Mechanical characterization and modeling stress relaxation behavior of acrylic–polyurethane-based graft-interpenetrating polymer networks

The stress relaxation behavior of acrylic–polyurethane (PU)-based graft-interpenetrating polymer networks (IPNs) was characterized via dynamic mechanical analysis (DMA) and modeled using finite element method (FEM) analysis. Stress relaxation of glassy IPN specimens was experimentally studied under flexural testing, while rubbery IPN specimens were tested in tension. The effects of varying the styrene content in the acrylic copolymer phase, compatibility of the two phases in IPNs, and changing the concentration of acrylic copolymer and PU were studied. A higher percentage of styrene content resulted in higher homogeneity of IPN specimens, and decrease in initial modulus for acrylic copolymer specimens. Additionally, glassy IPN specimens with 90% styrene shows resistance to relaxation as high as acrylic copolymer samples. Experimental results were used to develop a numerical model to study stress relaxation response of specimens. While polymer systems have been studied computationally, numerical modeling of IPN systems is still in its infancy. A three-dimensional FEM model was developed using the Generalized Maxwell model and four-term Prony series constants, which were extracted from the stress relaxation experiments. With four terms in the Prony series, a good match was observed between experimental observations and results from the FEM model.     

Multiresolution approach for multiple human detection using moments and local binary patterns

Human detection is a central problem in development of any surveillance application. In this study, we present a simple and efficient, multi-resolution gray scale invariant approach for multiple human detection. The multiresolution is important for objects of different size and gray scale invariance is important due to uneven illumination and within-class variability. The proposed method is based on integration of central moments upon multi-resolution gray scale invariant local binary patterns operator. Since, the local binary patterns operator is invariant against different resolutions of space scale and monotonic change in gray scale, therefore the proposed method is robust in terms of variations in space scale as well as gray scale. Another advantage is high computational accuracy of the method due to use of moment operator which enhances the efficiency of the proposed method. Moreover, the proposed method is simple, as these operations can be performed within a few steps in a small neighborhood and a lookup table. The proposed method is tested on multiple human images and experimentally found appropriate for multiple human detection. The proposed method has been evaluated over two datasets, one is our own created dataset and the other is standard INRIA human detection dataset. Experimental results obtained from the proposed method demonstrate that better discrimination can be achieved for human and non-human objects in real scenes.     

The dark side of information technology use

A large body of research has considered the positive aspects of information technology (IT) use. However, emerging research and practice commentary is beginning to focus on complex and often alarming ways in which use of IT affects organisational and social life. This Special Issue focuses on these ‘dark’ effects of IT use.     

Microalgae growth for nutrient recovery from sludge liquor and production of renewable bioenergy

Proof-of-concept has been demonstrated for a process that will utilize nutrients from sludge liquor, natural light, and CO2 from biogas to grow microalgae at wastewater treatment plants. This process will reduce the impact of returning side-streams to the head of the plant. The produced algae will be fed to anaerobic digesters for increased biogas production. Dewatering of anaerobically digested sludge in centrifuges produces reject water with extremely low transmittance of light. A pretreatment procedure was developed that improved light transmittance for reject water from the FREVAR, Norway, wastewater treatment plant from 0.1% T to 77% T (670 nm, 1 cm path). Chlorella sp. microalgae were found to be suitable for growth in this pre-treated reject water. Typical nitrogen removal was 80-90 g N/kg TSS of produced microalgae. The microalgae were successfully harvested by chemically assisted flocculation followed by straining through a 33 microm sieve cloth, achieving up to 99% recovery. Harvested algae were anaerobically co-digested with wastewater sludge. The specific methane gas production (mL CH4/g VS fed) for the algae varied from less than 65% to 90% of the specific methane gas production for the wastewater sludge, depending on digester temperature, retention time and pre-treatment of the algae biomass.     

Multi-objective reactive power market clearing in competitive electricity market using HFMOEA

This paper presents an application of a hybrid fuzzy multi-objective evolutionary algorithm (HFMOEA) for solving a highly constraint, mixed integer type, complex multi-objective reactive power market clearing (RPMC) problem for the competitive electricity market environment. In HFMOEA based multi-objective optimization approach, based on the output of a fuzzy logic controller crossover and mutation probabilities are varied dynamically. It enhances stochastic search capabilities of HFMOEA. In multi-objective RPMC optimization framework, two objective functions namely the total payment function (TPF) for reactive power support from generators and synchronous condensers and the total real transmission loss (TRTL) are minimized simultaneously for clearing the reactive power market. The proposed HFMOEA based multi-objective RPMC scheme is tested on a standard IEEE 24 bus reliability test system and its performance is compared with five other multi-objective evolutionary techniques such as MOPBIL, NSGA-II, UPS-EMOA and SPEA-2 and a new extended form of NSGA (ENSGA-II). Applying all these six evolutionary techniques, a detailed statistical analysis using T-test and boxplots is carried out on three performance metrics (spacing, spread and hypervolume) data for RPMC problem. The obtained simulation results confirm the overall superiority of HFMOEA to generate better Pareto-optimal solutions with higher convergence rate as compared to above mentioned algorithms. Further, TPF and TRTL values corresponding to the best compromise solutions are obtained using said multi-objective evolutionary techniques. These values are compared with one another to take better market clearing decisions in competitive electricity environment.     

Fault tolerant multiple event detection in a wireless sensor network

With an increasing acceptance of Wireless Sensor Networks (WSNs), the health of individual sensor is becoming critical in identifying important events in the region of interest. One of the key challenges in detecting event in a WSN is how to detect it accurately transmitting minimum information providing sufficient details about the event. At the same time, it is also important to devise a strategy to handle multiple events occurring simultaneously. In this paper, we propose a Polynomial-based scheme that addresses these problems of Event Region Detection (PERD) by having a aggregation tree of sensor nodes. We employ a data aggregation scheme, TREG (proposed in our earlier work) to perform function approximation of the event using a multivariate polynomial regression. Only coefficients of the polynomial (P$P$) are passed instead of aggregated data. PERD includes two components: event recognition and event report with boundary detection. This can be performed for multiple simultaneously occurring events. We also identify faulty sensor(s) using the aggregation tree. Performing further mathematical operations on the calculated P$P$ can identify the maximum (max) and minimum (min) values of the sensed attribute and their locations. Therefore, if any sensor reports a data value outside the [min, max] range, it can be identified as a faulty sensor. Since PERD is implemented over a polynomial tree on a WSN in a distributed manner, it is easily scalable and computation overhead is marginal. Results reveal that event(s) can be detected by PERD with error in detection remaining almost constant achieving a percentage error within a threshold of 10%$10\%$ with increase in communication range. Results also show that a faulty sensor can be detected with an average accuracy of 94%$94\%$ and it increases with increase in node density.     

Range-Free Localization Using Expected Hop Progress in Wireless Sensor Networks

Localization algorithm continues to be an important and challenging topic in today's wireless sensor networks (WSNs). In this paper, a novel range-free localization algorithm using expected hop progress (LAEP) to predict the location of any sensor in a WSN is proposed. This algorithm is based on an accurate analysis of hop progress in a WSN with randomly deployed sensors and arbitrary node density. By deriving the expected hop progress from a network model for WSNs in terms of network parameters, the distance between any pair of sensors can be accurately computed. Since the distance estimation is a key issue in localization systems for WSNs, the proposed range-free LAEP achieves better performance and less communication overhead as compared to some existent schemes like DV-Hop and RAW. In addition, we study the effect of anchor placement on the algorithm performance by deriving the corresponding mean position error range. Extensive simulations are performed and the results are observed to be in good agreement with the theoretical analysis.     

Adaptive state-based multi-radio multi-channel multi-path routing in Wireless Mesh Networks

Wireless Mesh Networks (WMNs) are envisioned to seamlessly extend the network connectivity to end users by forming a wireless backbone that requires minimal infrastructure. Unfortunately for WMNs, frequent link quality fluctuations, excessive load on selective links, congestion, and limited capacity due to the half-duplex nature of radios are some key limiting factors that hinder their deployment. To address these problems, we propose a novel Adaptive State-based Multi-path Routing Protocol (ASMRP), which constructs Directed Acyclic Graphs (DAGs) from each Mesh Router (MR) to Internet Gateways (IGWs) and effectively discovers multiple optimal path set between any given MR-IGW pair. A congestion aware traffic splitting algorithm to balance traffic over these multiple paths is presented which synergistically improves the overall performance of the WMNs. We design a novel Neighbor State Maintenance module that innovatively employs a state machine at each MR to monitor the quality of links connecting its neighbors in order to cope with unreliable wireless links. We also employ a 4-radio architecture for MRs, which allows them to communicate over multiple radios tuned to non-overlapping channels and better utilize the available spectrum. Through extensive simulations using ns-2, we observe that ASMRP substantially improves the achieved throughput (～5 times gain in comparison to AODV), and significantly minimizes end-to-end latencies. We also show that ASMRP ensures fairness in the network under varying traffic load conditions.     

Ionic transport in the (Agl∶AgCl) mixed-system

Ionic mobility, conductivity, number of charge carriers and transference number are reported for annealed and rapidly quenched [xAgl(1–x)AgCl] mixed systems, these results are compared with Agl. Of these three materials the quenched mixed-system had the highest Ag⁺ ion conductivity. Further, preliminary studies are reported where the high conductivity system (i.e. quenched 0.75 Agl0.25 AgCl) has been used as host material for the preparation of Ag⁺ ion conducting glasses and composites. Detailed thermal/phase diagram studies have also been carried out on the quenched composition. The conductivity enhancement in the quenched system is attributed to the formation of new disordered phases and the introduction of amorphisity, the simple space-charge model applicable to the annealed system failed to explain these results.     

Fuzzy and Deep Belief Network Based Malicious Vehicle Identification and Trust Recommendation Framework in VANETs

Wireless Personal Communications - This work aims to implement a clustering scheme to separate vehicles into a cluster that is based on various parameters, such as the total number of relay nodes,...