A disease network-based recommender system framework for predictive risk modelling of chronic diseases and their comorbidities

The prediction of chronic diseases and their comorbidities is an essential task in healthcare, aiming to predict patients’ future disease risk based on their previous medical records. The accumulation of administrative data has laid a solid foundation for applying deep learning approaches in healthcare. Existing studies focused on the patients’ characteristics such as gender, age and location to predict the risk of the different diseases. However, there are high dimensional, incomplete and noisy problems in the administrative data. In this research, using administrative health data, we implemented graph theory and content-based recommender system approaches to analyse and predict chronic diseases and their comorbidities. Firstly, we used bipartite graphs to represent the relationships between patients and diseases. Then, we projected this graph to a one-mode graph, namely ‘disease network’. After that, six recommender system models with patient features and network features were trained. The outputs of these models are the severity levels of diseases and the predicted diseases with rank. Finally, we evaluated the performance of these models against the same models without network features. The results demonstrated that the models with network features have lower prediction error and better performances for predicting chronic diseases and their latent comorbidities on large administrative data. Among these models, the graph convolution matrix completion model reveals the least amount of error and the best performance for prediction. Further, using a case study of a specific patient, we demonstrated the application of these models in predictive disease risk analysis. Thus, this study showed the potential application of the recommender system approaches to the health sector utilising administrative claim data, which could significantly contribute to healthcare services and stakeholders.

     

Royal Crown Shaped Polarization Insensitive Perfect Metamaterial Absorber for C-, X-, and Ku-Band Applications

This study proposed a new royal crown-shaped polarisation insensitive double negative triple band microwave range electromagnetic metamaterial absorber (MA). The primary purpose of this study is to utilise the exotic characteristics of this perfect metamaterial absorber (PMA) for microwave wireless communications. The fundamental unit cell of the proposed MA consists of two pentagonal-shaped resonators and two inverse C-shaped metallic components surrounded by a split ring resonator (SRR). The bottom thin copper deposit and upper metallic resonator surface are disjoined by an FR-4 dielectric substrate with 1.6 mm thickness. The CST MW studio, a high-frequency electromagnetic simulator has been deployed for numerical simulation of the unit cell in the frequency range of 4 to 14 GHz. In the TE mode, the offered MA structure demonstrated three different absorption peaks at 6.85 GHz (C-band), 8.87 GHz (X-band), and 12.03 GHz (Ku-band), with 96.82%, 99.24%, and 99.43% absorptivity, respectively. The electric field, magnetic field, and surface current distribution were analysed using Maxwell’s-Curl equations, whereas the angle sensitivity was investigated to comprehend the absorption mechanism of the proposed absorber. The numerical results were verified using the Ansys HFSS (high-frequency structure simulator) and ADS (advanced design system) for equivalent circuit models. Moreover, the proposed MA is polarisation and incident angle independent. Hence, the application of this MA can be extended to a great extent, including airborne radar applications, defence, and stealth-coating technology.     

Estimation of the surface velocity of debris flow with computer-based spatial filtering

A computer-based spatial-filtering velocimeter to measure the surface velocity of natural debris flow is described. This is a simple and interesting technique implemented with a spatial filter constructed as a software program that processes the video image of debris flow instead of a hardware implementation. The surface velocity of the debris flow at the Mt. Yakedake Volcano, Japan, was estimated by this computer-based spatial-filtering method, and the results were compared with those obtained by a hardware-based spatial-filtering method. Computer-based spatial filtering has the important advantage of a capability for tuning the spatial-filter parameters to the target flow.     

A recursive state estimator in the presence of state inequality constraints

This paper proposes an optimal recursive estimator to estimate the states of a stochastic discrete time linear dynamic system when the states of the system are constrained with inequality constraints. The case when the constraints are strictly satisfied is treated independently from the case when some of the constraints are violated. For the first case, the well known Kalman filter estimator is used. In the second case, an algorithm which uses a series of successive orthogonalizations on the measurement subspaces is employed to obtain the optimal estimate. It is shown that the proposed estimator has several attractive properties such that it is an unbiased estimator. More importantly, compared to other estimator found in the literature, the proposed estimator needs less computational efforts, is numerically more stable and it leads to a smaller variance. To show the effectiveness of the proposed estimator, several simulation results are presented and discussed.     

A portable toolkit for supporting end-user personalization and control in context-aware applications

A context-aware application in the pervasive computing environment provides intuitive user centric services using implicit context cues. Personalization and control are important issues for this class of application as they enable end-users to understand and configure the behavior of an application. However most development efforts for building context-aware applications focus on the sensor fusion and machine learning algorithms to generate and distribute context cues that drive the application with little emphasis on user-centric issues. We argue that, to elevate user experiences with context-aware applications, it is very important to address these personalization and control issues at the system interface level in parallel to context centric design. Towards this direction, we present Persona, a toolkit that provides support for extending context-aware applications with end-user personalization and control features. Specifically, Persona exposes a few application programming interfaces that abstract end-user customization and control mechanisms and enables developers to integrate these user-centric aspects with rest of the application seamlessly. There are two primary advantages of Persona. First, it can be used with various existing middlewares as a ready-to-use plug-in to build customizable and controllable context-aware applications. Second, existing context-aware applications can easily be augmented to provide end-user personalization and control support. In this paper, we discuss the design and implementation of Persona and demonstrate its usefulness through the development and augmentation of a range of common context-aware applications.     

Development and implementation of a hybrid intelligent controller for interior permanent-magnet synchronous motor drives

A hybrid neuro-fuzzy scheme for online tuning of a genetic-based proportional-integral (PI) controller for an interior permanent-magnet synchronous motor (IPMSM) drive is presented in this paper. The proposed controller is developed for accurate speed control of the IPMSM drive under various system disturbances. In this work, initially different operating conditions are obtained based on motor dynamics incorporating uncertainties. At each operating condition a genetic algorithm is used to optimize the PI controller parameters in a closed-loop vector control scheme. In the optimization procedure a performance index is developed to reflect the minimum speed deviation, minimum settling time and zero steady-state error. A fuzzy basis function network (FBFN) is utilized for online tuning of the PI controller parameters to ensure optimum drive performance under different disturbances. The proposed FBFN-based PI controller provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed controller is successfully implemented in real time using a digital signal processor board DS 1102 for a laboratory 1-hp IPMSM. The effectiveness of the proposed controller is verified by simulation as well as experimental results at different dynamic operating conditions. The proposed controller is found to be robust for applications in an IPMSM drive.     

Microbial fuel cells for bioelectricity generation through reduction of hexavalent chromium in wastewater: A review

The study proposes the use of microbial fuel cell (MFC) technology to reduce toxic Cr(VI) present in industrial wastewater to less toxic trivalent chromium [Cr(III)], while generating electricity through a bioelectrochemical oxidation-reduction process. Factors influencing the treatment process and electricity generation include the concentration of Cr(VI) in wastewater, substrate types used for anodes, types of microorganisms involved, types of cathode and anode, surface area of the cathode and anode, and pH and temperature of cathodic and anodic solutions. While other heavy metals in wastewater may be removed by MFC technology, Cr(VI) removal is more efficient in terms of electricity generation. Previous research indicated that the maximum electrical power generated by Cr(VI) removal through the use of MFCs is 1600 mW/m², which is expected to increase as the factors affecting this process are optimized. Based on current data, MFC-based electricity generation along with Cr(VI) removal is a potential future source of sustainable energy. However, research priorities need to focus on reducing the cost of MFC technology by using economical and effective materials and increasing electricity production.     

High dielectric permittivity and percolative behavior of polyvinyl alcohol/potassium dihydrogen phosphate composites

A new kind of anhydrous, transparent, and flexible potassium dihydrogen phosphate (KH₂PO₄ or KDP)/polyvinyl alcohol (PVA) composite in the form of film (0.10 mm) has been prepared by solution casting technique. KDP is well dispersed in the polymer matrix as observed from the microstructural studies. Frequency and temperature dependent dielectric properties of the composites have been studied with varying KDP concentrations. The PVA/KDP composite films exhibited extraordinarily high relative permittivity ε′ ∼ 430 (80 times higher compared with pure PVA and even higher than KDP) near the percolation threshold (ϕ_C = 2.5 wt % KDP) with low dielectric losses (∼ 0.15) at 1 kHz and room temperature. Such flexible, low loss and high dielectric permittivity material has enormous importance for application in devices. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Improved quadtree image segmentation approach to region information

Images are full of information and most often, little information is desired for subsequent processing. Hence, region of interest has key importance in image processing. Quadtree image segmentation has been widely used in many image processing applications to locate the region of interest for further processing. There are also variable block-size image coding techniques to effectively reduce the number of transmitted parts. This paper presents quadtree partition technique as a pre-processing step in image processing to determine what part should be more heterogeneous than the others. It also introduces an idea to solve the problem of squared images. Finally, proposed approach is implemented and analysed. The simulation of the Matlab code of the quadtree is represented by all algorithms and the figures. Thus, achieved results are promising in the state of the art.