Effect of compaction conditions on aggregate packing using 2-dimensional image analysis and the relation to performance of HMA

Characterization of the asphalt mixture microstructure using two dimensional (i.e., 2-D) imaging techniques could be an economically efficient approach. However, the features that have been captured and quantified using 2-D imaging techniques in most published research are limited to simplistic analyses of aggregate structure. This paper focuses on introducing a more elaborate method for characterization of the internal structure of aggregates to define performance related parameters that could be used as quality indicators of mixes. These indicators are proposed as important properties that complement the volumetric properties so wide relied on for acceptance of mixture designs. The results of the study show that aggregate structure can be characterized using a combination of newly developed image analysis indices namely: number of aggregate-to-aggregate proximity zones, total proximity zone length, and proximity zone plane orientation. A software developed in a previous study and significantly modified for this study, is used to process digital images of a set of asphalt mixtures with different gradations, binder contents, types of modification, compaction efforts, compaction temperatures, and methods. The results demonstrate that the internal structure indices correlate well with rutting performance, as well as with low temperature thermal contraction of asphalt mixtures. Additionally, the indices can be successfully used to show the effects of compaction effort, compaction method and temperature, gradation of aggregates, and binder modification on the mixture internal structure. The results indicate potential for using this method for quality control of mixtures during production.     

QoS in Wireless Multimedia Sensor Networks: A Layered and Cross-Layered Approach

The emergence of wireless multimedia sensor networks (WMSN) has given birth to several civilian as well as defense applications. Some of the interesting applications employing low cost sensor nodes to manipulate rich multimedia content include traffic monitoring, border surveillance, smart homes, environment and habitat monitoring. Unlike the traditional sensor networks which are aimed at maximizing network lifetime by decreasing energy utilization, the main objective of WMSNs is optimized delivery of multimedia content along with energy efficiency. Multimedia communications in WMSNs, has stringent delay and high bandwidth requirement as compared to scalar data transfer in WSNs. Fulfilling these constraints in resource and energy constrained WMSNs is a huge challenge. In WMSNs, each layer of the protocol stack is responsible and fully involved in providing QoS guarantees. There is a need for new schemes at each layer of the protocol stack- from advanced coding techniques that reduce encoder complexity and achieve maximum compression to dynamic routing and MAC protocols that provide service differentiation and reduce end-to-end latency. In wireless sensor networks, where all layers have dependency on each other, QoS guarantees are possible through the cross layer interaction of different layers. This paper gives an overview of the different existing layered schemes in WMSNs, followed by a discussion on the significance and efficiency gains that can be achieved from cross layer interactions in WMSNs along with the review of the existing cross layer approaches. Finally, we identify the open research issues which have not been adequately addressed so far.     

Effect of green synthesised silver nanoparticles on morphogenic and biochemical variations in callus cultures of kinnow mandarin (Citrus reticulata L.)

Citrus reticulata is economically important tree fruit crop in Pakistan, fortified with various nutrients and minerals including Vitamin C and secondary metabolites. Nanotechnology is a twenty‐first century science and deals with production of minute particles termed as nanoparticles. In present study, silver nanoparticles (AgNPs) were synthesised through green method by utilising leaves of Olea europea as main reducing and capping agent. The synthesised AgNPs were characterised through UV visible spectroscopy, SEM, and energy dispersive X‐ray. Furthermore, different concentrations of AgNPs (10, 20, 30 ppm) in combination with Thidiazuron (0.5, 1.0 mg/l) were added onto MS medium to study development and secondary metabolites production in callus culture of C. reticulata. Callus induction percentage (96%) was more in 20 ppm AgNPs and 1 mg/l TDZ concentration. Moreover, high total phenolic, flavonoid contents, and antioxidant activity was observed in 20 ppm AgNPs combined with 0.5 and 1 mg/l TDZ. Enzymatic components (SOD, POD and CAT) were increased in MS medium augmented with 30 ppm AgNPs and TDZ. The total protein content (TPC) was significant in callus cultures treated with TDZ only. This study provides the first evidence of green synthesised AgNPs on callus culture developments and further quantification of biochemical profiling in C. reticula. Inspec keywords: nanoparticles, silver, ultraviolet spectra, scanning electron microscopy, antibacterial activity, agricultural products, nanotechnology, biotechnology, agricultural engineering, nanofabrication, genetic engineering, cropsOther keywords: green synthesised silver nanoparticles, morphogenic variations, biochemical variations, kinnow mandarin, citrus reticulata L, nutrients, minerals, green method, UV visible spectroscopy, energy dispersive X‐ray, MS medium, secondary metabolites production, callus induction percentage, TDZ, flavonoid contents, callus culture developments, capping agent, phenolic contents, tree fruit crop     

Investigation of Structural,Electronics, Optical,Mechanical and Thermodynamic Properties of YRu2P2 Compound for Superconducting Application

Journal of Superconductivity and Novel Magnetism - The structural, electronic, optical, and thermodynamic properties of the YRu2P2 superconductor are investigated theoretically using the CASTEP...     

Optimization of steel bar manufacturing process using six sigma

Optimization of a manufacturing process results in higher productivity and reduced wastes. Production parameters of a local steel bar manufacturing industry of Pakistan is optimized by using six Sigma-Define, measure, analyze, improve, and control- methodology. Production data is collected and analyzed. After analysis, experimental design result is used to identify significant factors affecting process performance. The significant factors are controlled to optimized level using two-level factorial design method. A regression model is developed that helps in the estimation of response under multi variable input values. Model is tested, verified, and validated by using industrial data collected at a local steel bar manufacturing industry of Peshawar(Khyber Pakhtunkhwa, Pakistan). The sigma level of the manufacturing process is improved to 4.01 from 3.58. The novelty of the research is the identification of the significant factors along with the optimum levels that affects the process yield, and the methodology to optimize the steel bar manufacturing process.     

SIMULATION AND EXPERIMENTAL IMPLEMENTATION OF A NEURAL-NETWORK-BASED INTERNAL-MODEL CONTROL STRATEGY ON A REACTOR SYSTEM

The use of inverse-model-based control strategy for nonlinear system has been increasing lately. However it is hampered by the difficulty in obtaining the inverse of nonlinear systems analytically. Since neural networks has the ability to model such inverses, it has become a viable alternative. Although many simulations using neural network inverse models For controls have been reported recently, no actual experimental application has been reported on a reactor system. In this paper we describe a novel experimental application of a neural network inverse-model based control method on a partially simulated pilot plant reactor, exhibiting steady state parametric sensitivity and designed to test the use of such nonlinear algorithms. The implementation involved the control of the reactor temperature under set point changes, disturbance rejection and set point regulation with plant/model mismatches. Simulation tests on the model of the system were also carried out to enable better design of the neural network models and to highlight the differences between simulation and actual online results. The online implementation results obtained were sufficient to demonstrate the capability of applying these neural-network-based control methods in real systems.     

Synthesis of biodegradable Mg-Zn alloy by mechanical alloying: Statistical prediction of elastic modulus and mass loss using fractional factorial design

Biodegradable Mg-Zn alloy was synthesized using mechanical alloying where a statistical model was developed using fractional factorial design to predict elastic modulus and mass loss of the bulk alloy. The effects of mechanical alloying parameters (i.e., milling time, milling speed, ball-to-powder mass ratio and Zn content) and their interactions were investigated involving 4 numerical factors with 2 replicates, thus 16 runs of two-level fractional factorial design. Results of analysis of variance (ANOVA), regression analysis and R² test indicated good accuracy of the model. The statistical model determined that the elastic modulus of biodegradable Mg-Zn alloy was between 40.18 and 47.88 GPa, which was improved and resembled that of natural bone (30–57 GPa). Corrosion resistance (mass loss of pure Mg, 33.74 mg) was enhanced with addition of 3%–10% Zn (between 9.32 and 15.38 mg). The most significant independent variable was Zn content, and only the interaction of milling time and ball-to-powder mass ratio was significant as P-value was less than 0.05. Interestingly, mechanical properties (represented by elastic modulus) and corrosion resistance (represented by mass loss) of biodegradable Mg-Zn alloy can be statistically predicted according to the developed models.