Search results for: neuron network

Authors: Zainal Arifin, Avanti Fontana

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Considering that TOE framework is the most useful instrument for studying technology adoption in firm context, this paper will analyze the influence of technological, organizational and environmental (TOE) factors to the Dynamic capabilities (DCs) associated with technology adoption strategy for improving the firm’s performance. Focusing on the determinant factors of technology adoption at the firm level, the study will contribute to the broader study of resource base view (RBV) and dynamic capability (DC). There is no study connecting directly the TOE factors to the DCs, this paper proposes technology adoption as a functional competence/capability which mediates a relationship between technology adoptions with firm’s performance. The study wants to show a conceptual model of the indirect effects of DCs at the firm level, which can be key predictors of firm performance in dynamic business environment. The results of this research is mostly relevant to top corporate executives (BOD) or top management team (TMT) who seek to provide some supporting ‘hardware’ content and condition such as technological factors, organizational factors, environmental factors, and to improve firm's ‘software ‘ ability such as adaptive capability, absorptive capability and innovative capability, in order to achieve a successful technology adoption in organization. There are also mediating factors which are elaborated at this paper; timing and external network. A further research for showing its empirical results is highly recommended.

Keywords: technology adoption, TOE framework, dynamic capability, resources based view

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Authors: Ruchi Makani, B. V. R. Reddy

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Mobile Adhoc Networks (MANETs) are gaining popularity due to their potential of providing low-cost mobile connectivity solutions to real-world communication problems. Integrating Intrusion Detection Systems (IDS) in MANETs is a tedious task by reason of its distinctive features such as dynamic topology, de-centralized authority and highly controlled/limited resource environment. IDS primarily use automated soft-computing techniques to monitor the inflow/outflow of traffic packets in a given network to detect intrusion. Use of machine learning techniques in IDS enables system to make decisions on intrusion while continuous keep learning about their dynamic environment. An appropriate IDS model is essential to be selected to expedite this application challenges. Thus, this paper focused on fuzzy-logic based machine learning IDS technique for MANETs and presented their applicability for achieving effectiveness in identifying the intrusions. Further, the selection of appropriate protocol attributes and fuzzy rules generation plays significant role for accuracy of the fuzzy-logic based IDS, have been discussed. This paper also presents the critical attributes of MANET’s routing protocol and its applicability in fuzzy logic based IDS.

Keywords: AODV, mobile adhoc networks, intrusion detection, anomaly detection, fuzzy logic, fuzzy membership function, fuzzy inference system

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Authors: Vishal Kumar Gupta

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Purpose: This study has been undertaken to develop novel pH sensitive interpenetrating network hydrogel beads. Methods: The pH sensitive PAAM-g-Guar gum copolymer was synthesized by free radical polymerization followed by alkaline hydrolysis. Beads of guar gum-grafted-polyacrylamide and sodium Carboxy methyl cellulose (Na CMC) loaded with Pantoprazole sodium were prepared and evaluated for pH sensitivity, swelling properties, drug entrapment efficiency and in vitro drug release characteristics. Seven formulations were prepared for the drug with varying polymer and cross linker concentrations. Results: The grafting and alkaline hydrolysis reactions were confirmed by FT-IR spectroscopy. Differential scanning calorimetry was carried out to know the compatibility of encapsulated drug with the polymers. Scanning electron microscopic study revealed that the IPN beads were spherical. The entrapment efficiency was found to be in the range of 85-92%. Particle size analysis was carried out by optical microscopy. As the pH of the medium was changed from 1.2 to 7.4, a considerable increase in swelling was observed for all beads. Increase in the copolymer concentration showed sustained the drug release up to 12 hrs. Drug release from the beads followed super case II transport mechanism. Conclusion: It was concluded that guar gum-acrylamide beads, cross-linked with aluminum chloride offer an opportunity for controlled drug release of pantoprazole sodium.

Keywords: IPN, hydrogels, DSC, SEM

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Authors: Yilin Wang, Ayumi Goto

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Although most females will experience menopause due to social value habits of cultural factors, some Chinese women may lack the confidence to talk about the problems they are experiencing while going through menopause. Also, sometimes the inappropriateness of medical terminology leads to panic when women face the symptoms associated with menopause. On top of that, when women avoid discussing menopause as a topic, others are less likely to pay attention to the needs of menopausal women as their bodies change. This research will compare the experience of Chinese menopausal women and diasporic Chinese women's perceptions of menopause. A qualitative study will be conducted by collecting and analyzing experiences and perceptions to compare differences in women's perceptions of menopause, considering cultural and social factors. In addition, the study will gather information on the differences in the conceptualization of menopause between the Chinese and Canadian medical fields. Co-design sessions will be held to establish how to bring menopause to the attention of people other than women. Furthermore, a support network for menopause women will be created through these co-design sessions. It is hoped that this research will contribute to a proper understanding of menopause and provide support for Chinese women. This research is built upon feminist standpoint theory and inclusive design theory. The results of this study will be presented in this paper.

Keywords: menopause, feminist standpoint theory, Chinese national & diasporic women, inclusive design

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Authors: Giovanni Cicceri, Roberta Maisano, Nathalie Morey, Salvatore Distefano

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The main goal of this paper is to present a solution for a water purification system based on an Environmental Internet of Things (EIoT) platform to monitor and control water quality and machine learning (ML) models to support decision making and speed up the processes of purification of water. A real case study has been implemented by deploying an EIoT platform and a network of devices, called Gramb meters and belonging to the Gramb project, on wastewater purification systems located in Calabria, south of Italy. The data thus collected are used to control the wastewater quality, detect anomalies and predict the behaviour of the purification system. To this extent, three different statistical and machine learning models have been adopted and thus compared: Autoregressive Integrated Moving Average (ARIMA), Long Short Term Memory (LSTM) autoencoder, and Facebook Prophet (FP). The results demonstrated that the ML solution (LSTM) out-perform classical statistical approaches (ARIMA, FP), in terms of both accuracy, efficiency and effectiveness in monitoring and controlling the wastewater purification processes.

Keywords: environmental internet of things, EIoT, machine learning, anomaly detection, environment monitoring

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Authors: Shan Li, Qiuwen Ma

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Integrated project delivery (IPD) is a project delivery method distinguished by a shared risk/rewards mechanism and multiparty agreement. IPD has drawn increasing attention from construction industry due to its reliability to deliver high-performing buildings. However, unavailable IPD specific insurance concerns the industry participants who are interested in IPD implementation. Even though the risk management capability can be enhanced using shared risk mechanism, some risks may occur when the partners do not commit themselves into the integrated practices in a desired manner. This is because the intense collaboration and close integration can not only create added value but bring new opportunistic behaviors and disputes. The study is aimed to investigate the risks of implementing IPD using Bayesian theory. IPD risk taxonomy is presented to identify all potential risks of implementing IPD and a risk network map is developed to capture the interdependencies between IPD risks. The conditional relations between risk occurrences and the impacts of IPD risks on project performances are evaluated and simulated based on Bayesian theory. The probability of project outcomes is predicted by simulation. In addition, it is found that some risks caused by integration are most possible occurred risks. This study can help the IPD project participants identify critical risks of adopting IPD to improve project performances. In addition, it is helpful to develop IPD specific insurance when the pertinent risks can be identified.

Keywords: Bayesian theory, integrated project delivery, project risks, project performances

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Authors: Jessica Lopez Espejel, Mahaman Sanoussi Yahaya Alassan, Walid Dahhane, El Hassane Ettifouri

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Pretrained transformer-based models have shown high performance in natural language generation tasks. However, a new wave of interest has surged: automatic programming language code generation. This task consists of translating natural language instructions to a source code. Despite the fact that well-known pre-trained models on language generation have achieved good performance in learning programming languages, effort is still needed in automatic code generation. In this paper, we introduce JaCoText, a model based on Transformer neural network. It aims to generate java source code from natural language text. JaCoText leverages the advantages of both natural language and code generation models. More specifically, we study some findings from state of the art and use them to (1) initialize our model from powerful pre-trained models, (2) explore additional pretraining on our java dataset, (3) lead experiments combining the unimodal and bimodal data in training, and (4) scale the input and output length during the fine-tuning of the model. Conducted experiments on CONCODE dataset show that JaCoText achieves new state-of-the-art results.

Keywords: java code generation, natural language processing, sequence-to-sequence models, transformer neural networks

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Authors: Michał Witas

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This paper presents a technical approach to analysis of security of SCADA systems. Main goal of the paper is to make SCADA administrators aware of risks resulting from SCADA systems usage and to familiarize with methods that can be adopt to existing or planned system, to increase overall system security level. Because SCADA based systems become a industrial standard, more attention should be paid to the security of that systems. Industrial Control Systems (ICS) like SCADA are responsible for controlling crucial aspects of wide range of industrial processes. In pair with that responsibility, goes a lot of money that can be earned or lost – this fact is main reason of increased interest of attackers. Additionally ICS are often responsible for maintaining resources strategic from the point of view of national economy, like electricity (including nuclear power plants), heating, water resources or military facilities, so they can be targets of terrorist cybernetic attacks. Without proper risk analysis and management, vulnerabilities resulting from the usage of SCADA can be easily exploited by potential attacker. Paper is based mostly on own experience in systems security, gathered during academic studies and professional work in international company. As title suggests, it will cover only basics of topic, because every of points mentioned in the document can be base for additional research and papers.

Keywords: denial of service, SCADA, security policy, distributed network

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Authors: Amin Kamal Akhnoukh, Lois Zaki Kamel, Magued Mourad Barsoum

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The objective of this research is to mitigate and prevent the alkali silica reactivity (ASR) in highway construction projects. ASR is a deleterious reaction initiated when the silica content of the aggregate reacts with alkali hydroxides in cement in the presence of relatively high moisture content. The ASR results in the formation of an expansive white colored gel-like material which forms the destructive tensile stresses inside hardened concrete. In this research, different types of local aggregates available in the State of Arkansas were mixed and mortar bars were poured according to the ASTM specifications. Mortar bars expansion was measured versus time and aggregates with potential ASR problems were detected. Different types of supplementary cementitious materials (SCMs) were used in remixing mortar bars with highly reactive aggregates. Length changes for remixed bars proved that different types of SCMs can be successfully used in reducing the expansive effect of ASR. SCMs percentage by weight is highly dependent on the SCM type. The result of this study will help avoiding future losses due to ASR cracking in construction project and reduce the maintenance, repair, and replacement budgets required for highways network.

Keywords: alkali silica reaction, aggregates, misture, cracks, Mortar Bar Test, supplementary cementitious materials

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Authors: Ali Babaei

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There is a worldwide growing tendency toward construction of infrastructures with the possibility of private sector participation instead of free exploitation of public infrastructures. The construction and development of roads through private sector participation is performed by different countries because of appropriate results and benefits such as compensation of public budget deficit in road construction and maintenance and responding to traffic growth (demand). Toll is the most definite form of budget provision in road development. There are two issues in the toll rate assignment: A. costing of transport, B. Cost allocation and distribution of cost between different types of vehicles as each vehicle pay its own share. There can be different goals in toll collection and its extent is variable according to the strategy of toll collection. Costing principles in different countries are based on inclusion of the whole transport and not peculiar to the toll roads. For example, fuel tax policy functions where the road network users pay transportation cost (not just users of toll road). Whereas transportation infrastructures in Iran are free, these methods are not applicable. In Iran, different toll freeways have built by public investment and government provides participation in the road construction through encouragement of financial institutions. In this paper, the existing policies about the toll roads are studied and then the appropriate method of costing and cost allocation to different vehicles is introduced.

Keywords: toll allocation, road pricing, transportation, financial and industrial systems

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Authors: Joseph C. Chen, Venkata Mohan Kudapa

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Development of two real-time surface roughness (Ra) prediction systems for milling operations was attempted. The systems used not only cutting parameters, such as feed rate and spindle speed, but also the cutting current generated and corrected by a clamp type energy sensor. Two different approaches were developed. First, a fuzzy inference system (FIS), in which the fuzzy logic rules are generated by experts in the milling processes, was used to conduct prediction modeling using current cutting data. Second, a neuro-fuzzy system (ANFIS) was explored. Neuro-fuzzy systems are adaptive techniques in which data are collected on the network, processed, and rules are generated by the system. The inference system then uses these rules to predict Ra as the output. Experimental results showed that the parameters of spindle speed, feed rate, depth of cut, and input current variation could predict Ra. These two systems enable the prediction of Ra during the milling operation with an average of 91.83% and 94.48% accuracy by FIS and ANFIS systems, respectively. Statistically, the ANFIS system provided better prediction accuracy than that of the FIS system.

Keywords: surface roughness, input current, fuzzy logic, neuro-fuzzy, milling operations

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Authors: Simon Mwima, Kasule Solomon Kibirige, Evans Jennifer Mann, Bosco Mukuba, Edson Chipalo, Agnes Nzomene, Eusebius Small, Moses Okumu

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Introduction: Social workers must create, implement, and evaluate client-centered psychosocial interventions (CCPI) to reduce the impact of intersectional stigma on HIV service utilization among sexual minorities. We contribute to the scarcity of evidence about sexual minorities in Uganda by using social support theory to explore clients' perceptions that shape CCPI. Based on Focused Group Discussion (FGD) with 31 adolescents recruited from Kampala's HIV clinics in 2021, our findings reveal the positive influence of instrumental, informational, esteem, emotional, and social network support as intersectional stigma reduction interventions. Men who have sex with men, lesbians, and bisexual women used such strategies to navigate a heavily criminalized and stigmatizing setting during the COVID-19 pandemic in Uganda. Conclusion: This study provides evidence for the social work profession to develop and implement psychosocial interventions that reduce HIV stigma and discrimination among MSM, lesbians, and bisexual young people living with HIV in Uganda.

Keywords: pyschosocial interventions, social work, intersectional stigma, HIV/AIDS, adolescents, sexual minorities, Uganda

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Authors: Sumaya Iqbal, Aijaz Ahmad Reshi

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The advances in sensor and embedded technologies have led to rapid developments in Wireless Sensor Networks (WSNs). Presently researchers focus on the integration of WSNs to Internet for their pervasive availability to access these network resources as the interoperable subsystems. The recent computing technologies like cloud computing has made the resource sharing as a converged infrastructure with required service interfaces for the shared resources over the Internet. This paper presents application architecture for wireless Sensor and Actuator Networks (WSANS) following web of things, which allows easy integration of each node to the Internet in order to provide them web accessibility. The architecture enables the sensors and actuator nodes accessed and controlled using cloud interface on WWW. The application architecture was implemented using existing web and its emerging technologies. In particular Representational State Transfer protocol (REST) was extended for the specific requirements of the application. Cloud computing environment has been used as a development platform for the application to assess the possibility of integrating the WSAN nodes to Cloud services. The mushroom farm environment monitoring and control using WSANs has been taken as a research use case.

Keywords: WSAN, REST, web of things, ZigBee, cloud interface, PaaS, sensor gateway

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Authors: Amir Farouk Ahmed Ali Hussin

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Social media, such as Facebook, LinkedIn and Ex-Twitter have experienced exponential growth and a remarkable adoption rate in recent years. They give fantastic means of online social interactions and communications with family, friends, and colleagues from around the corner or across the globe, and they have become an important part of daily digital interactions for more than one and a half billion users around the world. The personal information sharing practices that social network providers encourage have led to their success as innovative social interaction platforms. Moreover, these practices have outcome in concerns with respect to privacy and security from different stakeholders. Guiding these privacy and security concerns in social networks is a must for these networks to be sustainable. Real security and privacy tools may not be enough to address existing concerns. Some points should be followed to protect users from the existing risks. In this research, we have checked the various privacy and security issues and concerns pertaining to social media. However, we have classified these privacy and security issues and presented a thorough discussion of the effects of these issues and concerns on the future of the social networks. In addition, we have presented a set of points as precaution measures that users can consider to address these issues.

Keywords: international legal, consultation mix, legal research, small and medium-sized enterprises, strategic International law, strategy alignment, house of laws, deployment, production strategy, legal strategy, business strategy

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Authors: Ceren Kaya, Okan Erkaymaz, Orhan Ayar, Mahmut Özer

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Diabetes Mellitus (Diabetes) is a disease based on insulin hormone disorders and causes high blood glucose. Clinical findings determine that diabetes can be diagnosed by electrophysiological signals obtained from the vital organs. 'Diabetic Retinopathy' is one of the most common eye diseases resulting on diabetes and it is the leading cause of vision loss due to structural alteration of the retinal layer vessels. In this study, features of horizontal and vertical Video-Oculography (VOG) signals have been used to classify non-proliferative and proliferative diabetic retinopathy disease. Twenty-five features are acquired by using discrete wavelet transform with VOG signals which are taken from 21 subjects. Two models, based on multi-layer perceptron and radial basis function, are recommended in the diagnosis of Diabetic Retinopathy. The proposed models also can detect level of the disease. We show comparative classification performance of the proposed models. Our results show that proposed the RBF model (100%) results in better classification performance than the MLP model (94%).

Keywords: diabetic retinopathy, discrete wavelet transform, multi-layer perceptron, radial basis function, video-oculography (VOG)

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Authors: Sawan Kumar Jangid

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The paper attempts to study various facets of urbanization and regional planning in the framework of the present conditions and future needs. Urbanization is a dynamic system in which development and changes are prominent features; which implies population growth and changes in the primary, secondary and tertiary sector in the economy. Urban population is increasing day by day due to a natural increase in population and migration from rural areas, and the impact is bound to have in urban areas in terms of infrastructure, environment, water supply and other vital resources. For the organized way of planning and monitoring the implementation of Physical urban and regional plans high-resolution satellite imagery is the potential solution. Now the Remote Sensing data is widely used in urban as well as regional planning, infrastructure planning mainly telecommunication and transport network planning, highway development, accessibility to market area development in terms of catchment and population built-up area density. With Remote Sensing it is possible to identify urban growth, which falls outside the formal planning control. Remote Sensing and GIS technique combined together facilitate the planners, in making a decision, for general public and investors to have relevant data for their use in minimum time. This paper sketches out the Urbanization modal for the future development of Urban and Regional Planning. The paper suggests, a dynamic approach towards regional development strategy.

Keywords: development, dynamic, migration, resolution

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Authors: Foad Nazari, Seyed Mahmood Hosseini, Mohammad Hossein Abolbashari, Mohammad Hassan Abolbashari

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The present study is concerned with the optimal design of functionally graded plates using particle swarm optimization (PSO) algorithm. In this study, meshless local Petrov-Galerkin (MLPG) method is employed to obtain the functionally graded (FG) plate’s natural frequencies. Effects of two parameters including thickness to height ratio and volume fraction index on the natural frequencies and total mass of plate are studied by using the MLPG results. Then the first natural frequency of the plate, for different conditions where MLPG data are not available, is predicted by an artificial neural network (ANN) approach which is trained by back-error propagation (BEP) technique. The ANN results show that the predicted data are in good agreement with the actual one. To maximize the first natural frequency and minimize the mass of FG plate simultaneously, the weighted sum optimization approach and PSO algorithm are used. However, the proposed optimization process of this study can provide the designers of FG plates with useful data.

Keywords: optimal design, natural frequency, FG plate, hybrid meshless method, MLPG method, ANN approach, particle swarm optimization

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Authors: M. T. Tsepav, Y. Adamu, M. A. Umar

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A geoelectric survey was carried out in some parts of Angwan Gwari, an outskirt of Lapai Local Government Area on Niger State which belongs to the Nigerian Basement Complex, with the aim of evaluating the soil corrosivity, aquifer transmissivity and protective capacity of the area from which aquifer characterisation was made. The G41 Resistivity Meter was employed to obtain fifteen Schlumberger Vertical Electrical Sounding data along profiles in a square grid network. The data were processed using interpex 1-D sounding inversion software, which gives vertical electrical sounding curves with layered model comprising of the apparent resistivities, overburden thicknesses and depth. This information was used to evaluate longitudinal conductance and transmissivities of the layers. The results show generally low resistivities across the survey area and an average longitudinal conductance variation from 0.0237Siemens in VES 6 to 0.1261 Siemens in VES 15 with almost the entire area giving values less than 1.0 Siemens. The average transmissivity values range from 96.45 Ω.m2 in VES 4 to 299070 Ω.m2 in VES 1. All but VES 4 and VES14 had an average overburden greater than 400 Ω.m2, these results suggest that the aquifers are highly permeable to fluid movement within, leading to the possibility of enhanced migration and circulation of contaminants in the groundwater system and that the area is generally corrosive.

Keywords: geoelectric survey, corrosivity, protective capacity, transmissivity

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Authors: Ahmed Elbeheri, Tarek Zayed

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Steel bridges deterioration has been one of the problems in North America for the last years. Steel bridges deterioration mainly attributed to the difficult weather conditions. Steel bridges suffer fatigue cracks and corrosion, which necessitate immediate inspection. Visual inspection is the most common technique for steel bridges inspection, but it depends on the inspector experience, conditions, and work environment. So many Non-destructive Evaluation (NDE) models have been developed use Non-destructive technologies to be more accurate, reliable and non-human dependent. Non-destructive techniques such as The Eddy Current Method, The Radiographic Method (RT), Ultra-Sonic Method (UT), Infra-red thermography and Laser technology have been used. Digital Image processing will be used for Corrosion detection as an Alternative for visual inspection. Different models had used grey-level and colored digital image for processing. However, color image proved to be better as it uses the color of the rust to distinguish it from the different backgrounds. The detection of the rust is an important process as it’s the first warning for the corrosion and a sign of coating erosion. To decide which is the steel element to be repainted and how urgent it is the percentage of rust should be calculated. In this paper, an image processing approach will be developed to detect corrosion and its severity. Two models were developed 1st to detect rust and 2nd to detect rust percentage.

Keywords: steel bridge, bridge inspection, steel corrosion, image processing

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Authors: Yavuz Unal, Kemal Polat, H. Erdinc Kocer

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In this study, a decision support system comprising three stages has been proposed to detect the disc abnormalities of the lumbar region. In the first stage named the feature extraction, T2-weighted sagittal and axial Magnetic Resonance Images (MRI) were taken from 55 people and then 27 appearance and shape features were acquired from both sagittal and transverse images. In the second stage named the feature weighting process, k-means clustering based feature weighting (KMCBFW) proposed by Gunes et al. Finally, in the third stage named the classification process, the classifier algorithms including multi-layer perceptron (MLP- neural network), support vector machine (SVM), Naïve Bayes, and decision tree have been used to classify whether the subject has lumbar disc or not. In order to test the performance of the proposed method, the classification accuracy (%), sensitivity, specificity, precision, recall, f-measure, kappa value, and computation times have been used. The best hybrid model is the combination of k-means clustering based feature weighting and decision tree in the detecting of lumbar disc disease based on both sagittal and axial MR images.

Keywords: lumbar disc abnormality, lumbar MRI, lumbar spine, hybrid models, hybrid features, k-means clustering based feature weighting

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Authors: Talal Alshammari, Nasser Alshammari, Mohamed Sedky, Chris Howard

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With the widespread adoption of the Internet-connected devices, and with the prevalence of the Internet of Things (IoT) applications, there is an increased interest in machine learning techniques that can provide useful and interesting services in the smart home domain. The areas that machine learning techniques can help advance are varied and ever-evolving. Classifying smart home inhabitants’ Activities of Daily Living (ADLs), is one prominent example. The ability of machine learning technique to find meaningful spatio-temporal relations of high-dimensional data is an important requirement as well. This paper presents a comparative evaluation of state-of-the-art machine learning techniques to classify ADLs in the smart home domain. Forty-two synthetic datasets and two real-world datasets with multiple inhabitants are used to evaluate and compare the performance of the identified machine learning techniques. Our results show significant performance differences between the evaluated techniques. Such as AdaBoost, Cortical Learning Algorithm (CLA), Decision Trees, Hidden Markov Model (HMM), Multi-layer Perceptron (MLP), Structured Perceptron and Support Vector Machines (SVM). Overall, neural network based techniques have shown superiority over the other tested techniques.

Keywords: activities of daily living, classification, internet of things, machine learning, prediction, smart home

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Authors: Ahmad E. Aldousaria, Abdulla Al Kafy

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Urbanization is an incessant process that involves the transformation of land use/land cover (LULC), resulting in a reduction of cool land covers and thermal comfort zones (TCZs). This study explores the directional shrinkage of TCZs in Kuwait using Landsat satellite data from 1991 – 2021 to predict the future LULC and TCZ distribution for 2026 and 2031 using cellular automata (CA) and artificial neural network (ANN) algorithms. Analysis revealed a rapid urban expansion (40 %) in SE, NE, and NW directions and TCZ shrinkage in N – NW and SW directions with 25 % of the very uncomfortable area. The predicted result showed an urban area increase from 44 % in 2021 to 47 % and 52 % in 2026 and 2031, respectively, where uncomfortable zones were found to be concentrated around urban areas and bare lands in N – NE and N – NW directions. This study proposes an effective and sustainable framework to control TCZ shrinkage, including zero soil policies, planned landscape design, manmade water bodies, and rooftop gardens. This study will help urban planners and policymakers to make Kuwait an eco–friendly, functional, and sustainable country.

Keywords: land cover change, thermal environment, green cover loss, machine learning, remote sensing

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Authors: Mina Beheshti, Vida Shams, Mojtaba Esfandiari, Farzaneh Abdollahi, Abdolreza Ohadi

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Most of the people who survive a stroke need rehabilitation tools to regain their mobility. The core function of these devices is a brake actuator. The goal of this study is to design and control a magnetorheological brake which can be used as a rehabilitation tool. In fact, the fluid used in this brake is called magnetorheological fluid or MR that properties can change by variation of the magnetic field. The braking properties can be set as control by using this feature of the fluid. In this research, different MR brake designs are first introduced in each design, and the dimensions of the brake have been determined based on the required torque for foot movement. To calculate the brake dimensions, it is assumed that the shear stress distribution in the fluid is uniform and the fluid is in its saturated state. After designing the rehabilitation brake, the mathematical model of the healthy movement of a healthy person is extracted. Due to the nonlinear nature of the system and its variability, various adaptive controllers, neural networks, and robust have been implemented to estimate the parameters and control the system. After calculating torque and control current, the best type of controller in terms of error and control current has been selected. Finally, this controller is implemented on the experimental data of the patient's movements, and the control current is calculated to achieve the desired torque and motion.

Keywords: rehabilitation, magnetorheological fluid, knee, brake, adaptive control, robust control, neural network control, torque control

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Authors: Saziye Ugur

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In this study, optical, film formation, morphological and the magnetic properties of a nanocomposite system, composed of polystyrene (PS) latex polymer and core-shell magnetic nanoparticles (MNPs) is presented. Nine different mixtures were prepared by mixing of PS latex dispersion with different amount of MNPs in the range of (0- 100 wt%). PS/MNPs films were prepared from these mixtures on glass substrates by drop casting method. After drying at room temperature, each film sample was separately annealed at temperatures from 100 to 250 °C for 10 min. In order to monitor film formation process, the transmittance of these composites was measured after each annealing step as a function of MNPs content. Below a critical MNPs content (30 wt%), it was found that PS percolates into the MNPs hard phase and forms an interconnected network upon annealing. The transmission results showed above this critical value, PS latexes were no longer film forming at all temperatures. Besides, the PS/MNPs composite films also showed excellent magnetic properties. All composite films showed superparamagnetic behaviors. The saturation magnetisation (Ms) first increased up to 0.014 emu in the range of (0-50) wt% MNPs content and then decreased to 0.010 emu with increasing MNPs content. The highest value of Ms was approximately 0.020 emu and was obtained for the film filled with 85 wt% MNPs content. These results indicated that the optical, film formation and magnetic properties of PS/MNPs composite films can be readily tuned by varying loading content of MNPs nanoparticles.

Keywords: composite film, film formation, magnetic nanoparticles, ps latex, transmission

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Authors: Hassan S. Hussein, Rania A. Abul Seoud, Amr M. Refaat

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Scientific researchers face in the analysis of very large data sets that is increasing noticeable rate in today’s and tomorrow’s technologies. Hadoop and Spark are types of software that developed frameworks. Hadoop framework is suitable for many Different hardware platforms. In this research, a scientific Linux cluster for Big Data analysis (SLBD) is presented. SLBD runs open source software with large computational capacity and high performance cluster infrastructure. SLBD composed of one cluster contains identical, commodity-grade computers interconnected via a small LAN. SLBD consists of a fast switch and Gigabit-Ethernet card which connect four (nodes). Cloudera Manager is used to configure and manage an Apache Hadoop stack. Hadoop is a framework allows storing and processing big data across the cluster by using MapReduce algorithm. MapReduce algorithm divides the task into smaller tasks which to be assigned to the network nodes. Algorithm then collects the results and form the final result dataset. SLBD clustering system allows fast and efficient processing of large amount of data resulting from different applications. SLBD also provides high performance, high throughput, high availability, expandability and cluster scalability.

Keywords: big data platforms, cloudera manager, Hadoop, MapReduce

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Authors: Tayyaba Hussain, Sobia Jangsher, Saqib Ali, Saqib Ejaz

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Unmanned Aerial vehicles (UAV’s) have gained great popularity due to their remoteness, ease of deployment and high maneuverability in different applications like real-time surveillance, image capturing, weather atmospheric studies, disaster site monitoring and mapping. These applications can involve a real-time communication with the ground station. However, altitude and mobility possess a few challenges for the communication. UAV’s at high altitude usually require more transmit power. One possible solution can be with the use of multi hops (UAV’s acting as relays) and exploiting the mobility pattern of the UAV’s. In this paper, we studied a relay (UAV’s acting as relays) selection for a reliable transmission to a destination UAV. We exploit the mobility information of the UAV’s to propose a Mobility-Aware Relay Selection (MARS) algorithm with the objective of giving improved data rates. The results are compared with Non Mobility-Aware relay selection scheme and optimal values. Numerical results show that our proposed MARS algorithm gives 6% better achievable data rates for the mobile UAV’s as compared with Non MobilityAware relay selection scheme. On average a decrease of 20.2% in data rate is achieved with MARS as compared with SDP solver in Yalmip.

Keywords: mobility aware, relay selection, time division multiple acess, unmanned aerial vehicle

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Authors: Ekaterina Razuvalova, Konstantin Rudenko

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In this article the most successful examples of international visual and virtual reconstructions of historical and culture objects, which are based on informative and communicative technologies, are represented. 3D reconstructions can demonstrate outward appearance, visualize different hypothesis, connected to represented object. Virtual reality can give us any daytime and season, any century and environment. We can see how different people from different countries and different era lived; we can get different information about any object; we can see historical complexes in real city environment, which are damaged or vanished. These innovations confirm the fact, that 3D reconstruction is important in museum development. Considering the most interesting examples of visual and virtual reconstructions, we can notice, that visual reconstruction is a 3D image of different objects, historical complexes, buildings and phenomena. They are constant and we can see them only as momentary objects. And virtual reconstruction is some environment with its own time, rules and phenomena. These reconstructions are continuous; seasons, daytime and natural conditions can change there. They can demonstrate abilities of virtual world existence. In conclusion: new technologies give us opportunities to expand the boundaries of museum space, improve abilities of museum expositions, create emotional atmosphere of game immersion, which can interest visitor. Usage of network sources allows increasing the number of visitors and virtual reconstruction opportunities show creative side of museum business.

Keywords: computer technologies, historical reconstruction, museums, museum expositions, virtual reconstruction

Procedia PDF Downloads 328

Authors: Nurul Bin Ibrahim

Abstract:

Advancements in neural networks have offered valuable insights into Fluid Dynamics, notably in addressing turbulence-related challenges. In this research, we introduce multiple applications of models of neural networks, namely Feed-Forward and Recurrent Neural Networks, to explore the relationship between jet formations and stratified turbulence within stochastically excited Boussinesq systems. Using machine learning tools like TensorFlow and PyTorch, the study has created models that effectively mimic and show the underlying features of the complex patterns of jet formation and stratified turbulence. These models do more than just help us understand these patterns; they also offer a faster way to solve problems in stochastic systems, improving upon traditional numerical techniques to solve stochastic differential equations such as the Euler-Maruyama method. In addition, the research includes a thorough comparison with the Statistical State Dynamics (SSD) approach, which is a well-established method for studying chaotic systems. This comparison helps evaluate how well neural networks can help us understand the complex relationship between jet formations and stratified turbulence. The results of this study underscore the potential of neural networks in computational physics and fluid dynamics, opening up new possibilities for more efficient and accurate simulations in these fields.

Keywords: neural networks, machine learning, computational fluid dynamics, stochastic systems, simulation, stratified turbulence

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Authors: Amal Hussain Alkhaiwani, Ghadah Abdullah Almalki

Abstract:

Recently human errors have increasingly become a very high factor in security breaches that may affect confidential data, and most of the cyber data breaches are caused by human errors. With one individual mistake, the attacker will gain access to the entire network and bypass the implemented access controls without any immediate detection. Unaware employees will be vulnerable to any social engineering cyber-attacks. Providing security awareness to People is part of the company protection process; the cyber risks cannot be reduced by just implementing technology; the human awareness of security will significantly reduce the risks, which encourage changes in staff cyber-awareness. In this paper, we will focus on Human Awareness, human needs to continue the required security education level; we will review human errors and introduce a proposed solution to avoid the breach from occurring again. Recently Saudi Arabia faced many attacks with different methods of social engineering. As Saudi Arabia has become a target to many countries and individuals, we needed to initiate a defense mechanism that begins with awareness to keep our privacy and protect the confidential data against possible intended attacks.

Keywords: cybersecurity, human aspects, human errors, human mistakes, security awareness, Saudi Arabia, security program, security education, social engineering

Procedia PDF Downloads 159

Authors: Maryam Maleki, Esther Mead, Mohammad Arani, Nitin Agarwal

Abstract:

This study is conducted to examine how opposing viewpoints related to COVID-19 were diffused on Twitter. To accomplish this, six datasets using two epidemiological models, SIR (Susceptible, Infected, Recovered) and SEIZ (Susceptible, Exposed, Infected, Skeptics), were analyzed. The six datasets were chosen because they represent opposing viewpoints on the COVID-19 pandemic. Three of the datasets contain anti-subject hashtags, while the other three contain pro-subject hashtags. The time frame for all datasets is three years, starting from January 2020 to December 2022. The findings revealed that while both models were effective in evaluating the propagation trends of these polarizing viewpoints, the SEIZ model was more accurate with a relatively lower error rate (6.7%) compared to the SIR model (17.3%). Additionally, the relative error for both models was lower for anti-subject hashtags compared to pro-subject hashtags. By leveraging epidemiological models, insights into the propagation trends of polarizing viewpoints on Twitter were gained. This study paves the way for the development of methods to prevent the spread of ideas that lack scientific evidence while promoting the dissemination of scientifically backed ideas.

Keywords: mathematical modeling, epidemiological model, seiz model, sir model, covid-19, twitter, social network analysis, social contagion

Procedia PDF Downloads 59