Search results for: high-dimensional data analysis

Authors: Abdulrahman K. Alshegaifi, Chun-Hsi Huang

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Treating data based on its location in memory has received much attention in recent years due to its different properties, which offer important aspects for cache utilization. Stack data and non-stack data may interfere with each other’s locality in the data cache. One of the important aspects of stack data is that it has high spatial and temporal locality. In this work, we simulate non-unified cache design that split data cache into stack and non-stack caches in order to maintain stack data and non-stack data separate in different caches. We observe that the overall hit rate of non-unified cache design is sensitive to the size of non-stack cache. Then, we investigate the appropriate size and associativity for stack cache to achieve high hit ratio especially when over 99% of accesses are directed to stack cache. The result shows that on average more than 99% of stack cache accuracy is achieved by using 2KB of capacity and 1-way associativity. Further, we analyze the improvement in hit rate when adding small, fixed, size of stack cache at level1 to unified cache architecture. The result shows that the overall hit rate of unified cache design with adding 1KB of stack cache is improved by approximately, on average, 3.9% for Rijndael benchmark. The stack cache is simulated by using SimpleScalar toolset.

Keywords: hit rate, locality of program, stack cache, stack data

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Authors: M. K. Witek-Hajduk, T. M. Napiórkowski

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A number of studies discussed the topic of benefits of retailers-manufacturers cooperation and coopetition. However, there are only few publications focused on the benefits of cooperation and coopetition between retailers and their suppliers of durable consumer goods; especially in the context of business model of cooperating partners. This paper aims to provide a clustering approach to segment retailers selling consumer durables according to the benefits they obtain from their cooperation with key manufacturers and differentiate the said retailers’ in term of the business models of cooperating partners. For the purpose of the study, a survey (with a CATI method) collected data on 603 consumer durables retailers present on the Polish market. Retailers are clustered both, with hierarchical and non-hierarchical methods. Five distinctive groups of consumer durables’ retailers are (based on the studied benefits) identified using the two-stage clustering approach. The clusters are then characterized with a set of exogenous variables, key of which are business models employed by the retailer and its partnering key manufacturer. The paper finds that the a combination of a medium sized retailer classified as an Integrator with a chiefly domestic capital and a manufacturer categorized as a Market Player will yield the highest benefits. On the other side of the spectrum is medium sized Distributor retailer with solely domestic capital – in this case, the business model of the cooperating manufactrer appears to be irreleveant. This paper is the one of the first empirical study using cluster analysis on primary data that defines the types of cooperation between consumer durables’ retailers and manufacturers – their key suppliers. The analysis integrates a perspective of both retailers’ and manufacturers’ business models and matches them with individual and joint benefits.

Keywords: benefits of cooperation, business model, cluster analysis, retailer-manufacturer cooperation

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Authors: Qianqian Chen

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The article intends to design a new research perspective on a linguistic landscape with the research background on multilingual urban tourism by analyzing the collected data, including a number of surveys on current urban tourism and the possibility of internationalization. The language usage analysis focuses on terms of English, Japanese and Spanish, which is based on the previous investigations. The analysis highlights the fact that contemporary tourism management and planning emphasizes cultural memories and heritage, and the combination between culture and tourism recalls the importance of "re-humanity" inhuman activities.

Keywords: multilingualism, culture, linguistic landscape, Hangzhou

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Authors: Mohammed R. Elkobaisi, Abdelsalam Maatuk

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The search engines are the large scale information retrieval tools from the Web that are currently freely available to all. This paper explains how to convert the raw resulted number of search engines into useful information. This represents a new method for data gathering comparing with traditional methods. When a query is submitted for a multiple numbers of keywords, this take a long time and effort, hence we develop a user interface program to automatic search by taking multi-keywords at the same time and leave this program to collect wanted data automatically. The collected raw data is processed using mathematical and statistical theories to eliminate unwanted data and converting it to usable data.

Keywords: search engines, information extraction, agent system

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Authors: Desmond Wade, Alfredo Ormazabal

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Background: Ever since it commenced its registration process of pre-hospital practitioners in the year 2000 through the Irish Government Statute Instrument (SI 109 of 2000) process, the approach to education of its professionals has changed drastically. The progression from the traditional behaviouristic to the current constructivist approach has been based on experiences from other sectors and industries, nationally and internationally. Today, the delivery of a safe and efficient ambulance service heavily depends on its practitioners’ range of technical skills, academic knowledge, and overall competences. As these increase, so does the level of complexity of paramedics’ everyday practice. This has made it inevitable to consider the 'Human Factor' as a source of potential risk and made formative institutions like the National Ambulance Service College to include it in their curriculum. Methods: This paper used a mixed-method approach, where both, an online questionnaire and a set of semi-structured interviews were the source of primary data. An analysis of this data was carried out using qualitative and quantitative data analysis. Conclusions: The evidence presented leads to the conclusion that in the National Ambulance Service there is a considerable lack of education of Human Factors and the levels in understanding of how to manage Human Factors in practice vary across its spectrum. Paramedic Practitioners in Ireland seem to understand that the responsibility of patient care lies on the team, rather than on the most hierarchically senior practitioner present in the scene.

Keywords: human factors, ergonomics, stress, decision making, pre-hospital care, paramedic, education

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Authors: Hacer Unver, Rukuye Aylaz

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Aim: This study was conducted in order to determine the effects of exercising on quality of life in pregnancy. Material and Method: The population of the study was formed by 580 pregnants who were registered to 10 Family Health Center located in the city center of Malatya. The sample of the study, on the other hand, was formed by 230 pregnants who had minimal sample size according to known population sample size calculation. The data of this descriptive study was collected between October 2013 and September 2014 from the Family Health Centers located in the city center of Malatya. The data were collected using pregnant introductory form, exercise benefit and barrier scale, quality of life scale. Percentage distributions, t-test, Variance Analysis (ANOVA), Kruskal-Wallis, Mann-Whitney U and Pearson Correlation tests were used in the analysis of the data. Result: It was determined that 69.1% of the pregnants participating to the study did not know the benefits of exercising and 89.6% did not exercise. Quality of life mental health scores of those who exercised were determined to be higher and statistically significant (p<0.05). A positive correlation was determined between the exercise benefit scala and physical quality of life scores of the pregnants in this study (0.268, p=0.001). It was also detected that the more exercise performed led to higher total quality of life scores. Conclusion: In consequence, exercising was determined to positively affect the quality of life in pregnants. Therefore, it is recommended that nurses should give education regarding the importance and benefits of exercise during pregnancy in order to increase the quality of life.

Keywords: exercise, midwife, pregnant woman, quality of life

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Authors: Ho Yeon Park, Kyoung-Jae Kim

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Recently, user’s interests for location-based social network service increases according to the advances of social web and location-based technologies. It may be easy to recommend preferred items if we can use user’s preference, context and social network information simultaneously. In this study, we propose context-aware POI (point-of-interests) recommender systems using location-based network analysis and sentiment analysis which consider context, social network information and implicit user’s preference score. We propose a context-aware POI recommendation system consisting of three sub-modules and an integrated recommendation system of them. First, we will develop a recommendation module based on network analysis. This module combines social network analysis and cluster-indexing collaboration filtering. Next, this study develops a recommendation module using social singular value decomposition (SVD) and implicit SVD. In this research, we will develop a recommendation module that can recommend preference scores based on the frequency of POI visits of user in POI recommendation process by using social and implicit SVD which can reflect implicit feedback in collaborative filtering. We also develop a recommendation module using them that can estimate preference scores based on the recommendation. Finally, this study will propose a recommendation module using opinion mining and emotional analysis using data such as reviews of POIs extracted from location-based social networks. Finally, we will develop an integration algorithm that combines the results of the three recommendation modules proposed in this research. Experimental results show the usefulness of the proposed model in relation to the recommended performance.

Keywords: sentiment analysis, network analysis, recommender systems, point-of-interests, business analytics

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Authors: Anushtha Saxena

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This paper examines the process of data monetisation bye-commerce companies operating in India. Data monetisation is collecting, storing, and analysing consumers’ data to use further the data that is generated for profits, revenue, etc. Data monetisation enables e-commerce companies to get better businesses opportunities, innovative products and services, a competitive edge over others to the consumers, and generate millions of revenues. This paper analyses the issues and challenges that are faced due to the process of data monetisation. Some of the issues highlighted in the paper pertain to the right to privacy, protection of data of e-commerce consumers. At the same time, data monetisation cannot be prohibited, but it can be regulated and monitored by stringent laws and regulations. The right to privacy isa fundamental right guaranteed to the citizens of India through Article 21 of The Constitution of India. The Supreme Court of India recognized the Right to Privacy as a fundamental right in the landmark judgment of Justice K.S. Puttaswamy (Retd) and Another v. Union of India . This paper highlights the legal issue of how e-commerce businesses violate individuals’ right to privacy by using the data collected, stored by them for economic gains and monetisation and protection of data. The researcher has mainly focused on e-commerce companies like online shopping websitesto analyse the legal issue of data monetisation. In the Internet of Things and the digital age, people have shifted to online shopping as it is convenient, easy, flexible, comfortable, time-consuming, etc. But at the same time, the e-commerce companies store the data of their consumers and use it by selling to the third party or generating more data from the data stored with them. This violatesindividuals’ right to privacy because the consumers do not know anything while giving their data online. Many times, data is collected without the consent of individuals also. Data can be structured, unstructured, etc., that is used by analytics to monetise. The Indian legislation like The Information Technology Act, 2000, etc., does not effectively protect the e-consumers concerning their data and how it is used by e-commerce businesses to monetise and generate revenues from that data. The paper also examines the draft Data Protection Bill, 2021, pending in the Parliament of India, and how this Bill can make a huge impact on data monetisation. This paper also aims to study the European Union General Data Protection Regulation and how this legislation can be helpful in the Indian scenarioconcerning e-commerce businesses with respect to data monetisation.

Keywords: data monetization, e-commerce companies, regulatory framework, GDPR

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Authors: Antonella Falanga, Armando Cartenì

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Today, an unprecedented amount of data coming from several sources, including mobile devices, sensors, tracking systems, and online platforms, characterizes our lives. The term “big data” not only refers to the quantity of data but also to the variety and speed of data generation. These data hold valuable insights that, when extracted and analyzed, facilitate informed decision-making. The 4Vs of big data - velocity, volume, variety, and value - highlight essential aspects, showcasing the rapid generation, vast quantities, diverse sources, and potential value addition of these kinds of data. This surge of information has revolutionized many sectors, such as business for improving decision-making processes, healthcare for clinical record analysis and medical research, education for enhancing teaching methodologies, agriculture for optimizing crop management, finance for risk assessment and fraud detection, media and entertainment for personalized content recommendations, emergency for a real-time response during crisis/events, and also mobility for the urban planning and for the design/management of public and private transport services. Big data's pervasive impact enhances societal aspects, elevating the quality of life, service efficiency, and problem-solving capacities. However, during this transformative era, new challenges arise, including data quality, privacy, data security, cybersecurity, interoperability, the need for advanced infrastructures, and staff training. Within the transportation sector (the one investigated in this research), applications span planning, designing, and managing systems and mobility services. Among the most common big data applications within the transport sector are, for example, real-time traffic monitoring, bus/freight vehicle route optimization, vehicle maintenance, road safety and all the autonomous and connected vehicles applications. Benefits include a reduction in travel times, road accidents and pollutant emissions. Within these issues, the proper transport demand estimation is crucial for sustainable transportation planning. Evaluating the impact of sustainable mobility policies starts with a quantitative analysis of travel demand. Achieving transportation decarbonization goals hinges on precise estimations of demand for individual transport modes. Emerging technologies, offering substantial big data at lower costs than traditional methods, play a pivotal role in this context. Starting from these considerations, this study explores the usefulness impact of big data within transport demand estimation. This research focuses on leveraging (big) data collected during the COVID-19 pandemic to estimate the evolution of the mobility demand in Italy. Estimation results reveal in the post-COVID-19 era, more than 96 million national daily trips, about 2.6 trips per capita, with a mobile population of more than 37.6 million Italian travelers per day. Overall, this research allows us to conclude that big data better enhances rational decision-making for mobility demand estimation, which is imperative for adeptly planning and allocating investments in transportation infrastructures and services.

Keywords: big data, cloud computing, decision-making, mobility demand, transportation

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Authors: Arbolino Roberta, Boffardi Raffaele

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Achieving the highest effectiveness for the National Plans for Recovery and Resilience (NPRR) while strengthening the objectives of cohesion and reduction of intra-EU unbalances is only possible by means of strategic, coordinated, and coherent policy planning. Therefore, the present research aims at assessing and quantifying the potential impact of NPRRs across the twenty-seven European Member States in terms of economic convergence, considering disaggregated data on industrial, construction, and service sectors. The first step of the research involves a performance analysis of the main macroeconomic indicators describing the trends of twenty-seven EU economies before the pandemic outbreak. Subsequently, in order to define the potential effect of the resources allocated, we perform an impact analysis of previous similar EU investment policies, estimating national-level sectoral elasticity associated with the expenditure of the 2007-2013 and 2014-2020 Cohesion programmes funds. These coefficients are then exploited to construct adjustment scenarios. Finally, convergence analysis is performed on the data used for constructing scenarios in order to understand whether the expenditure of funds might be useful to foster economic convergence besides driving recovery. The results of our analysis show that the allocation of resources largely mirrors the aims of the policy framework underlying the NPRR, thus reporting the largest investments in both those sectors most affected by the economic shock (services) and those considered fundamental for the digital and green transition. Notwithstanding an overall positive effect, large differences exist among European countries, while no convergence process seems to be activated or fostered by these interventions.

Keywords: NPRR, policy evaluation, cohesion policy, scenario Nalsysi

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Authors: Mehdi Habibagahi, Shami Nejadi, Ata Aminfar

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A significant degradation factor that impacts the durability of concrete structures is the alkali-silica reaction. Engineers are frequently charged with the challenges of conducting a thorough safety assessment of concrete structures that have been impacted by ASR. The alkali-silica reaction has a major influence on the structural capacities of structures. In most cases, the reduction in compressive strength, tensile strength, and modulus of elasticity is expressed as a function of free expansion and crack widths. Predicting the effect of ASR on flexural strength is also relevant. In this paper, a nonlinear three-dimensional (3D) finite-element model was proposed to describe the flexural strength degradation induced byASR.Initial strains, initial stresses, initial cracks, and deterioration of material characteristics were all considered ASR factors in this model. The effects of ASR on structural performance were evaluated by focusing on initial flexural stiffness, force–deformation curve, and load-carrying capacity. Degradation of concrete mechanical properties was correlated with ASR growth using material test data conducted at Tech Lab, UTS, and implemented into the FEM for various expansions. The finite element study revealed a better understanding of the ASR-affected RC beam's failure mechanism and capacity reduction as a function of ASR expansion. Furthermore, in this study, decreasing of the residual mechanical properties due to ASRisreviewed, using as input data for the FEM model. Finally, analysis techniques and a comparison of the analysis and the experiment results are discussed. Verification is also provided through analyses of reinforced concrete beams with behavior governed by either flexural or shear mechanisms.

Keywords: alkali-silica reaction, analysis, assessment, finite element, nonlinear analysis, reinforced concrete

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Authors: Marylene N. Tizon

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English instructors pay attention on errors committed by students as errors show whether they know or master their oral skills and what difficulties they may have in the process of learning the English language. This descriptive quantitative study aimed at identifying and categorizing the oral presentation errors of the purposively chosen 118 English 2A students enrolled during the first semester of school year 2013 – 2014. The analysis of the data for this study was undertaken using the errors committed by the students in their presentation. Marking and classifying of errors were made by first classifying them into linguistic grammatical errors then all errors were categorized further into Surface Structure Errors Taxonomy with the use of Frequency and Percentage distribution. From the analysis of the data, the researcher found out: Errors in tenses of the verbs (71 or 16%) and in addition 167 or 37% were most frequently uttered by the students. And Question and negation mistakes (12 or 3%) and misordering errors (28 or 7%) were least frequently enunciated by the students. Thus, the respondents in this study most frequently enunciated errors in tenses and in addition while they uttered least frequently the errors in question, negation, and misordering.

Keywords: grammatical error, oral presentation error, surface structure errors taxonomy, descriptive quantitative design, Philippines, Asia

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Authors: Dammak Saida, Mbarek Sonia

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This paper aims to examine the impact of public interest commitment, the attitude towards independence enforcement, and organizational ethical culture on auditors' ethical behavior. It also tests the moderating effect of gender diversity on these relationships. The sample consisted of 100 Tunisian chartered accountants. An online survey was used to collect the data. Data analysis techniques used to test hypotheses The findings of this study provide practical implications for accounting professionals, regulators, and audit firms as they help understand auditors' beliefs and behaviors, which implies more effective mechanisms for improving their ethical values.

Keywords: public interest, independence, organizational culture, professional behavior, Tunisian auditors

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Authors: Hang Wang, Xiaoming Gao

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The rapid development of e-commerce has significantly transformed consumer behavior and urban consumption patterns worldwide. This study explores the impact of e-commerce on the development and spatial distribution of consumption centers, with a particular focus on Jinan City, China. Traditionally, urban consumption centers are defined by physical commercial spaces, such as shopping malls and markets. However, the rise of e-commerce has introduced a shift towards virtual consumption hubs, with a corresponding impact on physical retail locations. Utilizing Gaode POI (Point of Interest) data, this research aims to provide a comprehensive analysis of the spatial distribution of consumption centers in Jinan, comparing e-commerce-driven virtual consumption hubs with traditional physical consumption centers. The study methodology involves gathering and analyzing POI data, focusing on logistics distribution for e-commerce activities and mobile charging point locations to represent offline consumption behavior. A spatial clustering technique is applied to examine the concentration of commercial activities and to identify emerging trends in consumption patterns. The findings reveal a clear differentiation between e-commerce and physical consumption centers in Jinan. E-commerce activities are dispersed across a wider geographic area, correlating closely with residential zones and logistics centers, while traditional consumption hubs remain concentrated around historical and commercial areas such as Honglou and the old city center. Additionally, the research identifies an ongoing transition within Jinan’s consumption landscape, with online and offline retail coexisting, though at different spatial and functional levels. This study contributes to urban planning by providing insights into how e-commerce is reshaping consumption behaviors and spatial structures in cities like Jinan. By leveraging big data analytics, the research offers a valuable tool for urban designers and planners to adapt to the evolving demands of digital commerce and to optimize the spatial layout of city infrastructure to better serve the needs of modern consumers.

Keywords: big data, consumption centers, e-commerce, urban planning, jinan

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Authors: Yan Cheng, Yijun Shao, James Rudolph, Charlene R. Weir, Beth Sahlmann, Qing Zeng-Treitler

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Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data, i.e., a ‘gold standard’, is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the labeled data creates a performance ceiling for the trained model. In this study, we trained several models to recognize the presence of delirium in clinical documents using data with annotations that are not completely accurate (i.e., weakly-supervised learning). In the external evaluation, the support vector machine model with a linear kernel performed best, achieving an area under the curve of 89.3% and accuracy of 88%, surpassing the 80% accuracy of the training sample. We then generated a set of simulated data and carried out a series of experiments which demonstrated that models trained on imperfect data can (but do not always) outperform the accuracy of the training data, e.g., the area under the curve for some models is higher than 80% when trained on the data with an error rate of 40%. Our experiments also showed that the error resistance of linear modeling is associated with larger sample size, error type, and linearity of the data (all p-values < 0.001). In conclusion, this study sheds light on the usefulness of imperfect data in clinical research via weakly-supervised learning.

Keywords: weakly-supervised learning, support vector machine, prediction, delirium, simulation

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Authors: Khizar Hayat Bhatti, Fiza Javed, Misbah Zafar

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Oat (Avena sativa L.) is a major cereal plant belonging to the family Poaceae. It is a very important source of carbohydrates, starch, minerals, vitamins and proteins that are beneficial for general health. Plants grow in the heavy metals contaminated soils that results in decline in growth. Glycine betaine application may improve plant growth, survival and resistance to metabolic disturbances due to stresses. Heavy metals, like nickels, have been accumulated for a long time in the soil because of industrial waste and sewage. The experiment was intended to alleviate the detrimental effects of heavy metal nickel stress on two oat varieties ‘Sgd-2011 and Hay’ using Glycine betain. Nickel was induced through soil application while GB was applied as foliar spray. After 10 days of nickel treatment, an exogenous spray of glycine betaine on the intact plant leaves. Data analysis was carried out using a Completely Randomized Design (CRD) with three replications in this study. For the analysis of all the data of the current research, Mini-Tab 19 software was used to compare the mean value of all treatments and Microsoft Excel software for generating the bars graphs. Significant accelerated plant growth was recorded when Ni exposed plants were treated with GB. Based on data findings, 3mM GB caused significant recovery from Ni stress doses. Overall results also demonstrated that the sgd-2011 variety of oats had the greatest outcomes for all parameters.

Keywords: CRD, foliar spray method, glycine betaine, heavy metals, nickel, ROS

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Authors: Arya Farjand

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This study delves into the Early Pleistocene fauna of the Yuanmou Basin, highlighting two significant findings. The first is the discovery of exceptionally well-preserved canid coprolites, which provide a rare glimpse into the diet and ecological niche of these ancient carnivores. The analysis of these coprolites has revealed a diet rich in diverse prey species, suggesting a complex food web and a dynamic ecological environment. This discovery not only sheds light on the dietary habits of these canids but also offers broader insights into the region's ecological dynamics during the Early Pleistocene. Additionally, the preservation of these coprolites allows for detailed study of the carnivore's role in the ecosystem, including their interactions with other species and the overall health of the environment. The second major finding is the identification of a mustelid species, Eirictis yuanmouensis, from the same fossil horizon as the coprolites. This discovery is crucial for understanding the diversity and evolution of Mustelidae in the region. The detailed analysis of cranial and dental morphology of Eirictis yuanmouensis indicates unique adaptations that suggest a specialized ecological niche. This finding, in conjunction with the coprolite analysis, provides a comprehensive view of the ecological niches occupied by both mustelids and hyenas, enhancing our understanding of their adaptations and interactions within this paleoenvironment. The study's significance is further amplified by the analysis of pollen data from the same horizon, which indicates a paleoenvironment characterized by rapid climatic changes and a dominant semiarid climate. This combination of faunal and floral data paints a detailed picture of the Early Pleistocene environment in the Yuanmou Basin, offering valuable insights into the interactions between different carnivore species and their adaptation strategies in response to changing environmental conditions.

Keywords: Yuanmou Basin, coprolite, Hyaena, eirictis yuanmouensis, early pleistocene

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Authors: Adheesh Budree, Olaf Jacob, Louis CH Fourie, James Njenga, Gabriel D Hoffman

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Business Intelligence (BI) is implemented by organisations for many reasons and chief among these is improved data support, decision support and savings. The main purpose of this study is to determine BI requirements and availability within South African organisations. The study addresses the following areas as identified as part of a literature review; assessing BI practices in businesses over a range of industries, sectors and managerial functions, determining the functionality of BI (technologies, architecture and methods). It was found that the overall satisfaction with BI in larger organisations is low due to lack of ability to meet user requirements.

Keywords: business intelligence, business value, data management, South Africa

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Authors: Kenneth Harper

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Blockchain technology presents solutions for managing healthcare data, addressing critical challenges in privacy, integrity, and access. This paper explores how privacy-preserving technologies, such as zero-knowledge proofs (ZKPs) and homomorphic encryption (HE), enhance decentralized healthcare platforms by enabling secure computations and patient data protection. An examination of the mathematical foundations of these methods, their practical applications, and how they meet the evolving demands of healthcare data security is unveiled. Using real-world examples, this research highlights industry-leading implementations and offers a roadmap for future applications in secure, decentralized healthcare ecosystems.

Keywords: blockchain, cryptography, data privacy, decentralized data management, differential privacy, healthcare, healthcare data security, homomorphic encryption, privacy-preserving technologies, secure computations, zero-knowledge proofs

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Authors: Housein Deli, Loui Al-Shrouf, Hammoud Al Joumaa, Mohieddine Jelali

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When forming metallic materials, fluctuations in material properties, process conditions, and wear lead to deviations in the component geometry. Several hundred features sometimes need to be measured, especially in the case of functional and safety-relevant components. These can only be measured offline due to the large number of features and the accuracy requirements. The risk of producing components outside the tolerances is minimized but not eliminated by the statistical evaluation of process capability and control measurements. The inspection intervals are based on the acceptable risk and are at the expense of productivity but remain reactive and, in some cases, considerably delayed. Due to the considerable progress made in the field of condition monitoring and measurement technology, permanently installed sensor systems in combination with machine learning and artificial intelligence, in particular, offer the potential to independently derive forecasts for component geometry and thus eliminate the risk of defective products - actively and preventively. The reliability of forecasts depends on the quality, completeness, and timeliness of the data. Measuring all geometric characteristics is neither sensible nor technically possible. This paper, therefore, uses the example of car seat rail production to discuss the necessary first step of feature selection and reduction by correlation analysis, as otherwise, it would not be possible to forecast components in real-time and inline. Four different car seat rails with an average of 130 features were selected and measured using a coordinate measuring machine (CMM). The run of such measuring programs alone takes up to 20 minutes. In practice, this results in the risk of faulty production of at least 2000 components that have to be sorted or scrapped if the measurement results are negative. Over a period of 2 months, all measurement data (> 200 measurements/ variant) was collected and evaluated using correlation analysis. As part of this study, the number of characteristics to be measured for all 6 car seat rail variants was reduced by over 80%. Specifically, direct correlations for almost 100 characteristics were proven for an average of 125 characteristics for 4 different products. A further 10 features correlate via indirect relationships so that the number of features required for a prediction could be reduced to less than 20. A correlation factor >0.8 was assumed for all correlations.

Keywords: long-term SHM, condition monitoring, machine learning, correlation analysis, component prediction, wear prediction, regressions analysis

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Authors: Mahbuba Rahman, Sabri Boughorbel, Scott Presnell, Charlie Quinn, Darawan Rinchai, Damien Chaussabel, Nico Marr

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Collections of large-scale datasets made available in public repositories can be used to identify and fill gaps in biomedical knowledge. But first, these data need to be made readily accessible to researchers for analysis and interpretation. Here a collection of transcriptome datasets was made available to investigate the functional programming of human hematopoietic cells in early life. Thirty two datasets were retrieved from the NCBI Gene Expression Omnibus (GEO) and loaded in a custom, interactive web application called the Gene Expression browser (GXB), designed for visualization and query of integrated large-scale data. Multiple sample groupings and gene rank lists were created based on the study design and variables in each dataset. Web links to customized graphical views can be generated by users and subsequently be used to graphically present data in manuscripts for publication. The GXB tool also enables browsing of a single gene across datasets, which can provide information on the role of a given molecule across biological systems. The dataset collection is available online. As a proof-of-principle, one of the datasets (GSE25087) was re-analyzed to identify genes that are differentially expressed by regulatory T cells in early life. Re-analysis of this dataset and a cross-study comparison using multiple other datasets in the above mentioned collection revealed that PMCH, a gene encoding a precursor of melanin-concentrating hormone (MCH), a cyclic neuropeptide, is highly expressed in a variety of other hematopoietic cell types, including neonatal erythroid cells as well as plasmacytoid dendritic cells upon viral infection. Our findings suggest an as yet unrecognized role of MCH in immune regulation, thereby highlighting the unique potential of the curated dataset collection and systems biology approach to generate new hypotheses which can be tested in future mechanistic studies.

Keywords: early-life, GEO datasets, PMCH, interactive query, systems biology

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Authors: Adeela Abu Bakar, Minder Kaur, Parthaman Singh

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In the Malaysian education system, a formal setting of English language learning takes place in a content-based classroom (CBC). Until recently, there is less study in Malaysia, which researched the effects of code-switching (CS) behaviour towards the students’ knowledge sharing (KS) with their peers. The aim of this study is to investigate the frequency, reasons, and effect that CS, from the English language to Bahasa Melayu, has among local STEM and N-STEM undergraduates towards KS in a content-based classroom. The study implies a mixed-method research design with questionnaire and interviews as the instruments. The data is collected through distribution of questionnaires and interviews with the undergraduates. The quantitative data is analysed using SPSS in simple frequencies and percentages, whereas qualitative data involves organizing the data into themes, followed by analysis. Findings found that N-STEM undergraduates code-switch more as compared to STEM undergraduates. In addition to that, both the STEM and N-STEM undergraduates agree that CS acts as a catalyst towards KS in a content-based classroom. However, they also acknowledge that excess use of CS can be a hindrance towards KS. The findings of the study can benefit STEM and N-STEM undergraduates, education policymakers, language teachers, university educators, and students with significant insights into the role of CS towards KS in a content-based classroom. Some of the recommendations that can be applied for future studies are that the number of participants can be increased, an observation to be included for the data collection.

Keywords: switching, content-based classroom, content and language integrated learning, knowledge sharing, STEM and N-STEM undergraduates

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Authors: Mohamed El Kory Yacoub, Ahmed Bezeid El Mamy Beyatt, Djibril Barry, Yanogo Pauline, Nicolas Meda

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Introduction: Rift Valley Fever is a zoonotic arbovirosis that severely affects ruminants, as well as humans. It causes abortions in pregnant females and deaths in young animals. The disease occurs during heavy rains followed by large numbers of mosquito vectors. The objective of this work is to evaluate the surveillance system for Rift Valley Fever. Methods: We conducted an evaluation of the Rift Valley Fiver surveillance system. Data were collected from the analysis of the national database of the Mauritanian Network of Animal Disease Epidemiological Surveillance at the Ministry of Rural Development, of RVF cases notified from the whole national territory, of questionnaires and interviews with all persons involved in RVF surveillance at the central level. The quality of the system was assessed by analyzing the quantitative attributes defined by the Centers for Disease Control and Prevention. Results: In 2019, 443 cases of RVF were notified by the surveillance system, of which 36 were positive. Among the notified cases of Rift Valley Fever, the 0- to the 3-year-old age group of small ruminants was the most represented with 49.21% of cases, followed by 33.33%, which was recorded in large ruminants in the 0 to 7-year-old age group, 11.11% of cases were older than seven years. The completeness of the data varied between 14.2% (age) and 100% (species). Most positive cases were recorded between October and November 2019 in seven different regions. Attribute analysis showed that 87% of the respondents were able to use the case definition well, and 78.8% said they were familiar with the reporting and feedback loop of the Rift Valley Fever data. 90.3% of the respondents found it easy, while 95% of them responded that it was easy for them to transmit their data to the next level. Conclusions: The epidemiological surveillance system for Rift Valley Fever in Mauritania is simple and representative. However, data quality, stability, and responsiveness are average, as the diagnosis of the disease requires laboratory confirmation and the average delay for this confirmation is long (13 days). Consequently, the lack of completeness of the recorded data and of description of cases in terms of time-place-animal, associated with the delay between the stages of the surveillance system can make prevention, early detection of epidemics, and the initiation of measures for an adequate response difficult.

Keywords: evaluation, epidemiological surveillance system, rift valley fever, mauritania, ruminants

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Authors: K. Tunde Olagunju, C. Scott Allen, Freek Van Der Meer

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Identification of hydrocarbon oil in remote sensing images is often the first step in monitoring oil during spill events. Most remote sensing methods adopt techniques for hydrocarbon identification to achieve detection in order to model an appropriate cleanup program. Identification on optical sensors does not only allow for detection but also for characterization and quantification. Until recently, in optical remote sensing, quantification and characterization are only potentially possible using high-resolution laboratory and airborne imaging spectrometers (hyperspectral data). Unlike multispectral, hyperspectral data are not freely available, as this data category is mainly obtained via airborne survey at present. In this research, two (2) operational high-resolution multispectral satellites (WorldView-3 and Sentinel-2) are theoretically assessed for their suitability for hydrocarbon characterization, using the hydrocarbon spectral slope model (HYSS). This method utilized the two most persistent hydrocarbon diagnostic/absorption features at 1.73 µm and 2.30 µm for hydrocarbon mapping on multispectral data. In this research, spectra measurement of seven (7) different hydrocarbon oils (crude and refined oil) taken on ten (10) different substrates with the use of laboratory ASD Fieldspec were convolved to Sentinel-2 and WorldView-3 resolution, using their full width half maximum (FWHM) parameter. The resulting hydrocarbon slope values obtained from the studied samples enable clear qualitative discrimination of most hydrocarbons, despite the presence of different background substrates, particularly on WorldView-3. Due to close conformity of central wavelengths and narrow bandwidths to key hydrocarbon bands used in HYSS, the statistical significance for qualitative analysis on WorldView-3 sensors for all studied hydrocarbon oil returned with 95% confidence level (P-value ˂ 0.01), except for Diesel. Using multifactor analysis of variance (MANOVA), the discriminating power of HYSS is statistically significant for most hydrocarbon-substrate combinations on Sentinel-2 and WorldView-3 FWHM, revealing the potential of these two operational multispectral sensors as rapid response tools for hydrocarbon mapping. One notable exception is highly transmissive hydrocarbons on Sentinel-2 data due to the non-conformity of spectral bands with key hydrocarbon absorptions and the relatively coarse bandwidth (> 100 nm).

Keywords: hydrocarbon, oil spill, remote sensing, hyperspectral, multispectral, hydrocarbon-substrate combination, Sentinel-2, WorldView-3

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Authors: Joanne Moyer

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El Paso, Texas is a southwest border city that has experienced continuous growth within the last 15-years. Understanding the urban growth trends and patterns using data from the National Land Cover Database (NLCD) and landscape metrics, provides a quantitative description of growth. Past urban growth provided a basis to predict 2031 future land-use for El Paso using the CA-Markov model. As a consequence of growth, an increase in demand of resources follows. Using panel data analysis, an understanding of the relation between landscape metrics and electricity consumption is further analyzed. The studies’ findings indicate that past growth focused within three districts within the City of El Paso. The landscape metrics suggest as the city has grown, fragmentation has decreased. Alternatively, the landscape metrics for the projected 2031 land-use indicates possible fragmentation within one of these districts. Panel data suggests electricity consumption and mean patch area landscape metric are positively correlated. The study provides local decision makers to make informed decisions for policies and urban planning to ensure a future sustainable community.

Keywords: landscape metrics, CA-Markov, El Paso, Texas, panel data

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Authors: Susana Marques, Cleide Abreu

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Presently, more than ever, men’s grooming is seen as a broad category. The problem comes when the previous research about male consumer behavior have neglected some aspects in this subject. The purpose of this investigation is to examine the role of celebrity endorsement in men’s grooming communication to Generation Y. After identifying some gaps in the literature, with regard to this contemporary subject, the most important variables were defined in order to develop the investigation and draw conclusions through statistical analysis and validation, about the role celebrity endorsement as source of credibility in men’s grooming communication. According to the design and methodology, this research was sustained through in depth marketing analysis (secondary data), and primary data collection via online questionnaire, whereby 168 male respondents, from Brazil and Portugal, were exposed to some advertisement pieces in order to express their opinion and feelings. The findings reveal all the relationships among the variables, suggested by the literature, have occurred, presenting a significant relationship in terms of Source Credibility scale dimensions – attractiveness, trustworthiness and expertise. This paper aims to contribute to the existing literature with important conclusions about the role of celebrity endorsement and its credibility in men’s grooming advertisement.

Keywords: communication, celebrity endorsement, men’s grooming, consumer behavior

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Authors: Lokesh Harshe

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The building construction sector is using 36% of global energy consumption with 39% of CO₂ emission. Professionals in the Built Environment Sector have long been aware of the industry’s contribution towards CO₂ emissions and are now moving towards more sustainable practices. As a result of this, many organizations have introduced rating systems to address the issue of global warming in the construction sector by ranking construction projects based on sustainability parameters. The pre-construction phase of any building project is the most essential time to make decisions for addressing the sustainability aspects. Traditionally, it is very difficult to collect data from different stakeholders and bring it together to form a decision based on factual data to perform sustainability analyses in the pre-construction phase. Building Information Modelling (BIM) is the solution where one single model is the result of the collaborative approach of BIM processes where all the information is shared, extracted, communicated, and stored on a single platform that everyone can access and make decisions based on real-time data. The focus of this research is on the Indian Green Rating System IGBC® with the objective of understanding IGBC® requirements and developing a framework to create the relationship between the rating processes and BIM. A Hypothetical (Architectural) model of a hostel building is developed using AutoCAD 2019 & Revit Arch. 2019, where the framework is applied to generate results on sustainability analysis using Green Building Studio (GBS) and Revit Add-ins. The results of any sustainability analysis are generated within a fraction of a minute, which is very quick in comparison with traditional sustainability analysis. This may save a considerable amount of time as well as cost. The future scope is to integrate Architectural, Structural, and MEP Models to perform accurate sustainability analyses with inputs from industry professionals working on real-life Green BIM projects.

Keywords: sustainability analyses, BIM, green rating systems, IGBC®, LEED

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Authors: Tabassum Riaz

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This study provides a complete examination of the stock prices behavior in the Karachi stock exchange. It examines that whether Karachi stock exchange can be described as mean reversion or not. For this purpose daily, weekly and monthly index data from Karachi stock exchange ranging from period July 1, 1997 to July 2, 2011 was taken. After employing the Multiple variance ratio and unit root tests it is concluded that stock market follow mean reversion behavior and hence have reverting trend which opens the door for the active invest management. Thus technical analysis may be help to identify the potential areas for value creation.

Keywords: mean reversion, random walk, technical analysis, Karachi stock exchange

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Authors: Ruixia Liu

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Qinghai-Tibet Plateau has an important influence on the precipitation of its lower reaches. Data from remote sensing has itself advantage and numerical prediction model which assimilates RS data will be better than other. We got the assimilation data of MHS and terrestrial and sounding from GSI, and introduced the result into WRF, then got the result of RH and precipitation forecast. We found that assimilating MHS and terrestrial and sounding made the forecast on precipitation, area and the center of the precipitation more accurate by comparing the result of 1h,6h,12h, and 24h. Analyzing the difference of the initial field, we knew that the data assimilating about Qinghai-Tibet Plateau influence its lower reaches forecast by affecting on initial temperature and RH.

Keywords: Qinghai-Tibet Plateau, precipitation, data assimilation, GSI

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Authors: Irene Yi

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Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Computational linguistics is a growing field dealing with such issues of data collection for technological development. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Computational analysis on such linguistic data is used to find patterns of misogyny. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: computational analysis, gendered grammar, misogynistic language, neural networks

Procedia PDF Downloads 119