An Intelligent Threat Prevention Framework with Heterogeneous Information

Three issues usually are associated with threat prevention intelligent surveillance systems. First, the fusion and interpretation of large scale incomplete heterogeneous information; second, the demand of effectively predicting suspects’ intention and ranking the potential threats posed by each suspect; third, strategies of allocating limited security resources (e.g., the dispatch of security team) to prevent a suspect’s further actions towards critical assets. However, in the literature, these three issues are seldomly considered together in a sensor network based intelligent surveillance framework. To address
this problem, in this paper, we propose a multi-level decision support framework for in-time reaction in intelligent surveillance. More specifically, based on a multi-criteria event modeling framework, we design a method to predict the most plausible intention of a suspect. Following this, a decision support model is proposed to rank each suspect based on their threat severity and to determine resource allocation strategies. Finally, formal properties are discussed to justify our framework.

Intelligent potentiostat for identification of heavy metals in situ

This article presents a low-cost portable electrochemical instrument capable of on-site identification of heavy metals. The instrument acquires metal-specific voltage and current signals by the application of differential pulse anodic stripping voltammetry. This technique enhances the analytical current and rejects the background current, resulting in a higher signal-to-noise ratio for a better detection limit. The identification of heavy metals is based on an intelligent machine-based method using a multilayer perceptron neural network consisting of three layers of neurons. The neural network is implemented using a 16 bit microcontroller. The system is developed for use in the field in order to avoid expensive and time-consuming procedures and can be used in a variety of situations to help environmental assessment and control. 

AgriSense: Multichannel disposable sensors for animal health disease diagnostics

Rapid and sensitive detection of viral infections associated with Bovine Respiratory Disease (BRD) in live animals is recognized as key to minimizing the impact of this disease. ELISA-based testing is limited as it typically relies on the detection of a single viral antibody subtype within an individual test sample and testing is relatively slow and expensive. We have recently initiated a new project entitled AgriSense to develop a novel low-cost and label-free, integrated bimodal electronic biosensor system for BRD. The biosensor system will consist of an integrated multichannel thin-film-transistor biosensor and an electrochemical impedance spectroscopy biosensor, interfaced with PDMS-based microfluidic sample delivery channels. By using both sensors in tandem, nonspecific binding biomolecules must have the same mass to charge ratio as the target analyte to elicit equivalent responses from both sensors. The system will target simultaneous multiplexed sensing of the four primary viral agents involved in the development of BRD: bovine herpesvirus-1 (BHV-1), bovine parainfluenza virus-3 (BPIV-3), bovine respiratory syncytial virus (BRSV), and bovine viral diarrhea (BVD). Optimized experimental conditions derived through model antigen-antibody studies will be applied to the detection of serological markers of BRD-related infections based on IgG interaction with a panel of sensor-immobilized viral proteins. This rapid, “cowside” multiplex sensor capability presents a major step forward in disease diagnosis, helping to ensure the integrity of the agri-food supply chain by reducing the risk of disease spread during animal movement and transport.

Molecular diagnostics of myeloproliferative neoplasms

Since the discovery of the JAK2 V617F mutation in the majority of the myeloproliferative neoplasms (MPN) of polycythemia vera, essential thrombocythemia and primary myelofibrosis ten years ago, further MPN-specific mutational events, notably in JAK2 exon 12, MPL exon 10 and CALR exon 9 have been identified. These discoveries have been rapidly incorporated into evolving molecular diagnostic algorithms. While many of these mutations appear to have prognostic implications, establishing MPN diagnosis is of immediate clinical importance with selection, implementation and the continual evaluation of the appropriate laboratory methodology to achieve this diagnosis similarly vital. The advantages and limitations of these approaches in identifying and quantitating the common MPN-associated mutations is considered herein with particular regard to their clinical utility. The evolution of molecular diagnostic applications and platforms has occurred in parallel with the discovery of MPN-associated mutations and it therefore appears likely that emerging technologies such as next-generation sequencing and digital PCR will in the future, play an increasing role in the molecular diagnosis of MPN. 

Handling Sequential Observations in Intelligent Surveillance

Demand for intelligent surveillance in public transport systems is growing due to the increased threats of terrorist attack, vandalism and litigation. The aim of intelligent surveillance is in-time reaction to information received from various monitoring devices, especially CCTV systems. However, video analytic algorithms can only provide static assertions, whilst in reality, many related events happen in sequence and hence should be modeled sequentially. Moreover, analytic algorithms are error-prone, hence how to correct the sequential analytic results based on new evidence (external information or later sensing discovery) becomes an interesting issue. In this paper, we introduce a high-level sequential observation modeling framework which can support revision and update on new evidence. This framework adapts the situation calculus to deal with uncertainty from analytic results. The output of the framework can serve as a foundation for event composition. We demonstrate the significance and usefulness of our framework with a case study of a bus surveillance project.

Gamma-ray diagnostics of Type Ia supernovae. Predictions of observables from three-dimensional modeling

Context. Although the question of progenitor systems and detailed explosion mechanisms still remains a matter of discussion, it is commonly believed that Type Ia supernovae (SNe Ia) are production sites of large amounts of radioactive nuclei. Even though the gamma-ray emission due to radioactive decays is responsible for powering the light curves of SNe Ia, gamma rays themselves are of particular interest as a diagnostic tool because they directly lead to deeper insight into the nucleosynthesis and the kinematics of these explosion events. Aims: We study the evolution of gamma-ray line and continuum emission of SNe Ia with the objective of analyzing the relevance of observations in this energy range. We seek to investigate the chances for the success of future MeV missions regarding their capabilities for constraining the intrinsic properties and the physical processes of SNe Ia. Methods: Focusing on two of the most broadly discussed SN Ia progenitor scenarios - a delayed detonation in a Chandrasekhar-mass white dwarf (WD) and a violent merger of two WDs - we used three-dimensional explosion models and performed radiative transfer simulations to obtain synthetic gamma-ray spectra. Both chosen models produce the same mass of 56Ni and have similar optical properties that are in reasonable agreement with the recently observed supernova SN 2011fe. We examine the gamma-ray spectra with respect to their distinct features and draw connections to certain characteristics of the explosion models. Applying diagnostics, such as line and hardness ratios, the detection prospects for future gamma-ray missions with higher sensitivities in the MeV energy range are discussed. Results: In contrast to the optical regime, the gamma-ray emission of our two chosen models proves to be quite different. The almost direct connection of the emission of gamma rays to fundamental physical processes occurring in SNe Ia permits additional constraints concerning several explosion model properties that are not easily accessible within other wavelength ranges. Proposed future MeV missions such as GRIPS will resolve all spectral details only for nearby SNe Ia, but hardness ratio and light curve measurements still allow for a distinction of the two different models at 10 Mpc and 16 Mpc for an exposure time of 106 s. The possibility of detecting the strongest line features up to the Virgo distance will offer the opportunity to build up a first sample of SN Ia detections in the gamma-ray energy range and underlines the importance of future space observatories for MeV gamma rays.

A potentiometric biosensor for rapid on-site disease diagnostics

Quantitative point-of-care (POC) devices are the next generation for serological disease diagnosis. Whilst pathogen serology is typically performed by centralized laboratories using Enzyme-Linked ImmunoSorbent Assay (ELISA), faster on-site diagnosis would infer improved disease management and treatment decisions. Using the model pathogen Bovine Herpes Virus-1 (BHV-1) this study employs an extended-gate field-effect transistor (FET) for direct potentiometric serological diagnosis. BHV-1 is a major viral pathogen of Bovine Respiratory Disease (BRD), the leading cause of economic loss ($2 billion annually in the US only) to the cattle and dairy industry. To demonstrate the sensor capabilities as a diagnostic tool, BHV-1 viral protein gE was expressed and immobilized on the sensor surface to serve as a capture antigen for a BHV-1-specific antibody (anti-gE), produced in cattle in response to viral infection. The gE-coated immunosensor was shown to be highly sensitive and selective to anti-gE present in commercially available anti-BHV-1 antiserum and in real serum samples from cattle with results being in excellent agreement with Surface Plasmon Resonance (SPR) and ELISA. The FET sensor is significantly faster than ELISA (<10 min), a crucial factor for successful disease intervention. This sensor technology is versatile, amenable to multiplexing, easily integrated to POC devices, and has the potential to impact a wide range of human and animal diseases.