Search results for: earthquake disaster data collection

Authors: Jacqueline Kunzelman, Val Anderson, Robert Johnson, Nicholas Anderson, Rebecca Bates

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In an effort to protect honeybees from colony collapse disorder, beekeepers are filing for government permits to use natural lands as summer pasture for honeybees under the multiple-use management regime in the United States. Utilizing natural landscapes in high mountain ranges may help strengthen honeybee colonies, as this natural setting is generally void of chemical pollutants and pesticides that are found in agricultural and urban settings. However, the introduction of a competitive species could greatly impact the native species occupying these natural landscapes. While honeybees and butterflies have different life histories, behavior, and foraging strategies, they compete for the same nectar resources. Few, if any, studies have focused on the potential population effects of commercial honeybees on native butterfly abundance and diversity. This study attempts to observe this impact using a paired before-after control-impact (BACI) design. Over the course of two years, malaise trap samples were collected every week during the months of the flowering season in two similar areas separated by 11 kilometers. Each area contained nine malaise trap sites for replication. In the first year, samples were taken to analyze and establish trends within the pollinating communities. In the second year, honeybees were introduced to only one of the two areas, and a change in trends between the two areas was assessed. Contrary to the original hypothesis, the resulting observation was an overall significant increase in the mean butterfly abundance in the impact areas after honeybees were introduced, while control areas remained relatively stable. This overall increase in abundance over the season can be attributed to an increase in butterflies during the first and second periods of the data collection when populations were near their peak. Several potential theories are 1) Honeybees are deterring a natural predator/competitor of butterflies that previously limited population growth. 2) Honeybees are consuming resources regularly used by butterflies, which may extend the foraging time and consequent capture rates of butterflies. 3) Environmental factors such as number of rainy days were inconsistent between control and impact areas, biasing capture rates. This ongoing research will help determine the suitability of high mountain ranges for the summer pasturing of honeybees and the population impacts on many different pollinators.

Keywords: butterfly, competition, honeybee, pollinator

Procedia PDF Downloads 138

Authors: Jude Opara, Esemokumo Perewarebo Akpos

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This paper is on non-parametric linear regression over its parametric counterparts with large sample size. Data set on anthropometric measurement of primary school pupils was taken for the analysis. The study used 50 randomly selected pupils for the study. The set of data was subjected to normality test, and it was discovered that the residuals are not normally distributed (i.e. they do not follow a Gaussian distribution) for the commonly used least squares regression method for fitting an equation into a set of (x,y)-data points using the Anderson-Darling technique. The algorithms for the nonparametric Theil’s regression are stated in this paper as well as its parametric OLS counterpart. The use of a programming language software known as “R Development” was used in this paper. From the analysis, the result showed that there exists a significant relationship between the response and the explanatory variable for both the parametric and non-parametric regression. To know the efficiency of one method over the other, the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) are used, and it is discovered that the nonparametric regression performs better than its parametric regression counterparts due to their lower values in both the AIC and BIC. The study however recommends that future researchers should study a similar work by examining the presence of outliers in the data set, and probably expunge it if detected and re-analyze to compare results.

Keywords: Theil’s regression, Bayesian information criterion, Akaike information criterion, OLS

Procedia PDF Downloads 297

Authors: D. Prangchumpol

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This research presents a forecasting method of requisition document demands for Military units by using Exponential Smoothing methods to analyze data. The data used in the forecast is an actual data requisition document of The Adjutant General Department. The results of the forecasting model to forecast the requisition of the document found that Holt–Winters’ trend and seasonality method of α=0.1, β=0, γ=0 is appropriate and matches for requisition of documents. In addition, the researcher has developed a requisition online system to improve the performance of requisition documents of The Adjutant General Department, and also ensuring that the operation can be checked.

Keywords: requisition, holt–winters, time series, royal thai army

Procedia PDF Downloads 295

Authors: Ibrahim Mohammed, Suleiman Taofiq, Muhammad Naziru Yahya

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Geoelectrical measurements using Schlumberger Vertical Electrical Sounding (VES) method were carried out in Waziri Umaru Federal Polytechnic, Birnin Kebbi, Nigeria, with the aim of determining the groundwater potential in the area. Twelve (12) Vertical Electric Sounding (VES) data were collected using Terrameter (ABEM SAS 300c) and analyzed using computer software (IPI2win), which gives an automatic interpretation of the apparent resistivity. The results of the interpretation of VES data were used in the characterization of three to five geo-electric layers from which the aquifer units were delineated. Data analysis indicated that water bearing formation exists in the third and fourth layers having resistivity range of 312 to 767 Ωm and 9.51 to 681 Ωm, respectively. The thickness of the formation ranges from 14.7 to 41.8 m, while the depth is from 8.22 to 53.7 m. Based on the result obtained from the interpretation of the data, five (5) VES stations were recommended as the most viable locations for groundwater exploration in the study area. The VES stations include VES A4, A5, A6, B1, and B2. The VES results of the entire area indicated that the water bearing formation occurs at maximum depth of 53.7 m at the time of this survey.

Keywords: aquifer, depth, groundwater, resistivity, Schlumberger

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Authors: Dalvin D. Hill, Hector M. Castro Garcia

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A growing number of individuals utilize wearable devices on a daily basis. The usage and functionality of these wearable devices vary from user to user. One popular usage of said devices is to track health-related activities that are typically stored on a device’s memory or uploaded to an account in the cloud; based on the current trend, the data accumulated from the wearable device are stored in a standalone location. In many of these cases, this health related datum is not a factor when considering the holistic view of a user’s health lifestyle or record. This health-related data generated from wearable and Internet of Things (IoT) devices can serve as empirical information to a medical provider, as the standalone data can add value to the holistic health record of a patient. This paper proposes a solution to incorporate the data gathered from these wearable and IoT devices, with that a patient’s Personal Health Record (PHR) stored within the confines of a Health Information Exchange (HIE).

Keywords: electronic health record, health information exchanges, internet of things, personal health records, wearable devices, wearables

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Authors: Chao Yu, Qing-Guo Wang, Dan Zhang

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The outlier detection problem for dynamic systems is formulated as a matrix decomposition problem with low-rank, sparse matrices and further recast as a semidefinite programming (SDP) problem. A fast algorithm is presented to solve the resulting problem while keeping the solution matrix structure and it can greatly reduce the computational cost over the standard interior-point method. The computational burden is further reduced by proper construction of subsets of the raw data without violating low rank property of the involved matrix. The proposed method can make exact detection of outliers in case of no or little noise in output observations. In case of significant noise, a novel approach based on under-sampling with averaging is developed to denoise while retaining the saliency of outliers and so-filtered data enables successful outlier detection with the proposed method while the existing filtering methods fail. Use of recovered “clean” data from the proposed method can give much better parameter estimation compared with that based on the raw data.

Keywords: outlier detection, system identification, matrix decomposition, low-rank matrix, sparsity, semidefinite programming, interior-point methods, denoising

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Authors: Walter Bernhofer, Peter Haber, Tobias Mayer, Manfred Mayr, Markus Ziegler

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Current preventive maintenance measures are cost intensive and not efficient. With the available sensor data of state of the art internet of things devices new possibilities of automated data processing emerge. Current advances in data science and in machine learning enable new, so called predictive maintenance technologies, which empower data scientists to forecast possible system failures. The goal of this approach is to cut expenses in preventive maintenance by automating the detection of possible failures and to improve efficiency and quality of maintenance measures. Additionally, a centralization of the sensor data monitoring can be achieved by using this approach. This paper describes the approach of three students to define a reference architecture for a predictive maintenance solution in the internet of things domain with a connected smartphone app for service technicians. The reference architecture is validated by a case study. The case study is implemented with current Microsoft Azure cloud technologies. The results of the case study show that the reference architecture is valid and can be used to achieve a system for predictive maintenance execution with the cloud components of Microsoft Azure. The used concepts are technology platform agnostic and can be reused in many different cloud platforms. The reference architecture is valid and can be used in many use cases, like gas station maintenance, elevator maintenance and many more.

Keywords: case study, internet of things, predictive maintenance, reference architecture

Procedia PDF Downloads 233

Authors: Vasileiou Apostolos, Theodosiou Chrysa, Tsitroulis Ioannis, Maris Fotios

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The severity of extreme phenomena is due to their ability to cause severe damage in a small amount of time. It has been observed that floods affect the greatest number of people and induce the biggest damage when compared to the total of annual natural disasters. The detection of potential flood-prone areas constitutes one of the fundamental components of the European Natural Disaster Management Policy, directly connected to the European Directive 2007/60. The aim of the present paper is to develop a new methodology that combines geographical information, fuzzy logic and multi-criteria evaluation methods so that the most vulnerable areas are defined. Therefore, ten factors related to geophysical, morphological, climatological/meteorological and hydrological characteristics of the basin were selected. Afterwards, two models were created to detect the areas pronest to flooding. The first model defined the gravitas of each factor using Analytical Hierarchy Process (AHP) and the final map of possible flood spots were created using GIS and Boolean Algebra. The second model made use of the fuzzy logic and GIS combination and a respective map was created. The application area of the aforementioned methodologies was in Ardas basin due to the frequent and important floods that have taken place these last years. Then, the results were compared to the already observed floods. The result analysis shows that both models can detect with great precision possible flood spots. As the fuzzy logic model is less time-consuming, it is considered the ideal model to apply to other areas. The said results are capable of contributing to the delineation of high risk areas and to the creation of successful management plans dealing with floods.

Keywords: analytical hierarchy process, flood prone areas, fuzzy logic, geographic information system

Procedia PDF Downloads 367

Authors: Yan Zhang

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Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

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Authors: Thien Van Ngo

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The engineering design process is a systematic approach to solving problems. It involves identifying a problem, brainstorming solutions, prototyping and testing solutions, and evaluating the results. The engineering design process can be used to teach students how to solve problems in a creative and innovative way. The research aim of this study was to investigate the effectiveness of using the engineering design process to enhance student learning outcomes in a physics course. A mixed research method was used in this study. The quantitative data were collected using a pretest-posttest control group design. The qualitative data were collected using semi-structured interviews. The sample was 150 first-year students in the Department of Mechanical Engineering Technology at Cao Thang Technical College in Vietnam in the 2022-2023 school year. The quantitative data were collected using a pretest-posttest control group design. The pretest was administered to both groups at the beginning of the study. The posttest was administered to both groups at the end of the study. The qualitative data were collected using semi-structured interviews with a sample of eight students in the experimental group. The interviews were conducted after the posttest. The quantitative data were analyzed using independent sample T-tests. The qualitative data were analyzed using thematic analysis. The quantitative data showed that students in the experimental group, who were taught using the engineering design process, had significantly higher post-test scores on physics problem-solving than students in the control group, who were taught using the conventional method. The qualitative data showed that students in the experimental group were more motivated and engaged in the learning process than students in the control group. Students in the experimental group also reported that they found the engineering design process to be a more effective way of learning physics. The findings of this study suggest that the engineering design process can be an effective way of enhancing student learning outcomes in physics courses. The engineering design process engages students in the learning process and helps them to develop problem-solving skills.

Keywords: engineering design process, problem-solving, learning outcome of physics, students’ physics competencies, deep learning

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Authors: Han-Mei Chen, Rongxin Zhou, Taige Wang

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Glass is widely used in everyday life, from glass bottles for beverages to architectural glass for various forms of glazing. Although the mainstream of used glass is recycled in the UK, the single-use and then recycling procedure results in a lot of waste as it incorporates intact glass with smashing, re-melting, and remanufacturing. These processes bring massive energy consumption with a huge loss of high embodied energy and economic value, compared to re-use, which’s towards a ‘zero carbon’ target. As a tourism city, Liverpool has more glass bottle consumption than most less leisure-focused cities. It’s therefore vital for Liverpool to find an upgrading approach for the single-use glass bottles with low carbon output. This project aims to assess the feasibility of industrial symbiosis and upgrading the framework of glass and to investigate the ways of achieving them. It is significant to Liverpool’s future industrial strategy since it provides an opportunity to target economic recovery for post-COVID by industry symbiosis and up-grading waste management in Liverpool to respond to the climate emergency. In addition, it will influence the local government policy for glass bottle reuse and recycling in North West England and as a good practice to be further recommended to other areas of the UK. First, a critical literature review of glass waste strategies has been conducted in the UK and worldwide industrial symbiosis practices. Second, mapping, data collection, and analysis have shown the current life cycle chain and the strong links of glass reuse and upgrading potentials via site visits to 16 local waste recycling centres. The results of this research have demonstrated the understanding of the influence of key factors on the development of a circular industrial symbiosis business model for beverage glass bottles. The current waste management procedures of the glass bottle industry, its business model, supply chain, and material flow have been reviewed. The various potential opportunities for glass bottle up-valuing have been investigated towards an industrial symbiosis in Liverpool. Finally, an up-valuing business model has been developed for an industrial symbiosis framework of glass in Liverpool. For glass bottles, there are two possibilities 1) focus on upgrading processes towards re-use rather than single-use and recycling and 2) focus on ‘smart’ re-use and recycling, leading to optimised values in other sectors to create a wider industry symbiosis for a multi-level and circular economy.

Keywords: glass bottles, industry symbiosis, smart re-use, waste upgrading

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Authors: Jalal Haghighat Monfared, Zahra Akbari

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Today, business executives need to have useful information to make better decisions. Banks have also been using information tools so that they can direct the decision-making process in order to achieve their desired goals by rapidly extracting information from sources with the help of business intelligence. The research seeks to investigate whether there is a relationship between the quality of decision making and the business intelligence capabilities of Mellat Bank. Each of the factors studied is divided into several components, and these and their relationships are measured by a questionnaire. The statistical population of this study consists of all managers and experts of Mellat Bank's General Departments (including 190 people) who use commercial intelligence reports. The sample size of this study was 123 randomly determined by statistical method. In this research, relevant statistical inference has been used for data analysis and hypothesis testing. In the first stage, using the Kolmogorov-Smirnov test, the normalization of the data was investigated and in the next stage, the construct validity of both variables and their resulting indexes were verified using confirmatory factor analysis. Finally, using the structural equation modeling and Pearson's correlation coefficient, the research hypotheses were tested. The results confirmed the existence of a positive relationship between decision quality and business intelligence capabilities in Mellat Bank. Among the various capabilities, including data quality, correlation with other systems, user access, flexibility and risk management support, the flexibility of the business intelligence system was the most correlated with the dependent variable of the present research. This shows that it is necessary for Mellat Bank to pay more attention to choose the required business intelligence systems with high flexibility in terms of the ability to submit custom formatted reports. Subsequently, the quality of data on business intelligence systems showed the strongest relationship with quality of decision making. Therefore, improving the quality of data, including the source of data internally or externally, the type of data in quantitative or qualitative terms, the credibility of the data and perceptions of who uses the business intelligence system, improves the quality of decision making in Mellat Bank.

Keywords: business intelligence, business intelligence capability, decision making, decision quality

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Authors: Rahul Patel

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Ahmedabad, a city located in western India, is experiencing rapid growth due to urbanization and industrialization. It is projected to become a metropolitan city in the near future, resulting in various construction activities. Soil testing is necessary before construction can commence, requiring construction companies and contractors to periodically conduct soil testing. The focus of this study is on the process of creating a spatial database that is digitally formatted and integrated with geotechnical data and a Geographic Information System (GIS). Building a comprehensive geotechnical (Geo)-database involves three steps: collecting borehole data from reputable sources, verifying the accuracy and redundancy of the data, and standardizing and organizing the geotechnical information for integration into the database. Once the database is complete, it is integrated with GIS, allowing users to visualize, analyze, and interpret geotechnical information spatially. Using a Topographic to Raster interpolation process in GIS, estimated values are assigned to all locations based on sampled geotechnical data values. The study area was contoured for SPT N-Values, Soil Classification, Φ-Values, and Bearing Capacity (T/m2). Various interpolation techniques were cross-validated to ensure information accuracy. This GIS map enables the calculation of SPT N-Values, Φ-Values, and bearing capacities for different footing widths and various depths. This study highlights the potential of GIS in providing an efficient solution to complex phenomena that would otherwise be tedious to achieve through other means. Not only does GIS offer greater accuracy, but it also generates valuable information that can be used as input for correlation analysis. Furthermore, this system serves as a decision support tool for geotechnical engineers.

Keywords: ArcGIS, borehole data, geographic information system, geo-database, interpolation, SPT N-value, soil classification, Φ-Value, bearing capacity

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Authors: S. M. Mahdi Niktabara, K. Seshagiri Raob, Amit Kumar Shrivastavac, Jiří Ščučkaa

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The presence of the discontinuity in the form of joints is one of the most significant factors causing instability in the rock mass. On the other hand, dynamic loads, including earthquake and blasting induce cyclic shear loads along the joints in rock masses; therefore, failure of rock mass exacerbates along the joints due to changing shear resistance. Joints are under constant normal load (CNL) and constant normal stiffness (CNS) conditions. Normal stiffness increases on the joints with increasing depth, and it can affect shear resistance. For correct assessment of joint shear resistance under varying normal stiffness and number of cycles, advanced laboratory shear machine is essential for the shear test. Conventional direct shear equipment has limitations such as boundary conditions, working under monotonic movements only, or cyclic shear loads with constant frequency and amplitude of shear loads. Hence, a large-scale servo-controlled direct shear testing machine was designed and fabricated to perform shear test under the both CNL and CNS conditions with varying normal stiffness at different frequencies and amplitudes of shear loads. In this study, laboratory cyclic shear tests were conducted on non-planar joints under varying normal stiffness. In addition, the effects of different frequencies and amplitudes of shear loads were investigated. The test results indicate that shear resistance increases with increasing normal stiffness at the first cycle, but the influence of normal stiffness significantly decreases with an increase in the number of shear cycles. The frequency of shear load influences on shear resistance, i.e. shear resistance increases with increasing frequency. However, at low shear amplitude the number of cycles does not affect shear resistance on the joints, but it decreases with higher amplitude.

Keywords: cyclic shear load, frequency of load, amplitude of displacement, normal stiffness

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Authors: B. R. Shen, J. R. Wang, J. H. Yang, S. W. Chen, C. Shih, Y. Chiang, Y. F. Chang, Y. H. Huang

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In this research, TRACE code with the interface code-SNAP was used to simulate and analyze the SBO (station blackout) accident which occurred in Maanshan PWR (pressurized water reactor) nuclear power plant (NPP). There are four main steps in this research. First, the SBO accident data of Maanshan NPP were collected. Second, the TRACE/SNAP model of Maanshan NPP was established by using these data. Third, this TRACE/SNAP model was used to perform the simulation and analysis of SBO accident. Finally, the simulation and analysis of SBO with mitigation equipments was performed. The analysis results of TRACE are consistent with the data of Maanshan NPP. The mitigation equipments of Maanshan can maintain the safety of Maanshan in the SBO according to the TRACE predictions.

Keywords: pressurized water reactor (PWR), TRACE, station blackout (SBO), Maanshan

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Authors: Jojo Y. C. Mo

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Memories are good. They remind us of people, places and experiences that we cherish. But memories cannot be changed and there may well be memories that we do not want to remember. This is particularly true in relation to information which causes us embarrassment and humiliation or simply because it is private – we all want to erase or delete such information. This desire to delete is recently recognised by the Court of Justice of the European Union in the 2014 case of Google Spain SL, Google Inc. v Agencia Española de Protección de Datos, Mario Costeja González in which the court ordered Google to remove links to some information about the complainant which he wished to be removed. This so-called ‘right to be forgotten’ received serious attention and significantly, the European Council and the European Parliament enacted the General Data Protection Regulation (GDPR) to provide a more structured and normative framework for implementation of right to be forgotten across the EU. This development in data protection laws will, undoubtedly, have significant impact on companies and co-operations not just within the EU but outside as well. Hong Kong, being one of the world’s leading financial and commercial center as well as one of the first jurisdictions in Asia to implement a comprehensive piece of data protection legislation, is therefore a jurisdiction that is worth looking into. This article/project aims to investigate the following: a) whether there is a right to be forgotten under the existing Hong Kong data protection legislation b) if not, whether such a provision is necessary and why. This article utilises a comparative methodology based on a study of primary and secondary resources, including scholarly articles, government and law commission reports and working papers and relevant international treaties, constitutional documents, case law and legislation. The author will primarily engage literature and case-law review as well as comparative and doctrinal analyses. The completion of this article will provide privacy researchers with more concrete principles and data to conduct further research on privacy and data protection in Hong Kong and internationally and will provide a basis for policy makers in assessing the rationale and need for a right to be forgotten in Hong Kong.

Keywords: privacy, right to be forgotten, data protection, Hong Kong

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Authors: Hyeongju Jeon, Yonghyun Kim, Yongil Kim

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Synthetic Aperture Radar (SAR) is an effective tool for damage assessment induced by disasters due to its all-weather and night/day acquisition capability. In this paper, the 2017 Colombia landslide was observed using full-polarimetric ALOS/PALSAR-2 data. Polarimetric decompositions, including the Freeman-Durden decomposition and the Cloude decomposition, are utilized to analyze the scattering mechanisms changes before and after-landslide. These analyses are used to detect the damaged areas induced by the landslide. Experimental results validate the efficiency of the full polarimetric SAR data since the damaged areas can be well discriminated. Thus, we can conclude the proposed method using full polarimetric data has great potential for damage assessment of landslides.

Keywords: Synthetic Aperture Radar (SAR), polarimetric decomposition, damage assessment, landslide

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Authors: Sofia Stoica

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In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices in the past five years of ten major tech companies – Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We experimented with a variety of models– a linear regressor model, K nearest Neighbors (KNN), a sequential neural network – and algorithms - Multiplicative Weight Update, and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement.

Keywords: finance, machine learning, opening price, stock market

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Authors: Saurabh Parkhedkar, Rajanikant Tenguria

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The paper proposes revamping the public transportation system in Mumbai with the use of Radio Frequency Identification (RFID) technology in order to provide better integration and compatibility across various modes of transport. In Mumbai, mass transport system suffers from poor inter-compatible ticketing system, subpar money collection techniques, and lack of planning for optimum utilization of resources. Development of suburbs and growth in population will result in growing demand for mass transportation networks. Hence, the growing demand for the already overburdened public transportation system is only going to worsen the scenario. Thus, a superior system is essential in order to regulate, manage and supervise future transportation needs. The proposed RFID based system integrates Mumbai Suburban Railway, BEST (Brihanmumbai Electric Supply and Transport Undertaking transport wing) Bus, Mumbai Monorail and Mumbai Metro systems into a Unified Public Transportation System (UPTS). The UTPS takes into account various drawbacks of the present day system and offers solution, suitable for the modern age Mumbai.

Keywords: urbanization, transportation, RFID, Mumbai, public transportation, smart city.

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Authors: Jihen Bennaceur, Wissem Zouaghi, Ali Mabrouk

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The major aim of cyber threat intelligence (CTI) is to provide sophisticated knowledge about cybersecurity threats to ensure internal and external safeguards against modern cyberattacks. Inaccurate, incomplete, outdated, and invaluable threat intelligence is the main problem. Therefore, data analysis based on AI algorithms is one of the emergent solutions to overcome the threat of information-sharing issues. In this paper, we propose a supervised machine learning-based algorithm to improve threat information sharing by providing a sophisticated classification of cyber threats and data. Extensive simulations investigate the accuracy, precision, recall, f1-score, and support overall to validate the designed algorithm and to compare it with several supervised machine learning algorithms.

Keywords: threat information sharing, supervised learning, data classification, performance evaluation

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Authors: Iryna P. Dzieciuch, Michael D. Putman

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Humidity is known to degrade Navy ship electronic equipment, especially in hot moist environments. If left untreated, it can cause significant and permanent damage. Even rigorous inspection and frequent clean-up would not prevent further equipment contamination and degradation because of the constant presence of favorable growth conditions for many microorganisms. Generally, relative humidity levels of less than 60% will inhibit corrosion in electronic equipment, but because NAVY electronics often operate in hot and humid environments, prevention via dehumidification is not always possible. Currently, there is no defined research that fully describes key mechanisms which cause electronics and its coating degradation. The corrosive action of most bacteria is mainly developed through (i) mycelium adherence to the metal plates, (ii) facilitation the formation of pitting areas, (iii) production of organic acids such as citric, iso-citric, cis-aconitic, alpha-ketoglutaric, which are corrosive to electronic equipment and its components. Our approach studies corrosive action in electronic equipment: circuit-board, wires and connections that are exposed in the humid environment that gets worse during condensation. In our new approach the technical task is built on work with the bacterial communities in public areas, bacterial genetics, bioinformatics, biostatistics and Scanning Electron Microscopy (SEM) of corroded circuit boards. Based on these methods, we collect and examine environmental samples from biofilms of the corroded and non-corroded sites, where bacterial contamination of electronic equipment, such as machine racks and shore boats, is an ongoing concern. Sample collection and sample analysis is focused on addressing the key questions identified above through the following tasks: laboratory sample processing and evaluation under scanning electron microscopy, initial sequencing and data evaluation; bioinformatics and data analysis. Preliminary results from scanning electron microscopy (SEM) have revealed that metal particulates and alloys in corroded samples consists mostly of Tin ( < 40%), Silicon ( < 4%), Sulfur ( < 1%), Aluminum ( < 2%), Magnesium ( < 2%), Copper ( < 1%), Bromine ( < 2%), Barium ( <1%) and Iron ( < 2%) elements. We have also performed X 12000 magnification of the same sites and that proved existence of undisrupted biofilm organelles and crystal structures. Non-corrosion sites have revealed high presence of copper ( < 47%); other metals remain at the comparable level as on the samples with corrosion. We have performed X 1000 magnification on the non-corroded at the sites and have documented formation of copper crystals. The next step of this study, is to perform metagenomics sequencing at all sites and to compare bacterial composition present in the environment. While copper is nontoxic to the living organisms, the process of bacterial adhesion creates acidic environment by releasing citric, iso-citric, cis-aconitic, alpha-ketoglutaric acidics, which in turn release copper ions Cu++, which that are highly toxic to the bacteria and higher order living organisms. This phenomenon, might explain natural “antibiotic” properties that are lacking in elements such as tin. To prove or deny this hypothesis we will use next - generation sequencing (NGS) methods to investigate types and growth cycles of bacteria that from bacterial biofilm the on corrosive and non-corrosive samples.

Keywords: bacteria, biofilm, circuit board, copper, corrosion, electronic equipment, organic acids, tin

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Authors: Julian Y. Raheema, Michael R. Hess, Raymond C. Provost, Mark Bilinski

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The world is rapidly moving towards advancing and utilizing artificial intelligence and autonomous robotics. The ground-breaking Boston Dynamics quadruped robot, SPOT, was designed for industrial and commercial tasks requiring limited autonomous navigation. Out of the box, SPOT has route memorization and playback – it can repeat a path that it has been manually piloted through, but it cannot autonomously navigate an area that has not been previously explored. The presented SPOT payload package is built on ROS framework to support autonomous navigation and mapping of an unexplored environment. The package is fully integrated with SPOT to take advantage of motor controls and collision avoidance that comes natively with the robot. The payload runs all computations onboard, takes advantage of visual odometry SLAM and uses an Intel RealSense depth camera and Velodyne LiDAR sensor to generate 2D and 3D maps while in autonomous navigation mode. These maps are fused into the navigation stack to generate a costmap to enable the robot to safely navigate the environment without causing damage to the surroundings or the robot. The operator defines the operational zone and start location and then sends the explore command to have SPOT explore, generate 2D and 3D maps of the environment and return to the start location to await the operator's next command. The benefit of the presented package is that it is much lighter weight and less expensive than previous approaches and, importantly, operates in GPS-denied scenarios, which is ideal for indoor mapping. There are numerous applications that are hazardous to humans for SPOT enhanced with the autonomy payload, including disaster response, nuclear inspection, mine inspection, and so on. Other less extreme uses cases include autonomous 3D and 2D scanning of facilities for inspection, engineering and construction purposes.

Keywords: autonomous, SLAM, quadruped, mapping, exploring, ROS, robotics, navigation

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Authors: Armenio Lopes, Carlos Ferreira

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In the specific context of the Iberian Union between 1580 and 1640, characteristics emerged in Portuguese architecture that stood out from the main architectural production of the period. Recognised and identified aspects that had begun making their appearance decades before (1521) became significantly more marked during the Hapsburg-Spanish occupation. Distinctive even from the imperialist language of Spain, this trend would endure even after the restoration of independence (1706), continuing through to the start of the age of absolutism. Or perhaps not. This trend, recognised as Plain Style (Kubler), associated with a certain scarcity of resources, involved a certain formal and decorative simplification, as well as a particular set of conventions that would subsequently mark the landscape. This expression could also be seen as a means of asserting a certain spirit of independence as the Iberian Union breathed its last. The image of a simple, bare-bones architecture with purer design lines is associated by various authors –most notably Kubler– with the narratives of modernism, to whose principles it is similar, in a context-specific to the period. There is a contrast with some of the exuberance of the baroque or its expression in the Manueline period, in a similar fashion to modernism's responses to nineteenth-century eclecticism. This assertion and practice of simple architecture, drafted from the interpretation of the treaties, and highlighting a certain classical inspiration, was to become a benchmark in the theory of architecture, spanning the Baroque and Mannerism, until achieving contemporary recognition within certain originality and modernity. At a time when the baroque and its scenography became generally very widespread, it is important also to recognise the role played by plain style architecture in the construction of a rather complex and contradictory waterfront landscape, featuring promises of exuberance and more discrete practices.

Keywords: Carlos Mardel, Lisbon's waterfront, plain style, urban image on the waterfront

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Authors: Srijanani Anurag Prasad

Abstract:

The Coalescence Hidden-variable Fractal Interpolation Surface (CHFIS) was built by combining interpolation data from the Iterated Function System (IFS). The interpolation data in a CHFIS comprises a row and/or column of uncertain values when a single point is entered. Alternatively, a row and/or column of additional points are placed in the given interpolation data to demonstrate the node added CHFIS. There are three techniques for inserting new points that correspond to the row and/or column of nodes inserted, and each method is further classified into four types based on the values of the inserted nodes. As a result, numerous forms of node insertion can be found in a CHFIS.

Keywords: fractal, interpolation, iterated function system, coalescence, node insertion, knot insertion

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Authors: Moses Emetere, Marvel Akinyemi, S. E. Sanni

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At the moment, AERONET or AMMA database shows a large volume of data loss. With only about 47% data set available to the scientist, it is evident that accurate nowcast or forecast cannot be guaranteed. The calibration constants of most radiosonde or weather stations are not compatible with the atmospheric conditions of the West African climate. A dispersion model was developed to incorporate salient mathematical representations like a Unified number. The Unified number was derived to describe the turbulence of the aerosols transport in the frictional layer of the lower atmosphere. Fourteen years data set from Multi-angle Imaging SpectroRadiometer (MISR) was tested using the dispersion model. A yearly estimation of the atmospheric constants over Ouagadougou using the model was obtained with about 87.5% accuracy. It further revealed that the average atmospheric constant for Ouagadougou-Niger is a_1 = 0.626, a_2 = 0.7999 and the tuning constants is n_1 = 0.09835 and n_2 = 0.266. Also, the yearly atmospheric constants affirmed the lower atmosphere of Ouagadougou is very dynamic. Hence, it is recommended that radiosonde and weather station manufacturers should constantly review the atmospheric constant over a geographical location to enable about eighty percent data retrieval.

Keywords: aerosols retention, aerosols loading, statistics, analytical technique

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Authors: Adilah Abdul Ghapor, Yong Zulina Zubairi, Rahmatullah Imon

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Missing value problem is common in statistics and has been of interest for years. This article considers two modern techniques in handling missing data for linear functional relationship model (LFRM) namely the Expectation-Maximization (EM) algorithm and Expectation-Maximization with Bootstrapping (EMB) algorithm using three performance indicators; namely the mean absolute error (MAE), root mean square error (RMSE) and estimated biased (EB). In this study, we applied the methods of imputing missing values in the LFRM. Results of the simulation study suggest that EMB algorithm performs much better than EM algorithm in both models. We also illustrate the applicability of the approach in a real data set.

Keywords: expectation-maximization, expectation-maximization with bootstrapping, linear functional relationship model, performance indicators

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Authors: Alireza Vafaei Sadr, Vida Abedi, Jiang Li, Ramin Zand

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Missing data is a common challenge in medical research and can lead to biased or incomplete results. When the data bias leaks into models, it further exacerbates health disparities; biased algorithms can lead to misclassification and reduced resource allocation and monitoring as part of prevention strategies for certain minorities and vulnerable segments of patient populations, which in turn further reduce data footprint from the same population – thus, a vicious cycle. This study compares the performance of six imputation techniques grouped into Linear and Non-Linear models on two different realworld electronic health records (EHRs) datasets, representing 17864 patient records. The mean absolute percentage error (MAPE) and root mean squared error (RMSE) are used as performance metrics, and the results show that the Linear models outperformed the Non-Linear models in terms of both metrics. These results suggest that sometimes Linear models might be an optimal choice for imputation in laboratory variables in terms of imputation efficiency and uncertainty of predicted values.

Keywords: EHR, machine learning, imputation, laboratory variables, algorithmic bias

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Authors: Mohammad H. Yarmohammadian, Mehdi Nasr Isfahani, Elham Anbari

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Introduction: The awareness of using chemical, biological, and nuclear agents in everyday industrial and non-industrial incidents has increased recently; release of these materials can be accidental or intentional. Since hospitals are the forefronts of confronting Chemical, Biological, Radiological and Nuclear( CBRN) incidents, the goal of the present research was to provide a generic protocol for CBRN incidents through a comparative review of CBRN protocols and guidelines of different countries and reviewing various books, handbooks and papers. Method: The integrative approach or research synthesis was adopted in this study. First a simple narrative review of programs, books, handbooks, and papers about response to CBRN incidents in different countries was carried out. Then the most important and functional information was discussed in the form of a generic protocol in focus group sessions and subsequently confirmed. Results: Findings indicated that most of the countries had various protocols, guidelines, and handbooks for hazardous materials or CBRN incidents. The final outcome of the research synthesis was a 50 page generic protocol whose main topics included introduction, definition and classification of CBRN agents, four major phases of incident and disaster management cycle, hospital response management plan, equipment, and recommended supplies and antidotes for decontamination (radiological/nuclear, chemical, biological); each of these also had subtopics. Conclusion: In the majority of international protocols, guidelines, handbooks and also international and Iranian books and papers, there is an emphasis on the importance of incident command system, determining the safety degree of decontamination zones, maps of decontamination zones, decontamination process, triage classifications, personal protective equipment, and supplies and antidotes for decontamination; these are the least requirements for such incidents and also consistent with the provided generic protocol.

Keywords: hospital, CBRN, decontamination, generic protocol, CBRN Incidents

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Authors: Gichun Cha, Changgil Lee, Seunghee Park

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The objective of the study is to construct the shape management method contributing to the safety of the large structure. In Korea, the research of the shape management is lack because of the new attempted technology. Terrestrial Laser Scanning (TLS) is used for measurements of large structures. TLS provides an efficient way to actively acquire accurate the point clouds of object surfaces or environments. The point clouds provide a basis for rapid modeling in the industrial automation, architecture, construction or maintenance of the civil infrastructures. TLS produce a huge amount of point clouds. Registration, Extraction and Visualization of data require the processing of a massive amount of scan data. The octree can be applied to the shape management of the large structure because the scan data is reduced in the size but, the data attributes are maintained. The octree space partitioning generates the voxel of 3D space, and the voxel is recursively subdivided into eight sub-voxels. The point cloud of scan data was converted to voxel and sampled. The experimental site is located at Sungkyunkwan University. The scanned structure is the steel-frame bridge. The used TLS is Leica ScanStation C10/C5. The scan data was condensed 92%, and the octree model was constructed with 2 millimeter in resolution. This study presents octree space partitioning for handling the point clouds. The basis is created by shape management of the large structures such as double-deck tunnel, building and bridge. The research will be expected to improve the efficiency of structural health monitoring and maintenance. "This work is financially supported by 'U-City Master and Doctor Course Grant Program' and the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIP) (NRF- 2015R1D1A1A01059291)."

Keywords: 3D scan data, octree space partitioning, shape management, structural health monitoring, terrestrial laser scanning

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Authors: L. Parisi

Abstract:

Kinematic data wisely correlate vector quantities in space to scalar parameters in time to assess the degree of symmetry between the intact limb and the amputated limb with respect to a normal model derived from the gait of control group participants. Furthermore, these particular data allow a doctor to preliminarily evaluate the usefulness of a certain rehabilitation therapy. Kinetic curves allow the analysis of ground reaction forces (GRFs) to assess the appropriateness of human motion. Electromyography (EMG) allows the analysis of the fundamental lower limb force contributions to quantify the level of gait asymmetry. However, the use of this technological tool is expensive and requires patient’s hospitalization. This research work suggests overcoming the above limitations by applying artificial neural networks.

Keywords: kinetics, kinematics, cyclograms, neural networks, transtibial amputation

Procedia PDF Downloads 434