Search results for: Privacy and Data Protection Law

Authors: Kuo Chieh Chao, Thishani Amarathunga, Sangam Shrestha

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Rainfall induced slope failures are one of the most damaging and disastrous natural hazards which occur frequently in the world. This type of sliding mainly occurs in the zone above the groundwater level in silty/sandy soils. When the rainwater begins to infiltrate into the vadose zone of the soil, the negative pore-water pressure tends to decrease and reduce the shear strength of soil material. Climate change has resulted in excessive and unpredictable rainfall in all around the world, resulting in landslides with dire consequences to human lives and infrastructure. Such problems could be overcome by examining in detail the causes for such slope failures and recommending effective repair plans for vulnerable locations by considering future climatic change. The selected area for this study is located in the road rehabilitation section from Maubara to Mota Ain road in Timor-Leste. Slope failures and cracks have occurred in 2013 and after repairs reoccurred again in 2017 subsequent to heavy rains. Both observed and future predicted climate data analyses were conducted to understand the severe precipitation conditions in past and future. Observed climate data were collected from NOAA global climate data portal. CORDEX data portal was used to collect Regional Climate Model (RCM) future predicted climate data. Both observed and RCM data were extracted to location-based data using ArcGIS Software. Linear scaling method was used for the bias correction of future data and bias corrected climate data were assigned to GeoStudio Software. Precipitations of wet seasons (December to March ) in 2007 to 2013 is higher than 2001-2006 period and it is more than nearly 40% higher precipitation than usual monthly average precipitation of 160mm.The results of seepage analyses which were carried out using SEEP/W model with observed climate, clearly demonstrated that the pore water pressure within the fill slope was significantly increased due to the increase of the infiltration during the wet season of 2013.One main Regional Climate Models (RCM) was analyzed in order to predict future climate variation under two Representative Concentration Pathways (RCPs).In the projected period of 76 years ahead from 2014, shows that the amount of precipitation is considerably getting higher in the future in both RCP 4.5 and RCP 8.5 emission scenarios. Critical pore water pressure conditions during 2014-2090 were used in order to recommend appropriate remediation methods. Results of slope stability analyses indicated that the factor of safety of the fill slopes was reduced from 1.226 to 0.793 during the dry season to wet season in 2013.Results of future slope stability which were obtained using SLOPE/W model for the RCP emissions scenarios depict that, the use of tieback anchors and geogrids in slope protection could be effective in increasing the stability of slopes to an acceptable level during the wet seasons. Moreover, methods and procedures like monitoring of slopes showing signs or susceptible for movement and installing surface protections could be used to increase the stability of slopes.

Keywords: climate change, precipitation, SEEP/W, SLOPE/W, unsaturated soil

Procedia PDF Downloads 122

Authors: Hasik Lee, Seung-Young Kho

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Nowadays, automatic fare collection (AFC) system is widely used in many countries. However, smart card data from many of cities does not contain alighting information which is necessary to build OD matrices. Therefore, in order to utilize smart card data, destinations of passengers should be estimated. In this paper, kernel density estimation was used to forecast probabilities of alighting stations of bus passengers and applied to smart card data in Seoul, Korea which contains boarding and alighting information. This method was also validated with actual data. In some cases, stochastic method was more accurate than deterministic method. Therefore, it is sufficiently accurate to be used to build OD matrices.

Keywords: destination estimation, Kernel density estimation, smart card data, validation

Procedia PDF Downloads 335

Authors: Sher Muhammad, Mirza Muhammad Waqar

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It is necessary to monitor and identify mangroves types and spatial extent near coastal areas because it plays an important role in coastal ecosystem and environmental protection. This research aims at identifying and mapping mangroves types along Karachi coast ranging from 24.79 to 24.85 degree in latitude and 66.91 to 66.97 degree in longitude using hyperspectral remote sensing data and techniques. Image acquired during February, 2012 through Hyperion sensor have been used for this research. Image preprocessing includes geometric and radiometric correction followed by Minimum Noise Fraction (MNF) and Pixel Purity Index (PPI). The output of MNF and PPI has been analyzed by visualizing it in n-dimensions for end-member extraction. Well-distributed clusters on the n-dimensional scatter plot have been selected with the region of interest (ROI) tool as end members. These end members have been used as an input for classification techniques applied to identify and map mangroves species including Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF), and Spectral Information Diversion (SID). Only two types of mangroves namely Avicennia Marina (white mangroves) and Avicennia Germinans (black mangroves) have been observed throughout the study area.

Keywords: mangrove, hyperspectral, hyperion, SAM, SFF, SID

Procedia PDF Downloads 349

Authors: Jae Won Shin

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We develop an evaluated nuclear data based photonuclear reaction model of GEANT4 for a more accurate simulation of photon-induced neutron production. The evaluated photonuclear data libraries from the ENDF/B-VII.1 are taken as input. Incident photon energies up to 140 MeV which is the threshold energy for the pion production are considered. For checking the validity of the use of the data-based model, we calculate the photoneutron production cross-sections and yields and compared them with experimental data. The results obtained from the developed model are found to be in good agreement with the experimental data for (γ,xn) reactions.

Keywords: ENDF/B-VII.1, GEANT4, photoneutron, photonuclear reaction

Procedia PDF Downloads 264

Authors: Rashi Bhalla, Russel Pears, M. Asif Naeem

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In this 'Information Explosion Era' analyzing data 'a critical commodity' and mining knowledge from vertically distributed data stream incurs huge communication cost. However, an effort to decrease the communication in the distributed environment has an adverse influence on the classification accuracy; therefore, a research challenge lies in maintaining a balance between transmission cost and accuracy. This paper proposes a method based on Bayesian inference to reduce the communication volume in a heterogeneous distributed environment while retaining prediction accuracy. Our experimental evaluation reveals that a significant reduction in communication can be achieved across a diverse range of dataset types.

Keywords: big data, bayesian inference, distributed data stream mining, heterogeneous-distributed data

Procedia PDF Downloads 144

Authors: Deepak Singh, Rail Kuliev

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The abstract highlights the critical importance of optimizing data integration and management strategies in the upstream oil and gas industry. With its complex and dynamic nature generating vast volumes of data, efficient data integration and management are essential for informed decision-making, cost reduction, and maximizing operational performance. Challenges such as data silos, heterogeneity, real-time data management, and data quality issues are addressed, prompting the proposal of several strategies. These strategies include implementing a centralized data repository, adopting industry-wide data standards, employing master data management (MDM), utilizing real-time data integration technologies, and ensuring data quality assurance. Training and developing the workforce, “reskilling and upskilling” the employees and establishing robust Data Management training programs play an essential role and integral part in this strategy. The article also emphasizes the significance of data governance and best practices, as well as the role of technological advancements such as big data analytics, cloud computing, Internet of Things (IoT), and artificial intelligence (AI) and machine learning (ML). To illustrate the practicality of these strategies, real-world case studies are presented, showcasing successful implementations that improve operational efficiency and decision-making. In present study, by embracing the proposed optimization strategies, leveraging technological advancements, and adhering to best practices, upstream oil and gas companies can harness the full potential of data-driven decision-making, ultimately achieving increased profitability and a competitive edge in the ever-evolving industry.

Keywords: master data management, IoT, AI&ML, cloud Computing, data optimization

Procedia PDF Downloads 55

Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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Authors: Margaret Denise P. Del Rosario, Geraldine Alcantara

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Background: The declaration of the COVID-19 pandemic has impacted hospital visits of child abuse cases with less consults but more severe injuries. Objective: The study aims to identify the clinical profiles of patients seen in the hospital ng Makati Women and Children Protection Unit before and during the pandemic. Design: A cross-sectional analytic study design through review of records that underwent quantitative analysis. Results: 264 cases pre-pandemic and 208 cases during the pandemic were reviewed. Most reported cases were neglect comprising of 47% of the pre-pandemic cases and 68% of cases during the pandemic. Supervisory neglect was most commonly reported. An equal distribution between males and females were seen among victims and alleged perpetrators. The age group of both victims and alleged perpetrators during the pandemic was significantly younger compared to the pre-pandemic period. Children belonging to larger family groups were commonly encountered with most of them being the eldest amongst siblings. Alleged perpetrators were mostly secondary graduates for both time periods. A significant increase of cases during the pandemic occurred at home. More patients required hospitalization during the pandemic period with 37% compared to the 23% of admissions prior to the pandemic. Furthermore, a three-fold increase of injuries sustained during the pandemic required intensive care. Conclusion: The study reflects increased severity of injuries related to abuse during the pandemic compared to pre-pandemic times. A significant increase in injuries requiring intensive care were also seen despite less reported cases.

Keywords: child abuse, COVID-19, violence against children, WCPU, neglect

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Authors: Apkar Salatian

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To relieve the burden of reasoning on a point to point basis, in many domains there is a need to reduce large and noisy data sets into trends for qualitative reasoning. In this paper we propose and describe a new architectural design pattern called REDUCER for reducing large and noisy data sets that can be tailored for particular situations. REDUCER consists of 2 consecutive processes: Filter which takes the original data and removes outliers, inconsistencies or noise; and Compression which takes the filtered data and derives trends in the data. In this seminal article, we also show how REDUCER has successfully been applied to 3 different case studies.

Keywords: design pattern, filtering, compression, architectural design

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Authors: Mengxin Feng, Feng Xu, Zhiyong Lai

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As a typical representative of the countryside, traditional Chinese villages are rich in cultural landscape resources and historical information, but they are still in continuous decline. The problems of people's weak protection awareness and low cultural recognition are still serious, and the protection of cultural heritage is imminent. At the same time, with the rapid development of rural tourism, its cultural value has been explored and paid attention to again. From the perspective of tourists, this study aimed to explore people's perception and identity of cultural landscape resources under the current cultural tourism development background. We selected eleven typical landscape elements of Lingshui Village, a traditional village in Beijing, as research objects and conducted a questionnaire survey with two scales of perception and identity to explore the characteristics of people's perception and identification of landscape elements. We found that there was a strong positive correlation between the perception and identity of each element and that geographical location influenced visitors' overall perception. The perception dimensions scored the highest in location, and the lowest in history and culture, and the identity dimensions scored the highest in meaning and lowest in emotion. We analyzed the impact of visitors' backgrounds on people's perception and identity characteristics and found that age and education were two important factors. The elderly had a higher degree of perceived identity, as the familiarity effect increased their attention. Highly educated tourists had more stringent criteria for perception and identification. The above findings suggest strategies for conserving and optimizing landscape elements in the traditional village to improve the acceptance and recognition of cultural information in traditional villages, which will inject new vitality into the development of traditional villages.

Keywords: traditional village, tourist perception, landscape elements, perception and identity

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Authors: Kimutai Joan Jepkorir, Moturi Wilkister Nyaora

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The practice of open defecation can be devastating for human health as well as the environment, and this practice persistence could be due to ingrained habits that individuals continue to engage in despite having a better alternative. Safe disposal of human excreta is essential for public health protection. This study sought to find if open defecation relates to fecal-oral infections in Burat and Ngaremara Wards in Isiolo County. This was achieved through conducting a cross-sectional study. Simple random sampling technique was used to select 385 households that were used in the study. Data collection was done by use of questionnaires and observation checklists. The result show that 66% of the respondents disposed-off fecal matter in a safe manner, whereas 34% disposed-off fecal matter in unsafe manner through open defecation. The prevalence proportions per 1000 of diarrhea and intestinal worms among children under-5 years of age were 142 and 21, respectively. The prevalence proportions per 1000 of diarrhea and typhoid among children over-5 years of age were 20 and 20, respectively.

Keywords: faecal-oral infections, open defecation, prevalence proportion, sanitation

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Authors: Mario M. Figueroa de la Cruz, Claudia I. Solorzano, Raul Acosta, Ignacio Funes

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This technological development project seeks to create a tool that allows companies, in need of implementing a Data Center, intelligently determining factors for allocating resources support cooling and power supply (UPS) in its conception. The results should show clearly the speed, robustness and reliability of a system designed for deployment in environments where they must manage and protect large volumes of data.

Keywords: telecommunications, data center, fuzzy logic, expert systems

Procedia PDF Downloads 332

Authors: E. Tugba Topcu

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The emergency services are usually the first places to encounter forensic cases. Hence, it is important to consider forensics from the perspective of the emergency services staff and the physiological and psychological consequences that may arise as a result of behaviour by itself or another person. Accurate and detailed documentation of the situation in which the patient first arrives at the emergency service and preservation of the forensic findings is pivotal for the subsequent forensic investigation. The first step in determining whether or not a forensic case exists is to perform a medical examination of the patient. For each individual suspected to be part of a forensic case, police officers should be informed at the same time as the medical examination is being conducted. Violent events are increasing every year and with an increase in the number of forensic cases, emergency service workers have increasing responsibility and consequently play a key role in protecting, collecting and arranging the forensic evidence. In addition, because the emergency service workers involved in forensic events typically have information about the accused and/or victim, as well as evidence related to the events and the cause of injuries, police officers often require their testimony. However, both nurses and other health care personnel do not typically have adequate expertise in forensic medicine. Emergency nurses should take an active role for determining that whether any patient admitted to the emergency services is a clinical forensic patient the emergency service with injury and requiring possible punishment and knowing of their roles and responsibilities in this area provides legal protection as well as the protection of the judicial affair. Particularly, in emergency services, where rapid patient turnover and high workload exists, patient registration and case reporting may not exist. In such instances, the witnesses, typically the nurses, are often consulted for information. Knowledge of forensic medical matters plays a vital role in achieving justice. According to the Criminal Procedure Law, Article 75, Paragraph 3, ‘an internal body examination or the taking of blood or other biological samples from the body can be performed only by a doctor or other health professional member’. In favour of this item, the clinic nurse and doctor are mainly responsible for evaluating forensic cases in emergency departments, performing the examination, collecting evidence, and storing and reporting data. The courts place considerable importance on determining whether a suspect is the victim or accused and, thus, in terms of illuminating events, it is crucial that any evidence is gathered carefully and appropriately. All the evidence related to the forensic case including the forensic report should be handed over to the police officers. In instances where forensic evidence cannot be collected and the only way to obtain the evidence is the hospital environment, health care personnel in emergency services need to have knowledge about the diagnosis of forensic evidence, the collection of evidence, hiding evidence and provision of the evidence delivery chain.

Keywords: emergency department, emergency nursing, forensic cases, forensic nursing

Procedia PDF Downloads 232

Authors: Mohammad Derakhshani, Saeed Reza Allahkaram, Michael Isakani-Zakaria, Masoud Samadian, Hojat Sharifi Rasaey

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Dynamic stray currents tend to change their magnitude and polarity with time at their source which will create anodic and cathodic spots on a nearby interfered structure. To date, one of the biggest known dynamic stray current sources are DC traction systems. Laboratory simulation is a suitable method to apply theoretical principles in order to identify effective parameters in dynamic stray current influenced corrosion. Simulation techniques can be utilized for various mitigation methods applied in a small scales for selection of the most efficient method with regards to field applications. In this research, laboratory simulation of potential fluctuations caused by dynamic stray current on a cathodically protected structure was investigated. A lab model capable of generating DC static and dynamic stray currents and simulating its effects on cathodically protected samples were developed based on stray current induced (contact-less) polarization technique. Stray current pick-up and discharge spots on an influenced structure were simulated by inducing fluctuations in the sample’s stationary potential. Two mitigation methods for dynamic stray current interference on buried structures namely application of sacrificial anodes as preferred discharge point for the stray current and potentially controlled cathodic protection was investigated. Results showed that the application of sacrificial anodes can be effective in reducing interference only in discharge spot. But cathodic protection through potential controlling is more suitable for mitigating dynamic stray current effects.

Keywords: simulation, dynamic stray current, fluctuating potentials, sacrificial anode

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Authors: Irfan Mohiuddin, Ahmad Al Mogren

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In recent years, cloud computing has earned substantial attention in the Oil and Gas Industry and provides services in all the phases of the industry lifecycle. Oil and gas supply infrastructure, in particular, is more vulnerable to accidental, natural and intentional threats because of its widespread distribution. Numerous surveys have been conducted on cloud security and privacy. However, to the best of our knowledge, hardly any survey is carried out that reviews cyber security in all phases with a cloud computing perspective. Moreover, a distinctive classification is performed for all the cloud-based cyber security measures based on the cloud component in use. The classification approach will enable researchers to identify the required technique used to enhance the security in specific cloud components. Also, the limitation of each component will allow the researchers to design optimal algorithms. Lastly, future directions are given to point out the imminent challenges that can pave the way for researchers to further enhance the resilience to cyber security threats in the oil and gas industry.

Keywords: cyber security, cloud computing, safety and security, oil and gas industry, security threats, oil and gas pipelines

Procedia PDF Downloads 127

Authors: Rian O. Miranda, Lidia V. de Sa, Julio C. Suita

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The Brazilian Nuclear Energy Commission (CNEN) is the main manufacturer of radiopharmaceuticals in Brazil. The Nuclear Engineering Institute (IEN), located at Rio de Janeiro, is one of its main centers of research and production, attending public and private hospitals in the state. This radiopharmaceutical production is used in diagnostic and therapy procedures and allows one and a half million nuclear medicine procedures annually. Despite this, the country is not self-sufficient to meet national demand, creating the need for importation and consequent dependence on other countries. However, IEN facilities were designed in the 60's, and today its structure is inadequate in relation to the good manufacturing practices established by sanitary regulator (ANVISA) and radiological protection leading to the need for a new project. In order to adapt and increase production in the country, a new plant will be built and integrated to the existing facilities with a new 30 MeV Cyclotron that is actually in project detailing process. Thus, it is proposed to survey current CNEN and ANVISA standards for radiopharmaceutical production facilities, as well as the radiological protection analysis of each area of the plant, following good manufacturing practices recommendations adopted nationally besides licensing exigencies for radioactive facilities. In this way, the main requirements for proper operation, equipment location, building materials, area classification, and maintenance program have been implemented. The access controls, interlocks, segregation zones and pass-through boxes integrated into the project were also analyzed. As a result, IEN will in future have the flexibility to produce all necessary radioisotopes for nuclear medicine application, more efficiently by simultaneously bombarding two targets, allowing the simultaneous production of two different radioisotopes, minimizing radiation exposure and saving operating costs.

Keywords: cyclotron, legislation, norms, production, radiopharmaceuticals

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Authors: Amy Gooden, Meshandren Naidoo

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Genetic research is not confined to a particular jurisdiction. Using direct-to-consumer genetic testing (DTC-GT) as an example, this research assesses the status of data sharing into and out of South Africa (SA). While SA laws cover the sending of genetic data out of SA, prohibiting such transfer unless a legal ground exists, the position where genetic data comes into the country depends on the laws of the country from where it is sent – making the legal position less clear.

Keywords: cross-border, data, genetic testing, law, regulation, research, sharing, South Africa

Procedia PDF Downloads 142

Authors: B. Rusyidi, D. Mariana

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Although reforms in trafficking regulations have taken place since 2007, Indonesia is still struggling to fight child trafficking. This study aimed to identify and assess risk factors and vulnerabilities in the life of trafficked children prior to, during, and after being trafficked in order to inform the child protection system and its policies. The study was qualitative and utilized in-depth interviews to collect data. Data were gathered in 2014 and 2015 from 15 trafficked and sexually exploited girls aged 14 to 17 years originating from West Java. Social workers, safe home personnel and parents were also included as informants. Data analysis was guided by the ecological perspective and theme analyses. The study found that risks and vulnerabilities of the victims were associated with conditions at various levels of the environment. At the micro level, risk factors and vulnerabilities included young age, family conflict/violence, involvement with the “wrong” circle of friends/peers, family poverty, lack of social and economic support for the victim’s family, and psychological damages due to trafficking experiences. At the mezzo level, the lack of structured activities after school, economic inequality, stigma towards victims, lack of services for victims, and minimum public education on human trafficking were among the community hazards that increased the vulnerability and risks. Gender inequality, consumerism, the view of children as assets, corruption, weak law enforcement, the lack of institutional support, and community-wide ignorance regarding trafficking were found as factors that increased risks and vulnerabilities at the macro level. The findings from the study underline the necessity to reduce risk factors and promote protective factors at the individual, family, community and societal levels. Shifting the current focus from tertiary to primary/prevention policies and improving institutional efforts are pressing needs in the context of reducing child trafficking in Indonesia. The roles of human service providers including social work also should be promoted.

Keywords: child trafficking, child sexual exploitation, ecological perspective, risks and vulnerabilities

Procedia PDF Downloads 257

Authors: Suyeon Bae, Abimbola Asojo, Denise Guerin, Caren Martin

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Post-occupancy evaluations (POEs) have been recognized for documenting occupant well-being and responses to indoor environmental quality (IEQ) factors such as thermal, lighting, and acoustic conditions. Sustainable Post-Occupancy evaluation survey (SPOES) developed by an interdisciplinary team at a Midwest University provides an evidence-based quantitative analysis of occupants’ satisfaction in office, classroom, and residential spaces to help direct attention to successful areas and areas that need improvement in buildings. SPOES is a self-administered and Internet-based questionnaire completed by building occupants. In this study, employees in three different office buildings rated their satisfaction on a Likert-type scale about 12 IEQ criteria including thermal condition, indoor air quality, acoustic quality, daylighting, electric lighting, privacy, view conditions, furnishings, appearance, cleaning and maintenance, vibration and movement, and technology. Employees rated their level of satisfaction on a Likert-type scale from 1 (very dissatisfied) to 7 (very satisfied). They also rate the influence of their physical environment on their perception of their work performance and the impact of their primary workspaces on their health on a scale from 1 (hinders) to 7 (enhances). Building A is a three-story building that includes private and group offices, classrooms, and conference rooms and amounted to 55,000 square-feet for primary workplace (N=75). Building B, a six-story building, consisted of private offices, shared enclosed office, workstations, and open desk areas for employees and amounted to 14,193 square-feet (N=75). Building C is a three-story 56,000 square-feet building that included classrooms, therapy rooms, an outdoor playground, gym, restrooms, and training rooms for clinicians (N=76). The results indicated that 10 IEQs for Building A except acoustic quality and privacy showed statistically significant correlations on the impact of the primary workspace on health. In Building B, 11 IEQs except technology showed statistically significant correlations on the impact of the primary workspace on health. Building C had statistically significant correlations between all 12 IEQ and the employees’ perception of the impact of their primary workspace on their health in two-tailed correlations (P ≤ 0.05). Out of 33 statistically significant correlations, 25 correlations (76%) showed at least moderate relationship (r ≥ 0.35). For the three buildings, daylighting, furnishings, and indoor air quality IEQs ranked highest on the impact on health. IEQs about vibration and movement, view condition, and electric lighting ranked second, followed by IEQs about cleaning and maintenance and appearance. These results imply that 12 IEQs developed in SPOES are highly related to employees’ perception of how their primary workplaces impact their health. The IEQs in this study offer an opportunity for improving occupants’ well-being and the built environment.

Keywords: post-occupancy evaluation, built environment, sustainability, well-being, indoor air quality

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Authors: Irina Mikajlo, Jaroslav Záhora, Helena Dvořáčková, Jaroslav Hynšt, Jakub Elbl

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Strong anthropogenic impact has uncontrolled consequences on the nature of the soil. Hence, up-to-date sustainable methods of soil state improvement are essential. Investigators provide the evidence that biochar can positively effects physical, chemical and biological soil properties and the abundance of mycorrhizal fungi which are in the focus of this study. The main aim of the present investigation is to demonstrate the effect of two types of plant growth promoting bacteria (PGPB) inoculums along with the beech wood biochar and mineral N additives on mycorrhizal colonization. Experiment has been set up in laboratory conditions with containers filled with arable soil from the protection zone of the main water source ‘Brezova nad Svitavou’. Lactuca sativa (lettuce) has been selected as a model plant. Based on the obtained data, it can be concluded that mycorrhizal colonization increased as the result of combined influence of biochar and PGPB inoculums amendment. In addition, correlation analyses showed that the numbers of main groups of cultivated bacteria were dependent on the degree of mycorrhizal colonization.

Keywords: Arbuscular mycorrhiza, biochar, PGPB inoculum, soil microorganisms

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Authors: Baris Can Yalcin

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Motion sensors have been commonly used as a valuable component in mechatronic systems, however, many mechatronic designs and applications that need motion sensors cost enormous amount of money, especially high-tech systems. Design of a software for communication protocol between data acquisition card and motion sensor is another issue that has to be solved. This study presents how to design a low cost motion data acquisition setup consisting of MPU 6050 motion sensor (gyro and accelerometer in 3 axes) and Arduino Mega2560 microcontroller. Design parameters are calibration of the sensor, identification and communication between sensor and data acquisition card, interpretation of data collected by the sensor.

Keywords: design, mechatronics, motion sensor, data acquisition

Procedia PDF Downloads 569

Authors: Ashish Dhamaniya, Satish Chandra

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Speed and traffic volume data are collected on different sections of four lane and six lane roads in three metropolitan cities in India. Speed data are analyzed to fit the statistical distribution to individual vehicle speed data and all vehicles speed data. It is noted that speed data of individual vehicle generally follows a normal distribution but speed data of all vehicle combined at a section of urban road may or may not follow the normal distribution depending upon the composition of traffic stream. A new term Speed Spread Ratio (SSR) is introduced in this paper which is the ratio of difference in 85^th and 50^th percentile speed to the difference in 50^th and 15^th percentile speed. If SSR is unity then speed data are truly normally distributed. It is noted that on six lane urban roads, speed data follow a normal distribution only when SSR is in the range of 0.86 – 1.11. The range of SSR is validated on four lane roads also.

Keywords: normal distribution, percentile speed, speed spread ratio, traffic volume

Procedia PDF Downloads 402

Authors: Ming Wei

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Over the past two decades, Location Based Service (LBS) data have been increasingly applied to urban and transportation studies due to their comprehensiveness and consistency. However, compared to other LBS data including mobile phone data, GPS and social networking platforms, smart card data collected from public transport users have arguably yet to be fully exploited in urban systems analysis. By using five weekdays of passenger travel transaction data taken from go card – Southeast Queensland’s transit smart card – this paper analyses the spatiotemporal distribution of passenger movement with regard to the land use patterns in Brisbane. Work and residential places for public transport commuters were identified after extracting journeys-to-work patterns. Our results show that the locations of the workplaces identified from the go card data and residential suburbs are largely consistent with those that were marked in the land use map. However, the intensity for some residential locations in terms of population or commuter densities do not match well between the map and those derived from the go card data. This indicates that the misalignment between residential areas and workplaces to a certain extent, shedding light on how enhancements to service management and infrastructure expansion might be undertaken.

Keywords: big data, smart card data, travel pattern, land use

Procedia PDF Downloads 270

Authors: El-Said. I. Shabana, Mohammad S. Tayeb, Maher M. T. Qutub, Abdulraheem A. Kinsara

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Usually, phosphate deposits contain ²³⁸U and ²³²Th in addition to their decay products. Due to their different pathways in the environment, the ²³⁸U/²³²Th activity concentration ratio usually found to be greater than unity in phosphate sediments. The presence of these radionuclides creates a potential need to control exposure of workers in the mining and processing activities of the phosphate minerals in accordance with IAEA safety standards. The greatest dose to workers comes from exposure to radon, especially ²²²Rn from the uranium series, and has to be controlled. In this regard, radon (²²²Rn) was measured in the atmosphere (indoor and outdoor) of Al-Jalamid phosphate-mines working area using a portable radon-measurement instrument RAD7, in a purpose of radiation protection. Radon was measured in 61 sites inside the open phosphate mines, the phosphate upgrading facility (offices and rooms of the workers, and in some open-air sites) and in the dwellings of the workers residence-village that lies at about 3 km from the mines working area. The obtained results indicated that the average indoor radon concentration was about 48.4 Bq/m³. Inside the upgrading facility, the average outdoor concentrations were 10.8 and 9.7 Bq/m³ in the concentrate piles and crushing areas, respectively. It was 12.3 Bq/m³ in the atmosphere of the open mines. These values are comparable with the global average values. Based on the average values, the annual effective dose due to radon inhalation was calculated and risk estimates have been done. The average annual effective dose to workers due to the radon inhalation was estimated by 1.32 mSv. The potential excess risk of lung cancer mortality that could be attributed to radon, when considering the lifetime exposure, was estimated by 53.0x10^-4. The results have been discussed in detail.

Keywords: dosimetry, environmental monitoring, phosphate deposits, radiation protection, radon

Procedia PDF Downloads 257

Authors: Md. Abu Sufian, Robiqul Islam, Imam Hossain Shajid, Mahesh Hanumanthu, Jarasree Varadarajan, Md. Sipon Miah, Mingbo Niu

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Coronary Heart Disease (CHD) remains a principal cause of global morbidity and mortality, characterized by atherosclerosis—the build-up of fatty deposits inside the arteries. The study introduces an innovative methodology that leverages cloud-based platforms like AWS Live Streaming and Artificial Intelligence (AI) to early detect and prevent CHD symptoms in web applications. By employing novel simulation processes and AI algorithms, this research aims to significantly mitigate the health and societal impacts of CHD. Methodology: This study introduces a novel simulation process alongside a multi-phased model development strategy. Initially, health-related data, including heart rate variability, blood pressure, lipid profiles, and ECG readings, were collected through user interactions with web-based applications as well as API Integration. The novel simulation process involved creating synthetic datasets that mimic early-stage CHD symptoms, allowing for the refinement and training of AI algorithms under controlled conditions without compromising patient privacy. AWS Live Streaming was utilized to capture real-time health data, which was then processed and analysed using advanced AI techniques. The novel aspect of our methodology lies in the simulation of CHD symptom progression, which provides a dynamic training environment for our AI models enhancing their predictive accuracy and robustness. Model Development: it developed a machine learning model trained on both real and simulated datasets. Incorporating a variety of algorithms including neural networks and ensemble learning model to identify early signs of CHD. The model's continuous learning mechanism allows it to evolve adapting to new data inputs and improving its predictive performance over time. Results and Findings: The deployment of our model yielded promising results. In the validation phase, it achieved an accuracy of 92% in predicting early CHD symptoms surpassing existing models. The precision and recall metrics stood at 89% and 91% respectively, indicating a high level of reliability in identifying at-risk individuals. These results underscore the effectiveness of combining live data streaming with AI in the early detection of CHD. Societal Implications: The implementation of cloud-based AI for CHD symptom detection represents a significant step forward in preventive healthcare. By facilitating early intervention, this approach has the potential to reduce the incidence of CHD-related complications, decrease healthcare costs, and improve patient outcomes. Moreover, the accessibility and scalability of cloud-based solutions democratize advanced health monitoring, making it available to a broader population. This study illustrates the transformative potential of integrating technology and healthcare, setting a new standard for the early detection and management of chronic diseases.

Keywords: coronary heart disease, cloud-based ai, machine learning, novel simulation techniques, early detection, preventive healthcare

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Authors: Chun-Wei Huang, Jyh-Cherng Gu, Ming-Ta Yang

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Non-synchronous breakage or line failure in power systems with light or no loads can lead to core saturation in transformers or potential transformers. This can cause component and capacitance matching resulting in the formation of resonant circuits, which trigger ferroresonance. This study employed a wavelet transform for the detection of ferroresonance. Simulation results demonstrate the efficacy of the proposed method.

Keywords: ferroresonance, wavelet transform, intelligent electronic device, transformer

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Authors: Seung Hwan Park, Cheng-Sool Park, Jun Seok Kim, Youngji Yoo, Daewoong An, Jun-Geol Baek

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Depending on the big data analysis becomes important, yield prediction using data from the semiconductor process is essential. In general, yield prediction and analysis of the causes of the failure are closely related. The purpose of this study is to analyze pattern affects the final test results using a die map based clustering. Many researches have been conducted using die data from the semiconductor test process. However, analysis has limitation as the test data is less directly related to the final test results. Therefore, this study proposes a framework for analysis through clustering using more detailed data than existing die data. This study consists of three phases. In the first phase, die map is created through fail bit data in each sub-area of die. In the second phase, clustering using map data is performed. And the third stage is to find patterns that affect final test result. Finally, the proposed three steps are applied to actual industrial data and experimental results showed the potential field application.

Keywords: die-map clustering, feature extraction, pattern recognition, semiconductor manufacturing process

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Authors: Afiq Juazer Rizal, Siti Zaleha Misnan, M. Zairi M. Yusof

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Seismic data is the fundamental tool utilized by exploration companies to determine potential hydrocarbon. However, the importance of seismic trace data will be undermined unless the geo-spatial component of the data is understood. Deriving a proposed well to be drilled from data that has positional ambiguity will jeopardize business decision and millions of dollars’ investment that every oil and gas company would like to avoid. Spatial integrity QC workflow has been introduced in PETRONAS to ensure positional errors within the seismic data are recognized throughout the exploration’s lifecycle from acquisition, processing, and seismic interpretation. This includes, amongst other tests, quantifying that the data is referenced to the appropriate coordinate reference system, survey configuration validation, and geometry loading verification. The direct outcome of the workflow implementation helps improve reliability and integrity of sub-surface geological model produced by geoscientist and provide important input to potential hazard assessment where positional accuracy is crucial. This workflow’s development initiative is part of a bigger geospatial integrity management effort, whereby nearly eighty percent of the oil and gas data are location-dependent.

Keywords: oil and gas exploration, PETRONAS, seismic data, spatial integrity QC workflow

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Authors: Zhenqiu Liu

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Visualizing the heterogeneity within cell-populations for single-cell RNA-seq data is crucial for studying the functional diversity of a cell. However, because of the high level of noises, outlier, and dropouts, it is very challenging to measure the cell-to-cell similarity (distance), visualize and cluster the data in a low-dimension. Minimum volume embedding (MVE) projects the data into a lower-dimensional space and is a promising tool for data visualization. However, it is computationally inefficient to solve a semi-definite programming (SDP) when the sample size is large. Therefore, it is not applicable to single-cell RNA-seq data with thousands of samples. In this paper, we develop an efficient algorithm with an accelerated proximal gradient method and visualize the single-cell RNA-seq data efficiently. We demonstrate that the proposed approach separates known subpopulations more accurately in single-cell data sets than other existing dimension reduction methods.

Keywords: single-cell RNA-seq, minimum volume embedding, visualization, accelerated proximal gradient method

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Authors: Sara Ibn El Ahrache, Tajje-eddine Rachidi, Hassan Badir, Abderrahmane Sbihi

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Recently, there has been increasing confidence for a favorable usage of big data drawn out from the huge amount of information deposited in a cloud computing system. Data kept on such systems can be retrieved through the network at the user’s convenience. However, the data that users send include private information, and therefore, information leakage from these data is now a major social problem. The usage of secret sharing schemes for cloud computing have lately been approved to be relevant in which users deal out their data to several servers. Notably, in a (k,n) threshold scheme, data security is assured if and only if all through the whole life of the secret the opponent cannot compromise more than k of the n servers. In fact, a number of secret sharing algorithms have been suggested to deal with these security issues. In this paper, we present a Mapreduce implementation of Shamir’s secret sharing scheme to increase its performance and to achieve optimal security for cloud data. Different tests were run and through it has been demonstrated the contributions of the proposed approach. These contributions are quite considerable in terms of both security and performance.

Keywords: cloud computing, data security, Mapreduce, Shamir's secret sharing

Procedia PDF Downloads 288