Search results for: abnormal activity detection

Authors: Enrico Dorigatti

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The growing pile of hazardous e-waste produced especially by those developed and wealthy countries gets relentlessly bigger, composed of the EEDs (Electric and Electronic Device) that are often thrown away although still well functioning, mainly due to (programmed) obsolescence. As a consequence, e-waste has taken, over the last years, the shape of a frightful, uncontrollable, and unstoppable phenomenon, mainly fuelled by market policies aiming to maximize sales—and thus profits—at any cost. Against it, governments and organizations put some efforts in developing ambitious frameworks and policies aiming to regulate, in some cases, the whole lifecycle of EEDs—from the design to the recycling. Incidentally, however, such regulations sometimes make the disposal of the devices economically unprofitable, which often translates into growing illegal e-waste trafficking—an activity usually undertaken by criminal organizations. It seems that nothing, at least in the near future, can stop the phenomenon of e-waste production and accumulation. But while, from a practical standpoint, a solution seems hard to find, much can be done regarding people's education, which translates into informing and promoting good practices such as reusing and repurposing. This research argues that circuit bending—an activity rooted in neo-materialist philosophy and post-digital aesthetic, and based on repurposing EEDs into novel music instruments and sound generators—could have a great potential in this. In particular, it asserts that circuit bending could expose ecological, environmental, and social criticalities related to the current market policies and economic model. Not only thanks to its practical side (e.g., sourcing and repurposing devices) but also to the artistic one (e.g., employing bent instruments for ecological-aware installations, performances). Currently, relevant literature and debate lack interest and information about the ecological aspects and implications of the practical and artistic sides of circuit bending. This research, therefore, although still at an early stage, aims to fill in this gap by investigating, on the one side, the ecologic potential of circuit bending and, on the other side, its capacity of sensitizing people, through artistic practice, about e-waste-related issues. The methodology will articulate in three main steps. Firstly, field research will be undertaken—with the purpose of understanding where and how to source, in an ecologic and sustainable way, (discarded) EEDs for circuit bending. Secondly, artistic installations and performances will be organized—to sensitize the audience about environmental concerns through sound art and music derived from bent instruments. Data, such as audiences' feedback, will be collected at this stage. The last step will consist in realising workshops to spread an ecologically-aware circuit bending practice. Additionally, all the data and findings collected will be made available and disseminated as resources.

Keywords: circuit bending, ecology, sound art, sustainability

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Authors: Marc Sader, Michiel Stock, Bernard De Baets

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Considering a large amount of adsorption data of adsorbate gases on adsorbent materials in literature, it is interesting to predict such adsorption data without experimentation. A quantitative structure-activity relationship (QSAR) is developed to correlate molecular characteristics of gases and existing knowledge of materials with their respective adsorption properties. The application of Random Forest, a machine learning method, on a set of adsorption isotherms at a wide range of partial pressures and concentrations is studied. The predicted adsorption isotherms are fitted to several adsorption equations to estimate the adsorption properties. To impute the adsorption properties of desired gases on desired materials, leave-one-out cross-validation is employed. Extensive experimental results for a range of settings are reported.

Keywords: adsorption, predictive modeling, QSAR, random forest

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Authors: Amira Shakila Razali, Faridah Lisa Supian, Muhammad Mat Salleh, Suriani Abu Bakar

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Water contamination by toxic compound is one of the serious environmental problems today. These toxic compounds mostly originated from industrial effluents, agriculture, natural sources and human waste. These study are focused on modification of multiwalled carbon nanotube (MWCNTs) with nanoparticle of calixarene and explore the possibility of using this nanocomposites for the remediation of cadmium in water. The nanocomposites were prepared by dissolving calixarene in chloroform solution as solvent, followed by additional multiwalled carbon nanotube (MWCNTs) then sonication process for 3 hour and fabricated the nanocomposites on substrate by spin coating method. Finally, the nanocomposites were tested on cadmium ion (10 mg/ml). The morphology of nanocomposites was investigated by FESEM showing the formation of calixarene on the outer walls of carbon nanotube and cadmium ion also clearly seen from the micrograph. This formation was supported by using energy dispersive x-ray (EDX). The presence of cadmium ions in the films, leads to some changes in the surface potential and Fourier Transform Infrared spectroscopy (FTIR).This nanocomposites have potential for development of sensor for pollutant monitoring and nanoelectronics devices applications

Keywords: calixarene, multiwalled carbon nanotubes, cadmium, surface potential

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Authors: J. K. Adedeji, M. O. Oyekanmi

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This paper has critically examined the use of Machine Learning procedures in curbing unauthorized access into valuable areas of an organization. The use of passwords, pin codes, user’s identification in recent times has been partially successful in curbing crimes involving identities, hence the need for the design of a system which incorporates biometric characteristics such as DNA and pattern recognition of variations in facial expressions. The facial model used is the OpenCV library which is based on the use of certain physiological features, the Raspberry Pi 3 module is used to compile the OpenCV library, which extracts and stores the detected faces into the datasets directory through the use of camera. The model is trained with 50 epoch run in the database and recognized by the Local Binary Pattern Histogram (LBPH) recognizer contained in the OpenCV. The training algorithm used by the neural network is back propagation coded using python algorithmic language with 200 epoch runs to identify specific resemblance in the exclusive OR (XOR) output neurons. The research however confirmed that physiological parameters are better effective measures to curb crimes relating to identities.

Keywords: biometric characters, facial recognition, neural network, OpenCV

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Authors: Görkem Cemali̇, Avram Aruh, Gamze Torun Köse, Erde Can ŞAfak

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In order to heal bone disorders, the conventional methods which involve the use of autologous and allogenous bone grafts or permanent implants have certain disadvantages such as limited supply, disease transmission, or adverse immune response. A biodegradable material that acts as structural support to the damaged bone area and serves as a scaffold that enhances bone regeneration and guides bone formation is one desirable solution. Poly(propylene fumarate) (PPF) which is an unsaturated polyester that can be copolymerized with appropriate vinyl monomers to give biodegradable network structures, is a promising candidate polymer to prepare bone tissue engineering scaffolds. In this study, hydroxyl-terminated PPF was synthesized and thermally cured with vinyl phosphonic acid (VPA) and diethyl vinyl phosphonate (VPES) in the presence of radical initiator benzoyl peroxide (BP), with changing co-monomer weight ratios (10-40wt%). In addition, the synthesized PPF was cured with VPES comonomer at body temperature (37oC) in the presence of BP initiator, N, N-Dimethyl para-toluidine catalyst and varying amounts of Beta-tricalcium phosphate (0-20 wt% ß-TCP) as filler via radical polymerization to prepare composite materials that can be used in injectable forms. Thermomechanical properties, compressive properties, hydrophilicity and biodegradability of the PPF/VPA and PPF/VPES copolymers were determined and analyzed with respect to the copolymer composition. Biocompatibility of the resulting polymers and their composites was determined by the MTS assay and osteoblast activity was explored with von kossa, alkaline phosphatase and osteocalcin activity analysis and the effects of VPA and VPES comonomer composition on these properties were investigated. Thermally cured PPF/VPA and PPF/VPES copolymers with different compositions exhibited compressive modulus and strength values in the wide range of 10–836 MPa and 14–119 MPa, respectively. MTS assay studies showed that the majority of the tested compositions were biocompatible and the overall results indicated that PPF/VPA and PPF/VPES network polymers show significant potential for applications as bone tissue engineering scaffolds where varying PPF and co-monomer ratio provides adjustable and controllable properties of the end product. The body temperature cured PPF/VPES/ß-TCP composites exhibited significantly lower compressive modulus and strength values than the thermal cured PPF/VPES copolymers and were therefore found to be useful as scaffolds for cartilage tissue engineering applications.

Keywords: biodegradable, bone tissue, copolymer, poly(propylene fumarate), scaffold

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Authors: Royan Dawud Aldian, Endah Purwanti, Soegianto Soelistiono

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In this research we have been developed an automatic investigation to classify normal children voice or autistic by using modern computation technology that is computation based on artificial neural network. The superiority of this computation technology is its capability on processing and saving data. In this research, digital voice features are gotten from the coefficient of linear-predictive coding with auto-correlation method and have been transformed in frequency domain using fast fourier transform, which used as input of artificial neural network in back-propagation method so that will make the difference between normal children and autistic automatically. The result of back-propagation method shows that successful classification capability for normal children voice experiment data is 100% whereas, for autistic children voice experiment data is 100%. The success rate using back-propagation classification system for the entire test data is 100%.

Keywords: autism, artificial neural network, backpropagation, linier predictive coding, fast fourier transform

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Authors: Abraham Motlhabane

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In most practical-related science lessons, the focus is on completing the experimental procedure as directed by the teacher. However, the scientific discourse among learners themselves and teacher–learner discourse about scientific processes, scientific inquiry and the nature of science should play an important role in the teaching and learning of science. This means the incorporation of inquiry-based activities aimed at sparking debates about scientific concepts. This article analyses a science lesson presented by a teacher to his colleagues acting as learners. Six lessons were presented and transcribed. One of the lessons has been used for this study as the basis for the events as they unfolded during the lesson. Data was obtained through direct observations and the use of a predetermined observation schedule. Field notes were compiled during teacher preparations and the presentation of the lessons.

Keywords: discourse, inquiry, practical work, science, scientific

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Authors: Hamed Alqahtani, Manolya Kavakli-Thorne

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The large pose discrepancy is one of the critical challenges in face recognition during video surveillance. Due to the entanglement of pose attributes with identity information, the conventional approaches for pose-independent representation lack in providing quality results in recognizing largely posed faces. In this paper, we propose a practical approach to disentangle the pose attribute from the identity information followed by synthesis of a face using a classifier network in latent space. The proposed approach employs a modified generative adversarial network framework consisting of an encoder-decoder structure embedded with a classifier in manifold space for carrying out factorization on the latent encoding. It can be further generalized to other face and non-face attributes for real-life video frames containing faces with significant attribute variations. Experimental results and comparison with state of the art in the field prove that the learned representation of the proposed approach synthesizes more compelling perceptual images through a combination of adversarial and classification losses.

Keywords: disentanglement, face detection, generative adversarial networks, video surveillance

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Authors: Juhi Kaushik, Abhishek Agarwal, Manoj Sarda, Vatsal Sanjay, Arup Kumar Das

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Fire accidents in trains bring huge disaster to human life and property. Evacuation becomes a major challenge in such incidents owing to confined spaces, large passenger density and trains moving at high speeds. The pantry car in Indian Railways trains carry inflammable materials like cooking fuel and LPG and electrical fittings. The pantry car is therefore highly susceptible to fire accidents. Numerical simulations have been done in a pantry car of Indian locomotive train using computational fluid dynamics based software. Different scenarios of a fire outbreak have been explored by varying Heat Release Rate per Unit Area (HRRPUA) of the fire source, introduction of exhaust in the cooking area, and taking a case of an air conditioned pantry car. Temporal statures of flame and soot have been obtained for each scenario and differences have been studied and reported. Inputs from this study can be used to assess casualties in fire accidents in locomotive trains and development of smoke control/detection systems in Indian trains.

Keywords: fire propagation, flame contour, pantry fire, soot flow

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Authors: Uduak Umoh, Imo Eyoh, Emmauel Nyoho

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This paper compares fuzzy-machine learning algorithms such as Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) for the predicting cases of fire outbreak. The paper uses the fire outbreak dataset with three features (Temperature, Smoke, and Flame). The data is pre-processed using Interval Type-2 Fuzzy Logic (IT2FL) algorithm. Min-Max Normalization and Principal Component Analysis (PCA) are used to predict feature labels in the dataset, normalize the dataset, and select relevant features respectively. The output of the pre-processing is a dataset with two principal components (PC1 and PC2). The pre-processed dataset is then used in the training of the aforementioned machine learning models. K-fold (with K=10) cross-validation method is used to evaluate the performance of the models using the matrices – ROC (Receiver Operating Curve), Specificity, and Sensitivity. The model is also tested with 20% of the dataset. The validation result shows KNN is the better model for fire outbreak detection with an ROC value of 0.99878, followed by SVM with an ROC value of 0.99753.

Keywords: Machine Learning Algorithms , Interval Type-2 Fuzzy Logic, Fire Outbreak, Support Vector Machine, K-Nearest Neighbour, Principal Component Analysis

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Authors: Hafsa Kamran, Asma Afreen, Zaheer Ahmed

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Internet addiction, an emerging mental health disorder from last two decades, is manifested by the inability in the controlled use of internet leading to academics, social, physiological and/or psychological difficulties. The present study aimed to assess the levels of internet addiction among university students in Lahore and to explore the effects of internet addiction on their dietary behavior and lifestyle. It was an analytical cross-sectional study. Data was collected from October to December 2016 from students of four universities selected through two-stage sampling method. The numbers of participants were 500 and 13 questionnaires were rejected due to incomplete information. Levels of Internet Addiction (IA) were calculated using Young Internet Addiction Test (YIAT). Data was also collected on students’ demographics, lifestyle factors and dietary behavior using self-reported questionnaire. Data was analyzed using SPSS (version 21). Chi-square test was applied to evaluate the relationship between variables. Results of the study revealed that 10% of the population had severe internet addiction while moderate Internet Addiction was present in 42%. High prevalence was found among males (11% vs. 8%), private sector university students (p = 0.008) and engineering students (p = 0.000). The lifestyle habits of internet addicts were significantly of poorer quality than normal users (p = 0.05). Internet addiction was found associated with lesser physically activity (p = 0.025), had shorter duration of physical activity (p = 0.016), had more disorganized sleep pattern (p = 0.023), had less duration of sleep (p = 0.019), reported being more tired and sleepy in class (p = 0.033) and spending more time on internet as compared to normal users. Severe and moderate internet addicts also found to be more overweight and obese than normal users (p = 0.000). The dietary behavior of internet addicts was significantly poorer than normal users. Internet addicts were found to skip breakfast more than a normal user (p = 0.039). Common reasons for meal skipping were lack of time and snacking between meals (p = 0.000). They also had increased meal size (p = 0.05) and habit of snacking while using the internet (p = 0.027). Fast food (p = 0.016) and fried items (p = 0.05) were most consumed snacks, while carbonated beverages (p = 0.019) were most consumed beverages among internet addicts. Internet Addicts were found to consume less than recommended daily servings of dairy (p = 0.008) and fruits (p = 0.000) and more servings of meat group (p = 0.025) than their no internet addict counterparts. In conclusion, in this study, it was demonstrated that internet addicts have unhealthy dietary behavior and inappropriate lifestyle habits. University students should be educated regarding the importance of balanced diet and healthy lifestyle, which are critical for effectual primary prevention of numerous chronic degenerative diseases. Furthermore, it is necessary to raise awareness concerning adverse effects of internet addiction among youth and their parents.

Keywords: dietary behavior, internet addiction, lifestyle, university students

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Authors: A.D. García-Soto, Miguel Jaimes, J.G. Valdés-Vázquez, A. Hernández-Martínez

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The seismic reliability of two-degree-of-freedom (2DOF) systems with and without supplemental damping are computed. The used records are scaled from realistic records using standard incremental dynamic Analysis (IDA). The total normalized shear base is computed for both cases using different scaling factors, and it is considered as the demand. The seismic reliability is computed using codified design to stipulate the capacity and, after some assumptions, applying the first-order reliability method (FORM). The 2DOF considered can be thought as structures with non-linear behavior, with and without seismic protection, subjected to earthquake activity in Mexico City. It is found that the reliability of 2DOF structures retrofitted with supplemental damper at its first story is generally higher than the reliability of 2DOF structures without supplemental damping.

Keywords: 2DOF structures, IDA, FORM, seismic reliability

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Authors: Chien-Liang Chao, Yun-Sheng Lin

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Secondary metabolites are applied in the human life of the Chinese herbal medicine. Many drugs are originally extracted from natural products with combination of pharmaceutical and chemical studies. Crude extract of the leaves from Zamia furfureace L. has been shown to exhibit anticancer activities. The first chemical investigation of this plant was carried out by our group. In this study, four known compounds were isolated from Zamia furfureace L. with three lignins (Sesamin (1), Wodeshiol (2) and Paulownin (3)), and one dipeptide (Aurantiamide acetate (4)). The structures of these compounds were analyzed through the 1D-NMR(1H-NMR,13C-NMR)、2D-NMR(COSY、HMQC、HMBC、NOESY) spectroscopic analysis, and by comparison of variety of physical data (IR, mass spectrometry, ultraviolet, optical rotation). Among them, Aurantiamide acetate (4) exhibited weak cytotoxic activity against human gastric cancer cells.

Keywords: Zamia furfureace L., AGS, sesamin, Aurantiamide acetate, secondary metabolites

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Authors: Arianna Zhu, Thomas Bakupog

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Taxol (generic name paclitaxel) is an antineoplastic drug used to treat breast, lung, and ovarian cancer. It performs exceptionally well against a wide variety of tumors, including B16 melanoma, L1210 and P388 leukemias, MX-1 mammary tumors, and CX-1 colon tumor xenografts. However, despite taxol’s efficacy in antitumor activity, its aqueous solubility is extremely poor, decreasing its bioavailability and making it difficult for the body to absorb. The objective of this study is to improve the solubility of taxol, thus increasing the bioavailability of the drug in preventing cancer. By modifying the structure of taxol, four novel taxol derivatives were created with improved solubilities. Two of the derivatives were given an additional hydrogen donor and acceptor and thus showed a pronounced positive change in solubility. The results of this work solve the issue of taxol’s inadequate solubility and show potential in increasing the absorption of the drug.

Keywords: Taxol, Solubility, improving bioavailability, logP

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Authors: Maryam Kiani

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The self-sensing capability opens up new possibilities for structural health monitoring, offering real-time information on the condition and performance of constructions. The synthesis and characterization of these functional 2D material geopolymers will be explored in this study. Various fabrication techniques, including mixing, dispersion, and coating methods, will be employed to ensure uniform distribution and integration of the 2D materials within the geopolymers. The resulting composite materials will be evaluated for their mechanical strength, electrical conductivity, and sensing capabilities through rigorous testing and analysis. The potential applications of these self-sensing geopolymers are vast. They can be used in infrastructure projects, such as bridges, tunnels, and buildings, to provide continuous monitoring and early detection of structural damage or degradation. This proactive approach to maintenance and safety can significantly improve the lifespan and efficiency of constructions, ultimately reducing maintenance costs and enhancing overall sustainability. In conclusion, the development of functional 2D material geopolymers as self-sensing materials presents an exciting advancement in the construction industry. By integrating these innovative materials into structures, we can create a new generation of intelligent, self-monitoring constructions that can adapt and respond to their environment.

Keywords: 2D materials, geopolymers, electrical properties, self-sensing

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Authors: Osama Terra, Hatem Hussein, Adriaan Van Brakel

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Dense wavelength division multiplexing (DWDM) technology requires tight specification and therefore measurement of wavelength accuracy and stability of the telecommunication lasers. Thus, calibration of the used Optical Spectrum Analyzers (OSAs) that are used to measure wavelength is of a great importance. Proficiency testing must be performed on such measuring activity to insure the accuracy of the measurement results. In this paper, a new comparison scheme is introduced to test the performance of such calibrations. This comparison scheme is implemented between NIS-Egypt and NMISA-South Africa for the calibration of the wavelength scale of an OSA. Both institutes employ reference gas cell to calibrate OSA according to the standard IEC/ BS EN 62129 (2006). The result of this comparison is compiled in this paper.

Keywords: OSA calibration, HCN gas cell, DWDM technology, wavelength measurement

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Authors: Wael Khalil, Elias Rahal, Ghassan Matar

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Tooth related infections or commonly named dental infections have been described as the most common causes of tooth loss in adults. These pathologies were mostly periodontitis, pericoronitis, and periapical infection. The involvement of various bacteria in the pathogenesis of these pathologies has been thoroughly mentioned and approved in the literature. However, the variability in the severity and prognosis of these lesions among patients suggests the association of other pathogens, like viruses and fungi, in the pathogenesis of these lesions. Several studies in the literature investigated the association of multiple viruses and fungi with the above-mentioned lesions, yet, a vast controversy was reached concerning this subject.Aim: Our study aims to fill the gap in the literature concerning the contribution of adenovirus, HPV-16, EBV, fungi, and candida in the pathogenesis of periodontitis, pericoronitis, and periapical infection. For this purpose, we utilized the quantitative PCR for pathogen detection in saliva, gingival, and lesions samples of involved subjects. Results: Some of these pathogens appeared to have an association with the investigated dental pathologies, while others showed no contribution to the pathogenesis of these lesions. Further investigation is required in order to identify the subtype of the involved pathogens in these tooth related oral pathology.

Keywords: periodontitis, pericoronitis, dental abscess, PCR, microbiology

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Authors: Nuruddeen Usman, Usman Mohammed Gidado

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A construction and post construction activity in buildings contributes to environmental degradation, because of the generation of solid waste during construction to the production of carbon dioxide by the occupants during utilization. These problems were caused as a result of lack of adopting green building technology during and after construction. However, this study aims at conceptualizing the factors that are affecting the adoption of green building technology with a view to suggest better ways for its successful adoption in the construction industry through developing a green building technology model. Thus, the research findings show that: Economic, social, cultural, and technological progresses are the factors affecting Green Building Technology Adoption. Therefore, identifying these factors and developing the model might help in the successful adoption of green building technology.

Keywords: green building technology, construction, post construction, degradation

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Authors: Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević

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The present study deals with the selection of the most suitable quantitative structure-retention relationship (QSRR) models which should be used in prediction of the retention behavior of basic, neutral, acidic and phenolic pesticides which belong to different classes: fungicides, herbicides, metabolites, insecticides and plant growth regulators. Sum of ranking differences (SRD) approach can give a different point of view on selection of the most consistent QSRR model. SRD approach can be applied not only for ranking of the QSRR models, but also for detection of similarity or dissimilarity among them. Applying the SRD analysis, the most similar models can be found easily. In this study, selection of the best model was carried out on the basis of the reference ranking (“golden standard”) which was defined as the row average values of logarithm of retention time (logtr) defined by high performance liquid chromatography (HPLC). Also, SRD analysis based on experimental logtr values as reference ranking revealed similar grouping of the established QSRR models already obtained by hierarchical cluster analysis (HCA).

Keywords: chemometrics, chromatography, pesticides, sum of ranking differences

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Authors: Mohammad Y. Abualhoul, Edgar Talavera Munoz, Fawzi Nashashibi

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The new emerging Visible Light Communication (VLC) technology has been subjected to intensive investigation, evaluation, and lately, deployed in the context of convoy-based applications for Intelligent Transportations Systems (ITS). The technology limitations were defined and supported by different solutions proposals to enhance the crucial alignment and mobility limitations. In this paper, we propose the incorporation of VLC technology and Lane-Centering (LC) technique to assure the VLC-connectivity by keeping the autonomous vehicle aligned to the lane center using vision-based lane detection in a convoy-based formation. Such combination can ensure the optical communication connectivity with a lateral error less than 30 cm. As soon as the road lanes are detectable, the evaluated system showed stable behavior independently from the inter-vehicle distances and without the need for any exchanged information of the remote vehicles. The evaluation of the proposed system is verified using VLC prototype and an empirical result of LC running application over 60 km in Madrid M40 highway.

Keywords: visible light communication, lane-centerin, platooning, intelligent transportation systems, road safety applications

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Authors: Mohammed S. Al-Rawi, J. Bastos, J. Rodriguez

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Object detection and object recognition are essential components of every computer vision system. Despite the high computational complexity and other problems related to numerical stability and accuracy, Zernike moments of 2D images (ZMs) have shown resilience when used in object recognition and have been used in various image analysis applications. In this work, we propose a novel method for computing ZMs via Fast Fourier Transform (FFT). Notably, this is the first algorithm that can generate ZMs up to extremely high orders accurately, e.g., it can be used to generate ZMs for orders up to 1000 or even higher. Furthermore, the proposed method is also simpler and faster than the other methods due to the availability of FFT software and/or hardware. The accuracies and numerical stability of ZMs computed via FFT have been confirmed using the orthogonality property. We also introduce normalizing ZMs with Neumann factor when the image is embedded in a larger grid, and color image reconstruction based on RGB normalization of the reconstructed images. Astonishingly, higher-order image reconstruction experiments show that the proposed methods are superior, both quantitatively and subjectively, compared to the q-recursive method.

Keywords: Chebyshev polynomial, fourier transform, fast algorithms, image recognition, pseudo Zernike moments, Zernike moments

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Authors: Shaeel A. Al Thabaiti, Sulaiman N. Basahel, Salem M. Bawaked, Mohamed Mokhtar

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Nanosheets for cobalt-layered double hydroxide (Co-Al-LDH)/GO were successfully synthesized with different Co:M g:Al ratios (0:3:1, 1.5:1.5:1, and 3:0:1). The layered double hydroxide structure and morphology were determined using x-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). Temperature prgrammed reduction (TPR) of Co-Al-LDH showed reduction peaks at lower temperature which indicates the ease reducibility of this particular sample. The thermal behaviour was studied using thermal graviemetric technique (TG), and the BET-surface area was determined using N2 physisorption at -196°C. The C-C coupling reaction was carried out over all the investigated catalysts. The Mg–Al LDH catalyst without Co ions is inactive, but the isomorphic substitution of Mg by Co ions (Co:Mg:Al = 1.5:1.5:1) in the cationic sheet resulted in 88% conversion of iodobenzene under reflux. LDH/GO hybrid is up to 2 times higher activity than for the unsupported LDH.

Keywords: adsorption, co-precipitation, graphene oxide, layer double hydroxide

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Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

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Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

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Authors: Catarina Cruz, Ana Breda

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In the early years, until the children’s entrance at the elementary school, they are stimulated by their educators, through rich and attractive contexts, to explore and develop skills in different domains, from the socio-emotional to the cognitive. Many of these contexts trigger real or imaginary situations, familiar or not, through resources or pedagogical practices that incite children's curiosity, questioning, expression of ideas or emotions, social interaction, among others. Later, when children enter at the elementary school, their activity at school becomes more focused on developing skills in the cognitive domain, namely acquiring learning from different subject areas, such as Mathematics, Natural Sciences, History, among others. That is, to ensure that children develop the standardized learning recommended in the guiding curriculum documents, they spend part of their time applying formulas, memorizing information, following instructions, and so on, and in this way not much time is left to listen children, to learn about their interests and likes, as well as their perspective and questions about the surround world. In Elementary School, especially in the 1st Cycle, children are naturally curious, however, sometimes this skill is subtly conditioned by adults. Curious children learn more, since they have an intrinsic desire to know more, especially about what is unknown. When children think on subjects or themes that they are interested in or curious about, they attribute more meaning to this learning and retain it for longer. Therefore, it is important to approach subjects in the classroom that seduce or captivate children's attention, trigger them curiosity, and allow to hear their ideas. There are several resources, strategies and pedagogical practices to awaken children's curiosity, to explore their knowledge, to understand their perspectives and their way of thinking, to know a little more about their personality and to provide space for dialogue. The storytelling, its narrative’s exploration and interpretation is one of those pedagogical practices. Children’s literature, about real or imaginary subjects, stimulate children’s insights supported into their experiences, emotions, learnings and personality, and promote opportunities for children express freely their feelings and thoughts. This work focuses on a session developed with children in the 3rd year of schooling, from a Portuguese 1st Cycle Basic School, in which the story "From the Outside In and From the Inside Out" was presented. The story’s presentation was mainly centred on children’s activity, who read excerpts and interpreted/explored them through a dialogue led by one of the authors. The study presented here intends to show an example of how an exploration of a children's story can trigger ideas, thoughts, emotions or attitudes in children in the 3rd year of elementary school. To answer the research question, this work aimed to: identify ideas, thoughts, emotions or attitudes that emerged from the exploration of story; analyse aspects of the story and the orchestration/conduction of dialogue with/between children that facilitated or inhibited the emergence of ideas, thoughts, emotions or attitudes by children,

Keywords: storytelling, children’s perspectives, soft skills, non-formal learning contexts, orchestration

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Authors: S. Abedi, M. R. Soroush, M. Mousakhani

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According to studies in the field of technology capabilities we identify the most important indicators to evaluate the level of "Design and Engineering" capabilities. Since the technology development correlates with the level of technology capabilities trying to promote its key importance. In this research by using FDM, the right combination of D&E capabilities indicators according to the auto industry is presented. Finally, with modeling evaluation of D&E capabilities by using FIS and check its reliability, five levels were determined to evaluate the D&E capabilities. We have analyzed 80 companies in auto industry and determined D&E capabilities of each level. Field of company activity indicators has been divided into four categories, Suspension group, Electrical group, Engine groups and trims group. The results show that half of the surveyed companies had D&E capabilities in Level 1 and 2 or in other words very low and low level of D&E.

Keywords: developing countries, D&E capabilities, technology capabilities, auto industry

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Authors: Chaluntorn Vichasilp, Sutee Wangtueai

Abstract:

This study aims to develop a new method to detect active compounds in Thai herbs (1-deoxynojirimycin (DNJ) in mulberry leave, anthocyanin in Mao and curcumin in turmeric) using near infrared spectroscopy (NIRs). NIRs is non-destructive technique that rapid, non-chemical involved and low-cost determination. By NIRs and chemometrics technique, it was found that the DNJ prediction equation conducted with partial least square regression with cross-validation had low accuracy R2 (0.42) and SEP (31.87 mg/100g). On the other hand, the anthocyanin prediction equation showed moderate good results (R2 and SEP of 0.78 and 0.51 mg/g) with Multiplication scattering correction at wavelength of 2000-2200 nm. The high absorption could be observed at wavelength of 2047 nm and this model could be used as screening level. For curcumin prediction, the good result was obtained when applied original spectra with smoothing technique. The wavelength of 1400-2500 nm was created regression model with R2 (0.68) and SEP (0.17 mg/g). This model had high NIRs absorption at a wavelength of 1476, 1665, 1986 and 2395 nm, respectively. NIRs showed prospective technique for detection of some active compounds in Thai herbs.

Keywords: anthocyanin, curcumin, 1-deoxynojirimycin (DNJ), near infrared spectroscopy (NIRs)

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Authors: Jihan Albayati, Kim Abdullah

Abstract:

This poster focuses on the significance of active learning strategies and their usage in the ESL classroom. Active learning is a big shift from traditional lecturing to active student engagement which can enhance and enrich student learning; therefore, engaging students is the core of this approach. Students learn more when they participate in the process of learning such as discussions, debates, analysis, synthesis, or any form of activity that requires student involvement. In order to achieve active learning, teachers can use different instructional strategies that are conducive to learning and the selection of these strategies depends on student learning outcomes. Active learning techniques must be carefully designed and integrated into the classroom to increase critical thinking and student participation. This poster provides a concise definition of active learning and its importance, instructional strategies, active learning techniques and their impact on student engagement. Also, it demonstrates the differences between passive and active learners.

Keywords: active learning, learner engagement, student-centered, teaching strategies

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Authors: Howard Chitimira

Abstract:

In Australia, the market abuse prohibition is generally well accepted by the investing and non-investing public as well as by the government. This co-operative and co-ordinated approach on the part of all the relevant stakeholders has to date given rise to an increased awareness and commendable combating of market abuse activities in the Australian corporations, companies, and securities markets. It is against this background that this article seeks to comparatively explore the general enforcement approaches that are employed to combat market abuse (insider trading and market manipulation) activity in Australia and South Africa. In relation to this, the role of selected enforcement authorities and possible enforcement methods which may be learnt from both the Australian and South African experiences will be isolated where necessary for consideration by such authorities, especially, in the South African market abuse regulatory framework.

Keywords: insider trading, market abuse, market manipulation, regulation

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Authors: Jose Neri, Fabrice Canto, Alastair Magnaldo, Laurent Guillerme, Vincent Dugas

Abstract:

A miniaturized and automated approach for the volumetric titration of free nitric acid in U(VI) solutions is presented. Free acidity measurement refers to the acidity quantification in solutions containing hydrolysable heavy metal ions such as U(VI), U(IV) or Pu(IV) without taking into account the acidity contribution from the hydrolysis of such metal ions. It is, in fact, an operation having an essential role for the control of the nuclear fuel recycling process. The main objective behind the technical optimization of the actual ‘beaker’ method was to reduce the amount of radioactive substance to be handled by the laboratory personnel, to ease the instrumentation adjustability within a glove-box environment and to allow a high-throughput analysis for conducting more cost-effective operations. The measurement technique is based on the concept of the Taylor-Aris dispersion in order to create inside of a 200 μm x 5cm circular cylindrical micro-channel a linear concentration gradient in less than a second. The proposed analytical methodology relies on the actinide complexation using pH 5.6 sodium oxalate solution and subsequent alkalimetric titration of nitric acid with sodium hydroxide. The titration process is followed with a CCD camera for fluorescence detection; the neutralization boundary can be visualized in a detection range of 500nm- 600nm thanks to the addition of a pH sensitive fluorophore. The operating principle of the developed device allows the active generation of linear concentration gradients using a single cylindrical micro channel. This feature simplifies the fabrication and ease of use of the micro device, as it does not need a complex micro channel network or passive mixers to generate the chemical gradient. Moreover, since the linear gradient is determined by the liquid reagents input pressure, its generation can be fully achieved in faster intervals than one second, being a more timely-efficient gradient generation process compared to other source-sink passive diffusion devices. The resulting linear gradient generator device was therefore adapted to perform for the first time, a volumetric titration on a chip where the amount of reagents used is fixed to the total volume of the micro channel, avoiding an important waste generation like in other flow-based titration techniques. The associated analytical method is automated and its linearity has been proven for the free acidity determination of U(VI) samples containing up to 0.5M of actinide ion and nitric acid in a concentration range of 0.5M to 3M. In addition to automation, the developed analytical methodology and technique greatly improves the standard off-line oxalate complexation and alkalimetric titration method by reducing a thousand fold the required sample volume, forty times the nuclear waste per analysis as well as the analysis time by eight-fold. The developed device represents, therefore, a great step towards an easy-to-handle nuclear-related application, which in the short term could be used to improve laboratory safety as much as to reduce the environmental impact of the radioanalytical chain.

Keywords: free acidity, lab-on-a-chip, linear concentration gradient, Taylor-Aris dispersion, volumetric titration

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Authors: Mirko Sailio

Abstract:

Border crossing automatization reduces required human resources in handling people crossing borders. As technology replaces and augments the work done by border officers, new cyber threats arise to threaten border security. This research analyses the current cyber threat actors and their capabilities. The analysis is conducted by gathering the threat actor data from a wide range of public sources. A model for a general border automatization system is presented, and its most significant cyber-security attributes are then compared to threat actor activity and capabilities in order to predict priorities in securing such systems. Organized crime and nation-state actors present the clearest threat to border cyber-security, and additional focus is given to their motivations and activities.

Keywords: border automation, cyber-security, threat actors, border cyber-security

Procedia PDF Downloads 207