Search results for: negative data

Authors: Farzaneh Khorram

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This paper endeavors to construct a block model to assess grinding energy consumption (CCE) and pinpoint blocks with the highest potential for energy usage during the grinding process within a specified region. Leveraging geostatistical techniques, particularly joint estimation, or simulation, based on geometallurgical data from various mineral processing stages, our objective is to forecast CCE across the study area. The dataset encompasses variables obtained from 2754 drill samples and a block model comprising 4680 blocks. The initial analysis encompassed exploratory data examination, variography, multivariate analysis, and the delineation of geological and structural units. Subsequent analysis involved the assessment of contacts between these units and the estimation of CCE via cokriging, considering its correlation with SPI. The selection of blocks exhibiting maximum CCE holds paramount importance for cost estimation, production planning, and risk mitigation. The study conducted exploratory data analysis on lithology, rock type, and failure variables, revealing seamless boundaries between geometallurgical units. Simulation methods, such as Plurigaussian and Turning band, demonstrated more realistic outcomes compared to cokriging, owing to the inherent characteristics of geometallurgical data and the limitations of kriging methods.

Keywords: geometallurgy, multivariate analysis, plurigaussian, turning band method, cokriging

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Authors: Alan C. Lin, Tzu-Kuan Lin, Tsong Der Lin

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This paper proposes a new method to find the equations of transformation matrix for the rotation angles of the two rotational axes and the coordinates of the three linear axes of an orthogonal multi-axis milling machine. This approach provides intuitive physical meanings for rotation angles of multi-axis machines, which can be used to evaluate the accuracy of the conversion from CL data to NC data.

Keywords: CAM, multi-axis milling machining, transformation matrix, rotation angles

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Authors: Manisha Mukherjee, Diptarka Saha

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Reliable forecasts of univariate time series data are often necessary for several contexts. ARIMA models are quite popular among practitioners in this regard. Hence, choosing correct parameter values for ARIMA is a challenging yet imperative task. Thus, a stepwise algorithm is introduced to provide automatic and robust estimates for parameters (p; d; q)(P; D; Q) used in seasonal ARIMA models. This process is focused on improvising the overall quality of the estimates, and it alleviates the problems induced due to the unidimensional nature of the methods that are currently used such as auto.arima. The fast and automated search of parameter space also ensures reliable estimates of the parameters that possess several desirable qualities, consequently, resulting in higher test accuracy especially in the cases of noisy data. After vigorous testing on real as well as simulated data, the algorithm doesn’t only perform better than current state-of-the-art methods, it also completely obviates the need for human intervention due to its automated nature.

Keywords: time series, ARIMA, auto.arima, ARIMA parameters, forecast, R function

Procedia PDF Downloads 158

Authors: Yingxiao Ye, Christopher Doehring, Angelos Georghiou, Hugh Robinson, Phebe Vayanos

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Human development is a threat to biodiversity, and conservation organizations (COs) are purchasing land to protect areas for biodiversity preservation. However, COs have limited budgets and thus face hard prioritization decisions that are confounded by uncertainty in future human land use. This research proposes a data-driven sequential planning model to help COs choose land parcels that minimize the uncertain human impact on biodiversity. The proposed model is robust to uncertain development, and the sequential decision-making process is adaptive, allowing land purchase decisions to adapt to human land use as it unfolds. The cellular automata model is leveraged to simulate land use development based on climate data, land characteristics, and development threat index from NASA Socioeconomic Data and Applications Center. This simulation is used to model uncertainty in the problem. This research leverages state-of-the-art techniques in the robust optimization literature to propose a computationally tractable reformulation of the model, which can be solved routinely by off-the-shelf solvers like Gurobi or CPLEX. Numerical results based on real data from the Jaguar in Central and South America show that the proposed method reduces conservation loss by 19.46% on average compared to standard approaches such as MARXAN used in practice for biodiversity conservation. Our method may better help guide the decision process in land acquisition and thereby allow conservation organizations to maximize the impact of limited resources.

Keywords: data-driven robust optimization, biodiversity conservation, uncertainty simulation, adaptive sequential planning

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Authors: L. Krpalkova, N. O' Mahony, A. Carvalho, S. Campbell, G. Corkery, E. Broderick, J. Walsh

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Farm management and operations will drastically change due to access to real-time data, real-time forecasting, and tracking of physical items in combination with Internet of Things developments to further automate farm operations. Dairy farms have embraced technological innovations and procured vast amounts of permanent data streams during the past decade; however, the integration of this information to improve the whole farm-based management and decision-making does not exist. It is now imperative to develop a system that can collect, integrate, manage, and analyse on-farm and off-farm data in real-time for practical and relevant environmental and economic actions. The developed systems, based on machine learning and artificial intelligence, need to be connected for useful output, a better understanding of the whole farming issue, and environmental impact. Evolutionary computing can be very effective in finding the optimal combination of sets of some objects and, finally, in strategy determination. The system of the future should be able to manage the dairy farm as well as an experienced dairy farm manager with a team of the best agricultural advisors. All these changes should bring resilience and sustainability to dairy farming as well as improving and maintaining good animal welfare and the quality of dairy products. This review aims to provide an insight into the state-of-the-art of big data applications and evolutionary computing in relation to smart dairy farming and identify the most important research and development challenges to be addressed in the future. Smart dairy farming influences every area of management, and its uptake has become a continuing trend.

Keywords: big data, evolutionary computing, cloud, precision technologies

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Authors: Yooseo Pang, Jongwoong Choi, Yong Cho, Yongchae Jeong

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Climate crisis, such as environmental problems related to energy supply, is getting emerged issues, so the use of renewable energy is essentially required to solve these problems, which are mainly managed by the Paris Agreement, the international treaty on climate change. The government of the Republic of Korea announced that the key long-term goal for a low-carbon strategy is “Carbon neutrality by 2050”. It is focused on the role of the internet data centers (IDC) in which large amounts of data, such as artificial intelligence (AI) and big data as an impact of the 4th industrial revolution, are managed. The demand for the cooling system market for IDC was about 9 billion US dollars in 2020, and 15.6% growth a year is expected in Korea. It is important to control the temperature in IDC with an efficient air conditioning system, so hydrothermal energy is one of the best options for saving energy in the cooling system. In order to save energy and optimize the operating conditions, it has been considered to apply ‘the dam deep water air conditioning system. Deep water at a specific level from the dam can supply constant water temperature year-round. It will be tested & analyzed the amount of energy saving with a pilot plant that has 100RT cooling capacity. Also, a target of this project is 1.2 PUE (Power Usage Effectiveness) which is the key parameter to check the efficiency of the cooling system.

Keywords: hydrothermal energy, HVAC, internet data center, free-cooling

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Authors: Kien-Trinh Thi Bui, Cuong Manh Nguyen

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In order to simulate and reproduce the operational characteristics of a dam visually, it is necessary to capture the displacement at different measurement points and analyze the observed movement data promptly to forecast the dam safety. The accuracy of forecasts is further improved by applying machine learning methods to data analysis progress. In this study, the horizontal displacement monitoring data of the Ialy hydroelectric dam was applied to machine learning algorithms: Gaussian processes, multi-layer perceptron neural networks, and the M5-rules algorithm for modelling and forecasting of horizontal displacement of the Ialy hydropower dam (Vietnam), respectively, for analysing. The database which used in this research was built by collecting time series of data from 2006 to 2021 and divided into two parts: training dataset and validating dataset. The final results show all three algorithms have high performance for both training and model validation, but the MLPs is the best model. The usability of them are further investigated by comparison with a benchmark models created by multi-linear regression. The result show the performance which obtained from all the GP model, the MLPs model and the M5-Rules model are much better, therefore these three models should be used to analyze and predict the horizontal displacement of the dam.

Keywords: Gaussian processes, horizontal displacement, hydropower dam, Ialy dam, M5-Rules, multi-layer perception neural networks

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Authors: Thilina Ranaweera, Enes Makalic, John L. Hopper, Adrian Bickerstaffe

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Data are the primary asset of biomedical researchers, and the engine for both discovery and research translation. As the volume and complexity of research datasets increase, especially with new technologies such as large single nucleotide polymorphism (SNP) chips, so too does the requirement for software to manage, process and analyze the data. Researchers often need to execute complicated queries and conduct complex analyzes of large-scale datasets. Existing tools to analyze such data, and other types of high-dimensional data, unfortunately suffer from one or more major problems. They typically require a high level of computing expertise, are too simplistic (i.e., do not fit realistic models that allow for complex interactions), are limited by computing power, do not exploit the computing power of large-scale parallel architectures (e.g. supercomputers, GPU clusters etc.), or are limited in the types of analysis available, compounded by the fact that integrating new analysis methods is not straightforward. Solutions to these problems, such as those developed and implemented on parallel architectures, are currently available to only a relatively small portion of medical researchers with access and know-how. The past decade has seen a rapid expansion of data management systems for the medical domain. Much attention has been given to systems that manage phenotype datasets generated by medical studies. The introduction of heterogeneous genomic data for research subjects that reside in these systems has highlighted the need for substantial improvements in software architecture. To address this problem, we have developed SPARK, an enabling and translational system for medical research, leveraging existing high performance computing resources, and analysis techniques currently available or being developed. It builds these into The Ark, an open-source web-based system designed to manage medical data. SPARK provides a next-generation biomedical data management solution that is based upon a novel Micro-Service architecture and Big Data technologies. The system serves to demonstrate the applicability of Micro-Service architectures for the development of high performance computing applications. When applied to high-dimensional medical datasets such as genomic data, relational data management approaches with normalized data structures suffer from unfeasibly high execution times for basic operations such as insert (i.e. importing a GWAS dataset) and the queries that are typical of the genomics research domain. SPARK resolves these problems by incorporating non-relational NoSQL databases that have been driven by the emergence of Big Data. SPARK provides researchers across the world with user-friendly access to state-of-the-art data management and analysis tools while eliminating the need for high-level informatics and programming skills. The system will benefit health and medical research by eliminating the burden of large-scale data management, querying, cleaning, and analysis. SPARK represents a major advancement in genome research technologies, vastly reducing the burden of working with genomic datasets, and enabling cutting edge analysis approaches that have previously been out of reach for many medical researchers.

Keywords: biomedical research, genomics, information systems, software

Procedia PDF Downloads 263

Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

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Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as vital physiological signs, images, and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision-making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful in influencing the judgement of clinical sentiment in ICU clinical notes. This paper introduces two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of clinical transformer models that can reliably predict clinical sentiment for notes of COVID patients in the ICU. We train the model on clinical notes for COVID-19 patients, a type of notes that were not previously seen by clinicalBERT, and Bio_Discharge_Summary_BERT. The model, which was based on clinicalBERT achieves higher predictive accuracy (Acc 93.33%, AUC 0.98, and precision 0.96 ). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and precision 0.92 ).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation

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Authors: Georges Bou Ghantous

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This literature review examines the transformative impact of Artificial Intelligence (AI) on higher education, highlighting both the potential benefits and challenges associated with its adoption. The review reveals that AI significantly enhances personalized learning by tailoring educational experiences to individual student needs, thereby boosting engagement and learning outcomes. Automated grading systems streamline assessment processes, allowing educators to focus on improving instructional quality and student interaction. AI's data-driven insights provide valuable analytics, helping educators identify trends in at-risk students and refine teaching strategies. Moreover, AI promotes enhanced instructional innovation through the adoption of advanced teaching methods and technologies, enriching the educational environment. Administrative efficiency is also improved as AI automates routine tasks, freeing up time for educators to engage in research and curriculum development. However, the review also addresses the challenges that accompany AI integration, such as data privacy concerns, algorithmic bias, dependency on technology, reduced human interaction, and ethical dilemmas. This balanced exploration underscores the need for careful consideration of both the advantages and potential hurdles in the implementation of AI in higher education.

Keywords: administrative efficiency, data-driven insights, data privacy, ethical dilemmas, higher education, personalized learning

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Authors: M. S. Turan, Dimple

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Due to implementation of IFRS by several developed and developing countries, India has no option other than to converge their accounting standards with IFRS. There are over 10,000 listed companies required to implement IFRS in India. IFRS based financial information presented by a company is different from the same information provided by Indian GAAPs. In this study, we have brought out and analyzed the effect of IFRS reporting on the financial statements of selected companies. The results reveal that convergence with IFRS brought prominent positive variations in the values of quick ratio, debt/equity ratio, proprietary ratio and net profit ratio, while negative variation is brought in the values of current ratio, debt to total assets ratio, operating profit ratio, return on capital employed and return on shareholders’ equity ratios. It also presents significant changes in the values of items of balance sheet, profit and loss account and cash flow statement.

Keywords: IFRS, reporting standards, convergence process, results

Procedia PDF Downloads 326

Authors: D. Benatiallah, A. Benatiallah, K. Bouchouicha, A. Harouz

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The development and operation of renewable energy known an important development in the world with significant growth potential. Estimate the solar radiation on terrestrial geographic locality is of extreme importance, firstly to choose the appropriate site where to place solar systems (solar power plants for electricity generation, for example) and also for the design and performance analysis of any system using solar energy. In addition, solar radiation measurements are limited to a few areas only in Algeria. Thus, we use theoretical approaches to assess the solar radiation on a given location. The Adrar region is one of the most favorable sites for solar energy use with a medium flow that exceeds 7 kWh / m2 / d and saddle of over 3500 hours per year. Our goal in this work focuses on the creation of a data bank for the given data in the energy field of the Adrar region for the period of the year and the month then the integration of these data into a geographic Information System (GIS) to estimate the solar flux on a location on the map.

Keywords: Adrar, flow, GIS, deposit potential

Procedia PDF Downloads 369

Authors: Gaya Bin Noon, Thoko Hanjahanja-Phiri, Laura Xavier Fadrique, Plinio Pelegrini Morita, Hélène Vaillancourt, Jennifer Teague, Tania Donovska

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Active Assisted Living (AAL) refers to systems designed to improve the quality of life, aid in independence, and create healthier lifestyles for care recipients. As the population ages, there is a pressing need for non-intrusive, continuous, adaptable, and reliable health monitoring tools to support aging in place. AAL has great potential to support these efforts with the wide variety of solutions currently available, but insufficient efforts have been made to address concerns arising from the integration of AAL into care. The purpose of this research was to (1) explore the integration of AAL technologies and data into the clinical pathway, and (2) map data access and governance for AAL technology in order to develop standards for use by policy-makers, technology manufacturers, and developers of smart communities for seniors. This was done through four successive research phases: (1) literature search to explore existing work in this area and identify lessons learned; (2) modeling of the continuum of care; (3) adapting a framework for data governance into the AAL context; and (4) interviews with stakeholders to explore the applicability of previous work. Opportunities for standards found in these research phases included a need for greater consistency in language and technology requirements, better role definition regarding who can access and who is responsible for taking action based on the gathered data, and understanding of the privacy-utility tradeoff inherent in using AAL technologies in care settings.

Keywords: active assisted living, aging in place, internet of things, standards

Procedia PDF Downloads 127

Authors: Y. Fekir, K. Mederbal, M. A. Hammadouche, D. Anteur

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The cultural park of the Tassili, occupying a large area of Algeria, is characterized by a rich vegetative biodiversity to be preserved and managed both in time and space. The management of a large area (case of Tassili), by its complexity, needs large amounts of data, which for the most part, are spatially localized (DEM, satellite images and socio-economic information etc.), where the use of conventional and traditional methods is quite difficult. The remote sensing, by its efficiency in environmental applications, became an indispensable solution for this kind of studies. Multispectral imaging sensors have been very useful in the last decade in very interesting applications of remote sensing. They can aid in several domains such as the de¬tection and identification of diverse surface targets, topographical details, and geological features. In this work, we try to extract vegetative areas using fusion techniques between data acquired from sensor on-board the Earth Observing 1 (EO-1) satellite and Landsat ETM+ and TM sensors. We have used images acquired over the Oasis of Djanet in the National Park of Tassili in the south of Algeria. Fusion technqiues were applied on the obtained image to extract the vegetative fraction of the different classes of land use. We compare the obtained results in vegetation end member extraction with vegetation indices calculated from both Hyperion and other multispectral sensors.

Keywords: Landsat ETM+, EO1, data fusion, vegetation, Tassili, Algeria

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Authors: Nyaris Pambudiyatno, Asto Buditjahjanto, Eppy Yundra, Arie Wardhono, Eko Hariadi

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Vocational education is part of the national education system that is prepared to produce graduates who have the skills and knowledge according to the needs and requirements required by the job. Vocational Education is a secondary education that prepares students to work in a particular field. The purpose of this study was to analyze and find out the effect of industrial work practice experience and career information on work readiness through work motivation. This type of research is causal research with a quantitative approach. The population in this study was 359 cadets of Aviation Polytechnic Surabaya. While the number of samples calculates using slovin calculations obtained by 189 cadets of Surabaya Aviation Polytechnic. The type of data used is quantitative data with the primary data source. Data collection techniques are by distributing questionnaires. Analysis of this study is with Lisrel. The findings prove that: (1) Industrial Work Experience experience has a positive and significant effect on work motivation; (2) Industrial Work Experience has a positive and significant impact on work readiness; (3) Career information has a positive and significant effect on job readiness; (4) Career information has a positive and significant impact on job readiness; Dan (5) Work motivation has a positive and significant effect on work readiness.

Keywords: career information, increase work readiness, industrial work experience, work motivation

Procedia PDF Downloads 135

Authors: Ranajoy Bhattcharyya

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We estimate international product cycles as shifting product spaces by using 1976 to 2010 UN Comtrade data on all differentiated tradable products in all countries. We use a product space approach to identify the representative product baskets of high-, middle and low-income countries and then use these baskets to identify the patterns of change in comparative advantage of countries over time. We find evidence of a product cycle in two senses: First, high-, middle- and low-income countries differ in comparative advantage, and high-income products migrate to the middle-income basket. A similar pattern is observed for middle- and low-income countries. Our estimation of the lag shows that middle-income countries tend to quickly take up the products of high-income countries, but low-income countries take a longer time absorbing these products. Thus, the gap between low- and middle-income countries is considerably higher than that between middle- and high-income nations.

Keywords: product cycle, comparative advantage, representative product basket, ex-post data

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Authors: Muhammad Naeem, Abdus Salam, Sumera Yasmin, Abir Ishtiaq

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In the present study, eighty-three wild Oreochromis aureus of different body size ranging 5.3-14.6 cm in total length were collected from the River Chenab, District Muzzafer Garh, Pakistan to investigate the parameters of length –weight, length-length relationships and condition factor in relation to size. Each fish was measured and weighed on arrival at laboratory. Log transformed regressions were used to test the allometric growth. Length-weight relationship was found highly significant (r = 0.964; P < 0.01). The values of exponent “ b” in Length–weight regression (W=aL^b), deviated from 3, showing isometric growth (b = 2.75). Results for LLRs indicated that these are highly correlated (P < 0.001). Condition factor (K) found constant with increasing body weight, however, showed negative influence with increasing total length.

Keywords: lenght-weight, Oreochromis aureus, morphometric study

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Authors: Zahide Tepeli Temiz, Itir Tari Comert

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Attachment patterns based on early emotional interactions between infant and primary caregiver continue to be influential in adult life, in terms of mental health and behaviors of individuals. Several studies reveal that infant-caregiver relationships have impressed the affect regulation, coping with stressful and negative situations, general satisfaction of life, and self image in adulthood, besides the attachment styles. The present study aims to examine the relationships between university students’ attachment style and their self-esteem, alexithymic features, satisfaction of life, and level of resilience. In line with this aim, the hypothesis of the prediction of attachment styles (anxious and avoidant) over life satisfaction, self-esteem, alexithymia, and psychological resilience was tested. Additionally, in this study Structural Equational Modeling was conducted to investigate the mediational role of self-esteem in the relationship between attachment styles and alexithymia, life satisfaction, and resilience. This model was examined with path analysis. The sample of the research consists of 425 university students who take education from several region of Turkey. The participants who sign the informed consent completed the Demographic Information Form, Experiences in Close Relationships-Revised, Rosenberg Self-Esteem Scale, The Satisfaction with Life Scale, Toronto Alexithymia Scale, and Resilience Scale for Adults. According to results, anxious, and avoidant dimensions of insecure attachment predicted the self-esteem score and alexithymia in positive direction. On the other hand, these dimensions of attachment predicted life satisfaction in negative direction. The results of linear regression analysis indicated that anxious and avoidant attachment styles didn’t predict the resilience. This result doesn’t support the theory and research indicating the relationship between attachment style and psychological resilience. The results of path analysis revealed the mediational role self esteem in the relation between anxious, and avoidant attachment styles and life satisfaction. In addition, SEM analysis indicated the indirect effect of attachment styles over alexithymia and resilience besides their direct effect. These findings support the hypothesis of this research relation to mediating role of self-esteem. Attachment theorists suggest that early attachment experiences, including supportive and responsive family interactions, have an effect on resilience to harmful situations in adult life, ability to identify, describe, and regulate emotions and also general satisfaction with life. Several studies examining the relationship between attachment styles and life satisfaction, alexithymia, and psychological resilience draw attention to mediational role of self-esteem. Results of this study support the theory of attachment patterns with the mediation of self-image influence the emotional, cognitive, and behavioral regulation of person throughout the adulthood. Therefore, it is thought that any intervention intended for recovery in attachment relationship will increase the self-esteem, life satisfaction, and resilience level, on the one side, decrease the alexithymic features, on the other side.

Keywords: alexithymia, anxious attachment, avoidant attachment, life satisfaction, path analysis, resilience, self-esteem, structural equation

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Authors: Emir Sunguroglu, Merve Sunguroglu, Yesim Aliefendioglu, Harun Tanrivermis

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Basic human needs range from physiological needs such as food, water and shelter to safety needs such as security, protection from natural disasters and even urban terrorism which are extant and not fulfilled even in urban areas where people live civilly in large communities. These basic needs when arose in urban life lead to a different kind of crime set defined as urban crimes. Urban crimes mostly result from differences between socioeconomic conditions in society. Income inequality increases tendency towards urban crimes. Especially in slum areas and suburbs, urban crimes not only threaten public security but they also affect deliverance of public services. It is highlighted that, construction of urban security against problems caused by urban crimes is not only achieved by involvement of urban security in security of the community but also comprises juridical development and staying above a level of legal standards concurrently. The idea of urban transformation emerged as interventions to demolishment and rebuilding of built environment to solve the unhealthy urban environment, inadequate infrastructure and socioeconomic problems came up during the industrialization process. Considering the probability of urbanization process driving citizens to commit crimes, The United Nations Commission on Human Security’s focus on this theme is conferred to be a proper approach. In this study, the analysis and change in security before, through and after urban transformation, which is one of the tools related to urbanization process, is strived to be discussed through the case of Sincan County Saraycik District. The study also aims to suggest improvements to current legislation on public safety, urban resilience, and urban transformation. In spite of Saraycik District residing in a developing County in Ankara, Turkey, from urbanization perspective as well as socioeconomic and demographic indicators the District exhibits a negative view throughout the County and the country. When related to the county, rates of intentional harm reports, burglary reports, the offense of libel and threat reports and narcotic crime reports are higher. The District is defined as ‘crime hotspot’. Interviews with residents of Saraycik claim that the greatest issue of the neighborhood is Public Order and Security (82.44 %). The District becomes prominent with negative aspects, especially with the presence of unlicensed constructions, occurrence of important social issues such as crime and insecurity and complicated lives of inhabitants from poverty and low standard conditions of living. Additionally, the social structure and demographic properties and crime and insecurity of the field have been addressed in this study. Consequently, it is claimed that urban crime rates were related to level of education, employment and household income, poverty trap, physical condition of housing and structuration, accessibility of public services, security, migration, safety in terms of disasters and emphasized that urban transformation is one of the most important tools in order to provide urban security.

Keywords: urban security, urban crimes, urban transformation, Saraycik district

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Authors: Jonathan Turner, Julie Doyle, Laura O’Philbin, Dympna O’Sullivan

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People living with dementia should be at the centre of decision-making regarding goals for daily living. These goals include basic activities (dressing, hygiene, and mobility), advanced activities (finances, transportation, and shopping), and meaningful activities that promote well-being (pastimes and intellectual pursuits). However, there is limited involvement of people living with dementia in the design of technology to support their goals. A project is described that is co-designing intelligent computer-based support for, and with, people affected by dementia and their carers. The technology will support self-management, empower participation in shared decision-making with carers and help people living with dementia remain healthy and independent in their homes for longer. It includes information from the patient’s care plan, which documents medications, contacts, and the patient's wishes on end-of-life care. Importantly for this work, the plan can outline activities that should be maintained or worked towards, such as exercise or social contact. The authors discuss how to integrate care goal information from such a care plan with data collected from passive sensors in the patient’s home in order to deliver individualized planning and interventions for persons with dementia. A number of scientific challenges are addressed: First, to co-design with dementia patients and their carers computerized support for shared decision-making about their care while allowing the patient to share the care plan. Second, to develop a new and open monitoring framework with which to configure sensor technologies to collect data about whether goals and actions specified for a person in their care plan are being achieved. This is developed top-down by associating care quality types and metrics elicited from the co-design activities with types of data that can be collected within the home, from passive and active sensors, and from the patient’s feedback collected through a simple co-designed interface. These activities and data will be mapped to appropriate sensors and technological infrastructure with which to collect the data. Third, the application of machine learning models to analyze data collected via the sensing devices in order to investigate whether and to what extent activities outlined via the care plan are being achieved. The models will capture longitudinal data to track disease progression over time; as the disease progresses and captured data show that activities outlined in the care plan are not being achieved, the care plan may recommend alternative activities. Disease progression may also require care changes, and a data-driven approach can capture changes in a condition more quickly and allow care plans to evolve and be updated.

Keywords: care goals, decision-making, dementia, self-care, sensors

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Authors: Mohamed H. Khalil

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Water is one of the most crucial elements which influence human lives and development. Noteworthy, over the last few years, GIS plays a significant role in optimizing water management systems, especially after exponential developing in this sector. In this context, the Egyptian government initiated an advanced ‘GIS-Web Based System’. This system is efficiently designed to tangibly assist and optimize the complement and integration of data between departments of Call Center, Operation and Maintenance, and laboratory. The core of this system is a unified ‘Data Model’ for all the spatial and tabular data of the corresponding departments. The system is professionally built to provide advanced functionalities such as interactive data collection, dynamic monitoring, multi-user editing capabilities, enhancing data retrieval, integrated work-flow, different access levels, and correlative information record/track. Noteworthy, this cost-effective system contributes significantly not only in the completeness of the base-map (93%), the water network (87%) in high level of details GIS format, enhancement of the performance of the customer service, but also in reducing the operating costs/day-to-day operations (~ 5-10 %). In addition, the proposed system facilitates data exchange between different departments (Call Center, Operation and Maintenance, and laboratory), which allowed a better understanding/analyzing of complex situations. Furthermore, this system reflected tangibly on: (i) dynamic environmental monitor/water quality indicators (ammonia, turbidity, TDS, sulfate, iron, pH, etc.), (ii) improved effectiveness of the different water departments, (iii) efficient deep advanced analysis, (iv) advanced web-reporting tools (daily, weekly, monthly, quarterly, and annually), (v) tangible planning synthesizing spatial and tabular data; and finally, (vi) scalable decision support system. It is worth to highlight that the proposed future plan (second phase) of this system encompasses scalability will extend to include integration with departments of Billing and SCADA. This scalability will comprise advanced functionalities in association with the existing one to allow further sustainable contributions.

Keywords: GIS Web-Based, base-map, water network, decision support system

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Authors: A. Harrouz, O. Harrouz, A. Benatiallah

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This paper is to review the essential definitions, roles, and characteristics of communication of metering system. We discuss measurement, data acquisition, and metrological control of a signal sensor from dynamic metering system. After that, we present control of instruments of metering system of fluid with more detailed discussions to the reference standards.

Keywords: data acquisition, dynamic metering system, reference standards, metrological control

Procedia PDF Downloads 487

Authors: Eduardo Padilla, Luis Daniel Rodriguez, Ivan Sanchez, Angelica Sofia Go

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The synthesis and application of metallic nanoparticles have increased in recent years. Biological methods go beyond the chemical and physical synthesis that is expensive and not friendly to the environment. Therefore, in this study, silver nanoparticles were synthesized biologically in an environmentally friendly way by Handroanthus chrysanthus flower aqueous extract (AgNPs) that contains phytochemicals capable of reducing silver nitrate. AgNPs were characterized visually by UV-visible spectroscopy and TEM. The antimicrobial activity of the AgNPs was tested by determining the minimum inhibitory concentration (MIC), and minimal bactericidal concentration (MBC) in Escherichia coli and Staphylococcus aureus strains AgNPs showed potent antimicrobial activity against gram-negative and gram-positive bacteria. MIC and MBC values were as low as 41.6, and 83.2 ug/mL using AgNPs biosynthesized by H. chrysanthus flower extract. This nanoparticle could be the basis for the formulation of disinfectants for use in the food and pharmaceutical industry.

Keywords: antimicrobial, silver nanoparticles, flower extract, Handroanthus

Procedia PDF Downloads 101

Authors: Angelo Lerro, Manuela Battipede, Piero Gili, Alberto Brandl

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Redundancy requirements for UAV (Unmanned Aerial Vehicle) are hardly faced due to the generally restricted amount of available space and allowable weight for the aircraft systems, limiting their exploitation. Essential equipment as the Air Data, Attitude and Heading Reference Systems (ADAHRS) require several external probes to measure significant data as the Angle of Attack or the Sideslip Angle. Previous research focused on the analysis of a patented technology named Smart-ADAHRS (Smart Air Data, Attitude and Heading Reference System) as an alternative method to obtain reliable and accurate estimates of the aerodynamic angles. This solution is based on an innovative sensor fusion algorithm implementing soft computing techniques and it allows to obtain a simplified inertial and air data system reducing external devices. In fact, only one external source of dynamic and static pressures is needed. This paper focuses on the benefits which would be gained by the implementation of this system in UAV applications. A simplification of the entire ADAHRS architecture will bring to reduce the overall cost together with improved safety performance. Smart-ADAHRS has currently reached Technology Readiness Level (TRL) 6. Real flight tests took place on ultralight aircraft equipped with a suitable Flight Test Instrumentation (FTI). The output of the algorithm using the flight test measurements demonstrates the capability for this fusion algorithm to embed in a single device multiple physical and virtual sensors. Any source of dynamic and static pressure can be integrated with this system gaining a significant improvement in terms of versatility.

Keywords: aerodynamic angles, air data system, flight test, neural network, unmanned aerial vehicle, virtual sensor

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Authors: J. R. Wang, Y. Chiang, W. Y. Li, H. T. Lin, H. C. Chen, C. Shih, S. W. Chen

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The dry-storage systems of nuclear power plants (NPPs) in Taiwan have become one of the major safety concerns. There are two steps considered in this study. The first step is the verification of the TRACE by using VSC-17 experimental data. The results of TRACE were similar to the VSC-17 data. It indicates that TRACE has the respectable accuracy in the simulation and analysis of the dry-storage systems. The next step is the application of TRACE in the dry-storage system of Kuosheng NPP (BWR/6). Kuosheng NPP is the second BWR NPP of Taiwan Power Company. In order to solve the storage of the spent fuels, Taiwan Power Company developed the new dry-storage system for Kuosheng NPP. In this step, the dry-storage system model of Kuosheng NPP was established by TRACE. Then, the steady state simulation of this model was performed and the results of TRACE were compared with the Kuosheng NPP data. Finally, this model was used to perform the safety analysis of Kuosheng NPP dry-storage system. Besides, FRAPTRAN was used tocalculate the transient performance of fuel rods.

Keywords: BWR, TRACE, FRAPTRAN, dry-storage

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Authors: Huie-Wen Lin

Abstract:

This study integrated various teaching factors -based on the idea of a flipped classroom- in a financial management course. The study’s aim was to establish an effective teaching implementation strategy and evaluation mechanism with respect to learning outcomes, which can serve as a reference for the future modification of teaching methods. This study implemented a teaching method in five stages and estimated the learning efficiencies of 22 students (in the teaching scenario and over two semesters). Subsequently, data envelopment analysis (DEA) was used to compare, for each student, between the learning efficiencies before and after participation in the flipped classroom -in the first and second semesters, respectively- to identify the crucial external factors influencing learning efficiency. According to the results, the average overall student learning efficiency increased from 0.901 in the first semester to 0.967 in the second semester, which demonstrate that the flipped classroom approach can improve teaching effectiveness and learning outcomes. The results also revealed a difference in learning efficiency between male and female students.

Keywords: data envelopment analysis, flipped classroom, learning outcome, teaching and learning

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Authors: J. R. Wang, Y. Chiang, W. S. Hsu, H. C. Chen, S. H. Chen, J. H. Yang, S. W. Chen, C. Shih

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In this study, the RASCAL and ALOHA codes are used to establish an analysis methodology for hydrogen fluoride (HF) evaluation. There are three main steps in this study. First, the UF₆ data were collected. Second, one postulated case was analyzed by using the RASCAL and UF₆ data. This postulated case assumes that fire occurring and UF₆ is releasing from a building. Third, the results of RASCAL for HF mass were as the input data of ALOHA. Two postulated cases of HF were analyzed by using ALOHA code and the results of RASCAL. These postulated cases assume fire occurring and HF is releasing with no raining (Case 1) or raining (Case 2) condition. According to the analysis results of ALOHA, the HF concentration of Case 2 is smaller than Case 1. The results can be a reference for the preparing of emergency plans for the release of HF.

Keywords: RASCAL, ALOHA, UF₆, hydrogen fluoride

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Authors: Abhigyan Hedau, Priya Shelke, Riddhi Mirajkar, Shreyash Chaple, Mrunali Gadekar, Himanshu Akula

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The heart is the coordinating centre of the major endocrine glandular structure of the body, which produces hormones that profoundly affect the operations of the body, and diagnosing cardiovascular disease is a difficult but critical task. By extracting knowledge and information about the disease from patient data, data mining is a more practical technique to help doctors detect disorders. We use a variety of machine learning methods here, including logistic regression and support vector classifiers (SVC), K-nearest neighbours Classifiers (KNN), Decision Tree Classifiers, Random Forest classifiers and Gradient Boosting classifiers. These algorithms are applied to patient data containing 13 different factors to build a system that predicts heart disease in less time with more accuracy.

Keywords: logistic regression, support vector classifier, k-nearest neighbour, decision tree, random forest and gradient boosting

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Authors: Oscar Bareiro, Johannes Sackmann, Thomas Gries

Abstract:

Carbon fiber reinforced polymer composites (CFRP) are used in a wide variety of applications due to its advantageous properties and design versatility. The braiding process enables the manufacture of components with good toughness and fatigue strength. However, failure mechanisms of CFRPs are complex and still present challenges associated with their maintenance and repair. Within the broad scope of structural health monitoring (SHM), strain monitoring can be applied to composite materials to improve reliability, reduce maintenance costs and safely exhaust service life. Traditional SHM systems employ e.g. fiber optics, piezoelectrics as sensors, which are often expensive, time consuming and complicated to implement. A cost-efficient alternative can be the exploitation of the conductive properties of fiber-based sensors such as carbon, copper, or constantan - a copper-nickel alloy – that can be utilized as sensors within composite structures to achieve strain monitoring. This allows the structure to provide feedback via electrical signals to a user which are essential for evaluating the structural condition of the structure. This work presents a strategy for the in-process integration of resistance-based sensors (Elektrisola Feindraht AG, CuNi23Mn, Ø = 0.05 mm) into textile preforms during its manufacture via the braiding process (Herzog RF-64/120) to achieve strain monitoring of braided composites. For this, flat samples of instrumented composite laminates of carbon fibers (Toho Tenax HTS40 F13 24K, 1600 tex) and epoxy resin (Epikote RIMR 426) were manufactured via vacuum-assisted resin infusion. These flat samples were later cut out into test specimens and the integrated sensors were wired to the measurement equipment (National Instruments, VB-8012) for data acquisition during the execution of mechanical tests. Quasi-static tests were performed (tensile, 3-point bending tests) following standard protocols (DIN EN ISO 527-1 & 4, DIN EN ISO 14132); additionally, dynamic tensile tests were executed. These tests were executed to assess the sensor response under different loading conditions and to evaluate the influence of the sensor presence on the mechanical properties of the material. Several orientations of the sensor with regards to the applied loading and sensor placements inside the laminate were tested. Strain measurements from the integrated sensors were made by programming a data acquisition code (LabView) written for the measurement equipment. Strain measurements from the integrated sensors were then correlated to the strain/stress state for the tested samples. From the assessment of the sensor integration approach it can be concluded that it allows for a seamless sensor integration into the textile preform. No damage to the sensor or negative effect on its electrical properties was detected during inspection after integration. From the assessment of the mechanical tests of instrumented samples it can be concluded that the presence of the sensors does not alter significantly the mechanical properties of the material. It was found that there is a good correlation between resistance measurements from the integrated sensors and the applied strain. It can be concluded that the correlation is of sufficient accuracy to determinate the strain state of a composite laminate based solely on the resistance measurements from the integrated sensors.

Keywords: braiding process, in-process sensor integration, instrumented composite material, resistance-based sensor, strain monitoring

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Authors: Bruno Diniz Fecchio

Abstract:

Operations have been demanding increasingly challenging operational targets for the dewatering process, requiring lower humidity for concentrates. Chemical dewatering agents are able to improve solid/liquid separation processes, allowing operations to deal with increased complexity caused by either mineralogical changes or seasonal events that present operations with challenging moisture requirements for transportation and downstream steps. These chemicals reduce water retention by reducing the capillary pressure of the mineral and contributing to improved water drainage. This current study addresses the reagent effects on pile dewatering for Bauxite. Such chemicals were able to decrease the moisture of granulated Bauxite (particle size of 5 – 50 mm). The results of the laboratory scale tests and industrial trials presented the obtention of up to 11% relative moisture reduction, which reinforced the strong interaction between dewatering agents and the particle surface of granulated Bauxite. The evaluated dewatering agents, however, did not present any negative impact on these operations.

Keywords: bauxite, dewatering agents, pile dewatering, moisture reduction

Procedia PDF Downloads 78