Detection of Parkinson’s disease tremor-onset with a frequency analysing artificial neural network

Deep Brain Stimulator devices are becoming widely used for therapeutic benefits in movement disorders such as Parkinson's disease. Prolonging the battery life span of such devices could dramatically reduce the risks and accumulative costs associated with surgical replacement. This paper demonstrates how an artificial neural network can be trained using pre-processing frequency analysis of deep brain electrode recordings to detect the onset of tremor in Parkinsonian patients. Implementing this solution into an 'intelligent' neurostimulator device will remove the need for continuous stimulation currently used, and open up the possibility of demand-driven stimulation. Such a methodology could potentially decrease the power consumption of a deep brain pulse generator.

Animal health challenges and veterinary aspects of organic livestock farming identified through a 3 year EU network project

From 2003-2006, an EU network project ‘Sustaining Animal Health and Food Safety in Organic Farming' (SAFO), was carried out with 26 partners from 20 EU-countries and 4 related partners from 4 candidate or new member states. The focus was the integration of animal health and welfare issues in organic farming with food safety aspects. Four very consistent conclusions became apparent: 1) The climatic, physical and socio-economic conditions vary considerably throughout Europe, leading to different livestock farming systems. This limits the possibility for technology transfer between regions, and creates several challenges for a harmonised regulation, 2) Implementing organic standards at farm level does not always ensure that animal health and welfare reach the high ideals of the organic principles, 3) To overcome these deficiencies, organic farmers and farmer organisations need to take ownership of organic values and, 4) In all participating countries, a strong need for training of farmers and in particular veterinarians in animal health promotion and organic principles was identified. The article presents a summary of papers presented at the five SAFO workshops.

Reconstruction of geomagnetic activity and near-Earth interplanetary conditions over the past 167 yr - Part 4: Near-Earth solar wind speed, IMF, and open solar flux

In the concluding paper of this tetralogy, we here use the different geomagnetic activity indices to reconstruct the near-Earth interplanetary magnetic field (IMF) and solar wind flow speed, as well as the open solar flux (OSF) from 1845 to the present day. The differences in how the various indices vary with near-Earth interplanetary parameters, which are here exploited to separate the effects of the IMF and solar wind speed, are shown to be statistically significant at the 93% level or above. Reconstructions are made using four combinations of different indices, compiled using different data and different algorithms, and the results are almost identical for all parameters. The correction to the aa index required is discussed by comparison with the Ap index from a more extensive network of mid-latitude stations. Data from the Helsinki magnetometer station is used to extend the aa index back to 1845 and the results confirmed by comparison with the nearby St Petersburg observatory. The optimum variations, using all available long-term geomagnetic indices, of the near-Earth IMF and solar wind speed, and of the open solar flux, are presented; all with ±2sigma� uncertainties computed using the Monte Carlo technique outlined in the earlier papers. The open solar flux variation derived is shown to be very similar indeed to that obtained using the method of Lockwood et al. (1999).

Centennial variations in sunspot number, open solar flux and streamer belt width: 1. correction of the sunspot number record since 1874

We analyse the widely-used international/ Zürich sunspot number record, R, with a view to quantifying a suspected calibration discontinuity around 1945 (which has been termed the “Waldmeier discontinuity” [Svalgaard, 2011]). We compare R against the composite sunspot group data from the Royal Greenwich Observatory (RGO) network and the Solar Optical Observing Network (SOON), using both the number of sunspot groups, N{sub}G{\sub}, and the total area of the sunspots, A{sub}G{\sub}. In addition, we compare R with the recently developed interdiurnal variability geomagnetic indices IDV and IDV(1d). In all four cases, linearity of the relationship with R is not assumed and care is taken to ensure that the relationship of each with R is the same before and after the putative calibration change. It is shown the probability that a correction is not needed is of order 10{sup}−8{\sup} and that R is indeed too low before 1945. The optimum correction to R for values before 1945 is found to be 11.6%, 11.7%, 10.3% and 7.9% using A{sub}G{\sub}, N{sub)G{\sub}, IDV, and IDV(1d), respectively. The optimum value obtained by combining the sunspot group data is 11.6% with an uncertainty range 8.1-14.8% at the 2σ level. The geomagnetic indices provide an independent yet less stringent test but do give values that fall within the 2σ uncertainty band with optimum values are slightly lower than from the sunspot group data. The probability of the correction needed being as large as 20%, as advocated by Svalgaard [2011], is shown to be 1.6 × 10{sup}−5{\sup}.

Network optimization for enhanced resilience of urban heat island measurements

The urban heat island is a well-known phenomenon that impacts a wide variety of city operations. With greater availability of cheap meteorological sensors, it is possible to measure the spatial patterns of urban atmospheric characteristics with greater resolution. To develop robust and resilient networks, recognizing sensors may malfunction, it is important to know when measurement points are providing additional information and also the minimum number of sensors needed to provide spatial information for particular applications. Here we consider the example of temperature data, and the urban heat island, through analysis of a network of sensors in the Tokyo metropolitan area (Extended METROS). The effect of reducing observation points from an existing meteorological measurement network is considered, using random sampling and sampling with clustering. The results indicated the sampling with hierarchical clustering can yield similar temperature patterns with up to a 30% reduction in measurement sites in Tokyo. The methods presented have broader utility in evaluating the robustness and resilience of existing urban temperature networks and in how networks can be enhanced by new mobile and open data sources.

The Birmingham Urban Climate Laboratory: an open meteorological test bed and challenges of the smart city

Existing urban meteorological networks have an important role to play as test beds for inexpensive and more sustainable measurement techniques that are now becoming possible in our increasingly smart cities. The Birmingham Urban Climate Laboratory (BUCL) is a near-real-time, high-resolution urban meteorological network (UMN) of automatic weather stations and inexpensive, nonstandard air temperature sensors. The network has recently been implemented with an initial focus on monitoring urban heat, infrastructure, and health applications. A number of UMNs exist worldwide; however, BUCL is novel in its density, the low-cost nature of the sensors, and the use of proprietary Wi-Fi networks. This paper provides an overview of the logistical aspects of implementing a UMN test bed at such a density, including selecting appropriate urban sites; testing and calibrating low-cost, nonstandard equipment; implementing strict quality-assurance/quality-control mechanisms (including metadata); and utilizing preexisting Wi-Fi networks to transmit data. Also included are visualizations of data collected by the network, including data from the July 2013 U.K. heatwave as well as highlighting potential applications. The paper is an open invitation to use the facility as a test bed for evaluating models and/or other nonstandard observation techniques such as those generated via crowdsourcing techniques.

A computation trust model with trust network in multi-agent systems

Trust is a fundamental issue in multi-agent systems, especially when they are applied in e-commence. The computational models of trust play an important role in determining who and how to interact in open and dynamic environments. To this end, a computation trust model is proposed in which the confidence information based on direct prior interactions with the target agent and the reputation information from trust network are used. In this way, agents can autonomously deal with deception and identify trustworthy parties in multi-agent systems. The ontological property of trust is also considered in the model. A case study is provided to show the effectiveness of the proposed model.

Information theory based detection against network behavior mimicking DDoS attacks

DDoS is a spy-on-spy game between attackers and detectors. Attackers are mimicking network traffic patterns to disable the detection algorithms which are based on these features. It is an open problem of discriminating the mimicking DDoS attacks from massive legitimate network accessing. We observed that the zombies use controlled function(s) to pump attack packages to the victim, therefore, the attack flows to the victim are always share some properties, e.g. packages distribution behaviors, which are not possessed by legitimate flows in a short time period. Based on this observation, once there appear suspicious flows to a server, we start to calculate the distance of the package distribution behavior among the suspicious flows. If the distance is less than a given threshold, then it is a DDoS attack, otherwise, it is a legitimate accessing. Our analysis and the preliminary experiments indicate that the proposed method- can discriminate mimicking flooding attacks from legitimate accessing efficiently and effectively.

Architecture of fiber network : from understanding to engineering of molecular gels

A new approach of engineering of molecular gels was established on the basis of a nucleation-initiated network formation mechanism. A variety of gel network structures can be obtained by regulating the starting temperature of the sol−gel transition. This enables us to tune the network from the spherulitic domains pattern to the extensively interconnected fibrillar network. As the consequence of fibrous network structure turning, desirable rheological and other in-use properties of the materials can be obtained accordingly. This approach of micro-/nanostructural fabrication may open up a new route for micro-/nanofunctional materials engineering in general.

Quality control of sensor network data

A wireless sensor network (WSN) is a group of sensors that are geographically distributed and interconnected by wireless networks. Sensors gather information about the state of physical world. Then, after processing forward them to the main destination. To be able to provide this service, there are many aspects of communication techniques that need to be explored. Supporting quality of service (QoS) will be of critical importance for pervasive WSNs that serve as the network infrastructure of diverse applications. To illustrate new research and development interests in this field, this paper examines and discusses the requirements, critical challenges, and open research issues on QoS management in WSNs. A brief overview of recent progress is given.

Hierarchical workflow management in wireless sensor network

To build the service-oriented applications in a wireless sensor network (WSN), the workflow can be utilized to compose a set of atomic services and execute the corresponding pre-designed processes. In general, WSN applications rely closely on the sensor data which are usually inaccurate or even incomplete in the resource-constrained WSN. Then, the erroneous sensor data will affect the execution of atomic services and furthermore the workflows, which form an important part in the bottom-to-up dynamics of WSN applications. In order to alleviate this issue, it is necessary to manage the workflow hierarchically. However, the hierarchical workflow management remains an open and challenging problem. In this paper, by adopting the Bloom filter as an effective connection between the sensor node layer and the upper application layer, a hierarchical workflow management approach is proposed to ensure the QoS of workflow-based WSN application . The case study and experimental evaluations demonstrate the capability of the proposed approach.