Facilitative responses to species-specific calls in cortical FM-FM neurons of the mustached bat

FM-FM neurons in the auditory cortex of the mustached bat are highly specialized for echolocation, responding facilitatively to the combination of frequency modulated (FM) components of biosonar pulse and its echo. Here we propose they are also specialized for processing bat communication calls. FM-FM neurons respond facilitatively to natural call syllable pairs, and exhibit inter-syllable interval tuning to the natural range of intervals. Our results support a role for 'combination-sensitive' neurons in communication, and suggest that cortical neurons can possess multiple distinct specialized modes of response.     

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.     

基于微生物和纳米TiO2的双负载 核壳水凝胶的制备和性能

摘要：设计了能同时利用微生物和光催化剂，分步逐级降解有机污染物的多功能双负载核壳水凝胶，核为聚乙烯醇（PVA）和羟乙基纤维素（HEC）包覆酵母菌（YE），壳为海藻酸钠（SA）和钙离子（Ca2+）负载纳米二氧化钛（TiO2 NPs）。采用SEM观察形貌可知，核壳结构清晰可辩，显微镜照片和荧光标记照片证明了核中YE的存在，XRD分析结果证明了壳中TiO2 NPs的负载，同时FTIR、XPS验证了核壳结构的分子间存在着相互作用力。并且在不同负载量下，研究了核壳水凝胶对亚甲基蓝（MB）染料去除的分步降解效果，壳中TiO2 NPs负载量越高，对MB的降解最高，可达96.65%； YE负载量的增加而MB的降解率先增大后减小，最高可达92.15%。在循环降解4次后，对MB染料的降解率仍有68.97%。本研究为分步多次降解多种有机污染物，提供并拓展了研究思路和新方法。     

Multi‐agent collaboration based on enhanced cognitive awareness: an architecture for agents' profiling on the semantic web

This paper applies cognitive models, inspired by cognitive science, with the aim to propose architectural and knowledge‐based requirements to structure ontological models for the cognitive profiling of agents. The proposed architecture aims to address the lack of flexibility that most agent‐based collaborations are affected by. The resulting agents, equipped with advanced cognitive profiling, have an increased cognitive awareness of themselves and are more capable of interacting with other agents in a multi‐agents based environment. In this research, cognitive awareness identifies the ability of the web agents to diagnose their processing limitations and to establish interactions with the external environment. The outcome is the enhanced flexibility, reusability and predictability of the agent behaviour; thus contributing towards minimizing human cognitive demands. The concept of cognitive profiling presented in this paper considers the semantic web as an action mediating space, where ontological models provide affordances for improving cognitive awareness through shared knowledge‐base. The conceptual model for the cognitive profile architecture is developed with Protégé Ontology editor to generate OWL Ontology and evaluated through a proof of concept. The results show that agents equipped with cognitive awareness can undertake complex tasks more dynamically.     

Bug Prioritization to Facilitate Bug Report Triage

The large number of new bug reports received in bug repositories of software systems makes their management a challenging task.Handling these reports manually is time consuming,and often results in delaying the resolution of important bugs.To address this issue,a recommender may be developed which automatically prioritizes the new bug reports.In this paper,we propose and evaluate a classification based approach to build such a recommender.We use the Na¨ ve Bayes and Support Vector Machine (SVM) classifiers,and present a comparison to evaluate which classifier performs better in terms of accuracy.Since a bug report contains both categorical and text features,another evaluation we perform is to determine the combination of features that better determines the priority of a bug.To evaluate the bug priority recommender,we use precision and recall measures and also propose two new measures,Nearest False Negatives (NFN) and Nearest False Positives (NFP),which provide insight into the results produced by precision and recall.Our findings are that the results of SVM are better than the Na¨ ve Bayes algorithm for text features,whereas for categorical features,Na¨ ve Bayes performance is better than SVM.The highest accuracy is achieved with SVM when categorical and text features are combined for training.     

Ear recognition with feed-forward artificial neural networks

Biometrics has become one of the most important techniques in recognizing a person’s identity. A person’s face, iris and fingerprint are mostly used in biometrics today. It has been established that there are no two ears exactly alike, even in the cases of identical twins. In this paper, we define a 7-element ear feature set and design and train a feed-forward artificial neural network to recognize a human ear. We train and test the network with 51 ear pictures from 51 different persons. Simulation experiments with various networks with various number of layers and number of neurons per layer and with and without noise are conducted. Results indicate that a 95 % ear recognition accuracy is achieved with a simple 3-layer feed-forward neural network with only a total of 18 neurons even in the presence of some noise. This design outstands previous work in simplicity and implementation cost.     

Low temperature synthesis of <Emphasis Type="Italic">Manganese tungstate</Emphasis> nanoflowers with antibacterial potential: Future material for water purification

Pure water is the fundamental requisite for human life. The water has been recycled naturally but not in an adequate amount for consumption. Nanotechnology with extraordinary applications provides competent ways for the decontamination of contaminated water. In the present study MnWO₄ nanoflowers endorsed with inherent antibacterial activity were successfully synthesized by facile hydrothermal approach. XRD, SEM, EDX spectroscopy and UVDRS were used to characterize the as-synthesized nanoflowers. Gram negative Escherichia coli ATCC 52922 bacterium was used as model organism to test antibacterial activity of as-synthesized MnWO₄ nanoflowers. This study was conducted to optimize minimum concentration of MnWO₄ nanoflowers and maximum contact time required to achieve complete inactivation of bacteria present in contaminated water. Minimum inhibitory concentration (MIC) of MnWO₄ nanoflowers was found to be 10 μg/ml. The assessment and interpretation of bacterial viability was done using dual fluorescent staining. The synthesized 3D-nanoflowers were found as potent bactericides. Thus, MnWO₄ nanoflowers emerged to be very good future material for disinfection of biological pollutants present in the contaminated water reservoirs and as an anti-biofouling agent.     

Rheology of natural fibers thermoplastic compounds: Flow length and fiber distribution

The presented study investigates the flow length and the corresponding fiber content distribution in the injection‐moulded natural fiber reinforced thermoplastics and its relation to fiber type and processing parameters such as injection pressure, temperature, injection rate and mould tempering by increasing die temperature. In this research, polypropylene compounds with nominally 30 wt % hemp and sisal fibers are investigated. The influence of the injection pressure (500 and 1000 bar), melt temperature (180°C, 200°C, and 220°C), and die temperature (23°C and 80°C) on the fiber content distribution all over the sample is investigated. An increasing linear trend of fiber content along the spiral length is observed as an evidence of a fiber/polymer multiflow system. A pattern for fiber content distribution with respect to the fiber length along the injected spiral can be distinguished, where the longer fibers are usually found at the end of the injected part and the shorter fibers remain near mould entrance point. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 39861.