An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing

In recent years, vehicular cloud computing (VCC) has emerged as a new technology which is being used in wide range of applications in the area of multimedia-based healthcare applications. In VCC, vehicles act as the intelligent machines which can be used to collect and transfer the healthcare data to the local, or global sites for storage, and computation purposes, as vehicles are having comparatively limited storage and computation power for handling the multimedia files. However, due to the dynamic changes in topology, and lack of centralized monitoring points, this information can be altered, or misused. These security breaches can result in disastrous consequences such as-loss of life or financial frauds. Therefore, to address these issues, a learning automata-assisted distributive intrusion detection system is designed based on clustering. Although there exist a number of applications where the proposed scheme can be applied but, we have taken multimedia-based healthcare application for illustration of the proposed scheme. In the proposed scheme, learning automata (LA) are assumed to be stationed on the vehicles which take clustering decisions intelligently and select one of the members of the group as a cluster-head. The cluster-heads then assist in efficient storage and dissemination of information through a cloud-based infrastructure. To secure the proposed scheme from malicious activities, standard cryptographic technique is used in which the auotmaton learns from the environment and takes adaptive decisions for identification of any malicious activity in the network. A reward and penalty is given by the stochastic environment where an automaton performs its actions so that it updates its action probability vector after getting the reinforcement signal from the environment. The proposed scheme was evaluated using extensive simulations on ns-2 with SUMO. The results obtained indicate that the proposed scheme yields an improvement of 10 % in detection rate of malicious nodes when compared with the existing schemes.

A Green Approach for Selfish Misbehavior Detection in 802.11-Based Wireless Networks

IEEE 802.11 is one of the most well-established and widely used standard for wireless LAN. Its Medium Access control (MAC) layer assumes that the devices adhere to the standard’s rules and timers to assure fair access and sharing of the medium. However, wireless cards driver flexibility and configurability make it possible for selfish misbehaving nodes to take advantages over the other well-behaving nodes. The existence of selfish nodes degrades the QoS for the other devices in the network and may increase their energy consumption. In this paper we propose a green solution for selfish misbehavior detection in IEEE 802.11-based wireless networks. The proposed scheme works in two phases: Global phase which detects whether the network contains selfish nodes or not, and Local phase which identifies which node or nodes within the network are selfish. Usually, the network must be frequently examined for selfish nodes during its operation since any node may act selfishly. Our solution is green in the sense that it saves the network resources as it avoids wasting the nodes energy by examining all the individual nodes of being selfish when it is not necessary. The proposed detection algorithm is evaluated using extensive OPNET simulations. The results show that the Global network metric clearly indicates the existence of a selfish node while the Local nodes metric successfully identified the selfish node(s). We also provide mathematical analysis for the selfish misbehaving and derived formulas for the successful channel access probability.

Oriented Tailoring of Plastic Antibodies for Prostate Specific Antigen and Application of the Imprinted Material as Ionophore in Potentiometric Detection

Prostate Specific Antigen (PSA) is the biomarker of choice for screening prostate cancer throughout the population, with PSA values above 10 ng/mL pointing out a high probability of associated cancer1. According to the most recent World Health Organization (WHO) data, prostate cancer is the commonest form of cancer in men in Europe2. Early detection of prostate cancer is thus very important and is currently made by screening PSA in men over 45 years old, combined with other alterations in serum and urine parameters. PSA is a glycoprotein with a molecular mass of approximately 32 kDa consisting of one polypeptide chain, which is produced by the secretory epithelium of human prostate. Currently, the standard methods available for PSA screening are immunoassays like Enzyme-Linked Immunoabsorbent Assay (ELISA). These methods are highly sensitive and specific for the detection of PSA, but they require expensive laboratory facilities and high qualify personal resources. Other highly sensitive and specific methods for the detection of PSA have also become available and are in its majority immunobiosensors1,3-5, relying on antibodies. Less expensive methods producing quicker responses are thus needed, which may be achieved by synthesizing artificial antibodies by means of molecular imprinting techniques. These should also be coupled to simple and low cost devices, such as those of the potentiometric kind, one approach that has been proven successful6. Potentiometric sensors offer the advantage of selectivity and portability for use in point-of-care and have been widely recognized as potential analytical tools in this field. The inherent method is simple, precise, accurate and inexpensive regarding reagent consumption and equipment involved. Thus, this work proposes a new plastic antibody for PSA, designed over the surface of graphene layers extracted from graphite. Charged monomers were used to enable an oriented tailoring of the PSA rebinding sites. Uncharged monomers were used as control. These materials were used as ionophores in conventional solid-contact graphite electrodes. The obtained results showed that the imprinted materials displayed a selective response to PSA. The electrodes with charged monomers showed a more stable and sensitive response, with an average slope of -44.2 mV/decade and a detection limit of 5.8X10-11 mol/L (2 ng/mL). The corresponding non-imprinted sensors showed smaller sensitivity, with average slopes of -24.8 mV/decade. The best sensors were successfully applied to the analysis of serum samples, with percentage recoveries of 106.5% and relatives errors of 6.5%.

The Interpretation of Verbal Probability Expressions Used in the IAS/IFRS: the auditors Rrgistered with the portuguese securities market commission

One of the main arguments in favour of the adoption and convergence with the international accounting standards published by the IASB (i.e. IAS/IFRS) is that these will allow comparability of financial reporting across countries. However, because these standards use verbal probability expressions (v.g. “probable”) when establishing the recognition and disclosure criteria for accounting elements, they require professional accountants to interpret and classify the probability of an outcome or event taking into account those terms and expressions and to best decide in terms of financial reporting. This paper reports part of a research we carried out on the interpretation of “in context” verbal probability expressions used in the IAS/IFRS by the auditors registered with the Portuguese Securities Market Commission, the Comissão do Mercado de Valores Mobiliários (CMVM). Our results provide support for the hypothesis that culture affects the CMVM registered auditors’ interpretation of verbal probability expressions through its influence on the accounting value (or attitude) of conservatism. Our results also suggest that there are significant differences in their interpretation of the term “probable”, which is consistent with literature in general. Since “probable” is the most frequent verbal probability expression used in the IAS/IFRS, this may have a negative impact on financial statements comparability.

Gestão de risco: caso da Sonae Indústria

Dissertação para a obtenção do Grau de Mestre em Auditoria Orientador: Mestre Carlos Manuel Antunes Mendes

Increase of the delivered energy probability in DES using a fuzzy probabilistic modeling

The paper proposes a methodology to increase the probability of delivering power to any load point by identifying new investments in distribution energy systems. The proposed methodology is based on statistical failure and repair data of distribution components and it uses a fuzzy-probabilistic modeling for the components outage parameters. The fuzzy membership functions of the outage parameters of each component are based on statistical records. A mixed integer nonlinear programming optimization model is developed in order to identify the adequate investments in distribution energy system components which allow increasing the probability of delivering power to any customer in the distribution system at the minimum possible cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 180 bus distribution network.

Celiac disease detection using a transglutaminase electrochemical immunosensor fabricated on nanohybrid screen-printed carbon electrodes

Celiac disease is a gluten-induced autoimmune enteropathy characterized by the presence of tissue tranglutaminase (tTG) autoantibodies. A disposable electrochemical immunosensor (EI) for the detection of IgA and IgG type anti-tTG autoantibodies in real patient’s samples is presented. Screen-printed carbon electrodes (SPCE) nanostructurized with carbon nanotubes and gold nanoparticles were used as the transducer surface. This transducer exhibits the excellent characteristics of carbon–metal nanoparticle hybrid conjugation and led to the amplification of the immunological interaction. The immunosensing strategy consisted of the immobilization of tTG on the nanostructured electrode surface followed by the electrochemical detection of the autoantibodies present in the samples using an alkaline phosphatase (AP) labelled anti-human IgA or IgG antibody. The analytical signal was based on the anodic redissolution of enzymatically generated silver by cyclic voltammetry. The results obtained were corroborated with a commercial ELISA kit indicating that the electrochemical immunosensor is a trustful analytical screening tool.

Molecular imprinted nanoelectrodes for ultra sensitive detection of ovarian cancer marker

The relentless discovery of cancer biomarkers demands improved methods for their detection. In this work, we developed protein imprinted polymer on three-dimensional gold nanoelectrode ensemble (GNEE) to detect epithelial ovarian cancer antigen-125 (CA 125), a protein biomarker associated with ovarian cancer. CA 125 is the standard tumor marker used to follow women during or after treatment for epithelial ovarian cancer. The template protein CA 125 was initially incorporated into the thin-film coating and, upon extraction of protein from the accessible surfaces on the thin film, imprints for CA 125 were formed. The fabrication and analysis of the CA 125 imprinted GNEE was done by using cyclic voltammetry (CV), differential pulse voltammetry (DPV) and electrochemical impedance spectroscopy (EIS) techniques. The surfaces of the very thin, protein imprinted sites on GNEE are utilized for immunospecific capture of CA 125 molecules, and the mass of bound on the electrode surface can be detected as a reduction in the faradic current from the redox marker. Under optimal conditions, the developed sensor showed good increments at the studied concentration range of 0.5–400 U mL−1. The lowest detection limit was found to be 0.5 U mL−1. Spiked human blood serum and unknown real serum samples were analyzed. The presence of non-specific proteins in the serum did not significantly affect the sensitivity of our assay. Molecular imprinting using synthetic polymers and nanomaterials provides an alternative approach to the trace detection of biomarker proteins.

Ultrasensitive detection of ovarian cancer marker using immunoliposomes and gold nanoelectrodes

Mucin-16 (MUC16) is the established ovarian cancer marker used to follow the disease during or after treatment for epithelial ovarian cancer. The emerging science of cancer markers also demands for the new sensitive detection methods. In this work, we have developed an electrochemical immunosensor for antigen MUC16 using gold nanoelectrode ensemble (GNEE) and ferrocene carboxylic acid encapsulated liposomes tethered with monoclonal anti-Mucin-16 antibodies ( MUC16). GNEEs were fabricated by electroless deposition of the gold within the pores of polycarbonate track-etched membranes. Afterwards, MUC16 were immobilized on preformed self-assembled monolayer of cysteamine on the GNEE via cross-linking with EDC-Sulfo-NHS. A sandwich immunoassay was performed on MUC16 functionalized GNEE with MUC16 and immunoliposomes. The differential pulse voltammetry was employed to quantify the faradic redox response of ferrocene carboxylic acid released from immunoliposomes. The dose–response curve for MUC16 concentration was found between the range of 0.001–300 U mL−1. The lowest detection limit was found to be 5 × 10−4 U mL−1 (S/N = 3). We evaluated the performance of this developed immunosensor with commercial ELISA assay by comparing results obtained from spiked serum samples and real blood serum samples from volunteers.

Electrochemical immunosensor for multiplexed detection of food-borne pathogens using nanocrystal bioconjugates and MWCNT screen-printed electrode

Bacterial food poisoning is an ever-present threat that can be prevented with proper care and handling of food products. A disposable electrochemical immunosensor for the simultaneous measurements of common food pathogenic bacteria namely Escherichia coli O157:H7 (E. coli), campylobacter and salmonella were developed. The immunosensor was fabricated by immobilizing the mixture of anti-E. coli, anticampylobacter and anti-salmonella antibodies with a ratio of 1:1:1 on the surface of the multiwall carbon nanotube-polyallylamine modified screen printed electrode (MWCNT-PAH/SPE). Bacteria suspension became attached to the immobilized antibodies when the immunosensor was incubated in liquid samples. The sandwich immunoassay was performed with three antibodies conjugated with specific nanocrystal ( -E. coli-CdS, -campylobacter-PbS and -salmonella-CuS) which has releasable metal ions for electrochemical measurements. The square wave anodic stripping voltammetry (SWASV) was employed to measure released metal ions from bound antibody nanocrystal conjugates. The calibration curves for three selected bacteria were found in the range of 1 × 103 – 5 × 105 cells mL−1 with the limit of detection (LOD) 400 cells mL−1 for salmonella, 400 cells mL−1 for campylobacter and 800 cells mL−1 for E. coli. The precision and sensitivity of this method show the feasibility of multiplexed determination of bacteria in milk samples.

Enzymatic determination of choline in milk using a FIA system with potentiometric detection

The development of a FIA system for the determination of total choline content in several types of milk is described. The samples were submitted to hydrochloric acid digestion before injection into the system and passed through an enzymatic reactor containing choline oxidase immobilised on glass beads. This enzymatic reaction releases hydrogen peroxide which then reacts with a solution of iodide. The decrease in the concentration of iodide ion is quantified using an iodide ion selective tubular electrode based on a homogeneous crystalline membrane. Validation of the results obtained with this system was performed by comparison with results from a method described in the literature and applied to the determination of total choline in milks. The relative deviation was always < 5%. The repeatability of the method developed was assessed by calculation of the relative standard deviation (RSD) for 12 consecutive injections of one sample. The RSD obtained was < 0.6%.

Sensors for the detection and quantification of bacterial contamination in water for human use

The deterioration of water quality by Cyanobacteria cause outbreaks and epidemics associated with harmful diseases in Humans and animals because of the toxins that they release. Microcystin-LR is one of the hepatotoxins most widely studied and the World Health Organization, recommend a maximum value of 1mgL 1 in drinking water. Highly specific recognition molecules, such as molecular imprinted polymers are developed to quantify microcystins in waters for human use and shown to be of great potential in the analysis of these kinds of samples. The obtained results were auspicious, the detection limit found, 1.5mgL 1, being of the same order of magnitude as the guideline limit recommended by the WHO. This technology is very promising because the sensors are stable and specific, and the technology is inexpensive and allows for rapid on-site monitoring.

A multiresidue method for the analysis of carbamate and urea pesticides from soils by microwave-assisted extraction and liquid chromatography with photodiode array detection

A multiresidue approach using microwave-assisted extraction and liquid chromatography with photodiode array detection was investigated for the determination of butylate, carbaryl, carbofuran, chlorpropham, ethiofencarb, linuron,metobromuron, and monolinuron in soils. The critical parameters of the developed methodology were studied. Method validation was performed by analyzing freshly and aged spiked soil samples. The recoveries and relative standard deviations reached using the optimized conditions were between 77.0 ± 0.46% and 120 ± 2.9% except for ethiofencarb (46.4 ± 4.4% to 105 ± 1.6%) and butylate (22.1 ± 7.6% to 49.2 ± 11%). Soil samples from five locations of Portugal were analysed.

Square-wave adsorptive-stripping voltammetric detection in the quality control of fluoxetine

Electroanalytical methods based on square-wave adsorptive-stripping voltammetry (SWAdSV) and flow-injection analysis with square-wave adsorptive-stripping voltammetric detection (FIA-SWAdSV) were developed for the determination of fluoxetine (FXT). The methods were based on the reduction of FXT at a mercury drop electrode at -1.2 V versus Ag/AgCl, in a phosphate buffer of pH 12.0, and on the possibility of accumulating the compound at the electrode surface. The SWAdSV method was successfully applied in the quantification of FXT in pharmaceutical products, human serum samples, and in drug dissolution studies. Because the presence of dissolved oxygen did not interfere significantly with the analysis, it was possible to quantify FXT in several pharmaceutical products using FIA-SWAdSV. This method enables analysis of up to 120 samples per hour at reduced costs.

Development of an FIA system with amperometric detection for determination of bentazone in estuarine waters

On the basis of its electrochemical behaviour a new flow-injection analysis (FIA) method with amperometric detection has been developed for quantification of the herbicide bentazone (BTZ) in estuarine waters. Standard solutions and samples (200 µL) were injected into a water carrier stream and both pH and ionic strength were automatically adjusted inside the manifold. Optimization of critical FIA conditions indicated that the best analytical results were obtained at an oxidation potential of 1.10 V, pH 4.5, and an overall flow-rate of 2.4 mL min–1. Analysis of real samples was performed by means of calibration curves over the concentration range 2.5x10–6 to 5.0x10–5 mol L–1, and results were compared with those obtained by use of an independent method (HPLC). The accuracy of the amperometric determinations was ascertained; errors relative to the comparison method were below 4% and sampling rates were approximately 100 samples h–1. The repeatability of the proposed method was calculated by assessing the relative standard deviation (%) of ten consecutive determinations of one sample; the value obtained was 2.1%.