Search results for: wealth status prediction

Authors: Najmeh Mohsenifar, Narjes Mohsenifar, Abbas Kargar

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In this paper, has been presented a stable method for predicting the ECG signals through the RBF neural networks, by the PSO algorithm. In spite of quasi-periodic ECG signal from a healthy person, there are distortions in electro cardiographic data for a patient. Therefore, there is no precise mathematical model for prediction. Here, we have exploited neural networks that are capable of complicated nonlinear mapping. Although the architecture and spread of RBF networks are usually selected through trial and error, the PSO algorithm has been used for choosing the best neural network. In this way, 2 second of a recorded ECG signal is employed to predict duration of 20 second in advance. Our simulations show that PSO algorithm can find the RBF neural network with minimum MSE and the accuracy of the predicted ECG signal is 97 %.

Keywords: electrocardiogram, RBF artificial neural network, PSO algorithm, predict, accuracy

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Authors: Yuichi Kida, Takuro Kida

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In a new smart society supported by the recent development of 5G and 6G Communication systems, the im- portance of wireless power transmission is increasing. These systems contain discrete sampling systems in the middle of the transmission path and equivalent circuit representation of lossless or lossy power transmission through these systems is an important issue in circuit theory. In this paper, for the given weight function, we show that a lossless power transmission system with the given weight is expressed by an equivalent circuit representation of the Kida’s optimal signal prediction system followed by a reactance multi-port circuit behind it. Further, it is shown that, when the system is lossy, the system has an equivalent circuit in the form of connecting a multi-port positive-real circuit behind the Kida’s optimal signal prediction system. Also, for the convenience of the reader, in this paper, the equivalent circuit expression of the reactance multi-port circuit and the positive- real multi-port circuit by Cauer and Ohno, whose information is currently being lost even in the world of the Internet.

Keywords: signal prediction, pseudo inverse matrix, artificial intelligence, power transmission

Procedia PDF Downloads 119

Authors: Omar M. Elmabrouk

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The cutting tool, in the high-speed machining process, is consistently dealing with high localized stress at the tool tip, tip temperature exceeds 800°C and the chip slides along the rake face. These conditions are affecting the tool wear, the cutting tool performances, the quality of the produced parts and the tool life. Therefore, a thin film coating on the cutting tool should be considered to improve the tool surface properties while maintaining its bulks properties. One of the general coating processes in applying thin film for hard coating purpose is PVD magnetron sputtering. In this paper, the prediction of the effects of PVD magnetron sputtering coating process parameters, sputter power in the range of (4.81-7.19 kW), bias voltage in the range of (50.00-300.00 Volts) and substrate temperature in the range of (281.08-600.00 °C), were studied using artificial neural network (ANN). The results were compared with previously published results using RSM model. It was found that the ANN is more accurate in prediction of tool hardness, and hence, it will not only improve the tool life of the tool but also significantly enhances the efficiency of the machining processes.

Keywords: artificial neural network, hardness, prediction, titanium aluminium nitrate coating

Procedia PDF Downloads 550

Authors: Saima Kalsoom, Sobia Masood, Muhammad Faran

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This study aimed to examine the association between emotional intelligence and gender role attitudes of married individuals. Another aim of this study was to test the moderating role of gender work status of married individuals for predicting gender role attitudes from emotional intelligence. A sample of (N = 500) married working men and women (both working & housewives) was approached through purposive convenience sampling technique. The data was collected employing cross-sectional research design. The indigenous versions of the Gender Role Attitudes Scale and perceived Emotional Intelligence Scale were used. The results of alpha coefficients for both the scales and subscales used in this study designated satisfactory evidence for internal consistency and reliability. Assessment of correlation coefficients showed significant positive correlation between gender role attitudes and emotional intelligence, subfactors of emotional intelligence i.e., emotional self-regulation, emotional self-awareness, and interpersonal skills with gender role attitudes. Results of model testing revealed that gender (the effect was significant for women) and work status (the effect was more significant for married working women than married working men and housewives) of the married individuals significantly moderated the relationship between emotional intelligence and gender role attitudes into the positive direction. Further, it was also found that gender and work status also moderated the relationship between emotional self-regulation (as sub factor of emotional intelligence) and gender role attitudes in a positive direction. In conclusion, this empirical evidence is vital contribution derived from the traditional and collectivistic socio-cultural background of Pakistan.

Keywords: gender role attitudes, emotional intelligence, emotional self-regulation, gender, work status, married working women

Procedia PDF Downloads 102

Authors: Shatabdi Goon

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OBJECTIVE: To determine the prevalence and associations between breakfast skipping and health status for university professionals in Bangladesh. DESIGN: A cross-sectional descriptive study design was performed using information on respondent’s sociodemographic status and eating behavior. Factors associated with breakfast skipping were identified using multivariate regression models. SETTINGS: Data obtained from a representative sample (n 120) of university professionals randomly selected from two distinct universities in Dhaka city, Bangladesh. SUBJECT: A total number of one hundred and twenty university professionals with a mean age of 29 years. RESULT: Results indicated that approximately 35.8% of the sample skipped breakfast. Gender was the only statistically significant sociodemographic variable, with females skipping at over two times the rate of males (OR 95% CI: 1.9; 0.90-4.13). The reasons given for skipping breakfast were almost exclusively habit (39.5%), work pressure (23.2%) and lack of time (16.2%). Skippers were significantly more likely to be obese (OR 2.4; 95% CI 1.02- 5.7), less energetic (OR 3.5; 95% CI 1.5-8.6), associated with health problems (OR 4.3; 95% CI 1.8- 10.17) and eating tendency of fast food (OR 2.5; 95% CI 1.13 - 5.5). Gastric and heart burn (X2=4.19, p<0.05) and high blood pressure (X2=5.027, p<0.05) were detected among 34.9% and 27.9 % of those employees respectively identified as breakfast skippers and they showed significantly high prevalence. CONCLUSION: Breakfast skipping is highly prevalent among university professionals with significant association of different health problems in Bangladesh. Health promotion strategies should be used to encourage all adults to eat breakfast regularly.

Keywords: breakfast, healthy lifestyle, breakfast skipping, health status, university professionals

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Authors: Praveen Chandramenon, Andrew Gascoyne, Fideline Tchuenbou-Magaia

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Aquaponics offers a simple conclusive solution to the food and environmental crisis of the world. This approach combines the idea of Aquaculture (growing fish) to Hydroponics (growing vegetables and plants in a soilless method). Smart Aquaponics explores the use of smart technology including artificial intelligence and IoT, to assist farmers with better decision making and online monitoring and control of the system. Identification of different growth stages of Swiss Chard plants and predicting its harvest time is found to be important in Aquaponic yield management. This paper brings out the comparative analysis of a standard Aquaponics with a Vermiponics (Aquaponics with worms), which was grown in the controlled environment, by implementing IoT and deep learning-based growth stage segregation and harvest time prediction of Swiss Chards before and after applying an optimal freshwater replenishment. Data collection, Growth stage classification and Harvest Time prediction has been performed with and without water replenishment. The paper discusses the experimental design, IoT and sensor communication with architecture, data collection process, image segmentation, various regression and classification models and error estimation used in the project. The paper concludes with the results comparison, including best models that performs growth stage segregation and harvest time prediction of the Aquaponic and Vermiponic testbed with and without freshwater replenishment.

Keywords: aquaponics, deep learning, internet of things, vermiponics

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Authors: Waqas Ahmed Khan Afridi, Subhas Chandra Mukhopadhyay, Bandita Mainali

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The research paper presents a unique approach to evapotranspiration (ET) prediction using a Support Vector Machine (SVM) learning algorithm. The study leverages real-time sensor node data to develop an accurate and adaptable prediction model, addressing the inherent challenges of traditional ET estimation methods. The integration of the SVM algorithm with real-time sensor node data offers great potential to improve spatial and temporal resolution in ET predictions. In the model development, key input features are measured and computed using mathematical equations such as Penman-Monteith (FAO56) and soil water balance (SWB), which include soil-environmental parameters such as; solar radiation (Rs), air temperature (T), atmospheric pressure (P), relative humidity (RH), wind speed (u2), rain (R), deep percolation (DP), soil temperature (ST), and change in soil moisture (∆SM). The one-year field data are split into combinations of three proportions i.e. train, test, and validation sets. While kernel functions with tuning hyperparameters have been used to train and improve the accuracy of the prediction model with multiple iterations. This paper also outlines the existing methods and the machine learning techniques to determine Evapotranspiration, data collection and preprocessing, model construction, and evaluation metrics, highlighting the significance of SVM in advancing the field of ET prediction. The results demonstrate the robustness and high predictability of the developed model on the basis of performance evaluation metrics (R2, RMSE, MAE). The effectiveness of the proposed model in capturing complex relationships within soil and environmental parameters provide insights into its potential applications for water resource management and hydrological ecosystem.

Keywords: evapotranspiration, FAO56, KNIME, machine learning, RStudio, SVM, sensors

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Authors: Lise Retat, Maria Carmen Huerta, Laura Webber, Franco Sassi

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Background: Intra-generational social mobility (ISM) can be defined as the extent to which individuals change their socio-economic position over a period of time or during their entire life course. The relationship between poor health and ISM is established. Therefore, quantifying the impact that potential health policies have on ISM now and into the future would provide evidence for how social inequality could be reduced. This paper takes the condition of overweight and obesity as an example and estimates the mean earning change per individual if the UK were to introduce policies to effectively reduce overweight and obesity. Methods: The HealthLumen individual-based model was used to estimate the impact of obesity on social mobility measures, such as earnings, occupation, and wealth. The HL tool models each individual's probability of experiencing downward ISM as a result of their overweight and obesity status. For example, one outcome of interest was the cumulative mean earning per person of implementing a policy which would reduce adult overweight and obesity by 1% each year between 2020 and 2030 in the UK. Results: Preliminary analysis showed that by reducing adult overweight and obesity by 1% each year between 2020 and 2030, the cumulative additional mean earnings would be ~1,000 Euro per adult by 2030. Additional analysis will include other social mobility indicators. Conclusions: These projections are important for illustrating the role of health in social mobility and for providing evidence for how health policy can make a difference to social mobility outcomes and, in turn, help to reduce inequality.

Keywords: modelling, social mobility, obesity, health

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Authors: Vaidehi Iyer, Konstantin Borozdin

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Time series prediction problems have many important practical applications, but are notoriously difficult for statistical modeling. Recently, machine learning methods have been attracted significant interest as a practical tool applied to a variety of problems, even though developments in this field tend to be semi-empirical. This paper explores application of Long Short Term Memory based Recurrent Neural Networks to the one-step prediction of time series for both trend and stochastic components. Two types of data are analyzed - daily stock prices, that are often considered to be a typical example of a random walk, - and weather patterns dominated by seasonal variations. Results from both analyses are compared, and reinforced learning framework is used to select more efficient between Recurrent Neural Networks and more traditional auto regression methods. It is shown that both methods are able to follow long-term trends and seasonal variations closely, but have difficulties with reproducing day-to-day variability. Future research directions and potential real world applications are briefly discussed.

Keywords: long short term memory, prediction methods, recurrent neural networks, reinforcement learning

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Authors: Klaudia Tomala, Elzbieta Grochowska-Niedworok, Katarzyna Brukalo, Marek Kardas, Beata Calyniuk, Renata Polaniak

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Background. Nutrition plays an important role in the psychophysical growth of children and has effects on their health. Providing children with the appropriate supply of macro- and micro-nutrients requires dietary diversity across every food group. Meals in kindergartens should provide 70-75% of their daily food requirement. Aim. The aim of this study was to determine the vitamin content in the food rations of children attending kindergarten in the wider aspect of nutritional status. Material and Methods. Kindergarten menus from the spring and autumn seasons of 2015 were analyzed. In these meals, fat content and levels of water-soluble vitamins were estimated. The vitamin content was evaluated using the diet calculator “Aliant”. Statistical analysis was done in MS Office Excel 2007. Results. Vitamin content in the analyzed menus in many cases is too high with reference to dietary intake, with only vitamin D intake being insufficient. Vitamin E intake was closest to the dietary reference intake. Conclusion. The results show that vitamin intake is usually too high, and menus should, therefore, be modified. Also, nutrition education among kindergarten staff is needed. The identified errors in the composition of meals will affect the nutritional status of children and their proper composition in the body.

Keywords: children, nutrition status, vitamins, preschool

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Authors: Jens Friedrich, Matthias A. Gebele, Armin Lechler, Alexander Verl

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Chatter vibrations and process instabilities are the most important factors limiting the productivity of the milling process. Chatter can leads to damage of the tool, the part or the machine tool. Therefore, the estimation and prediction of the process stability is very important. The process stability depends on the spindle speed, the depth of cut and the width of cut. In milling, the process conditions are defined in the NC-program. While the spindle speed is directly coded in the NC-program, the depth and width of cut are unknown. This paper presents a new simulation based approach for the prediction of the depth and width of cut of a milling process. The prediction is based on a material removal simulation with an analytically represented tool shape and a multi-dexel approach for the work piece. The new calculation method allows the direct estimation of the depth and width of cut, which are the influencing parameters of the process stability, instead of the removed volume as existing approaches do. The knowledge can be used to predict the stability of new, unknown parts. Moreover with an additional vibration sensor, the stability lobe diagram of a milling process can be estimated and improved based on the estimated depth and width of cut.

Keywords: dexel, process stability, material removal, milling

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Authors: Diqin Qi, Jiaming Li, Siman Li

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Based on the use of the three-point smoothing method for selectively processing original data columns, this paper establishes a group of grey GM(1,1) models to predict the concentration ranges of four major air pollutants in Shanghai from 2023 to 2024. The results indicate that PM₁₀, SO₂, and NO₂ maintain the national Grade I standards, while the concentration of PM₂.₅ has decreased but still remains within the national Grade II standards. Combining the forecast results, recommendations are provided for the Shanghai municipal government's efforts in air pollution prevention and control.

Keywords: atmospheric pollutant prediction, Grey GM(1, 1), model group, three-point smoothing method

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Authors: Chala Elhassen

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City planning and urban management seem more complex our days compared to past times. The interaction of many factors both endogenous and exogenous made more difficult the urban fact. The city has changed status with the demographic bulge. It passed the primary status meeting limited requirements to a multidisciplinary status marked by the diversity of needs. These increase with the increase in population and living standard. Our era is marked by urbanization, complex phenomenon that develops both in industrialized countries in those of the third world. Human concentrations increasingly have significant multiplier effects on the social and economic structure of a region or a country. On the whole, the issue of urban planning revolved around questions related firstly to the understanding of the phenomena of urbanization; and also in search of the most appropriate ways to ensure control, the efficiency and consistency of the urbanization process. Urban planning remains an ambiguous area that mixes scientific contributions, technical, artistic, administrative and legal in varying proportions. What is the founder of specificity is that it always presupposes the existence of a will to act, itself supported by a thorough knowledge of will.

Keywords: urbanization, urban planning, management, industrialized countries

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Authors: Wenfen Bi, Zeng Lin

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As a developing country, China is badly in need of capital and talents to develop the socialist country with Chinese characteristics. However, a large proportion of high income people with know-how technique, wealth and management experience have immigrated or plan to immigrate to other countries. Of course, this phenomenon has attracted the attention from both the government and researchers. One explanation might be that these high-income people lack confidence in China’s social development. Based on the data on W city’s comprehensive social situation surveyed by center for the social survey research of Wuhan university (CSSR) in 2014, this paper employed the structural equation model (SEM) to evaluate whether personal income affects social confidence, via the mediating effect of the sense of social equity (sense of right equity and sense of distributive equity). Bootstrap mediation analysis revealed that after controlling Demographic variables, personal income had a significant negative influence on sense of right equity and in turn, sense of rights equity can significantly positively predict social confidence. While personal income had no significant effect on sense of distributive equity, and sense of distributive equity did not significantly affect macro social confidence. Also, the direct effects of personal income on social confidence became not significant. These findings revealed the inner mechanism of the relationship between the personal income and social confidence in contemporary China, which was caused by mediating effect of sense of rights equity. That is, the higher the personal income, the lower the sense of rights equity, the lower the social confidence. Thus, the boost of the social confidence, especially for the rich, does not only depend on the equitable distribution of material wealth, but also on the right equity and making people feel rights equally in common life.

Keywords: personal income, sense of right equity, sense of social equity, social confidence

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Authors: Aidan J. Bowes, Reaz Hasan

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The automotive industry is continually aiming to develop the aerodynamics of car body design. This may be for a variety of beneficial reasons such as to increase speed or fuel efficiency by reducing drag. However recently there has been a greater amount of focus on wind noise produced while driving. Designers in this industry seek a combination of both simplicity of approach and overall effectiveness. This combined with the growing availability of commercial CFD (Computational Fluid Dynamics) packages is likely to lead to an increase in the use of RANS (Reynolds Averaged Navier-Stokes) based CFD methods. This is due to these methods often being simpler than other CFD methods, having a lower demand on time and computing power. In this investigation the effectiveness of turbulent flow and acoustic noise prediction using RANS based methods has been assessed for different wing mirror geometries. Three different RANS based models were used, standard k-ε, realizable k-ε and k-ω SST. The merits and limitations of these methods are then discussed, by comparing with both experimental and numerical results found in literature. In general, flow prediction is fairly comparable to more complex LES (Large Eddy Simulation) based methods; in particular for the k-ω SST model. However acoustic noise prediction still leaves opportunities for more improvement using RANS based methods.

Keywords: acoustics, aerodynamics, RANS models, turbulent flow

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Authors: Andrei Contan, Richard Torkar

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Aim: The process of test automation and its practices in industry have to be better understood, both for the industry itself and for the research community. Method: We conducted a quantitative industry survey by asking IT professionals to answer questions related to the area of test automation. Results: Test automation needs and practices vary greatly between organizations at different stages of the software development life cycle. Conclusions: Most of the findings are general test automation challenges and are specific to small- to medium-sized companies, developing software applications in the web, desktop or mobile domain.

Keywords: survey, testing, test automation, status of test automation

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Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

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Authors: Nishank Raisinghani

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Efficiently predicting drug response remains a challenge in the realm of drug discovery. To address this issue, we propose four model architectures that combine graphical representation with varying positions of multiheaded self-attention mechanisms. By leveraging two types of multi-omics data, transcriptomics and genomics, we create a comprehensive representation of target cells and enable drug response prediction in precision medicine. A majority of our architectures utilize multiple transformer models, one with a graph attention mechanism and the other with a multiheaded self-attention mechanism, to generate latent representations of both drug and omics data, respectively. Our model architectures apply an attention mechanism to both drug and multiomics data, with the goal of procuring more comprehensive latent representations. The latent representations are then concatenated and input into a fully connected network to predict the IC-50 score, a measure of cell drug response. We experiment with all four of these architectures and extract results from all of them. Our study greatly contributes to the future of drug discovery and precision medicine by looking to optimize the time and accuracy of drug response prediction.

Keywords: drug discovery, transformers, graph neural networks, multiomics

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

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This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

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Authors: Dona Ghosh, Zakir Husain

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In India, the increase in share of aged has generated many social and economic issues, of which health concerns is a major challenge that society must confront in coming years. Self-reported health (SRH) is a popular health measure in this regard but has been questioned in recent years due to its heavy dependence on the socioeconomic status. So, the validity of SRH, as a measure of health status during old age, is needed to be verified. This paper emphasizes on the self-reported health and related inconsistent responses among elderly in India. The objective of the study is bifurcated into two parts: firstly, to identify the socioeconomic determinants of subjective health status and its change over time; and secondly, to analyse the role of the socioeconomic components in providing inconsistent responses regarding the health status of elderly. Inconsistency in response can rise in two ways: positive response bias (if an individual has a health problem but reports his/her health as good) and negative response bias (if bad health is reported even if there is no health problem). However, in the present study, we focus only on the negative response bias of elderly individuals. To measure the inconsistencies in responses, self-reported health is compared with two types of physical health conditions – existence of chronicle ailment and physical immobility. Using NSS dataset of 60th and 71st rounds, the study found that subjective health has worsened over time in both rural and urban areas. Findings suggest that inconsistency in responses, related to chronic ailment, vary across social classes, living environments, geographical regions, age groups and education levels. On the contrary, variation in inconsistent responses regarding physical mobility is quite rare and difficult to explain by socioeconomic characteristics because most of the indicators are found to be insignificant in this regard. The findings indicate that in case of chronicle ailment, inconsistency between objective and subjective health status largely depends on socioeconomic conditions but the importance of such factors disappears for physical immobility.

Keywords: India, aging, self-reported health, inconsistent responses

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Authors: Mona Francesca B. Dela Cruz, Katrina Marie R. Mamaril, Mariah Hannah Kassandra Salazar, Emerald Jay D. Ilac

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One key factor that should be considered when determining sustainable solutions to various environmental problems is the potential impact of individual human beings. In order to understand an individual, there is a need to examine cognitive, emotional, dispositional, and behavioral factors which are all indicative of one’s environmental consciousness. This quantitative study explored the moderated mediation between environmental consciousness, socio-economic status, social norms as a mediator, and the perceived role of government as a moderator for 381 Filipinos, aged 25 to 65, in urban and suburban settings. Results showed social norms do not have a mediating effect between socio-economic status and environmental consciousness. This may be influenced by the collectivist culture of the Philippines and the tendency for people to copy behaviors according to the descriptive norm effect. Meanwhile, there exists a moderating effect of the perceived role of government between the relationship of social norms and environmental consciousness which can be explained by the government’s ability to impose social norms that can induce a person to think and act pro-environmentally. Practical applications of this study can be used to tap the ability of the government to strengthen their influence and control over environmental protection and to provide a basis for the development of class-specific environmental solutions that can be done by individuals depending on their socioeconomic status.

Keywords: environmental consciousness, role of government, social norms, socio-economic status

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Authors: Amira Abdelrasoul, Huu Doan, Ali Lohi

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An efficient and well-planned ultrafiltration process is becoming a necessity for monetary returns in the industrial settings. The aim of the present study was to develop a mathematical model for an accurate prediction of ultrafiltration membrane fouling of latex effluent applied to homogeneous and heterogeneous membranes with uniform and non-uniform pore sizes, respectively. The models were also developed for an accurate prediction of power consumption that can handle the large-scale purposes. The model incorporated the fouling attachments as well as chemical and physical factors in membrane fouling for accurate prediction and scale-up application. Both Polycarbonate and Polysulfone flat membranes, with pore sizes of 0.05 µm and a molecular weight cut-off of 60,000, respectively, were used under a constant feed flow rate and a cross-flow mode in ultrafiltration of the simulated paint effluent. Furthermore, hydrophilic ultrafilic and hydrophobic PVDF membranes with MWCO of 100,000 were used to test the reliability of the models. Monodisperse particles of 50 nm and 100 nm in diameter, and a latex effluent with a wide range of particle size distributions were utilized to validate the models. The aggregation and the sphericity of the particles indicated a significant effect on membrane fouling.

Keywords: membrane fouling, mathematical modeling, power consumption, attachments, ultrafiltration

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Authors: Ruth Estrada Javier - Reyes

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The study assessed the status of HTM program of selected private higher education institutions for internationalization across the eleven regions of the country. The descriptive survey method of research was used in this study. A devised survey questionnaire was utilized to gather information about the status of Philippine Higher Education Institutions’ internationalization of hospitality and tourism management education programs. The respondents were 12 administrators, 17 deans and program heads, 104 faculty members and 860 HTM students. Frequency, percentage, mean, standard deviation, t-test and F-test were used to treat the data. The results of the study are as follows: HEIs’ HTM education had complied with the policies/standards of CHED as per CMO No. 30 S. 2006. The respondents of the HTM education program were qualified for internationalization as assessed both by administrators and faculty. The private HEIs are ready to apply for international certification of their HTM education programs. The curriculum of HTM education programs in private HEIs are enriched by internationalization requirements. The administrators and faculty of HTM education programs are qualified educators but have limited participation in collaborative international research and linkages. The HEIs are qualified to apply for the internationalization of the Hospitality and Tourism Management education program in preparation to the ASEAN 2015.

Keywords: status, Hospitality and Tourism Management Program, internationalization, Private Higher Education Institutions

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Authors: Adebusoye Michael, Okunola Olayinka, Owolabi Abdulateef, Jacob Anayo

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Introduction: Child undernutrition remains one of Africa’s most fundamental challenges for improved human development because the time and capacities of caregivers are limited; far too many children are unable to access effectively amenities they need for a healthy life. Methods and procedures: This cross-sectional, descriptive study evaluated the maternal care practices on nutritional status of pre-school children, 150 mothers were selected by systematic random sampling in Dass L.G.A., Bauchi-State, Nigeria. Information on relevant parameters were collected by questionaire, analysed by various indices of descriptive statistics using SPSS version 16.0.Spearman’s rank correlation was used to test for associations between the variables. Results: Thirty-five (23.3%) of the respondents were aged 21-25 years. Thirty-three (28.0%) had secondary education, while forty-nine (32.7%) were full housewives. Majority 79(52.7) earned NI,000- N10,000 monthly versus 10(6.7%) who earned N11,000- N20,000.113(75.3%) married while 7(4.7%) of respondents were separated. Sixty-one (40.7%) practiced exclusive breastfeeding within six months. Only seventy-one (47.3%) initiated breastfeeding between 7 and 13 months. Five (3.3%) of children were mildly underweight while nine (6.0%) were severely stunted. Conclusion: The outcome suggested that working time of mothers is a major determinant on their child nutritional status. However, there is a significant relationship on the working time of mothers, income level and educational level of mothers to the nutritional status of their children (P<0.05). Recommendation: Good policy programmes should aim at eradicating poverty, better child care practices that would reduce malnutrition among under-five children.

Keywords: maternal care, nutritional status, preschool children, Dass L.G.A.

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Authors: Erdinc Ozturk, Gizem Akcan

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The aim of this study was to determine some psychosocial variables that predict childhood trauma and dissociation in university students. These psychosocial variables were perceived social support, relationship status, gender and life satisfaction. 250 (125 males, 125 females) university students (bachelor, master and postgraduate degree) were enrolled in this study. They were chosen from universities in Istanbul at the education year of 2016-2017. Dissociative Experiences Scale (DES), Childhood Trauma Questionnaire (CTQ), Multidimensional Perceived Social Support Scale, Life Satisfaction Scale and Relationship Scales Questionnaire were used to assess related variables. Demographic information form was given to students in order to have their demographic information. Frequency distribution, multiple linear regression, and t-test analysis were used for statistical analysis. As together, perceived social support, relationship status and life satisfaction were found to have predictive value on trauma among university students. However, as together, these psychosocial variables did not have predictive value on dissociation. Only, trauma and relationship status had significant predictive value on dissociation. Moreover, there was significant difference between males and females in terms of trauma; however, dissociation scores of participants were not significantly different in terms of gender.

Keywords: childhood trauma, dissociation, perceived social support, relationship status, life satisfaction

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Authors: Espinosa Carlos, Nobrega Doris

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Children are at high risk for lead (Pb) exposure. The objective of this study was to identify risk factors that contribute to high blood lead (PbB) levels in Venezuelan children. The concentration of PbB was determined in 60 children (ages 4-9 years old), coming from the Michelena sector, Valencia District, Carabobo State. The relationship between these concentrations and socio-economical parameters (A: high quality life; B: fair quality life; C: critic poverty), Pb levels of faucet water (Pb-water) and dust Pb levels of floor (Pb-dust) of their houses, was established. Living areas were classified according to sectors and socio-economical status. Forty [40=66.7%] children resulted with PbB levels above the permissible concentration (LAPC). Average PbB was not significantly higher than the permissible levels. Odds ratio proved that children from status C are 7.28 times more likely to have LAPC of PbB than the ones coming from A or B. Thirty-four percent (34%) of the children with LAPC come from status C which could be considered the most critical status from the exposure risk point of view. The 76,3% of the sampled houses reported VSLP of Pb-water, being the Pb-water average in 35 ± 25.5 ug/L. This average significantly went superior to the permissible limit established by Venezuela and international organisms (10 ug/L). When grouping the results of PbB and Pb-water by sex, were that 50,8% of the children who presented/displayed VSLP of Pb-water and PbB. Was a significant relation (p ≤ 0.05), between masculine sex and the VSLP of PbB and Pb-water (x² = 3,672). In relation to the Pb-Dust analyses, were not statistically significant differences with respect to their permissible limit value (40 ug/pie²). This study shows that by correlating geographical and health data, we can identify 'high risk' areas, leading to a proactive public health action. The results of this study are excellent, in order to take preventive measures for the care from the health. Later studies are suggested predicting main to determine of more conclusive form of levels elevated of PbB in the investigated population.

Keywords: demographic, lead, risk, socio-economical status

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Authors: Marzieh Karimihaghighi, Carlos Castillo

Abstract:

This work studies how Machine Learning (ML) may be used to increase the effectiveness of a criminal recidivism risk assessment tool, RisCanvi. The two key dimensions of this analysis are predictive accuracy and algorithmic fairness. ML-based prediction models obtained in this study are more accurate at predicting criminal recidivism than the manually-created formula used in RisCanvi, achieving an AUC of 0.76 and 0.73 in predicting violent and general recidivism respectively. However, the improvements are small, and it is noticed that algorithmic discrimination can easily be introduced between groups such as national vs foreigner, or young vs old. It is described how effectiveness and algorithmic fairness objectives can be balanced, applying a method in which a single error disparity in terms of generalized false positive rate is minimized, while calibration is maintained across groups. Obtained results show that this bias mitigation procedure can substantially reduce generalized false positive rate disparities across multiple groups. Based on these results, it is proposed that ML-based criminal recidivism risk prediction should not be introduced without applying algorithmic bias mitigation procedures.

Keywords: algorithmic fairness, criminal risk assessment, equalized odds, recidivism

Procedia PDF Downloads 143

Authors: Mohamed Elhakeem, A. N. Thanos Papanicolaou, Christopher Wilson, Yi-Jia Chang

Abstract:

In this study, a physically-based, modelling framework was developed to predict saturated hydraulic conductivity (KSAT) dynamics in the Clear Creek Watershed (CCW), Iowa. The modelling framework integrated selected pedotransfer functions and watershed models with geospatial tools. A number of pedotransfer functions and agricultural watershed models were examined to select the appropriate models that represent the study site conditions. Models selection was based on statistical measures of the models’ errors compared to the KSAT field measurements conducted in the CCW under different soil, climate and land use conditions. The study has shown that the predictions of the combined pedotransfer function of Rosetta and the Water Erosion Prediction Project (WEPP) provided the best agreement to the measured KSAT values in the CCW compared to the other tested models. Therefore, Rosetta and WEPP were integrated with the Geographic Information System (GIS) tools for visualization of the data in forms of geospatial maps and prediction of KSAT variability in CCW due to the seasonal changes in climate and land use activities.

Keywords: saturated hydraulic conductivity, pedotransfer functions, watershed models, geospatial tools

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Authors: Tomas Berhanu Bekele

Abstract:

Traffic congestion is one of the main problems related to transportation in developed as well as developing countries. Traffic control systems are based on the idea of avoiding traffic instabilities and homogenizing traffic flow in such a way that the risk of accidents is minimized and traffic flow is maximized. Lately, Intelligent Transport Systems (ITS) has become an important area of research to solve such road traffic-related issues for making smart decisions. It links people, roads and vehicles together using communication technologies to increase safety and mobility. Moreover, accurate prediction of road traffic is important to manage traffic congestion. The aim of this study is to develop an ANN model for the prediction of traffic flow and to compare the ANN model with the linear regression model of traffic flow predictions. Data extraction was carried out in intervals of 15 minutes from the video player. Video of mixed traffic flow was taken and then counted during office work in order to determine the traffic volume. Vehicles were classified into six categories, namely Car, Motorcycle, Minibus, mid-bus, Bus, and Truck vehicles. The average time taken by each vehicle type to travel the trap length was measured by time displayed on a video screen.

Keywords: intelligent transport system (ITS), traffic flow prediction, artificial neural network (ANN), linear regression

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Authors: Andrey Lebedev, Ilya Dmitrenok, JooYoung Lee, Leonard Johard

Abstract:

Top-k shortest path routing problem is an extension of finding the shortest path in a given network. Shortest path is one of the most essential measures as it reveals the relations between two nodes in a network. However, in many real world networks, whose diameters are small, top-k shortest path is more interesting as it contains more information about the network topology. Many variations to compute top-k shortest paths have been studied. In this paper, we apply an efficient top-k shortest distance routing algorithm to the link prediction problem and test its efficacy. We compare the results with other base line and state-of-the-art methods as well as with the shortest path. Then, we also propose a top-k distance based graph matching algorithm.

Keywords: graph matching, link prediction, shortest path, similarity

Procedia PDF Downloads 353