Search results for: Cox proportional hazard regression

Authors: Saheed Olakunle Sanusi, Israel Ajibade Adedeji

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The study analysed the effect of microfinance on the profit level of small and medium scale enterprises in Lagos. The data for the study were obtained by simple random sampling, and total of one hundred and fifty (150) small and medium scale enterprises (SMEs) were sampled for the study. Seventy-five (75) each are microfinance users and non-users. Data were analysed using descriptive statistics, logit model, t-test and ordinary least square (OLS) regression. The mean profit of the enterprises using microfinance is ₦16.8m, while for the non-users of microfinance is ₦5.9m. The mean profit of microfinance users is statistically different from the non-users. The result of the logit model specified for the determinant of access to microfinance showed that three of specified variables- educational status of the enterprise head, credit utilisation and volume of business investment are significant at P < 0.01. Enterprises with many years of experience, highly educated enterprise heads and high volume of business investment have more potential access to microfinance. The OLS regression model indicated that three parameters namely number of school years, the volume of business investment and (dummy) participation in microfinance were found to be significant at P < 0.05. These variables are therefore significant determinants of impacts of microfinance on profit level in the study area. The study, therefore, concludes and recommends that to improve the status of small and medium scale enterprises for an increase in profit, the full benefit of access to microfinance can be enhanced through investment in social infrastructure and human capital development. Also, concerted efforts should be made to encouraged non-users of microfinance among SMEs to use it in order to boost their profit.

Keywords: credit utilisation, logit model, microfinance, small and medium enterprises

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Authors: Maria Anjelica P. Ancheta, Roberto S. Soriano, Erickson L. Llaguno

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The main purpose of this study is to examine whether there is a significant relationship between socio-economic class and household water supply demand, through determining or verifying the factors governing water use consumption patterns of households from a sampling from different socio-economic classes in Metro Manila, the national capital region of the Philippines. This study is also an opportunity to augment the lack of local academic literature due to the very few publications on urban household water demand after 1999. In over 600 Metro Manila households, a rapid survey was conducted on their average monthly water consumption and habits on household water usage. The questions in the rapid survey were based on an extensive review of literature on urban household water demand. Sample households were divided into socio-economic classes A-B and C-D. Cluster analysis, dummy coding and outlier tests were done to prepare the data for regression analysis. Subsequently, backward stepwise regression analysis was used in order to determine different statistical models to describe the determinants of water consumption. The key finding of this study is that the socio-economic class of a household in Metro Manila is a significant factor in water consumption. A-B households consume more water in contrast to C-D families based on the mean average water consumption for A-B and C-D households are 36.75 m3 and 18.92 m3, respectively. The most significant proxy factors of socio-economic class that were related to household water consumption were examined in order to suggest improvements in policy formulation and household water demand management.

Keywords: household water uses, socio-economic classes, urban planning, urban water demand management

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Authors: Sayyed Yaghoub Zolfegharifar, Khairul Anuar Kassim, Hossein Khoramrooz, Khodayar Farhadiasl, Sadegh Jahan

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Iran is one of the world's seismically active countries so that it experiences many small to medium earthquakes annually and a large earthquake every ten years. Due to seism tectonic conditions and special geographical and climatic position, Iran has the potential to create numerous severe earthquakes. Therefore, seismicity studies and seismic zonation of seismic zones of the country are necessary. In this article, the effect of local site conditions on the characteristics of seismic design in Rahmormoz will be examined. After analyzing the seismic hazard for Rahmormoz through deterministic and statistical methods and preparing the necessary geotechnical models based on available data, the ground response will be analyzed for different parts of the city based on four inputs and acceleration level estimated for bedrock through the equivalent linear method and by means of Deep Soil program. Finally, through the analysis of the obtained results, the seismic profiles of the ground surface for different parts of the city will be presented.

Keywords: seismic microzonation, ground response, resonance spectrum, period, site conditions

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Authors: Jamil Ahmed, Li P. Wong, Yan P. Chua

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The United Nation's sustainable development goals include the target to ensure access to water and sanitation for all; however, very few studies have assessed school-based drinking water in Pakistan. The purpose of this study was to characterize water quality in primary schools of Pakistan and to characterize how recent WASH interventions were associated with school water quality. We conducted a representative cross-sectional study of primary schools in the Sindh province of Pakistan. We used structured observations and structured interviews to ascertain the school’s WASH conditions. Our primary exposures of interest were the implementation of previous WASH interventions in the school and the water source type. Outcomes of interest included water quality (measured by various chemical and microbiological indicators) and water availability at the school’s primary drinking water source. We used log-binomial regression to characterize how WASH exposures were associated with water quality outcomes. We collected data from 256 schools. Groundwater was the primary drinking water source at most schools (87%). Water testing showed that 14% of the school’s water had arsenic above the WHO recommendations, and over 50% of the water samples exceeded recommendations for both lead and cadmium. A majority of the water sources (52%) had fecal coliform contamination. None of the schools had nitrate contamination (0%), and few had fluoride contamination (5%). Regression results indicated that having a recent WASH intervention at the school was not associated with either arsenic contamination (prevalence ratio=0.97; 95% CI: 0.46-2.1) or with fecal coliform contamination (PR=0.88; 95% CI: 0.67-1.17). Our assessment unveiled several water quality gaps that exist, including high heavy metal and fecal contamination. Our findings will help various stakeholders to take suitable action to improve water quality in Pakistani schools.

Keywords: WASH interventions, water quality, primary school children, heavy metals

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Authors: Marulasiddappa H. B., Pushparajesh Viswanathan

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Day by day, the uses of internal combustion engines (ICE) are deteriorating because of pollution and less fuel availability. In the present scenario, the electric vehicle (EV) plays a major role in the place of an ICE vehicle. The performance of EVs can be improved by the proper selection of electric motors. Initially, EV preferred induction motors for traction purposes, but due to complexity in controlling induction motor, permanent magnet synchronous motor (PMSM) is replacing induction motor in EV due to its advantages. Direct torque control (DTC) is one of the known techniques for PMSM drive in EV to control the torque and speed. However, the presence of torque ripple is the main drawback of this technique. Many control strategies are followed to reduce the torque ripples in PMSM. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) controller technique is proposed to reduce torque ripples and settling time. Here the performance parameters like torque, speed and settling time are compared between conventional proportional-integral (PI) controller with ANFIS controller.

Keywords: direct torque control, electric vehicle, torque ripple, PMSM

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Authors: Xu Zhang

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This article unveils an indicator of language contact between English and Cantonese in one of the Outer Circle Englishes, Hong Kong (HK) English, through an empirical investigation into 1000 tokens from the Global Web-based English (GloWbE) corpus, employing frequency analysis and logistic regression analysis. It is perceived that Cantonese and general Chinese are contextually marked by an integral underlying thinking pattern. Chinese speakers exhibit a reliance on semantic context over syntactic rules and lexical forms. This linguistic trait carries over to their use of English, affording greater flexibility to formal elements in constructing English sentences. The study focuses on the syntactic positioning of the focusing subjunct ‘also’, a linguistic element used to add new or contrasting prominence to specific sentence constituents. The English language generally allows flexibility in the relative position of 'also’, while there is a preference for close marking relationships. This article shifts attention to Hong Kong, where Cantonese and English converge, and 'also' finds counterparts in Cantonese ‘jaa’ and Mandarin ‘ye’. Employing a corpus-based data-driven method, we investigate the syntactic position of 'also' in both HK and GB English. The study aims to ascertain whether HK English exhibits a greater 'syntactic freedom,' allowing for a more distant marking relationship with 'also' compared to GB English. The analysis involves a random extraction of 500 samples from both HK and GB English from the GloWbE corpus, forming a dataset (N=1000). Exclusions are made for cases where 'also' functions as an additive conjunct or serves as a copulative adverb, as well as sentences lacking sufficient indication that 'also' functions as a focusing particle. The final dataset comprises 820 tokens, with 416 for GB and 404 for HK, annotated according to the focused constituent and the relative position of ‘also’. Frequency analysis reveals significant differences in the relative position of 'also' and marking relationships between HK and GB English. Regression analysis indicates a preference in HK English for a distant marking relationship between 'also' and its focused constituent. Notably, the subject and other constituents emerge as significant predictors of a distant position for 'also.' Together, these findings underscore the nuanced linguistic dynamics in HK English and contribute to our understanding of language contact. It suggests that future pedagogical practice should consider incorporating the syntactic variation within English varieties, facilitating leaners’ effective communication in diverse English-speaking environments and enhancing their intercultural communication competence.

Keywords: also, Cantonese, English, focus marker, frequency analysis, language contact, logistic regression analysis

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Authors: Abdulmajeed Dabwan, Saqib Anwar, Ali Al-Samhan

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Electron Beam Melting (EBM) is a metal powder bed-based Additive Manufacturing (AM) technology, which uses computer-controlled electron beams to create fully dense three-dimensional near-net-shaped parts from metal powder. It gives the ability to produce any complex parts directly from a computer-aided design (CAD) model without tools and dies, and with a variety of materials. However, the quality of the surface finish in EBM process has limitations to meeting the performance requirements of additively manufactured components. The aim of this study is to investigate the cutting forces induced during milling Ti6Al4V produced by EBM as well as the surface quality of the milled surfaces. The effects of cutting speed and radial depth of cut on the cutting forces, surface roughness, and surface morphology were investigated. The results indicated that the cutting speed was found to be proportional to the resultant cutting force at any cutting conditions while the surface roughness improved significantly with the increase in cutting speed and radial depth of cut.

Keywords: electron beam melting, additive manufacturing, Ti6Al4V, surface morphology

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Authors: Md. Anayet U. Patwari, Mohammad Ahsan Habib, Md. Tanzib Ehsan, Md Golam Ahnaf, Md. S. I. Chowdhury

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Surface roughness is one of the key quality parameters of the finished product. During any machining operation, high temperatures are generated at the tool-chip interface impairing surface quality and dimensional accuracy of products. Cutting fluids are generally applied during machining to reduce temperature at the tool-chip interface. However, usages of cutting fluids give rise to problems such as waste disposal, pollution, high cost, and human health hazard. Researchers, now-a-days, are opting towards dry machining and other cooling techniques to minimize use of coolants during machining while keeping surface roughness of products within desirable limits. In this paper, a concept of using peltier cooling effects during aluminium milling operation has been presented and adopted with an aim to improve surface roughness of the machined surface. Experimental evidence shows that peltier cooling effect provides better surface roughness of the machined surface compared to dry machining.

Keywords: aluminium, milling operation, peltier cooling effect, surface roughness

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Authors: Ali Borhani Manesh, Sirus Mohammadi

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Harmonic distortion is caused by nonlinear devices in the power system. A nonlinear device is one in which the current is not proportional to the applied voltage. Harmonic distortion is present to some degree on all power systems. Proactive monitoring of power quality disturbance levels by electricity utilities is vital to allow cost-effective mitigation when disturbances are perceived to be approaching planning levels and also to protect the security of customer installations. Ensuring that disturbance levels are within limits at the HV and EHV points of supply of the network is essential if satisfactory levels downstream are to be maintained. This paper presents discussion on a power quality monitoring campaign performed at the sub-transmission point of supply of a distribution network with the objective of benchmarking background disturbance levels prior to modifications to the substation and to ensure emissions from HV customers and the downstream MV networks are within acceptable levels. Some discussion on the difficulties involved in such a study is presented. This paper presents a survey of voltage and current harmonic distortion levels at transmission system in Kohgiloye and Boyrahmad. The effects of harmonics on capacitors and power transformers are discussed.

Keywords: power quality, harmonics, flicker, measurement, substation

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Authors: Nguyen J. M., Lucas G., Brunner M., Ruan S., Antonioli D.

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Deep learning based on convolutional neural networks (CNN) is a very powerful technique for classifying information from an image. We propose a new method, DeepNic, to transform each variable of a tabular dataset into an image where each pixel represents a set of conditions that allow the variable to make an error-free prediction. The contrast of each pixel is proportional to its prediction performance and the color of each pixel corresponds to a sub-family of NICs. NICs are probabilities that depend on the number of inputs to each neuron and the range of coefficients of the inputs. Each variable can therefore be expressed as a function of a matrix of 2 vectors corresponding to an image whose pixels express predictive capabilities. Our objective is to transform each variable of tabular data into images into an image that can be analysed by CNNs, unlike other methods which use all the variables to construct an image. We analyse the NIC information of each variable and express it as a function of the number of neurons and the range of coefficients used. The predictive value and the category of the NIC are expressed by the contrast and the color of the pixel. We have developed a pipeline to implement this technology and have successfully applied it to genomic expressions on an Affymetrix chip.

Keywords: tabular data, deep learning, perfect trees, NICS

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Authors: Shorena Pharjiani

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The Present empirical paper investigates the relationship between FDI and economic growth by 10 selected industries in 10 Central and Eastern European countries from the period 1995 to 2012. Different estimation approaches were used to explore the connection between FDI and economic growth, for example OLS, RE, FE with and without time dummies. Obtained empirical results leads to some main consequences: First, the Central and East European countries (CEEC) attracted foreign direct investment, which raised the productivity of industries they entered in. It should be concluded that the linkage between FDI and output growth by industries is positive and significant enough to suggest that foreign firm’s participation enhanced the productivity of the industries they occupied. There had been an endogeneity problem in the regression and fixed effects estimation approach was used which partially corrected the regression analysis in order to make the results less biased. Second, it should be stressed that the results show that time has an important role in making FDI operational for enhancing output growth by industries via total factor productivity. Third, R&D positively affected economic growth and at the same time, it should take some time for research and development to influence economic growth. Fourth, the general trends masked crucial differences at the country level: over the last 20 years, the analysis of the tables and figures at the country level show that the main recipients of FDI of the 11 Central and Eastern European countries were Hungary, Poland and the Czech Republic. The main reason was that these countries had more open door policies for attracting the FDI. Fifth, according to the graphical analysis, while Hungary had the highest FDI inflow in this region, it was not reflected in the GDP growth as much as in other Central and Eastern European countries.

Keywords: central and East European countries (CEEC), economic growth, FDI, panel data

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Authors: Sizar Abed Mohammed, Zahraa Ghazi Sadeeq

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The use of the classic method, least squares (OLS) to estimate the linear regression parameters, when they are available assumptions, and capabilities that have good characteristics, such as impartiality, minimum variance, consistency, and so on. The development of alternative statistical techniques to estimate the parameters, when the data are contaminated with outliers. These are powerful methods (or resistance). In this paper, three of robust methods are studied, which are: Maximum likelihood type estimate M-estimator, Modified Maximum likelihood type estimate MM-estimator and Least Trimmed Squares LTS-estimator, and their results are compared with OLS method. These methods applied to real data taken from Duhok company for manufacturing furniture, the obtained results compared by using the criteria: Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Mean Sum of Absolute Error (MSAE). Important conclusions that this study came up with are: a number of typical values detected by using four methods in the furniture line and very close to the data. This refers to the fact that close to the normal distribution of standard errors, but typical values in the doors line data, using OLS less than that detected by the powerful ways. This means that the standard errors of the distribution are far from normal departure. Another important conclusion is that the estimated values of the parameters by using the lifeline is very far from the estimated values using powerful methods for line doors, gave LTS- destined better results using standard MSE, and gave the M- estimator better results using standard MAPE. Moreover, we noticed that using standard MSAE, and MM- estimator is better. The programs S-plus (version 8.0, professional 2007), Minitab (version 13.2) and SPSS (version 17) are used to analyze the data.

Keywords: Robest, LTS, M estimate, MSE

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Authors: O.O. Ehinmowo, A.I. Fatuase, D.F. Oke

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Nigeria is endowed with resources that could boost the economy as well as generate income and provide jobs to the teaming populace. One of the keys of attaining this is by making the environment conducive for the entrepreneurs to excel in their respective enterprises so that more income could be accrued to the entrepreneurs. This study therefore examines income inequality among selected entrepreneurs in Ondo State, Nigeria using primary data. A multistage sampling technique was used to select 200 respondents for the study with the aid of structured questionnaire and personal interview. The data collected were subjected to descriptive statistics, Lorenz curve, Gini coefficient and Double - Log regression model. Results revealed that majority of the entrepreneurs (63%) were males and 90% were married with an average age of 44 years. About 40% of the respondents spent at most 12 years in school with 81% of the respondents had 4-6 members per household, while hair dressing (43.5%) and fashion designing (31.5%) were the most common enterprises among the sampled respondents. The findings also showed that majority of the entrepreneurs in hairdressing, fashion designing and laundry service earned below N200,000 per annum while the majority of those in restaurant and food vending earned between N400,000 – N600,000 followed by the entrepreneurs in pure water enterprise where majority earned N800,000 and above per annum. The result of the Gini coefficient (0.58) indicated that there was presence of inequality among the entrepreneurs which was also affirmed by the Lorenz curve. The Regression results showed that gender, household size and number of employees significantly affected the income of the entrepreneurs in the study area. Therefore, more female households should be encouraged into entrepreneurial businesses and government should give incentive cum conductive environment that could bridge the disparity in the income of the entrepreneurs in their various enterprises.

Keywords: entrepreneurs, Gini coefficient, income inequality, Lorenz curve

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Authors: Yadollah Hezarjaribi, Mahdie Yari Esboi

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In this paper, a novel MEMS capacitive pressure sensor with small size and high sensitivity is presented. This sensor has the separated clamped square diaphragm and the movable plate. The diaphragm material is polysilicon. The movable and fixed plates and mechanical coupling are gold. The substrate and diaphragm are pyrex glass and polysilicon, respectively. In capacitive sensor the sensitivity is proportional to deflection and capacitance changes with pressure for this reason with this design is improved the capacitance and sensitivity with small size. This sensor is designed for low pressure between 0-60 mmHg that is used for medical application such as treatment of an incurable disease called glaucoma. The size of this sensor is 350×350 µm2 and the thickness of the diaphragm is 2µm with 1μ air gap. This structure is designed by intellisuite software. In this MEMS capacitive pressure sensor the sensor sensitivity, diaphragm mechanical sensitivity for polysilicon diaphragm are 0.0469Pf/mmHg, 0.011 μm/mmHg, respectively. According to the simulating results for low pressure, the structure with polysilicon diaphragm has more change of the displacement and capacitance, this leads to high sensitivity than other diaphragms.

Keywords: glaucoma, MEMS capacitive pressure sensor, square clamped diaphragm, polysilicon

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Authors: Muhammet Baldan, Emel Timuçin

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Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications.

Keywords: random forest, machine learning, comparison, feature extraction

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Authors: Raquel Dafouz Neus Cáceres, Javier Fernandez-Rubio, Belinda Huerta José Luis Rodríguez-Gil, Nicola Mastroianni, Miren López de Alda, Damià Barceló, Yolanda Valcárcel

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The region of Galicia, found in north-western (NW) Spain, is a national and world leader in shellfish, especially mussel production, and recognized for its fishing industry. Few studies have evaluated the presence of emerging contaminants in NW Spain, with those published mainly concerning the continental aquatic environment. The objective of this study was to identify the environmental risk posed by the presence of pharmaceuticals and drugs of abuse in this important coastal region. The presence of sixteen pharmaceuticals (benzodiazepines, anxiolytics, and caffeine), and 19 drugs of abuse (cocainics, amphetamine-like compounds, opiates and opioids, lysergic compounds, and cannabinoids) was assessed in 23 sites located in the Rías (Coastal inlets) of Muros, Arousa, and Pontevedra (NW Spain). Twenty-two of these locations were affected by waste-water treatment plant (WWTP) effluents, and one represented the effluent of one of these WWTPs. Venlafaxine was the pharmaceutical compound detected at higher concentration in the three Rías, with a maximum value of 291 ng/L at the site Porto do Son (Ría de Muros). Total concentration in the three Rías was 819,26 ng/L. Next, citalopram and lorazepam were the most prevalent compounds detected. Metabolite of cocaine benzoylecgonine was the drug of abuse with the highest concentration, measured at 972 ng/L in the Ría of Noia WWTP (no dilution). This compound was also detected at 142 ng/L in the site La Isla de Aros, Ría of Pontevedra. Total concentration for the three Rías was 1210 ng/L. Ephedrine was also detected at high level in the three Rías, with a total concentration of 579,28 ng/L. The results obtained for caffeine show maximum and average concentrations of 857 ng/L Isla de Arosa, Ría de Pontevedra the highest measured in seawater in Spain. A preliminary hazard assessment was carried out by comparing these measured environmental concentrations (MEC) to predicted no-effect concentrations (PNECs) for aquatic organisms. Six out of the 22 seawater samples resulted in a Hazard Quotient (HQ) from chronic exposure higher than 1 with the highest being 17.14, indicating a high probability of adverse effects in the aquatic environment. In addition, the risk was assessed on the basis of persistence, bioaccumulation, and toxicity (PBT). This work was financially supported by the Spanish Ministry of Economy and Competitiveness through the Carlos III Health Institute and the program 'Proyectos de Investigacion en Salud 2015-2017' FIS (PI14/00516), the European Regional Development Fund (ERDF), the Catalan Government (Consolidated Research Groups '2014 SGR 418 - Water and Soil Quality Unit' and 2014 SGR 291 - ICRA), and the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 603437. The poster entitled 'Environmental Risk of Pharmaceuticals, Drugs of Abuse and Stimulant Caffeine in Marine Water: A Case Study in the North-Western of Spain'.

Keywords: drug of abuse, pharmaceuticals, caffeine, environmental risk, seawater

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Authors: Nervana Elbakary, Sami Ouanes, Sadaf Riaz, Oraib Abdallah, Islam Mahran, Noriya Al-Khuzaei, Yassin Eltorki

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Major Depressive Disorder (MDD) requires therapeutic interventions during the initial month after being diagnosed for better disease outcomes. International guidelines recommend a duration of 4–12 weeks for an initial antidepressant (IAD) trial at an optimized dose to get a response. If depressive symptoms persist after this duration, guidelines recommend switching, augmenting, or combining strategies as the next step. Most patients with MDD in the mental health setting have been labeled incorrectly as treatment-resistant where in fact they have not been subjected to an adequate trial of guideline-recommended therapy. Premature discontinuation of IAD due to ineffectiveness can cause unfavorable consequences. Avoiding irrational practices such as subtherapeutic doses of IAD, premature switching between the ADs, and refraining from unjustified polypharmacy can help the disease to go into a remission phase We aimed to determine the prevalence and the patterns of strategies applied after an IAD was changed because of a suboptimal response as a primary outcome. Secondary outcomes included the median survival time on IAD before any change; and the predictors that were associated with IAD change. This was a retrospective cross- sectional study conducted in Mental Health Services in Qatar. A dataset between January 1, 2018, and December 31, 2019, was extracted from the electronic health records. Inclusion and exclusion criteria were defined and applied. The sample size was calculated to be at least 379 patients. Descriptive statistics were reported as frequencies and percentages, in addition, to mean and standard deviation. The median time of IAD to any change strategy was calculated using survival analysis. Associated predictors were examined using two unadjusted and adjusted cox regression models. A total of 487 patients met the inclusion criteria of the study. The average age for participants was 39.1 ± 12.3 years. Patients with first experience MDD episode 255 (52%) constituted a major part of our sample comparing to the relapse group 206(42%). About 431 (88%) of the patients had an occurrence of IAD change to any strategy before end of the study. Almost half of the sample (212 (49%); 95% CI [44–53%]) had their IAD changed less than or equal to 30 days. Switching was consistently more common than combination or augmentation at any timepoint. The median time to IAD change was 43 days with 95% CI [33.2–52.7]. Five independent variables (age, bothersome side effects, un-optimization of the dose before any change, comorbid anxiety, first onset episode) were significantly associated with the likelihood of IAD change in the unadjusted analysis. The factors statistically associated with higher hazard of IAD change in the adjusted analysis were: younger age, un-optimization of the IAD dose before any change, and comorbid anxiety. Because almost half of the patients in this study changed their IAD as early as within the first month, efforts to avoid treatment failure are needed to ensure patient-treatment targets are met. The findings of this study can have direct clinical guidance for health care professionals since an optimized, evidence-based use of AD medication can improve the clinical outcomes of patients with MDD; and also, to identify high-risk factors that could worsen the survival time on IAD such as young age and comorbid anxiety

Keywords: initial antidepressant, dose optimization, major depressive disorder, comorbid anxiety, combination, augmentation, switching, premature discontinuation

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Authors: Ahmet Parlak, Celalettin Bilgen

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In order to create an effective early warning system, Identification of the risks, preparation and carrying out risk modeling of risk scenarios, taking into account the shortcomings of the old disaster scenarios should be used to improve the system. In the light of this, the importance of risk modeling in creating an effective early warning system is understood. In the scope of TAFRISK project risk modeling trend analysis report on risk modeling developed and a demonstration was conducted for Risk Modeling for flood and mass movements. For risk modeling R&D, studies have been conducted to determine the information, and source of the information, to be gathered, to develop algorithms and to adapt the current algorithms to Turkey’s conditions for determining the risk score in the high disaster risk areas. For each type of the disaster; Disaster Deficit Index (DDI), Local Disaster Index (LDI), Prevalent Vulnerability Index (PVI), Risk Management Index (RMI) have been developed as disaster indices taking danger, sensitivity, fragility, and vulnerability, the physical and economic damage into account in the appropriate scale of the respective type.

Keywords: disaster, hazard, risk modeling, sensor

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Authors: H. Akbari Khorami, P. Wild, N. Djilali

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This paper reports on the response of a fiber-optic sensing probe to small concentrations of hydrogen peroxide (H2O2) vapor at room temperature. H2O2 has extensive applications in industrial and medical environments. Conversely, H2O2 can be a health hazard by itself. For example, H2O2 induces cellular damage in human cells and its presence can be used to diagnose illnesses such as asthma and human breast cancer. Hence, development of reliable H2O2 sensor is of vital importance to detect and measure this species. Ferric ferrocyanide, referred to as Prussian blue (PB), was deposited on the tip of a multimode optical fiber through the single source precursor technique and served as an indicator of H2O2 in a spectroscopic manner. Sensing tests were performed in H2O2-H2O vapor mixtures with different concentrations of H2O2. The results of sensing tests show the sensor is able to detect H2O2 concentrations in the range of 50.6 ppm to 229.5 ppm. Furthermore, the sensor response to H2O2 concentrations is linear in a log-log scale with the adjacent R-square of 0.93. This sensing behavior allows us to detect and quantify the concentration of H2O2 in the vapor phase.

Keywords: chemical deposition, fiber-optic sensor, hydrogen peroxide vapor, prussian blue

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Authors: X. L. Zhou, S. Tunmee, I. Toda, K. Komatsu, S. Ohshio, H. Saitoh

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Hydrogenated amorphous carbon (a-C:H) films have been synthesized by a radio frequency plasma enhanced chemical vapor deposition (rf-PECVD) technique with different bias voltage from 0.0 to -0.5 kV. The Raman spectra displayed the polymer-like hydrogenated amorphous carbon (PLCH) film with 0.0 to -0.1 and a-C:H films with -0.2 to -0.5 kV of bias voltages. The surface chemical information of all films were studied by X-ray photo electron spectroscopy (XPS) technique, presented to C-C (sp2 and sp3) and C-O bonds, and relative carbon (C) and oxygen (O) atomics contents. The O contamination had affected on structure and optical properties. The true density of PLCH and a-C:H films were characterized by X-ray refractivity (XRR) method, showed the result as in the range of 1.16-1.73 g/cm3 that depending on an increasing of bias voltage. The hardness was proportional to the true density of films. In addition, the optical properties i.e. refractive index (n) and extinction coefficient (k) of these films were determined by a spectroscopic ellipsometry (SE) method that give formation to in 1.62-2.10 (n) and 0.04-0.15 (k) respectively. These results indicated that the optical properties confirmed the Raman results as presenting the structure changed with applied bias voltage increased.

Keywords: negative bias voltage, a-C:H film, oxygen contamination, optical properties

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Authors: Solmaz Jahed Shiran, John Hearne, Tayebeh Saghapour

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Daily travel behavior is strongly influenced by the location of the places of residence, education, and employment. Hence a change in those locations due to a move or changes in an occupation leads to a change in travel behavior. Given the interventions of housing mobility and travel behaviors, the hypothesis is that a mobile housing market allows households to move as a result of any change in their life course, allowing them to be closer to central services, public transport facilities and workplace and hence reducing the time spent by individuals on daily travel. Conversely, household’s immobility may lead to longer commutes of residents, for example, after a change of a job or a need for new services such as schools for children who have reached their school age. This paper aims to investigate the association between residential mobility and travel behavior. The Victorian Integrated Survey of Travel and Activity (VISTA) data is used for the empirical analysis. Car ownership and journey to work time and distance of employed people are used as indicators of travel behavior. Change of usual residence within the last five years used to identify movers and non-movers. Statistical analysis, including regression models, is used to compare the travel behavior of movers and non-movers. The results show travel time, and the distance does not differ for movers and non-movers. However, this is not the case when taking into account the residence tenure-type. In addition, car ownership rate and number found to be significantly higher for non-movers. It is hoped that the results from this study will contribute to a better understanding of factors other than common socioeconomic and built environment features influencing travel behavior.

Keywords: journey to work, regression models, residential mobility, commute mode, car ownership

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Authors: Shaibu Ali, Sherif Heiman Shaban, Musah Ismaila, Imoro Alhassan, Yusif Ali

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Purpose: Islamic banking and finance is one of the most rapidly growing segments of the global finance industry. Starting with the Dubai Islamic Bank in 1975, the number of Islamic financial institutions worldwide has shot up astronomically, to over three hundred, with operations in seventy-five countries and assets in excess of US$400 billion. The purpose of this study is to explore the prospects and challenges of Islamic banking introduction in a non-Islamic country like Ghana. Design/Methodology: Data for the study was collected via an expert opinion of three Islamic scholars on Islamic banking from Ghana. Findings: Findings from this study indicates some of the benefits of Islamic banking includes connecting financial markets and economic activity, promoting the principle of financial justice, greater stability, avoiding economic bubbles (and bursts) and reducing the impact of harmful products and practices. The study also identified lack of experts in various fields of Islamic banking, product innovation, moral hazard, and need for experienced staff in Islamic banking as some of the challenges to Islamic banking system’s introduction. Contribution: The study contributes to literature on Islamic banking from a non-Islamic country like Ghana.

Keywords: Islamic banking, Shari’ah, Riba, conventional banking

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Authors: Rajesh Kondareddy, Prakash Kumar Nayak, Maunash Das, Vrinatri Velentina Boro

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Today, there is a huge global concern for sustainable development which would include minimizing the consumption of non-renewable energies without affecting the basic global economy. Solar drying is one of the important processes used for extending the shelf life of agricultural products. The performance of a low cost automated solar dryer fitted with cascade control scheme and modified PI controller for drying chilli was investigated. The dryer was composed of designed solar collector (air heater) fitted with cylindrical pipes to improve the air velocity and a solar drying chamber containing rack of two cheese cloth (net) trays both being integrated together. The air allowed in through air inlet is heated up in the solar collector and channelled through the drying chamber where it is utilized in drying (removing the moisture content from the food substance or agricultural produce loaded). Here, to maintain the temperature in the heating chambers and to improve performance, a modified PI (Proportional–Integral) controller was used due its simplicity and robustness. Drying time for drying chilli from the initial moisture content of 88.5% (wb) to 7.3% (wb) was estimated to be 14 hours in solar dryer whereas 32 h was observed in the open sun drying.

Keywords: cascade control, chilli, PI controller, solar dryer

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Authors: Septiany Trisnaningtyas

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Purpose: This research aims to examine the factors affecting the profitability of pharmaceutical company during the Covid-19 Pandemic in Indonesia. A sharp decline in the number of patients coming to the hospital for treatment during the pandemic has an impact on the growth of the pharmaceutical sector and brought major changes in financial position and business performance. Pharmaceutical companies that provide products related to the Covid-19 pandemic can survive and continue to grow. This study investigates the factors affecting the profitability of pharmaceutical company during the Covid-19 Pandemic in Indonesia associated with the number of Covid-19 cases. Design/methodology/approach: This study uses panel-data regression models to evaluate the influence of the number of Covid-19 confirmed cases on profitability of ninelisted pharmaceuticalcompanies in Indonesia. This research is based on four independent variables that were empirically examined for their relationship with profitability. These variables are liquidity (current ratio), growth rate (sales growth), firm size (total sales), and market power (the Lerner index). Covid-19 case is used as moderating variable. Data of nine pharmaceutical companies listed on the Indonesia Stock Exchange covering the period of 2018–2021 were extracted from companies’ quarterly annual reports. Findings: In the period during Covid-19, company growth (sales growth) and market power (lerner index) have a positive and significant relationship to ROA and ROE. Total of confirmed Covid-19 cases has a positive and significant relationship to ROA and is proven to have a moderating effect between company’s growth (sales growth) to ROA and ROE and market power (Lerner index) to ROA. Research limitations/implications: Due to data availability, this study only includes data from nine listed pharmaceutical companies in Indonesian Stock exchange and quarterly annual reportscovering the period of 2018-2021. Originality/value: This study focuses onpharmaceutical companies in Indonesia during Covid-19 pandemic. Previous study analyzes the data from pharmaceutical companies’ annual reports since 2014 and focus on universal health coverage (national health insurance) implementation from the Indonesian government. This study analyzes the data using fixed effect panel-data regression models to evaluate the influence of Covid-19 confirmed cases on profitability. Pooled ordinary least squares regression and fixed effects were used to analyze the data in previous study. This study also investigate the moderating effect of Covid-19 confirmed cases to profitability in relevant with the pandemic situation.

Keywords: profitability, indonesia, pharmaceutical, Covid-19

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Authors: Anjelika Kremer, Irina Nachimov, Dan Justo

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Background: C-reactive protein (CRP) serum levels are commonly measured in hospitalized patients. Elevated admission CRP serum levels and in-hospital mortality has been seldom studied in the general population of elderly patients admitted to the acute Geriatrics department. Methods: A retrospective cross-sectional study was conducted at a tertiary medical center. Included were all elderly patients (age 65 years or more) admitted to a single acute Geriatrics department from the emergency room between April 2014 and January 2015. CRP serum levels were measured routinely in all patients upon the first 24 hours of admission. A logistic regression analysis was used to study if admission CRP serum levels were associated with in-hospital mortality independent of age, gender, functional status, and co-morbidities. Results: Overall, 498 elderly patients were included in the analysis: 306 (61.4%) female patients and 192 (38.6%) male patients. The mean age was 84.8±7.0 years (median: 85 years; IQR: 80-90 years). The mean admission CRP serum levels was 43.2±67.1 mg/l (median: 13.1 mg/l; IQR: 2.8-51.7 mg/l). Overall, 33 (6.6%) elderly patients died during the hospitalization. A logistic regression analysis showed that in-hospital mortality was independently associated with history of stroke (p < 0.0001), heart failure (p < 0.0001), and admission CRP serum levels (p < 0.0001) – and to a lesser extent with age (p = 0.042), collagen vascular disease (p=0.011), and recent venous thromboembolism (p=0.037). Receiver operating characteristic (ROC) curve showed that admission CRP serum levels predict in-hospital mortality fairly with an area under the curve (AUC) of 0.694 (p < 0.0001). Cut-off value with maximal sensitivity and specificity was 19.7 mg/L. Conclusions: Admission CRP serum levels may be used to predict in-hospital mortality in the general population of elderly patients admitted to the acute Geriatrics department.

Keywords: c-reactive protein, elderly, mortality, prediction

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Authors: Mohammad E. Nikoofal, Mehmet Gumus

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Besides the many benefits of outsourcing, firms are still concerned about the lack of critical information regarding both the risk levels and actions of their suppliers that are just a few links away. In this paper, we study the effectiveness of audit for the manufacturer in managing her supplier’s process improvement effort when the supplier is privately informed about his disruption risk and actions. By comparing the agency costs associated with the optimal menu of contracts with and without audit, we completely characterize the value of audit for all the cases from the perspectives of both manufacturer, and supplier as well as total supply chain. First, the analysis of value of audit from the manufacturer’s perspective shows that she can strictly benefit from auditing her supplier’s actions. To the best of our knowledge, this result has not been documented before in the principal-agent literature under a standard setting where the agent is assumed to be risk-neutral and not protected by limited liability constraints. Second, we find that not only the manufacturer but also the supplier can strictly benefit from audit. Third, the audit enables the manufacturer to customize her contract offerings based on the reliability of the supplier. Finally, by analyzing the impact of problem parameters on the value of audit, we identify the conditions under which an audit would be beneficial for individual supply chain parties as well as total supply chain.

Keywords: supply disruption, adverse selection, moral hazard incentives, audit

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Authors: Anand Kishore Kola, G. Uday Bhaskar Babu, Kotturi Ajay Kumar

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The modern dynamic systems used in industries are complex in nature and hence the fractional order controllers have been contemplated as a fresh approach to control system design that takes the complexity into account. Traditional integer order controllers use integer derivatives and integrals to control systems, whereas fractional order controllers use fractional derivatives and integrals to regulate memory and non-local behavior. This study provides a method based on the maximumsensitivity (Ms) methodology to discover all resilient fractional filter Internal Model Control - proportional integral derivative (IMC-PID) controllers that stabilize the closed-loop system and deliver the highest performance for a time delay system with a Smith predictor configuration. Additionally, it helps to enhance the range of PID controllers that are used to stabilize the system. This study also evaluates the effectiveness of the suggested controller approach for minimum phase system in comparison to those currently in use which are based on Integral of Absolute Error (IAE) and Total Variation (TV).

Keywords: modern dynamic systems, fractional order controllers, maximum-sensitivity, IMC-PID controllers, Smith predictor, IAE and TV

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Authors: Arpine Maghakyan

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The purpose of this paper is to study the relationship between the level of industry digitalization and audit fees, especially, the relationship between Big 4 auditor fees and industry digitalization level. On the one hand, automation of business processes decreases internal control weakness and manual mistakes; increases work effectiveness and integrations. On the other hand, it may cause serious misstatements, high business risks or even bankruptcy, typically in early stages of automation. Incomplete automation can bring high audit risk especially if the auditor does not fully understand client’s business automation model. Higher audit risk consequently will cause higher audit fees. Higher audit fees for clients with high automation level are more highlighted in Big 4 auditor’s behavior. Using data of US firms from 2005-2015, we found that industry level digitalization is an interaction for the auditor quality on audit fees. Moreover, the choice of Big4 or non-Big4 is correlated with client’s industry digitalization level. Big4 client, which has higher digitalization level, pays more than one with low digitalization level. In addition, a high-digitalized firm that has Big 4 auditor pays higher audit fee than non-Big 4 client. We use audit fees and firm-specific variables from Audit Analytics and Compustat databases. We analyze collected data by using fixed effects regression methods and Wald tests for sensitivity check. We use fixed effects regression models for firms for determination of the connections between technology use in business and audit fees. We control for firm size, complexity, inherent risk, profitability and auditor quality. We chose fixed effects model as it makes possible to control for variables that have not or cannot be measured.

Keywords: audit fees, auditor quality, digitalization, Big4

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Authors: B. L. Ho, L. Shi, D. F. Wang, V. C. T. Mok

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The brain’s functional connectivity while temporally non-stationary does express consistency at a macro spatial level. The study of stable resting state connectivity patterns hence provides opportunities for identification of diseases if such stability is severely perturbed. A mathematical model replicating the brain’s spatial connections will be useful for understanding brain’s representative geometry and complements the empirical model where it falls short. Empirical computations tend to involve large matrices and become infeasible with fine parcellation. However, the proposed analytical model has no such computational problems. To improve replicability, 92 subject data are obtained from two open sources. The proposed methodology, inspired by financial theory, uses multivariate regression to find relationships of every cortical region of interest (ROI) with some pre-identified hubs. These hubs acted as representatives for the entire cortical surface. A variance-covariance framework of all ROIs is then built based on these relationships to link up all the ROIs. The result is a high level of match between model and empirical correlations in the range of 0.59 to 0.66 after adjusting for sample size; an increase of almost forty percent. More significantly, the model framework provides an intuitive way to delineate between systemic drivers and idiosyncratic noise while reducing dimensions by more than 30 folds, hence, providing a way to conduct attribution analysis. Due to its analytical nature and simple structure, the model is useful as a standalone toolkit for network dependency analysis or as a module for other mathematical models.

Keywords: functional magnetic resonance imaging, multivariate regression, network hubs, resting state functional connectivity

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Authors: Adewale Oladapo, C. A. Afolami

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The study analyzed the effect of micro credit access on poverty reduction among small scale women entrepreneurs in Ondo state, Nigeria. Primary data were collected in a cross-sectional survey of 100 randomly selected woman entrepreneurs. These were drawn in multistage sampling process covering four local government areas (LGAS). Data collected include socio economics characteristics of respondents, access to micro credit, sources of micro credit, and constraints faced by the entrepreneur in sourcing for micro credit. Data were analyzed using descriptive statistics, Foster, Greer and Thorbecke (FGT) index of poverty measure, Gini coefficients and probit regression analysis. The study found that respondents sampled for the survey were within the age range of 31-40 years with mean age 38.6%. Mostly (56.0%) of the respondents were educated to the tune of primary school. Majority (87.0%) of the respondents were married with fairly large household size of (4-5). The poverty index analysis revealed that most (67%) of the sample respondents were poor. The result of the Probit regression analyzed showed that income was a significant variable in micro credit access, while the result of the Gini coefficient revealed a very high income inequality among the respondents. The study concluded that most of the respondents were poor and return on investment (income) was an important variable that increased the chance of respondents in sourcing for micro-credit loan and recommended that income realized by entrepreneur should be properly documented to facilitate loan accessibility.

Keywords: entrepreneurs, income, micro-credit, poverty

Procedia PDF Downloads 130