Life cycle energy and carbon footprint analysis of photovoltaic battery microgrid system in India

Electricity supply in India is from a centralized grid. Many parts of the country experience grid interruptions. Life cycle energy and environmental analysis has been done for a 27 kWp photovoltaic system which acts as grid backup for 3 h outage in an Indian urban residential scenario. This paper discusses energy requirements and carbon emission for a PV storage system for five different battery technologies in Indian context. This can be used as a metric for comparative analysis for new batteries, with an undeveloped market. The energy requirements for the components are quantified and are compared in terms of energy payback time (EPBT) and Net Energy Ratio (NER). All the calculations are done for Indian context. EPBT is found to be in the range of 2–4.5 years for all the systems, while NER is in the range of 6.6–2.52. NaS has the highest emission factor of 0.67 kgCO₂/kWh and the least for NiCd (0.091 kgCO₂/kWh). These factors can be used to select a PV battery option and to target selection of materials and systems based on the reported values.     

The role of protein kinase in C ischemic/reperfused preconditioned isolated rat hearts

Protein kinase C (PKC) has been implicated in the preconditioning-induced cardiac protection in ischemic/reperfused myocardium. We studied the effect of PKC inhibition with calphostin C (25, 50, 100, 200, 400, and 800 nM), a potent and specific inhibitor of PKC, in isolated working nonpreconditioned and preconditioned ischemic/reperfused hearts. In the nonpreconditioned groups, all hearts underwent 30 min of normothermic global ischemia followed by 30 min of reperfusion. In the preconditioned groups, hearts were subjected to four cycles of ischemic preconditioning by using 5 min of ischemia followed by 10 min reperfusion, before the induction of 30 min ischemia and reperfusion. At low concentrations of calphostin C (25, 50, and 100 nM), the PKC inhibitor had no effect on the incidence or arrhythmias or postischemic cardiac function in the nonpreconditioned ischemic/reperfused groups. With 200 and 400 nM of calphostin C, a significant increase in postischemic function and a reduction in the incidence of arrhythmias were observed in the nonpreconditioned ischemic/reperfused groups. Increasing the concentration of calphostin C to 800 NM, the recovery of postischemic cardiac function was similar to that of the drug-free control group. In preconditioned hearts, lower concentrations (< 100 nM) of calphostin C did not change the response of the myocardium to ischemia and reperfusion in comparison to the preconditioned drug-free myocardium. Two hundred and 400 nM of the PKC inhibitor further reduced the incidence of ventricular fibrillation (VF) from the preconditioned drug-free value of 50% to 0 (p < 0.05) and 0 (p < 0.05), respectively, indicating that the combination of the two, preconditioning and calphostin C, affords significant additional protection. Increasing the concentration of calphostin C to 800 nM blocked the cardioprotective effect of preconditioning (100% incidence of VF). The recovery of cardiac function was similarly improved at calphostin C doses of 200 and 400 nM and was reduced at 800 nM (p < 0.05). With 200 and 400 nM of calphostin C, both cytosolic and particulate PKC activity were reduced by approximately 40 and 60%, respectively, in both preconditioned and preconditioned/ischemic/reperfused hearts. The highest concentration of calphostin C (800 nM) resulted in almost a complete inhibition of cytosolic (100%) and particulate (85%) PKC activity correlated with the abolition of preconditioning-induced cardiac protection. In conclusion, calphostin C protects the ischemic myocardium obtained from intact animals, provides significant additional protection to preconditioning at moderate doses, and blocks the protective effect of preconditioning at high concentrations. The dual effects of calphostin C appear to be strictly dose and "enzyme inhibition" related.     

Cooperative adaptive control for synchronization of second‐order systems with unknown nonlinearities

This paper studies synchronization to a desired trajectory for multi‐agent systems with second‐order integrator dynamics and unknown nonlinearities and disturbances. The agents can have different dynamics and the treatment is for directed graphs with fixed communication topologies. The command generator or leader node dynamics is also nonlinear and unknown. Cooperative tracking adaptive controllers are designed based on each node maintaining a neural network parametric approximator and suitably tuning it to guarantee stability and performance. A Lyapunov‐based proof shows the ultimate boundedness of the tracking error. A simulation example with nodes having second‐order Lagrangian dynamics verifies the performance of the cooperative tracking adaptive controller. Copyright © 2010 John Wiley & Sons, Ltd.     

A local asynchronous distributed privacy preserving feature selection algorithm for large peer-to-peer networks

In this paper we develop a local distributed privacy preserving algorithm for feature selection in a large peer-to-peer environment. Feature selection is often used in machine learning for data compaction and efficient learning by eliminating the curse of dimensionality. There exist many solutions for feature selection when the data are located at a central location. However, it becomes extremely challenging to perform the same when the data are distributed across a large number of peers or machines. Centralizing the entire dataset or portions of it can be very costly and impractical because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, dynamic nature of the data/network, and privacy concerns. The solution proposed in this paper allows us to perform feature selection in an asynchronous fashion with a low communication overhead where each peer can specify its own privacy constraints. The algorithm works based on local interactions among participating nodes. We present results on real-world dataset in order to test the performance of the proposed algorithm.     

A genetic algorithm based heuristic for scheduling of virtual manufacturing cells (VMCs)

We present a genetic algorithm (GA) based heuristic approach for job scheduling in virtual manufacturing cells (VMCs). In a VMC, machines are dedicated to a part as in a regular cell, but machines are not physically relocated in a contiguous area. Cell configurations are therefore temporary, and assignments are made to optimize the scheduling objective under changing demand conditions. We consider the case where there are multiple jobs with different processing routes. There are multiple machine types with several identical machines in each type and are located in different locations in the shop floor. Scheduling objective is weighted makespan and total traveling distance minimization. The scheduling decisions are the (i) assignment of jobs to the machines, and (ii) the job start time at each machine. To evaluate the effectiveness of the GA heuristic we compare it with a mixed integer programming (MIP) solution. This is done on a wide range of benchmark problem. Computational results show that GA is promising in finding good solutions in very shorter times and can be substituted in the place of MIP model.     

Affine-based registration of CT and MR modality images of human brain using multiresolution approaches: comparative study on genetic algorithm and particle swarm optimization

We present a non-linear 2-D/2-D affine registration technique for MR and CT modality images of section of human brain. Automatic registration is achieved by maximization of a similarity metric, which is the correlation function of two images. The proposed method has been implemented by choosing a realistic, practical transformation and optimization techniques. Correlation-based similarity metric should be maximal when two images are perfectly aligned. Since similarity metric is a non-convex function and contains many local optima, choice of search strategy for optimization is important in registration problem. Many optimization schemes are existing, most of which are local and require a starting point. In present study we have implemented genetic algorithm and particle swarm optimization technique to overcome this problem. A comparative study shows the superiority and robustness of swarm methodology over genetic approach.     

Hadamard quantum broadcast channels

We consider three different communication tasks for quantum broadcast channels, and we determine the capacity region of a Hadamard broadcast channel for these various tasks. We define a Hadamard broadcast channel to be such that the channel from the sender to one of the receivers is entanglement-breaking and the channel from the sender to the other receiver is complementary to this one. As such, this channel is a quantum generalization of a degraded broadcast channel, which is well known in classical information theory. The first communication task we consider is classical communication to both receivers, the second is quantum communication to the stronger receiver and classical communication to other, and the third is entanglement-assisted classical communication to the stronger receiver and unassisted classical communication to the other. The structure of a Hadamard broadcast channel plays a critical role in our analysis: The channel to the weaker receiver can be simulated by performing a measurement channel on the stronger receiver’s system, followed by a preparation channel. As such, we can incorporate the classical output of the measurement channel as an auxiliary variable and solve all three of the above capacities for Hadamard broadcast channels, in this way avoiding known difficulties associated with quantum auxiliary variables.     

A synopsis on node compromise detection in wireless sensor networks using sequential analysis (Invited Review Article)

By exploiting the unattended nature of the wireless sensor networks, an attacker can physically capture and compromise sensor nodes and then launch a variety of attacks. He can additionally create many replicas of a few compromised nodes and spread these replicas over the network, thus launching further attacks with their help. In order to minimize the damage incurred by compromised and replicated nodes, it is very important to detect such malicious nodes as quickly as possible. In this review article, we synthesize our previous works on node compromise detection in sensor networks while providing the extended analysis in terms of performance comparison to the related work. More specifically, we use the methodology of the sequential analysis to detect static and mobile compromised nodes, as well as mobile replicated nodes in sensor networks. With the help of analytical and simulation results, we also demonstrate that our schemes provide robust and efficient node compromise detection capability.