Search results for: greener cloud
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 737

Search results for: greener cloud

227 Reduction of Energy Consumption Using Smart Home Techniques in the Household Sector

Authors: Ahmed Al-Adaileh, Souheil Khaddaj

Abstract:

Outcomes of exhaustion of natural resources started influencing each spirit on this planet. Energy is an essential factor in this aspect. To restore the circumstance to the appropriate track, all attempts must focus on two fundamental branches: producing electricity from clean and renewable reserves and decreasing the overall unnecessary consumption of energy. The focal point of this paper will be on lessening the power consumption in the household's segment. This paper is an attempt to give a clear understanding of a framework called Reduction of Energy Consumption in Household Sector (RECHS) and how it should help householders to reduce their power consumption by substituting their household appliances, turning-off the appliances when stand-by modus is detected, and scheduling their appliances operation periods. Technically, the framework depends on utilizing Z-Wave compatible plug-ins which will be connected to the usual house devices to gauge and control them remotely and semi-automatically. The suggested framework underpins numerous quality characteristics, for example, integrability, scalability, security and adaptability.

Keywords: smart energy management systems, internet of things, wireless mesh networks, microservices, cloud computing, big data

Procedia PDF Downloads 171
226 The Incesant Subversion of Judiciary by African Political Leaders

Authors: Joy Olayemi Gbala, Fatai Olatokunbo, Philip Cloud

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Catastrophic dictatorship has been discovered to be the major leadership challenge that orchestrates stagnated and contrasted economy with dysfunctional democracy in Africa through willful misappropriation of resources and egregious subversion of the rule of law. Almost invariably, most African leaders inexplicably often become power drunk and addicted which usually leads to abuse of state power, abdication of constitutional duties, unjustly withdrawal of business license of operation, human right violation, election malpractices, financial corruption, disruptions of policies of democratic government transition, annulment of free and fair election, and disruptions of legal electoral procedures and unachievable dividends of democracy and many more. Owing to this, most African nations have gone and still go through political unrest and insurgencies leading to loss of lives and property, violent protests, detention of detractors and political activists and massive human displacement. This research work is concerned with, and investigates the causes, menace, consequences and impacts of subverting the rule of law in Africa on the economy and the development of the continent with a suggested practical solution to the plights.

Keywords: corruption, law, leadership, violation

Procedia PDF Downloads 130
225 Application Methodology for the Generation of 3D Thermal Models Using UAV Photogrammety and Dual Sensors for Mining/Industrial Facilities Inspection

Authors: Javier Sedano-Cibrián, Julio Manuel de Luis-Ruiz, Rubén Pérez-Álvarez, Raúl Pereda-García, Beatriz Malagón-Picón

Abstract:

Structural inspection activities are necessary to ensure the correct functioning of infrastructures. Unmanned Aerial Vehicle (UAV) techniques have become more popular than traditional techniques. Specifically, UAV Photogrammetry allows time and cost savings. The development of this technology has permitted the use of low-cost thermal sensors in UAVs. The representation of 3D thermal models with this type of equipment is in continuous evolution. The direct processing of thermal images usually leads to errors and inaccurate results. A methodology is proposed for the generation of 3D thermal models using dual sensors, which involves the application of visible Red-Blue-Green (RGB) and thermal images in parallel. Hence, the RGB images are used as the basis for the generation of the model geometry, and the thermal images are the source of the surface temperature information that is projected onto the model. Mining/industrial facilities representations that are obtained can be used for inspection activities.

Keywords: aerial thermography, data processing, drone, low-cost, point cloud

Procedia PDF Downloads 119
224 Removal of Heavy Metal Using Continous Mode

Authors: M. Abd elfattah, M. Ossman, Nahla A. Taha

Abstract:

The present work explored the use of Egyptian rice straw, an agricultural waste that leads to global warming problem through brown cloud, as a potential feedstock for the preparation of activated carbon by physical and chemical activation. The results of this study showed that it is feasible to prepare activated carbons with relatively high surface areas and pore volumes from the Egyptian rice straw by direct chemical and physical activation. The produced activated carbon from the two methods (AC1 and AC2) could be used as potential adsorbent for the removal of Fe(III) from aqueous solution contains heavy metals and polluted water. The adsorption of Fe(III) was depended on the pH of the solution. The optimal Fe(III) removal efficiency occurs at pH 5. Based on the results, the optimum contact time is 60 minutes and adsorbent dosage is 3 g/L. The adsorption breakthrough curves obtained at different bed depths indicated increase of breakthrough time with increase in bed depths. A rise in inlet Fe(III) concentration reduces the throughput volume before the packed bed gets saturated. AC1 showed higher affinity for Fe(III) as compared to Raw rice husk.

Keywords: rice straw, activated carbon, Fe(III), fixed bed column, pyrolysis

Procedia PDF Downloads 232
223 Power Recovery from Waste Air of Mine Ventilation Fans Using Wind Turbines

Authors: Soumyadip Banerjee, Tanmoy Maity

Abstract:

The recovery of power from waste air generated by mine ventilation fans presents a promising avenue for enhancing energy efficiency in mining operations. This abstract explores the feasibility and benefits of utilizing turbine generators to capture the kinetic energy present in waste air and convert it into electrical power. By integrating turbine generator systems into mine ventilation infrastructures, the potential to harness and utilize the previously untapped energy within the waste air stream is realized. This study examines the principles underlying turbine generator technology and its application within the context of mine ventilation systems. The process involves directing waste air from ventilation fans through specially designed turbines, where the kinetic energy of the moving air is converted into rotational motion. This mechanical energy is then transferred to connected generators, which convert it into electrical power. The recovered electricity can be employed for various on-site applications, including powering mining equipment, lighting, and control systems. The benefits of power recovery from waste air using turbine generators are manifold. Improved energy efficiency within the mining environment results in reduced dependence on external power sources and associated cost savings. Additionally, this approach contributes to environmental sustainability by utilizing a previously wasted resource for power generation. Resource conservation is further enhanced, aligning with modern principles of sustainable mining practices. However, successful implementation requires careful consideration of factors such as waste air characteristics, turbine design, generator efficiency, and integration into existing mine infrastructure. Maintenance and monitoring protocols are necessary to ensure consistent performance and longevity of the turbine generator systems. While there is an initial investment associated with equipment procurement, installation, and integration, the long-term benefits of reduced energy costs and environmental impact make this approach economically viable. In conclusion, the recovery of power from waste air from mine ventilation fans using turbine generators offers a tangible solution to enhance energy efficiency and sustainability within mining operations. By capturing and converting the kinetic energy of waste air into usable electrical power, mines can optimize resource utilization, reduce operational costs, and contribute to a greener future for the mining industry.

Keywords: waste to energy, wind power generation, exhaust air, power recovery

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222 Data Security: An Enhancement of E-mail Security Algorithm to Secure Data Across State Owned Agencies

Authors: Lindelwa Mngomezulu, Tonderai Muchenje

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Over the decades, E-mails provide easy, fast and timely communication enabling businesses and state owned agencies to communicate with their stakeholders and with their own employees in real-time. Moreover, since the launch of Microsoft office 365 and many other clouds based E-mail services, many businesses have been migrating from the on premises E-mail services to the cloud and more precisely since the beginning of the Covid-19 pandemic, there has been a significant increase of E-mails utilization, which then leads to the increase of cyber-attacks. In that regard, E-mail security has become very important in the E-mail transportation to ensure that the E-mail gets to the recipient without the data integrity being compromised. The classification of the features to enhance E-mail security for further from the enhanced cyber-attacks as we are aware that since the technology is advancing so at the cyber-attacks. Therefore, in order to maximize the data integrity we need to also maximize security of the E-mails such as enhanced E-mail authentication. The successful enhancement of E-mail security in the future may lessen the frequency of information thefts via E-mails, resulting in the data of South African State-owned agencies not being compromised.

Keywords: e-mail security, cyber-attacks, data integrity, authentication

Procedia PDF Downloads 108
221 Multi-Modal Visualization of Working Instructions for Assembly Operations

Authors: Josef Wolfartsberger, Michael Heiml, Georg Schwarz, Sabrina Egger

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Growing individualization and higher numbers of variants in industrial assembly products raise the complexity of manufacturing processes. Technical assistance systems considering both procedural and human factors allow for an increase in product quality and a decrease in required learning times by supporting workers with precise working instructions. Due to varying needs of workers, the presentation of working instructions leads to several challenges. This paper presents an approach for a multi-modal visualization application to support assembly work of complex parts. Our approach is integrated within an interconnected assistance system network and supports the presentation of cloud-streamed textual instructions, images, videos, 3D animations and audio files along with multi-modal user interaction, customizable UI, multi-platform support (e.g. tablet-PC, TV screen, smartphone or Augmented Reality devices), automated text translation and speech synthesis. The worker benefits from more accessible and up-to-date instructions presented in an easy-to-read way.

Keywords: assembly, assistive technologies, augmented reality, manufacturing, visualization

Procedia PDF Downloads 147
220 A Numerical Description of a Fibre Reinforced Concrete Using a Genetic Algorithm

Authors: Henrik L. Funke, Lars Ulke-Winter, Sandra Gelbrich, Lothar Kroll

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This work reports about an approach for an automatic adaptation of concrete formulations based on genetic algorithms (GA) to optimize a wide range of different fit-functions. In order to achieve the goal, a method was developed which provides a numerical description of a fibre reinforced concrete (FRC) mixture regarding the production technology and the property spectrum of the concrete. In a first step, the FRC mixture with seven fixed components was characterized by varying amounts of the components. For that purpose, ten concrete mixtures were prepared and tested. The testing procedure comprised flow spread, compressive and bending tensile strength. The analysis and approximation of the determined data was carried out by GAs. The aim was to obtain a closed mathematical expression which best describes the given seven-point cloud of FRC by applying a Gene Expression Programming with Free Coefficients (GEP-FC) strategy. The seven-parametric FRC-mixtures model which is generated according to this method correlated well with the measured data. The developed procedure can be used for concrete mixtures finding closed mathematical expressions, which are based on the measured data.

Keywords: concrete design, fibre reinforced concrete, genetic algorithms, GEP-FC

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219 Evaluation of Thrombolytic Activity of Zingiber cassumunar Roxb. and Thai Herbal Prasaplai Formula

Authors: Warachate Khobjai, Suriyan Sukati, Khemjira Jarmkom, Pattaranut Eakwaropas, Surachai Techaoei

Abstract:

The propose of this study was to investigate in vitro thrombolytic activity of Zingiber cassumunar Roxb. and Prasaplai, a Thai herbal formulation of Z. cassumunar Roxb. Herbs were extracted with boiling water and concentrated by lyophilization. To observe their thrombolytic potential, an in vitro clot lysis method was applied where streptokinase and sterile distilled water were used as positive and negative controls, respectively. Crude aqueous extracts from Z. cassumunar Roxb. and Prasaplai formula showed significant thrombolytic activity by clot lysis of 17.90% and 25.21%, respectively, compared to the negative control water (5.16%) while the standard streptokinase revealed 64.78% clot lysis. These findings suggest that Z. cassumunar Roxb. exhibits moderate thrombolytic activity and cloud play an important role in the thrombolytic properties of Prasaplai formula. However, further study should be done to observe in vivo clot dissolving potential and to isolate active component(s) of these extracts.

Keywords: thrombolytic activity, clot lysis, Zingiber cassumunar Roxb., Prasaplai formula, aqueous extract

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218 Genesis of Entrepreneur Business Models in New Ventures

Authors: Arash Najmaei, Jo Rhodes, Peter Lok, Zahra Sadeghinejad

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In this article, we endeavor to explore how a new business model comes into existence in the Australian cloud-computing eco-system. Findings from multiple case study methodology reveal that to develop a business model new ventures adopt a three-phase approach. In the first phase, labelled as business model ideation (BMID) various ideas for a viable business model are generated from both internal and external networks of the entrepreneurial team and the most viable one is chosen. Strategic consensus and commitment are generated in the second phase. This phase is a business modelling strategic action phase. We labelled this phase as business model strategic commitment (BMSC) because through commitment and the subsequent actions of executives resources are pooled, coordinated and allocated to the business model. Three complementary sets of resources shape the business model: managerial (MnRs), marketing (MRs) and technological resources (TRs). The third phase is the market-test phase where the business model is reified through the delivery of the intended value to customers and conversion of revenue into profit. We labelled this phase business model actualization (BMAC). Theoretical and managerial implications of these findings will be discussed and several directions for future research will be illuminated.

Keywords: entrepreneur business model, high-tech venture, resources, conversion of revenue

Procedia PDF Downloads 419
217 A Simple Algorithm for Real-Time 3D Capturing of an Interior Scene Using a Linear Voxel Octree and a Floating Origin Camera

Authors: Vangelis Drosos, Dimitrios Tsoukalos, Dimitrios Tsolis

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We present a simple algorithm for capturing a 3D scene (focused on the usage of mobile device cameras in the context of augmented/mixed reality) by using a floating origin camera solution and storing the resulting information in a linear voxel octree. Data is derived from cloud points captured by a mobile device camera. For the purposes of this paper, we assume a scene of fixed size (known to us or determined beforehand) and a fixed voxel resolution. The resulting data is stored in a linear voxel octree using a hashtable. We commence by briefly discussing the logic behind floating origin approaches and the usage of linear voxel octrees for efficient storage. Following that, we present the algorithm for translating captured feature points into voxel data in the context of a fixed origin world and storing them. Finally, we discuss potential applications and areas of future development and improvement to the efficiency of our solution.

Keywords: voxel, octree, computer vision, XR, floating origin

Procedia PDF Downloads 115
216 A Monocular Measurement for 3D Objects Based on Distance Area Number and New Minimize Projection Error Optimization Algorithms

Authors: Feixiang Zhao, Shuangcheng Jia, Qian Li

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High-precision measurement of the target’s position and size is one of the hotspots in the field of vision inspection. This paper proposes a three-dimensional object positioning and measurement method using a monocular camera and GPS, namely the Distance Area Number-New Minimize Projection Error (DAN-NMPE). Our algorithm contains two parts: DAN and NMPE; specifically, DAN is a picture sequence algorithm, NMPE is a relatively positive optimization algorithm, which greatly improves the measurement accuracy of the target’s position and size. Comprehensive experiments validate the effectiveness of our proposed method on a self-made traffic sign dataset. The results show that with the laser point cloud as the ground truth, the size and position errors of the traffic sign measured by this method are ± 5% and 0.48 ± 0.3m, respectively. In addition, we also compared it with the current mainstream method, which uses a monocular camera to locate and measure traffic signs. DAN-NMPE attains significant improvements compared to existing state-of-the-art methods, which improves the measurement accuracy of size and position by 50% and 15.8%, respectively.

Keywords: monocular camera, GPS, positioning, measurement

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215 The Influence of Students’ Learning Factor and Parents’ Involvement in Their Learning and Suspension: The Application of Big Data Analysis of Internet of Things Technology

Authors: Chih Ming Kung

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This study is an empirical study examining the enrollment rate and dropout rate of students from the perspectives of students’ learning, parents’ involvement and the learning process. Methods: Using the data collected from the entry website of Internet of Things (IoT), parents’ participation and the installation pattern of exit poll website, an investigation was conducted. Results: This study discovered that in the aspect of the degree of involvement, the attractiveness of courses, self-performance and departmental loyalty exerts significant influences on the four aspects: psychological benefits, physical benefits, social benefits and educational benefits of learning benefits. Parents’ participation also exerts a significant influence on the learning benefits. A suitable tool on the cloud was designed to collect the dynamic big data of students’ learning process. Conclusion: This research’s results can be valuable references for the government when making and promoting related policies, with more macro view and consideration. It is also expected to be contributory to schools for the practical study of promotion for enrollment.

Keywords: students’ learning factor, parents’ involvement, involvement, technology

Procedia PDF Downloads 129
214 Statistical Shape Analysis of the Human Upper Airway

Authors: Ramkumar Gunasekaran, John Cater, Vinod Suresh, Haribalan Kumar

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The main objective of this project is to develop a statistical shape model using principal component analysis that could be used for analyzing the shape of the human airway. The ultimate goal of this project is to identify geometric risk factors for diagnosis and management of Obstructive Sleep Apnoea (OSA). Anonymous CBCT scans of 25 individuals were obtained from the Otago Radiology Group. The airways were segmented between the hard-palate and the aryepiglottic fold using snake active contour segmentation. The point data cloud of the segmented images was then fitted with a bi-cubic mesh, and pseudo landmarks were placed to perform PCA on the segmented airway to analyze the shape of the airway and to find the relationship between the shape and OSA risk factors. From the PCA results, the first four modes of variation were found to be significant. Mode 1 was interpreted to be the overall length of the airway, Mode 2 was related to the anterior-posterior width of the retroglossal region, Mode 3 was related to the lateral dimension of the oropharyngeal region and Mode 4 was related to the anterior-posterior width of the oropharyngeal region. All these regions are subjected to the risk factors of OSA.

Keywords: medical imaging, image processing, FEM/BEM, statistical modelling

Procedia PDF Downloads 485
213 A Machine Learning Based Method to Detect System Failure in Resource Constrained Environment

Authors: Payel Datta, Abhishek Das, Abhishek Roychoudhury, Dhiman Chattopadhyay, Tanushyam Chattopadhyay

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Machine learning (ML) and deep learning (DL) is most predominantly used in image/video processing, natural language processing (NLP), audio and speech recognition but not that much used in system performance evaluation. In this paper, authors are going to describe the architecture of an abstraction layer constructed using ML/DL to detect the system failure. This proposed system is used to detect the system failure by evaluating the performance metrics of an IoT service deployment under constrained infrastructure environment. This system has been tested on the manually annotated data set containing different metrics of the system, like number of threads, throughput, average response time, CPU usage, memory usage, network input/output captured in different hardware environments like edge (atom based gateway) and cloud (AWS EC2). The main challenge of developing such system is that the accuracy of classification should be 100% as the error in the system has an impact on the degradation of the service performance and thus consequently affect the reliability and high availability which is mandatory for an IoT system. Proposed ML/DL classifiers work with 100% accuracy for the data set of nearly 4,000 samples captured within the organization.

Keywords: machine learning, system performance, performance metrics, IoT, edge

Procedia PDF Downloads 175
212 The Integration of Patient Health Record Generated from Wearable and Internet of Things Devices into Health Information Exchanges

Authors: Dalvin D. Hill, Hector M. Castro Garcia

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A growing number of individuals utilize wearable devices on a daily basis. The usage and functionality of these wearable devices vary from user to user. One popular usage of said devices is to track health-related activities that are typically stored on a device’s memory or uploaded to an account in the cloud; based on the current trend, the data accumulated from the wearable device are stored in a standalone location. In many of these cases, this health related datum is not a factor when considering the holistic view of a user’s health lifestyle or record. This health-related data generated from wearable and Internet of Things (IoT) devices can serve as empirical information to a medical provider, as the standalone data can add value to the holistic health record of a patient. This paper proposes a solution to incorporate the data gathered from these wearable and IoT devices, with that a patient’s Personal Health Record (PHR) stored within the confines of a Health Information Exchange (HIE).

Keywords: electronic health record, health information exchanges, internet of things, personal health records, wearable devices, wearables

Procedia PDF Downloads 108
211 Electrodeposition of Silicon Nanoparticles Using Ionic Liquid for Energy Storage Application

Authors: Anjali Vanpariya, Priyanka Marathey, Sakshum Khanna, Roma Patel, Indrajit Mukhopadhyay

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Silicon (Si) is a promising negative electrode material for lithium-ion batteries (LiBs) due to its low cost, non-toxicity, and a high theoretical capacity of 4200 mAhg⁻¹. The primary challenge of the application of Si-based LiBs is large volume expansion (~ 300%) during the charge-discharge process. Incorporation of graphene, carbon nanotubes (CNTs), morphological control, and nanoparticles was utilized as effective strategies to tackle volume expansion issues. However, molten salt methods can resolve the issue, but high-temperature requirement limits its application. For sustainable and practical approach, room temperature (RT) based methods are essentially required. Use of ionic liquids (ILs) for electrodeposition of Si nanostructures can possibly resolve the issue of temperature as well as greener media. In this work, electrodeposition of Si nanoparticles on gold substrate was successfully carried out in the presence of ILs media, 1-butyl-3-methylimidazolium-bis (trifluoromethyl sulfonyl) imide (BMImTf₂N) at room temperature. Cyclic voltammetry (CV) suggests the sequential reduction of Si⁴⁺ to Si²⁺ and then Si nanoparticles (SiNs). The structure and morphology of the electrodeposited SiNs were investigated by FE-SEM and observed interconnected Si nanoparticles of average particle size ⁓100-200 nm. XRD and XPS data confirm the deposition of Si on Au (111). The first discharge-charge capacity of Si anode material has been found to be 1857 and 422 mAhg⁻¹, respectively, at current density 7.8 Ag⁻¹. The irreversible capacity of the first discharge-charge process can be attributed to the solid electrolyte interface (SEI) formation via electrolyte decomposition, and trapped Li⁺ inserted into the inner pores of Si. Pulverization of SiNs results in the creation of a new active site, which facilitates the formation of new SEI in the subsequent cycles leading to fading in a specific capacity. After 20 cycles, charge-discharge profiles have been stabilized, and a reversible capacity of 150 mAhg⁻¹ is retained. Electrochemical impedance spectroscopy (EIS) data shows the decrease in Rct value from 94.7 to 47.6 kΩ after 50 cycles of charge-discharge, which demonstrates the improvements of the interfacial charge transfer kinetics. The decrease in the Warburg impedance after 50 cycles of charge-discharge measurements indicates facile diffusion in fragmented and smaller Si nanoparticles. In summary, Si nanoparticles deposited on gold substrate using ILs as media and characterized well with different analytical techniques. Synthesized material was successfully utilized for LiBs application, which is well supported by CV and EIS data.

Keywords: silicon nanoparticles, ionic liquid, electrodeposition, cyclic voltammetry, Li-ion battery

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210 Synthesized Doped TiO2 Photocatalysts for Mineralization of Quinalphos from Aqueous Streams

Authors: Nidhi Sharotri, Dhiraj Sud

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Water pollution by pesticides constitutes a serious ecological problem due to their potential toxicity and bioaccumulation. The widespread use of pesticides in industry and agriculture along with their resistance to natural decomposition, biodegradation, chemical and photochemical degradation under typical environmental conditions has resulted in the emergence of these chemicals and their transformed products in natural water. Among AOP’s, heterogeneous photocatalysis using TiO2 as photocatalyst appears as the most emerging destructive technology for mineralization of the pollutant in aquatic streams. Among the various semiconductors (TiO2, ZnO, CdS, FeTiO3, MnTiO3, SrTiO2 and SnO2), TiO2 has proven to be the most efficient photocatalyst for environmental applications due to its biological and chemical inertness, high photo reactivity, non-toxicity, and photo stability. Semiconductor photocatalysts are characterized by an electronic band structure in which valence band and conduction band are separated by a band gap, i.e. a region of forbidden energy. Semiconductor based photocatalysts produces e-/h+ pairs which have been employed for degradation of organic pollutants. The present paper focuses on modification of TiO2 photocatalyst in order to shift its absorption edge towards longer wavelength to make it active under natural light. Semiconductor TiO2 photocatalysts was prepared by doping with anion (N), cation (Mn) and double doped (Mn, N) using greener approach. Titanium isopropoxide is used as titania precursor and ethanedithiol, hydroxyl amine hydrochloride, manganous chloride as sulphur, nitrogen and manganese precursors respectively. Synthesized doped TiO2 nanomaterials are characterized for surface morphology (SEM, TEM), crystallinity (XRD) and optical properties (absorption spectra and band gap). EPR data confirms the substitutional incorporation of Mn2+ in TiO2 lattice. The doping influences the phase transformation of rutile and anatase phase crystal and thereby the absorption spectrum changes were observed. The effect of variation of reaction parameters such as solvent, reaction time and calcination temperature on the yield, surface morphology and optical properties was also investigated. The TEM studies show the particle size of nanomaterials varies from 10-50 nm. The calculated band gap of nanomaterials varies from 2.30-2.60 eV. The photocatalytic degradation of organic pollutant organophosphate pesticide (Quinalphos) has been investigated by studying the changes in UV absorption spectrum and the promising results were obtained under visible light. The complete mineralization of quinalphos has occurred as no intermediates were recorded after 8 hrs of degradation confirmed from the HPLC studies.

Keywords: quinalphos, doped-TiO2, mineralization, EPR

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209 IoT Based Approach to Healthcare System for a Quadriplegic Patient Using EEG

Authors: R. Gautam, P. Sastha Kanagasabai, G. N. Rathna

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The proposed healthcare system enables quadriplegic patients, people with severe motor disabilities to send commands to electronic devices and monitor their vitals. The growth of Brain-Computer-Interface (BCI) has led to rapid development in 'assistive systems' for the disabled called 'assistive domotics'. Brain-Computer-Interface is capable of reading the brainwaves of an individual and analyse it to obtain some meaningful data. This processed data can be used to assist people having speech disorders and sometimes people with limited locomotion to communicate. In this Project, Emotiv EPOC Headset is used to obtain the electroencephalogram (EEG). The obtained data is processed to communicate pre-defined commands over the internet to the desired mobile phone user. Other Vital Information like the heartbeat, blood pressure, ECG and body temperature are monitored and uploaded to the server. Data analytics enables physicians to scan databases for a specific illness. The Data is processed in Intel Edison, system on chip (SoC). Patient metrics are displayed via Intel IoT Analytics cloud service.

Keywords: brain computer interface, Intel Edison, Emotiv EPOC, IoT analytics, electroencephalogram

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208 A Real Time Monitoring System of the Supply Chain Conditions, Products and Means of Transport

Authors: Dimitris E. Kontaxis, George Litainas, Dimitris P. Ptochos

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Real-time monitoring of the supply chain conditions and procedures is a critical element for the optimal coordination and safety of the deliveries, as well as for the minimization of the delivery time and cost. Real-time monitoring requires IoT data streams, which are related to the conditions of the products and the means of transport (e.g., location, temperature/humidity conditions, kinematic state, ambient light conditions, etc.). These streams are generated by battery-based IoT tracking devices, equipped with appropriate sensors, and are transmitted to a cloud-based back-end system. Proper handling and processing of the IoT data streams, using predictive and artificial intelligence algorithms, can provide significant and useful results, which can be exploited by the supply chain stakeholders in order to enhance their financial benefits, as well as the efficiency, security, transparency, coordination, and sustainability of the supply chain procedures. The technology, the features, and the characteristics of a complete, proprietary system, including hardware, firmware, and software tools -developed in the context of a co-funded R&D programme- are addressed and presented in this paper.

Keywords: IoT embedded electronics, real-time monitoring, tracking device, sensor platform

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207 Intelligent Technology for Real-Time Monitor and Data Analysis of the Aquaculture Toxic Water Concentration

Authors: Chin-Yuan Hsieh, Wei-Chun Lu, Yu-Hong Zeng

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The situation of a group of fish die is frequently found due to the fish disease caused by the deterioration of aquaculture water quality. The toxic ammonia is produced by animals as a byproduct of protein. The system is designed by the smart sensor technology and developed by the mathematical model to monitor the water parameters 24 hours a day and predict the relationship among twelve water quality parameters for monitoring the water quality in aquaculture. All data measured are stored in cloud server. In productive ponds, the daytime pH may be high enough to be lethal to the fish. The sudden change of the aquaculture conditions often results in the increase of PH value of water, lack of oxygen dissolving content, water quality deterioration and yield reduction. From the real measurement, the system can send the message to user’s smartphone successfully on the bad conditions of water quality. From the data comparisons between measurement and model simulation in fish aquaculture site, the difference of parameters is less than 2% and the correlation coefficient is at least 98.34%. The solubility rate of oxygen decreases exponentially with the elevation of water temperature. The correlation coefficient is 98.98%.

Keywords: aquaculture, sensor, ammonia, dissolved oxygen

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206 Machine Learning Assisted Performance Optimization in Memory Tiering

Authors: Derssie Mebratu

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As a large variety of micro services, web services, social graphic applications, and media applications are continuously developed, it is substantially vital to design and build a reliable, efficient, and faster memory tiering system. Despite limited design, implementation, and deployment in the last few years, several techniques are currently developed to improve a memory tiering system in a cloud. Some of these techniques are to develop an optimal scanning frequency; improve and track pages movement; identify pages that recently accessed; store pages across each tiering, and then identify pages as a hot, warm, and cold so that hot pages can store in the first tiering Dynamic Random Access Memory (DRAM) and warm pages store in the second tiering Compute Express Link(CXL) and cold pages store in the third tiering Non-Volatile Memory (NVM). Apart from the current proposal and implementation, we also develop a new technique based on a machine learning algorithm in that the throughput produced 25% improved performance compared to the performance produced by the baseline as well as the latency produced 95% improved performance compared to the performance produced by the baseline.

Keywords: machine learning, bayesian optimization, memory tiering, CXL, DRAM

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205 Developing Fault Tolerance Metrics of Web and Mobile Applications

Authors: Ahmad Mohsin, Irfan Raza Naqvi, Syda Fatima Usamn

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Applications with higher fault tolerance index are considered more reliable and trustworthy to drive quality. In recent years application development has been shifted from traditional desktop and web to native and hybrid application(s) for the web and mobile platforms. With the emergence of Internet of things IOTs, cloud and big data trends, the need for measuring Fault Tolerance for these complex nature applications has increased to evaluate their performance. There is a phenomenal gap between fault tolerance metrics development and measurement. Classic quality metric models focused on metrics for traditional systems ignoring the essence of today’s applications software, hardware & deployment characteristics. In this paper, we have proposed simple metrics to measure fault tolerance considering general requirements for Web and Mobile Applications. We have aligned factors – subfactors, using GQM for metrics development considering the nature of mobile we apps. Systematic Mathematical formulation is done to measure metrics quantitatively. Three web mobile applications are selected to measure Fault Tolerance factors using formulated metrics. Applications are then analysed on the basis of results from observations in a controlled environment on different mobile devices. Quantitative results are presented depicting Fault tolerance in respective applications.

Keywords: web and mobile applications, reliability, fault tolerance metric, quality metrics, GQM based metrics

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204 Polarimetric Synthetic Aperture Radar Data Classification Using Support Vector Machine and Mahalanobis Distance

Authors: Najoua El Hajjaji El Idrissi, Necip Gokhan Kasapoglu

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Polarimetric Synthetic Aperture Radar-based imaging is a powerful technique used for earth observation and classification of surfaces. Forest evolution has been one of the vital areas of attention for the remote sensing experts. The information about forest areas can be achieved by remote sensing, whether by using active radars or optical instruments. However, due to several weather constraints, such as cloud cover, limited information can be recovered using optical data and for that reason, Polarimetric Synthetic Aperture Radar (PolSAR) is used as a powerful tool for forestry inventory. In this [14paper, we applied support vector machine (SVM) and Mahalanobis distance to the fully polarimetric AIRSAR P, L, C-bands data from the Nezer forest areas, the classification is based in the separation of different tree ages. The classification results were evaluated and the results show that the SVM performs better than the Mahalanobis distance and SVM achieves approximately 75% accuracy. This result proves that SVM classification can be used as a useful method to evaluate fully polarimetric SAR data with sufficient value of accuracy.

Keywords: classification, synthetic aperture radar, SAR polarimetry, support vector machine, mahalanobis distance

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203 Modeling the Elastic Mean Free Path of Electron Collision with Pyrimidine: The Screen Corrected Additivity Rule Method

Authors: Aouina Nabila Yasmina, Chaoui Zine El Abiddine

Abstract:

This study presents a comprehensive investigation into the elastic mean free path (EMFP) of electrons colliding with pyrimidine, a precursor to the pyrimidine bases in DNA, employing the Screen Corrected Additivity Rule (SCAR) method. The SCAR method is introduced as a novel approach that combines classical and quantum mechanical principles to elucidate the interaction of electrons with pyrimidine. One of the most fundamental properties characterizing the propagation of a particle in the nuclear medium is its mean free path. Knowledge of the elastic mean free path is essential to accurately predict the effects of radiation on biological matter, as it contributes to the distances between collisions. Additionally, the mean free path plays a role in the interpretation of almost all experiments in which an excited electron moves through a solid. Pyrimidine, the precursor of the pyrimidine bases of DNA, has interesting physicochemical properties, which make it an interesting molecule to study from a fundamental point of view. These include a relatively large dipole polarizability and dipole moment and an electronic charge cloud with a significant spatial extension, which justifies its choice in this present study.

Keywords: elastic mean free path, elastic collision, pyrimidine, SCAR

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202 AI-Based Autonomous Plant Health Monitoring and Control System with Visual Health-Scoring Models

Authors: Uvais Qidwai, Amor Moursi, Mohamed Tahar, Malek Hamad, Hamad Alansi

Abstract:

This paper focuses on the development and implementation of an advanced plant health monitoring system with an AI backbone and IoT sensory network. Our approach involves addressing the critical environmental factors essential for preserving a plant’s well-being, including air temperature, soil moisture, soil temperature, soil conductivity, pH, water levels, and humidity, as well as the presence of essential nutrients like nitrogen, phosphorus, and potassium. Central to our methodology is the utilization of computer vision technology, particularly a night vision camera. The captured data is then compared against a reference database containing different health statuses. This comparative analysis is implemented using an AI deep learning model, which enables us to generate accurate assessments of plant health status. By combining the AI-based decision-making approach, our system aims to provide precise and timely insights into the overall health and well-being of plants, offering a valuable tool for effective plant care and management.

Keywords: deep learning image model, IoT sensing, cloud-based analysis, remote monitoring app, computer vision, fuzzy control

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201 Internal Product Management: The Key to Achieving Digital Maturity and Business Agility for Manufacturing IT Organizations

Authors: Frederick Johnson

Abstract:

Product management has a long and well-established history within the consumer goods industry, despite being one of the most obscure aspects of brand management. Many global manufacturing organizations are now opting for external cloud-based Manufacturing Execution Systems (MES) to replace costly and outdated monolithic MES solutions. Other global manufacturing leaders are restructuring their organizations to support human-centered values, agile methodologies, and fluid operating principles. Still, industry-leading organizations struggle to apply the appropriate framework for managing evolving external MES solutions as internal "digital products." Product management complements these current trends in technology and philosophical thinking in the market. This paper discusses the central problems associated with adopting product management processes by analyzing its traditional theories and characteristics. Considering these ideas, the article then constructs a translated internal digital product management framework by combining new and existing approaches and principles. The report concludes by demonstrating the framework's capabilities and potential effectiveness in achieving digital maturity and business agility within a manufacturing environment.

Keywords: internal product management, digital transformation, manufacturing information technology, manufacturing execution systems

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200 Biorefinery as Extension to Sugar Mills: Sustainability and Social Upliftment in the Green Economy

Authors: Asfaw Gezae Daful, Mohsen Alimandagari, Kathleen Haigh, Somayeh Farzad, Eugene Van Rensburg, Johann F. Görgens

Abstract:

The sugar industry has to 're-invent' itself to ensure long-term economic survival and opportunities for job creation and enhanced community-level impacts, given increasing pressure from fluctuating and low global sugar prices, increasing energy prices and sustainability demands. We propose biorefineries for re-vitalisation of the sugar industry using low value lignocellulosic biomass (sugarcane bagasse, leaves, and tops) annexed to existing sugar mills, producing a spectrum of high value platform chemicals along with biofuel, bioenergy, and electricity. Opportunity is presented for greener products, to mitigate climate change and overcome economic challenges. Xylose from labile hemicellulose remains largely underutilized and the conversion to value-add products a major challenge. Insight is required on pretreatment and/or extraction to optimize production of cellulosic ethanol together with lactic acid, furfural or biopolymers from sugarcane bagasse, leaves, and tops. Experimental conditions for alkaline and pressurized hot water extraction dilute acid and steam explosion pretreatment of sugarcane bagasse and harvest residues were investigated to serve as a basis for developing various process scenarios under a sugarcane biorefinery scheme. Dilute acid and steam explosion pretreatment were optimized for maximum hemicellulose recovery, combined sugar yield and solids digestibility. An optimal range of conditions for alkaline and liquid hot water extraction of hemicellulosic biopolymers, as well as conditions for acceptable enzymatic digestibility of the solid residue, after such extraction was established. Using data from the above, a series of energy efficient biorefinery scenarios are under development and modeled using Aspen Plus® software, to simulate potential factories to better understand the biorefinery processes and estimate the CAPEX and OPEX, environmental impacts, and overall viability. Rigorous and detailed sustainability assessment methodology was formulated to address all pillars of sustainability. This work is ongoing and to date, models have been developed for some of the processes which can ultimately be combined into biorefinery scenarios. This will allow systematic comparison of a series of biorefinery scenarios to assess the potential to reduce negative impacts on and maximize the benefits of social, economic, and environmental factors on a lifecycle basis.

Keywords: biomass, biorefinery, green economy, sustainability

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199 Flood Monitoring Using Active Microwave Remote Sensed Synthetic Aperture Radar Data

Authors: Bikramjit Goswami, Manoranjan Kalita

Abstract:

Active microwave remote sensing is useful in remote sensing applications in cloud-covered regions in the world. Because of high spatial resolution, the spatial variations of land cover can be monitored in greater detail using synthetic aperture radar (SAR). Inundation is studied using the SAR images obtained from Sentinel-1A in both VH and VV polarizations in the present experimental study. The temporal variation of the SAR scattering coefficient values for the area gives a good indication of flood and its boundary. The study area is the district of Morigaon in the state of Assam in India. The period of flood monitoring study is the monsoon season of the year 2017, during which high flood occurred in the state of Assam. The variation of microwave scattering value shows a distinctive indication of flood from the non-flooded period. Frequent monitoring of flood in a large area (10 km x 10 km) using passive microwave sensing and pin-pointing the actual flooded portions (5 m x 5 m) within the flooded area using active microwave sensing, can be a highly useful combination, as revealed by the present experimental results.

Keywords: active remote sensing, flood monitoring, microwave remote sensing, synthetic aperture radar

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198 Fish Oil and Its Methyl Ester as an Alternate Fuel in the Direct Injection Diesel Engine

Authors: Pavan Pujar

Abstract:

Mackerel Fish oil was used as the raw material to produce the biodiesel in this study. The raw oil (RO) was collected from discarded fish products. This oil was filtered and heated to 110oC and made it moisture free. The filtered and moisture free RO was transesterified to produce biodiesel. The experimental results showed that oleic acid and lauric acid were the two major components of the fish oil biodiesel (FOB). Palmitic acid and linoleic acid were found approximately same in the quantity. The fuel properties kinematic viscosity, flash point, fire point, specific gravity, calorific value, cetane number, density, acid value, saponification value, iodine value, cloud point, pour point, ash content, Cu strip corrosion, carbon residue, API gravity were determined for FOB. A comparative study of the properties was carried out with RO and Neat diesel (ND). It was found that Cetane number was 59 for FOB which was more than RO, which showed 57. Blends (B20, B40, B60, B80: example: B20: 20% FOB + 80% ND) of FOB and ND were prepared on volume basis and comparative study was carried out with ND and FOB. Performance parameters BSFE, BSEC, A:F Ratio, Break thermal efficiency were analyzed and it was found that complete replacement of neat diesel (ND) is possible without any engine modifications.

Keywords: fish oil biodiesel, raw oil, blends, performance parameters

Procedia PDF Downloads 395