Search results for: Dynamic Data Envelopment Analysis

Authors: Qing K. Zhu

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Very Large Scale Integration (VLSI) design becomes very complex due to the continuous integration of millions of gates in one chip based on Moore’s law. Designers have encountered numerous report files during design iterations using timing and noise analysis tools. This paper presented our work using data mining techniques combined with HTML tables to extract and represent critical timing/noise data. When we apply this data-mining tool in real applications, the running speed is important. The software employs table look-up techniques in the programming for the reasonable running speed based on performance testing results. We added several advanced features for the application in one industry chip design.

Keywords: VLSI design, data mining, big data, HTML forms, web, VLSI, EDA, timing, noise

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Authors: Layal Mansour

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This paper breaks down Egypt’s financial crisis by constructing a customized financial stress index by including the vulnerable economic indicator “dollarization” as a vulnerable indicator in the credit and exchange sector. The Financial Stress Index for Egypt (FSIE) includes informative vulnerable indicators of the main financial sectors: the banking sector, the equities market, and the foreign exchange market. It is calculated on a monthly basis from 2010 to December 2022, so to report the two recent world’s most devastating financial crises: Covid 19 crisis and Ukraine-Russia War, in addition to the local 2016 and 2022 financial crises. We proceed first by a graphical analysis then by empirical analysis in running under Vector Autoregression (VAR) Model, dynamic causality tests between foreign reserves, dollarization rate, and FSIE. The graphical analysis shows that unexpectedly, Egypt’s economy seems to be immune to internal economic/political instabilities, however it is highly exposed to the foreign and exchange market. Empirical analysis confirms the graphical observations and proves that dollarization, or more precisely debt in foreign currency seems to be the main trigger of Egypt’s current financial crisis.

Keywords: egypt, financial crisis, financial stress index, dollarization, VAR model, causality tests

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Authors: Amin Asgarian, Ghyslaine McClure

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Experiences in the past earthquakes have clearly demonstrated the necessity of seismic design and assessment of Non-Structural Components (NSCs) particularly in post-disaster structures such as hospitals, power plants, etc. as they have to be permanently functional and operational. Meeting this objective is contingent upon having proper seismic performance of both structural and non-structural components. Proper seismic design, analysis, and assessment of NSCs can be attained through generation of Floor Design Spectrum (FDS) in a similar fashion as target spectrum for structural components. This paper presents the developed methodology to generate FDS directly from corresponding Uniform Hazard Spectrum (UHS) (i.e. design spectra for structural components). The methodology is based on the experimental and numerical analysis of a database of 27 real Reinforced Concrete (RC) buildings which are located in Montreal, Canada. The buildings were tested by Ambient Vibration Measurements (AVM) and their dynamic properties have been extracted and used as part of the approach. Database comprises 12 low-rises, 10 medium-rises, and 5 high-rises and they are mostly designated as post-disaster\emergency shelters by the city of Montreal. The buildings are subjected to 20 compatible seismic records to UHS of Montreal and Floor Response Spectra (FRS) are developed for every floors in two horizontal direction considering four different damping ratios of NSCs (i.e. 2, 5, 10, and 20 % viscous damping). Generated FRS (approximately 132’000 curves) are statistically studied and the methodology is proposed to generate the FDS directly from corresponding UHS. The approach is capable of generating the FDS for any selection of floor level and damping ratio of NSCs. It captures the effect of: dynamic interaction between primary (structural) and secondary (NSCs) systems, higher and torsional modes of primary structure. These are important improvements of this approach compared to conventional methods and code recommendations. Application of the proposed approach are represented here through two real case-study buildings: one low-rise building and one medium-rise. The proposed approach can be used as practical and robust tool for seismic assessment and design of NSCs especially in existing post-disaster structures.

Keywords: earthquake engineering, operational and functional components, operational modal analysis, seismic assessment and design

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Authors: Addisu Tadesse Sahile, Mussie Wubshet Teka, Solomon Muluken Ayehu

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Background: Diabetes is one of the common public health problems of the century that was estimated to affect one in a tenth of the world population by the year 2030, where diabetes ketoacidosis is one of its common acute complications. Objectives: The aim of this review was to assess the magnitude of diabetes ketoacidosis among patients with type 1 diabetes in Ethiopia. Methods: A systematic data search was done across Google Scholar, PubMed, Web of Science, and African Online Journals. Two reviewers carried out the selection, reviewing, screening, and extraction of the data independently by using a Microsoft Excel Spreadsheet. The Joanna Briggs Institute's prevalence critical appraisal tool was used to assess the quality of evidence. All studies conducted in Ethiopia that reported diabetes ketoacidosis rates among type 1 diabetes were included. The extracted data was imported into the comprehensive meta-analysis version 3.0 for further analysis. Heterogeneity was checked by Higgins’s method, whereas the publication bias was checked by using Beggs and Eggers’s tests. A random-effects meta-analysis model with a 95% confidence interval was computed to estimate the pooled prevalence. Furthermore, subgroup analysis based on the study area (Region) and the sample size was carried out. Result and Conclusion: After review made across a total of 51 articles, of which 12 articles fulfilled the inclusion criteria and were included in the meta-analysis. The pooled prevalence of diabetes ketoacidosis among type 1 diabetes in Ethiopia was 53.2% (95%CI: 43.1%-63.1%). The highest prevalence of DKA was reported in the Tigray region of Ethiopia, whereas the lowest was reported in the Southern region of Ethiopia. Concerned bodies were suggested to work on the escalated burden of diabetes ketoacidosis in Ethiopia.

Keywords: DKA, Type 1 diabetes, Ethiopia, systematic review, meta-analysis

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Authors: Saima Nazir, Mustansar Ali Ghazanfar, Sanay Muhammad Umar Saeed, Muhammad Awais Azam, Saad Ali Alahmari

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Terrorism is an important and challenging concern. The entire world is threatened by only few sophisticated terrorist groups and especially in Gulf Region and Pakistan, it has become extremely destructive phenomena in recent years. Predicting the pattern of attack type, attack group and target type is an intricate task. This study offers new insight on terrorist group’s attack type and its chosen target. This research paper proposes a framework for prediction of terrorist attacks using the historical data and making an association between terrorist group, their attack type and target. Analysis shows that the number of attacks per year will keep on increasing, and Al-Harmayan in Saudi Arabia, Al-Qai’da in Gulf Region and Tehreek-e-Taliban in Pakistan will remain responsible for many future terrorist attacks. Top main targets of each group will be private citizen & property, police, government and military sector under constant circumstances.

Keywords: data mining, counter terrorism, machine learning, SVM

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Authors: Janet Holland

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Eye tracking is a great way to triangulate multiple data sources for deeper, more complete knowledge of how instructional materials are really being used and emotional connections made. Using sensor based biometrics provides a detailed local analysis in real time expanding our ability to collect science based data for a more comprehensive level of understanding, not previously possible, for teaching and learning. The knowledge gained will be used to make future improvements to instructional materials, tools, and interactions. The literature has been examined and a preliminary pilot test was implemented to develop a methodology for research in Instructional Design and Technology. Eye tracking now offers the addition of objective metrics obtained from eye tracking and other biometric data collection with analysis for a fresh perspective.

Keywords: area of interest, eye tracking, biometrics, fixation, fixation count, fixation sequence, fixation time, gaze points, heat map, saccades, time to first fixation

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Authors: Christian Kapuku, Seung-Young Kho

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An accurate representation of the transportation system serving the region is one of the important aspects of transportation modeling. Such representation often requires developing an abstract model of the system elements, which also requires important amount of data, surveys and time. However, in some cases such as in developing countries, data deficiencies, time and budget constraints do not always allow such accurate representation, leaving opportunities to assumptions that may negatively affect the quality of the analysis. With the emergence of Internet open source data especially in the mapping technologies as well as the advances in Geography Information System, opportunities to tackle these issues have raised. Therefore, the objective of this paper is to demonstrate such application through a practical case of the development of the transportation network for the city of Kinshasa. The GIS geo-referencing was used to construct the digitized map of Transportation Analysis Zones using available scanned images. Centroids were then dynamically placed at the center of activities using an activities density map. Next, the road network with its characteristics was built using OpenStreet data and other official road inventory data by intersecting their layers and cleaning up unnecessary links such as residential streets. The accuracy of the final network was then checked, comparing it with satellite images from Google and Bing. For the validation, the final network was exported into Emme3 to check for potential network coding issues. Results show a high accuracy between the built network and satellite images, which can mostly be attributed to the use of open source data.

Keywords: geographic information system (GIS), network construction, transportation database, open source data

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Authors: Lubna Eljabu, Mohammad Etemad, Stan Matwin

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Port authorities have many challenges in congested ports to allocate their resources to provide a safe and secure loading/ unloading procedure for cargo vessels. Selecting a destination port is the decision of a vessel master based on many factors such as weather, wavelength and changes of priorities. Having access to a tool which leverages AIS messages to monitor vessel’s movements and accurately predict their next destination port promotes an effective resource allocation process for port authorities. In this research, we propose a method, namely, Reference Route of Trajectory (RRoT) to assist port authorities in predicting inflow and outflow traffic in their local environment by monitoring Automatic Identification System (AIS) messages. Our RRoT method creates a reference route based on historical AIS messages. It utilizes some of the best trajectory similarity measure to identify the destination of a vessel using their recent movement. We evaluated five different similarity measures such as Discrete Fr´echet Distance (DFD), Dynamic Time Warping (DTW), Partial Curve Mapping (PCM), Area between two curves (Area) and Curve length (CL). Our experiments show that our method identifies the destination port with an accuracy of 98.97% and an fmeasure of 99.08% using Dynamic Time Warping (DTW) similarity measure.

Keywords: spatial temporal data mining, trajectory mining, trajectory similarity, resource optimization

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Authors: Yogendra Singh, Mayur Pisode

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Many times it is observed that in multi-storeyed buildings the dynamic properties in the two directions are similar due to which there may be a coupling between the two orthogonal modes of the building. This is particularly observed in bi-symmetric buildings (buildings with structural properties and periods approximately equal in the two directions). There is a swapping of vibrational energy between the modes in the two orthogonal directions. To avoid this coupling the draft revision of IS:1893 proposes a minimum separation of more than 15% between the frequencies of the fundamental modes in the two directions. This study explores the seismic behaviour of bi-symmetrical buildings under uniaxial and bi-axial ground motions. For this purpose, three different types of 8 storey buildings symmetric in plan are modelled. The first building has square columns, resulting in identical periods in the two directions. The second building, with rectangular columns, has a difference of 20% in periods in orthogonal directions, and the third building has half of the rectangular columns aligned in one direction and other half aligned in the other direction. The numerical analysis of the seismic response of these three buildings is performed by using a set of 22 ground motions from PEER NGA database and scaled as per FEMA P695 guidelines to represent the same level of intensity corresponding to the Design Basis Earthquake. The results are analyzed in terms of the displacement-time response of the buildings at roof level and corresponding maximum inter-storey drift ratios.

Keywords: bi-symmetric buildings, design code, dynamic coupling, multi-storey buildings, seismic response

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Authors: Zahid Ullah, Atlas Khan

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The rapid advancements in data collection, storage, and processing capabilities have led to an explosion of data in various domains. In this era of big data, mathematical sciences play a crucial role in uncovering valuable insights and driving informed decision-making through data analytics. The purpose of this abstract is to present the latest advances in mathematical sciences and their application in harnessing the power of data analytics. This abstract highlights the interdisciplinary nature of data analytics, showcasing how mathematics intersects with statistics, computer science, and other related fields to develop cutting-edge methodologies. It explores key mathematical techniques such as optimization, mathematical modeling, network analysis, and computational algorithms that underpin effective data analysis and interpretation. The abstract emphasizes the role of mathematical sciences in addressing real-world challenges across different sectors, including finance, healthcare, engineering, social sciences, and beyond. It showcases how mathematical models and statistical methods extract meaningful insights from complex datasets, facilitating evidence-based decision-making and driving innovation. Furthermore, the abstract emphasizes the importance of collaboration and knowledge exchange among researchers, practitioners, and industry professionals. It recognizes the value of interdisciplinary collaborations and the need to bridge the gap between academia and industry to ensure the practical application of mathematical advancements in data analytics. The abstract highlights the significance of ongoing research in mathematical sciences and its impact on data analytics. It emphasizes the need for continued exploration and innovation in mathematical methodologies to tackle emerging challenges in the era of big data and digital transformation. In summary, this abstract sheds light on the advances in mathematical sciences and their pivotal role in unveiling the power of data analytics. It calls for interdisciplinary collaboration, knowledge exchange, and ongoing research to further unlock the potential of mathematical methodologies in addressing complex problems and driving data-driven decision-making in various domains.

Keywords: mathematical sciences, data analytics, advances, unveiling

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Authors: Lukas Vierus, Thomas Schuster

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A general framework is examined for dynamic tensor field tomography within an inhomogeneous medium characterized by refraction and absorption, treated as an inverse source problem concerning the associated transport equation. Guided by Fermat’s principle, the Riemannian metric within the specified domain is determined by the medium's refractive index. While considerable literature exists on the inverse problem of reconstructing a tensor field from its longitudinal ray transform within a static Euclidean environment, limited inversion formulas and algorithms are available for general Riemannian metrics and time-varying tensor fields. It is established that tensor field tomography, akin to an inverse source problem for a transport equation, persists in dynamic scenarios. Framing dynamic tensor tomography as an inverse source problem embodies a comprehensive perspective within this domain. Ensuring well-defined forward mappings necessitates establishing existence and uniqueness for the underlying transport equations. However, the bilinear forms of the associated weak formulations fail to meet the coercivity condition. Consequently, recourse to viscosity solutions is taken, demonstrating their unique existence within suitable Sobolev spaces (in the static case) and Sobolev-Bochner spaces (in the dynamic case), under a specific assumption restricting variations in the refractive index. Notably, the adjoint problem can also be reformulated as a transport equation, with analogous results regarding uniqueness. Analytical solutions are expressed as integrals over geodesics, facilitating more efficient evaluation of forward and adjoint operators compared to solving partial differential equations. Certainly, here's the revised sentence in English: Numerical experiments are conducted using a Nesterov-accelerated Landweber method, encompassing various fields, absorption coefficients, and refractive indices, thereby illustrating the enhanced reconstruction achieved through this holistic modeling approach.

Keywords: attenuated refractive dynamic ray transform of tensor fields, geodesics, transport equation, viscosity solutions

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Authors: P. Sankar, P. James Daniel Paul, Siddhant Sahu

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A brief review of the empirical studies on the methodology of the stock market decision support would indicate that they are at a threshold of validating the accuracy of the traditional and the fuzzy, artificial neural network and the decision trees. Many researchers have been attempting to compare these models using various data sets worldwide. However, the research community is on the way to the conclusive confidence in the emerged models. This paper attempts to use the automotive sector stock prices from National Stock Exchange (NSE), India and analyze them for the intra-sectorial support for stock market decisions. The study identifies the significant variables and their lags which affect the price of the stocks using OLS analysis and decision tree classifiers.

Keywords: Indian automotive sector, stock market decisions, equity portfolio analysis, decision tree classifiers, statistical data analysis

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Authors: K. Y. Chen, H. Chua, C. W. Kan

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Environmental investment is an important issue in many countries. In this study, we will first review the environmental issues related to India and their effect on the economical development. Secondly, economic data would be collected from government yearly statistics. The statistics would also include the environmental investment information of India. Finally, we would co-relate the data in order to find out the relationship between environmental investment and sustainable development in India. Therefore, in the paper, we aim to analyse the effect of an environmental investment on the sustainable development in India. Based on the economic data collected, India is in development status with fast population and GDP growth speed. India is facing the environment problems due to its high-speed development. However, the environment investment could give a positive impact on the sustainable development in India. The environmental investment is keeping in the same growth rate with GDP. Acknowledgment: Authors would like to thank the financial support from the Hong Kong Polytechnic University for this work.

Keywords: India, environmental investment, sustainable development, analysis

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Authors: Kholil, Laksanto Utomo

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The food needs of the Indonesian people will continue increase year to year due to the increase of population growth. For ensuring food securityand and resilience, the government has developed a program food self-resilience village since 2006. Food resilience is a complex system, consisting of subsystem availability, distribution and consumption of the sufficiency of food consumed both in quantity and quality. Low access, and limited assets to food sources is the dominant factor vulnerable of food. Women have a major role in supporting the productive activities of the family to meet food sufficiency and resilience. The purpose of this paper is to discuss the model of food self-resilience village wich gender responsive by using a dynamic system model. Model will be developed into 3 level: family, vilage, and regency in accordance with the concept of village food resilience model wich has been developed by ministry of agriculture. Model development based on the results of experts discussion and field study. By some scenarios and simulation models we will able to develop appropriate policy strategies for family food resilience. The result of study show that food resilience was influenced by many factors: goverment policies, technology, human resource, and in the same time it will be a feed back for goverment policies and number of poor family.

Keywords: food availability, food sufficiency, gender, model dynamic, law enfrocement

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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: Kai Warsoenke, Maik Mackiewicz

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To achieve a high quality of assembled car body structures, tolerancing is used to ensure a geometric accuracy of the single car body parts. There are two main techniques to determine the required tolerances. The first is tolerance analysis which describes the influence of individually tolerated input values on a required target value. Second is tolerance synthesis to determine the location of individual tolerances to achieve a target value. Both techniques are based on classical statistical methods, which assume certain probability distributions. To ensure competitiveness in both saturated and dynamic markets, production processes in vehicle manufacturing must be flexible and efficient. The dimensional specifications selected for the individual body components and the resulting assemblies have a major influence of the quality of the process. For example, in the manufacturing of forming tools as operating equipment or in the higher level of car body assembly. As part of the metrological process monitoring, manufactured individual parts and assemblies are recorded and the measurement results are stored in databases. They serve as information for the temporary adjustment of the production processes and are interpreted by experts in order to derive suitable adjustments measures. In the production of forming tools, this means that time-consuming and costly changes of the tool surface have to be made, while in the body shop, uncertainties that are difficult to control result in cost-intensive rework. The stored measurement results are not used to intelligently design tolerances in future processes or to support temporary decisions based on real-world geometric data. They offer potential to extend the tolerancing methods through data analysis and machine learning models. The purpose of this paper is to examine real-world measurement data from individual car body components, as well as assemblies, in order to develop an approach for using the data in short-term actions and future projects. For this reason, the measurement data will be analyzed descriptively in the first step in order to characterize their behavior and to determine possible correlations. In the following, a database is created that is suitable for developing machine learning models. The objective is to create an intelligent way to determine the position and number of measurement points as well as the local tolerance range. For this a number of different model types are compared and evaluated. The models with the best result are used to optimize equally distributed measuring points on unknown car body part geometries and to assign tolerance ranges to them. The current results of this investigation are still in progress. However, there are areas of the car body parts which behave more sensitively compared to the overall part and indicate that intelligent tolerancing is useful here in order to design and control preceding and succeeding processes more efficiently.

Keywords: automotive production, machine learning, process optimization, smart tolerancing

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Authors: Weiliang Wen, Xinyu Guo, Ying Zhang, Jianjun Du, Boxiang Xiao

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Reconstructing and analyzing three-dimensional (3D) models from situ measured data is important for a number of researches and applications in plant science, including plant phenotyping, functional-structural plant modeling (FSPM), plant germplasm resources protection, agricultural technology popularization. It has many scales like cell, tissue, organ, plant and canopy from micro to macroscopic. The techniques currently used for data capture, feature analysis, and 3D reconstruction are quite different of different scales. In this context, morphological data acquisition, 3D analysis and modeling of plants on different scales are introduced systematically. The commonly used data capture equipment for these multiscale is introduced. Then hot issues and difficulties of different scales are described respectively. Some examples are also given, such as Micron-scale phenotyping quantification and 3D microstructure reconstruction of vascular bundles within maize stalks based on micro-CT scanning, 3D reconstruction of leaf surfaces and feature extraction from point cloud acquired by using 3D handheld scanner, plant modeling by combining parameter driven 3D organ templates. Several application examples by using the 3D models and analysis results of plants are also introduced. A 3D maize canopy was constructed, and light distribution was simulated within the canopy, which was used for the designation of ideal plant type. A grape tree model was constructed from 3D digital and point cloud data, which was used for the production of science content of 11th international conference on grapevine breeding and genetics. By using the tissue models of plants, a Google glass was used to look around visually inside the plant to understand the internal structure of plants. With the development of information technology, 3D data acquisition, and data processing techniques will play a greater role in plant science.

Keywords: plant, three dimensional modeling, multi-scale, plant phenotyping, three dimensional data acquisition

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Authors: Annapureddy Srikant Reddy, Atthanti Mahendra, Samala Chinni Krishna, N. Neelima

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In our present world, we are generating a lot of data and we, need a specific device to store all these data. Generally, we store data in pen drives, hard drives, etc. Sometimes we may loss the data due to the corruption of devices. To overcome all these issues, we implemented a cloud space for storing the data, and it provides more security to the data. We can access the data with just using the internet from anywhere in the world. We implemented all these with the java using Net beans IDE. Once user uploads the data, he does not have any rights to change the data. Users uploaded files are stored in the cloud with the file name as system time and the directory will be created with some random words. Cloud accepts the data only if the size of the file is less than 2MB.

Keywords: cloud space, AES, FTP, NetBeans IDE

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Authors: Chao He, Shunhua Zhou, Peijun Guo

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The vast majority of existing underground railway lines consist of twin tunnels. In this paper, the dynamic interaction between two neighboring tunnels in a layered half-space is investigated by an analytical model. The two tunnels are modelled as cylindrical thin shells, while the soil in the form of a layered half-space with two cylindrical cavities is simulated by the elastic continuum theory. The transfer matrix method is first used to derive the relationship between the plane wave vectors in arbitrary layers and the source layer. Thereafter, the wave translation and transformation are introduced to determine the plane and cylindrical wave vectors in the source layer. The solution for the dynamic interaction between twin tunnels in a layered half-space is obtained by means of the compatibility of displacements and equilibrium of stresses on the two tunnel–soil interfaces. By coupling the proposed model with a fully track model, the train-induced vibrations from twin tunnels in a multi-layered half-space are investigated. The numerical results demonstrate that the existence of a neighboring tunnel has a significant effect on ground vibrations.

Keywords: underground railway, twin tunnels, wave translation and transformation, transfer matrix method

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Authors: Lynndee Kemmet

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The U.S. military plays an increasing role in supporting political stability efforts, and this includes efforts to prevent the food insecurity that can trigger political and social instability. This paper presents a model that assists military commanders in identifying variables that impact food production and distribution in their areas of operation (AO), in identifying connections between variables and in assessing the impacts of those variables on food production and distribution. Through use of the model, military units can better target their data collection efforts and can categorize and analyze data within the data categorization framework most widely-used by military forces—PMESII-PT (Political, Military, Economic, Infrastructure, Information, Physical Environment and Time). The model provides flexibility of analysis in that commanders can target analysis to be highly focused on a specific PMESII-PT domain or variable or conduct analysis across multiple PMESII-PT domains. The model is also designed to assist commanders in mapping food systems in their AOs and then identifying components of those systems that must be strengthened or protected.

Keywords: food security, food system model, political stability, US Military

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Authors: Eleonora Sofilda, Ria Amalia, Muhammad Zilal Hamzah

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Foreign direct investment is one of the sources of financing or capital that important for a country, especially for developing countries. This investment also provides a great contribution to development through the transfer of assets, management improving, and transfer of technology in enhancing the economy of a country. In the other side currently in ASEAN countries emerge the interesting phenomenon where some big producers are re-locate their basic production among those countries. This research is aimed to analyze the factors that affect capital inflows of foreign direct investment into the 6 ASEAN countries (Indonesia, Malaysia, Singapore, Thailand, Philippines, and Vietnam) in period 2004-2012. This study uses panel data analysis to determine the factors that affect of foreign direct investment in 6 ASEAN. The factors that affect of foreign direct investment (FDI) are the gross domestic product (GDP), global competitiveness (GCI), interest rate, exchange rate and trade openness (TO). Result of panel data analysis show that three independent variables (GCI, GDP, and TO) have a significant effect to the FDI in 6 ASEAN Countries.

Keywords: foreign direct investment, the gross domestic product, global competitiveness, interest rate, exchange rate, trade openness, panel data analysis

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Authors: Siriporn Meenanan

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The objective of this study is to investigate the attitude of Thai students towards the Thai series "Hormones the Series Season 2". This study was conducted in the quantitative research, and the questionnaires were used to collect data from 400 people of the sample group. Descriptive statistics were used in data analysis. The findings reveal that most participants have positive comments regarding the series. They strongly agreed that the series reflects on the way of life and problems of teenagers in Thailand. Hence, the participants believe that if adults have a chance to watch the series, they will have the better understanding of the teenagers. In addition, the participants also agreed that the contents of the play are appropriate and satisfiable as the contents of “Hormones the Series Season 2” will raise awareness among the teens and use it as a guide to prevent problems that might happen during their teenage life.

Keywords: content analysis, attitude, Thai series, hormones the Series

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Authors: Matodzi Rapalalani

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In context with Spencer's (2018) classic labelling theory, labels influence the perceptions of both the individual and other members of society. That is, once labelled, the individual act in ways that confirm the stereotypes attached to the label. This study, therefore, investigates the understanding of gendered labelling and its effects on Vhavenda women. Gender socialization and patriarchy have been viewed as the core causes of the problem. The literature presented the development of gendered labelling, forms of it, and other aspects. A qualitative method of data collection was used in this study, and semi-structural interviews were conducted. A total of 6 participants were used as it is easy to deal with a small sample. Thematic analysis was used as the data was interpreted and analyzed. Ethical issues such as confidentiality, informed consent, and voluntary participation were considered. Through the analysis and data interpretation, causes such as lack of Christian values, insecurities, and lust were mentioned as well as some of the effects such as frustrations, increased divorce, and low self-esteem.

Keywords: gender, naming, Venda, women, African culture

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Authors: Iacopo Testi, Diego Pajarito, Nicoletta Roberto, Carmen Greco

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Today, a consistent segment of the world’s population lives in urban areas, and this proportion will vastly increase in the next decades. Therefore, understanding the key trends in urbanization, likely to unfold over the coming years, is crucial to the implementation of sustainable urban strategies. In parallel, the daily amount of digital data produced will be expanding at an exponential rate during the following years. The analysis of various types of data sets and its derived applications have incredible potential across different crucial sectors such as healthcare, housing, transportation, energy, and education. Nevertheless, in city development, architects and urban planners appear to rely mostly on traditional and analogical techniques of data collection. This paper investigates the prospective of the data science field, appearing to be a formidable resource to assist city managers in identifying strategies to enhance the social, economic, and environmental sustainability of our urban areas. The collection of different new layers of information would definitely enhance planners' capabilities to comprehend more in-depth urban phenomena such as gentrification, land use definition, mobility, or critical infrastructural issues. Specifically, the research results correlate economic, commercial, demographic, and housing data with the purpose of defining the youth economic discomfort index. The statistical composite index provides insights regarding the economic disadvantage of citizens aged between 18 years and 29 years, and results clearly display that central urban zones and more disadvantaged than peripheral ones. The experimental set up selected the city of Rome as the testing ground of the whole investigation. The methodology aims at applying statistical and spatial analysis to construct a composite index supporting informed data-driven decisions for urban planning.

Keywords: data science, spatial analysis, composite index, Rome, urban planning, youth economic discomfort index

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Authors: Saul Ngarava, Leocadia Zhou, Nomakhaya Monde

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Water crises have the fourth most significant societal impact after weapons of mass destruction, climate change, and extreme weather conditions, ahead of natural disasters. Intricacies between women and water are central to achieving the 2030 Sustainable Development Goals (SDGs). The majority of the 1.2 billion poor people worldwide, with two-thirds being women, and mostly located in Sub Sahara Africa (SSA) and South Asia, do not have access to safe and reliable sources of water. There exist gendered differences in water security based on the division of labour associating women with water. Globally, women and girls are responsible for water collection in 80% of the households which have no water on their premises. Women spend 16 million hours a day collecting water, while men and children spend 6 million and 4 million per day, respectively, which is time foregone in the pursuit of other livelihood activities. Due to their proximity and activities concerning water, women are vulnerable to water insecurity through exposures to water-borne diseases, fatigue from physically carrying water, and exposure to sexual and physical harassment, amongst others. Proximity to treated water and their wellbeing also has an effect on their sensitivity and adaptive capacity to water insecurity. The great distances, difficult terrain and heavy lifting expose women to vulnerabilities of water insecurity. However, few studies have quantified the vulnerabilities and burdens on women, with a few taking a phenomenological qualitative approach. Vulnerability studies have also been scanty in the water security realm, with most studies taking linear forms of either quantifying exposures, sensitivities or adaptive capacities in climate change studies. The current study argues for the need for a water insecurity vulnerability assessment, especially for women into research agendas as well as policy interventions, monitoring, and evaluation. The study sought to identify and provide pathways through which female-headed households were water insecure in South Africa, the 30th driest country in the world. This was through linking the drinking water decision as well as the vulnerability frameworks. Secondary data collected during the 2016 General Household Survey (GHS) was utilised, with a sample of 5928 female-headed households. Principal Component Analysis and Structural Equation Modelling were used to analyse the data. The results show dynamic relationships between water characteristics and water treatment. There were also associations between water access and wealth status of the female-headed households. Association was also found between water access and water treatment as well as between wealth status and water treatment. The study concludes that there are dynamic relationships in water insecurity (exposure, sensitivity, and adaptive capacity) for female-headed households in South Africa. The study recommends that a multi-prong approach is required in tackling exposures, sensitivities, and adaptive capacities to water insecurity. This should include capacitating and empowering women for wealth generation, improve access to water treatment equipment as well as prioritising the improvement of infrastructure that brings piped and safe water to female-headed households.

Keywords: gender, principal component analysis, structural equation modelling, vulnerability, water insecurity

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Authors: Nini Johana Marin Rodriguez

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This paper applies DCC, EWMA and OGARCH models to compare the dynamic correlations between exchange rates, oil prices, exchange rates and stock markets to examine the time-varying conditional correlations to the daily oil prices and index returns in relation to the US dollar/local currency for developed (Canada and Mexico) and emerging Latin American markets (Brazil, Chile, Colombia and Peru). Changes in correlation interactions are indicative of structural changes in market linkages with implications to contagion and interdependence. For each pair of stock price-exchange rate and oil price-US dollar/local currency, empirical evidence confirms of a strengthening negative correlation in the last decade. Methodologies suggest only two events have significatively impact in the countries analyzed: global financial crisis and Europe crisis, both events are associated with shifts of correlations to stronger negative level for most of the pairs analyzed. While, the first event has a shifting effect on mainly emerging members, the latter affects developed members. The identification of these relationships provides benefits in risk diversification and inflation targeting.

Keywords: crude oil, dynamic conditional correlation, exchange rates, interdependence, stock prices

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Authors: Patricia Portugal Marques de Carvalho Lourenco

Abstract:

A three-phase methodological multi-lingual path was designed, constructed and carried out using the 2020 Chinese Internet Drama Series We Are All Alone as a case study. Phase one, the backbone of the research, comprised of secondary data analysis, providing the structure on which the next two phases would be built on. Phase one incorporated a Google Scholar and a Baidu Index analysis, Star Network Influence Index and Mydramalist.com top two drama reviews, along with an article written about the drama and scrutiny of Chinese related blogs and websites. Phase two was field research elaborated across Latin Europe, and phase three was social media focused, having into account that perceptions are going to be memory conditioned based on past ideas recall. Overall, research has shown the poor cultural expression of Chinese entertainment in Latin Europe and demonstrated the inexistence of Chinese content in French, Italian, Portuguese and Spanish Business to Consumer retailers; a reflection of their low significance in Latin European markets and the short-life cycle of entertainment products in general, bubble-gum, disposable goods without a mid to long-term effect in consumers lives. The process of conducting comprehensive international research was complex and time-consuming, with data not always available in Mandarin, the researcher’s linguistic deficiency, limited Chinese Cultural Knowledge and cultural equivalence. Despite steps being taken to minimize the international proposed research, theoretical limitations concurrent to Latin Europe and China still occurred. Data accuracy was disputable; sampling, data collection/analysis methods are heterogeneous; ascertaining data requirements and the method of analysis to achieve a construct equivalence was challenging and morose to operationalize. Secondary data was also not often readily available in Mandarin; yet, in spite of the array of limitations, research was done, and results were produced.

Keywords: research methodologies, international research, primary data, secondary data, research limitations, online dramas, china, latin europe

Procedia PDF Downloads 59

Authors: S. K. Ashiquer Rahman

Abstract:

the paper concentrations on the importance of hourly compensation, emphasizing the significance of the unemployment rate. There are the two most important factors of a nation these are its unemployment rate and hourly compensation. These are not merely statistics but they have profound effects on individual, families, and the economy. They are inversely related to one another. When we consider the unemployment rate that will probably decline as hourly compensations in manufacturing rise. But when we reduced the unemployment rates and increased job prospects could result from higher compensation. That’s why, the increased hourly compensation in the manufacturing sector that could have a favorable effect on job changing issues. Moreover, the relationship between hourly compensation and unemployment is complex and influenced by broader economic factors. In this paper, we use panel data regression models to evaluate the expected link between hourly compensation and unemployment rate in order to determine the effect of hourly compensation on unemployment rate. We estimate the fixed effects model, evaluate the error components, and determine which model (the FEM or ECM) is better by pooling all 60 observations. We then analysis and review the data by comparing 3 several countries (United States, Canada and the United Kingdom) using panel data regression models. Finally, we provide result, analysis and a summary of the extensive research on how the hourly compensation effects on the unemployment rate. Additionally, this paper offers relevant and useful informational to help the government and academic community use an econometrics and social approach to lessen on the effect of the hourly compensation on Unemployment rate to eliminate the problem.

Keywords: hourly compensation, Unemployment rate, panel data regression models, dummy variables, random effects model, fixed effects model, the linear regression model

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Authors: A. H. Meysami

Abstract:

In this paper, the hot compression tests were carried on X45CrNiW189 valve steel (X45) in the temperature range of 1000–1200°C and the strain rate range of 0.004–0.5 s^(-1) in order to study the high temperature softening behavior of the steel. For the exact prediction of flow stress, the effective stress - effective strain curves were obtained from experiments under various conditions. On the basis of experimental results, the dynamic recrystallization fraction (DRX), AGS, hot deformation and activation energy behavior were investigated. It was found that the calculated results were in a good agreement with the experimental flow stress and microstructure of the steel for different conditions of hot deformation.

Keywords: X45CrNiW189, valve steel, hot compression test, dynamic recrystallization, hot deformation

Procedia PDF Downloads 260

Authors: A. Şahinoğlu, R. Gürbüz, A. Güllü, M. Karhan

Abstract:

This study in production machines, it is aimed that machine will automatically perceive cutting data and alter cutting parameters. The two most important parameters have to be checked in machine control unit are progress feed rate and speeds. These parameters are aimed to be controlled by sounds of machine. Optimum sound’s features introduced to computer. During process, real time data is received and converted by Matlab software. Data is converted into numerical values. According to them progress and speeds decreases/increases at a certain rate and thus optimum sound is acquired. Cutting process is made in respect of optimum cutting parameters. During chip remove progress, features of cutting tools, kind of cut material, cutting parameters and used machine; affects on various parameters. Instead of required parameters need to be measured such as temperature, vibration, and tool wear that emerged during cutting process; detailed analysis of the sound emerged during cutting process will provide detection of various data that included in the cutting process by the much more easy and economic way. The relation between cutting parameters and sound is being identified.

Keywords: cutting process, sound processing, intelligent late, sound analysis

Procedia PDF Downloads 315