Antihyperglycemic, antihyperlipidemic, and antioxidant effects of Chaenomeles sinensis fruit extract in streptozotocin-induced diabetic rats

The rising incidence of diabetes mellitus (DM) is alarming and becoming a major health problem worldwide, which is mainly associated with hyperglycemia, abnormal lipid, and antioxidant profiles. Herbal medicines are being used by about 80% of the world population primarily in the developing countries for primary health care, including DM. Based on these facts, in this study, the antihyperglycemic, antihyperlipidemic, and antioxidant properties of Chaenomeles sinensis fruits extract (CSFE) were investigated in streptozotocin (STZ)-induced (55 mg/kg body weight) diabetic rats. CSFE was found to be rich in total phenolics and flavonoid contents. The following assays were performed: fasting blood glucose, hemoglobin (Hb), blood urea nitrogen (BUN), serum total cholesterol (TC), serum triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), serum glutamic pyruvic transaminase/alanine aminotransferase (SGPT/ALT), serum glutamic oxaloacetic transaminase/aspartate aminotransaminase (SGOT/AST), liver glycogen content and superoxide dismutase (SOD), reduced glutathione (GSH), and catalase (CAT) contents in liver and kidney. Oral administration of CSFE (500 mg/kg body weight) significantly decreased fasting blood glucose, BUN, and serum TC, TG, LDL-C, SGPT/ALT, and SGOT/AST, while it increased blood Hb content and HDL-C in diabetic rats. Furthermore, CSFE treatment significantly increased liver glycogen content, SOD, GSH, and CAT levels in diabetic rats. The results showed that CSFE significantly inhibited the progression of diabetes induced by STZ, and the inhibitory effect of CSFE on diabetes might be associated with its hypoglycemic effect, modulation of lipid metabolism, and its ability to scavenge free radicals.     

Extrusion of highly filled flexible polymer sheet

Highly-filled polymer systems include color masterbatches, feedstocks for powder injection molding, and rigid sheets with high levels of flame retardants, but they have not been explored for flexible sheet. This work investigated the (a) selecting a polymer matrix with enough melt strength and flexibility to form a stable sheet with high filler loading, (b) the maximum achievable filler loading for the sheet, and (c) optimizing the process of extruding a highly-filled flexible polymer system. Extrusion grade low-density polyethylene (LDPE) provided sufficient flexibility and permitted a maximum filler loading of 36 vol% (~78 wt%). Good dispersion of the nanoparticle filler, however, required two passes through multiple screw extruders and a small reduction in the viscosity of the LDPE. Sheet with thickness of 415 μm, surface roughness of <1 μm, and sufficient flexibility was extruded continuously at a rate of 10 m/min., but it required a more traditional coat hanger manifold to prevent filler hang up in the sheet die. The filler particles were distributed uniformly through the core and skin of the sheet, giving the sheet good mechanical properties.     

Metal–organic frameworks for electronics: emerging second order nonlinear optical and dielectric materials

Metal–organic frameworks (MOFs) have been intensively studied over the past decade because they represent a new category of hybrid inorganic–organic materials with extensive surface areas, ultrahigh porosity, along with the extraordinary tailorability of structure, shape and dimensions. In this highlight, we summarize the current state of MOF research and report on structure–property relationships for nonlinear optical (NLO) and dielectric applications. We focus on the design principles and structural elements needed to develop potential NLO and low dielectric (low-κ) MOFs with an emphasis on enhancing material performance. In addition, we highlight experimental evidence for the design of devices for low-dielectric applications. These results motivate us to develop better low-dielectric and NLO materials and to perform in-depth studies related to deposition techniques, patterning and the mechanical performance of these materials in the future.     

Analytical Modeling for Translating Statistical Changes to Circuit Variability by Ultra-Deep Submicron Digital Circuit Design

This paper presents a physics-based compact gate delay model that includes all short-channel phenomena prevalent at the ultra-deep submicron technology node of 32 nm. To simplify calculations, the proposed model is connected to a compact α-power law-based (Sakurai-Newton) model. The model has been tested on a wide range of supply voltages. The model accurately predicts nominal delays and the delays under process variations. It has been shown that at lower technology nodes, the delay is more sensitive to threshold voltage variations, specifically at the sub-threshold operating region as compared with effective channel length variations above the threshold region.     

Application of ameliorated Harris Hawks optimizer for designing of low-power signed floating-point MAC architecture

Neural Computing and Applications - Recently established Harris Hawks optimization (HHO) has natural behaviour for finding an optimum solution in global search space without getting trapped in...     

Investigating the performance of satellite and reanalysis rainfall products at monthly timescales across different rainfall regimes of Ethiopia

Continuous availability of a variety of satellite and reanalysis rainfall products have triggered the use of such products as an alternate source of rainfall data in sparsely gauge networked areas. However, before utilizing them a detailed validation of these datasets are essential to have some level of guarantee. In many parts of Africa in general and most parts of Ethiopia particularly in the lowland areas, gauge stations are very sparse and unevenly distributed. In addition, due to the nature of complex topography and geographical location, Ethiopian rainfall shows high variability both temporally and spatially. In view of the above, the present study is aimed at statistically evaluating such rainfall products across different rainfall regimes (regions with different rainfall characteristics as defined by National Meteorological Agency (NMA) of Ethiopia). In the current study, five satellite and two reanalysis rainfall products such as African Rainfall Climatology version 2 (ARC2), Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT), Tropical Rainfall Measuring Mission-3B43 version 7 (TRMM 3B43v7), Climate Prediction Center Morphing Technique (CMORPH), Climate Hazards Group Infrared Precipitation with Stations version 2 (CHIRPSv2), the Climate Forecast System Reanalysis (CFSR) and the European Center for Medium Range Weather Forecast Reanalysis (ERA-Interim) are considered based on their spatial coverage, spatial resolution, temporal resolution, latency period and length of data records. Evaluation is done at monthly and seasonal time scales against the observed gauge rainfall data provided by the National Meteorological Agency of Ethiopia across entire Ethiopia in two different manners, first by considering the entire country as one homogeneous unit and secondly in a distributed manner across the three rainfall regimes of Ethiopia. The obtained results show that: (i) CHIRPSv2 and TRMM 3B43v7 show better performance during June to September (the main rainfall season) and during February to May (the smaller rainfall season) in regimes 1 and 2. (ii) In regime 3 these products show good performance from October to November (smaller rainy season of this regime) and March to May (main rainy season of this regime); (iii).CMORPH, TAMSAT and ARC2 show moderate performance in all three regimes; (iv) CFSR and ERA-Interim exhibit poor performance in all rainfall regimes. Overall, the detailed analysis of statistical evaluation results of the rainfall products at monthly timescale shows that CHIRPSv2 performs comparatively better than the other tested rainfall products across all rainfall regimes. However, the best performance of CHIRPSv2 is obtained in regime 2 followed by regime 1 and regime 3.     

An improvement in creep strength of thermo-mechanical treated modified 9Cr-1Mo steel weld joint

Modified 9Cr-1Mo steel weld joints generally experience the type IV premature failure in the intercritical region (ICR) of HAZ under long term creep exposure at high temperature. Possibility of improving the resistance of this joint to type IV cracking through thermo-mechanical treatment (TMT) of the steel has been explored. Weld joints have been fabricated from the TMT and conventional normalized and tempered (NT) steels using electron beam (EB) welding process. Creep tests have been carried out on NT and TMT steels joint at 923 K (650°C) and 110–100 MPa applied stress. Creep rupture life of the TMT weld joint was significantly higher than the NT steel weld joint. Significant variations of microstructural constituents such as M₂₃C₆ precipitate; lath structure and hardness across the joint have been examined in both the joints. The coarser M₂₃C₆ precipitate and lath, and subgrain formation in the ICR resulted in the soft zone formation and was predominant in the ICR of NT steel joint. The enhanced MX precipitation through TMT processing and reduction in coarsening of M₂₃C₆ precipitate under thermal cycle resulted in improved creep rupture strength of TMT steel weld joint.     

A semi-supervised domain adaptation assembling approach for image classification

Automatic annotation of images is one of the fundamental problems in computer vision applications. With the increasing amount of freely available images, it is quite possible that the training data used to learn a classifier has different distribution from the data which is used for testing. This results in degradation of the classifier performance and highlights the problem known as domain adaptation. Framework for domain adaptation typically requires a classification model which can utilize several classifiers by combining their results to get the desired accuracy. This work proposes depth-based and iterative depth-based fusion methods which are basically rank-based fusion methods and utilize rank of the predicted labels from different classifiers. Two frameworks are also proposed for domain adaptation. The first framework uses traditional machine learning algorithms, while the other works with metric learning as well as transfer learning algorithm. Motivated from ImageCLEF’s 2014 domain adaptation task, these frameworks with the proposed fusion methods are validated and verified by conducting experiments on the images from five domains having varied distributions. Bing, Caltech, ImageNet, and PASCAL are used as source domains and the target domain is SUN. Twelve object categories are chosen from these domains. The experimental results show the performance improvement not only over the baseline system, but also over the winner of the ImageCLEF’s 2014 domain adaptation challenge.     

Density Based Fuzzy C Means Clustering to prolong Network Lifetime in Smart Grids

Wireless sensor networks (WSN's) are preferred for industrial applications due to progressive increase of sensor electronics. One such application is deployment of WSN's in smart grids. Smart Grid integrates information and communication techniques with electricity network. Smart grids utilize sophisticated control and monitoring devices for improving the efficiency of the grid. For energy efficient, low cost monitoring and control in smart grid WSN's is treated as a promising technology. Advanced Metering Infrastructure (AMI) is the key technology in the distribution networks of Smart Grid. The AMI is composed of various sensors for metering purpose. The meter data is also useful for the distribution operators to manage the demand response. The network involves smart meters, smart electric gas and water meters along with digital network management appliances for optimizing the electric network with real time data management. The smart sensors are limited in terms of battery, operational power and memory. These sensors communicate with the base station in restricted range. The communication between smart grid nodes and base station (sink) is multi-hop in nature. The communication takes place within limited range of communication so the security concerns that are involved in the network are to be handled by the routing protocols. So as to make the bidirectional communication efficient between the smart sensors and utility an effective routing scheme is required for these energy limited devices to handle the heavy network traffic in smart grids. Here energy efficient routing for WSN's in NAN networks to attain load balancing is proposed through density based Fuzzy C means clustering (DFCM). The obtained simulation results show that DFCM can provide a satisfactory performance for enhancing the network life span.