Synthesis of highly pure and dense 0.9(KNbO3)-0.1(BaNi1/2Nb1/2O3-δ) ceramic with superior magnetic properties

Residual NiO phase is generally detected in 0.9(KNbO₃)–0.1(BaNi_1/2Nb_1/2O_3-δ) (KBNNO) synthesized using NiO as a nickel precursor by solid-state reaction. In this work, NiO phase is found to exist in the form of the residual NiO particles with a size of 100-200 nm using energy dispersive X-ray elemental mapping. These NiO residual particles are eliminated by using nickel acetate as a nickel precursor and a 100% perovskite phase KBNNO is successfully synthesized at as low as 600°C temperature. Furthermore, using the two-step sintering technique, 100% relative density is achieved in this material. The nickel acetate–based KBNNO shows a robust ferromagnetism with the saturation magnetization of 11.42 memu/g and the remanent magnetization of 3.89 memu/g which is 38 times higher than that of previously reported value in NiO-based KBNNO. Thus, a highly pure and fully dense KBNNO ceramic with superior magnetic properties is obtained using nickel acetate and by the two-step sintering method. This is a key step forward in the processing of KBNNO and is likely to have a significant impact on other physical properties of this newly invented and promising photovoltaic perovskite material.     

Investigation of Dissipation,Adsorption, Degradation,and Leaching of Triazophos Pesticide in Various Soils

This study concerns the investigation of dissipation, adsorption, and degradation of triazophos in different soils from Pakistan. These processes help in the disappearance of pesticide from the environment. Gas chromatography was used for dissipation and adsorption analysis while for degradation study mass spectrometer was used in addition of gas chromatography (GC-MS). The dissipation rate of triazophos in three different soils was 90% over 30 days with average half-life of 9.059 days. From dissipation study it was inferred that rate is variable in each soil due to climatic changes, soil nature and soil-pesticide interactions. Adsorption experiment has revealed that the adsorption of this pesticide to soil follows the pseudo first order kinetic model with rate constant value of 0.479/h and Freundlich isotherm with adsorption capacity of 1.832 mol/g. Degradation study has displayed two major metabolites, one is phosphorothioic acid, S-[2-[(1-cyano-1-methylethyl) amino]-2-oxoethyl] O,O-diethyl ester at retention time of 9.136 and the other is sulfotep at 14.304 min. The leaching potential of triazophos was also calculated from its half-life and organic carbon content present in soil which was 1.688 representing it as non leacher pesticide.     

Thermally Resistive Electrospun Composite Membranes for Low‐Grade Thermal Energy Harvesting

In this work, thermally insulating composite mats of poly(vinylidene fluoride) (PVDF) and polyacrylonitrile (PAN) blends are used as the separator membranes. The membranes improve the thermal‐to‐electrical energy conversion efficiency of a thermally driven electrochemical cell (i.e., thermocell) up to 95%. The justification of the improved performance is an intricate relationship between the porosity, electrolyte uptake, electrolyte uptake rate of the electrospun fibrous mat, and the actual temperature gradient at the electrode surface. When the porosity is too high (87%) in PAN membranes, the electrolyte uptake and electrolyte uptake rate are significantly high as 950% and 0.53 µL s^?1, respectively. In such a case, the convective heat flow within the cell is high and the power density is limited to 32.7 mW m^?2. When the porosity is lesser (up to 81%) in PVDF membranes, the electrolyte uptake and uptake rate are relatively low as 434% and 0.13 µL s^?1, respectively. In this case, the convective flow shall be low, however, the maximum power density of 63.5 mW m^?2 is obtained with PVDF/PAN composites as the aforementioned parameters are optimized. Furthermore, multilayered membrane structures are also investigated for which a bilayered architecture produces highest power density of 102.7 mW m^?2.     

PMSS: A programmable memory system and scheduler for complex memory patterns

HPC industry demands more computing units on FPGAs, to enhance the performance by using task/data parallelism. FPGAs can provide its ultimate performance on certain kernels by customizing the hardware for the applications. However, applications are getting more complex, with multiple kernels and complex data arrangements, generating overhead while scheduling/managing system resources. Due to this reason all classes of multi threaded machines–minicomputer to supercomputer–require to have efficient hardware scheduler and memory manager that improves the effective bandwidth and latency of the DRAM main memory. This architecture could be a very competitive choice for supercomputing systems that meets the demand of parallelism for HPC benchmarks. In this article, we proposed a Programmable Memory System and Scheduler (PMSS), which provides high speed complex data access pattern to the multi threaded architecture. This proposed PMSS system is implemented and tested on a Xilinx ML505 evaluation FPGA board. The performance of the system is compared with a microprocessor based system that has been integrated with the Xilkernel operating system. Results show that the modified PMSS based multi-accelerator system consumes 50% less hardware resources, 32% less on-chip power and achieves approximately a 19x speedup compared to the MicroBlaze based system.     

Designing genetic regulatory networks using fuzzy Petri nets approach

In this paper, we have successfully presented a fuzzy Petri net (FPN) model to design the genetic regulatory network. Based on the FPN model, an efficient algorithm is proposed to automatically reason about imprecise and fuzzy information. By using the reasoning algorithm for the FPN, we present an alternative approach that is more promising than the fuzzy logic. The proposed FPN approach offers more flexible reasoning capability because it is able to obtain results with fuzzy intervals rather than point values. In this paper, a novel model with a new concept of hidden fuzzy transition (HFT) to design the genetic regulatory network is developed. We have built the FPN model and classified the input data in terms of time point and obtained the output data, so the system can be viewed as the two-input and one output system. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. The experimental results show the proposed approach is feasible and acceptable to design the genetic regulatory network and investigate the dynamical behaviors of gene network.     

Efficient single-pass frequent pattern mining using a prefix-tree

The FP-growth algorithm using the FP-tree has been widely studied for frequent pattern mining because it can dramatically improve performance compared to the candidate generation-and-test paradigm of Apriori. However, it still requires two database scans, which are not consistent with efficient data stream processing. In this paper, we present a novel tree structure, called CP-tree (compact pattern tree), that captures database information with one scan (insertion phase) and provides the same mining performance as the FP-growth method (restructuring phase). The CP-tree introduces the concept of dynamic tree restructuring to produce a highly compact frequency-descending tree structure at runtime. An efficient tree restructuring method, called the branch sorting method, that restructures a prefix-tree branch-by-branch, is also proposed in this paper. Moreover, the CP-tree provides full functionality for interactive and incremental mining. Extensive experimental results show that the CP-tree is efficient for frequent pattern mining, interactive, and incremental mining with a single database scan.     

Fast attack detection using correlation and summarizing of security alerts in grid computing networks

Due to the extensive growth of grid computing networks, security is becoming a challenge. Usual solutions are not enough to prevent sophisticated attacks fabricated by multiple users especially when the number of nodes connected to the network is changing over the time. Attackers can use multiple nodes to launch DDoS attacks which generate a large amount of security alerts. On the one hand, this large number of security alerts degrades the overall performance of the network and creates instability in the operation of the security management solutions. On the other hand, they can help in camouflaging other real attacks. To address these issues, a?correlation mechanism is proposed which reduces the security alerts and continue detecting attacks in grid computing networks. To obtain the more accurate results, a?major portion of the experiments are performed by launching DDoS and Brute Force (BF) attacks in real grid environment, i.e., the Grid??5000 (G5K) network.     

Identification of probe request attacks in WLANs using neural networks

Any sniffer can see the information sent through unprotected ‘probe request messages’ and ‘probe response messages’ in wireless local area networks (WLAN). A station (STA) can send probe requests to trigger probe responses by simply spoofing a genuine media access control (MAC) address to deceive access point (AP) controlled access list. Adversaries exploit these weaknesses to flood APs with probe requests, which can generate a denial of service (DoS) to genuine STAs. The research examines traffic of a WLAN using supervised feed-forward neural network classifier to identify genuine frames from rogue frames. The novel feature of this approach is to capture the genuine user and attacker training data separately and label them prior to training without network administrator’s intervention. The model’s performance is validated using self-consistency and fivefold cross-validation tests. The simulation is comprehensive and takes into account the real-world environment. The results show that this approach detects probe request attacks extremely well. This solution also detects an attack during an early stage of the communication, so that it can prevent any other attacks when an adversary contemplates to start breaking into the network.     

Fault tolerance aware scheduling technique for cloud computing environment using dynamic clustering algorithm

In cloud computing, resources are dynamically provisioned and delivered to users in a transparent manner automatically on-demand. Task execution failure is no longer accidental but a common characteristic of cloud computing environment. In recent times, a number of intelligent scheduling techniques have been used to address task scheduling issues in cloud without much attention to fault tolerance. In this research article, we proposed a dynamic clustering league championship algorithm (DCLCA) scheduling technique for fault tolerance awareness to address cloud task execution which would reflect on the current available resources and reduce the untimely failure of autonomous tasks. Experimental results show that our proposed technique produces remarkable fault reduction in task failure as measured in terms of failure rate. It also shows that the DCLCA outperformed the MTCT, MAXMIN, ant colony optimization and genetic algorithm-based NSGA-II by producing lower makespan with improvement of 57.8, 53.6, 24.3 and 13.4 % in the first scenario and 60.0, 38.9, 31.5 and 31.2 % in the second scenario, respectively. Considering the experimental results, DCLCA provides better quality fault tolerance aware scheduling that will help to improve the overall performance of the cloud environment.