Interactive smart battery storage for a PV and wind hybrid energy management control based on conservative power theory

This paper presents interactive smart battery-based storage (BBS) for wind generator (WG) and photovoltaic (PV) systems. The BBS is composed of an asymmetric cascaded H-bridge multilevel inverter (ACMI) with staircase modulation. The structure is parallel to the WG and PV systems, allowing the ACMI to have a reduction in power losses compared to the usual solution for storage connected at the DC-link of the converter for WG or PV systems. Moreover, the BBS is embedded with a decision algorithm running real-time energy costs, plus a battery state-of-charge manager and power quality capabilities, making the described system in this paper very interactive, smart and multifunctional. The paper describes how BBS interacts with the WG and PV and how its performance is improved. Experimental results are presented showing the efficacy of this BBS for renewable energy applications.     

Development of active matrix LCD for use in high‐resolution adaptive headlights

Conventional adaptive driving beam headlamps are limited in achieving still higher quantities of switchable pixels by the number of LEDs and movable elements needed. In this paper, it is shown that by integrating an active matrix liquid crystal display module, it is possible to realize fully adaptive high‐resolution headlights without mechanical elements and a finite number of LED with 30 k switchable pixels.     

Forensic taxonomy of android productivity apps

Android productivity apps have provided the facility of having a constantly accessible and productive workforce to the information and work capabilities needed by the users. With hundreds of productivity apps available in the Android app market, it is necessary to develop a taxonomy for the forensic investigators and the end users to allow them to know what personal data remnants are available from the productivity apps. In this paper, 30 popular Android productivity apps were examined. A logical extraction of the Android phone was collected by using a well-known mobile forensic tool- XRY to extract various information of forensic interest such as user email ID and list of tasks. Based on the findings, a two-dimensional taxonomy of the forensic artefacts of the productivity apps is proposed with the app categories in one dimension and the classes of artefacts in the other dimension. The artefacts identified in the study of the apps are summarised using the taxonomy. In addition, a comparison with the existing forensic taxonomies of different categories of Android apps is provided to facilitate timely collection and analysis of evidentiary materials from mobile devices.     

Efficient schemes for similarity-aware refinement of aggregation queries

Interactive data exploration platforms in Web, business and scientific domains are becoming increasingly popular. Typically, users without prior knowledge of data interact with these platforms in an exploratory manner hoping they might retrieve the results they are looking for. One way to explore large-volume data is by posing aggregate queries which group values of multiple rows by an aggregate operator to form a single value: an aggregated value. Though, when a query fails, i.e., returns undesired aggregated value, users will have to undertake a frustrating trial-and-error process to refine their queries, until a desired result is attained. This data exploration process, however, is growing rather difficult as the underlying data is typically of large-volume and high-dimensionality. While heuristic-based techniques are fairly successful in generating refined queries that meet specified requirements on the aggregated values, they are rather oblivious to the (dis)similarity between the input query and its corresponding refined version. Meanwhile, enforcing a similarity-aware query refinement is rather a non-trivial challenge, as it requires a careful examination of the query space while maintaining a low processing cost. To address this challenge, we propose an innovative scheme for efficient Similarity-Aware Refinement of Aggregation Queries called (EAGER) which aims to balance the tradeoff between satisfying the aggregate and similarity constraints imposed on the refined query to maximize its overall benefit to the user. To achieve that goal, EAGER implements efficient strategies to minimize the costs incurred in exploring the available search space by utilizing similarity-based and monotonic-based pruning techniques to bound the search space and quickly find a refined query that meets users’ expectations. Our extensive experiments show the scalability exhibited by EAGER under various workload settings, and the significant benefits it provides.     

Bifurcation control in a delayed two-neuron fractional network

The issue of bifurcation control for a delayed fractional network involving two neurons is concerned. Delay-dependent stability conditions and the bifurcation point are established by discussing the associated characteristic equation of the proposed network. Then, a delayed feedback controller is firstly designed to stabilize the Hopf bifurcation, and desirable dynamics is achieved. It is indicated that the designed controller is extremely effective which can postpone the onset of bifurcation by carefully selecting the feedback gain. Finally, simulation results are given to verify the efficiency of the theoretical results.     

An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows

Binary image representation is essential format for document analysis. In general, different available binarization techniques are implemented for different types of binarization problems. The majority of binarization techniques are complex and are compounded from filters and existing operations. However, the few simple thresholding methods available cannot be applied to many binarization problems. In this paper, we propose a local binarization method based on a simple, novel thresholding method with dynamic and flexible windows. The proposed method is tested on selected samples called the DIBCO 2009 benchmark dataset using specialized evaluation techniques for binarization processes. To evaluate the performance of our proposed method, we compared it with the Niblack, Sauvola and NICK methods. The results of the experiments show that the proposed method adapts well to all types of binarization challenges, can deal with higher numbers of binarization problems and boosts the overall performance of the binarization.     

Learning mixtures of point distribution models with the EM algorithm

This paper demonstrates how the EM algorithm can be used for learning and matching mixtures of point distribution models. We make two contributions. First, we show how shape-classes can be learned in an unsupervised manner. We present a fast procedure for training point distribution models using the EM algorithm. Rather than estimating the class means and covariance matrices needed to construct the PDM, the method iteratively refines the eigenvectors of the covariance matrix using a gradient ascent technique. Second, we show how recognition by alignment can be realised by fitting a mixture of linear shape deformations. We evaluate the method on the problem of learning the class-structure and recognising Arabic characters.     

Effects of helium massive gas injection level on disruption mitigation on EAST

In this study, NIMROD simulations are performed to investigate the effects of massive helium gas injection level on the induced disruption on EAST tokamak. It is demonstrated in simulations that two different scenarios of plasma cooling(complete cooling and partial cooling) take place for different amounts of injected impurities. For the impurity injection above a critical level, a single MHD activity is able to induce a complete core temperature collapse. For impurity injection below the critical level, a series of multiple minor disruptions occur before the complete thermal quench.     

Green Synthesis of Silymarin–Chitosan Nanoparticles as a New Nano Formulation with Enhanced Anti-Fibrotic Effects against Liver Fibrosis

Background: Silymarin (SIL) has long been utilized to treat a variety of liver illnesses, but due to its poor water solubility and low membrane permeability, it has a low oral bioavailability, limiting its therapeutic potential. Aim: Design and evaluate hepatic-targeted delivery of safe biocompatible formulated SIL-loaded chitosan nanoparticles (SCNPs) to enhance SIL’s anti-fibrotic effectiveness in rats with CCl₄-induced liver fibrosis. Methods: The SCNPs and chitosan nanoparticles (CNPs) were prepared by ionotropic gelation technique and are characterized by physicochemical parameters such as particle size, morphology, zeta potential, and in vitro release studies. The therapeutic efficacy of successfully formulated SCNPs and CNPs were subjected to in vivo evaluation studies. Rats were daily administered SIL, SCNPs, and CNPs orally for 30 days. Results: The in vivo study revealed that the synthesized SCNPs demonstrated a significant antifibrotic therapeutic action against CCl₄-induced hepatic injury in rats when compared to treated groups of SIL and CNPs. SCNP-treated rats had a healthy body weight, with normal values for liver weight and liver index, as well as significant improvements in liver functions, inflammatory indicators, antioxidant pathway activation, and lipid peroxidation reduction. The antifibrotic activities of SCNPs were mediated by suppressing the expression of the main fibrosis mediators TGFβR1, COL3A1, and TGFβR2 by boosting the hepatic expression of protective miRNAs; miR-22, miR-29c, and miR-219a, respectively. The anti-fibrotic effects of SCNPs were supported by histopathology and immunohistochemistry (IHC) study. Conclusions: According to the above results, SCNPs might be the best suitable carrier to target liver cells in the treatment of liver fibrosis.