Search results for: acoustic source detection

Authors: Shriya Shukla, Lachin Fernando

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Pneumonia, a leading cause of mortality in young children globally, presents significant diagnostic challenges, particularly in resource-limited settings. This study presents an approach to diagnosing pediatric pneumonia using Chest X-Ray (CXR) images, employing a lightweight MobileNetV2 model enhanced with synthetic data augmentation. Addressing the challenge of dataset scarcity and imbalance, the study used a Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to generate synthetic CXR images, improving the representation of normal cases in the pediatric dataset. This approach not only addresses the issues of data imbalance and scarcity prevalent in medical imaging but also provides a more accessible and reliable diagnostic tool for early pneumonia detection. The augmented data improved the model’s accuracy and generalization, achieving an overall accuracy of 95% in pneumonia detection. These findings highlight the efficacy of the MobileNetV2 model, offering a computationally efficient yet robust solution well-suited for resource-constrained environments such as mobile health applications. This study demonstrates the potential of synthetic data augmentation in enhancing medical image analysis for critical conditions like pediatric pneumonia.

Keywords: pneumonia, MobileNetV2, image classification, GAN, VAE, deep learning

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Authors: Badr M. Alshammari, A. Rabeh, A. K. Mohamed

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This paper presents the modeling and the control of a grid-connected photovoltaic system (PVS). Firstly, the MPPT control of the PVS and its associated DC/DC converter has been analyzed in order to extract the maximum of available power. Secondly, the control system of the grid side converter (GSC) which is a three-phase voltage source inverter (VSI) has been presented. A special attention has been paid to the control algorithms of the GSC converter during grid voltages imbalances. Especially, three different control objectives are to achieve; the mitigation of the grid imbalance adverse effects, at the point of common coupling (PCC), on the injected currents, the elimination of double frequency oscillations in active power flow, and the elimination of double frequency oscillations in reactive power flow. Simulation results of two control strategies have been performed via MATLAB software in order to demonstrate the particularities of each control strategy according to power quality standards.

Keywords: renewable energies, photovoltaic systems, dc link, voltage source inverter, space vector SVPWM, unbalanced voltage dips, symmetrical components

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Authors: Nor Zaidi Haron, Fauziyah Salehuddin, Norsuhaidah Arshad, Sani Irwan Salim

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Resistive random access memories (RRAMs) are one of the main candidates for future computer memories. However, due to their tiny size and immature device technology, the quality of the outgoing RRAM chips is seen as a serious issue. Defective RRAM cells might behave differently than existing semiconductor memories (Dynamic RAM, Static RAM, and Flash), meaning that they are difficult to be detected using existing test schemes. This paper presents an enhanced test scheme, referred to as Programmable Short Write Time (PSWT) that is able to improve the detection of faulty RRAM cells. It is developed by applying multiple weak write operations, each with different time durations. The test circuit embedded in the RRAM chip is made programmable in order to supply different weak write times during testing. The RRAM electrical model is described using Verilog-AMS language and is simulated using HSPICE simulation tools. Simulation results show that the proposed test scheme offers better open-resistive fault detection compared to existing test schemes.

Keywords: memory fault, memory test, design-for-testability, resistive random access memory

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Authors: Hossein Keshtkar, Ali Nasiri Toosi

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With the growing concerns about gasoline environmental pollution and also the need for a more widely available fuel source, natural gas is finding its way to the automotive engines. But before this could happen industrially, simulations of natural gas direct injection need to take place to maximize and optimize power output. KIVA is one of the most powerful tools when it comes to engine simulation. Widely accepted by both researchers and the industry, KIVA an open-source code, offers great in-depth simulation and analyzation. KIVA can compute complex phenomena’s which can occur inside the chamber before, whilst and after ignition. One downside to KIVA, is its in-capability of simulating gaseous injections, making it useful for only liquidized fuel. In this study, we developed a numerical code, to enable the simulation of gaseous injection within the KIVA code. By introducing our code as a subroutine, we modified the original KIVA program. To ensure the correct application of gaseous fuel injection using our modified KIVA code, we simulated two different cases and compared them with their experimental data. We concluded our modified version of KIVA’s simulation results came in very close to those measured experimentally.

Keywords: gaseous injections, KIVA, natural gas direct injection, numerical code, simulation

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Authors: Khalida S. Rijab, Mohammed E. Safi, Ayad A. Ibrahim

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When electrical electrodes are mounted on the skin surface of the muscle, a signal is detected when a skeletal muscle undergoes contraction; the signal is known as surface electromyographic signal (EMG). This signal has a noise-like interference pattern resulting from the temporal and spatial summation of action potentials (AP) of all active motor units (MU) near electrode detection. By appropriate processing (Decomposition), the surface EMG signal may be used to give an estimate of motor unit action potential. In this work, a denoising technique is applied to the MUAP signals extracted from the spatial filter (IB2). A set of signals from a non-invasive two-dimensional grid of 16 electrodes from different types of subjects, muscles, and sex are recorded. These signals will acquire noise during recording and detection. A digital fourth order band- pass Butterworth filter is used for denoising, with a tuned band-pass frequency of suitable choice of cutoff frequencies is investigated, with the aim of obtaining a suitable band pass frequency. Results show an improvement of (1-3 dB) in the signal to noise ratio (SNR) have been achieved, relative to the raw spatial filter output signals for all cases that were under investigation. Furthermore, the research’s goal included also estimation and reconstruction of the mean shape of the MUAP.

Keywords: EMG, Motor Unit, Digital Filter, Denoising

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Authors: Busayo Fashoto, Abdulhakeem Shaibu, Justice Agbadu, Samuel Aiyeoribe

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Data has gone from just rows and columns to being an infrastructure itself. The traditional medium of data infrastructure has been managed by individuals in different industries and saved on personal work tools; one of such is the laptop. This hinders data sharing and Sustainable Development Goal (SDG) 9 for infrastructure sustainability across all countries and regions. However, there has been a constant demand for data across different agencies and ministries by investors and decision-makers. The rapid development and adoption of open-source technologies that promote the collection and processing of data in new ways and in ever-increasing volumes are creating new data infrastructure in sectors such as lands and health, among others. This paper examines the process of developing data infrastructure and, by extension, a data portal to provide baseline data for sustainable development and decision making in Nigeria. This paper employs the FAIR principle (Findable, Accessible, Interoperable, and Reusable) of data management using open-source technology tools to develop data portals for public use. eHealth Africa, an organization that uses technology to drive public health interventions in Nigeria, developed a data portal which is a typical data infrastructure that serves as a repository for various datasets on administrative boundaries, points of interest, settlements, social infrastructure, amenities, and others. This portal makes it possible for users to have access to datasets of interest at any point in time at no cost. A skeletal infrastructure of this data portal encompasses the use of open-source technology such as Postgres database, GeoServer, GeoNetwork, and CKan. These tools made the infrastructure sustainable, thus promoting the achievement of SDG 9 (Industries, Innovation, and Infrastructure). As of 6th August 2021, a wider cross-section of 8192 users had been created, 2262 datasets had been downloaded, and 817 maps had been created from the platform. This paper shows the use of rapid development and adoption of technologies that facilitates data collection, processing, and publishing in new ways and in ever-increasing volumes. In addition, the paper is explicit on new data infrastructure in sectors such as health, social amenities, and agriculture. Furthermore, this paper reveals the importance of cross-sectional data infrastructures for planning and decision making, which in turn can form a central data repository for sustainable development across developing countries.

Keywords: data portal, data infrastructure, open source, sustainability

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Authors: García López

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The Government Departments of Health have the task of informing and educating citizens about public health issues. For this, they use channels like Twitter, key in the search for health information and the propagation of content. The tweets, important in the virality of the content, may contain emotions that influence the contagion and exchange of knowledge. The goal of this study is to perform an analysis of the emotional projection of health information shared on Twitter by official American accounts: the disease control account CDCgov, National Institutes of Health, NIH, the government agency HHSGov, and the professional organization PublicHealth. For this, we used Tone Analyzer, an International Business Machines Corporation (IBM) tool specialized in emotion detection in text, corresponding to the categorical model of emotion representation. For 15 days, all tweets from these accounts were analyzed with the emotional analysis tool in text. The results showed that their tweets contain an important emotional load, a determining factor in the success of their communications. This exposes that official accounts also use subjective language and contain emotions. The predominance of emotion joy over sadness and the strong presence of emotions in their tweets stimulate the virality of content, a key in the work of informing that government health departments have.

Keywords: emotions in tweets, emotion detection in the text, health information on Twitter, American health official accounts, emotions on Twitter, emotions and content

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Authors: Coline Bretz, Andrea Vaccaro, Dario Leumann

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The food, personal care, and cosmetic industries are seeing increased consumer demand for more sustainable and innovative ingredients. When developing new formulations incorporating such ingredients, stability is one of the first criteria that must be assessed, and it is thus of great importance to have a method that can detect instabilities early and quickly. Diffusing Wave Spectroscopy (DWS) is a light scattering technique that probes the motion,i.e., the mean square displacement (MSD), of colloids, such as nanoparticles in a suspension or droplets in emulsions. From the MSD, the rheological properties of the surrounding medium can be determined via the so-called microrheology approach. In the case of purely viscous media, it is also possible to obtain information about particle size. DWS can thus be used to monitor the size evolution of particles, droplets, or bubbles in aging dispersions, emulsions, or foams. In the context of early instability detection in emulsions, DWS offers considerable advantages, as the samples are measured in a contact-free manner, using only small quantities of samples loaded in a sealable cuvette. The sensitivity and rapidity of the technique are key to detecting and following the ageing of emulsions reliably. We present applications of DWS focused on the characterization of emulsions. In particular, we demonstrate the ability to record very subtle changes in the structural properties early on. We also discuss the various mechanisms at play in the destabilization of emulsions, such as coalescence or Ostwald ripening, and how to identify them through this technique.

Keywords: instrumentation, emulsions, stability, DWS

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Authors: Clara Tramuta, Lucia Decastelli, Elisa Barcucci, Sandra Fragassi, Samantha Lupi, Enrico Arletti, Melissa Bizzarri, Daniela Manila Bianchi

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Introdution: Food allergies occurs, in the Western World, 2% of adults and up to 8% of children. The protection of allergic consumers is guaranted, in Eurrope, by Regulation (EU) No 1169/2011 of the European Parliament which governs the consumer's right to information and identifies 14 food allergens to be mandatory indicated on the label. Among these, mustard is a popular spice added to enhance the flavour and taste of foods. It is frequently present as an ingredient in spice blends, marinades, salad dressings, sausages, and other products. Hypersensitivity to mustard is a public health problem since the ingestion of even low amounts can trigger severe allergic reactions. In order to protect the allergic consumer, high performance methods are required for the detection of allergenic ingredients. Food safety laboratories rely on validated methods that detect hidden allergens in food to ensure the safety and health of allergic consumers. Here we present the test results for the validation and accreditation of a Real time PCR assay (RT-PCR: SPECIALfinder MC Mustard, Generon), for the detection of mustard traces in food. Materials and Methods. The method was tested on five classes of food matrices: bakery and pastry products (chocolate cookies), meats (ragù), ready-to-eat (mixed salad), dairy products (yogurt), grains, and milling products (rice and barley flour). Blank samples were spiked starting with the mustard samples (Sinapis Alba), lyophilized and stored at -18 °C, at a concentration of 1000 ppm. Serial dilutions were then prepared to a final concentration of 0.5 ppm, using the DNA extracted by ION Force FAST (Generon) from the blank samples. The Real Time PCR reaction was performed by RT-PCR SPECIALfinder MC Mustard (Generon), using CFX96 System (BioRad). Results. Real Time PCR showed a limit of detection (LOD) of 0.5 ppm in grains and milling products, ready-to-eat, meats, bakery, pastry products, and dairy products (range Ct 25-34). To determine the exclusivity parameter of the method, the ragù matrix was contaminated with Prunus dulcis (almonds), peanut (Arachis hypogaea), Glycine max (soy), Apium graveolens (celery), Allium cepa (onion), Pisum sativum (peas), Daucus carota (carrots), and Theobroma cacao (cocoa) and no cross-reactions were observed. Discussion. In terms of sensitivity, the Real Time PCR confirmed, even in complex matrix, a LOD of 0.5 ppm in five classes of food matrices tested; these values are compatible with the current regulatory situation that does not consider, at international level, to establish a quantitative criterion for the allergen considered in this study. The Real Time PCR SPECIALfinder kit for the detection of mustard proved to be easy to use and particularly appreciated for the rapid response times considering that the amplification and detection phase has a duration of less than 50 minutes. Method accuracy was rated satisfactory for sensitivity (100%) and specificity (100%) and was fully validated and accreditated. It was found adequate for the needs of the laboratory as it met the purpose for which it was applied. This study was funded in part within a project of the Italian Ministry of Health (IZS PLV 02/19 RC).

Keywords: allergens, food, mustard, real time PCR

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Authors: Sandeep Mehmi, Harsh Verma, A. L. Sangal

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Ever since the idea of using computing services as commodity that can be delivered like other utilities e.g. electric and telephone has been floated, the scientific fraternity has diverted their research towards a new area called utility computing. New paradigms like cluster computing and grid computing came into existence while edging closer to utility computing. With the advent of internet the demand of anytime, anywhere access of the resources that could be provisioned dynamically as a service, gave rise to the next generation computing paradigm known as cloud computing. Today, cloud computing has become one of the most aggressively growing computer paradigm, resulting in growing rate of applications in area of IT outsourcing. Besides catering the computational and storage demands, cloud computing has economically benefitted almost all the fields, education, research, entertainment, medical, banking, military operations, weather forecasting, business and finance to name a few. Smart grid is another discipline that direly needs to be benefitted from the cloud computing advantages. Smart grid system is a new technology that has revolutionized the power sector by automating the transmission and distribution system and integration of smart devices. Cloud based smart grid can fulfill the storage requirement of unstructured and uncorrelated data generated by smart sensors as well as computational needs for self-healing, load balancing and demand response features. But, security issues such as confidentiality, integrity, availability, accountability and privacy need to be resolved for the development of smart grid cloud. In recent years, a number of intrusion prevention techniques have been proposed in the cloud, but hackers/intruders still manage to bypass the security of the cloud. Therefore, precise intrusion detection systems need to be developed in order to secure the critical information infrastructure like smart grid cloud. Considering the success of artificial neural networks in building robust intrusion detection, this research proposes an artificial neural network based model for detecting attacks in smart grid cloud.

Keywords: artificial neural networks, cloud computing, intrusion detection systems, security issues, smart grid

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Authors: Laila Montaser, Hala Gabr, Maha El-Bassuony, Gehan Tawfeek

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Orthotropic liver transplantation (OLT) is the final procedure of both end stage and metabolic liver diseases. Hepatocyte transplantation is an alternative for OLT, but the sources of hepatocytes are limited. Bone marrow mesenchymal stem cells (BM-MSCs) can differentiate into hepatocyte-like cells and are a potential alternative source for hepatocytes. The MSCs from bone marrow are a promising target population as they are capable of differentiating along multiple lineages and, at least in vitro, have significant expansion capability. MSCs from bone marrow may have the potential to differentiate in vitro and in vivo into hepatocytes. Our study examined whether mesenchymal stem cells (MSCs), which are stem cells originated from human bone marrow, are able to differentiate into functional hepatocyte-like cells in vitro. Our aim was to investigate the differentiation potential of BM-MSCs into hepatocyte-like cells. Adult stem cell therapy could solve the problem of degenerative disorders, including liver disease.

Keywords: bone marrow, differentiation, hepatocyte, stem cells

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Authors: Md. Abdul Aziz

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Sulfide ion is water soluble, highly corrosive, toxic and harmful to the human beings. As a result, knowing the exact concentration of sulfide in water is very important. However, the existing detection and quantification methods have several shortcomings, such as high cost, low sensitivity, and massive instrumentation. Consequently, the development of novel sulfide sensor is relevant. Nevertheless, electrochemical methods gained enormous popularity due to a vast improvement in the technique and instrumentation, portability, low cost, rapid analysis and simplicity of design. Successful field application of electrochemical devices still requires vast improvement, which depends on the physical, chemical and electrochemical aspects of the working electrode. The working electrode made of bulk gold (Au) and platinum (Pt) are quite common, being very robust and endowed with good electrocatalytic properties. High cost, and electrode poisoning, however, have so far hindered their practical application in many industries. To overcome these obstacles, we developed a sulfide sensor based on an indium tin oxide nanoparticle (ITONP)-modified ITO electrode. To prepare ITONP-modified ITO, various methods were tested. Drop-drying of ITONPs (aq.) on aminopropyltrimethoxysilane-functionalized ITO (APTMS/ITO) was found to be the best method on the basis of voltammetric analysis of the sulfide ion. ITONP-modified APTMS/ITO (ITONP/APTMS/ITO) yielded much better electrocatalytic properties toward sulfide electro-οxidation than did bare or APTMS/ITO electrodes. The ITONPs and ITONP-modified ITO were also characterized using transmission electron microscopy and field emission scanning electron microscopy, respectively. Optimization of the type of inert electrolyte and pH yielded an ITONP/APTMS/ITO detector whose amperometrically and chronocoulοmetrically determined limits of detection for sulfide in aqueous solution were 3.0 µM and 0.90 µM, respectively. ITONP/APTMS/ITO electrodes which displayed reproducible performances were highly stable and were not susceptible to interference by common contaminants. Thus, the developed electrode can be considered as a promising tool for sensing sulfide.

Keywords: amperometry, chronocoulometry, electrocatalytic properties, ITO-nanoparticle-modified ITO, sulfide sensor

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Authors: N. Drir, L. Barazane, M. Loudini

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It is well known that photovoltaic (PV) cells are an attractive source of energy. Abundant and ubiquitous, this source is one of the important renewable energy sources that have been increasing worldwide year by year. However, in the V-P characteristic curve of GPV, there is a maximum point called the maximum power point (MPP) which depends closely on the variation of atmospheric conditions and the rotation of the earth. In fact, such characteristics outputs are nonlinear and change with variations of temperature and irradiation, so we need a controller named maximum power point tracker MPPT to extract the maximum power at the terminals of photovoltaic generator. In this context, the authors propose here to study the modeling of a photovoltaic system and to find an appropriate method for optimizing the operation of the PV generator using two intelligent controllers respectively to track this point. The first one is based on artificial neural networks and the second on fuzzy logic. After the conception and the integration of each controller in the global process, the performances are examined and compared through a series of simulation. These two controller have prove by their results good tracking of the MPPT compare with the other method which are proposed up to now.

Keywords: maximum power point tracking, neural networks, photovoltaic, P&O

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Authors: Prashanth Pillai, Purnaprajna Mangsuli

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Oil exploration and production companies invest considerable time and efforts to extract essential well attributes (like well status, surface, and target coordinates, wellbore depths, event timelines, etc.) from unstructured data sources like technical reports, which are often non-standardized, multimodal, and highly domain-specific by nature. It is also important to consider the context when extracting attribute values from reports that contain information on multiple wells/wellbores. Moreover, semantically similar information may often be depicted in different data syntax representations across multiple pages and document sources. We propose a hierarchical multi-source fact extraction workflow based on a deep learning framework to extract essential well attributes at scale. An information retrieval module based on the transformer architecture was used to rank relevant pages in a document source utilizing the page image embeddings and semantic text embeddings. A question answering framework utilizingLayoutLM transformer was used to extract attribute-value pairs incorporating the text semantics and layout information from top relevant pages in a document. To better handle context while dealing with multi-well reports, we incorporate a dynamic query generation module to resolve ambiguities. The extracted attribute information from various pages and documents are standardized to a common representation using a parser module to facilitate information comparison and aggregation. Finally, we use a probabilistic approach to fuse information extracted from multiple sources into a coherent well record. The applicability of the proposed approach and related performance was studied on several real-life well technical reports.

Keywords: natural language processing, deep learning, transformers, information retrieval

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Authors: J. C. Colmenares, M. Paszkiewicz-Gawron, D. Lomot, S. R. Pradhan, A. Qayyum

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This work is a report of recent selected experiments of photocatalysis intensification using flow microphotoreactors (fabricated by an ultrasound-based technique) for photocatalytic selective oxidation of benzyl alcohol (BnOH) to benzaldehyde (PhCHO) (in the frame of the concept of lignin valorization), and the proof of concept of intensifying a flow selective photocatalytic oxidation process by acoustic cavitation. The synthesized photocatalysts were characterized by using different techniques such as UV-Vis diffuse reflectance spectroscopy, X-ray diffraction, nitrogen sorption, thermal gravimetric analysis, and transmission electron microscopy. More specifically, the work will be on: a Design and development of metal-containing TiO₂ coated microflow reactor for photocatalytic partial oxidation of benzyl alcohol: The current work introduces an efficient ultrasound-based metal (Fe, Cu, Co)-containing TiO₂ deposition on the inner walls of a perfluoroalkoxy alkanes (PFA) microtube under mild conditions. The experiments were carried out using commercial TiO₂ and sol-gel synthesized TiO₂. The rough surface formed during sonication is the site for the deposition of these nanoparticles in the inner walls of the microtube. The photocatalytic activities of these semiconductor coated fluoropolymer based microreactors were evaluated for the selective oxidation of BnOH to PhCHO in the liquid flow phase. The analysis of the results showed that various features/parameters are crucial, and by tuning them, it is feasible to improve the conversion of benzyl alcohol and benzaldehyde selectivity. Among all the metal-containing TiO₂ samples, the 0.5 at% Fe/TiO₂ (both, iron and titanium, as cheap, safe, and abundant metals) photocatalyst exhibited the highest BnOH conversion under visible light (515 nm) in a microflow system. This could be explained by the higher crystallite size, high porosity, and flake-like morphology. b. Designing/fabricating photocatalysts by a sonochemical approach and testing them in the appropriate flow sonophotoreactor towards sustainable selective oxidation of key organic model compounds of lignin: Ultrasonication (US)-assitedprecipitaion and US-assitedhydrosolvothermal methods were used for the synthesis of metal-oxide-based and metal-free-carbon-based photocatalysts, respectively. Additionally, we report selected experiments of intensification of a flow photocatalytic selective oxidation through the use of ultrasonic waves. The effort of our research is focused on the utilization of flow sonophotocatalysis for the selective transformation of lignin-based model molecules by nanostructured metal oxides (e.g., TiO₂), and metal-free carbocatalysts. A plethora of parameters that affects the acoustic cavitation phenomena, and as a result the potential of sonication were investigated (e.g. ultrasound frequency and power). Various important photocatalytic parameters such as the wavelength and intensity of the irradiated light, photocatalyst loading, type of solvent, mixture of solvents, and solution pH were also optimized.

Keywords: heterogeneous photo-catalysis, metal-free carbonaceous materials, selective redox flow sonophotocatalysis, titanium dioxide

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Authors: Moftah BinHasan

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Induction motors (IM) are vital components in industrial processes whose failure may yield to an unexpected interruption at the industrial plant, with highly incurred consequences in costs, product quality, and safety. Among different detection approaches proposed in the literature, that based on stator current monitoring termed as Motor Current Signature Analysis (MCSA) is the most preferred. MCSA is advantageous due to its non-invasive properties. The popularity of motor current signature analysis comes from being that the current consists of motor harmonics, around the supply frequency, which show some properties related to different situations of healthy and faulty conditions. One of the techniques used with machine line current resorts to spectrum analysis. Besides discussing the fundamentals of MCSA and its applications in the condition monitoring arena, this paper shows a summary of the most frequent faults and their consequence signatures on the stator current spectrum of an induction motor. In addition, this article presents different case studies of induction motor fault diagnosis. These faults were seeded in the machine which was run for more than an hour for each test before the results were recorded for the faulty situations. These results are then compared with those for the healthy cases that were recorded earlier.

Keywords: induction motor, condition monitoring, fault diagnosis, MCSA, rotor, stator, bearing, eccentricity

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Authors: Janella Salamania, Marcedon Fernandez, Matthew Villanueva Henry Ramos

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Titanium nitrite (TiN) a popular functional and decorative coating because of its golden yellow color, high hardness and superior wear resistance. It is also being studied as a diffusion barrier in integrated circuits due to its known chemical stability and low resistivity. While there have been numerous deposition methods done for TiN, most required the heating of substrates at high temperatures. In this work, TiN films are deposited on silicon (111) and (100) substrates without substrate heating using a patented magnetized sheet plasma source. Films were successfully deposited without substrate heating at various target bias, while maintaining a constant 25% N2 to Ar ratio, and deposition of time of 30 minutes. The resulting films exhibited a golden yellow color which is characteristic of TiN. X-ray diffraction patterns show the formation of TiN predominantly oriented in the (111) direction regardless of substrate used. EDX data also confirms the 1:1 stoichiometry of titanium an nitrogen. Ellipsometry measurements estimate the thickness to range from 28 nm to 33 nm. SEM images were also taken to observe the morphology of the film.

Keywords: coatings, nitrides, coatings, reactive magnetron sputtering, thin films

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Authors: Zhiwen Pan, Jesus Pacheco, Salim Hariri, Yiqiang Chen, Bozhi Liu

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The Internet of Things (IoT) will lead to the development of advanced Smart Home services that are pervasive, cost-effective, and can be accessed by home occupants from anywhere and at any time. However, advanced smart home applications will introduce grand security challenges due to the increase in the attack surface. Current approaches do not handle cybersecurity from a holistic point of view; hence, a systematic cybersecurity mechanism needs to be adopted when designing smart home applications. In this paper, we present a generic intrusion detection methodology to detect and mitigate the anomaly behaviors happened in Smart Home Systems (SHS). By utilizing our Smart Home Context Data Structure, the heterogeneous information and services acquired from SHS are mapped in context attributes which can describe the context of smart home operation precisely and accurately. Runtime models for describing usage patterns of home assets are developed based on characterization functions. A threat-aware action management methodology, used to efficiently mitigate anomaly behaviors, is proposed at the end. Our preliminary experimental results show that our methodology can be used to detect and mitigate known and unknown threats, as well as to protect SHS premises and services.

Keywords: Internet of Things, network security, context awareness, intrusion detection

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Authors: D. Nagalakshmi, S. Parashuramulu K. Sadasiva Rao, G. Aruna, L. Vikram

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The zinc (Zn) is the second most abundant trace element in mammals and birds, forming structural component of over 300 enzymes, playing an important role in anti-oxidant defense, immune response and reproduction. Organic trace minerals are more readily absorbed from the digestive tract and more biologically available compared with its inorganic salt. Thus, the present study was undertaken on 60 adult female Sprague Dawley rats (275±2.04 g) for experimental duration of 12 weeks to investigate the effect of dietary Zn supplementation from various organic sources on immunity, reproduction, oxidative defense mechanism and blood biochemical profile. The rats were randomly allotted to 30 replicates (2 per replicate) which were in turn randomly allotted to 5 dietary treatments varying in Zn source i.e., one inorganic source (Zn carbonate) and 4 organic sources (Zn-proteinate, Zn-propionate, Zn-amino acid complex and Zn-methionine) so as to supply NRC recommended Zn concentration (12 ppm Zn). Supplementation of organic Zn had no effect on various haematological and serum biochemical constituents compared to inorganic Zn fed rats. The TBARS and protein carbonyls concentration in liver indicative of oxidative stress was comparable between various organic and inorganic groups. The glutathione reductase activity in haemolysate (P<0.05) and reduced glutathione concentration in liver (P<0.01) was higher when fed organic Zn and RBC catalase activity was higher (P<0.01) on Zn methionine compared to other organic sources tested and the inorganic source. The humoral immune response assessed as antibody titres against sheep RBC was higher (P<0.05) when fed organic sources of zinc compared to inorganic source. The cell mediated immune response expressed as delayed type hypersensitivity reaction was higher (P<0.05) in rats fed Zn propionate with no effect of other organic Zn sources. The serum progesterone concentration was higher (P<0.05) in rats fed organic Zn sources compared to inorganic zinc. The data on ovarian folliculogenesis indicated that organic Zn supplementation increased (P<0.05) the number of graafian follicles and corpus luteum with no effect on primary, secondary and tertiary follicle number. The study indicated that rats fed organic sources of Zn had higher antioxidant enzyme activities, immune response and serum progesterone concentration with higher number of mature follicles. Though the effect of feeding various organic sources were comparable, rats fed zinc methionine had higher antioxidant activity and cell mediated immune response was higher in rats on Zn propionate.

Keywords: organic zinc, immune, rats, reproductive

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Authors: Morteza Taherinezhad, Ahmad Reza Rabbani, Morteza Asemani, Rudy Swennen

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Oil-oil correlation is one of the most important issues in geochemical studies that enables to classify oils genetically. Oil-oil correlation is generally estimated based on non-polar fractions of crude oil (e.g., saturate and aromatic compounds). Despite several advantages, the drawback of using these compounds is their susceptibility of being affected by secondary processes. The polar fraction of crude oil (e.g., asphaltenes) has similar characteristics to kerogen, and this structural similarity is preserved during migration, thermal maturation, biodegradation, and water washing. Therefore, these structural characteristics can be considered as a useful correlation parameter, and it can be concluded that asphaltenes from different reservoirs with the same genetic signatures have a similar origin. Hence in this contribution, an integrated study by using both non-polar and polar fractions of oil was performed to use the merits of both fractions. Therefore, five oil samples from oil fields in the Persian Gulf were studied. Structural characteristics of extracted asphaltenes were investigated by Fourier transform infrared (FTIR) spectroscopy. Graphs based on aliphatic and aromatic compounds (predominant compounds in asphaltenes structure) and sulphoxide and carbonyl functional groups (which are representatives of sulphur and oxygen abundance in asphaltenes) were used for comparison of asphaltenes structures in different samples. Non-polar fractions were analyzed by GC-MS. The study of asphaltenes showed the studied oil samples comprise two oil families with distinct genetic characteristics. The first oil family consists of Salman and Reshadat oil samples, and the second oil family consists of Resalat, Siri E, and Siri D oil samples. To validate our results, biomarker parameters were employed, and this approach completely confirmed previous results. Based on biomarker analyses, both oil families have a marine source rock, whereby marl and carbonate source rocks are the source rock for the first and the second oil family, respectively.

Keywords: biomarker, non-polar fraction, oil-oil correlation, petroleum geochemistry, polar fraction

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Authors: Shakir H. Mohammed Al-Alwany, Saad Hasan M. Ali, Ibrahim Mohammed S. Shnawa

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The study was aimed to define the percentage of detection of high-oncogenic risk types of HPV and their genotyping in archival tissue specimens that ranged from apparently healthy tissue to invasive breast cancer by using one of the recent versions of In Situ Hybridization(ISH) 0.2. To find out rational significance of such genotypes as well as over expressed products of mutants P53 and RB genes on the severity of underlying breast cancers. The DNA of HPV was detected in 46.5 % of tissues from breast cancers while HPV DNA in the tissues from benign breast tumours was detected in 12.5%. No HPV positive–ISH reaction was detected in healthy breast tissues of the control group. HPV DNA of genotypes (16, 18, 31 and 33) was detected in malignant group in frequency of 25.6%, 27.1%, 30.2% and 12.4%, respectively. Over expression of p53 was detected by IHC in 51.2% breast cancer cases and in 50% benign breast tumour group, while none of control group showed P53- over expression. Retinoblastoma protein was detected by IHC test in 49.7% of malignant breast tumours, 54.2% of benign breast tumours but no signal was reported in the tissues of control group. The significance prevalence of expression of mutated p53 & Rb genes as well as detection of high-oncogenic HPV genotypes in patients with breast cancer supports the hypothesis of an etiologic role for the virus in breast cancer development.

Keywords: human papilloma virus, P53, RB, breast cancer

Procedia PDF Downloads 464

Authors: Illyas Md Isa, Maryam Musfirah Che Sobry, Mohamad Syahrizal Ahmad, Nurashikin Abd Azis

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A single-walled carbon nanotube (SWCNT) that has been modified with magnesium layered hydroxide 3-(4-methoxyphenyl)propionic acid nanocomposite was proposed for the determination of uric acid and bisphenol A by square wave voltammetry. The results obtained denote that MLH-MPP nanocomposites enhance the sensitivity of the voltammetry detection responses. The best performance is shown by the modified carbon nanotube paste electrode (CNTPE) with the composition of single-walled carbon nanotube: magnesium layered hydroxide 3-(4-methoxyphenyl)propionic acid nanocomposite at 100:15 (% w/w). The linear range where the sensor works well is within the concentration 1.0 10-7 – 1.0 10-4 and 3.0 10-7 – 1.0 10-4 for uric acid and bisphenol A respectively with the limit of detection of 1.0 10-7 M for both organics. The interferences of uric acid and bisphenol A with other organic were studied and most of them did not interfere. The results shown for each experimental parameter on the proposed CNTPE showed that it has high sensitivity, good selectivity, repeatability and reproducibility. Therefore, the modified CNTPE can be used for the determination of uric acid and bisphenol A in real samples such as blood, plastic bottles and foods.

Keywords: bisphenol A, magnesium layered hydroxide 3-(4-methoxyphenyl)propionic acid nanocomposite, Nanocomposite, uric acid

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Authors: Yanjie Zhu, André Jesus, Irwanda Laory

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Structural Health Monitoring (SHM), involving data acquisition, data interpretation and decision-making system aim to continuously monitor the structural performance of civil infrastructures under various in-service circumstances. The main value and purpose of SHM is identifying damages through data interpretation system. Research on SHM has been expanded in the last decades and a large volume of data is recorded every day owing to the dramatic development in sensor techniques and certain progress in signal processing techniques. However, efficient and reliable data interpretation for damage detection under environmental variations is still a big challenge. Structural damages might be masked because variations in measured data can be the result of environmental variations. This research reports a novel method based on single-channel Blind Signal Separation (BSS), which extracts environmental effects from measured data directly without any prior knowledge of the structure loading and environmental conditions. Despite the successful application in audio processing and bio-medical research fields, BSS has never been used to detect damage under varying environmental conditions. This proposed method optimizes and combines Ensemble Empirical Mode Decomposition (EEMD), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) together to separate structural responses due to different loading conditions respectively from a single channel input signal. The ICA is applying on dimension-reduced output of EEMD. Numerical simulation of a truss bridge, inspired from New Joban Line Arakawa Railway Bridge, is used to validate this method. All results demonstrate that the single-channel BSS-based method can recover temperature effects from mixed structural response recorded by a single sensor with a convincing accuracy. This will be the foundation of further research on direct damage detection under varying environment.

Keywords: damage detection, ensemble empirical mode decomposition (EEMD), environmental variations, independent component analysis (ICA), principal component analysis (PCA), structural health monitoring (SHM)

Procedia PDF Downloads 290

Authors: Ilana Margalith, Adaya Meirowitz, Sigalit Cohavi

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Violence against health professionals by patients and their families have recently become a disturbing phenomenon worldwide, exacting psychological as well as economic tolls. Health workplaces in Israel (e.g. hospitals and H.M.O clinics) provide workshops for their employees, supplying them with coping strategies. However, these workshops do not focus on nursing students, who are also subjected to this violence. Their learning environment is no longer as protective as it used to be. Furthermore, coping with violence was not part of the curriculum for Israeli nursing students. Thus, based on human aggression theories which depict the pivotal role of the professional's correct response in preventing the onset of an aggressive response or the escalation of violence, a workshop was developed for undergraduate nursing students at the Clalit Nursing Academy, Rabin Campus (Dina), Israel. The workshop aimed at reducing students' anxiety vis a vis the aggressive patient or family in addition to strengthening their ability to cope with such situations. The students practiced interpersonal skills, especially relevant to early detection of potential violence, as well as ‘a correct response’ reaction to the violence, thus developing the necessary steps to be implemented when encountering violence in the workplace. In order to assess the efficiency of the workshop, the participants filled out a questionnaire comprising knowledge and self-efficacy scales. Moreover, the replies of the 23 participants in this workshop were compared with those of 24 students who attended a standard course on interpersonal communication. Students' self-efficacy and knowledge were measured in both groups before and after the course. A statistically significant interaction was found between group (workshop/standard course) and time (before/after) as to the influence on students' self-efficacy (p=0.004) and knowledge (p=0.007). Nursing students, who participated in this ‘coping with workplace violence’ workshop, gained knowledge, confidence and a sense of self-efficacy with regard to workplace violence. Early detection of signs of imminent violence amongst patients or families and the prevention of its escalation, as well as the ability to manage the threatening situation when occurring, are acquired skills. Encouraging nursing students to learn and practice these skills may enhance their ability to cope with these unfortunate occurrences.

Keywords: early detection of violence, nursing students, patient aggression, self-efficacy, workplace violence

Procedia PDF Downloads 124

Authors: K. Thiagarajan, K. Saranya, A. Veeraiah, B. Sudha

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This paper presents a novel algorithm for secure, reliable and flexible transmission of big data in two hop wireless networks using cooperative jamming scheme. Two hop wireless networks consist of source, relay and destination nodes. Big data has to transmit from source to relay and from relay to destination by deploying security in physical layer. Cooperative jamming scheme determines transmission of big data in more secure manner by protecting it from eavesdroppers and malicious nodes of unknown location. The novel algorithm that ensures secure and energy balance transmission of big data, includes selection of data transmitting region, segmenting the selected region, determining probability ratio for each node (capture node, non-capture and eavesdropper node) in every segment, evaluating the probability using binary based evaluation. If it is secure transmission resume with the two- hop transmission of big data, otherwise prevent the attackers by cooperative jamming scheme and transmit the data in two-hop transmission.

Keywords: big data, two-hop transmission, physical layer wireless security, cooperative jamming, energy balance

Procedia PDF Downloads 468

Authors: Chang-Fa Liu, Yi-Ting Wang, Yuan Liu, Hai-Feng Wei, Lei Fang, Jin Li

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Heavy metals in sediments can cause adverse ecological effects while it exceeds a given criteria. The present study investigated sediment environmental quality, pollutant enrichment, ecological risk, and source identification for copper, cadmium, lead, zinc, mercury, and arsenic in the sediments collected from tidal flat marsh of Shuangtai estuary, China. The arithmetic mean integrated pollution index, geometric mean integrated pollution index, fuzzy integrated pollution index, and principal component score were used to characterize sediment environmental quality; fuzzy similarity and geo-accumulation Index were used to evaluate pollutant enrichment; correlation matrix, principal component analysis, and cluster analysis were used to identify source of pollution; environmental risk index and potential ecological risk index were used to assess ecological risk. The environmental qualities of sediment are classified to very low degree of contamination or low contamination. The similar order to element background of soil in the Liaohe plain is region of Sanjiaozhou, Honghaitan, Sandaogou, Xiaohe by pollutant enrichment analysis. The source identification indicates that correlations are significantly among metals except between copper and cadmium. Cadmium, lead, zinc, mercury, and arsenic will be clustered in the same clustering as the first principal component. Copper will be clustered as second principal component. The environmental risk assessment level will be scaled to no risk in the studied area. The order of potential ecological risk is As > Cd > Hg > Cu > Pb > Zn.

Keywords: ecological risk assessment, heavy metals, sediment, marsh, Shuangtai estuary

Procedia PDF Downloads 330

Authors: Surajit Chakrabarty, Piyush Chauhan, Subhasis Panda, Sujoy Bhattacharya

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A large proportion of roads in India are not well maintained as per the laid down public safety guidelines leading to loss of direction control and fatal accidents. We propose a technique to detect speed breakers and potholes using mobile sensor data captured from multiple vehicles and provide a profile of the road. This would, in turn, help in monitoring roads and revolutionize digital maps. Incorporating randomness in the model formulation for detection of speed breakers and potholes is crucial due to substantial heterogeneity observed in data obtained using a mobile application from multiple vehicles driven by different drivers. This is accomplished with Hidden Markov Models, whose hidden state sequence is found for each time step given the observables sequence, and are then fed as input to LSTM network with peephole connections. A precision score of 0.96 and 0.63 is obtained for classifying bumps and potholes, respectively, a significant improvement from the machine learning based models. Further visualization of bumps/potholes is done by converting time series to images using Markov Transition Fields where a significant demarcation among bump/potholes is observed.

Keywords: deep learning, hidden Markov model, pothole, speed breaker

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Authors: Antonio Horta, Renato Marinho, Raimir Holanda

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Considered as an evolution of the Blockchain, the Ethereum platform, besides allowing transactions of its cryptocurrency named Ether, it allows the programming of decentralised applications (DApps) and smart contracts. However, this functionality into blockchains has raised other types of threats, and the exploitation of smart contracts vulnerabilities has taken companies to experience big losses. This research intends to figure out the number of contracts that are under risk of being drained. Through a deep investigation, more than two hundred thousand smart contracts currently available in the Ethereum platform were scanned and estimated how much money is at risk. The experiment was based in a query run on Google Big Query in July 2022 and returned 50,707,133 contracts published on the Ethereum platform. After applying the filtering criteria, the experimentgot 430,584 smart contracts to download and analyse. The filtering criteria consisted of filtering out: ERC20 and ERC721 contracts, contracts without transactions, and contracts without balance. From this amount of 430,584 smart contracts selected, only 268,103 had source codes published on Etherscan, however, we discovered, using a hashing process, that there were contracts duplication. Removing the duplicated contracts, the process ended up with 20,417 source codes, which were analysed using the open source SAST tool smartbugswith oyente and securify algorithms. In the end, there was nearly $100,000 at risk of being drained from the potentially vulnerable smart contracts. It is important to note that the tools used in this study may generate false positives, which may interfere with the number of vulnerable contracts. To address this point, our next step in this research is to develop an application to test the contract in a parallel environment to verify the vulnerability. Finally, this study aims to alert users and companies about the risk on not properly creating and analysing their smart contracts before publishing them into the platform. As any other application, smart contracts are at risk of having vulnerabilities which, in this case, may result in direct financial losses.

Keywords: blockchain, reentrancy, static application security testing, smart contracts

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Authors: Nusrat Praween, Krishna Thej Pammi Guru, Palash Kumar Basu

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Designing non-invasive biosensors for cancer diagnosis is essential for developing an affordable and specific tool to measure cancer-related exosome biomarkers. Exosomes, released by healthy as well as cancer cells, contain valuable information about the biomarkers of various diseases, including cancer. Despite the availability of various isolation techniques, ultracentrifugation is the standard technique that is being employed. Post isolation, exosomes are traditionally exposed to detergents for extracting their proteins, which can often lead to protein degradation. Further to this, it is very essential to develop a sensing platform for the quantification of clinically relevant proteins in a wider range to ensure practicality. In this study, exosomes were immobilized on the Au Screen Printed Electrode (SPE) using EDC/NHS chemistry to facilitate binding. After immobilizing the exosomes on the screen-printed electrode (SPE), we investigated the impact of the electric field by applying various voltages to induce exosome lysis and release their contents. The lysed solution was used for sensing TSG101, a crucial biomarker associated with various cancers, using both faradaic and non-faradaic electrochemical impedance spectroscopy (EIS) methods. The results of non-faradaic and faradaic EIS were comparable and showed good consistency, indicating that non-faradaic sensing can be a reliable alternative. Hence, the non-faradaic sensing technique was used for label-free quantification of the TSG101 biomarker. The results were validated using ELISA. Our electrochemical immunosensor demonstrated a consistent response of TSG101 from 125 pg/mL to 8000 pg/mL, with a detection limit of 0.125 pg/mL at room temperature. Additionally, since non-faradic sensing is label-free, the ease of usage and cost of the final sensor developed can be reduced. The proposed immunosensor is capable of detecting the TSG101 protein at low levels in healthy serum with good sensitivity and specificity, making it a promising platform for biomarker detection.

Keywords: biosensor, exosomes isolation on SPE, electric field lysis of exosome, EIS sensing of TSG101

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Authors: Htay Htay Yi, Zin May Aye

Abstract:

Network security is role of the ICT environment because malicious users are continually growing that realm of education, business, and then related with ICT. The network security contravention is typically described and examined centrally based on a security event management system. The firewalls, Intrusion Detection System (IDS), and Intrusion Prevention System are becoming essential to monitor or prevent of potential violations, incidents attack, and imminent threats. In this system, the firewall rules are set only for where the system policies are needed. Dataset deployed in this system are derived from the testbed environment. The traffic as in DoS and PortScan traffics are applied in the testbed with firewall and IDS implementation. The network traffics are classified as normal or attacks in the existing testbed environment based on six machine learning classification methods applied in the system. It is required to be tested to get datasets and applied for DoS and PortScan. The dataset is based on CICIDS2017 and some features have been added. This system tested 26 features from the applied dataset. The system is to reduce false positive rates and to improve accuracy in the implemented testbed design. The system also proves good performance by selecting important features and comparing existing a dataset by machine learning classifiers.

Keywords: false negative rate, intrusion detection system, machine learning methods, performance

Procedia PDF Downloads 105