Search results for: transition regression model

Authors: Waleed El-Said Abou El-Amaiem

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Sole hemorrhages are considered as a main cause for sub clinical laminitis. In this study we aimed at discussing the most prominent risk factors associated with sole hemorrhages causing lameness in Egyptian water buffaloes and native breed cows. The final multivariate logistic regression model showed, a significant association between sub acute ruminal acidosis (P< 0.05), limb affected (P< 0.05) and weight (P< 0.05) and sole hemorrhages causing lameness in Egyptian water buffaloes and native breed cows. According to our knowledge, this is the first paper to discuss the risk factors associated with sole hemorrhages causing lameness in Egyptian water buffaloes and native breed cows.

Keywords: lameness, buffalo, sole hemorrhages, breed cows

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Authors: Abhishek Kumar, Nilam

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As we know that, time delay exists almost in every biological phenomenon. Therefore, in the present study, we propose a susceptible–infected–recovered (SIR) epidemic model along with time delay, modulated incidence rate of infection, and Holling Type II nonlinear treatment rate. The present model aims to provide a strategy to control the spread of epidemics. In the mathematical study of the model, it has been shown that the model has two equilibriums which are named as disease-free equilibrium (DFE) and endemic equilibrium (EE). Further, stability analysis of the model is discussed. To prove the stability of the model at DFE, we derived basic reproduction number, denoted by (R₀). With the help of basic reproduction number (R₀), we showed that the model is locally asymptotically stable at DFE when the basic reproduction number (R₀) less than unity and unstable when the basic reproduction number (R₀) is greater than unity. Furthermore, stability analysis of the model at endemic equilibrium has also been discussed. Finally, numerical simulations have been done using MATLAB 2012b to exemplify the theoretical results.

Keywords: time delayed SIR epidemic model, modulated incidence rate, Holling type II nonlinear treatment rate, stability

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Authors: Mehrdad Shahrbanozadeh, Gholam Abbas Barani, Saeed Shojaee

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In this study, a finite element (Seep3D model) and an artificial neural network (ANN) model were developed to simulate flow through dam foundation. Seep3D model is capable of simulating three-dimensional flow through a heterogeneous and anisotropic, saturated and unsaturated porous media. Flow through the Shahid Abbaspour dam foundation has been used as a case study. The FEM with 24960 triangular elements and 28707 nodes applied to model flow through foundation of this dam. The FEM being made denser in the neighborhood of the curtain screen. The ANN model developed for Shahid Abbaspour dam is a feedforward four layer network employing the sigmoid function as an activator and the back-propagation algorithm for the network learning. The water level elevations of the upstream and downstream of the dam have been used as input variables and the piezometric heads as the target outputs in the ANN model. The two models are calibrated and verified using the Shahid Abbaspour’s dam piezometric data. Results of the models were compared with those measured by the piezometers which are in good agreement. The model results also revealed that the ANN model performed as good as and in some cases better than the FEM.

Keywords: seepage, dam foundation, finite element method, neural network, seep 3D model

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Authors: Naima Nur, Safa Islam, Saeema Islam, Md. Faridul Alam

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Background: The worldwide increase in pediatric presumptive tuberculosis (TB) is the most life-threatening challenge in effectively controlling TB. The objective of this study was to determine the proportion of presumptive TB and the factors associated with it. Methods: A cross-sectional study was conducted between March and November 2013 at ICDDR-Bangladesh. Two hundred twelve pulmonary and extra-pulmonary specimens were collected from 84 suspected pediatric patients diagnosed with TB based on their clinical symptoms/radiological findings. Presumptive TB and confirmed TB were considered presumptive TB and non-presumptive TB and were isolated by smear-microscopy, culture, and GeneXpert. Logistic regression was used to analyze associations between outcome and predictor variables. Results: The proportion of presumptive TB was 85.7%, and 14.3% of non-presumptive TB. In presumptive TB, vaccine scars, family TB history, and school-going children were 16.6%, 33.3%, and 56.9%, respectively. In contrast, vaccine scars and family TB history were 8.3%, and school-going children were 58.3% in non-presumptive TB. Significant factors did not appear in the logistic regression analysis. Conclusion: Despite the high proportion of presumptive TB, there was no statistically significant between presumptive TB and non-presumptive TB.

Keywords: presumptive tuberculosis, confirmed tuberculosis, patient's characteristics, diagnosis

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Authors: Mohsen Ziaee

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In this paper, a mixed integer linear programming (MILP) model is presented to solve the flexible job shop scheduling problem (FJSP). This problem is one of the hardest combinatorial problems. The objective considered is the minimization of the makespan. The computational results of the proposed MILP model were compared with those of the best known mathematical model in the literature in terms of the computational time. The results show that our model has better performance with respect to all the considered performance measures including relative percentage deviation (RPD) value, number of constraints, and total number of variables. By this improved mathematical model, larger FJS problems can be optimally solved in reasonable time, and therefore, the model would be a better tool for the performance evaluation of the approximation algorithms developed for the problem.

Keywords: scheduling, flexible job shop, makespan, mixed integer linear programming

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Authors: D. Mohammadzadeh S., J. Bolouri Bazaz

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This paper presents a new prediction model for compression index of fine-grained soils using multi-gene genetic programming (MGGP) technique. The proposed model relates the soil compression index to its liquid limit, plastic limit and void ratio. Several laboratory test results for fine-grained were used to develop the models. Various criteria were considered to check the validity of the model. The parametric and sensitivity analyses were performed and discussed. The MGGP method was found to be very effective for predicting the soil compression index. A comparative study was further performed to prove the superiority of the MGGP model to the existing soft computing and traditional empirical equations.

Keywords: new prediction model, compression index soil, multi-gene genetic programming, MGGP

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Authors: Lie-Fen Lin, Tzu-Hsuan Lin, Ching-Heng Lin

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Hospice care for terminally ill patients provides not only a better quality of life but also cost-saving benefits. However, the utilization of home-based hospice care (HBH care) remains low even for countries covered by National Health Insurance (NHI) programs in Taiwan. In the current commercial insurance policy, only hospital-based hospice benefits were covered. It may have an influence on the insureds chosen to receive end-of-life care in a hospitalized manner. Thus, how to propose a feasible method to advocate HBH care utilization rate of public health policies is an important issue. A total of 130,219 cancer decedents in the year 2011-2013 from the National Health Insurance Research Database (NHIRD) in Taiwan were included in this study. By adding a day volume pays benefits of HBH care as a commercial insurance rider, will provide alternative benefits for the insureds. A multiple-state Markov chain model was incorporated to estimate the transition intensities of patients in different states at the end of their lives (Non-hospice, HBH, hospital-based hospice), and the premiums were estimated. HBH care insurance benefits provide financial support and reduce the burden of care for patients. The rate-making of this product is very sensitive while the utilization rate is rising, especially for high ages. The proposed HBH care insurance is a feasible way to reduce the financial burden, enhance the care quality and family satisfaction of insureds. Meanwhile, insurance companies can participate in advocating a good medical policy to enhance the social image. In addition, the medical costs of NHI can reduce effectively.

Keywords: home-based hospice care, commercial insurance, Markov chain model, the day volume pays

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Authors: Harshit Vallecha, Prabha Bhola

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‘Energy for all’, is a global issue of concern for the past many years. Despite the number of technological advancements and innovations, significant numbers of people are living without access to electricity around the world. India, an emerging economy, tops the list of nations having the maximum number of residents living off the grid, thus raising global attention in past few years to provide clean and sustainable energy access solutions to all of its residents. It is evident from developed economies that centralized planning and electrification alone is not sufficient for meeting energy security. Implementation of off-grid and consumer-driven energy models like Decentralized Renewable Energy (DRE) systems have played a significant role in meeting the national energy demand in developed nations. Cases of DRE systems have been reported in developing countries like India for the past few years. This paper attempts to profile the status of DRE projects in the Indian context with their scope and relevance to ensure universal electrification. Diversified cases of DRE projects, particularly solar, biomass and micro hydro are identified in different Indian states. Critical factors affecting the sustainability of DRE projects are extracted with their interlinkages in the context of developers, beneficiaries and promoters involved in such projects. Socio-techno-economic indicators are identified through similar cases in the context of DRE projects. Exploratory factor analysis is performed to evaluate the critical sustainability factors followed by regression analysis to establish the relationship between the dependent and independent factors. The generated EFA-Regression model provides a basis to develop the sustainability and replicability framework for broader coverage of DRE projects in developing nations in order to attain the goal of universal electrification with least carbon emissions.

Keywords: climate change, decentralized generation, electricity access, renewable energy

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Authors: Gang Liu, Can Wang, Runnan Zhang, Quan Wang, Huimin Song, Shaomin Ji

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Biba model can protect information integrity but might deny various non-malicious access requests of the subjects, thereby decreasing the availability in the system. Therefore, a mechanism that allows exceptional access control is needed. Break the Glass (BTG) strategies refer an efficient means for extending the access rights of users in exceptional cases. These strategies help to prevent a system from stagnation. An approach is presented in this work for integrating Break the Glass strategies into the Biba model. This research proposes a model, BTG-Biba, which provides both an original Biba model used in normal situations and a mechanism used in emergency situations. The proposed model is context aware, can implement a fine-grained type of access control and primarily solves cross-domain access problems. Finally, the flexibility and availability improvement with the use of the proposed model is illustrated.

Keywords: Biba model, break the glass, context, cross-domain, fine-grained

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Authors: Ferhat Demir

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Even if strategic management is highly critical for all types of organizations, only a few maturity models have been proposed in business literature for the area of strategic management activities. This paper updates previous studies and presents a new conceptual model for assessing the maturity of strategic management in any organization. Strategic management maturity model (S-3M) is basically composed of 6 maturity levels with 7 dimensions. The biggest contribution of S-3M is to put innovation into agenda of strategic management. The main objective of this study is to propose a model to align innovation with business strategies. This paper suggests that innovation (breakthrough new products/services and business models) is the only way of creating sustainable growth and strategy studies cannot ignore this aspect. Maturity models should embrace innovation to respond dynamic business environment and rapidly changing customer behaviours.

Keywords: strategic management, innovation, business model, maturity model

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Authors: Shilpi Tyagi, D. K. Nauriyal

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This study investigates the firm level determinants of profitability of Indian drug and pharmaceutical industry. The study uses inflation adjusted panel data for a period 2000-2013 and applies OLS regression model with Driscoll-Kraay standard errors. It has been found that export intensity, A&M intensity, firm’s market power and stronger patent regime dummy have exercised positive influence on profitability. The negative and statistically significant influence of R&D intensity and raw material import intensity points to the need for firms to adopt suitable investment strategies. The study suggests that firms are required to pay far more attention to optimize their operating expenditures, advertisement and marketing expenditures and improve their export orientation, as part of the long term strategy.

Keywords: Indian pharmaceutical industry, profits, TRIPS, performance

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Authors: Husnil Khatimah, Fairol Halim

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This research examines the moderating impact of security on intention to use e-money that adapted from some variables of the TAM (Technology Acceptance Model) and TPB (Theory of Planned Behavior). This study will use security as moderating variable and finds these relationship depends on customer intention to use e-money as payment tools. The conceptual framework of e-money transactions was reviewed to understand behavioral intention of consumers from perceived usefulness, perceived ease of use, perceived behavioral control and security. Quantitative method will be utilized as sources of data collection. A total of one thousand respondents will be selected using quota sampling method in Medan, Indonesia. Descriptive analysis and Multiple Regression analysis will be conducted to analyze the data. The article ended with suggestion for future studies.

Keywords: e-money transaction, TAM & TPB, moderating variable, behavioral intention, conceptual paper

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Authors: Elnaz Bandeh

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This study aimed to predict marital burnout based on irrational beliefs and sexual dysfunction of couples. The research method was descriptive-correlational, and the statistical population included all couples who consulted to counseling clinics in the fall of 2016. The sample consisted of 200 people who were selected by convenience sampling and answered the Ahwaz Irrational Beliefs Questionnaire, Pines Couple Burnout, and Hudson Marital Satisfaction Questionnaire. The data were analyzed using regression coefficient. The results of regression analysis showed that there was a linear relationship between irrational beliefs and couple burnout and dimensions of helplessness toward change, expectation of approval from others, and emotional irresponsibility were positive and significant predictors of couple burnout. However, after avoiding the problem of power, it was not a significant predictor of marital dissatisfaction. There was also a linear relationship between sexual dysfunction and couple burnout, and sexual dysfunction was a positive and significant predictor of couple burnout. Based on the findings, it can be concluded that irrational beliefs and sexual dysfunction play a role in couple dysfunction.

Keywords: couple burnout, irrational beliefs, sexual dysfunction, marital relationship

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Authors: Bo Keun Lee, Doo Yeon Kwon, Ji Hoon Park, Gun Hee Lee, Ji Hye Baek, Heung Jae Chun, Young Joo Koh, Moon Suk Kim

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Thermo-responsive materials are viscoelastic materials that undergo a sol-to-gel phase transition at a specific temperature and many materials have been developed. MPEG-b-PCL (MPC) as a thermo-responsive material contained hydrophilic and hydrophobic segments and it formed an ordered crystalline structure of hydrophobic PCL segments in aqueous solutions. The ordered crystalline structure packed tightly or aggregated and finally induced an aggregated gel through intra- and inter-molecular interactions as a function of temperature. Thus, we introduced anionic and cationic groups into the end positions of the PCL chain to alter the hydrophobicity of the PCL segment. Introducing anionic and cationic groups into the PCL end position altered their solubility by changing the crystallinity and hydrophobicity of the PCL block domains. These results indicated that the properties of the end group in the hydrophobic PCL blockand the balance between hydrophobicity and hydrophilicity affect thermo-responsivebehavior of the copolymers in aqueous solutions. Thus, we concluded that determinant of the temperature-dependent thermo-responsive behavior of MPC depend on the ionic end group in the PCL block. So, we introduced zwitterionic end groups to investigate the thermo-responsive behavior of MPC. Methoxypoly(ethylene oxide) and ε-caprolactone (CL) were randomly copolymerized that introduced varying hydrophobic PCL lengths and an MPC featuring a zwitterionic sulfobetaine (MPC-ZW) at the chain end of the PCL segment. The MPC and MPC-ZW copolymers were obtained formed sol-state at room temperature when prepared as 20-wt% aqueous solutions. The solubility of MPC decreased when the PCL block was increased from molecular weight. The solubilization time of MPC-2.4k was around 20 min and MPC-2.8k, MPC-3.0k increased to 30 min and 1 h, respectively. MPC-3.6k was not solubilized. In case of MPC-ZW 3.6k, However, the zwitterion-modified MPC copolymers were solubilized in 3–5 min. This result indicates that the zwitterionic end group of the MPC-ZW diblock copolymer increased the aqueous solubility of the diblock copolymer even when the length of the hydrophobic PCL segment was increased. MPC and MPC-ZW diblock copolymers that featuring zwitterionic end groups were synthesized successfully. The sol-to-gel phase-transition was formed that specific temperature depend on the length of the PCL hydrophobic segments introduced and on the zwitterion groups attached to the MPC chain end. This result indicated that the zwitterionic end groups reduced the hydrophobicity in the PCL block and changed the solubilization. The MPC-ZW diblock copolymer can be utilized as a potential injectable drug and cell carrier.

Keywords: thermo-responsive material, zwitterionic, hydrophobic, crystallization, phase transition

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Authors: Mehmet Izzettin Yilmazer, Emin Cadirli

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Zn-7wt.%Al-2.96wt.%Cu eutectic alloy was directionally solidified upwards with different temperature gradients (from 6.70 K/mm to 10.67 K/mm) at a constant growth rate (16.4 Km/s) and also different growth rate (from 8.3 micron/s to 166 micron/s) at a constant temperature gradient (10.67 K/mm) using a Bridgman–type growth apparatus.The values of eutectic spacing were measured from longitudinal and transverse sections of the samples. The dependency of microstructures on the G and V were determined with linear regression analysis and experimental equations were found as λl=8.953xVexp-0.49, λt=5.942xVexp-0.42 and λl=0.008xGexp-1.23, λt=0.024xGexp-0.93. The measurements of microhardness of directionally solidified samples were obtained by using a microhardness test device. The dependence of microhardness HV on temperature gradient and growth rate were analyzed. The dependency of microhardness on the G and V were also determined with linear regression analysis as HVl=110.66xVexp0.02, HVt=111.94xVexp0.02 and HVl=69.66xGexp0.17, HVt=68.86xGexp0.18. The experimental results show that the microhardness of the directionally solidified Zn-Al-Cu alloy increases with increasing the growth rate. The results obtained in this work were compared with the previous similar experimental results.

Keywords: directional solidification, eutectic alloys, microstructure, microhardness

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Authors: B. Sudhakara Reddy

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The transition to a clean and green energy system is an economic and social transformation that is exciting as well as challenging. The world today faces a formidable challenge in transforming its economy from being driven primarily by fossil fuels, which are non-renewable and a major source of global pollution, to becoming an economy that can function effectively using renewable energy sources and by achieving high energy efficiency levels. In the present study, a green economy scenario is developed for India using a bottom-up approach. The results show that the penetration rate of renewable energy resources will reduce the total primary energy demand by 23% under GE. Improvements in energy efficiency (e.g. households, industrial and commercial sectors) will result in reduced demand to the tune of 318 MTOE. The volume of energy-related CO2 emissions decline to 2,218 Mt in 2030 from 3,440 under the BAU scenario and the per capita emissions will reduce by about 35% (from 2.22 to 1.45) under the GE scenario. The reduction in fossil fuel demand and focus on clean energy will reduce the energy intensity to 0.21 (TOE/US$ of GDP) and carbon intensity to 0.42 (ton/US$ of GDP) under the GE scenario. total import bill (coal and oil) will amount to US$ 334 billion by 2030 (at 2010/11 prices), but as per the GE scenario, it would be US$ 194.2 billion, a saving of about US$ 140 billion. The building of a green energy economy can also serve another purpose: to develop new ‘pathways out of poverty’ by creating more than 10 million jobs and thus raise the standard of living of low-income people. The differences between the baseline and green energy scenarios are not so much the consequence of the diffusion of various technologies. It is the result of the active roles of different actors and the drivers that become dominant.

Keywords: emissions, green energy, fossil fuels, green jobs, renewables, scenario

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Authors: Athmane Bakhta, Sebastien Leclaire, David Vidal, Francois Bertrand, Mohamed Cheriet

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This work presents a new three-dimensional variational model proposed for the simulation of ink seepage into paper sheets at the fiber level. The model, inspired by the Hising model, takes into account a finite volume of ink and describes the system state through gravity, cohesion, and adhesion force interactions. At the mesoscopic scale, the paper substrate is modeled using a discretized fiber structure generated using a numerical deposition procedure. A modified Monte Carlo method is introduced for the simulation of the ink dynamics. Besides, a multiphase lattice Boltzmann method is suggested to fine-tune the mesoscopic variational model parameters, and it is shown that the ink seepage behaviors predicted by the proposed model can resemble those predicted by a method relying on first principles.

Keywords: fibrous media, lattice Boltzmann, modelling and simulation, Monte Carlo, variational model

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Authors: L. Agneni, F. Carta, C. Micheletta, V. Tersigni

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The development of a qualitative method to profile jobseekers is needed to improve the quality of the Public Employment Services (PES) in Italy. This is why the National Agency for Active Labour Market Policies (ANPAL) decided to introduce a Qualitative Profiling Service in the context of the activities carried out by local employment offices’ operators. The qualitative profiling service provides information and data regarding the jobseeker’s personal transition status, through a semi-structured questionnaire administered to PES clients during the guidance interview. The questionnaire responses allow PES staff to identify, for each client, proper activities and policy measures to support jobseekers in their reintegration into the labour market. Data and information gathered by the qualitative profiling tool are the following: frequency, modalities and motivations for clients to apply to local employment offices; clients’ expectations and skills; difficulties that they have faced during the previous working experiences; strategies, actions undertaken and activated channels for job search. These data are used to assess jobseekers’ personal and career characteristics and to measure their employability level (qualitative profiling index), in order to develop and deliver tailor-made action programmes for each client. This paper illustrates the use of the above-mentioned qualitative profiling service on the national territory and provides an overview of the main findings of the survey: concerning the difficulties that unemployed people face in finding a job and their perception of different aspects related to the transition in the labour market. The survey involved over 10.000 jobseekers registered with the PES. Most of them are beneficiaries of the “citizens' income”, a specific active labour policy and social inclusion measure. Furthermore, data analysis allows classifying jobseekers into a specific group of clients with similar features and behaviours, on the basis of socio-demographic variables, customers' expectations, needs and required skills for the profession for which they seek employment. Finally, the survey collects PES staff opinions and comments concerning clients’ difficulties in finding a new job and also their strengths. This is a starting point for PESs’ operators to define adequate strategies to facilitate jobseekers’ access or reintegration into the labour market.

Keywords: labour market transition, public employment services, qualitative profiling, vocational guidance

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Authors: Carol Anne Hargreaves

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A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.

Keywords: machine learning, stock market trading, logistic regression, cluster analysis, factor analysis, decision trees, neural networks, automated stock investment system

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Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

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Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

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Authors: Batuhan Kocaoglu

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Although using powerful systems like ERP (Enterprise Resource Planning), companies still cannot benchmark their processes and measure their process performance easily based on predefined SCOR (Supply Chain Operation References) terms. The purpose of this research is to identify common and corresponding processes to present a conceptual model to model and measure the purchasing process of an organization. The main steps for the research study are: Literature review related to 'procure to pay' process in ERP system; Literature review related to 'sourcing' process in SCOR model; To develop a conceptual model integrating 'sourcing' of SCOR model and 'procure to pay' of ERP model. In this study, we examined the similarities and differences between these two models. The proposed framework is based on the assumptions that are drawn from (1) the body of literature, (2) the authors’ experience by working in the field of enterprise and logistics information systems. The modeling framework provides a structured and systematic way to model and decompose necessary information from conceptual representation to process element specification. This conceptual model will help the organizations to make quality purchasing system measurement instruments and tools. And offered adaptation issues for ERP systems and SCOR model will provide a more benchmarkable and worldwide standard business process.

Keywords: SCOR, ERP, procure to pay, sourcing, reference model

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Authors: Vidishaa Jali, Amit Sinha, Kameshwar Prasad

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Background and Objective: Cerebral microbleeds are frequently found in healthy elderly individuals. We performed a meta- analysis to determine the prevalence of cerebral microbleeds in apparently healthy, elderly population and to determine the effect of age, smoking and hypertension on the occurrence of cerebral microbleeds. Methods: Relevant literature was searched using electronic databases such as MEDLINE, EMBASE, PubMed, Cochrane database, Google scholar to identify studies on the prevalence of cerebral microbleeds in general elderly population till March 2016. STATA version 13 software was used for analysis. Fixed effect model was used if heterogeneity was less than 50%. Otherwise, random effect model was used. Meta- regression analysis was performed to check any effect of important variables such as age, smoking, hypertension. Selection Criteria: We included cross-sectional studies performed in apparently healthy elderly population, who had age more than 50 years. Results: The pooled proportion of cerebral microbleeds in healthy population is 12% (95% CI, 0.11 to 0.13). No significant effect of age was found on the prevalence of cerebral microbleeds (p= 0.99). A linear relationship between increase in hypertension and the prevalence of cerebral microbleeds was found, however, this linear relationship was not statistically significant (p=0.16). Similarly, A linear relationship between increase in smoking and the prevalence of cerebral microbleeds was found, however, this linear relationship was also not statistically significant (p=0.21). Conclusion: Presence of cerebral microbleeds is evident in apparently healthy, elderly population, in more than 10% of individuals.

Keywords: apparently healthy, elderly, prevalence, cerebral microbleeds

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Authors: Redvan Ghasemlounia, Elham Ansari, Hikmet Kerem Cigizoglu

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Hydrological modeling in arid and semi-arid regions is very important. Iran has many regions with these climate conditions such as Chaharmahal and Bakhtiari province that needs lots of attention with an appropriate management. Forecasting of hydrological parameters and estimation of hydrological events of catchments, provide important information that used for design, management and operation of water resources such as river systems, and dams, widely. Discharge in rivers is one of these parameters. This study presents the application and comparison of some estimation methods such as Feed-Forward Back Propagation Neural Network (FFBPNN), Multi Linear Regression (MLR), Gene Expression Programming (GEP) and Bayesian Network (BN) to predict the daily flow discharge of the Soolegan River, located at Chaharmahal and Bakhtiari province, in Iran. In this study, Soolegan, station was considered. This Station is located in Soolegan River at 51° 14՜ Latitude 31° 38՜ longitude at North Karoon basin. The Soolegan station is 2086 meters higher than sea level. The data used in this study are daily discharge and daily precipitation of Soolegan station. Feed Forward Back Propagation Neural Network(FFBPNN), Multi Linear Regression (MLR), Gene Expression Programming (GEP) and Bayesian Network (BN) models were developed using the same input parameters for Soolegan's daily discharge estimation. The results of estimation models were compared with observed discharge values to evaluate performance of the developed models. Results of all methods were compared and shown in tables and charts.

Keywords: ANN, multi linear regression, Bayesian network, forecasting, discharge, gene expression programming

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Authors: Ahmed BenSaïda

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This paper investigates the association between financial technology (FinTech) growth and bank performance in the Gulf Cooperation Council (GCC) region. Application is conducted on a panel dataset containing the annual observations of banks covering the period from 2012 to 2021. FinTech growth is set as an explanatory variable on three proxies of bank performance. These proxies are the return on assets (ROA), return on equity (ROE), and net interest margin (NIM). Moreover, several control variables are added to the model, including bank-specific and macroeconomic variables. The results are significant as all the proxies of the bank performance are negatively affected by the growth of FinTech startups. Consequently, banks are urged to proactively invest in FinTech startups and engage in partnerships to avoid the risk of disruption.

Keywords: financial technology, bank performance, GCC countries, panel regression

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Authors: M. Kumpugdee-Vollrath, T. G. D. Phu, M. Helmis

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A suitable model membrane to study the pharmacological effect of pharmaceutical products is human stratum corneum because this layer of human skin is the outermost layer and it is an important barrier to be passed through. Other model membranes which were also used are for example skins from pig, mouse, reptile or fish. We are interested in fish skins in this project. The advantages of the fish skins are, that they can be obtained from the supermarket or fish shop. However, the fish skins should be freshly prepared and used directly without storage. In order to understand the effect of different model drugs e.g. lidocaine HCl, resveratrol, paracetamol, ibuprofen, acetyl salicylic acid on the properties of the model membrane from various types of fishes e.g. trout, salmon, cod, plaice permeation tests were performed and differential scanning calorimetry was applied.

Keywords: fish skin, model membrane, permeation, DSC, lidocaine HCl, resveratrol, paracetamol, ibuprofen, acetyl salicylic acid

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Authors: Rahele Mosleh

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This paper gives a survey of results on global stability of extended Ross model for malaria by constructing some elegant Lyapunov functions for two cases of epidemic, including disease-free and endemic occasions. The model is a nonlinear seven-dimensional system of ordinary differential equations that simulates this phenomenon in a more realistic fashion. We discuss the existence of positive disease-free and endemic equilibrium points of the model. It is stated that extended Ross model possesses invariant solutions for human and mosquito in a specific domain of the system.

Keywords: global stability, invariant solutions, Lyapunov function, stationary points

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Authors: P. Byrnes, F. A. DiazDelaO

Abstract:

The Support Vector Machine (SVM) has become widely recognised as one of the leading algorithms in machine learning for both regression and binary classification. It expresses predictions in terms of a linear combination of kernel functions, referred to as support vectors. Despite its popularity amongst practitioners, SVM has some limitations, with the most significant being the generation of point prediction as opposed to predictive distributions. Stemming from this issue, a probabilistic model namely, Probabilistic Classification Vector Machines (PCVM), has been proposed which respects the original functional form of SVM whilst also providing a predictive distribution. As physical system designs become more complex, an increasing number of classification tasks involving industrial applications consist of more than two classes. Consequently, this research proposes a framework which allows for the extension of PCVM to a multi class setting. Additionally, the original PCVM framework relies on the use of type II maximum likelihood to provide estimates for both the kernel hyperparameters and model evidence. In a high dimensional multi class setting, however, this approach has been shown to be ineffective due to bad scaling as the number of classes increases. Accordingly, we propose the application of Markov Chain Monte Carlo (MCMC) based methods to provide a posterior distribution over both parameters and hyperparameters. The proposed framework will be validated against current multi class classifiers through synthetic and real life implementations.

Keywords: probabilistic classification vector machines, multi class classification, MCMC, support vector machines

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Authors: Stephanie Watson, Fabrice Stephenson, Conrad Pilditch, Carolyn Lundquist

Abstract:

Spatial data detailing both the distribution and density of functionally important marine species are needed to inform management decisions. Species distribution models (SDMs) have proven helpful in this regard; however, models often focus only on species occurrences derived from spatially expansive datasets and lack the resolution and detail required to inform regional management decisions. Boosted regression trees (BRT) were used to produce high-resolution SDMs (250 m) at two spatial scales predicting probability of occurrence, abundance (count per sample unit), density (count per km2) and uncertainty for seven coastal seafloor taxa that vary in habitat usage and distribution to examine prediction differences and implications for coastal management. We investigated if small scale regionally focussed models (82,000 km2) can provide improved predictions compared to data-rich national scale models (4.2 million km2). We explored the variability in predictions across model type (occurrence vs abundance) and model scale to determine if specific taxa models or model types are more robust to geographical variability. National scale occurrence models correlated well with broad-scale environmental predictors, resulting in higher AUC (Area under the receiver operating curve) and deviance explained scores; however, they tended to overpredict in the coastal environment and lacked spatially differentiated detail for some taxa. Regional models had lower overall performance, but for some taxa, spatial predictions were more differentiated at a localised ecological scale. National density models were often spatially refined and highlighted areas of ecological relevance producing more useful outputs than regional-scale models. The utility of a two-scale approach aids the selection of the most optimal combination of models to create a spatially informative density model, as results contrasted for specific taxa between model type and scale. However, it is vital that robust predictions of occurrence and abundance are generated as inputs for the combined density model as areas that do not spatially align between models can be discarded. This study demonstrates the variability in SDM outputs created over different geographical scales and highlights implications and opportunities for managers utilising these tools for regional conservation, particularly in data-limited environments.

Keywords: Benthic ecology, spatial modelling, multi-scalar modelling, marine conservation.

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Authors: Sina Saadati, Mohammadreza Razzazi

Abstract:

Since Java is an object-oriented language, It can be used to solve a wide range of problems. One of the considerable usages of this language can be found in Agent-based modeling and simulation. Despite the significant power of Java, There is not an easy method to render a 3-dimensional human model. In this article, we are about to develop a library which helps modelers present a 3D human model and control it with Java. The library runs two server programs. The first one is a web page server that can connect to any browser and present an HTML code. The second server connects to the browser and controls the movement of the model. So, the modeler will be able to develop a simulation and display a good-looking human model without any knowledge of any graphical tools.

Keywords: agent-based modeling and simulation, human model, graphics, Java, distributed systems

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Authors: Wei Wang, Yaohua Zhao, Yanhua Diao

Abstract:

The exponential growth of the lighting industry has rendered traditional thermal technologies inadequate for addressing the thermal management challenges inherent to high-power light-emitting diode (LED) technology. To enhance the thermal management of LEDs, this study proposes a heat sink configuration that integrates a miniature heat pipe array based on phase change technology with rectangular channels. The thermal performance of the heat sink was evaluated through experimental testing, and the results demonstrated that when the input power was 100W, 150W, and 200W, the temperatures of the LED substrate were 47.64℃, 56.78℃, and 69.06℃, respectively. Additionally, the maximum temperature difference of the MHPA in the vertical direction was observed to be 0.32℃, 0.30℃, and 0.30℃, respectively. The results demonstrate that the heat sink not only effectively dissipates the heat generated by the LEDs, but also exhibits excellent temperature uniformity. In consideration of the experimental measurement outcomes, a corresponding numerical model was developed as part of this study. Following the model validation, the effect of the structural parameters of the heat sink on its heat dissipation efficacy was examined through the use of response surface methodology (RSM) analysis. The rectangular channel width, channel height, channel length, number of channel cross-sections, and channel cross-section spacing were selected as the input parameters, while the LED substrate temperature and the total mass of the heat sink were regarded as the response variables. Subsequently, the response was subjected to an analysis of variance (ANOVA), which yielded a regression model that predicted the response based on the input variables. This offers some direction for the design of the radiator.

Keywords: light-emitting diodes, heat transfer, heat pipe, natural convection, response surface methodology

Procedia PDF Downloads 34