Search results for: restructuring digital factory model

Authors: B. Khorram, M. Taslikh, R. Sattarzadeh, M. Ghazanfari

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Bitterness is one of the most important problems in cheese processing industry all over the world. There are several reasons that bitterness may develop in cheese. With a few exceptions bitterness is generally associated with proteolysis. In this investigation, anti-stick as a neutral substance in proteolysis were considered and studied for reducing the problem. This vast survey was conducted in a big cheese production factory (in Neyshabur) and in the same procedure anti-stick as interested factor in cheeses packaging compared to standard cheeses production, one line productions (65200 packs with anti-stick were tested by 2953 persons for bitterness and another line was included the same procedure with standard cheese. In this investigate: 83% of standard packaging cheeses, compared with only28% of consumers cheese with anti-stick which confirmed bitterness. Although bitterness is generally associated with proteolysis and Microbial factors, Somatic cell, Starters play important role in generating bitterness in ultra filtrated cheeses, but based on the results the other factors such as anti-stick in packaging can be effective methods for reducing and removing unfavorable bitterness in cheese production.

Keywords: bitterness, uf cheese, anti-stick, single packaged

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Authors: Budi Nurani Ruchjana, Atje Setiawan Abdullah, I. Gede Nyoman Mindra Jaya, Eddy Hermawan

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In this paper, we propose the application package to estimate parameters of spatiotemporal model based on the multivariate time series analysis using the R open-source software. We build packages mainly to estimate the parameters of the Generalized Space Time Autoregressive (GSTAR) model. GSTAR is a combination of time series and spatial models that have parameters vary per location. We use the method of Ordinary Least Squares (OLS) and use the Mean Average Percentage Error (MAPE) to fit the model to spatiotemporal real phenomenon. For case study, we use oil production data from volcanic layer at Jatibarang Indonesia or climate data such as rainfall in Indonesia. Software R is very user-friendly and it is making calculation easier, processing the data is accurate and faster. Limitations R script for the estimation of model parameters spatiotemporal GSTAR built is still limited to a stationary time series model. Therefore, the R program under windows can be developed either for theoretical studies and application.

Keywords: GSTAR Model, MAPE, OLS method, oil production, R software

Procedia PDF Downloads 243

Authors: Watcharin Sangma, Onsiri Chanmuang, Pitsanu Tongkhow

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A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method.

Keywords: forecasting model, steel demand uncertainty, hierarchical Bayesian framework, exponential smoothing method

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Authors: Soroor K. H. Al-Khafaji, Manal Mohammad Abed

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The research aim of this paper is to evaluate the reliability of a complex engineering system and to design a fuzzy model for the reliability estimation. The designed model has been applied on Vegetable Oil Purification System (neutralization system) to help the specialist user based on the concept of FMEA (Failure Mode and Effect Analysis) to estimate the reliability of the repairable system at the vegetable oil industry. The fuzzy model has been used to predict the system reliability for a future time period, depending on a historical database for the two past years. The model can help to specify the system malfunctions and to predict its reliability during a future period in more accurate and reasonable results compared with the results obtained by the traditional method of reliability estimation.

Keywords: fuzzy logic, reliability, repairable systems, FMEA

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Authors: Fatima Ali Abbass, Hassan Ibrahim Rkein

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The number of entities worldwide that currently accept digital currency as payment is increasing; however, digital currency still is not widely accepted as a medium of exchange, nor they represent legal tender. At the same time, this makes accounting for cryptocurrency, as cash (Currency) is not possible under IAS 7 and IAS 32, Cryptocurrency also cannot be accounted for as Financial Assets at fair value through profit or loss under IFRS 9. Therefore, this paper studies the possible means to account for Cryptocurrency, since, as of today, there is not yet an accounting standard that deals with cryptocurrency. The request to have a specific accounting standard is increasing from top accounting firms and from professional accounting bodies. This study uses a mixture of qualitative and quantitative analysis in its quest to explore the best possible way to account for cryptocurrency. Interviews and surveys were conducted targeting accounting professionals. This study highlighted the deficiencies in the current way of accounting for Cryptocurrency as intangible Assets with an indefinite life. The deficiency becomes well highlighted, as the asset will then be subject to impairment, where under GAAP, only depreciation in the value of the intangible asset is recognized. On the other hand, appreciation in the value of the asset is ignored, and this prohibits the reporting entity from showing the true value of the cryptocurrency asset. This research highlights the gap that arises due to using accounting standards that are not specific for Cryptocurrency and this study confirmed that there is an urgent need to call upon the accounting standards setters (IASB and FASB) to issue accounting standards specifically for Cryptocurrency.

Keywords: cryptocurrency, accounting, IFRS, GAAP, classification, measurement

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Authors: Daban Mohammed Haji

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This paper explores the application of constructive alignment in digital education at the University of Sulaimani, focusing specifically on the Language and Culture Center, Translation Department, and English Department. Constructive alignment, an outcome-based pedagogical framework developed by John Biggs, ensures that learning activities and assessments are directly aligned with the intended learning outcomes (ILOs). The study's findings reveal a significant gap in awareness and understanding of this pedagogical concept among lecturers. Many instructors are unfamiliar with constructive alignment, and those who have some knowledge of it face considerable challenges. These challenges include aligning learning activities and assessments with the ILOs and fostering higher-order cognitive skills as outlined in the SOLO taxonomy and revised Bloom’s taxonomy. To address this issue, the existing pedagogy center at the University of Sulaimani could play a pivotal role. This center has the potential to foster faculty development and promote the adoption of constructive alignment in online teaching. By leveraging the center's expertise and resources, a tailored program can be designed to enhance faculty understanding and application of this pedagogical framework.

Keywords: constructive alignment, student-centerdness, pedagogy, bologna process

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Authors: Kuan-Chou Chen

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This paper will demonstrate a simulation model of an information security system by using the systems dynamic approach. The relationships in the system model are designed to be simple and functional and do not necessarily represent any particular information security environments. The purpose of the paper aims to develop a generic system dynamic information security system model with implications on information security research. The interrelated and interdependent relationships of five primary sectors in the system dynamic model will be presented in this paper. The integrated information security systems model will include (1) information security characteristics, (2) users, (3) technology, (4) business functions, and (5) policy and management. Environments, attacks, government and social culture will be defined as the external sector. The interactions within each of these sectors will be depicted by system loop map as well. The proposed system dynamic model will not only provide a conceptual framework for information security analysts and designers but also allow information security managers to remove the incongruity between the management of risk incidents and the management of knowledge and further support information security managers and decision makers the foundation for managerial actions and policy decisions.

Keywords: system thinking, information security systems, security management, simulation

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Authors: Madhu Babu Cherukuri, Tamoghna Ghosh

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Factory supply chain management spends billions of dollars every year to procure and manage equipment spare parts. Due to technology -and processes changes some of these spares become obsolete/dead inventory. Factories have huge dead inventory worth millions of dollars accumulating over time. This is due to lack of a scientific methodology to identify them and send the inventory back to the suppliers on a timely basis. The standard approach followed across industries to deal with this is: if a part is not used for a set pre-defined period of time it is declared dead. This leads to accumulation of dead parts over time and these parts cannot be sold back to the suppliers as it is too late as per contract agreement. Our main idea is the time period for identifying a part as dead cannot be a fixed pre-defined duration across all parts. Rather, it should depend on various properties of the part like historical consumption pattern, type of part, how many machines it is being used in, whether it- is a preventive maintenance part etc. We have designed a predictive algorithm which predicts part obsolescence well in advance with reasonable accuracy and which can help save millions.

Keywords: obsolete inventory, machine learning, big data, supply chain analytics, dead inventory

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Authors: Semih Sözer

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One of the most common issues in economic systems is understanding characteristics of economic activities in cities and regions. Although there are critics to economic base models in conceptual and empirical aspects, these models are useful tools to examining the economic structure of a nation, regions or cities. This paper uses one of the methodologies of economic base models namely the location quotients model. Data for this model includes employment numbers of provinces and NUTS II regions in Turkey. Time series of data covers the years of 1990, 2000, 2003, and 2009. Aim of this study is finding which sectors are export-base and which sectors are import-base in provinces and regions. Model results show that big provinces or powerful regions (population, size etc.) mostly have basic sectors in their economic system. However, interesting facts came from different sectors in different provinces and regions in the model results.

Keywords: economic base, location quotients model, regional economics, regional development

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Authors: Ankita Singh

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Sex trafficking and child pornography are those kinds of borderless crimes which can not be effectively prevented only through the laws and efforts of one country because it requires a proper and smooth collaboration among countries. Eradication of international human trafficking syndicates, criminalisation of international cyber offenders, and effective ban on child pornography is not possible without applying effective universal laws; hence, continuous collaboration of all countries is much needed to adopt and routinely update these universal laws. Congregation of countries on an international platform is very necessary from time to time, where they can simultaneously adopt international agendas and create powerful universal laws to prevent sex trafficking and child pornography in this modern digital era. In the past, some international steps have been taken through The Convention on the Rights of the Child (CRC) and through The Optional Protocol to the Convention on the Rights of the Child on the Sale of Children, Child Prostitution, and Child Pornography, but in reality, these measures are quite weak and are not capable in effectively protecting children from sexual abuse in this modern & highly advanced digital era. The uncontrolled growth of artificial intelligence (AI) and its misuse, lack of proper legal jurisdiction over foreign child abusers and difficulties in their extradition, improper control over international trade of digital child pornographic content, etc., are some prominent issues which can only be controlled through some new, effective and powerful universal laws. Due to a lack of effective international standards and a lack of improper collaboration among countries, Indian laws are also not capable of taking effective actions against child abusers. This research will be conducted through both doctrinal as well as empirical methods. Various literary sources will be examined, and a questionnaire survey will be conducted to analyse the effectiveness of international standards and Indian laws against child pornography. Participants in this survey will be Indian University students. In this work, the existing international norms made for protecting children from sexual abuse will be critically analysed. It will explore why effective and strong collaboration between countries is required in modern times. It will be analysed whether existing international steps are enough to protect children from getting trafficked or being subjected to pornography, and if these steps are not found to be sufficient enough, then suggestions will be given on how international standards and protections can be made more effective and powerful in this digital era. The approach of India towards the existing international standards, the Indian laws to protect children from being subjected to pornography, and the contributions & capabilities of India in strengthening the international standards will also be analysed.

Keywords: child pornography, prevention of children from sexual offences act, the optional protocol to the convention on the rights of the child on the sale of children, child prostitution and child pornography, the convention on the rights of the child

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Authors: Ahmad Alsaber

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This paper aims to understand the intermediary role of the payment platform applications by analyzing the various factors that can influence the desirability of utilizing said payment services in Kuwait, as well as to determine the effect of the presence of different types of payment platforms on the variables of the “Unified Theory of Acceptance and Use of Technology” (UTAUT) model. The UTAUT model's findings will provide an important understanding of the moderating role of payment platform mobile applications. This study will explore the influence of payment platform mobile applications on customer purchase intentions in Kuwait by employing a quantitative survey of 200 local customers. Questions will cover their usage of payment platforms, purchase intent, and overall satisfaction. The information gathered is then analyzed using descriptive statistics and correlation analysis in order to gain insights. The research hopes to provide greater insight into the effect of mobile payment platforms on customer purchase intentions in Kuwait. This research will provide important implications to marketers and customer service providers, informing their strategies and initiatives, as well as offer recommendations to payment platform providers on how to improve customer satisfaction and security. The study results suggest that the likelihood of a purchase is affected by performance expectancy, effort expectancy, social influence, risk, and trust. The purpose of this research is to understand the advancements in the different variables that Kuwaiti customers consider while dealing with mobile banking applications. With the implementation of stronger security measures, progressively more payment platform applications are being utilized in the Kuwaiti marketplace, making them more desirable with their accessibility and usability. With the development of the Kuwaiti digital economy, it is expected that mobile banking will have a greater impact on banking transactions and services in the future.

Keywords: purchase intention, UTAUT, performance expectancy, social influence, risk, trust

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Authors: Albert Gyamfi

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Cocoa plays critical role in the socio-economic development of Ghana. Meanwhile, smallholder farmers most of whom are illiterate dominate the industry. According to the cocoa-based agricultural knowledge and information system (AKIS) model knowledge is created and transferred to the industry between three key actors: cocoa researchers, extension experts, and cocoa farmers. Dwelling on the SECI model, the media richness theory (MRT), and the AKIS model, a conceptual model of web 2.0-based AKIS model (AKIS 2.0) is developed and used to assess the possible effects of social media usage for knowledge creation in the Ghanaian cocoa industry. A mixed method approach with a survey questionnaire was employed, and a second-order multi-group structural equation model (SEM) was used to analyze the data. The study concludes that the use of web 2.0 applications for knowledge creation would lead to sustainable interactions among the key knowledge actors for effective knowledge creation in the cocoa industry in Ghana.

Keywords: agriculture, cocoa, knowledge, media, web 2.0

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Authors: Subhadip Biswas, Shivendra Maurya, Satish Chandra, Indrajit Ghosh

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Developing speed model is a challenging task particularly under mixed traffic condition where the traffic composition plays a significant role in determining vehicular speed. The present research has been conducted to model individual vehicular speed in the context of mixed traffic on an urban arterial. Traffic speed and volume data have been collected from three midblock arterial road sections in New Delhi. Using the field data, a volume based speed prediction model has been developed adopting the methodology of Artificial Neural Network (ANN). The model developed in this work is capable of estimating speed for individual vehicle category. Validation results show a great deal of agreement between the observed speeds and the predicted values by the model developed. Also, it has been observed that the ANN based model performs better compared to other existing models in terms of accuracy. Finally, the sensitivity analysis has been performed utilizing the model in order to examine the effects of traffic volume and its composition on individual speeds.

Keywords: speed model, artificial neural network, arterial, mixed traffic

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Authors: Sadia Basar, Naila Habib, Awais Adnan

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The paper presents the Technique for Image Segmentation Using K-Means Clustering Classification. The presented algorithms were specific, however, missed the neighboring information and required high-speed computerized machines to run the segmentation algorithms. Clustering is the process of partitioning a group of data points into a small number of clusters. The proposed method is content-aware and feature extraction method which is able to run on low-end computerized machines, simple algorithm, required low-quality streaming, efficient and used for security purpose. It has the capability to highlight the boundary and the object. At first, the user enters the data in the representation of the input. Then in the next step, the digital image is converted into groups clusters. Clusters are divided into many regions. The same categories with same features of clusters are assembled within a group and different clusters are placed in other groups. Finally, the clusters are combined with respect to similar features and then represented in the form of segments. The clustered image depicts the clear representation of the digital image in order to highlight the regions and boundaries of the image. At last, the final image is presented in the form of segments. All colors of the image are separated in clusters.

Keywords: clustering, image segmentation, K-means function, local and global minimum, region

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Authors: Toby Gibbs, Suwaree Yordchim

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This research analyzes factors affecting the success of Litecoin Value within Thailand and develops a guideline for self-reliance for effective business implementation. Samples in this study included 119 people through surveys. The results revealed four main factors affecting the success as follows: 1) Future Career training should be pursued in applied Litecoin development. 2) Didn't grasp the concept of a digital currency or see the benefit of a digital currency. 3) There is a great need to educate the next generation of learners on the benefits of Litecoin within the community. 4) A great majority didn't know what Litecoin was. The guideline for self-reliance planning consisted of 4 aspects: 1) Development planning: by arranging meet up groups to conduct further education on Litecoin and share solutions on adoption into every day usage. Local communities need to develop awareness of the usefulness of Litecoin and share the value of Litecoin among friends and family. 2) Computer Science and Business Management staff should develop skills to expand on the benefits of Litecoin within their departments. 3) Further research should be pursued on how Litecoin Value can improve business and tourism within Thailand. 4) Local communities should focus on developing Litecoin awareness by encouraging street vendors to accept Litecoin as another form of payment for services rendered.

Keywords: bitcoin, cryptocurrency, decentralized, business implementation

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Authors: Ali Kazemi

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The digital evolution of financial services, while offering unprecedented convenience and accessibility, has also escalated the vulnerabilities to fraudulent activities. In this study, we introduce a distinct approach to real-time anomaly detection in financial transactions, aiming to fortify the defenses of banking and financial institutions against such threats. Utilizing unsupervised machine learning algorithms, specifically autoencoders and isolation forests, our research focuses on identifying irregular patterns indicative of fraud within transactional data, thus enabling immediate action to prevent financial loss. The data we used in this study included the monetary value of each transaction. This is a crucial feature as fraudulent transactions may have distributions of different amounts than legitimate ones, such as timestamps indicating when transactions occurred. Analyzing transactions' temporal patterns can reveal anomalies (e.g., unusual activity in the middle of the night). Also, the sector or category of the merchant where the transaction occurred, such as retail, groceries, online services, etc. Specific categories may be more prone to fraud. Moreover, the type of payment used (e.g., credit, debit, online payment systems). Different payment methods have varying risk levels associated with fraud. This dataset, anonymized to ensure privacy, reflects a wide array of transactions typical of a global banking institution, ranging from small-scale retail purchases to large wire transfers, embodying the diverse nature of potentially fraudulent activities. By engineering features that capture the essence of transactions, including normalized amounts and encoded categorical variables, we tailor our data to enhance model sensitivity to anomalies. The autoencoder model leverages its reconstruction error mechanism to flag transactions that deviate significantly from the learned normal pattern, while the isolation forest identifies anomalies based on their susceptibility to isolation from the dataset's majority. Our experimental results, validated through techniques such as k-fold cross-validation, are evaluated using precision, recall, and the F1 score alongside the area under the receiver operating characteristic (ROC) curve. Our models achieved an F1 score of 0.85 and a ROC AUC of 0.93, indicating high accuracy in detecting fraudulent transactions without excessive false positives. This study contributes to the academic discourse on financial fraud detection and provides a practical framework for banking institutions seeking to implement real-time anomaly detection systems. By demonstrating the effectiveness of unsupervised learning techniques in a real-world context, our research offers a pathway to significantly reduce the incidence of financial fraud, thereby enhancing the security and trustworthiness of digital financial services.

Keywords: anomaly detection, financial fraud, machine learning, autoencoders, isolation forest, transactional data analysis

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Authors: Alisher Ibragimov, Sofya Senotrusova, Aleksandra Beliaeva, Egor Ushakov, Yuri Markin

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Mammography plays a critical role in screening for breast cancer in women, and artificial intelligence has enabled the automatic detection of diseases in medical images. Many of the current techniques used for mammogram analysis focus on a single view (mediolateral or craniocaudal view), while in clinical practice, radiologists consider multiple views of mammograms from both breasts to make a correct decision. Consequently, computer-aided diagnosis (CAD) systems could benefit from incorporating information gathered from multiple views. In this study, the introduce a method based on a Siamese neural network (SNN) model that simultaneously analyzes mammographic images from tri-view: bilateral and ipsilateral. In this way, when a decision is made on a single image of one breast, attention is also paid to two other images – a view of the same breast in a different projection and an image of the other breast as well. Consequently, the algorithm closely mimics the radiologist's practice of paying attention to the entire examination of a patient rather than to a single image. Additionally, to the best of our knowledge, this research represents the first experiments conducted using the recently released Vietnamese dataset of digital mammography (VinDr-Mammo). On an independent test set of images from this dataset, the best model achieved an AUC of 0.87 per image. Therefore, this suggests that there is a valuable automated second opinion in the interpretation of mammograms and breast cancer diagnosis, which in the future may help to alleviate the burden on radiologists and serve as an additional layer of verification.

Keywords: breast cancer, computer-aided diagnosis, deep learning, multi-view mammogram, siamese neural network

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Authors: Xincheng He, Weijun Gao, Gangwei Cai

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In the process of rapid urbanization, large-scale industrial production, and unreasonable planning and construction have caused various ecological and environmental problems, while hindered the sustainable development of cities. The ecological district not only realizes the coordinated development of society, economy, and environment but also conforms to the trend of smart contraction of the development of cities in China from the periphery to the center. This paper reviews the development of China's ecological district, including the full life cycle process of policy, planning, implementation, and operation. Based on sorting out the concept, connotation, and development status of China’s ecological district, the relationship between the construction of the ecological district and the sustainable city is discussed. Summarizing the development trend of the ecological district, the ecological district should combine the construction of smart cities, actively respond to the digital information era, and improve the construction of the ecological district system. It proposes that the future direction of city's sustainable development needs to change from a thematic focus on ecology to the common urbanization of humanity, society, and nature. Focusing on people-oriented, ecological, and digital future communities will become an important construction method for the city's sustainable smart contraction.

Keywords: eco-district, smart contraction, sustainable development, future community

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Authors: Ye Wei, Sitalakshmi Venkatraman, Fahri Benli, Fiona Wahr

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The present digital world poses many challenges to various stakeholders in the education sector. In particular, lecturers of higher education (HE) are faced with the problem of ensuring that students are able to achieve the required learning outcomes despite rapid changes taking place worldwide. Different strategies are adopted to retain student engagement and commitment in classrooms to address the differences in learning habits, preferences, and styles of the digital generation of students recently. Further, the onset of the coronavirus disease (COVID-19) pandemic has resulted in online teaching being mandatory. These changes have compounded the problems in the learning engagement and short attention span of HE students. New agile methodologies that have been successfully employed to manage projects in different fields are gaining prominence in the education domain. In this paper, we present the application of Scrum as an agile methodology to enhance student learning and engagement in an online smart classroom environment. We demonstrate the use of our proposed approach using a case study to teach key topics in information technology that require students to gain technical and business-related data analytics skills.

Keywords: agile methodology, Scrum, online learning, smart classroom environment, student engagement, active learning

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Authors: Mohammad Zahidul Islam Bhuiyan, Shanyong Wang, Scott William Sloan, Daichao Sheng

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Nowadays soil pressure transducers are widely used to measure the soil stress states in laboratory and field experiments. The soil pressure transducers, investigated here, are traditional diaphragm-type earth pressure cells (DEPC) based on strain gauge principle. It is found that the output of these sensors varies with the soil conditions as well as the position of a sensor. Therefore, it is highly recommended to calibrate the pressure sensors based on the similar conditions of their intended applications. The factory calibration coefficients of the EPCs are not reliable to use since they are normally calibrated by applying fluid (a special type of oil) pressure only over load sensing zone, which does not represent the actual field conditions. Thus, the calibration of these sensors is utmost important, and they play a pivotal role for assessing earth pressures precisely. In the present study, TML soil pressure sensor is used to compare its sensitivity under different calibration systems, for example, fluid calibration, and static load calibration with or without soil. The results report that the sensor provides higher sensitivity (more accurate results) under soil calibration system.

Keywords: calibration, soil pressure, earth pressure cell, sensitivity

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Authors: Mohammed Ibahrine

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One of the prominent trends in the Saudi digital space in recent years is the boom in the use of social networking sites such as Facebook, YouTube, and Twitter. As of 2016, Twitter has over six million users in Saudi Arabia. In the wake of the recent political instability in the Arab region, digital platforms have gained importance for both, personal and professional purposes. A conspicuously observable tide of social activism has risen, with Twitter playing an increasingly important role. One of their primary goals is to enforce the logic of public visibility, social mobility and civic participation in the Saudi society. Saudi women use Twitter to disseminate specific and relevant information and promote their social agenda that remained unrecognized and invisible in the mainstream media and thus in the public sphere. The question is to what extent does Twitter empower Saudi women or reinforces their social immobility and invisibility? This paper focuses on three kinds of empowerment through Twitter in the religiously conservative and socially patriarchal Saudi society. It traces and analyses how Saudi female hashtactivism is increasingly becoming a site of struggle over visibility, mobility, control, and civic participation. The underlying thesis is that Twitter makes a contribution to the development of participatory culture, especially in the lives of women.

Keywords: civic, hashtactivism, Saudi Arabia, Twiterverse

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Authors: Myriam Ferreira

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Websites and social networks allow museums to divulge their works by new and attractive means. Besides, these technologies provide tools to generate a new history of art’s contents and promote visits to their installations. At the same time, museums are proposing more and more activities to families, children and young people. However, these activities usually take place in the museum’s physical installations, while websites and social networks seem to be mainly targeted to adults. The problem is that being children and young people digital natives, they feel apart from museums, so they need a presence of museums in digital means to feel attracted to them. Some institutions are making efforts to fill this vacuum. In this paper, resources designed specifically for children and teenagers have been selected from websites and social networks of five Spanish Museums: Prado Museum, Thyssen Museum, Guggenheim Museum, America Museum and Cerralbo Museum. After that, we have carried out an investigation in a school with children and teenagers between 11 and 15 years old. Those young people have been asked about their valuation of those web pages and social networks, with quantitative-qualitative questions. The results show that the least rated resources were videos and social networks because they were considered ‘too serious’, while the most rated were games and augmented reality. These ratings confirm theoretical papers that affirm that the future of technologies applied to museums is edutainment and interaction.

Keywords: children, museums, social networks, teenagers, websites

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Authors: Anderson Ngowa Chembe, John Olukuru

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Greenhouse emissions by human activities are known to irreversibly increase global temperatures through the greenhouse effect. This study seeks to propose a mortality model with sensitivity to heat-change effects as one of the underlying parameters in the model. As such, the study sought to establish the relationship between greenhouse emissions and mortality indices in five OECD countries (USA, UK, Japan, Canada & Germany). Upon the establishment of the relationship using correlation analysis, an additional parameter that accounts for the sensitivity of heat-changes to mortality rates was incorporated in the Lee-Carter model. Based on the proposed model, new parameter estimates were calculated using iterative algorithms for optimization. Finally, the goodness of fit for the original Lee-Carter model and the proposed model were compared using deviance comparison. The proposed model provides a better fit to mortality rates especially in USA, UK and Germany where the mortality indices have a strong positive correlation with the level of greenhouse emissions. The results of this study are of particular importance to actuaries, demographers and climate-risk experts who seek to use better mortality-modeling techniques in the wake of heat effects caused by increased greenhouse emissions.

Keywords: climate risk, greenhouse emissions, Lee-Carter model, OECD

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Authors: S. Kealy, J. Louys, S. O’Connor

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The island archipelago that stretches between the continents of Sunda (Southeast Asia) and Sahul (Australia - New Guinea) and comprising much of modern-day Indonesia as well as Timor-Leste, represents the biogeographic region of Wallacea. The islands of Wallaea are significant archaeologically as they have never been connected to the mainlands of either Sunda or Sahul, and thus the colonization by early modern humans of these islands and subsequently Australia and New Guinea, would have necessitated some form of water crossings. Accurate palaeogeographic reconstructions of the Wallacean Archipelago for this time are important not only for modeling likely routes of colonization but also for reconstructing likely landscapes and hence resources available to the first colonists. Here we present five digital reconstructions of coastal outlines of Wallacea and Sahul (Australia and New Guinea) for the periods 65, 60, 55, 50, and 45,000 years ago using the latest bathometric chart and a sea-level model that is adjusted to account for the average uplift rate known from Wallacea. This data was also used to reconstructed island areal extent as well as topography for each time period. These reconstructions allowed us to determine the distance from the coast and relative elevation of the earliest archaeological sites for each island where such records exist. This enabled us to approximate how much effort exploitation of coastal resources would have taken for early colonists, and how important such resources were. These reconstructions also allowed us to estimate visibility for each island in the archipelago, and to model how intervisible each island was during the period of likely human colonisation. We demonstrate how these models provide archaeologists with an important basis for visualising this ancient landscape and interpreting how it was originally viewed, traversed and exploited by its earliest modern human inhabitants.

Keywords: Wallacea, palaeogeographic reconstructions, islands, intervisibility

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Authors: Ibrahim A. Aref, Tarek El-Mihoub, Khadiga Ben Musa

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This paper presents Carrier Sense Multiple Access (CSMA) communication model based on SoC design methodology. Such model can be used to support the modelling of the complex wireless communication systems, therefore use of such communication model is an important technique in the construction of high performance communication. SystemC has been chosen because it provides a homogeneous design flow for complex designs (i.e. SoC and IP based design). We use a swarm system to validate CSMA designed model and to show how advantages of incorporating communication early in the design process. The wireless communication created through the modeling of CSMA protocol that can be used to achieve communication between all the agents and to coordinate access to the shared medium (channel).

Keywords: systemC, modelling, simulation, CSMA

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Authors: Vinayaka Gude Divya Sampath

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The research introduces an integrated prediction model to assess the susceptibility of roads in a future flooding event. The model consists of deep learning algorithm for forecasting gauge height data and Flood Inundation Mapper (FIM) for spatial flooding. An optimal architecture for Long short-term memory network (LSTM) was identified for the gauge located on Tangipahoa River at Robert, LA. Dropout was applied to the model to evaluate the uncertainty associated with the predictions. The estimates are then used along with FIM to identify the spatial flooding. Further geoprocessing in ArcGIS provides the susceptibility values for different roads. The model was validated based on the devastating flood of August 2016. The paper discusses the challenges for generalization the methodology for other locations and also for various types of flooding. The developed model can be used by the transportation department and other emergency response organizations for effective disaster management.

Keywords: deep learning, disaster management, flood prediction, urban flooding

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Authors: Oscar Javier Herrera Ochoa, Ivan Dario Romero Fonseca

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This paper presents a design of a model for planning the distribution logistics operation. The significance of this work relies on the applicability of this fact to the analysis of small and medium enterprises (SMEs) of dry freight in Bogotá. Two stages constitute this implementation: the first one is the place where optimal planning is achieved through a hybrid model developed with mixed integer programming, which considers the transhipment operation based on a combined load allocation model as a classic transshipment model; the second one is the specific routing of that operation through the heuristics of Clark and Wright. As a result, an integral model is obtained to carry out the step by step planning of the distribution of dry freight for SMEs in Bogotá. In this manner, optimum assignments are established by utilizing transshipment centers with that purpose of determining the specific routing based on the shortest distance traveled.

Keywords: transshipment model, mixed integer programming, saving algorithm, dry freight transportation

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Authors: Ma Lanting, S. Eguilior, A. Hurtado, Juan F. Llamas Borrajo

Abstract:

Horizontal hydraulic fracturing is a technology to increase natural gas flow and improve productivity in the low permeability formation. During this drilling operation tons of flowback and produced water which contains many organic compounds return to the surface with a potential risk of influencing the surrounding environment and human health. A mathematical model is urgently needed to represent organic compounds in water transportation process behavior and the concentration change with time throughout the hydraulic fracturing operation life cycle. A comprehensive model combined Organic Matter Transport Dynamic Model with Two-Compartment First-order Model Constant (TFRC) Model has been established to quantify the organic compounds concentration. This algorithm model is composed of two transportation parts based on time factor. For the fast part, the curve fitting technique is applied using flowback water data from the Marcellus shale gas site fracturing and the coefficients of determination (R2) from all analyzed compounds demonstrate a high experimental feasibility of this numerical model. Furthermore, along a decade of drilling the concentration ratio curves have been estimated by the slow part of this model. The result shows that the larger value of Koc in chemicals, the later maximum concentration in water will reach, as well as all the maximum concentrations percentage would reach up to 90% of initial concentration from shale formation within a long sufficient period.

Keywords: model, shale gas, concentration, organic compounds

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Authors: Supunmali Ahangama, Danny Chiang Choon Poo

Abstract:

Health analytics (HA) is used in healthcare systems for effective decision-making, management, and planning of healthcare and related activities. However, user resistance, the unique position of medical data content, and structure (including heterogeneous and unstructured data) and impromptu HA projects have held up the progress in HA applications. Notably, the accuracy of outcomes depends on the skills and the domain knowledge of the data analyst working on the healthcare data. The success of HA depends on having a sound process model, effective project management and availability of supporting tools. Thus, to overcome these challenges through an effective process model, we propose an HA process model with features from the rational unified process (RUP) model and agile methodology.

Keywords: agile methodology, health analytics, unified process model, UML

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Authors: Pius A. Onobhayedo, Eugene A. Ohu

Abstract:

Nigeria has one of the fastest growing mobile telecommunications industry in the world. In the absence of fixed connection access to the Internet, access to the Internet is primarily via mobile devices. It, therefore, provides a test case for how to penetrate the mobile market in an emerging economy. We also hope to contribute to a sparse literature on strategies employed in building successful data-driven mobile businesses in emerging economies. We, therefore, sought to identify and analyse the strategic approach taken in a successful locally born mobile data-driven business in Nigeria. The analysis was carried out through the framework of strategic intent and competitive advantages developed from the conception of the company to date. This study is based on an exploratory investigation of an innovative digital company based in Nigeria specializing in the mobile advertising business. The projected growth and high adoption of mobile in this African country, coinciding with the smartphone revolution triggered by the launch of iPhone in 2007 opened a new entrepreneurial horizon for the founder of the company, who reached the conclusion that ‘the future is mobile’. This dream led to the establishment of three digital businesses, designed for convergence and complementarity of medium and content. The mobile Ad subsidiary soon grew to become a truly African network with operations and campaigns across West, East and South Africa, successfully delivering campaigns in several African countries including Nigeria, Kenya, South Africa, Ghana, Uganda, Zimbabwe, and Zambia amongst others. The company recently declared a 40% year-end profit which was nine times that of the previous financial year. This study drew from an in-depth interview with the company’s founder, analysis of primary and secondary data from and about the business, as well as case studies of digital marketing campaigns. We hinge our analysis on the strategic intent concept which has been proposed to be an engine that drives the quest for sustainable strategic advantage in the global marketplace. Our goal was specifically to identify the strategic intents of the founder and how these were transformed creatively into processes that may have led to some distinct competitive advantages. Along with the strategic intents, we sought to identify the respective absorptive capacities that constituted favourable antecedents to the creation of such competitive advantages. Our recommendations and findings will be pivotal information for anybody wishing to invest in the world’s fastest technology business space - Africa.

Keywords: Africa, competitive advantage, competitive strategy, digital, mobile business, marketing, strategic intent

Procedia PDF Downloads 436