Search results for: violation data discovery

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: 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

Procedia PDF Downloads 387

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: 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: 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: 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: 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

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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

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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

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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: Victor Pesco Kassa

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This research deals with the assessment of the contribution of GIS Technology in NRW. This research was conducted at Dar, Kawe Mzimuni Street. The data collection was obtained from existing source which is DAWASA HQ. The interpretation of the data was processed by using ArcGIS software. The data collected from the existing source reveals a good coverage of DAWASA’s water network at Mzimuni Street. Most of residents are connected to the DAWASA’s customer service. Also the collected data revealed that by using GIS DAWASA’s customer Geodatabase has been improved. Through GIS we can prepare customer location map purposely for site surveying also this map will be able to show different type of customer that are connected to DAWASA’s water service. This is a perfect contribution of the GIS Technology to address and manage the problem of NRW in DAWASA. Finally, the study recommends that the same study should be conducted in other DAWASA’s zones such as Temeke, Boko and Bagamoyo not only at Kawe Mzimuni Street. Through this study it is observed that ArcGIS software can offer powerful tools for managing and processing information geographically and in water and sanitation authorities such as DAWASA.

Keywords: DAWASA, NRW, Esri, EURA, ArcGIS

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Authors: Osamu Komori, Shinto Eguchi, Hiroshi Okamura, Momoko Ichinokawa

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The long time-series data on population assessments are essential for global ecosystem assessment because the temporal change of biomass in such a database reflects the status of global ecosystem properly. However, the available assessment data usually have limited sample sizes and the ratio of populations with low abundance of biomass (collapsed) to those with high abundance (non-collapsed) is highly imbalanced. To allow for the imbalance and uncertainty involved in the ecological data, we propose a binary regression model with mixed effects for inferring ecosystem status through an asymmetric logistic model. In the estimation equation, we observe that the weights for the non-collapsed populations are relatively reduced, which in turn puts more importance on the small number of observations of collapsed populations. Moreover, we extend the asymmetric logistic regression model using propensity score to allow for the sample biases observed in the labeled and unlabeled datasets. It robustified the estimation procedure and improved the model fitting.

Keywords: double robust estimation, ecological binary data, mixed effect logistic regression model, propensity score

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Authors: Chen Wang, Qiuxiao Chen

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With the advent of the information age and the era of big data, urban planning research is no longer satisfied with the analysis and application of traditional data. Because of the limitations of traditional urban commercial center system research, big data provides new opportunities for urban research. Therefore, based on the quantitative evaluation method of big data, the commercial center system of the main city of Hangzhou is analyzed and evaluated, and the scale and hierarchical structure characteristics of the urban commercial center system are studied. In order to make up for the shortcomings of the existing POI extraction method, it proposes a POI extraction method based on adaptive adjustment of search window, which can accurately and efficiently extract the POI data of commercial business in the main city of Hangzhou. Through the visualization and nuclear density analysis of the extracted Point of Interest (POI) data, the current situation of the commercial center system in the main city of Hangzhou is evaluated. Then it compares with the commercial center system structure of 'Hangzhou City Master Plan (2001-2020)', analyzes the problems existing in the planned urban commercial center system, and provides corresponding suggestions and optimization strategy for the optimization of the planning of Hangzhou commercial center system. Then get the following conclusions: The status quo of the commercial center system in the main city of Hangzhou presents a first-level main center, a two-level main center, three third-level sub-centers, and multiple community-level business centers. Generally speaking, the construction of the main center in the commercial center system is basically up to standard, and there is still a big gap in the construction of the sub-center and the regional-level commercial center, further construction is needed. Therefore, it proposes an optimized hierarchical functional system, organizes commercial centers in an orderly manner; strengthens the central radiation to drive surrounding areas; implements the construction guidance of the center, effectively promotes the development of group formation and further improves the commercial center system structure of the main city of Hangzhou.

Keywords: business center system, business format, main city of Hangzhou, POI extraction method

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Authors: Thanomsin Chakreeves, Atichat Preittigun, Ajchara Phu-ang

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This paper presents a stakeholder analysis of agricultural drone policies that meet the government's goal of building an agricultural drone ecosystem in Thailand. Firstly, case studies from other countries are reviewed. The stakeholder analysis method and qualitative data from the interviews are then presented including data from the Institute of Innovation and Management, the Office of National Higher Education Science Research and Innovation Policy Council, agricultural entrepreneurs and farmers. Study and interview data are then employed to describe the current ecosystem and to guide the implementation of agricultural drone policies that are suitable for the ecosystem of Thailand. Finally, policy recommendations are then made that the Thai government should adopt in the future.

Keywords: drone public policy, drone ecosystem, policy development, agricultural drone

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Authors: Jillian M. Hagel, Joseph E. Tucker, Peter J. Facchini

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With the aim of establishing a holistic approach for the treatment of central nervous system (CNS) disorders, we are pursuing a drug development program rapidly progressing through discovery and characterization phases. The drug candidates identified in this program are referred to as neuroplastogens owing to their ability to mediate neuroplasticity, which can be beneficial to patients suffering from anxiety, depression, or posttraumatic stress disorder. These and other related neuropsychiatric conditions are associated with the onset of neuronal atrophy, which is defined as a reduction in the number and/or productivity of neurons. The stimulation of neuroplasticity results in an increase in the connectivity between neurons and promotes the restoration of healthy brain function. We have synthesized a substantial catalogue of proprietary indolethylamine derivatives based on the general structures of serotonin (5-hydroxytryptamine) and psychedelic molecules such as N,N-dimethyltryptamine (DMT) and psilocin (4-hydroxy-DMT) that function as neuroplastogens. A primary objective in our screening protocol is the identification of derivatives associated with a significant reduction in hallucination, which will allow administration of the drug at a dose that induces neuroplasticity and triggers other efficacious outcomes in the treatment of targeted CNS disorders but which does not cause a psychedelic response in the patient. Both neuroplasticity and hallucination are associated with engagement of the 5HT2A receptor, requiring drug candidates differentially coupled to these two outcomes at a molecular level. We use novel and proprietary artificial intelligence algorithms to predict the mode of binding to the 5HT2A receptor, which has been shown to correlate with the hallucinogenic response. Hallucination is tested using the mouse head-twitch response model, whereas mouse marble-burying and sucrose preference assays are used to evaluate anxiolytic and anti-depressive potential. Neuroplasticity is assays using dendritic outgrowth assays and cell-based ELISA analysis. Pharmacokinetics and additional receptor-binding analyses also contribute the selection of lead candidates. A summary of the program is presented.

Keywords: neuroplastogen, non-hallucinogenic, drug development, anxiety, depression, PTSD, indolethylamine derivatives, psychedelic-inspired, 5-HT2A receptor, computational chemistry, head-twitch response behavioural model, neurite outgrowth assay

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Authors: Boudene Maamar, Xu Mai

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Optical Intersatellite Links (OISLs) are wireless communications using optical signals to interconnect satellites. It is expected to be the next generation wireless communication technology according to its inherent characteristics like: an increased bandwidth, a high data rate, a data transmission security, an immunity to interference, and an unregulated spectrum etc. Optical space links are the best choice for the classical communication schemes due to its distinctive properties; high frequency, small antenna diameter and lowest transmitted power, which are critical factors to define a space communication. This paper discusses the development of free space technology and analyses the parameters and factors to establish a reliable intersatellite links using an optical signal to exchange data between satellites.

Keywords: optical intersatellite links, optical wireless communications, free space optical communications, next generation wireless communication

Procedia PDF Downloads 447

Authors: Chen-Chang Lee, Po-Chou Chen, Jo-Chi Jao, Chun-Chung Lui, Leung-Chit Tsang, Lain-Chyr Hwang

Abstract:

Fetal Magnetic Resonance Imaging (MRI) is a challenge task because the fetal movements could cause motion artifact in MR images. The remedy to overcome this problem is to use fast scanning pulse sequences. The Single-Shot Fast Spin-Echo (SSFSE) T2-weighted imaging technique is routinely performed and often used as a gold standard in clinical examinations. Fast spoiled gradient-echo (FSPGR) T1-Weighted Imaging (T1WI) is often used to identify fat, calcification and hemorrhage. Fast Imaging Employing Steady-State Acquisition (FIESTA) is commonly used to identify fetal structures as well as the heart and vessels. The contrast of FIESTA image is related to T1/T2 and is different from that of SSFSE. The advantages and disadvantages of these two scanning sequences for fetal imaging have not been clearly demonstrated yet. This study aimed to compare these three rapid MRI techniques (SSFSE, FIESTA, and FSPGR) for fetal MRI examinations. The image qualities and influencing factors among these three techniques were explored. A 1.5T GE Discovery 450 clinical MR scanner with an eight-channel high-resolution abdominal coil was used in this study. Twenty-five pregnant women were recruited to enroll fetal MRI examination with SSFSE, FIESTA and FSPGR scanning. Multi-oriented and multi-slice images were acquired. Afterwards, MR images were interpreted and scored by two senior radiologists. The results showed that both SSFSE and T2W-FIESTA can provide good image quality among these three rapid imaging techniques. Vessel signals on FIESTA images are higher than those on SSFSE images. The Specific Absorption Rate (SAR) of FIESTA is lower than that of the others two techniques, but it is prone to cause banding artifacts. FSPGR-T1WI renders lower Signal-to-Noise Ratio (SNR) because it severely suffers from the impact of maternal and fetal movements. The scan times for these three scanning sequences were 25 sec (T2W-SSFSE), 20 sec (FIESTA) and 18 sec (FSPGR). In conclusion, all these three rapid MR scanning sequences can produce high contrast and high spatial resolution images. The scan time can be shortened by incorporating parallel imaging techniques so that the motion artifacts caused by fetal movements can be reduced. Having good understanding of the characteristics of these three rapid MRI techniques is helpful for technologists to obtain reproducible fetal anatomy images with high quality for prenatal diagnosis.

Keywords: fetal MRI, FIESTA, FSPGR, motion artifact, SSFSE

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Authors: Lazhar Zourgui

Abstract:

Opuntia species are used as local medicinal interventions for chronic diseases and as food sources, mainly because they possess nutritional properties and biological activities. Opuntia ficus-indica (L.) Mill, commonly known as prickly pear or nopal cactus, is the most economically valuable plant in the Cactaceae family worldwide. It is a tropical or subtropical plant native to tropical and subtropical America, which can grow in arid and semi-arid climates. It belongs to the family of angiosperms dicotyledons Cactaceae of which about 1500 species of cacti are known. The Opuntia plant is distributed throughout the world and has great economic potential. There are differences in the phytochemical composition of Opuntia species between wild and domesticated species and within the same species. It is an interesting source of plant bioactive compounds. Bioactive compounds are compounds with nutritional benefits and are generally classified into phenolic and non-phenolic compounds and pigments. Opuntia species are able to grow in almost all climates, for example, arid, temperate, and tropical climates, and their bioactive compound profiles change depending on the species, cultivar, and climatic conditions. Therefore, there is an opportunity for the discovery of new compounds from different Opuntia cultivars. Health benefits of prickly pear are widely demonstrated: There is ample evidence of the health benefits of consuming prickly pear due to its source of nutrients and vitamins and its antioxidant properties due to its content of bioactive compounds. In addition, prickly pear is used in the treatment of hyperglycemia and high cholesterol levels, and its consumption is linked to a lower incidence of coronary heart disease and certain types of cancer. It may be effective in insulin-independent type 2 diabetes mellitus. Opuntia ficus-Indica seed oil has shown potent antioxidant and prophylactic effects. Industrial applications of these bioactive compounds are increasing. In addition to their application in the pharmaceutical industries, bioactive compounds are used in the food industry for the production of nutraceuticals and new food formulations (juices, drinks, jams, sweeteners). In my lecture, I will review in a comprehensive way the phytochemical, nutritional, and bioactive compound composition of the different aerial and underground parts of Opuntia species. The biological activities and applications of Opuntia compounds are also discussed.

Keywords: medicinal plants, cactus, Opuntia, actives biomolecules, biological activities

Procedia PDF Downloads 107

Authors: Emi Yuda, Yutaka Yoshida, Junichiro Hayano

Abstract:

Distinct circadian rhythm of activity, i.e., high activity during the day and deep rest at night are a typical feature of a healthy lifestyle. Exposure to the skylight is thought to be an important factor to increase arousal level and maintain normal circadian rhythm. To examine whether sunshine hours influence the day-night contract of activity, we analyzed the relationship between 24-hour heart rate (HR) and weather data of the recording day. We analyzed data in 36,500 males and 49,854 females of Allostatic State Mapping by Ambulatory ECG Repository (ALLSTAR) database in Japan. Median (IQR) sunshine duration was 5.3 (2.8-7.9) hr. While sunshine hours had only modest effects of increasing 24-hour average HR in either gender (P=0.0282 and 0.0248 for male and female) and no significant effects on nighttime HR in either gender, it increased daytime HR (P = 0.0007 and 0.0015) and day-night HF difference in both genders (P < 0.0001 for both) even after adjusting for the effects of average temperature, atmospheric pressure, and humidity. Our observations support for the hypothesis that longer sunshine hours enhance circadian rhythm of activity.

Keywords: big data, circadian rhythm, heart rate, sunshine

Procedia PDF Downloads 165

Authors: Yassine Bouzouita, Oumaima Belghith, Cyrine Zitoun, Charles Bonneau

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Performance measurement framework (PMF) is an essential tool in any organization to assess the performance of its processes. It guides businesses to stay on track with their objectives and benchmark themselves from the market. With the growing trend of the digital transformation of business processes, led by innovations in artificial intelligence (AI) & Big Data applications, developing a mature system capable of capturing the impact of digital solutions across different industries became a necessity. Based on the conducted research, no such system has been developed in academia nor the industry. In this context, this paper covers a variety of methodologies on performance measurement, overviews the major AI and big data applications in the banking sector, and covers an exhaustive list of relevant metrics. Consequently, this paper is of interest to both researchers and practitioners. From an academic perspective, it offers a comparative analysis of the reviewed performance measurement frameworks. From an industry perspective, it offers exhaustive research, from market leaders, of the major applications of AI and Big Data technologies, across the different departments of an organization. Moreover, it suggests a standardized classification model with a well-defined structure of intelligent digital solutions. The aforementioned classification is mapped to a centralized library that contains an indexed collection of potential metrics for each application. This library is arranged in a manner that facilitates the rapid search and retrieval of relevant metrics. This proposed framework is meant to guide professionals in identifying the most appropriate AI and big data applications that should be adopted. Furthermore, it will help them meet their business objectives through understanding the potential impact of such solutions on the entire organization.

Keywords: AI and Big Data applications, impact assessment, metrics, performance measurement

Procedia PDF Downloads 198