Search results for: data reduction

Authors: Gabriel Gomez

Abstract:

The very concept of information seeking behavior, and the means by which librarians teach users to gain information, that is information literacy, are at the heart of how libraries deliver information, but big data will forever change human interaction with information and the way such behavior is both studied and taught. Just as importantly, big data will orient the study of behavior towards commercial ends because of a tendency towards instrumentalist views of human behavior, something one might also call a trend towards behaviorism. This oral presentation seeks to explore how the impact of big data on understandings of human behavior might impact a library information science (LIS) view of human behavior and information literacy, and what this might mean for social justice aims and concomitant community action normally at the center of librarianship. The methodology employed here is a non-empirical examination of current understandings of LIS in regards to social justice alongside an examination of the benefits and dangers foreseen with the growth of big data analysis. The rise of big data within the ever-changing information environment encapsulates a shift to a more mechanistic view of human behavior, one that can easily encompass information seeking behavior and information use. As commercial aims displace the important political and ethical aims that are often central to the missions espoused by libraries and the social sciences, the very altruism and power relations found in LIS are at risk. In this oral presentation, an examination of the social justice impulses of librarians regarding power and information demonstrates how such impulses can be challenged by big data, particularly as librarians understand user behavior and promote information literacy. The creeping behaviorist impulse inherent in the emphasis big data places on specific solutions, that is answers to question that ask how, as opposed to larger questions that hint at an understanding of why people learn or use information threaten library information science ideals. Together with the commercial nature of most big data, this existential threat can harm the social justice nature of librarianship.

Keywords: big data, library information science, behaviorism, librarianship

Procedia PDF Downloads 380

Authors: Min Kyung An

Abstract:

In this paper, we study the data collection problem in Wireless Sensor Networks (WSNs) adopting the two interference models: The graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR). The main issue of the problem is to compute schedules with the minimum number of timeslots, that is, to compute the minimum latency schedules, such that data from every node can be collected without any collision or interference to a sink node. While existing works studied the problem with unit-sized and unbounded-sized message models, we investigate the problem with the bounded-sized message model, and introduce a constant factor approximation algorithm. To the best known of our knowledge, our result is the first result of the data collection problem with bounded-sized model in both interference models.

Keywords: data collection, collision-free, interference-free, physical interference model, SINR, approximation, bounded-sized message model, wireless sensor networks

Procedia PDF Downloads 217

Authors: Z. Jamalzadeh, A. Niaei, H. Erfannia, S. G. Hosseini, A. S. Razmgir

Abstract:

Due to developing the industries, the emission of pollutants such as NOx, SOx, and CO2 are rapidly increased. Generally, NOx is attributed to the mono nitrogen oxides of NO and NO2 that is one of the most important atmospheric contaminants. Hence, controlling the emission of nitrogen oxides is urgent environmentally. Selective Catalytic Reduction of NOx is one of the most common techniques for NOx removal in which Zeolites have wide application due to their high performance. In zeolitic processes, the catalytic reaction occurs mostly in the pores. Therefore, investigation the adsorption phenomena of the molecules in order to gain an insight and understand the catalytic cycle is of important. Hence, in current study, molecular simulations is applied for studying the adsorption phenomena in nanocatalysts applied for SCR of NOx process. The effect of cation addition to the support in the catalysts’ behavior through adsorption step was explored by Mont Carlo (MC). Simulation time of 1 Ns accompanying 1 fs time step, COMPASS27 Force Field and the cut off radios of 12.5 Ȧ was applied for performed runs. It was observed that the adsorption capacity increases in the presence of cations. The sorption isotherms demonstrated the behavior of type I isotherm categories and sorption capacity diminished with increase in temperature whereas an increase was observed at high pressures. Besides, NO sorption showed higher sorption capacity than NH3 in H–ZSM5. In this respect, the Energy distributions signified that the molecules could adsorb in just one sorption site at the catalyst and the sorption energy of NO was stronger than the NH3 in H-ZSM5. Furthermore, the isosteric heat of sorption data showed nearly same values for the molecules; however, it indicated stronger interactions of NO molecules with H-ZSM5 Zeolite compared to the isosteric heat of NH3 which was low in value.

Keywords: Monte Carlo simulation, adsorption, NOx, ZSM5

Procedia PDF Downloads 372

Authors: Chen Xi, Lao Xuerui, Li Junjie, Jiang Yike, Wang Hanwei, Zeng Zihao

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 behaviour recognition models, to provide empirical data such as 'pedestrian flow data and human behavioural 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, convolutional neural network

Procedia PDF Downloads 137

Authors: Esam Mohammed Al-shaebi

Abstract:

One of the most crucial approaches for treating human diseases, particularly parasite infections, is nanomedicine. One of the most significant protozoan diseases that impact farm and domestic animals is coccidiosis. While, amprolium is one of the traditional anticoccidial medication, the advent of drug-resistant strains of Eimeria necessitates the development of novel treatments. The goal of the current investigation was to determine whether biosynthesized selenium nanoparticles (Bio-SeNPs) using Azadirachta indica leaves extract might treat mice with Eimeria papillata infection in the jejunal tissue. Five groups of seven mice each were used, as follows: Group 1: Non-infected-non-treated (negative control). Group 2: Non-infected treated group with Bio-SeNPs (0.5 mg/kg of body weight). Groups 3-5 were orally inoculated with 1×103 sporulated oocysts of E. papillata. Group 3: Infected-non-treated (positive control). Group 4: Infected and treated group with Bio-SeNPs (0.5 mg/kg). Group 5: Infected and treated group with the Amprolium. Groups 4 and 5 daily received oral administration (for 5 days) of Bio-SeNPs and anticoccidial medication, respectively, after infection. Bio-SeNPs caused a considerable reduction in oocyst output in mice feces (97.21%). This was also accompanied by a significant reduction in the number of developmental parasitic stages in the jejunal tissues. Glutathione reduced (GSH), glutathione peroxidase (GPx), and superoxide dismutase (SOD) levels were dramatically reduced by the Eimeria parasite, whereas, nitric oxide (NO) and malonaldehyde (MDA) levels were markedly elevated. The amount of goblet cells and MUC2 gene expression were used as apoptotic indicators, and both were considerably downregulated by infection. However, infection markedly increased the expression of inflammatory cytokines (IL-6 and TNF-α) and the apoptotic genes (Caspase-3 and BCL2). Bio-SeNPs were administrated to mice to drastically lower body weight, oxidative stress, and inflammatory and apoptotic indicators in the jejunal tissue. Our research thus showed the involvement of Bio-SeNPs in protecting mice with E. papillata infections against jejunal damage.

Keywords: coccidiosis, nanoparticles, azadirachta indica, oxidative stress

Procedia PDF Downloads 90

Authors: Yunos Zahri, Ariffin Aswami

Abstract:

Cyber terrorism represents the convergence of two worlds: virtual and physical. The virtual world is a place in which computer programs function and data move, whereas the physical world is where people live and function. The merging of these two domains is the interface being targeted in the incidence of cyber terrorism. To better understand why cyber terrorism acts are committed, this study presents the context of cyber terrorism from motivational perspectives. Motivational forces behind cyber terrorism can be social, political, ideological and economic. In this research, data are analyzed using a qualitative method. A semi-structured interview with purposive sampling was used for data collection. With the growing interconnectedness between critical infrastructures and Information & Communication Technology (ICT), selecting targets that facilitate maximum disruption can significantly influence terrorists. This work provides a baseline for defining the concept of cyber terrorism from motivational perspectives.

Keywords: cyber terrorism, terrorism, motivation, qualitative analysis

Procedia PDF Downloads 415

Authors: Sheheryar Khan, Weidong Li, Fanqian Meng

Abstract:

The Urban Heat Island (UHI) effect is one of the foremost problems out of other ecological and socioeconomic issues in urbanization. Due to this phenomenon that human-made urban areas have replaced the rural landscape with the surface that increases thermal conductivity and urban warmth; as a result, the temperature in the city is higher than in the surrounding rural areas. To affect the evidence of this phenomenon in the Zhengzhou city area, an observation of the temperature variations in the urban area is done through a scientific method that has been followed. Landsat 8 satellite images were taken from 2013 to 2015 to calculate the effect of Urban Heat Island (UHI) along with the NPP-VRRIS night-time remote sensing data to analyze the result for a better understanding of the center of the built-up area. To further support the evidence, the correlation between land surface temperatures and the normalized difference vegetation index (NDVI) was calculated using the Red band 4 and Near-infrared band 5 of the Landsat 8 data. Mono-window algorithm was applied to retrieve the land surface temperature (LST) distribution from the Landsat 8 data using Band 10 and 11 accordingly to convert the top-of-atmosphere radiance (TOA) and to convert the satellite brightness temperature. Along with Landsat 8 data, NPP-VIIRS night-light data is preprocessed to get the research area data. The analysis between Landsat 8 data and NPP night-light data was taken to compare the output center of the Built-up area of Zhengzhou city.

Keywords: built-up area, land surface temperature, mono-window algorithm, NDVI, remote sensing, threshold method, Zhengzhou

Procedia PDF Downloads 135

Authors: Robab Abdolkhani, Farzin Halabchi, Reza Safdari, Goli Arji

Abstract:

Background and purpose: The quality of health record depends on the quality of its content and proper documentation. Minimum data set makes a standard method for collecting key data elements that make them easy to understand and enable comparison. The aim of this study was to determine the minimum data set for Iranian athletes’ health records. Methods: This study is an applied research of a descriptive comparative type which was carried out in 2013. By using internal and external forms of documentation, a checklist was created that included data elements of athletes health record and was subjected to debate in Delphi method by experts in the field of sports medicine and health information management. Results: From 97 elements which were subjected to discussion, 85 elements by more than 75 percent of the participants (as the main elements) and 12 elements by 50 to 75 percent of the participants (as the proposed elements) were agreed upon. In about 97 elements of the case, there was no significant difference between responses of alumni groups of sport pathology and sports medicine specialists with medical record, medical informatics and information management professionals. Conclusion: Minimum data set of Iranian athletes’ health record with four information categories including demographic information, health history, assessment and treatment plan was presented. The proposed model is available for manual and electronic medical records.

Keywords: Documentation, Health record, Minimum data set, Sports medicine

Procedia PDF Downloads 472

Authors: Abdellatif Chebboub

Abstract:

Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change’s representative concentration pathway with end-of-century radiative forcing of 8.5 W/m2. The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

Keywords: climate change, agriculture, weather change, danger of climate change

Procedia PDF Downloads 310

Authors: Tingting Song

Abstract:

Wetlands are significant ecosystems that provide a variety of ecosystem services for humans, such as, providing water and food resources, purifying water quality, regulating climate, protecting biodiversity, and providing cultural, recreational, and educational resources. Wetlands also provide benefits, such as reduction of flood, storm damage, and soil erosion. The flood protection ecosystem services of wetlands are often ignored. Due to climate change, the flood caused by extreme weather in recent years occur frequently. Flood has a great impact on people's production and life with more and more economic losses. This study area is in the Tone river basin in the Kanto area, Japan. It is the second-longest river with the largest basin area in Japan, and it is still suffering heavy economic losses from floods. Tone river basin is one of the rivers that provide water for Tokyo and has an important impact on economic activities in Japan. The purpose of this study was to investigate land-use changes of wetlands in the Tone River Basin, and whether there are spatial differences in the value of wetland functions in mitigating economic losses caused by floods. This study analyzed the land-use change of wetland in Tone River, based on the Landsat data from 1980 to 2020. Combined with flood economic loss, wetland area, GDP, population density, and other social-economic data, a geospatial weighted regression model was constructed to analyze the spatial difference of wetland ecosystem service value. Now, flood protection mainly relies on such a hard project of dam and reservoir, but excessive dependence on hard engineering will cause the government huge financial pressure and have a big impact on the ecological environment. However, natural wetlands can also play a role in flood management, at the same time they can also provide diverse ecosystem services. Moreover, the construction and maintenance cost of natural wetlands is lower than that of hard engineering. Although it is not easy to say which is more effective in terms of flood management. When the marginal value of a wetland is greater than the economic loss caused by flood per unit area, it may be considered to rely on the flood storage capacity of the wetland to reduce the impact of the flood. It can promote the sustainable development of wetlands ecosystem. On the other hand, spatial analysis of wetland values can provide a more effective strategy for flood management in the Tone river basin.

Keywords: wetland, geospatial weighted regression, ecosystem services, environment valuation

Procedia PDF Downloads 99

Authors: Maria Antonieta Erna Castillo Holly

Abstract:

During the last decade, data analysis, strategic decision making, and the use of artificial intelligence (AI) tools in Latin American agriculture have been a challenge. In some countries, the availability, quality, and reliability of historical data, in addition to the current data recording methodology in the field, makes it difficult to use information systems, complete data analysis, and their support for making the right strategic decisions. This is something essential in Agriculture 4.0. where the increase in the global demand for fresh agricultural products of tropical origin, during all the seasons of the year requires a change in the production model and greater agility in the responses to the consumer market demands of quality, quantity, traceability, and sustainability –that means extensive data-. Having quality information available and updated in real-time on what, how much, how, when, where, at what cost, and the compliance with production quality standards represents the greatest challenge for sustainable and profitable agriculture in the region. The objective of this work is to present a methodological proposal for the collection of georeferenced data from the protected agriculture sector, specifically in production units (UP) with tall structures (Greenhouses), initially for Venezuela, taking the state of Mérida as the geographical framework, and horticultural products as target crops. The document presents some background information and explains the methodology and tools used in the 3 phases of the work: diagnosis, data collection, and analysis. As a result, an evaluation of the process is carried out, relevant data and dashboards are displayed, and the first satellite maps integrated with layers of information in a geographic information system are presented. Finally, some improvement proposals and tentatively recommended applications are added to the process, understanding that their objective is to provide better qualified and traceable georeferenced data for subsequent analysis of the information and more agile and accurate strategic decision making. One of the main points of this study is the lack of quality data treatment in the Latin America area and especially in the Caribbean basin, being one of the most important points how to manage the lack of complete official data. The methodology has been tested with horticultural products, but it can be extended to other tropical crops.

Keywords: greenhouses, protected agriculture, data analysis, geographic information systems, Venezuela

Procedia PDF Downloads 127

Authors: Arie Yanwar Kapriadi

Abstract:

This research is aiming to develop deeper understanding of the scale of power that shaped the biofuel policy. This research is important for the following reasons. Firstly, this research will enrich the body of literature within the field of political ecology, scale and environmental governance. Secondly, by focussing on energy transition policies, this research offers a critical perspective on how government policy, aimed at delivering low carbon sustainable energy systems, being scaled and implemented through multi variate stakeholders. Finally, the research could help the government of Indonesia as a policy evaluation on delivering low carbon sustainable energy systems at the macro level that (possibility) being unable to be delivered at different scale and instead being perceived differently by different stakeholders. Qualitative method is applied particularly an in depth interview with government officials as well as policy stakeholders outside of government and people in positions of responsibility with regards to policy delivery. There are 4 field study location where interview took place as well as sites visit to some biofuel refining facilities. There are some major companies which involve on the production and distribution of biofuel and its relation with biofuel feedstock industry as the source of data. The research investigates how the government biofuel policies correlated with other policy issues such as land reclassification and carbon emission reduction which also influenced plantations expansion as well as its impact on the local people. The preliminary result shows tension of power between governing authorities caused the Indonesian biofuel policy being unfocused which led to failing to meet its mandatory blending target despite the abundance of its feedstock.

Keywords: biofuel, energy transition, renewable energy, political ecology

Procedia PDF Downloads 194

Authors: Gustavo Cruz-Bello

Abstract:

Progreso is a coastal community in Yucatan, Mexico, highly exposed to floods produced by severe storms and tropical cyclones. A participatory cartography approach was conducted to help to reduce floods disasters and assess social vulnerability within the community. The first step was to engage local authorities in risk management to facilitate the process. Two workshop were conducted, in the first, a poster size printed high spatial resolution satellite image of the town was used to gather information from the participants: eight women and seven men, among them construction workers, students, government employees and fishermen, their ages ranged between 23 and 58 years old. For the first task, participants were asked to locate emblematic places and place them in the image to familiarize with it. Then, they were asked to locate areas that get flooded, the buildings that they use as refuges, and to list actions that they usually take to reduce vulnerability, as well as to collectively come up with others that might reduce disasters. The spatial information generated at the workshops was digitized and integrated into a GIS environment. A printed version of the map was reviewed by local risk management experts, who validated feasibility of proposed actions. For the second workshop, we retrieved the information back to the community for feedback. Additionally a survey was applied in one household per block in the community to obtain socioeconomic, prevention and adaptation data. The information generated from the workshops was contrasted, through T and Chi Squared tests, with the survey data in order to probe the hypothesis that poorer or less educated people, are less prepared to face floods (more vulnerable) and live near or among higher presence of floods. Results showed that a great majority of people in the community are aware of the hazard and are prepared to face it. However, there was not a consistent relationship between regularly flooded areas with people’s average years of education, house services, or house modifications against heavy rains to be prepared to hazards. We could say that the participatory cartography intervention made participants aware of their vulnerability and made them collectively reflect about actions that can reduce disasters produced by floods. They also considered that the final map could be used as a communication and negotiation instrument with NGO and government authorities. It was not found that poorer and less educated people are located in areas with higher presence of floods.

Keywords: climate change, floods, Mexico, participatory mapping, social vulnerability

Procedia PDF Downloads 112

Authors: S. H. Lee, M. J. Park, O. M. Kwon

Abstract:

In this paper, reliable consensus of multi-agent systems with sampled-data is investigated. By using a suitable Lyapunov-Krasovskii functional and some techniques such as Wirtinger Inequality, Schur Complement and Kronecker Product, the results of this systems are obtained by solving a set of Linear Matrix Inequalities(LMIs). One numerical example is included to show the effectiveness of the proposed criteria.

Keywords: multi-agent, linear matrix inequalities (LMIs), kronecker product, sampled-data, Lyapunov method

Procedia PDF Downloads 524

Authors: Yusuf Ziya Ayık, Ferhat Kahveci

Abstract:

Currently, database management systems have various tools such as backup and maintenance, and also provide statistical information such as resource usage and security. In terms of query performance, this paper covers query optimization, views, indexed tables, pre-computation materialized view, query performance analysis in which query plan alternatives can be created and the least costly one selected to optimize a query. Indexes and views can be created for related table columns. The literature review of this study showed that, in the course of time, despite the growing capabilities of the database management system, only database administrators are aware of the need for dealing with archival and transactional data types differently. These data may be constantly changing data used in everyday life, and also may be from the completed questionnaire whose data input was completed. For both types of data, the database uses its capabilities; but as shown in the findings section, instead of repeating similar heavy calculations which are carrying out same results with the same query over a survey results, using materialized view results can be in a more simple way. In this study, this performance difference was observed quantitatively considering the cost of the query.

Keywords: cost of query, database management systems, materialized view, query performance

Procedia PDF Downloads 275

Authors: Chia-Hau Liu, Tai-Yue Wang

Abstract:

Traditional multivariate control charts assume that measurement from manufacturing processes follows a multivariate normal distribution. However, this assumption may not hold or may be difficult to verify because not all the measurement from manufacturing processes are normal distributed in practice. This study develops a new multivariate control chart for monitoring the processes with non-normal data. We propose a mechanism based on integrating the one-class classification method and the adaptive technique. The adaptive technique is used to improve the sensitivity to small shift on one-class classification in statistical process control. In addition, this design provides an easy way to allocate the value of type I error so it is easier to be implemented. Finally, the simulation study and the real data from industry are used to demonstrate the effectiveness of the propose control charts.

Keywords: multivariate control chart, statistical process control, one-class classification method, non-normal data

Procedia PDF Downloads 420

Authors: Samuel Munaf, Kevin Swingler, Franz Brülisauer, Anthony O’Hare, George Gunn, Aaron Reeves

Abstract:

Web scraping and text mining are popular computer science methods deployed by public health researchers to augment traditional epidemiological surveillance. However, within veterinary disease surveillance, such techniques are still in the early stages of development and have not yet been fully utilised. This study presents an exploration into the utility of incorporating internet-based data to better understand the smallholder farming communities within Scotland by using online text extraction and the subsequent mining of this data. Web scraping of the livestock fora was conducted in conjunction with text mining of the data in search of common themes, words, and topics found within the text. Results from bi-grams and topic modelling uncover four main topics of interest within the data pertaining to aspects of livestock husbandry: feeding, breeding, slaughter, and disposal. These topics were found amongst both the poultry and pig sub-forums. Topic modeling appears to be a useful method of unsupervised classification regarding this form of data, as it has produced clusters that relate to biosecurity and animal welfare. Internet data can be a very effective tool in aiding traditional veterinary surveillance methods, but the requirement for human validation of said data is crucial. This opens avenues of research via the incorporation of other dynamic social media data, namely Twitter and Facebook/Meta, in addition to time series analysis to highlight temporal patterns.

Keywords: veterinary epidemiology, disease surveillance, infodemiology, infoveillance, smallholding, social media, web scraping, sentiment analysis, geolocation, text mining, NLP

Procedia PDF Downloads 92

Authors: M. Koray Cetin, Mehmet Mert

Abstract:

The purpose of the study is to examine impacts on tourism revenues of the exchange rate and country overall security level. There are numerous studies that examine the bidirectional relation between macroeconomic factors and tourism revenues and tourism demand. Most of the studies support the existence of impact of tourism revenues on growth rate but not vice versa. Few studies examine the impact of factors like real exchange rate or purchasing power parity on the tourism revenues. In this context, firstly impact of real exchange rate on tourism revenues examination is aimed. Because exchange rate is one of the main determinants of international tourism services price in guests currency unit. Another determinant of tourism demand for a country is country’s overall security level. This issue can be handled in the context of the relationship between tourism revenues and overall security including turmoil, terrorism, border problem, political violence. In this study, factors are handled for several countries which have tourism revenues on a certain level. With this structure, it is a panel data, and it is evaluated with panel data analysis techniques. Panel data have at least two dimensions, and one of them is time dimensions. The panel data analysis techniques are applied to data gathered from Worldbank data web page. In this study, it is expected to find impacts of real exchange rate and security factors on tourism revenues for the countries that have noteworthy tourism revenues.

Keywords: exchange rate, panel data analysis, security, tourism revenues

Procedia PDF Downloads 346

Authors: Insaf Mehani, Bachir Bouchekima

Abstract:

The necessary reduction and progressive consumption of fossil fuels, whose scarcity is inevitable, involves mobilizing a set of alternatives.Renewable energy, including bio energy are an alternative to fossil fuel depletion and a way to fight against the harmful effects of climate change. It is possible to develop common dates of low commercial value, and put on the local and international market a new generation of products with high added values such as bio ethanol. Besides its use in chemical synthesis, bio ethanol can be blended with gasoline to produce a clean fuel while improving the octane.

Keywords: bioenergy, dates, bioethanol, renewable energy, south Algeria

Procedia PDF Downloads 486

Authors: Sumedha Ganjoo, Santosh Goswami

Abstract:

The rising reliance on technology places national security at the forefront of 21st-century issues. It complicates the efforts of emerging and developed countries to combat cyber threats and increases the inherent risk factors connected with technology. The inability to preserve data securely might have devastating repercussions on a massive scale. Consequently, it is vital to establish national, regional, and global data protection rules and regulations that penalise individuals who participate in immoral technology usage and exploit the inherent vulnerabilities of technology. This study paper seeks to analyse GDPR-inspired Bills in the South Asian Region and determine their suitability for the development of a worldwide data protection framework, considering that Asian countries are much more diversified than European ones. In light of this context, the objectives of this paper are to identify GDPR-inspired Bills in the South Asian Region, identify their similarities and differences, as well as the obstacles to developing a regional-level data protection mechanism, thereby satisfying the need to develop a global-level mechanism. Due to the qualitative character of this study, the researcher did a comprehensive literature review of prior research papers, journal articles, survey reports, and government publications on the aforementioned topics. Taking into consideration the survey results, the researcher conducted a critical analysis of the significant parameters highlighted in the literature study. Many nations in the South Asian area are in the process of revising their present data protection measures in accordance with GDPR, according to the primary results of this study. Consideration is given to the data protection laws of Thailand, Malaysia, China, and Japan. Significant parallels and differences in comparison to GDPR have been discussed in detail. The conclusion of the research analyses the development of various data protection legislation regimes in South Asia.

Keywords: data privacy, GDPR, Asia, data protection laws

Procedia PDF Downloads 81

Authors: Kenza Aida Amara, Bachir Djebbar

Abstract:

We treat the two-dimensional bin packing problem which involves packing a given set of rectangles into a minimum number of larger identical rectangles called bins. This combinatorial problem is NP-hard. We propose a pretreatment for the oriented version of the problem that allows the valorization of the lost areas in the bins and the reduction of the size problem. A heuristic method based on the strategy first-fit adapted to this problem is presented. We present an approach of resolution by bee colony optimization. Computational results express a comparison of the number of bins used with and without pretreatment.

Keywords: bee colony optimization, bin packing, heuristic algorithm, pretreatment

Procedia PDF Downloads 630

Authors: Musab Isah

Abstract:

This paper presents a longitudinal analysis of Internet speed data in the Gulf Cooperation Council (GCC) region, focusing on the most populous cities of each of the six countries – Riyadh, Saudi Arabia; Dubai, UAE; Kuwait City, Kuwait; Doha, Qatar; Manama, Bahrain; and Muscat, Oman. The study utilizes data collected from the Measurement Lab (M-Lab) infrastructure over a five-year period from January 1, 2019, to December 31, 2023. The analysis includes downstream and upstream throughput data for the cities, covering significant events such as the launch of 5G networks in 2019, COVID-19-induced lockdowns in 2020 and 2021, and the subsequent recovery period and return to normalcy. The results showcase substantial increases in Internet speeds across the cities, highlighting improvements in both download and upload throughput over the years. All the GCC countries have achieved above-average Internet speeds that can conveniently support various online activities and applications with excellent user experience.

Keywords: internet data science, internet performance measurement, throughput analysis, internet speed, measurement lab, network diagnostic tool

Procedia PDF Downloads 57

Authors: Rose A. Yemson, Ping Jiang, Oyedeji L. Inumoh

Abstract:

The deployment of wireless sensor network has rapidly increased, however with the increased capacity and diversity of sensors, and applications ranging from biological, environmental, military etc. generates tremendous volume of data’s where more attention is placed on the distributed sensing and little on how to manage, analyze, retrieve and understand the data generated. This makes it more quite difficult to process live sensor data, run concurrent control and update because sensor data are either heavyweight, complex, and slow. This work will focus on developing a web service platform for automatic detection of sensors, acquisition of sensor data, storage of sensor data into a database, processing of sensor data using reconfigurable software components. This work will also create a web service based sensor data management system to monitor physical movement of an individual wearing wireless network sensor technology (SunSPOT). The sensor will detect movement of that individual by sensing the acceleration in the direction of X, Y and Z axes accordingly and then send the sensed reading to a database that will be interfaced with an internet platform. The collected sensed data will determine the posture of the person such as standing, sitting and lying down. The system is designed using the Unified Modeling Language (UML) and implemented using Java, JavaScript, html and MySQL. This system allows real time monitoring an individual closely and obtain their physical activity details without been physically presence for in-situ measurement which enables you to work remotely instead of the time consuming check of an individual. These details can help in evaluating an individual’s physical activity and generate feedback on medication. It can also help in keeping track of any mandatory physical activities required to be done by the individuals. These evaluations and feedback can help in maintaining a better health status of the individual and providing improved health care.

Keywords: HTML, java, javascript, MySQL, sunspot, UML, web-based, wireless network sensor

Procedia PDF Downloads 210

Authors: Yi Li, Rui Lu, Lianjun Wang

Abstract:

With the rapid development of chemical industry, the consumption of volatile organic compounds (VOCs) has increased extensively. In the process of VOCs production and application, plenty of them have been transferred to environment. As a result, it has led to pollution problems not only in soil and ground water but also to human beings. Thus, it is important to develop a sensitive and cost-effective analytical method for trace VOCs detection in environment. Surface-enhanced Raman Spectroscopy (SERS), as one of the most sensitive optical analytical technique with rapid response, pinpoint accuracy and noninvasive detection, has been widely used for ultratrace analysis. Based on the plasmon resonance on the nanoscale metallic surface, SERS technology can even detect single molecule due to abundant nanogaps (i.e. 'hot spots') on the nanosubstrate. In this work, a self-supported flexible silver nitrate (AgNO3)/ethylene-propylene copolymer (EPM) hybrid nanofibers was fabricated by electrospinning. After an in-situ chemical reduction using ice-cold sodium borohydride as reduction agent, numerous silver nanoparticles were formed on the nanofiber surface. By adjusting the reduction time and AgNO3 content, the morphology and dimension of silver nanoparticles could be controlled. According to the principles of solid-phase extraction, the hydrophobic substance is more likely to partition into the hydrophobic EPM membrane in an aqueous environment while water and other polar components are excluded from the analytes. By the enrichment of EPM fibers, the number of hydrophobic molecules located on the 'hot spots' generated from criss-crossed nanofibers is greatly increased, which further enhances SERS signal intensity. The as-prepared Ag/EPM hybrid nanofibers were first employed to detect common SERS probe molecule (p-aminothiophenol) with the detection limit down to 10-12 M, which demonstrated an excellent SERS performance. To further study the application of the fabricated substrate for monitoring hydrophobic substance in water, several typical VOCs, such as benzene, toluene and p-xylene, were selected as model compounds. The results showed that the characteristic peaks of these target analytes in the mixed aqueous solution could be distinguished even at a concentration of 10-6 M after multi-peaks gaussian fitting process, including C-H bending (850 cm-1), C-C ring stretching (1581 cm-1, 1600 cm-1) of benzene, C-H bending (844 cm-1 ,1151 cm-1), C-C ring stretching (1001 cm-1), CH3 bending vibration (1377 cm-1) of toluene, C-H bending (829 cm-1), C-C stretching (1614 cm-1) of p-xylene. The SERS substrate has remarkable advantages which combine the enrichment capacity from EPM and the Raman enhancement of Ag nanoparticles. Meanwhile, the huge specific surface area resulted from electrospinning is benificial to increase the number of adsoption sites and promotes 'hot spots' formation. In summary, this work provides powerful potential in rapid, on-site and accurate detection of trace VOCs using a portable Raman.

Keywords: electrospinning, ethylene-propylene copolymer, silver nanoparticles, SERS, VOCs

Procedia PDF Downloads 157

Authors: Ayeshmanthe Rathnayake, Ashutosh Hardikar

Abstract:

Introduction: Lung cancer is the most common cause of cancer death in Australia, with more than 13000 cases per year. Until now, there has been a major deficiency of national comprehensive thoracic surgery data. The thoracic workload for surgeons as well as caseload per unit, is highly variable, with some centres performing less than 15 cases per annum, thus raising concerns about optimal care at low-volume sites. This is an attempt to review the outcomes of lung cancer surgery in Tasmania. Method: The objective of this study is to determine the surgical outcomes of lung cancer surgery at Royal Hobart Hospital (RHH) with the primary outcome of surgical mortality. Four hundred fifty-one cases were analysed retrospectively from 2010 to May 2022. Results: A total of 451 patients underwent thoracic surgery with a primary diagnosis of lung cancer. The primary outcome of 30-day mortality was <0.5%. The mean age was 65.3 years, with male predominance and a 4.2% prevalence of Indigenous Australians. The mean LOS was 7.5 days. The surgical approach was either VATS (50.3%) or Thoracotomy (49.7%), with a trend towards the former in recent years with an increase in the proportion of VATS from 18.2% to 51% (p<0.05) in complex resections since 2019. A corresponding reduction in conversion rate to open was observed (18% vs. 5.5%), and there were no deaths within this subgroup. Lung resections were divided into lobectomy (55.4%), wedge resection (36.8%), segmentectomy (2.9%) and pneumonectomy (4.9%). The RHH demonstrates good surgical outcomes for lung cancer and provides a sustainable service for Tasmania. Conclusion: This retrospective study reports the surgical outcomes of lung cancer surgery at the Royal Hobart Hospital, thereby providing insight into the surgical management of lung cancer in the state thus far. The state has been slow to catch up on the minimally invasive program, but the overall results have been comparable to most peers.

Keywords: lung cancer, thoracic surgery, lung resection, surgical outcomes

Procedia PDF Downloads 92

Authors: Melkamu A. Zeru, Yamral M. Warkaw, Aweke A. Mitku, Muluwerk Ayele

Abstract:

Introduction: Malaria is a severe health threat in the World, mainly in Africa. It is the major cause of health problems in which the risk of morbidity and mortality associated with malaria cases are characterized by spatial variations across the county. This study aimed to investigate the spatial patterns and predictors of malaria distribution in Ethiopia. Methods: A weighted sample of 15,239 individuals with rapid diagnosis tests was obtained from the Central Statistical Agency and Ethiopia malaria indicator survey of 2015. Global Moran's I and Moran scatter plots were used in determining the distribution of malaria cases, whereas the local Moran's I statistic was used in identifying exposed areas. In data manipulation, machine learning was used for variable reduction and statistical software R, Stata, and Python were used for data management and analysis. The auto logistics spatial binary regression model was used to investigate the predictors of malaria. Results: The final auto logistics regression model reported that male clients had a positive significant effect on malaria cases as compared to female clients [AOR=2.401, 95 % CI: (2.125 - 2.713)]. The distribution of malaria across the regions was different. The highest incidence of malaria was found in Gambela [AOR=52.55, 95%CI: (40.54-68.12)] followed by Beneshangul [AOR=34.95, 95%CI: (27.159 - 44.963)]. Similarly, individuals in Amhara [AOR=0.243, 95% CI:(0.1950.303],Oromiya[AOR=0.197,95%CI:(0.1580.244)],DireDawa[AOR=0.064,95%CI(0.049-0.082)],AddisAbaba[AOR=0.057,95%CI:(0.044-0.075)], Somali[AOR=0.077,95%CI:(0.059-0.097)], SNNPR[OR=0.329, 95%CI: (0.261- 0.413)] and Harari [AOR=0.256, 95%CI:(0.201 - 0.325)] were less likely to had low incidence of malaria as compared with Tigray. Furthermore, for a one-meter increase in altitude, the odds of a positive rapid diagnostic test (RDT) decrease by 1.6% [AOR = 0.984, 95% CI :( 0.984 - 0.984)]. The use of a shared toilet facility was found as a protective factor for malaria in Ethiopia [AOR=1.671, 95% CI: (1.504 - 1.854)]. The spatial autocorrelation variable changes the constant from AOR = 0.471 for logistic regression to AOR = 0.164 for auto logistics regression. Conclusions: This study found that the incidence of malaria in Ethiopia had a spatial pattern that is associated with socio-economic, demographic, and geographic risk factors. Spatial clustering of malaria cases had occurred in all regions, and the risk of clustering was different across the regions. The risk of malaria was found to be higher for those who live in soil floor-type houses as compared to those who live in cement or ceramics floor type. Similarly, households with thatched, metal and thin, and other roof-type houses have a higher risk of malaria than ceramic tiles roof houses. Moreover, using a protected anti-mosquito net reduced the risk of malaria incidence.

Keywords: malaria, Ethiopia, auto logistics, spatial model, spatial clustering

Procedia PDF Downloads 27

Authors: Silvia A. Magalhaes, Vasco P. De Freitas, Jose L. Alexandre

Abstract:

Most of the European literature concerning energy efficiency and thermal comfort of dwellings assumes permanent heating and focuses on energy-saving measures. European National regulations are designed for those permanent comfort conditions. On the other hand, very few studies focus on the effect of the improvement measures in comfort reduction, for free-floating conditions or intermittent heating, in fuel poverty vulnerable countries. In Portugal, only 21% of the household energy consumptions (and 10% of the cost) are spent in space heating, while, on average European bills, this value rises to 67%. The mild climate, but mainly fuel poverty and cultural background, justifies these low heating practices. This study proposes a “passive discomfort” index definition, considering free-floating temperatures or with intermittent heating profiles (more realistic conditions), putting the focus on comfort rather than energy consumption (which is low for these countries). The aim is to compare both energy (regarding the legal framework of national regulation) and comfort (considering realistic conditions of use) to identify some correlation. It was developed an experimental campaign of indoor thermal conditions in a 19th building located in Porto with several apartments. One dwelling was chosen as a case study to carry out a sensitivity analysis. The results are discussed comparing both theoretical energy consumption (energy rates from national regulation) and discomfort (new index defined), for different insulation thicknesses, orientations, and intermittent heating profiles. The results show that the different passive options (walls insulation and glazing options) have a small impact on winter discomfort, which is always high for low heating profiles. Moreover, it was shown that the insulation thickness on walls has no influence, and the minimum insulation thickness considered is enough to achieve the same impact on discomfort reduction. Plus, for these low heating profiles, other conditions are critical, as the orientation. Finally, there isn’t an unequivocal relation between the energy label and the discomfort index. These and other results are surprising when compared with the most usual approaches, which assume permanent heating.

Keywords: dwellings in historical buildings, low-heating countries, mild climates, thermal comfort

Procedia PDF Downloads 143

Authors: Valentina Marutyan

Abstract:

Healthcare data mining is a rapidly developing field at the intersection of technology and medicine that has the potential to change our understanding and approach to providing healthcare. Healthcare and data mining is the process of examining huge amounts of data to extract useful information that can be applied in order to improve patient care, treatment effectiveness, and overall healthcare delivery. This field looks for patterns, trends, and correlations in a variety of healthcare datasets, such as electronic health records (EHRs), medical imaging, patient demographics, and treatment histories. To accomplish this, it uses advanced analytical approaches. Predictive analysis using historical patient data is a major area of interest in healthcare data mining. This enables doctors to get involved early to prevent problems or improve results for patients. It also assists in early disease detection and customized treatment planning for every person. Doctors can customize a patient's care by looking at their medical history, genetic profile, current and previous therapies. In this way, treatments can be more effective and have fewer negative consequences. Moreover, helping patients, it improves the efficiency of hospitals. It helps them determine the number of beds or doctors they require in regard to the number of patients they expect. In this project are used models like logistic regression, random forests, and neural networks for predicting diseases and analyzing medical images. Patients were helped by algorithms such as k-means, and connections between treatments and patient responses were identified by association rule mining. Time series techniques helped in resource management by predicting patient admissions. These methods improved healthcare decision-making and personalized treatment. Also, healthcare data mining must deal with difficulties such as bad data quality, privacy challenges, managing large and complicated datasets, ensuring the reliability of models, managing biases, limited data sharing, and regulatory compliance. Finally, secret code of data mining in healthcare helps medical professionals and hospitals make better decisions, treat patients more efficiently, and work more efficiently. It ultimately comes down to using data to improve treatment, make better choices, and simplify hospital operations for all patients.

Keywords: data mining, healthcare, big data, large amounts of data

Procedia PDF Downloads 73

Authors: Ozge Tahran, Sevgi Sevi Yesilyaprak

Abstract:

Objective: The aim of the study was to investigate the effect of the Wilk’s modified two different stretching exercises on posterior shoulder tightness, pain, and dysfunction in patients with subacromial impingement syndrome (SIS). Method: This study was carried out on 67 patients who have more than 15° difference in shoulder internal rotation range of motion between two sides and had been diagnosed as SIS. Before treatment, all patients were randomly assigned into three groups. Standard physiotherapy programme was applied to the Group 3 (n=23), standard physiotherapy program with Wilk’s modified cross-body stretching exercises were applied to Group 1 (n=22), and standard physiotherapy program with Wilk’s modified sleeper stretching exercises were applied to Group 2 (n= 23). All the patients received 20 sessions of physiotherapy during 4 weeks, 5 days in a week by a physiotherapist. The patients continued their exercises at home at the weekends. Pain severity, shoulder rotation range of motion, posterior shoulder tightness, upper extremity functionality with Constant and Murley Score (CMS) and disability level with The Disabilities of the Arm, Shoulder and Hand Score (QuickDASH) were evaluated before and after physiotherapy programme. Results: Before treatment, demographic and anthropometric characteristics were similar in groups and there was no statistical difference (p > 0.05). It was determined that pain severity decreased, shoulder rotation range of motion, posterior shoulder tightness, upper extremity functionality, and disability were improved after physiotherapy in both groups (p < 0.05). Group 1 and 2 had better results in terms of reduction of pain severity during activity, increase in shoulder rotation range of motion, posterior shoulder mobility and upper extremity functionality and improvement in upper extremity disability, compared to Group 3 (p < 0.05). Conclusion: Modified posterior shoulder stretching exercises in addition to standard physiotherapy programme is more effective for reduction of pain during activity, to improve shoulder rotation range of motion, posterior shoulder mobility, and upper extremity functionality in patients with SIS compared to standard physiotherapy programme alone.

Keywords: modified posterior shoulder stretching exercises, posterior shoulder tightness, shoulder complex, subacromial impingement syndrome

Procedia PDF Downloads 171

Authors: Bushra Zafar, Usman Qamar

Abstract:

Large data sample size and dimensions render the effectiveness of conventional data mining methodologies. A data mining technique are important tools for collection of knowledgeable information from variety of databases and provides supervised learning in the form of classification to design models to describe vital data classes while structure of the classifier is based on class attribute. Classification efficiency and accuracy are often influenced to great extent by noisy and undesirable features in real application data sets. The inherent natures of data set greatly masks its quality analysis and leave us with quite few practical approaches to use. To our knowledge first time, we present a new approach for investigation of structure and quality of datasets by providing a targeted analysis of localization of noisy and irrelevant features of data sets. Machine learning is based primarily on feature selection as pre-processing step which offers us to select few features from number of features as a subset by reducing the space according to certain evaluation criterion. The primary objective of this study is to trim down the scope of the given data sample by searching a small set of important features which may results into good classification performance. For this purpose, a heuristic for wrapper-based feature selection using genetic algorithm and for discriminative feature selection an external classifier are used. Selection of feature based on its number of occurrence in the chosen chromosomes. Sample dataset has been used to demonstrate proposed idea effectively. A proposed method has improved average accuracy of different datasets is about 95%. Experimental results illustrate that proposed algorithm increases the accuracy of prediction of different diseases.

Keywords: data mining, generic algorithm, KNN algorithms, wrapper based feature selection

Procedia PDF Downloads 314