Search results for: robust regression

Authors: H. Gonzalez-Rivera, J. L. Palmeros-Torres

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This paper describes the application of traditional computer vision techniques as a procedure for automatic measurement of the secondary dendrite arm spacing (SDAS) from microscopic images. The algorithm is capable of finding the lineal or curve-shaped secondary column of the main microstructure, measuring its length size in a micro-meter and counting the number of spaces between dendrites. The automatic characterization was compared with a set of 1728 manually characterized images, leading to an accuracy of −0.27 µm for the length size determination and a precision of ± 2.78 counts for dendrite spacing counting, also reducing the characterization time from 7 hours to 2 minutes.

Keywords: dendrite arm spacing, microstructure inspection, pattern recognition, polynomial regression

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Authors: Anna Romanova, Mojtaba Mahmoodian, Upul Chandrasekara, Morteza A. Alani

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Corrosion of concrete sewer pipes induced by sulphuric acid attack is a recognised problem worldwide, which is not only an attribute of countries with hot climate conditions as thought before. The significance of this problem is by far only realised when the pipe collapses causing surface flooding and other severe consequences. To change the existing post-reactive attitude of managing companies, easy to use and robust models are required to be developed which currently lack reliable data to be correctly calibrated. This paper focuses on laboratory experiments of establishing concrete pipe corrosion rate by submerging samples in to 0.5 pH sulphuric acid solution for 56 days under 10ºC, 20ºC and 30ºC temperature regimes. The result showed that at very early stage of the corrosion process the samples gained overall mass, at 30ºC the corrosion progressed quicker than for other temperature regimes, however with time the corrosion level for 10ºC and 20ºC regimes tended towards those at 30ºC. Overall, at these conditions the corrosion rates of 10 mm/year, 13,5 mm/year, and 17 mm/year were observed.

Keywords: sewer pipes, concrete corrosion, sulphuric acid, concrete coupons, corrosion rate

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Authors: Truong Nguyen Luan Vu, Le Hieu Giang, Le Linh

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The FOPI controller is proposed based on the main properties of the decoupling control scheme, as well as the fractional calculus. By using the simplified decoupling technique, the transfer function of decoupled apparent process is firstly separated into a set of n equivalent independent processes in terms of a ratio of the diagonal elements of original open-loop transfer function to those of dynamic relative gain array and the fraction – order PI controller is then developed for each control loops due to the Bode’s ideal transfer function that gives the desired fractional closed-loop response in the frequency domain. The simulation studies were carried out to evaluate the proposed design approach in a fair compared with the other existing methods in accordance with the structured singular value (SSV) theory that used to measure the robust stability of control systems under multiplicative output uncertainty. The simulation results indicate that the proposed method consistently performs well with fast and well-balanced closed-loop time responses.

Keywords: ideal transfer function of bode, fractional calculus, fractional order proportional integral (FOPI) controller, decoupling control system

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Authors: Saad Bin Nasir

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The study identifies the impact of inflation and foreign exchange rate on foreign direct investment in Pakistan. Inflation and exchange rates are used as independent variables and foreign direct investment is taken as dependent variable. Discreet time series data has been used from the period of 1999 to 2009. The results of regression analysis reveal that high inflation has negative impact on foreign direct investment and higher exchange rates has positive impact on foreign direct investment in Pakistan. The inflation and foreign exchange rates both are insignificant in the analysis.

Keywords: inflation rate, foreign exchange rate, foreign direct investment, foreign assets

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Authors: Haon-Yao Chen, Cheng-Fang Lin, Pui-Kwan Andy Hong, Ping-Yi Yang, Kok Kwang Ng, Sheng-Fu Yang

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Simultaneous nitrification and denitrification (SND) are a natural phenomenon in the soil environment that can be applied in wastewater treatment. At a domestic wastewater treatment plant, we performed a pilot test of installing bioplates with entrapped biomass into a conventional aeration basin for SND, and investigated the effects of bioplate packing ratio, hydraulic retention time, dissolved oxygen level, on/off aeration mode, and supplemental carbon and alkalinity on nitrogen removal. With the pilot aeration basin of 1.3 m3 loaded with mixed liquor suspended solids of 1500-2500 mg/L and bioplates at PR of 3.2% (3.2% basin volume) operated at HRT of 6 h and DO of 4-6 mg/L without supplemental carbon or alkalinity, nitrogen in the wastewater was removed to an effluent total nitrogen (TN) of 7.3 mg/L from an influent TN of 28 mg/L. The bioplate robust cellulose triacetate structure carrying the biomass shows promise in retrofitting conventional aeration basins for enhanced nutrient removal.

Keywords: immobilization, nitrification/denitrification, nutrient removal, total nitrogen

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Authors: Triana Fitriastuti, Pipiet Larasatie, Alex Vanderstraten

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Many scholars have demonstrated the negative impacts of the perception of organizational politics on organizational outcomes. The model proposed in this study analyzes the impact of the perception of organizational politics on job satisfaction. In the same way, ingratiation as a moderator variable is tested. We applied regression analysis to test the hypothesis. The findings of the current research, which was conducted with 240 employees in the public sector in Indonesia, show that the perception of organizational politics has a negative effect on job satisfaction. In contrast, ingratiation plays a role that fully moderates the relationship between organizational politics and organizational outcomes and changes the correlation between the perception of organizational politics on job satisfaction. Employees who use ingratiation as a coping mechanism tend to do so when they perceive a high degree of organizational politics.

Keywords: ingratiation, impression management, job satisfaction, perception of organizational politics

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Authors: Romulo Damasclin Chaves dos Santos, Jorge Henrique de Oliveira Sales

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This paper presents a rigorous study of advanced functional spaces, with a focus on Sobolev and Besov spaces, to investigate key aspects of fluid dynamics, including the regularity of solutions to the Navier-Stokes equations, hypercomplex bifurcations, and turbulence. We offer a comprehensive analysis of Sobolev embedding theorems in fractional spaces and apply bifurcation theory within quaternionic dynamical systems to better understand the complex behaviors in fluid systems. Additionally, the research delves into energy dissipation mechanisms in turbulent flows through the framework of Besov spaces. Key mathematical tools, such as interpolation theory, Littlewood-Paley decomposition, and energy cascade models, are integrated to develop a robust theoretical approach to these problems. By addressing challenges related to the existence and smoothness of solutions, this work contributes to the ongoing exploration of the open Navier-Stokes problem, providing new insights into the intricate relationship between fluid dynamics and functional spaces.

Keywords: navier-stokes equations, hypercomplex bifurcations, turbulence, sobolev and besov space

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Authors: F. Pari, E. Fathiazar, T. Hashemi, M. Pari

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The present study aimed to examine the role of self-efficacy and school belonging in the academic achievement of Tabriz high school students in grade 11. Therefore, using a random cluster method, 377 subjects were selected from the whole students of Tabriz high schools. They filled in the School Belonging Questionnaire (SBQ) and General Self-Efficacy Scale. Data were analyzed using correlational as well as multiple regression methods. Findings demonstrate self-efficacy and school belonging have significant roles in the prediction of academic achievement. On the other hand, the results suggest that considering the gender variable there is no significant difference between self-efficacy and school belonging. On the whole, cognitive approaches could be effective in the explanation of academic achievement.

Keywords: school belonging, self-efficacy, academic achievement, high school

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Authors: Ghada Moussa, Mamoud Owais

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Transit Route Network Design Problem (TrNDP) is the most important component in Transit planning, in which the overall cost of the public transportation system highly depends on it. The main purpose of this study is to develop a novel solution methodology for the TrNDP, which goes beyond pervious traditional sophisticated approaches. The novelty of the solution methodology, adopted in this paper, stands on the deterministic operators which are tackled to construct bus routes. The deterministic manner of the TrNDP solution relies on using linear and integer mathematical formulations that can be solved exactly with their standard solvers. The solution methodology has been tested through Mandl’s benchmark network problem. The test results showed that the methodology developed in this research is able to improve the given network solution in terms of number of constructed routes, direct transit service coverage, transfer directness and solution reliability. Although the set of routes resulted from the methodology would stand alone as a final efficient solution for TrNDP, it could be used as an initial solution for meta-heuristic procedures to approach global optimal. Based on the presented methodology, a more robust network optimization tool would be produced for public transportation planning purposes.

Keywords: integer programming, transit route design, transportation, urban planning

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Authors: Zine Souhila Rached, Mazari Benyounes Bouzid, Mohamed Amine, Allaoui Tayeb

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Wind energy has many advantages, it does not pollute and it is an inexhaustible source. However, its high cost is a major constraint, especially on the less windy sites. The purpose of wind energy systems is to maximize energy efficiency, and extract maximum power from the wind speed. In other words, having a power coefficient is maximum and therefore the maximum power point tracking. In this case, the MPPT control becomes important.To realize this control, strategy conventional proportional and integral (PI) controller is usually used. However, this strategy cannot achieve better performance. This paper proposes a robust control of a turbine which optimizes its production, that is improve the quality and energy efficiency, namely, a strategy of sliding mode control. The proposed sliding mode control strategy presents attractive features such as robustness to parametric uncertainties of the turbine; the proposed sliding mode control approach has been simulated on three-blade wind turbine. The simulation result under Matlab\Simulink has validated the performance of the proposed MPPT strategy.

Keywords: wind turbine, maximum power point tracking, sliding mode, energy conversion systems

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Authors: Houcine Naim, Abdelatif Hassini, Noureddine Benabadji, Alex Van Den Bossche

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In this paper, two models to estimate the overall monthly average daily radiation on a horizontal surface were applied to the site of Oran (35.38 ° N, 0.37 °W). We present a comparison between the first one is a regression equation of the Angstrom type and the second model is developed by the present authors some modifications were suggested using as input parameters: the astronomical parameters as (latitude, longitude, and altitude) and meteorological parameters as (relative humidity). The comparisons are made using the mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE), and mean absolute bias error (MABE). This comparison shows that the second model is closer to the experimental values that the model of Angstrom.

Keywords: meteorology, global radiation, Angstrom model, Oran

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Authors: A. M. Benomair, M. O. Tokhi

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This paper presents the investigation on a system model for the stabilization of a Magnetic Levitation System (Maglev’s). The magnetic levitation system is a challenging nonlinear mechatronic system in which an electromagnetic force is required to suspend an object (metal sphere) in air space. The electromagnetic force is very sensitive to the noise which can create acceleration forces on the metal sphere, causing the sphere to move into the unbalanced region. Maglev’s give the contribution in industry and this system has reduce the power consumption, has increase the power efficiency and reduce the cost maintenance. The common applications for Maglev’s Power Generation (e.g. wind turbine), Maglev’s trains and Medical Device (e.g. Magnetically suspended Artificial Heart Pump). This paper presents the comparison between dynamic response and robust characteristic for both conventional PD and Fuzzy PD controller. The main contribution of this paper is the proof of fuzzy PD type stabilization and robustness. By use of a method to tune the scaling factors of the linear PD type fuzzy controller from an equivalent tuned conventional PD.

Keywords: magnetic levitation system, PD controller, Fuzzy Logic Control, Fuzzy PD

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Authors: Matthew Cardeiro, Amalia D. Ardeljan, Lexi Frankel, Dianela Prado Escobar, Catalina Molnar, Omar M. Rashid

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Introduction: Enterococci comprise the natural flora of nearly all animals and are ubiquitous in food manufacturing and probiotics. However, its role in the microbiome remains controversial. The gut microbiome has shown to play an important role in immunology and cancer. Further, recent data has suggested a relationship between gut microbiota and breast cancer. These studies have shown that the gut microbiome of patients with breast cancer differs from that of healthy patients. Research regarding enterococcus infection and its sequala is limited, and further research is needed in order to understand the relationship between infection and cancer. Enterococcus may prevent the development of breast cancer (BC) through complex immunologic and microbiotic adaptations following an enterococcus infection. This study investigated the effect of enterococcus infection and the incidence of BC. Methods: A retrospective study (January 2010- December 2019) was provided by a Health Insurance Portability and Accountability Act (HIPAA) compliant national database and conducted using a Humans Health Insurance Database. International Classification of Disease (ICD) 9th and 10th codes, Current Procedural Terminology (CPT), and National Drug Codes were used to identify BC diagnosis and enterococcus infection. Patients were matched for age, sex, Charlson Comorbidity Index (CCI), antibiotic treatment, and region of residence. Chi-squared, logistic regression, and odds ratio were implemented to assess the significance and estimate relative risk. Results: 671 out of 28,518 (2.35%) patients with a prior enterococcus infection and 1,459 out of 28,518 (5.12%) patients without enterococcus infection subsequently developed BC, and the difference was statistically significant (p<2.2x10⁻¹⁶). Logistic regression also indicated enterococcus infection was associated with a decreased incidence of BC (RR=0.60, 95% CI [0.57, 0.63]). Treatment for enterococcus infection was analyzed and controlled for in both enterococcus infected and noninfected populations. 398 out of 11,523 (3.34%) patients with a prior enterococcus infection and treated with antibiotics were compared to 624 out of 11,523 (5.41%) patients with no history of enterococcus infection (control) and received antibiotic treatment. Both populations subsequently developed BC. Results remained statistically significant (p<2.2x10-16) with a relative risk of 0.57 (95% CI [0.54, 0.60]). Conclusion & Discussion: This study shows a statistically significant correlation between enterococcus infection and a decrease incidence of breast cancer. Further exploration is needed to identify and understand not only the role of enterococcus in the microbiome but also the protective mechanism(s) and impact enterococcus infection may have on breast cancer development. Ultimately, further research is needed in order to understand the complex and intricate relationship between the microbiome, immunology, bacterial infections, and carcinogenesis.

Keywords: breast cancer, enterococcus, immunology, infection, microbiome

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Authors: Majid A. AlSayari

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The study reported here investigated social cognitive theory (SCT) in the context of Vocational Rehab (VR) for people with disabilities. The prime purpose was to increase knowledge of VR phenomena and make recommendations for improving VR services. The sample consisted of 242 persons with Spinal Cord Injuries (SCI) who completed questionnaires. A further 32 participants were Trainers. Analysis of questionnaire data was carried out using factor analysis, multiple regression analysis, and thematic analysis. The analysis suggested that, in motivational terms, and consistent with research carried out in other academic contexts, self-efficacy was the best predictor of VR performance. The author concludes that that VR self-efficacy predicted VR training performance.

Keywords: people with physical disabilities, social cognitive theory, self-efficacy, vocational training

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Authors: Min Woo Lee, Kyoung Seok Kim

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We made an effort to resolve a research question, which is about the relationship between employees’ corporate social responsibility (CSR) participation and their organizational citizenship behavior (OCB), and an effect of profiles of job characteristics. To test the question, we divided sample into two groups that have the profiles of each job characteristic. One group had high level on the five dimensions of job characteristic (D group), whereas another group had low level on the dimensions (R group). As a result, regression analyses showed that the relationship between CSR participation and OCB is positive in the D group, but the relationship is not significant in the R group. The results raise a question to the argument of recent studies showing that there is positive relationship between the CSR and the OCB. Implications and limitations are demonstrated in the conclusion.

Keywords: CSR, OCB, job characteristics, cluster analysis

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Authors: Liubov Evstigneeva

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In this paper an attempt is made to establish causal nexuses between innovation and international trade in Russia. The topicality of this issue is determined by the necessity of choosing policy instruments for economic modernization and transition to innovative development. The vector auto regression (VAR) model and Granger test are applied for the Russian monthly data from 2005 until the second quartile of 2015. Both lagged import and export at the national level cause innovation, the latter starts to stimulate foreign trade since it is a remote lag. In comparison to aggregate data, the results by patent’s categories are more diverse. Importing technologies from foreign countries stimulates patent activity, while innovations created in Russia are only Granger causality for import to Commonwealth of Independent States.

Keywords: export, import, innovation, patents

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Authors: F. Guspi, E. García

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This exploratory study gives an overview of the evolution of the main financial and performance indicators of the Academic Spin-Off’s and High Growth Academic Spin-Off’s in year 3 and year 6 after its creation in the region of Catalonia in Spain. The study compares and evaluates results of these different measures of performance and the degree of success of these companies for each University. We found that the average Catalonian Academic Spin-Off is small and have not achieved the sustainability stage at year 6. On the contrary, a small group of High Growth Academic Spin-Off’s exhibit robust performance with high profits in year 6. Our results support the need to increase selectivity and support for these companies especially near year 3, because are the ones that will bring wealth and employment. University role as an investor has rigid norms and habits that impede an efficient economic return from their ASO investment. Universities with high performance on sales and employment in year 3 not always could sustain this growth in year 6 because their ASO’s are not profitable. On the contrary, profitable ASO exhibit superior performance in all measurement indicators in year 6. We advocate the need of a balanced growth (with profits) as a way to obtain subsequent continuous growth.

Keywords: Academic Spin-Off (ASO), university entrepreneurship, entrepreneurial university, high growth, New Technology Based Companies (NTBC), University Spin-Off

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Authors: Mahmood Khalghollah, Mohammad Tavallaeinejad, Mohammad Eghtesad

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The method of Controlled Lagrangian is an energy shaping control technique for under actuated Lagrangian systems. Energy shaping control design methods are appealing as they retain the underlying nonlinear dynamics and can provide stability results that hold over larger domain than can be obtained using linear design and analysis. In the present study, controlled lagrangian is employed for designing a controller in an under actuated rotating flexible plate system. In the system of rotating flexible plate, due to its nonlinear characteristics and coupled dynamics of rigid and flexible components, controller design is a known challenge. In this paper, controller objectives are considered to be vibration reduction of flexible component and position control of the tip of the plate. To achieve the goals, a method based on both kinetic and potential energy shaping is introduced. The stability of the closed-loop system is investigated and proved around its equilibrium points. Moreover, the proposed controller is shown to be robust against disturbance and plant uncertainties.

Keywords: controlled lagrangian, underactuated system, flexible rotating plate, disturbance

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Authors: Dijiang Lyu, Shaoying Li, Zhangzhi Tan, Zhifeng Wu, Feng Gao

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Nowadays, China is experiencing rapidly urban rail transit expansions in the world. The purpose of this study is to finely model factors influencing transit ridership at multi-time dimensions within transit stations’ pedestrian catchment area (PCA) in Guangzhou, China. This study was based on multi-sources spatial data, including smart card data, high spatial resolution images, points of interest (POIs), real-estate online data and building height data. Eight multiple linear regression models using backward stepwise method and Geographic Information System (GIS) were created at station-level. According to Chinese code for classification of urban land use and planning standards of development land, residential land-use were divided into three categories: first-level (e.g. villa), second-level (e.g. community) and third-level (e.g. urban villages). Finally, it concluded that: (1) four factors (CBD dummy, number of feeder bus route, number of entrance or exit and the years of station operation) were proved to be positively correlated with transit ridership, but the area of green land-use and water land-use negative correlated instead. (2) The area of education land-use, the second-level and third-level residential land-use were found to be highly connected to the average value of morning peak boarding and evening peak alighting ridership. But the area of commercial land-use and the average height of buildings, were significantly positive associated with the average value of morning peak alighting and evening peak boarding ridership. (3) The area of the second-level residential land-use was rarely correlated with ridership in other regression models. Because private car ownership is still large in Guangzhou now, and some residents living in the community around the stations go to work by transit at peak time, but others are much more willing to drive their own car at non-peak time. The area of the third-level residential land-use, like urban villages, was highly positive correlated with ridership in all models, indicating that residents who live in the third-level residential land-use are the main passenger source of the Guangzhou Metro. (4) The diversity of land-use was found to have a significant impact on the passenger flow on the weekend, but was non-related to weekday. The findings can be useful for station planning, management and policymaking.

Keywords: fine-scale modeling, Guangzhou city, multi-time dimensions, multi-sources spatial data, transit ridership

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Authors: Sam Weber, Deepak Mishra, Susan Wilde, Elizabeth Kramer

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The frequency and severity of cyanobacteria harmful blooms (CyanoHABs) have been increasing over time, with point and non-point source eutrophication and shifting climate paradigms being blamed as the primary culprits. Excessive nutrients, warm temperatures, quiescent water, and heavy and less regular rainfall create more conducive environments for CyanoHABs. CyanoHABs have the potential to produce a spectrum of toxins that cause gastrointestinal stress, organ failure, and even death in humans and animals. To promote enhanced, proactive CyanoHAB management, risk modeling using geospatial tools can act as predictive mechanisms to supplement current CyanoHAB monitoring, management and mitigation efforts. The risk maps would empower water managers to focus their efforts on high risk water bodies in an attempt to prevent CyanoHABs before they occur, and/or more diligently observe those waterbodies. For this research, exploratory spatial data analysis techniques were used to identify the strongest predicators for CyanoHAB blooms based on remote sensing-derived cyanobacteria cell density values for 771 waterbodies in the Georgia Piedmont and landscape characteristics of their watersheds. In-situ datasets for cyanobacteria cell density, nutrients, temperature, and rainfall patterns are not widely available, so free gridded geospatial datasets were used as proxy variables for assessing CyanoHAB risk. For example, the percent of a watershed that is agriculture was used as a proxy for nutrient loading, and the summer precipitation within a watershed was used as a proxy for water quiescence. Cyanobacteria cell density values were calculated using atmospherically corrected images from the European Space Agency’s Sentinel-2A satellite and multispectral instrument sensor at a 10-meter ground resolution. Seventeen explanatory variables were calculated for each watershed utilizing the multi-petabyte geospatial catalogs available within the Google Earth Engine cloud computing interface. The seventeen variables were then used in a multiple linear regression model, and the strongest predictors of cyanobacteria cell density were selected for the final regression model. The seventeen explanatory variables included land cover composition, winter and summer temperature and precipitation data, topographic derivatives, vegetation index anomalies, and soil characteristics. Watershed maximum summer temperature, percent agriculture, percent forest, percent impervious, and waterbody area emerged as the strongest predictors of cyanobacteria cell density with an adjusted R-squared value of 0.31 and a p-value ~ 0. The final regression equation was used to make a normalized cyanobacteria cell density index, and a Jenks Natural Break classification was used to assign waterbodies designations of low, medium, or high risk. Of the 771 waterbodies, 24.38% were low risk, 37.35% were medium risk, and 38.26% were high risk. This study showed that there are significant relationships between free geospatial datasets representing summer maximum temperatures, nutrient loading associated with land use and land cover, and the area of a waterbody with cyanobacteria cell density. This data analytics approach to CyanoHAB risk assessment corroborated the literature-established environmental triggers for CyanoHABs, and presents a novel approach for CyanoHAB risk mapping in waterbodies across the greater southeastern United States.

Keywords: cyanobacteria, land use/land cover, remote sensing, risk mapping

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Authors: Sayantan Khanra, Rojers P. Joseph

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Background and Significance: This study is aimed at analyzing the role of demographic and service quality variables in the adoption and diffusion of e-government services among the users in India. The study proposes to examine the users' perception about e-Government services and investigate the key variables that are most salient to the Indian populace. Description of the Basic Methodologies: The methodology to be adopted in this study is Hierarchical Regression Analysis, which will help in exploring the impact of the demographic variables and the quality dimensions on the willingness to use e-government services in two steps. First, the impact of demographic variables on the willingness to use e-government services is to be examined. In the second step, quality dimensions would be used as inputs to the model for explaining variance in excess of prior contribution by the demographic variables. Present Status: Our study is in the data collection stage in collaboration with a highly reliable, authentic and adequate source of user data. Assuming that the population of the study comprises all the Internet users in India, a massive sample size of more than 10,000 random respondents is being approached. Data is being collected using an online survey questionnaire. A pilot survey has already been carried out to refine the questionnaire with inputs from an expert in management information systems and a small group of users of e-government services in India. The first three questions in the survey pertain to the Internet usage pattern of a respondent and probe whether the person has used e-government services. If the respondent confirms that he/she has used e-government services, then an aggregate of 15 indicators are used to measure the quality dimensions under consideration and the willingness of the respondent to use e-government services, on a five-point Likert scale. If the respondent reports that he/she has not used e-government services, then a few optional questions are asked to understand the reason(s) behind the same. Last four questions in the survey are dedicated to collect data related to the demographic variables. An indication of the Major Findings: Based on the extensive literature review carried out to develop several propositions; a research model is prescribed to start with. A major outcome expected at the completion of the study is the development of a research model that would help to understand the relationship involving the demographic variables and service quality dimensions, and the willingness to adopt e-government services, particularly in an emerging economy like India. Concluding Statement: Governments of emerging economies and other relevant agencies can use the findings from the study in designing, updating, and promoting e-government services to enhance public participation, which in turn, would help to improve efficiency, convenience, engagement, and transparency in implementing these services.

Keywords: adoption and diffusion of e-government services, demographic variables, hierarchical regression analysis, service quality dimensions

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Authors: Randy Price, Harbir Antil, Rainald Löhner, Fumiya Togashi

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Chemically reacting flows are common in engineering applications such as hypersonic flow, combustion, explosions, manufacturing process, and environmental assessments. The number of reactions in combustion simulations can exceed 100, making a large number of flow and combustion problems beyond the capabilities of current supercomputers. Motivated by this, deep neural networks (DNNs) will be introduced with the goal of eventually replacing the existing chemistry software packages with DNNs. The DNNs used in this paper are motivated by the Residual Neural Network (ResNet) architecture. In the continuum limit, ResNets become an optimization problem constrained by an ODE. Such a feature allows the use of ODE control techniques to enhance the DNNs. In this work, DNNs are constructed, which update the species un at the nᵗʰ timestep to uⁿ⁺¹ at the n+1ᵗʰ timestep. Parallel DNNs are trained for each species, taking in uⁿ as input and outputting one component of uⁿ⁺¹. These DNNs are applied to multiple species and reactions common in chemically reacting flows such as H₂-O₂ reactions. Experimental results show that the DNNs are able to accurately replicate the dynamics in various situations and in the presence of errors.

Keywords: chemical reacting flows, computational fluid dynamics, ODEs, residual neural networks, ResNets

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Authors: Pradyumna Mohanty

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The paper aims to explore whether accounting information of Indian companies in the Steel sector are value relevant or not. Ohlson’s model which usually takes into consideration book value per share (BV) and earnings per share (EARN) has been used and the same has been expanded to include two more variables such as cash flow from operations (CFO) and return on equity (ROE). The data were collected from CMIE-Prowess data base in respect of BSE-listed steel companies and the time frame spans from 2010 to 2014. OLS regression has been used to test the value relevance of these accounting numbers. Results indicate that both CFO and BV are having significant influence on the stock price in two out of five years of study. But, BV is emerging as the most significant and highly value relevant of all the four variables during the entire period of study.

Keywords: value relevance, accounting information, book value per share, earnings per share

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Authors: Jose Funes, Jeff Sauer, Laixiang Sun

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This research examines determinants of multifamily housing development and spillovers in the District of Columbia. A range of socioeconomic factors related to income distribution, productivity, and land use policies are thought to influence the development in contemporary U.S. multifamily housing markets. The analysis leverages data from the American Community Survey to construct panel datasets spanning from 2010 to 2019. Using spatial regression, we identify several socioeconomic measures and land use policies both positively and negatively associated with new housing supply. We contextualize housing estimates related to race in relation to uneven development in the contemporary D.C. housing supply.

Keywords: neighborhood effect, sorting, spatial spillovers, multifamily housing

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Authors: Abdelrahman A. Elsehsah, Nada A. El-Kordy

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The "Investing in Minds: A Financial Framework for Mental Health and Well-Being in the Workplace" project aims to address the pressing mental health challenges faced by employees in modern workplaces. Recognizing the significant impact of mental health on productivity, employee engagement, and overall organizational performance, this initiative proposes a structured approach that includes comprehensive needs assessments, evidence-based interventions, and robust evaluation frameworks. Key components, such as employee assistance programs (EAPs) and wellness initiatives, are designed to foster a supportive workplace culture and promote healthy behaviors. By prioritizing mental health, organizations can expect substantial benefits, including reduced healthcare costs, improved employee retention, and enhanced productivity. This project aligns with the goals of the International Labour Organization (ILO) and emphasizes the importance of integrating mental health strategies into organizational policies. Through effective communication and a commitment to continuous improvement, "Investing in Minds" seeks to create a sustainable framework for mental health support, ultimately contributing to a healthier workforce and better business outcomes.

Keywords: mental health, workplace well-being, employee assistance programs, evaluation framework

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Authors: Soheila Sadeghi

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In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Cross-validation techniques are employed to assess the robustness and generalization ability of the models. The performance of the models is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The feature importance analysis reveals the relative significance of different project attributes in predicting the impact on cost and schedule. Key factors such as productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are identified as influential predictors. The study highlights the importance of considering both cost and schedule implications when managing scope changes. The developed predictive models provide project managers with a data-driven tool to proactively assess the potential impact of scope changes on project cost and schedule. By leveraging these insights, project managers can make informed decisions, optimize resource allocation, and develop effective mitigation strategies. The findings of this research contribute to improved project planning, risk management, and overall project success.

Keywords: cost impact, machine learning, predictive modeling, schedule impact, scope changes

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Authors: Juanee Cilliers

Abstract:

Urban citizens have come to expect more from their cities, demanding optimal conditions for business creativity and professional development, along with efficient, sustainable transportation and energy systems that feed robust economic development and healthy job markets. Urban public spaces are an important part of the urban environment, creating the framework for public life and quality thereof. The transformation of space into successful public places are crucial in this regard as planning must safeguard flexibility towards future changes, whilst simultaneously be capable of acting on short-term demands in order to address the complexity of public spaces within urban areas. This research evaluated two case studies of different cities in Belgium which successfully transformed spaces into lively public places. The transformation was illustrated and evaluated by means of visual analyses and space usage analyses of the original and redesigned space, along with the experience and value that the redesign brought to the area. Selected design elements were identified and evaluated based on the role in the transformation process, in an attempt to draw conclusions with regards to theory-practice relevance and to guide the transformation of space to place of (similar) public spaces.

Keywords: space, place, transformation, case studies

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Authors: Rodrigo De A. Ferrazza, Henry D. M. Garcia, Viviana H. V. Aristizabal, Camilla De S. Nogueira, Cecilia J. Verissimo, Jose Roberto Sartori, Roberto Sartori, Joao Carlos P. Ferreira

Abstract:

Environmental factors adversely influence sustainability in livestock production system. Dairy herds are the most affected by heat stress among livestock industries. This clearly implies in development of new strategies for mitigating heat, which should be based on physiological and metabolic adaptations of the animal. In this study, we incorporated the effect of climate variables and heat exposure time on the thermoregulatory responses in order to clarify the adaptive mechanisms for bovine heat dissipation under intense thermal stress induced experimentally in climate chamber. Non-lactating Holstein cows were contemporaneously and randomly assigned to thermoneutral (TN; n=12) or heat stress (HS; n=12) treatments during 16 days. Vaginal temperature (VT) was measured every 15 min with a microprocessor-controlled data logger (HOBO®, Onset Computer Corporation, Bourne, MA, USA) attached to a modified vaginal controlled internal drug release insert (Sincrogest®, Ourofino, Brazil). Rectal temperature (RT), respiratory rate (RR) and heart rate (HR) were measured twice a day (0700 and 1500h) and dry matter intake (DMI) was estimated daily. The ambient temperature and air relative humidity were 25.9±0.2°C and 73.0±0.8%, respectively for TN, and 36.3± 0.3°C and 60.9±0.9%, respectively for HS. Respiratory rate of HS cows increased immediately after exposure to heat and was higher (76.02±1.70bpm; P<0.001) than TN (39.70±0.71bpm), followed by rising of RT (39.87°C±0.07 for HS versus 38.56±0.03°C for TN; P<0.001) and VT (39.82±0.10°C for HS versus 38.26±0.03°C for TN; P<0.001). A diurnal pattern was detected, with higher (P<0.01) afternoon temperatures than morning and this effect was aggravated for HS cows. There was decrease (P<0.05) of HR for HS cows (62.13±0.99bpm) compared to TN (66.23±0.79bpm), but the magnitude of the differences was not the same over time. From the third day, there was a decrease of DMI for HS in attempt to maintain homeothermy, while TN cows increased DMI (8.27kg±0.33kg d-1 for HS versus 14.03±0.29kg d-1 for TN; P<0.001). By regression analysis, RT and RR better reflected the response of cows to changes in the Temperature Humidity Index and the effect of climate variables from the previous day to influence the physiological parameters and DMI was more important than the current day, with ambient temperature the most important factor. Comparison between acute (0 to 3 days) and chronic (13 to 16 days) exposure to heat stress showed decreasing of the slope of the regression equations for RR and DMI, suggesting an adaptive adjustment, however with no change for RT. In conclusion, intense heat stress exerted strong influence on the thermoregulatory mechanisms, but the acclimation process was only partial.

Keywords: acclimation, bovine, climate chamber, hyperthermia, thermoregulation

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Authors: Zahra Okba, Hassan El Ouizgani

Abstract:

Anticipating an individual’s behavior to the environmental factors allows for having relevant ecological forecasts. The Dynamic Energy Budget model facilitates prediction, and it is mechanically dependent on biology to abiotic factors but is generally field verified under relatively stable physical conditions. Dynamic Energy Budget Theory (DEB) is a robust framework that can link the individual state to environmental factors, and in our work, we have tested its ability to account for variability by looking at model predictions in the Agadir Bay, which is characterized by a semi-arid climate and temperature is strongly influenced by the trade winds front and nutritional availability. From previous works in our laboratory, we have collected different biological DEB model parameters of Mytilus galloprovincialis mussel in Agadir Bay. We mathematically formulated the equations that make up the DEB model and then adjusted our analytical functions with the observed biological data of our local species. We also assumed the condition of constant immersion, and then we integrated the details of the tidal cycles to calculate the metabolic depression at low tide. Our results are quite satisfactory concerning the length and shape of the shell in one part and the gonadosomatic index in another part.

Keywords: dynamic energy budget, mussels, mytilus galloprovincialis, agadir bay, DEB model

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Authors: Thohidul Islam, Md. Hamidul Haque, Robin Kumar Biswas

Abstract:

Floods are one of the deadliest natural disasters which are very complex to model; however, machine learning is opening the door for more reliable and accurate flood prediction. In this research, a multilayer perceptron neural network (MLP) is developed to model the rainfall-water level relation, in a subtropical monsoon climatic region of the Bangladesh-India border. Our experiments show promising empirical results to forecast the water level for 1 day lead time. Our best performing MLP model achieves 98.7% coefficient of determination with lower model complexity which surpasses previously reported results on similar forecasting problems.

Keywords: flood forecasting, machine learning, multilayer perceptron network, regression

Procedia PDF Downloads 174