Search results for: urban growth model
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 24179

Search results for: urban growth model

2579 Barnard Feature Point Detector for Low-Contractperiapical Radiography Image

Authors: Chih-Yi Ho, Tzu-Fang Chang, Chih-Chia Huang, Chia-Yen Lee

Abstract:

In dental clinics, the dentists use the periapical radiography image to assess the effectiveness of endodontic treatment of teeth with chronic apical periodontitis. Periapical radiography images are taken at different times to assess alveolar bone variation before and after the root canal treatment, and furthermore to judge whether the treatment was successful. Current clinical assessment of apical tissue recovery relies only on dentist personal experience. It is difficult to have the same standard and objective interpretations due to the dentist or radiologist personal background and knowledge. If periapical radiography images at the different time could be registered well, the endodontic treatment could be evaluated. In the image registration area, it is necessary to assign representative control points to the transformation model for good performances of registration results. However, detection of representative control points (feature points) on periapical radiography images is generally very difficult. Regardless of which traditional detection methods are practiced, sufficient feature points may not be detected due to the low-contrast characteristics of the x-ray image. Barnard detector is an algorithm for feature point detection based on grayscale value gradients, which can obtain sufficient feature points in the case of gray-scale contrast is not obvious. However, the Barnard detector would detect too many feature points, and they would be too clustered. This study uses the local extrema of clustering feature points and the suppression radius to overcome the problem, and compared different feature point detection methods. In the preliminary result, the feature points could be detected as representative control points by the proposed method.

Keywords: feature detection, Barnard detector, registration, periapical radiography image, endodontic treatment

Procedia PDF Downloads 433
2578 The Impact of the Enron Scandal on the Reputation of Corporate Social Responsibility Rating Agencies

Authors: Jaballah Jamil

Abstract:

KLD (Peter Kinder, Steve Lydenberg and Amy Domini) research & analytics is an independent intermediary of social performance information that adopts an investor-pay model. KLD rating agency does not have an explicit monitoring on the rated firm which suggests that KLD ratings may not include private informations. Moreover, the incapacity of KLD to predict accurately the extra-financial rating of Enron casts doubt on the reliability of KLD ratings. Therefore, we first investigate whether KLD ratings affect investors' perception by studying the effect of KLD rating changes on firms' financial performances. Second, we study the impact of the Enron scandal on investors' perception of KLD rating changes by comparing the effect of KLD rating changes on firms' financial performances before and after the failure of Enron. We propose an empirical study that relates a number of equally-weighted portfolios returns, excess stock returns and book-to-market ratio to different dimensions of KLD social responsibility ratings. We first find that over the last two decades KLD rating changes influence significantly and negatively stock returns and book-to-market ratio of rated firms. This finding suggests that a raise in corporate social responsibility rating lowers the firm's risk. Second, to assess the Enron scandal's effect on the perception of KLD ratings, we compare the effect of KLD rating changes before and after the Enron scandal. We find that after the Enron scandal this significant effect disappears. This finding supports the view that the Enron scandal annihilates the KLD's effect on Socially Responsible Investors. Therefore, our findings may question results of recent studies that use KLD ratings as a proxy for Corporate Social Responsibility behavior.

Keywords: KLD social rating agency, investors' perception, investment decision, financial performance

Procedia PDF Downloads 425
2577 The Cloud Systems Used in Education: Properties and Overview

Authors: Agah Tuğrul Korucu, Handan Atun

Abstract:

Diversity and usefulness of information that used in education are have increased due to development of technology. Web technologies have made enormous contributions to the distance learning system especially. Mobile systems, one of the most widely used technology in distance education, made much easier to access web technologies. Not bounding by space and time, individuals have had the opportunity to access the information on web. In addition to this, the storage of educational information and resources and accessing these information and resources is crucial for both students and teachers. Because of this importance, development and dissemination of web technologies supply ease of access to information and resources are provided by web technologies. Dynamic web technologies introduced as new technologies that enable sharing and reuse of information, resource or applications via the Internet and bring websites into expandable platforms are commonly known as Web 2.0 technologies. Cloud systems are one of the dynamic web technologies that defined as a model provides approaching the demanded information independent from time and space in appropriate circumstances and developed by NIST. One of the most important advantages of cloud systems is meeting the requirements of users directly on the web regardless of hardware, software, and dealing with install. Hence, this study aims at using cloud services in education and investigating the services provided by the cloud computing. Survey method has been used as research method. In the findings of this research the fact that cloud systems are used such studies as resource sharing, collaborative work, assignment submission and feedback, developing project in the field of education, and also, it is revealed that cloud systems have plenty of significant advantages in terms of facilitating teaching activities and the interaction between teacher, student and environment.

Keywords: cloud systems, cloud systems in education, online learning environment, integration of information technologies, e-learning, distance learning

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2576 The Population Death Model and Influencing Factors from the Data of The "Sixth Census": Zhangwan District Case Study

Authors: Zhou Shangcheng, Yi Sicen

Abstract:

Objective: To understand the mortality patterns of Zhangwan District in 2010 and provide the basis for the development of scientific and rational health policy. Methods: Data are collected from the Sixth Census of Zhangwan District and disease surveillance system. The statistical analysis include death difference between age, gender, region and time and the related factors. Methods developed for the Global Burden of Disease (GBD) Study by the World Bank and World Health Organization (WHO) were adapted and applied to Zhangwan District population health data. DALY rate per 1,000 was calculated for varied causes of death. SPSS 16 is used by statistic analysis. Results: From the data of death population of Zhangwan District we know the crude mortality rate was 6.03 ‰. There are significant differences of mortality rate in male and female population which was respectively 7.37 ‰ and 4.68 ‰. 0 age group population life expectancy in Zhangwan District in 2010 was 78.40 years old(Male 75.93, Female 81.03). The five leading causes of YLL in descending order were: cardiovascular diseases(42.63DALY/1000), malignant neoplasm (23.73DALY/1000), unintentional injuries (5.84DALY/1000), Respiratory diseases(5.43 DALY/1000), Respiratory infections (2.44DALY/1000). In addition, there are strong relation between the marital status , educational level and mortality in some to a certain extend. Conclusion Zhangwan District, as city level, is at lower mortality levels. The mortality of the total population of Zhangwan District has a downward trend and life expectancy is rising.

Keywords: sixth census, Zhangwan district, death level differences, influencing factors, cause of death

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2575 Resilience in the Face of Environmental Extremes through Networking and Resource Mobilization

Authors: Abdullah Al Mohiuddin

Abstract:

Bangladesh is one of the poorest countries in the world, and ranks low on almost all measures of economic development, thus leaving the population extremely vulnerable to natural disasters and climate events. 20% of GDP come from agriculture but more than 60% of the population relies on agriculture as their main source of income making the entire economy vulnerable to climate change and natural disasters. High population density exacerbates the exposure to and effect of climate events, and increases the levels of vulnerability, as does the poor institutional development of the country. The most vulnerable sectors to climate change impacts in Bangladesh are agriculture, coastal zones, water resources, forestry, fishery, health, biomass, and energy. High temperatures, heavy rainfall, high humidity and fairly marked seasonal variations characterize the climate in Bangladesh: Mild winter, hot humid summer and humid, warm rainy monsoon. Much of the country is flooded during the summer monsoon. The Department of Environment (DOE) under the Ministry of Environment and Forestry (MoEF) is the focal point for the United Nations Framework Convention on Climate Change (UNFCCC) and coordinates climate related activities in the country. Recently, a Climate Change Cell (CCC) has been established to address several issues including adaptation to climate change. The climate change focus started with The National Environmental Management Action Plan (NEMAP) which was prepared in 1995 in order to initiate the process to address environmental and climate change issues as long-term environmental problems for Bangladesh. Bangladesh was one of the first countries to finalise a NAPA (Preparation of a National Adaptation Plan of Action) which addresses climate change issues. The NAPA was completed in 2005, and is the first official initiative for mainstreaming adaptation to national policies and actions to cope with climate change and vulnerability. The NAPA suggests a number of adaptation strategies, for example: - Providing drinking water to coastal communities to fight the enhanced salinity caused by sea level rise, - Integrating climate change in planning and design of infrastructure, - Including climate change issues in education, - Supporting adaptation of agricultural systems to new weather extremes, - Mainstreaming CCA into policies and programmes in different sectors, e.g. disaster management, water and health, - Dissemination of CCA information and awareness raising on enhanced climate disasters, especially in vulnerable communities. Bangladesh has geared up its environment conservation steps to save the world’s poorest countries from the adverse effects of global warming. Now it is turning towards green economy policies to save the degrading ecosystem. Bangladesh is a developing country and always fights against Natural Disaster. At the same time we also fight for establishing ecological environment through promoting Green Economy/Energy by Youth Networking. ANTAR is coordinating a big Youth Network in the southern part of Bangladesh where 30 Youth group involved. It can be explained as the economic development based on sustainable development which generates growth and improvement in human’s lives while significantly reducing environmental risks and ecological scarcities. Green economy in Bangladesh promotes three bottom lines – sustaining economic, environment and social well-being.

Keywords: resilience, networking, mobilizing, resource

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2574 Fractal Nature of Granular Mixtures of Different Concretes Formulated with Different Methods of Formulation

Authors: Fatima Achouri, Kaddour Chouicha, Abdelwahab Khatir

Abstract:

It is clear that concrete of quality must be made with selected materials chosen in optimum proportions that remain after implementation, a minimum of voids in the material produced. The different methods of formulations what we use, are based for the most part on a granular curve which describes an ‘optimal granularity’. Many authors have engaged in fundamental research on granular arrangements. A comparison of mathematical models reproducing these granular arrangements with experimental measurements of compactness have to verify that the minimum porosity P according to the following extent granular exactly a power law. So the best compactness in the finite medium are obtained with power laws, such as Furnas, Fuller or Talbot, each preferring a particular setting between 0.20 and 0.50. These considerations converge on the assumption that the optimal granularity Caquot approximates by a power law. By analogy, it can then be analyzed as a granular structure of fractal-type since the properties that characterize the internal similarity fractal objects are reflected also by a power law. Optimized mixtures may be described as a series of installments falling granular stuff to better the tank on a regular hierarchical distribution which would give at different scales, by cascading effects, the same structure to the mix. Likely this model may be appropriate for the entire extent of the size distribution of the components, since the cement particles (and silica fume) correctly deflocculated, micrometric dimensions, to chippings sometimes several tens of millimeters. As part of this research, the aim is to give an illustration of the application of fractal analysis to characterize the granular concrete mixtures optimized for a so-called fractal dimension where different concretes were studying that we proved a fractal structure of their granular mixtures regardless of the method of formulation or the type of concrete.

Keywords: concrete formulation, fractal character, granular packing, method of formulation

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2573 Machine Learning Prediction of Compressive Damage and Energy Absorption in Carbon Fiber-Reinforced Polymer Tubular Structures

Authors: Milad Abbasi

Abstract:

Carbon fiber-reinforced polymer (CFRP) composite structures are increasingly being utilized in the automotive industry due to their lightweight and specific energy absorption capabilities. Although it is impossible to predict composite mechanical properties directly using theoretical methods, various research has been conducted so far in the literature for accurate simulation of CFRP structures' energy-absorbing behavior. In this research, axial compression experiments were carried out on hand lay-up unidirectional CFRP composite tubes. The fabrication method allowed the authors to extract the material properties of the CFRPs using ASTM D3039, D3410, and D3518 standards. A neural network machine learning algorithm was then utilized to build a robust prediction model to forecast the axial compressive properties of CFRP tubes while reducing high-cost experimental efforts. The predicted results have been compared with the experimental outcomes in terms of load-carrying capacity and energy absorption capability. The results showed high accuracy and precision in the prediction of the energy-absorption capacity of the CFRP tubes. This research also demonstrates the effectiveness and challenges of machine learning techniques in the robust simulation of composites' energy-absorption behavior. Interestingly, the proposed method considerably condensed numerical and experimental efforts in the simulation and calibration of CFRP composite tubes subjected to compressive loading.

Keywords: CFRP composite tubes, energy absorption, crushing behavior, machine learning, neural network

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2572 A Study of Body Weight and Type Traits Recorded on Hairy Goat in Punjab, Pakistan

Authors: A. Qayyum, G. Bilal, H. M. Waheed

Abstract:

The objectives of the study were to determine phenotypic variations in Hairy goats for quantitative and qualitative traits and to analyze the relationship between different body measurements and body weight in Hairy goats. Data were collected from the Barani Livestock Production Research Institute (BLPRI) at Kherimurat, Attock and potential farmers who were raising hairy goats in the Potohar region. Twelve (12) phenotypic parameters were measured on 99 adult Hairy goat (18 male and 81 female). Four qualitative and 8 quantitative traits were investigated. Qualitative traits were visually observed and expressed as percentages. Descriptive analysis was done on quantitative variables. All hairy goats had predominately black body coat color (72%), whereas white (11%) and brown (11%) body coat color were also observed. Both the pigmented (45.5%) and non-pigmented (54.5%) type of body skin were observed in the goat breed. Horns were present in the majority (91%) of animals. Most of the animals (83%) had straight facial head profiles. Analysis was performed in SAS On-Demand for Academics using PROC mixed model procedure. Overall means ± SD of body weight (BW), body length (BL), height at wither (HAW), ear length (EL), head length (HL), heart girth (HG), tail length (TL) and MC (muzzle circumference) were 41.44 ± 12.21 kg, 66.40 ± 7.87 cm, 75.17 ± 7.83 cm, 22.99 ± 6.75 cm, 15.07 ± 3.44 cm, 76.54 ± 8.80 cm, 18.28 ± 4.18 cm, and 26.24 ± 5.192 cm, respectively. Sex had a significant effect on BL and HG (P < 0.05), whereas BW, HAW, EL, HL, TL, and MC were not significantly affected (P > 0.05). The herd had a significant effect on BW, BL, HAW, HL, HG, and TL (P < 0.05) except EL and MC (P > 0.05). Hairy goats appear to have the potential for selection as mutton breeds in the Potohar region of Punjab. The findings of the present study would help in the characterization and conservation of hairy goats using genetic and genomic tools in the future.

Keywords: body weight, Hairy goat, type traits Punjab, Pakistan

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2571 The Effect of an Infill on the Bearing Capacity and Stiffness of Infilled Frames

Authors: Goran Baloevic, Jure Radnic, Nikola Grgic

Abstract:

The application of frames with masonry or panel infill is common in the engineering practice. In these cases, a frame is often considered to be a primary structure, while an infill is considered to be a secondary structure. In past calculations, the infill was rarely included in the design of frame structures in terms of their bearing capacity and safety. Recent calculations of such structures necessarily include the effect of infill since it contributes to stiffness and bearing capacity of overall system, especially under horizontal loads. In certain cases, if the infill is not included in the seismic design of frame structures, the result can be lower design safety. However, since the different configuration of the infill through the building’s height can be made, it is possible that contribution of such infill to the overall bearing capacity can be lower and seismic forces on the building can be increased due to greater stiffness of the structure. So far, many experimental and numerical researches on the behavior of infilled frames under horizontal static forces and earthquake have been performed. In this paper, several masonry-infilled concrete and steel frames under horizontal static forces and earthquake are analysed. The experimental results by shake-table and numerical results are compared in terms of the bearing capacity of bare and infilled frames. Herein, the stiffness of frames and infill were varied, with different position of the infill and different types of openings. Cases with positive and negative effects of the infill to the bearing capacity of the frames were considered. Finally, main conclusions and recommendations for practical application and design of masonry-infilled concrete and steel frames are given.

Keywords: bearing capacity, infilled frame, numerical model, shake table

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2570 Modified Clusterwise Regression for Pavement Management

Authors: Mukesh Khadka, Alexander Paz, Hanns de la Fuente-Mella

Abstract:

Typically, pavement performance models are developed in two steps: (i) pavement segments with similar characteristics are grouped together to form a cluster, and (ii) the corresponding performance models are developed using statistical techniques. A challenge is to select the characteristics that define clusters and the segments associated with them. If inappropriate characteristics are used, clusters may include homogeneous segments with different performance behavior or heterogeneous segments with similar performance behavior. Prediction accuracy of performance models can be improved by grouping the pavement segments into more uniform clusters by including both characteristics and a performance measure. This grouping is not always possible due to limited information. It is impractical to include all the potential significant factors because some of them are potentially unobserved or difficult to measure. Historical performance of pavement segments could be used as a proxy to incorporate the effect of the missing potential significant factors in clustering process. The current state-of-the-art proposes Clusterwise Linear Regression (CLR) to determine the pavement clusters and the associated performance models simultaneously. CLR incorporates the effect of significant factors as well as a performance measure. In this study, a mathematical program was formulated for CLR models including multiple explanatory variables. Pavement data collected recently over the entire state of Nevada were used. International Roughness Index (IRI) was used as a pavement performance measure because it serves as a unified standard that is widely accepted for evaluating pavement performance, especially in terms of riding quality. Results illustrate the advantage of the using CLR. Previous studies have used CLR along with experimental data. This study uses actual field data collected across a variety of environmental, traffic, design, and construction and maintenance conditions.

Keywords: clusterwise regression, pavement management system, performance model, optimization

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2569 The Essence of Culture and Religion in Creating Disaster Resilient Societies through Corporate Social Responsibility

Authors: Repaul Kanji, Rajat Agrawal

Abstract:

In this era where issues like climate change and disasters are the topics of discussion at national and international forums, it is very often that humanity questions the causative role of corporates in such events. It is beyond any doubt that rapid industrialisation and development has taken a toll in the form of climate change and even disasters, in some case. Thus, demanding to fulfill a corporate's responsibilities in the form of rescue and relief in times of disaster, rehabilitation and even mitigation and preparedness to adapt to the oncoming changes is obvious. But how can the responsibilities of the corporates be channelised to ensure all this, i.e., develop a resilient society? More than that, which factors, when emphasised upon, can lead to the holistic development of the society. To answer this query, an extensive literature review was done to identify several enablers like legislations of a nation, the role of brand and reputation, ease of doing Corporate Social Responsibility, mission and vision of an organisation, religion and culture, etc. as a tool for building disaster resilience. A questionnaire survey, interviews with experts and academicians followed by interpretive structural modelling (ISM) were used to construct a multi-hierarchy model depicting the contextual relationship among the identified enablers. The study revealed that culture and religion are the most powerful driver, which affects other enablers either directly or indirectly. Taking cognisance of the fact that an idea of separation between religion and workplace (business) resides subconsciously within the society, the study tries to interpret the outcome of the ISM through the lenses of past researches (The Integrating Box) and explores how it can be leveraged to build a resilient society.

Keywords: corporate social responsibility, interpretive structural modelling, disaster resilience and risk reduction, the integration box (TIB)

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2568 Role of Transient Receptor Potential Vanilloid 1 in Electroacupuncture Analgesia on Chronic Inflammatory Pain in Mice

Authors: Jun Yang, Ching-Liang Hsieh, Yi-Wen Lin

Abstract:

Chronic inflammatory pain results from peripheral tissue injury or local inflammation to increase the release of protons, histamines, adenosine triphosphate, and several proinflammatory cytokines. Transient receptor potential vanilloid 1 (TRPV1) is involved in fibromyalgia, neuropathic, and inflammatory pain; however, its exact mechanisms in chronic inflammatory pain are still unclear. We investigate the analgesic effect of EA by injecting complete Freund’s adjuvant (CFA) in the hind paw of mice to induce chronic inflammatory pain ( > 14 d). Our results showed that EA significantly reduced chronic mechanical and thermal hyperalgesia in the chronic inflammatory pain model. Chronic mechanical and thermal hyperalgesia was also abolished in TRPV1−/− mice. TRPV1 increased in the dorsal root ganglion (DRG) and spinal cord (SC) at 2 weeks after CFA injection. The expression levels of downstream molecules such as pPKA, pPI3K, and pPKC increased, as did those of pERK, pp38, and pJNK. Transcription factors (pCREB and pNFκB) and nociceptive ion channels (Nav1.7 and Nav1.8) were involved in this process. Inflammatory mediators such as GFAP (Glial fibrillary acidic protein), S100B, and RAGE (Receptor for advanced glycation endproducts) were also involved. The expression levels of these molecules were reduced in EA (electroacupuncture) and TRPV1−/−mice but not in the sham EA group. The present study demonstrated that EA or TRPV1 gene deletion reduced chronic inflammatory pain through TRPV1 and related molecules. In addition, our data provided evidence to support the clinical use of EA for treating chronic inflammatory pain.

Keywords: auricular electric-stimulation, epileptic seizures, anti-inflammation, electroacupuncture

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2567 Political Deprivations, Political Risk and the Extent of Skilled Labor Migration from Pakistan: Finding of a Time-Series Analysis

Authors: Syed Toqueer Akhter, Hussain Hamid

Abstract:

Over the last few decades an upward trend has been observed in the case of labor migration from Pakistan. The emigrants are not just economically motivated and in search of a safe living environment towards more developed countries in Europe, North America and Middle East. The opportunity cost of migration comes in the form of brain drain that is the loss of qualified and skilled human capital. Throughout the history of Pakistan, situations of political instability have emerged ranging from violation of political rights, political disappearances to political assassinations. Providing security to the citizens is a major issue faced in Pakistan due to increase in crime and terrorist activities. The aim of the study is to test the impact of political instability, appearing in the form of political terror, violation of political rights and civil liberty on skilled migration of labor. Three proxies are used to measure the political instability; political terror scale (based on a scale of 1-5, the political terror and violence that a country encounters in a particular year), political rights (a rating of 1-7, that describes political rights as the ability for the people to participate without restraint in political process) and civil liberty (a rating of 1-7, civil liberty is defined as the freedom of expression and rights without government intervention). Using time series data from 1980-2011, the distributed lag models were used for estimation because migration is not a onetime process, previous events and migration can lead to more migration. Our research clearly shows that political instability appearing in the form of political terror, political rights and civil liberty all appeared significant in explaining the extent of skilled migration of Pakistan.

Keywords: skilled labor migration, political terror, political rights, civil liberty, distributed lag model

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2566 Typical Characteristics and Compositions of Solvent System in Application of Maceration Technology to Isolate Antioxidative Activated Extract of Natural Products

Authors: Yohanes Buang, Suwari

Abstract:

Increasing interest of society in use and creation of herbal medicines has encouraged scientists/researchers to establish an ideal method to produce the best quality and quantity of pharmaceutical extracts. To have highest the antioxidative extracts, the method used must be at optimum conditions. Hence, the best method is not only able to provide highest quantity and quality of the isolated pharmaceutical extracts but also it has to be easy to do, simple, fast, and cheap. The characterization of solvents in maceration technique, in present study, involved various variables influencing quantity and quality of the pharmaceutical extracts, such as solvent’s optimum acidity-alkalinity (pH), temperature, concentration, and contact time. The shifting polarity of the solvent by combinations of water with ethanol (70:30) and (50:50) were also performed to completely record the best solvent system in application of maceration technology. Among those three solvents threated within Myrmecodia pendens, as a model of natural product, the results showed that water solvent system with conditions of alkalinity pH, optimum temperature, concentration, and contact time, is the best system to perform the maceration in order to have the highest isolated antioxidative activated extracts. The optimum conditions of the water solvent are at the alkalinity pH 9 up, 30 mg/mL of concentration, 40 min of contact time, 100 °C of temperature, and no ethanol used to replace parts of the water solvent. The present study strongly recommended the best conditions of solvent system to isolate the pharmaceutical extracts of natural products in application of the maceration technology.

Keywords: extracts, herbal medicine, natural product, maceration technique

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2565 Lithium Ion Supported on TiO2 Mixed Metal Oxides as a Heterogeneous Catalyst for Biodiesel Production from Canola Oil

Authors: Mariam Alsharifi, Hussein Znad, Ming Ang

Abstract:

Considering the environmental issues and the shortage in the conventional fossil fuel sources, biodiesel has gained a promising solution to shift away from fossil based fuel as one of the sustainable and renewable energy. It is synthesized by transesterification of vegetable oils or animal fats with alcohol (methanol or ethanol) in the presence of a catalyst. This study focuses on synthesizing a high efficient Li/TiO2 heterogeneous catalyst for biodiesel production from canola oil. In this work, lithium immobilized onto TiO2 by the simple impregnation method. The catalyst was evaluated by transesterification reaction in a batch reactor under moderate reaction conditions. To study the effect of Li concentrations, a series of LiNO3 concentrations (20, 30, 40 wt. %) at different calcination temperatures (450, 600, 750 ºC) were evaluated. The Li/TiO2 catalysts are characterized by several spectroscopic and analytical techniques such as XRD, FT-IR, BET, TG-DSC and FESEM. The optimum values of impregnated Lithium nitrate on TiO2 and calcination temperature are 30 wt. % and 600 ºC, respectively, along with a high conversion to be 98 %. The XRD study revealed that the insertion of Li improved the catalyst efficiency without any alteration in structure of TiO2 The best performance of the catalyst was achieved when using a methanol to oil ratio of 24:1, 5 wt. % of catalyst loading, at 65◦C reaction temperature for 3 hours of reaction time. Moreover, the experimental kinetic data were compatible with the pseudo-first order model and the activation energy was (39.366) kJ/mol. The synthesized catalyst Li/TiO2 was applied to trans- esterify used cooking oil and exhibited a 91.73% conversion. The prepared catalyst has shown a high catalytic activity to produce biodiesel from fresh and used oil within mild reaction conditions.

Keywords: biodiesel, canola oil, environment, heterogeneous catalyst, impregnation method, renewable energy, transesterification

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2564 Memory Based Reinforcement Learning with Transformers for Long Horizon Timescales and Continuous Action Spaces

Authors: Shweta Singh, Sudaman Katti

Abstract:

The most well-known sequence models make use of complex recurrent neural networks in an encoder-decoder configuration. The model used in this research makes use of a transformer, which is based purely on a self-attention mechanism, without relying on recurrence at all. More specifically, encoders and decoders which make use of self-attention and operate based on a memory, are used. In this research work, results for various 3D visual and non-visual reinforcement learning tasks designed in Unity software were obtained. Convolutional neural networks, more specifically, nature CNN architecture, are used for input processing in visual tasks, and comparison with standard long short-term memory (LSTM) architecture is performed for both visual tasks based on CNNs and non-visual tasks based on coordinate inputs. This research work combines the transformer architecture with the proximal policy optimization technique used popularly in reinforcement learning for stability and better policy updates while training, especially for continuous action spaces, which are used in this research work. Certain tasks in this paper are long horizon tasks that carry on for a longer duration and require extensive use of memory-based functionalities like storage of experiences and choosing appropriate actions based on recall. The transformer, which makes use of memory and self-attention mechanism in an encoder-decoder configuration proved to have better performance when compared to LSTM in terms of exploration and rewards achieved. Such memory based architectures can be used extensively in the field of cognitive robotics and reinforcement learning.

Keywords: convolutional neural networks, reinforcement learning, self-attention, transformers, unity

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2563 An Assessment into Impact of Regional Conflicts upon Socio-Political Sustainability in Pakistan

Authors: Syed Toqueer Akhter, Muhammad Muzaffar Abbas

Abstract:

Conflicts in Pakistan are a result of a configuration of factors, which are directly related to the system of the state, the unstable regional setting, and the geo-strategic location of Pakistan at large. This paper examines the impact of regional conflict onto the socio-political sustainability of Pakistan. The magnitude of the spillover from a conflicted region is similar in size of the equivalent increase in domestic conflict. Pakistan has gone at war three times with India; the border with India is named as the tensest borderlines of the world. Disagreements with India and lack of dispute settlement mechanisms have negatively effected the peace in the region, influx of illegal weapons and refugees from Afghanistan as an outcome of 9/11 incidence, have exasperated the criticality of levels of internal conflict in Pakistan. Our empirical findings are based on the data collected on regional conflict levels, regional trade, global trade, comparative defence capabilities of the region in contrast to Pakistan and the government regime (Autocratic, Democratic) over 1972-2007. It has been proposed in this paper that the intent of domestic conflict is associated with the conflict in the region, regional trade, global trade and the government regime of Pakistan. The estimated model (OLS) implies that domestic conflict is effected positively and significantly with long term impact of conflict in the region. Also, if defence capabilities of the region are better than that of Pakistan it effects domestic conflict positively and significantly. Conflict in neighbouring countries are found as a source of domestic conflict in Pakistan, whereas the regional trade as well as type of government regimes in Pakistan lowered the intensity of domestic conflict significantly, while globalized trade imply risk of domestic conflict to be reduced but not significantly.

Keywords: conflict, regional trade, socio-politcal instability

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2562 Towards a Multilevel System of Talent Management in Small And Medium-Sized Enterprises: French Context Exploration

Authors: Abid Kousay

Abstract:

Appeared and developed essentially in large companies and multinationals, Talent Management (TM) in Small and Medium-Sized Enterprises (SMEs) has remained an under-explored subject till today. Although the literature on TM in the Anglo-Saxon context is developing, it remains monopolized in non-European contexts, especially in France. Therefore, this article aims to address these shortcomings through contributing to TM issues, by adopting a multilevel approach holding the goal of reaching a global holistic vision of interactions between various levels, while applying TM. A qualitative research study carried out within 12 SMEs in France, built on the methodological perspective of grounded theory, will be used in order to go beyond description, to generate or discover a theory or even a unified theoretical explanation. Our theoretical contributions are the results of the grounded theory, the fruit of context considerations and the dynamic of the multilevel approach. We aim firstly to determine the perception of talent and TM in SMEs. Secondly, we formalize TM in SME through the empowerment of all 3 levels in the organization (individual, collective, and organizational). And we generate a multilevel dynamic system model, highlighting the institutionalization dimension in SMEs and the managerial conviction characterized by the domination of the leader's role. Thirdly, this first study shed the light on the importance of rigorous implementation of TM in SMEs in France by directing CEO and HR and TM managers to focus on elements that upstream TM implementation and influence the system internally. Indeed, our systematic multilevel approach policy reminds them of the importance of the strategic alignment while translating TM policy into strategies and practices in SMEs.

Keywords: French context, institutionalization, talent, multilevel approach, talent management system

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2561 Research on Level Adjusting Mechanism System of Large Space Environment Simulator

Authors: Han Xiao, Zhang Lei, Huang Hai, Lv Shizeng

Abstract:

Space environment simulator is a device for spacecraft test. KM8 large space environment simulator built in Tianjing Space City is the largest as well as the most advanced space environment simulator in China. Large deviation of spacecraft level will lead to abnormally work of the thermal control device in spacecraft during the thermal vacuum test. In order to avoid thermal vacuum test failure, level adjusting mechanism system is developed in the KM8 large space environment simulator as one of the most important subsystems. According to the level adjusting requirements of spacecraft’s thermal vacuum tests, the four fulcrums adjusting model is established. By means of collecting level instruments and displacement sensors data, stepping motors controlled by PLC drive four supporting legs simultaneous movement. In addition, a PID algorithm is used to control the temperature of supporting legs and level instruments which long time work under the vacuum cold and black environment in KM8 large space environment simulator during thermal vacuum tests. Based on the above methods, the data acquisition and processing, the analysis and calculation, real time adjustment and fault alarming of the level adjusting mechanism system are implemented. The level adjusting accuracy reaches 1mm/m, and carrying capacity is 20 tons. Debugging showed that the level adjusting mechanism system of KM8 large space environment simulator can meet the thermal vacuum test requirement of the new generation spacecraft. The performance and technical indicators of the level adjusting mechanism system which provides important support for the development of spacecraft in China have been ahead of similar equipment in the world.

Keywords: space environment simulator, thermal vacuum test, level adjusting, spacecraft, parallel mechanism

Procedia PDF Downloads 232
2560 Harnessing Deep-Level Metagenomics to Explore the Three Dynamic One Health Areas: Healthcare, Domiciliary and Veterinary

Authors: Christina Killian, Katie Wall, Séamus Fanning, Guerrino Macori

Abstract:

Deep-level metagenomics offers a useful technical approach to explore the three dynamic One Health axes: healthcare, domiciliary and veterinary. There is currently limited understanding of the composition of complex biofilms, natural abundance of AMR genes and gene transfer occurrence in these ecological niches. By using a newly established small-scale complex biofilm model, COMBAT has the potential to provide new information on microbial diversity, antimicrobial resistance (AMR)-encoding gene abundance, and their transfer in complex biofilms of importance to these three One Health axes. Shotgun metagenomics has been used to sample the genomes of all microbes comprising the complex communities found in each biofilm source. A comparative analysis between untreated and biocide-treated biofilms is described. The basic steps include the purification of genomic DNA, followed by library preparation, sequencing, and finally, data analysis. The use of long-read sequencing facilitates the completion of metagenome-assembled genomes (MAG). Samples were sequenced using a PromethION platform, and following quality checks, binning methods, and bespoke bioinformatics pipelines, we describe the recovery of individual MAGs to identify mobile gene elements (MGE) and the corresponding AMR genotypes that map to these structures. High-throughput sequencing strategies have been deployed to characterize these communities. Accurately defining the profiles of these niches is an essential step towards elucidating the impact of the microbiota on each niche biofilm environment and their evolution.

Keywords: COMBAT, biofilm, metagenomics, high-throughput sequencing

Procedia PDF Downloads 39
2559 Using Open Source Data and GIS Techniques to Overcome Data Deficiency and Accuracy Issues in the Construction and Validation of Transportation Network: Case of Kinshasa City

Authors: Christian Kapuku, Seung-Young Kho

Abstract:

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

Procedia PDF Downloads 154
2558 Numerical Studies on 2D and 3D Boundary Layer Blockage and External Flow Choking at Wing in Ground Effect

Authors: K. Dhanalakshmi, N. Deepak, E. Manikandan, S. Kanagaraj, M. Sulthan Ariff Rahman, P. Chilambarasan C. Abhimanyu, C. A. Akaash Emmanuel Raj, V. R. Sanal Kumar

Abstract:

In this paper using a validated double precision, density-based implicit standard k-ε model, the detailed 2D and 3D numerical studies have been carried out to examine the external flow choking at wing-in-ground (WIG) effect craft. The CFD code is calibrated using the exact solution based on the Sanal flow choking condition for adiabatic flows. We observed that at the identical WIG effect conditions the numerically predicted 2D boundary layer blockage is significantly higher than the 3D case and as a result, the airfoil exhibited an early external flow choking than the corresponding wing, which is corroborated with the exact solution. We concluded that, in lieu of the conventional 2D numerical simulation, it is invariably beneficial to go for a realistic 3D simulation of the wing in ground effect, which is analogous and would have the aspects of a real-time parametric flow. We inferred that under the identical flying conditions the chances of external flow choking at WIG effect is higher for conventional aircraft than an aircraft facilitating a divergent channel effect at the bottom surface of the fuselage as proposed herein. We concluded that the fuselage and wings integrated geometry optimization can improve the overall aerodynamic performance of WIG craft. This study is a pointer to the designers and/or pilots for perceiving the zone of danger a priori due to the anticipated external flow choking at WIG effect craft for safe flying at the close proximity of the terrain and the dynamic surface of the marine.

Keywords: boundary layer blockage, chord dominated ground effect, external flow choking, WIG effect

Procedia PDF Downloads 255
2557 The Effect of Corporate Governance on Financial Stability and Solvency Margin for Insurance Companies in Jordan

Authors: Ghadeer A.Al-Jabaree, Husam Aldeen Al-Khadash, M. Nassar

Abstract:

This study aimed at investigating the effect of well-designed corporate governance system on the financial stability of insurance companies listed in ASE. Further, this study provides a comprehensive model for evaluating and analyzing insurance companies' financial position and prospective for comparing the degree of corporate governance application provisions among Jordanian insurance companies. In order to achieve the goals of the study, a whole population that consist of (27) listed insurance companies was introduced through the variables of (board of director, audit committee, internal and external auditor, board and management ownership and block holder's identities). Statistical methods were used with alternative techniques by (SPSS); where descriptive statistical techniques such as means, standard deviations were used to describe the variables, while (F) test and ANOVA analysis of variance were used to test the hypotheses of the study. The study revealed the existence of significant effect of corporate governance variables except local companies that are not listed in ASE on financial stability within control variables especially debt ratio (leverage),where it's also showed that concentration in motor third party doesn't have significant effect on insurance companies' financial stability during study period. Moreover, the study concludes that Global financial crisis affect the investment side of insurance companies with insignificant effect on the technical side. Finally, some recommendations were presented such as enhancing the laws and regulation that help the appropriate application of corporate governance, and work on activating the transparency in the disclosures of the financial statements and focusing on supporting the technical provisions for the companies, rather than focusing only on profit side.

Keywords: corporate governance, financial stability and solvency margin, insurance companies, Jordan

Procedia PDF Downloads 479
2556 Revenue Management of Perishable Products Considering Freshness and Price Sensitive Customers

Authors: Onur Kaya, Halit Bayer

Abstract:

Global grocery and supermarket sales are among the largest markets in the world and perishable products such as fresh produce, dairy and meat constitute the biggest section of these markets. Due to their deterioration over time, the demand for these products depends highly on their freshness. They become totally obsolete after a certain amount of time causing a high amount of wastage and decreases in grocery profits. In addition, customers are asking for higher product variety in perishable product categories, leading to less predictable demand per product and to more out-dating. Effective management of these perishable products is an important issue since it is observed that billions of dollars’ worth of food is expired and wasted every month. We consider coordinated inventory and pricing decisions for perishable products with a time and price dependent random demand function. We use stochastic dynamic programming to model this system for both periodically-reviewed and continuously-reviewed inventory systems and prove certain structural characteristics of the optimal solution. We prove that the optimal ordering decision scenario has a monotone structure and the optimal price value decreases by time. However, the optimal price changes in a non-monotonic structure with respect to inventory size. We also analyze the effect of 1 different parameters on the optimal solution through numerical experiments. In addition, we analyze simple-to-implement heuristics, investigate their effectiveness and extract managerial insights. This study gives valuable insights about the management of perishable products in order to decrease wastage and increase profits.

Keywords: age-dependent demand, dynamic programming, perishable inventory, pricing

Procedia PDF Downloads 238
2555 Experimental Monitoring of the Parameters of the Ionosphere in the Local Area Using the Results of Multifrequency GNSS-Measurements

Authors: Andrey Kupriyanov

Abstract:

In recent years, much attention has been paid to the problems of ionospheric disturbances and their influence on the signals of global navigation satellite systems (GNSS) around the world. This is due to the increase in solar activity, the expansion of the scope of GNSS, the emergence of new satellite systems, the introduction of new frequencies and many others. The influence of the Earth's ionosphere on the propagation of radio signals is an important factor in many applied fields of science and technology. The paper considers the application of the method of transionospheric sounding using measurements from signals from Global Navigation Satellite Systems to determine the TEC distribution and scintillations of the ionospheric layers. To calculate these parameters, the International Reference Ionosphere (IRI) model of the ionosphere, refined in the local area, is used. The organization of operational monitoring of ionospheric parameters is analyzed using several NovAtel GPStation6 base stations. It allows performing primary processing of GNSS measurement data, calculating TEC and fixing scintillation moments, modeling the ionosphere using the obtained data, storing data and performing ionospheric correction in measurements. As a result of the study, it was proved that the use of the transionospheric sounding method for reconstructing the altitude distribution of electron concentration in different altitude range and would provide operational information about the ionosphere, which is necessary for solving a number of practical problems in the field of many applications. Also, the use of multi-frequency multisystem GNSS equipment and special software will allow achieving the specified accuracy and volume of measurements.

Keywords: global navigation satellite systems (GNSS), GPstation6, international reference ionosphere (IRI), ionosphere, scintillations, total electron content (TEC)

Procedia PDF Downloads 168
2554 Customer Churn Prediction by Using Four Machine Learning Algorithms Integrating Features Selection and Normalization in the Telecom Sector

Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

Abstract:

A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

Procedia PDF Downloads 122
2553 A Context Aware Mobile Learning System with a Cognitive Recommendation Engine

Authors: Jalal Maqbool, Gyu Myoung Lee

Abstract:

Using smart devices for context aware mobile learning is becoming increasingly popular. This has led to mobile learning technology becoming an indispensable part of today’s learning environment and platforms. However, some fundamental issues remain - namely, mobile learning still lacks the ability to truly understand human reaction and user behaviour. This is due to the fact that current mobile learning systems are passive and not aware of learners’ changing contextual situations. They rely on static information about mobile learners. In addition, current mobile learning platforms lack the capability to incorporate dynamic contextual situations into learners’ preferences. Thus, this thesis aims to address these issues highlighted by designing a context aware framework which is able to sense learner’s contextual situations, handle data dynamically, and which can use contextual information to suggest bespoke learning content according to a learner’s preferences. This is to be underpinned by a robust recommendation system, which has the capability to perform these functions, thus providing learners with a truly context-aware mobile learning experience, delivering learning contents using smart devices and adapting to learning preferences as and when it is required. In addition, part of designing an algorithm for the recommendation engine has to be based on learner and application needs, personal characteristics and circumstances, as well as being able to comprehend human cognitive processes which would enable the technology to interact effectively and deliver mobile learning content which is relevant, according to the learner’s contextual situations. The concept of this proposed project is to provide a new method of smart learning, based on a capable recommendation engine for providing an intuitive mobile learning model based on learner actions.

Keywords: aware, context, learning, mobile

Procedia PDF Downloads 234
2552 Near Optimal Closed-Loop Guidance Gains Determination for Vector Guidance Law, from Impact Angle Errors and Miss Distance Considerations

Authors: Karthikeyan Kalirajan, Ashok Joshi

Abstract:

An optimization problem is to setup to maximize the terminal kinetic energy of a maneuverable reentry vehicle (MaRV). The target location, the impact angle is given as constraints. The MaRV uses an explicit guidance law called Vector guidance. This law has two gains which are taken as decision variables. The problem is to find the optimal value of these gains which will result in minimum miss distance and impact angle error. Using a simple 3DOF non-rotating flat earth model and Lockheed martin HP-MARV as the reentry vehicle, the nature of solutions of the optimization problem is studied. This is achieved by carrying out a parametric study for a range of closed loop gain values and the corresponding impact angle error and the miss distance values are generated. The results show that there are well defined lower and upper bounds on the gains that result in near optimal terminal guidance solution. It is found from this study, that there exist common permissible regions (values of gains) where all constraints are met. Moreover, the permissible region lies between flat regions and hence the optimization algorithm has to be chosen carefully. It is also found that, only one of the gain values is independent and that the other dependent gain value is related through a simple straight-line expression. Moreover, to reduce the computational burden of finding the optimal value of two gains, a guidance law called Diveline guidance is discussed, which uses single gain. The derivation of the Diveline guidance law from Vector guidance law is discussed in this paper.

Keywords: Marv guidance, reentry trajectory, trajectory optimization, guidance gain selection

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2551 An Efficient Machine Learning Model to Detect Metastatic Cancer in Pathology Scans Using Principal Component Analysis Algorithm, Genetic Algorithm, and Classification Algorithms

Authors: Bliss Singhal

Abstract:

Machine learning (ML) is a branch of Artificial Intelligence (AI) where computers analyze data and find patterns in the data. The study focuses on the detection of metastatic cancer using ML. Metastatic cancer is the stage where cancer has spread to other parts of the body and is the cause of approximately 90% of cancer-related deaths. Normally, pathologists spend hours each day to manually classifying whether tumors are benign or malignant. This tedious task contributes to mislabeling metastasis being over 60% of the time and emphasizes the importance of being aware of human error and other inefficiencies. ML is a good candidate to improve the correct identification of metastatic cancer, saving thousands of lives and can also improve the speed and efficiency of the process, thereby taking fewer resources and time. So far, the deep learning methodology of AI has been used in research to detect cancer. This study is a novel approach to determining the potential of using preprocessing algorithms combined with classification algorithms in detecting metastatic cancer. The study used two preprocessing algorithms: principal component analysis (PCA) and the genetic algorithm, to reduce the dimensionality of the dataset and then used three classification algorithms: logistic regression, decision tree classifier, and k-nearest neighbors to detect metastatic cancer in the pathology scans. The highest accuracy of 71.14% was produced by the ML pipeline comprising of PCA, the genetic algorithm, and the k-nearest neighbor algorithm, suggesting that preprocessing and classification algorithms have great potential for detecting metastatic cancer.

Keywords: breast cancer, principal component analysis, genetic algorithm, k-nearest neighbors, decision tree classifier, logistic regression

Procedia PDF Downloads 65
2550 More Precise: Patient-Reported Outcomes after Stroke

Authors: Amber Elyse Corrigan, Alexander Smith, Anna Pennington, Ben Carter, Jonathan Hewitt

Abstract:

Background and Purpose: Morbidity secondary to stroke is highly heterogeneous, but it is important to both patients and clinicians in post-stroke management and adjustment to life after stroke. The consideration of post-stroke morbidity clinically and from the patient perspective has been poorly measured. The patient-reported outcome measures (PROs) in morbidity assessment help improve this knowledge gap. The primary aim of this study was to consider the association between PRO outcomes and stroke predictors. Methods: A multicenter prospective cohort study assessed 549 stroke patients at 19 hospital sites across England and Wales during 2019. Following a stroke event, demographic, clinical, and PRO measures were collected. Prevalence of morbidity within PRO measures was calculated with associated 95% confidence intervals. Predictors of domain outcome were calculated using a multilevel generalized linear model. Associated P -values and 95% confidence intervals are reported. Results: Data were collected from 549 participants, 317 men (57.7%) and 232 women (42.3%) with ages ranging from 25 to 97 (mean 72.7). PRO morbidity was high post-stroke; 93.2% of the cohort report post-stroke PRO morbidity. Previous stroke, diabetes, and gender are associated with worse patient-reported outcomes across both the physical and cognitive domains. Conclusions: This large-scale multicenter cohort study illustrates the high proportion of morbidity in PRO measures. Further, we demonstrate key predictors of adverse outcomes (Diabetes, previous stroke, and gender) congruence with clinical predictors. The PRO has been demonstrated to be an informative and useful stroke when considering patient-reported outcomes and has wider implications for considerations of PROs in clinical management. Future longitudinal follow-up with PROs is needed to consider association of long-term morbidity.

Keywords: morbidity, patient-reported outcome, PRO, stroke

Procedia PDF Downloads 116