Search results for: advanced b-spline

Authors: Opata Obinna Johnpaul

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Wastewater is one of the day-to–day concerns of humans and the environment, in general, due to its importance to the environment. This is because of the presence of various contaminants that are involved in waste water. Wastewater treatment can be defined as the proportion of wastewater that is treated, in order to reduce pollutants before being discharged to the environment, by the level of treatment. This work discusses wastewater treatment, its contaminants, as well as the technologies, involved.The major focus is to analyze Okomu Oil Palm Company Plc, their effluent treatment facility. Okomu Oil Palm Company is based in Nigeria, which is one of the developing countries of the world. Okomu Oil Palm Company uses aquatic treatment technology for their effluent treatment and applies the physio-chemical level of advanced chemical treatment of wastewater treatment process. This work will discuss the outcome of the laboratory sample taken on the 30th January, 2015 and analyzed between 30th January- 4th February 2015.

Keywords: wastewater treatment, contaminants, physio-chemical process, Okomu oil palm

Procedia PDF Downloads 356

Authors: Rohan Sarker, Cory Smith

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ROMIC Group UK is a leading provider of diesel electric locomotives, specializing in innovative and efficient solutions for rail transportation. With a strong focus on sustainability and performance, ROMIC's diesel electric locomotives are engineered to reduce fuel consumption while maximizing operational efficiency. The company's locomotives incorporate advanced technologies such as regenerative braking systems and precision control software, which enhance fuel economy and minimize environmental impact. ROMIC's extensive experience in the rail sector enables it to deliver reliable and powerful locomotives that meet the demands of both freight and passenger services. These locomotives are designed for heavy-duty applications, offering superior traction and load-carrying capacity across various terrains. The company's commitment to cutting-edge engineering and customer satisfaction ensures that its locomotives are a trusted choice for rail operators looking for high performance and low operational costs. ROMIC Group UK continues to drive innovation in the rail industry with its sustainable locomotive solutions.

Keywords: diesel, electric, engines, locomotives

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Authors: Ramiz M. Shubbar, Dong In Lee, Hatem A. Gzar, Arthur S. Rood

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Air pollution is one of the biggest environmental problems in Baghdad, Iraq. The Daura refinery located nearest the center of Baghdad, represents the largest industrial area, which transmits enormous amounts of pollutants, therefore study the gaseous pollutants and particulate matter are very important to the environment and the health of the workers in refinery and the people whom leaving in areas around the refinery. Actually, some studies investigated the studied area before, but it depended on the basic Gaussian equation in a simple computer programs, however, that kind of work at that time is very useful and important, but during the last two decades new largest production units were added to the Daura refinery such as, PU_3 (Power unit_3 (Boiler 11&12)), CDU_1 (Crude Distillation unit_70000 barrel_1), and CDU_2 (Crude Distillation unit_70000 barrel_2). Therefore, it is necessary to use new advanced model to study air pollution at the region for the new current years, and calculation the monthly emission rate of pollutants through actual amounts of fuel which consumed in production unit, this may be lead to accurate concentration values of pollutants and the behavior of dispersion or transport in study area. In this study to the best of author’s knowledge CALPUFF model was used and examined for first time in Iraq. CALPUFF is an advanced non-steady-state meteorological and air quality modeling system, was applied to investigate the pollutants concentration of SO2, NO2, CO, and PM1-10μm, at areas adjacent to Daura refinery which located in the center of Baghdad in Iraq. The CALPUFF modeling system includes three main components: CALMET is a diagnostic 3-dimensional meteorological model, CALPUFF (an air quality dispersion model), CALPOST is a post processing package, and an extensive set of preprocessing programs produced to interface the model to standard routinely available meteorological and geophysical datasets. The targets of this work are modeling and simulation the four pollutants (SO2, NO2, CO, and PM1-10μm) which emitted from Daura refinery within one year. Emission rates of these pollutants were calculated for twelve units includes thirty plants, and 35 stacks by using monthly average of the fuel amount consumption at this production units. Assess the performance of CALPUFF model in this study and detect if it is appropriate and get out predictions of good accuracy compared with available pollutants observation. CALPUFF model was investigated at three stability classes (stable, neutral, and unstable) to indicate the dispersion of the pollutants within deferent meteorological conditions. The simulation of the CALPUFF model showed the deferent kind of dispersion of these pollutants in this region depends on the stability conditions and the environment of the study area, monthly, and annual averages of pollutants were applied to view the dispersion of pollutants in the contour maps. High values of pollutants were noticed in this area, therefore this study recommends to more investigate and analyze of the pollutants, reducing the emission rate of pollutants by using modern techniques and natural gas, increasing the stack height of units, and increasing the exit gas velocity from stacks.

Keywords: CALPUFF, daura refinery, Iraq, pollutants

Procedia PDF Downloads 196

Authors: Lassaad Smirani

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In this paper we propose a Neurosymbolic Learning System (NLS) as a channel estimator for Long Term Evolution Advanced (LTE-A) uplink. The proposed system main idea based on Neural Network has modules capable of performing bidirectional information transfer between symbolic module and connectionist module. We demonstrate various strengths of the NLS especially the ability to integrate theoretical knowledge (rules) and experiential knowledge (examples), and to make an initial knowledge base (rules) converted into a connectionist network. Also to use empirical knowledge witch by learning will have the ability to revise the theoretical knowledge and acquire new one and explain it, and finally the ability to improve the performance of symbolic or connectionist systems. Compared with conventional SC-FDMA channel estimation systems, The performance of NLS in terms of complexity and quality is confirmed by theoretical analysis and simulation and shows that this system can make the channel estimation accuracy improved and bit error rate decreased.

Keywords: channel estimation, SC-FDMA, neural network, hybrid system, BER, LTE-A

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Authors: Orestis Κ. Efthymiou, Stavros T. Ponis

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In the last decade, a new industrial revolution seems to be emerging, supported -once again- by the rapid advancements of Information Technology in the areas of Machine-to-Machine (M2M) communication permitting large numbers of intelligent devices, e.g. sensors to communicate with each other and take decisions without any or minimum indirect human intervention. The advent of these technologies have triggered the emergence of a new category of hybrid (cyber-physical) manufacturing systems, combining advanced manufacturing techniques with innovative M2M applications based on the Internet of Things (IoT), under the umbrella term Industry 4.0. Even though the topic of Industry 4.0 has attracted much attention during the last few years, the attempts of providing a systematic literature review of the subject are scarce. In this paper, we present the authors’ initial study of the field with a special focus on the use and applications of Industry 4.0 principles in material handling automations and in-house logistics. Research shows that despite the vivid discussion and attractiveness of the subject, there are still many challenges and issues that have to be addressed before Industry 4.0 becomes standardized and widely applicable.

Keywords: Industry 4.0, internet of things, manufacturing systems, material handling, logistics

Procedia PDF Downloads 126

Authors: Khoi A. Nguyen, Rodney A. Stewart, Hong Zhang

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Advanced metering technologies coupled with informatics creates an opportunity to form digital multi-utility service providers. These providers will be able to concurrently collect a customers’ medium-high resolution water, electricity and gas demand data and provide user-friendly platforms to feed this information back to customers and supply/distribution utility organisations. With the emergence of blockchain technology, a new research area has been explored which helps bring this multi-utility service provider concept to a much higher level. This study aims at introducing a breakthrough system architecture where smart metering technology in water, energy, and gas (WEG) are combined with blockchain technology to provide customer a novel real-time consumption report and decentralized resource trading platform. A pilot study on 4 properties in Australia has been undertaken to demonstrate this system, where benefits for customers and utilities are undeniable.

Keywords: blockchain, digital multi-utility, end use, demand forecasting

Procedia PDF Downloads 170

Authors: Rodrigo C. Belleza, Jr., Roselyn A. Maaño, Karl Patrick E. Camota, Darwin Kim Q. Bulawan

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Eyes are an essential and conspicuous organ of the human body. Human eyes are outward and inward portals of the body that allows to see the outside world and provides glimpses into ones inner thoughts and feelings. Inevitable blindness and visual impairments may result from eye-related disease, trauma, or congenital or degenerative conditions that cannot be corrected by conventional means. The study emphasizes innovative tools that will serve as an aid to the blind and visually impaired (VI) individuals. The researchers fabricated a prototype that utilizes the Microsoft Kinect for Windows and Arduino microcontroller board. The prototype facilitates advanced gesture recognition, voice recognition, obstacle detection and indoor environment navigation. Open Computer Vision (OpenCV) performs image analysis, and gesture tracking to transform Kinect data to the desired output. A computer vision technology device provides greater accessibility for those with vision impairments.

Keywords: algorithms, blind, computer vision, embedded systems, image analysis

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Authors: Pan Long, Bi Dongjie, Li Xifeng, Xie Yongle

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The near-field synthetic aperture radar (SAR) imaging is an advanced nondestructive testing and evaluation (NDT&E) technique. This paper investigates the complex-valued signal processing related to the near-field SAR imaging system, where the measurement data turns out to be noncircular and improper, meaning that the complex-valued data is correlated to its complex conjugate. Furthermore, we discover that the degree of impropriety of the measurement data and that of the target image can be highly correlated in near-field SAR imaging. Based on these observations, A modified generalized sparse Bayesian learning algorithm is proposed, taking impropriety and noncircularity into account. Numerical results show that the proposed algorithm provides performance gain, with the help of noncircular assumption on the signals.

Keywords: complex-valued signal processing, synthetic aperture radar, 2-D radar imaging, compressive sensing, sparse Bayesian learning

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Authors: Mansouri Nabila, Ben Jemaa Yousra, Motamed Cina, Watelain Eric

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Research and development efforts in intelligent Advanced Driver Assistance Systems (ADAS) seek to save lives and reduce the number of on-road fatalities. For traffic and emergency monitoring, the essential but challenging task is vehicle detection and tracking in reasonably short time. This purpose needs first of all a powerful dynamic car detector model. In fact, this paper presents an optimized HOG process based on shape and motion parameters fusion. Our proposed approach mains to compute HOG by bloc feature from foreground blobs using configurable research window and pathway in order to overcome the shortcoming in term of computing time of HOG descriptor and improve their dynamic application performance. Indeed we prove in this paper that HOG by bloc descriptor combined with motion parameters is a very suitable car detector which reaches in record time a satisfactory recognition rate in dynamic outside area and bypasses several popular works without using sophisticated and expensive architectures such as GPU and FPGA.

Keywords: car-detector, HOG, motion, computing time

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Authors: Mohamed Laamim, Abdelilah Rochd, Aboubakr Benazzouz, Abderrahim El Fadili

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Smart grids have recently sparked a lot of interest in the energy sector as they allow for the modernization and digitization of the existing power infrastructure. Smart grids have several advantages in terms of reducing the environmental impact of generating power from fossil fuels due to their capacity to integrate large amounts of distributed energy resources. On the other hand, smart grid technologies necessitate many field investigations and requirements. This paper focuses on the major difficulties that governments face around the world and compares them to the situation in Morocco. Also presented in this study are the current works and projects being developed to improve the penetration of smart grid technologies into the electrical system. Furthermore, the findings of this study will be useful to promote the smart grid revolution in Morocco, as well as to construct a strong foundation and develop future needs for better penetration of technologies that aid in the integration of smart grid features.

Keywords: smart grids, microgrids, virtual power plants, digital twin, distributed energy resources, vehicle-to-grid, advanced metering infrastructure.

Procedia PDF Downloads 137

Authors: Youn-Sung Kim, Mi-Sung Kim, Jae-Kun Lee

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This paper deals with the accelerated life cycle test method of the motor for home appliances that demand high reliability. Life Cycle of parts in home appliances also should be 10 years because life cycle of the home appliances such as washing machine, refrigerator, TV is at least 10 years. In case of washing machine, the life cycle test method of motor is advanced for 3000 cycle test (1cycle = 2hours). However, 3000 cycle test incurs loss for the time and cost. Objectives of this study are to reduce the life cycle test time and the number of test samples, which could be realized by using acceleration factor for the test time and reduction factor for the number of sample.

Keywords: accelerated life cycle test, motor reliability test, motor for washing machine, BLDC motor

Procedia PDF Downloads 633

Authors: R. B. Knudsen, O. T. Rasmussen, R. A. Alphinas

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The reason for conducting this research is to develop an algorithm that is capable of classifying news articles from the automobile industry, according to the competitive actions that they entail, with the use of Text Mining (TM) methods. It is needed to test how to properly preprocess the data for this research by preparing pipelines which fits each algorithm the best. The pipelines are tested along with nine different classification algorithms in the realm of regression, support vector machines, and neural networks. Preliminary testing for identifying the optimal pipelines and algorithms resulted in the selection of two algorithms with two different pipelines. The two algorithms are Logistic Regression (LR) and Artificial Neural Network (ANN). These algorithms are optimized further, where several parameters of each algorithm are tested. The best result is achieved with the ANN. The final model yields an accuracy of 0.79, a precision of 0.80, a recall of 0.78, and an F1 score of 0.76. By removing three of the classes that created noise, the final algorithm is capable of reaching an accuracy of 94%.

Keywords: Artificial Neural network, Competitive dynamics, Logistic Regression, Text classification, Text mining

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Authors: Zeynep R. Ege, Aydin Akan, Faik N. Oktar, Betul Karademir, Oguzhan Gunduz

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Early detection of cancer could save human life and quality in insidious cases by advanced biomedical imaging techniques. Designing targeted detection system is necessary in order to protect of healthy cells. Electrospun nanofibers are efficient and targetable nanocarriers which have important properties such as nanometric diameter, mechanical properties, elasticity, porosity and surface area to volume ratio. In the present study, indocyanine green (ICG) organic dye was stabilized and encapsulated in polymer matrix which polyethylene oxide (PEO) and chitosan (CHI) multilayer nanofibers via co-axial electrospinning method at one step. The co-axial electrospun nanofibers were characterized as morphological (SEM), molecular (FT-IR), and entrapment efficiency of Indocyanine Green (ICG) (confocal imaging). Controlled release profile of PEO/CHI/ICG nanofiber was also evaluated up to 40 hours.

Keywords: chitosan, coaxial electrospinning, controlled releasing, drug delivery, indocyanine green, polyethylene oxide

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Authors: Azene Zenebe

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Deep learning is a subset of machine learning which incorporates techniques for the construction of artificial neural networks and found to be useful for modeling complex problems with large dataset. Deep learning requires a very high power computational and longer time for training. By aggregating computing power, high performance computer (HPC) has emerged as an approach to resolving advanced problems and performing data-driven research activities. Cyber threat intelligence (CIT) is actionable information or insight an organization or individual uses to understand the threats that have, will, or are currently targeting the organization. Results of review of literature will be presented along with results of experimental study that compares the performance of tree-based and function-base machine learning including deep learning algorithms using secondary dataset collected from darknet.

Keywords: deep-learning, cyber security, cyber threat modeling, tree-based machine learning, function-based machine learning, data science

Procedia PDF Downloads 152

Authors: R. Dakir, J. Zbitou, Ahmed Mouhsen, A. Errkik, A. Tajmouati, M. Latrach

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The design and analysis of a new compact and miniaturized monopole antenna structure for ultra wideband (UWB) wireless applications are presented and suggested in this paper. The proposed antenna structure is based on corner radiator patch with T-shaped slot and fed by mictostrip feed line with a partial ground plane combined a periodic rectangular slot and inverted T-stub tuning to increase the bandwidth. The design parameters and the performance of the suggested antenna are investigated by using 'CST Microwave Studio' and Advanced Design System. The final prototype of the proposed antenna operates from 3GHZ to 25GHz, corresponding to wide input impedance bandwidth around (157.14%) with a size of 16*24mm2 and can be easily integrated with radio-frequency or microwave circuits with low cost manufacturing. Details of the UWB antenna design and both simulated and measured results are described and discussed.

Keywords: UWB, T-shaped slots, improvement, bandwidth, stub tuning

Procedia PDF Downloads 291

Authors: Hilal Turkoglu Sasmazel, Ozan Ozkan, Seda Surucu

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Tissue engineering is the field of treating defects caused by injuries, trauma or acute/chronic diseases by using artificial scaffolds that mimic the extracellular matrix (ECM), the natural biological support for the tissues and cells within the body. The main aspects of a successful artificial scaffold are (i) large surface area in order to provide multiple anchorage points for cells to attach, (ii) suitable porosity in order to achieve 3 dimensional growth of the cells within the scaffold as well as proper transport of nutrition, biosignals and waste and (iii) physical, chemical and biological compatibility of the material in order to obtain viability throughout the healing process. By hybrid scaffolds where two or more different materials were combined with advanced fabrication techniques into complex structures, it is possible to combine the advantages of individual materials into one single structure while eliminating the disadvantages of each. Adding this to the complex structure provided by advanced fabrication techniques enables obtaining the desired aspects of a successful artificial tissue scaffold. In this study, fibroblast compatibility of poly(ε-caprolactone) (PCL)/chitosan core-shell electrospun hybrid scaffolds with proper mechanical, chemical and physical properties successfully developed in our previous study was investigated. Standard 7-day cell culture was carried out with L929 fibroblast cell line. The viability of the cells cultured with the scaffolds was monitored with 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) viability assay for every 48 h starting with 24 h after the initial seeding. In this assay, blank commercial tissue culture polystyrene (TCPS) Petri dishes, single electrospun PCL and single electrospun chitosan mats were used as control in order to compare and contrast the performance of the hybrid scaffolds. The adhesion, proliferation, spread and growth of the cells on/within the scaffolds were observed visually on the 3rd and the 7th days of the culture period with confocal laser scanning microscopy (CSLM) and scanning electron microscopy (SEM). The viability assay showed that the hybrid scaffolds caused no toxicity for fibroblast cells and provided a steady increase in cell viability, effectively doubling the cell density for every 48 h for the course of 7 days, as compared to TCPS, single electrospun PCL or chitosan mats. The cell viability on the hybrid scaffold was ~2 fold better compared to TCPS because of its 3D ECM-like structure compared to 2D flat surface of commercially cell compatible TCPS, and the performance was ~2 fold and ~10 fold better compared to single PCL and single chitosan mats, respectively, even though both fabricated similarly with electrospinning as non-woven fibrous structures, because single PCL and chitosan mats were either too hydrophobic or too hydrophilic to maintain cell attachment points. The viability results were verified with visual images obtained with CSLM and SEM, in which cells found to achieve characteristic spindle-like fibroblast shape and spread on the surface as well within the pores successfully at high densities.

Keywords: chitosan, core-shell, fibroblast, electrospinning, PCL

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Authors: A. Al Drees, A. Alahmari, R. Almuwayshir

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The advanced information technology is becoming an important factor in the development of financial services industry, especially the banking industry. It has introduced new ways of delivering banking to the customer, such as Internet Banking. Banks began to look at electronic banking (e-banking) as a means to replace some of their traditional branch functions using the Internet as a new distribution channel. Some consumers have at least more than one account, and across banks, and access these accounts using e-banking services. To look at the current net worth position, customers have to login to each of their accounts and get the details and work on consolidation. This not only takes ample time but it is a repetitive activity at a specified frequency. To address this point, an account aggregation concept is added as a solution. E-banking account aggregation, as one of the e-banking types, appeared to build a stronger relationship with customers. Account Aggregation Service generally refers to a service that allows customers to manage their bank accounts maintained in different institutions through a common Internet banking operating a platform, with a high concern to security and privacy. This paper presents an overview of an e-banking account aggregation approach as a new service in the e-banking field.

Keywords: e-banking, account aggregation, security, enterprise development

Procedia PDF Downloads 323

Authors: Niloufar Yadgari

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GANs are a potent form of deep learning models that have found success in various fields. They are part of the larger group of generative techniques, which aim to produce authentic data using a probabilistic model that learns distributions from actual samples. In clinical settings, GANs have demonstrated improved abilities in capturing spatially intricate, nonlinear, and possibly subtle disease impacts in contrast to conventional generative techniques. This review critically evaluates the current research on how GANs are being used in imaging studies of different neurological conditions like Alzheimer's disease, brain tumors, aging of the brain, and multiple sclerosis. We offer a clear explanation of different GAN techniques for each use case in neuroimaging and delve into the key hurdles, unanswered queries, and potential advancements in utilizing GANs in this field. Our goal is to connect advanced deep learning techniques with neurology studies, showcasing how GANs can assist in clinical decision-making and enhance our comprehension of the structural and functional aspects of brain disorders.

Keywords: GAN, pathology, generative adversarial network, neuro imaging

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Authors: Daniel O'Shea, Mario M. Attard, David C. Kellermann

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In this paper, we propose a hyperelastic strain energy function that maps isotopic hyperelastic constitutive laws for the use of orthotropic materials without the use of structural tensors or any kind of fiber vector, or the use of standard invariants. In particular, we focus on neo-Hookean class of models and represent them using an invariant-free formulation. To achieve this, we revise the invariant-free formulation of isotropic hyperelasticity. The formulation uses quadruple contractions between fourth-order tensors, rather than scalar products of scalar invariants. We also propose a new decomposition of the orthotropic Hookean stiffness tensor into two fourth-order Lamé tensors that collapse down to the classic Lamé parameters for isotropic continua. The resulting orthotropic hyperelastic model naturally maintains all of the advanced properties of the isotropic counterparts, and similarly collapse back down to their isotropic form by nothing more than equality of parameters in all directions (isotropy). Comparisons are made with large strain experimental results for transversely isotropic rubber type materials under tension.

Keywords: finite strain, hyperelastic, invariants, orthotropic

Procedia PDF Downloads 445

Authors: S. Sudhakar, Geetha Manjunath, Siva Teja Kakileti, Himanshu Madhu

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Diagnosis of breast cancer at early stages has seen better clinical and survival outcomes. Survival rates in developing countries like India are very low due to accessibility and affordability issues of screening tests such as Mammography. In addition, Mammography is not much effective in younger women with dense breasts. This leaves a gap in current screening methods. Thermalytix is a new technique for detecting breast abnormality in a non-contact, non-invasive way. It is an AI-enabled computer-aided diagnosis solution that automates interpretation of high resolution thermal images and identifies potential malignant lesions. The solution is low cost, easy to use, portable and is effective in all age groups. This paper presents the results of a retrospective comparative analysis of Thermalytix over Mammography and Clinical Breast Examination for breast cancer screening. Thermalytix was found to have better sensitivity than both the tests, with good specificity as well. In addition, Thermalytix identified all malignant patients without palpable lumps.

Keywords: breast cancer screening, radiology, thermalytix, artificial intelligence, thermography

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Authors: Benghenia Hadj Abd El Kader

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Sleep apnea events as obstructive, central, mixed or hypopnea are characterized by frequent breathing cessations or reduction in upper airflow during sleep. An advanced method for analyzing the patterning of biomedical signals to recognize obstructive sleep apnea and hypopnea is presented. In the aim to extract characteristic parameters, which will be used for classifying the above stated (obstructive, central, mixed) sleep apnea and hypopnea, the proposed method is based first on the analysis of polysomnography signals such as electrocardiogram signal (ECG) and electromyogram (EMG), then classification of the (obstructive, central, mixed) sleep apnea and hypopnea. The analysis is carried out using the wavelet transform technique in order to extract characteristic parameters whereas classification is carried out by applying the SVM (support vector machine) technique. The obtained results show good recognition rates using characteristic parameters.

Keywords: obstructive, central, mixed, sleep apnea, hypopnea, ECG, EMG, wavelet transform, SVM classifier

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Authors: Sakura Ganegama Bogodage, Andrew Yee Tat Leung

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This paper describes experimental, Computational Fluid Dynamics (CFD) and theoretical analysis of a cyclone performance, operated 1.0 g/m3 solid loading rate, at two different inlet velocities (5 m/s and 10 m/s). Comparing experimental results with theoretical and CFD simulation results, it is pronounced that the influence of solid in processing flow is significant than expected. Experimental studies based on gas- solid flows of cyclone separators are complicated as they required advanced sensitive measuring techniques, especially flow characteristics. Thus, CFD modelling and theoretical analysis are economical in analyzing cyclone separator performance but detailed clarifications of the application of these in cyclone separator performance evaluation is not yet discussed. The present study shows the limitations of influencing parameters of CFD and theoretical considerations, comparing experimental results and flow characteristics from CFD modelling.

Keywords: cyclone performance, inlet velocity, pressure drop, solid loading rate

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Authors: Xiang Zhang, David Rey, S. Travis Waller

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Stochastic User Equilibrium (SUE) model is a widely used traffic assignment model in transportation planning, which is regarded more advanced than Deterministic User Equilibrium (DUE) model. However, a problem exists that the performance of the SUE model depends on its error term parameter. The objective of this paper is to propose a systematic method of determining the appropriate error term parameter value for the SUE model. First, the significance of the parameter is explored through a numerical example. Second, the parameter calibration method is developed based on the Logit-based route choice model. The calibration process is realized through multiple nonlinear regression, using sequential quadratic programming combined with least square method. Finally, case analysis is conducted to demonstrate the application of the calibration process and validate the better performance of the SUE model calibrated by the proposed method compared to the SUE models under other parameter values and the DUE model.

Keywords: parameter calibration, sequential quadratic programming, stochastic user equilibrium, traffic assignment, transportation planning

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Authors: Ting (Grace) Lin, Jianhong (Cecilia) Xia, Todd Robinson

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Accessibility analysis, examining people’s ability to access facilities and destinations, is a fundamental assessment for transport planning, policy making, and social exclusion research. Dynamic accessibility which measures accessibility in real-time traffic environment has been an advanced accessibility indicator in transport research. It is also a useful indicator to help travelers to understand travel time daily variability, assists traffic engineers to monitor traffic congestions, and finally develop effective strategies in order to mitigate traffic congestions. This research involved real-time traffic information by collecting travel time data with 15-minute interval via the TomTom^® API. A framework for measuring dynamic accessibility was then developed based on the gravity theory and accessibility dichotomy theory through space and time interpolation. Finally, the dynamic accessibility can be derived at any given time and location under dynamic accessibility spatial analysis framework.

Keywords: dynamic accessibility, hot spot, transport research, TomTom® API

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Authors: Gaurav Bansod, Swapnil Sutar, Abhijit Patil, Jagdish Patil

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This paper proposes an ultra-lightweight cipher NUX. NUX is a generalized Feistel network. It supports 128/80 bit key length and block length of 64 bit. For 128 bit key length, NUX needs only 1022 GEs which is less as compared to all existing cipher design. NUX design results into less footprint area and minimal memory size. This paper presents security analysis of NUX cipher design which shows cipher’s resistance against basic attacks like Linear and Differential Cryptanalysis. Advanced attacks like Biclique attack is also mounted on NUX cipher design. Two different F function in NUX cipher design results in high diffusion mechanism which generates large number of active S-boxes in minimum number of rounds. NUX cipher has total 31 rounds. NUX design will be best-suited design for critical application like smart grid, IoT, wireless sensor network, where memory size, footprint area and the power dissipation are the major constraints.

Keywords: lightweight cryptography, Feistel cipher, block cipher, IoT, encryption, embedded security, ubiquitous computing

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Authors: Derick M. Rakgoale, Topside E. Mathonsi, Vusumuzi Malele

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Cloud computing offers distributed online and on-demand computational services from anywhere in the world. Cloud computing services have grown immensely over the past years, especially in the past year due to the Coronavirus pandemic. Cloud computing has changed the working environment and introduced work from work phenomenon, which enabled the adoption of technologies to fulfill the new workings, including cloud services offerings. The increased cloud computing adoption has come with new challenges regarding data privacy and its integrity in the cloud environment. Previously advanced encryption algorithms failed to reduce the memory space required for cloud computing performance, thus increasing the computational cost. This paper reviews the existing encryption algorithms used in cloud computing. In the future, artificial neural networks (ANN) algorithm design will be presented as a security solution to ensure data integrity, confidentiality, privacy, and availability of user data in cloud computing. Moreover, MATLAB will be used to evaluate the proposed solution, and simulation results will be presented.

Keywords: cloud computing, data integrity, confidentiality, privacy, availability

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Authors: Rodrigo Zepeda

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Transaction cost analysis (TCA) is a way of analyzing the relative performance of different intermediaries and different trading strategies for trades undertaken in financial instruments. It is a way for an investor to determine the overall quality of execution of a particular trade, and there are many different approaches to undertaking TCA. Under the updated Markets in Financial Instruments Directive (2014/65/EU) (MiFID II), investment firms are required when executing orders, to take all sufficient steps to obtain the best possible result for their clients. This requirement for 'Best Execution' must take into account price, costs, speed, likelihood of execution and settlement, size, nature or any other consideration relevant to the execution of the order. The new regulatory compliance framework under MiFID II will now also apply across a very broad range of financial instruments. This article will provide a comprehensive technical analysis of how TCA and Best Execution will significantly change under MiFID II. It will also explain why harmonization of post-trade reporting requirements under MiFID II could potentially support the development of peer group analysis, which in turn could provide a new and highly advanced framework for TCA that could more effectively support Best Execution requirements under MiFID II. The study is significant because there are no studies that have dealt with TCA and Best Execution under MiFID II in the literature.

Keywords: transaction cost analysis, execution quality, best execution, MiFID II, financial instruments

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Authors: Haley Hrymak

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For decades drug addiction has been understood to be a health problem and not a social problem. While research has advanced to allow for a more comprehensive understanding of the factors affecting addiction, the justice system has lagged behind. Given all that is known about addiction as a health issue and the need for effective rehabilitation to prevent further involvement with crime, there is a need for a dramatic shift in order to ensure individual's human right to health is being upheld within the Canadian criminal justice system. This research employs the qualitative methodology to interview advocates who work with substance users within the Greater Vancouver area to explore best practices for representing individuals with substance abuse issues within the Canadian justice system. The research shows that treatment, not punishment, is what is needed in order for recidivism to be reduced for individuals with substance abuse issues. The creative options that advocates employ to work within the current system are intended to provide a guide for lawyers working within the current criminal justice system.

Keywords: addiction, criminal law, right to health, rehabilitation

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Authors: Abdelhadi Adel, Kadri Ouahab

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This paper proposes an improved approach based on MAS Architecture and Heuristic Algorithm for systematic maintenance to minimize makespan. We have implemented a problem-solving approach for optimizing the processing time, methods based on metaheuristics. The proposed approach is inspired by the behavior of the human body. This hybridization is between a multi-agent system and inspirations of the human body, especially genetics. The effectiveness of our approach has been demonstrated repeatedly in this paper. To solve such a complex problem, we proposed an approach which we have used advanced operators such as uniform crossover set and single point mutation. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling.

Keywords: multi-agent systems, emergence, genetic algorithm, makespan, systematic maintenance, scheduling, hybrid flow shop scheduling

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Authors: Loha Hashimy, Isabella Castillo

Abstract:

In today's rapidly evolving technological landscape, the importance of precise navigation and mapping systems cannot be understated. As various sectors undergo transformative changes, the market potential for Advanced Mapping and Management Systems (AMMS) emerges as a critical focus area. The Galileo/GNSS-Based Autonomous Mobile Mapping System (GAMMS) project, specifically targeted toward high-definition mapping (HDM), endeavours to provide insights into this market within the broader context of the geomatics and navigation fields. With the growing integration of Autonomous Vehicles (AVs) into our transportation systems, the relevance and demand for sophisticated mapping solutions like HDM have become increasingly pertinent. The research employed a meticulous, lean, stepwise, and interconnected methodology to ensure a comprehensive assessment. Beginning with the identification of pivotal project results, the study progressed into a systematic market screening. This was complemented by an exhaustive desk research phase that delved into existing literature, data, and trends. To ensure the holistic validity of the findings, extensive consultations were conducted. Academia and industry experts provided invaluable insights through interviews, questionnaires, and surveys. This multi-faceted approach facilitated a layered analysis, juxtaposing secondary data with primary inputs, ensuring that the conclusions were both accurate and actionable. Our investigation unearthed a plethora of drivers steering the HD maps landscape. These ranged from technological leaps, nuanced market demands, and influential economic factors to overarching socio-political shifts. The meteoric rise of Autonomous Vehicles (AVs) and the shift towards app-based transportation solutions, such as Uber, stood out as significant market pull factors. A nuanced PESTEL analysis further enriched our understanding, shedding light on political, economic, social, technological, environmental, and legal facets influencing the HD maps market trajectory. Simultaneously, potential roadblocks were identified. Notable among these were barriers related to high initial costs, concerns around data quality, and the challenges posed by a fragmented and evolving regulatory landscape. The GAMMS project serves as a beacon, illuminating the vast opportunities that lie ahead for the HD mapping sector. It underscores the indispensable role of HDM in enhancing navigation, ensuring safety, and providing pinpoint, accurate location services. As our world becomes more interconnected and reliant on technology, HD maps emerge as a linchpin, bridging gaps and enabling seamless experiences. The research findings accentuate the imperative for stakeholders across industries to recognize and harness the potential of HD mapping, especially as we stand on the cusp of a transportation revolution heralded by Autonomous Vehicles and advanced geomatic solutions.

Keywords: high-definition mapping (HDM), autonomous vehicles, PESTEL analysis, market drivers

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