Search results for: data integrity and privacy

Authors: Mohammad Parvaresh, Mahdi Babaee, Bahareh Arghand, Davood Nourmohammadi

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Environmental degradation is caused by social inequalities and the inappropriate use of nature and a factor in the violation of human rights. Indeed, the right to a safe, healthy and ecologically-balanced environment is an independent human right. Therefore, the relationship between human rights and environmental protection is crucial for the study of social justice and sustainable development, and environmental problems are a result of the failure to realize social and economic justice. In this regard, 'article 50 of the constitution of the Islamic Republic of Iran as a general principle have many of the concepts of sustainable development, including: the growth and improvement of human life, the rights of present and future generations, and the integrity of the inner and outer generation, the prohibition of any environmental degradation'. Also, Charter on Citizen’s Rights, which was conveyed by the President of the Islamic Republic of Iran, Mr. Rouhani refers to the right to a healthy environment and sustainable development. In this regard in 2013, Tehran Province Water and Wastewater Co. defined a plan called 'Tehran’s Health Line' was includes Western and Eastern part by about 26 kilometers of water transferring pipelines varied 1000 to 2000 mm diameters. This project aims to: (1) Transfer water from the northwest water treatment plant to the southwest areas, which suffer from qualitative and quantitative water, in order to mix with the improper wells’ water; (2) Reducing the water consumption provided by harvesting from wells which results in improving the underground water resources, causing the large settlements and stopping the immigrating slums into the center or north side of the city. All of the financial resources accounted for 53,000,000 US$ which is mobilized by Tehran Province Water and Wastewater Co. to expedite the work. The present study examines the Tehran Health Line plan and the purpose of implementation of this plan to achieve environmental protection, environmental justice and citizenship rights for all people who live in Tehran.

Keywords: environmental justice, international environmental law, erga omnes, charter on citizen's rights, Tehran health line

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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

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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

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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

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Authors: Ali D. Omur, Betul Apaydin Yildirim, Yavuz S. Saglam, Selim Comakli, Mustafa Ozkaraca

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Twenty-eight male Sprague Dawley rats (aged 3 months) were used in the study. The animals were given feed and water as ad libitum. Sprague Dawley rats were randomly divided into 4 groups as 7 rats in each group. Aflatoxin B1 (7.5 μg/200 g), resveratrol (60 mg/kg) was administered to rats in groups other than the control group. At the end of the 16th day, blood, semen and tissue specimens were taken by decapitation under ether anesthesia. The effects of aflatoxin B1 and resveratrol on spermatological, pathological and biochemical parameters were determined in rats. When we evaluate the spermatological parameters, it is understood that resveratrol has a statistically significant difference in terms of sperm motility and viability (membrane integrity) compared to the control group and aflatoxin B1 administration groups, indicating a protective effect on spermatological parameters (groups: control, resveratrol, aflatoxin B1 and Afb1 + res; respectively, values of motility: 71,42 ± 0,52b, 72,85 ± 1, 48c , 60,71 ± 1,30a, 57,14 ± 2, 40a; values of viability: 63,85 ± 1,33b, 70,42 ± 2,61c, 55,00 ± 1,54a, 56,57 ± 0,89a. In terms of pathological parameters -histopathological examination- in the control and resveratrol groups, seminiferous tubules were observed to be in normal structure. In the group treated with aflatoxin, the regular structure of the spermatogenic cells deteriorated, and the seminiferous tubules became necrotic and degenerative. In the group treated with Afb1 + res, the decreasing of necrotic and degenerative changes were determined compared with in the group treated with aflatoxin. As immunohistochemical examination, cleaved caspase 3 expression was found to be very low in the control and resveratrol groups. Cleaved caspase 3 expression was severely exacerbated in seminiferous tubules in aflatoxin group but cleaved caspase 3 expression level decreased in Afb1 + res. In the biochemical direction, resveratrol has been shown to inhibit the adverse effects of aflatoxin on antioxidant levels (GSH-mmol/L, CAT-kU/L, GPx-U/mL, SOD-EU/mL) and to show a protective effect. For this purpose, the use of resveratrol with antioxidant activity was investigated in preventing or ameliorating damage to aflatoxin B1. It has been concluded that resveratrol effectively prevents the aflatoxin-induced testicular damage and lipid peroxidation. It has also been shown that resveratrol has protective effects on sperm motility and viability.

Keywords: Aflatoxin B1, rat, resveratrol, sperm

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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: Judith Mwakalonge, Geophrey Mbatta, Saidi Siuhi, Gurcan Comert, Cuthbert Ruseruka

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Transportation agencies worldwide continuously monitor their roads' conditions to minimize road maintenance costs and maintain public safety and rideability quality. Existing methods for carrying out road condition surveys involve manual observations of roads using standard survey forms done by qualified road condition surveyors or engineers either on foot or by vehicle. Automated road condition survey vehicles exist; however, they are very expensive since they require special vehicles equipped with sensors for data collection together with data processing and computing devices. The manual methods are expensive, time-consuming, infrequent, and can hardly provide real-time information for road conditions. This study contributes to this arena by utilizing built-in vehicle technologies, drones, and deep learning to automate road condition surveys while using low-cost technology. A single model is trained to capture flexible pavement distresses (Potholes, Rutting, Cracking, and raveling), thereby providing a more cost-effective and efficient road condition monitoring approach that can also provide real-time road conditions. Additionally, data fusion is employed to enhance the road condition assessment with data from vehicles and drones.

Keywords: road conditions, built-in vehicle technology, deep learning, drones

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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: 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: Kalina Alina Aidarovna, Khayrullina Yulia Sergeevna

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The question of forming quality housing and living environment remains a vexed problem in the current situation of high-rise apartment building in big cities of Russia. At this start up stage of the modern so-called "mass housing" market it needs to identify key quality characteristics on a different scale from apartments to the district. This paper describes the methodology of qualitative assessment of modern mass housing construction, made on the basis of the ITMO university in cooperation with the institute of spatial planning "Urbanika," based on the case study of St. Petersburg’s residential mass housing built in 2011-2014. The methodology of the study of housing and living environment goes back to the native and foreign urbanists of 60s - 80s, such Jane Jacobs, Jan Gehl, Oscar Newman, Krasheninnikov, as well as Sommer, Stools, Kohnen and Sherrod, Krasilnikova, Sychev, Zhdanov, Tinyaeva considering spatial features of living environment in a wide range of its characteristics (environmental control, territorial and personalization, privacy, etc.). Assessment is carrying out on the proposed system of criteria developed for each residential environment scale-district, quarter, courtyard, building surrounding grounds, houses, and flats. Thus the objects of study are planning unit of residential development areas (residential area, neighborhood, quarter) residential units areas (living artist, a house), and households (apartments) consisting of residential units. As a product of identified methodology, after the results of case studies of more than 700 residential complexes in St. Petersburg, we intend the creation of affordable online resource that would allow conducting a detailed qualitative evaluation or comparative characteristics of residential complexes for all participants of the construction market-developers, designers, realtors and buyers. Thereby the main objective of the rating may be achieved to improve knowledge, requirements, and demand for quality housing and living environment among the major stakeholders of the construction market.

Keywords: methodology of living environment, qualitative assessment of mass housing, scale-district, vexed problem

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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: 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: 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: Patricia Portugal Marques de Carvalho Lourenco

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A three-phase methodological multi-lingual path was designed, constructed and carried out using the 2020 Chinese Internet Drama Series We Are All Alone as a case study. Phase one, the backbone of the research, comprised of secondary data analysis, providing the structure on which the next two phases would be built on. Phase one incorporated a Google Scholar and a Baidu Index analysis, Star Network Influence Index and Mydramalist.com top two drama reviews, along with an article written about the drama and scrutiny of Chinese related blogs and websites. Phase two was field research elaborated across Latin Europe, and phase three was social media focused, having into account that perceptions are going to be memory conditioned based on past ideas recall. Overall, research has shown the poor cultural expression of Chinese entertainment in Latin Europe and demonstrated the inexistence of Chinese content in French, Italian, Portuguese and Spanish Business to Consumer retailers; a reflection of their low significance in Latin European markets and the short-life cycle of entertainment products in general, bubble-gum, disposable goods without a mid to long-term effect in consumers lives. The process of conducting comprehensive international research was complex and time-consuming, with data not always available in Mandarin, the researcher’s linguistic deficiency, limited Chinese Cultural Knowledge and cultural equivalence. Despite steps being taken to minimize the international proposed research, theoretical limitations concurrent to Latin Europe and China still occurred. Data accuracy was disputable; sampling, data collection/analysis methods are heterogeneous; ascertaining data requirements and the method of analysis to achieve a construct equivalence was challenging and morose to operationalize. Secondary data was also not often readily available in Mandarin; yet, in spite of the array of limitations, research was done, and results were produced.

Keywords: research methodologies, international research, primary data, secondary data, research limitations, online dramas, china, latin europe

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Authors: Tingting Huang, Sha Li, Geoffrey Griffiths, Martin Lukac, Jianning Zhu

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Territorial landscapes have experienced a gradual loss of their typical characteristics during long-term human activities. In order to protect the integrity of regional landscapes, it is necessary to characterize, evaluate and protect them in a graded manner. The study takes Fujian, China, as an example and classifies the landscape characters of the site at the regional scale, middle scale, and detailed scale. A multi-scale approach combining parametric and holistic approaches is used to classify and partition the landscape character types (LCTs) and landscape character areas (LCAs) at different scales, and a multi-element landscape assessment approach is adopted to explore the conservation strategies of the landscape character. Firstly, multiple fields and multiple elements of geography, nature and humanities were selected as the basis of assessment according to the scales. Secondly, the study takes a parametric approach to the classification and partitioning of landscape character, Principal Component Analysis, and two-stage cluster analysis (K-means and GMM) in MATLAB software to obtain LCTs, combines with Canny Operator Edge Detection Algorithm to obtain landscape character contours and corrects LCTs and LCAs by field survey and manual identification methods. Finally, the study adopts the Landscape Sensitivity Assessment method to perform landscape character conservation analysis and formulates five strategies for different LCAs: conservation, enhancement, restoration, creation, and combination. This multi-scale identification approach can efficiently integrate multiple types of landscape character elements, reduce the difficulty of broad-scale operations in the process of landscape character conservation, and provide a basis for landscape character conservation strategies. Based on the natural background and the restoration of regional characteristics, the results of landscape character assessment are scientific and objective and can provide a strong reference in regional and national scale territorial spatial planning.

Keywords: parameterization, multi-scale, landscape character identify, landscape character assessment

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Authors: Marianna Maiaru, Gregory M. Odegard, Josh Kemppainen, Ivan Gallegos, Michael Olaya

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Ceramic matrix composites (CMCs) are typically used in applications that require long-term mechanical integrity at elevated temperatures. CMCs are usually fabricated using a polymer precursor that is initially polymerized in situ with fiber reinforcement, followed by a series of cycles of pyrolysis to transform the polymer matrix into a rigid glass or ceramic. The pyrolysis step typically generates volatile gasses, which creates porosity within the polymer matrix phase of the composite. Subsequent cycles of monomer infusion, polymerization, and pyrolysis are often used to reduce the porosity and thus increase the durability of the composite. Because of the significant expense of such iterative processing cycles, new generations of CMCs with improved durability and manufacturability are difficult and expensive to develop using standard Edisonian approaches. The goal of this research is to develop a computational process-modeling-based approach that can be used to design the next generation of CMC materials with optimized material and processing parameters for maximum strength and efficient manufacturing. The process modeling incorporates computational modeling tools, including molecular dynamics (MD), to simulate the material at multiple length scales. Results from MD simulation are used to inform the continuum-level models to link molecular-level characteristics (material structure, temperature) to bulk-level performance (strength, residual stresses). Processing parameters are optimized such that process-induced residual stresses are minimized and laminate strength is maximized. The multiscale process modeling method developed with this research can play a key role in the development of future CMCs for high-temperature and high-strength applications. By combining multiscale computational tools and process modeling, new manufacturing parameters can be established for optimal fabrication and performance of CMCs for a wide range of applications.

Keywords: digital engineering, finite elements, manufacturing, molecular dynamics

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Authors: Srijanani Anurag Prasad

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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: Yasir Memon

Abstract:

The complexity and challenges in the offshore industry are increasing more than the past. The oil and gas industry is expanding every day by accomplishing these challenges. More challenging wells such as longer and deeper are being drilled in today’s environment. Blowout prevention phenomena hold a worthy importance in oil and gas biosphere. In recent, so many past years when the oil and gas industry was growing drilling operation were extremely dangerous. There was none technology to determine the pressure of reservoir and drilling hence was blind operation. A blowout arises when an uncontrolled reservoir pressure enters in wellbore. A potential of blowout in the oil industry is the danger for the both environment and the human life. Environmental damage, state/country regulators, and the capital investment causes in loss. There are many cases of blowout in the oil the gas industry caused damage to both human and the environment. A huge capital investment is being in used to stop happening of blowout through all over the biosphere to bring damage at the lowest level. The objective of this study is to promote safety and good resources to assure safety and environmental integrity in all operations during drilling. This study shows that human errors and management failure is the main cause of blowout therefore proper management with the wise use of precautions, prevention methods or controlling techniques can reduce the probability of blowout to a minimum level. It also discusses basic procedures, concepts and equipment involved in well control methods and various steps using at various conditions. Furthermore, another aim of this study work is to highlight management role in oil gas operations. Moreover, this study analyze the causes of Blowout of Macondo well occurred in the Gulf of Mexico on April 20, 2010, and deliver the recommendations and analysis of various aspect of well control methods and also provides the list of mistakes and compromises that British Petroleum and its partner were making during drilling and well completion methods and also the Macondo well disaster happened due to various safety and development rules violation. This case study concludes that Macondo well blowout disaster could be avoided with proper management of their personnel’s and communication between them and by following safety rules/laws it could be brought to minimum environmental damage.

Keywords: energy, environment, oil and gas industry, Macondo well accident

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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: Gichun Cha, Changgil Lee, Seunghee Park

Abstract:

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

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Authors: Mansoor Imam, Amber Afshan, Sumbul Mujeeb, Kamran Gill

Abstract:

An investigative approach has been made to study the quality of living prevailing in the squatter slums of Karachi city that is influencing the urbanization trends and environmental degeneration of built environment. The paper identifies the issues and aspects that have directly and indirectly impacted the degeneration owing to inadequate basic infrastructural amenities, substandard housing, overcrowding, poor ventilation in homes and workplaces, and noncompliance with building bye-laws and regulations, etc. Primarily, secondary data has been critically examined and analyzed which was however not limited to census data, demographic / socioeconomic data, official documents and other relevant secondary data were obtained from existing literature and GIS. It is observed that the poor and sub-standard housing / living quality have serious adverse impacts on the environment and the health of city residents. Hence strategies for improving the quality of built environment for sustainable living are mandated. It is, therefore, imperative to check and prevent further degradation and promote harmonious living and sustainable urbanization.

Keywords: squatter slums, urbanization, degenerations, living quality, built environment

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Authors: Ntandoni Gloria Biyela

Abstract:

During traditional times, there were no formal institutions of learning as they are today, where children attend classes to acquire or develop knowledge. This does not mean that there was no learning in indigenous African societies. Grandparents used to tell their grandchildren stories or teach them educational games around the fireplace, which this study refers to as a ‘traditional classroom’. A story recreated in symbolic or allegorical way, forms a base for a society’s beliefs, customs, accepted norms and language learning. Through folklore narratives, a society develops its own self awareness and education. So narrative characters, especially animals may be mythical products of the pre-literate folklore world and thus show the closeness that the Zulu society had with the wildlife. Oral cultures strive to create new facets of meaning by the use of animal metaphors to reflect the relationship of humans with the animal realm and to contribute to the language learning or literature in cross-cultural studies. Although animal metaphors are widespread in Zulu language because of the Zulu nation’s traditional closeness to wildlife, little field-research has been conducted on the social behavior of animals on the way in which their characteristics were transferred with precision to depictions of King Shaka’s behavior and activities during the amalgamation of Nguni clans into a Zulu kingdom. This study attempts to fill the gap by using first-hand interviews with local informants in areas traditionally linked to the king in KwaZulu-Natal province, South Africa. Departing from the conceptual metaphor theory, the study concentrates on King Shaka’s praise-poems in which the praise-poet describes his physical and dispositional characteristics through bold animal metaphors of the ‘Big Five’; namely, the lion, the leopard, the buffalo, the rhinoceros and the elephant, which are often referred to as Zulu royal favorites. These metaphors are still learnt by young and old in the 21st century because they reflect the responsibilities, status, and integrity of the king and the respect in which he is held by his people. They also project the crescendo growth of the Zulu nation, which, through the fulfillment of his ambitions, grew from a small clan to a mighty kingdom.

Keywords: animal, indigenous, learning, metaphor

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