Search results for: computational neural networks

Authors: Carlos Caro, Ernest Blade, Nestor Rojas

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This study determined the relationship between basic geo-technical parameters and parameters of the hydro logical model Thomas for water balance of rural watersheds, as a methodological calibration application, applicable in distributed models as IBER model, which represents a distributed system simulation models for unsteady flow numerical free surface. There was an exploration in 25 points (on 15 sub) basin of Rio Piedras (Boy.) obtaining soil samples, to which geo-technical characterization was performed by laboratory tests. Thomas model has a physical characterization of the input area by only four parameters (a, b, c, d). Achieve measurable relationship between geo technical parameters and 4 values of hydro logical parameters helps to determine subsurface, underground and surface flow more agile manner. It is intended in this way to reach some solutions regarding limits initial model parameters on the basis of Thomas geo-technical characterization. In hydro geological models of rural watersheds, calibration is an important process in the characterization of the study area. This step can require a significant computational cost and time, especially if the initial values or parameters before calibration are outside of the geo-technical reality. A better approach in these initial values means optimization of these process through a geo-technical materials area, where is obtained an important approach to the study as in the starting range of variation for the calibration parameters.

Keywords: distributed hydrology, hydrological and geotechnical characterization, Iber model

Procedia PDF Downloads 520

Authors: S. Thaiudom, W. Toommuangpak

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Yoghurt ice cream (YIC) containing prebiotics and probiotics seems to be much more recognized among consumers who concern for their health. Not only can it be a benefit on consumers’ health but also its taste and freshness provide people easily accept. However, the survival of such probiotic especially Bifidobacterium longum, found in human gastrointestinal tract and to be benefit to human gut, was still needed to study in the severe condition as whipping and freezing in ice cream process. Low and full-fat yoghurt ice cream containing 2 and 10% (w/w) fat content (LYIC and FYIC), respectively was produced by mixing 20% yoghurt containing B. longum into milk ice cream mix. Fructo-oligosaccharides (FOS) or galacto-oligosaccharides (GOS) at 0, 1, and 2% (w/w) were separately used as prebiotic in order to improve the survival of B. longum. Survival of this bacteria as a function of ice cream storage time and ice cream properties were investigated. The results showed that prebiotic; especially FOS could improve viable count of B. longum. The more concentration of prebiotic used, the more is the survival of B. Longum. These prebiotics could prolong the survival of B. longum up to 60 days, and the amount of survival number was still in the recommended level (106 cfu per gram). Fat content and prebiotic did not significantly affect the total acidity and the overrun of all samples, but an increase of fat content significantly increased the fat particle size which might be because of partial coalescence found in FYIC rather than in LYIC. However, addition of GOS or FOS could reduce the fat particle size, especially in FYIC. GOS seemed to reduce the hardness of YIC rather than FOS. High fat content (10% fat) significantly influenced on lowering the melting rate of YIC better than 2% fat content due to the 3-dimension networks of fat partial coalescence theoretically occurring more in FYIC than in LYIC. However, FOS seemed to retard the melting rate of ice cream better than GOS. In conclusion, GOS and FOS in YIC with different fat content can enhance the survival of B. longum and affect physical and chemical properties of such yoghurt ice cream.

Keywords: Bifidobacterium longum, prebiotic, survival, yoghurt ice cream

Procedia PDF Downloads 160

Authors: A. Dinelka Panagoda, Bathiya Bandara, Chamod Wijetunga, Chathura Malinda, Lakmal Rupasinghe, Chethana Liyanapathirana

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This research takes us forward with the concepts of Federated Learning and Software-Defined Networking (SDN) to introduce an efficient malware detection technique and provide a mitigation mechanism to give birth to a resilient and automated healthcare sector network system by also adding the feature of extended privacy preservation. Due to the daily transformation of new malware attacks on hospital Integrated Clinical Environment (ICEs), the healthcare industry is at an undefinable peak of never knowing its continuity direction. The state of blindness by the array of indispensable opportunities that new medical device inventions and their connected coordination offer daily, a factor that should be focused driven is not yet entirely understood by most healthcare operators and patients. This solution has the involvement of four clients in the form of hospital networks to build up the federated learning experimentation architectural structure with different geographical participation to reach the most reasonable accuracy rate with privacy preservation. While the logistic regression with cross-entropy conveys the detection, SDN comes in handy in the second half of the research to stack up the initial development phases of the system with malware mitigation based on policy implementation. The overall evaluation sums up with a system that proves the accuracy with the added privacy. It is no longer needed to continue with traditional centralized systems that offer almost everything but not privacy.

Keywords: software-defined network, federated learning, privacy, integrated clinical environment, decentralized learning, malware detection, malware mitigation

Procedia PDF Downloads 186

Authors: Vakamalla Teja Reddy, Narasimha Mangadoddy

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Hydrocyclones are used to separate particles into different size fractions in the mineral processing, chemical and metallurgical industries. High speed video imaging, Laser Doppler Anemometry (LDA), X-ray and Gamma ray tomography are previously used to measure the two-phase flow characteristics in the cyclone. However, investigation of solids flow characteristics inside the cyclone is often impeded by the nature of the process due to slurry opaqueness and solid metal wall vessels. In this work, a dual-plane high speed Electrical resistance tomography (ERT) is used to measure hydrocyclone internal flow dynamics in situ. Experiments are carried out in 3 inch hydrocyclone for feed solid concentrations varying in the range of 0-50%. ERT data analysis through the optimized FEM mesh size and reconstruction algorithms on air-core and solid concentration tomograms is assessed. Results are presented in terms of the air-core diameter and solids volume fraction contours using Maxwell’s equation for various hydrocyclone operational parameters. It is confirmed by ERT that the air core occupied area and wall solids conductivity levels decreases with increasing the feed solids concentration. Algebraic slip mixture based multi-phase computational fluid dynamics (CFD) model is used to predict the air-core size and the solid concentrations in the hydrocyclone. Validation of air-core size and mean solid volume fractions by ERT measurements with the CFD simulations is attempted.

Keywords: air-core, electrical resistance tomography, hydrocyclone, multi-phase CFD

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Authors: Soheila Sadeghi

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Accurate forecasting of project performance metrics is crucial for successfully managing and delivering urban road reconstruction projects. Traditional methods often rely on static baseline plans and fail to consider the dynamic nature of project progress and external factors. This research proposes a machine learning-based approach to forecast project performance metrics, such as cost variance and earned value, for each Work Breakdown Structure (WBS) category in an urban road reconstruction project. The proposed model utilizes time series forecasting techniques, including Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, to predict future performance based on historical data and project progress. The model also incorporates external factors, such as weather patterns and resource availability, as features to enhance the accuracy of forecasts. By applying the predictive power of machine learning, the performance forecasting model enables proactive identification of potential deviations from the baseline plan, which allows project managers to take timely corrective actions. The research aims to validate the effectiveness of the proposed approach using a case study of an urban road reconstruction project, comparing the model's forecasts with actual project performance data. The findings of this research contribute to the advancement of project management practices in the construction industry, offering a data-driven solution for improving project performance monitoring and control.

Keywords: project performance forecasting, machine learning, time series forecasting, cost variance, earned value management

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Authors: Sarayu Vanga, Jorge Galeano-Cabral, Kaya Wei

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Cerebral hypoxia refers to a condition in which there is a decrease in oxygen supply to the brain. Patients suffering from this condition experience a decrease in their body temperature. While there isn't any cure to treat cerebral hypoxia as of date, certain procedures are utilized to help aid in the treatment of the condition. Regulating the body temperature is an example of one of those procedures. Hypoxia is well known to reduce the body temperature of mammals, although the neural origins of this response remain uncertain. In order to speed recovery from this condition, it is necessary to maintain a stable body temperature. In this study, we present an approach to regulating body temperature for patients who suffer from cerebral hypoxia or other similar conditions. After a thorough literature study, we propose the use of thermoelectric blankets, which are temperature-controlled thermal blankets based on thermoelectric devices. These blankets are capable of heating up and cooling down the patient to stabilize body temperature. This feature is possible through the reversible effect that thermoelectric devices offer while behaving as a thermal sensor, and it is an effective way to stabilize temperature. Thermoelectricity is the direct conversion of thermal to electrical energy and vice versa. This effect is now known as the Seebeck effect, and it is characterized by the Seebeck coefficient. In such a configuration, the device has cooling and heating sides with temperatures that can be interchanged by simply switching the direction of the current input in the system. This design integrates various aspects, including a humidifier, ventilation machine, IV-administered medication, air conditioning, circulation device, and a body temperature regulation system. The proposed design includes thermocouples that will trigger the blanket to increase or decrease a set temperature through a medical temperature sensor. Additionally, the proposed design allows an efficient way to control fluctuations in body temperature while being cost-friendly, with an expected cost of 150 dollars. We are currently working on developing a prototype of the design to collect thermal and electrical data under different conditions and also intend to perform an optimization analysis to improve the design even further. While this proposal was developed for treating cerebral hypoxia, it can also aid in the treatment of other related conditions, as fluctuations in body temperature appear to be a common symptom that patients have for many illnesses.

Keywords: body temperature regulation, cerebral hypoxia, thermoelectric, blanket design

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Authors: Suresh Sahu, Abhijeet M. Vaidya, Naresh K. Maheshwari

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The efforts to understand the heat transfer behavior of supercritical water in supercritical water cooled reactor (SCWR) are ongoing worldwide to fulfill the future energy demand. The higher thermal efficiency of these reactors compared to a conventional nuclear reactor is one of the driving forces for attracting the attention of nuclear scientists. In this work, a solution procedure has been described for solving supercritical fluid flow problems in complex geometries. The solution procedure is based on non-staggered grid. All governing equations are discretized by finite volume method (FVM) in curvilinear coordinate system. Convective terms are discretized by first-order upwind scheme and central difference approximation has been used to discretize the diffusive parts. k-ε turbulence model with standard wall function has been employed. SIMPLE solution procedure has been implemented for the curvilinear coordinate system. Based on this solution method, 3-D Computational Fluid Dynamics (CFD) code has been developed. In order to demonstrate the capability of this CFD code in supercritical fluid flows, heat transfer to supercritical water in circular tubes has been considered as a test problem. Results obtained by code have been compared with experimental results reported in literature.

Keywords: curvilinear coordinate, body-fitted mesh, momentum interpolation, non-staggered grid, supercritical fluids

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Authors: Armana Huq

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Railway networks have a significant role in the economy of any country. Similar to other transportation modes, many lives suffer from fatalities or injuries caused by accidents related to the railway. Railway accidents are not as common as roadway accidents yet they are more devastating and damaging than other roadway accidents. Despite that, issues related to railway accidents are not taken into consideration with significant attention as a major threat because of their less frequency compared to other accident categories perhaps. However, the Federal Railroad Administration reported nearly twelve thousand train accidents related to the railroad in the year 2014, resulting in more than eight hundred fatalities and thousands of injuries in the United States alone of which nearly one third fatalities resulted from railway crossing accidents. From an analysis of railway accident data of six years (2005-2010), it has been revealed that 344 numbers of the collision were occurred resulting 200 people dead and 443 people injured in Bangladesh. This paper includes a comprehensive overview of the railway safety situation in Bangladesh from 1998 to 2015. Each year on average, eight fatalities are reported in at-grade level crossings due to railway accidents in Bangladesh. In this paper, the number of railway accidents that occurred in Bangladesh has been presented and a fatality rate of 58.62% has been estimated as the percentage of total at-grade railway level crossing accidents. For this study, analysis of railway accidents in Bangladesh for the period 1998 to 2015 was obtained from the police reported accident database using MAAP (Microcomputer Accident Analysis Package). Investigation of the major contributing factors to the railway accidents has been performed using the Multinomial Logit model. Furthermore, hotspot analysis has been conducted using ArcGIS. Eventually, some suggestions have been provided to mitigate those accidents.

Keywords: safety, human factors, multinomial logit model, railway

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Authors: Shazia Mannan, Zunera Khalid, Hammad-Ul-Mubeen

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Wingless type Mouse mammary tumor virus (MMTV) Integration site-10B (Wnt10B) is an important member of the Wnt protein family that functions as cellular messenger in paracrine manner. Aberrant Wnt10B activity is the cause of several abnormalities including cancers of breast, cervix, liver, gastric tract, esophagus, pancreas as well as physiological problems like obesity, and osteoporosis. The objective of this study was to determine the possible inhibitors against aberrant expression of Wnt10B in order to prevent and treat the physiological disorders associated with it. Wnt10B3D structure was predicted by using comparative modeling and then analyzed by PROCHECK, Verify3D, and Errat. The model having 84.54% quality value was selected and acylated to satisfy the hydrophobic nature of Wnt10B. For search of inhibitors, virtual screening was performed on Natural Products (NP) database. The compounds were filtered and ligand-based screening was performed using the antagonist for mouse Wnt-3A. This resulted in a library of 272 unique compounds having most potent drug like activities for Wnt-4. Out of the 271 molecules analyzed three small molecules ZINC35442871, ZINC85876388, and ZINC00754234 having activity against Wnt4 abbarent expression were found common through docking experiment of Wnt10B. It is concluded that the three molecules ZINC35442871, ZINC85876388, and ZINC00754234 can be considered as lead compounds for performing further drug designing experiments against aberrant Wnt expressions.

Keywords: Wnt10B inhibitors, comparative computational studies, proto-oncogene, molecular docking

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Authors: Shatha Ghareeb, Rawaa Al-Jumeily, Thar Baker

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In Abu Dhabi, there are many different education curriculums where sector of private schools and quality assurance is supervising many private schools in Abu Dhabi for many nationalities. As there are many different education curriculums in Abu Dhabi to meet expats’ needs, there are different requirements for registration and success. In addition, there are different age groups for starting education in each curriculum. In fact, each curriculum has a different number of years, assessment techniques, reassessment rules, and exam boards. Currently, students that transfer curriculums are not being placed in the right year group due to different start and end dates of each academic year and their date of birth for each year group is different for each curriculum and as a result, we find students that are either younger or older for that year group which therefore creates gaps in their learning and performance. In addition, there is not a way of storing student data throughout their academic journey so that schools can track the student learning process. In this paper, we propose to develop a computational framework applicable in multicultural countries such as UAE in which multi-education systems are implemented. The ultimate goal is to use cloud and fog computing technology integrated with Artificial Intelligence techniques of Machine Learning to aid in a smooth transition when assigning students to their year groups, and provide leveling and differentiation information of students who relocate from a particular education curriculum to another, whilst also having the ability to store and access student data from anywhere throughout their academic journey.

Keywords: admissions, algorithms, cloud computing, differentiation, fog computing, levelling, machine learning

Procedia PDF Downloads 141

Authors: Songwu Shen, Garrett Drysdale, Veerendranath Mannepalli, Qihua Dai, Yuan Wang, Yuli Chen, David Qian, Utkarsh Kakaiya

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Hardware accelerator has been a promising solution to reduce the cost of cloud data centers. This paper investigates the QoS enhancement of the acceleration of an important datacenter workload: the webserver (or proxy) that faces high computational consumption originated from secure sockets layer (SSL) or transport layer security (TLS) procession in the cloud environment. Our study reveals that for the accelerator maintenance cases—need to upgrade driver/firmware or hardware reset due to hardware hang; we still can provide cryptography services by switching to software during maintenance phase and then switching back to accelerator after maintenance. The switching is seamless to server application such as Nginx that runs inside a VM on top of the server. To achieve this high availability goal, we propose a comprehensive fallback solution based on Intel® QuickAssist Technology (QAT). This approach introduces an architecture that involves the collaboration between physical function (PF) and virtual function (VF), and collaboration among VF, OpenSSL, and web application Nginx. The evaluation shows that our solution could provide high reliability, availability, and scalability (RAS) of hardware cryptography service in a 7x24x365 manner in the cloud environment.

Keywords: accelerator, cryptography service, RAS, secure sockets layer/transport layer security, SSL/TLS, virtualization fallback architecture

Procedia PDF Downloads 153

Authors: Jigg Pelayo, Ricardo Villar

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Leaning on the development of aerial laser scanning in the Philippine geospatial industry, researches about remote sensing and machine vision technology became a trend. Object detection via template matching is one of its application which characterized to be fast and in real time. The paper purposely attempts to provide application for robust pattern matching algorithm based on the normalized cross correlation (NCC) criterion function subjected in Object-based image analysis (OBIA) utilizing high-resolution aerial imagery and low density LiDAR data. The height information from laser scanning provides effective partitioning order, thus improving the hierarchal class feature pattern which allows to skip unnecessary calculation. Since detection is executed in the object-oriented platform, mathematical morphology and multi-level filter algorithms were established to effectively avoid the influence of noise, small distortion and fluctuating image saturation that affect the rate of recognition of features. Furthermore, the scheme is evaluated to recognized the performance in different situations and inspect the computational complexities of the algorithms. Its effectiveness is demonstrated in areas of Misamis Oriental province, achieving an overall accuracy of 91% above. Also, the garnered results portray the potential and efficiency of the implemented algorithm under different lighting conditions.

Keywords: algorithm, LiDAR, object recognition, OBIA

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Authors: F. M. Pisano, M. Ciminello

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Aerospace, mechanical, and civil engineering infrastructures can take advantages from damage detection and identification strategies in terms of maintenance cost reduction and operational life improvements, as well for safety scopes. The challenge is to detect so called “barely visible impact damage” (BVID), due to low/medium energy impacts, that can progressively compromise the structure integrity. The occurrence of any local change in material properties, that can degrade the structure performance, is to be monitored using so called Structural Health Monitoring (SHM) systems, in charge of comparing the structure states before and after damage occurs. SHM seeks for any "anomalous" response collected by means of sensor networks and then analyzed using appropriate algorithms. Independently of the specific analysis approach adopted for structural damage detection and localization, textual reports, tables and graphs describing possible outlier coordinates and damage severity are usually provided as artifacts to be elaborated for information extraction about the current health conditions of the structure under investigation. Visual Analytics can support the processing of monitored measurements offering data navigation and exploration tools leveraging the native human capabilities of understanding images faster than texts and tables. Herein, a SHM system enrichment by integration of a Visual Analytics component is investigated. Analytical dashboards have been created by combining worksheets, so that a useful Visual Analytics tool is provided to structural analysts for exploring the structure health conditions examined by a Principal Component Analysis based algorithm.

Keywords: interactive dashboards, optical fibers, structural health monitoring, visual analytics

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Authors: S. Hutasavi, D. Chen

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The built-up area is a significant proxy to measure regional economic growth and reflects the Gross Provincial Product (GPP). However, an up-to-date and reliable database of built-up areas is not always available, especially in developing countries. The cloud-based geospatial analysis platform such as Google Earth Engine (GEE) provides an opportunity with accessibility and computational power for those countries to generate the built-up data. Therefore, this study aims to extract the built-up areas in Eastern Economic Corridor (EEC), Thailand using day and nighttime satellite imagery based on GEE facilities. The normalized indices were generated from Landsat 8 surface reflectance dataset, including Normalized Difference Built-up Index (NDBI), Built-up Index (BUI), and Modified Built-up Index (MBUI). These indices were applied to identify built-up areas in EEC. The result shows that MBUI performs better than BUI and NDBI, with the highest accuracy of 0.85 and Kappa of 0.82. Moreover, the overall accuracy of classification was improved from 79% to 90%, and error of total built-up area was decreased from 29% to 0.7%, after night-time light data from the Visible and Infrared Imaging Suite (VIIRS) Day Night Band (DNB). The results suggest that MBUI with night-time light imagery is appropriate for built-up area extraction and be utilize for further study of socioeconomic impacts of regional development policy over the EEC region.

Keywords: built-up area extraction, google earth engine, adaptive thresholding method, rapid mapping

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Authors: Shan Zhang, Peter Suechting

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Predicting trade patterns is critical to decision-making in public and private domains, especially in the current context of trade disputes among major economies. In the past, U.S. recycling has relied heavily on strong demand for recyclable materials overseas. However, starting in 2017, a series of new recycling policies (bans and higher inspection standards) was enacted by multiple countries that were the primary importers of recyclables from the U.S. prior to that point. As the global trade flow of recycling shifts, some new importers, mostly developing countries in South and Southeast Asia, have been overwhelmed by the sheer quantities of scrap materials they have received. As the leading exporter of recyclable materials, the U.S. now has a pressing need to build its recycling industry domestically. With respect to the global trade in scrap materials used for recycling, the interest in this paper is (1) predicting how the export of recyclable materials from the U.S. might vary over time, and (2) predicting how international trade flows for recyclables might change in the future. Focusing on three major recyclable materials with a history of trade, this study uses data-driven and machine learning (ML) algorithms---supervised (shrinkage and tree methods) and unsupervised (neural network method)---to decipher the international trade pattern of recycling. Forecasting the potential trade values of recyclables in the future could help importing countries, to which those materials will shift next, to prepare related trade policies. Such policies can assist policymakers in minimizing negative environmental externalities and in finding the optimal amount of recyclables needed by each country. Such forecasts can also help exporting countries, like the U.S understand the importance of healthy domestic recycling industry. The preliminary result suggests that gravity models---in addition to particular selection macroeconomic predictor variables--are appropriate predictors of the total export value of recyclables. With the inclusion of variables measuring aspects of the political conditions (trade tariffs and bans), predictions show that recyclable materials are shifting from more policy-restricted countries to less policy-restricted countries in international recycling trade. Those countries also tend to have high manufacturing activities as a percentage of their GDP.

Keywords: environmental economics, machine learning, recycling, international trade

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Authors: Sultan Alharby, Nick Harris, Alex Weddell, Jeff Reeve

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The concept of the Internet of Things (IoT) has received much attention over the last five years. It is predicted that the IoT will influence every aspect of our lifestyles in the near future. Wireless Sensor Networks are one of the key enablers of the operation of IoTs, allowing data to be collected from the surrounding environment. However, due to limited resources, nature of deployment and unattended operation, a WSN is vulnerable to various types of attack. Security is paramount for reliable and safe communication between IoT embedded devices, but it does, however, come at a cost to resources. Nodes are usually equipped with small batteries, which makes energy conservation crucial to IoT devices. Nevertheless, security cost in terms of energy consumption has not been studied sufficiently. Previous research has used a security specification of 802.15.4 for IoT applications, but the energy cost of each security level and the impact on quality of services (QoS) parameters remain unknown. This research focuses on the cost of security at the IoT media access control (MAC) layer. It begins by studying the energy consumption of IEEE 802.15.4 security levels, which is followed by an evaluation for the impact of security on data latency and throughput, and then presents the impact of transmission power on security overhead, and finally shows the effects of security on memory footprint. The results show that security overhead in terms of energy consumption with a payload of 24 bytes fluctuates between 31.5% at minimum level over non-secure packets and 60.4% at the top security level of 802.15.4 security specification. Also, it shows that security cost has less impact at longer packet lengths, and more with smaller packet size. In addition, the results depicts a significant impact on data latency and throughput. Overall, maximum authentication length decreases throughput by almost 53%, and encryption and authentication together by almost 62%.

Keywords: energy consumption, IEEE 802.15.4, IoT security, security cost evaluation

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Authors: Joanna Smoluk-Sikorska, Władysława Łuczka

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Organic farming is an important element of sustainable agriculture. It has been developing very dynamically in Poland, especially since Poland’s accession to the EU. Nevertheless, properly functioning organic market is a necessary condition justifying development of organic agriculture. Despite significant improvement, this market in Poland is still in the initial stage of growth. An important element of the market is distribution, especially retail, which offers specified product range to consumers. Therefore, there is a need to investigate retail outlets offering organic food in order to improve functioning of this part of the market. The inquiry research conducted in three types of outlets offering organic food, between 2011 and 2012 in the 8 largest Polish cities, shows that the majority of outlets offer cereals, processed fruit and vegetables as well as spices and the least shops – meat and sausages. The distributors mostly indicate unsatisfactory product range of suppliers as the reason for this situation. The main providers of the outlets are wholesalers, particularly in case of processed products, and in fresh products – organic farms. A very important distribution obstacle is dispersion of producers, which generates high transportation costs and what follows that, high price of organics. In the investigated shops, the most often used price calculation method is a cost method. The majority of the groceries and specialist shops apply margins between 21 and 40%. The margin in specialist outlets is the highest, in regard to the qualified service and advice. In turn, most retail networks declare the margin between 0 and 20%, which is consistent with low-price strategy applied in these shops. Some lacks in the product range of organics and in particular high prices cause that the demand volume is rather low. Therefore there is a need to support certain market actions, e.g. on-farm processing or promotion.

Keywords: organic food, retail, product range, supply sources

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Authors: Aisha F. Bushager

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Today we are more exposed than ever to cyber threats and attacks at personal, community, organizational, national, and international levels. More aspects of our lives are operating on computer networks simply because we are living in the fifth domain, which is called the Cyberspace. One of the most sensitive areas that are vulnerable to cyber threats and attacks is the Electronic Banking (e-Banking) area, where the banking sector is providing online banking services to its clients. To be able to obtain the clients trust and encourage them to practice e-Banking, also, to maintain the services provided by the banks and ensure safety, cyber security and risks control should be given a high priority in the e-banking area. The aim of the study is to carry out risk assessment on the e-banking services and determine the cyber threats, cyber attacks, and vulnerabilities that are facing the e-banking area specifically in the Kingdom of Bahrain. To collect relevant data, structured interviews were taken place with e-banking experts in different banks. Then, collected data where used as in input to the risk management framework provided by the National Institute of Standards and Technology (NIST), which was the model used in the study to assess the risks associated with e-banking services. The findings of the study showed that the cyber threats are commonly human errors, technical software or hardware failure, and hackers, on the other hand, the most common attacks facing the e-banking sector were phishing, malware attacks, and denial-of-service. The risks associated with the e-banking services were around the moderate level, however, more controls and countermeasures must be applied to maintain the moderate level of risks. The results of the study will help banks discover their vulnerabilities and maintain their online services, in addition, it will enhance the cyber security and contribute to the management and control of risks that are facing the e-banking sector.

Keywords: cyber security, e-banking, risk assessment, threats identification

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Authors: Esam I. Jassim

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The main aim of this paper is to demonstrate the prediction of the model capability of predicting the nucleation process, the growth rate, and the deposition potential of second phase particles in gas flowlines. The primary objective of the research is to predict the risk hazards involved in the marine transportation of compressed natural gas. However, the proposed model can be equally used for other applications including production and transportation of natural gas in any high-pressure flow-line. The proposed model employs the following three main components to approach the problem: computational fluid dynamics (CFD) technique is used to configure the flow field; the nucleation model is developed and incorporated in the simulation to predict the incipient hydrate particles size and growth rate; and the deposition of the gas/particle flow is proposed using the concept of the particle deposition velocity. These components are integrated in a comprehended model to locate the hydrate deposition in natural gas flowlines. The present research is prepared to foresee the deposition location of solid particles that could occur in a real application in Compressed Natural Gas loading and offloading. A pipeline with 120 m length and different sizes carried a natural gas is taken in the study. The location of particle deposition formed as a result of restriction is determined based on the procedure mentioned earlier and the effect of water content and downstream pressure is studied. The critical flow speed that prevents such particle to accumulate in the certain pipe length is also addressed.

Keywords: hydrate deposition, compressed natural gas, marine transportation, oceanography

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Authors: Dongqi Li, Yunjia Huang, Tomo Inoue, Kohei Inoue

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With urbanization, urban cultural heritage is facing the impact and destruction of modernization and urbanization. Many historical areas are losing their historical information and regional cultural characteristics, so it is necessary to carry out systematic color planning for historical areas in conservation. As an early focus on urban color planning, Japan has a systematic approach to urban color planning. Hence, this paper selects five merchant towns from the category of important traditional building preservation areas in Japan as the subject of this study to explore the color structure and emotion of this type of historic area. First, the image semantic segmentation method identifies the buildings, roads, and landscape environments. Their color data were extracted for color composition and emotion analysis to summarize their common features. Second, the obtained Internet evaluations were extracted by natural language processing for keyword extraction. The correlation analysis of the color structure and keywords provides a valuable reference for conservation decisions for this historic area in the town. This paper also combines the color structure and Internet evaluation results with generative adversarial networks to generate predicted images of color structure improvements and color improvement schemes. The methods and conclusions of this paper can provide new ideas for the digital management of environmental colors in historic districts and provide a valuable reference for the inheritance of local traditional culture.

Keywords: historic districts, color planning, semantic segmentation, natural language processing

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Authors: Yuning Guan

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Under the background of rapid urbanization, mountainous counties need to break through mountain barriers for urban expansion due to undulating topography, resulting in ecological problems such as landscape fragmentation and reduced biodiversity. Green infrastructure networks are constructed to alleviate the contradiction between urban expansion and ecological protection, promoting the healthy and sustainable development of urban ecosystems. This study applies the MSPA model, the MCR model and Linkage Mapper Tools to identify eco-sources and eco-corridors in the Shapingba District of Chongqing and combined with landscape connectivity assessment and circuit theory to delineate the importance levels to extract ecological pinch point areas on the corridors. The results show that: (1) 20 ecological sources are identified, with a total area of 126.47 km², accounting for 31.88% of the study area, and showing a pattern of ‘one core, three corridors, multi-point distribution’. (2) 37 ecological corridors are formed in the area, with a total length of 62.52km, with a ‘more in the west, less in the east’ pattern. (3) 42 ecological pinch points are extracted, accounting for 25.85% of the length of the corridors, which are mainly distributed in the eastern new area. Accordingly, this study proposes optimization strategies for sub-area protection of ecological sources, grade-level construction of ecological corridors, and precise restoration of ecological pinch points.

Keywords: green infrastructure network, morphological spatial pattern, minimal cumulative resistance, mountainous counties, circuit theory, shapingba district

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Authors: Kelvin Lo

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Nowadays, numerical modelling in geotechnical engineering is very common but sophisticated. Many advanced input settings and considerable computational efforts are required to optimize the design to reduce the construction cost. To optimize a design, it usually requires huge numerical models. If the optimization is conducted manually, there is a potentially dangerous consequence from human errors, and the time spent on the input and data extraction from output is significant. This paper presents an automation process introduced to numerical modelling (Plaxis 2D) of a trench excavation supported by a secant-pile retaining structure for a top-down tunnel project. Python code is adopted to control the process, and numerical modelling is conducted automatically in every 20m chainage along the 200m tunnel, with maximum retained height occurring in the middle chainage. Python code continuously changes the geological stratum and excavation depth under groundwater flow conditions in each 20m section. It automatically conducts trial and error to determine the required pile length and the use of props to achieve the required factor of safety and target displacement. Once the bending moment of the pile exceeds its capacity, it will increase in size. When the pile embedment reaches the default maximum length, it will turn on the prop system. Results showed that it saves time, increases efficiency, lowers design costs, and replaces human labor to minimize error.

Keywords: automation, numerical modelling, Python, retaining structures

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Authors: Olumide Simeon Ogunnusi, Shukor Abd Razak, Michael Kolade Adu

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Inter-agent communication manager facilitates communication among mobile agents via message passing mechanism. Until now, all Foundation for Intelligent Physical Agents (FIPA) compliant agent systems are capable of exchanging messages following the standard format of sending and receiving messages. Previous works tend to secure messages to be exchanged among a community of collaborative agents commissioned to perform specific tasks using cryptosystems. However, the approach is characterized by computational complexity due to the encryption and decryption processes required at the two ends. The proposed approach to secure agent communication allows only agents that are created by the host agent server to communicate via the agent communication channel provided by the host agent platform. These agents are assumed to be harmless. Therefore, to secure communication of legitimate agents from intrusion by external agents, a 2-phase policy enforcement system was developed. The first phase constrains the external agent to run only on the network server while the second phase confines the activities of the external agent to its execution environment. To implement the proposed policy, a controller agent was charged with the task of screening any external agent entering the local area network and preventing it from migrating to the agent execution host where the legitimate agents are running. On arrival of the external agent at the host network server, an introspector agent was charged to monitor and restrain its activities. This approach secures legitimate agent communication from Man-in-the Middle and Replay attacks.

Keywords: agent communication, introspective agent, isolation of agent, policy enforcement system

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Authors: Florian Monetti, Brett Beamsley, Peter McComb, Simon Weppe

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Land reclamation from the ocean has considerably increased over past decades to support worldwide rapid urban growth. Reshaping the coastline, however, inevitably affects coastal systems. One of the main challenges for coastal oceanographers is to predict the physical and morphological responses for nearshore systems to man-made changes over multiple time-scales. Fully-coupled numerical models are powerful tools for simulating the wide range of interactions between flow field and bedform morphology. Restricted and inconsistent measurements, combined with limited computational resources, typically make this exercise complex and uncertain. In the present study, we investigate the impact of proposed land reclamation within a wave-dominated bay in New Zealand. For this purpose, we first calibrated our morphological model based on the long-term evolution of the bay resulting from land reclamation carried out in the 1950s. This included the application of sedimentological spin-up and reduction techniques based on historical bathymetry datasets. The updated bathymetry, including the proposed modifications of the bay, was then used to predict the effect of the proposed land reclamation on the wave climate and morphology of the bay after one decade. We show that reshaping the bay induces a distinct symmetrical response of the shoreline which likely will modify the nearshore wave patterns and consequently recreational activities in the area.

Keywords: coastal waves, impact of land reclamation, long-term coastal evolution, morphodynamic modeling

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Authors: Surah Aldakhl, Dafer Alali, Mohamed Zohdy

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The next generation of electricity grid infrastructure, known as the "smart grid," integrates smart ICT (information and communication technology) into existing grids in order to alleviate the drawbacks of existing one-way grid systems. Future power systems' efficiency and dependability are anticipated to significantly increase thanks to the Smart Grid, especially given the desire for renewable energy sources. The security of the Smart Grid's cyber infrastructure is a growing concern, though, as a result of the interconnection of significant power plants through communication networks. Since cyber-attacks can destroy energy data, beginning with personal information leaking from grid members, they can result in serious incidents like huge outages and the destruction of power network infrastructure. We shall thus propose a secure smart energy management system based on the Blockchain as a remedy for this problem. The power transmission and distribution system may undergo a transformation as a result of the inclusion of optical fiber sensors and blockchain technology in smart grids. While optical fiber sensors allow real-time monitoring and management of electrical energy flow, Blockchain offers a secure platform to safeguard the smart grid against cyberattacks and unauthorized access. Additionally, this integration makes it possible to see how energy is produced, distributed, and used in real time, increasing transparency. This strategy has advantages in terms of improved security, efficiency, dependability, and flexibility in energy management. An in-depth analysis of the advantages and drawbacks of combining blockchain technology with optical fiber is provided in this paper.

Keywords: smart grids, blockchain, fiber optic sensor, security

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Authors: Asharaf Abdul Salam

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Arabian Gulf countries have norms and rules different from others: having an exodus of male immigrant labor contract holders of age 20-60 years as a floating population. Such a demographic scenario camouflages population ageing. However, it is observed on examining vigilantly, not only in the native population but also in the general population. This research on population ageing in the Arabian Gulf examines ageing scenario and concerns through analyses of international databases (US Census Bureau and United Nations) and national level databases (Censuses and Surveys) apart from a review of published research. Transitions in demography and epidemiology lead to gains in life expectancy and thereby reductions in fertility, leading to ageing of the population in the region. Even after bringing adult immigrants, indices and age pyramids show an increasing ageing trend in the total population, demonstrating an ageing workforce. Besides, the exclusive native population analysis reveals a trend of expansive pyramids (pre-transitional stage) turning to constrictive (transition stage) and cylindrical (post-transition stage) shapes. Age-based indices such as the index of ageing, age dependency ratio, and median age confirm this trend. While the feminine nature of ageing is vivid, gains in life expectancy and causes of death in old age, indicating co-morbidity compression, are concerns to ageing. Preparations are in demand to cope with ageing from different dimensions, as explained in the United Nations Plans of Action. A strategy of strengthening informal care with supportive semi-formal and supplementary formal care networks would alleviate this crisis associated with population ageing.

Keywords: total versus native population, indices of ageing, age pyramids, feminine nature, comorbidity compression, strategic interventions

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Authors: Sameen Tariq, Saira Bano, Sayyada Ghufrana Nadeem

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The Ocean, which covers over 70% of the world's surface, is wealthy in biodiversity as well as a rich wellspring of microorganisms with huge potential. The oceanic climate is home to an expansive scope of plants, creatures, and microorganisms. Marine microbial networks, which incorporate microscopic organisms, infections, and different microorganisms, enjoy different benefits in biotechnological processes. Samples were collected from marine environments, including soil and water samples, to cultivate the uncultured marine organisms by using Zobell’s medium, Sabouraud’s dextrose agar, and casein media for this purpose. Following isolation, we conduct microscopy and biochemical tests, including gelatin, starch, glucose, casein, catalase, and carbohydrate hydrolysis for further identification. The results show that more gram-positive and gram-negative bacteria. The isolation process of marine organisms is essential for understanding their ecological roles, unraveling their biological secrets, and harnessing their potential for various applications. Marine organisms exhibit remarkable adaptations to thrive in the diverse and challenging marine environment, offering vast potential for scientific, medical, and industrial applications. The isolation process plays a crucial role in unlocking the secrets of marine organisms, understanding their biological functions, and harnessing their valuable properties. They offer a rich source of bioactive compounds with pharmaceutical potential, including antibiotics, anticancer agents, and novel therapeutics. This study is an attempt to explore the diversity and dynamics related to marine microflora and their role in biofilm formation.

Keywords: marine microorganisms, ecosystem, fungi, biofilm, gram-positive, gram-negative

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Authors: Zhuoneng Li, Zeeshan A. Rana, Karl W. Jenkins

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In industry, to validate a case, often a multitude of simulation are required and in order to demonstrate confidence in the process where users tend to use a coarser mesh. Therefore, it is imperative to establish the coarsest mesh that could be used while keeping reasonable simulation accuracy. To date, the two most reliable, affordable and broadly used advanced simulations are the hybrid RANS (Reynolds Averaged Navier Stokes)/LES (Large Eddy Simulation) and wall modelled LES. The potentials in these two simulations will still be developed in the next decades mainly because the unaffordable computational cost of a DNS (Direct Numerical Simulation). In the wall modelled LES, the turbulence model is applied as a sub-grid scale model in the most inner layer near the wall. The RANS turbulence models cover the entire boundary layer region in a hybrid RANS/LES (Detached Eddy Simulation) and its variants, therefore, the RANS still has a very important role in the state of art simulations. This research focuses on the turbulence model mesh sensitivity analysis where various turbulence models such as the S-A (Spalart-Allmaras), SSG (Speziale-Sarkar-Gatski), K-Omega transitional SST (Shear Stress Transport), K-kl-Omega, γ-Reθ transitional model, v2f are evaluated within the OpenFOAM. The simulations are conducted on a fully developed turbulent flow over a flat plate where the skin friction coefficient as well as velocity profiles are obtained to compare against experimental values and DNS results. A concrete conclusion is made to clarify the mesh sensitivity for different turbulence models.

Keywords: mesh sensitivity, turbulence models, OpenFOAM, RANS

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Authors: Nesrine Boussaoui

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Background: International students often encounter challenges while studying in the UK, including communication and language barriers, lack of social networks, and socio-cultural differences that adversely impact on their mental health. For Algerian international students (AISs), these challenges may be heightened as English is not their first language and the culture of their homeland is substantially different from British culture, yet research has to incorporate their experiences and perspectives. Aim: The current study aimed to explore AISs’ 1) understandings of mental health; 2) issues of disclosure for mental health difficulties; and 3) mental health help-seeking and coping strategies. Method: In-depth, audio recorded semi-structured interviews (n = 20) with AISs in UK universities were conducted. An inductive, reflective thematic approach analysis was used. Finding: The following themes and associated sub-themes were developed: (1) Algerian cultural influences on mental health understanding(socio-cultural comparisons); (2) the paradox of the family (pressure vs. support); (3) stigma and fear of disclosure; (4) Barriers to formal help-seeking (informal disclosure as first step to seeking help); (5) Communication barriers (resort to mother tongue to disclose); (6) Self-reliance and religious coping. Conclusion: Recognising and understanding the challenges faced by AISs in terms of disclosure and mental health help-seeking is essential to reduce barriers to formal help-seeking. Informal disclosure among peers is often the first step to seeking help. Enhancing practitioners’ cultural competences and awareness of diverse understandings of mental health and the role of religious coping among AISs’ may have transferable benefits to a wider international student population.

Keywords: mental health, stegma, coping, disclosure

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Authors: Zachary Huffman, Joana Rocha

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Wall-pressure fluctuations induced by the turbulent boundary layer (TBL) developed over aircraft are a significant source of aircraft cabin noise. Since the power spectral density (PSD) of these pressure fluctuations is directly correlated with the amount of sound radiated into the cabin, the development of accurate empirical models that predict the PSD has been an important ongoing research topic. The sound emitted can be represented from the pressure fluctuations term in the Reynoldsaveraged Navier-Stokes equations (RANS). Therefore, early TBL empirical models (including those from Lowson, Robertson, Chase, and Howe) were primarily derived by simplifying and solving the RANS for pressure fluctuation and adding appropriate scales. Most subsequent models (including Goody, Efimtsov, Laganelli, Smol’yakov, and Rackl and Weston models) were derived by making modifications to these early models or by physical principles. Overall, these models have had varying levels of accuracy, but, in general, they are most accurate under the specific Reynolds and Mach numbers they were developed for, while being less accurate under other flow conditions. Despite this, recent research into the possibility of using alternative methods for deriving the models has been rather limited. More recent studies have demonstrated that an artificial neural network model was more accurate than traditional models and could be applied more generally, but the accuracy of other machine learning techniques has not been explored. In the current study, an original model is derived using a stepwise regression algorithm in the statistical programming language R, and TBL wall-pressure fluctuations PSD data gathered at the Carleton University wind tunnel. The theoretical advantage of a stepwise regression approach is that it will automatically filter out redundant or uncorrelated input variables (through the process of feature selection), and it is computationally faster than machine learning. The main disadvantage is the potential risk of overfitting. The accuracy of the developed model is assessed by comparing it to independently sourced datasets.

Keywords: aircraft noise, machine learning, power spectral density models, regression models, turbulent boundary layer wall-pressure fluctuations

Procedia PDF Downloads 134