Search results for: the health belief model

Authors: William Coburn, Dylan Hauchard, Amel Naouali

Abstract:

Background: The nature of the HIV epidemic in Puerto Rico (PR) is less understood than in the continental U.S. There is evidence to suggest that there are differences in health care access based on geographical location, such that rural areas are less underserved and have less immediate access to HIV prevention resources. Methods: The current study consists of a cross-sectional online survey of self-reporting HIV-negative sexual minority men (SMM) residing in PR. Results: In this sample, there were no differences between urban and rural-based services for SMM. However, more than half of the sample reported that they have never disclosed their gender identity and sexual practices to a physician. Conclusion: HIV is a significant public health concern affecting Latinos/Hispanics in the U.S. Findings in this paper can have implications for HIV prevention services in PR specifically, as few studies have directly focused on the impact of HIV and health care services in PR outside of the continental U.S.

Keywords: HIV, Puerto Rico, infectious diseases , public health

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Authors: Mahnaz Hosseinzadeh, Shaghayegh Rezaee Amiri

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This paper presents a methodology for serial production planning problem in wire and cable manufacturing process that addresses the problem of input-output imbalance in different consecutive stations, hoping to minimize the halt of machines in each stage. To this end, a linear Goal Programming (GP) model is developed, in which four main categories of constraints as per the number of runs per machine, machines’ sequences, acceptable inventories of machines at the end of each period, and the necessity of fulfillment of the customers’ orders are considered. The model is formulated based upon on the real data obtained from IKO TAK Company, an important supplier of wire and cable for oil and gas and automotive industries in Iran. By solving the model in GAMS software the optimal number of runs, end-of-period inventories, and the possible minimum idle time for each machine are calculated. The application of the numerical results in the target company has shown the efficiency of the proposed model and the solution in decreasing the lead time of the end product delivery to the customers by 20%. Accordingly, the developed model could be easily applied in wire and cable companies for the aim of optimal production planning to reduce the halt of machines in manufacturing stages.

Keywords: goal programming approach, GP, production planning, serial manufacturing process, wire and cable industry

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Authors: F. Benhamida, A. Graa, L. Benameur, I. Ziane

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The state estimation of the electrical power system operation state is very important for supervising task. With the nonlinearity of the AC power flow model, the state estimation problem (SEP) is a nonlinear mathematical problem with many local optima. This paper treat the mathematical model for the SEP and the monitoring of the nonlinear systems of great dimensions with an application on power electrical system, the modelling, the analysis and state estimation synthesis in order to supervise the power system behavior. in fact, it is very difficult, to see impossible, (for reasons of accessibility, techniques and/or of cost) to measure the excessive number of the variables of state in a large-sized system. It is thus important to develop software sensors being able to produce a reliable estimate of the variables necessary for the diagnosis and also for the control.

Keywords: power system, state estimation, robustness, observability

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Authors: Bhim Kumar Dahal

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Transportation network development in the developing country is in rapid pace. The majority of the network belongs to railway and expressway which passes through diverse topography, landform and geological conditions despite the avoidance principle during route selection. Construction of such networks demand many low to high embankment which required improvement in the foundation soil. This paper is mainly focused on the various advanced ground improvement techniques used to improve the soft soil, modelling approach and its predictability for embankments construction. The ground improvement techniques can be broadly classified in to three groups i.e. densification group, drainage and consolidation group and reinforcement group which are discussed with some case studies.  Various methods were used in modelling of the embankments from simple 1-dimensional to complex 3-dimensional model using variety of constitutive models. However, the reliability of the predictions is not found systematically improved with the level of sophistication.  And sometimes the predictions are deviated more than 60% to the monitored value besides using same level of erudition. This deviation is found mainly due to the selection of constitutive model, assumptions made during different stages, deviation in the selection of model parameters and simplification during physical modelling of the ground condition. This deviation can be reduced by using optimization process, optimization tools and sensitivity analysis of the model parameters which will guide to select the appropriate model parameters.

Keywords: cement, improvement, physical properties, strength

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Authors: B. F. Nteumagne, E. Pindza, E. Mare

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We apply Lie symmetries analysis to price and hedge options in the fractional Brownian framework. The reputation of Lie groups is well spread in the area of Mathematical sciences and lately, in Finance. In the presence of transactions costs and under fractional Brownian motions, analytical solutions become difficult to obtain. Lie symmetries analysis allows us to simplify the problem and obtain new analytical solution. In this paper, we investigate the use of symmetries to reduce the partial differential equation obtained and obtain the analytical solution. We then proposed a hedging procedure and calibration technique for these types of options, and test the model on real market data. We show the robustness of our methodology by its application to the pricing of digital options.

Keywords: fractional brownian model, symmetry, transaction cost, option pricing

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Authors: Leila Rashidian

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Temperature rise on Earth has had harmful effects on the Earth's surface and has led to change in precipitation patterns all around the world. The present research was aimed to study the process of climate change according to the data simulation in future and compare these parameters with current situation in the studied stations in Semnan province including Garmsar, Shahrood and Semnan. In this regard, LARS-WG software, HADCM₃ model and A₂ scenario were used for the 2010-2030 period. In this model, climatic parameters such as maximum and minimum temperature, precipitation and radiation were used daily. The obtained results indicated that there will be a 4.4% increase in precipitation in Semnan province compared with the observed data, and in general, there will be a 1.9% increase in temperature. This temperature rise has significant impact on precipitation patterns. Most of precipitation will be raining (torrential rains in some cases). According to the results, from west to east, the country will experience more temperature rise and will be warmer.

Keywords: climate change, Semnan province, Lars.WG model, climate parameters, HADCM₃ model

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Authors: O. P. Oladipo, O. A. Falode

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The determination of Recovery Factor is of great importance to the reservoir engineer since it relates reserves to the initial oil in place. Reserves are the producible portion of reservoirs and give an indication of the profitability of a field Development. The core objective of this project is to develop an artificial neural network model using selected reservoir data to predict Recovery Factors (RF) of hydrocarbon reservoirs and compare the model with a couple of the existing correlations. The type of Artificial Neural Network model developed was the Single Layer Feed Forward Network. MATLAB was used as the network simulator and the network was trained using the supervised learning method, Afterwards, the network was tested with input data never seen by the network. The results of the predicted values of the recovery factors of the Artificial Neural Network Model, API Correlation for water drive reservoirs (Sands and Sandstones) and Guthrie and Greenberger Correlation Equation were obtained and compared. It was noted that the coefficient of correlation of the Artificial Neural Network Model was higher than the coefficient of correlations of the other two correlation equations, thus making it a more accurate prediction tool. The Artificial Neural Network, because of its accurate prediction ability is helpful in the correct prediction of hydrocarbon reservoir factors. Artificial Neural Network could be applied in the prediction of other Petroleum Engineering parameters because it is able to recognise complex patterns of data set and establish a relationship between them.

Keywords: recovery factor, reservoir, reserves, artificial neural network, hydrocarbon, MATLAB, API, Guthrie, Greenberger

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Authors: Guanhua Zhou, Zhongqi Ma

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Submerged aquatic vegetation (SAV) in wetlands can absorb nitrogen and phosphorus effectively to prevent the eutrophication of water. It is feasible to monitor the distribution of SAV through remote sensing, but for the reason of weak vegetation signals affected by water body, traditional terrestrial vegetation indices are not applicable. This paper aims at constructing SAV index to enhance the vegetation signals and distinguish SAV from water body. The methodology is as follows: (1) select the bands sensitive to the vegetation parameters based on global sensitivity analysis of SAV canopy radiative transfer model; (2) take the soil line concept as reference, analyze the distribution of SAV and water reflectance simulated by SAV canopy model and semi-analytical water model in the two-dimensional space built by different sensitive bands; (3)select the band combinations which have better separation performance between SAV and water, and use them to build the SAVI indices in the form of normalized difference vegetation index(NDVI); (4)analyze the sensitivity of indices to the water and vegetation parameters, choose the one more sensitive to vegetation parameters. It is proved that index formed of the bands with central wavelengths in 705nm and 842nm has high sensitivity to chlorophyll content in leaves while it is less affected by water constituents. The model simulation shows a general negative, little correlation of SAV index with increasing water depth. Moreover, the index enhances capabilities in separating SAV from water compared to NDVI. The SAV index is expected to have potential in parameter inversion of wetland remote sensing.

Keywords: global sensitivity analysis, radiative transfer model, submerged aquatic vegetation, vegetation indices

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Authors: S. Khamtree, T. Ratanawilai, C. Nuntadusit

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Rubberwood is a crucial commercial timber in Southern Thailand. All processes in a rubberwood production depend on the knowledge and expertise of the technicians, especially the drying process. This research aims to develop an empirical model for drying kinetics in rubberwood. During the experiment, the temperature of the hot air and the average air flow velocity were kept at 80-100 °C and 1.75 m/s, respectively. The moisture content in the samples was determined less than 12% in the achievement of drying basis. The drying kinetic was simulated using an empirical solver. The experimental results illustrated that the moisture content was reduced whereas the drying temperature and time were increased. The coefficient of the moisture ratio between the empirical and the experimental model was tested with three statistical parameters, R-square (R²), Root Mean Square Error (RMSE) and Chi-square (χ²) to predict the accuracy of the parameters. The experimental moisture ratio had a good fit with the empirical model. Additionally, the results indicated that the drying of rubberwood using the Henderson and Pabis model revealed the suitable level of agreement. The result presented an excellent estimation (R² = 0.9963) for the moisture movement compared to the other models. Therefore, the empirical results were valid and can be implemented in the future experiments.

Keywords: empirical models, rubberwood, moisture ratio, hot air drying

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Authors: Meriem Bahij, Ahmed Nafidi, Boujemâa Achchab, Sílvio M. A. Gama, José A. O. Matos

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Stochastic modeling concerns the use of probability to model real-world situations in which uncertainty is present. Therefore, the purpose of stochastic modeling is to estimate the probability of outcomes within a forecast, i.e. to be able to predict what conditions or decisions might happen under different situations. In the present study, we present a model of a stochastic diffusion process based on the bi-Weibull distribution function (its trend is proportional to the bi-Weibull probability density function). In general, the Weibull distribution has the ability to assume the characteristics of many different types of distributions. This has made it very popular among engineers and quality practitioners, who have considered it the most commonly used distribution for studying problems such as modeling reliability data, accelerated life testing, and maintainability modeling and analysis. In this work, we start by obtaining the probabilistic characteristics of this model, as the explicit expression of the process, its trends, and its distribution by transforming the diffusion process in a Wiener process as shown in the Ricciaardi theorem. Then, we develop the statistical inference of this model using the maximum likelihood methodology. Finally, we analyse with simulated data the computational problems associated with the parameters, an issue of great importance in its application to real data with the use of the convergence analysis methods. Overall, the use of a stochastic model reflects only a pragmatic decision on the part of the modeler. According to the data that is available and the universe of models known to the modeler, this model represents the best currently available description of the phenomenon under consideration.

Keywords: diffusion process, discrete sampling, likelihood estimation method, simulation, stochastic diffusion process, trends functions, bi-parameters weibull density function

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Authors: Elham Taghizadeh, Mostafa Abedzadeh, Mostafa Setak

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Logistics network is expected that opened facilities work continuously for a long time horizon without any failure; but in real world problems, facilities may face disruptions. This paper studies a reliable joint inventory location problem to optimize cost of facility locations, customers’ assignment, and inventory management decisions when facilities face failure risks and doesn’t work. In our model we assume when a facility is out of work, its customers may be reassigned to other operational facilities otherwise they must endure high penalty costs associated with losing service. For defining the model closer to real world problems, the model is proposed based on p-median problem and the facilities are considered to have limited capacities. We define a new binary variable (Z_is) for showing that customers are not assigned to any facilities. Our problem involve a bi-objective model; the first one minimizes the sum of facility construction costs and expected inventory holding costs, the second one function that mention for the first one is minimizes maximum expected customer costs under normal and failure scenarios. For solving this model we use NSGAII and MOSS algorithms have been applied to find the pareto- archive solution. Also Response Surface Methodology (RSM) is applied for optimizing the NSGAII Algorithm Parameters. We compare performance of two algorithms with three metrics and the results show NSGAII is more suitable for our model.

Keywords: joint inventory-location problem, facility location, NSGAII, MOSS

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Authors: Khalid Hassan Abdesamed, Kalsom Abd Wahab

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Economic development and growth are significantly linked to the consistent and sustainable sector of small and medium enterprises (SMEs). Banks are the frontrunners in financing and advising SMEs. The main objective of the study is to assess the tendency of SMEs to use the Islamic bank. Model was developed using quantitative method with a hypothetical-deductive testing approach. Model (N = 364) used primary data on the tendency of SMEs to use Islamic banks gathered from questionnaire. It is found by Mann-Whitney test that the tendency to use Islamic bank varies between those firms which consider formal financing with the ones relying on informal financing with the latter tends more to use Islamic bank. This study can serve academic researchers, policy makers, and developing countries as a model of SMEs’ desirability to Islamic banking.

Keywords: formal financing, informal financing, Islamic bank, SMEs

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Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Hamidrza Joodaki

Abstract:

The most complex issue in urban land use planning is site selection that needs to assess the verity of elements and factors. Multi Criteria Decision Making (MCDM) methods are the best approach to deal with complex problems. In this paper, combination of the analytical hierarchy process (AHP) model and FUZZY logic was used as MCDM methods to select the best site for gas station in the 4th gas zone of Tehran. The first and the most important step in FUZZY-AHP model is selection of criteria and sub-criteria. Population, accessibility, proximity and natural disasters were considered as the main criteria in this study. After choosing the criteria, they were weighted based on AHP by EXPERT CHOICE software, and FUZZY logic was used to enhance accuracy and to approach the reality. After these steps, criteria layers were produced and weighted based on FUZZY-AHP model in GIS. Finally, through ARC GIS software, the layers were integrated and the 4th gas zone in TEHRAN was selected as the best site to locate gas station.

Keywords: multiple criteria decision making (MCDM), analytic hierarchy process (AHP), FUZZY logic, geographic information system (GIS)

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Authors: Pedram Saadati, Zahra Nazari

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In the last decade, the role and importance of knowledge-based technological businesses in the insurance industry has greatly increased, and due to the weakness of previous studies in Iran, the current research deals with the design of the InsurTech empowerment model. In order to obtain the conceptual model of the research, a hybrid framework has been used. The statistical population of the research in the qualitative part were experts, and in the quantitative part, the InsurTech activists. The tools of data collection in the qualitative part were in-depth and semi-structured interviews and structured self-interaction matrix, and in the quantitative part, a researcher-made questionnaire. In the qualitative part, 55 indicators, 20 components and 8 concepts (dimensions) were obtained by the content analysis method, then the relationships of the concepts with each other and the levels of the components were investigated. In the quantitative part, the information was analyzed using the descriptive analytical method in the way of path analysis and confirmatory factor analysis. The proposed model consists of eight dimensions of supporter capability, supervisor of insurance innovation ecosystem, managerial, financial, technological, marketing, opportunity identification, innovative InsurTech capabilities. The results of statistical tests in identifying the relationships of the concepts with each other have been examined in detail and suggestions have been presented in the conclusion section.

Keywords: insurTech, knowledge-base, empowerment model, factor analysis, insurance

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Authors: Sara Mostofian

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The nuclear industry is a technology that can fulfill future energy needs but requires special attention to ensure safety and reliability while minimizing any environmental impact. To meet these expectations, the nuclear industry is exploring different reactor technologies for power production. Several designs are under development and the technical viability of these new designs is the subject of many ongoing studies. One of these studies considers the radioactive emissions and radioactive waste generated during the life of a nuclear power production plant to allow a successful license process. For all the modern technologies, a good understanding of the radioactivity generated in the process systems of the plant is essential. Some of that understanding may be gleaned from the performance of some prototype reactors of similar design that operated decades ago. This paper presents how, with that understanding, a model can be developed to estimate the emissions as well as the radioactive waste during the normal operation of a nuclear power plant. The model would predict the radioactive material concentrations in different waste streams. Using this information, the radioactive emission and waste generated during the life of these new technologies can be estimated during the early stages of the design of the plant.

Keywords: SMRs, activity transport, model, radioactive waste

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Authors: Chayanika Rout, Debjani Chakraborty, Adrijit Goswami

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Our work presents an inventory model which illustrates imperfect production and imperfect inspection processes for deteriorating items. A cost-minimizing model is studied considering two types of inspection errors, namely, Type I error of falsely screening out a proportion of non-defects, thereby passing them on for rework and Type II error of falsely not screening out a proportion of defects, thus selling those to customers which incurs a penalty cost. The screened items are reworked; however, no returns are entertained due to deteriorating nature of the items. In more practical situations, certain parameters such as the demand rate and the deterioration rate of inventory cannot be accurately determined, and therefore, they are assumed to be triangular fuzzy numbers in our model. We calculate the optimal lot size that must be produced in order to minimize the total inventory cost for both the crisp and the fuzzy models. A numerical example is also considered to exemplify the procedure which is followed by the analysis of sensitivity of various parameters on the decision variable and the objective function.

Keywords: deteriorating items, EPQ, imperfect quality, rework, type I and type II inspection errors

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Authors: Yogesh Shukla

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Many attempts have been made to study temperature distribution problem in human tissues under normal environmental and physiological conditions at constant arterial blood temperature. But very few attempts have been made to investigate temperature distribution in human tissues under different arterial blood temperature. In view of above, a finite element model has been developed to unsteady temperature distribution in dermal region in human body. The model has been developed for one dimension unsteady state case. The variation in parameters like thermal conductivity, blood mass flow and metabolic activity with respect to position and time has been incorporated in the model. Appropriate boundary conditions have been framed. The central difference approach has been used in space variable and trapezoidal rule has been employed a long time variable. Numerical results have been obtained to study relationship among temperature and time.

Keywords: rate of metabolism, blood mass flow rate, thermal conductivity, heat generation, finite element method

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Authors: Shariful Islam, Sharun Akter Khushbu, S. M. Shaqib, Shahriar Sultan Ramit

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In the construction sector, ensuring worker safety is of the utmost significance. In this study, a deep learning-based technique is presented for identifying safety gear worn by construction workers, such as helmets, goggles, jackets, gloves, and footwear. The suggested method precisely locates these safety items by using the YOLO v7 (You Only Look Once) object detection algorithm. The dataset utilized in this work consists of labeled images split into training, testing and validation sets. Each image has bounding box labels that indicate where the safety equipment is located within the image. The model is trained to identify and categorize the safety equipment based on the labeled dataset through an iterative training approach. We used custom dataset to train this model. Our trained model performed admirably well, with good precision, recall, and F1-score for safety equipment recognition. Also, the model's evaluation produced encouraging results, with a [email protected] score of 87.7%. The model performs effectively, making it possible to quickly identify safety equipment violations on building sites. A thorough evaluation of the outcomes reveals the model's advantages and points up potential areas for development. By offering an automatic and trustworthy method for safety equipment detection, this research contributes to the fields of computer vision and workplace safety. The proposed deep learning-based approach will increase safety compliance and reduce the risk of accidents in the construction industry.

Keywords: deep learning, safety equipment detection, YOLOv7, computer vision, workplace safety

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Authors: Yixin Zhu, Lishengsa Yue, Jian Sun, Lanyue Tang

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Modeling driving distraction in microscopic traffic simulation is crucial for enhancing simulation accuracy. Current driving distraction models are mainly derived from physical motion constraints under distracted states, in which distraction-related error terms are added to existing microscopic driver models. However, the model accuracy is not very satisfying, due to a lack of modeling the cognitive mechanism underlying the distraction. This study models driving distraction based on the Queueing Network Human Processor model (QN-MHP). This study utilizes the queuing structure of the model to perform task invocation and switching for distracted operation and control of the vehicle under driver distraction. Based on the assumption of the QN-MHP model about the cognitive sub-network, server F is a structural bottleneck. The latter information must wait for the previous information to leave server F before it can be processed in server F. Therefore, the waiting time for task switching needs to be calculated. Since the QN-MHP model has different information processing paths for auditory information and visual information, this study divides driving distraction into two types: auditory distraction and visual distraction. For visual distraction, both the visual distraction task and the driving task need to go through the visual perception sub-network, and the stimuli of the two are asynchronous, which is called stimulus on asynchrony (SOA), so when calculating the waiting time for switching tasks, it is necessary to consider it. In the case of auditory distraction, the auditory distraction task and the driving task do not need to compete for the server resources of the perceptual sub-network, and their stimuli can be synchronized without considering the time difference in receiving the stimuli. According to the Theory of Planned Behavior for drivers (TPB), this study uses risk entropy as the decision criterion for driver task switching. A logistic regression model is used with risk entropy as the independent variable to determine whether the driver performs a distraction task, to explain the relationship between perceived risk and distraction. Furthermore, to model a driver’s perception characteristics, a neurophysiological model of visual distraction tasks is incorporated into the QN-MHP, and executes the classical Intelligent Driver Model. The proposed driving distraction model integrates the psychological cognitive process of a driver with the physical motion characteristics, resulting in both high accuracy and interpretability. This paper uses 773 segments of distracted car-following in Shanghai Naturalistic Driving Study data (SH-NDS) to classify the patterns of distracted behavior on different road facilities and obtains three types of distraction patterns: numbness, delay, and aggressiveness. The model was calibrated and verified by simulation. The results indicate that the model can effectively simulate the distracted car-following behavior of different patterns on various roadway facilities, and its performance is better than the traditional IDM model with distraction-related error terms. The proposed model overcomes the limitations of physical-constraints-based models in replicating dangerous driving behaviors, and internal characteristics of an individual. Moreover, the model is demonstrated to effectively generate more dangerous distracted driving scenarios, which can be used to construct high-value automated driving test scenarios.

Keywords: computational cognitive model, driving distraction, microscopic traffic simulation, psychological-physical constraints

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Authors: Henry Venter, Murali Thyloth, Alceu Casseb

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Historically, mental and substance use disorders were not a global health priority. Since the 1993 World Development Report, the importance of the contribution of mental health and substance abuse on the relative global burden associated with disease morbidity has been recognized with 300 million people worldwide suffering from depression alone. This led to an international effort to improve the mental health of populations around the world. Despite these efforts some countries remain at the top of the list of countries with the highest rate of mental illness. Important research questions were asked: Would there be commonalities regarding mental health between these countries; would there be common factors leading to the high prevalence of mental illness; and how prepared are these countries with mental health delivery? Findings from this research can aid organizations and institutions preparing mental health service providers to focus training and preparation to address specific needs revealed by the study. Methods: Researchers decided to compare three distinctly different countries at the top of the list of countries with the highest rate of mental illness, the USA, India and Brazil, situated on three different continents with different economies and lifestyles. Data were collected using archival research methodology, reviewing records and findings of international and national health and mental health studies to subtract and compare data and findings. Results: The findings indicated that India is the most depressed country in the world, followed by the USA (and China) with Brazil in Latin America with the greatest number of depressed individuals. By 2020 roughly 20% of India, acountry of over one billion citizens, will suffer from some form of mental illnees, yet there are less than 4,000 experts available. In the USA 164.8 million people were substance abusers and an estimate of 47.6 million adults, 18 or older, had any mental illness in 2018. That means that about one in five adults in the USA experiences some form of mental illness each year, but only 41% of those affected received mental health care or services in the past year. Brazil has the greatest number of depressed individuals, in Latin America. Adults living in Sao Paulo megacity has prevalence of mental disorders at greater levels than similar surveys conducted in other areas of the world with more than one million adults with serious impairment levels. Discussion: The results show that, despite the vast socioeconomic differences between the three countries, there are correlations regarding mental health prevalence and difficulty to provide adequate services including a lack of awareness of how serious mental illness is, stigma for seeking mental illness, with comorbidity a common phenomenon, and a lack of partnership between different levels of service providers, which weakens mental health service delivery. The findings also indicate that mental health training institutions have a monumental task to prepare personnel to address the future mental health needs in each of the countries compared, which will constitute the next phase of the research.

Keywords: mental health epidemiology, mental health disorder, mental health prevalence, mental health treatment

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Authors: Maryam Haghi, Ivan Contreras, Nadia Bhuiyan

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Chemotherapy is one of the most popular and effective cancer treatments offered to patients in outpatient oncology centers. In such clinics, patients first consult with an oncologist and the oncologist may prescribe a chemotherapy treatment plan for the patient based on the blood test results and the examination of the health status. Then, when the plan is determined, a set of chemotherapy and consultation appointments should be scheduled for the patient. In this work, a comprehensive mathematical formulation for planning and scheduling different types of chemotherapy patients over a planning horizon considering blood test, consultation, pharmacy and treatment stages has been proposed. To be more realistic and to provide an applicable model, this study is focused on a case study related to a major outpatient cancer treatment clinic in Montreal, Canada. Comparing the results of the proposed model with the current practice of the clinic under study shows significant improvements regarding different performance measures. These major improvements in the patients’ schedules reveal that using optimization techniques in planning and scheduling of patients in such highly demanded cancer treatment clinics is an essential step to provide a good coordination between different involved stages which ultimately increases the efficiency of the entire system and promotes the staff and patients' satisfaction.

Keywords: chemotherapy patients scheduling, integer programming, integrated scheduling, staff balancing

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Authors: Anuj Abraham, N. Pappa, Daniel Honc, Rahul Sharma

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In this paper, an analysis of some model order reduction techniques is presented. A new hybrid algorithm for model order reduction of linear time invariant systems is compared with the conventional techniques namely Balanced Truncation, Hankel Norm reduction and Dominant Pole Algorithm (DPA). The proposed hybrid algorithm is known as Clustering Dominant Pole Algorithm (CDPA) is able to compute the full set of dominant poles and its cluster center efficiently. The dominant poles of a transfer function are specific eigenvalues of the state space matrix of the corresponding dynamical system. The effectiveness of this novel technique is shown through the simulation results.

Keywords: balanced truncation, clustering, dominant pole, Hankel norm, model reduction

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Authors: Peter Adly Hamdy Fahmy

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The aim of the study was to examine the effects of a physical education program on student learning by combining the teaching of personal and social responsibility (TPSR) with a physical education model and TPSR with a traditional teaching model, these learning outcomes involving self-learning. -Study. Athletic performance, enthusiasm for sport, group cohesion, sense of responsibility and game performance. The participants were 3 secondary school physical education teachers and 6 physical education classes, 133 participants with students from the experimental group with 75 students and the control group with 58 students, and each teacher taught the experimental group and the control group for 16 weeks. The research methods used surveys, interviews and focus group meetings. Research instruments included the Personal and Social Responsibility Questionnaire, Sports Enthusiasm Scale, Group Cohesion Scale, Sports Self-Efficacy Scale, and Game Performance Assessment Tool. Multivariate analyzes of covariance and repeated measures ANOVA were used to examine differences in student learning outcomes between combining the TPSR with a physical education model and the TPSR with a traditional teaching model. The research findings are as follows: 1) The TPSR sports education model can improve students' learning outcomes, including sports self-efficacy, game performance, sports enthusiasm, team cohesion, group awareness and responsibility. 2) A traditional teaching model with TPSR could improve student learning outcomes, including sports self-efficacy, responsibility, and game performance. 3) The sports education model with TPSR could improve learning outcomes more than the traditional teaching model with TPSR, including sports self-efficacy, sports enthusiasm, responsibility and game performance. 4) Based on qualitative data on teachers' and students' learning experience, the physical education model with TPSR significantly improves learning motivation, group interaction and sense of play. The results suggest that physical education with TPSR could further improve learning outcomes in the physical education program. On the other hand, the hybrid model curriculum projects TPSR - Physical Education and TPSR - Traditional Education are good curriculum projects for moral character education that can be used in school physics.

Keywords: approach competencies, physical, education, teachers employment, graduate, physical education and sport sciences, SWOT analysis character education, sport season, game performance, sport competence

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Authors: Vishant Gahlaut, A Mercy Latha, Sanjay Kumar Ghosh

Abstract:

Multi-stage depressed collectors (MDC) are used as an efficiency enhancement technique in traveling wave tubes the high-energy electron beam, after its interaction with the RF signal, gets velocity sorted and collected at various depressed electrodes of the MDC. The ultimate goal is to identify an optimum thermal management scheme (cooling mechanism) that could extract the heat efficiently from the electrodes. Careful thermal analysis, incorporating the cooling mechanism is required to ensure that the maximum temperature does not exceed the safe limits. A simple analytical model for quick prediction of the thermal has been developed. The model has been developed for the worst-case un-modulated DC condition, where all the thermal power is dissipated in the last electrode (typically, fourth electrode in the case of the four-stage depressed collector). It considers the thermal contact resistances at various braze joints accounting for the practical non-uniformities. Analytical results obtained from the model have been validated with simulated and experimental results.

Keywords: multi-stage depressed collector, TWTs, thermal contact resistance, thermal management

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Authors: Yong-Nam Kwon

Abstract:

In the present study, we have designed the hydro-mechanical forming in which AZ31 sheet was drawn to a kind of preform step following gas blow forming for accurate geometry. In order to judge a formability enhancement of AZ31 sheet, model geometry came from a practical automotive part which had quite depth with complicated curvatures, which was proven that a single sheet forming could not gave a successful part. Experimentally, we succeeded to make the model part with accurate dimension. The optimum forming conditions for respective forming steps were considered most important technical features of this hydro-mechanical and would be discussed in details. Also, the effort to avoid detrimental abnormal grain growth was given and discussed for a practical application.

Keywords: hydro-mechanical forming, AZ31, abnormal grain growth, model geometry

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Authors: Sonal Tuljaram Kame

Abstract:

Growth and development is based on proper diet. A balanced diet contains all the nutrients in required quantum. Although physical growth is completed by young adulthood, the body tissues remain in a dynamic state with catabolism slightly exceeding anabolism, resulting in a net decrease in the number of cells. After the years of adolescence which cause upheavals in the life of the person, the individual struggle to emerge as an adult who know who he is and what his goals are. During this period nutrients are needed for maintaining the health and energy is required for physical functions and physical activities. The nutritional requirement in young adulthood differs from other periods of life. Iron is needed for haemoglobin synthesis and necessitates by the considerable examination of blood volume. Young adult girls need to ensure adequate intake of iron as they loose 0.5 mg/day by way of menstruation. This is complete awareness about nutritional and health on the other side there is widespread ignorance about nutrition and health among young adult girls. The young adult girls who are aware about nutrition and health seem to be very conscious about nutritional intake and health. Figure consciousness and fear of obesity leads to self imposed intake of nutrients. It may result in various health problems. The study was planned to investigate nutrient intake, find relation between nutritional intake, clinical examination score and health status of young adult girls. The present study is based on the data collected from 120 young adult girls studying in four different competitive exams coaching academies in Akola city of Maharashtra. It was found that nutritional intake of these young adult girls was below the recommended level, nutritional knowledge level and nutritional intake are associated attributes, calories, calcium and protein intake is positively correlated with clinical examination and health status. It was concluded that well planned nutritional counseling for the young adult girls can help prevent nutritional deficiency diseases and disorders which may lead to anaemic condition in young adult girls. Girls need to be educated on intake of iron and vitamin B12.

Keywords: nutritional intake, health status, young adult girls, correlation studies

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Authors: Babanyara Y. Y., Ibrahim D. B., Garba T., Bogoro A. G., Abubakar, M. Y.

Abstract:

Medical care is vital for our life, health, and well-being. However, the waste generated from medical activities can be hazardous, toxic, and even lethal because of their high potential for diseases transmission. The hazardous and toxic parts of waste from healthcare establishments comprising infectious, medical, and radioactive material as well as sharps constitute a grave risks to mankind and the environment, if these are not properly treated/disposed or are allowed to be mixed with other municipal waste. In Nigeria, practical information on this aspect is inadequate and research on the public health implications of poor management of medical wastes is few and limited in scope. Findings drawn from Literature particularly in the third world countries highlights financial problems, lack of awareness of risks involved in MWM, lack of appropriate legislation and lack of specialized MWM staff. The paper recommends how MWM practices can be improved in medical facilities.

Keywords: environmental pollution, infectious, management, medical waste, public health

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Authors: Marian Sorin Ionescu, Olivia Negoita, Cosmin Dobrin

Abstract:

This scientific communication reports and discusses learning models adaptable to modern business problems and models specific to digital concepts and paradigms. In the PAC (probably approximately correct) learning model approach, in which the learning process begins by receiving a batch of learning examples, the set of learning processes is used to acquire a hypothesis, and when the learning process is fully used, this hypothesis is used in the prediction of new operational examples. For complex business models, a lot of models should be introduced and evaluated to estimate the induced results so that the totality of the results are used to develop a predictive rule, which anticipates the choice of new models. In opposition, for online learning-type processes, there is no separation between the learning (training) and predictive phase. Every time a business model is approached, a test example is considered from the beginning until the prediction of the appearance of a model considered correct from the point of view of the business decision. After choosing choice a part of the business model, the label with the logical value "true" is known. Some of the business models are used as examples of learning (training), which helps to improve the prediction mechanisms for future business models.

Keywords: machine learning, business models, convex analysis, online learning

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Authors: Sanjeev Kumar Sharma, Arnab Mondal, Ranjit Kumar Upadhyay

Abstract:

Excitable cells often produce different oscillatory activities that help us to understand the transmitting and processing of signals in the neural system. We consider a FitzHugh-Rinzel (FH-R) model and studied the different dynamics of the model by considering the parameter c as the predominant parameter. The model exhibits different types of neuronal responses such as regular spiking, mixed-mode bursting oscillations (MMBOs), elliptic bursting, etc. Based on the bifurcation diagram, we consider the three regimes (MMBOs, elliptic bursting, and quiescent state). An analytical treatment for the occurrence of the supercritical Hopf bifurcation is studied. Further, we extend our study to a network of a hundred neurons by considering the bi-directional synaptic coupling between them. In this article, we investigate the alternation of spiking propagation and bursting phenomena of an uncoupled and coupled FH-R neurons. We explore that the complete graph of heterogenous desynchronized neurons can exhibit different types of bursting oscillations for certain coupling strength. For higher coupling strength, all the neurons in the network show complete synchronization.

Keywords: excitable neuron model, spiking-bursting, stability and bifurcation, synchronization networks

Procedia PDF Downloads 128