Search results for: earth observation data cube
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 26914

Search results for: earth observation data cube

24184 Students with Severe Learning Disabilities in Mainstream Classes: A Study of Comprehensions amongst School Staff and Parents Built on Observations and Interviews in a Phenomenological Framework

Authors: Inger Eriksson, Lisbeth Ohlsson, Jeremias Rosenqvist

Abstract:

Ingress: Focus in the study is directed towards phenomena and concepts of segregation, integration, and inclusion of students attending a special school form in Sweden, namely compulsory school for pupils with learning disabilities (in Swedish 'särskola') as an alternative to mainstream compulsory school. Aim: The aim of the study is to examine the school situation for students attending särskola from a historical perspective focussing the 1980s, 1990s and the 21st century, from an integration perspective, and from a perspective of power. Procedure: Five sub-studies are reported, where integration and inclusion are looked into by observation studies and interviews with school leaders, teachers, special and remedial teachers, psychologists, coordinators, and parents in the special schools/särskola. In brief, the study about special school students attending mainstream classes from 1998 takes its point of departure in the idea that all knowledge development takes place in a social context. A special interest is taken in the school’s role for integration generally, and the role of special education particularly and on whose conditions the integration is taking place – the special school students' or the other students,' or may be equally, in the class. Pedagogical and social conditions for so called individually integrated special school students in elementary school classes were studied in eleven classes. Results: The findings are interpreted in a power perspective supported by Foucault and relationally by Vygotsky. The main part of the data consists of extensive descriptions of the eleven cases, here called integration situations. Conclusions: In summary, this study suggests that the possibilities for a special school student to get into the class community and fellowship and thereby be integrated with the class are to a high degree dependant on to what extent the student can take part in the pedagogical processes. The pedagogical situation for the special school student is affected not only by the class teacher and the support and measures undertaken but also by the other students in the class as they, in turn, are affected by how the special school student is acting. This mutual impact, which constitutes the integration process in itself, might result in a true integration if the special school student attains the status of being accepted on his/her own terms not only being cared for or cherished by some classmates. A special school student who is not accepted even on the terms of the class will often experience severe problems in the contacts with classmates and the school situation might thus be a mere placement.

Keywords: integration/inclusion, mainstream school, power, special school students

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24183 Detectability Analysis of Typical Aerial Targets from Space-Based Platforms

Authors: Yin Zhang, Kai Qiao, Xiyang Zhi, Jinnan Gong, Jianming Hu

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In order to achieve effective detection of aerial targets over long distances from space-based platforms, the mechanism of interaction between the radiation characteristics of the aerial targets and the complex scene environment including the sunlight conditions, underlying surfaces and the atmosphere are analyzed. A large simulated database of space-based radiance images is constructed considering several typical aerial targets, target working modes (flight velocity and altitude), illumination and observation angles, background types (cloud, ocean, and urban areas) and sensor spectrums ranging from visible to thermal infrared. The target detectability is characterized by the signal-to-clutter ratio (SCR) extracted from the images. The influence laws of the target detectability are discussed under different detection bands and instantaneous fields of view (IFOV). Furthermore, the optimal center wavelengths and widths of the detection bands are suggested, and the minimum IFOV requirements are proposed. The research can provide theoretical support and scientific guidance for the design of space-based detection systems and on-board information processing algorithms.

Keywords: space-based detection, aerial targets, detectability analysis, scene environment

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24182 Probing The Electronic Excitation Induced Structural Phase Transition In Nd2zr2o7 Using X-ray Techniques

Authors: Yogendar Singh, Parasmani Rajput, Pawan Kumar Kulriya

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Understanding the radiation response of the pyrochlore structured ceramics in the nuclear reactor core-like environment is of quite an interest for their utilization as host matrices. Electronic excitation (100 MeV I7+) induced crystalline to amorphous phase transition in Nd2Zr2O7 pyrochlore synthesized through three steps solid-state sintering method was investigated. The x-ray diffraction, along with Raman spectroscopy and x-ray absorption spectroscopy experiments conducted on pristine and irradiated pyrochlore, showed an increase in the rate of amorphization with ion fluence. XRD results indicate that specimen is completely amorphized on irradiation at the highest fluence of 5×1013 ions/cm2. The EXAFS spectra of the K-Zr edge and the Nd LIII edge confirmed a significant change in the chemical environment of Nd upon swift heavy ion irradiation. Observation of a large change in the intensity of K-Zr pre-edge spectra is also a good indicator of the phase transition from pyrochlore to the amorphous phase, which is supported by the FT modulus of the LIII-Nd edge. However, the chemical environment of Zr is less affected by irradiation, but it clearly exhibits an increase in the degree of disorder.

Keywords: nuclear host matrices, swift heavy ion irradiation, x-ray absorption spectroscopy, pyrochlore oxides

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24181 Machine Learning Strategies for Data Extraction from Unstructured Documents in Financial Services

Authors: Delphine Vendryes, Dushyanth Sekhar, Baojia Tong, Matthew Theisen, Chester Curme

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Much of the data that inform the decisions of governments, corporations and individuals are harvested from unstructured documents. Data extraction is defined here as a process that turns non-machine-readable information into a machine-readable format that can be stored, for instance, in a database. In financial services, introducing more automation in data extraction pipelines is a major challenge. Information sought by financial data consumers is often buried within vast bodies of unstructured documents, which have historically required thorough manual extraction. Automated solutions provide faster access to non-machine-readable datasets, in a context where untimely information quickly becomes irrelevant. Data quality standards cannot be compromised, so automation requires high data integrity. This multifaceted task is broken down into smaller steps: ingestion, table parsing (detection and structure recognition), text analysis (entity detection and disambiguation), schema-based record extraction, user feedback incorporation. Selected intermediary steps are phrased as machine learning problems. Solutions leveraging cutting-edge approaches from the fields of computer vision (e.g. table detection) and natural language processing (e.g. entity detection and disambiguation) are proposed.

Keywords: computer vision, entity recognition, finance, information retrieval, machine learning, natural language processing

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24180 Regression Approach for Optimal Purchase of Hosts Cluster in Fixed Fund for Hadoop Big Data Platform

Authors: Haitao Yang, Jianming Lv, Fei Xu, Xintong Wang, Yilin Huang, Lanting Xia, Xuewu Zhu

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Given a fixed fund, purchasing fewer hosts of higher capability or inversely more of lower capability is a must-be-made trade-off in practices for building a Hadoop big data platform. An exploratory study is presented for a Housing Big Data Platform project (HBDP), where typical big data computing is with SQL queries of aggregate, join, and space-time condition selections executed upon massive data from more than 10 million housing units. In HBDP, an empirical formula was introduced to predict the performance of host clusters potential for the intended typical big data computing, and it was shaped via a regression approach. With this empirical formula, it is easy to suggest an optimal cluster configuration. The investigation was based on a typical Hadoop computing ecosystem HDFS+Hive+Spark. A proper metric was raised to measure the performance of Hadoop clusters in HBDP, which was tested and compared with its predicted counterpart, on executing three kinds of typical SQL query tasks. Tests were conducted with respect to factors of CPU benchmark, memory size, virtual host division, and the number of element physical host in cluster. The research has been applied to practical cluster procurement for housing big data computing.

Keywords: Hadoop platform planning, optimal cluster scheme at fixed-fund, performance predicting formula, typical SQL query tasks

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24179 Model Predictive Controller for Pasteurization Process

Authors: Tesfaye Alamirew Dessie

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Our study focuses on developing a Model Predictive Controller (MPC) and evaluating it against a traditional PID for a pasteurization process. Utilizing system identification from the experimental data, the dynamics of the pasteurization process were calculated. Using best fit with data validation, residual, and stability analysis, the quality of several model architectures was evaluated. The validation data fit the auto-regressive with exogenous input (ARX322) model of the pasteurization process by roughly 80.37 percent. The ARX322 model structure was used to create MPC and PID control techniques. After comparing controller performance based on settling time, overshoot percentage, and stability analysis, it was found that MPC controllers outperform PID for those parameters.

Keywords: MPC, PID, ARX, pasteurization

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24178 Carolina Maria De Jesus' Narrative in a Fundamental Rights Perspective

Authors: Eliziane Fernanda Navarro, Aparecida Eleonora Sitta

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Child of the Dark is the work of the Brazilian author Carolina Maria de Jesus, published at the first time by Ática and Francisco Alves in 1960. It is, mostly, a story of lack of rights. It lacks to men who live in the slums what is essential in order to take advantage of the privilege of rationality to develop themselves as civilized humans. It is, therefore, in the withholding of the basic rights that inequality finds space to build itself to be the main misery on Earth. Antonio Candido, a Brazilian sociologist claims that it is the right to literature has the ability to humanize men, once the aptitude to create fiction and fable is essential to the social balance. Hence, for the forming role that literature holds, it must be thought as the number of rights that assure human dignity, such as housing, education, health, freedom, etc. When talking about her routine, Carolina puts in evidence something that has great influence over the formation of human beings, contributing to the way they live: the slum. Even though it happens in a distinct way and using her own linguistics variation, Carolina writes about something that will only be discussed later on Brazil’s Cities Statute and Erminia Maricato: the right to the city, and how the slums are, although inserted in the city, an attachment, an illegal city, a dismissing room. It interests ourselves, for that matter, in this work, to analyse how the deprivation of the rights to the city and literature, detailed in Carolina’s journal, conditions human beings to a life where the instincts overcome the social values.

Keywords: Child of the Dark, slum, literature, architecture and urbanism, fundamental rights, Brazil

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24177 Point Estimation for the Type II Generalized Logistic Distribution Based on Progressively Censored Data

Authors: Rana Rimawi, Ayman Baklizi

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Skewed distributions are important models that are frequently used in applications. Generalized distributions form a class of skewed distributions and gain widespread use in applications because of their flexibility in data analysis. More specifically, the Generalized Logistic Distribution with its different types has received considerable attention recently. In this study, based on progressively type-II censored data, we will consider point estimation in type II Generalized Logistic Distribution (Type II GLD). We will develop several estimators for its unknown parameters, including maximum likelihood estimators (MLE), Bayes estimators and linear estimators (BLUE). The estimators will be compared using simulation based on the criteria of bias and Mean square error (MSE). An illustrative example of a real data set will be given.

Keywords: point estimation, type II generalized logistic distribution, progressive censoring, maximum likelihood estimation

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24176 Sustainability of High-Rise Affordable Housing: Critical Issues in Applying Green Building Rating Tools

Authors: Poh Im. Lim, Hillary Yee Qin. Tan

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Nowadays, going green has become a trend, and being emphasized in the construction industry. In Malaysia, there are several green rating tools available in the industry and among these, GBI and GreenRE are considered as the most common tools adopted for residential buildings. However, being green is not equal to or making something sustainable. Being sustainable is to take economic, environmental and social aspects into consideration. This is particularly essential in the affordable housing sector as the end-users belong to lower-income and places importance on many socio-economic needs beyond the environmental criteria. This paper discusses the arguments in proposing a sustainability framework that is tailor-made for high-rise affordable housing. In-depth interviews and observation mapping methods were used in gathering inputs from the end-users, non-governmental organisations (NGOs) as well as the professionals. ‘Bottom-up’ approach was applied in this research to show the significance of participation from the local community in the decision-making process. The proposed sustainability framework illustrates the discrepancies between user priorities and what the industry is providing. The outcome of this research suggests that integrating sustainability into high-rise affordable housing is achievable and beneficial to the industry, society, and the environment.

Keywords: green building rating tools, high-rise affordable housing, sustainability framework, sustainable development

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24175 Omni: Data Science Platform for Evaluate Performance of a LoRaWAN Network

Authors: Emanuele A. Solagna, Ricardo S, Tozetto, Roberto dos S. Rabello

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Nowadays, physical processes are becoming digitized by the evolution of communication, sensing and storage technologies which promote the development of smart cities. The evolution of this technology has generated multiple challenges related to the generation of big data and the active participation of electronic devices in society. Thus, devices can send information that is captured and processed over large areas, but there is no guarantee that all the obtained data amount will be effectively stored and correctly persisted. Because, depending on the technology which is used, there are parameters that has huge influence on the full delivery of information. This article aims to characterize the project, currently under development, of a platform that based on data science will perform a performance and effectiveness evaluation of an industrial network that implements LoRaWAN technology considering its main parameters configuration relating these parameters to the information loss.

Keywords: Internet of Things, LoRa, LoRaWAN, smart cities

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24174 Impact of Gases Derived from Sargassum Algae Biodegradation on Copper Atmospheric Corrosion

Authors: M. Said Ahmed, M. Lebrini, J. Pellé, S. Rioual, B. Lescop, C. Roos

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The corrosion behavior of copper exposed in a marine atmosphere polluted and unpolluted by gases, mainly hydrogen sulphide (H2S), from the decomposition of Sargassum algae was studied using the mass loss method and electrochemical measurements. MEB/EDX and XRD were also used for the observation of morphology and surface analysis. To study the impact of this on copper corrosion, four sites more or less impacted by Sargassum algae strandings were selected. The samples were exposed for up to six months. The mass loss results showed that the average corrosion rate of copper was 528 µm/year for the site most affected by Sargassum algae and 9.4 µm/year for the least impacted site after three months of exposure, implying that the presence of Sargassum algae caused an important copper degradation. The morphological structures and properties of the corrosion products obtained at the impacted and non-impacted sites differed significantly. In the absence of Sargassum algae, we obtained mainly Cu2O and Cu2Cl(OH)3. Whereas in the atmosphere with Sargassum algae, CuS product is the main corrosion product obtained. Electrochemical analyses showed that the protection offered by the corrosion product layer was more important and improved with time for the non-impacted sites, whereas on the impacted sites, this protection deteriorated.

Keywords: atmospheric-corrosion, sargassum algae, copper, electrochemical techniques, SEM/EDX and XRD

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24173 Cybervetting and Online Privacy in Job Recruitment – Perspectives on the Current and Future Legislative Framework Within the EU

Authors: Nicole Christiansen, Hanne Marie Motzfeldt

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In recent years, more and more HR professionals have been using cyber-vetting in job recruitment in an effort to find the perfect match for the company. These practices are growing rapidly, accessing a vast amount of data from social networks, some of which is privileged and protected information. Thus, there is a risk that the right to privacy is becoming a duty to manage your private data. This paper investigates to which degree a job applicant's fundamental rights are protected adequately in current and future legislation in the EU. This paper argues that current data protection regulations and forthcoming regulations on the use of AI ensure sufficient protection. However, even though the regulation on paper protects employees within the EU, the recruitment sector may not pay sufficient attention to the regulation as it not specifically targeting this area. Therefore, the lack of specific labor and employment regulation is a concern that the social partners should attend to.

Keywords: AI, cyber vetting, data protection, job recruitment, online privacy

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24172 Sequential Pattern Mining from Data of Medical Record with Sequential Pattern Discovery Using Equivalent Classes (SPADE) Algorithm (A Case Study : Bolo Primary Health Care, Bima)

Authors: Rezky Rifaini, Raden Bagus Fajriya Hakim

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This research was conducted at the Bolo primary health Care in Bima Regency. The purpose of the research is to find out the association pattern that is formed of medical record database from Bolo Primary health care’s patient. The data used is secondary data from medical records database PHC. Sequential pattern mining technique is the method that used to analysis. Transaction data generated from Patient_ID, Check_Date and diagnosis. Sequential Pattern Discovery Algorithms Using Equivalent Classes (SPADE) is one of the algorithm in sequential pattern mining, this algorithm find frequent sequences of data transaction, using vertical database and sequence join process. Results of the SPADE algorithm is frequent sequences that then used to form a rule. It technique is used to find the association pattern between items combination. Based on association rules sequential analysis with SPADE algorithm for minimum support 0,03 and minimum confidence 0,75 is gotten 3 association sequential pattern based on the sequence of patient_ID, check_Date and diagnosis data in the Bolo PHC.

Keywords: diagnosis, primary health care, medical record, data mining, sequential pattern mining, SPADE algorithm

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24171 Estimation of Reservoirs Fracture Network Properties Using an Artificial Intelligence Technique

Authors: Reda Abdel Azim, Tariq Shehab

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The main objective of this study is to develop a subsurface fracture map of naturally fractured reservoirs by overcoming the limitations associated with different data sources in characterising fracture properties. Some of these limitations are overcome by employing a nested neuro-stochastic technique to establish inter-relationship between different data, as conventional well logs, borehole images (FMI), core description, seismic attributes, and etc. and then characterise fracture properties in terms of fracture density and fractal dimension for each data source. Fracture density is an important property of a system of fracture network as it is a measure of the cumulative area of all the fractures in a unit volume of a fracture network system and Fractal dimension is also used to characterize self-similar objects such as fractures. At the wellbore locations, fracture density and fractal dimension can only be estimated for limited sections where FMI data are available. Therefore, artificial intelligence technique is applied to approximate the quantities at locations along the wellbore, where the hard data is not available. It should be noted that Artificial intelligence techniques have proven their effectiveness in this domain of applications.

Keywords: naturally fractured reservoirs, artificial intelligence, fracture intensity, fractal dimension

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24170 Governance, Risk Management, and Compliance Factors Influencing the Adoption of Cloud Computing in Australia

Authors: Tim Nedyalkov

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A business decision to move to the cloud brings fundamental changes in how an organization develops and delivers its Information Technology solutions. The accelerated pace of digital transformation across businesses and government agencies increases the reliance on cloud-based services. They are collecting, managing, and retaining large amounts of data in cloud environments makes information security and data privacy protection essential. It becomes even more important to understand what key factors drive successful cloud adoption following the commencement of the Privacy Amendment Notifiable Data Breaches (NDB) Act 2017 in Australia as the regulatory changes impact many organizations and industries. This quantitative correlational research investigated the governance, risk management, and compliance factors contributing to cloud security success. The factors influence the adoption of cloud computing within an organizational context after the commencement of the NDB scheme. The results and findings demonstrated that corporate information security policies, data storage location, management understanding of data governance responsibilities, and regular compliance assessments are the factors influencing cloud computing adoption. The research has implications for organizations, future researchers, practitioners, policymakers, and cloud computing providers to meet the rapidly changing regulatory and compliance requirements.

Keywords: cloud compliance, cloud security, data governance, privacy protection

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24169 Banking and Accounting Analysis Researches Effect on Environment

Authors: Michael Saad Thabet Azrek

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The advanced facts era is becoming a vital element within the improvement of financial offerings enterprise, in particular, the banking enterprise. It has introduced new approaches to delivering banking to the patron, including Internet Banking. Banks started to observe digital banking (e-banking) as a means to update a number of their conventional branch features using the net as a brand-new distribution channel. A few purchasers have, as a minimum, a couple of accounts across banks and get the right of entry to these accounts using e-banking offerings. To study the contemporary net really worth role, clients ought to log in to each of their debts and get the info and paintings on consolidation. This not simplest takes enough time, but it's also a repetitive hobby at a specific frequency. To cope with this point, an account aggregation idea is introduced as an answer. E-banking account aggregation, as one of the e-banking types, appeared to construct a more potent dating with clients. Account Aggregation provider usually refers to a provider that permits clients to manage their financial institution debts maintained in distinct establishments through a not unusual net banking operating a platform, with an excessive situation to protection and privateness. This paper gives an outline of an e-banking account aggregation method as a new provider in the e-banking field.

Keywords: compatibility, complexity, mobile banking, observation, risk banking technology, internet banks, modernization of banks, banks, account aggregation, security, enterprise development

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24168 Efficient Pre-Processing of Single-Cell Assay for Transposase Accessible Chromatin with High-Throughput Sequencing Data

Authors: Fan Gao, Lior Pachter

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The primary tool currently used to pre-process 10X Chromium single-cell ATAC-seq data is Cell Ranger, which can take very long to run on standard datasets. To facilitate rapid pre-processing that enables reproducible workflows, we present a suite of tools called scATAK for pre-processing single-cell ATAC-seq data that is 15 to 18 times faster than Cell Ranger on mouse and human samples. Our tool can also calculate chromatin interaction potential matrices, and generate open chromatin signal and interaction traces for cell groups. We use scATAK tool to explore the chromatin regulatory landscape of a healthy adult human brain and unveil cell-type specific features, and show that it provides a convenient and computational efficient approach for pre-processing single-cell ATAC-seq data.

Keywords: single-cell, ATAC-seq, bioinformatics, open chromatin landscape, chromatin interactome

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24167 21st Century Gunboat Diplomacy and Strategic Sea Areas

Authors: Mustafa Avsever

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Throughout history, states have attached great importance to seas in terms of economic and security. Advanced civilizations have always founded in coastal regions. Over time, human being has tended to trade and naturally always aimed get more and more. Seas by covering 71% of the earth, provide the greatest economic opportunities for access to raw material resources and the world market. As a result, seas have become the most important areas of conflict over the course of time. Coastal states, use seas as a tool for defense zone, trade, marine transportation and power transfer, they have acquired colonies overseas and increased their capital, raw materials and labor. Societies, have increased their economic prosperity, though their navies in order to retain their welfare and achieve their foreign policy objectives. Sometimes they have imposed their demands through the use or threat of limited naval force in accordance with their interests that is gunboat diplomacy. Today we can see samples of gunboat diplomacy used in the Eastern Mediterranean, during Ukraine crisis, in dispute between North Korea and South Korea and the ongoing power struggle in Asia-Pacific. Gunboat diplomacy has been and continues to be applied consistently in solving problems by the stronger side of the problem. The purpose of this article is to examine using navy under the gunboat diplomacy as an active instrument of foreign policy and security policy and reveal the strategic sea areas in which gunboat diplomacy is used effectively in the matrix of international politics in the 21st century.

Keywords: gunboat diplomacy, maritime strategy, sea power, strategic sea lands

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24166 Affordable Housing and Economic Sustainability: The Case of the Poorest of the Poor Housing in Debre Markos City

Authors: Michael Menberu

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Housing affordability is a crucial policy objective. Incorporating economic sustainability in affordable housing in the development of the poorest of the poor's housing programs has become a major concern. This paper examines the affordability and economic sustainability of the poorest of the poor affordable housing in Debre Markos city. To address this, this study uses both quantitative and qualitative methods, Using a questionnaire survey, Interviews, field observation, household survey, and Pearson correlation analysis. The findings show that households in the expansion area have lower monthly incomes than in the past, but they are satisfied with the housing quality, rental pricing, and tenure security of their homes. This demonstrates in providing affordable housing for the poorest of the poor the location of the houses must be considered in order to have affordable and economically sustainable development. The findings show that housing is not truly affordable if it is in an inaccessible place with a long commute to work, high transportation expenditures, and land use that is homogeneous. Increasing the supply of affordable housing in accessible locations helps the poorest of the poor achieve multiple planning objectives: it reduces transportation costs, improves incomes, provides infrastructure, and reduces the distance to the work area.

Keywords: housing, affordable housing, economic sustainability, the poorest of the poor housing

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24165 Meta Mask Correction for Nuclei Segmentation in Histopathological Image

Authors: Jiangbo Shi, Zeyu Gao, Chen Li

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Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks by using a small amount of clean meta-data. Then the corrected masks are used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. In particular, in some noise scenarios, it even exceeds the performance of training on supervised data.

Keywords: deep learning, histopathological image, meta-learning, nuclei segmentation, weak annotations

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24164 Computational Fluid Dynamics Simulation of Reservoir for Dwell Time Prediction

Authors: Nitin Dewangan, Nitin Kattula, Megha Anawat

Abstract:

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

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

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24163 Risk-taking and Avoidance Decisions in Pandemic Agriculture in Georgia

Authors: Nino Damenia

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The paper discusses the risks arising in agriculture in Georgia, the possibilities of their acceptance and prevention, the threat created by the pandemic crisis, and the state programs for overcoming them. The share of agriculture in the country's GDP is 8.3%. Over the past five years, Georgia has imported $ 5.9 billion worth of agri-food products. Despite these figures, agriculture has become an important sector for the Georgian government since 2012, as evidenced by the more than 1.5 billion GEL spent from the 2012-2020 budget for agricultural development. Any field of agriculture, be it poultry, livestock, cereals, fruits, or vegetables, is very sensitive to various climatic and viral risks. Avoiding these risks requires additional investment. It is noteworthy that small farms are mainly affected by the risks, while relatively large farms face fewer problems because they are relatively prepared to face the problems and can avoid them more easily. An example of viral risk in the article is the export of hazelnuts, which has quite a lot of potential. Due to the spoilage of the crop caused by Brown Marmorated Stink Bug (BMSB), hazelnut exports have declined considerably over the years. If the volume of hazelnuts exported in 2016 was 179 378 thousand USD, due to the deficit caused by Brown Marmorated Stink Bug (BMSB) in 2018, it became 57 124 thousand USD. And after the situation was relatively settled, hazelnut seedlings were poisoned. By 2020, this figure improved to 91,088 thousand US dollars. The development of the agricultural sector and the reduction of risks require technological development, investor interest, and even more state support to enable more small farms to have the potential for greater production and sustainable development. The aim of the study is to identify the risks arising in the agricultural sector of Georgia before and after the pandemic, to evaluate them, compare them with the agriculture of some European countries, and to develop the necessary recommendations to avoid the emerging risks. The research uses methods of analysis and synthesis, observation, induction, deduction, and analysis of statistics. The paper is based on both Georgian and foreign scientific research, as well as state-published documentation on agricultural assistance programs. The research is based on the analysis of data published by the European Statistics Office, the National Statistics Office of Georgia, and many other organizations. The results of the study and the recommendations will help reduce the risks in agriculture in Georgia and, in general, to identify the existing potential and the development of the sector as a whole.

Keywords: risk, agriculture, pandemi, brown marmorated stink bug (BMSB)

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24162 One Year Follow up of Head and Neck Paragangliomas: A Single Center Experience

Authors: Cecilia Moreira, Rita Paiva, Daniela Macedo, Leonor Ribeiro, Isabel Fernandes, Luis Costa

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Background: Head and neck paragangliomas are a rare group of tumors with a large spectrum of clinical manifestations. The approach to evaluate and treat these lesions has evolved over the last years. Surgery was the standard for the approach of these patients, but nowadays new techniques of imaging and radiation therapy changed that paradigm. Despite advances in treating, the growth potential and clinical outcome of individual cases remain largely unpredictable. Objectives: Characterization of our institutional experience with clinical management of these tumors. Methods: This was a cross-sectional study of patients followed in our institution between 01 January and 31 December 2017 with paragangliomas of the head and neck and cranial base. Data on tumor location, catecholamine levels, and specific imaging modalities employed in diagnostic workup, treatment modality, tumor control and recurrence, complications of treatment and hereditary status were collected and summarized. Results: A total of four female patients were followed between 01 January and 31 December 2017 in our institution. The mean age of our cohort was 53 (± 16.1) years. The primary locations were at the level of the tympanic jug (n=2, 50%) and carotid body (n=2, 50%), and only one of the tumors of the carotid body presented pulmonary metastasis at the time of diagnosis. None of the lesions were catecholamine-secreting. Two patients underwent genetic testing, with no mutations identified. The initial clinical presentation was variable highlighting the decrease of visual acuity and headache as symptoms present in all patients. In one of the cases, loss of all teeth of the lower jaw was the presenting symptomatology. Observation with serial imaging, surgical extirpation, radiation, and stereotactic radiosurgery were employed as treatment approaches according to anatomical location and resectability of lesions. As post-therapeutic sequels the persistence of tinnitus and disabling pain stands out, presenting one of the patients neuralgia of the glossopharyngeal. Currently, all patients are under regular surveillance with a median follow up of 10 months. Conclusion: Ultimately, clinical management of these tumors remains challenging owing to heterogeneity in clinical presentation, the existence of multiple treatment alternatives, and potential to cause serious detriment to critical functions and consequently interference with the quality of life of the patients.

Keywords: clinical outcomes, head and neck, management, paragangliomas

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24161 Feature Selection Approach for the Classification of Hydraulic Leakages in Hydraulic Final Inspection using Machine Learning

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

Abstract:

Manufacturing companies are facing global competition and enormous cost pressure. The use of machine learning applications can help reduce production costs and create added value. Predictive quality enables the securing of product quality through data-supported predictions using machine learning models as a basis for decisions on test results. Furthermore, machine learning methods are able to process large amounts of data, deal with unfavourable row-column ratios and detect dependencies between the covariates and the given target as well as assess the multidimensional influence of all input variables on the target. Real production data are often subject to highly fluctuating boundary conditions and unbalanced data sets. Changes in production data manifest themselves in trends, systematic shifts, and seasonal effects. Thus, Machine learning applications require intensive pre-processing and feature selection. Data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets. Within the used real data set of Bosch hydraulic valves, the comparability of the same production conditions in the production of hydraulic valves within certain time periods can be identified by applying the concept drift method. Furthermore, a classification model is developed to evaluate the feature importance in different subsets within the identified time periods. By selecting comparable and stable features, the number of features used can be significantly reduced without a strong decrease in predictive power. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. In this research, the ada boosting classifier is used to predict the leakage of hydraulic valves based on geometric gauge blocks from machining, mating data from the assembly, and hydraulic measurement data from end-of-line testing. In addition, the most suitable methods are selected and accurate quality predictions are achieved.

Keywords: classification, achine learning, predictive quality, feature selection

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24160 Secure Data Sharing of Electronic Health Records With Blockchain

Authors: Kenneth Harper

Abstract:

The secure sharing of Electronic Health Records (EHRs) is a critical challenge in modern healthcare, demanding solutions to enhance interoperability, privacy, and data integrity. Traditional standards like Health Information Exchange (HIE) and HL7 have made significant strides in facilitating data exchange between healthcare entities. However, these approaches rely on centralized architectures that are often vulnerable to data breaches, lack sufficient privacy measures, and have scalability issues. This paper proposes a framework for secure, decentralized sharing of EHRs using blockchain technology, cryptographic tokens, and Non-Fungible Tokens (NFTs). The blockchain's immutable ledger, decentralized control, and inherent security mechanisms are leveraged to improve transparency, accountability, and auditability in healthcare data exchanges. Furthermore, we introduce the concept of tokenizing patient data through NFTs, creating unique digital identifiers for each record, which allows for granular data access controls and proof of data ownership. These NFTs can also be employed to grant access to authorized parties, establishing a secure and transparent data sharing model that empowers both healthcare providers and patients. The proposed approach addresses common privacy concerns by employing privacy-preserving techniques such as zero-knowledge proofs (ZKPs) and homomorphic encryption to ensure that sensitive patient information can be shared without exposing the actual content of the data. This ensures compliance with regulations like HIPAA and GDPR. Additionally, the integration of Fast Healthcare Interoperability Resources (FHIR) with blockchain technology allows for enhanced interoperability, enabling healthcare organizations to exchange data seamlessly and securely across various systems while maintaining data governance and regulatory compliance. Through real-world case studies and simulations, this paper demonstrates how blockchain-based EHR sharing can reduce operational costs, improve patient outcomes, and enhance the security and privacy of healthcare data. This decentralized framework holds great potential for revolutionizing healthcare information exchange, providing a transparent, scalable, and secure method for managing patient data in a highly regulated environment.

Keywords: blockchain, electronic health records (ehrs), fast healthcare interoperability resources (fhir), health information exchange (hie), hl7, interoperability, non-fungible tokens (nfts), privacy-preserving techniques, tokens, secure data sharing,

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24159 The Twin Terminal of Pedestrian Trajectory Based on City Intelligent Model (CIM) 4.0

Authors: Chen Xi, Liu Xuebing, Lao Xueru, Kuan Sinman, Jiang Yike, Wang Hanwei, Yang Xiaolang, Zhou Junjie, Xie Jinpeng

Abstract:

To further promote the development of smart cities, the microscopic "nerve endings" of the City Intelligent Model (CIM) are extended to be more sensitive. In this paper, we develop a pedestrian trajectory twin terminal based on the CIM and CNN technology. It also uses 5G networks, architectural and geoinformatics technologies, convolutional neural networks, combined with deep learning networks for human behavior recognition models, to provide empirical data such as 'pedestrian flow data and human behavioral characteristics data', and ultimately form spatial performance evaluation criteria and spatial performance warning systems, to make the empirical data accurate and intelligent for prediction and decision making.

Keywords: urban planning, urban governance, CIM, artificial intelligence, sustainable development

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24158 Enhancing Learning Ability among Deaf Students by Using Photographic Images

Authors: Aidah Alias, Mustaffa Halabi Azahari, Adzrool Idzwan Ismail, Salasiah Ahmad

Abstract:

Education is one of the most important elements in a human life. Educations help us in learning and achieve new things in life. The ability of hearing gave us chances to hear voices and it is important in our communication. Hearing stories told by others; hearing news and music to create our creative and sense; seeing and hearing make us understand directly the message trying to deliver. But, what will happen if we are born deaf or having hearing loss while growing up? The objectives of this paper are to identify the current practice in teaching and learning among deaf students and to analyse an appropriate method in enhancing learning process among deaf students. A case study method was employed by using methods of observation and interview to selected deaf students and teachers. The findings indicated that the suitable method of teaching for deaf students is by using pictures and body movement. In other words, by combining these two medium of images and body movement, the best medium that the study suggested is by using video or motion pictures. The study concluded and recommended that video or motion pictures is recommended medium to be used in teaching and learning for deaf students.

Keywords: deaf, photographic images, visual communication, education, learning ability

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24157 The Impact of Hosting an On-Site Vocal Concert in Preschool on Music Inspiration and Learning Among Preschoolers

Authors: Meiying Liao, Poya Huang

Abstract:

The aesthetic domain is one of the six major domains in the Taiwanese preschool curriculum, encompassing visual arts, music, and dramatic play. Its primary objective is to cultivate children’s abilities in exploration and awareness, expression and creation, and response and appreciation. The purpose of this study was to explore the effects of hosting a vocal music concert on aesthetic inspiration and learning among preschoolers in a preschool setting. The primary research method employed was a case study focusing on a private preschool in Northern Taiwan that organized a school-wide event featuring two vocalists. The concert repertoires included children’s songs, folk songs, and arias performed in Mandarin, Hakka, English, German, and Italian. In addition to professional performances, preschool teachers actively participated by presenting a children’s song. A total of 5 classes, comprising approximately 150 preschoolers, along with 16 teachers and staff, participated in the event. Data collection methods included observation, interviews, and documents. Results indicated that both teachers and children thoroughly enjoyed the concert, with high levels of acceptance when the program was appropriately designed and hosted. Teachers reported that post-concert discussions with children revealed the latter’s ability to recall people, events, and elements observed during the performance, expressing their impressions of the most memorable segments. The concert effectively achieved the goals of the aesthetic domain, particularly in fostering response and appreciation. It also inspired preschoolers’ interest in music. Many teachers noted an increased desire for performance among preschoolers after exposure to the concert, with children imitating the performers and their expressions. Remarkably, one class extended this experience by incorporating it into the curriculum, autonomously organizing a high-quality concert in the music learning center. Parents also reported that preschoolers enthusiastically shared their concert experiences at home. In conclusion, despite being a single event, the positive responses from preschoolers towards the music performance suggest a meaningful impact. These experiences extended into the curriculum, as firsthand exposure to performances allowed teachers to deepen related topics, fostering a habit of autonomous learning in the designated learning centers.

Keywords: concert, early childhood music education, aesthetic education, music develpment

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24156 Application of Groundwater Model for Optimization of Denitrification Strategies to Minimize Public Health Risk

Authors: Mukesh A. Modi

Abstract:

High-nitrate concentration in groundwater of unconfined aquifers has been a serious issue for public health risk at a global scale. Various anthropogenic activities in agricultural land and urban land of alluvial soil have been observed to be responsible for the increment of nitrate in groundwater. The present study was designed to identify suitable denitrification strategies to minimize the effects of high nitrate in groundwater near the Mahi River of Vadodara block, Gujarat. There were 11 wells of Jal Jeevan Mission, Ministry of Jal Shakti, along with 3 observation wells of Gujarat Water Resources Development Corporation have been used for the duration of 21 years. MODFLOW and MT3DMS codes have been used to simulate solute transport phenomena along with attempted effectively for optimization. Current research is one step ahead by optimizing various denitrification strategies with the simulation of the model. The in-situ and ex-situ denitrification strategies viz. NAS (No Action Scenario), CAS (Crop Alternation Scenario), PS (Phytoremediation Scenario), and CAS + PS (Crop Alternation Scenario + Phytoremediation Scenario) have been selected for the optimization. The groundwater model simulates the most suitable denitrification strategy considering the hydrogeological characteristics at the targeted well.

Keywords: groundwater, high nitrate, MODFLOW, MT3DMS, optimization, denitrification strategy

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24155 Composite Electrodes Containing Ni-Fe-Cr as an Activatable Oxygen Evolution Catalyst

Authors: Olga A. Krysiak, Grzegorz Cichowicz, Wojciech Hyk, Michal Cyranski, Jan Augustynski

Abstract:

Metal oxides are known electrocatalyst in water oxidation reaction. Due to the fact that it is desirable for efficient oxygen evolution catalyst to contain numerous redox-active metal ions to guard four electron water oxidation reaction, mixed metal oxides exhibit enhanced catalytic activity towards oxygen evolution reaction compared to single metal oxide systems. On the surface of fluorine doped tin oxide coated glass slide (FTO) deposited (doctor blade technique) mixed metal oxide layer composed of nickel, iron, and chromium. Oxide coating was acquired by heat treatment of the aqueous precursors' solutions of the corresponding salts. As-prepared electrodes were photosensitive and acted as an efficient oxygen evolution catalyst. Our results showed that obtained by this method electrodes can be activated which leads to achieving of higher current densities. The recorded current and photocurrent associated with oxygen evolution process were at least two orders of magnitude higher in the presence of oxide layer compared to bare FTO electrode. The overpotential of the process is low (ca. 0,2 V). We have also checked the activity of the catalyst at different known photoanodes used in sun-driven water splitting. Herein, we demonstrate that we were able to achieve efficient oxygen evolution catalysts using relatively cheap precursor consisting of earth abundant metals and simple method of preparation.

Keywords: chromium, electrocatalysis, iron, metal oxides, nickel, oxygen evolution

Procedia PDF Downloads 211