Guideline on the design and conduct of cystic fibrosis clinical trials: The European Cystic Fibrosis Society–Clinical Trials Network (ECFS-CTN)

We describe the rationale for disease specific research networks in general as well as the aims and function of the European Cystic Fibrosis Society-Clinical Trials Network (ECFS-CTN) specifically. The ECFS-CTN was founded in 2009 with the aim of improving the quality and quantity of clinical research in the area of cystic fibrosis (CF) in Europe. A network of 18 clinical trial sites in 8 European countries was established according to uniform state-of-the-art quality criteria. To support the ECFS-CTN in the acquisition, planning and conduct of clinical trials, the network is equipped with a coordinating centre, steering and executive committees, and committees for protocol review, standardization, training and networking as well as a data safety monitoring board. A strong partnership with European CF patient parent organizations aims to increase awareness of the need for efficient clinical research and the participation of patients in clinical trials.

Intrusion Detection System for Network Security in Synchrophasor Systems

Synchrophasor systems will play a crucial role in next generation Smart Grid monitoring, protection and control. However these systems also introduce a multitude of potential vulnerabilities from malicious and inadvertent attacks, which may render erroneous operation or severe damage. This paper proposes a Synchrophasor Specific Intrusion Detection System (SSIDS) for malicious cyber attack and unintended misuse. The SSIDS comprises a heterogeneous whitelist and behavior-based approach to detect known attack types and unknown and so-called ‘zero-day’ vulnerabilities and attacks. The paper describes reconnaissance, Man-in-the-Middle (MITM) and Denial-of-Service (DoS) attack types executed against a practical synchrophasor system which are used to validate the real-time effectiveness of the proposed SSIDS cyber detection method.

Fluorescence based fibre optic pH sensor for the pH 10–13 range suitable for corrosion monitoring in concrete structures

The design, development and evaluation of an optical fibre pH sensor for monitoring pH in the alkaline region are discussed in detail in this paper. The design of this specific pH sensor is based on the pH induced change in fluorescence intensity of a coumarin imidazole dye which is covalently attached to a polymer network and then fixed to the distal end of an optical fibre. The sensor provides a response over a pH range of 10.0–13.2 with an acceptable response rate of around 50 min, having shown a very good stability over a period of longer than 20 months thus far. The sensor has also demonstrated little cross-sensitivity to ionic strength (IS) and also excellent photostability through a series of laboratory tests. These features make this type of sensor potentially well suited for in situ long term monitoring of pH in concrete structures, to enhance structural monitoring in the civil engineering sector

Low-cost Process monitoring for polymer extrusion

Polymer extrusion is regarded as an energy-intensive production process, and the real-time monitoring of both energy consumption and melt quality has become necessary to meet new carbon regulations and survive in the highly competitive plastics market. The use of a power meter is a simple and easy way to monitor energy, but the cost can sometimes be high. On the other hand, viscosity is regarded as one of the key indicators of melt quality in the polymer extrusion process. Unfortunately, viscosity cannot be measured directly using current sensory technology. The employment of on-line, in-line or off-line rheometers is sometimes useful, but these instruments either involve signal delay or cause flow restrictions to the extrusion process, which is obviously not suitable for real-time monitoring and control in practice. In this paper, simple and accurate real-time energy monitoring methods are developed. This is achieved by looking inside the controller, and using control variables to calculate the power consumption. For viscosity monitoring, a ‘soft-sensor’ approach based on an RBF neural network model is developed. The model is obtained through a two-stage selection and differential evolution, enabling compact and accurate solutions for viscosity monitoring. The proposed monitoring methods were tested and validated on a Killion KTS-100 extruder, and the experimental results show high accuracy compared with traditional monitoring approaches.

Retinal microvascular network attenuation in Alzheimer's disease

INTRODUCTION:Cerebral small-vessel disease has been implicated in the development of Alzheimer’sdisease (AD). The retinal microvasculature enables non-invasive visualization andevaluation of the systemic microcirculation. We evaluated retinal microvascular parametersin a case-control study of AD patients and cognitively-normal controls. 

METHODS:Retinal images were computationally analyzed and quantitative retinal parameters (caliber,fractal dimension, tortuosity, and bifurcation) measured. Regression models were used tocompute odds ratios (OR) and confidence intervals (CI) for AD with adjustment forconfounders. 

RESULTS:Retinal images were available in 213 AD participants and 294 cognitively-normal controls.Persons with lower venular fractal dimension (OR per standard deviation [SD] increase, 0.77[CI: 0.62–0.97]) and lower arteriolar tortuosity (OR per SD increase, 0.78 [CI: 0.63–0.97])were more likely to have AD following appropriate adjustment. 
DISCUSSION:Patients with AD have a sparser retinal microvascular network and retinal microvascularvariation may represent similar pathophysiological events within the cerebralmicrovasculature of patients with AD.

Monitoring Bridge Dynamic Behaviour using an Instrumented Two Axle Vehicle

Highway structures such as bridges are subject to continuous degradation primarily due to ageing, loading and environmental factors. A rational transport policy must monitor and provide adequate maintenance to this infrastructure to guarantee the required levels of transport service and safety. Increasingly in recent years, bridges are being instrumented and monitored on an ongoing basis due to the implementation of Bridge Management Systems. This is very effective and provides a high level of protection to the public and early warning if the bridge becomes unsafe. However, the process can be expensive and time consuming, requiring the installation of sensors and data acquisition electronics on the bridge. This paper investigates the use of an instrumented 2-axle vehicle fitted with accelerometers to monitor the dynamic behaviour of a bridge network in a simple and cost-effective manner. A simplified half car-beam interaction model is used to simulate the passage of a vehicle over a bridge. This investigation involves the frequency domain analysis of the axle accelerations as the vehicle crosses the bridge. The spectrum of the acceleration record contains noise, vehicle, bridge and road frequency components. Therefore, the bridge dynamic behaviour is monitored in simulations for both smooth and rough road surfaces. The vehicle mass and axle spacing are varied in simulations along with bridge structural damping in order to analyse the sensitivity of the vehicle accelerations to a change in bridge properties. These vehicle accelerations can be obtained for different periods of time and serve as a useful tool to monitor the variation of bridge frequency and damping with time.

Time-lapse monitoring of climate effects on earthworks using surface waves

The UK’s transportation network is supported by critical geotechnical assets (cuttings/embankments/dams) that require sustainable, cost-effective management, while maintaining an appropriate service level to meet social, economic, and environmental needs. Recent effects of extreme weather on these geotechnical assets have highlighted their vulnerability to climate variations. We have assessed the potential of surface wave data to portray the climate-related variations in mechanical properties of a clay-filled railway embankment. Seismic data were acquired bimonthly from July 2013 to November 2014 along the crest of a heritage railway embankment in southwest England. For each acquisition, the collected data were first processed to obtain a set of Rayleigh-wave dispersion and attenuation curves, referenced to the same spatial locations. These data were then analyzed to identify a coherent trend in their spatial and temporal variability. The relevance of the observed temporal variations was also verified with respect to the experimental data uncertainties. Finally, the surface wave dispersion data sets were inverted to reconstruct a time-lapse model of S-wave velocity for the embankment structure, using a least-squares laterally constrained inversion scheme. A key point of the inversion process was constituted by the estimation of a suitable initial model and the selection of adequate levels of spatial regularization. The initial model and the strength of spatial smoothing were then kept constant throughout the processing of all available data sets to ensure homogeneity of the procedure and comparability among the obtained VS sections. A continuous and coherent temporal pattern of surface wave data, and consequently of the reconstructed VS models, was identified. This pattern is related to the seasonal distribution of precipitation and soil water content measured on site.

Time-lapse monitoring of fluid induced geophysical property variations within an unstable earthwork using P-wave refraction

A significant portion of the UK’s transportation system relies on a network of geotechnical earthworks (cuttings and embankments) that were constructed more than 100 years ago, whose stability is affected by the change in precipitation patterns experienced over the past few decades. The vulnerability of these structures requires a reliable, cost- and time-effective monitoring of their geomechanical condition. We have assessed the potential application of P-wave refraction for tracking the seasonal variations of seismic properties within an aged clay-filled railway embankment, located in southwest England. Seismic data were acquired repeatedly along the crest of the earthwork at regular time intervals, for a total period of 16 months. P-wave first-break times were picked from all available recorded traces, to obtain a set of hodocrones referenced to the same spatial locations, for various dates along the surveyed period of time. Traveltimes extracted from each acquisition were then compared to track the pattern of their temporal variability. The relevance of such variations over time was compared with the data experimental uncertainty. The multiple set of hodocrones was subsequently inverted using a tomographic approach, to retrieve a time-lapse model of VPVP for the embankment structure. To directly compare the reconstructed VPVP sections, identical initial models and spatial regularization were used for the inversion of all available data sets. A consistent temporal trend for P-wave traveltimes, and consequently for the reconstructed VPVP models, was identified. This pattern could be related to the seasonal distribution of precipitation and soil-water content measured on site.

Feature Study on a Programmable Network Traffic Classifier

Monitoring and tracking of IP traffic flows are essential for network services (i.e. packet forwarding). Packet header lookup is the main part of flow identification by determining the predefined matching action for each incoming flow. In this paper, an improved header lookup and flow rule update solution is investigated. A detailed study of several well-known lookup algorithms reveals that searching individual packet header field and combining the results achieve high lookup speed and flexibility. The proposed hybrid lookup architecture is comprised of various lookup algorithms, which are selected based on the user applications and system requirements.

Detecting stealthy attacks: Efficient monitoring of suspicious activities on computer networks

Stealthy attackers move patiently through computer networks - taking days, weeks or months to accomplish their objectives in order to avoid detection. As networks scale up in size and speed, monitoring for such attack attempts is increasingly a challenge. This paper presents an efficient monitoring technique for stealthy attacks. It investigates the feasibility of proposed method under number of different test cases and examines how design of the network affects the detection. A methodological way for tracing anonymous stealthy activities to their approximate sources is also presented. The Bayesian fusion along with traffic sampling is employed as a data reduction method. The proposed method has the ability to monitor stealthy activities using 10-20% size sampling rates without degrading the quality of detection.