Search results for: automated diagnoses

Authors: Nuraddeen Usman, Khiruddin Abdullah, Mohd Nawawi, Amin Khalil Ismail

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A magnetic survey is carried out in order to locate the remains of construction items, specifically concrete pillars. The conventional Euler deconvolution technique can perform the task but it requires the use of fixed structural index (SI) and the construction items are made of materials with different shapes which require different SI (unknown). A Euler deconvolution technique that estimate background, horizontal coordinate (xo and yo), depth and structural index (SI) simultaneously is prepared and used for this task. The synthetic model study carried indicated the new methodology can give a good estimate of location and does not depend on magnetic latitude. For field data, both the total magnetic field and gradiometer reading had been collected simultaneously. The computed vertical derivatives and gradiometer readings are compared and they have shown good correlation signifying the effectiveness of the method. The filtering is carried out using automated procedure, analytic signal and other traditional techniques. The clustered depth solutions coincided with the high amplitude/values of analytic signal and these are the possible target positions of the concrete pillars being sought. The targets under investigation are interpreted to be located at the depth between 2.8 to 9.4 meters. More follow up survey is recommended as this mark the preliminary stage of the work.

Keywords: concrete pillar, magnetic survey, geotechnical investigation, Euler Deconvolution

Procedia PDF Downloads 252

Authors: Hayder H. Kareem, Ayad K. Hussein, Aseel A. Alkatib

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Groundwater accumulated at geological formations constitutes a worldwide vital water resource component which can be used to supply agriculture, industry, and domestic uses. The subsurface environment is affected by human activities; consequently, planning and sustainable management of aquifers require serious attention, especially as the world is exposed to the problem of global warming. Establishing accurate and efficient groundwater models will provide confident results for the behavior of the aquifer's system. The new approach, 'Distributed Value Property Zones,' available in Visual MODFLOW, is used to reconstruct the subsurface zones of the Al-Najaf region aquifer, and then its effect is compared with those manual and automated (PEST) approaches. Results show that the model has become more accurate with the use of the new approach, as the calibration and results analyses revealed. The assessment of the Al-Najaf region groundwater aquifer has revealed a degree of insufficiency of the required pumping demand, which reflects dry areas in both of the aquifer's layers. In addition, with pumping, the Euphrates River loses water of 7458 m³/day to the aquifer, while without pumping, it gains 28837 m³/day from the rainfall's recharge. The distributed value property zones approach achieves a precise groundwater model to assess the state of the Al-Najaf region aquifer.

Keywords: Al-Najaf region, distributed value property zones approach, hydraulic conductivity, groundwater modelling using visual MODFLOW

Procedia PDF Downloads 165

Authors: Clement Leroy, Guillaume Boitel

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This paper presents a comparative benchmarking review of a steady and transient three-dimensional (3D) flow computations in centrifugal pump using commercial (AnsysCFX) and open source (OpenFOAM) computational fluid dynamics (CFD) software. In centrifugal rotor-dynamic pump, the fluid enters in the impeller along to the rotating axis to be accelerated in order to increase the pressure, flowing radially outward into another stage, vaned diffuser or volute casing, from where it finally exits into a downstream pipe. Simulations are carried out at the best efficiency point (BEP) and part load, for single-phase flow with several turbulence models. The results are compared with overall performance report from experimental data. The use of CFD technology in industry is still limited by the high computational costs, and even more by the high cost of commercial CFD software and high-performance computing (HPC) licenses. The main objectives of the present study are to define OpenFOAM methodology for high-quality 3D steady and transient turbomachinery CFD simulation to conduct a thorough time-accurate performance analysis. On the other hand a detailed comparisons between computational methods, features on latest Ansys release 18 and OpenFOAM is investigated to assess the accuracy and industrial applications of those solvers. Finally an automated connected workflow (IoT) for turbine blade applications is presented.

Keywords: benchmarking, CFX, internet of things, openFOAM, time-accurate, turbomachinery

Procedia PDF Downloads 194

Authors: Zahow Muoftah

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Traditional manual visual grading of fruits has been one of the agricultural industry’s major challenges due to its laborious nature as well as inconsistency in the inspection and classification process. The main requirements for computer vision and visual processing are some effective techniques for identifying defects and estimating defect areas. Automated defect detection using computer vision and machine learning has emerged as a promising area of research with a high and direct impact on the visual inspection domain. Grading, sorting, and disease detection are important factors in determining the quality of fruits after harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have been conducted to identify diseases and pests that affect the fruits of agricultural crops. However, most previous studies concentrated solely on the diagnosis of a lesion or disease. This study focused on a comprehensive study to identify pests and diseases of apple and banana fruits using detection and classification defects on Banana and Apples by Computer Vision. As a result, the current article includes research from these domains as well. Finally, various pattern recognition techniques for detecting apple and banana defects are discussed.

Keywords: computer vision, banana, apple, detection, classification

Procedia PDF Downloads 90

Authors: Ayush Shrivastava, Arpit Chaudhary, Devang Kulshreshtha, Vibhav Prakash Singh, Rajeev Srivastava

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Early diagnosis of breast cancer can improve the survival rate by detecting cancer at an early stage. Breast region segmentation is an essential step in the analysis of digital mammograms. Accurate image segmentation leads to better detection of cancer. It aims at separating out Region of Interest (ROI) from rest of the image. The procedure begins with removal of labels, annotations and tags from the mammographic image using morphological opening method. Pectoral Muscle Sliding Window Algorithm (PMSWA) is used for removal of pectoral muscle from mammograms which is necessary as the intensity values of pectoral muscles are similar to that of ROI which makes it difficult to separate out. After removing the pectoral muscle, Dispersed Region Growing Algorithm (DRGA) is used for segmentation of mammogram which disperses seeds in different regions instead of a single bright region. To demonstrate the validity of our segmentation method, 322 mammographic images from Mammographic Image Analysis Society (MIAS) database are used. The dataset contains medio-lateral oblique (MLO) view of mammograms. Experimental results on MIAS dataset show the effectiveness of our proposed method.

Keywords: CAD, dispersed region growing algorithm (DRGA), image segmentation, mammography, pectoral muscle sliding window algorithm (PMSWA)

Procedia PDF Downloads 299

Authors: Itamir de Morais Barroca Filho, Cephas A. S. Barreto, Ramon Malaquias, Cezar Miranda Paula de Souza, Arthur Costa Gorgônio, João C. Xavier-Júnior, Mateus Firmino, Fellipe Matheus Costa Barbosa

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Information and Communication Technologies (ICT) in healthcare are crucial for efficiently delivering medical healthcare services to patients. These ICTs are also known as e-health and comprise technologies such as electronic record systems, telemedicine systems, and personalized devices for diagnosis. The focus of e-health is to improve the quality of health information, strengthen national health systems, and ensure accessible, high-quality health care for all. All the data gathered by these technologies make it possible to help clinical staff with automated decisions using machine learning. In this context, we collected patient data, such as heart rate, oxygen saturation (SpO2), blood pressure, respiration, and others. With this data, we were able to develop machine learning models for patients’ risk of death and estimate the length of stay in ICU beds. Thus, this paper presents the methodology for applying machine learning techniques to develop these models. As a result, although we implemented these models on an IoT healthcare platform, helping clinical staff in healthcare in an ICU, it is essential to create a robust clinical validation process and monitoring of the proposed models.

Keywords: ICT, e-health, machine learning, ICU, healthcare

Procedia PDF Downloads 91

Authors: R. Tariq, R. Lee

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Background: Additional demands placed on senior clinical teams with ongoing COVID-19 management has accelerated the need to harness the wider healthcare professional resources and upskill them to take on greater clinical responsibility safely. The UK NHS Long Term Plan (2019)¹ emphasises the importance of expanding Advanced Practitioners’ (APs) roles to take on more clinical diagnostic responsibilities to cope with increased demand. In acute settings, APs are often the first point of care for patients and require training to take on initial triage responsibilities efficiently and safely. Critically, their roles include determining which onward services the patients may require, and assessing whether they can be treated at home, avoiding unnecessary admissions to the hospital. Dem Dx is a Clinical Reasoning Platform (CRP) that claims to help frontline healthcare professionals independently assess and triage patients. It guides the clinician from presenting complaints through associated symptoms to a running list of differential diagnoses, media, national and institutional guidelines. The objective of this study was to compare the clinical referral rates and guidelines adherence registered by the HMR Urgent Community Care Team (UCCT)² and Dem Dx recommendations using retrospective cases. Methodology: 192 cases seen by the UCCT were anonymised and reassessed using Dem Dx clinical pathways. We compared the UCCT’s performance with Dem Dx regarding the appropriateness of onward referrals. We also compared the clinical assessment regarding adherence to NICE guidelines recorded on the clinical notes and the presence of suitable guidance in each case. The cases were audited by two medical doctors. Results: Dem Dx demonstrated appropriate referrals in 85% of cases, compared to 47% in the UCCT team (p<0.001). Of particular note, Dem Dx demonstrated an almost 65% (p<0.001) improvement in the efficacy and appropriateness of referrals in a highly experienced clinical team. The effectiveness of Dem Dx is in part attributable to the relevant NICE and local guidelines found within the platform's pathways and was found to be suitable in 86% of cases. Conclusion: This study highlights the potential of clinical decision support, as Dem Dx, to improve the quality of onward clinical referrals delivered by a multidisciplinary team in primary care. It demonstrated that it could support healthcare professionals in making appropriate referrals, especially those that may be overlooked by providing suitable clinical guidelines directly embedded into cases and clear referral pathways. Further evaluation in the clinical setting has been planned to confirm those assumptions in a prospective study.

Keywords: advanced practitioner, clinical reasoning, clinical decision-making, management, multidisciplinary team, referrals, triage

Procedia PDF Downloads 140

Authors: Kavita Bodke

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Structural health monitoring (SHM) is experiencing growth, necessitating the development of distinct methodologies to address its expanding scope effectively. In this study, we developed automatic structure damage identification, which incorporates three unique types of a building’s structural integrity. The first pertains to the presence of fractures within the structure, the second relates to the issue of dampness within the structure, and the third involves corrosion inside the structure. This study employs image classification techniques to discern between intact and impaired structures within structural data. The aim of this research is to find automatic damage detection with the probability of each damage class being present in one image. Based on this probability, we know which class has a higher probability or is more affected than the other classes. Utilizing photographs captured by a mobile camera serves as the input for an image classification system. Image classification was employed in our study to perform multi-class and multi-label classification. The objective was to categorize structural data based on the presence of cracks, moisture, and corrosion. In the context of multi-class image classification, our study employed three distinct methodologies: Random Forest, Multilayer Perceptron, and CNN. For the task of multi-label image classification, the models employed were Rasnet, Xceptionet, and Inception.

Keywords: SHM, CNN, deep learning, multi-class classification, multi-label classification

Procedia PDF Downloads 26

Authors: Alvaro Javier Ortega

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A large number of companies face the challenge of matching highly-skilled professionals to high-end positions by human resource deployment professionals. However, when the professional list and tasks to be matched are larger than a few dozens, this process result is far from optimal and takes a long time to be made. Therefore, an automated assignment algorithm for this workforce management problem is needed. The majority of companies are divided into several sectors or departments, where trained employees with different experience levels deal with a large number of tasks daily. Also, the execution order of all tasks is of mater consequence, due to some of these tasks just can be run it if the result of another task is provided. Thus, a wrong execution order leads to large waiting times between consecutive tasks. The desired goal is, therefore, creating accurate matches and a near-optimal execution order that maximizes the number of tasks performed and minimizes the idle time of the expensive skilled employees. The problem described before can be model as a mixed-integer non-linear programming (MINLP) as it will be shown in detail through this paper. A large number of MINLP algorithms have been proposed in the literature. Here, genetic algorithm solutions are considered and a comparison between two different mutation approaches is presented. The simulated results considering different complexity levels of assignment decisions show the appropriateness of the proposed model.

Keywords: employees, genetic algorithm, industry management, workforce

Procedia PDF Downloads 161

Authors: Sagri Sharma

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Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms and statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data.

Keywords: artificial intelligence, biomarker, gene expression datasets, hepatocellular carcinoma, machine learning, supervised learning algorithms, support vector machine

Procedia PDF Downloads 420

Authors: Farid Eltom A. E., Mustafa Abdul-Halim, Abdalla Markaz, Sami Atta, Mohamed Azhari, Ahmed Rashed

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Smart environment sensors integrated with ‘Internet of Things’ (IoT) technology can provide a new concept in tracking, sensing, and monitoring objects in the environment. The aim of the study is to evaluate the farm environmental monitoring system based on (IoT) and to realize the automated management of agriculture and the implementation of precision production. Until now, irrigation monitoring operations in Sudan have been carried out using traditional methods, which is a very costly and unreliable mechanism. However, by utilizing soil moisture sensors, irrigation can be conducted only when needed without fear of plant water stress. The result showed that software application allows farmers to display current and historical data on soil moisture and nutrients in the form of line charts. Design measurements of the soil factors: moisture, electrical, humidity, conductivity, temperature, pH, phosphorus, and potassium; these factors, together with a timestamp, are sent to the data server using the Lora WAN interface. It is considered scientifically agreed upon in the modern era that artificial intelligence works to arrange the necessary procedures to take care of the terrain, predict the quality and quantity of production through deep analysis of the various operations in agricultural fields, and also support monitoring of weather conditions.

Keywords: smart environment, monitoring systems, IoT, LoRa Gateway, center pivot

Procedia PDF Downloads 41

Authors: S. Mantzoukis, M. Gerasimou, P. Christodoulou, N. Varsamis, G. Kolliopoulou, N. Zotos

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Purpose: Patients in Intensive Care Units (ICU) are exposed to a different spectrum of microorganisms relative to the hospital. Due to the fact that the majority of these patients are intubated, bronchial secretions should be examined. Material and Method: Bronchial secretions should be taken with care so as not to be mixed with sputum or saliva. The bronchial secretions are placed in a sterile container and then inoculated into blood, Mac Conkey No2, Chocolate, Mueller Hinton, Chapman and Saboureaud agar. After this period, if any number of microbial colonies are detected, gram staining is performed and then the isolated organisms are identified by biochemical techniques in the automated Microscan system (Siemens) followed by a sensitivity test in the same system using the minimum inhibitory concentration MIC technique. The sensitivity test is verified by a Kirby Bauer test. Results: In 2017 the Laboratory of Microbiology received 365 samples of bronchial secretions from the Intensive Care Unit. 237 were found positive. S. epidermidis was identified in 1 specimen, A. baumannii in 60, K. pneumoniae in 42, P. aeruginosa in 50, C. albicans in 40, P. mirabilis in 4, E. coli in 4, S. maltophilia in 6, S. marcescens in 6, S. aureus in 12, S. pneumoniae in 1, S. haemolyticus in 4, P. fluorescens in 1, E. aerogenes in 1, E. cloacae in 5. Conclusions: The majority of ICU patients appear to be a fertile ground for the development of infections. The nature of the findings suggests that a significant part of the bacteria found comes from the unit (nosocomial infection).

Keywords: bronchial secretions, cultures, infections, intensive care units

Procedia PDF Downloads 179

Authors: Walter Bernhofer, Peter Haber, Tobias Mayer, Manfred Mayr, Markus Ziegler

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Current preventive maintenance measures are cost intensive and not efficient. With the available sensor data of state of the art internet of things devices new possibilities of automated data processing emerge. Current advances in data science and in machine learning enable new, so called predictive maintenance technologies, which empower data scientists to forecast possible system failures. The goal of this approach is to cut expenses in preventive maintenance by automating the detection of possible failures and to improve efficiency and quality of maintenance measures. Additionally, a centralization of the sensor data monitoring can be achieved by using this approach. This paper describes the approach of three students to define a reference architecture for a predictive maintenance solution in the internet of things domain with a connected smartphone app for service technicians. The reference architecture is validated by a case study. The case study is implemented with current Microsoft Azure cloud technologies. The results of the case study show that the reference architecture is valid and can be used to achieve a system for predictive maintenance execution with the cloud components of Microsoft Azure. The used concepts are technology platform agnostic and can be reused in many different cloud platforms. The reference architecture is valid and can be used in many use cases, like gas station maintenance, elevator maintenance and many more.

Keywords: case study, internet of things, predictive maintenance, reference architecture

Procedia PDF Downloads 235

Authors: Judith Mwakalonge, Geophrey Mbatta, Saidi Siuhi, Gurcan Comert, Cuthbert Ruseruka

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Transportation agencies worldwide continuously monitor their roads' conditions to minimize road maintenance costs and maintain public safety and rideability quality. Existing methods for carrying out road condition surveys involve manual observations of roads using standard survey forms done by qualified road condition surveyors or engineers either on foot or by vehicle. Automated road condition survey vehicles exist; however, they are very expensive since they require special vehicles equipped with sensors for data collection together with data processing and computing devices. The manual methods are expensive, time-consuming, infrequent, and can hardly provide real-time information for road conditions. This study contributes to this arena by utilizing built-in vehicle technologies, drones, and deep learning to automate road condition surveys while using low-cost technology. A single model is trained to capture flexible pavement distresses (Potholes, Rutting, Cracking, and raveling), thereby providing a more cost-effective and efficient road condition monitoring approach that can also provide real-time road conditions. Additionally, data fusion is employed to enhance the road condition assessment with data from vehicles and drones.

Keywords: road conditions, built-in vehicle technology, deep learning, drones

Procedia PDF Downloads 111

Authors: C. Manjula, Lilly Florence

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Software technology is developing rapidly which leads to the growth of various industries. Now-a-days, software-based applications have been adopted widely for business purposes. For any software industry, development of reliable software is becoming a challenging task because a faulty software module may be harmful for the growth of industry and business. Hence there is a need to develop techniques which can be used for early prediction of software defects. Due to complexities in manual prediction, automated software defect prediction techniques have been introduced. These techniques are based on the pattern learning from the previous software versions and finding the defects in the current version. These techniques have attracted researchers due to their significant impact on industrial growth by identifying the bugs in software. Based on this, several researches have been carried out but achieving desirable defect prediction performance is still a challenging task. To address this issue, here we present a machine learning based hybrid technique for software defect prediction. First of all, Genetic Algorithm (GA) is presented where an improved fitness function is used for better optimization of features in data sets. Later, these features are processed through Decision Tree (DT) classification model. Finally, an experimental study is presented where results from the proposed GA-DT based hybrid approach is compared with those from the DT classification technique. The results show that the proposed hybrid approach achieves better classification accuracy.

Keywords: decision tree, genetic algorithm, machine learning, software defect prediction

Procedia PDF Downloads 324

Authors: Nalini C. Iyer, Shraddha H., Anagha B. Varahamurthy, Dikshith C. S., Ishwar G. Kubasad, Vinayak I. Karalatti, Pavan B. Mulimani

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The current scenario of the pandemic due to COVID-19 has led to the awareness among the people to avoid unneces-sary contact in public places. There is a need to avoid contact with physical objects to stop the spreading of infection. The contactless feature has to be included in the systems in public places wherever possible. For example, attendance monitoring systems with fingerprint biometric can be replaced with a contactless feature. One more important protocol followed in the current situation is temperature monitoring and screening. The paper describes an attendance system with a contactless feature and temperature screening for the university. The system displays a QR code to scan, which redirects to the student login web page only if the location is valid (the location where the student scans the QR code should be the location of the display of the QR code). Once the student logs in, the temperature of the student is scanned by the contactless temperature sensor (mlx90614) with an error of 0.5°C. If the temperature falls in the range of the desired value (range of normal body temperature), then the attendance of the student is marked as present, stored in the database, and the door opens automatically. The attendance is marked as absent in the other case, alerted with the display of temperature, and the door remains closed. The door is automated with the help of a servomotor. To avoid the proxy, IR sensors are used to count the number of students in the classroom. The hardware system consisting of a contactless temperature sensor and IR sensor is implemented on the microcontroller, NodeMCU.

Keywords: NodeMCU, IR sensor, attendance monitoring, contactless, temperature

Procedia PDF Downloads 179

Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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Interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis applications. Segmentation of liver, spleen and kidneys is regarded as a major primary step in the computer-aided diagnosis of abdominal organ diseases. In this paper, a semi-automated method for medical image data is presented for the abdominal organ segmentation data using mathematical morphology. Our proposed method is based on hierarchical segmentation and watershed algorithm. In our approach, a powerful technique has been designed to suppress over-segmentation based on mosaic image and on the computation of the watershed transform. Our algorithm is currency in two parts. In the first, we seek to improve the quality of the gradient-mosaic image. In this step, we propose a method for improving the gradient-mosaic image by applying the anisotropic diffusion filter followed by the morphological filters. Thereafter, we proceed to the hierarchical segmentation of the liver, spleen and kidney. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work.

Keywords: anisotropic diffusion filter, CT images, morphological filter, mosaic image, simultaneous organ segmentation, the watershed algorithm

Procedia PDF Downloads 429

Authors: Vani Chintapally

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The expansion of modest and minimized Global Positioning System (GPS) beneficiaries has prompted most Automatic Vehicle Location (AVL) frameworks today depending solely on satellite-based finding frameworks, as GPS is the most stable usage of these. This paper shows the attributes of a proposed framework for following and dissecting open transport in a run of the mill medium-sized city and complexities the qualities of such a framework to those of broadly useful AVL frameworks. Particular properties of the courses broke down by the AVL framework utilized for the examination of open transport in our study incorporate cyclic vehicle courses, the requirement for particular execution reports, and so forth. This paper particularly manages vehicle movement forecasts and the estimation of station landing time, combined with consequently produced reports on timetable conformance and other execution measures. Another side of the watched issue is proficient exchange of information from the vehicles to the control focus. The pervasiveness of GSM bundle information exchange advancements combined with decreased information exchange expenses have brought on today's AVL frameworks to depend predominantly on parcel information exchange administrations from portable administrators as the correspondences channel in the middle of vehicles and the control focus. This methodology brings numerous security issues up in this conceivably touchy application field.

Keywords: automatic vehicle location (AVL), expectation of landing times, AVL security, data administrations, wise transport frameworks (ITS), guide coordinating

Procedia PDF Downloads 372

Authors: Volker Wannack

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Regional, national, and international strategies focusing on hydrogen (H₂) and blockchain are driving significant advancements in hydrogen and blockchain technology worldwide. These strategies lay the foundation for the groundbreaking "Blockchain Based Hydrogen Market (BBH₂)" project. The primary goal of this project is to develop a functional Blockchain Minimum Viable Product (B-MVP) for the hydrogen market. The B-MVP will leverage blockchain as an enabling technology with a common database and platform, facilitating secure and automated transactions through smart contracts. This innovation will revolutionize logistics, trading, and transactions within the hydrogen market. The B-MVP has transformative potential across various sectors. It benefits renewable energy producers, surplus energy-based hydrogen producers, hydrogen transport and distribution grid operators, and hydrogen consumers. By implementing standardized, automated, and tamper-proof processes, the B-MVP enhances cost efficiency and enables transparent and traceable transactions. Its key objective is to establish the verifiable integrity of climate-friendly "green" hydrogen by tracing its supply chain from renewable energy producers to end users. This emphasis on transparency and accountability promotes economic, ecological, and social sustainability while fostering a secure and transparent market environment. A notable feature of the B-MVP is its cross-border operability, eliminating the need for country-specific data storage and expanding its global applicability. This flexibility not only broadens its reach but also creates opportunities for long-term job creation through the establishment of a dedicated blockchain operating company. By attracting skilled workers and supporting their training, the B-MVP strengthens the workforce in the growing hydrogen sector. Moreover, it drives the emergence of innovative business models that attract additional company establishments and startups and contributes to long-term job creation. For instance, data evaluation can be utilized to develop customized tariffs and provide demand-oriented network capacities to producers and network operators, benefitting redistributors and end customers with tamper-proof pricing options. The B-MVP not only brings technological and economic advancements but also enhances the visibility of national and international standard-setting efforts. Regions implementing the B-MVP become pioneers in climate-friendly, sustainable, and forward-thinking practices, generating interest beyond their geographic boundaries. Additionally, the B-MVP serves as a catalyst for research and development, facilitating knowledge transfer between universities and companies. This collaborative environment fosters scientific progress, aligns with strategic innovation management, and cultivates an innovation culture within the hydrogen market. Through the integration of blockchain and hydrogen technologies, the B-MVP promotes holistic innovation and contributes to a sustainable future in the hydrogen industry. The implementation process involves evaluating and mapping suitable blockchain technology and architecture, developing and implementing the blockchain, smart contracts, and depositing certificates of origin. It also includes creating interfaces to existing systems such as nomination, portfolio management, trading, and billing systems, testing the scalability of the B-MVP to other markets and user groups, developing data formats for process-relevant data exchange, and conducting field studies to validate the B-MVP. BBH₂ is part of the "Technology Offensive Hydrogen" funding call within the research funding of the Federal Ministry of Economics and Climate Protection in the 7th Energy Research Programme of the Federal Government.

Keywords: hydrogen, blockchain, sustainability, innovation, structural change

Procedia PDF Downloads 155

Authors: Nitin Dewangan, Nitin Kattula, Megha Anawat

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Hydraulic reservoir is the key component in the mobile construction vehicles; most of the off-road earth moving construction machinery requires bigger side hydraulic reservoirs. Their reservoir construction is very much non-uniform and designers used such design to utilize the space available under the vehicle. There is no way to find out the space utilization of the reservoir by oil and validity of design except virtual simulation. Computational fluid dynamics (CFD) helps to predict the reservoir space utilization by vortex mapping, path line plots and dwell time prediction to make sure the design is valid and efficient for the vehicle. The dwell time acceptance criteria for effective reservoir design is 15 seconds. The paper will describe the hydraulic reservoir simulation which is carried out using CFD tool acuSolve using automated mesh strategy. The free surface flow and moving reference mesh is used to define the oil flow level inside the reservoir. The first baseline design is not able to meet the acceptance criteria, i.e., dwell time below 15 seconds because the oil entry and exit ports were very close. CFD is used to redefine the port locations for the reservoir so that oil dwell time increases in the reservoir. CFD also proposed baffle design the effective space utilization. The final design proposed through CFD analysis is used for physical validation on the machine.

Keywords: reservoir, turbulence model, transient model, level set, free-surface flow, moving frame of reference

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Authors: Ran Etgar, Yuval Cohen, Erel Avineri

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One of the major challenges of transportation in medium sized inner-cities (such as Tel-Aviv) is the last-mile solution. Personal rapid transit (PRT) seems like an applicable candidate for this, as it combines the benefits of personal (car) travel with the operational benefits of transit. However, the investment required for large area PRT grid is significant and there is a need to economically justify such investment by correctly evaluating the grid capacity. PRT main elements are small automated vehicles (sometimes referred to as podcars) operating on a network of specially built guideways. The research is looking at a specific concept of elevated PRT system. Literature review has revealed the drawbacks PRT modelling and simulation approaches, mainly due to the lack of consideration of technical and operational features of the system (such as headways, acceleration, safety issues); the detailed design of infrastructure (guideways, stations, and docks); the stochastic and sessional characteristics of demand; and safety regulations – all of them have a strong effect on the system performance. A highly detailed model of the system, developed in this research, is applying a discrete event simulation combined with an agent-based approach, to represent the system elements and the podecars movement logic. Applying a case study approach, the simulation model is used to study the capacity of the system, the expected throughput of the system, the utilization, and the level of service (journey time, waiting time, etc.).

Keywords: capacity, productivity measurement, PRT, simulation, transportation

Procedia PDF Downloads 157

Authors: Vishaal Subramanian, Annuatha Vinod Kumar, Santosh Kumar Budankayala, M. Senthil Kumar

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This paper discusses the aerodynamic and structural design, simulation and optimization of a mini-High Altitude Long Endurance (HALE) UAV. The applications of this mini HALE UAV vary from aerial topological surveys, quick first aid supply, emergency medical blood transport, search and relief activates to border patrol, surveillance and estimation of forest fire progression. Although classified as a mini UAV according to UVS International, our design is an amalgamation of the features of ‘mini’ and ‘HALE’ categories, combining the light weight of the ‘mini’ and the high altitude ceiling and endurance of the HALE. Designed with the idea of implementation in India, it is in strict compliance with the UAS rules proposed by the office of the Director General of Civil Aviation. The plane can be completely automated or have partial override control and is equipped with an Infra-Red camera and a multi coloured camera with on-board storage or live telemetry, GPS system with Geo Fencing and fail safe measures. An additional of 1.5 kg payload can be attached to three major hard points on the aircraft and can comprise of delicate equipment or releasable payloads. The paper details the design, optimization process and the simulations performed using various software such as Design Foil, XFLR5, Solidworks and Ansys.

Keywords: aircraft, endurance, HALE, high altitude, long range, UAV, unmanned aerial vehicle

Procedia PDF Downloads 390

Authors: Rajvir Kaur, Jeewani Anupama Ginige

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With recent trends in Big Data and advancements in Information and Communication Technologies, the healthcare industry is at the stage of its transition from clinician oriented to technology oriented. Many people around the world die of cancer because the diagnosis of disease was not done at an early stage. Nowadays, the computational methods in the form of Machine Learning (ML) are used to develop automated decision support systems that can diagnose cancer with high confidence in a timely manner. This paper aims to carry out the comparative evaluation of a selected set of ML classifiers on two existing datasets: breast cancer and cervical cancer. The ML classifiers compared in this study are Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression, Ensemble (Bagged Tree) and Artificial Neural Networks (ANN). The evaluation is carried out based on standard evaluation metrics Precision (P), Recall (R), F1-score and Accuracy. The experimental results based on the evaluation metrics show that ANN showed the highest-level accuracy (99.4%) when tested with breast cancer dataset. On the other hand, when these ML classifiers are tested with the cervical cancer dataset, Ensemble (Bagged Tree) technique gave better accuracy (93.1%) in comparison to other classifiers.

Keywords: artificial neural networks, breast cancer, classifiers, cervical cancer, f-score, machine learning, precision, recall

Procedia PDF Downloads 268

Authors: Marina Lazar Chandy, N. Kannan, Rajendra Patil, Vinod Mathew, Ajmal Mohamed, P. K. Sreeja, Renju Jose

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Introduction- Arecoline, a major constituent of arecanut has shown to have some effect on liver. The use of arecanut is found to be the most common etiological factor for the development of Oral Submucous fibrosis (O.S.M.F). The effect of arecanut usage on liver in patients with O.S.M.F needs to be assessed. Lipids play a role in structural maintenance of cell. Alterations of lipid profile were noted in cancer patients. O.S.M.F being a precancerous lesion can have some effect on the level of lipids in the body. Objectives: This study was done to assess the alterations in liver enzymes (Serum Glutamic Pyruvic Transaminase(S.G.P.T ,Serum Glutamic Oxaloacetic Transaminase(S.G.O.T)) and lipid metabolism (High Density Lipoprotien(H.D.L) and Apo Lipoprotien A1 (Apo A1)) in patients with O.S.M.F. Methods-130 patients were taken for the study,100 patients with O.S.M.F and 30 as control group without O.S.M.F. Fasting blood sugar levels were taken, centrifuged and analyzed for S.G.P.T,S.G.O.T, H.D.L and Apo A1 using semi automated spectrophotometer. Results: After statistical analysis, it was concluded that there is an elevation of levels of S.G.P.T, S.G.O.T, and decreased levels of H.D.L, Apo A1 for O.S.M.F group when compared with control group. With increased grade of O.S.M.F. and duration of habit, S.G.P.T. & S.G.O.T. increased whereas, H.D.L. & Apo A1 decreased. All the values were statistically significant at p<0.01.

Keywords: apolipoprotien A1, high density lipoprotien, oral submucous fibrosis, serum glutamic oxaloacetic transaminase

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Authors: Dercan Gencbas, Hatice Bebis, Sue Moorhead

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In the nursing process, many of the nursing classification systems were created to be used in international. From these, NANDA-I, Nursing Outcomes Classification (NOC) and Nursing Interventions Classification (NIC). In this direction, the main objective of this study is to establish a model for caregivers in hospitals and communities in Turkey and to ensure that nursing outputs are assessed by NOC-based measures. There are many scales to measure Urinary Incontinence (UI), which is very common in children, in old age, vaginal birth, NOC scales are ideal for use in the nursing process for comprehensive and holistic assessment, with surveys available. For this reason, the purpose of this study is to evaluate the validity of the NOC outputs and indicators used for UI NANDA-I. This research is a methodological study. In addition to the validity of scale indicators in the study, how much they will contribute to recovery after the nursing intervention was assessed by experts. Scope validations have been applied and calculated according to Fehring 1987 work model. According to this, nursing inclusion criteria and scores were determined. For example, if experts have at least four years of clinical experience, their score was 4 points or have at least one year of the nursing classification system, their score was 1 point. The experts were a publication experience about nursing classification, their score was 1 point, or have a doctoral degree in nursing, their score was 2 points. If the expert has a master degree, their score was 1 point. Total of 55 experts rated Fehring as a “senior degree” with a score of 90 according to the expert scoring. The nursing interventions to be applied were asked to what extent these indicators would contribute to recovery. For coverage validity tailored to Fehring's model, each NOC and NOC indicator from specialists was asked to score between 1-5. Score for the significance of indicators was from 1=no precaution to 5=very important. After the expert opinion, these weighted scores obtained for each NOC and NOC indicator were classified as 0.8 critical, 0.8 > 0.5 complements, > 0.5 are excluded. In the NANDA-I / NOC / NIC system (guideline), 5 NOCs proposed for nursing diagnoses for UI were proposed. These outputs are; Urinary Continence, Urinary Elimination, Tissue Integrity, Self CareToileting, Medication Response. After the scales are translated into Turkish, the weighted average of the scores obtained from specialists for the coverage of all 5 NOCs and the contribution of nursing initiatives exceeded 0.8. After the opinions of the experts, 79 of the 82 indicators were calculated as critical, 3 of the indicators were calculated as supplemental. Because of 0.5 > was not obtained, no substance was removed. All NOC outputs were identified as valid and usable scales in Turkey. In this study, five NOC outcomes were verified for the evaluation of the output of individuals who have received nursing knowledge of UI and variant types. Nurses in Turkey can benefit from the outputs of the NOC scale to perform the care of the elderly incontinence.

Keywords: nursing outcomes, content validity, nursing diagnosis, urinary incontinence

Procedia PDF Downloads 119

Authors: U. Borasi, Amit Kr. Jain, Rakesh, Piyush Jain

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Data collection, data preparation, model training, model evaluation, and deployment are all processes in a typical machine learning workflow. Training data needs to be gathered and organised. This often entails collecting a sizable dataset and cleaning it to remove or correct any inaccurate or missing information. Preparing the data for use in the machine learning model requires pre-processing it after it has been acquired. This often entails actions like scaling or normalising the data, handling outliers, selecting appropriate features, reducing dimensionality, etc. This pre-processed data is then used to train a model on some machine learning algorithm. After the model has been trained, it needs to be assessed by determining metrics like accuracy, precision, and recall, utilising a test dataset. Every time a new model is built, both data pre-processing and model training—two crucial processes in the Machine learning (ML) workflow—must be carried out. Thus, there are various Machine Learning algorithms that can be employed for every single approach to data pre-processing, generating a large set of combinations to choose from. Example: for every method to handle missing values (dropping records, replacing with mean, etc.), for every scaling technique, and for every combination of features selected, a different algorithm can be used. As a result, in order to get the optimum outcomes, these tasks are frequently repeated in different combinations. This paper suggests a simple architecture for organizing this largely produced “combination set of pre-processing steps and algorithms” into an automated workflow which simplifies the task of carrying out all possibilities.

Keywords: machine learning, automation, AUTOML, architecture, operator pool, configuration, scheduler

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Authors: Erika R. Chambriard, Sandro C. Izidoro, Davidson P. Mendes, Douglas E. V. Pires

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Work-related illnesses and disorders have been a constant aspect of work. Although their nature has changed over time, from musculoskeletal disorders to illnesses related to psychosocial aspects of work, its impact on the life of workers remains significant. Despite significant efforts worldwide to protect workers, the disparity between changes in work legislation and actual benefit for workers’ health has been creating a significant economic burden for social security and health systems around the world. In this context, this study aims to propose, test and validate a modular prototype that allows for work environmental aspects to be assessed, monitored and better controlled. The main focus is also to provide a historical record of working conditions and the means for workers to obtain comprehensible and useful information regarding their work environment and legal limits of occupational exposure to different types of environmental variables, as means to improve prevention of work-related accidents and disorders. We show the developed prototype provides useful and accurate information regarding the work environmental conditions, validating them with standard occupational hygiene equipment. We believe the proposed prototype is a cost-effective and adequate approach to work environment monitoring that could help elucidate the links between work and occupational illnesses, and that different industry sectors, as well as developing countries, could benefit from its capabilities.

Keywords: Arduino prototyping, occupational health and hygiene, work environment, work-related disorders prevention

Procedia PDF Downloads 118

Authors: Mayank Mundhra, Chester Rebeiro

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Cryptocurrencies are rapidly finding wide application in areas such as Real Time Gross Settlements and Payments Systems. Ripple is a cryptocurrency that has gained prominence with banks and payment providers. It solves the Byzantine General’s Problem with its Ripple Protocol Consensus Algorithm (RPCA), where each server maintains a list of servers, called Unique Node List (UNL) that represents the network for the server, and will not collectively defraud it. The server believes that the network has come to a consensus when members of the UNL come to a consensus on a transaction. In this paper we improve Ripple to achieve better speed, security, last mile connectivity and ease of use. We implement guidelines and automated systems for building and maintaining UNLs for resilience, robustness, improved security, and efficient information propagation. We enhance the system so as to ensure that each server receives information from across the whole network rather than just from the UNL members. We also introduce the paradigm of UNL overlap as a function of information propagation and the trust a server assigns to its own UNL. Our design not only reduces vulnerabilities such as eclipse attacks, but also makes it easier to identify malicious behaviour and entities attempting to fraudulently Double Spend or stall the system. We provide experimental evidence of the benefits of our approach over the current Ripple scheme. We observe ≥ 4.97x and 98.22x in speedup and success rate for information propagation respectively, and ≥ 3.16x and 51.70x in speedup and success rate in consensus.

Keywords: Ripple, Kelips, unique node list, consensus, information propagation

Procedia PDF Downloads 135

Authors: Takamitsu Kodama, Eiji Kawamoto

Abstract:

Introduction: Ultrasound in emergency room has advantages of safer, faster, repeatable and noninvasive. Especially focused Point-Of-Care Ultrasound (POCUS) is used daily for prompt and accurate diagnoses, for quickly identifying critical and life-threatening conditions. That is why ultrasound has demonstrated its usefulness in emergency medicine. The true value of ultrasound has been once again recognized in recent years. It is thought that all resident physicians working at emergency room should perform an ultrasound scan to interpret signs and symptoms of deteriorating patients in the emergency room. However, a practical education on ultrasound is still in development. To resolve this issue, we established a new educational program using high-fidelity simulators and evaluated the efficacy of this course. Methods: Educational program includes didactic lectures and skill stations in half-day course. Instructor gives a lecture on POCUS such as Rapid Ultrasound in Shock (RUSH) and/or Focused Assessment Transthoracic Echo (FATE) protocol at the beginning of the course. Then, attendees are provided for training of scanning with cooperation of normal simulated patients. In the end, attendees learn how to apply focused POCUS skills at clinical situation using high-fidelity simulators such as SonoSim® (SonoSim, Inc) and SimMan® 3G (Laerdal Medical). Evaluation was conducted through surveillance questionnaires to 19 attendees after two pilot courses. The questionnaires were focused on understanding course concept and satisfaction. Results: All attendees answered the questionnaires. With respect to the degree of understanding, 12 attendees (number of valid responses: 13) scored four or more points out of five points. High-fidelity simulators, especially SonoSim® was highly appreciated to enhance learning how to handle ultrasound at an actual practice site by 11 attendees (number of valid responses: 12). All attendees encouraged colleagues to take this course because the high level of satisfaction was achieved. Discussion: Newly introduced educational course using high-fidelity simulators realizes the circle of learning to deepen the understanding on focused POCUS by gradual stages. SonoSim® can faithfully reproduce scan images with pathologic findings of ultrasound and provide experimental learning for a growth number of beginners such as resident physicians. In addition, valuable education can be provided if it is used combined with SimMan® 3G. Conclusions: Newly introduced educational course using high-fidelity simulators is supposed to be effective and helps in providing better education compared with conventional courses for emergency physicians.

Keywords: point-of-care ultrasound, high-fidelity simulators, education, circle of learning

Procedia PDF Downloads 272

Authors: Dario Basile, Manuela De Maddis, Raffaella Sesana, Pasquale Russo Spena, Roberto Maiorano

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Nowadays, the reduction of CO₂ emissions and the decrease in energy consumption are the main aims of several industries, especially in the automotive sector. To comply with the increasingly restrictive regulations, the automotive industry is constantly looking for innovative techniques to produce lighter, more efficient, and less polluting vehicles. One of the latest technologies, and still developing, is based on the fabrication of the body-in-white and car parts through the stamping of Aluminum Tailor Welded Blanks. Tailor Welded Blanks (TWBs) are generally the combination of two/three metal sheets with different thicknesses and/or mechanical strengths, which are commonly butt-welded together by laser sources. The use of aluminum TWBs has several advantages such as low density and corrosion resistance adequate. However, their use is still limited by the lower formability with respect to the parent materials and the more intrinsic difficulty of laser welding of aluminum sheets (i.e., internal porosity) that, although its use in automated industries is constantly growing, remains a process to be further developed and improved. This study has investigated the effect of the main laser welding process parameters (laser power, welding speed, and focal distance) on the mechanical properties of aluminum TWBs made of 6xxx series. The research results show that a narrow weldability window can be found to ensure welded joints with high strength and limited or no porosity.

Keywords: aluminum sheets, automotive industry, laser welding, mechanical properties, tailor welded blanks

Procedia PDF Downloads 101