Search results for: estimated model

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: Liaquat Ali, Khurshid Natasha

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Low resource countries are not usually equipped with the organizational tools to implement health care for chronic diseases, and thus, providing effective diabetes care in such countries is a challenging task. Diabetic Association of Bangladesh (BADAS in Bengali acronym) has created a stimulating example to meet this challenge. Starting its journey in 1956 with 39 patients in a small tin shed clinic BADAS, and its affiliated associations now operate 90 hospitals and health centres all over the country. Together, these facilities provide integrated health care to about 1.5 million registered diabetic patients which constitute about 20% of the estimated diabetic population in the country. BADAS has also become a pioneer in health manpower generation in Bangladesh. Along with its affiliates, it now runs 3 Medical Colleges (to generate graduate physicians), 2 Nursing Institutes, and 2 Postgraduate Institutes which conduct 25 postgraduate courses (under the University of Dhaka) in various basic, clinical and public health disciplines. BADAS gives great emphasis on research, which encompasses basic, clinical as well as public health areas. BADAS is an ideal example of public-private partnership in health as most of its infrastructure has been created through government support but it is almost self-reliant in managing its revenue budget which approached approximately 40 million US dollar during 2010. BADAS raises resources by providing high-quality services to the people, both diabetic and non-diabetic. At the same time, BADAS has developed a cross financing model, to support diabetic patients in general and poor diabetic patients (identified through a social welfare network) in particular, through redistribution of the resources. Along with financial sustainability BADAS ensure organizational sustainability through a process of decentralization, community ownership, and democratic management. Presently a large scale pilot project (named as a Health Care Development Project or HCDP) is under implementation under BADAS umbrella with an objective to transform the diabetes care model to a health care model in general. It is expected to create further evidence on providing sustainable (with social safety net) health care delivery for diabetes, and other chronic illnesses as an integral part of general health care delivery in a resource constrained setting.

Keywords: Bangladesh, self sustain, health care, constrain

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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: 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: 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: 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

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Authors: Bill D. Geis

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Suicide wrongful death forensic cases are the fastest rising tort in mental health law. It is estimated that suicide-related cases have accounted for 15% of U.S. malpractice claims since 2006. Most suicide-related personal injury claims fall into the legal category of “wrongful death.” Though mental health experts may be called on to address a range of forensic questions in wrongful death cases, the central consultation that most experts provide is about the negligence element—specifically, the issue of whether the clinician met the clinical standard of care in assessing, treating, and managing the deceased person’s mental health care. Standards of care, varying from U.S. state to state, are broad and address what a reasonable clinician might do in a similar circumstance. This fact leaves the issue of the suicide standard of care, in each case, up to forensic experts to put forth a reasoned estimate of what the standard of care should have been in the specific case under litigation. Because the general state guidelines for standard of care are broad, forensic experts are readily retained to provide scientific and clinical opinions about whether or not a clinician met the standard of care in their suicide assessment, treatment, and management of the case. In the past and in much of current practice, the assessment of suicide has centered on the elicitation of verbalized suicide ideation. Research in recent years, however, has indicated that the majority of persons who end their lives do not say they are suicidal at their last medical or psychiatric contact. Near-term risk assessment—that goes beyond verbalized suicide ideation—is needed. Our previous research employed structural equation modeling to predict lethal suicide risk--eight negative thought patterns (feeling like a burden on others, hopelessness, self-hatred, etc.) mediated by nine transdiagnostic clinical factors (mental torment, insomnia, substance abuse, PTSD intrusions, etc.) were combined to predict acute lethal suicide risk. This structural equation model, the Lethal Suicide Risk Pattern (LSRP), Acute model, had excellent goodness-of-fit [χ2(df) = 94.25(47)***, CFI = .98, RMSEA = .05, .90CI = .03-.06, p(RMSEA = .05) = .63. AIC = 340.25, ***p < .001.]. A further SEQ analysis was completed for this paper, adding a measure of Acute Suicide Ideation to the previous SEQ. Acceptable prediction model fit was no longer achieved [χ2(df) = 3.571, CFI > .953, RMSEA = .075, .90% CI = .065-.085, AIC = 529.550].This finding suggests that, in this additional study, immediate verbalized suicide ideation information was unhelpful in the assessment of lethal risk. The LSRP and other dynamic, near-term risk models (such as the Acute Suicide Affective Disorder Model and the Suicide Crisis Syndrome Model)—going beyond elicited suicide ideation—need to be incorporated into current clinical suicide assessment training. Without this training, the standard of care for suicide assessment is out of sync with current research—an emerging dilemma for the forensic evaluation of suicide wrongful death cases.

Keywords: forensic evaluation, standard of care, suicide, suicide assessment, wrongful death

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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

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Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

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Authors: S. O. Oyamakin, A. U. Chukwu

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Richard's growth equation being a generalized logistic growth equation was improved upon by introducing an allometric parameter using the hyperbolic sine function. The integral solution to this was called hyperbolic Richard's growth model having transformed the solution from deterministic to a stochastic growth model. Its ability in model prediction was compared with the classical Richard's growth model an approach which mimicked the natural variability of heights/diameter increment with respect to age and therefore provides a more realistic height/diameter predictions using the coefficient of determination (R2), Mean Absolute Error (MAE) and Mean Square Error (MSE) results. The Kolmogorov-Smirnov test and Shapiro-Wilk test was also used to test the behavior of the error term for possible violations. The mean function of top height/Dbh over age using the two models under study predicted closely the observed values of top height/Dbh in the hyperbolic Richard's nonlinear growth models better than the classical Richard's growth model.

Keywords: height, Dbh, forest, Pinus caribaea, hyperbolic, Richard's, stochastic

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Authors: Tengqin Han

Abstract:

Delinquency has been a crucial factor in economics throughout the years. Commonly seen in credit card and mortgage, it played one of the crucial roles in causing the most recent financial crisis in 2008. In each case, a delinquency is a sign of the loaner being unable to pay off the debt, and thus may cause a lost of property in the end. Individually, one case of delinquency seems unimportant compared to the entire credit system. China, as an emerging economic entity, the national strength and economic strength has grown rapidly, and the gross domestic product (GDP) growth rate has remained as high as 8% in the past decades. However, potential risks exist behind the appearance of prosperity. Among the risks, the credit system is the most significant one. Due to long term and a large amount of balance of the mortgage, it is critical to monitor the risk during the performance period. In this project, about 300,000 mortgage account data are analyzed in order to develop a predictive model to predict the probability of delinquency. Through univariate analysis, the data is cleaned up, and through bivariate analysis, the variables with strong predictive power are detected. The project is divided into two parts. In the first part, the analysis data of 2005 are split into 2 parts, 60% for model development, and 40% for in-time model validation. The KS of model development is 31, and the KS for in-time validation is 31, indicating the model is stable. In addition, the model is further validation by out-of-time validation, which uses 40% of 2006 data, and KS is 33. This indicates the model is still stable and robust. In the second part, the model is improved by the addition of macroeconomic economic indexes, including GDP, consumer price index, unemployment rate, inflation rate, etc. The data of 2005 to 2010 is used for model development and validation. Compared with the base model (without microeconomic variables), KS is increased from 41 to 44, indicating that the macroeconomic variables can be used to improve the separation power of the model, and make the prediction more accurate.

Keywords: delinquency, mortgage, model development, model validation

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Authors: Eunmi Koh, Bogyoung Choi, Dayeon Ryu, Jee-Yeon Lee, Sungok Kwon, Cho-Il Kim

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Ethyl carbamate(EC) is a probable human carcinogen (Group 2A) found in alcoholic beverages and fermented foods. A total of 351 samples including fermented foods and alcoholic beverages were chosen from 734 foods appeared in the pooled intake data of 2008, 2009, 2010, and 2011 Korea National Health & Nutrition Examination Survey (KNHANES). Sampling was carried out from September 2013 to July 2016 in 18 supermarkets of 9 metropolitan cities in Korea. The samples were pooled, prepared according to various cooking methods, and analyzed. A total of 1245 samples were analyzed using gas chromatograph-mass spectrometer. EC was detected in 13 items (1.0%), which ranged from not-detected to 151 g/kg. Alcoholic beverages (maesilju, whisky, and bokbunjaju) and fermented soy products (soy sauce and soybean paste) were the food items with relatively higher EC levels. Dietary intake of EC in the Korean population was estimated to be 2.11 ng/kg body weight (bw) per day for average population and 8.42 ng/kg bw per day for high consumers (the 97.5th percentile). When the estimated average dietary exposure to EC was compared with the Benchmark Dose Lower Confidence Limit 10% (BMDL10) of 0.3 mg/kg bw per day, margin of exposure (MOE) values of 1420000 to 28000000 were observed. This indicates that there is no health concern for the Korean population.

Keywords: ethyl carbamate, total diet study, dietary exposure, margin of exposure

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Authors: Mustafa Kamoona, Mohamed El-Sharkawy

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Although the latest Wireless Local Area Network technology Wi-Fi 802.11i standard addresses many of the security weaknesses of the antecedent Wired Equivalent Privacy (WEP) protocol, there are still scenarios where the network security are still vulnerable. The first security model that 802.11i offers is the Personal model which is very cheap and simple to install and maintain, yet it uses a Pre Shared Key (PSK) and thus has a low to medium security level. The second model that 802.11i provide is the Enterprise model which is highly secured but much more expensive and difficult to install/maintain and requires the installation and maintenance of an authentication server that will handle the authentication and key management for the wireless network. A central issue with the personal model is that the PSK needs to be shared with all the devices that are connected to the specific Wi-Fi network. This pre-shared key, unless changed regularly, can be cracked using offline dictionary attacks within a matter of hours. The key is burdensome to change in all the connected devices manually unless there is some kind of algorithm that coordinate this PSK update. The key idea of this paper is to propose a new algorithm that proactively and effectively coordinates the pre-shared key generation, management, and distribution in the cheap WPA/WPA2 personal security model using only a DD-WRT router.

Keywords: Wi-Fi, WPS, TLS, DD-WRT

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Authors: Syazreen N. Shair, Saiful A. Ishak, Aida Y. Yusof, Azizah Murad

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In this paper, we forecast age-specific Malaysian mortality rates and life expectancy at births by gender and ethnic groups including Malay, Chinese and Indian. Two mortality forecasting models are adopted the original Lee-Carter model and its recent modified version, the product ratio coherent model. While the first forecasts the mortality rates for each subpopulation independently, the latter accounts for the relationship between sub-populations. The evaluation of both models is performed using the out-of-sample forecast errors which are mean absolute percentage errors (MAPE) for mortality rates and mean forecast errors (MFE) for life expectancy at births. The best model is then used to perform the long-term forecasts up to the year 2030, the year when Malaysia is expected to become an aged nation. Results suggest that in terms of overall accuracy, the product ratio model performs better than the original Lee-Carter model. The association of lower mortality group (Chinese) in the subpopulation model can improve the forecasts of high mortality groups (Malay and Indian).

Keywords: coherent forecasts, life expectancy at births, Lee-Carter model, product-ratio model, mortality rates

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Authors: Keerthi S. Shetty, Annappa Basava

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In recent years, modelling of biological systems represented by biochemical reactions has become increasingly important in Systems Biology. Biological systems represented by biochemical reactions are highly stochastic in nature. Probabilistic model is often used to describe such systems. One of the main challenges in Systems biology is to combine absolute experimental data into probabilistic model. This challenge arises because (1) some molecules may be present in relatively small quantities, (2) there is a switching between individual elements present in the system, and (3) the process is inherently stochastic on the level at which observations are made. In this paper, we describe a novel idea of combining absolute experimental data into probabilistic model using tool R2. Through a case study of the Transcription Process in Prokaryotes we explain how biological systems can be written as probabilistic program to combine experimental data into the model. The model developed is then analysed in terms of intrinsic noise and exact sampling of switching times between individual elements in the system. We have mainly concentrated on inferring number of genes in ON and OFF states from experimental data.

Keywords: systems biology, probabilistic model, inference, biology, model

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Authors: Elena Puica

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This paper aims to investigate the impact of Supply Chain Management (SCM) on the environment by applying a Machine Learning model while pointing out the efficiency of the technology used. The Machine Learning model was used to derive the efficiency and optimization of technology used in SCM and the environmental impact of SCM processes. The model applied is a predictive classification model and was trained firstly to determine which stage of the SCM has more outputs and secondly to demonstrate the efficiency of using advanced technology in SCM instead of recuring to traditional SCM. The outputs are the emissions generated in the environment, the consumption from different steps in the life cycle, the resulting pollutants/wastes emitted, and all the releases to air, land, and water. This manuscript presents an innovative approach to applying advanced technology in SCM and simultaneously studies the efficiency of technology and the SCM's impact on the environment. Identifying the conceptual relationships between SCM practices and their impact on the environment is a new contribution to the research. The authors can take a forward step in developing recent studies in SCM and its effects on the environment by applying technology.

Keywords: machine-learning model in SCM, SCM processes, SCM and the environmental impact, technology in SCM

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Authors: Ana Rahma Yuniarti, Ki Moo Lim

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Slowed myocardial conduction velocity (CV) and shortened action potential duration (APD) due to some reason are associated with an increased risk of re-entrant excitation, predisposing to cardiac arrhythmia. That is because both of CV reduction and APD shortening induces shortening of wavelength. In this study, we investigated quantitatively the cardiac mechanical responses under various CV and APD using multi-scale computational model of the heart. The model consisted of electrical model coupled with the mechanical contraction model together with a lumped model of the circulatory system. The electrical model consisted of 149.344 numbers of nodes and 183.993 numbers of elements of tetrahedral mesh, whereas the mechanical model consisted of 356 numbers of nodes and 172 numbers of elements of hexahedral mesh with hermite basis. We performed the electrical simulation with two scenarios: 1) by varying the CV values with constant APD and 2) by varying the APD values with constant CV. Then, we compared the electrical and mechanical responses for both scenarios. Our simulation showed that faster CV and longer APD induced largest resultants wavelength and generated better cardiac pumping efficacy by increasing the cardiac output and consuming less energy. This is due to the long wave propagation and faster conduction generated more synchronous contraction of whole ventricle.

Keywords: conduction velocity, action potential duration, mechanical contraction model, circulatory model

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Authors: M. Mohsin Munir, Taimoor Ahmad, Javed Munir, Usman Rashid

Abstract:

Determination of maximum elevation of a flowing fluid due to sudden rejection of load in a hydropower facility is of great interest to hydraulic engineers to ensure safety of the hydraulic structures. Several mathematical models exist that employ one-dimensional modeling for the determination of surge but none of these perfectly simulate real-time circumstances. The paper envisages investigation of surge inception and propagation for a Low Head Hydropower project using Computational Fluid Dynamics (CFD) analysis on FLOW-3D software package. The fluid dynamic model utilizes its analysis for surge by employing Reynolds’ Averaged Navier-Stokes Equations (RANSE). The CFD model is designed for a case study at Taunsa hydropower Project in Pakistan. Various scenarios have run through the model keeping in view upstream boundary conditions. The prototype results were then compared with the results of physical model testing for the same scenarios. The results of the numerical model proved quite accurate coherence with the physical model testing and offers insight into phenomenon which are not apparent in physical model and shall be adopted in future for the similar low head projects limiting delays and cost incurred in the physical model testing.

Keywords: surge, FLOW-3D, numerical model, Taunsa, RANSE

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Authors: Ali Kadivar, Kaveh Niayesh

Abstract:

This work simulates the voltage drop and resistance of the explosion of copper wires of diameters 25, 40, and 100 µm surrounded by 1 bar nitrogen exposed to a 150 A current and before plasma formation. The absorption of electrical energy in an exploding wire is greatly diminished when the plasma is formed. This study shows the importance of considering radiation and heat conductivity in the accuracy of the circuit simulations. The radiation of the dense plasma formed on the wire surface is modeled with the Net Emission Coefficient (NEC) and is mixed with heat conductivity through PLASIMO® software. A time-transient code for analyzing wire explosions driven by a slow current rise rate is developed. It solves a circuit equation coupled with one-dimensional (1D) equations for the copper electrical conductivity as a function of its physical state and Net Emission Coefficient (NEC) radiation. At first, an initial voltage drop over the copper wire, current, and temperature distribution at the time of expansion is derived. The experiments have demonstrated that wires remain rather uniform lengthwise during the explosion and can be simulated utilizing 1D simulations. Data from the first stage are then used as the initial conditions of the second stage, in which a simplified 1D model for high-Mach-number flows is adopted to describe the expansion of the core. The current was carried by the vaporized wire material before it was dispersed in nitrogen by the shock wave. In the third stage, using a three-dimensional model of the test bench, the streamer threshold is estimated. Electrical breakdown voltage is calculated without solving a full-blown plasma model by integrating Townsend growth coefficients (TdGC) along electric field lines. BOLSIG⁺ and LAPLACE databases are used to calculate the TdGC at different mixture ratios of nitrogen/copper vapor. The simulations show both radiation and heat conductivity should be considered for an adequate description of wire resistance, and gaseous discharges start at lower voltages than expected due to ultraviolet radiation and the exploding shocks, which may have ionized the nitrogen.

Keywords: exploding wire, Townsend breakdown mechanism, streamer, metal vapor, shock waves

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Authors: Haggay Etkes

Abstract:

This study provides evidence for the impact of monthly interviews conducted for the Israeli Labor Force Surveys (LFSs) on estimated flows between labor force (LF) statuses and on coefficients in fixed-effects estimations. The study uses the natural experiment of parallel interviews for the quarterly and the monthly LFSs in Israel in 2011 for demonstrating that the Labor Force Participation (LFP) rate of Jewish persons who participated in the monthly LFS increased between interviews, while in the quarterly LFS it decreased. Interestingly, the estimated impact on the LFP rate of self-reporting individuals is 2.6–3.5 percentage points while the impact on the LFP rate of individuals whose data was reported by another member of their household (a proxy), is lower and statistically insignificant. The relative increase of the LFP rate in the monthly survey is a result of a lower rate of exit from the LF and a somewhat higher rate of entry into the LF relative to these flows in the quarterly survey. These differing flows have a bearing on labor search models as the monthly survey portrays a labor market with less friction and a “steady state” LFP rate that is 5.9 percentage points higher than the quarterly survey. The study also demonstrates that monthly interviews affect a specific group (45–64 year-olds); thus the sign of coefficient of age as an explanatory variable in fixed-effects regressions on LFP is negative in the monthly survey and positive in the quarterly survey.

Keywords: measurement error, surveys, search, LFSs

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Authors: S. Abdulkadir, I. Y. Moh’d, S. U. Kunya, U. Nuruddeen

Abstract:

The basis of any contract formation between the client and contractor is the budgeted cost and the estimated duration of projects. These variables are paramount important to project's sponsor in a construction projects and in assessing the success or viability of construction projects. Despite the availability of various techniques of cost and time control, many projects failed to achieve their initial estimated cost and time. The paper evaluate the inhibiting factors of cost and time control in Nigerian construction practice and comparing the result with the United Kingdom practice as identified by one researcher. The populations of the study are construction professionals within Bauchi and Gombe state, Nigeria, a judgmental sampling employed in determining the size of respondents. Descriptive statistics used in analyzing the data in SPSS. Design change, project fraud and corruption, financing and payment of completed work found to be common among the top five inhibiting factors of cost and time control in the study area. Furthermore, the result had shown some comprising with slight contrast as in the case of United Kingdom practice. Study recommend the adaptation of mitigation measures developed in the UK prior to assessing its effectiveness and so also developing a mitigating measure for other top factors that are not within the one developed in United Kingdom practice. Also, it recommends a wider assessing comparison on the modify inhibiting factors of cost and time control as revealed by the study to cover almost all part of Nigeria.

Keywords: comparison, cost, inhibiting factor, United Kingdom, time

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Authors: Anisul Islam

Abstract:

The U.S. has a very strong trade relationship with China but with a large and persistent trade deficit. Some has argued that the undervalued Chinese Yuan is to be blamed for the persistent trade deficit. The empirical results are mixed at best. This paper empirically estimates the U.S. export function along with the U.S. import function with its trade with China with the purpose of testing for the existence of the Marshall-Lerner (ML) condition as well for the possible existence of the J-curve hypothesis. Annual export and import data will be utilized for as long as the time series data exists. The export and import functions will be estimated using advanced econometric techniques, along with appropriate diagnostic tests performed to examine the validity and reliability of the estimated results. The annual time-series data covers from 1975 to 2022 with a sample size of 48 years, the longest period ever utilized before in any previous study. The data is collected from several sources, such as the World Bank’s World Development Indicators, IMF Financial Statistics, IMF Direction of Trade Statistics, and several other sources. The paper is expected to shed important light on the ongoing debate regarding the persistent U.S. trade deficit with China and the policies that may be useful to reduce such deficits over time. As such, the paper will be of great interest for the academics, researchers, think tanks, global organizations, and policy makers in both China and the U.S.

Keywords: exports, imports, marshall-lerner condition, j-curve hypothesis, united states, china

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Authors: Yungtai Lo

Abstract:

The current study follows an educational intervention on bottle-weaning to simultaneously evaluate the effect of the bottle-weaning intervention on reducing bottle use, daily milk intake from bottles, and daily energy intake in toddlers aged 11 to 13 months. A shared parameter model and a random effects model are used to jointly model bottle use, daily milk intake from bottles, and daily energy intake. We show in the two joint models that the bottle-weaning intervention promotes bottleweaning, and reduces daily milk intake from bottles in toddlers not off bottles and daily energy intake. We also show that the odds of drinking from a bottle were positively associated with the amount of milk intake from bottles and increased daily milk intake from bottles was associated with increased daily energy intake. The effect of bottle use on daily energy intake is through its effect on increasing daily milk intake from bottles that in turn increases daily energy intake.

Keywords: two-part model, semi-continuous variable, joint model, gamma regression, shared parameter model, random effects model

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Authors: T. M. Ismail, M. A. El-Salam

Abstract:

A mathematical model study was carried out to investigate gasification of biomass fuels using high-temperature air and steam as a gasifying agent using high-temperature air up to 1000°C. In this study, a 2D computational fluid dynamics model was developed to study the gasification process in an updraft gasifier, considering drying, pyrolysis, combustion, and gasification reactions. The gas and solid phases were resolved using a Euler−Euler multiphase approach, with exchange terms for the momentum, mass, and energy. The standard k−ε turbulence model was used in the gas phase, and the particle phase was modeled using the kinetic theory of granular flow. The results show that the present model giving a promising way in its capability and sensitivity for the parameter effects that influence the gasification process.

Keywords: computational fluid dynamics, gasification, biomass fuel, fixed bed gasifier

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Authors: Dheyaa Wajid Abbood, Hanan Hussien Abood

Abstract:

Stepped cascade has been utilized as a hydraulic structure for years. It has proven to be the least costly aeration system in replenishing dissolved oxygen. Numerical modeling of stepped cascade with end sill is very complicated and challenging because of the high roughness and velocity re circulation regions. Volume of fluid multiphase flow model (VOF) is used .The realizable k-ξ model is chosen to simulate turbulence. The computational results are compared with lab-scale stepped cascade data. The lab –scale model was constructed in the hydraulic laboratory, Al-Mustansiriya University, Iraq. The stepped cascade was 0.23 m wide and consisted of 3 steps each 0.2m high and 0.6 m long with variable end sill. The discharge was varied from 1 to 4 l/s. ANSYS has been employed to simulate the experimental data and their related results. This study shows that ANSYS is able to predict results almost the same as experimental findings in some regions of the structure.

Keywords: stepped cascade weir, aeration, multiphase flow model, ansys

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Authors: R. Monteiro, A. Abarca

Abstract:

Large scale seismic risk assessment has become increasingly popular to evaluate the physical vulnerability of a given region to seismic events, by putting together hazard, exposure and vulnerability components. This study, developed within the scope of the EU-funded project ITERATE (Improved Tools for Disaster Risk Mitigation in Algeria), explains the steps and expected results for the development of an integrated seismic risk model for assessment of the vulnerability of residential buildings in Northern Algeria. For this purpose, the model foresees the consideration of an updated seismic hazard model, as well as ad-hoc exposure and physical vulnerability models for local residential buildings. The first results of this endeavor, such as the hazard model and a specific taxonomy to be used for the exposure and fragility components of the model are presented, using as starting point the province of Blida, in Algeria. Specific remarks and conclusions regarding the characteristics of the Northern Algerian in-built are then made based on these results.

Keywords: Northern Algeria, risk, seismic hazard, vulnerability

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Authors: M. Kharazmi, M. Zakeri, M. Packirisamy, J. Faraji

Abstract:

Micro/Nanorobotics or manipulation of nanoparticles by Atomic Force Microscopic (AFM) is one of the most important solutions for controlling the movement of atoms, particles and micro/nano metrics components and assembling of them to design micro/nano-meter tools. Accurate modelling of manipulation requires identification of forces and mechanical knowledge in the Nanoscale which are different from macro world. Due to the importance of the adhesion forces and the interaction of surfaces at the nanoscale several friction models were presented. In this research, friction and normal forces that are applied on the AFM by using of the dynamic bending-torsion model of AFM are obtained based on Hurtado-Kim friction model (HK), Johnson-Kendall-Robert contact model (JKR) and Greenwood-Williamson roughness model (GW). Finally, the effect of standard deviation of asperities height on the normal load, friction force and friction coefficient are studied.

Keywords: atomic force microscopy, contact model, friction coefficient, Greenwood-Williamson model

Procedia PDF Downloads 186