Search results for: time series data mining

Authors: Tomasz L. Nawrocki

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Due to the growing global interest of investment society in innovative enterprises, as the objective of this research was adopted to examine the investment efficiency in shares of companies with innovative characteristics in the risk-return layout. The research was carried out for companies listed on the Warsaw Stock Exchange taking into various consideration time ranges of investment. Obtained results show that in shorter periods of time, investors buy expectations connected with innovative companies and therefore the efficiency of investment in their shares is relatively high, but in the longer term expectations are revised by companies financial results, which in turn negatively affects the efficiency of investment in their shares.

Keywords: capital market, innovative company, investment strategies, risk and return analysis

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Authors: Christian Bucio Pacheco, Miriam Magnolia Sosa Castro, María Alejandra Cabello Rosales

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This paper aims to analyze the contagion effect among the stock markets of the BRIC+M block (Brazil, Russia, India, China plus Mexico). The contagion effect is proved through increasing on dependence parameters during crisis periods. The dependence parameters are estimated through copula approach in a period of time from July 1997 to December 2015. During this period there are instability and calm episodes, allowing to analyze changes in the relations of dependence. Empirical results show strong evidence of time-varying dependence among the BRIC+M markets and an increasing dependence relation during global financial crisis period.

Keywords: BRIC+M Block, Contagion effect, Copula, dependence

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Authors: Hammam Aljabri, Hong G. Im

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Hydrogen is a promising future fuel to support the transition of the energy sector toward carbon neutrality. The direct utilization of H2 in Internal Combustion Engines (ICEs) is possible, and this technology faces mainly two challenges; high NOx emissions and severe knocking at mid to high loads. In this study, we numerically investigated the potential of H2 combustion in a truck-size engine operated in SI mode. To mitigate the knocking nature of H2 combustion, we have focused on studying the effects of three primary parameters; the compression ratio (CR), the air-fuel ratio, and the spark time. The baseline case was set using a CR of 16.5 and an equivalence ratio of 0.35. In simulations, the auto-ignition tendency was evaluated based on the maximum pressure rise rate and the local pressure fluctuations at the monitoring points set along the wall of the combustion chamber. To mitigate the auto-ignition tendency while enabling a wider range of engine operation, the effect of lowering the compression ratio was assessed. The results indicate that by lowering the compression ratio from 16.5:1 to 12.5:1, an indicated thermal efficiency of 47.5% can be achieved. Aiming to restrain the auto-ignition while maintaining good efficiency, a reduction in the equivalence ratio was examined under different compression ratios. The result indicates that higher compression ratios will require lower equivalence ratios, and due to practical limitations, a lower equivalence ratio of 0.25 was set as the limit. Using a compression ratio of 13.5 combined with an equivalence ratio of 0.3 resulted in an indicated thermal efficiency of 48.6%, that is, at a fixed spark time. It is found that under such lean conditions, the incomplete combustion losses and exhaust losses were high. Thus, advancing the spark time was assessed as a possible solution. The results demonstrated the advantages of advancing the spark time, where an indicated thermal efficiency exceeding 50% was achieved using a compression ratio of 14.5:1 and an equivalence ratio of 0.25.

Keywords: hydrogen, combustion, engine knock, SI engine

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

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

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

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Authors: Juan F. Muñoz-Rosas, Rosa M. García-Fernández, Encarnación Álvarez-Verdejo, Pablo J. Moya-Fernández

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Poverty measurement is a very important topic in many studies in social sciences. One of the most important indicators when measuring poverty is the low income proportion. This indicator gives the proportion of people of a population classified as poor. This indicator is generally unknown, and for this reason, it is estimated by using survey data, which are obtained by official surveys carried out by many statistical agencies such as Eurostat. The main feature of the mentioned survey data is the fact that they contain several variables. The variable used to estimate the low income proportion is called as the variable of interest. The survey data may contain several additional variables, also named as the auxiliary variables, related to the variable of interest, and if this is the situation, they could be used to improve the estimation of the low income proportion. In this paper, we use Monte Carlo simulation studies to analyze numerically the performance of estimators based on several auxiliary variables. In this simulation study, we considered real data sets obtained from the 2011 European Union Survey on Income and Living Condition. Results derived from this study indicate that the estimators based on auxiliary variables are more accurate than the naive estimator.

Keywords: inclusion probability, poverty, poverty line, survey sampling

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Authors: G. A. Asciak, C. Camilleri, A. Rizzo

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The waste management in Malta is a national challenge. Coupled with Malta’s recent economic boom, which has seen massive growth in several sectors, especially the construction industry, drastic actions need to be taken. Wood waste, currently being dumped in landfills, is one type of waste which has increased astronomically. This research study aims to carry out a thorough examination on the possibility of using this waste as a biomass resource and adopting a waste-to-energy technology in order to generate electrical energy. This study is composed of three distinct yet interdependent phases, namely, data collection from the local SMEs, thermal analysis using the bomb calorimeter, and generation of energy from wood waste using a micro biomass plant. Data collection from SMEs specializing in wood works was carried out to obtain information regarding the available types of wood waste, the annual weight of imported wood, and to analyse the manner in which wood shavings are used after wood is manufactured. From this analysis, it resulted that five most common types of wood available in Malta which would suitable for generating energy are Oak (hardwood), Beech (hardwood), Red Beech (softwood), African Walnut (softwood) and Iroko (hardwood). Subsequently, based on the information collected, a thermal analysis using a 6200 Isoperibol calorimeter on the five most common types of wood was performed. This analysis was done so as to give a clear indication with regards to the burning potential, which will be valuable when testing the wood in the biomass plant. The experiments carried out in this phase provided a clear indication that the African Walnut generated the highest gross calorific value. This means that this type of wood released the highest amount of heat during the combustion in the calorimeter. This is due to the high presence of extractives and lignin, which accounts for a slightly higher gross calorific value. This is followed by Red Beech and Oak. Moreover, based on the findings of the first phase, both the African Walnut and Red Beech are highly imported in the Maltese Islands for use in various purposes. Oak, which has the third highest gross calorific value is the most imported and common wood used. From the five types of wood, three were chosen for use in the power plant on the basis of their popularity and their heating values. The PP20 biomass plant was used to burn the three types of shavings in order to compare results related to the estimated feedstock consumed by the plant, the high temperatures generated, the time taken by the plant to produce gasification temperatures, and the projected electrical power attributed to each wood type. From the experiments, it emerged that whilst all three types reached the required gasification temperature and thus, are feasible for electrical energy generation. African Walnut was deemed to be the most suitable fast-burning fuel. This is followed by Red-beech and Oak, which required a longer period of time to reach the required gasification temperatures. The results obtained provide a clear indication that wood waste can not only be treated instead of being dumped in dumped in landfill but coupled.

Keywords: biomass, isoperibol calorimeter, waste-to-energy technology, wood

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Authors: Jonas Hamann, Siavash Saki, Tobias Hagen

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Discretization of urban areas is a crucial aspect in many spatial analyses. The process of discretization of space into subspaces without overlaps and gaps is called tessellation. It helps understanding spatial space and provides a framework for analyzing geospatial data. Tessellation methods can be divided into two groups: regular tessellations and irregular tessellations. While regular tessellation methods, like squares-grids or hexagons-grids, are suitable for addressing pure geometry problems, they cannot take the unique characteristics of different subareas into account. However, irregular tessellation methods allow the border between the subareas to be defined more realistically based on urban features like a road network or Points of Interest (POI). Even though Python is one of the most used programming languages when it comes to spatial analysis, there is currently no library that combines different tessellation methods to enable users and researchers to compare different techniques. To close this gap, we are proposing TessPy, an open-source Python package, which combines all above-mentioned tessellation methods and makes them easily accessible to everyone. The core functions of TessPy represent the five different tessellation methods: squares, hexagons, adaptive squares, Voronoi polygons, and city blocks. By using regular methods, users can set the resolution of the tessellation which defines the finesse of the discretization and the desired number of tiles. Irregular tessellation methods allow users to define which spatial data to consider (e.g., amenity, building, office) and how fine the tessellation should be. The spatial data used is open-source and provided by OpenStreetMap. This data can be easily extracted and used for further analyses. Besides the methodology of the different techniques, the state-of-the-art, including examples and future work, will be discussed. All dependencies can be installed using conda or pip; however, the former is more recommended.

Keywords: geospatial data science, geospatial data analysis, tessellations, urban studies

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Authors: Dmitry V. Fomichev, Vladimir V. Solonin

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This paper presents the findings from a numerical simulation of the flow in 37-rod fuel assembly models spaced by a double-wire trapezoidal wrapping as applied to the BREST-OD-300 experimental nuclear reactor. Data on a high static pressure distribution within the models, and equations for determining the fuel bundle flow friction factors have been obtained. Recommendations are provided on using the closing turbulence models available in the ANSYS Fluent. A comparative analysis has been performed against the existing empirical equations for determining the flow friction factors. The calculated and experimental data fit has been shown. An analysis into the experimental data and results of the numerical simulation of the BREST-OD-300 fuel rod assembly hydrodynamic performance are presented.

Keywords: BREST-OD-300, ware-spaces, fuel assembly, computation fluid dynamics

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Authors: Ishtiaq A. Chaudhry, Z. R. Tahir, F. A. Siddiqui, Z. Anwar, F. Valenzuelacalva

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A series of experiments involving the particle image velocimetry technique are carried out to analyse the quantitative effectiveness of the synthesized vortical structures towards actual flow separation control. The streamwise vortices are synthesized from the synthetic jet actuator and introduced into the attached and separating boundary layer developed on the flat plate surface. Two types of actuators with different geometrical set up are used to analyse the evolution of vortical structures in the near wall region and their impact towards achieving separation delay on the actual aircraft wing. Firstly a single circular jet is synthesized at varying actuator operating parameters and issued into the boundary layer to evaluate the dynamics of the interaction between the vortical structures and the near wall low momentum fluid in the separated region. Secondly, an array of jets has been issued into the artificially separated region to assess the effectiveness of various vortical structures towards achieving the reattachment of the separated flow in the streamwise direction.

Keywords: boundary layer, flow separation, streamwise vortices, synthetic jet actuator

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Authors: Ali Alshumrani, Nathan Clarke, Bogdan Ghite, Stavros Shiaeles

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Digital forensics has become an essential tool in the investigation of cyber and computer-assisted crime. Arguably, given the prevalence of technology and the subsequent digital footprints that exist, it could have a significant role across almost all crimes. However, the variety of technology platforms (such as computers, mobiles, Closed-Circuit Television (CCTV), Internet of Things (IoT), databases, drones, cloud computing services), heterogeneity and volume of data, forensic tool capability, and the investigative cost make investigations both technically challenging and prohibitively expensive. Forensic tools also tend to be siloed into specific technologies, e.g., File System Forensic Analysis Tools (FS-FAT) and Network Forensic Analysis Tools (N-FAT), and a good deal of data sources has little to no specialist forensic tools. Increasingly it also becomes essential to compare and correlate evidence across data sources and to do so in an efficient and effective manner enabling an investigator to answer high-level questions of the data in a timely manner without having to trawl through data and perform the correlation manually. This paper proposes a Unified Forensic Analysis Tool (U-FAT), which aims to establish a common language for electronic information and permit multi-source forensic analysis. Core to this approach is the identification and development of forensic analyses that automate complex data correlations, enabling investigators to investigate cases more efficiently. The paper presents a systematic analysis of major crime categories and identifies what forensic analyses could be used. For example, in a child abduction, an investigation team might have evidence from a range of sources including computing devices (mobile phone, PC), CCTV (potentially a large number), ISP records, and mobile network cell tower data, in addition to third party databases such as the National Sex Offender registry and tax records, with the desire to auto-correlate and across sources and visualize in a cognitively effective manner. U-FAT provides a holistic, flexible, and extensible approach to providing digital forensics in technology, application, and data-agnostic manner, providing powerful and automated forensic analysis.

Keywords: digital forensics, evidence correlation, heterogeneous data, forensics tool

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Authors: Hassan Baji, Chun-Qing Li, Wei Yang

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Water seepage is one of the most common causes of damage in tunnel structures, which can cause direct and indirect e.g. reinforcement corrosion and calcium leaching damages. Estimation of water seepage or inflow is one of the main challenges in probabilistic assessment of tunnels. The methodology proposed in this study is an attempt for mathematically modeling the water seepage in tunnel structures and further predicting its service life. Using the time-dependent reliability, water seepage is formulated as a failure mode, which can be used for prediction of service life. Application of the formulated seepage failure mode to a case study tunnel is presented.

Keywords: water seepage, tunnels, time-dependent reliability, service life

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Authors: Beatriz Lafuente Alcázar, Yash Wani, Amit J. Nimunkar

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Cardiovascular Diseases (CVDs) are the leading cause of death globally, and around 80% of sudden cardiac deaths are due to arrhythmias or irregular heartbeats. The majority of these pathologies are revealed by either short-term or long-term alterations in the electrocardiogram (ECG) morphology. The ECG is the main diagnostic tool in cardiology. It is a non-invasive, pain free procedure that measures the heart’s electrical activity and that allows the detecting of abnormal rhythms and underlying conditions. A cardiologist can diagnose a wide range of pathologies based on ECG’s form alterations, but the human interpretation is subjective and it is contingent to error. Moreover, ECG records can be quite prolonged in time, which can further complicate visual diagnosis, and deeply retard disease detection. In this context, deep learning methods have risen as a promising strategy to extract relevant features and eliminate individual subjectivity in ECG analysis. They facilitate the computation of large sets of data and can provide early and precise diagnoses. Therefore, the cardiology field is one of the areas that can most benefit from the implementation of deep learning algorithms. In the present study, a deep learning algorithm is trained following a novel approach, using a combination of different databases as the training set. The goal of the algorithm is to achieve the detection of R-peaks in ECG signals. Its performance is further evaluated in ECG signals with different origins and features to test the model’s ability to generalize its outcomes. Performance of the model for detection of R-peaks for clean and noisy ECGs is presented. The model is able to detect R-peaks in the presence of various types of noise, and when presented with data, it has not been trained. It is expected that this approach will increase the effectiveness and capacity of cardiologists to detect divergences in the normal cardiac activity of their patients.

Keywords: arrhythmia, deep learning, electrocardiogram, machine learning, R-peaks

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Authors: N. F. Hanna, A. M. Haridy

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One of the main drawbacks of the Modal Pushover Analysis (MPA) is the need to perform nonlinear time-history analysis, which complicates the analysis method and time. A simplified version of the MPA has been proposed based on the concept of the inelastic deformation ratio. Furthermore, the effect of the higher modes of vibration is considered by assuming linearly-elastic responses, which enables the use of standard elastic response spectrum analysis. In this thesis, the simplified MPA (SMPA) method is applied to determine the target global drift and the inter-story drifts of steel frame building. The effect of the higher vibration modes is considered within the framework of the SMPA. A comprehensive survey about the inelastic deformation ratio is presented. After that, a suitable expression from literature is selected for the inelastic deformation ratio and then implemented in the SMPA. The estimated seismic demands using the SMPA, such as target drift, base shear, and the inter-story drifts, are compared with the seismic responses determined by applying the standard MPA. The accuracy of the estimated seismic demands is validated by comparing with the results obtained by the nonlinear time-history analysis using real earthquake records.

Keywords: modal analysis, pushover analysis, seismic performance, target displacement

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Authors: Yeganeh Jabbari, Sepideh Khalatabad

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This study explores the development of virtual reality (VR) technology for training applications, specifically its the potential benefits of VR technology for employee training and its ability to simulate real-world scenarios in a safe and controlled environment are highlighted, along with the associated cost and time savings. The adoption of VR technology in high-risk industrial organizations such as the oil and gas industry is discussed, with a focus on its ability to improve worker performance. Additionally, the use of VR technology in activities such as simulation and data visualization in the oil and gas industry is explored, leading to enhanced safety measures and collaboration between teams. The integration of advanced technologies such as robotics is mentioned as a way to further promote efficiency and sustainability. Also, the study mentions that the digital transformation of the oil and gas industry is revolutionizing operations and promoting safety, efficiency, and sustainability through the use of VR technology.

Keywords: virtual reality training, virtual reality benefits, high-risk industries, digital transformation

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Authors: Elmamy Ahmedsalem

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The world has witnessed the collapse of many corporate giants as a result of unethical behavior in recent decades. This has induced a series of questions by the global community on why such occurrences could happen, even with corporate governance in place. This paper attempts to propose a philosophical approach from an Islamic perspective to be consolidated with current corporate governance in order to confront contemporary dilemmas. In this paper, ethical theories are presented as a discussion followed by their applications to modern cases of financial collapses. Virtue ethics by Aristotle, justice and fairness by John Rawls, deontology by Immanuel Kant, and utilitarianism by John Stuart Mill, are the four theories which can then be contrasted with the paradigm of Muslim scholars. Despite the differences between the fundamental principles of Islamic and Western worldviews, their ethical theories are aimed at making right decisions and solving ethical dilemmas based on what is good for society. Therefore, Islamic principles should be synthesized with Western philosophy to form a more coherent framework. The integration of Islamic and western ethical theories into business is important for sound corporate governance.

Keywords: business ethics, Islamic philosophy, western philosophy, Western and Islamic worldview of ethics

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Authors: Tahseen Saad, Halim Ceylan, Jonathan Weaver, Osman Nuri Çelik, Onur Gungor Sahin

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A new concept of uniform delay offset dependent mathematical optimization problem is derived as the main objective for this study using a differential evolution algorithm. To control the coordination problem, which depends on offset selection and to estimate uniform delay based on the offset choice in a traffic signal network. The assumption is the periodic sinusoidal function for arrival and departure patterns. The cycle time is optimized at the entry links and the optimized value is used in the non-entry links as a common cycle time. The offset optimization algorithm is used to calculate the uniform delay at each link. The results are illustrated by using a case study and are compared with the canonical uniform delay model derived by Webster and the highway capacity manual’s model. The findings show new model minimizes the total uniform delay to almost half compared to conventional models. The mathematical objective function is robust. The algorithm convergence time is fast.

Keywords: area traffic control, traffic flow, differential evolution, sinusoidal periodic function, uniform delay, offset variable

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Authors: Jonardan Koner, Basabi Bhattacharya, Avinash Purandare

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The study is focused to find out the impact of infrastructural investment on economic development in Indian states. The study uses panel data analysis to measure the impact of infrastructural investment on Real Gross Domestic Product in Indian States. Panel data analysis incorporates Unit Root Test, Cointegration Teat, Pooled Ordinary Least Squares, Fixed Effect Approach, Random Effect Approach, Hausman Test. The study analyzes panel data (annual in frequency) ranging from 1991 to 2012 and concludes that infrastructural investment has a desirable impact on economic development in Indian. Finally, the study reveals that the infrastructural investment significantly explains the variation of economic indicator.

Keywords: infrastructural investment, real GDP, unit root test, cointegration teat, pooled ordinary least squares, fixed effect approach, random effect approach, Hausman test

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Authors: Migael van Zyl, Stefanie Visser, Awelani Phaswana

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The electricity landscape in South Africa is characterized by frequent occurrences of loadshedding, a measure implemented by Eskom to manage electricity generation shortages by curtailing demand. Loadshedding, classified into stages ranging from 1 to 8 based on severity, involves the systematic rotation of power cuts across municipalities according to predefined schedules. However, this practice introduces distortions in recorded electricity demand, posing challenges to accurate forecasting essential for budgeting, network planning, and generation scheduling. Addressing this challenge requires the development of a methodology to quantify the impact of loadshedding and integrate it back into metered electricity demand data. Fortunately, comprehensive records of loadshedding impacts are maintained in a database, enabling the alignment of Loadshedding effects with hourly demand data. This adjustment ensures that forecasts accurately reflect true demand patterns, independent of loadshedding's influence, thereby enhancing the reliability of electricity supply management in South Africa. This paper presents a methodology for determining the hourly impact of load scheduling and subsequently adjusting historical demand data to account for it. Furthermore, two forecasting models are developed: one utilizing the original dataset and the other using the adjusted data. A comparative analysis is conducted to evaluate forecast accuracy improvements resulting from the adjustment process. By implementing this methodology, stakeholders can make more informed decisions regarding electricity infrastructure investments, resource allocation, and operational planning, contributing to the overall stability and efficiency of South Africa's electricity supply system.

Keywords: electricity demand forecasting, load shedding, demand side management, data science

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Authors: Fatemeh Behbahani, Rubiyah Yusof

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To avoid collision between following vehicles and vehicles in front, it is vital to keep appropriate, safe spacing between both vehicles over all speeds. Therefore, the following vehicle needs to have exact information regarding the speed and spacing between vehicles. This project is conducted to simulate the tuning of controller gain for a vehicle-following system through the selected control strategy, spacing control policy and fixed-time headway policy. In addition, the paper simulates and designs an adaptive gain controller for a road-vehicle-following system which uses information on the spacing, velocity and also acceleration of a preceding vehicle in the proposed one-vehicle look-ahead strategy. The mathematical model is implemented using Kirchhoff and Newton’s Laws, and stability simulated. The trial-error method was used to obtain a suitable value of controller gain. However, the adaptive-based controller system was able to optimize the gain value automatically. Model Reference Adaptive Control (MRAC) is designed and utilized and based on firstly the Gradient and secondly the Lyapunov approach. The Lyapunov approach considers stability. The Gradient approach was found to improve the best value of gain in the controller system with fixed-time headway.

Keywords: one-vehicle look-ahead, model reference adaptive, stability, tuning gain controller, MRAC

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Authors: Frederic Heymes, Michael Albrecht Birk, Roland Eyssette

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Boiling Liquid Expanding Vapor Explosion (BLEVE) has been a well know industrial accident for over 6 decades now, and yet it is still poorly predicted and avoided. BLEVE is created when a vessel containing a pressure liquefied gas (PLG) is engulfed in a fire until the tank rupture. At this time, the pressure drops suddenly, leading the liquid to be in a superheated state. The vapor expansion and the violent boiling of the liquid produce several shock waves. This works aimed at understanding the contribution of vapor ad liquid phases in the overpressure generation in the near field. An experimental work was undertaken at a small scale to reproduce realistic BLEVE explosions. Key parameters were controlled through the experiments, such as failure pressure, fluid mass in the vessel, and weakened length of the vessel. Thirty-four propane BLEVEs were then performed to collect data on scenarios similar to common industrial cases. The aerial overpressure was recorded all around the vessel, and also the internal pressure changed during the explosion and ground loading under the vessel. Several high-speed cameras were used to see the vessel explosion and the blast creation by shadowgraph. Results highlight how the pressure field is anisotropic around the cylindrical vessel and highlights a strong dependency between vapor content and maximum overpressure from the lead shock. The time chronology of events reveals that the vapor phase is the main contributor to the aerial overpressure peak. A prediction model is built upon this assumption. Secondary flow patterns are observed after the lead. A theory on how the second shock observed in experiments forms is exposed thanks to an analogy with numerical simulation. The phase change dynamics are also discussed thanks to a window in the vessel. Ground loading measurements are finally presented and discussed to give insight into the order of magnitude of the force.

Keywords: phase change, superheated state, explosion, vapor expansion, blast, shock wave, pressure liquefied gas

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Authors: Raif Parlakkaya, Ahmet Diken, Erkan Kara

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This paper examines the relationship between corporate governance rating and stock prices of 26 Turkish firms listed in Turkish stock exchange (Borsa Istanbul) by using panel data analysis over five-year period. The paper also investigates the stock performance of firms with governance rating with regards to the market portfolio (i.e. BIST 100 Index) both prior and after governance scoring began. The empirical results show that there is no relation between corporate governance rating and stock prices when using panel data for annual variation in both rating score and stock prices. Further analysis indicates surprising results that while the selected firms outperform the market significantly prior to rating, the same performance does not continue afterwards.

Keywords: corporate governance, stock price, performance, panel data analysis

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Authors: Jiandong Lv, Xingang Wang, Cuiling Shao

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The rapid development of social media platforms has made it one of the important news sources. While it provides people with convenient real-time communication channels, fake news and rumors are also spread rapidly through social media platforms, misleading the public and even causing bad social impact in view of the slow speed and poor consistency of artificial rumor detection. We propose an end-to-end rumor detection model-TIMF, which captures the dependencies between multimodal data based on the interactive attention mechanism, uses a transformer for cross-modal feature sequence mapping and combines hybrid fusion strategies to obtain decision results. This paper verifies two multi-modal rumor detection datasets and proves the superior performance and early detection performance of the proposed model.

Keywords: hybrid fusion, multimodal fusion, rumor detection, social media, transformer

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Authors: Ayodeji Emmanuel Oke, Mathew O. Ikuabe, Clinton O. Aigbavboa, Douglas O. Aghimien

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The call for sustainable construction has received significant drive in recent time considering the overwhelming impacts of its adoption. However, not much has been deliberated on this subject with regards to the roles of organisational leadership in delivering sustainable construction. To this end, the study empirically scrutinised the roles of organisational leadership in delivering sustainable construction. The study adopted a quantitative approach while construction professionals formed the population of the study. A well-articulated questionnaire was used in eliciting responses from the respondents, while appropriate methods of data analysis were used. Findings from the study depicted that the major role of organisational leadership in the delivery of sustainable construction is acting as sustainability integrators. Equally revealed are the internal and external factors affecting organisational leadership in delivering sustainable construction. The study concluded by emphasizing the core roles for delivering sustainable construction by organisational leadership and further recommended that sustainable construction should serve as a prominent and focal organisation goal by organisational leadership when steering the organisation towards meeting its objectives

Keywords: organisational leadership, project delivery, roles, sustainable construction

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Authors: Umut Çınar

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The fact that ozone gas has color bleaching properties has made the use of ozone gas widespread in the textile sector as well as in many other sectors. Ozone gas, which is a strong oxidative agent on the fabric, causes the paint on the fabric to wear off and lighten its color with an aged appearance. Within the scope of this thesis, parameters affecting the bleaching properties of ozone gas on reactive dyed knitted fabric, which is rare in the literature, were investigated. Ozone concentration, time, and pH values were analyzed with the Box Behnken experimental design method, and optimum conditions were determined. After the experiments, wear and opacity values were measured with the help of a spectrophotometer. With the help of the Design Expert program, the graphics related to the data were prepared and interpreted with Box Behnken and ANOVA. These experiments on reactive dyed knitted fabric were tested on these parameters, and the spectrophotometric values of the fabric and optimum parameters in abrasion and opacity were revealed.

Keywords: ozone, reactive dye, bleaching, textile, garment wash, sustainability, washing, Box–Behnken, experimental design

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Authors: Junze Li, M. Li

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Laser diodes fabricated from the III-nitride material system are emerging solutions for the next generation telecommunication systems and optical clocks based on Ca at 397nm, Rb at 420.2nm and Yb at 398.9nm combined 556 nm. Most of the applications require single longitudinal optical mode lasers, with very narrow linewidth and compact size, such as communication systems and laser cooling. In this case, the GaN based distributed feedback (DFB) laser diode is one of the most effective candidates with gratings are known to operate with narrow spectra as well as high power and efficiency. Given the wavelength range, the period of the first-order diffraction grating is under 100 nm, and the realization of such gratings is technically difficult due to the narrow line width and the high quality nitride overgrowth based on the Bragg grating. Some groups have reported GaN DFB lasers with high order distributed feedback surface gratings, which avoids the overgrowth. However, generally the strength of coupling is lower than that with Bragg grating embedded into the waveguide within the GaN laser structure by two-step-epitaxy. Therefore, the overgrowth on the grating technology need to be studied and optimized. Here we propose to fabricate the fine step shape structure of first-order grating by the nanoimprint combined inductively coupled plasma (ICP) dry etching, then carry out overgrowth high quality AlGaN film by metalorganic chemical vapor deposition (MOCVD). Then a series of gratings with different period, depths and duty ratios are designed and fabricated to study the influence of grating structure to the nano-heteroepitaxy. Moreover, we observe the nucleation and growth process by step-by-step growth to study the growth mode for nitride overgrowth on grating, under the condition that the grating period is larger than the mental migration length on the surface. The AFM images demonstrate that a smooth surface of AlGaN film is achieved with an average roughness of 0.20 nm over 3 × 3 μm2. The full width at half maximums (FWHMs) of the (002) reflections in the XRD rocking curves are 278 arcsec for the AlGaN film, and the component of the Al within the film is 8% according to the XRD mapping measurement, which is in accordance with design values. By observing the samples with growth time changing from 200s, 400s to 600s, the growth model is summarized as the follow steps: initially, the nucleation is evenly distributed on the grating structure, as the migration length of Al atoms is low; then, AlGaN growth alone with the grating top surface; finally, the AlGaN film formed by lateral growth. This work contributed to carrying out GaN DFB laser by fabricating grating and overgrowth on the nano-grating patterned substrate by wafer scale, moreover, growth dynamics had been analyzed as well.

Keywords: DFB laser, MOCVD, nanoepitaxy, III-niitride

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Authors: Lina Wu, Ye Li

Abstract:

An equivalent relation between a harmonic form and a closed co-closed form is established on a complete non-compact manifold. This equivalence has been generalized for a differential k-form ω from Lq spaces to non-Lq spaces when q=2 in the context of p-balanced growth where p=2. Especially for a simple differential k-form on a complete non-compact manifold, the equivalent relation has been verified with the extended scope of q for from finite q-energy in Lq spaces to infinite q-energy in non-Lq spaces when with 2-balanced growth. Generalized Hadamard Theorem, Cauchy-Schwarz Inequality, and Calculus skills including Integration by Parts as well as Convergent Series have been applied as estimation techniques to evaluate growth rates for a differential form. In particular, energy growth rates as indicated by an appropriate power range in a selected test function lead to a balance between a harmonic differential form and a closed co-closed differential form. Research ideas and computational methods in this paper could provide an innovative way in the study of broadening Lq spaces to non-Lq spaces with a wide variety of infinite energy growth for a differential form.

Keywords: closed forms, co-closed forms, harmonic forms, L^q spaces, p-balanced growth, simple differential k-forms

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Authors: A. Calabrese, V. F. Uricchio, D. di Noia, S. Favale, C. Caiati, G. P. Maggi, G. Donvito, D. Diacono, S. Tangaro, A. Italiano, E. Riezzo, M. Zippitelli, M. Toriello, E. Celiberti, D. Festa, A. Colaianni

Abstract:

Climate change has a severe impact on human health. There is a vast literature demonstrating temperature increase is causally related to cardiovascular problem and represents a high risk for human health, but there are not study that improve a solution. In this work, it is studied how the clime influenced the human parameter through the analysis of climatic conditions in an area of the Apulia Region: Capurso Municipality. At the same time, medical personnel involved identified a set of variables useful to define an index describing health condition. These scientific studies are the base of an innovative alert system, IPPOCRATE, whose aim is to asses climate risk and share information to population at risk to support prevention and mitigation actions. IPPOCRATE is an e-health system, it is designed to provide technological support to analysis of health risk related to climate and provide tools for prevention and management of critical events. It is the first integrated system of prevention of human risk caused by climate change. IPPOCRATE calculates risk weighting meteorological data with the vulnerability of monitored subjects and uses mobile and cloud technologies to acquire and share information on different data channels. It is composed of four components: Multichannel Hub. Multichannel Hub is the ICT infrastructure used to feed IPPOCRATE cloud with a different type of data coming from remote monitoring devices, or imported from meteorological databases. Such data are ingested, transformed and elaborated in order to be dispatched towards mobile app and VoIP phone systems. IPPOCRATE Multichannel Hub uses open communication protocols to create a set of APIs useful to interface IPPOCRATE with 3rd party applications. Internally, it uses non-relational paradigm to create flexible and highly scalable database. WeHeart and Smart Application The wearable device WeHeart is equipped with sensors designed to measure following biometric variables: heart rate, systolic blood pressure and diastolic blood pressure, blood oxygen saturation, body temperature and blood glucose for diabetic subjects. WeHeart is designed to be easy of use and non-invasive. For data acquisition, users need only to wear it and connect it to Smart Application by Bluetooth protocol. Easy Box was designed to take advantage from new technologies related to e-health care. EasyBox allows user to fully exploit all IPPOCRATE features. Its name, Easy Box, reveals its purpose of container for various devices that may be included depending on user needs. Territorial Registry is the IPPOCRATE web module reserved to medical personnel for monitoring, research and analysis activities. Territorial Registry allows to access to all information gathered by IPPOCRATE using GIS system in order to execute spatial analysis combining geographical data (climatological information and monitored data) with information regarding the clinical history of users and their personal details. Territorial Registry was designed for different type of users: control rooms managed by wide area health facilities, single health care center or single doctor. Territorial registry manages such hierarchy diversifying the access to system functionalities. IPPOCRATE is the first e-Health system focused on climate risk prevention.

Keywords: climate change, health risk, new technological system

Procedia PDF Downloads 854

Authors: V. L. M. F. Capellini, A. P. P. M. Maturana, N. C. M. Brondino, M. B. C. L. B. M. Peixoto, A. J. Broughton

Abstract:

A paradigm shift is a process. The process of implementing inclusive education, a system constructed to support all learners, requires planning, identification, experimentation, and evaluation. In this vein, the purpose of the present study was to evaluate the capacity of one Brazilian state school systems to provide special education students with a quality inclusive education. This study originated at the behest of concerned families of students with special needs who filed complaints with the Municipality of Bauru, São Paulo. These families claimed, 1) children with learning differences and educational needs had not been identified for services, and 2) those who had been identified had not received sufficient specialized educational assistance (SEA) in schools across the City of Bauru. Hence, the Office of Civil Rights for the state of São Paulo (Ministério Público de São Paulo) summoned the local higher education institution, UNESP, to design a research study to investigate these allegations. In this exploratory study, descriptive data were gathered from all elementary and middle schools including 58 state schools and 17 city schools, for a total of 75 schools overall. Data collection consisted of each school's annual strategic action plan, surveys and interviews with all school stakeholders to determine their perceptions of the inclusive education available to students with Special Education Needs (SEN). The data were collected as one of four stages in a larger study which also included field observations of a focal students' experience and a continuing education course for all teachers and administrators in both state and city schools. For the purposes of this study, the researchers were interested in understanding the perceptions of school staff, parents, and students across all schools. Therefore, documents and surveys from 75 schools were analyzed for adherence to federal legislation guaranteeing students with SEN the right to special education assistance within the regular school setting. Results shows that while some schools recognized the legal rights of SEN students to receive special education, the plans to actually deliver services were absent. In conclusion, the results of this study revealed both school staff and families have insufficient planning and accessibility resources, and the schools have inadequate infrastructure for full-time support to SEN students, i.e., structures and systems to support the identification of SEN and delivery of services within schools of Bauru, SP. Having identified the areas of need, the city is now prepared to take next steps in the process toward preparing all schools to be inclusive.

Keywords: inclusion, school, special education, special needs

Procedia PDF Downloads 149

Authors: Nissa Phloimontri

Abstract:

This research explores the current physical conditions of motorcycle taxi stands located near the BTS stations in the central business district (CBD) and the comfort conditions of motorcycle taxi drivers. The criteria set up for physical stand survey and assessment are the integration of multimodal access design guidelines. After the survey, stands that share similar characteristics are classified into a series of typologies. Based on the environmental comfort model, questionnaires and in-depth interviews are conducted to evaluate the comfort levels of drivers including physical, functional, and psychological comfort. The results indicate that there are a number of motorcycle taxi stands that are not up to standard and are not conducive to the work-related activities of drivers. The study concludes by recommending public policy for integrated paratransit stops that support the multimodal transportation and seamless mobility concepts within the specific context of Bangkok as well as promote the quality of work life of motorcycle taxi drivers.

Keywords: motorcycle taxi, paratransit stops, environmental comfort, quality of work life

Procedia PDF Downloads 86

Authors: Kenneth Kapalu Muzata, Dikeledi Mahlo, Pinkie Mabunda Mabunda

Abstract:

This paper presents results of a study conducted to establish special education teachers’ knowledge and application of curriculum adaptation of the 2013 revised curriculum in Zambia. From a sample of 134 respondents (120 special education teachers, 12 education officers, and 2 curriculum specialists), the study collected both quantitative and qualitative data to establish whether teachers understood and applied the concept of curriculum adaptation in teaching learners with special education needs. To obtain data validity and reliability, the researchers collected data by use of mixed methods. Semi-structured questionnaires and interviews were administered. Lesson Observations and post-lesson discussions were conducted on 12 selected teachers from the 120 sample that answered the questionnaires. Frequencies, percentages, and significant differences were derived through the statistical package for social sciences. Qualitative data were analyzed with the help of NVIVO qualitative software to create themes and obtain coding density to help with conclusions. Both quantitative and qualitative data were concurrently compared and related. The results revealed that special education teachers lacked a thorough understanding of the concept of curriculum adaptation, thus denying learners with special education needs the opportunity to benefit from the revised curriculum. The teachers were not oriented on the revised curriculum and hence facing numerous challenges trying to adapt the curriculum. The study recommended training of special education teachers in curriculum adaptation.

Keywords: curriculum adaptation, special education, learners with special education needs, special education teachers

Procedia PDF Downloads 163