Ferromagnetic nano model study for the peristaltic flow in a plumb duct with permeable walls

Microsystem Technologies - The purpose of the present enquiry is to analyse the mechanics of an incompressible fluid, with water as base fluid, through a radially symmetric plumb duct with...     

A fog-based security framework for intelligent traffic light control system

Multimedia Tools and Applications - The world is facing many problems including that of traffic congestion. To highlight the issue of traffic congestion worldwide specially in urban areas and to...     

A Novel DUF569 Gene Is a Positive Regulator of the Drought Stress Response in Arabidopsis

In the last two decades, global environmental change has increased abiotic stress on plants and severely affected crops. For example, drought stress is a serious abiotic stress that rapidly and substantially alters the morphological, physiological, and molecular responses of plants. In Arabidopsis, several drought-responsive genes have been identified; however, the underlying molecular mechanism of drought tolerance in plants remains largely unclear. Here, we report that the “domain of unknown function” novel gene DUF569 (AT1G69890) positively regulates drought stress in Arabidopsis. The Arabidopsis loss-of-function mutant atduf569 showed significant sensitivity to drought stress, i.e., severe wilting at the rosette-leaf stage after water was withheld for 3 days. Importantly, the mutant plant did not recover after rewatering, unlike wild-type (WT) plants. In addition, atduf569 plants showed significantly lower abscisic acid accumulation under optimal and drought-stress conditions, as well as significantly higher electrolyte leakage when compared with WT Col-0 plants. Spectrophotometric analyses also indicated a significantly lower accumulation of polyphenols, flavonoids, carotenoids, and chlorophylls in atduf569 mutant plants. Overall, our results suggest that novel DUF569 is a positive regulator of the response to drought in Arabidopsis.     

Modeling and solving mixed-model assembly line balancing problem with setups. Part I: A mixed integer linear programming model

This paper is the first one of the two papers entitled “modeling and solving mixed-model assembly line balancing problem with setups”, which has the aim of developing the mathematical programming formulation of the problem and solving it with a hybrid meta-heuristic approach. In this current part, a mixed-integer linear mathematical programming (MILP) model for mixed-model assembly line balancing problem with setups is developed. The proposed MILP model considers some particular features of the real world problems such as parallel workstations, zoning constraints, and sequence dependent setup times between tasks, which is an actual framework in assembly line balancing problems. The main endeavor of Part-I is to formulate the sequence dependent setup times between tasks in type-I mixed-model assembly line balancing problem. The proposed model considers the setups between the tasks of the same model and the setups because of the model switches in any workstation. The capability of our MILP is tested through a set of computational experiments. Part-II tackles the problem with a multiple colony hybrid bees algorithm. A set of computational experiments is also carried out for the proposed approach in Part-II.     

Antibody purification by concanavalin A affinity chromatography

Concanavalin A (Con A) immobilized poly(2‐hydroxyethyl methacrylate) (PHEMA) beads in a spherical form (100–150 μm in diameter) were used for the affinity chromatography purification of human immunoglobulin G (IgG) from aqueous solutions and human plasma. PHEMA adsorbents were prepared by suspension polymerization. Bioligand Con A was then immobilized by covalent binding onto PHEMA beads. The maximum IgG adsorption on the PHEMA/Con A beads was observed at pH 6.0. The nonspecific IgG adsorption onto the plain PHEMA adsorbents was very low (ca. 0.17 mg/g). Higher adsorption values (up to 54.3 mg/g) were obtained when the PHEMA/Con A beads were used from aqueous solutions. A higher adsorption capacity was observed for human plasma (up to 69.4 mg/g) with a purity of 82.5%. The adsorption capacities of other blood proteins were 2.0 mg/g for fibrinogen and 4.2 mg/g for albumin. The total protein adsorption was determined to be 76.0 mg/g. IgG molecules could be repeatedly adsorbed and desorbed with the PHEMA/Con A beads without noticeable loss in the IgG adsorption capacity. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 97: 1202–1208, 2005     

Toward an efficient and scalable feature selection approach for internet traffic classification

There is significant interest in the network management and industrial security community about the need to identify the “best” and most relevant features for network traffic in order to properly characterize user behaviour and predict future traffic. The ability to eliminate redundant features is an important Machine Learning (ML) task because it helps to identify the best features in order to improve the classification accuracy as well as to reduce the computational complexity related to the construction of the classifier. In practice, feature selection (FS) techniques can be used as a preprocessing step to eliminate irrelevant features and as a knowledge discovery tool to reveal the “best” features in many soft computing applications. In this paper, we investigate the advantages and disadvantages of such FS techniques with new proposed metrics (namely goodness, stability and similarity). We continue our efforts toward developing an integrated FS technique that is built on the key strengths of existing FS techniques. A novel way is proposed to identify efficiently and accurately the “best” features by first combining the results of some well-known FS techniques to find consistent features, and then use the proposed concept of support to select a smallest set of features and cover data optimality. The empirical study over ten high-dimensional network traffic data sets demonstrates significant gain in accuracy and improved run-time performance of a classifier compared to individual results produced by some well-known FS techniques.     

Nigella sativa oil entrapped polycaprolactone nanoparticles for leishmaniasis treatment

This study is the first to investigate the antileishmanial activities of Nigella sativa oil (NSO) entrapped poly‐ɛ ‐caprolactone (PCL) nanoparticles on Leishmania infantum promastigotes and amastigotes in vitro. NSO molecules with variable initial doses of 50, 100, 150, and 200 mg were successfully encapsulated into PCL nanoparticles identified as formulations NSO1, NSO2, NSO3, and NSO4, respectively. This process was characterised by scanning electron microscope, dynamic light scattering, Fourier transform infrared, encapsulation efficiency measurements, and release profile evaluations. The resulting synthetised nanoparticles had sizes ranging between 200 and 390 nm. PCL nanoparticles encapsulated 98% to 80% of initial doses of NSO and after incubation released approximately 85% of entrapped oil molecules after 288 h. All investigated formulations demonstrated strong antileishmanial effects on L. infantum promastigotes by inhibiting up to 90% of parasites after 192 h. The tested formulations decreased infection indexes of macrophages in a range between 2.4‐ and 4.1‐fold in contrast to control, thus indicating the strong anti‐amastigote activities of NSO encapsulated PCL nanoparticles. Furthermore, NSO‐loaded PCL nanoparticles showed immunomodulatory effects by increasing produced nitric oxide amounts within macrophages by 2–3.5‐fold in contrast to use of free oil. The obtained data showed significant antileishmanial effects of NSO encapsulated PCL nanoparticles on L. infantum promastigotes and amastigotes.Inspec keywords: antibacterial activity, drug delivery systems, nanofabrication, nitrogen compounds, nanomedicine, microorganisms, cellular biophysics, diseases, scanning electron microscopy, oils, polymers, biomedical materials, nanoparticles, encapsulation, Fourier transform infrared spectraOther keywords: encapsulation efficiency measurements, entrapped oil molecules, investigated formulations, NSO‐loaded PCL nanoparticles, Nigella sativa oil entrapped polycaprolactone nanoparticles, antileishmanial activities, poly‐ε‐caprolactone nanoparticles, scanning electron microscope, DLS, Fourier transform infrared, release profile evaluations, Leishmania infantum promastigotes, Leishmania infantum amastigotes, parasites, infection, infection indexes, macrophages, immunomodulatory effects, time 288.0 hour, time 192.0 hour, mass 50.0 mg, mass 100.0 mg, mass 150.0 mg, mass 200.0 mg, size 200.0 nm to 390.0 nm     

A single tri-axial accelerometer-based real-time personal life log system capable of human activity recognition and exercise information generation

Recording a personal life log (PLL) of daily activities in a ubiquitous environment is an emerging application of information technology. In this work, we present a single tri-axial accelerometer-based PLL system capable of human activity recognition and exercise information generation. Our PLL system exhibits two main functions: activity recognition and exercise information generation. For activity recognition, the system first recognizes a state of daily activities based on the statistical and spectral features of the accelerometer signals. An activity within the recognized state is then recognized using a set of augmented features, including autoregressive coefficients, signal magnitude area, and tilt angle, via linear discriminant analysis and hierarchical artificial neural networks. Upon the recognition of each activity, the system further estimates exercise information that includes energy expenditure based on metabolic equivalents, stride length, step count, walking distance, and walking speed. Our PLL system operates in real-time, and the life log information it generates is archived in a daily log database. We have validated our PLL system for six daily activities (i.e., lying, standing, walking, going-upstairs, going-downstairs, and driving) via subject-independent and subject-dependent recognition on a total of twenty subjects, achieving an average recognition accuracy of 94.43 and 96.61%, respectively. Our results demonstrate the feasibility of a portable real-time PLL system that could be used for u-lifecare and u-healthcare services in the near future.     

Rule extraction from artificial neural networks to discover causes of quality defects in fabric production

In this paper, a novel classification rule extraction algorithm which has been recently proposed by authors is employed to determine the causes of quality defects in a fabric production facility in terms of predetermined parameters like machine type, warp type etc. The proposed rule extraction algorithm works on the trained artificial neural networks in order to discover the hidden information which is available in the form of connection weights in them. The proposed algorithm is mainly based on a swarm intelligence metaheuristic which is known as Touring Ant Colony Optimization (TACO). The algorithm has a hierarchical structure with two levels. In the first level, a multilayer perceptron type neural network is trained and its weights are extracted. After obtaining the weights, in the second level, the TACO-based algorithm is applied to extract classification rules. The main purpose of the present work is to determine and analyze the most effective parameters on the quality defects in fabric production. The parameters and their levels which give the best quality results are tried to be discovered and evaluated by making use of the proposed algorithm. It is also aimed to compare the accuracy of proposed algorithm with several other rule-based algorithms in order to present its competitiveness.