Search results for: market data

Authors: D. P. Rajesh, H. N. Anjanayappa, P. Nayana, S. Benakappa

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The present study deals with length-weight relationship of Atropus atropos for which no information is available on this aspect from Mangalore coast. Therefore the present investigation was undertaken. Fish samples were collected from fish landing center (Mangalore) and fish market. The regression co-efficient of male was found to be lower than female. From this observation it may be opined that female gained more weight with increase in length compared to male. Data on seasonal variation in condition factor (Kn) showed that Kn values were more or less similar in both the sexes, indicating almost identical metabolic activity. Gonadal development and high feeding intensity are the factors which influenced the condition factor. The seasonal fluctuations in the relative condition factor of both the sexes could be attributed to the sexual cycle, food intake and environmental factors. From the present study, it can be inferred that the variation in the condition of Atropus atropos was due to feeding activity and gonadal maturity.

Keywords: Atropus atropos, length-weight relationship, Mangalore coast, relative condition factor, Kn

Procedia PDF Downloads 344

Authors: Madaki Umar Yusuf, Mohd Rizam B. Abubakar

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The survival censored data with incomplete covariate data is a common occurrence in many studies in which the outcome is survival time. With model when the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM by the method of weights. The survival outcome for the class of generalized linear model is applied and this method requires the estimation of the parameters of the distribution of the covariates. In this paper, we propose some clinical trials with ve covariates, four of which have some missing values which clearly show that they were fully censored data.

Keywords: EM algorithm, incomplete categorical covariates, ignorable missing data, missing at random (MAR), Weibull Distribution

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Authors: Abdeljalil Beniiche

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The limitation with smart contracts is that they cannot access external data that might be required to control the execution of business logic. Oracles can be used to provide external data to smart contracts. An oracle is an interface that delivers data from external data outside the blockchain to a smart contract to consume. Oracle can deliver different types of data depending on the industry and requirements. In this paper, we study and describe the widely used blockchain oracles. Then, we elaborate on his potential role, technical architecture, and design patterns. Finally, we discuss the human oracle and its key role in solving the truth problem by reaching a consensus about a certain inquiry and tasks.

Keywords: blockchain, oracles, oracles design, human oracles

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Authors: Kingkan Pongsiri

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This paper aims to study the role and activities of the participants and the impact of activities created in the local area in order to sustainably develop the local areas. This study applied both qualitative and quantitative approaches presented in descriptive style; the data was collected via survey, observation and in-depth interviews with samples. The results illustrated five sorts of roles of participants of the Cicada Walking-street and four types of creative activities; recreation based, art based, cultural based, and live events. Integration of local characteristics, arts and cultures were presented creatively and interestingly. Participants are various. The roles of the participants found in the Cicada Market are group of the property and area management, entrepreneurs, leisure (entertaining persons), local people, and tourists. The good impacts on local communities are those in terms of economy, environmental friendly and local arts and cultures promoting. On the other hand, the traffic congestion, waste and the increasing of energy consumption are negative impacts from area development.

Keywords: creative tourism activity, destination development, sustainable development, walking street

Procedia PDF Downloads 245

Authors: Abdoullah Nyassi, Golam Sarwar, Sarra Baldeh, Mamadou S. K. Jallow, Bai Lamin Dondeh, Isaac Osei, Grant A. Mackenzie

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A randomized controlled trial is the "gold standard" for evaluating the efficacy of an intervention. Large-scale, cluster-randomized trials are expensive and difficult to conduct, though. To guarantee the validity and generalizability of findings, high-quality, dependable, and accurate data management systems are necessary. Robust data management systems are crucial for optimizing and validating the quality, accuracy, and dependability of trial data. Regarding the difficulties of data gathering in clinical trials in low-resource areas, there is a scarcity of literature on this subject, which may raise concerns. Effective data management systems and implementation goals should be part of trial procedures. Publicizing the creative clinical data management techniques used in clinical trials should boost public confidence in the study's conclusions and encourage further replication. In the ongoing pneumococcal vaccine schedule study in rural Gambia, this report details the development and deployment of multi-data management systems and methodologies. We implemented six different data management, synchronization, and reporting systems using Microsoft Access, RedCap, SQL, Visual Basic, Ruby, and ASP.NET. Additionally, data synchronization tools were developed to integrate data from these systems into the central server for reporting systems. Clinician, lab, and field data validation systems and methodologies are the main topics of this report. Our process development efforts across all domains were driven by the complexity of research project data collected in real-time data, online reporting, data synchronization, and ways for cleaning and verifying data. Consequently, we effectively used multi-data management systems, demonstrating the value of creative approaches in enhancing the consistency, accuracy, and reporting of trial data in a poor resource setting.

Keywords: data management, data collection, data cleaning, cluster-randomized trial

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Authors: Alessandro Andre Leme

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To analyze three Brazilian think tanks: a) Fernando Henrique Foundation; b) Celso Furtado International Center; c) Millennium Institute and how they dispute interpretations about the type of development and State that should be adopted in Brazil. We will make use of Network and content analysis of the sites. The analyzes show a dispute that goes from a defense of ultraliberalism to developmentalism, going through a hybrid between State and Market voiced in each of the Think Tanks.

Keywords: sociopolitical and economic thinking, development, strategies, intellectuals, state

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Authors: Alhadi Bustaman, Soeganda Formalidin, Titin Siswantining

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DNA microarray technology is used to analyze thousand gene expression data simultaneously and a very important task for drug development and test, function annotation, and cancer diagnosis. Various clustering methods have been used for analyzing gene expression data. However, when analyzing very large and heterogeneous collections of gene expression data, conventional clustering methods often cannot produce a satisfactory solution. Biclustering algorithm has been used as an alternative approach to identifying structures from gene expression data. In this paper, we introduce a transform technique based on singular value decomposition to identify normalized matrix of gene expression data followed by Mixed-Clustering algorithm and the Lift algorithm, inspired in the node-deletion and node-addition phases proposed by Cheng and Church based on Agglomerative Hierarchical Clustering (AHC). Experimental study on standard datasets demonstrated the effectiveness of the algorithm in gene expression data.

Keywords: agglomerative hierarchical clustering (AHC), biclustering, gene expression data, lymphoma, singular value decomposition (SVD)

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Authors: Ramya P, Lino Abraham Varghese, S. Bose

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By cloud storage services, the data can be stored in the cloud, and can be shared across multiple users. Due to the unexpected hardware/software failures and human errors, which make the data stored in the cloud be lost or corrupted easily it affected the integrity of data in cloud. Some mechanisms have been designed to allow both data owners and public verifiers to efficiently audit cloud data integrity without retrieving the entire data from the cloud server. But public auditing on the integrity of shared data with the existing mechanisms will unavoidably reveal confidential information such as identity of the person, to public verifiers. Here a privacy-preserving mechanism is proposed to support public auditing on shared data stored in the cloud. It uses group signatures to compute verification metadata needed to audit the correctness of shared data. The identity of the signer on each block in shared data is kept confidential from public verifiers, who are easily verifying shared data integrity without retrieving the entire file. But on demand, the signer of the each block is reveal to the owner alone. Group private key is generated once by the owner in the static group, where as in the dynamic group, the group private key is change when the users revoke from the group. When the users leave from the group the already signed blocks are resigned by cloud service provider instead of owner is efficiently handled by efficient proxy re-signature scheme.

Keywords: data integrity, dynamic group, group signature, public auditing

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Authors: Uwalomwa Uwuigbe, Olubukola Ranti Uwuigbe, Jinadu Olugbenga, Otekunrin Adegbola

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Corporate reporting has evolved over the years resulting from criticisms of the precedent by shareholders, stakeholders and other relevant financial institutions. Integrated reporting has become a globalized corporate reporting style, with its adoption around the world occurring rapidly to bring about an improvement in the quality of corporate reporting. While some countries have swiftly clinched into reporting in an integrated manner, others have not. In addition, there are ample research that has been conducted on the benefits of adopting integrated reporting, however, the same is not true in developing economies like Nigeria. Hence, this study basically examined the factors determining the adoption of integrated reporting in Nigeria. One hundred (100) copies of questionnaire was administered to financial managers of 20 selected listed companies in the Nigeria stock exchange market. The data obtained was analysed using the Spearman Rank Order Correlation via the Statistical Package for Social Science. This study observed that there is a significant relationship between the social pressures of isomorphic changes and integrated reporting adoption in Nigeria. The study recommends the need for an enforcement mechanism to be put in place while considering the adoption of integrated reporting in Nigeria, enforcement mechanisms should put into consideration the investors demand, the level of economic development, and the degree of corporate social responsibility.

Keywords: corporate social responsibility, isomorphic, integrated reporting, Nigeria, sustainability

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Authors: Christopher Lama, Alix Rieser, Aleksandra Molchanova, Charles Thangaraj

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New technologies like Tesla’s Dojo have made high-performance embedded computing more available. Although automobile computing has developed and benefited enormously from these more recent technologies, the costs are still high, prohibitively high in some cases for broader adaptation, particularly for the after-market and enthusiast markets. This project aims to implement a Raspberry Pi-based low-power (under one hundred Watts) highly mobile computing cluster for a neural network. The computing cluster built from off-the-shelf components is more affordable and, therefore, makes wider adoption possible. The paper describes the design of the neural network, Raspberry Pi-based cluster, and applications the cluster will run. The neural network will use input data from sensors and cameras to project a live view of the road state as the user drives. The neural network will be trained to predict traffic behavior and generate warnings when potentially dangerous situations are predicted. The significant outcomes of this study will be two folds, firstly, to implement and test the low-cost cluster, and secondly, to ascertain the effectiveness of the predictive AI implemented on the cluster.

Keywords: CS pedagogy, student research, cluster computing, machine learning

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Authors: Jerlin George, R. Chitra

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The devices with sensors that can monitor your temperature, heart rate, and other vital signs and link to the internet, known as the Internet of Things (IoT), have completely transformed the way we control health. Providing real-time health data, these sensors improve diagnostics and treatment outcomes. Security and privacy matters when IoT comes into play in healthcare. Cyberattacks on centralized database systems are also a problem. To solve these challenges, the study uses blockchain technology coupled with proxy re-encryption to secure health data. ThingSpeak IoT cloud analyzes the collected data and turns them into blockchain transactions which are safely kept on the DriveHQ cloud. Transparency and data integrity are ensured by blockchain, and secure data sharing among authorized users is made possible by proxy re-encryption. This results in a health monitoring system that preserves the accuracy and confidentiality of data while reducing the safety risks of IoT-driven healthcare applications.

Keywords: internet of things, healthcare, sensors, electronic health records, blockchain, proxy re-encryption, data privacy, data security

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Authors: Nathainail Bashir, Neil Anderson

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The objective of this study site was to investigate the current state of the practice with regards to karst detection methods and recommend the best method and pattern of arrays to acquire the desire results. Proper site investigation in karst prone regions is extremely valuable in determining the location of possible voids. Two geophysical techniques were employed: multichannel analysis of surface waves (MASW) and electric resistivity tomography (ERT).The MASW data was acquired at each test location using different array lengths and different array orientations (to increase the probability of getting interpretable data in karst terrain). The ERT data were acquired using a dipole-dipole array consisting of 168 electrodes. The MASW data was interpreted (re: estimated depth to physical top of rock) and used to constrain and verify the interpretation of the ERT data. The ERT data indicates poorer quality MASW data were acquired in areas where there was significant local variation in the depth to top of rock.

Keywords: dipole-dipole, ERT, Karst terrains, MASW

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Authors: H. W. Meerveld, R. H. A. Lindelauf

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In contemporary warfare, data science is crucial for the military in achieving information superiority. Yet, to the authors’ knowledge, no extensive literature survey on data science in military decision-making has been conducted so far. In this study, 156 peer-reviewed articles were analysed through an integrative, semi-systematic literature review to gain an overview of the topic. The study examined to what extent literature is focussed on the opportunities or risks of data science in military decision-making, differentiated per level of war (i.e. strategic, operational, and tactical level). A relatively large focus on the risks of data science was observed in social science literature, implying that political and military policymakers are disproportionally influenced by a pessimistic view on the application of data science in the military domain. The perceived risks of data science are, however, hardly addressed in formal science literature. This means that the concerns on the military application of data science are not addressed to the audience that can actually develop and enhance data science models and algorithms. Cross-disciplinary research on both the opportunities and risks of military data science can address the observed research gaps. Considering the levels of war, relatively low attention for the operational level compared to the other two levels was observed, suggesting a research gap with reference to military operational data science. Opportunities for military data science mostly arise at the tactical level. On the contrary, studies examining strategic issues mostly emphasise the risks of military data science. Consequently, domain-specific requirements for military strategic data science applications are hardly expressed. Lacking such applications may ultimately lead to a suboptimal strategic decision in today’s warfare.

Keywords: data science, decision-making, information superiority, literature review, military

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Authors: Babak Bashari Rad

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A metamorphic computer virus uses different code transformation techniques to mutate its body in duplicated instances. Characteristics and function of new instances are mostly similar to their parents, but they cannot be easily detected by the majority of antivirus in market, as they depend on string signature-based detection techniques. The purpose of this research is to propose a Hidden Markov Model for classification of metamorphic viruses in executable files. In the proposed solution, portable executable files are inspected to extract the instructions opcodes needed for the examination of code. A Hidden Markov Model trained on portable executable files is employed to classify the metamorphic viruses of the same family. The proposed model is able to generate and recognize common statistical features of mutated code. The model has been evaluated by examining the model on a test data set. The performance of the model has been practically tested and evaluated based on False Positive Rate, Detection Rate and Overall Accuracy. The result showed an acceptable performance with high average of 99.7% Detection Rate.

Keywords: malware classification, computer virus classification, metamorphic virus, metamorphic malware, Hidden Markov Model

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Authors: Mehdi Rostamzadeh

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Genetic Algorithms (GAs) are an adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. In this study, we apply GAs for fundamental and technical models of exchange rate determination in exchange rate market. In this framework, we estimated absolute and relative purchasing power parity, Mundell-Fleming, sticky and flexible prices (monetary models), equilibrium exchange rate and portfolio balance model as fundamental models and Auto Regressive (AR), Moving Average (MA), Auto-Regressive with Moving Average (ARMA) and Mean Reversion (MR) as technical models for Iranian Rial against European Union’s Euro using monthly data from January 1992 to December 2014. Then, we put these models into the genetic algorithm system for measuring their optimal weight for each model. These optimal weights have been measured according to four criteria i.e. R-Squared (R2), mean square error (MSE), mean absolute percentage error (MAPE) and root mean square error (RMSE).Based on obtained Results, it seems that for explaining of Iranian Rial against EU Euro exchange rate behavior, fundamental models are better than technical models.

Keywords: exchange rate, genetic algorithm, fundamental models, technical models

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Authors: Cai Qianyi

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In the midst of global digital revolution, the flow of data poses security threats that call China's existing legislative framework for protecting personal information into question. As a preliminary procedure for risk analysis and prevention, the risk assessment of personal data transmission lacks detailed guidelines for support. Existing provisions reveal unclear responsibilities for network operators and weakened rights for data subjects. Furthermore, the regulatory system's weak operability and a lack of industry self-regulation heighten data transmission hazards. This paper aims to compare the regulatory pathways for data information transmission risks between China and Europe from a legal framework and content perspective. It draws on the “Data Protection Impact Assessment Guidelines” to empower multiple stakeholders, including data processors, controllers, and subjects, while also defining obligations. In conclusion, this paper intends to solve China's digital security shortcomings by developing a more mature regulatory framework and industry self-regulation mechanisms, resulting in a win-win situation for personal data protection and the development of the digital economy.

Keywords: personal information data transmission, risk assessment, DPIA, internet service provider, personal information data transimission, risk assessment

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Authors: Md. Nuru Miah, A. F. M. Akhter Uddin

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Medicinal plants are grown naturally in a tropical environment in Bangladesh and used as drug and therapeutic agents in the health care system. According to Bangladesh Agricultural Research Institute (BARI), there are 722 species of medicinal plants in the country. Of them, 255 plants are utilized by the manufacturers of Ayurvedic and Unani medicines. Medicinal plants like Aloevera, Ashwagonda, shotomul,Tulsi, Vuikumra, Misridana are extensively cultivated in some selected areas as well; where Aloevera scored the highest position in production. In the early 1980, Ayurvedic and Unani companies procured 80 percent of medicinal plants from natural forests, and the rest 20 percent was imported. Now the scenario has changed; 80 percent is imported, and the rest 20 percent is collected from local products(Source: Astudy on sectorbased need assessment of Business promotion council-Herbal products and medicinal plants, page-4). Uttara Development Program Society, a leading Non- Government development organization in Bangladesh, has been implementing a value chain development project under promoting Agricultural commercialization and Enterprises of Pally Karma Sahayak Foundation (PKSF) funded by the International Fund for Agricultural Development (IFAD) in Natore Sadar Upazila from April 2017 to sustainably develop the technological interventions for products and market development. The ultimate goal of the project is to increase income, generate employment and develop this sector as a sustainable business enterprise. Altogether 10,000 farmers (Nursery owners, growers, input supplier, processors, whole sellers, and retailers) are engaged in different activities of the project. The entrepreneurs engaged in medicinal plant cultivation did not know and follow environmental and good agricultural practices. They used to adopt traditional methodology in production and processing. Locally the farmers didn’t have any positive initiative to expand their business as well as developvalue added products. A lot of diversified products could be possible to develop and marketed with the introduction of post-harvest processing technology and market linkage with the local and global buyer. Training is imparted to the nursery owners and herbal growers on production technologies, sowing method, use of organic fertilizers/compost/pesticides, harvesting procedures, and storage facilities. Different types of herbal tea like Rosella, Moringa, Tulshi, and Basak are being produced and packed locally with a good scope of its marketing in different cities of the country. The project has been able to achieve a significant impact in the development of production technologies, but still, there is room for further improvement in processing, packaging, and marketing level. The core intervention of the current project to develop some entrepreneurs for branding, packaging, promotion, and marketing while considering environment friendly practices. The present strategies will strengthen the knowledge and skills of the entrepreneurs for the production and marketing of their products, maintaining worldwide accepted compliance system for easy access to the global market.

Keywords: aloe vera, herbs and shrubs, market, interventions

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Authors: Habiba Ben Abdessalem

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The emergence of giant set of textual data was the push that has encouraged researchers to invest in this field. The purpose of textual data analysis methods is to facilitate access to such type of data by providing various graphic visualizations. Applying these methods requires a corpus pretreatment step, whose standards are set according to the objective of the problem studied. This step determines the forms list contained in contingency table by keeping only those information carriers. This step may, however, lead to noisy contingency tables, so the use of wavelet denoising function. The validity of the proposed approach is tested on a text database that offers economic and political events in Tunisia for a well definite period.

Keywords: textual data, wavelet, denoising, contingency table

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Authors: Uttam Kumar Sahoo, K. Lalhmingsangi, J. H. Lalremruati

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Non-timber forest resources particularly the high-value, low volume NTFPs has drawn interest as an activity all over the world during the past three decades that could raise standards of living for the rural folks while being compatible with forest conservation. This is particularly true for the people living in and around or fringes of protected areas. However, the economics that plays between resources’ stock and its utilization by the humans is yet to be validated and evaluated logistically. A study was therefore designed to understand the linkages between resource (especially NTFPs) availability and their utilization, existing threats to this biodiversity conservation and the role of NTFPs within the livelihood systems of those households that are most directly involved in creating conservation threats. About 25% of the households were sampled from the two sites ‘resource-rich’ and ‘resource poor’ area of Dampa Tiger Reserve (Western boundary). Our preliminary findings suggest that the collection of relatively high-volume and low value NTFPs such as fuelwood, fodder has caused degradation of forest resources while the low-volume and high-value NTFPs such as wild edible mushrooms, vegetables, other specialty food products, inputs to crafts, medicinal plants have resulted into species promotion/conservation through their domestication in traditional agroforestry systems including home gardens and/or collateral protection of the Tiger Reserve. It is thus suggested that proper assessment of these biodiversities, their direct and indirect valuation, market and non-market profits etc be carried out in greater details which would result in prescribing effective management plans around the park.

Keywords: household economy, livelihood strategies, non-timber forest products, species conservation

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Authors: Burcu Oralhan, Zeki Oralhan, Nilsun Sariyer, Kumru Uyar

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Data mining has been becoming more and more important and a wide range of applications in recent years. Data mining is the process of find hidden and unknown patterns in big data. One of the applied fields of data mining is Customer Relationship Management. Understanding the relationships between products and customers is crucial for every business. Customer Relationship Management is an approach to focus on customer relationship development, retention and increase on customer satisfaction. In this study, we made an application of a data mining methods in telecommunication customer relationship management side. This study aims to determine the customers profile who likely to leave the system, develop marketing strategies, and customized campaigns for customers. Data are clustered by applying classification techniques for used to determine the churners. As a result of this study, we will obtain knowledge from international telecommunication industry. We will contribute to the understanding and development of this subject in Customer Relationship Management.

Keywords: customer churn analysis, customer relationship management, data mining, telecommunication industry

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Authors: N. R. N. Idris, S. Baharom

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A meta-analysis may be performed using aggregate data (AD) or an individual patient data (IPD). In practice, studies may be available at both IPD and AD level. In this situation, both the IPD and AD should be utilised in order to maximize the available information. Statistical advantages of combining the studies from different level have not been fully explored. This study aims to quantify the statistical benefits of including available IPD when conducting a conventional summary-level meta-analysis. Simulated meta-analysis were used to assess the influence of the levels of data on overall meta-analysis estimates based on IPD-only, AD-only and the combination of IPD and AD (mixed data, MD), under different study scenario. The percentage relative bias (PRB), root mean-square-error (RMSE) and coverage probability were used to assess the efficiency of the overall estimates. The results demonstrate that available IPD should always be included in a conventional meta-analysis using summary level data as they would significantly increased the accuracy of the estimates. On the other hand, if more than 80% of the available data are at IPD level, including the AD does not provide significant differences in terms of accuracy of the estimates. Additionally, combining the IPD and AD has moderating effects on the biasness of the estimates of the treatment effects as the IPD tends to overestimate the treatment effects, while the AD has the tendency to produce underestimated effect estimates. These results may provide some guide in deciding if significant benefit is gained by pooling the two levels of data when conducting meta-analysis.

Keywords: aggregate data, combined-level data, individual patient data, meta-analysis

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Authors: Adil Basuki Ahza, Michael Liong, Subarna Suryatman

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Healthy chips now dominating the snack market shelves. More than 80% processed snack foods in the market are chips. This research takes the advantages of twin extrusion technology to produce two types of product, i.e. directly expanded and intermediate ready-to-fry or microwavable chips. To improve the functional quality, the cereal-tuber based mix was enriched with antioxidant rich mix of temurui, celery, carrot and isolated soy protein (ISP) powder. Objectives of this research were to find best composite cassava-sorghum ratio, i.e. 60:40, 70:30 and 80:20, to optimize processing conditions of extrusion and study the microstructural, physical and sensorial characteristics of the final products. Optimization was firstly done by applying metering section of extruder barrel temperatures of 120, 130 and 140 °C with screw speeds of 150, 160 and 170 rpm to produce direct expanded product. The intermediate product was extruded in 100 °C and 100 rpm screw speed with feed moisture content of 35, 40 and 45%. The directly expanded products were analyzed for color, hardness, density, microstructure, and organoleptic properties. The results showed that interaction of ratio of cassava-sorghum and cooking methods affected the product's color, hardness, and bulk density (p<0.05). Extrusion processing conditions also significantly affected product's microstructure (p<0.05). The direct expanded snacks of 80:20 cassava-sorghum ratio and fried expanded one 70:30 and 80:20 ratio shown the best organoleptic score (slightly liked) while baking the intermediate product with microwave were resulted sensorial not acceptable quality chips.

Keywords: cassava-sorghum composite, extrusion, microstructure, physical characteristics

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Authors: Hyun-Woo Cho

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The monitoring of industrial production quality has to be implemented to alarm early warning for unusual operating conditions. Furthermore, identification of their assignable causes is necessary for a quality control purpose. For such tasks many multivariate statistical techniques have been applied and shown to be quite effective tools. This work presents a process data-based monitoring scheme for production processes. For more reliable results some additional steps of noise filtering and preprocessing are considered. It may lead to enhanced performance by eliminating unwanted variation of the data. The performance evaluation is executed using data sets from test processes. The proposed method is shown to provide reliable quality control results, and thus is more effective in quality monitoring in the example. For practical implementation of the method, an on-line data system must be available to gather historical and on-line data. Recently large amounts of data are collected on-line in most processes and implementation of the current scheme is feasible and does not give additional burdens to users.

Keywords: detection, filtering, monitoring, process data

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Authors: Muhammad Awais Shafique, Eiji Hato

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Household trip data is of crucial importance for managing present transportation infrastructure as well as to plan and design future facilities. It also provides basis for new policies implemented under Transportation Demand Management. The methods used for household trip data collection have changed with passage of time, starting with the conventional face-to-face interviews or paper-and-pencil interviews and reaching to the recent approach of employing smartphones. This study summarizes the step-wise evolution in the travel data collection methods. It provides a comprehensive review of the topic, for readers interested to know the changing trends in the data collection field.

Keywords: computer, smartphone, telephone, travel survey

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Authors: R. Alothaim, T. Mishra

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Different researchers have reviewed the gender wage gap since early days between women and men to point out their difference to help bring about equality in production among them. Many fingers have been pointed out towards culture and religion as one of the major factors contributing to the gender wage gap throughout the years passed. Recent research has been done to give out equalization to this gap between men and women. The gender wage gap has raised serious concerns among nations and societies. Additionally, data, methodology and time periods have been affected by the gender wage gap, thus needing special decision making to help in the meta-study in the provision of quantitative review. Quality indicators have played a crucial role towards the education through stressing on enough consideration to help give a solution of equality and worth in the research study. The different research reviewed have given enough evidence and impact to point out that the major causes of this gender wage gap has resulted due to culture. On the other pedestal, religion may play a role to the issues of gender wage gap but with more emphasis on culture playing the bigger part. Furthermore, social status of individual has contributed to the wage gap difference between men and women. Labor market has played a vital role in empowering women, leading to the lower rate of the raw wage difference in the recent years.

Keywords: culture, gender wage gap, social, religion

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Authors: Hiroaki Nasu, Ryota Miyamoto, Yuta Kodera, Yasuyuki Nogami

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To promote Industry 4.0 and Society 5.0 and so on, it is important to connect and share data so that every member can trust it. Blockchain (BC) technology is currently attracting attention as the most advanced tool and has been used in the financial field and so on. However, the data collaboration using BC has not progressed sufficiently among companies on the supply chain of manufacturing industry that handle sensitive data such as product quality, manufacturing conditions, etc. There are two main reasons why data utilization is not sufficiently advanced in the industrial supply chain. The first reason is that manufacturing information is top secret and a source for companies to generate profits. It is difficult to disclose data even between companies with transactions in the supply chain. In the blockchain mechanism such as Bitcoin using PKI (Public Key Infrastructure), in order to confirm the identity of the company that has sent the data, the plaintext must be shared between the companies. Another reason is that the merits (scenarios) of collaboration data between companies are not specifically specified in the industrial supply chain. For these problems this paper proposes a Business to Business (B2B) collaboration system using homomorphic encryption and BC technique. Using the proposed system, each company on the supply chain can exchange confidential information on encrypted data and utilize the data for their own business. In addition, this paper considers a scenario focusing on quality data, which was difficult to collaborate because it is a top secret. In this scenario, we show a implementation scheme and a benefit of concrete data collaboration by proposing a comparison protocol that can grasp the change in quality while hiding the numerical value of quality data.

Keywords: business to business data collaboration, industrial supply chain, blockchain, homomorphic encryption

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Authors: Helen L. Hein, Joachim Schwarte

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As a pillar of sustainable development, ecology has become an important milestone in research community, especially due to global challenges like climate change. The ecological performance of products can be scientifically conducted with life cycle assessments. In the construction sector, significant amounts of CO₂ emissions are assigned to the energy used for building heating purposes. Therefore, sustainable construction materials for insulating purposes are substantial, whereby aerogels have been explored intensively in the last years due to their low thermal conductivity. Therefore, the WALL-ACE project aims to develop an aerogel-based thermal insulating plaster that would achieve minor thermal conductivities. But as in the early stage of development phases, a lot of information is still missing or not yet accessible, the ecological performance of innovative products bases increasingly on uncertain data that can lead to significant deviations in the results. To be able to predict realistically how meaningful the results are and how viable the developed products may be with regard to their corresponding respective market, these deviations however have to be considered. Therefore, a classification method is presented in this study, which may allow comparing the ecological performance of modern products with already established and competitive materials. In order to achieve this, an alternative calculation method was used that allows computing with lower and upper bounds to consider all possible values without precise data. The life cycle analysis of the considered products was conducted with an interval arithmetic based calculation method. The results lead to the conclusion that the interval solutions describing the possible environmental impacts are so wide that the result usability is limited. Nevertheless, a further optimization in reducing environmental impacts of aerogels seems to be needed to become more competitive in the future.

Keywords: aerogel-based, insulating material, early development phase, interval arithmetic

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Authors: Ali Rashash Alzahrani, Elizabeth Stojanovski

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Data on various aspects of education are collected at the institutional and government level regularly. In Australia, for example, students at various levels of schooling undertake examinations in numeracy and literacy as part of NAPLAN testing, enabling longitudinal assessment of such data as well as comparisons between schools and states within Australia. Another source of educational data collected internationally is via the PISA study which collects data from several countries when students are approximately 15 years of age and enables comparisons in the performance of science, mathematics and English between countries as well as ranking of countries based on performance in these standardised tests. As well as student and school outcomes based on the tests taken as part of the PISA study, there is a wealth of other data collected in the study including parental demographics data and data related to teaching strategies used by educators. Overall, an abundance of educational data is available which has the potential to be used to help improve educational attainment and teaching of content in order to improve learning outcomes. A multivariate assessment of such data enables multiple variables to be considered simultaneously and will be used in the present study to help develop profiles of students based on performance in mathematics using data obtained from the PISA study.

Keywords: cluster analysis, education, mathematics, profiles

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Authors: Sakda Loetpiparwanich, Preecha Vichitthamaros

Abstract:

Nowadays, poor data quality is considered one of the majority costs for a data project. The data project with data quality awareness almost as much time to data quality processes while data project without data quality awareness negatively impacts financial resources, efficiency, productivity, and credibility. One of the processes that take a long time is defining the expectations and measurements of data quality because the expectation is different up to the purpose of each data project. Especially, big data project that maybe involves with many datasets and stakeholders, that take a long time to discuss and define quality expectations and measurements. Therefore, this study aimed at developing meaningful indicators to describe overall data quality for each dataset to quick comparison and priority. The objectives of this study were to: (1) Develop a practical data quality indicators and measurements, (2) Develop data quality dimensions based on statistical characteristics and (3) Develop Composite Indicator that can describe overall data quality for each dataset. The sample consisted of more than 500 datasets from public sources obtained by random sampling. After datasets were collected, there are five steps to develop the Dataset Quality Index (SDQI). First, we define standard data quality expectations. Second, we find any indicators that can measure directly to data within datasets. Thirdly, each indicator aggregates to dimension using factor analysis. Next, the indicators and dimensions were weighted by an effort for data preparing process and usability. Finally, the dimensions aggregate to Composite Indicator. The results of these analyses showed that: (1) The developed useful indicators and measurements contained ten indicators. (2) the developed data quality dimension based on statistical characteristics, we found that ten indicators can be reduced to 4 dimensions. (3) The developed Composite Indicator, we found that the SDQI can describe overall datasets quality of each dataset and can separate into 3 Level as Good Quality, Acceptable Quality, and Poor Quality. The conclusion, the SDQI provide an overall description of data quality within datasets and meaningful composition. We can use SQDI to assess for all data in the data project, effort estimation, and priority. The SDQI also work well with Agile Method by using SDQI to assessment in the first sprint. After passing the initial evaluation, we can add more specific data quality indicators into the next sprint.

Keywords: data quality, dataset quality, data quality management, composite indicator, factor analysis, principal component analysis

Procedia PDF Downloads 139

Authors: Gulmira Zhakupova, Tamara Tultabayeva, Aknur Muldasheva, Assem Sagandyk

Abstract:

The development of technology and production of products with increased biological value based on the use of natural food raw materials are important tasks in the policy of the food market of the Republic of Kazakhstan. For Kazakhstan, livestock farming, in particular sheep farming, is the most ancient and developed industry and way of life. The history of the Kazakh people is largely connected with this type of agricultural production, with established traditions using dairy products from sheep's milk. Therefore, the development of new technologies from sheep’s milk remains relevant. In addition, one of the most promising areas for the development of food technology for therapeutic and prophylactic purposes is sheep milk products as a source of protein, immunoglobulins, minerals, vitamins, and other biologically active compounds. This article presents the results of research on the study of milk processing technology. The objective of the study is to study the possibilities of processing sheep milk and its role in human nutrition, as well as the results of research to improve the technology of sheep milk products. The studies were carried out on the basis of sanitary and hygienic requirements for dairy products in accordance with the following test methods. To perform microbiological analysis, we used the method for identifying Salmonella bacteria (Horizontal method for identifying, counting, and serotyping Salmonella) in a certain mass or volume of product. Nutritional value is a complex of properties of food products that meet human physiological needs for energy and basic nutrients. The protein mass fraction was determined by the Kjeldahl method. This method is based on the mineralization of a milk sample with concentrated sulfuric acid in the presence of an oxidizing agent, an inert salt - potassium sulfate, and a catalyst - copper sulfate. In this case, the amino groups of the protein are converted into ammonium sulfate dissolved in sulfuric acid. The vitamin composition was determined by HPLC. To determine the content of mineral substances in the studied samples, the method of atomic absorption spectrophotometry was used. The study identified the technological parameters of sheep milk products and determined the prospects for researching sheep milk products. Microbiological studies were used to determine the safety of the study product. According to the results of the microbiological analysis, no deviations from the norm were identified. This means high safety of the products under study. In terms of nutritional value, the resulting products are high in protein. Data on the positive content of amino acids were also obtained. The results obtained will be used in the food industry and will serve as recommendations for manufacturers.

Keywords: dairy, milk processing, nutrition, colostrum

Procedia PDF Downloads 57