Search results for: nonparametric geographically weighted regression

Authors: Nasim Hossein Hamzeh, Karim Shukurov, Abbas Ranjbar Saadat Abadi, Alaa Mhawish, Christian Opp

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Deserts are the main dust sources in the world. Also, recently driedLake beds have caused environmental problems inthe surrounding areas in the world. In this study, the Urmia Lake was the source of dustfromApril 24 to April 25, 2017.The local dust storm was combined with another large-scale dust storm that originated from Saudi Arabia and Iraq 1-2 days earlier. Synoptic investigation revealed that the severe dust storm was made by a strong Black Sea cyclone and a low-pressure system over the Middle East and Central Iraq in conjunction a high-pressure system and associated with a high gradient contour and a quasi-stationary long-wave trough over the east and south of the Mediterranean Sea. Based on HYSPLIT 72 hours backward and forward trajectories, the most probable dust transport routes to and from the Urmia Lake region are estimated. Using the concentration weighted trajectory (CWT) method based on 24 hours backward and 24 hours forward trajectories, the spatial distributions of potential sources of PM10 observed in the Urmia Lake region on April 23-26, 2017. Also, the vertical profile of dust particles using the WRF-Chem model with two dust schemes showed dust ascending up to 5 km from the lake. Also, the dust schemes outputs shows that the PM10 fluctuating changes are 12 hours earlier than the measured surface PM10 at five air pollution monitoring stations around the Urmia Lake in 23-26 April 2017.

Keywords: dust storm, synoptic investigation, WRF-chem model, urmia lake, lagrangian trajectory

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Authors: H. Aissa, L. Mouzai, M. Bouhadef

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The effects of down slope steepness on soil splash distribution under a water drop impact have been investigated in this study. The equipment used are the burette to simulate a water drop, a splash cup filled with sandy soil which forms the source area and a splash board to collect the ejected particles. The results found in this study have shown that the apparent mass increased with increasing downslope angle following a linear regression equation with high coefficient of determination. In the same way, the radial soil splash distribution over the distance has been analyzed statistically, and an exponential function was the best fit of the relationship for the different slope angles. The curves and the regressions equations validate the well known FSDF and extend the theory of Van Dijk.

Keywords: splash distribution, water drop, slope steepness, soil detachment

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Authors: Maria Venus G. Solares

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The ancient Filipinos had their own spelling or alphabet that differed from the modern Roman alphabet brought by the Spaniards. It consists of seventeen letters, three vowels, and fourteen consonants and is called Baybayin. The word Baybayin is a Tagalog word that refers to all the letters used in writing a language, an alphabet; however, it is also a syllable. The House Bill 4395, first proposed by Rep. Leopoldo Bataoil of the second district of Pangasinan in 2011, which later became House Bill 1022 of what he called The Declaration of the Baybayin as the National Writing System of the Philippines, prompted the researcher to conduct a study on the topic. The main objective of this study was to develop and assess the proposed module for enhancing the reading and writing skills in Baybayin of the students. The researchers wanted to ensure the acceptability of the Baybayin using the proposed module and meet the needs of students in developing their ability to read and write Baybayin through the module. The researchers used quasi-experimental research in this study. The data was collected through the initial and final analysis of the students of Adamson University's ABM 1102 using convenient sampling techniques. Based on statistical analysis of data using weighted mean, standard deviation, and paired t-tests, the proposed module helped improve the students' literacy skills, and the response exercises in the proposed module changed the acceptability of the Baybayin in their minds. The study showed that there was an important difference in the scores of students before and after the use of the module. The student's response to the assessment of their reading and writing skills on Baybayin was highly acceptable. This study will help develop the reading and writing skills of the students in Baybayin and teach Baybayin in response to the revival of a part of Philippine culture that has been long forgotten.

Keywords: Baybayin, proposed module, skill, acceptability

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Authors: Teodorica G. Ani

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Economic activity requires an encouraging regulatory environment and effective rules that are transparent and accessible to all. The World Bank has been publishing the annual Doing Business reports since 2004 to investigate the scope and manner of regulations that enhance business activity and those that constrain it. A streamlined business environment supporting the development of competitive small and medium enterprises (SMEs) may expand employment opportunities and improve the living conditions of low income households. Asia has emerged as one of the most attractive markets in the world. Economies in East Asia and the Pacific were among the most active in making it easier for local firms to do business. The study aimed to describe the ease of doing business and its effect to economic growth among selected economies in Asia for the year 2014. The study covered 29 economies in East Asia, Southeast Asia, South Asia and Middle Asia. Ease of doing business is measured by the Doing Business indicators (DBI) of the World Bank. The indicators cover ten aspects of the ease of doing business such as starting a business, dealing with construction permits, getting electricity, registering property, getting credit, protecting investors, paying taxes, trading across borders, enforcing contracts and resolving insolvency. In the study, Gross Domestic Product (GDP) was used as the proxy variable for economic growth. Descriptive research was the research design used. Graphical analysis was used to describe the income and doing business among selected economies. In addition, multiple regression was used to determine the effect of doing business to economic growth. The study presented the income among selected economies. The graph showed China has the highest income while Maldives produces the lowest and that observation were supported by gathered literatures. The study also presented the status of the ten indicators of doing business among selected economies. The graphs showed varying trends on how easy to start a business, deal with construction permits and to register property. Starting a business is easiest in Singapore followed by Hong Kong. The study found out that the variations in ease of doing business is explained by starting a business, dealing with construction permits and registering property. Moreover, an explanation of the regression result implies that a day increase in the average number of days it takes to complete a procedure will decrease the value of GDP in general. The research proposed inputs to policy which may increase the awareness of local government units of different economies on the simplification of the policies of the different components used in measuring doing business.

Keywords: doing business, economic growth, gross domestic product, Asia

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Authors: Awais Ahmed, Atif Rana, Haseeb Zia, Maham Jahangir, Rashed Nazir, Faisal Dar

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Purpose: Biliary complications represent the most common cause of morbidity in living related liver donor transplantation and detailed preoperative evaluation of biliary anatomic variants is crucial for safe patient selection and improved surgical outcomes. Purpose of this study is to determine the accuracy of preoperative MRCP in predicting biliary variations when compared to intraoperative cholangiography in living related liver donors. Materials and Methods: From 44 potential donors, 40 consecutive living related liver donors (13 females and 28 males) underwent donor hepatectomy at our centre from April 2012 to August 2013. MRCP and IOC of all patients were retrospectively reviewed separately by two radiologists and a transplant surgeon.MRCP was performed on 1.5 Tesla MR magnets using breath-hold heavily T2 weighted radial slab technique. One patient was excluded due to suboptimal MRCP. The accuracy of MRCP for variant biliary anatomy was calculated. Results: MRCP accurately predicted the biliary anatomy in 38 of 39 cases (97 %). Standard biliary anatomy was predicted by MRCP in 25 (64 %) donors (100% sensitivity). Variant biliary anatomy was noted in 14 (36 %) IOCs of which MRCP predicted precise anatomy of 13 variants (93 % sensitivity). The two most common variations were drainage of the RPSD into the LHD (50%) and the triple confluence of the RASD, RPSD and LHD (21%). Conclusion: MRCP is a sensitive imaging tool for precise pre-operative mapping of biliary variations which is critical to surgical decision making in living related liver transplantation.

Keywords: intraoperative cholangiogram, liver transplantation, living related donors, magnetic resonance cholangio-pancreaticogram (MRCP)

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Authors: Mohammad Hossein Pazandeh, Monir Doudi, Sona Rostampour Yasouri

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Irregular consumption of current antibiotic makes increases of antibiotic resistance between urin pathogens on all worlds. This study selected based on this great community problem. The aim of this study was the biosynthesis of silver nanoparticles from Zataria multiflora extract and then to investigate its antibacterial effect on gram-negative bacilli common in Urinary Tract Infections (UTI) and MDR. The plant used in the present research was Zataria multiflora whose extract was prepared through Soxhlet extraction method. Green synthesis condition of silver nanoparticles was investigated in terms of three parameters including the extract amount, concentration of silver nitrate salt, and temperature. The seizes of nanoparticles were determined by Zetasizer. In order to identify synthesized silver nanoparticles Transmission Electron Microscopy (TEM) and X-ray Diffraction (XRD) methods were used. For evaluating the antibacterial effects of nanoparticles synthesized through biological method different concentrations of silver nanoparticles were studied on 140 cases of Muliple Drug Resistance (MDR) bacteria strains Escherichia coli, Klebsiella pneumoniae, Enterobacter aerogenes, Proteus vulgaris,Citrobacter freundii, Acinetobacter bumanii and Pseudomonas aeruginosa, (each genus of bacteria, 20 samples), which all were MDR and cause urinary tract infections , for identification of bacteria were used of Polymerase Chain Reaction (PCR) test and laboratory methods (Agar well diffusion and Microdilution methods) to assess their sensitivity to Nanoparticles. The data were analyzed using SPSS software by nonparametric Kruskal-Wallis and Mann-Whitney tests. Significant results were found about the effects of silver nitrate concentration, different amounts of Zataria multiflora extract, and temperature on nanoparticles; that is, by increasing the concentration of silver nitrate, extract amount, and temperature, the sizes of synthesized nanoparticles declined. However, the effect of above mentioned factors on particles diffusion index was not significant. Based on the TEM results, particles were mainly spherical shape with a diameter range of 25 to 50 nm. The results of XRD Analysis indicated the formation of Nanostructures and Nanocrystals of silver.. The obtained results of antibacterial effects of different concentrations of silver nanoparticles on according to agar well diffusion and microdilution method, biologically synthesized nanoparticles showed 1000 mg /ml highest and lowest mean inhibition zone diameter in E.coli , Acinetobacter bumanii 23 and 15mm, respectively. MIC was observed for all of bacteria 125mg/ml and for Acinetobacter bumanii 250mg/ml.Comparing the growth inhibitory effect of chemically synthesized Nanoparticles and biologically synthesized Nanoparticles showed that in the chemical method the highest growth inhibition belonged to the concentration of 62.5 mg /ml. The inhibitory effect on the growth all of bacteria causes of urine infection and MDR was observed and by increasing silver ion concentration in Nanoparticles, antibacterial activity increased. Generally, the biological synthesis can be considered an efficient way not only in making Nanoparticles but also for having anti-bacterial properties. It is more biocompatible and may be possess less toxicity than the Nanoparticles synthesized chemically.

Keywords: biosynthesis, MDR bacteria, silver nanoparticles, UTI

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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: 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: 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: Melkamu A. Zeru, Aweke A. Mitiku, Endashaw Amuka

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Background: Under-five mortality is the likelihood that a baby will pass away before turning exactly 5 years old, represented as a percentage per 1,000 live births. Exploring the spatial distribution and identifying the temporal pattern is important to reducing under-five child mortality globally, including in Ethiopia. Thus, this study aimed to identify the risk factors of under-five mortality and the spatiotemporal variation in Ethiopian administrative zones. Method: This study used the 2000-2016 Ethiopian Demographic and Health Survey (EDHS) data, which were collected using a two-stage sampling method. A total of 43,029 (10,873 in 2000, 9,861 in 2005, 11,654 in 2011, and 10,641 in 2016) weighted sample under-five child mortality was used. The space-time dynamic model was employed to account for spatial and time effects in 65 administrative zones in Ethiopia. Results: From the result of a general nesting spatial-temporal dynamic model, there was a significant space-time interaction effect [γ = -0.1444, 95 % CI (-0.6680, -0.1355)] for under-five mortality. The increase in the percentages of mothers illiteracy [𝛽 = 0.4501, 95% CI (0.2442, 0.6559)], not vaccinated[𝛽= 0.7681, 95% CI (0.5683, 0.9678)], unimproved water[𝛽= 0.5801, CI (0.3793, 0.7808)] were increased death rates for under five children while increased percentage of contraceptive use [𝛽= -0.6609, 95% CI (-0.8636, -0.4582)] and ANC visit > 4 times [𝛽= -0.1585, 95% CI(-0.1812, -0.1357)] were contributed to the decreased under-five mortality rate at the zone in Ethiopia. Conclusions: Even though the mortality rate for children under five has decreased over time, still there is still higher in different zones of Ethiopia. There exists spatial and temporal variation in under-five mortality among zones. Therefore, it is very important to consider spatial neighbourhoods and temporal context when aiming to avoid under-five mortality.

Keywords: under-five children mortality, space-time dynamic, spatiotemporal, Ethiopia

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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: 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: 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: 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: Kuo-Kai Lin, Po-Lun Chang

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Research Background and Purpose: Following the development of the Internet and multimedia, the Internet and information technology have become crucial avenues of modern communication and knowledge acquisition. The advantages of using mobile devices for learning include making learning borderless and accessible. Mobile learning has become a trend in disease management and health promotion in recent years. End-stage renal disease (ESRD) is an irreversible chronic disease, and patients who do not receive kidney transplants can only rely on hemodialysis or peritoneal dialysis to survive. Due to the complexities in caregiving for patients with ESRD that stem from their advanced age and other comorbidities, the patients’ incapacity of self-care leads to an increase in the need to rely on their families or primary caregivers, although whether the primary caregivers adequately understand and implement patient care is a topic of concern. Therefore, this study explored whether primary caregivers’ health care provisions can be improved through the intervention of an automatic speech intelligent system, thereby improving the objective health outcomes of patients undergoing long-term dialysis. Method: This study developed an automatic speech intelligent system with healthcare functions such as health information voice prompt, two-way feedback, real-time push notification, and health information delivery. Convenience sampling was adopted to recruit eligible patients from a hemodialysis center at a regional teaching hospital as research participants. A one-group pretest-posttest design was adopted. Descriptive and inferential statistics were calculated from the demographic information collected from questionnaires answered by patients and primary caregivers, and from a medical record review, a health care scale (recorded six months before and after the implementation of intervention measures), a subjective health assessment, and a report of objective physiological indicators. The changes in health care behaviors, subjective health status, and physiological indicators before and after the intervention of the proposed automatic speech intelligent system were then compared. Conclusion and Discussion: The preliminary automatic speech intelligent system developed in this study was tested with 20 pretest patients at the recruitment location, and their health care capacity scores improved from 59.1 to 72.8; comparisons through a nonparametric test indicated a significant difference (p < .01). The average score for their subjective health assessment rose from 2.8 to 3.3. A survey of their objective physiological indicators discovered that the compliance rate for the blood potassium level was the most significant indicator; its average compliance rate increased from 81% to 94%. The results demonstrated that this automatic speech intelligent system yielded a higher efficacy for chronic disease care than did conventional health education delivered by nurses. Therefore, future efforts will continue to increase the number of recruited patients and to refine the intelligent system. Future improvements to the intelligent system can be expected to enhance its effectiveness even further.

Keywords: automatic speech intelligent system for health care, primary caregiver, long-term hemodialysis, health care capabilities, health outcomes

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Authors: Pius Marthin, Nihal Ata Tutkun

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Survival analysis has become one of the paramount procedures in the modeling of time-to-event data. When we encounter complex survival problems, the traditional approach remains limited in accounting for the complex correlational structure between the covariates and the outcome due to the strong assumptions that limit the inference and prediction ability of the resulting models. Several studies exist on the deep learning approach to survival modeling; moreover, the application for the case of complex survival problems still needs to be improved. In addition, the existing models need to address the data structure's complexity fully and are subject to noise and redundant information. In this study, we design a deep learning technique (CmpXRnnSurv_AE) that obliterates the limitations imposed by traditional approaches and addresses the above issues to jointly predict the risk-specific probabilities and survival function for recurrent events with competing risks. We introduce the component termed Risks Information Weights (RIW) as an attention mechanism to compute the weighted cumulative incidence function (WCIF) and an external auto-encoder (ExternalAE) as a feature selector to extract complex characteristics among the set of covariates responsible for the cause-specific events. We train our model using synthetic and real data sets and employ the appropriate metrics for complex survival models for evaluation. As benchmarks, we selected both traditional and machine learning models and our model demonstrates better performance across all datasets.

Keywords: cumulative incidence function (CIF), risk information weight (RIW), autoencoders (AE), survival analysis, recurrent events with competing risks, recurrent neural networks (RNN), long short-term memory (LSTM), self-attention, multilayers perceptrons (MLPs)

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Authors: Korpapa Srisamai, Pawee Siriruk

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The purpose of this study is to forecast product demands and develop appropriate and adequate procurement plans to meet customer needs and reduce costs. When the product exceeds customer demands or does not move, it requires the company to support insufficient storage spaces. Moreover, some items, when stored for a long period of time, cause deterioration to dead stock. A case study of the wholesale company of electronic equipment and components, which has uncertain customer demands, is considered. The actual purchasing orders of customers are not equal to the forecast provided by the customers. In some cases, customers have higher product demands, resulting in the product being insufficient to meet the customer's needs. However, some customers have lower demands for products than estimates, causing insufficient storage spaces and dead stock. This study aims to reduce the loss of sales opportunities and the number of remaining goods in the warehouse, citing 30 product samples of the company's most popular products. The data were collected during the duration of the study from January to October 2022. The methods used to forecast are simple moving averages, weighted moving average, and exponential smoothing methods. The economic ordering quantity and reorder point are used to calculate to meet customer needs and track results. The research results are very beneficial to the company. The company can reduce the loss of sales opportunities by 20% so that the company has enough products to meet customer needs and can reduce unused products by up to 10% dead stock. This enables the company to order products more accurately, increasing profits and storage space.

Keywords: demand forecast, reorder point, lost sale, dead stock

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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: 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: Abolghasem Yousefi-Babadi, Ali Bozorgi-Amiri, Aida Kazempour, Reza Tavakkoli-Moghaddam, Maryam Irani

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All over the world, natural disasters (e.g., earthquakes, floods, volcanoes and hurricanes) causes a lot of deaths. Earthquakes are introduced as catastrophic events, which is accident by unusual phenomena leading to much loss around the world. Such could be replaced by disasters or any other synonyms strongly demand great long-term help and relief, which can be hard to be managed. Supplies and facilities are very important challenges after any earthquake which should be prepared for the disaster regions to satisfy the people's demands who are suffering from earthquake. This paper proposed disaster response facility allocation problem for disaster relief operations as a mathematical programming model. Not only damaged people in the earthquake victims, need the consumable commodities (e.g., food and water), but also they need non-consumable commodities (e.g., clothes) to protect themselves. Therefore, it is concluded that paying attention to disaster points and people's demands are very necessary. To deal with this objective, both commodities including consumable and need non-consumable commodities are considered in the presented model. This paper presented the multi-objective multi-period mathematical programming model regarding the minimizing the average of the weighted response times and minimizing the total operational cost and penalty costs of unmet demand and unused commodities simultaneously. Furthermore, a Chebycheff multi-objective solution procedure as a powerful solution algorithm is applied to solve the proposed model. Finally, to illustrate the model applicability, a case study of the Tehran earthquake is studied, also to show model validation a sensitivity analysis is carried out.

Keywords: facility location, multi-objective model, disaster response, commodity

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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

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

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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

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Authors: Heba Abdelmotaal, Magdy Abdel-Kader

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Purpose: This study explores whether a firm’s effective political risk management is preventing real and accrual earnings management . Design/methodology/approach: Based on a sample of 130 firms operating in Egypt during the period 2008-2013, two hypotheses are tested using the panel data regression models. Findings: The empirical findings indicate a significant relation between real and accrual earnings management and political risk. Originality/value: This paper provides a statistically evidence on the effects of the political risk management failure on the mangers’ engagement in the real and accrual earnings management practices, and its impact on the firm’s performance.

Keywords: political risk, risk management failure, real activities manipulation, accrual earnings management

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Authors: Ebenezer Kwesi Armah-Ansah, Kenneth Fosu Oteng, Esther Selasi Avinu, Eugene Budu, Edward Kwabena Ameyaw

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Background: Maternal mortality, particularly in Benin, is a major public health concern in Sub-Saharan Africa. To provide a positive pregnancy experience and reduce maternal morbidities, all pregnant women must get appropriate and timely prenatal support. However, many pregnant women in developing countries, including Benin, begin antenatal care late. There is a paucity of empirical literature on the prevalence and predictors of early antenatal care visits in Benin. As a result, the purpose of this study is to investigate the prevalence and predictors of early antenatal care visits among women of productive age in Benin. Methods: This is a secondary analysis of the 2017/2018 Benin Demographic and Health Survey (BDHS) data. The study involved 6,919 eligible women. Data analysis was conducted using Stata version 14.2 for Mac OS. We adopted a multilevel logistic regression to examine the predictors of early ANC visits in Benin. The results were presented as odds ratios (ORs) associated with 95% confidence intervals (CIs) and p-value <0.05 to determine the significant associations. Results: The prevalence of early ANC visits among pregnant women in Benin was 57.03% [95% CI: 55.41-58.64]. In the final multilevel logistic regression, early ANC visit was higher among women aged 30-34 [aOR=1.60, 95% CI=1.17-2.18] compared to those aged 15-19, women with primary education [aOR=1.22, 95% CI=1.06-142] compared to the non-educated women, women who were covered by health insurance [aOR=3.03, 95% CI=1.35-6.76], women without a big problem in getting the money needed for treatment [aOR=1.31, 95% CI=1.16-1.49], distance to the health facility, not a big problem [aOR=1.23, 95% CI=1.08-1.41], and women whose partners had secondary/higher education [aOR=1.35, 95% CI=1.15-1.57] compared with those who were not covered by health insurance, had big problem in getting money needed for treatment, distance to health facility is a big problem and whose partners had no education respectively. However, women who had four or more births [aOR=0.60, 95% CI=0.48-0.74] and those in Atacora Region [aOR=0.50, 95% CI=0.37-0.68] had lower odds of early ANC visit. Conclusion: This study revealed a relatively high prevalence of early ANC visits among women of reproductive age in Benin. Women's age, educational status of women and their partners, parity, health insurance coverage, distance to health facilities, and region were all associated with early ANC visits among women of reproductive in Benin. These factors ought to be taken into account when developing ANC policies and strategies in order to boost early ANC visits among women in Benin. This will significantly reduce maternal and newborn mortality and help achieve the World Health Organization’s recommendation that all pregnant women should initiate early ANC visits within the first three months of pregnancy.

Keywords: antenatal care, Benin, maternal health, pregnancy, DHS, public health

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