Search results for: urban modeling

Authors: Hasibe Caballero-Gomez, Richard Pepino

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Childhood lead poisoning is an issue that continues to plague major U.S. cities. Lead poisoning has been linked to decreases in academic achievement and IQ at levels as low as 5 ug/dL. Despite efforts from the Philadelphia Health Department to curtail systemic childhood lead poisoning, children continue to be identified with elevated blood lead levels (EBLLs) above the CDC reference level for diagnosis. This problem disproportionately affects low-income Black communities. At the moment, remediation is costly, and with the current policies in place, comprehensive remediation seems unrealistic. This research investigates community engagement policy and the ways pre-exisiting resources in target communities can be adjusted to decrease childhood lead poisoning. The study was done with two methods: content analysis and case studies. The content analysis includes 12 interviews from stakeholders and five published policy recommendations. The case studies focus on Baltimore, Chicago, Rochester, and St. Louis, four cities with significant childhood lead poisoning. Target communities were identified by mapping five factors that indicate a higher risk for lead poisoning. Seven priority zipcodes were identified for the model developed in this study. For these urban centers, 28 policy solutions and suggestions were identified, with three being identified at least four times in the content analysis and case studies. These three solutions create an interdependent model that offers increased community awareness and engagement with the issue that could potentially improve health and social outcomes for at-risk children.

Keywords: at-risk populations, community engagement, environmental justice, policy translation

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Authors: Imme Petersen, Wiebke Sick, Regine Kollek

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In systems medicine, high-throughput technologies produce large amounts of data on different biological and pathological processes, including (disturbed) gene expressions, metabolic pathways and signaling. The large volume of data of different types, stored in separate databases and often located at different geographical sites have posed new challenges regarding data handling and processing. Tools based on bioinformatics have been developed to resolve the upcoming problems of systematizing, standardizing and integrating the various data. However, the heterogeneity of data gathered at different levels of biological complexity is still a major challenge in data analysis. To build multilayer disease modules, large and heterogeneous data of disease-related information (e.g., genotype, phenotype, environmental factors) are correlated. Therefore, a great deal of attention in systems medicine has been put on data standardization, primarily to retrieve and combine large, heterogeneous datasets into standardized and incorporated forms and structures. However, this data-centred concept of standardization in systems medicine is contrary to the debate in science and technology studies (STS) on standardization that rather emphasizes the dynamics, contexts and negotiations of standard operating procedures. Based on empirical work on research consortia that explore the molecular profile of diseases to establish systems medical approaches in the clinic in Germany, we trace how standardized data are processed and shaped by bioinformatics tools, how scientists using such data in research perceive such standard operating procedures and which consequences for knowledge production (e.g. modeling) arise from it. Hence, different concepts and meanings of standardization are explored to get a deeper insight into standard operating procedures not only in systems medicine, but also beyond.

Keywords: data, science and technology studies (STS), standardization, systems medicine

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Authors: Ralph Lugon, Randolf Ramseyer

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The beauty and quality of the natural heritage can be appreciated in different ways by different users, but the delicate balance of the environment in a nature reserve must be respected. The case of the territorial anchorage of the Grangettes natural reserve gives an interesting insight into the users' perception of the environmental constraints and standards of tourist activities. The nature reserve was once conceived as a sanctuary of natural heritage, a place where flora and fauna could flourish with minimal human interference. However, over time and with the transition to modernity, the values and meanings of the reserve have changed for visitors and the people living in the surrounding area. Today, The Grangettes nature reserve is a place of relaxation for urban dwellers with limited knowledge of nature and a lack of awareness of conservation issues. As a result, the reserve is now threatened by the negative impacts of human activities and mass tourism on its environment. Les Grangettes is a nature reserve that faces the challenge of preserving biodiversity while managing tourist flows. Ways must be found to accommodate new types of visitors from towns and cities who are looking for new activities, quality services and facilities, as well as aesthetic inspiration. To ensure the long-term conservation of the area, the flow of tourists must be carefully controlled. Through a dual qualitative-quantitative approach in 2021-22, this paper explores new visitor trends, changes in the reserve, and potential consequences for other stakeholders in the ecosystem. The purpose of this research is to assess users' perceptions of environmental constraints and standards on tourist activities in a nature reserve.

Keywords: outdoor recreation, nature-based tourism, over tourism, protected area, user's perceptions

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Authors: Ibrahim Rassoul, E-K. Si Ahmed

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The growth, deformation, and detachment of fluid droplets adherent to solid substrates is a problem of fundamental interest with numerous practical applications. Specific interest in this proposal is the problem of a droplet on a fibrous, hydrophobic substrate subjected to body or external forces (gravity, convection). The past decade has seen tremendous advances in proton exchange membrane fuel cell (PEMFC) technology. However, there remain many challenges to bring commercially viable stationary PEMFC products to the market. PEMFCs are increasingly emerging as a viable alternative clean power source for automobile and stationary applications. Before PEMFCs can be employed to power automobiles and homes, several key technical challenges must be properly addressed. One technical challenge is elucidating the mechanisms underlying water transport in and removal from PEMFCs. On the one hand, sufficient water is needed in the polymer electrolyte membrane or PEM to maintain sufficiently high proton conductivity. On the other hand, too much liquid water present in the cathode can cause 'flooding' (that is, pore space is filled with excessive liquid water) and hinder the transport of the oxygen reactant from the gas flow channel (GFC) to the three-phase reaction sites. The aim of this work is to investigate the stability of a liquid water droplet emerging form a GDL pore, to gain fundamental insight into the instability process leading to detachment. The approach will combine analytical and numerical modeling with experimental visualization and measurements.

Keywords: polymer electrolyte fuel cell, water droplet, gas diffusion layer, contact angle, surface tension

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Authors: Marko Batic, Nikola Tomasevic, Sanja Vranes

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One of the strongholds in the ubiquitous efforts related to the energy conservation and energy efficiency improvement is represented by the retrofit of high energy consumers in buildings. In general, HVAC systems represent the highest energy consumers in buildings. However they usually suffer from mal-operation and/or malfunction, causing even higher energy consumption than necessary. Various Fault Detection and Diagnosis (FDD) systems can be successfully employed for this purpose, especially when it comes to the application at a single device/unit level. In the case of more complex systems, where multiple devices are operating in the context of the same building, significant energy efficiency improvements can only be achieved through application of comprehensive FDD systems relying on additional higher level knowledge, such as their geographical location, served area, their intra- and inter- system dependencies etc. This paper presents a comprehensive FDD system that relies on the utilization of common knowledge repository that stores all critical information. The discussed system is deployed as a test-bed platform at the two at Fiumicino and Malpensa airports in Italy. This paper aims at presenting advantages of implementation of the knowledge base through the utilization of ontology and offers improved functionalities of such system through examples of typical queries and reasoning that enable derivation of high level energy conservation measures (ECM). Therefore, key SPARQL queries and SWRL rules, based on the two instantiated airport ontologies, are elaborated. The detection of high level irregularities in the operation of airport heating/cooling plants is discussed and estimation of energy savings is reported.

Keywords: airport ontology, knowledge management, ontology modeling, reasoning

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Authors: Pavan K. Emani, Shashank Kothari, V. S. Phanikanth

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The ultimate load analysis of RC pile groups has assumed a lot of significance under liquefying soil conditions, especially due to post-earthquake studies of 1964 Niigata, 1995 Kobe and 2001 Bhuj earthquakes. The present study reports the results of numerical simulations on pile groups subjected to monotonically increasing lateral loads under design amounts of pile axial loading. The soil liquefaction has been considered through the non-linear p-y relationship of the soil springs, which can vary along the depth/length of the pile. This variation again is related to the liquefaction potential of the site and the magnitude of the seismic shaking. As the piles in the group can reach their extreme deflections and rotations during increased amounts of lateral loading, a precise modeling of the inelastic behavior of the pile cross-section is done, considering the complete stress-strain behavior of concrete, with and without confinement, and reinforcing steel, including the strain-hardening portion. The possibility of the inelastic buckling of the individual piles is considered in the overall collapse modes. The model is analysed using Riks analysis in finite element software to check the post buckling behavior and plastic collapse of piles. The results confirm the kinds of failure modes predicted by centrifuge test results reported by researchers on pile group, although the pile material used is significantly different from that of the simulation model. The extension of the present work promises an important contribution to the design codes for pile groups in liquefying soils.

Keywords: collapse load analysis, inelastic buckling, liquefaction, pile group

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Authors: Malay Kumar Pramanik

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Climate change induced sea level rise increases storm surge, erosion, and inundation, which are stirred by an intricate interplay of physical environmental components at the coastal region. The Mumbai coast is much vulnerable to accelerated regional sea level change due to its highly dense population, highly developed economy, and low topography. To determine the significant causes behind coastal vulnerability, this study analyzes four different iterations of CVI by incorporating the pixel-based differentially weighted rank values of the selected five geological (CVI5), three physical (CVI8 with including geological variables), and four socio-economic variables (CVI4). However, CVI5 and CVI8 results yielded broadly similar natures, but after including socio-economic variables (CVI4), the results CVI (CVI12) has been changed at Mumbai and Kurla coastal portion that indicates the study coastal areas are mostly sensible with socio-economic variables. Therefore, the results of CVI12 show that out of 274.1 km of coastline analyzed, 55.83 % of the coast is very low vulnerable, 60.91 % of the coast is moderately vulnerable while 50.75 % is very high vulnerable. Finding also admits that in the context of growing urban population and the increasing rate of economic activities, socio-economic variables are most important variable to use for validating and testing the CVI. Finally, some recommendations are presented for concerned decision makers and stakeholders to develop appropriate coastal management plans, nourishment projects and mitigation measures considering socio-economic variables.

Keywords: coastal vulnerability index, sea level change, Mumbai coast, geospatial approach, coastal management, climate change

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Authors: Anisa Duraj

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As usually acknowledged, cultural heritage is the weakest component in EIA studies. The role of heritage impact assessment (HIA) in development projects is not often accounted for, and in those cases where it is, HIA is considered as a reactive response and not as a solutions provider. Because of continuous development projects, in most cases, heritage is unconsidered and often put under threat. Cultural protection and development challenges ask for prudent legal regulation and appropriate policy implementation. The challenges become even more peculiar in underdeveloped countries or endangered areas, which are generally characterized by numerous legal constraints. Therefore, the need for strategic proposals for HIA is of high importance. In order to trigger HIA as a proactive operation in the IA process and make sure to cover cultural heritage in the whole EIA framework, an appropriate system of evaluation of impacts should be provided. To obtain the required results for HIA, this last must be part of a regional policy, which will address and guide development projects toward a proper evaluation of their impacts affecting heritage. In order to get a clearer picture of existing gabs but also new possibilities for HIA, this paper will focus on the Western Balkans region and the undergoing changes that it faces. Concerning continuous development pressure in the region and within the aspiration of the Western Balkans countries to join the European Union (EU) as member states, attention should be paid to new development policies under the EU directives for conducting EIAs, and accurate support is required for the restructuration of existing policies as well as for the implementation of the UN Agenda for SDGs. In the framework of new emerging needs, if HIA is taken into account, the outcome would be an inclusive regional program that would help to overcome marginality issues of spaces and people.

Keywords: cultural heritage, impact assessment, SDGs, urban development, western Balkans, regional policy, HIA, EIA

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Authors: Songul Cinaroglu

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Regional planning in health has gained speed for developing countries in recent years. In Turkey, 89 different Public Hospital Unions (PHUs) were conducted based on provincial levels. In this study technical efficiencies of 89 PHUs were examined by using Data Envelopment Analysis (DEA) and machine learning techniques by dividing them into two clusters in terms of similarities of input and output indicators. Number of beds, physicians and nurses determined as input variables and number of outpatients, inpatients and surgical operations determined as output indicators. Before performing DEA, PHUs were grouped into two clusters. It is seen that the first cluster represents PHUs which have higher population, demand and service density than the others. The difference between clusters was statistically significant in terms of all study variables (p ˂ 0.001). After clustering, DEA was performed for general and for two clusters separately. It was found that 11% of PHUs were efficient in general, additionally 21% and 17% of them were efficient for the first and second clusters respectively. It is seen that PHUs, which are representing urban parts of the country and have higher population and service density, are more efficient than others. Random forest decision tree graph shows that number of inpatients is a determinative factor of efficiency of PHUs, which is a measure of service density. It is advisable for public health policy makers to use statistical learning methods in resource planning decisions to improve efficiency in health care.

Keywords: public hospital unions, efficiency, data envelopment analysis, random forest

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Authors: Neeraj Kumar, J. P. Narayan

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The effects of multiple Structure-Soil-Structure Interactions (SSSI) on the seismic wave-field is generally disregarded by earthquake engineers, particularly the surface waves which cause more damage to buildings. Closely built high rise buildings exchange substantial seismic energy with each other and act as a full-coupled dynamic system. In this paper, SSI effects on the building responses and the free field motion due to a small city consisting 25- homogenous buildings blocks of 10-storey are quantified. The rocking and translational behavior of building under Rayleigh wave loading is studied for different dimensions of the building. The obtained dynamic parameters of buildings revealed a reduction in building roof drift with an increase in number of buildings ahead of the considered building. The strain developed by vertical component of Rayleigh may cause tension in structural components of building. A matching of fundamental frequency of building for the horizontal component of Rayleigh wave with that for vertically incident SV-wave is obtained. Further, the fundamental frequency of building for the vertical vibration is approximately twice to that for horizontal vibration. The city insulation has caused a reduction of amplitude of Rayleigh wave up to 19.3% and 21.6% in the horizontal and vertical components, respectively just outside the city. Further, the insulating effect of city was very large at fundamental frequency of buildings for both the horizontal and vertical components. Therefore, it is recommended to consider the insulating effects of city falling in the path of Rayleigh wave propagation in seismic hazard assessment for an area.

Keywords: structure-soil-structure interactions, Rayleigh wave propagation, finite difference simulation, dynamic response of buildings

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Authors: Yepeng Cheng, Yasuhiko Morimoto

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Customer relationship analysis is vital for retail stores, especially for supermarkets. The point of sale (POS) systems make it possible to record the daily purchasing behaviors of customers as an identification point of sale (ID-POS) database, which can be used to analyze customer behaviors of a supermarket. The customer value is an indicator based on ID-POS database for detecting the customer loyalty of a store. In general, there are many supermarkets in a city, and other nearby competitor supermarkets significantly affect the customer value of customers of a supermarket. However, it is impossible to get detailed ID-POS databases of competitor supermarkets. This study firstly focused on the customer value and distance between a customer's home and supermarkets in a city, and then constructed the models based on logistic regression analysis to analyze correlations between distance and purchasing behaviors only from a POS database of a supermarket chain. During the modeling process, there are three primary problems existed, including the incomparable problem of customer values, the multicollinearity problem among customer value and distance data, and the number of valid partial regression coefficients. The improved customer value, Huff’s gravity model, and inverse attractiveness frequency are considered to solve these problems. This paper presents three types of models based on these three methods for loyal customer classification and competitors’ influence analysis. In numerical experiments, all types of models are useful for loyal customer classification. The type of model, including all three methods, is the most superior one for evaluating the influence of the other nearby supermarkets on customers' purchasing of a supermarket chain from the viewpoint of valid partial regression coefficients and accuracy.

Keywords: customer value, Huff's Gravity Model, POS, Retailer

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Authors: Longondjo Etambakonga Clement

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Local municipality responsible for solid waste management (SWM) in many developing countries is facing real challenge. This is even more critical in the country facing political instability. Few decades ago, it has emerged new urban governance including partnerships and involvement of formal and informal actors for an effective and sustainable solid waste management. This paper identifies SWM actors and analyzes their roles to sustainable development in Kinshasa. An attempt has been to examine the challenges facing the actors in managing effectively waste in the city. The study is based on the empirical data gathered in the years 2009 and 2014 in Kinshasa using expert interviews, observation and documentation. The findings indicate that solid waste in the city is poorly managed, activities not coordinated and fragmented, as consequence severe public health and environmental problems. Five group actors are involved in SWM in the city including government, private business, NGOs/CBOs/donors, household, scavengers, in which, scavengers are more visible in collection and recycling activities. The results suggest that recognition of informal collectors and recyclers (scavengers) and strengthening alliances among all SWM stakeholders can lead to greater effective SWM in the city. The key lessons learned include lack of city’s SWM culture over SWM, unwillingness to pay and lack of environmental consciences are the main obstructions to sustainable SWM, therefore there is a need for social capital approach to empower individual and group actors as to create capabilities for an sustainable SWM.

Keywords: challenges, institutions, political instability, scavengers, solid waste management, sustainable development

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Authors: Rajarshi Sharma, Rashidul Alam, Visavino Seleyi, Yuvila Sangtam

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The Intensity-Duration-Frequency (IDF) relationship of rainfall amounts is one of the most commonly used tools in water resources engineering for planning, design and operation of water resources project, or for various engineering projects against design floods. The establishment of such relationships was reported as early as in 1932 (Bernard). Since then many sets of relationships have been constructed for several parts of the globe. The objective of this research is to derive IDF relationship of rainfall for western watershed of Guwahati, Assam. These relationships are useful in the design of urban drainage works, e.g. storm sewers, culverts and other hydraulic structures. In the study, rainfall depth for 10 years viz. 2001 to 2010 has been collected from the Regional Meteorological Centre Borjhar, Guwahati. Firstly, the data has been used to construct the mass curve for duration of more than 7 hours rainfall to calculate the maximum intensity and to form the intensity duration curves. Gumbel’s frequency analysis technique has been used to calculate the probable maximum rainfall intensities for a period of 2 yr, 5 yr, 10 yr, 50 yr, 100 yr from the maximum intensity. Finally, regression analysis has been used to develop the intensity-duration-frequency (IDF) curve. Thus, from the analysis the values for the constants ‘a’,‘b’ &‘c’ have been found out. The values of ‘a’ for which the sum of the squared deviation is minimum has been found out to be 40 and when the corresponding value of ‘c’ and ‘b’ for the minimum squared deviation of ‘a’ are 0.744 and 1981.527 respectively. The results obtained showed that in all the cases the correlation coefficient is very high indicating the goodness of fit of the formulae to estimate IDF curves in the region of interest.

Keywords: intensity-duration-frequency relationship, mass curve, regression analysis, correlation coefficient

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Authors: Tianyue Wan

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Building a child-friendly city (CFC) contributes to improving the quality of urbanization. It also forms a local system committed to fulfilling children's rights and development. Yet, the work related to CFC is still at the initial stage in China. Therefore, taking Wuhan, the most populous city in central China, as the pilot city would offer some reference for other cities. Based on the analysis of theories and practice examples, this study puts forward the challenges of building a child-friendly city under the particularity of China's national conditions. To handle these challenges, this study uses four methods to collect status data: literature research, site observation, research inquiry, and semantic differential (SD). And it adopts three data analysis methods: case analysis, geographic information system (GIS) analysis, and analytic hierarchy process (AHP) method. Through data analysis, this study identifies the evaluation system and appraises the current situation of Wuhan. According to the status of Wuhan's child-friendly city, this study proposes three strategies: 1) construct the evaluation system; 2) establish a child-friendly space system integrating 'point-line-surface'; 3) build a digitalized service platform. At the same time, this study suggests building a long-term mechanism for children's participation and multi-subject supervision from laws, medical treatment, education, safety protection, social welfare, and other aspects. Finally, some conclusions of strategies about CFC are tried to be drawn to promote the highest quality of life for all citizens in Wuhan.

Keywords: action plan, child friendly city, construction strategy, urban space

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Authors: Julide Yildirim, Nadir Ocal

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Even though there are many studies investigating the extent and determinants of poverty, there is paucity of research investigating the issue of energy poverty in Turkey. The aim of this paper is threefold: First to investigate the extend of energy poverty in Turkey by using Household Budget Survey datasets belonging to 2005 - 2016 period. Second, to examine the risk factors for energy poverty. Finally, to assess the impact of social assistance program participation on energy poverty. Existing literature employs alternative methods to measure energy poverty. In this study energy poverty is measured by employing expenditure approach, where people are considered as energy poor if they disburse more than 10 per cent of their income to meet their energy requirements. Empirical results indicate that energy poverty rate is around 20 per cent during the time period under consideration. Since Household Budget Survey panel data is not available for 2005 - 2016 period, a pseudo panel has been constructed. Panel logistic regression method is utilized to determine the risk factors for energy poverty. The empirical results demonstrate that there is a statistically significant impact of work status and education level on energy poverty likelihood. In the final part of the paper the impact of social transfers on energy poverty has been examined by utilizing panel biprobit model, where social transfer participation and energy poverty incidences are jointly modeled. The empirical findings indicate that social transfer program participation reduces energy poverty. The negative association between energy poverty and social transfer program participation is more pronounced in urban areas compared with the rural areas.

Keywords: energy poverty, social transfers, panel data models, Turkey

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Authors: Rogelio Omar Corona-Núñez, Alma Mendoza-Ponce

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The Tropical Dry Forest not only is one of the most important tropical ecosystems in terms of area, but also it is one of the most degraded ecosystems. However, little is known about the degradation impacts on carbon stocks, therefore in carbon emissions. There are different studies which explain its deforestation dynamics, but there is still a lack of understanding of how they correlate to carbon losses. Recently different authors have built current biomass maps for the tropics and Mexico. However, it is not clear how well they predict at the local scale, and how they can be used to estimate carbon emissions. This study quantifies the forest net carbon losses by comparing the potential carbon stocks and the different current biomass maps in the Southern Pacific coast in Oaxaca, Mexico. The results show important differences in the current biomass estimates with not a clear agreement. However, by the aggregation of the information, it is possible to infer the general patterns of biomass distribution and it can identify the driving forces of the carbon emissions. This study estimated that currently ~44% of the potential carbon stock estimated for the region is still present. A total of 6,764 GgC has been emitted due to deforestation and degradation of the forest at a rate of above ground biomass loss of 66.4 Mg ha-1. Which, ~62% of the total carbon emissions can be regarded as being due to forest degradation. Most of carbon losses were identified in places suitable for agriculture, close to rural areas and to roads while the lowest losses were accounted in places with high water stress and within the boundaries of the National Protected Area. Moreover, places not suitable for agriculture, but close to the coast showed carbon losses as a result of urban settlements.

Keywords: above ground biomass, deforestation, degradation, driving forces, tropical deciduous forest

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Authors: Vahid Nourani, Atefeh Ashrafi

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Prediction of treated wastewater quality is a matter of growing importance in water treatment procedure. In this way artificial neural network (ANN), as a robust data-driven approach, has been widely used for forecasting the effluent quality of wastewater treatment. However, developing ANN model based on appropriate input variables is a major concern due to the numerous parameters which are collected from treatment process and the number of them are increasing in the light of electronic sensors development. Various studies have been conducted, using different clustering methods, in order to classify most related and effective input variables. This issue has been overlooked in the selecting dominant input variables among wastewater treatment parameters which could effectively lead to more accurate prediction of water quality. In the presented study two ANN models were developed with the aim of forecasting effluent quality of Tabriz city’s wastewater treatment plant. Biochemical oxygen demand (BOD) was utilized to determine water quality as a target parameter. Model A used Principal Component Analysis (PCA) for input selection as a linear variance-based clustering method. Model B used those variables identified by the mutual information (MI) measure. Therefore, the optimal ANN structure when the result of model B compared with model A showed up to 15% percent increment in Determination Coefficient (DC). Thus, this study highlights the advantage of PCA method in selecting dominant input variables for ANN modeling of wastewater plant efficiency performance.

Keywords: Artificial Neural Networks, biochemical oxygen demand, principal component analysis, mutual information, Tabriz wastewater treatment plant, wastewater treatment plant

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Authors: Danielle Schneider, Ying Xie

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Middle school science, technology, engineering, and math (STEM) teachers experience an exceptional challenge in the expectation to incorporate curricula that builds strong spatial reasoning skills on rudimentary geometry concepts. Because spatial ability is so closely tied to STEM students’ success, researchers are tasked to determine effective instructional practices that create an authentic learning environment within the immersive virtual reality learning environment (IVRLE). This study looked to investigate the effect of the IVRLE on middle school STEM students’ spatial reasoning skills as a methodology to benefit the STEM middle school students’ spatial reasoning skills. This experimental study was comprised of thirty 7th-grade STEM students divided into a treatment group that was engaged in an immersive VR platform where they engaged in building an object in the virtual realm by applying spatial processing and visualizing its dimensions and a control group that built the identical object using a desktop computer-based, computer-aided design (CAD) program. Before and after the students participated in the respective “3D modeling” environment, their spatial reasoning abilities were assessed using the Middle Grades Mathematics Project Spatial Visualization Test (MGMP-SVT). Additionally, both groups created a physical 3D model as a secondary measure to measure the effectiveness of the IVRLE. The results of a one-way ANOVA in this study identified a negative effect on those in the IVRLE. These findings suggest that with middle school students, virtual reality (VR) proved an inadequate tool to benefit spatial relation skills as compared to desktop-based CAD.

Keywords: virtual reality, spatial reasoning, CAD, middle school STEM

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Authors: Jingwen Shi, Qi Wu

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We study the role played interest rate risk in carry trade return in order to understand the forward premium puzzle. In this study, our goal is to investigate to what extent carry trade return is indeed due to compensation for risk taking and, more important, to reveal the nature of these risks. Using option data not only on exchange rates but also on interest rate swaps (swaptions), our first finding is that, besides the consensus currency risks, interest rate risks also contribute a non-negligible portion to the carry trade return. What strikes us is our second finding. We find that large downside risks of future exchange rate movements are, in fact, priced significantly in option market on interest rates. The role played by interest rate risk differs structurally from the currency risk. There is a unique premium associated with interest rate risk, though seemingly small in size, which compensates the tail risks, the left tail to be precise. On the technical front, our study relies on accurately retrieving implied distributions from currency options and interest rate swaptions simultaneously, especially the tail components of the two. For this purpose, our major modeling work is to build a new international asset pricing model where we use an orthogonal setup for pricing kernels and specify non-Gaussian dynamics in order to capture three sets of option skew accurately and consistently across currency options and interest rate swaptions, domestic and foreign, within one model. Our results open a door for studying forward premium anomaly through implied information from interest rate derivative market.

Keywords: carry trade, forward premium anomaly, FX option, interest rate swaption, implied volatility skew, uncovered interest rate parity

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Authors: Omar Bouledroua, Djamel Zelmati, Zahreddine Hafsi, Milos B. Djukic

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Transporting hydrogen gas through the existing natural gas pipeline network offers an efficient solution for energy storage and conveyance. Hydrogen generated from excess renewable electricity can be conveyed through the API 5L steel-made pipelines that already exist. In recent years, there has been a growing demand for the transportation of hydrogen through existing gas pipelines. Therefore, numerical and experimental tests are required to verify and ensure the mechanical integrity of the API 5L steel pipelines that will be used for pressurized hydrogen transportation. Internal pressure loading is likely to accelerate hydrogen diffusion through the internal pipe wall and consequently accentuate the hydrogen embrittlement of steel pipelines. Furthermore, pre-cracked pipelines are susceptible to quick failure, mainly under a time-dependent cyclic pressure loading that drives fatigue crack propagation. Meanwhile, after several loading cycles, the initial cracks will propagate to a critical size. At this point, the remaining service life of the pipeline can be estimated, and inspection intervals can be determined. This paper focuses on the hydrogen embrittlement of API 5L steel-made pipeline under cyclic pressure loading. Pressurized hydrogen gas is transported through a network of pipelines where demands at consumption nodes vary periodically. The resulting pressure profile over time is considered a cyclic loading on the internal wall of a pre-cracked pipeline made of API 5L steel-grade material. Numerical modeling has allowed the prediction of fatigue crack evolution and estimation of the remaining service life of the pipeline. The developed methodology in this paper is based on the ASME B31.12 standard, which outlines the guidelines for hydrogen pipelines.

Keywords: hydrogen embrittlement, pipelines, transient flow, cyclic pressure, fatigue crack growth

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Authors: A. S. Strait, J. A. Bryk-Lucy, L. M. Ritchie

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Thrush is a common disease of ungulates primarily affecting the frog and sulci, caused by the anaerobic bacteria Fusobacterium necrophorum. Thrush accounts for approximately 45.0% of hoof disorders in horses. Prevention and treatment of thrush are essential to prevent horses from developing severe infections and becoming lame. Proper knowledge of hoof care and thrush treatments is crucial to avoid financial costs, unsoundness and lost training time. Research on the effectiveness of numerous commercial and homemade thrush treatments is limited in the equine industry. The objective of this study was to compare the effectiveness of three common thrush treatments for horses: weekly application of Thrush Buster, daily dilute bleach solution spray, or Metronidazole pastes every other day. Cases of thrush diagnosed by a veterinarian or veterinarian-trained researcher were given a score, from 0 to 4, based on the severity of the thrush in each hoof (n=59) and randomly assigned a treatment. Cases were rescored each week of the three-week treatment, and the final and initial scores were compared to determine effectiveness. The thrush treatments were compared with Thrush Buster as the reference at a significance level of α=.05. Binomial Logistic Regression Modeling was performed, finding that the odds of a hoof treated with Metronidazole to be thrush-free was 6.1 times greater than a hoof treated with Thrush Buster (p=0.001), while the odds of a hoof that was treated with bleach to be thrush-free was only 0.97 times greater than a hoof treated with Thrush Buster (p=0.970), after adjustment for treatment week. Of the three treatments utilized in this study, Metronidazole paste applied to the affected areas every other day was the most effective treatment for thrush in horses. There are many other thrush remedies available, and further research is warranted to determine the efficacy of additional treatment options.

Keywords: fusobacterium necrophorum, thrush, equine, horse, lameness

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Authors: V. Churkin, M. Lopatin

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The purpose of the paper is to estimate the US small wind turbines market potential and forecast the small wind turbines sales in the US. The forecasting method is based on the application of the Bass model and the generalized Bass model of innovations diffusion under replacement purchases. In the work an exponential distribution is used for modeling of replacement purchases. Only one parameter of such distribution is determined by average lifetime of small wind turbines. The identification of the model parameters is based on nonlinear regression analysis on the basis of the annual sales statistics which has been published by the American Wind Energy Association (AWEA) since 2001 up to 2012. The estimation of the US average market potential of small wind turbines (for adoption purchases) without account of price changes is 57080 (confidence interval from 49294 to 64866 at P = 0.95) under average lifetime of wind turbines 15 years, and 62402 (confidence interval from 54154 to 70648 at P = 0.95) under average lifetime of wind turbines 20 years. In the first case the explained variance is 90,7%, while in the second - 91,8%. The effect of the wind turbines price changes on their sales was estimated using generalized Bass model. This required a price forecast. To do this, the polynomial regression function, which is based on the Berkeley Lab statistics, was used. The estimation of the US average market potential of small wind turbines (for adoption purchases) in that case is 42542 (confidence interval from 32863 to 52221 at P = 0.95) under average lifetime of wind turbines 15 years, and 47426 (confidence interval from 36092 to 58760 at P = 0.95) under average lifetime of wind turbines 20 years. In the first case the explained variance is 95,3%, while in the second –95,3%.

Keywords: bass model, generalized bass model, replacement purchases, sales forecasting of innovations, statistics of sales of small wind turbines in the United States

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Authors: Bhavna Rajagopal, Mrinmoyi Kulkarni

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Today women experience more menstrual cycles than their ancestors did a hundred years ago, owing to early puberty, fewer pregnancies and dietary changes. Much of the research in menstruation is located in the medical domain with a focus on physical symptoms. The research in psychology is largely concerned with premenstrual syndrome (PMS), whereas the focus in sociology is on social and cultural practices relating to menstruation. Research that simultaneously studies the physical, psychological, social and cultural aspects is lacking. Therefore, in this study, an attempt has been made to identify socio-cultural, psychological and physical factors that interact to influence a woman’s experience of menstruation in the urban setting. The study included seven unmarried women in the age group of 24-30 and data was obtained through a focus group discussion. The transcript of the focus group discussion was thematically analysed. Two major themes relating to the self and social experience of menstruation emerged. Themes relating to the self included menarcheal experiences, self-perception, mood and management of menstrual hygiene and symptoms while themes relating to social experience included the construction of menstruation by family and peers, and cultural factors. Attitudes towards the menstrual cycle appeared to be primarily influenced by severity of symptoms and the resulting disruption to daily life. Outcomes of this study have indicated that future research needs to study menstruation and its impact on women’s wellbeing by adopting a socio-ecological approach and by collecting data using the whole cycle approach across a woman’s reproductive years.

Keywords: India, menstrual cycle, psychosocial approach, wellbeing

Procedia PDF Downloads 131

Authors: Rachel Y. Zhang, Christopher K. Anderson

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A critical aspect of revenue management is a firm’s ability to predict demand as a function of price. Historically hotels have used simple time series models (regression and/or pick-up based models) owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand – focusing on the opportunities created by machine learning approaches, including K-Nearest-Neighbors, Support vector machine, Regression Tree, and Artificial Neural Network algorithms. The out-of-sample performances of above approaches to forecasting hotel demand are illustrated by using a proprietary sample of the market level (24 properties) transactional data for Las Vegas NV. Causal predictive models can be built and evaluated owing to the availability of market level (versus firm level) data. This research also compares and contrast model accuracy of firm-level models (i.e. predictive models for hotel A only using hotel A’s data) to models using market level data (prices, review scores, location, chain scale, etc… for all hotels within the market). The prospected models will be valuable for hotel revenue prediction given the basic characters of a hotel property or can be applied in performance evaluation for an existed hotel. The findings will unveil the features that play key roles in a hotel’s revenue performance, which would have considerable potential usefulness in both revenue prediction and evaluation.

Keywords: hotel revenue, k-nearest-neighbors, machine learning, neural network, prediction model, regression tree, support vector machine

Procedia PDF Downloads 127

Authors: Melkamu A. Zeru, Aweke A. Mitiku, Endashaw Amuka

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Background: Under-five mortality is the likelihood that a baby will pass away before turning exactly 5 years old, represented as a percentage per 1,000 live births. Exploring the spatial distribution and identifying the temporal pattern is important to reducing under-five child mortality globally, including in Ethiopia. Thus, this study aimed to identify the risk factors of under-five mortality and the spatiotemporal variation in Ethiopian administrative zones. Method: This study used the 2000-2016 Ethiopian Demographic and Health Survey (EDHS) data, which were collected using a two-stage sampling method. A total of 43,029 (10,873 in 2000, 9,861 in 2005, 11,654 in 2011, and 10,641 in 2016) weighted sample under-five child mortality was used. The space-time dynamic model was employed to account for spatial and time effects in 65 administrative zones in Ethiopia. Results: From the result of a general nesting spatial-temporal dynamic model, there was a significant space-time interaction effect [γ = -0.1444, 95 % CI (-0.6680, -0.1355)] for under-five mortality. The increase in the percentages of mothers illiteracy [𝛽 = 0.4501, 95% CI (0.2442, 0.6559)], not vaccinated[𝛽= 0.7681, 95% CI (0.5683, 0.9678)], unimproved water[𝛽= 0.5801, CI (0.3793, 0.7808)] were increased death rates for under five children while increased percentage of contraceptive use [𝛽= -0.6609, 95% CI (-0.8636, -0.4582)] and ANC visit > 4 times [𝛽= -0.1585, 95% CI(-0.1812, -0.1357)] were contributed to the decreased under-five mortality rate at the zone in Ethiopia. Conclusions: Even though the mortality rate for children under five has decreased over time, still there is still higher in different zones of Ethiopia. There exists spatial and temporal variation in under-five mortality among zones. Therefore, it is very important to consider spatial neighbourhoods and temporal context when aiming to avoid under-five mortality.

Keywords: under-five children mortality, space-time dynamic, spatiotemporal, Ethiopia

Procedia PDF Downloads 32

Authors: N. D. Iswarya, M. A. Maluk Mohamed, N. Vijaya

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Grid is infrastructure that allows the deployment of distributed data in large size from multiple locations to reach a common goal. Scheduling data intensive applications becomes challenging as the size of data sets are very huge in size. Only two solutions exist in order to tackle this challenging issue. First, computation which requires huge data sets to be processed can be transferred to the data site. Second, the required data sets can be transferred to the computation site. In the former scenario, the computation cannot be transferred since the servers are storage/data servers with little or no computational capability. Hence, the second scenario can be considered for further exploration. During scheduling, transferring huge data sets from one site to another site requires more network bandwidth. In order to mitigate this issue, this work focuses on incorporating cognitive science in scheduling. Cognitive Science is the study of human brain and its related activities. Current researches are mainly focused on to incorporate cognitive science in various computational modeling techniques. In this work, the problem solving approach of human brain is studied and incorporated during the data intensive scheduling in grid environments. Here, a cognitive engine is designed and deployed in various grid sites. The intelligent agents present in CE will help in analyzing the request and creating the knowledge base. Depending upon the link capacity, decision will be taken whether to transfer data sets or to partition the data sets. Prediction of next request is made by the agents to serve the requesting site with data sets in advance. This will reduce the data availability time and data transfer time. Replica catalog and Meta data catalog created by the agents assist in decision making process.

Keywords: data grid, grid workflow scheduling, cognitive artificial intelligence

Procedia PDF Downloads 391

Authors: Gülay Dalgıç, Gildis Tachir

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Architectural design studios are ambiencein where architecture design is realized as a palpable product in architectural education. In the design studios that the architect candidate will use in the design processthe information, the methods of approaching the design problem, the solution proposals, etc., are set uptogetherwith the studio coordinators. The architectural design process, on the other hand, is complex and uncertain.Candidate architects work in a process that starts with abstre and ill-defined problems. This process starts with the generation of alternative solutions with the help of representation tools, continues with the selection of the appropriate/satisfactory solution from these alternatives, and then ends with the creation of an acceptable design/result product. In the studio ambience, many designs and thought relationships are evaluated, the most important step is the early design phase. In the early design phase, the first steps of converting the information are taken, and converted information is used in the constitution of the first design decisions. This phase, which positively affects the progress of the design process and constitution of the final product, is complex and fuzzy than the other phases of the design process. In this context, the aim of the study is to investigate the effects of face-to-face learning model and e-learning model on the early design phase. In the study, the early design phase was defined by literature research. The data of the defined early design phase criteria were obtained with the feedback graphics created for the architect candidates who performed e-learning in the first year of architectural education and continued their education with the face-to-face learning model. The findings of the data were analyzed with the common graphics program. It is thought that this research will contribute to the establishment of a contemporary architectural design education model by reflecting the evaluation of the data and results on architectural education.

Keywords: education modeling, architecture education, design education, design process

Procedia PDF Downloads 133

Authors: Ejaz Ali M. Phil

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Enterprise Resource Planning (ERP) systems are playing a vital role in effective management of business functions in large and complex organizations. Human Resource Information System (HRIS) is a core module of ERP, providing concrete solutions to implement Human Resource Management (HRM) Practices in an innovative and efficient manner. Over the last decade, there has been considerable increase in the studies on HRIS. Nevertheless, previous studies relatively lacked to examine the moderating role of HRIS in performing HRM practices that may affect the firms’ performance. The current study was carried out to examine the impact of HRM practices (training, performance appraisal) on perceived organizational performance, with moderating role of HRIS, where the system is in place. The study based on Resource Based View (RBV) and Ability Motivation Opportunity (AMO) Theories, advocating that strengthening of human capital enables an organization to achieve and sustain competitive advantage which leads to improved organizational performance. Data were collected through structured questionnaire based upon adopted instruments after establishing reliability and validity. The structural equation modeling (SEM) were used to assess the model fitness, hypotheses testing and to establish validity of the instruments through Confirmatory Factor Analysis (CFA). A total 220 employees of 25 firms in corporate sector were sampled through non-probability sampling technique. Path analysis revealing that HRM practices and HRIS have significant positive impact on organizational performance. The results further showed that the HRIS moderated the relationships between training, performance appraisal and organizational performance. The interpretation of the findings and limitations, theoretical and managerial implications are discussed.

Keywords: enterprise resource planning, human resource, information system, human capital

Procedia PDF Downloads 391

Authors: Donghwan Lee, Gichun Cha, Jooyoung Park, Junkyeong Kim, Seunghee Park

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3D shape models of the existing structure are required for many purposes such as safety and operation management. The traditional 3D modeling methods are based on manual or semi-automatic reconstruction from close-range images. It occasions great expense and time consuming. The Terrestrial Laser Scanner (TLS) is a common survey technique to measure quickly and accurately a 3D shape model. This TLS is used to a construction site and cultural heritage management. However there are many limits to process a TLS point cloud, because the raw point cloud is massive volume data. So the capability of carrying out useful analyses is also limited with unstructured 3-D point. Thus, segmentation becomes an essential step whenever grouping of points with common attributes is required. In this paper, members segmentation algorithm was presented to separate a raw point cloud which includes only 3D coordinates. This paper presents a clustering approach based on a fuzzy method for this objective. The Fuzzy C-Means (FCM) is reviewed and used in combination with a similarity-driven cluster merging method. It is applied to the point cloud acquired with Lecia Scan Station C10/C5 at the test bed. The test-bed was a bridge which connects between 1st and 2nd engineering building in Sungkyunkwan University in Korea. It is about 32m long and 2m wide. This bridge was used as pedestrian between two buildings. The 3D point cloud of the test-bed was constructed by a measurement of the TLS. This data was divided by segmentation algorithm for each member. Experimental analyses of the results from the proposed unsupervised segmentation process are shown to be promising. It can be processed to manage configuration each member, because of the segmentation process of point cloud.

Keywords: fuzzy c-means (FCM), point cloud, segmentation, terrestrial laser scanner (TLS)

Procedia PDF Downloads 228

Authors: Sai AKhila Budaraju

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Heritage sites are an integral part of cities and carry a sense of identity to the cities/ towns, but the process of urbanization is a carrying potential threat for the loss of these heritage sites/monuments. Both Central and State Governments listed the historic Golconda fort as National Important Monument and the Heritage precinct with eight heritage-listed buildings and two historical sites respectively, for conservation and preservation, due to the presence of IT Corridor 6kms away accommodating more people in the precinct is under constant pressure. The heritage precinct possesses high property values, being a prime location connecting the IT corridor and CBD (central business district )areas. The primary objective of the study was to assess and identify the factors that are affecting the heritage precinct through Mapping and documentation, Identifying and assessing the factors through empirical analysis, Ordinal regression analysis and Hedonic Pricing Model. Ordinal regression analysis was used to identify the factors that contribute to the changes in the precinct due to urbanization. Hedonic Pricing Model was used to understand and establish a relation whether the presence of historical monuments is also a contributing factor to the property value and to what extent this influence can contribute. The above methods and field visit indicates the Physical, socio-economic factors and the neighborhood characteristics of the precinct contributing to the property values. The outturns and the potential elements derived from the analysis of the Development Control Rules were derived as recommendations to Integrate both Old and newly built environments.

Keywords: heritage planning, heritage conservation, hedonic pricing model, ordinal regression analysis

Procedia PDF Downloads 186