Search results for: data protection act

Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.

Keywords: apartment complex, big data, life-cycle building value analysis, machine learning

Procedia PDF Downloads 357

Authors: Omid Amini

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Nowadays, technology plays the most important role in the life of human beings because people use technology to share data and to communicate with each other, but the challenge is the security of this data. For instance, as more people turn to technology in the world, more data is generated, and more hackers try to steal or infiltrate data. In addition, the data is under the control of the central authority, which can trigger the challenge of losing information and changing information; this can create widespread anxiety for different people in different communities. In this paper, we sought to investigate Blockchain technology that can guarantee information security and eliminate the challenge of central authority access to information. Now a day, people are suffering from the current voting system. This means that the lack of transparency in the voting system is a big problem for society and the government in most countries, but blockchain technology can be the best alternative to the previous voting system methods because it removes the most important challenge for voting. According to the results, this research can be a good start to getting acquainted with this new technology, especially on the security part and familiarity with how to use a voting system based on blockchain in the world. At the end of this research, it is concluded that the use of blockchain technology can solve the major security problem and lead to a secure and transparent election.

Keywords: blockchain, technology, security, information, voting system, transparency

Procedia PDF Downloads 109

Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 148

Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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Authors: K. W. Nam, B. J. Kim, S. J. Lee

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Along with the development of network infrastructures, such as high-speed Internet and mobile environment, the explosion of multimedia data is expanding the range of multimedia services beyond voice and data services. Amid this flow, research is actively being done on the creation, management, and transmission of metadata on digital content to provide different services to users. This paper proposes a system for the insertion, storage, and retrieval of metadata about digital content. The metadata server with Binary XML was implemented for efficient storage space and retrieval speeds, and the transport data size required for metadata retrieval was simplified. With the proposed system, the metadata could be inserted into the moving objects in the video, and the unnecessary overlap could be minimized by improving the storage structure of the metadata. The proposed system can assemble metadata into one relevant topic, even if it is expressed in different media or in different forms. It is expected that the proposed system will handle complex network types of data.

Keywords: video, multimedia, metadata, editing tool, XML

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Authors: Sarthak Kumar Jena, Chandra Sekhar Mishra, Prabina Rajib

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Purpose: This paper aims to derive undervaluation signal from the insiders trading of Indian companies where the ownership is complex and concentrated, investors protection is weak, and the insider rules and regulations are not stringent like developed country. This study examines the relationship between insider trading with short term and long term abnormal return. The study also examines the relationship between insider trading and the actual share repurchase by the firm. Methodology: A sample of 78 companies over the period 2008-2013 are analyzed in the study due to not availability of insider data in Indian context. For preliminary analysis T-test and Wilcoxon rank sum test is used to find the difference between the insider trading before and after the share repurchase announcement. Tobit model is used to find out whether insider trading influence shares repurchase decisions or not. Return on the basis of market model and buy hold are calculated in the previous year and the following year of share repurchase announcement. Findings: The paper finds that insider trading around share repurchase is more than control firms and there is positive and significant difference in insider buying between the previous year of share buyback announcement and the following year of buyback announcement. Insider buying before share repurchase announcement has a positive influence on share repurchase decisions. We find insider buying has a positive and significant relationship with announcement return, whereas insider selling has a negative significant relationship with announcement return. Actual share repurchase and program completion also depend on insider trading before share repurchase. Research limitation: The study is constrained by the small sample size, so the results should be viewed by keeping this limitation in mind. Originality: The paper is to our best knowledge the first study based on Indian context to extend the insider trading literature to share repurchase event and examine insider trading to find out undervaluation signal associated with insider buying.

Keywords: insider trading, buyback, open market share repurchase, signalling

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Authors: Luís Pina

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The conservation of marine biodiversity keeps ecosystems in balance and ensures the sustainable use of resources. In this context, technological resources have been used for monitoring marine species to allow biologists to obtain data in real-time. There are different mobile applications developed for data collection for monitoring purposes, but these systems are designed to be utilized only on third-generation (3G) phones or smartphones with Internet access and in rural parts of the developing countries, Internet services and smartphones are scarce. Thus, the objective of this work is to develop a system to monitor marine turtles using Unstructured Supplementary Service Data (USSD), which users can access through basic mobile phones. The system aims to improve the data collection mechanism and enhance the effectiveness of current systems in monitoring sea turtles using any type of mobile device without Internet access. The system will be able to report information related to the biological activities of marine turtles. Also, it will be used as a platform to assist marine conservation entities to receive reports of illegal sales of sea turtles. The system can also be utilized as an educational tool for communities, providing knowledge and allowing the inclusion of communities in the process of monitoring marine turtles. Therefore, this work may contribute with information to decision-making and implementation of contingency plans for marine conservation programs.

Keywords: GSM, marine biology, marine turtles, unstructured supplementary service data (USSD)

Procedia PDF Downloads 190

Authors: Oussama Hafferssas, Hiba Benyahia, Amina Madani, Nassima Zeriri

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Although data nowadays has multiple forms; from text to images, and from audio to videos, yet text is still the most used one at a public level. At an academical and research level, and unlike other forms, text can be considered as the easiest form to process. Therefore, a brunch of Data Mining researches has been always under its shadow, called "Text Mining". Its concept is just like data mining’s, finding valuable patterns in data, from large collections and tremendous volumes of data, in this case: Text. Named entity recognition (NER) is one of Text Mining’s disciplines, it aims to extract and classify references such as proper names, locations, expressions of time and dates, organizations and more in a given text. Our approach "Octopub" does not aim to find new ways to improve named entity recognition process, rather than that it’s about finding a new, and yet smart way, to use NER in a way that we can extract sentiments of millions of people using Social Networks as a limitless information source, and Marketing for product promotion as the main domain of application.

Keywords: textmining, named entity recognition(NER), sentiment analysis, social media networks (SN, SMN), business intelligence(BI), marketing

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Authors: Mohammadreza Ashouri, Majid Bayatian

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Analysis of fire data is a way for the implementation of any plan to improve the level of safety in cities. Such an analysis is able to reveal signs of changes in a given period and can be used as a measure of safety. The information of about 66,341 fires (from 2002 to 2012) released by Tehran Safety Services and Fire-Fighting Organization and data on the population and the number of households provided by Tehran Municipality and the Statistical Yearbook of Iran were extracted. Using the data, the fire changes, the rate of injuries, and mortality rate were determined and analyzed. The rate of injuries and mortality rate of fires per one million population of Tehran were 59.58% and 86.12%, respectively. During the study period, the number of fires and fire stations increased by 104.38% and 102.63%, respectively. Most fires (9.21%) happened in the 4th District of Tehran. The results showed that the recorded fire data have not been systematically planned for fire prevention since one of the ways to reduce injuries caused by fires is to develop a systematic plan for necessary actions in emergency situations. To determine a reliable source for fire prevention, the stages, definitions of working processes and the cause and effect chains should be considered. Therefore, a comprehensive statistical system should be developed for reported and recorded fire data.

Keywords: fire statistics, fire analysis, accident prevention, Tehran

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Authors: Nam Don Kim, Jungho Moon, Tae Yun Chung

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This paper proposes an Internet of Things (IoT) based virtualization platform for providing smart tourism services. The virtualization platform provides a consistent access interface to various types of data by naming IoT devices and legacy information systems as pathnames in a virtual file system. In the other words, the IoT virtualization platform functions as a middleware which uses the metadata for underlying collected data. The proposed platform makes it easy to provide customized tourism information by using tourist locations collected by IoT devices and additionally enables to create new interactive smart tourism services focused on the tourist locations. The proposed platform is very efficient so that the provided tourism services are isolated from changes in raw data and the services can be modified or expanded without changing the underlying data structure.

Keywords: internet of things (IoT), IoT platform, serviceplatform, virtual file system (VSF)

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Authors: Ava Zarif Sanayei, Sedigheh Sina

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During fluoroscopically guided procedures, healthcare workers, especially radiology trainees, are at risk of exposure to elevated radiation exposure. It is vital to prioritize their safety in such settings. However, there is limited data on their monthly or annual doses. This study aimed to evaluate the equivalent dose to the eyes of the student trainee, utilizing LiF: Mg, Ti (TLD-100) chips at the radiology department of a hospital in Shiraz, Iran. Initially, the dosimeters underwent calibration procedures with the assistance of ISO-PTW calibrated phantoms. Following this, a set of dosimeters was prepared To determine HP(3) value for a trainee involved in the main operation room and controlled area utilized for two months. Three TLD chips were placed in a holder and attached to her eyeglasses. Upon completion of the duration, the TLDs were read out using a Harshaw TLD reader. Results revealed that Hp(3) value was 0.31±0.04 mSv. Based on international recommendations, students in radiology training above 18 have an annual dose limit of 0.6 rem (6 mSv). Assuming a 12-month workload, staff radiation exposure stayed below the annual limit. However, the Trainee workload may vary due to different deeds. This study's findings indicate the need for consistent, precise dose monitoring in IR facilities. Students can undertake supervised internships for up to 500 hours, depending on their institution. These internships take place in health-focused environments offering radiology services, such as clinics, diagnostic imaging centers, and hospitals. Failure to do so might result in exceeding occupational radiation dose limits. A 0.5 mm lead apron effectively absorbs 99% of radiation. To ensure safety, technologists and staff need to wear this protective gear whenever they are in the room during procedures. Furthermore, maintaining a safe distance from the primary beam is crucial. In cases where patients need assistance and must be held for imaging, additional protective equipment, including lead goggles, gloves, and thyroid shields, should be utilized for optimal safety.

Keywords: annual dose limits, Hp(3), individual monitoring, radiation protection, TLD-100

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Authors: Slim Namouchi, Bruno Vallet, Imed Riadh Farah

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3D urban models provide powerful tools for decision making, urban planning, and smart city services. The accuracy of this 3D based systems is directly related to the quality of these models. Since manual large-scale modeling, such as cities or countries is highly time intensive and very expensive process, a fully automatic 3D building generation is needed. However, 3D modeling process result depends on the input data, the proprieties of the captured objects, and the required characteristics of the reconstructed 3D model. Nowadays, producing 3D real-world model is no longer a problem. Remotely sensed data had experienced a remarkable increase in the recent years, especially data acquired using unmanned aerial vehicles (UAV). While the scanning techniques are developing, the captured data amount and the resolution are getting bigger and more precise. This paper presents a literature review, which aims to identify different methods of automatic 3D buildings extractions either from LiDAR or the combination of LiDAR and satellite or aerial images. Then, we present open source technologies, and data models (e.g., CityGML, PostGIS, Cesiumjs) used to integrate these models in geospatial base layers for smart city services.

Keywords: CityGML, LiDAR, remote sensing, SIG, Smart City, 3D urban modeling

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Authors: Ahmed Noor Al-qayyim, Barlas Özden Çağlayan

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Structural failure is caused mainly by damage that often occurs on structures. Many researchers focus on obtaining very efficient tools to detect the damage in structures in the early state. In the past decades, a subject that has received considerable attention in literature is the damage detection as determined by variations in the dynamic characteristics or response of structures. This study presents a new damage identification technique. The technique detects the damage location for the incomplete structure system using output data only. The method indicates the damage based on the free vibration test data by using “Two Points - Condensation (TPC) technique”. This method creates a set of matrices by reducing the structural system to two degrees of freedom systems. The current stiffness matrices are obtained from optimization of the equation of motion using the measured test data. The current stiffness matrices are compared with original (undamaged) stiffness matrices. High percentage changes in matrices’ coefficients lead to the location of the damage. TPC technique is applied to the experimental data of a simply supported steel beam model structure after inducing thickness change in one element. Where two cases are considered, the method detects the damage and determines its location accurately in both cases. In addition, the results illustrate that these changes in stiffness matrix can be a useful tool for continuous monitoring of structural safety using ambient vibration data. Furthermore, its efficiency proves that this technique can also be used for big structures.

Keywords: damage detection, optimization, signals processing, structural health monitoring, two points–condensation

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Authors: P. Senthil Kumari

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Main concerns in data mining investigation are social controls of data mining for handling ambiguity, noise, or incompleteness on text data. We describe an innovative approach for unplanned text data detection of community networks achieved by classification mechanism. In a tangible domain claim with humble secrecy backgrounds provided by community network for evading annoying content is presented on consumer message partition. To avoid this, mining methodology provides the capability to unswervingly switch the messages and similarly recover the superiority of ordering. Here we designated learning-centered mining approaches with pre-processing technique to complete this effort. Our involvement of work compact with rule-based personalization for automatic text categorization which was appropriate in many dissimilar frameworks and offers tolerance value for permits the background of comments conferring to a variety of conditions associated with the policy or rule arrangements processed by learning algorithm. Remarkably, we find that the choice of classifier has predicted the class labels for control of the inadequate documents on community network with great value of effect.

Keywords: text mining, data classification, community network, learning algorithm

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Authors: Gertraud Koch, Teresa Stumpf, Alejandra Tijerina García

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Discourse analysis research approaches belong to the central research strategies applied throughout the humanities; they focus on the countless forms and ways digital texts and images shape present-day notions of the world. Despite the constantly growing number of relevant digital, multimodal discourse resources, digital humanities (DH) methods are thus far not systematically developed and accessible for discourse analysis approaches. Specifically, the significance of multimodality and meaning plurality modelling are yet to be sufficiently addressed. In order to address this research gap, the D-WISE project aims to develop a prototypical working environment as digital support for the sociology of knowledge approach to discourse analysis and new IT-analysis approaches for the use of context-oriented embedding representations. Playing an essential role throughout our research endeavor is the constant optimization of hermeneutical methodology in the use of (semi)automated processes and their corresponding epistemological reflection. Among the discourse analyses, the sociology of knowledge approach to discourse analysis is characterised by the reconstructive and accompanying research into the formation of knowledge systems in social negotiation processes. The approach analyses how dominant understandings of a phenomenon develop, i.e., the way they are expressed and consolidated by various actors in specific arenas of discourse until a specific understanding of the phenomenon and its socially accepted structure are established. This article presents insights and initial findings from D-WISE, a joint research project running since 2021 between the Institute of Anthropological Studies in Culture and History and the Language Technology Group of the Department of Informatics at the University of Hamburg. As an interdisciplinary team, we develop central innovations with regard to the availability of relevant DH applications by building up a uniform working environment, which supports the procedure of the sociology of knowledge approach to discourse analysis within open corpora and heterogeneous, multimodal data sources for researchers in the humanities. We are hereby expanding the existing range of DH methods by developing contextualized embeddings for improved modelling of the plurality of meaning and the integrated processing of multimodal data. The alignment of this methodological and technical innovation is based on the epistemological working methods according to grounded theory as a hermeneutic methodology. In order to systematically relate, compare, and reflect the approaches of structural-IT and hermeneutic-interpretative analysis, the discourse analysis is carried out both manually and digitally. Using the example of current discourses on digitization in the healthcare sector and the associated issues regarding data protection, we have manually built an initial data corpus of which the relevant actors and discourse positions are analysed in conventional qualitative discourse analysis. At the same time, we are building an extensive digital corpus on the same topic based on the use and further development of entity-centered research tools such as topic crawlers and automated newsreaders. In addition to the text material, this consists of multimodal sources such as images, video sequences, and apps. In a blended reading process, the data material is filtered, annotated, and finally coded with the help of NLP tools such as dependency parsing, named entity recognition, co-reference resolution, entity linking, sentiment analysis, and other project-specific tools that are being adapted and developed. The coding process is carried out (semi-)automated by programs that propose coding paradigms based on the calculated entities and their relationships. Simultaneously, these can be specifically trained by manual coding in a closed reading process and specified according to the content issues. Overall, this approach enables purely qualitative, fully automated, and semi-automated analyses to be compared and reflected upon.

Keywords: entanglement of structural IT and hermeneutic-interpretative analysis, multimodality, plurality of meaning, sociology of knowledge approach to discourse analysis

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Authors: Ivan Balachin, Geanette Polanco, Jiang Xingliang, Hu Qin

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The problem of global warming raised up interest towards renewable energy sources. To reduce cost of wind energy is a challenge. Before building of wind park conditions such as: average wind speed, direction, time for each wind, probability of icing, must be considered in the design phase. Operation values used on the setting of control systems also will depend on mentioned variables. Here it is proposed a procedure to be include in the evaluation of the performance of a wind turbine, based on the amplitude of wind changes, the number of changes and their duration. A generic study case based on actual data is presented. Data analysing techniques were applied to model the power required for yaw system based on amplitude and data amount of wind changes. A theoretical model between time, amplitude of wind changes and angular speed of nacelle rotation was identified.

Keywords: field data processing, regression determination, wind turbine performance, wind turbine placing, yaw system losses

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Authors: Masoud Charkhabi

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We examine trade theory with this motivation. The full set of World Trade Organization data are organized into country-year pairs, each treated as a different entity. Topological Data Analysis reveals that among the 16 region and 240 region-year pairs there exists in fact a distinguishable group of region-period pairs. The generally accepted periods of shifts from dissimilar-dissimilar to similar-similar trade in goods among regions are examined from this new perspective. The period breaks are treated as cumulative and are flexible. This type of all-subsets analysis is motivated from computer science and is made possible with Lossy Compression and Graph Theory. The results question many patterns in similar-similar to dissimilar-dissimilar trade. They also show indications of economic shifts that only later become evident in other economic metrics.

Keywords: econometrics, globalization, network science, topological data, analysis, trade theory, visualization, world trade

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Authors: Natalie A. Sterk, Bernard van Vuuren, Petrie Marais, Bongani Mthombeni

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Injuries to the internal structures of the thorax and abdomen remain a leading cause of death among soldiers. Body armour is a standard issue piece of military equipment designed to protect the vital organs against ballistic and stab threats. When configured for maximum protection, the excessive weight and size of the armour may limit soldier mobility and increase physical fatigue and discomfort. Providing soldiers with more armour than necessary may, therefore, hinder their ability to react rapidly in life-threatening situations. The capability to determine the optimal trade-off between the amount of essential anatomical coverage and hindrance on soldier performance may significantly enhance the design of armour systems. The current study aimed to develop and pilot a methodology for relating internal anatomical structures with actual armour plate coverage in real-time using low-dose diagnostic X-ray scanning. Several pilot scanning sessions were held at Lodox Systems (Pty) Ltd head-office in South Africa. Testing involved using the Lodox eXero-dr to scan dummy trunk rigs at various degrees and heights of measurement; as well as human participants, wearing correctly fitted body armour while positioned in supine, prone shooting, seated and kneeling shooting postures. The verification of sizing and metrics obtained from the Lodox eXero-dr were then confirmed through a verification board with known dimensions. Results indicated that the low-dose diagnostic X-ray has the capability to clearly identify the vital internal structures of the aortic arch, heart, and lungs in relation to the position of the external armour plates. Further testing is still required in order to fully and accurately identify the inferior liver boundary, inferior vena cava, and spleen. The scans produced in the supine, prone, and seated postures provided superior image quality over the kneeling posture. The X-ray-source and-detector distance from the object must be standardised to control for possible magnification changes and for comparison purposes. To account for this, specific scanning heights and angles were identified to allow for parallel scanning of relevant areas. The low-dose diagnostic X-ray provides a non-invasive, safe, and rapid technique for relating vital internal structures with external structures. This capability can be used for the re-evaluation of anatomical coverage required for essential protection while optimising armour design and fit for soldier performance.

Keywords: body armour, low-dose diagnostic X-ray, scanning, vital organ coverage

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Authors: Muhammad Awais Shafique, Eiji Hato, Hideki Yaginuma

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Recently GPS data is used in a lot of studies to automatically reconstruct travel patterns for trip survey. The aim is to minimize the use of questionnaire surveys and travel diaries so as to reduce their negative effects. In this paper data acquired from GPS and accelerometer embedded in smart phones is utilized to predict the mode of transportation used by the phone carrier. For prediction, Support Vector Machine (SVM) and Adaptive boosting (AdaBoost) are employed. Moreover a unique method to improve the prediction results from these algorithms is also proposed. Results suggest that the prediction accuracy of AdaBoost after improvement is relatively better than the rest.

Keywords: accelerometer, AdaBoost, GPS, mode prediction, support vector machine

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Authors: Eric Kumar, Erica Cochran, Zhiang Zhang, Wei Liang, Ronak Mody

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The objective of this article was to create a modelling framework that exposes the marginal costs of shifting workloads across geographically distributed data-centers. Geographical distribution of internet services helps to optimize their performance for localized end users with lowered communications times and increased availability. However, due to the geographical and temporal effects, the physical embodiments of a service's data center infrastructure can vary greatly. In this work, we first identify that the sources of variances in the physical infrastructure primarily stem from local weather conditions, specific user traffic profiles, energy sources, and the types of IT hardware available at the time of deployment. Second, we create a traffic simulator that indicates the IT load at each data-center in the set as an approximator for user traffic profiles. Third, we implement a framework that quantifies the global level energy demands using building energy models and the traffic profiles. The results of the model provide a time series of energy demands that can be used for further life cycle analysis of internet services.

Keywords: data-centers, energy, life cycle, network simulation

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Authors: Vaibhav Singh Parihar

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Incidents of serious offenses being committed by children are increasing day by day thereby becoming a matter of great concern. The involvement of a 17-year-old boy in the incident that took place on 16th December 2012 (most commonly known as ‘Nirbhaya Case’)wherein a 23-year-old girl was brutally gang-raped and thrown out of the moving bus, took the entire nation by shock. Previously, the legislation dealing with juvenile delinquency in India considered a child to be juvenile if he/she was below the age of 18 years. As a consequence, the accused who was just six months short of attaining the age of 18 years was convicted for only three years. The primary objective of the study is to understand the gravity as to why the need for distinguishing a child and juvenile arose in this time and to what extent legislations are successful in this regard. It initially explains the history and evolution of juvenile legislation in India and the provisions contained in the Indian Constitution. It then goes on to explain the causes of juvenile delinquency in India. Further, the study focuses on the latest trends that have developed in juvenile delinquency, explaining how the Nirbhaya Case led to the amendments made to the Juvenile Justice Act, 2010. Also, it focuses on the Child Rights and Child Protection and the stand taken by the National Human Rights Commission and the international community. An attempt has been made to settle the debate as to whether the juvenile justice system in India is reformative or punitive. The need for amendment in the Juvenile Justice Act is also highlighted. The outcome of the study suggests that the legislation relating to juvenile delinquency have not been able to achieve the desired results. The age determination method in our system has been given paramount importance. The maximum punishment prescribed, even for heinous crimes, is only three years. Also, the reformative style of punishment is not adequate and more emphasis should be laid on penalization. Finally, the author concludes that the legislation has failed at creating a deterrent effect. It is suggested to strengthen the role of government authorities and to sensitize people in this regard to increase community participation. A non-doctrinal and analytical approach has been adopted and secondary sources of data have been relied upon by the author for conducting the research for the study.

Keywords: child, delinquency, juvenile, Nirbhaya case

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Authors: Marcho Setiawan Handok, Samuel S. Lemma, Abdoulaye Fofana, Naseef Mansoor

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This paper is proposing a method to predict the outcome of the National Football League match with data from 2019 to 2022 and compare it with other popular models. The model uses open-source statistical data of each team, such as passing yards, rushing yards, fumbles lost, and scoring. Each statistical data has offensive and defensive. For instance, a data set of anticipated values for a specific matchup is created by comparing the offensive passing yards obtained by one team to the defensive passing yards given by the opposition. We evaluated the model’s performance by contrasting its result with those of established prediction algorithms. This research is using a neural network to predict the score of a National Football League match and then predict the winner of the game.

Keywords: game prediction, NFL, football, artificial neural network

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Authors: Mehdi Khaki, Ehsan Forootan, Joseph Awange, Michael Kuhn

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Terrestrial water storage, as a source of freshwater, plays an important role in human lives. Hydrological models offer important tools for simulating and predicting water storages at global and regional scales. However, their comparisons with 'reality' are imperfect mainly due to a high level of uncertainty in input data and limitations in accounting for all complex water cycle processes, uncertainties of (unknown) empirical model parameters, as well as the absence of high resolution (both spatially and temporally) data. Data assimilation can mitigate this drawback by incorporating new sets of observations into models. In this effort, we use multi-mission satellite-derived remotely sensed observations to improve the performance of World-Wide Water Resources Assessment system (W3RA) hydrological model for estimating terrestrial water storages. For this purpose, we assimilate total water storage (TWS) data from the Gravity Recovery And Climate Experiment (GRACE) and surface soil moisture data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) into W3RA. This is done to (i) improve model estimations of water stored in ground and soil moisture, and (ii) assess the impacts of each satellite of data (from GRACE and AMSR-E) and their combination on the final terrestrial water storage estimations. These data are assimilated into W3RA using the Ensemble Square-Root Filter (EnSRF) filtering technique over Mississippi Basin (the United States) and Murray-Darling Basin (Australia) between 2002 and 2013. In order to evaluate the results, independent ground-based groundwater and soil moisture measurements within each basin are used.

Keywords: data assimilation, GRACE, AMSR-E, hydrological model, EnSRF

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Authors: Zhao Haoyue

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Due to the combined effects of climate change, urban expansion, and population growth, various environmental issues, such as urban heat islands and pollution, arise. Therefore, reliable information on urban environmental climate is needed to address and mitigate the negative effects. The emergence of urban climate maps provides a practical basis for urban climate regulation and improvement. This article takes Shapingba District, Chongqing City, as an example to study the construction method of urban thermal environment climate maps based on GIS spatial analysis technology. The thermal load, ventilation potential analysis map, and thermal environment comprehensive analysis map were obtained. Based on the classification criteria obtained from the climate map, corresponding protection and planning mitigation measures have been proposed.

Keywords: urban climate, GIS, heat island analysis, urban thermal environment

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Authors: Shinn-Dar Wu

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This study aims to synthesize lithium iron phosphate cathode material using a recycling technology involving non-protective gas calcination. The advantages include lower cost and easier production than traditional methods that require a large amount of protective gas. The novel technology may have extensive industrial applications. Given that the traditional gas calcination has a large number of protection free Fe3+ production, this study developed a precursor iron phosphate (Fe2+) material recycling technology and conducted related tests and analyses. It focused on flow field design of calcination and new technology as well as analyzed the best conditions for powder calcination combination. The electrical properties were determined by button batteries and exhibited a capacity of 118 mAh/g (The use of new materials synthesis, capacitance is about 122 mAh/g). The cost reduced to 50% of the original.

Keywords: lithium battery, lithium iron phosphate, calcined technology, recycling technology

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Authors: Jacob Wahl, Jane Zhang

Abstract:

This paper describes the development and characterization of a prototype IoT network for use with agricultural imaging and monitoring applications. The sensor and gateway nodes are designed using the ESP32 SoC with integrated Bluetooth Low Energy 4.2 and Wi-Fi. A development board, the Arducam IoTai ESP32, is used for prototyping, testing, and power measurements. Google’s Firebase is used as the cloud storage site for image data collected by the sensor. The sensor node captures images using the OV2640 2MP camera module and transmits the image data to the gateway via Bluetooth Low Energy. The gateway then uploads the collected images to Firebase via a known nearby Wi-Fi network connection. This image data can then be processed and analyzed by computer vision and machine learning pipelines to assess crop growth or other needs. The sensor node achieves a wireless transmission data throughput of 220kbps while consuming 150mA of current; the sensor sleeps at 162µA. The sensor node device lifetime is estimated to be 682 days on a 6600mAh LiPo battery while acquiring five images per day based on the development board power measurements. This network can be utilized by any application that requires high data rates, low power consumption, short-range communication, and large amounts of data to be transmitted at low-frequency intervals.

Keywords: Bluetooth low energy, ESP32, firebase cloud, IoT, smart farming

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Authors: Yu Chen, Xiaoguang Yang, Lin Lin

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Traffic engineering construction is an important infrastructure for economic and social development. In the process of construction and operation, the ability to make a correct evaluation of the project's environmental impact appears to be crucial to the rational operation of existing transportation projects, the correct development of transportation engineering construction and the adoption of corresponding measures to scientifically carry out environmental protection work. Most of the existing research work on ecological and environmental impact assessment is limited to individual aspects of the environment and less to the overall evaluation of the environmental system; in terms of research conclusions, there are more qualitative analyses from the technical and policy levels, and there is a lack of quantitative research results and quantitative and operable evaluation models. In this paper, a comprehensive analysis of the ecological and environmental impacts of transportation construction projects is conducted, and factors such as the accessibility of data and the reliability of calculation results are comprehensively considered to extract indicators that can reflect the essence and characteristics. The qualitative evaluation indicators were screened using the expert review method, the qualitative indicators were measured using the fuzzy statistics method, the quantitative indicators were screened using the principal component analysis method, and the quantitative indicators were measured by both literature search and calculation. An environmental impact evaluation index system with the general objective layer, sub-objective layer and indicator layer was established, dividing the environmental impact of the transportation construction project into two periods: the construction period and the operation period. On the basis of the evaluation index system, the index weights are determined using the hierarchical analysis method, and the individual indicators to be evaluated are dimensionless, eliminating the influence of the original background and meaning of the indicators. Finally, the thesis uses the above research results, combined with the actual engineering practice, to verify the correctness and operability of the evaluation method.

Keywords: transportation construction projects, ecological and environmental impact, analysis and evaluation, indicator evaluation system

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Authors: Adamantini Loukodimou, David Weston, Shiladitya Paul

Abstract:

Offshore structures are subjected to harsh environments. It is documented that carbon steel needs protection from corrosion. The combined effect of UV radiation, seawater splash, and fluctuating temperatures diminish the integrity of these structures. In addition, the possibility of damage caused by floating ice, seaborne debris, and maintenance boats make them even more vulnerable. Their inspection and maintenance when far out in the sea are difficult, risky, and expensive. The most known method of mitigating corrosion of offshore structures is the use of cathodic protection. There are several zones in an offshore wind turbine. In the atmospheric zone, due to the lack of a continuous electrolyte (seawater) layer between the structure and the anode at all times, this method proves inefficient. Thus, the use of protective coatings becomes indispensable. This research focuses on the atmospheric zone. The conversion of commercially available and conventional paint (epoxy) system to an autonomous self-healing paint system via the addition of suitable encapsulated healing agents and catalyst is investigated in this work. These coating systems, which can self-heal when damaged, can provide a cost-effective engineering solution to corrosion and related problems. When the damage of the paint coating occurs, the microcapsules are designed to rupture and release the self-healing liquid (monomer), which then will react in the presence of the catalyst and solidify (polymerization), resulting in healing. The catalyst should be compatible with the system because otherwise, the self-healing process will not occur. The carbon steel substrate will be exposed to a corrosive environment, so the use of a sacrificial layer of Zn is also investigated. More specifically, the first layer of this new coating system will be TSZA (Thermally Sprayed Zn85/Al15) and will be applied on carbon steel samples with dimensions 100 x 150 mm after being blasted with alumina (size F24) as part of the surface preparation. Based on the literature, it corrodes readily, so one additional paint layer enriched with microcapsules will be added. Also, the reaction and the curing time are of high importance in order for this bilayer system of coating to work successfully. For the first experiments, polystyrene microcapsules loaded with 3-octanoyltio-1-propyltriethoxysilane were conducted. Electrochemical experiments such as Electrochemical Impedance Spectroscopy (EIS) confirmed the corrosion inhibiting properties of the silane. The diameter of the microcapsules was about 150-200 microns. Further experiments were conducted with different reagents and methods in order to obtain diameters of about 50 microns, and their self-healing properties were tested in synthetic seawater using electrochemical techniques. The use of combined paint/electrodeposited coatings allows for further novel development of composite coating systems. The potential for the application of these coatings in offshore structures will be discussed.

Keywords: corrosion mitigation, microcapsules, offshore wind turbines, self-healing

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Authors: Lauren C. Porter, Andrew Curtis, Eric Jefferis, Susanne Mitchell

Abstract:

A wealth of research has been generated examining the variation in crime across neighborhoods. However, there is also a striking degree of crime concentration within neighborhoods. A number of studies show that a small percentage of street segments, intersections, or addresses account for a large portion of crime. Not surprisingly, a focus on these crime hot spots can be an effective strategy for reducing community level crime and related ills, such as health problems. However, research is also limited in an important respect. Studies tend to use official data to identify hot spots, such as 911 calls or calls for service. While the use of call data may be more representative of the actual level and distribution of crime than some other official measures (e.g. arrest data), call data still suffer from the 'dark figure of crime.' That is, there is most certainly a degree of error between crimes that occur versus crimes that are reported to the police. In this study, we present an alternative method of identifying crime hot spots, that does not rely on official data. In doing so, we highlight the potential utility of neighborhood-insiders to identify and understand crime dynamics within geographic spaces. Specifically, we use spatial video and geo-narratives to record the crime insights of 36 police, ex-offenders, and residents of a high crime neighborhood in northeast Ohio. Spatial mentions of crime are mapped to identify participant-identified hot spots, and these are juxtaposed with calls for service (CFS) data. While there are bound to be differences between these two sources of data, we find that one location, in particular, a corner store, emerges as a hot spot for all three groups of participants. Yet it does not emerge when we examine CFS data. A closer examination of the space around this corner store and a qualitative analysis of narrative data reveal important clues as to why this store may indeed be a hot spot, but not generate disproportionate calls to the police. In short, our results suggest that researchers who rely solely on official data to study crime hot spots may risk missing some of the most dangerous places.

Keywords: crime, narrative, video, neighborhood

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Authors: Ibrahim A. Adeniran, Rui F. Zhu, Man S. Wong

Abstract:

Owing to the insufficiency in the spatial representativeness and continuity of in situ temperature measurements from weather stations (WS), the use of temperature measurement from WS for large-range diurnal analysis in heterogenous landscapes has been limited. This has made the accurate estimation of land surface temperature (LST) from remotely sensed data more crucial. Moreover, the study of dynamic interaction between the atmosphere and the physical surface of the Earth could be enhanced at both annual and diurnal scales by using optimal LST data derived from satellite sensors. The tradeoff between the spatial and temporal resolution of LSTs from satellite’s thermal infrared sensors (TIRS) has, however, been a major challenge, especially when high spatiotemporal LST data are recommended. It is well-known from existing literature that polar satellites have the advantage of high spatial resolution, while geostationary satellites have a high temporal resolution. Hence, this study is aimed at designing a framework for the cross-comparison of LST data from polar and geostationary satellites in a heterogeneous landscape. This could help to understand the relationship between the LST estimates from the two satellites and, consequently, their integration in diurnal LST analysis. Landsat-8 satellite data will be used as the representative of the polar satellite due to the availability of its long-term series, while the Himawari-8 satellite will be used as the data source for the geostationary satellite because of its improved TIRS. For the study area, Hong Kong Special Administrative Region (HK SAR) will be selected; this is due to the heterogeneity in the landscape of the region. LST data will be retrieved from both satellites using the Split window algorithm (SWA), and the resulting data will be validated by comparing satellite-derived LST data with temperature data from automatic WS in HK SAR. The LST data from the satellite data will then be separated based on the land use classification in HK SAR using the Global Land Cover by National Mapping Organization version3 (GLCNMO 2013) data. The relationship between LST data from Landsat-8 and Himawari-8 will then be investigated based on the land-use class and over different seasons of the year in order to account for seasonal variation in their relationship. The resulting relationship will be spatially and statistically analyzed and graphically visualized for detailed interpretation. Findings from this study will reveal the relationship between the two satellite data based on the land use classification within the study area and the seasons of the year. While the information provided by this study will help in the optimal combination of LST data from Polar (Landsat-8) and geostationary (Himawari-8) satellites, it will also serve as a roadmap in the annual and diurnal urban heat (UHI) analysis in Hong Kong SAR.

Keywords: automatic weather station, Himawari-8, Landsat-8, land surface temperature, land use classification, split window algorithm, urban heat island

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