Search results for: networked model predictive control

Authors: Baharullah Khattak, Saifullah

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To study the efficacy of the selected Trichoderma isolates, field trials were conducted in the root-knot nematode-infested areas of Dargai and Swat, Pakistan. Four isolates of T. harzianum viz, Th-1, Th-2, Th-9 and Th-15 were tested against root knot nematodes on summer tomatoes under field conditions. The T. harzianum isolates, grown on wheat grains substrate, were applied @ 8 g plant-1, either alone or in different combinations. Root weight of tomato plants was reduced Th-9 as compared to 26.37 g in untreated control. Isolate Th-1 was found to enhance shoot and root lengths to the maximum levels of 78.76 cm and 19.59 cm, respectively. Tomato shoot weight was significantly increased (65.36g) in Th-1-treated plots as compared to 49.66 g in control. Maximum (156) number of flowers plant-1 and highest (48.18%) fruit set plant-1 was observed in Th-1 treated plots, while there were 87 flowers and 35.50% fruit set in the untreated control. Maximum fruit weight (70.97 g) plant-1 and highest (17.99 t ha-1) marketable yield were recorded in the treatments where T. harzianum isolate Th-1 was used, in comparison to 51.33 g tomato fruit weight and 9.90 t ha-1 yield was noted in the control plots. It was observed that T. harzianum isolates significantly reduced the nematode populations. The fungus enhanced plant growth and yield in all the treated plots. Jabban isolate (Th-1) was found as the most effective in nematode suppression followed by Shamozai (Th-9) isolate. It was concluded from the present findings that T. harzianum has a potential bio control capability against root-knot nematodes.

Keywords: biological control, Trichoderma harzianum, root-knot nematode, meloidogyne

Procedia PDF Downloads 498

Authors: Soni Nopembri

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The objective of this research is to evaluate the implementation of teaching games for Understanding model by conducting action to physical education teacher who have got long teaching experience. The research applied Participatory Action Research. The subjects of this research were 19 physical education teachers who had got training of Teaching Games for Understanding. Data collection was conducted intensively through a questionnaire, in-depth interview, Focus Group Discussion (FGD), observation, and documentation. The collected data was analysis zed qualitatively and quantitatively. The result showed that physical education teachers had got an appropriate interpretation on TGfU model. Some indicators that were the focus of this research indicated this points; they are: (1) physical education teachers had good understanding toward TGfU model, (2) PE teachers’ competence in applying TGfU model on Physical Education at school were adequate, though some improvement were needed, (3) the influence factors in the implementation of TGfU model, in sequence, were teacher, facilities, environment, and students factors, (4) PE teachers’ perspective toward TGfU model were positively good, although some teachers were less optimistic toward the development of TGfU model in the future.

Keywords: TGfU, physical education teacher, teaching games, FGD

Procedia PDF Downloads 548

Authors: Joshua Adan Valdez, Shawn Gallaher

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Global climate change has impacted atmospheric temperatures contributing to rising sea levels, decreasing sea ice, and increased freshening of high latitude oceans. This freshening has contributed to increased stratification inhibiting local mixing and nutrient transport, modifying regional circulations in polar oceans. In recent years, the Western Arctic has seen an increase in freshwater volume at an average rate of 397+-116km3/year across the Beaufort Gyre. The majority of the freshwater volume resides in the Beaufort Gyre surface lens driven by anticyclonic wind forcing, sea ice melt, and Arctic river runoff, and is typically defined as water fresher than 34.8. The near-isothermal nature of Arctic seawater and non-linearities in the equation of state for near-freezing waters result in a salinity-driven pycnocline as opposed to the temperature-driven density structure seen in the lower latitudes. In this study, we investigate the relationship between freshwater content and dynamic ocean topography (DOT). In situ measurements of freshwater content are useful in providing information on the freshening rate of the Beaufort Gyre; however, their collection is costly and time-consuming. Utilizing NASA’s ICESat-2’s DOT remote sensing capabilities and Air Expendable CTD (AXCTD) data from the Seasonal Ice Zone Reconnaissance Surveys (SIZRS), a linear regression model between DOT and freshwater content is determined along the 150° west meridian. Freshwater content is calculated by integrating the volume of water between the surface and a depth with a reference salinity of ~34.8. Using this model, we compare interannual variability in freshwater content within the gyre, which could provide a future predictive capability of freshwater volume changes in the Beaufort-Chukchi Sea using non-in situ methods. Successful employment of the ICESat-2’s DOT approximation of freshwater content could potentially demonstrate the value of remote sensing tools to reduce reliance on field deployment platforms to characterize physical ocean properties.

Keywords: Cryosphere, remote sensing, Arctic oceanography, climate modeling, Ekman transport

Procedia PDF Downloads 78

Authors: Khadijeh Abhari, Seyed Shahram Shekarforoush, Saeid Hosseinzadeh

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Probiotics have been considered as an approach to treat and prevent a wide range of inflammatory diseases. The spore forming probiotic strain Bacillus coagulans has demonstrated anti-inflammatory and immune-modulating effects in both animals and humans. The prebiotic, inulin, also potentially affects the immune system as a result of the change in the composition or fermentation profile of the gastrointestinal microbiota. An in vivo trial was conducted to evaluate the effects of probiotic B. coagulans, and inulin, either separately or in combination, on down regulate immune responses and progression of rheumatoid arthritis using induced arthritis rat model. Forty-eight male Wistar rats were randomly divided into 6 groups and fed as follow: 1) control: Normal healthy rats fed by standard diet, 2) Disease control (RA): Arthritic induced (RA) rats fed by standard diet, 3) Prebiotic (PRE): RA+ 5% w/w long chain inulin, 4) Probiotic (PRO): RA+ 109 spores/day B. coagulans by orogastric gavage, 5) Synbiotic (SYN): RA+ 5% w/w long chain inulin and 109 spores/day B. coagulans and 6) Treatment control: (INDO): RA+ 3 mg/kg/day indomethacin by orogastric gavage. Feeding with mentioned diets started on day 0 and continued to the end of study. On day 14, rats were injected with complete Freund’s adjuvant (CFA) to induce arthritis. Arthritis activity was evaluated by biochemical parameters and paw thickness. Biochemical assay for Fibrinogen (Fn), Serum Amyloid A (SAA), TNF-α and Alpha-1-acid glycoprotein (α1AGp) was performed on day 21, 28 and 35 (1, 2 and 3 weeks post RA induction). Pretreatment with PRE, PRO and SYN diets significantly inhibit SAA and Fn production in arthritic rats (P < 0.001). A significant decrease in production of pro-inflammatory cytokines, TNF-α, was seen in PRE, PRO and SYN groups (P < 0.001) which was similar to the effect of the anti-inflammatory drug Indomethacin. Further, there were no significant anti-inflammatory effects observed following different treatments using α1AGp as a RA indicator. Pretreatment with all supplied diets significantly inhibited the development of paw swelling induced by CFA (P < 0.001). Conclusion: Results of this study support that oral intake of probiotic B. coagulans and inulin are able to improve biochemical and clinical parameters of induced RA in rat.

Keywords: rheumatoid arthritis, bacillus coagulans, inulin, animal model

Procedia PDF Downloads 358

Authors: Mahnaz Khalafehnilsaz, Rozina Rahnama

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Introduction: The aging process has been linked to a wide range of non-communicable diseases that cause a loss of health-related quality of life. This process can be worsened if an active and healthy lifestyle is not followed by adults, especially in the workplace. This setting not only may create a sedentary lifestyle but will lead to obesity and overweight in the long term and create unhealthy and inactive aging. In addition, eating habits are always known to be associated with active aging. Therefore, it is very valuable to know the eating patterns of people at work in order to detect and prevent diseases in the coming years. This study aimed to design and test a model to improve eating habits among employees at an industrial complex as a practical strategy. Material and method: The present research was a mixed-method study with a subsequent exploratory design which was carried out in two phases, qualitative and quantitative, in 2018 year. In the first step, participants were selected by purposive sampling (n=34) to ensure representation of different job roles; hours worked, gender, grade, and age groups, and semi-structured interviews were used. All interviews were conducted in the workplace and were audio recorded, transcribed verbatim, and analyzed using the Strauss and Corbin approach. The interview question was, “what were their experiences of eating at work, and how could these nutritional habits affect their health in old age.” Finally, a total of 1500 basic codes were oriented at the open coding step, and they were merged together to create the 17 classes, and six concepts and a conceptual model were designed. The second phase of the study was conducted in the form of a cross-sectional study. After verification of the research tool, the developed questionnaire was examined in a group of employees. In order to test the conceptual model of the study, a total of 500 subjects were included in psychometry. Findings: Six main concepts have been known, including 1. undesirable control of stress, 2. lack of eating knowledge, 3. effect of the social network, 4. lack of motivation for healthy habits, 5. environmental-organizational intensifier, 6. unhealthy eating behaviors. The core concept was “Motivation Loss to do preventive behavior.” The main constructs of the motivational-based model for the promotion of eating habits are “modification and promote of eating habits,” increase of knowledge and competency, convey of healthy nutrition behavior culture and effecting of behavioral model especially in older age, desirable of control stress. Conclusion: A key factor for unhealthy eating behavior at the workplace is a lack of motivation, which can be an obstacle to conduct preventive behaviors at work that can affect the healthy aging process in the long term. The motivational-based model could be considered an effective conceptual framework and instrument for designing interventions for the promotion to create healthy and active aging.

Keywords: aging, eating habits, older age, workplace

Procedia PDF Downloads 102

Authors: Marzieh Zarei

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Well instability is one of the most fundamental challenges faced by the oil and gas industry. Well wall stability analysis is a gap to be filled in the oil industry. The collection of static data such as well logging leads to the construction of a geomechanical numerical model, which will help in assessing the probable risks in future drilling. In this paper, geomechanical model was designed, and mechanical properties of the rock was determined at all points of the model. It was found the safe mud window was determined and the minimum and maximum mud pressures were determined in the ranges of 70-60 MPa and 110-100 MPa, respectively.

Keywords: geomechanics, numerical model, well stability, in-situ stress, underbalanced drilling

Procedia PDF Downloads 130

Authors: Enyia James Diwa, Dodeye Ina Igbong, Archibong Archibong Eso

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Gas turbine operators have been faced with the dramatic financial setback resulting from compressor fouling. In a highly deregulated power industry where there is stiffness in the market competition, has made it imperative to improvise means of reducing maintenance cost in other to yield maximum profit. Compressor fouling results from the deposition of contaminants in the presence of oil and moisture on the compressor blade or annulus surfaces, which leads to a loss in flow capacity and compressor efficiency. These combined effects reduce power output, increase heat rate and cause creep life reduction. This paper also contains a model of two gas turbine engines via Cranfield University software known as TURBOMATCH, which is simulation software for detecting engine fouling rate. The model engines are of different configurations and capacities, and are operating in two different modes of constant output power and turbine inlet temperature for a two and three stage filter system. The idea is to investigate the more economically viable filtration systems by gas turbine users based on performance only. It has been demonstrated in the results that the two spool engine is a little more beneficial compared to the single spool. This is as a result of a higher pressure ratio of the two spools as well as the deceleration of the high-pressure compressor and high-pressure turbine speed in a constant TET. Meanwhile, the inlet filtration system was properly designed and balanced with a well-timed and economical compressor washing regime/scheme to control compressor fouling. The different technologies of inlet air filtration and compressor washing are considered and an attempt at optimization with respect to the cost of a combination of both control measures are made.

Keywords: inlet filtration, pressure loss, single spool, two spool

Procedia PDF Downloads 323

Authors: Bencheikh Abdellah

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In this paper, we present a mathematical model dedicated to the simulation breaks bars in a three-phase cage induction motor. This model is based on a mesh circuit representing the rotor cage. The tested simulation allowed us to demonstrate the effectiveness of this model to describe the behavior of the machine in a healthy state, failure.

Keywords: AC motors, squirrel cage, diagnostics, MATLAB, SIMULINK

Procedia PDF Downloads 438

Authors: Oleg Oborin

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This paper is dedicated to the development of the model, which can be used to evaluate the effectiveness of long-term corporate strategies and identify the best strategies. The theoretical model of the relatively homogenous product market (such as iron and steel industry, mobile services or road transport) has been developed. In the model, the market consists of a large number of companies with different internal characteristics and objectives. The companies can perform mergers and acquisitions in order to increase their market share. The model allows the simulation of long-time dynamics of the market (for a period longer than 20 years). Therefore, a large number of simulations on random input data was conducted in the framework of the model. After that, the results of the model were compared with the dynamics of real markets, such as the US steel industry from the beginning of the XX century to the present day, and the market of mobile services in Germany for the period between 1990 and 2015.

Keywords: Economic Modelling, Long-Time Strategy, Mergers and Acquisitions, Simulation

Procedia PDF Downloads 368

Authors: Ali Emre Öztürk, Ergun Ercelebi

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Autonomous robotic systems needs an equipment like a human eye for their movement. Robotic camera systems, distance sensors and 3D laser scanners have been used in the literature. In this study a 3D laser scanner has been produced for those autonomous robotic systems. In general 3D laser scanners are using 2 dimension laser range finders that are moving on one-axis (1D) to generate the model. In this study, the model has been obtained by a one-dimensional laser range finder that is moving in two –axis (2D) and because of this the laser scanner has been produced cheaper. Furthermore for the laser scanner a motor driver, an embedded system control board has been used and at the same time a user interface card has been used to make the communication between those cards and computer. Due to this laser scanner, the density of the objects, the distance between the objects and the necessary path ways for the robot can be calculated. The data collected by the laser scanner system is converted in to cartesian coordinates to be modeled in AutoCAD program. This study shows also the synchronization between the computer user interface, AutoCAD and the embedded systems. As a result it makes the solution cheaper for such systems. The scanning results are enough for an autonomous robot but the scan cycle time should be developed. This study makes also contribution for further studies between the hardware and software needs since it has a powerful performance and a low cost.

Keywords: 3D laser scanner, embedded system, 1D laser range finder, 3D model

Procedia PDF Downloads 278

Authors: Xiaogang Li, Jieqiong Miao

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As for the problem of the grey forecasting model prediction accuracy is low, an improved grey prediction model is put forward. Firstly, use trigonometric function transform the original data sequence in order to improve the smoothness of data , this model called SGM( smoothness of grey prediction model), then combine the improved grey model with support vector machine , and put forward the grey support vector machine model (SGM - SVM).Before the establishment of the model, we use trigonometric functions and accumulation generation operation preprocessing data in order to enhance the smoothness of the data and weaken the randomness of the data, then use support vector machine (SVM) to establish a prediction model for pre-processed data and select model parameters using genetic algorithms to obtain the optimum value of the global search. Finally, restore data through the "regressive generate" operation to get forecasting data. In order to prove that the SGM-SVM model is superior to other models, we select the battery life data from calce. The presented model is used to predict life of battery and the predicted result was compared with that of grey model and support vector machines．For a more intuitive comparison of the three models, this paper presents root mean square error of this three different models .The results show that the effect of grey support vector machine (SGM-SVM) to predict life is optimal, and the root mean square error is only 3.18%. Keywords: grey forecasting model, trigonometric function, support vector machine, genetic algorithms, root mean square error

Keywords: Grey prediction model, trigonometric functions, support vector machines, genetic algorithms, root mean square error

Procedia PDF Downloads 462

Authors: Yiea Funk Te, Daniel Mueller, Irena Pletikosa Cvijikj

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Small and medium enterprises (SMEs) play an important role in the economy of many countries. When the overall world economy is considered, SMEs represent 95% of all businesses in the world, accounting for 66% of the total employment. Existing studies show that the current business environment is characterized as highly turbulent and strongly influenced by modern information and communication technologies, thus forcing SMEs to experience more severe challenges in maintaining their existence and expanding their business. To support SMEs at improving their competitiveness, researchers recently turned their focus on applying data mining techniques to build risk and growth prediction models. However, data used to assess risk and growth indicators is primarily obtained via questionnaires, which is very laborious and time-consuming, or is provided by financial institutes, thus highly sensitive to privacy issues. Recently, web mining (WM) has emerged as a new approach towards obtaining valuable insights in the business world. WM enables automatic and large scale collection and analysis of potentially valuable data from various online platforms, including companies’ websites. While WM methods have been frequently studied to anticipate growth of sales volume for e-commerce platforms, their application for assessment of SME risk and growth indicators is still scarce. Considering that a vast proportion of SMEs own a website, WM bears a great potential in revealing valuable information hidden in SME websites, which can further be used to understand SME risk and growth indicators, as well as to enhance current SME risk and growth prediction models. This study aims at developing an automated system to collect business-relevant data from the Web and predict future growth trends of SMEs by means of WM and data mining techniques. The envisioned system should serve as an 'early recognition system' for future growth opportunities. In an initial step, we examine how structured and semi-structured Web data in governmental or SME websites can be used to explain the success of SMEs. WM methods are applied to extract Web data in a form of additional input features for the growth prediction model. The data on SMEs provided by a large Swiss insurance company is used as ground truth data (i.e. growth-labeled data) to train the growth prediction model. Different machine learning classification algorithms such as the Support Vector Machine, Random Forest and Artificial Neural Network are applied and compared, with the goal to optimize the prediction performance. The results are compared to those from previous studies, in order to assess the contribution of growth indicators retrieved from the Web for increasing the predictive power of the model.

Keywords: data mining, SME growth, success factors, web mining

Procedia PDF Downloads 269

Authors: S. Jakšić, B. Žmuk

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The paper investigates the key factors of export dynamics for a set of Central and Southeast European (CSEE) countries in the context of current economic and financial crisis. In order to model the export dynamics a Global Vector Auto Regressive (GVAR) model is defined. As opposed to models which model each country separately, the GVAR combines all country models in a global model which enables obtaining important information on spill-over effects in the context of globalization and rising international linkages. The results of the study indicate that for most of the CSEE countries, exports are mainly driven by domestic shocks, both in the short run and in the long run. This study is the first application of the GVAR model to studying the export dynamics in the CSEE countries and therefore the results of the study present an important empirical contribution.

Keywords: export, GFEVD, global VAR, international trade, weak exogeneity

Procedia PDF Downloads 302

Authors: Adem Peker, Yüksel Eroğlu, İsmail Ay

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As the information and communication technologies have become embedded in everyday life of adolescents, both their possible benefits and risks to adolescents are being identified. The information and communication technologies provide opportunities for adolescents to connect with peers and to access to information. However, as with other social connections, users of information and communication devices have the potential to meet and interact with in harmful ways. One emerging example of such interaction is cyber bullying. Cyber bullying occurs when someone uses the information and communication technologies to harass or embarrass another person. Cyber bullying can take the form of malicious text messages and e-mails, spreading rumours, and excluding people from online groups. Cyber bullying has been linked to psychological problems for cyber bullies and victims. Therefore, it is important to determine how internet addiction contributes to cyber bullying. Building on this question, this study takes a closer look at the relationship between internet addiction and cyber bullying. For this purpose, in this study, based on descriptive relational model, it was hypothesized that loss of control, excessive desire to stay online, and negativity in social relationships, which are dimensions of internet addiction, would be associated positively with cyber bullying and victimization. Participants were 383 high school students (176 girls and 207 boys; mean age, 15.7 years). Internet addiction was measured by using Internet Addiction Scale. The Cyber Victim and Bullying Scale was utilized to measure cyber bullying and victimization. The scales were administered to the students in groups in the classrooms. In this study, stepwise regression analyses were utilized to examine the relationships between dimensions of internet addiction and cyber bullying and victimization. Before applying stepwise regression analysis, assumptions of regression were verified. According to stepwise regression analysis, cyber bullying was predicted by loss of control (β=.26, p<.001) and negativity in social relationships (β=.13, p<.001). These variables accounted for 9 % of the total variance, with the loss of control explaining the higher percentage (8 %). On the other hand, cyber victimization was predicted by loss of control (β=.19, p<.001) and negativity in social relationships (β=.12, p<.001). These variables altogether accounted for 8 % of the variance in cyber victimization, with the best predictor loss of control (7 % of the total variance). The results of this study demonstrated that, as expected, loss of control and negativity in social relationships predicted cyber bullying and victimization positively. However, excessive desire to stay online did not emerge a significant predictor of both cyberbullying and victimization. Consequently, this study would enhance our understanding of the predictors of cyber bullying and victimization since the results proposed that internet addiction is related with cyber bullying and victimization.

Keywords: cyber bullying, internet addiction, adolescents, regression

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Authors: Niklas Panten, Eberhard Abele

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This paper presents a novel approach for real-time and near-optimal control of industrial smart grids by deep reinforcement learning (DRL). To achieve highly energy-efficient factory systems, the energetic linkage of machines, technical building equipment and the building itself is desirable. However, the increased complexity of the interacting sub-systems, multiple time-variant target values and stochastic influences by the production environment, weather and energy markets make it difficult to efficiently control the energy production, storage and consumption in the hybrid industrial smart grids. The studied deep reinforcement learning approach allows to explore the solution space for proper control policies which minimize a cost function. The deep neural network of the DRL agent is based on a multilayer perceptron (MLP), Long Short-Term Memory (LSTM) and convolutional layers. The agent is trained within multiple Modelica-based factory simulation environments by the Advantage Actor Critic algorithm (A2C). The DRL controller is evaluated by means of the simulation and then compared to a conventional, rule-based approach. Finally, the results indicate that the DRL approach is able to improve the control performance and significantly reduce energy respectively operating costs of industrial smart grids.

Keywords: industrial smart grids, energy efficiency, deep reinforcement learning, optimal control

Procedia PDF Downloads 197

Authors: Felix Oromena Egara, Moeketsi Mosia

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The study aimed to evaluate the impact of blended learning on mathematics retention among secondary school students. Conducted in the Isoko North Local Government Area of Delta State, Nigeria, the research involved 1,235 senior class one (SS 1) students. Employing a non-equivalent control group pre-test-post-test quasi-experimental design, a sample of 70 students was selected from two secondary schools with ICT facilities through purposive sampling. Random allocation of students into experimental and control groups was achieved through balloting within each selected school. The investigation included three assessment points: pre-Mathematics Achievement Test (MAT), post-MAT, and post-post-MAT (retention), administered systematically by the researchers. Data collection utilized the established MAT instrument, which demonstrated a high reliability score of 0.86. Statistical analysis was conducted using the Statistical Package for Social Sciences (SPSS) version 28, with mean and standard deviation addressing study questions and analysis of covariance scrutinizing hypotheses at a significance level of .05. Results revealed significantly greater improvements in mathematics retention scores among students exposed to blended learning compared to those instructed through conventional methods. Moreover, noticeable differences in mean retention scores were observed, with male students in the blended learning group exhibiting notably higher performance. Based on these findings, recommendations were made, advocating for mathematics educators to integrate blended learning, particularly in geometry teaching, to enhance students’ retention of mathematical concepts.

Keywords: blended learning, flipped classroom model, secondary school students, station rotation model

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Authors: Wei Hu, Wenguang Chen, Chong Dong

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In order to improve the efficiency and safety of photovoltaic power generation devices, this photovoltaic power generation system combines Artificial Intelligence (AI) and the Internet of Things (IoT) to control the chasing photovoltaic power generation device to track the sun to improve power generation efficiency and then convert energy management. The system uses artificial intelligence as the control terminal, the power generation device executive end uses the Linux system, and Exynos4412 is the CPU. The power generating device collects the sun image information through Sony CCD. After several power generating devices feedback the data to the CPU for processing, several CPUs send the data to the artificial intelligence control terminal through the Internet. The control terminal integrates the executive terminal information, time information, and environmental information to decide whether to generate electricity normally and then whether to convert the converted electrical energy into the grid or store it in the battery pack. When the power generation environment is abnormal, the control terminal authorizes the protection strategy, the power generation device executive terminal stops power generation and enters a self-protection posture, and at the same time, the control terminal synchronizes the data with the cloud. At the same time, the system is more intelligent, more adaptive, and longer life.

Keywords: photo-voltaic power generation, the pursuit of light, artificial intelligence, internet of things, photovoltaic array, power management

Procedia PDF Downloads 126

Authors: Yi Yau, Chia-Huei Hsiao

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The elderly society is already a problem of globalization, and Taiwan will enter a super-aged society in 2025. Therefore, how to improve the health of the elderly and reduce the government's social burden is an important issue at present. Exercise is the best medical care, and it is also a healthy activity for people to live a healthy life. Facing the super-aged society in the future, it is necessary to attract them to participate in sports voluntarily through sports promotion so that they can live healthy and independent lives and continue to participate in society to enhance the well-being of the elderly. Experiential marketing and sports participation are closely related. In the past, it was mainly aimed at consumer behavior at the commercial level. At present, there are not many study objects focusing on participant behavior and middle-aged and elderly people. Therefore, this study takes the news emerged sport-Pickleball that has been loved by silver-haired people in recent years as the research sport. It uses questionnaire surveys and intentional sampling methods. The purpose of the group is to understand the middle-aged and elderly people’s experience and behavior patterns of Pickleball, explore the relationship between experiential marketing and participants' intentional behaviors, and predict which aspects of experiential marketing will affect their intentional behaviors. The findings showed that experience marketing is highly positively correlated with behavioral intentions, and experience marketing has a positive predictive power for behavioral intentions. Among them, "ACT" and "SENSE" are predictive variables that effectively predict behavioral intentions. This study proves the feasibility of pickleball for middle-aged and senior sports. It is recommended that in the future curriculum planning, try to simplify the exercise steps, increase the chances of contact with the sphere, and enhance the sensory experience to enhance the sense of success during exercise, and then generate exercise motivation, and ultimately change the exercise mode or habits and promote health.

Keywords: newly emerged sports, middle age and elderly, health promotion, ACT, SENSE

Procedia PDF Downloads 157

Authors: Akhtar Nawaz, Sahar Noor, Iftikhar Hussain

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This paper considers advanced hybrid manufacturing planning both push and pull system in which each customer order has a due date by demand forecast and customer orders. We present a tool for model for tool development that requires an absolute due dates and customer orders in a manufacturing supply chain. It is vital for the manufacturing companies to face the problem of variations in demands, increase in varieties by maintaining safety stock and to minimize components obsolescence and uselessness. High inventory cost and low delivery lead time is expected in push type of system and on contrary high delivery lead time and low inventory cost is predicted in the pull type. For this tool for model we need an MRP system for the push and pull environment and control of inventories in push parts and lead time in the pull part. To retain process data quickly, completely and to improve responsiveness and minimize inventory cost, a tool is required to deal with the high product variance and short cycle parts. In practice, planning and scheduling are interrelated and should be solved simultaneously with supply chain to ensure that the due dates of customer orders are met. The proposed tool for model considers alternative process plans for job types, with precedence constraints for job operations. Such a tool for model has not been treated in the literature. To solve the model, tool was developed, so a new technique was required to deal with the issue of high product variance and short life cycles in assemble to order.

Keywords: hybrid manufacturing system, supply chain system, make to order, make to stock, assemble to order

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Authors: Maico R. Severino, Laura G. Caixeta, Nadine M. Costa, Raísa L. T. Napoleão, Éverton F. V. Valle, Diego D. Calixto, Danielle Oliveira

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One of the biggest barriers that companies find nowadays is the coordination of production flow in their Supply Chains (SC). In this study, coordination is understood as a mechanism for incorporating the entire production channel, with everyone involved focused on achieving the same goals. Sometimes, this coordination is attempted by the use of logistics practices or production plan and control methods. No papers were found in the literature that presented the combined use of logistics practices and production plan and control methods. The main objective of this paper is to propose solutions for six case studies combining logistics practices and Ordering Systems (OS). The methodology used in this study was a conceptual model of decision making. This model contains six phases: a) the analysis the types and characteristics of relationships in the SC; b) the choice of the OS; c) the choice of the logistics practices; d) the development of alternative proposals of combined use; e) the analysis of the consistency of the chosen alternative; f) the qualitative and quantitative assessment of the impact on the coordination of the production flow and the verification of applicability of the proposal in the real case. This study was conducted on six Brazilian SC of different sectors: footwear, food and beverages, garment, sugarcane, mineral and metal mechanical. The results from this study showed that there was improvement in the coordination of the production flow through the following proposals: a) for the footwear industry the use of Period Bath Control (PBC), Quick Response (QR) and Enterprise Resource Planning (ERP); b) for the food and beverage sector firstly the use of Electronic Data Interchange (EDI), ERP, Continuous Replenishment (CR) and Drum-Buffer-Rope Order (DBR) (for situations in which the plants of both companies are distant), and secondly EDI, ERP, Milk-Run and Review System Continues (for situations in which the plants of both companies are close); c) for the garment industry the use of Collaborative Planning, Forecasting, and Replenishment (CPFR) and Constant Work-In-Process (CONWIP) System; d) for the sugarcane sector the use of EDI, ERP and CONWIP System; e) for the mineral processes industry the use of Vendor Managed Inventory (VMI), EDI and MaxMin Control System; f) for the metal mechanical sector the use of CONWIP System and Continuous Replenishment (CR). It should be emphasized that the proposals are exclusively recommended for the relationship between client and supplier studied. Therefore, it cannot be generalized to other cases. However, what can be generalized is the methodology used to choose the best practices for each case. Based on the study, it can be concluded that the combined use of OS and logistics practices enable a better coordination of flow production on SC.

Keywords: supply chain management, production flow coordination, logistics practices, ordering systems

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Authors: Radwa Elshorbagy, Alaa Elden Balbaa, Khaled Ayad, Waleed Reda

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Introduction: Ankle sprain is a common lower limb injury that is complicated by high recurrence rate. The cause of recurrence is not clear; however, changes in motor control have been postulated. Objective: to determine the contribution of proximal hip strategy to dynamic postural control in patients with recurrent ankle sprain. Methods: Fifteen subjects with recurrent ankle sprain (group A) and fifteen healthy control subjects (group B) participated in this study. Abductor-adductors as well as flexor-extensor hip musculatures control was abolished by fatigue using the Biodex Isokinetic System. Dynamic postural control was measured before and after fatigue by the Biodex Balance System. Results: Repeated measures MANOVA was used to compare between and within group differences, in group A fatiguing of hip muscles (flexors-extensors and abductors-adductors) increased overall stability index (OASI), anteroposterior stability index (APSI) and mediolateral stability index (MLSI) significantly (p=0.00) whereas; in group B fatiguing of hip flexors-extensors increased significantly OASI and APSI only (p= 0.017, 0.010; respectively) while fatiguing of hip abductors-adductors has no significant effect on these variables. Moreover, patients with ankle sprain had significantly lower dynamic balance after hip muscles fatigue compared to the control group. Specifically, after hip flexor-extensor fatigue, the OASI, APSI and MLSI were increased significantly than those of the control values (p= 0.002, 0.011, and 0.003, respectively) whereas fatiguing of hip abductors-adductors increased significantly in OASI and APSI only (p=0.012, 0.026, respectively). Conclusion: To maintain dynamic balance, patients with recurrent ankle sprain seem to rely more on the hip strategy. This means that those patients depend on a top to down instead of down to top strategy clinical relevance: patients with recurrent ankle sprain less efficient in maintaining the dynamic postural control due to the change in motor strategies. Indicating that health care providers and rehabilitation specialists should treat CAI as a global/central and not just as a simple local or peripheral injury.

Keywords: hip strategy, ankle strategy, postural control, dynamic balance

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Authors: Yash Jain

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The US Center for Disease Control has recommended wearing masks to slow the spread of the virus. The research uses a video feed from a camera to conduct real-time classifications of whether or not a human is correctly wearing a mask, incorrectly wearing a mask, or not wearing a mask at all. Utilizing two distinct datasets from the open-source website Kaggle, a mask detection network had been trained. The first dataset that was used to train the model was titled 'Face Mask Detection' on Kaggle, where the dataset was retrieved from and the second dataset was titled 'Face Mask Dataset, which provided the data in a (YOLO Format)' so that the TinyYoloV3 model could be trained. Based on the data from Kaggle, two machine learning models were implemented and trained: a Tiny YoloV3 Real-time model and a two-stage neural network classifier. The two-stage neural network classifier had a first step of identifying distinct faces within the image, and the second step was a classifier to detect the state of the mask on the face and whether it was worn correctly, incorrectly, or no mask at all. The TinyYoloV3 was used for the live feed as well as for a comparison standpoint against the previous two-stage classifier and was trained using the darknet neural network framework. The two-stage classifier attained a mean average precision (MAP) of 80%, while the model trained using TinyYoloV3 real-time detection had a mean average precision (MAP) of 59%. Overall, both models were able to correctly classify stages/scenarios of no mask, mask, and incorrectly worn masks.

Keywords: datasets, classifier, mask-detection, real-time, TinyYoloV3, two-stage neural network classifier

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Authors: Alexander Buhr, Klaus Ehrenfried

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Trackside induced airflow velocities, also known as slipstream velocities, are an important criterion for the design of high-speed trains. The maximum permitted values are given by the Technical Specifications for Interoperability (TSI) and have to be checked in the approval process. For train manufactures it is of great interest to know in advance, how new train geometries would perform in TSI tests. The Reynolds number in moving model experiments is lower compared to full-scale. Especially the limited model length leads to a thinner boundary layer at the rear end. The hypothesis is that the boundary layer rolls up to characteristic flow structures in the train wake, in which the maximum flow velocities can be observed. The idea is to enlarge the boundary layer using roughness elements at the train model head so that the ratio between the boundary layer thickness and the car width at the rear end is comparable to a full-scale train. This may lead to similar flow structures in the wake and better prediction accuracy for TSI tests. In this case, the design of the roughness elements is limited by the moving model rig. Small rectangular roughness shapes are used to get a sufficient effect on the boundary layer, while the elements are robust enough to withstand the high accelerating and decelerating forces during the test runs. For this investigation, High-Speed Particle Image Velocimetry (HS-PIV) measurements on an ICE3 train model have been realized in the moving model rig of the DLR in Göttingen, the so called tunnel simulation facility Göttingen (TSG). The flow velocities within the boundary layer are analysed in a plain parallel to the ground. The height of the plane corresponds to a test position in the EN standard (TSI). Three different shapes of roughness elements are tested. The boundary layer thickness and displacement thickness as well as the momentum thickness and the form factor are calculated along the train model. Conditional sampling is used to analyse the size and dynamics of the flow structures at the time of maximum velocity in the train wake behind the train. As expected, larger roughness elements increase the boundary layer thickness and lead to larger flow velocities in the boundary layer and in the wake flow structures. The boundary layer thickness, displacement thickness and momentum thickness are increased by using larger roughness especially when applied in the height close to the measuring plane. The roughness elements also cause high fluctuations in the form factors of the boundary layer. Behind the roughness elements, the form factors rapidly are approaching toward constant values. This indicates that the boundary layer, while growing slowly along the second half of the train model, has reached a state of equilibrium.

Keywords: boundary layer, high-speed PIV, ICE3, moving train model, roughness elements

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Authors: Erika Tatiana Ruiz, Wilson Adarme Jaimes

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This abstract presents the methodology for improving the logistics processes of agricultural production units belonging to the coffee, cocoa, and fruit sectors, starting from the fundamental concepts and detailing each of the phases to carry out the diagnosis, which will be the basis for the formulation of its action plan and implementation of the maturity model. As a result of this work, the maturity model is formulated to improve logistics processes. This model seeks to: generate a progressive model that is useful for all productive units belonging to these sectors at the national level, regardless of their initial conditions, focus on the improvement of logistics processes as a strategy that contributes to improving the competitiveness of the agricultural sector in Colombia and spread the implementation of good logistics practices in postharvest in all departments of the country through autonomous tools. This model has been built through a series of steps that allow the evaluation and improvement of the logistics dimensions or indicators. The potential improvements for each dimension provide the foundation on which to advance to the next level. Within the maturity model, a methodology is indicated for the design and execution of strategies to improve its logistics processes, taking into account the current state of each production unit.

Keywords: agroindustrial, characterization, logistics, maturity model, processes

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Authors: Alvarez M., Chapela M. J., Balboa E., Rubianes D., Sinde E., Fernandez de Ana C., Rodríguez-Blanco A.

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The gut microbiota plays an important role as gut inflammation could contribute to colorectal cancer development; however, this role is still not fully understood, and tools able to prevent this progression are yet to be developed. The main objective of this study was to monitor the effects of a mushroom extracts formulation in gut microbial community composition of an Azoxymethane (AOM)/Dextran sodium sulfate (DSS) mice model of inflammation-linked carcinogenesis. For the in vivo study, 41 adult male mice of the C57BL / 6 strain were obtained. 36 of them have been induced in a state of colon carcinogenesis by a single intraperitoneal administration of AOM at a dose of 12.5 mg/kg; the control group animals received instead of the same volume of 0.9% saline. DSS is an extremely toxic polysaccharide sulfate that causes chronic inflammation of the colon mucosa, favoring the appearance of severe colitis and the production of tumors induced by AOM. Induction by AOM/DSS is an interesting platform for chemopreventive intervention studies. This time the model was used to monitor gut microbiota changes as a result of supplementation with a specific mushroom extracts formulation previously shown to have prebiotic activity. The animals have been divided into three groups: (i) Cancer + mushroom extracts formulation experimental group: to which the MicoDigest2.0 mushroom extracts formulation developed by Hifas da Terra S.L has been administered dissolved in drinking water at an estimated concentration of 100 mg / ml. (ii) Control group of animals with Cancer: to which normal water has been administered without any type of treatment. (iii) Control group of healthy animals: these are the animals that have not been induced cancer or have not received any treatment in drinking water. This treatment has been maintained for a period of 3 months, after which the animals were sacrificed to obtain tissues that were subsequently analyzed to verify the effects of the mushroom extract formulation. A microbiological analysis has been carried out to compare the microbial communities present in the intestines of the mice belonging to each of the study groups. For this, the methodology of massive sequencing by molecular analysis of the 16S gene has been used (Ion Torrent technology). Initially, DNA extraction and metagenomics libraries were prepared using the 16S Metagenomics kit, always following the manufacturer's instructions. This kit amplifies 7 of the 9 hypervariable regions of the 16S gene that will then be sequenced. Finally, the data obtained will be compared with a database that makes it possible to determine the degree of similarity of the sequences obtained with a wide range of bacterial genomes. Results obtained showed that, similarly to certain natural compounds preventing colorectal tumorigenesis, a mushroom formulation enriched the Firmicutes and Proteobacteria phyla and depleted Bacteroidetes. Therefore, it was demonstrated that the consumption of the mushroom extracts’ formulation developed could promote the recovery of the microbial balance that is disrupted in the mice model of carcinogenesis. More preclinical and clinical studies are needed to validate this promising approach.

Keywords: carcinogenesis, microbiota, mushroom extracts, inflammation

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Authors: Toby Li, Julian Zhu

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In this paper, a model based on a simplified geometry is introduced to give a very conservative collision probability prediction for the Starlink satellite in its most densely clustered region. Under the model in this paper, the probability of collision for Starlink satellite where it clustered most densely is found to be 8.484 ∗ 10^−4. It is found that the predicted collision probability increased nonlinearly with the increased safety distance set. This simple model provides evidence that the continuous development of maneuver avoidance systems is necessary for the future of the orbital safety of satellites under the harsher Lower Earth Orbit environment.

Keywords: Starlink, collision probability, debris, geometry model

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Authors: A. E. Kobryn

Abstract:

We present both descriptive and predictive modeling of structural properties of blends of PCBM or organic-inorganic hybrid perovskites of the type CH3NH3PbX3 (X=Cl, Br, I) with P3HT, P3BT or squaraine SQ2 dye sensitizer, including adsorption on TiO2 clusters having rutile (110) surface. In our study, we use a methodology that allows computing the microscopic structure of blends on the nanometer scale and getting insight on miscibility of its components at various thermodynamic conditions. The methodology is based on the integral equation theory of molecular liquids in the reference interaction site representation/model (RISM) and uses the universal force field. Input parameters for RISM, such as optimized molecular geometries and charge distribution of interaction sites, are derived with the use of the density functional theory methods. To compare the diffusivity of the PCBM in binary blends with P3HT and P3BT, respectively, the study is complemented with MD simulation. A very good agreement with experiment and the reports of alternative modeling or simulation is observed for PCBM in P3HT system. The performance of P3BT with perovskites, however, seems as expected. The calculated nanoscale morphologies of blends of P3HT, P3BT or SQ2 with perovskites, including adsorption on TiO2, are all new and serve as an instrument in rational design of organic/hybrid photovoltaics. They are used in collaboration with experts who actually make prototypes or devices for practical applications.

Keywords: multiscale theory and modeling, nanoscale morphology, organic-inorganic halide perovskites, three dimensional distribution

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Authors: Magda I. Hassan

Abstract:

This paper aims to investigate the health effects of aspartame on weanling male hamsters. 20 Golden Syrian hamsters drank only water (control) or water with 6, 11, and 18 mg aspartame/kg of body weight per day for 42 days. Food intake, weight gain, glucose blood level, and lipid profile were determined at the end of the experiment. The animals were sacrificed and histopathological examination of organs (liver, brain and heart) was done. Results revealed that animals in Asp.groups consumed significantly larger amount of food than the control (13.4±5.9, 8.6±2.5 and 8.8±3.0 vs 4.2±2.5 g/day, in succession). Hamsters in the control group showed higher total cholesterol and HDL levels than hamsters in aspartame 6, 11, 18 groups (160±19 vs 101±13, 130±22, 141±15 mg/dl & 144±9 vs 120±12, 118±13, 99±17 respectively (P<0•05)). The control group showed a glucose concentration below those of aspartame groups, indicating no effect of aspartame on glucose blood level. While, there were no significant differences in the triglycerides and LDL levels between control group and Asp.groups. Histopathological changes were observed, especially in brain and liver cells. Aspartame increases appetite and weight gain of young hamsters. Therefore, FDA should reconsider the acceptable daily intake (ADI) of aspartame for children.

Keywords: aspartame, brain, food intake, hamsters

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Authors: Bummo Ahn

Abstract:

Palpation is widely used to measure soft tissue firmness or stiffness in the living condition in order to apply detection, diagnosis, and treatment of tumors, scar tissue, abnormal muscle tone, or muscle spasticity. Since these methods are subjective and depend on the proficiency level, it is concluded that there are other diagnoses depending on the condition of the experts and the results are not objective. The mechanical property obtained by using the elasticity of the tissue is important to calculate a predictive variable for monitoring abnormal tissues. If the mechanical load such as reaction force on the foot increases in the same region under the same conditions, the mechanical property of the tissue is changed. Therefore, objective diagnosis is possible not only for experts but also for patients using this quantitative information. Furthermore, the portable system also allows non-experts to easily diagnose at home, not in hospitals or institutions. In this paper, we introduce a portable palpation system that can be used to measure the mechanical properties of human tissue, which can be applied to monitor diabetic foot ulceration patients with measuring the mechanical property change of foot tissue. The system was designed to be smaller and portable in comparison with the conventional palpation systems. It is consists of the probe, the force sensor, linear actuator, micro control unit, the display module, battery, and housing. Using this system, we performed validation experiments by applying different palpations (3 and 5 mm) to soft tissue (silicone rubber) and measured reaction forces. In addition, we estimated the elastic moduli of the soft tissue against different palpations and compare the estimated elastic moduli that show similar value even if the palpation depths are different.

Keywords: palpation probe, portable, diabetic foot ulceration, monitoring, mechanical property

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Authors: V. Mantey, N. Findlay, I. Maddox

Abstract:

The quality and quantity of global satellite data have been increasing exponentially in recent years as spaceborne systems become more affordable and the sensors themselves become more sophisticated. This is a valuable resource for many applications, including disaster management and relief. However, while more information can be valuable, the volume of data available is impossible to manually examine. Therefore, the question becomes how to extract as much information as possible from the data with limited manpower. Buildings are a key feature of interest in satellite imagery with applications including telecommunications, population models, and disaster relief. Machine learning tools are fast becoming one of the key resources to solve this problem, and models have been developed to detect buildings in optical satellite imagery. However, by and large, most models focus on affluent regions where buildings are generally larger and constructed further apart. This work is focused on the more difficult problem of detection in populated regions. The primary challenge with detecting small buildings in densely populated regions is both the spatial and spectral resolution of the optical sensor. Densely packed buildings with similar construction materials will be difficult to separate due to a similarity in color and because the physical separation between structures is either non-existent or smaller than the spatial resolution. This study finds that training models until they are overfitting the input sample can perform better in these areas than a more robust, generalized model. An overfitted model takes less time to fine-tune from a generalized pre-trained model and requires fewer input data. The model developed for this study has also been fine-tuned using existing, open-source, building vector datasets. This is particularly valuable in the context of disaster relief, where information is required in a very short time span. Leveraging existing datasets means that little to no manpower or time is required to collect data in the region of interest. The training period itself is also shorter for smaller datasets. Requiring less data means that only a few quality areas are necessary, and so any weaknesses or underpopulated regions in the data can be skipped over in favor of areas with higher quality vectors. In this study, a landcover classification model was developed in conjunction with the building detection tool to provide a secondary source to quality check the detected buildings. This has greatly reduced the false positive rate. The proposed methodologies have been implemented and integrated into a configurable production environment and have been employed for a number of large-scale commercial projects, including continent-wide DEM production, where the extracted building footprints are being used to enhance digital elevation models. Overfitted machine learning models are often considered too specific to have any predictive capacity. However, this study demonstrates that, in cases where input data is scarce, overfitted models can be judiciously applied to solve time-sensitive problems.

Keywords: building detection, disaster relief, mask-RCNN, satellite mapping

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