures and the electrospun nanofibre morphologies. The fractions of free ions and crystalline phases of the as‐spun electrolytes were estimated by obtaining Fourier transform infrared and XRD spectra, respectively. The results showed a significant improvement in the ionic conductivity of the nanofibrous electrolytes by increasing filler concentrations. The highest ionic conductivity of 0.28 mS cm^?1 was obtained by the introduction of 0.49 wt% Co‐POM@Ru‐rGO into the electrospun electrolyte at ambient temperature. Compared with solution‐cast polymeric electrolytes, the electrospun electrolytes present superior ionic conductivity. Moreover, the cycle stability of the as‐spun electrolytes was clearly improved by the addition of fillers. Furthermore, the mechanical strength was enhanced with the insertion of 0.07 wt% fillers to the electrospun electrolytes. The results implied that the prepared nanofibres are good candidates as solvent‐free electrolytes for lithium ion batteries. © 2020 Society of Chemical Industry     

“How do you know that I don’t understand?” A look at the future of intelligent tutoring systems

Many software systems would significantly improve performance if they could adapt to the emotional state of the user, for example if Intelligent Tutoring Systems (ITSs), ATM’s, ticketing machines could recognise when users were confused, frustrated or angry they could guide the user back to remedial help systems so improving the service. Many researchers now feel strongly that ITSs would be significantly enhanced if computers could adapt to the emotions of students. This idea has spawned the developing field of affective tutoring systems (ATSs): ATSs are ITSs that are able to adapt to the affective state of students. The term “affective tutoring system” can be traced back as far as Rosalind Picard’s book Affective Computing in 1997.This paper presents research leading to the development of Easy with Eve, an ATS for primary school mathematics. The system utilises a network of computer systems, mainly embedded devices to detect student emotion and other significant bio-signals. It will then adapt to students and displays emotion via a lifelike agent called Eve. Eve’s tutoring adaptations are guided by a case-based method for adapting to student states; this method uses data that was generated by an observational study of human tutors. This paper presents the observational study, the case-based method, the ATS itself and its implementation on a distributed computer systems for real-time performance, and finally the implications of the findings for Human Computer Interaction in general and e-learning in particular. Web-based applications of the technology developed in this research are discussed throughout the paper.     

A fast and noise resilient cluster-based anomaly detection

Clustering, while systematically applied in anomaly detection, has a direct impact on the accuracy of the detection methods. Existing cluster-based anomaly detection methods are mainly based on spherical shape clustering. In this paper, we focus on arbitrary shape clustering methods to increase the accuracy of the anomaly detection. However, since the main drawback of arbitrary shape clustering is its high memory complexity, we propose to summarize clusters first. For this, we design an algorithm, called Summarization based on Gaussian Mixture Model (SGMM), to summarize clusters and represent them as Gaussian Mixture Models (GMMs). After GMMs are constructed, incoming new samples are presented to the GMMs, and their membership values are calculated, based on which the new samples are labeled as “normal” or “anomaly.” Additionally, to address the issue of noise in the data, instead of labeling samples individually, they are clustered first, and then each cluster is labeled collectively. For this, we present a new approach, called Collective Probabilistic Anomaly Detection (CPAD), in which, the distance of the incoming new samples and the existing SGMMs is calculated, and then the new cluster is labeled the same as of the closest cluster. To measure the distance of two GMM-based clusters, we propose a modified version of the Kullback–Libner measure. We run several experiments to evaluate the performances of the proposed SGMM and CPAD methods and compare them against some of the well-known algorithms including ABACUS, local outlier factor (LOF), and one-class support vector machine (SVM). The performance of SGMM is compared with ABACUS using Dunn and DB metrics, and the results indicate that the SGMM performs superior in terms of summarizing clusters. Moreover, the proposed CPAD method is compared with the LOF and one-class SVM considering the performance criteria of (a) false alarm rate, (b) detection rate, and (c) memory efficiency. The experimental results show that the CPAD method is noise resilient, memory efficient, and its accuracy is higher than the other methods.     

A novel image encryption/decryption scheme based on chaotic neural networks

This paper presents a novel image encryption/decryption algorithm based on chaotic neural network (CNN). The employed CNN is comprised of two 3-neuron layers called chaotic neuron layer (CNL) and permutation neuron layer (PNL). The values of three RGB (Red, Green and Blue) color components of image constitute inputs of the CNN and three encoded streams are the network outputs. CNL is a chaotic layer where, three well-known chaotic systems i.e. Chua, Lorenz and Lü systems participate in generating weights and biases matrices of this layer corresponding to each pixel RGB features. Besides, a chaotic tent map is employed as the activation function of this layer, and makes the relationship between the plain image and cipher image nonlinear. The output of CNL, i.e. the diffused information, is the input of PNL, where three-dimensional permutation is applied to the diffused information. The overall process is repeated several times to make the encryption process more robust and complex. A 160-bit-long authentication code has been used to generate the initial conditions and the parameters of the CNL and PNL. Some security analysis are given to demonstrate that the key space of the new algorithm is large enough to make brute-force attacks infeasible and simulations have been carried out with detailed numerical analysis, demonstrating the high security of the new image encryption scheme.     

A comprehensive review of solar‐driven desalination technologies for off‐grid greenhouses

This paper is motivated by the crisis of freshwater in remote areas around the world and responds to the growing need for sustainable food production in arid lands. It focuses on utilizing solar energy to yield freshwater from the sea or brackish water with less environmental impacts, for greenhouses, which can produce sustainable food all over the year. The integration of various solar‐driven desalinations such as solar still, humidification‐dehumidification, reverse osmosis, electrodialysis, and multieffect and multistage flash with greenhouses are evaluated, for better sustainability towards greenization. The paper first discusses the specifications of solar‐driven desalinations and compares their advantages and limitations. Then, different types of greenhouses are introduced, and their total water requirement is discussed based on their locations, crop type, greenhouse technology, irrigation type, and environmental conditions, as well as their cooling and heating strategies. Later, the existing integration of solar‐driven desalinations with greenhouses are reviewed, and their advantages and limitations are deliberated. Finally, the paper discusses the criteria to be considered when selecting solar‐driven desalinations for greenhouses and presents a detailed comparison between the water production rate and cost as well as the energy consumption of these systems. In the end, the most appropriate combinations of solar‐driven desalinations with greenhouses are recommended based on their water requirement and production cost.     

A novel bilayer drug-loaded wound dressing of PVDF and PHB/Chitosan nanofibers applicable for post-surgical ulcers

A novel bilayer nanofibrous wound dressing, with enhanced mechanical properties is successfully fabricated. This membrane, consisted of polyvinylidenefluoride (PVDF) nanofibers for providing tensile strength and polyhydroxybutyrate/chitosan (PHB/CTS) nanofibers loaded with gentamicin with ability of controlled drug delivery, is a great choice for post-surgical ulcers. Mechanical properties showed dramatically improvement of tensile strength by addition of PVDF layer. Gentamycin release represented both an immediate and a sustained release of about 24?h and 1 week, respectively and release increment with increase of CTS ratio. Results also revealed that drug release in structures follow first order kinetic and Fickian release mechanism.     

The effects of food essential oils on cardiovascular diseases: A review

Essential oils (EO) are complex secondary metabolites, which are produced by aromatic plants and identified by their powerful odors. Present studies on EO and their isolated ingredients have drawn the attention of researchers to screen these natural products and evaluate their effect on the cardiovascular system. Some EO, and their active ingredients, have been reported to improve the cardiovascular system significantly by affecting vaso-relaxation, and decreasing the heart rate and exert a hypotension activity. Several mechanisms have been proposed for the role of EO and their main active components in promoting the health of the cardiovascular system. The objective of this review is to highlight the current state of knowledge on the functional role of EO extracted from plants for reducing the risk of cardiovascular diseases and their mechanisms of action. Research on EO has the potential to identify new bioactive compounds and formulate new functional products for the treatment of cardiovascular diseases such as arterial hypertension, angina pectoris, heart failure, and myocardial infarction.