Search results for: data security

Authors: Hamd Rezaeifar, Hamid Reza Sahriari

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Accidents are considered to be one of the challenges of modern life. Due to the fact that the victims of this problem and also internal transportations are getting increased day by day in Iran, studying effective factors of accidents and identifying suitable models and parameters about this issue are absolutely essential. The main purpose of this research has been studying the factors and spatial data affecting accidents of Mashhad during 2007- 2008. In this paper it has been attempted to – through matching spatial layers on each other and finally by elaborating them with the place of accident – at the first step by adding landmarks of the accident and through adding especial fields regarding the existence or non-existence of effective phenomenon on accident, existing information banks of the accidents be completed and in the next step by means of data mining tools and analyzing by neural network, the relationship between these data be evaluated and a logical model be designed for predicting accident-prone spots with minimum error. The model of this article has a very accurate prediction in low-accident spots; yet it has more errors in accident-prone regions due to lack of primary data.

Keywords: accident, data mining, neural network, GIS

Procedia PDF Downloads 47

Authors: Buse A. Ataç, Doğan K. Cenan, Arda Çetinkaya, Naz D. Şahin, Köksal Sanlı, Zeynep Koç, Akın Kısa

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With its spatial data reliability, interpretation and questioning capabilities, Geographical Information Systems make significant contributions to scientists, planners and practitioners. Geographic information systems have received great attention in today's digital world, growing rapidly, and increasing the efficiency of use. Access to and use of current and accurate geographical data, which are the most important components of the Geographical Information System, has become a necessity rather than a need for sustainable and economic development. This project aims to enable sharing of data collected by public institutions and organizations on a web-based platform. Within the scope of the project, INSPIRE (Infrastructure for Spatial Information in the European Community) data specifications are considered as a road-map. In this context, Turkey's National Geographic Information System (TUCBS) Integration Project supports sharing spatial data within 61 pilot public institutions as complied with defined national standards. In this paper, which is prepared by the project team members in the TUCBS Integration Project, the technical process with a detailed methodology is explained. In this context, the main technical processes of the Project consist of Geographic Data Analysis, Geographic Data Harmonization (Standardization), Web Service Creation (WMS, WFS) and Metadata Creation-Publication. In this paper, the integration process carried out to provide the data produced by 61 institutions to be shared from the National Geographic Data Portal (GEOPORTAL), have been trying to be conveyed with a detailed methodology.

Keywords: data specification, geoportal, GIS, INSPIRE, Turkish National Geographic Information System, TUCBS, Turkey's national geographic information system

Procedia PDF Downloads 144

Authors: Abbas Sheikhtaheri, Nasim Hashemi

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Health information systems have great benefits for clinical and managerial processes of health care organizations. However, identifying and removing constraints and barriers of implementing and using health information systems before any implementation is essential. Physicians are one of the main users of health information systems, therefore, identifying the causes of their resistance and concerns about the barriers of the implementation of these systems is very important. So the purpose of this study was to determine the barriers of the development and implementation of health information systems in terms of the Iranian physicians’ perspectives. In this study conducted in 8 selected hospitals affiliated to Tehran and Iran Universities of Medical Sciences, Tehran, Iran in 2014, physicians (GPs, residents, interns, specialists) in these hospitals were surveyed. In order to collect data, a research made questionnaire was used (Cronbach’s α = 0.95). The instrument included 25 about organizational (9), personal (4), moral and legal (3) and technical barriers (9). Participants were asked to answer the questions using 5 point scale Likert (completely disagree=1 to completely agree=5). By using a simple random sampling method, 200 physicians (from 600) were invited to study that eventually 163 questionnaires were returned. We used mean score and t-test and ANOVA to analyze the data using SPSS software version 17. 52.1% of respondents were female. The mean age was 30.18 ± 7.29. The work experience years for most of them were between 1 to 5 years (80.4 percent). The most important barriers were organizational ones (3.4 ± 0.89), followed by ethical (3.18 ± 0.98), technical (3.06 ± 0.8) and personal (3.04 ± 1.2). Lack of easy access to a fast Internet (3.67±1.91) and the lack of exchanging information (3.61±1.2) were the most important technical barriers. Among organizational barriers, the lack of efficient planning for the development and implementation systems (3.56±1.32) and was the most important ones. Lack of awareness and knowledge of health care providers about the health information systems features (3.33±1.28) and the lack of physician participation in planning phase (3.27±1.2) as well as concerns regarding the security and confidentiality of health information (3.15 ± 1.31) were the most important personal and ethical barriers, respectively. Women (P = 0.02) and those with less experience (P = 0.002) were more concerned about personal barriers. GPs also were more concerned about technical barriers (P = 0.02). According to the study, technical and ethics barriers were considered as the most important barriers however, lack of awareness in target population is also considered as one of the main barriers. Ignoring issues such as personal and ethical barriers, even if the necessary infrastructure and technical requirements were provided, may result in failure. Therefore, along with the creating infrastructure and resolving organizational barriers, special attention to education and awareness of physicians and providing solution for ethics concerns are necessary.

Keywords: barriers, development health information systems, implementation, physicians

Procedia PDF Downloads 345

Authors: Jamal Al-Zain, O. El Hajjaji, T. El Bardouni

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A (Th-U) O2 fuel pin benchmark made up of 25 w/o U and 75 w/o Th was used. In order to analyze the depletion and inventory of the fuel for the pressurized water reactor pin-cell model. The new version VIII.0 of the ENDF/B nuclear data library was used to create a data set in ACE format at various temperatures and process the data using the MAKXSF6.2 and NJOY2016 programs to process the data at the various temperatures in order to conduct this study and analyze cross-section data. The infinite multiplication factor, the concentrations and activities of the main fission products, the actinide radionuclides accumulated in the pin cell, and the total radioactivity were all estimated and compared in this study using the Monte Carlo N-Particle 6 (MCNP6.2) and DRAGON5 programs. Additionally, the behavior of the Pressurized Water Reactor (PWR) thorium pin cell that is dependent on burn-up (BU) was validated and compared with the reference data obtained using the Massachusetts Institute of Technology (MIT-MOCUP), Idaho National Engineering and Environmental Laboratory (INEEL-MOCUP), and CASMO-4 codes. The results of this study indicate that all of the codes examined have good agreements.

Keywords: PWR thorium pin cell, ENDF/B-VIII.0, MAKXSF6.2, NJOY2016, MCNP6.2, DRAGON5, fuel burn-up.

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Authors: Bastian Haarmann, Likas Sikorski

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In this paper, we introduce an NLG application for the automatic creation of ready-to-publish texts from big data. The fully automatic generated stories have a high resemblance to the style in which the human writer would draw up a news story. Topics may include soccer games, stock exchange market reports, weather forecasts and many more. The generation of the texts runs according to the human language production. Each generated text is unique. Ready-to-publish stories written by a computer application can help humans to quickly grasp the outcomes of big data analyses, save time-consuming pre-formulations for journalists and cater to rather small audiences by offering stories that would otherwise not exist.

Keywords: big data, natural language generation, publishing, robotic journalism

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Authors: Matthew Kostawich, Mohammed Elmorsy, Mohamed Sayed Sifat, Shoukry Shams, Mahmoud Elsaadany

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6G communication systems represent the nominal future extension of current wireless technology, where its impact is extended to touch upon all human activities, including medical, security, and entertainment applications. As a result, human needs are allocated among the highest priority aspects of the system design and requirements. 6G communications is expected to replace all the current video conferencing with interactive virtual reality meetings involving high data-rate transmission merged with massive distributed computing resources. In addition, the current expansion of IoT applications must be mitigated with significant network changes to provide a reasonable Quality of Service (QoS). This directly implies a high demand for Human-Computer Interaction (HCI) through mobile computing modules in future wireless communication systems. This article proposes the utilization of a Printed Ridge Gap Waveguide (PRGW) to host the wearable nodes. To the best of our knowledge, we propose for the first time a physical layer analysis within the context of a complete architecture. A thorough study is provided on the impact of the distortion of the guiding structure on the overall system performance. The proposed structure shows small latency and small losses, highlighting its compatibility with future applications.

Keywords: ridge gap waveguide, edge computing module, 6G, multimedia IoT applications

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Authors: Aswathi Thrivikraman, S. Advaith

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The study is aimed at designing encoders for deciphering intricacies in time series data by redescribing the dynamics operating on a lower-dimensional manifold. A semi-supervised LSTM autoencoder is devised and investigated to see if the latent representation of the time series data can better forecast the data. End-to-end training of the LSTM autoencoder, together with another LSTM network that is connected to the latent space, forces the hidden states of the encoder to represent the most meaningful latent variables relevant for forecasting. Furthermore, the study compares the predictions with those of a traditional seq2seq model.

Keywords: LSTM, autoencoder, forecasting, seq2seq model

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Authors: R. G. Ariyawansa, M. A. N. R. M. Perera

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“House” is a powerful symbol of socio-economic background of individuals and families. In fact, housing provides all types of needs/wants from basic needs to self-actualization needs. This phenomenon can be realized only having analyzed hidden motives of buyers and sellers of the housing market. Hence, the aim of this study is to examine the socio-economic insight of the secondary housing market in Colombo suburbs. This broader aim was achieved via analyzing the general pattern of the secondary housing market, identifying socio-economic motives of sellers of the secondary housing market, and reviewing sellers’ experience of buyer behavior. A purposive sample of 50 sellers from popular residential areas in Colombo such as Maharagama, Kottawa, Piliyandala, Punnipitiya, and Nugegoda was used to collect primary data instead of relevant secondary data from published and unpublished reports. The sample was limited to selling price ranging from Rs15 million to Rs25 million, which apparently falls into middle and upper-middle income houses in the context. Participatory observation and semi-structured interviews were adopted as key data collection tools. Data were descriptively analyzed. This study found that the market is mainly handled by informal agents who are unqualified and unorganized. People such as taxi/tree-wheel drivers, boutique venders, security personals etc. are engaged in housing brokerage as a part time career. Few fulltime and formally organized agents were found but they were also not professionally qualified. As far as housing quality is concerned, it was observed that 90% of houses was poorly maintained and illegally modified. They are situated in poorly maintained neighborhoods as well. Among the observed houses, 2% was moderately maintained and 8% was well maintained and modified. Major socio-economic motives of sellers were “migrating foreign countries for education and employment” (80% and 10% respectively), “family problems” (4%), and “social status” (3%). Other motives were “health” and “environmental/neighborhood problems” (3%). This study further noted that the secondary middle income housing market in the area directly related with the migrants who motivated for education in foreign countries, mainly Australia, UK and USA. As per the literature, families motivated for education tend to migrate Colombo suburbs from remote areas of the country. They are seeking temporary accommodation in lower middle income housing. However, the secondary middle income housing market relates with the migration from Colombo to major global cities. Therefore, final transaction price of this market may depend on migration related dates such as university deadlines, visa and other agreements. Hence, it creates a buyers’ market lowering the selling price. Also it was revealed that the buyers tend to trust more on this market as far as the quality of construction of houses is concerned than brand new houses which are built for selling purpose.

Keywords: informal housing market, hidden motives of buyers and sellers, secondary housing market, socio-economic insight

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Authors: Ewa M. Laskowska, Jorn Vatn

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Industry 4.0 focuses on digital optimization of industrial processes. The idea is to use extracted data in order to build a decision support model enabling use of those data for real time decision making. In terms of predictive maintenance, the desired decision support tool would be a model enabling prognostics of system's health based on the current condition of considered equipment. Within area of system prognostics and health management, a commonly used health indicator is Remaining Useful Lifetime (RUL) of a system. Because the RUL is a random variable, it has to be estimated based on available health indicators. Health indicators can be of different types and come from different sources. They can be process variables, equipment performance variables, data related to number of experienced failures, etc. The aim of this study is the analysis of performance variables of emergency shutdown valves (ESV) used in oil and gas industry. ESV is inspected periodically, and at each inspection torque and time of valve operation are registered. The data will be analyzed by means of machine learning or statistical analysis. The purpose is to investigate whether the available data could be used as a health indicator for a prognostic purpose. The second objective is to examine what is the most efficient way to incorporate the data into predictive model. The idea is to check whether the data can be applied in form of explanatory variables in Markov process or whether other stochastic processes would be a more convenient to build an RUL model based on the information coming from registered data.

Keywords: emergency shutdown valves, health indicator, prognostics, remaining useful lifetime, RUL

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Authors: Lucy Patricia Onundo, Wilfred Njoroge Mwema

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The tidal power potential available for electricity generation from Mombasa and Lamu sites in Kenya will be examined. Several African countries in the Western Indian Ocean endure insufficiencies in the power sector, including both generation and distribution. One important step towards increasing energy security and availability is to intensify the use of renewable energy sources. The access to cost-efficient hydropower is low in Mombasa and Lamu hence Ocean energy will play an important role. Global-Level resource assessments and oceanographic literature and data have been compiled in an analysis between technology-specific requirements for ocean energy technologies (salinity, tide, tidal current, wave, Ocean thermal energy conversion, wind and solar) and the physical resources in Lamu and Mombasa. The potential for tide and tidal current power is more restricted but may be of interest at some locations. The theoretical maximum power produced over a tidal cycle is determined by the product of the forcing tide and the undisturbed volumetric flow-rate. The extraction of the maximum power reduces the flow-rate, but a significant portion of the maximum power can be extracted with little change to the tidal dynamics. Two-dimensional finite-element, numerical simulations designed and developed agree with the theory. Temporal variations in resource intensity, as well as the differences between small-scale and large-scale applications, are considered.

Keywords: energy assessment, marine tidal power, renewable energy, tidal dynamics

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Authors: James Newman

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Process mining is an emerging technology that looks to serialize enterprise data in time series data. It has been used by many companies and has been the subject of a variety of research papers. However, the majority of current efforts have looked at how to best create process mining from standard relational databases. This paper is the first pass at outlining a database custom-built for the minimal viable product of process mining. We present Block Miner, a blockchain protocol to store process mining data across a distributed network. We demonstrate the feasibility of storing process mining data on the blockchain. We present a proof of concept and show how the intersection of these two technologies helps to solve a variety of issues, including but not limited to ransomware attacks, tax documentation, and conflict resolution.

Keywords: blockchain, process mining, memory optimization, protocol

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Authors: Tamar Trop, Boris Portnov

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Public Space Lighting (PSL) of outdoor urban areas promotes comfort, defines spaces and neighborhood identities, enhances perceived safety and security, and contributes to residential satisfaction and wellbeing. However, if excessive or misdirected, PSL leads to unnecessary energy waste and increased greenhouse gas emissions, poses a non-negligible threat to the nocturnal environment, and may become a potential health hazard. At present, PSL is designed according to international, regional, and national standards, which consolidate best practice. Yet, knowledge regarding the optimal light characteristics needed for creating a perception of personal comfort and safety in densely populated residential areas, and the factors associated with this perception, is still scarce. The presented study suggests a paradigm shift in designing PSL towards a user-centered approach, which incorporates pedestrians' perspectives into the process. The study is an ongoing joint research project between China and Israel Ministries of Science and Technology. Its main objectives are to reveal inhabitants' perceptions of and preferences for PSL in different densely populated neighborhoods in China and Israel, and to develop a model that links instrumentally measured parameters of PSL (e.g., intensity, spectra and glare) with its perceived comfort and quality, while controlling for three groups of attributes: locational, temporal, and individual. To investigate measured and perceived PSL, the study employed various research methods and data collection tools, developed a location-based mobile application, and used multiple data sources, such as satellite multi-spectral night-time light imagery, census statistics, and detailed planning schemes. One of the study’s preliminary findings is that higher sense of safety in the investigated neighborhoods is not associated with higher levels of light intensity. This implies potential for energy saving in brightly illuminated residential areas. Study findings might contribute to the design of a smart and adaptive PSL strategy that enhances pedestrians’ perceived safety and comfort while reducing light pollution and energy consumption.

Keywords: energy efficiency, light pollution, public space lighting, PSL, safety perceptions

Procedia PDF Downloads 133

Authors: Aniedi A. Udo, Magnus U. Igboekwe, Rasaaq Bello, Francis D. Eyenaka, Michael C. Ohakwere-Eze

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Groundwater vulnerability to pollution was assessed in Akwa Ibom State, Southern Nigeria, with the aim of locating areas with high potentials for resource contamination, especially due to anthropogenic influence. The electrical resistivity method was utilized in the collection of the initial field data. Additional data input, which included depth to static water level, drilled well log data, aquifer recharge data, percentage slope, as well as soil information, were sourced from secondary sources. The initial field data were interpreted both manually and with computer modeling to provide information on the geoelectric properties of the subsurface. Interpreted results together with the secondary data were used to develop the DRASTIC thematic maps. A vulnerability assessment was performed using the DRASTIC model in a GIS environment and areas with high vulnerability which needed immediate attention was clearly mapped out and presented using an aquifer vulnerability map. The model was subjected to validation and the rate of validity was 73% within the area of study.

Keywords: groundwater, vulnerability, DRASTIC model, pollution

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Authors: Okpanachi Linus Odiji, Chris Ogwu Attah

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Election is no doubt widely assumed as one of the most suitable means of resolving political quagmires even though it has never been bereft of conflict which can manifest before, during, or after polls. What, however, advances democracy and promotes electoral integrity is the existence and effectiveness of institutional frameworks for electoral conflict management. Electoral conflicts are no doubt unique in the sense that they represent the struggles of people over the control of public resources. In most cases, the stakes involved are high and emotional that they do not only undermine inter-group relationship but also threaten national security. The need, therefore, for an effectively functional conflict management apparatus becomes imperative. While at the State level, there exist numerous governmental initiatives at various electoral stages aimed at managing conflicts, this paper examines the activities of the mass media, which is another prominent stakeholder in the electoral process. Even though media influence has increased tremendously in the last decade, researchers are yet to agree on its utility in the management of conflicts. Guided by the social responsibility theory of media reporting and drawing data from observed trends in Kogi state, the paper, which context analyses the 2019 gubernatorial election coverage in the state, observes both conflict escalation and de-escalation roles in the media. To mitigate conflict reporting misrepresentation, therefore, a common approach to conflict reporting should be designed and ordered by the National Broadcasting Commission as well as the Nigerian Press Council. This should be garnished with the training of journalists on conflict reporting and development of a standard conflict reporting procedure.

Keywords: conflict management, electoral conflict, mass media, media reporting

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Authors: Anselm Ego Onyimonyi, Maduako Johnpaul O.

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The study was designed to investigate into the socio-ecological impact of migration of Fulani herders in Anambra state of Nigeria, through a climate justice lens. Nigeria is one of the world’s most densely populated countries with a population of over 284 million people, half of which are considered to be in abject poverty. There is no doubt that livestock production provides sustainable contributions to food security and poverty reduction to Nigeria economy, but not without some environmental implications like any other economic activities. Nigeria is recognized as being vulnerable to climate change. Climate change and global warming if left unchecked will cause adverse effects on livelihoods in Nigeria, such as livestock production, crop production, fisheries, forestry and post-harvest activities, because the rainfall regimes and patterns will be altered, floods which devastate farmlands would occur, increase in temperature and humidity which increases pest and disease would occur and other natural disasters like desertification, drought, floods, ocean and storm surges, which not only damage Nigerians’ livelihood but also cause harm to life and property, would occur. This and other climatic issue as it affects Fulani herdsmen was what this study investigated. In carrying out this research, a survey research design was adopted. A simple sampling technique was used. One local government area (LGA) was selected purposively from each of the four agricultural zone in the state based on its predominance of Fulani herders. For appropriate sampling, 25 respondents from each of the four Agricultural zones in the state were randomly selected making up the 100 respondent being sampled. Primary data were generated by using a set of structured 5-likert scale questionnaire. Data generated were analyzed using SPSS and the result presented using descriptive statistics. From the data analyzed, the study indentified; Unpredicted rainfall (mean = 3.56), Forest fire (mean = 4.63), Drying Water Source (mean = 3.99), Dwindling Grazing (mean 4.43), Desertification (mean = 4.44), Fertile land scarcity (mean = 3.42) as major factor predisposing Fulani herders to migrate southward while rejecting Natural inclination to migrate (mean = 2.38) and migration to cause trouble as a factor. On the reason why Fulani herders are trying to establish a permanent camp in Anambra state; Moderate temperature (mean= 3.60), Avoiding overgrazing (4.42), Search for fodder and water (mean = 4.81) and (mean = 4.70) respectively, Need for market (4.28), Favorable environment (mean = 3.99) and Access to fertile land (3.96) were identified. It was concluded that changing climatic variables necessitated the migration of herders from Northern Nigeria to areas in the South were the variables are most favorable to the herders and their animals.

Keywords: socio-ecological, migration, fulani, climate, justice, lens

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Authors: Nicolas Plamondon, Stuart Atkinson, Shuzi Zhou

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The insurance industry is usually not the first topic one has in mind when thinking about applications of data science. However, the use of data science in the finance and insurance industry is growing quickly for several reasons, including an abundance of reliable customer data, ferocious competition requiring more accurate pricing, etc. Among the top use cases of data science, we find pricing optimization, customer segmentation, customer risk assessment, fraud detection, marketing, and triage analytics. The objective of this paper is to present an application of the generalized additive model (GAM) on a commercial automobile insurance product: an individually rated commercial automobile. These are vehicles used for commercial purposes, but for which there is not enough volume to apply pricing to several vehicles at the same time. The GAM model was selected as an improvement over GLM for its ease of use and its wide range of applications. The model was trained using the largest split of the data to determine model parameters. The remaining part of the data was used as testing data to verify the quality of the modeling activity. We used the Gini coefficient to evaluate the performance of the model. For long-term monitoring, commonly used metrics such as RMSE and MAE will be used. Another topic of interest in the insurance industry is to process of producing the model. We will discuss at a high level the interactions between the different teams with an insurance company that needs to work together to produce a model and then monitor the performance of the model over time. Moreover, we will discuss the regulations in place in the insurance industry. Finally, we will discuss the maintenance of the model and the fact that new data does not come constantly and that some metrics can take a long time to become meaningful.

Keywords: insurance, data science, modeling, monitoring, regulation, processes

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Authors: Umego Chukwudi T., Ntui Valentine O., Uyoh Edak A.

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Phosphorus (P) deficiency has been identified as a major hindrance to rice production the world over. Eleven (11) rice genotypes predominantly used by local farmers in Nigeria were studied for their responses to P deficient conditions. The characterization was based on morpho-physiologic parameters. The genotypes were screened using a hydroponic system in a modified Hoagland’s solution. Morphological and physiologic parameters, including Plant height (PH), number of tillers per plant, shoot dry weight (SDW), shoot phosphate concentration (SPC), and chlorophyll content, were recorded after exposure to three levels of phosphate concentration (0µM, 400 µM, and 800 µM). The data obtained were subjected to analysis of variance (ANOVA), and the means were separated using least significance difference tests. The results obtained showed that P starvation caused a significant (p≤0.05) reduction in PH, SDW, and tillering and also triggered a significant (p≤0.05) increase in root length among the genotypes. The Pearsons correlation coefficient was used to estimate the relationships among studied parameters, and a significant negative correlation was observed between plant height and root length. FARO63 was identified as a highly tolerant genotype to P deficiency with a low (0.24) SPC and higher (4.81) phosphate utilization efficiency (PUE). This study has identified FARO63 as a true tolerant genotype to Phosphate deficiency, which will be useful in breeding for phosphate deficiency tolerance in rice and thus combating food insecurity.

Keywords: phosphate deficiency, rice genotypes, hydroponic system, food security

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Authors: Mehmet Erdem Uzun, Hande Şirin, Amela Kojic Ateş, Utku Beyazıt, Aynur Bütün Ayhan

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The aim of this study was to examine the health literacy of parents with children diagnosed with ADHD. The study group consisted of 394 parents with children diagnosed with ADHD who applied to the child and adolescent psychiatry outpatient clinic of a public hospital in Bursa, Turkey. 339 mothers and 52 fathers participated in the study. The parents were administered a questionnaire prepared by the researchers in addition to the European Health Literacy Scale-Short Form. Prior to the onset of the analyses, a normality test was performed, and it was determined that the data did not show normal distribution. In this regard, Mann-Whitney U and Kruskal Wallis tests were employed in the analyses. According to the results obtained, it was determined that the health literacy of the mothers (x2=21.015, p<.001) and fathers (x2=7.462, p<.05) differed according to their education levels; that is, the health literacy level of parents who graduated from primary school was lower than the other parents. In addition, it was determined that the level of health literacy differed according to the income level of the family (x2=14.308, p<.05), and the health literacy level of the parents in the low-income group was lower than the other parents. On the other hand, it was seen that the health literacy levels of mothers and fathers did not differ according to the variables of age, whether they had social security, whether the child diagnosed with ADHD was taking medication, and if so, how long the child had been taking medication; age and gender of the child; whether there were other individuals diagnosed with ADHD in the family, and whether the child or parents had a chronic disease (p>.05). The results obtained were discussed in the light of the literature findings.

Keywords: health literacy, parents, children, ADHD

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Authors: Hamidreza Ghazvinian, Khosro Ghazvinian, Touba Khodaiean

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The importance of evaporation estimation in water resources and agricultural studies is undeniable. Pan evaporation are used as an indicator to determine the evaporation of lakes and reservoirs around the world due to the ease of interpreting its data. In this research, intelligent models were investigated in estimating pan evaporation on a daily basis. Shahroud and Mayamey were considered as the studied cities. These two cities are located in Semnan province in Iran. The mentioned cities have dry weather conditions that are susceptible to high evaporation potential. Meteorological data of 11 years of synoptic stations of Shahrood and Mayamey cities were used. The intelligent models used in this study are Artificial Neural Network (ANN), Least Squares Support Vector Machine (LSSVM), and M5 tree models. Meteorological parameters of minimum and maximum air temperature (Tmax, Tmin), wind speed (WS), sunshine hours (SH), air pressure (PA), relative humidity (RH) as selected input data and evaporation data from pan (EP) to The output data was considered. 70% of data is used at the education level, and 30 % of the data is used at the test level. Models used with explanation coefficient evaluation (R2) Root of Mean Squares Error (RMSE) and Mean Absolute Error (MAE). The results for the two Shahroud and Mayamey stations showed that the above three models' operations are rather appropriate.

Keywords: pan evaporation, intelligent methods, shahroud, mayamey

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Authors: Allmin Susaiyah, Aki Härmä, Milan Petković

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Automatic generation of insights from data using insight mining systems (IMS) is useful in many applications, such as personal health tracking, patient monitoring, and business process management. Existing IMS face challenges in controlling insight extraction, scaling to large databases, and generalising to unseen domains. In this work, we propose a hybrid approach consisting of rule-based and neural components for generating insights from data while overcoming the aforementioned challenges. Firstly, a rule-based data 2CNL component is used to extract statistically significant insights from data and represent them in a controlled natural language (CNL). Secondly, a BERTSum-based CNL2NL component is used to convert these CNLs into natural language texts. We improve the model using task-specific and domain-specific fine-tuning. Our approach has been evaluated using statistical techniques and standard evaluation metrics. We overcame the aforementioned challenges and observed significant improvement with domain-specific fine-tuning.

Keywords: data mining, insight mining, natural language generation, pre-trained language models

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Authors: Huseyin Sinan Gunesli

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Process Safety Management is a disciplined framework for managing the integrity of systems and processes that handle hazardous substances. It relies on good design principles, well-implemented automation systems, and operating and maintenance practices. Alarm Management Systems play a critically important role in the safe and efficient operation of modern industrial plants. In that respect, Alarm Management is one of the critical factors feeding the safe operations of the plants in the manner of applying effective process safety principles. Trans Anatolian Natural Gas Pipeline (TANAP) is part of the Southern Gas Corridor, which extends from the Caspian Sea to Italy. TANAP transports Natural Gas from the Shah Deniz gas field of Azerbaijan, and possibly from other neighboring countries, to Turkey and through Trans Adriatic Pipeline (TAP) Pipeline to Europe. TANAP plays a crucial role in maintaining Energy Security for the region and Europe. In that respect, the application of Process Safety principles is vital to deliver safe, reliable and efficient Natural Gas delivery to Shippers both in the region and Europe. Effective Alarm Management is one of those Process Safety principles which feeds safe operations of the TANAP pipeline. Alarm Philosophy was designed and implemented in TANAP Pipeline according to the relevant standards. However, it is essential to manage the alarms received in the control room effectively to maintain safe operations. In that respect, TANAP has commenced Alarm Management & Rationalization program as of February 2022 after transferring to Plateau Regime, reaching the design parameters. While Alarm Rationalization started, there were more than circa 2300 alarms received per hour from one of the compressor stations. After applying alarm management principles such as reviewing and removal of bad actors, standing, stale, chattering, fleeting alarms, comprehensive review and revision of alarm set points through a change management principle, conducting alarm audits/design verification and etc., it has been achieved to reduce down to circa 40 alarms per hour. After the successful implementation of alarm management principles as specified above, the number of alarms has been reduced to industry standards. That significantly improved operator vigilance to focus on mainly important and critical alarms to avoid any excursion beyond safe operating limits leading to any potential process safety events. Following the ‟What Gets Measured, Gets Managed” principle, TANAP has identified key Performance Indicators (KPIs) to manage Process Safety principles effectively, where Alarm Management has formed one of the key parameters of those KPIs. However, review and analysis of the alarms were performed manually. Without utilizing Alarm Management Software, achieving full compliance with international standards is almost infeasible. In that respect, TANAP has started using one of the industry-wide known Alarm Management Applications to maintain full review and analysis of alarms and define actions as required. That actually significantly empowered TANAP’s process safety principles in terms of Alarm Management.

Keywords: process safety principles, energy security, natural gas pipeline operations, alarm rationalization, alarm management, alarm management application

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Authors: Manh-Dung Ho, Van-Giap Pham, Van-Doanh Ho, Quang-Thien Tran, Tuan-Anh Tran

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The k0-factors and related nuclear data, i.e. the Q0-factors and effective resonance energies (Ēr) of the selected radionuclides which are used in the k0-based neutron activation analysis (k0-NAA), were critically reviewed to be integrated in the “k0-DALAT” software. The k0- and Q0-factors of some short-lived radionuclides: 46mSc, 110Ag, 116m2In, 165mDy, and 183mW, were experimentally determined at the Dalat research reactor. The other radionuclides selected are: 20F, 36S, 49Ca, 60mCo, 60Co, 75Se, 77mSe, 86mRb, 115Cd, 115mIn, 131Ba, 134mCs, 134Cs, 153Gd, 153Sm, 159Gd, 170Tm, 177mYb, 192Ir, 197mHg, 239U and 239Np. The reviewed data as compared with the literature data were biased within 5.6-7.3% in which the experimental re-determined factors were within 6.1 and 7.3%. The NIST standard reference materials: Oyster Tissue (1566b), Montana II Soil (2711a) and Coal Fly Ash (1633b) were used to validate the new reviewed data showing that the new data gave an improved k0-NAA using the “k0-DALAT” software with a factor of 4.5-6.8% for the investigated radionuclides.

Keywords: neutron activation analysis, k0-based method, k0 factor, Q0 factor, effective resonance energy

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Authors: Tayyibah Khanam, Mohammad Saad Alam, Sanchari Deb, Yasser Rafat

Abstract:

Electric vehicles are considered as viable replacements to gasoline cars since they help in reducing harmful emissions and stimulate power generation through renewable energy sources, hence contributing to sustainability. However, one of the significant obstacles in the mass deployment of electric vehicles is the charging time anxiety among users and, thus, the subsequent large waiting times for available chargers at charging stations. Data analytics, on the other hand, has revolutionized the decision-making tasks of management and operating systems since its arrival. In this paper, we attempt to optimize the choice of EV charging stations for users in their vicinity by minimizing the time taken to reach the charging stations and the waiting times for available chargers. Time taken to travel to the charging station is calculated by the Google Maps API and the waiting times are predicted by polynomial regression of the historical data stored. The proposed framework utilizes real-time data and historical data from all operating charging stations in the city and assists the user in finding the best suitable charging station for their current situation and can be implemented in a mobile phone application. The algorithm successfully predicts the most optimal choice of a charging station and the minimum required time for various sample data sets.

Keywords: charging data, electric vehicles, machine learning, waiting times

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Authors: R. Shamsi, F. Sharifi

Abstract:

In this paper, we obtain the projection of inefficient units in data envelopment analysis (DEA) in the case of stochastic inputs and outputs using the multi-objective programming (MOP) structure. In some problems, the inputs might be stochastic while the outputs are deterministic, and vice versa. In such cases, we propose a multi-objective DEA-R model because in some cases (e.g., when unnecessary and irrational weights by the BCC model reduce the efficiency score), an efficient decision-making unit (DMU) is introduced as inefficient by the BCC model, whereas the DMU is considered efficient by the DEA-R model. In some other cases, only the ratio of stochastic data may be available (e.g., the ratio of stochastic inputs to stochastic outputs). Thus, we provide a multi-objective DEA model without explicit outputs and prove that the input-oriented MOP DEA-R model in the invariable return to scale case can be replaced by the MOP-DEA model without explicit outputs in the variable return to scale and vice versa. Using the interactive methods for solving the proposed model yields a projection corresponding to the viewpoint of the DM and the analyst, which is nearer to reality and more practical. Finally, an application is provided.

Keywords: DEA-R, multi-objective programming, stochastic data, data envelopment analysis

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Authors: Ademola Ibukunolu Atanda, Gbenga Nathaniel Adeola

Abstract:

The study examined awareness of child rights as a determinant of effective student personnel services in public secondary schools in Southwestern Nigeria. It was survey research. The sample comprised 433 teachers, 137 school administrators, and 968 students who were drawn by simple random sampling techniques. The respondents were given copies of questionnaires tagged “school administrator/teacher’s awareness of child’s rights and student personnel services elements inventory.” Key Informant Interview (KII) was also employed. The data were analysed using frequency count, percentages, weighted average, grand mean, standard deviation, and Pearson Product Moment Correlation, while KII was qualitatively analysed. The findings of the study revealed that public secondary school administrator awareness of child rights was at a moderate level, but the awareness of child rights was low among the teachers. The study equally revealed that student personnel services are moderately provided in public secondary schools in Southwestern Nigeria, but security remains a major challenge. It was also found that there was a significant relationship between awareness of child rights and effective student personnel services. It was therefore recommended, based on the findings, that attention should be given to heightening awareness of child rights among public secondary school administrators and teachers for effective student personnel services. Copies of the Child Right Act 2003 should also be made available in all public secondary schools in Southwestern Nigeria, as the study revealed that the documents were not available.

Keywords: student personnel, child right, administrator awareness, practice of child right

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Authors: Miguel Cañon, Darwin Mena, Ivan Cabeza

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In this work was compared and evaluated the applicability of statistical methods for the estimation of missing precipitations data in the basin of the river Lenguazaque located in the departments of Cundinamarca and Boyacá, Colombia. The methods used were the method of simple linear regression, distance rate, local averages, mean rates, correlation with nearly stations and multiple regression method. The analysis used to determine the effectiveness of the methods is performed by using three statistical tools, the correlation coefficient (r2), standard error of estimation and the test of agreement of Bland and Altmant. The analysis was performed using real rainfall values removed randomly in each of the seasons and then estimated using the methodologies mentioned to complete the missing data values. So it was determined that the methods with the highest performance and accuracy in the estimation of data according to conditions that were counted are the method of multiple regressions with three nearby stations and a random application scheme supported in the precipitation behavior of related data sets.

Keywords: statistical comparison, precipitation data, river subbasin, Bland and Altmant

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Authors: Li Qingjian, Li Ke, He Chun, Huang Yong

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In this paper, the method of combining the Pohl Seidman's deep belief network with the self-organizing neural network is proposed to classify the target. This method is mainly aimed at the high nonlinearity of the hyperspectral image, the high sample dimension and the difficulty in designing the classifier. The main feature of original data is extracted by deep belief network. In the process of extracting features, adding known labels samples to fine tune the network, enriching the main characteristics. Then, the extracted feature vectors are classified into the self-organizing neural network. This method can effectively reduce the dimensions of data in the spectrum dimension in the preservation of large amounts of raw data information, to solve the traditional clustering and the long training time when labeled samples less deep learning algorithm for training problems, improve the classification accuracy and robustness. Through the data simulation, the results show that the proposed network structure can get a higher classification precision in the case of a small number of known label samples.

Keywords: DBN, SOM, pattern classification, hyperspectral, data compression

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Authors: Masood Varshosaz, Kamyar Hasanpour

Abstract:

In recent years, we have seen growing interest in recognizing humans in drone images for post-disaster search and rescue operations. Deep learning algorithms have shown great promise in this area, but they often require large amounts of labeled data to train the models. To keep the data acquisition cost low, augmentation techniques can be used to create additional data from existing images. There are many techniques of such that can help generate variations of an original image to improve the performance of deep learning algorithms. While data augmentation is potentially assumed to improve the accuracy and robustness of the models, it is important to ensure that the performance gains are not outweighed by the additional computational cost or complexity of implementing the techniques. To this end, it is important to evaluate the impact of data augmentation on the performance of the deep learning models. In this paper, we evaluated the most currently available 2D data augmentation techniques on a standard convolutional network which was trained for recognizing humans in drone images. The techniques include rotation, scaling, random cropping, flipping, shifting, and their combination. The results showed that the augmented models perform 1-3% better compared to a base network. However, as the augmented images only contain the human parts already visible in the original images, a new data augmentation approach is needed to include the invisible parts of the human body. Thus, we suggest a new method that employs simulated 3D human models to generate new data for training the network.

Keywords: human recognition, deep learning, drones, disaster mitigation

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Authors: Emine Akar

Abstract:

The majority of privacy-related regulation has traditionally focused on concepts that are perceived to be well-understood or easily describable, such as certain categories of data and personal information or images. In the past century, such regulation appeared reasonably suitable for its purposes. However, technologies such as AI, combined with ever-increasing capabilities to collect, process, and store “big data”, not only require calibration of these traditional understandings but may require re-thinking of entire categories of privacy law. In the presentation, it will be explained, against the background of various emerging technologies under the umbrella term “emotional artificial intelligence”, why modern privacy law will need to embrace human emotions as potentially private subject matter. This argument can be made on a jurisprudential level, given that human emotions can plausibly be accommodated within the various concepts that are traditionally regarded as the underlying foundation of privacy protection, such as, for example, dignity, autonomy, and liberal values. However, the practical reasons for regarding human emotions as potentially private subject matter are perhaps more important (and very likely more convincing from the perspective of regulators). In that respect, it should be regarded as alarming that, according to most projections, the usefulness of emotional data to governments and, particularly, private companies will not only lead to radically increased processing and analysing of such data but, concerningly, to an exponential growth in the collection of such data. In light of this, it is also necessity to discuss options for how regulators could address this emerging threat.

Keywords: AI, privacy law, data protection, big data

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Authors: Reza Mohammadzadeh

Abstract:

The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

Procedia PDF Downloads 274