Search results for: catchment characteristics model

Authors: Abduxukur Zayit, Guo Rui Chen

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Universities play important role for develop and distribute sustainable development ideas. This research based on the current situation of large and widely distributed university campuses in China. In view of the deterioration of campus performance, the aging of function and facilities, the large consumption of energy and resources, a logic of "problem-oriented-goal-oriented- At the level, taking the problem orientation as the focus,this paper analyzes the main influencing factors of the existing characteristics of the university campuses, establishes the digital assessment methods and clarifies the key points of the rennovation. Based on the goal orientation, this paper puts forward the existing university campus design principles, builds the green transformation-carding model and sets up the post-use evaluation model. In the end, with dual guidance as the constraint, we will formulate green design standards for campus greening, construct a greening enhancement measure for campus environment, and develop and promote a green campus after-use assessment platform. It provides useful research methods and research ideas for the reconstruction of the existing campus in China, especially the urban universities.

Keywords: design method, existing university campus, green renovation, sustainable development

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Authors: Peter J. Riley

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Introductory courses in High Energy Physics (HEP) can be extended with the Tri-Preon (TP) model to both supplements and challenge the Standard Model (SM) theory. TP supplements by simplifying the tracking of Conserved Quantum Numbers at an interaction vertex, e.g., the lepton number can be seen as a di-preon current. TP challenges by proposing extended particle families to three generations of particle triplets for leptons, quarks, and weak bosons. There are extensive examples discussed at an introductory level in six arXiv publications, including supersymmetry, hyper color, and the Higgs. Interesting exercises include pion decay, kaon-antikaon mixing, neutrino oscillations, and K+ decay to muons. It is a revealing exercise for students to weigh the pros and cons of parallel theories at an early stage in their HEP journey.

Keywords: HEP, particle physics, standard model, Tri-Preon model

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Authors: Gayathri Nagarajan, L. D. Dhinesh Babu

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Health care is one of the prominent industries that generate voluminous data thereby finding the need of machine learning techniques with big data solutions for efficient processing and prediction. Missing data, incomplete data, real time streaming data, sensitive data, privacy, heterogeneity are few of the common challenges to be addressed for efficient processing and mining of health care data. In comparison with other applications, accuracy and fast processing are of higher importance for health care applications as they are related to the human life directly. Though there are many machine learning techniques and big data solutions used for efficient processing and prediction in health care data, different techniques and different frameworks are proved to be effective for different applications largely depending on the characteristics of the datasets. In this paper, we present a framework that uses ensemble machine learning technique gradient boosted trees for data classification in health care big data. The framework is built on Spark platform which is fast in comparison with other traditional frameworks. Unlike other works that focus on a single technique, our work presents a comparison of six different machine learning techniques along with gradient boosted trees on datasets of different characteristics. Five benchmark health care datasets are considered for experimentation, and the results of different machine learning techniques are discussed in comparison with gradient boosted trees. The metric chosen for comparison is misclassification error rate and the run time of the algorithms. The goal of this paper is to i) Compare the performance of gradient boosted trees with other machine learning techniques in Spark platform specifically for health care big data and ii) Discuss the results from the experiments conducted on datasets of different characteristics thereby drawing inference and conclusion. The experimental results show that the accuracy is largely dependent on the characteristics of the datasets for other machine learning techniques whereas gradient boosting trees yields reasonably stable results in terms of accuracy without largely depending on the dataset characteristics.

Keywords: big data analytics, ensemble machine learning, gradient boosted trees, Spark platform

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Authors: Habtamu Ayenew Asegie

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Wealth, as opposed to income or consumption, implies a more stable and permanent status. Due to natural and human-made difficulties, households' economies will be diminished, and their well-being will fall into trouble. Hence, governments and humanitarian agencies offer considerable resources for poverty and malnutrition reduction efforts. One key factor in the effectiveness of such efforts is the accuracy with which low-income or poor populations can be identified. As a result, this study aims to predict a household’s wealth status using ensemble Machine learning (ML) algorithms. In this study, design science research methodology (DSRM) is employed, and four ML algorithms, Random Forest (RF), Adaptive Boosting (AdaBoost), Light Gradient Boosted Machine (LightGBM), and Extreme Gradient Boosting (XGBoost), have been used to train models. The Ethiopian Demographic and Health Survey (EDHS) dataset is accessed for this purpose from the Central Statistical Agency (CSA)'s database. Various data pre-processing techniques were employed, and the model training has been conducted using the scikit learn Python library functions. Model evaluation is executed using various metrics like Accuracy, Precision, Recall, F1-score, area under curve-the receiver operating characteristics (AUC-ROC), and subjective evaluations of domain experts. An optimal subset of hyper-parameters for the algorithms was selected through the grid search function for the best prediction. The RF model has performed better than the rest of the algorithms by achieving an accuracy of 96.06% and is better suited as a solution model for our purpose. Following RF, LightGBM, XGBoost, and AdaBoost algorithms have an accuracy of 91.53%, 88.44%, and 58.55%, respectively. The findings suggest that some of the features like ‘Age of household head’, ‘Total children ever born’ in a family, ‘Main roof material’ of their house, ‘Region’ they lived in, whether a household uses ‘Electricity’ or not, and ‘Type of toilet facility’ of a household are determinant factors to be a focal point for economic policymakers. The determinant risk factors, extracted rules, and designed artifact achieved 82.28% of the domain expert’s evaluation. Overall, the study shows ML techniques are effective in predicting the wealth status of households.

Keywords: ensemble machine learning, households wealth status, predictive model, wealth status prediction

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Authors: Vinod Kumar Jaysaval, Prateek Agarwal

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Performance prediction of airborne radar is a challenging and cumbersome task in clutter scenario for different types of targets. A generalized model requires to predict the performance of Radar for air targets as well as ground moving targets. In this paper, we propose a generalized model to bring out the performance of airborne radar for different Pulsed Repetition Frequency (PRF) as well as different type of targets. The model provides a platform to bring out different subsystem parameters for different applications and performance requirements under different types of clutter terrain.

Keywords: airborne radar, blind zone, clutter, probability of detection

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Authors: Zhang Shuqi, Liu Dan

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For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

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Authors: Sender Dovchin

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Moving beyond two main frameworks of interlingual and intralingual discrimination, this paper will address the understanding of translingual discrimination. This concept refers to discrimination based on how one uses certain languages, linguistic and communicative repertoires, which are (il)legitimised by the interactants. Translingual discrimination contributes intensity to transnational migrations processes, where migrants with transitional backgrounds seem to illustrate two main characteristics of marginalizations – “name discrimination” and “accentism”. The lifelong accumulation of these characteristics of translingual discrimination may cause negative emotionality, such as translingual inferiority complexes, to its victims. As a result, these transnational migrants seem to adopt varied coping strategies such as CV-whitening and accent purifications while probing for translingual safe spaces. The presentation concludes with the socio-cultural and pedagogical implications of translingual discrimination in relation to the language usage of transnational migrants.

Keywords: accentism, discrimination, migrants, translingualism

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Authors: Ehsan Mehryaar, Seyed Armin Motahari Tabari

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Without a doubt, soil shear strength is the most important property of the soil. The majority of fatal and catastrophic geological accidents are related to shear strength failure of the soil. Therefore, its prediction is a matter of high importance. However, acquiring the shear strength is usually a cumbersome task that might need complicated laboratory testing. Therefore, prediction of it based on common and easy to get soil properties can simplify the projects substantially. In this paper, A hybrid model based on the classification and regression tree algorithm and logistic regression is proposed where each leaf of the tree is an independent regression model. A database of 189 points for clay soil, including Moisture content, liquid limit, plastic limit, clay content, and shear strength, is collected. The performance of the developed model compared to the existing models and equations using root mean squared error and coefficient of correlation.

Keywords: model tree, CART, logistic regression, soil shear strength

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Authors: Shishay Kidanu, Abdullah Alhaj

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Sinkholes in the karst terrain of Greene County, Missouri, pose significant geohazards, imposing challenges on construction and infrastructure development, with potential threats to lives and property. To address these issues, understanding the influencing factors and modeling sinkhole susceptibility is crucial for effective mitigation through strategic changes in land use planning and practices. This study utilizes geographic information system (GIS) software to collect and process diverse data, including topographic, geologic, hydrogeologic, and anthropogenic information. Nine key sinkhole influencing factors, ranging from slope characteristics to proximity to geological structures, were carefully analyzed. The Frequency Ratio method establishes relationships between attribute classes of these factors and sinkhole events, deriving class weights to indicate their relative importance. Weighted integration of these factors is accomplished using the Analytic Hierarchy Process (AHP) and the Weighted Linear Combination (WLC) method in a GIS environment, resulting in a comprehensive sinkhole susceptibility index (SSI) model for the study area. Employing Jenk's natural break classifier method, the SSI values are categorized into five distinct sinkhole susceptibility zones: very low, low, moderate, high, and very high. Validation of the model, conducted through the Area Under Curve (AUC) and Sinkhole Density Index (SDI) methods, demonstrates a robust correlation with sinkhole inventory data. The prediction rate curve yields an AUC value of 74%, indicating a 74% validation accuracy. The SDI result further supports the success of the sinkhole susceptibility model. This model offers reliable predictions for the future distribution of sinkholes, providing valuable insights for planners and engineers in the formulation of development plans and land-use strategies. Its application extends to enhancing preparedness and minimizing the impact of sinkhole-related geohazards on both infrastructure and the community.

Keywords: sinkhole, GIS, analytical hierarchy process, frequency ratio, susceptibility, Missouri

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Authors: S. A. Abdellatef, A. Taniguchi, Namiki, Tsukuba, Ibaraki

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The alterations in the physicochemical characteristics of bio-materials are renowned for their impact in cellular behaviors. Surface chemistry and substratum topography are separately considered as mutable characteristics with deep impact on the overall cell behaviors. In our recent work, we examined the manipulation of the physical cues on hepatic cellular behaviors. We have proven that the geometrical or dimensional characteristics of nano features are essential for the optimum hepatocellular functions. While here, the collective impact of both physical and chemical cues on hepatocellular behaviors was investigated. On which RGD peptide was immobilized on a TiO2 nano pattern that imitates the hierarchically extend collagen nano fibrillar structures. The hepatocytes morphological and functional changes induced by simultaneously combining the diversified cues were investigated. TiO2 substrates that integrate nano topography with the adhesive peptide motif (RGD) had showed an increase in the hepatocellular functionality to the maximum extent. While a significant enhancement in expression of these liver specific markers on RGD coated surfaces were observed compared to uncoated substrates regardless of topography. Consequently in depth understanding of the relationship between various kind of cues and hepatocytes behaviors would be a paving step in the application of tissue engineering and bio reactor technology.

Keywords: biomaterial, tiO2, hepG2, RGD

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: deep learning, convolutional neural network, LSTM, housing prediction

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Authors: Zainal Abu Zarim, Hafizah Omar Zaki

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The objective of this study is to analyse the success factors of entrepreneurs in the Malaysian SMEs in the urge to discover their entrepreneurial leadership characteristics. Data has been collected from top 50 SME award winning companies. The study has used the qualitative approach to data collection, where interviews are dispersed on these selected companies. From these 50 SMEs, only 25 accepted the interview request where one entrepreneur from each SME answered the questions. To successfully run this study, we administered some questions based on Hornaday 42 characteristics of an entrepreneurs, as well some structured questions to determine a successful of a company. The result shows that, entrepreneurs are confident, determine, diligent, flexible, responsive to challenges, responsible, foresight, courageous, aggressive, and committed. Consistent to this, several elements that makes the company successful includes (1) strong financial control, (2) continuous improvement, (3) product quality and product safety as top priority, (4) hard work and team work, and (5) eagerness in taking challenges. These results has deemed that entrepreneurs in many aspects are also leaders that are risk averse and determine, and are eager to work on continuous improvement in a financially strong company.

Keywords: characteristics of entrepreneurs, success of a company, key success factors, Malaysian SMEs

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Authors: Hadia Abdel Aziz, Raghda El Ebrashi

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Business models are shaped by their design space or the environment they are designed to be implemented in. The rapidly changing economic, technological, political, regulatory and market external environment severely affects business logic. This is particularly true for social enterprises whose core mission is to transform their environments, and thus, their whole business logic revolves around the interchange between the enterprise and the environment. The context in which social business operates imposes different business design constraints while at the same time, open up new design opportunities. It is also affected to a great extent by the impact that successful enterprises generate; a continuous loop of interaction that needs to be managed through a dynamic capability in order to generate a lasting powerful impact. This conceptual research synthesizes and analyzes literature on social enterprise, social enterprise business models, business model innovation, business model design, and the open system view theory to propose a new business model design process for social enterprises that takes into account the critical role of environmental factors. This process would help the social enterprise develop a dynamic capability that ensures the alignment of its business model to its environmental context, thus, maximizing its probability of success.

Keywords: social enterprise, business model, business model design, business model environment

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Authors: Abdel Hadi Ebraheim

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This paper introduces a new extension of the Inverse Pareto distribution in the framework of Marshal-Olkin (1997) family of distributions. This model is capable of modeling various shapes of aging and failure data. The statistical properties of the new model are discussed. Several methods are used to estimate the parameters involved. Explicit expressions are derived for different types of moments of value in reliability analysis are obtained. Besides, the order statistics of samples from the new proposed model have been studied. Finally, the usefulness of the new model for modeling reliability data is illustrated using two real data sets with simulation study.

Keywords: pareto distribution, marshal-Olkin, reliability, hazard functions, moments, estimation

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Authors: Dua K. S. Y. Klaas, Monzur A. Imteaz, Ika Sudiayem, Elkan M. E. Klaas, Eldav C. M. Klaas

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Due to its porous nature, karst terrains – geomorphologically developed from dissolved formations, is vulnerable to water shortage and deteriorated water quality. Therefore, a solid comprehension on sub-surface flow of karst landscape is essential to assess the long-term availability of groundwater resources. In this paper, a single-continuum model using a finite difference model, MODLFOW, was constructed to represent an unconfined carbonate aquifer in a tropical karst island of Rote in Indonesia. The model, spatially discretized in 20 x 20 m grid cells, was calibrated and validated using available groundwater level and atmospheric variables. In the calibration and validation steps, Parameter Estimation (PEST) and geostatistical pilot point methods were employed to estimate hydraulic conductivity and specific yield values. The results show that the model is able to represent the sub-surface flow indicated by good model performances both in calibration and validation steps. The final model can be used as a robust representation of the system for future study on climate and land use scenarios.

Keywords: carbonate aquifer, karst, sub-surface flow, groundwater model

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Authors: Kisik Song, Sungjoo Lee

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Recent years have seen an increasing number of patent disputes due to excessive competition in the global market and a reduced technology life-cycle; this has increased the risk of investment in technology development. While many global companies have started developing a methodology to identify promising technologies and assess for decisions, the existing methodology still has some limitations. Post hoc assessments of the new technology are not being performed, especially to determine whether the suggested technologies turned out to be promising. For example, in existing quantitative patent analysis, a patent’s citation information has served as an important metric for quality assessment, but this analysis cannot be applied to recently registered patents because such information accumulates over time. Therefore, we propose a new technology assessment model that can replace citation information and positively affect technological development based on post hoc analysis of the patents for promising technologies. Additionally, we collect customer reviews on a target technology to extract keywords that show the customers’ needs, and we determine how many keywords are covered in the new technology. Finally, we construct a portfolio (based on a technology assessment from patent information) and a customer-based marketability assessment (based on review data), and we use them to visualize the characteristics of the new technologies.

Keywords: technology assessment, patents, citation information, opinion mining

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Authors: H. K. Esfahani, B. Datta

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Groundwater is one of the most important natural resources in many parts of the world; however it is widely polluted due to human activities. Currently, effective and reliable groundwater management and remediation strategies are obtained using characterization of groundwater pollution sources, where the measured data in monitoring locations are utilized to estimate the unknown pollutant source location and magnitude. However, accurately identifying characteristics of contaminant sources is a challenging task due to uncertainties in terms of predicting source flux injection, hydro-geological and geo-chemical parameters, and the concentration field measurement. Reactive transport of chemical species in contaminated groundwater systems, especially with multiple species, is a complex and highly non-linear geochemical process. Although sufficient concentration measurement data is essential to accurately identify sources characteristics, available data are often sparse and limited in quantity. Therefore, this inverse problem-solving method for characterizing unknown groundwater pollution sources is often considered ill-posed, complex and non- unique. Different methods have been utilized to identify pollution sources; however, the linked simulation-optimization approach is one effective method to obtain acceptable results under uncertainties in complex real life scenarios. With this approach, the numerical flow and contaminant transport simulation models are externally linked to an optimization algorithm, with the objective of minimizing the difference between measured concentration and estimated pollutant concentration at observation locations. Concentration measurement data are very important to accurately estimate pollution source properties; therefore, optimal design of the monitoring network is essential to gather adequate measured data at desired times and locations. Due to budget and physical restrictions, an efficient and effective approach for groundwater pollutant source characterization is to design an optimal monitoring network, especially when only inadequate and arbitrary concentration measurement data are initially available. In this approach, preliminary concentration observation data are utilized for preliminary source location, magnitude and duration of source activity identification, and these results are utilized for monitoring network design. Further, feedback information from the monitoring network is used as inputs for sequential monitoring network design, to improve the identification of unknown source characteristics. To design an effective monitoring network of observation wells, optimization and interpolation techniques are used. A simulation model should be utilized to accurately describe the aquifer properties in terms of hydro-geochemical parameters and boundary conditions. However, the simulation of the transport processes becomes complex when the pollutants are chemically reactive. Three dimensional transient flow and reactive contaminant transport process is considered. The proposed methodology uses HYDROGEOCHEM 5.0 (HGCH) as the simulation model for flow and transport processes with chemically multiple reactive species. Adaptive Simulated Annealing (ASA) is used as optimization algorithm in linked simulation-optimization methodology to identify the unknown source characteristics. Therefore, the aim of the present study is to develop a methodology to optimally design an effective monitoring network for pollution source characterization with reactive species in polluted aquifers. The performance of the developed methodology will be evaluated for an illustrative polluted aquifer sites, for example an abandoned mine site in Queensland, Australia.

Keywords: monitoring network design, source characterization, chemical reactive transport process, contaminated mine site

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Authors: Julianne Cai, Weili Xue, Yibin Wu

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Social media retailing (SMR) platforms have become popular nowadays. It is characterized by a creative combination of content creation and product selling, which differs from traditional e-tailing (TE) with product selling alone. Motivated by real-world practices like social media platforms “TikTok” and douyin.com, we endeavor to study if the SMR model performs better than the TE model in a monopoly setting. By building a stylized economic model, we find that the SMR model does not always outperform the TE model. Specifically, when the SMR platform collects less commission from the seller than the TE platform, the seller, consumers, and social welfare all benefit more from the SMR model. In contrast, the platform benefits more from the SMR model if and only if the creator’s social influence is high enough or the cost of content creation is small enough. For the incentive structure of the content rewards in the SMR model, we found that a strong incentive mechanism (e.g., the quadratic form) is more powerful than a weak one (e.g., the linear form). The previous one will encourage the creator to choose a much higher quality level of content creation and meanwhile allowing the platform, consumers, and social welfare to become better off. Counterintuitively, providing more generous content rewards is not always helpful for the creator (seller), and it may reduce her profit. Our findings will guide the platform to effectively design incentive mechanisms to boost the content creation and retailing in the SMR model and help the influencers efficiently create content, engage their followers (fans), and price their products sold on the SMR platform.

Keywords: content creation, creator economy, incentive strategy, platform retailing

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Authors: Jana Abu Ahmada, Zaineb Mohamed, Ilyasse Aksikas

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The linear quadratic control system of hyperbolic first order partial differential equations (PDEs) are presented. The aim of this research is to control chemical reactions. This is achieved by converting the PDEs system to ordinary differential equations (ODEs) using the method of characteristics to reduce the system to control it by using the integral reinforcement learning. The designed controller is applied to a catalytic cracking reactor. Background—Transport-Reaction systems cover a large chemical and bio-chemical processes. They are best described by nonlinear PDEs derived from mass and energy balances. As a main application to be considered in this work is the catalytic cracking reactor. Indeed, the cracking reactor is widely used to convert high-boiling, high-molecular weight hydrocarbon fractions of petroleum crude oils into more valuable gasoline, olefinic gases, and others. On the other hand, control of PDEs systems is an important and rich area of research. One of the main control techniques is feedback control. This type of control utilizes information coming from the system to correct its trajectories and drive it to a desired state. Moreover, feedback control rejects disturbances and reduces the variation effects on the plant parameters. Linear-quadratic control is a feedback control since the developed optimal input is expressed as feedback on the system state to exponentially stabilize and drive a linear plant to the steady-state while minimizing a cost criterion. The integral reinforcement learning policy iteration technique is a strong method that solves the linear quadratic regulator problem for continuous-time systems online in real time, using only partial information about the system dynamics (i.e. the drift dynamics A of the system need not be known), and without requiring measurements of the state derivative. This is, in effect, a direct (i.e. no system identification procedure is employed) adaptive control scheme for partially unknown linear systems that converges to the optimal control solution. Contribution—The goal of this research is to Develop a characteristics-based optimal controller for a class of hyperbolic PDEs and apply the developed controller to a catalytic cracking reactor model. In the first part, developing an algorithm to control a class of hyperbolic PDEs system will be investigated. The method of characteristics will be employed to convert the PDEs system into a system of ODEs. Then, the control problem will be solved along the characteristic curves. The reinforcement technique is implemented to find the state-feedback matrix. In the other half, applying the developed algorithm to the important application of a catalytic cracking reactor. The main objective is to use the inlet fraction of gas oil as a manipulated variable to drive the process state towards desired trajectories. The outcome of this challenging research would yield the potential to provide a significant technological innovation for the gas industries since the catalytic cracking reactor is one of the most important conversion processes in petroleum refineries.

Keywords: PDEs, reinforcement iteration, method of characteristics, riccati equation, cracking reactor

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Authors: Irene Carter, Roy Hanes, Judy MacDonald

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Considering that disability is universal and people with disabilities are part of all societies; that there is a connection between the disabled individual and the societal; and that it is society and social arrangements that disable people with impairments, contemporary disability discourse emphasizes the social model of disability to counter medical and rehabilitative models of disability. However, the social model does not go far enough in addressing the issues of oppression and inclusion. The authors indicate that the social model does not specifically or adequately denote the oppression of persons with disabilities, which is a central component of progressive social work practice with people with disabilities. The social model of disability does not go far enough in deconstructing disability and offering social workers, as well as people with disabilities a way of moving forward in terms of practice anchored in individual, familial and societal change. The social model of disability is expanded by incorporating principles of anti-oppression social work practice. Although the contextual analysis of the social model of disability is an important component there remains a need for social workers to provide service to individuals and their families, which will be illustrated through anti-oppressive practice (AOP). By applying an anti-oppressive model of practice to the above definitions, the authors not only deconstruct disability paradigms but illustrate how AOP offers a framework for social workers to engage with people with disabilities at the individual, familial and community levels of practice, promoting an emancipatory focus in working with people with disabilities. An anti- social- oppression social work model of disability connects the day-to-day hardships of people with disabilities to the direct consequence of oppression in the form of ableism. AOP theory finds many of its basic concepts within social-oppression theory and the social model of disability. It is often the case that practitioners, including social workers and psychologists, define people with disabilities’ as having or being a problem with the focus placed upon adjustment and coping. A case example will be used to illustrate how an AOP paradigm offers social work a more comprehensive and critical analysis and practice model for social work practice with and for people with disabilities than the traditional medical model, rehabilitative and social model approaches.

Keywords: anti-oppressive practice, disability, people with disabilities, social model of disability

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Authors: Claudio Di Benedetto, Irene Bengo

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The objective of the paper is to study how large European companies develop social business (SB) at the base of the economic pyramid (BoP). BoP markets are defined as the four billions people living with an annual income below $3,260 in local purchasing power. Despite they are heterogeneous in terms of geographic range they present some common characteristics: the presence of significant unmet (social) needs, high level of informal economy and the so-called ‘poverty penalty’. As a result, most people living at BoP are excluded from the value created by the global market economy. But it is worth noting, that BoP population with an aggregate purchasing power of around $5 trillion a year, represent a huge opportunity for companies that want to enhance their long-term profitability perspective. We suggest that in this context, the development of SB is, for companies, an innovative and promising way to satisfy unmet social needs and to experience new forms of value creation. Indeed, SB can be considered a strategic model to develop CSR programs that fully integrate the social dimension into the business to create economic and social value simultaneously. Despite in literature many studies have been conducted on social business, only few have explicitly analyzed such phenomenon from a company perspective and their role in the development of such initiatives remains understudied with fragmented results. To fill this gap the paper analyzes the key characteristics of the social business initiatives developed by European companies at BoP. The study was performed analyzing 1475 European companies participating in the United Nation Global Compact, the world’s leading corporate social responsibility program. Through the analysis of the corporate websites the study identifies companies that actually do SB at BoP. For SB initiatives identified, information were collected according to a framework adapted from the SB model developed by preliminary results show that more than one hundred European companies have already implemented social businesses at BoP accounting for the 6,5% of the total. This percentage increases to 15% if the focus is on companies with more than 10.440 employees. In terms of geographic distribution 80% of companies doing SB at BoP are located in western and southern Europe. The companies more active in promoting SB belong to financial sector (20%), energy sector (17%) and food and beverage sector (12%). In terms of social needs addressed almost 30% of the companies develop SB to provide access to energy and WASH, 25% of companies develop SB to reduce local unemployment or to promote local entrepreneurship and 21% of companies develop SB to promote financial inclusion of poor. In developing SB companies implement different social business configurations ranging from forms of outsourcing to internal development models. The study identifies seven main configurations through which company develops social business and each configuration present distinguishing characteristics respect to the involvement of the company in the management, the resources provided and the benefits achieved. By performing different analysis on data collected the paper provides detailed insights on how European companies develop SB at BoP.

Keywords: base of the economic pyramid, corporate social responsibility, social business, social enterprise

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Authors: Saad M. Darwish

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Recently, software quality issues have come to be seen as important subject as we see an enormous growth of agencies involved in software industries. However, these agencies cannot guarantee the quality of their products, thus leaving users in uncertainties. Software certification is the extension of quality by means that quality needs to be measured prior to certification granting process. This research participates in solving the problem of software assessment by proposing a model for assessment and certification of software product that uses a fuzzy inference engine to integrate both of process–driven and application-driven quality assurance strategies. The key idea of the on hand model is to improve the compactness and the interpretability of the model’s fuzzy rules via employing an ant colony optimization algorithm (ACO), which tries to find good rules description by dint of compound rules initially expressed with traditional single rules. The model has been tested by case study and the results have demonstrated feasibility and practicability of the model in a real environment.

Keywords: software quality, quality assurance, software certification model, software assessment

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Authors: Hui Wei, Zheng Dong

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Object recognition has long been a challenging task in computer vision. Yet the human brain, with the ability to rapidly and accurately recognize visual stimuli, manages this task effortlessly. In the past decades, advances in neuroscience have revealed some neural mechanisms underlying visual processing. In this paper, we present a novel model inspired by the visual pathway in primate brains. This multi-layer neural network model imitates the hierarchical convergent processing mechanism in the visual pathway. We show that local image features generated by this model exhibit robust discrimination and even better generalization ability compared with some existing image descriptors. We also demonstrate the application of this model in an object recognition task on image data sets. The result provides strong support for the potential of this model.

Keywords: biological model, feature extraction, multi-layer neural network, object recognition

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Authors: H. Singh, H. Bhowmick

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Now-a-days, particle based lubricants have been widely used to enhance the lubrication performance. Use of tailor made micro/nanofluids can reduce the friction losses and dissipate heat in a better way. Use of Carbon Nanotubes (CNTs) has gained interests because of its structure that can endure much better in a system mechanically or thermally in comparison to any other additive in oil. On the other hand, nanoclays have been characterized mechanically and tribologically for the use of clay/polymer composite, and they have been gaining huge interest. Hence it is interesting to be investigated the effect of nanoclays as additive in oil. Thermophysical characteristics of lubricant play a predominant role in defining the friction and wear characteristics of lubricated contacts. However, very limited studies have been carried out to correlate the thermophysical properties of nanolubricants with their lubricity characteristics. Besides, most of the lubricant formulations till dates are found to be optimized for steel/steel contacts. In the present study, Multiwall Carbon Nanotube (MWCNT) and nanoclay are used as particle additives in mineral oil to develop nanofluids of various concentrations. The prepared lubricants are tested for their rheological, thermal and lubricity characteristics under aluminium-steel contacts. From the thermophysical investigation, it is observed that nanoclay particles significantly improve the viscosity of lubricant with an insignificant improvement in thermal conductivity. On the other hand, MWCNT particles moderately increase the viscosity but significantly increase the thermal conductivity of the base oil. Frictional responses of the nanofluids are characterized using a Pin-on-Disc tribometer which reveal some interesting facts. The findings from this study will greatly aid in formulating the particle based lubricants for cutting fluid in metal forming industries as well as fully developed nanolubricants for aluminium and Aluminium Metal Matrix Composite (AMMC) tribocontact for the use in the automotive and their allied industries.

Keywords: MWCNT, Multiwall Carbon Nanotube, nanoclay, nanolubricant, rheology, thermal conductivity

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Authors: Myungjin Lee, Daegun Han, Jongsung Kim, Soojun Kim, Hung Soo Kim

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Recently, the localized heavy rainfall and typhoons are frequently occurred due to the climate change and the damage is becoming bigger. Therefore, we may need a more accurate prediction of the rainfall and runoff. However, the gauge rainfall has the limited accuracy in space. Radar rainfall is better than gauge rainfall for the explanation of the spatial variability of rainfall but it is mostly underestimated with the uncertainty involved. Therefore, the ensemble of radar rainfall was simulated using error structure to overcome the uncertainty and gauge rainfall. The simulated ensemble was used as the input data of the rainfall-runoff models for obtaining the ensemble of runoff hydrographs. The previous studies discussed about the accuracy of the rainfall-runoff model. Even if the same input data such as rainfall is used for the runoff analysis using the models in the same basin, the models can have different results because of the uncertainty involved in the models. Therefore, we used two models of the SSARR model which is the lumped model, and the Vflo model which is a distributed model and tried to simulate the optimum runoff considering the uncertainty of each rainfall-runoff model. The study basin is located in Han river basin and we obtained one integrated runoff hydrograph which is an optimum runoff hydrograph using the blending methods such as Multi-Model Super Ensemble (MMSE), Simple Model Average (SMA), Mean Square Error (MSE). From this study, we could confirm the accuracy of rainfall and rainfall-runoff model using ensemble scenario and various rainfall-runoff model and we can use this result to study flood control measure due to climate change. Acknowledgements: This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 18AWMP-B083066-05).

Keywords: radar rainfall ensemble, rainfall-runoff models, blending method, optimum runoff hydrograph

Procedia PDF Downloads 280

Authors: S. Farid F. Mojtahedi, Ali Khosravi, Behnaz Naeimian, S. Adel A. Hosseini

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Land subsidence is a gradual settling or sudden sinking of the land surface from changes that take place underground. There are different causes of land subsidence; most notably, ground-water overdraft and severe weather conditions. Subsidence of the land surface due to ground water overdraft is caused by an increase in the intergranular pressure in unconsolidated aquifers, which results in a loss of buoyancy of solid particles in the zone dewatered by the falling water table and accordingly compaction of the aquifer. On the other hand, exploitation of underground water may result in significant changes in degree of saturation of soil layers above the water table, increasing the effective stress in these layers, and considerable soil settlements. This study focuses on estimation of soil moisture at surface using different methods. Specifically, different methods for the estimation of moisture content at the soil surface, as an important term to solve Richard’s equation and estimate soil moisture profile are presented, and their results are discussed through comparison with field measurements obtained from Yanco1 station in south-eastern Australia. Surface soil moisture is not easy to measure at the spatial scale of a catchment. Due to the heterogeneity of soil type, land use, and topography, surface soil moisture may change considerably in space and time.

Keywords: artificial neural network, empirical method, remote sensing, surface soil moisture, unsaturated soil

Procedia PDF Downloads 359

Authors: Tooraj Karimi, Mohammadreza Sadeghi Moghadam

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Grey set theory has the advantage of using fewer data to analyze many factors, and it is therefore more appropriate for system study rather than traditional statistical regression which require massive data, normal distribution in the data and few variant factors. So, in this paper grey clustering and entropy of coefficient vector of grey evaluations are used to analyze energy consumption in buildings of the Oil Ministry in Tehran. In fact, this article intends to analyze the results of energy audit reports and defines most favorable characteristics of system, which is energy consumption of buildings, and most favorable factors affecting these characteristics in order to modify and improve them. According to the results of the model, ‘the real Building Load Coefficient’ has been selected as the most important system characteristic and ‘uncontrolled area of the building’ has been diagnosed as the most favorable factor which has the greatest effect on energy consumption of building. Grey clustering in this study has been used for two purposes: First, all the variables of building relate to energy audit cluster in two main groups of indicators and the number of variables is reduced. Second, grey clustering with variable weights has been used to classify all buildings in three categories named ‘no standard deviation’, ‘low standard deviation’ and ‘non- standard’. Entropy of coefficient vector of Grey evaluations is calculated to investigate greyness of results. It shows that among the 38 buildings surveyed in terms of energy consumption, 3 cases are in standard group, 24 cases are in ‘low standard deviation’ group and 11 buildings are completely non-standard. In addition, clustering greyness of 13 buildings is less than 0.5 and average uncertainly of clustering results is 66%.

Keywords: energy audit, grey set theory, grey incidence matrixes, grey clustering, Iran oil ministry

Procedia PDF Downloads 373

Authors: Natia Gavardashvili

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On June 13-14 of 2015, a landslide in village Akhaldaba was formed as a result of the intense rains in the water catch basin of the river Vere. As a result of the landslide movement, freshets and mudflows originated, and unfortunately, there were victims: zoo animals and birds were drawn in the flood and 12 people died due to the flooded motor road. The goal of the study is to give the analysis of the results of the field and scientific research held in 2015-2017 and to generalize them to the water catch basins of the erosive-mudflow rivers of other mountain landscapes of Georgia. By considering the field and scientific works, the main geographic, geological, climatic, hydrological and hydraulic properties of the erosive-mudflow tributaries of the water catch basin of the river Vere were evaluated and the probabilities of mudflow formation by considering relevant risk-factors were identified. The typology of the water catch basins of erosive-mudflow rivers of Georgia was identified on the example of the river Vere based on the field and scientific study, and their genesis, frequency of mudflow formation and volume of the drift material was identified. By using the empirical and theoretical dependencies, the amount of solid admixtures in the mudflow formed in the gorge of the river Jokhona, the right tributary of the river Vere was identified by considering the shape of the stones.

Keywords: water catchment basin, erosion, mudflow, typology

Procedia PDF Downloads 276

Authors: Idrissa Kayijuka

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The logistic regression and Cox regression models (proportional hazard model) at present are being employed in the analysis of prospective epidemiologic research looking into risk factors in their application on chronic diseases. However, a theoretical relationship between the two models has been studied. By definition, Cox regression model also called Cox proportional hazard model is a procedure that is used in modeling data regarding time leading up to an event where censored cases exist. Whereas the Logistic regression model is mostly applicable in cases where the independent variables consist of numerical as well as nominal values while the resultant variable is binary (dichotomous). Arguments and findings of many researchers focused on the overview of Cox and Logistic regression models and their different applications in different areas. In this work, the analysis is done on secondary data whose source is SPSS exercise data on BREAST CANCER with a sample size of 1121 women where the main objective is to show the application difference between Cox regression model and logistic regression model based on factors that cause women to die due to breast cancer. Thus we did some analysis manually i.e. on lymph nodes status, and SPSS software helped to analyze the mentioned data. This study found out that there is an application difference between Cox and Logistic regression models which is Cox regression model is used if one wishes to analyze data which also include the follow-up time whereas Logistic regression model analyzes data without follow-up-time. Also, they have measurements of association which is different: hazard ratio and odds ratio for Cox and logistic regression models respectively. A similarity between the two models is that they are both applicable in the prediction of the upshot of a categorical variable i.e. a variable that can accommodate only a restricted number of categories. In conclusion, Cox regression model differs from logistic regression by assessing a rate instead of proportion. The two models can be applied in many other researches since they are suitable methods for analyzing data but the more recommended is the Cox, regression model.

Keywords: logistic regression model, Cox regression model, survival analysis, hazard ratio

Procedia PDF Downloads 455

Authors: Saba Rahman, Arvind K. Jain, S. D. Bharti, T. K. Datta

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The present study compares the semi-empirical wake-oscillator models that are used to predict vortex-induced vibration of structures. These models include those proposed by Facchinetti, Farshidian, and Dolatabadi, and Skop and Griffin. These models combine a wake oscillator model resembling the Van der Pol oscillator model and a single degree of freedom oscillation model. In order to use these models for estimating the top displacement of chimneys, the first mode vibration of the chimneys is only considered. The modal equation of the chimney constitutes the single degree of freedom model (SDOF). The equations of the wake oscillator model and the SDOF are simultaneously solved using an iterative procedure. The empirical parameters used in the wake-oscillator models are estimated using a newly developed approach, and response is compared with experimental data, which appeared comparable. For carrying out the iterative solution, the ode solver of MATLAB is used. To carry out the comparative study, a tall concrete chimney of height 210m has been chosen with the base diameter as 28m, top diameter as 20m, and thickness as 0.3m. The responses of the chimney are also determined using the linear model proposed by E. Simiu and the deterministic model given in Eurocode. It is observed from the comparative study that the responses predicted by the Facchinetti model and the model proposed by Skop and Griffin are nearly the same, while the model proposed by Fashidian and Dolatabadi predicts a higher response. The linear model without considering the aero-elastic phenomenon provides a less response as compared to the non-linear models. Further, for large damping, the prediction of the response by the Euro code is relatively well compared to those of non-linear models.

Keywords: chimney, deterministic model, van der pol, vortex-induced vibration

Procedia PDF Downloads 221