Search results for: data mining challenges

Authors: Deepak Singh, Rail Kuliev

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This research article explores the significance of advanced data visualization techniques in enhancing decision-making processes within the oil and gas exploration and production domain. With the oil and gas industry facing numerous challenges, effective interpretation and analysis of vast and diverse datasets are crucial for optimizing exploration strategies, production operations, and risk assessment. The article highlights the importance of data visualization in managing big data, aiding the decision-making process, and facilitating communication with stakeholders. Various advanced data visualization techniques, including 3D visualization, augmented reality (AR), virtual reality (VR), interactive dashboards, and geospatial visualization, are discussed in detail, showcasing their applications and benefits in the oil and gas sector. The article presents case studies demonstrating the successful use of these techniques in optimizing well placement, real-time operations monitoring, and virtual reality training. Additionally, the article addresses the challenges of data integration and scalability, emphasizing the need for future developments in AI-driven visualization. In conclusion, this research emphasizes the immense potential of advanced data visualization in revolutionizing decision-making processes, fostering data-driven strategies, and promoting sustainable growth and improved operational efficiency within the oil and gas exploration and production industry.

Keywords: augmented reality (AR), virtual reality (VR), interactive dashboards, real-time operations monitoring

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Authors: Chapman Eze Nnadozie

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Cloud computing, a technology that is made possible through virtualization within networks represents a shift from the traditional ownership of infrastructure and other resources by distinct organization to a more scalable pattern in which computer resources are rented online to organizations on either as a pay-as-you-use basis or by subscription. In other words, cloud computing entails the renting of computing resources (such as storage space, memory, servers, applications, networks, etc.) by a third party to its clients on a pay-as-go basis. It is a new innovative technology that is globally embraced because of its renowned benefits, profound of which is its cost effectiveness on the part of organizations engaged with its services. In Nigeria, the services are provided either directly to companies mostly by the key IT players such as Microsoft, IBM, and Google; or in partnership with some other players such as Infoware, Descasio, and Sunnet. This action enables organizations to rent IT resources on a pay-as-you-go basis thereby salvaging them from wastages accruable on acquisition and maintenance of IT resources such as ownership of a separate data centre. This paper intends to appraise the challenges of cloud computing adoption in Nigeria, bearing in mind the country’s peculiarities’ in terms of infrastructural development. The methodologies used in this paper include the use of research questionnaires, formulated hypothesis, and the testing of the formulated hypothesis. The major findings of this paper include the fact that there are some addressable challenges to the adoption of cloud computing in Nigeria. Furthermore, the country will gain significantly if the challenges especially in the area of infrastructural development are well addressed. This is because the research established the fact that there are significant gains derivable by the adoption of cloud computing by organizations in Nigeria. However, these challenges can be overturned by concerted efforts in the part of government and other stakeholders.

Keywords: cloud computing, data centre, infrastructure, it resources, virtualization

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Authors: Ali Mohammadi

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The construction of Embankment dams is considered one of the most challenging construction projects, for which several main reasons can be mentioned. Excavation and embankment must be done in a large area, and its design is based on preliminary studies, but at the time of construction, it is possible that excavation does not match with the stability or slope of the rock, or the design is incomplete, and corrections should be made in order to be able to carry out excavation and embankment. Also, the progress of the work depends on the main factors, the lack of each of which can slow down the construction of the dams, and lead to an increase in costs, and control of excavations and embankments and calculations of their volumes are done in this collection. In the following, we will investigate three Embankment dams in Iran that faced these challenges and how they overcame these challenges. KHODA AFARIN on the Aras River between the two countries of IRAN and AZARBAIJAN, SIAH BISHEH PUMPED STORAGE on CHALUS River and GOTVAND on KARUN River are among the most important dams built in Iran.

Keywords: section, data transfer, tunnel, free station

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Authors: Clifford Kwakwa Amoah, De-Graft Owusu-Manu, Prince Antwi-Afari

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Infrastructure is the lifeline for economic development of any country. Hence, obtaining infrastructure quality cannot be overemphasized. Nevertheless, challenges of infrastructure quality persist, and it is worse in developing countries despite the diverse study on the subject matter. Therefore, this study was formulated to identify the prevalent determinants of infrastructure quality using synthesis of extant literature (to identify key variables), and analysis of survey questionnaire of data collected by means of the inductive methodology approach, mean score ranking and descriptive statistics. The variables “partner with the private sector, growth stimulation and poverty reduction, and adherence to procurement core principles” were the most significant challenges that the government faces. Moreover, it would be of utmost concern to adopt some stringent measures to help improve and accelerate on the growth and development of the nation, thereby achieving the best quality required. This study is novel conducted to provide insight into some of the punitive measures, considered in ensuring that quality infrastructure is obtained in both developing (specifically) and developed economies. The research findings therefore provide some guidance for overcoming the accumulative challenges. Application of the stated findings will help bridge the gap of infrastructure challenges; this is because the study found strong empirical evidence that infrastructure plays a pivotal role in the productivity enhancement.

Keywords: challenges, development, economic growth, government, infrastructure quality

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Authors: S. McLean, J. A. Scott

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The research topic is focusing on improving sustainable operation through recovery and reuse of waste heat in process water streams, an area in the mining industry that is often overlooked. There are significant advantages to the application of this topic, including economic and environmental benefits. The smelting process in the mining industry presents an opportunity to recover waste heat and apply it to alternative uses, thereby enhancing the overall process. This applied research has been conducted at the Sudbury Integrated Nickel Operations smelter site, in particular on the water cooling towers. The aim was to determine and optimize methods for appropriate recovery and subsequent upgrading of thermally low-grade heat lost from the water cooling towers in a manner that makes it useful for repurposing in applications, such as within an acid plant. This would be valuable to mining companies as it would be an opportunity to reduce the cost of the process, as well as decrease environmental impact and primary fuel usage. The waste heat from the cooling towers needs to be upgraded before it can be beneficially applied, as lower temperatures result in a decrease of the number of potential applications. Temperature and flow rate data were collected from the water cooling towers at an acid plant over two years. The research includes process control strategies and the development of a model capable of determining if the proposed heat recovery technique is economically viable, as well as assessing any environmental impact with the reduction in net energy consumption by the process. Therefore, comprehensive cost and impact analyses are carried out to determine the best area of application for the recovered waste heat. This method will allow engineers to easily identify the value of thermal resources available to them and determine if a full feasibility study should be carried out. The rapid scoping model developed will be applicable to any site that generates large amounts of waste heat. Results show that heat pumps are an economically viable solution for this application, allowing for reduced cost and CO₂ emissions.

Keywords: environment, heat recovery, mining engineering, sustainability

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Authors: Dasom Kim, William Xiu Shun Wong, Yoonjin Hyun, Donghoon Lee, Minji Paek, Sungho Byun, Namgyu Kim

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There have been many researches on document classification for classifying voluminous documents automatically. Through document classification, we can assign a specific category to each unlabeled document on the basis of various machine learning algorithms. However, providing labeled documents manually requires considerable time and effort. To overcome the limitations, the semi-supervised learning which uses unlabeled document as well as labeled documents has been invented. However, traditional document classifiers, regardless of supervised or semi-supervised ones, cannot sufficiently explain the reason or the process of the classification. Thus, in this paper, we proposed a methodology to visualize major topics and class components of each document. We believe that our methodology for visualizing topics and classes of each document can enhance the reliability and explanatory power of document classifiers.

Keywords: data mining, document classifier, text mining, topic modeling

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Authors: Faiza Hussain, Usman Qamar

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Synonym-based searching is recognized to be a complicated problem as text mining from unstructured data of web is challenging. Finding useful information which matches user need from bulk of web pages is a cumbersome task. In this paper, a novel and practical synonym retrieval technique is proposed for addressing this problem. For replacement of semantics, user intent is taken into consideration to realize the technique. Parts-of-Speech tagging is applied for pattern generation of the query and a thesaurus for this experiment was formed and used. Comparison with Non-Context Based Searching, Context Based searching proved to be a more efficient approach while dealing with linguistic semantics. This approach is very beneficial in doing intent based searching. Finally, results and future dimensions are presented.

Keywords: natural language processing, text mining, information retrieval, parts-of-speech tagging, grammar, semantics

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Authors: Jason West

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Low profitability in agriculture production along with increasing scrutiny over environmental effects is limiting food production at scale. In contrast, the mining sector offers access to resources including energy, water, transport and chemicals for food production at low marginal cost. Scalable agricultural production can benefit from the nexus of resources (water, energy, transport) offered by mining activity in remote locations. A decision support bioeconomic model for controlled environment vertical farms was used. Four submodels were used: crop structure, nutrient requirements, resource-crop integration, and economic. They escalate to a macro mathematical model. A demonstrable dynamic systems framework is needed to prove productive outcomes are feasible. We demonstrate a generalized bioeconomic macro model for controlled environment production systems in minesites using systems dynamics modeling methodology. Despite the complexity of bioeconomic modelling of resource-agricultural dynamic processes and interactions, the economic potential greater than general economic models would assume. Scalability of production as an input becomes a key success feature.

Keywords: crop production systems, mathematical model, mining, agriculture, dynamic systems

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Authors: Babalola Sunday Oyetayo, Igbinomwanhia Uyi Osamwonyi, Idowu T. O., Herbert Tata

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This paper explores the potential benefits of adopting blockchain technology in Nigeria's land administration systems while also addressing the challenges and implications of its implementation in the country's unique context. Through a comprehensive literature review and analysis of existing research, the paper delves into the key attributes of blockchain that can revolutionize land administration practices, with a particular focus on simplifying land registration procedures, expediting land title issuance, and enhancing data transparency and security. The decentralized and immutable nature of blockchain offers unique advantages, instilling trust and confidence in land transactions, which are especially crucial in Nigeria's land governance landscape. However, integrating blockchain in Nigeria's land administration ecosystem presents specific challenges, necessitating a critical evaluation of technical, socio-economic, and infrastructural barriers. These challenges encompass data privacy concerns, scalability, interoperability with outdated systems, and gaining acceptance from various stakeholders. By synthesizing these insights, the paper proposes strategies tailored to Nigeria's context to optimize the benefits of blockchain adoption while addressing the identified challenges. The research findings contribute significantly to the ongoing discourse on blockchain technology in Nigeria's land governance, offering evidence-based recommendations to policymakers, land administrators, and stakeholders. Ultimately, the paper aims to promote the effective utilization of blockchain, fostering efficiency, transparency, and trust in Nigeria's land administration systems to drive sustainable development and societal progress.

Keywords: block-chain, technology, stakeholders, land registration

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Authors: Redie Bezabih Hailu

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Unemployment is a problem of nations that needs a continuous research. This study aimed to determine the collaborations and challenges of public employment service (PES) with special emphasis of stakeholders, employers and job seekers. The researcher used pragmatic philosophy, exploratory design and inductive approach to collect data from the respondents using interview and focused group discussion techniques. PES provides job market information, vocational counseling, and training. As PES is not fully furnished with man power, budget, modern technologies, it is providing less adequate services to the employers and job seekers. Matching job seekers with job vacancies is the major challenge for the center and using paper-based data management system too. There is also a number of job seekers in spite of very limited number of vacancies that the service provision is poor due to the fact that there is low level of vacancies and high level of job seekers. The center has collaboration with AFE, AYA, BoTVED, BoWCY, and CETU. The major challenges with this collaborations was the absence of operational guidelines to evaluate effectiveness and performance, lottery method of selecting candidates for vacancies and nepotism or favoritism were challenges for job seekers. On the other hand, (COVID-19) pandemic, inability to get skilled labor, absence of standardized payment, expectation of job seekers and less educational quality and mass graduation were another challenges for employment services. The study recommended quality education and training, operational guideline for collaboration, technology based labor market information system and suggested further studies on quality of PES.

Keywords: public employment service, collaborations, stakeholders, employers, job seekers

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Authors: Lu Si, Jie Yu, Shasha Li, Jun Ma, Lei Luo, Qingbo Wu, Yongqi Ma, Zhengji Liu

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Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rule, we propose a large data sets instance selection method with MapReduce framework. Besides ensuring the prediction accuracy and reduction rate, it has two desirable properties: First, it reduces the work load in the aggregation node; Second and most important, it produces the same result with the sequential version, which other parallel methods cannot achieve. We evaluate the performance of FCNN-MR on one small data set and two large data sets. The experimental results show that it is effective and practical.

Keywords: instance selection, data reduction, MapReduce, kNN

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Authors: Pavel Shcherban, Vlad Golovanov

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Nowadays, the depletion of hydrocarbon deposits on the land of the Kaliningrad region leads to active geological exploration and development of oil and natural gas reserves in the southeastern part of the Baltic Sea. LLC 'Lukoil-Kaliningradmorneft' implements a comprehensive program for the development of the region's shelf in 2014-2023. Due to heterogeneity of reservoir rocks in various open fields, as well as with ambiguous conclusions on the contours of deposits, additional geological prospecting and refinement of the recoverable oil reserves are carried out. The key element is use of an effective technique of computer stock modeling at the first stage of processing of the received data. The following step uses information for the cluster analysis, which makes it possible to optimize the field development approaches. The article analyzes the effectiveness of various methods for reserves' calculation and computer modelling methods of the offshore hydrocarbon fields. Cluster analysis allows to measure influence of the obtained data on the development of a technical and economic model for mining deposits. The relationship between the accuracy of the calculation of recoverable reserves and the need of modernization of existing mining infrastructure, as well as the optimization of the scheme of opening and development of oil deposits, is observed.

Keywords: cluster analysis, computer modelling of deposits, correction of the feasibility study, offshore hydrocarbon fields

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Authors: R. Khoza, K. K. Govender

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The aim of the study was to review and critically evaluate the supply chain management (SCM) strategies and challenges in the private residential construction sector in South Africa. The study was grounded in three theories, namely, theory of constraints, principal-agency theory, and stakeholder theory. A quantitative approach was used to survey 320 private residential construction companies which registered with the National Homebuilders Registration Council (NHBRC) within the Gauteng province. The data from 250 questionnaires returned were analysed using SPSS (Versions 23) and Smart PLS. It became evident that the SCM challenges included lack of trust between the supplier and the organization; lack of adoption of SCM system; lack of a sufficiently skilled SCM workforce; and poor implementation of contract management. The findings also indicate that there is a significant positive relationship between the performance of the private residential construction sector in South Africa and SCM challenges, SCM strategies and SCM processes. A framework is proposed comprising SCM practices and strategies of private residential construction sector in South Africa, which will enable them to enhance performance.

Keywords: management challenges, residential housing, South Africa, supply chain management

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Authors: Kuliya Muhammed

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This paper examines the potentials of online learning and the challenges to its adoption in Nigeria’s higher institutions of learning. The research would assist in tackling the challenges of online learning adoption and enlighten institutions on the numerous benefits of online learning in Nigeria. The researcher used survey method for the study and questionnaires were used to obtain the needed data from 230 respondents cut across 20 higher institutions in the country. The findings revealed that online learning has the prospect to boost access to learning tools, assist students’ to learn from the comfort of their offices or homes, reduce the cost of learning, and enable individuals to gain self-knowledge. The major challenges in the adoption of e-learning are poor Information and Communication Technology infrastructures, poor internet connectivity where available, lack of Information and Communication Technology background, problem of power supply, lack of commitment by institutions, poor maintenance of Information and Communication Technology tools, inadequate facilities, lack of government funding and fraud. Recommendations were also made at the end of the research work.

Keywords: electronic, ICT, institution, internet, learning, technology

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Authors: Mohammed Abdullah Alshahrani

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The research focuses on investigating the use of partial least squares (PLS) methodology for addressing challenges associated with high-dimensional correlated data. Recent technological advancements have led to experiments producing data characterized by a large number of variables compared to observations, with substantial inter-variable correlations. Such data patterns are common in chemometrics, where near-infrared (NIR) spectrometer calibrations record chemical absorbance levels across hundreds of wavelengths, and in genomics, where thousands of genomic regions' copy number alterations (CNA) are recorded from cancer patients. PLS serves as a widely used method for analyzing high-dimensional data, functioning as a regression tool in chemometrics and a classification method in genomics. It handles data complexity by creating latent variables (components) from original variables. However, applying PLS can present challenges. The study investigates key areas to address these challenges, including unifying interpretations across three main PLS algorithms and exploring unusual negative shrinkage factors encountered during model fitting. The research presents an alternative approach to addressing the interpretation challenge of predictor weights associated with PLS. Sparse estimation of predictor weights is employed using a penalty function combining a lasso penalty for sparsity and a Cauchy distribution-based penalty to account for variable dependencies. The results demonstrate sparse and grouped weight estimates, aiding interpretation and prediction tasks in genomic data analysis. High-dimensional data scenarios, where predictors outnumber observations, are common in regression analysis applications. Ordinary least squares regression (OLS), the standard method, performs inadequately with high-dimensional and highly correlated data. Copy number alterations (CNA) in key genes have been linked to disease phenotypes, highlighting the importance of accurate classification of gene expression data in bioinformatics and biology using regularized methods like PLS for regression and classification.

Keywords: partial least square regression, genetics data, negative filter factors, high dimensional data, high correlated data

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Authors: Veronica S. Moertini, Vinsensius Kevin, Gede Karya

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Customer reviews have been collected by many kinds of e-commerce websites selling products, services, hotel rooms, tickets and so on. Each website collects its own customer reviews. The reviews can be crawled, collected from those websites and stored as big data. Text analysis techniques can be used to analyze that data to produce summarized information, such as customer opinions. Then, these opinions can be published by independent service provider websites and used to help customers in choosing the most suitable products or services. As the opinions are analyzed from big data of reviews originated from many websites, it is expected that the results are more trusted and accurate. Indonesian customers write reviews in Indonesian language, which comes with its own structures and uniqueness. We found that most of the reviews are expressed with “daily language”, which is informal, do not follow the correct grammar, have many abbreviations and slangs or non-formal words. Hadoop is an emerging platform aimed for storing and analyzing big data in distributed systems. A Hadoop cluster consists of master and slave nodes/computers operated in a network. Hadoop comes with distributed file system (HDFS) and MapReduce framework for supporting parallel computation. However, MapReduce has weakness (i.e. inefficient) for iterative computations, specifically, the cost of reading/writing data (I/O cost) is high. Given this fact, we conclude that MapReduce function is best adapted for “one-pass” computation. In this research, we develop an efficient technique for extracting or mining opinions from big data of Indonesian reviews, which is based on MapReduce with one-pass computation. In designing the algorithm, we avoid iterative computation and instead adopt a “look up table” technique. The stages of the proposed technique are: (1) Crawling the data reviews from websites; (2) cleaning and finding root words from the raw reviews; (3) computing the frequency of the meaningful opinion words; (4) analyzing customers sentiments towards defined objects. The experiments for evaluating the performance of the technique were conducted on a Hadoop cluster with 14 slave nodes. The results show that the proposed technique (stage 2 to 4) discovers useful opinions, is capable of processing big data efficiently and scalable.

Keywords: big data analysis, Hadoop MapReduce, analyzing text data, mining Indonesian reviews

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Authors: Volker Mittendorf, Andre Schmale

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In this case study, we want to explore the Twitter-use of candidates during the state parliamentary elections-year 2018 in Bavaria, Germany. This paper focusses on the seven parties that probably entered the parliament. Against this background, the paper classifies the use of language as populism which itself is considered as a political communication style. First, we determine the election campaigns which started in the years 2017 on Twitter, after that we categorize the posting times of the different direct candidates in order to derive ideal types from our empirical data. Second, we have done the exploration based on the dictionary of concerned citizens which contains German political language of the right and the far right. According to that, we are analyzing the corpus with methods of text mining and social network analysis, and afterwards we display the results in a network of words of concerned citizen communication style (CCCS).

Keywords: populism, communication style, election, text mining, social media

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Authors: Jin Fan, M. Ala Saadeghvaziri

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Unmanned aerial vehicles (UAVs), also referred to as drones, equipped with various kinds of advanced detecting or surveying systems, are effective and low-cost in data acquisition, data delivery and sharing, which can benefit the building of infrastructures. This paper will give an overview of applications of drones in planning, designing, construction and maintenance of infrastructures. The drone platform, detecting and surveying systems, and post-data processing systems will be introduced, followed by cases with details of the applications. Challenges from different aspects will be addressed. Opportunities of drones in infrastructure include but not limited to the following. Firstly, UAVs equipped with high definition cameras or other detecting equipment are capable of inspecting the hard to reach infrastructure assets. Secondly, UAVs can be used as effective tools to survey and map the landscape to collect necessary information before infrastructure construction. Furthermore, an UAV or multi-UVAs are useful in construction management. UVAs can also be used in collecting roads and building information by taking high-resolution photos for future infrastructure planning. UAVs can be used to provide reliable and dynamic traffic information, which is potentially helpful in building smart cities. The main challenges are: limited flight time, the robustness of signal, post data analyze, multi-drone collaboration, weather condition, distractions to the traffic caused by drones. This paper aims to help owners, designers, engineers and architects to improve the building process of infrastructures for higher efficiency and better performance.

Keywords: bridge, construction, drones, infrastructure, information

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Authors: Rawaa El Ayoubi

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Many researchers, academics and practitioners have developed leadership theories during the 20th century. This substantial effort has built more leadership theories, generating considerable organisational research on leadership models in contemporary literature. This paper explores the stages and drivers of leadership theory evolution based on the researcher’s personal conclusions and review of leadership theories. The purpose of this paper is to create a Leadership Process Model (LPM) that can provide guidance in dealing with the key challenges within the organisation. This integrative model of organisational leadership is based on inner meaning, leader values and vision. It further addresses the relationships between leadership theory, practice and development, exploring why challenges exist within the field of leadership theory and how these challenges can be mitigated.

Keywords: leadership challenges, leadership process model, leadership |theories, organisational leadership, paradigm development

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Authors: Dila Ram Bhandari

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Throughout the 20th century, new governments were created where identities such as ethnic, religious, linguistic, caste, communal, tribal, and others played a part in the development of constitutions and the legal system of victim and criminal justice. Acute issues with extremism, poverty, environmental degradation, cybercrimes, human rights violations, crime against, and victimization of both individuals and groups have recently plagued South Asian nations. Everyday massive number of crimes are steadfast, these frequent crimes have made the lives of common citizens restless. Crimes are one of the major threats to society and also for civilization. Crime is a bone of contention that can create a societal disturbance. The old-style crime solving practices are unable to live up to the requirement of existing crime situations. Crime analysis is one of the most important activities of the majority of intelligent and law enforcement organizations all over the world. The South Asia region lacks such a regional coordination mechanism, unlike central Asia of Asia Pacific regions, to facilitate criminal intelligence sharing and operational coordination related to organized crime, including illicit drug trafficking and money laundering. There have been numerous conversations in recent years about using data mining technology to combat crime and terrorism. The Data Detective program from Sentient as a software company, uses data mining techniques to support the police (Sentient, 2017). The goals of this internship are to test out several predictive model solutions and choose the most effective and promising one. First, extensive literature reviews on data mining, crime analysis, and crime data mining were conducted. Sentient offered a 7-year archive of crime statistics that were daily aggregated to produce a univariate dataset. Moreover, a daily incidence type aggregation was performed to produce a multivariate dataset. Each solution's forecast period lasted seven days. Statistical models and neural network models were the two main groups into which the experiments were split. For the crime data, neural networks fared better than statistical models. This study gives a general review of the applied statistics and neural network models. A detailed image of each model's performance on the available data and generalizability is provided by a comparative analysis of all the models on a comparable dataset. Obviously, the studies demonstrated that, in comparison to other models, Gated Recurrent Units (GRU) produced greater prediction. The crime records of 2005-2019 which was collected from Nepal Police headquarter and analysed by R programming. In conclusion, gated recurrent unit implementation could give benefit to police in predicting crime. Hence, time series analysis using GRU could be a prospective additional feature in Data Detective.

Keywords: time series analysis, forecasting, ARIMA, machine learning

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Authors: Marlin Killen

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The American Association of University Professors (AAUP) describes academic freedom as essential to the purposes of research and teaching. The importance of academic freedom as a bedrock foundation that supports the work of the professoriate cannot be overstated, and there have been innumerable challenges that have attempted to curtail it. These challenges come from a variety of sources that span legal, ethical, cultural, institutional, and professional perspective and are amplified by social media, traditional media, and political action efforts. Because of these challenges, the development of a comprehensive institutional policy on academic freedom that addresses the principles, practices, and appropriate responses to modern challenges can be a daunting task. This presentation will focus on a case study of a university’s effort to develop an updated, evolving policy on academic freedom that provides a framework and remedies for contemporary challenges to this critical function in higher education.

Keywords: academic freedom, academic freedom policy, higher education policy

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Authors: Rachael Sullivan, Joshua Berman

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The number of satellites orbiting earth are in the thousands now. Commercial launches are increasing, and civilians are venturing into the outer reaches of the atmosphere. As the space industry continues to grow and evolve, so too will the demand on resources, the disparities amongst socio-economic groups, and space company governance standards. Outside of just ensuring that space operations are compliant with government regulations, export controls, and international sanctions, companies should also keep in mind the impact their operations will have on society and the environment. Those looking to expand their operations into outer space should remain mindful of both the opportunities and challenges that they could encounter along the way. From commercial launches promoting civilian space travel—like the recent launches from Blue Origin, Virgin Galactic, and Space X—to regulatory and policy shifts, the commercial landscape beyond the Earth's atmosphere is evolving. But practices will also have to become sustainable. Through a review and analysis of space industry trends, international government regulations, and empirical data, this research explores how Environmental, Social, and Governance (ESG) reporting and investing will manifest within a fast-changing space industry.Institutions, regulators, investors, and employees are increasingly relying on ESG. Those working in the space industry will be no exception. Companies (or investors) that are already engaging or plan to engage in space operations should consider 1) environmental standards and objectives when tackling space debris and space mining, 2) social standards and objectives when considering how such practices may impact access and opportunities for different socioeconomic groups to the benefits of space exploration, and 3) how decision-making and governing boards will function ethically, equitably, and sustainably as we chart new paths and encounter novel challenges in outer space.

Keywords: climate, environment, ESG, law, outer space, regulation

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Authors: C. B. Le, V. N. Pham

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In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

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Authors: Krish Batra

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The advent of open banking has revolutionized the financial services industry by fostering innovation, enhancing customer experience, and promoting competition. However, this paradigm shift towards more open and interconnected banking ecosystems has introduced complex cybersecurity challenges. This research paper delves into the multifaceted cybersecurity landscape of open banking, highlighting the vulnerabilities and threats inherent in sharing financial data across a network of banks and third-party providers. Through a detailed analysis of recent data breaches, phishing attacks, and other cyber incidents, the paper assesses the current state of cybersecurity within the open banking framework. It examines the effectiveness of existing security measures, such as encryption, API security protocols, and authentication mechanisms, in protecting sensitive financial information. Furthermore, the paper explores the regulatory response to these challenges, including the implementation of standards such as PSD2 in Europe and similar initiatives globally. By identifying gaps in current cybersecurity practices, the research aims to propose a set of robust, forward-looking strategies that can enhance the security and resilience of open banking systems. This includes recommendations for banks, third-party providers, regulators, and consumers on how to mitigate risks and ensure a secure open banking environment. The ultimate goal is to provide stakeholders with a comprehensive understanding of the cybersecurity implications of open banking and to outline actionable steps for safeguarding the financial ecosystem in an increasingly interconnected world.

Keywords: open banking, financial services industry, cybersecurity challenges, data breaches, phishing attacks, encryption, API security protocols, authentication mechanisms, regulatory response, PSD2, cybersecurity practices

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Authors: Celeste Labuschagne, Sam Ramaila, Thasmai Dhurumraj

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Although formative assessment is acknowledged as crucial for teachers to gauge students’ understanding of subject content, applying formative assessment in an online context is more challenging than in a traditional Physical Sciences classroom. This study examines challenges experienced by Grade 10 Physical Sciences teachers when enacting online formative assessment. The empirical investigation adopted a generic qualitative design and involved three purposively selected Grade 10 Physical Sciences teachers from three different schools and quintiles within the Tshwane North District in South Africa. Data were collected through individual and focus group interviews. Technological, pedagogical, and content knowledge (TPACK) was utilised as a theoretical framework underpinning the study. The study identified a myriad of challenges experienced by Grade 10 Physical Sciences teachers when enacting online formative assessment. These challenges include the utilisation of Annual Teaching Plans, lack of technological knowledge, and internet connectivity. The Department of Basic Education faces the key imperative to provide continuous teacher professional development and concomitant online learning materials that can facilitate meaningful enactment of online formative assessment in various educational settings.

Keywords: COVID-19, challenges, online formative assessment, physical sciences, TPACK

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Authors: John Michael Russo

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Honeybees are important to our food system and continue to suffer from high rates of colony loss. Precision feed has brought many benefits to livestock cultivation and these should transfer to apiculture. However, apiculture has unique challenges. The objective of this research is to understand how principles of precision agriculture, applied to apiculture and feed specifically, might effectively improve state-of-the-art cultivation. The methodology surveys apicultural practice to build a model for assessment. First, a review of apicultural motivators is made. Feed method is then evaluated. Finally, precision feed methods are examined as accelerants with potential to advance the effectiveness of feed practice. Six important motivators emerge: colony loss, disease, climate change, site variance, operational costs, and competition. Feed practice itself is used to compensate for environmental variables. The research finds that the current state-of-the-art in apiculture feed focuses on critical challenges in the management of feed schedules which satisfy requirements of the bees, preserve potency, optimize environmental variables, and manage costs. Many of the challenges are most acute when feed is used to dispense medication. Technology such as RNA treatments have even more rigorous demands. Precision feed solutions focus on strategies which accommodate specific needs of individual livestock. A major component is data; they integrate precise data with methods that respond to individual needs. There is enormous opportunity for precision feed to improve apiculture through the integration of precision data with policies to translate data into optimized action in the apiary, particularly through automation.

Keywords: precision agriculture, precision feed, apiculture, honeybees

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Authors: Karim Karmi

Abstract:

To keep pace with the requirements of globalization, a lot of universities across the globe have started teaching various academic subjects in English. In Tunisia, two higher education institutions have embarked on the experience of teaching in English instead of French. The aim of the present study was threefold. First, it sought to explore the stakeholders’ attitudes toward this shift. By stakeholders, we mean students and teachers. Second, it aimed at probing the challenges that might arise in the classroom. By challenges, we mean the linguistic and pedagogical difficulties that students and teachers might face. Third, the study investigated the reasons that led teachers and students to opt for English as a medium of instruction instead of French. The participants were 335 students and 14 teachers selected from two Tunisian universities teaching in English. Data was collected by means of questionnaires, interviews, and classroom observations. The findings showed that there is a positive attitude towards English, in contrast to French. In other words, both students and teachers are enjoying the experience, and they hope that English will officially become the medium of instruction in Tunisia. Students and teachers reported a number of linguistic and pedagogical challenges, and they mainly ascribed them to the abrupt transition from French to English. The vast majority of the respondents, be they students or teachers, opted for English as a medium of instruction to maximise their chances of getting a job abroad. It is also worth noting that most teachers stated that teaching through English helps them when it comes to publishing academic articles.

Keywords: attitudes, challenges, English as a medium of instruction, opportunities

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Authors: Ahmad. A. Kardan, Amir Narimani, Foozhan Ataiefard

Abstract:

Recommender Systems have been developed to provide contents and services compatible to users based on their behaviors and interests. Due to information overload in online discussion forums and users diverse interests, recommending relative topics and threads is considered to be helpful for improving the ease of forum usage. In order to lead learners to find relevant information in educational forums, recommendations are even more needed. We present a hybrid thread recommender system for MOOC forums by applying social network analysis and association rule mining techniques. Initial results indicate that the proposed recommender system performs comparatively well with regard to limited available data from users' previous posts in the forum.

Keywords: association rule mining, hybrid recommender system, massive open online courses, MOOCs, social network analysis

Procedia PDF Downloads 271

Authors: Fulya Gokalp Yavuz, Mark Daniel Ward

Abstract:

This study focuses on a contemporary and inevitable topic of Data Science and its exemplary application for early career building: Big Data and Leaving Learning Community (LLC). ‘Academia’ and ‘Industry’ have a common sense on the importance of Big Data. However, both of them are in a threat of missing the training on this interdisciplinary area. Some traditional teaching doctrines are far away being effective on Data Science. Practitioners needs some intuition and real-life examples how to apply new methods to data in size of terabytes. We simply explain the scope of Data Science training and exemplified its early stage application with LLC, which is a National Science Foundation (NSF) founded project under the supervision of Prof. Ward since 2014. Essentially, we aim to give some intuition for professors, researchers and practitioners to combine data science tools for comprehensive real-life examples with the guides of mentees’ feedback. As a result of discussing mentoring methods and computational challenges of Big Data, we intend to underline its potential with some more realization.

Keywords: Big Data, computation, mentoring, training

Procedia PDF Downloads 333

Authors: Shengnan Chen, Shuhua Wang

Abstract:

Successful production of hydrocarbon from unconventional tight oil reserves has changed the energy landscape in North America. The oil contained within these reservoirs typically will not flow to the wellbore at economic rates without assistance from advanced horizontal well and multi-stage hydraulic fracturing. Efficient and economic development of these reserves is a priority of society, government, and industry, especially under the current low oil prices. Meanwhile, society needs technological and process innovations to enhance oil recovery while concurrently reducing environmental impacts. Recently, big data analysis and artificial intelligence become very popular, developing data-driven insights for better designs and decisions in various engineering disciplines. However, the application of data mining in petroleum engineering is still in its infancy. The objective of this research aims to apply intelligent data analysis and data-driven models to exploit unconventional oil reserves both efficiently and economically. More specifically, a comprehensive database including the reservoir geological data, reservoir geophysical data, well completion data and production data for thousands of wells is firstly established to discover the valuable insights and knowledge related to tight oil reserves development. Several data analysis methods are introduced to analysis such a huge dataset. For example, K-means clustering is used to partition all observations into clusters; principle component analysis is applied to emphasize the variation and bring out strong patterns in the dataset, making the big data easy to explore and visualize; exploratory factor analysis (EFA) is used to identify the complex interrelationships between well completion data and well production data. Different data mining techniques, such as artificial neural network, fuzzy logic, and machine learning technique are then summarized, and appropriate ones are selected to analyze the database based on the prediction accuracy, model robustness, and reproducibility. Advanced knowledge and patterned are finally recognized and integrated into a modified self-adaptive differential evolution optimization workflow to enhance the oil recovery and maximize the net present value (NPV) of the unconventional oil resources. This research will advance the knowledge in the development of unconventional oil reserves and bridge the gap between the big data and performance optimizations in these formations. The newly developed data-driven optimization workflow is a powerful approach to guide field operation, which leads to better designs, higher oil recovery and economic return of future wells in the unconventional oil reserves.

Keywords: big data, artificial intelligence, enhance oil recovery, unconventional oil reserves

Procedia PDF Downloads 264