Search results for: inherent feature

Authors: Mohammad Hossein Khasmafkan Nezam, Nader Chavoshi Boroujeni, Mohamad Reza Veshaghi

Abstract:

Quality management is dominated by rational paradigms for the measurement and management of quality, but these paradigms start to ‘break down’, when faced with the inherent complexity of managing quality in intensely competitive changing environments. In this article, the various theoretical paradigms employed to manage quality are reviewed and the advantages and limitations of these paradigms are highlighted. A major implication of this review is that when faced with complexity, an ideological stance to any single strategy paradigm for total quality environmental management is ineffective. We suggest that as complexity increases and we envisage intensely competitive changing environments there will be a greater need to consider a multi-paradigm integrationist view of strategy for TQEM.

Keywords: total quality management (TQM), total quality environmental management (TQEM), ideologies (philosophy), theoretical paradigms

Procedia PDF Downloads 290

Authors: Sunico Armie Flores

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Early childhood education (ECE) is pivotal in shaping the future of individuals and society. This paper explores the collaborative efforts required among educators, families, and communities to create a collective impact on young children’s development. It delves into the importance of these partnerships, effective strategies for engagement, and the challenges and opportunities inherent in fostering such collaboration. By examining current research and practices, the paper aims to highlight the essential role of an integrated approach in achieving significant and sustainable improvements in early childhood outcomes.

Keywords: early childhood education, lifelong learning, cognitive development, socio-emotional development, educators, families, communities, collaborative efforts, collective impact, early learning environments, holistic development, high-quality ECE programs, investment in education

Procedia PDF Downloads 15

Authors: N. Seemuang, T. McLeay, T. Slatter

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Worn tools have a direct effect on the surface finish and part accuracy. Tool condition monitoring systems have been developed over a long period and used to avoid a loss of productivity resulting from using a worn tool. However, the majority of tool monitoring research has applied expensive sensing systems not suitable for production. In this work, the cutting sound in turning machine was studied using microphone. Machining trials using seven cutting conditions were conducted until the observable flank wear width (FWW) on the main cutting edge exceeded 0.4 mm. The cutting inserts were removed from the tool holder and the flank wear width was measured optically. A microphone with built-in preamplifier was used to record the machining sound of EN24 steel being face turned by a CNC lathe in a wet cutting condition using constant surface speed control. The sound was sampled at 50 kS/s and all sound signals recorded from microphone were transformed into the frequency domain by FFT in order to establish the frequency content in the audio signature that could be then used for tool condition monitoring. The extracted feature from audio signal was compared to the flank wear progression on the cutting inserts. The spectrogram reveals a promising feature, named as ‘spindle noise’, which emits from the main spindle motor of turning machine. The spindle noise frequency was detected at 5.86 kHz of regardless of cutting conditions used on this particular CNC lathe. Varying cutting speed and feed rate have an influence on the magnitude of power spectrum of spindle noise. The magnitude of spindle noise frequency alters in conjunction with the tool wear progression. The magnitude increases significantly in the transition state between steady-state wear and severe wear. This could be used as a warning signal to prepare for tool replacement or adapt cutting parameters to extend tool life.

Keywords: tool wear, flank wear, condition monitoring, spindle noise

Procedia PDF Downloads 310

Authors: Ilona Copik

Abstract:

The aim of this study is to analyze the representation of memories of the forced displacement of Poles and Germans from the eastern territories in 1945 as depicted by Polish and German feature films between the years 1945-1960. The aftermath of World War II and the Allied agreements concluded at Yalta and Potsdam (1945) resulted in changes in national borders in Central and Eastern Europe and the large-scale transfer of civilians. The westward migration became a symbol of the new post-war division of Europe, new spheres of influence separated by the Iron Curtain. For years it was a controversial topic in both Poland and Germany due to the geopolitical alignment (the socialist East and capitalist West of Europe), as well as the unfinished debate between the victims and perpetrators of the war. The research premise is to take a comparative view of the conflicted cultures of Polish and German memory, to reflect on the possibility of an international dialogue about the past recorded in film images, and to discover the potential of film as a narrative warning against totalitarian inclinations. Until now, films made between 1945 and 1960 in Poland and the German occupation zones have been analyzed mainly in the context of artistic strategies subordinated to ideology and historical politics. In this study, the intention is to take a critical approach leading to the recognition of how films work as collective memory media, how they reveal the mechanisms of memory/forgetting, and what settlement topoi and migration myths they contain. The main hypothesis is that feature films about forced displacement, in addition to the politics of history - separate in each country - reveal comparable transnational individual experiences: the chaos of migration, the trauma of losing one's home, the conflicts accompanying the familiar/foreign, the difficulty of cultural adaptation, the problem of lost identity, etc.

Keywords: forced displacement, Polish and German cinema, war victims, World War II

Procedia PDF Downloads 50

Authors: Mayank Gupta, Siba Prasad Samal, Vasu Kakkirala

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The internet world and its priorities have changed considerably in the last decade. Browsing on smart phones has increased manifold and is set to explode much more. Users spent considerable time browsing different websites, that gives a great deal of insight into user’s preferences. Instead of plain information classifying different aspects of browsing like Bookmarks, History, and Download Manager into useful categories would improve and enhance the user’s experience. Most of the classification solutions are server side that involves maintaining server and other heavy resources. It has security constraints and maybe misses on contextual data during classification. On device, classification solves many such problems, but the challenge is to achieve accuracy on classification with resource constraints. This on device classification can be much more useful in personalization, reducing dependency on cloud connectivity and better privacy/security. This approach provides more relevant results as compared to current standalone solutions because it uses content rendered by browser which is customized by the content provider based on user’s profile. This paper proposes a Naive Bayes based lightweight classification engine targeted for a resource constraint devices. Our solution integrates with Web Browser that in turn triggers classification algorithm. Whenever a user browses a webpage, this solution extracts DOM Tree data from the browser’s rendering engine. This DOM data is a dynamic, contextual and secure data that can’t be replicated. This proposal extracts different features of the webpage that runs on an algorithm to classify into multiple categories. Naive Bayes based engine is chosen in this solution for its inherent advantages in using limited resources compared to other classification algorithms like Support Vector Machine, Neural Networks, etc. Naive Bayes classification requires small memory footprint and less computation suitable for smartphone environment. This solution has a feature to partition the model into multiple chunks that in turn will facilitate less usage of memory instead of loading a complete model. Classification of the webpages done through integrated engine is faster, more relevant and energy efficient than other standalone on device solution. This classification engine has been tested on Samsung Z3 Tizen hardware. The Engine is integrated into Tizen Browser that uses Chromium Rendering Engine. For this solution, extensive dataset is sourced from dmoztools.net and cleaned. This cleaned dataset has 227.5K webpages which are divided into 8 generic categories ('education', 'games', 'health', 'entertainment', 'news', 'shopping', 'sports', 'travel'). Our browser integrated solution has resulted in 15% less memory usage (due to partition method) and 24% less power consumption in comparison with standalone solution. This solution considered 70% of the dataset for training the data model and the rest 30% dataset for testing. An average accuracy of ~96.3% is achieved across the above mentioned 8 categories. This engine can be further extended for suggesting Dynamic tags and using the classification for differential uses cases to enhance browsing experience.

Keywords: chromium, lightweight engine, mobile computing, Naive Bayes, Tizen, web browser, webpage classification

Procedia PDF Downloads 139

Authors: Getaneh Berie Tarekegn

Abstract:

A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Uche A. Nnawulezi

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This study evolved from the need to look at and evaluate the difficulties in the domestication of International Laws and Human Rights Laws in Nigeria. Essentially, the paper-based its examination on documentary evidence and depended much on secondary sources, for example, textbooks, journals, articles, periodicals and research reports emanating from suggestions of international law experts, jurists and human rights lawyers on the development challenges in domesticating international laws and human rights laws in Nigeria. These data were analyzed by the application of content analysis and careful observation of the current municipal laws which has posed great challenges in the domestication of International laws. This paper might follow the historical backdrop of the practices in the use of International law in Nigeria and should likewise consider the challenges inherent in these practices. The paper suggests that a sustainable domestication of International Laws and its application in Nigerian courts will ensure a better enforcement of human rights within the domestic jurisdiction.

Keywords: international law, human rights, domestication, challenges

Procedia PDF Downloads 217

Authors: Ogunsina Babatunde, Aregbesola Omotayo, Adebayo Adewale, Odunlami Johnson

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The physical properties of nine Nigerian staple food flours related to bulk handling and processing were investigated following standard procedures. The results showed that the moisture content, bulk density, angle of repose, water absorption capacity, swelling index, dispersability, pH and wettability of the flours ranged from 9.95 to 11.98%, 0.44 to 0.66 g/cm3, 31.43 to 39.65o, 198.3 to 291.7 g of water/100 g of sample, 5.53 to 7.63, 60.3 to 73.8%, 4.43 to 6.70, and 11 to 150 s. The particle size analysis of the flour samples indicated significant differences (p<0.05). The least gelation concentration of the flour samples ranged from 6 to 14%. The colour of the flours fell between light and saturated, with the exception of cassava, millet and maize flours which appear dark and dull. The properties of food flours depend largely on the inherent property of the food material and may influence their functional behaviour as food materials.

Keywords: properties, flours, staple food, bulk handling

Procedia PDF Downloads 453

Authors: Thomas Kipkurgat, Silahs Chemwaina

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Communication and ICT is a crucial component in realization of vision 2030, radio has played a key role in dissemination of information to mass audience. Since time immemorial, mass media has played a vital role in passing information on agricultural development issues both locally and internationally. This paper aimed at assessing the role of community radio stations in enhancing agricultural development in Kenya. The paper sought to identify the main contributions of KASS FM radio in the agricultural development especially in rural areas, the study also aimed to establish the appropriate adjustments in editorial policies of KASS FM radio in helping to promote agricultural development related programmes in rural areas. Despite some weaknesses in radio programming and the mode of interaction with the rural people, the findings of this study showed that the rural communities are better off today than in the old days when FM radios were non-existent. KASS FM has come up with different developmental programmes that have positively contributed to changing the rural people’s ways of life. These programmes include farming, health, marital values, environment, cultural issues, human rights, democracy, religious teachings, peace and reconciliation. Such programmes feature experts, professionals and opinion leaders who address numerous topics of interest to the community. The local people participate in the production of these programmes through letters to the editor, and phone-ins, among others. Programmes such as political talk shows, which feature in KASS FM, has become one of the most important ways of community participation. The interpretation and conclusions are based on the empirical data analysis and the theories of development advanced by international development communication scholars, as presented in the paper. The study ends with some recommendations on how KASS FM can best serve the interests of the poor people in rural areas, and helps improve their lives.

Keywords: agriculture, development, communication, KASS FM, radio, rural areas, Kenya

Procedia PDF Downloads 270

Authors: Thiago Spilborghs Bueno Meyer, Plinio Thomaz Aquino Junior

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Through speech, which privileges the functional and interactive nature of the text, it is possible to ascertain the spatiotemporal circumstances, the conditions of production and reception of the discourse, the explicit purposes such as informing, explaining, convincing, etc. These conditions allow bringing the interaction between humans closer to the human-robot interaction, making it natural and sensitive to information. However, it is not enough to understand what is said; it is necessary to recognize emotions for the desired interaction. The validity of the use of neural networks for feature selection and emotion recognition was verified. For this purpose, it is proposed the use of neural networks and comparison of models, such as recurrent neural networks and deep neural networks, in order to carry out the classification of emotions through speech signals to verify the quality of recognition. It is expected to enable the implementation of robots in a domestic environment, such as the HERA robot from the RoboFEI@Home team, which focuses on autonomous service robots for the domestic environment. Tests were performed using only the Mel-Frequency Cepstral Coefficients, as well as tests with several characteristics of Delta-MFCC, spectral contrast, and the Mel spectrogram. To carry out the training, validation and testing of the neural networks, the eNTERFACE’05 database was used, which has 42 speakers from 14 different nationalities speaking the English language. The data from the chosen database are videos that, for use in neural networks, were converted into audios. It was found as a result, a classification of 51,969% of correct answers when using the deep neural network, when the use of the recurrent neural network was verified, with the classification with accuracy equal to 44.09%. The results are more accurate when only the Mel-Frequency Cepstral Coefficients are used for the classification, using the classifier with the deep neural network, and in only one case, it is possible to observe a greater accuracy by the recurrent neural network, which occurs in the use of various features and setting 73 for batch size and 100 training epochs.

Keywords: emotion recognition, speech, deep learning, human-robot interaction, neural networks

Procedia PDF Downloads 137

Authors: Yan Zhang

Abstract:

Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

Procedia PDF Downloads 364

Authors: Carlos Gonzalez

Abstract:

This paper describes the use of the Internet as a feature to enhance the security of our software that is going to be distributed/sold to users potentially all over the world. By placing in a secure server some of the features of the secure software, we increase the security of such software. The communication between the protected software and the secure server is done by a double lock algorithm. This paper also includes an analysis of intruders and describes possible responses to detect threats.

Keywords: internet, secure software, threats, cryptography process

Procedia PDF Downloads 304

Authors: Manjula Sutagundar, B. G. Sheeparamatti, D. S. Jangamshetti

Abstract:

Micro Electro Mechanical Systems (MEMS) resonators have shown the potential of replacing quartz crystal technology for sensing and high frequency signal processing applications because of inherent advantages like small size, high quality factor, low cost, compatibility with integrated circuit chips. This paper presents the optimization and modelling and simulation of the optimized micro cantilever resonator. The objective of the work is to optimize the dimensions of a micro cantilever resonator for a specified range of resonant frequency and specific quality factor. Optimization is carried out using genetic algorithm. The genetic algorithm is implemented using MATLAB. The micro cantilever resonator is modelled in CoventorWare using the optimized dimensions obtained from genetic algorithm. The modeled cantilever is analysed for resonance frequency.

Keywords: MEMS resonator, genetic algorithm, modelling and simulation, optimization

Procedia PDF Downloads 527

Authors: Kyle Yatich Terik, Collins Oduor

Abstract:

The current healthcare system has made healthcare more accessible and efficient by the use of information technology through the implementation of computer algorithms that generate menus based on the diagnosis. While many systems just like these have been created over the years, their main objective is to help healthy individuals calculate their calorie intake and assist them by providing food selections based on a pre-specified calorie. That application has been proven to be useful in some ways, and they are not suitable for monitoring, planning, and managing hospital patients, especially that critical condition their dietary needs. The system also addresses a number of objectives, such as; the main objective is to be able to design, develop and implement an efficient, user-friendly as well as and interactive dietary management system. The specific design development objectives include developing a system that will facilitate a monitoring feature for users using graphs, developing a system that will provide system-generated reports to the users, dietitians, and system admins, design a system that allows users to measure their BMI (Body Mass Index), the system will also provide food template feature that will guide the user on a balanced diet plan. In order to develop the system, further research was carried out in Kenya, Nairobi County, using online questionnaires being the preferred research design approach. From the 44 respondents, one could create discussions such as the major challenges encountered from the manual dietary system, which include no easily accessible information of the calorie intake for food products, expensive to physically visit a dietitian to create a tailored diet plan. Conclusively, the system has the potential of improving the quality of life of people as a whole by providing a standard for healthy living and allowing individuals to have readily available knowledge through food templates that will guide people and allow users to create their own diet plans that consist of a balanced diet.

Keywords: DMS, dietitian, patient, administrator

Procedia PDF Downloads 141

Authors: Getaneh Berie Tarekegn

Abstract:

A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

Procedia PDF Downloads 47

Authors: Aarnav Singh, Jayesh Ghatate, Tarush Pandey

Abstract:

Non-Governmental Organizations (NGOs) play a crucial role in addressing societal challenges, relying heavily on financial transactions to fund their impactful initiatives. However, traditional financial systems can be cumbersome and lack transparency, hindering the efficiency and trustworthiness of NGO operations. The Ethereum main-net, while pioneering the decentralized finance landscape, grapples with inherent scalability challenges, restricting its transaction throughput to a range of 15-45 transactions per second (TPS). This limitation poses substantial obstacles for NGOs engaging in swift and dynamic financial transactions critical to their operational efficiency. This research is a comprehensive exploration of the intricacies of these scalability challenges and delves into the design and implementation of a purpose-built blockchain system explicitly crafted to surmount these constraints.

Keywords: non-governmental organizations, decentralized system, zero knowledge Ethereum virtual machine, decentralized application

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Authors: Altilio Rosa, Chirico Francesco, Foglia Goffredo

Abstract:

In recent years, the number of Unmanned Aerial Vehicles (UAVs) has significantly increased. The rapid development of commercial and recreational drones makes them an important part of our society. Despite the growing list of their applications, these vehicles pose a huge threat to civil and military installations: detection, classification and neutralization of such flying objects become an urgent need. Radar is an effective remote sensing tool for detecting and tracking flying objects, but scenarios characterized by the presence of a high number of tracks related to flying birds make especially challenging the drone detection task: operator PPI is cluttered with a huge number of potential threats and his reaction time can be severely affected. Flying birds compared to UAVs show similar velocity, RADAR cross-section and, in general, similar characteristics. Building from the absence of a single feature that is able to distinguish UAVs and birds, this paper uses a multiple features approach where an original feature selection technique is developed to feed binary classifiers trained to distinguish birds and UAVs. RADAR tracks acquired on the field and related to different UAVs and birds performing various trajectories were used to extract specifically designed target movement-related features based on velocity, trajectory and signal strength. An optimization strategy based on a genetic algorithm is also introduced to select the optimal subset of features and to estimate the performance of several classification algorithms (Neural network, SVM, Logistic regression…) both in terms of the number of selected features and misclassification error. Results show that the proposed methods are able to reduce the dimension of the data space and to remove almost all non-drone false targets with a suitable classification accuracy (higher than 95%).

Keywords: birds, classification, machine learning, UAVs

Procedia PDF Downloads 194

Authors: James Ladzekpo

Abstract:

Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

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Authors: Ebitisam K. Elberkawi, Mohamed M. Elammari

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Graphical User Interface (GUI) is essential to programming, as is any other characteristic or feature, due to the fact that GUI components provide the fundamental interaction between the user and the program. Thus, we must give more interest to GUI during building and development of systems. Also, we must give a greater attention to the user who is the basic corner in the dealing with the GUI. This paper introduces an approach for designing GUI from one of the models of business workflows which describe the workflow behavior of a system, specifically through activity diagrams (AD).

Keywords: activity diagram, graphical user interface, GUI components, program

Procedia PDF Downloads 440

Authors: Joseph Marzouk Gerais Abdelmalak

Abstract:

The interface between development and human rights has long been the subject of academic debate. Therefore, to understand the dynamics between the two concepts, a number of principles have been adopted, ranging from the right to development to a human rights-based approach to development. Despite these attempts, the exact connection between development and human rights is not yet fully understood. However, the inherent interdependence between these two concepts and the idea that development efforts should be undertaken with respect for human rights guarantees have gained momentum in recent years. It will then be examined whether the right to sustainable development is recognized.The article therefore concludes that the principles of sustainable development are recognized, directly or indirectly, in various human rights instruments, which represents a positive answer to the question posed above. Therefore, this work discusses international and regional human rights instruments as well as case law and interpretative guidelines from human rights bodies to demonstrate this hypothesis.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security

Procedia PDF Downloads 45

Authors: Gnanadeep Gudapati, V. Annie Grace

Abstract:

A broadband circular polarization (CP) feature is designed for a CPW-fed planar printed monopole antenna. A rectangle patch and an improved ground plane make up the antenna. The antenna's impedance bandwidth can be increased by adding a vertical stub and a horizontal slit in the ground plane. The measured results show that the proposed antenna has a wide 10-dB return loss bandwidth of 70.2% (4.35GHz, 3.7-8.1GHz) centered at 4.2 GHz.

Keywords: CPW-fed, circular polarised, FR4 epoxy, slit and stub

Procedia PDF Downloads 128

Authors: Nandy Putra, Imansyah Ibnu Hakim, Iwan Setyawan, Muhammad Zayd A.I

Abstract:

A two-phase cooling technology of passive system sometimes can no longer meet the cooling needs of an increasingly challenging due to the inherent limitations of the capillary pumping for example in terms of the heat flux that can lead to dry out. In this study, intended to overcome the dry out with the addition of a diaphragm, they pump to accelerate the fluid transportation from the condenser to the evaporator. Diaphragm pump installed on the bypass line. When it did not happen dry out then the hybrid loop heat pipe will be work passively using a capillary pressure of wick. Meanwhile, when necessary, hybrid loop heat pipe will be work actively, using diaphragm pump with temperature control installed on the evaporator. From the results, it can be said that the pump has been successfully overcome dry out and can distribute working fluid from the condenser to the evaporator and reduce the temperature of the evaporator from 143°C to 100°C as a temperature controlled where the pump start actively at set point 100°C.

Keywords: hybrid, heat pipe, dry out, assisted, pump

Procedia PDF Downloads 327

Authors: Subhash Chandra Sharma, Mohammad Soleimani

Abstract:

Selection of the correct lubricant for the top of rail application is a complex process. In this paper, the selection of the proper lubricant for a Top-Of-Rail (TOR) lubrication system based on graph theory and matrix approach has been developed. Attributes influencing the selection process and their influence on each other has been represented through a digraph and an equivalent matrix. A matrix function which is called the Permanent Function is derived. By substituting the level of inherent contribution of the influencing parameters and their influence on each other qualitatively, a criterion called Suitability Index is derived. Based on these indices, lubricants can be ranked for their suitability. The proposed model can be useful for maintenance engineers in selecting the best lubricant for a TOR application. The proposed methodology is illustrated step–by-step through an example.

Keywords: lubricant selection, top of rail lubrication, graph-theory, Ranking of lubricants

Procedia PDF Downloads 272

Authors: Hosam Gamil Sharoubim Abdalla

Abstract:

The interface between development and human rights has long been the subject of academic debate. Therefore, to understand the dynamics between the two concepts, a number of principles have been adopted, ranging from the right to development to a human rights-based approach to development. Despite these attempts, the exact connection between development and human rights is not yet fully understood. However, the inherent interdependence between these two concepts and the idea that development efforts should be undertaken with respect for human rights guarantees have gained momentum in recent years. It will then be examined whether the right to sustainable development is recognized. The article, therefore, concludes that the principles of sustainable development are recognized, directly or indirectly, in various human rights instruments, which represents a positive answer to the question posed above. Therefore, this work discusses international and regional human rights instruments as well as case law and interpretative guidelines from human rights bodies to demonstrate this hypothesis.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security

Procedia PDF Downloads 34

Authors: Marcelo Franzkowiak Stahlschmidt

Abstract:

This paper discusses the experiments carried out based on the wire drawing process analysis and later annealing on lead furnace on a galvanizing line. Using Design of Experiments methodology, the aim of this work is to understand the occurrence of lead entrainment originating from the annealed wires in order to decrease this problem. Wire samples were collected from wire drawing machines and galvanizing line and submitted to surface roughness analysis and its implications on lead drag out based on wire speed, wire diameter, lead bath temperature, thermal capacity of the lead kettle, wire surface condition, wire roughness and wire superficial cleanliness. Proposals to decrease lead drag out were made in order to increase wire drawing machines and galvanizing line performance.

Keywords: wire drawing process, galvanizing, heat treatment, lead

Procedia PDF Downloads 620

Authors: Ming-Chu Yin, Ping-An Du

Abstract:

The shielding ability of a shielding cavity is affected greatly by its resonances, which include resonance modes and frequencies. The equivalent circuit method and numerical method of transmission line matrix (TLM) are used to analyze the effect of aperture-cavity coupling on electromagnetic resonances of a cavity with an aperture in this paper. Both theoretical and numerical results show that the resonance modes of a shielding cavity with an aperture can be considered as the combination of cavity and aperture inherent resonance modes with resonance frequencies shifting, and the reason of this shift is aperture-cavity coupling. Because aperture sizes are important parameters to aperture-cavity coupling, variation rules of electromagnetic resonances of a shielding cavity with its aperture sizes are given, which will be useful for the design of shielding cavities.

Keywords: aperture-cavity coupling, equivalent circuit method, resonances, shielding equipment

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Authors: Bolatbek Rysbaiuly, Aliya S. Azhibekova

Abstract:

Problems concerning the interpretation of the well testing results belong to the class of inverse problems of subsurface hydromechanics. The distinctive feature of such problems is that additional information is depending on the capabilities of oilfield experiments. Another factor that should not be overlooked is the existence of errors in the test data. To determine reservoir properties, some inverse problems for parabolic equations were investigated. An approach to solving the inverse problems based on the method of regularization is proposed.

Keywords: iterative approach, inverse problem, parabolic equation, reservoir properties

Procedia PDF Downloads 405

Authors: Mina Rashad Saad Abdelnoor

Abstract:

The interface between development and human rights has long been the subject of academic debate. Therefore, to understand the dynamics between the two concepts, a number of principles have been adopted, ranging from the right to development to a human rights-based approach to development. Despite these attempts, the exact connection between development and human rights is not yet fully understood. However, the inherent interdependence between these two concepts and the idea that development efforts should be undertaken with respect for human rights guarantees have gained momentum in recent years. It will then be examined whether the right to sustainable development is recognized. The article therefore concludes that the principles of sustainable development are recognized, directly or indirectly, in various human rights instruments, which represents a positive answer to the question posed above. Therefore, this work discusses international and regional human rights instruments as well as case law and interpretative guidelines from human rights bodies to demonstrate this hypothesis.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security

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Authors: Thiru Vandeyar

Abstract:

This study sets out to explore how teachers’ beliefs and attitudes about ICT policy influence a consensus approach to the formulation of a school ICT policy. This case study proposes Q- methodology as an innovative method to facilitate a school’s capacity to develop policy reflecting teacher beliefs and attitudes. Q-methodology is used as a constructivist approach to the formulation of an ICT policy. Data capture was a mix of Q-methodology and qualitative principles. Data was analyzed by means of document, content and cluster analysis methods. Findings were threefold: First, teachers’ beliefs and attitudes about ICT policy influenced a consensus approach by including teachers as policy decision-makers. Second, given the opportunity, teachers have the inherent ability to deconstruct and critically engage with policy statements according to their own professional beliefs and attitudes. And third, an inclusive approach to policy formulation may inform the practice of school leaders and policymakers alike on how schools may develop their own policy.

Keywords: ICT, policy, teacher beliefs, consensus

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Authors: Mst Shapna Akter, Hossain Shahriar

Abstract:

One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.

Keywords: cyber security, vulnerability detection, neural networks, feature extraction

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