Search results for: spatial rainfall prediction

Authors: Živa Ravnikar, Alfonso Bahillo Martinez, Barbara Goličnik Marušić

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Climate change mitigation has been formally adopted and announced by countries over the globe, where cities are targeting carbon neutrality through various more or less successful, systematic, and fragmentary actions. The article is based on the fact that environmental conditions affect human comfort and the usage of space. Urban planning can, with its sustainable solutions, not only support climate mitigation in terms of a planet reduction of global warming but as well enabling natural processes that in the immediate vicinity produce environmental conditions that encourage people to walk or cycle. However, the article draws attention to the importance of integrating climate consideration into urban planning, where detailed environmental data play a key role, enabling urban planners to improve or monitor environmental conditions on cycle paths. In a practical aspect, this paper tests a particular ICT tool, a prototype used for environmental data. Data gathering was performed along the cycling lanes in Ljubljana (Slovenia), where the main objective was to assess the tool's data applicable value within the planning of comfortable cycling lanes. The results suggest that such transportable devices for in-situ measurements can help a researcher interpret detailed environmental information, characterized by fine granularity and precise data spatial and temporal resolution. Data can be interpreted within human comfort zones, where graphical representation is in the form of a map, enabling the link of the environmental conditions with a spatial context. The paper also provides preliminary results in terms of the potential of such tools for identifying the correlations between environmental conditions and different spatial settings, which can help urban planners to prioritize interventions in places. The paper contributes to multidisciplinary approaches as it demonstrates the usefulness of such fine-grained data for better consideration of microclimatology in urban planning, which is a prerequisite for creating climate-comfortable cycling lanes promoting active mobility.

Keywords: information and communication technology tools, urban planning, human comfort, microclimate, cycling lanes

Procedia PDF Downloads 120

Authors: Mohammed AL-Hasani

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The Pandemic COVID-19 altered the daily habits and affected the functional performance of the cities after this crisis leaving remarkable impacts on many metropolises worldwide. It is so obvious that having more densification in the city leads to more threats altering this main approach that was called for achieving sustainable development. The main goal to achieve resiliency in the cities, especially in forcing risks, is to deal with a planning system that is able to resist, absorb, accommodate and recover from the impacts that had been affected. Many Cities in London, Wuhan, New York, and others worldwide carried different planning approaches and varied in reaction to safeguard the impacts of the pandemic. The cities globally varied from the radiant pattern predicted by Le Corbusier, or having multi urban centers more like the approach of Frank Lloyd Wright’s Broadacre City, or having linear growth or gridiron expansion that was common by Doxiadis, compact pattern, and many other hygiene patterns. These urban patterns shape the spatial distribution and Identify both open and natural spaces with gentrified and gentrifying areas. This crisis paid attention to reassess many planning approaches and examine the existing urban patterns focusing more on the aim of continuity and resiliency in managing the crises within the rapid transformation and the power of market forces. According to that, this paper hypothesized that those urban planning patterns determine the method of reaction in assuring quarantine for the inhabitance and the performance of public services and need to be updated through carrying out an innovative urban management system and adopt further resilience patterns in prospective urban planning approaches. This paper investigates the adaptivity and resiliency of variant urban planning patterns regarding selected cities worldwide that affected by COVID-19 and their role in applying certain management strategies in controlling the pandemic spread, finding out the main potentials that should be included in prospective planning approaches. The examination encompasses the spatial arrangement, blocks definition, plots arrangement, and urban space typologies. This paper aims to investigate the urban patterns to deliberate also the debate between densification as one of the more sustainable planning approaches and disaggregation tendency that was followed after the pandemic by restructuring and managing its application according to the assessment of the spatial distribution and urban patterns. The biggest long-term threat to dense cities proves the need to shift to online working and telecommuting, creating a mixture between using cyber and urban spaces to remobilize the city. Reassessing spatial design and growth, open spaces, urban population density, and public awareness are the main solutions that should be carried out to face the outbreak in our current cities that should be managed from global to tertiary levels and could develop criteria for designing the prospective cities

Keywords: COVID-19, densification, resiliency, urban patterns

Procedia PDF Downloads 114

Authors: Dorota Miroslaw-Swiatek, Mateusz Grygoruk, Sylwia Szporak

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We applied a two-dimensional surface water flow model with irregular wet boundaries. In this model, flow equations are in the form of a 2-D, non-linear diffusion equations which allows to account spatial variations in flow resistance and topography. Calculation domain to simulate the flow pattern in the floodplain is congruent with a Digital Elevation Model (DEM) grid. The rate and direction of sheet flow in wetlands is affected by vegetation type and density, therefore the developed model take into account spatial distribution vegetation resistance to the water flow. The model was tested in a part of the Biebrza Valley, of an outstanding heterogeneity in the elevation and flow resistance distributions due to various ecohydrological conditions and management measures. In our approach we used the highest-possible quality of the DEM in order to obtain hydraulic slopes and vegetation distribution parameters for the modelling. The DEM was created from the cloud of points measured in the LiDAR technology. The LiDAR reflects both the land surface as well as all objects on top of it such as vegetation. Depending on the density of vegetation cover the ability of laser penetration is variable. Therefore to obtain accurate land surface model the “vegetation effect” was corrected using data collected in the field (mostly the vegetation height) and satellite imagery such as Ikonos (to distinguish different vegetation types of the floodplain and represent them spatially). Model simulation was performed for the spring thaw flood in 2009.

Keywords: floodplain flow, Biebrza valley, model simulation, 2D surface flow model

Procedia PDF Downloads 485

Authors: Julien Caudeville, Muriel Ismert

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Analyzing the relationship between environment and health has become a major preoccupation for public health as evidenced by the emergence of the French national plans for health and environment. These plans have identified the following two priorities: (1) to identify and manage geographic areas, where hotspot exposures are suspected to generate a potential hazard to human health; (2) to reduce exposure inequalities. At a regional scale and fine resolution of exposure outcome prerequisite, environmental monitoring networks are not sufficient to characterize the multidimensionality of the exposure concept. In an attempt to increase representativeness of spatial exposure assessment approaches, risk composite indicators could be built using additional available databases and theoretical framework approaches to combine factor risks. To achieve those objectives, combining data process and transfer modeling with a spatial approach is a fundamental prerequisite that implies the need to first overcome different scientific limitations: to define interest variables and indicators that could be built to associate and describe the global source-effect chain; to link and process data from different sources and different spatial supports; to develop adapted methods in order to improve spatial data representativeness and resolution. A GIS-based modeling platform for quantifying human exposure to chemical substances (PLAINE: environmental inequalities analysis platform) was used to build health risk indicators within the Lorraine region (France). Those indicators combined chemical substances (in soil, air and water) and noise risk factors. Tools have been developed using modeling, spatial analysis and geostatistic methods to build and discretize interest variables from different supports and resolutions on a 1 km2 regular grid within the Lorraine region. By example, surface soil concentrations have been estimated by developing a Kriging method able to integrate surface and point spatial supports. Then, an exposure model developed by INERIS was used to assess the transfer from soil to individual exposure through ingestion pathways. We used distance from polluted soil site to build a proxy for contaminated site. Air indicator combined modeled concentrations and estimated emissions to take in account 30 polluants in the analysis. For water, drinking water concentrations were compared to drinking water standards to build a score spatialized using a distribution unit serve map. The Lden (day-evening-night) indicator was used to map noise around road infrastructures. Aggregation of the different factor risks was made using different methodologies to discuss weighting and aggregation procedures impact on the effectiveness of risk maps to take decisions for safeguarding citizen health. Results permit to identify pollutant sources, determinants of exposure, and potential hotspots areas. A diagnostic tool was developed for stakeholders to visualize and analyze the composite indicators in an operational and accurate manner. The designed support system will be used in many applications and contexts: (1) mapping environmental disparities throughout the Lorraine region; (2) identifying vulnerable population and determinants of exposure to set priorities and target for pollution prevention, regulation and remediation; (3) providing exposure database to quantify relationships between environmental indicators and cancer mortality data provided by French Regional Health Observatories.

Keywords: health risk, environment, composite indicator, hotspot areas

Procedia PDF Downloads 237

Authors: Arun Goel

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The present paper attempts to investigate the prediction of air entrainment rate and aeration efficiency of a free over-fall jets issuing from a triangular sharp crested weir by using regression based modelling. The empirical equations, support vector machine (polynomial and radial basis function) models and the linear regression techniques were applied on the triangular sharp crested weirs relating the air entrainment rate and the aeration efficiency to the input parameters namely drop height, discharge, and vertex angle. It was observed that there exists a good agreement between the measured values and the values obtained using empirical equations, support vector machine (Polynomial and rbf) models, and the linear regression techniques. The test results demonstrated that the SVM based (Poly & rbf) model also provided acceptable prediction of the measured values with reasonable accuracy along with empirical equations and linear regression techniques in modelling the air entrainment rate and the aeration efficiency of a free over-fall jets issuing from triangular sharp crested weir. Further sensitivity analysis has also been performed to study the impact of input parameter on the output in terms of air entrainment rate and aeration efficiency.

Keywords: air entrainment rate, dissolved oxygen, weir, SVM, regression

Procedia PDF Downloads 418

Authors: Simone Jane Dennis

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This paper leverages the spatial and temporal investigative strategy utilized by crime scene investigators – blood spatter work– to engage with the real and metaphorical memorialization of blood-soaked places. It uses this key trope with phenomenological sensibility, to trace the physical and temporal movement of blood outbound from the human body to sites beyond. Working backward, as crime scene investigators do, this paper traces the importance of both space and time and their confluence, to developing a comprehensive theory of violence. To do this work, the paper engages a range of geo-violent scales, from murder scenes to genocides, to both engage an extraordinarily replete literature of bloodshed across history and to move beyond analyses of how significance is assigned to the sites in which blood comes to rest to instead consider the importance of space and time to the structure of violence itself. It is in this regard that the kind of investigative work upon which blood spatter analysis depends is crucial: it engages time and space in reverse to understand the microscopic relations between bodies, places, and numerous (biological, clock, and seasonal) temporalities. Considering the circumstances under which blood escaped a body, the details of its destination in place, and the temporal circumstances of corporal departure, is crucial to making new knowledge about the peculiar temporality and spatiality of violence itself.

Keywords: blood, crime scenes, temporality, violence

Procedia PDF Downloads 49

Authors: Ya Lv

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This article presents the application of the semi-analytic method (SAM) in the thermal management solution (TMS) of the energy storage system (ESS). The TMS studied in this work is fluid cooling. In fluid cooling, both effective heat conduction and heat convection are indispensable due to the heat transfer from solid to fluid. Correspondingly, an efficient TMS requires a design investigation of the following parameters: fluid inlet temperature, ESS initial temperature, fluid flow rate, working c rate, continuous working time, and materials properties. Their variation induces a change of thermal performance in the battery module, which is usually evaluated by numerical simulation. Compared to complicated computation resources and long computation time in simulation, the SAM is developed in this article to predict the thermal influence within a few seconds. In SAM, a fast prediction model is reckoned by combining numerical simulation with theoretical/empirical equations. The SAM can explore the thermal effect of boundary parameters in both steady-state and transient heat transfer scenarios within a short time. Therefore, the SAM developed in this work can simplify the design cycle of TMS and inspire more possibilities in TMS design.

Keywords: semi-analytic method, fast prediction model, thermal influence of boundary parameters, energy storage system

Procedia PDF Downloads 134

Authors: K. C. R.Perera, V. P. C Dassanayake, B. M. Hapuwatte, B. G. Smapath

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This paper presents a methodology to harvest the kinetic energy of the raindrops using piezoelectric devices. In the study 1m×1m PVDF (Polyvinylidene fluoride) piezoelectric membrane, which is fixed by the four edges, is considered for the numerical simulation on deformation of the membrane due to the impact of the raindrops. Then according to the drop size of the rain, the simulation is performed classifying the rainfall types into three categories as light stratiform rain, moderate stratiform rain and heavy thundershower. The impact force of the raindrop is dependent on the terminal velocity of the raindrop, which is a function of raindrop diameter. The results were then analyzed to calculate the harvestable energy from the deformation of the piezoelectric membrane.

Keywords: raindrop, piezoelectricity, deformation, terminal velocity

Procedia PDF Downloads 310

Authors: Ravi Verma

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Cryosorption pump is the best solution to achieve clean, vibration free ultra-high vacuum. Furthermore, the operation of cryosorption pump is free from the influence of electric and magnetic fields. Due to these attributes, this pump is used in the space simulation chamber to create the ultra-high vacuum. The cryosorption pump comprises of three parts (a) panel which is cooled with the help of cryogen or cryocooler, (b) an adsorbent which is used to adsorb the gas molecules, (c) an epoxy which holds the adsorbent and the panel together thereby aiding in heat transfer from adsorbent to the panel. The performance of cryosorption pump depends on the temperature of the adsorbent and hence, on the thermal conductivity of the epoxy. Therefore we have made an attempt to increase the thermal conductivity of epoxy adhesive by mixing nano-sized graphene filler particles. The thermal conductivity of epoxy-graphene composite adhesive is measured with the help of indigenously developed experimental setup in the temperature range from 4.5 K to 7 K, which is generally the operating temperature range of cryosorption pump for efficiently pumping of hydrogen and helium gas. In this article, we have presented the experimental results of epoxy-graphene composite adhesive in the temperature range from 4.5 K to 7 K. We have also proposed an analytical heat conduction model to find the thermal conductivity of the composite. In this case, the filler particles, such as graphene, are randomly distributed in a base matrix of epoxy. The developed model considers the complete spatial random distribution of filler particles and this distribution is explained by Binomial distribution. The results obtained by the model have been compared with the experimental results as well as with the other established models. The developed model is able to predict the thermal conductivity in both isotropic regions as well as in anisotropic region over the required temperature range from 4.5 K to 7 K. Due to the non-empirical nature of the proposed model, it will be useful for the prediction of other properties of composite materials involving the filler in a base matrix. The present studies will aid in the understanding of low temperature heat transfer which in turn will be useful towards the development of high performance cryosorption pump.

Keywords: composite adhesive, computational modelling, cryosorption pump, thermal conductivity

Procedia PDF Downloads 76

Authors: Syed Atif Hassan, Luv Mehta, Syed Asif Hassan

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Hit song science is a field of investigation wherein machine learning techniques are applied to music tracks in order to extract such features from audio signals which can capture information that could explain the popularity of respective tracks. Record companies invest huge amounts of money into recruiting fresh talents and churning out new music each year. Gaining insight into the basis of why a song becomes popular will result in tremendous benefits for the music industry. This paper aims to extract basic musical and more advanced, acoustic features from songs while also taking into account external factors that play a role in making a particular song popular. We use a dataset derived from popular Spotify playlists divided by genre. We use ten genres (blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, rock), chosen on the basis of clear to ambiguous delineation in the typical sound of their genres. We feed these features into three different classifiers, namely, SVM with RBF kernel, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model at the end. Predicting song popularity is particularly important for the music industry as it would allow record companies to produce better content for the masses resulting in a more competitive market.

Keywords: classifier, machine learning, music tracks, popularity, prediction

Procedia PDF Downloads 637

Authors: Sanja O. Podunavac-Kuzmanović, Lidija R. Jevrić, Strahinja Z. Kovačević

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Benzimidazoles are a group of compounds with significant antibacterial, antifungal and anticancer activity. The studied compounds consist of the main benzimidazole structure with different combinations of substituens. This study is based on the two-dimensional and three-dimensional molecular modeling and calculation of molecular descriptors (physicochemical and lipophilicity descriptors) of structurally diverse benzimidazoles. Molecular modeling was carried out by using ChemBio3D Ultra version 14.0 software. The obtained 3D models were subjected to energy minimization using molecular mechanics force field method (MM2). The cutoff for structure optimization was set at a gradient of 0.1 kcal/Åmol. The obtained set of molecular descriptors was used in principal component analysis (PCA) of possible similarities and dissimilarities among the studied derivatives. After the molecular modeling, the quantitative structure-property relationship (QSPR) analysis was applied in order to get the mathematical models which can be used in prediction of pKb values of structurally similar benzimidazoles. The obtained models are based on statistically valid multiple linear regression (MLR) equations. The calculated cross-validation parameters indicate the high prediction ability of the established QSPR models. This study is financially supported by COST action CM1306 and the project No. 114-451-347/2015-02, financially supported by the Provincial Secretariat for Science and Technological Development of Vojvodina.

Keywords: benzimidazoles, chemometrics, molecular modeling, molecular descriptors, QSPR

Procedia PDF Downloads 269

Authors: Meidesta Pitria, Akiko Okabe

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In high-density informal settlement, extension area at the outside of the houses could primarily happen as a spatial modification response. House extension in high-density informal settlement is not only becoming a physical spatial modification that makes a blur zone between private and public but also supporting the growth and existence of informal economy and other daily activities in both individuals and communities. This research took a case study in an informal settlement named Kampung Cikini, a densely populated area in Central Jakarta. The aim of this study is to identify and clarify house extension as a strategy in dealing with urbanization in an informal settlement. By using the perspective and information from housewives, the analysis is based on the assumption that land ownership transformation and the activities in house extension area influence the different kinds of house extension’s spatial modification and local planning policy in relation with the implementation of house extension strategy. The data collection was done in four sites, two sites are located in outer-wide alley and another two sites are located in inner-narrow alley. In this research, data of 104 housewives in 86 houses were collected through representatives of housewives and local leader of each sites. The research was started from participatory mapping process, deep interview with local leader, and initiated collaboration with housewives community in having a celebration as communal event to cultivate together the issue. This study shows that land ownership, activities, and alley are indispensable in the decision of extension space making. The more permanency status of land ownership the more permanent and various extension could be implemented. However, in some blocks, the existence of origin house or first land owner also has a significant role in coordination and agreement in using and modifying extension space. In outer-wide alley, the existence of more various activities in front area of the houses is significantly related with the chance given by having wider alley, particularly for informal income generating activities. In the inner-narrow alley, limited space in front of the houses affects more negotiations in the community for having more shared spaces, even inside their private space.

Keywords: house extension, housewives, informal settlement, kampung, high density

Procedia PDF Downloads 193

Authors: Heribert R. Kaijage, Samuel N. A. Codjoe, Simon H. D. Mamuya, Mangi J. Ezekiel

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There is strong evidence that climate has changed significantly affecting various sectors including public health. The recommended feasible solution is adopting development trajectories which combine both mitigation and adaptation measures for improving resilience pathways. This approach demands a consideration for complex interactions between climate and social-ecological systems. While other sectors such as agriculture and water have developed climate resilience indices, the public health sector in Tanzania is still lagging behind. The aim of this study was to find out how can we use Demographic and Health Surveys (DHS) linked with Remote Sensing (RS) technology and metrological information as tools to inform climate change resilient development and evaluation for the health sector. Methodological review was conducted whereby a number of studies were content analyzed to find appropriate indicators and indices for climate resilience household and their integration approach. These indicators were critically reviewed, listed, filtered and their sources determined. Preliminary identification and ranking of indicators were conducted using participatory approach of pairwise weighting by selected national stakeholders from meeting/conferences on human health and climate change sciences in Tanzania. DHS datasets were retrieved from Measure Evaluation project, processed and critically analyzed for possible climate change indicators. Other sources for indicators of climate change exposure were also identified. For the purpose of preliminary reporting, operationalization of selected indicators was discussed to produce methodological approach to be used in resilience comparative analysis study. It was found that household climate resilient index depends on the combination of three indices namely Household Adaptive and Mitigation Capacity (HC), Household Health Sensitivity (HHS) and Household Exposure Status (HES). It was also found that, DHS alone cannot complement resilient evaluation unless integrated with other data sources notably flooding data as a measure of vulnerability, remote sensing image of Normalized Vegetation Index (NDVI) and Metrological data (deviation from rainfall pattern). It can be concluded that if these indices retrieved from DHS data sets are computed and scientifically integrated can produce single climate resilience index and resilience maps could be generated at different spatial and time scales to enhance targeted interventions for climate resilient development and evaluations. However, further studies are need to test for the sensitivity of index in resilience comparative analysis among selected regions.

Keywords: climate change, resilience, remote sensing, demographic and health surveys

Procedia PDF Downloads 149

Authors: Jing Zhao, Dayong Liu, Shihao Wang, Xinghua Zhu, Delong Li

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Uhumanned Underwater Vehicles generally operate in the deep sea, which has its own unique working conditions. Lithium-ion power batteries should have the necessary stability and endurance for use as an underwater vehicle’s power source. Therefore, it is essential to accurately forecast how long lithium-ion batteries will last in order to maintain the system’s reliability and safety. In order to model and forecast lithium battery Remaining Useful Life (RUL), this research suggests a model based on Complete Ensemble Empirical Mode Decomposition with Adaptive noise-Temporal Convolutional Net (CEEMDAN-TCN). In this study, two datasets, NASA and CALCE, which have a specific gap in capacity data fluctuation, are used to verify the model and examine the experimental results in order to demonstrate the generalizability of the concept. The experiments demonstrate the network structure’s strong universality and ability to achieve good fitting outcomes on the test set for various battery dataset types. The evaluation metrics reveal that the CEEMDAN-TCN prediction performance of TCN is 25% to 35% better than that of a single neural network, proving that feature expansion and modal decomposition can both enhance the model’s generalizability and be extremely useful in industrial settings.

Keywords: lithium-ion battery, remaining useful life, complete EEMD with adaptive noise, temporal convolutional net

Procedia PDF Downloads 127

Authors: M. Abou-Amouna, A. Radwan, L. Al-kuwari, A. Hammuda, K. Al-Khalifa

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There is growing concern over increasing incidences of road accidents and consequent loss of human life in Qatar. In light to the future planned event in Qatar, World Cup 2022; Qatar should put into consideration the future deaths caused by road accidents, and past trends should be considered to give a reasonable picture of what may happen in the future. Qatar roads should be arranged and paved in a way that accommodate high capacity of the population in that time, since then there will be a huge number of visitors from the world. Qatar should also consider the risk issues of road accidents raised in that period, and plan to maintain high level to safety strategies. According to the increase in the number of road accidents in Qatar from 1995 until 2012, an analysis of elements affecting and causing road accidents will be effectively studied. This paper aims to identify and criticize the factors that have high effect on causing road accidents in the state of Qatar, and predict the total number of road accidents in Qatar 2022. Alternative methods are discussed and the most applicable ones according to the previous researches are selected for further studies. The methods that satisfy the existing case in Qatar were the multiple linear regression model (MLR) and artificial neutral network (ANN). Those methods are analyzed and their findings are compared. We conclude that by using MLR the number of accidents in 2022 will become 355,226 accidents, and by using ANN 216,264 accidents. We conclude that MLR gave better results than ANN because the artificial neutral network doesn’t fit data with large range varieties.

Keywords: road safety, prediction, accident, model, Qatar

Procedia PDF Downloads 240

Authors: Melody Yin

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Concussions impact a large amount of adolescents; they make up as much as half of the diagnosed concussions in America. This research proposes a hybrid machine learning model based on the combination of human/knowledge-based domains and computer-generated feature rankings to improve the accuracy of diagnosing sports related concussion (SRC). Using a data set of symptoms collected on the sideline post-SRC events, the symptom selection criteria method has been developed by using Google AutoML's important score function to identify the top 10 symptom features. In addition, symptom domains have been introduced as another parameter, categorizing the symptoms into physical, cognitive, sleep, and emotional domains. The hybrid machine learning model has been trained with a combination of the top 10 symptoms and 4 domains. From the results, the hybrid model was the best performer for symptom resolution time prediction in 2 and 4-week thresholds. This research is a proof of concept study in the use of domains along with machine learning in order to improve concussion prediction accuracy. It is also possible that the use of domains can make the model more efficient due to reduced training time. This research examines the use of a hybrid method in predicting sports-related concussion. This achievement is based on data preprocessing, using a hybrid method to select criteria to achieve high performance.

Keywords: hybrid model, machine learning, sports related concussion, symptom resolution time

Procedia PDF Downloads 152

Authors: Bi-Huei Tsai

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Previous studies on financial distress prediction choose the conventional failing and non-failing dichotomy; however, the distressed extent differs substantially among different financial distress events. To solve the problem, “non-distressed”, “slightly-distressed” and “reorganization and bankruptcy” are used in our article to approximate the continuum of corporate financial health. This paper explains different financial distress events using the two-stage method. First, this investigation adopts firm-specific financial ratios, corporate governance and market factors to measure the probability of various financial distress events based on multinomial logit models. Specifically, the bootstrapping simulation is performed to examine the difference of estimated misclassifying cost (EMC). Second, this work further applies macroeconomic factors to establish the credit cycle index and determines the distressed cut-off indicator of the two-stage models using such index. Two different models, one-stage and two-stage prediction models, are developed to forecast financial distress, and the results acquired from different models are compared with each other, and with the collected data. The findings show that the two-stage model incorporating financial ratios, corporate governance and market factors has the lowest misclassification error rate. The two-stage model is more accurate than the one-stage model as its distressed cut-off indicators are adjusted according to the macroeconomic-based credit cycle index.

Keywords: Multinomial logit model, corporate governance, company failure, reorganization, bankruptcy

Procedia PDF Downloads 364

Authors: Temitayo Abayomi Ewemoje, Isaac Olugbemiga Afolayan, Badmus Alao Tayo

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Researchers in developing countries have worked on improving cassava resistance to diseases and pests, high yielding and early maturity However, water management has received little or no attention as cassava cultivation in Sub-Saharan Africa depended on available precipitation (rain-fed condition). Therefore the need for water management in Agricultural crop production cannot be overemphasized. As other sectors compete with agricultural sector for fresh water (which is not readily available), there is need to increase water productivity in agricultural production. Experimentation was conducted to examine water use, growth and yield of improved cassava varieties under rain fed condition using Latin- square design with four replications. Four improved disease free stem cassava varieties TMS (30572, 980505, 920326 and 090581) were planted and growth parameters of the varieties were monitored for 90 and 120 days after planting (DAP). Effective rainfall useful for the plant growth was calculated using CROPWAT8 for Windows. Results indicated TMS090581 was having the highest tuber yield and plant height while TMS30572 had highest number of nodes. Tuber stem and leaf water productivities at 90 and 120 DAP of TMS (30572, 980505, 920326 and 090581) are (1.27 and 3.58, 1.44 and 2.35, 0.89 and 1.86, 1.64 and 3.77) kg/m3 (1.56 and 2.59, 1.95 and 2.02, 1.98 and 2.05, 1.95 and 2.18) kg/m3, and (1.34 and 2.32, 1.94 and 2.16, 1.57 and 1.40, 1.27 and 1.80) kg/m3 respectively. Based on tuber water productivity TMS090581 are recommended while TMS30572 are recommended based on leaf and stem productivity in water scarce regions.Experimentation was conducted to examine water use, growth and yield of improved cassava varieties under rain fed condition using Latin- square design with four replications. Four improved disease free stem cassava varieties TMS (30572, 980505, 920326 and 090581) were planted and growth parameters of the varieties were monitored for 90 and 120 days after planting (DAP). Effective rainfall useful for the plant growth was calculated using CROPWAT8 for Windows. Results indicated TMS090581 was having the highest tuber yield and plant height while TMS30572 had the highest number of nodes. Tuber, stem and leaf water productivities at 90 and 120 DAP of TMS (30572, 980505, 920326 and 090581) are (1.27 and 3.58, 1.44 and 2.35, 0.89 and 1.86, 1.64 and 3.77) kg/m3 (1.56 and 2.59, 1.95 and 2.02, 1.98 and 2.05, 1.95 and 2.18) kg/m3, and (1.34 and 2.32, 1.94 and 2.16, 1.57 and 1.40, 1.27 and 1.80) kg/m3 respectively. Based on tuber water productivity TMS090581 are recommended while TMS30572 are recommended based on leaf and stem productivity in water scarce regions

Keywords: improved TMS varieties, leaf productivity, rain-fed cassava production, stem productivity, tuber productivity

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Authors: Harshan Tee Pee

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Climate elements include surface temperatures, rainfall patterns, humidity, type and amount of cloudiness, air pressure and wind speed and direction. Change in one element can have an impact on the regional climate. The scientific predictions indicate that global climate change will increase the number of extreme events, leading to more frequent natural hazards. Global warming is likely to intensify the risk of drought in certain parts and also leading to increased rainfall in some other parts. Drought is a slow advancing disaster and creeping phenomenon– which accumulate slowly over a long period of time. Droughts are naturally linked with aridity. But droughts occur over most parts of the world (both wet and humid regions) and create severe impacts on agriculture, basic household welfare and ecosystems. Drought condition occurs at least every three years in India. India is one among the most vulnerable drought prone countries in the world. The economic impacts resulting from extreme environmental events and disasters are huge as a result of disruption in many economic activities. The focus of this paper is to develop a comprehensive understanding about the distributional impacts of disaster, especially impact of drought on agricultural production and income through a panel study (drought year and one year after the drought) in Raikhel village, Maharashtra, India. The major findings of the study indicate that cultivating area as well as the number of cultivating households reduced after the drought, indicating a shift in the livelihood- households moved from agriculture to non-agriculture. Decline in the gross cropped area and production of various crops depended on the negative income from these crops in the previous agriculture season. All the landholding categories of households except landlords had negative income in the drought year and also the income disparities between the households were higher in that year. In the drought year, the cost of cultivation was higher for all the landholding categories due to the increased cost for irrigation and input cost. In the drought year, agriculture products (50 per cent of the total products) were used for household consumption rather than selling in the market. It is evident from the study that livelihood which was based on natural resources became less attractive to the people to due to the risk involved in it and people were moving to less risk livelihood for their sustenance.

Keywords: climate change, drought, agriculture economics, disaster impact

Procedia PDF Downloads 101

Authors: C. E. Sutton, A. Varvani-Farahani

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Fatigue damage and life prediction of particle metal matrix composites (PMMCs) under uniaxial and multiaxial loading conditions were investigated. Three PMM composite materials of Al6061/Al2O3/20p-T6, Al6061/Al2O3/22p-T6 and Al6061/SiC/17w-T6 tested under tensile, torsion, and combined tension-torsion fatigue cycling were evaluated with various fatigue damage models. The fatigue damage models of Smith-Watson-Topper (S. W. T.), Ellyin, Brown-Miller, Fatemi-Socie, and Varvani were compared for their capability to assess the fatigue damage of materials undergoing various loading conditions. Fatigue life predication results were then evaluated by implementing material-dependent coefficients that factored in the effects of the particle reinforcement in the earlier developed Varvani model. The critical plane-energy approach incorporated the critical plane as the plane of crack initiation and early stage of crack growth. The strain energy density was calculated on the critical plane incorporating stress and strain components acting on the plane. This approach successfully evaluated fatigue damage values versus fatigue lives within a narrower band for both uniaxial and multiaxial loading conditions as compared with other damage approaches studied in this paper.

Keywords: fatigue damage, life prediction, critical plane approach, energy approach, PMM composites

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Authors: Sanjay Kumar Srivastava, Anu Rani Sharma, Kamna Sachdeva

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Every year, fog formation over the Indo-Gangetic Plains (IGPs) of Indian region during the winter months of December and January is believed to create numerous hazards, inconvenience, and economic loss to the inhabitants of this densely populated region of Indian subcontinent. The aim of the paper is to analyze the spatial and temporal variability of winter fog over IGPs. Long term ground observations of visibility and other meteorological parameters (1971-2010) have been analyzed to understand the formation of fog phenomena and its relevance during the peak winter months of January and December over IGP of India. In order to examine the temporal variability, time series and trend analysis were carried out by using the Mann-Kendall Statistical test. Trend analysis performed by using the Mann-Kendall test, accepts the alternate hypothesis with 95% confidence level indicating that there exists a trend. Kendall tau’s statistics showed that there exists a positive correlation between time series and fog frequency. Further, the Theil and Sen’s median slope estimate showed that the magnitude of trend is positive. Magnitude is higher during January compared to December for the entire IGP except in December when it is high over the western IGP. Decade wise time series analysis revealed that there has been continuous increase in fog days. The net overall increase of 99 % was observed over IGP in last four decades. Diurnal variability and average daily persistence were computed by using descriptive statistical techniques. Geo-statistical analysis of fog was carried out to understand the spatial variability of fog. Geo-statistical analysis of fog revealed that IGP is a high fog prone zone with fog occurrence frequency of more than 66% days during the study period. Diurnal variability indicates the peak occurrence of fog is between 06:00 and 10:00 local time and average daily fog persistence extends to 5 to 7 hours during the peak winter season. The results would offer a new perspective to take proactive measures in reducing the irreparable damage that could be caused due to changing trends of fog.

Keywords: fog, climatology, Mann-Kendall test, trend analysis, spatial variability, temporal variability, visibility

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Authors: Sri E. Wati, D. Roswidyatmoko, N. Maslahatun, Gunawan, Andhika B. Taji

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Natural resource is the main sources to fulfill human needs. Its utilization must consider not only human prosperity but also sustainability. Balance of natural resources is a tool to manage natural wealth and to control land use change. This tool is needed to organize land use planning as stated on spatial plan in a certain region. Balance of natural resources can be calculated by comparing two-series of natural resource data obtained at different year. In this case, four years data period of land and forest were used (2010 and 2014). Land use data were acquired through satellite image interpretation and field checking. By means of GIS analysis, its result was then assessed with land use plan. It is intended to evaluate whether existing land use is suitable with land use plan. If it is improper, what kind of efforts and policies must be done to overcome the situation. Subosukawonosraten is rapid developed areas in Central Java Province. This region consists of seven regencies/cities which are Sukoharjo Regency, Boyolali Regency, Surakarta City, Karanganyar Regency, Wonogiri Regency, Sragen Regency, and Klaten Regency. This region is regarding to several former areas under Karasidenan Surakarta and their location is adjacent to Surakarta. Balance of forest resources show that width of forest area is not significantly changed. Some land uses within the area are slightly changed. Some rice field areas are converted into settlement (0.03%) whereas water bodies become vacant areas (0.09%). On the other hand, balance of land resources state that there are many land use changes in this region. Width area of rice field decreases 428 hectares and more than 50% of them have been transformed into settlement area and 11.21% is converted into buildings such as factories, hotels, and other infrastructures. It occurs mostly in Sragen, Sukoharjo, and Karanganyar Regency. The results illustrate that land use change in this region is mostly influenced by increasing of population number. Some agricultural lands have been converted into built-up area since demand of settlement, industrial area, and other infrastructures also increases. Unfortunately, recent utilization of more than a half of total area is not appropriate with land use plan declared in spatial planning document. It means, local government shall develop a strict regulation and law enforcement related to any violation in land use management.

Keywords: balance, forest, land, spatial plan

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Authors: Ahmed M. AlHasem

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The Kuwaiti society has long been aware of climatic changes and their annual dates and trying to link them to astronomy in an attempt to forecast the future weather conditions. The reason for this concern is that many of the economic, social and living activities of the society depend deeply on the nature of the weather conditions directly and indirectly. In other words, Kuwaiti society, like the case of many human societies, has in the past tried to predict climatic conditions by linking them to astronomy or popular statements to indicate the timing of climate changes. Accordingly, this study was devoted to scientific investigation based on the statistical analysis of climatic data to show the accuracy and compatibility of some of the most important elements of the cultural heritage in relation to climate change and to relate it scientifically to precise climatic measurements for decades. The research has been divided into 10 topics, each topic has been focused on one legacy, whether by linking climate changes to the appearance/disappearance of star or a popular statement inherited through generations, through explain the nature and timing and thereby statistical analysis to indicate the proportion of accuracy based on official climatic data since 1962. The study's conclusion is that the relationship is weak and, in some cases, non-existent between the popular heritage and the actual climatic data. Therefore, it does not have a dependable relationship and a reliable scientific prediction between both the popular heritage and the forecast of weather conditions.

Keywords: astronomy, cultural heritage, statistical analysis, weather prediction

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Authors: Yilei Yu

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Modernism, emerging in the Western world of the 20th century, attempted to create a universal international style, which pulled the architectural and social systems created by classicism back to an initial pure state. However, out of the introspection of the Modernism, Regionalism attempted to restore a humanistic environment and create flexible buildings during the 1950s. Meanwhile, as the first generation of architects came back, the wind of the Regionalism blew to Taiwan. However, with the increasing of political influence and the tightening of free creative space, from the second half of the 1950s to the 1980s, the "real" Regional Architecture, which should have taken roots in Taiwan, becomes the "fake" Regional Architecture filled with the oriental charm. Through the Comparative Method, which includes description, interpretation, juxtaposition, and comparison, this study analyses the difference of the style of the Modernist Chinese Architecture between the period before the 1980s and the after. The paper aims at exploring the development of Regionalism Architecture in Taiwan, which includes three parts. First, the burgeoning period of the "modernist Chinese architecture" in Taiwan was the beginning of the Chinese Nationalist Party's coming to Taiwan to consolidate political power. The architecture of the "Ming and Qing Dynasty Palace Revival Style" dominated the architectural circles in Taiwan. These superficial "regional buildings" have nearly no combination with the local customs of Taiwan, which is difficult to evoke the social identity. Second, in the late 1970s, the second generation of architects headed by Baode Han began focusing on the research and preservation of traditional Taiwanese architecture, and creating buildings combined the terroirs of Taiwan through the imitation of styles. However, some scholars have expressed regret that very few regionalist architectural works that appeared in the 1980s can respond specifically to regional conditions and forms of construction. Instead, most of them are vocabulary-led representations. Third, during the 1990s, by the end of the period of martial law, community building gradually emerged, which made the object of Taiwan's architectural concern gradually extended to the folk and ethnic groups. In the Yilan area, there are many architects who care about the local environment, such as the Field office Architects. Compared with the hollow regionality of the passionate national spirits that emerged during the martial law period, the local practice of the architect team in Yilan can better link the real local environmental life and reflect the true regionality. In conclusion, with the local practice case of the huge construction team in Yilan area, this paper focuses on the Spatial Practice by the Field office Architects to explore the spatial representation of the space and the practical enlightenment in the process of modernist Chinese architecture development in Taiwan.

Keywords: regionalism, modernism, Chinese architecture, political landscape, spatial representation

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Authors: Amal Hentati, Mbarka Selmi, Tarek Kormi, Julien Baroth, Barthelemy Harthong

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The impact of uncertainties on the performance assessment of shallow foundations is often significant. The need of the geotechnical engineers to a more objective and rigorous description of soil variations permitting to quantify these uncertainties and to incorporate them into calculation methods led to the development of reliability approaches. In this context, a mechanical-reliability coupling was developed in this paper, using a program coded in Matlab and the finite element software Abaqus, for the bearing capacity assessment of shallow foundations. The reliability analysis, based on the finite element method, assumed both soil cohesion and friction angle as uncertain parameters characterized by normal or lognormal probability distributions. The inherent spatial variability of both soil properties was, then, taken into account using 1D stationary random fields. The application of the proposed methodology to a shallow foundation subjected to a centered vertical loading permitted to highlight the proposed process interest. Findings proved the insufficiency of the conventional approach to predict the foundation failure and a high sensitivity of the ultimate loads to the soil properties uncertainties, mainly those related to the friction angle, was noted. Moreover, an asymmetry of both displacement and velocity fields was obtained.

Keywords: mechanical-reliability coupling, finite element method, shallow foundation, random fields, spatial variability

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Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Melik Kara, Gulsah Tulger Kara

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The soil is a crucial component of the ecosystem, and in industrial and urban areas it receives large amounts of trace elements from several sources. Therefore, accumulated pollutants in surface soils can be transported to different environmental components, such as deep soil, water, plants, and dust particles. While elemental contamination of soils is caused mainly by atmospheric deposition, soil also affects the air quality since enriched trace elemental contents in atmospheric particulate matter originate from resuspension of polluted soils. The objectives of this study were to determine the total and leachate concentrations of trace elements in soils of city area in Izmir and characterize their spatial distribution and to identify the possible sources of trace elements in surface soils. The surface soil samples were collected from 20 sites. They were analyzed for total element concentrations and leachate concentrations. Analyses of trace elements (Ag, Al, As, B, Ba, Be, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Er, Eu, Fe, Ga, Gd, Hf, Ho, K, La, Li, Lu, Mg, Mn, Mo, Na, Nd, Ni, P, Pb, Pr, Rb, Sb, Sc, Se, Si, Sm, Sn, Sr, Tb, Th, Ti, Tl, Tm, U, V, W, Y, Yb, Zn and Zr) were carried out using ICP-MS (Inductively Coupled Plasma-Mass Spectrometer). The elemental concentrations were calculated along with overall median, kurtosis, and skewness statistics. Elemental composition indicated that the soil samples were dominated by crustal elements such as Si, Al, Fe, Ca, K, Mg and the sea salt element, Na which is typical for Aegean region. These elements were followed by Ti, P, Mn, Ba and Sr. On the other hand, Zn, Cr, V, Pb, Cu, and Ni (which are anthropogenic based elements) were measured as 61.6, 39.4, 37.9, 26.9, 22.4, and 19.4 mg/kg dw, respectively. The leachate element concentrations were showed similar sorting although their concentrations were much lower than total concentrations. In the study area, the spatial distribution patterns of elemental concentrations varied among sampling sites. The highest concentrations were measured in the vicinity of industrial areas and main roads. To determine the relationships among elements and to identify the possible sources, PCA (Principal Component Analysis) was applied to the data. The analysis resulted in six factors. The first factor exhibited high loadings of Co, K, Mn, Rb, V, Al, Fe, Ni, Ga, Se, and Cr. This factor could be interpreted as residential heating because of Co, K, Rb, and Se. The second factor associated positively with V, Al, Fe, Na, Ba, Ga, Sr, Ti, Se, and Si. Therefore, this factor presents mixed city dust. The third factor showed high loadings with Fe, Ni, Sb, As, Cr. This factor could be associated with industrial facilities. The fourth factor associated with Cu, Mo, Zn, Sn which are the marker elements of traffic. The fifth factor presents crustal dust, due to its high correlation with Si, Ca, and Mg. The last factor is loaded with Pb and Cd emitted from industrial activities.

Keywords: trace elements, surface soil, source apportionment, Izmir

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Authors: Salif Koné

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We analyze hydrometric data at the Koulikoro station on the Niger River; this basin drains 120600 km2 and covers three countries in West Africa, Guinea, Mali, and Ivory Coast. Two subsequent decadal cycles are highlighted (1925-1936 and 1929-1939) instead of the presumed single decadal one from literature. Moreover, the observed hydrometric data shows a multidecadal 40-year period that is confirmed when graphing a spatial coefficient of variation of runoff over decades (starting at 1901-1910). Spatial runoff data are produced on 48 grids (0.5 degree by 0.5 degree) and through semi-distributed versions of both SimulHyd model and GR2M model - variants of a French Hydrologic model – standing for Genie Rural of 2 parameters at monthly time step. Both extremal decades in terms of runoff coefficient of variation are confronted: 1951-1960 has minimal coefficient of variation, and 1981-1990 shows the maximal value of it during the three months of high-water level (August, September, and October). The mapping of the relative variation of these two decadal situations allows hypothesizing as following: the scale of variation between both extremal situations could serve to fix boundary conditions for further simulations using data from climate scenario.

Keywords: internal cycles, hydrometric data, niger river, gr2m and simulhyd framework, runoff coefficient of variation

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Authors: Walid Moudani

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The healthcare environment is generally perceived as being information rich yet knowledge poor. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. In fact, valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, a proficient methodology for the extraction of significant patterns from the coronary heart disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming. Therefore, we propose to validate the classification using Random Forest (RF) decision tree to identify the risky heart disease cases. This work is based on a large amount of data collected from several clinical institutions based on the medical profile of patient. Moreover, the experts’ knowledge in this field has been taken into consideration in order to define the disease, its risk factors, and to establish significant knowledge relationships among the medical factors. A computer-aided system is developed for this purpose based on a population of 525 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

Keywords: multi-classifier decisions tree, features reduction, dynamic programming, rough sets

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Authors: Sagri Sharma

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Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms and statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data.

Keywords: artificial intelligence, biomarker, gene expression datasets, hepatocellular carcinoma, machine learning, supervised learning algorithms, support vector machine

Procedia PDF Downloads 416