Search results for: post model selection

Authors: Adil Kaval, Digdem Muge Siyez

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With the negative effects of internet use in a person's life, the use of the Internet has become an issue. This subject was mostly considered as internet addiction, and it was investigated. In literature, it is noteworthy that some theoretical models have been proposed to explain the reasons for internet addiction. In addition to these theoretical models, it may be thought that the coping style for stressing events can be a predictor of internet addiction. It was aimed to test with logistic regression the effect of high school students' coping styles on internet addiction levels. Sample of the study consisted of 770 Turkish adolescents (471 girls, 299 boys) selected from high schools in the 2017-2018 academic year in İzmir province. Internet Addiction Test, Coping Scale for Child and Adolescents and a demographic information form were used in this study. The results of the logistic regression analysis indicated that the model of coping styles predicted internet addiction provides a statistically significant prediction of internet addiction. Gender does not predict whether or not to be addicted to the internet. The active coping style is not effective on internet addiction levels, while the avoiding and negative coping style are effective on internet addiction levels. With this model, % 79.1 of internet addiction in high school is estimated. The Negelkerke pseudo R2 indicated that the model accounted for %35 of the total variance. The results of this study on Turkish adolescents are similar to the results of other studies in the literature. It can be argued that avoiding and negative coping styles are important risk factors in the development of internet addiction.

Keywords: adolescents, coping, internet addiction, regression analysis

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Authors: Dino Carpentras, Alejandro Dinkelberg, Michael Quayle

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Within agent-based modeling, opinion dynamics is the field that focuses on modeling people's opinions. In this prolific field, most of the literature is dedicated to the exploration of the two 'degrees of freedom' and how they impact the model’s properties (e.g., the average final opinion, the number of final clusters, etc.). These degrees of freedom are (1) the interaction rule, which determines how agents update their own opinion, and (2) the network topology, which defines the possible interaction among agents. In this work, we show that the third degree of freedom exists. This can be used to change a model's output up to 100% of its initial value or to transform two models (both from the literature) into each other. Since opinion dynamics models are representations of the real world, it is fundamental to understand how people’s opinions can be measured. Even for abstract models (i.e., not intended for the fitting of real-world data), it is important to understand if the way of numerically representing opinions is unique; and, if this is not the case, how the model dynamics would change by using different representations. The process of measuring opinions is non-trivial as it requires transforming real-world opinion (e.g., supporting most of the liberal ideals) to a number. Such a process is usually not discussed in opinion dynamics literature, but it has been intensively studied in a subfield of psychology called psychometrics. In psychometrics, opinion scales can be converted into each other, similarly to how meters can be converted to feet. Indeed, psychometrics routinely uses both linear and non-linear transformations of opinion scales. Here, we analyze how this transformation affects opinion dynamics models. We analyze this effect by using mathematical modeling and then validating our analysis with agent-based simulations. Firstly, we study the case of perfect scales. In this way, we show that scale transformations affect the model’s dynamics up to a qualitative level. This means that if two researchers use the same opinion dynamics model and even the same dataset, they could make totally different predictions just because they followed different renormalization processes. A similar situation appears if two different scales are used to measure opinions even on the same population. This effect may be as strong as providing an uncertainty of 100% on the simulation’s output (i.e., all results are possible). Still, by using perfect scales, we show that scales transformations can be used to perfectly transform one model to another. We test this using two models from the standard literature. Finally, we test the effect of scale transformation in the case of finite precision using a 7-points Likert scale. In this way, we show how a relatively small-scale transformation introduces both changes at the qualitative level (i.e., the most shared opinion at the end of the simulation) and in the number of opinion clusters. Thus, scale transformation appears to be a third degree of freedom of opinion dynamics models. This result deeply impacts both theoretical research on models' properties and on the application of models on real-world data.

Keywords: degrees of freedom, empirical validation, opinion scale, opinion dynamics

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Authors: Gisele Ghanem Cardoso, Sandra Rech

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Despite recent advancements, the plus-size market still faces significant gaps, as individuals with larger bodies struggle to find clothing that fits well and meets their needs. Addressing this issue, this research aims to investigate the availability of fashion products for plus-size women in both physical and online stores in Florianópolis, as well as the quantity of products available in each size. The study employs content analysis based on Bardin's framework, examining data on store locations, size ranges, and target audiences of various brands alongside observations of visual elements such as hanger sizes and the branding of specialized labels. The findings reveal a concentration of plus-size stores in peripheral areas and a limited selection of diverse, high-quality products, contrasting sharply with the access standard-sized bodies have to more prestigious fashion hubs. These results highlight how the current market structure perpetuates social exclusion, underscoring the urgent need for inclusive policies and an expanded plus-size market to promote greater equity and representation in fashion consumption.

Keywords: plus size fashion, representation, consumption, Florianópolis, product availability, social exclusion

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Authors: Antoni Wibowo, Harry Pujianto, Dewi Retno Sari Saputro

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The stock market can provide huge profits in a relatively short time in financial sector; however, it also has a high risk for investors and traders if they are not careful to look the factors that affect the stock market. Therefore, they should give attention to the dynamic fluctuations and movements of the stock market to optimize profits from their investment. In this paper, we present a nonlinear autoregressive exogenous model (NARX) to predict the movements of stock market; especially, the movements of the closing price index. As case study, we consider to predict the movement of the closing price in Indonesia composite index (IHSG) and choose the best structures of NARX for IHSG’s prediction.

Keywords: NARX (Nonlinear Autoregressive Exogenous Model), prediction, stock market, time series

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Authors: T. Walczak, J. K. Grabski, P. Fritzkowski, M. Stopa

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Objectives of the study are i) to perform numerical simulations that permit an analysis of the dynamics of subretinal fluid when an implant has induced scleral intussusception and ii) assess the impact of the physical parameters of the model on the flow rate. Computer simulations were created using finite element method (FEM) based on a model that takes into account the interaction of a viscous fluid (subretinal fluid) with a hyperelastic body (retina). The purpose of the calculation was to investigate the dependence of the flow rate of subretinal fluid through a hole in the retina on different factors such as viscosity of subretinal fluid, material parameters of the retina, and the offset of the implant from the retina’s hole. These simulations were performed for different speeds of eye movement that reflect the behavior of the eye when reading, REM, and saccadic movements. Similar to other works in the field of subretinal fluid flow, it was assumed stationary, single sided, forced fluid flow in the considered area simulating the subretinal space. Additionally, a hyperelastic material model of the retina and parameterized geometry of the considered model was adopted. The calculations also examined the influence the direction of the force of gravity due to the position of the patient’s head on the trend of outflow of fluid. The simulations revealed that fluid outflow from the retina becomes significant with eyeball movement speed of 100°/sec. This speed is greater than in the case of reading but is four times less than saccadic movement. The increase of viscosity of the fluid increased beneficial effect. Further, the simulation results suggest that moderate eye movement speed is optimal and that the conventional prescription of the avoidance of routine eye movement following retinal detachment surgery should be relaxed. Additionally, to verify numerical results, some calculations were repeated with use of meshless method (method of fundamental solutions), which is relatively fast and easy to implement. The paper has been supported by 02/21/DSPB/3477 grant.

Keywords: CFD simulations, FEM analysis, meshless method, retinal detachment

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Authors: Amritee Bora, B. S. Mipun

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Land degradation is often used to describe the land environmental phenomena that reduce land’s original productivity both qualitatively and quantitatively. The study of land degradation vulnerability primarily deals with “Environmentally Sensitive Areas” (ESA) and the amount of topsoil loss due to erosion. In many studies, it is observed that the assessment of the existing status of land degradation is used to represent the vulnerability. Moreover, it is also noticed that in most studies, the primary emphasis of land degradation vulnerability is to assess its sensitivity to soil erosion only. However, the concept of land degradation vulnerability can have different objectives depending upon the perspective of the study. It shows the extent to which changes in land use land cover can imprint their effect on the land. In other words, it represents the susceptibility of a piece of land to degrade its productive quality permanently or in the long run. It is also important to mention that the vulnerability of land degradation is not a single factor outcome. It is a probability assessment to evaluate the status of land degradation and needs to consider both biophysical and human induce parameters. To avoid the complexity of the previous models in this regard, the present study has emphasized on to generate a simplified model to assess the land degradation vulnerability in terms of its current human population pressure, land use practices, and existing biophysical conditions. It is a “Mixed-Method” termed as the land degradation vulnerability index (LDVi). It was originally inspired by the MEDALUS model (Mediterranean Desertification and Land Use), 1999, and Farazadeh’s 2007 revised version of it. It has followed the guidelines of Space Application Center, Ahmedabad / Indian Space Research Organization for land degradation vulnerability. The model integrates the climatic index (Ci), vegetation index (Vi), erosion index (Ei), land utilization index (Li), population pressure index (Pi), and cover management index (CMi) by giving equal weightage to each parameter. The final result shows that the very high vulnerable zone primarily indicates three (3) prominent circumstances; land under continuous population pressure, high concentration of human settlement, and high amount of topsoil loss due to surface runoff within the study sites. As all the parameters of the model are amalgamated with equal weightage further with the help of regression analysis, the LDVi model also provides a strong grasp of each parameter and how far they are competent to trigger the land degradation process.

Keywords: population pressure, land utilization, soil erosion, land degradation vulnerability

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Authors: Michael Stewart

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Mutation testing is a type of software testing proposed in the 1970s where program statements are deliberately changed to introduce simple errors so that test cases can be validated to determine if they can detect the errors. Test cases are executed against the mutant code to determine if one fails, detects the error and ensures the program is correct. One major issue with this type of testing was it became intensive computationally to generate and test all possible mutations for complex programs. This paper used reinforcement learning and parallel processing within the context of mutation testing for the selection of mutation operators and test cases that reduced the computational cost of testing and improved test suite effectiveness. Experiments were conducted using sample programs to determine how well the reinforcement learning-based algorithm performed with one live mutation, multiple live mutations and no live mutations. The experiments, measured by mutation score, were used to update the algorithm and improved accuracy for predictions. The performance was then evaluated on multiple processor computers. With reinforcement learning, the mutation operators utilized were reduced by 50 – 100%.

Keywords: automated-testing, machine learning, mutation testing, parallel processing, reinforcement learning, software engineering, software testing

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Authors: Sherif Fayez Lewis Ghaly

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up to datefacupupdated planning has the mission of designing merchandise, plant life, procedures, enterprise, regions, and the development of a up to date. The requirements for up-to-date planning and the constructing of a updated have changed in recent years. everyday restructuring is turning inupupdated greater essential up-to-date hold the competitiveness of a manufacturing facilityupdated. restrictions in new regions, shorter existence cycles of product and manufacturing generation up-to-date a VUCA global (Volatility, Uncertainty, Complexity & Ambiguity) up-to-date greater frequent restructuring measures inside a manufacturing facilityupdated. A virtual up-to-date model is the making plans basis for rebuilding measures and up-to-date an fundamental up-to-date. short-time period rescheduling can now not be handled through on-web site inspections and manual measurements. The tight time schedules require 3177227fc5dac36e3e5ae6cd5820dcaa making plans fashions. updated the high variation fee of facup-to-dateries defined above, a method for rescheduling facupdatedries on the idea of a modern-day digital up to datery dual is conceived and designed for sensible software in updated restructuring projects. the point of interest is on rebuild approaches. The purpose is up-to-date preserve the planning basis (virtual up-to-date model) for conversions within a up to datefacupupdated updated. This calls for the application of a methodology that reduces the deficits of present techniques. The goal is up-to-date how a digital up to datery version may be up to date up to date during ongoing up to date operation. a method up-to-date on phoup to dategrammetry technology is presented. the focus is on developing a easy and fee-powerful up to date tune the numerous adjustments that arise in a manufacturing unit constructing in the course of operation. The method is preceded with the aid of a hardware and software assessment up-to-date become aware of the most cost effective and quickest version.

Keywords: building information modeling, digital factory model, factory planning, maintenance digital factory model, photogrammetry, restructuring

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Authors: Nisar Ahmed Memon

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A new medical image watermarking scheme delivering high embedding capacity is presented in this paper. Integer Wavelet Transform (IWT), Companding technique and adaptive thresholding are used in this scheme. The proposed scheme implants, recovers the hidden information and restores the input image to its pristine state at the receiving end. Magnetic Resonance Imaging (MRI) images are used for experimental purposes. The scheme first segment the MRI medical image into non-overlapping blocks and then inserts watermark into wavelet coefficients having a high frequency of each block. The scheme uses block-based watermarking adopting iterative optimization of threshold for companding in order to avoid the histogram pre and post processing. Results show that proposed scheme performs better than other reversible medical image watermarking schemes available in literature for MRI medical images.

Keywords: adaptive thresholding, companding technique, data authentication, reversible watermarking

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Authors: H. Namazi, H. T. N. Kuan

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Fick's first law relates the diffusive flux to the concentration field, by postulating that the flux goes from regions of high concentration to regions of low concentration, with a magnitude that is proportional to the concentration gradient (spatial derivative). It is clear that the diffusion of flux cannot be instantaneous and should be some time delay in this propagation. But Fick’s first law doesn’t consider this delay which results in some errors especially when there is a considerable time delay in the process. In this paper, we introduce a time delay to Fick’s first law. By this modification, we consider that the diffusion of flux cannot be instantaneous. In order to verify this claim an application sample in fluid diffusion is discussed and the results of modified Fick’s first law, Fick’s first law and the experimental results are compared. The results of this comparison stand for the accuracy of the modified model. The modified model can be used in any application where the time delay has considerable value and neglecting its effect reflects in undesirable results.

Keywords: Fick's first law, flux, diffusion, time delay, modified Fick’s first law

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Authors: Ji-Seok Koo, Hee‑Yong Kwon, Hui-Young Yun, Kyung-Hui Wang, Youn-Seo Koo

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This paper presents a forecasting system for PM2.5 levels in Seoul, South Korea, leveraging a combination of chemical transport modeling and ConvLSTM-DNN machine learning technology. Exposure to PM2.5 has known detrimental impacts on public health, making its prediction crucial for establishing preventive measures. Existing forecasting models, like the Community Multiscale Air Quality (CMAQ) and Weather Research and Forecasting (WRF), are hindered by their reliance on uncertain input data, such as anthropogenic emissions and meteorological patterns, as well as certain intrinsic model limitations. The system we've developed specifically addresses these issues by integrating machine learning and using carefully selected input features that account for local and distant sources of PM2.5. In South Korea, the PM2.5 concentration is greatly influenced by both local emissions and long-range transport from China, and our model effectively captures these spatial and temporal dynamics. Our PM2.5 prediction system combines the strengths of advanced hybrid machine learning algorithms, convLSTM and DNN, to improve upon the limitations of the traditional CMAQ model. Data used in the system include forecasted information from CMAQ and WRF models, along with actual PM2.5 concentration and weather variable data from monitoring stations in China and South Korea. The system was implemented specifically for Seoul's PM2.5 forecasting.

Keywords: PM2.5 forecast, machine learning, convLSTM, DNN

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Authors: Nouha Khediri, Mohammed Ben Ammar, Monji Kherallah

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Recently, facial emotion recognition (FER) has become increasingly essential to understand the state of the human mind. Accurately classifying emotion from the face is a challenging task. In this paper, we present a facial emotion recognition approach named CV-FER, benefiting from deep learning, especially CNN and VGG16. First, the data is pre-processed with data cleaning and data rotation. Then, we augment the data and proceed to our FER model, which contains five convolutions layers and five pooling layers. Finally, a softmax classifier is used in the output layer to recognize emotions. Based on the above contents, this paper reviews the works of facial emotion recognition based on deep learning. Experiments show that our model outperforms the other methods using the same FER2013 database and yields a recognition rate of 92%. We also put forward some suggestions for future work.

Keywords: CNN, deep-learning, facial emotion recognition, machine learning

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Authors: Asad Abbas, Muhammad Fezan Afzal, Muhammad Latif Anjum, Muhammad Azmat

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The Internet of Things (IoT) is used to link up real objects that use the internet to interact. IoT applications allow handling and operating the equipment in accordance with environmental needs, such as transportation and healthcare. IoT devices are linked together via a number of agents that act as a middleman for communications. The operation of a heat sensor differs indoors and outside because agent applications work with environmental variables. In this article, we suggest using Software Product Line (SPL) to model IoT agents and applications' features on an XML-based basis. The contextual diversity within the same domain of application can be handled, and the reusability of features is increased by XML-based feature modelling. For the purpose of managing contextual variability, we have embraced XML for modelling IoT applications, agents, and internet-connected devices.

Keywords: IoT agents, IoT applications, software product line, feature model, XML

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Authors: Kamel Al-Khaled

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Reaction-diffusion equations are commonly used in population biology to model the spread of biological species. In this paper, we propose a fractional reaction-diffusion equation, where the classical second derivative diffusion term is replaced by a fractional derivative of order less than two. Based on the symbolic computation system Mathematica, Adomian decomposition method, developed for fractional differential equations, is directly extended to derive explicit and numerical solutions of space fractional reaction-diffusion equations. The fractional derivative is described in the Caputo sense. Finally, the recent appearance of fractional reaction-diffusion equations as models in some fields such as cell biology, chemistry, physics, and finance, makes it necessary to apply the results reported here to some numerical examples.

Keywords: fractional partial differential equations, reaction-diffusion equations, adomian decomposition, biological species

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Authors: Ben D. Atkins

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This study will examine the impact of September 11, 2001, terrorist attacks on the organizational development of American federal law enforcement through focusing on the Federal Bureau of Investigation. Content analysis of discourse in a federal law enforcement practitioner publication along with official FBI statements will be used to gain a better understanding of FBI organizational changes that have taken place since the events of September 11, 2001. Analysis of content trends in the FBI Law Enforcement Bulletin and public discourse of FBI officials from 1999 to 2005 indicate that, in addition to structural changes, the bureau has also undergone a variety of cultural changes. The results offer some support for the institutional theoretical perspective, suggesting that post-9/11 organizational changes such as new mission priorities and the establishment of new branches were partially initiated due to a variety external pressures, which lends support for coercive isomorphism. Furthermore, structural changes are discussed in relation to the attainment and maintenance of organizational legitimacy.

Keywords: institutional theory, organizational theory, law enforcement, public administration

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Authors: Arunroong Wongkungwan

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This study aimed to investigate the influence of selected antecedents, which were tourists’ satisfaction towards attractions in Bangkok, perceived value of the attractions, feelings of engagement with the attractions, acquaintance with the attractions, push factors, pull factors and motivation to seek novelty, on foreign tourist’s loyalty towards tourist attractions in Bangkok. By using multi stage sampling technique, 400 international tourists were sampled. After that, Semi Structural Equation Model was utilized in the analysis stage by LISREL. The Semi Structural Equation Model of the selected antecedents of tourist’s loyalty attractions had a correlation with the empirical data through the following statistical descriptions: Chi- square = 3.43, df = 4, P- value = 0.48893; RMSEA = 0.000; CFI = 1.00; CN = 1539.75; RMR = 0.0022; GFI = 1.00 and AGFI = 0.98. The findings indicated that all antecedents were able together to predict the loyalty of the foreign tourists who visited Bangkok at 73 percent.

Keywords: antecedent, Bangkok, foreign tourists, loyalty, tourist attractions

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Authors: Sayantan Khanra, Rojers P. Joseph

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This study attempts to analyze the impact of demography and service quality on the adoption and diffusion of e-Government services in the context of India. The objective of this paper is to study the users' perception about e-Government services and investigate the key variables that are most salient to the Indian populace. At the completion of this study, a research model that would help to understand the relationship involving the demographic variables and service quality dimensions, and the willingness to adopt e-Government services is expected to be developed. Dedicated authorities, particularly those in developing economies, may use that model or its augmented versions to design and update e-Government services and promote their use among citizens. After all, enhanced public participation is required to improve efficiency, engagement and transparency in the implementation of the aforementioned services.

Keywords: adoption and diffusion of e-government services, demographic variables, hierarchical regression analysis, service quality dimensions

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

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

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

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Authors: Muhammad Rizwan Tabassum, Ao Xia, Jerry D. Murphy

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The purpose of this work is to provide a comprehensive overview of seaweed as the alternative feedstock for biofuel production and key conversion technologies. Resource depletion and climate change are the driving forces to hunt for renewable sources of energy. Macroalgae can be preferred over land based crops for biofuel production because they are not in competition with food crops for arable land, high growth rates and low lignin contents which require less energy-intensive pre-treatments. However, some disadvantages, such as high moisture content, seasonal variation in chemical composition and process inhibition limit its economic feasibility. Seaweed can be converted into gaseous and liquid fuel by different conversion technologies, but biogas via anaerobic digestion from seaweed is attracting increased attention due to its dual benefit of an economic source of bio-fuel and environment-friendly technology. Biodiesel and bioethanol conversion technologies from seaweed are still under development. A selection of high yielding seaweed species, optimal harvesting season and process optimization make them economically feasible for the alternative source of renewable and sustainable feedstock for biofuel in future.

Keywords: anaerobic digestion, biofuel, bio-methane, conversion technologies, seaweed

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Authors: Huimin Liu, Li Xiang, Yue Liu, Jing Wang

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Background: As one of typical case for the developing countries who are stepping into the aging times globally, more and more older people in China might face the problem of which they could not maintain normal life due to the functional disability. While the government take efforts to build long-term care system and further carry out related policies for the core concept, there is still lack of strong evidence to evaluating the profile of disability states in the elderly population and its transition rate. It has been proved that disability is a dynamic condition of the person rather than irreversible so it means possible to intervene timely on them who might be in a risk of severe disability. Objective: The aim of this study was to depict the picture of the disability transferring status of the older people in China, and then find out individual characteristics that change the state of disability to provide theory basis for disability prevention and early intervention among elderly people. Methods: Data for this study came from the 2011 baseline survey and the 2013 follow-up survey of the China Health and Retirement Longitudinal Study (CHARLS). Normal ADL function, 1~2 ADLs disability,3 or above ADLs disability and death were defined from state 1 to state 4. Multi-state Markov model was applied and the four-state homogeneous model with discrete states and discrete times from two visits follow-up data was constructed to explore factors for various progressive stages. We modeled the effect of explanatory variables on the rates of transition by using a proportional intensities model with covariate, such as gender. Result: In the total sample, state 2 constituent ratio is nearly about 17.0%, while state 3 proportion is blow the former, accounting for 8.5%. Moreover, ADL disability statistics difference is not obvious between two years. About half of the state 2 in 2011 improved to become normal in 2013 even though they get elder. However, state 3 transferred into the proportion of death increased obviously, closed to the proportion back to state 2 or normal functions. From the estimated intensities, we see the older people are eleven times as likely to develop at 1~2 ADLs disability than dying. After disability onset (state 2), progression to state 3 is 30% more likely than recovery. Once in state 3, a mean of 0.76 years is spent before death or recovery. In this model, a typical person in state 2 has a probability of 0.5 of disability-free one year from now while the moderate disabled or above has a probability of 0.14 being dead. Conclusion: On the long-term care cost considerations, preventive programs for delay the disability progression of the elderly could be adopted based on the current disabled state and main factors of each stage. And in general terms, those focusing elderly individuals who are moderate or above disabled should go first.

Keywords: Markov model, elderly people, disability, transition intensity

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Authors: Luz del Carmen Rubí Félix Peña, Jose Adan Felix-Ortiz, Ely Sara Lopez-Alvarez, Wenceslao Valenzuela-Quiñonez

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On a global scale, Mexico is the sixth largest producer of farmed white shrimp (Penaeus vannamei). The activity suffered significant economic losses due to acute hepatopancreatic necrosis (AHPND) caused by a strain of Vibrio parahaemolyticus. For control, the first option is the application of antibiotics in food, causing changes in the environment and bacterial communities, which has produced greater virulence and resistance of pathogenic bacteria. An alternative treatment is silver nanoparticles (AgNPs) generated by green synthesis, which have shown an antibacterial capacity by destroying the cell membrane or denaturing the cell. However, the doses at which these are effective are still unknown. The aim is to calculate the minimum inhibitory concentration (MIC) using the Gompertz, Richard, and Logistic model of biosynthesized AgNPs against a strain of V. parahaemolyticus. Through the testing of different formulations of AgNPs synthesized from Euphorbia prostrate (Ep) extracts against V. parahaemolyticus causing AHPND in white shrimp. Aqueous and ethanol extracts were obtained, and the concentration of phenols and flavonoids was quantified. In the antibiograms, AgNPs were formulated in ethanol extracts of Ep (20 and 30%). The inhibition halo at well dilution test were 18±1.7 and 17.67±2.1 mm against V. parahaemolyticus. A broth microdilution was performed with the inhibitory agents (aqueous and ethanolic extracts and AgNPs) and 20 μL of the inoculum of V. parahaemolyticus. The MIC for AgNPs was 6.2-9.3 μg/mL and for ethanol extract of 49-73 mg/mL. The Akaike index (AIC) was used to choose the Gompertz model for ethanol extracts of Ep as the best data descriptor (AIC=204.8, 10%; 45.5, 20%, and 204.8, 30%). The Richards model was at AgNPs ethanol extract with AIC=-9.3 (10%), -17.5 (20 and 30%). The MIC calculated for EP extracts with the modified Gompertz model were 20 mg/mL (10% and 20% extract) and 40 mg/mL at 30%, while Richard was winner for AgNPs-synthesized it was 5 μg/mL (10% and 20%) and 8 μg/mL (30%). The solver tool Excel was used for the calculations of the models and inhibition curves against V.parahaemolyticus.

Keywords: green synthesis, euphorbia prostata, phenols, flavonoids, bactericide

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Authors: Jaime E. Fernandez, Pablo J. Valverde

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This paper presents a methodological approach that aims to contrast/validate theoretical results from a corruption network game through probabilistic analysis of simulated microdata using Bayesian Networks (BNs). The research develops a public corruption model in a game theory framework. Theoretical results suggest a series of 'optimal settings' of model's exogenous parameters that boost the emergence of corruption. The paper contrasts these outcomes with probabilistic inference results based on BNs adjusted over simulated microdata. Principal findings indicate that probabilistic reasoning based on BNs significantly improves parameter specification and causal analysis in a public corruption game.

Keywords: Bayesian networks, probabilistic reasoning, public corruption, theoretical games

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Authors: A. Hany A. Khater, G. M. Mostafa, M. R. Badawy

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The techno-economic analysis of the nuclear desalination is a very important tool that enables studying of the mutual effects between the nuclear power plant and the coupled desalination plant under different operating conditions, and hence investigating the feasibility of safe and economical production of potable water. For this purpose, a comprehensive model for both technical and economic performance evaluation of the nuclear desalination has been prepared. The developed model has the capability to be used in performing a parametric study for the performance measuring parameters of the nuclear desalination system. Also a sensitivity analysis of varying important factors such as interest/discount rate, power plant availability, fossil fuel prices, purchased electricity price, nuclear fuel cost, and specific base cost for both power and water plant has been conducted.

Keywords: uclear desalination, PWR, MED, MED-TVC, MSF, RO

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Authors: Libo Jiang, Huan Li, Rongling Wu

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Ordinary differentiation equations (ODE) have proven to be powerful for reconstructing precise and informative gene regulatory networks (GRNs) from dynamic gene expression data. However, joint modeling and analysis of all genes, essential for the systematical characterization of genetic interactions, are challenging due to high dimensionality and a complex pattern of genetic regulation including activation, repression, and antitermination. Here, we address these challenges by unifying variable selection and game theory through ODE. Each gene within a GRN is co-expressed with its partner genes in a way like a game of multiple players, each of which tends to choose an optimal strategy to maximize its “fitness” across the whole network. Based on this unifying theory, we designed and conducted a real experiment to infer salt tolerance-related GRNs for Euphrates poplar, a hero tree that can grow in the saline desert. The pattern and magnitude of interactions between several hub genes within these GRNs were found to determine the capacity of Euphrates poplar to resist to saline stress.

Keywords: gene regulatory network, ordinary differential equation, game theory, LASSO, saline resistance

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Authors: Andreas Rüdiger

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The selection of appropriate wastewater treatment technology for decentralized coastal tourist areas is an important engineering challenge. The local situation in coastal tourist cities and villages is characterized by important daily and seasonal fluctuations in hydraulic flow and pollution, high annual temperature variations, scarcity of building area and high housing density. At the same time, coastal zones have to meet stringent effluent limits all over the year and need simple and easy technologies to operate. This article presents the innovative technology of standardized modular aerated up-flow biofiltration SMBF as an adapted solution for decentralized wastewater treatment in sensitive touristic coastal areas. As modular technology with several biofiltration units, the system is able to treat low and high loads with low energy consumption and low demands for operators. The article focuses on the climatic and tourist situation in Croatia. Full-scale plants in Eastern Europe and Croatia have presented as well as dimensioning parameters and outlet concentrations. Energy consumption as a function of load is demonstrated.

Keywords: wastewater treatment, biofiltration, touristic areas, energy saving

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Authors: Ould Bouamama, M. Bressel, D. Hissel, M. Hilairet

Abstract:

Fuel cell (FC) is one of the best alternatives of fossil energy. Recently, the research community of fuel cell has shown a considerable interest for diagnosis in view to ensure safety, security, and availability when faults occur in the process. The problematic for model based FC diagnosis consists in that the model is complex because of coupling of several kind of energies and the numerical values of parameters are not always known or are uncertain. The present paper deals with use of one tool: the Linear Fractional Transformation bond graph tool not only for uncertain modelling but also for monitorability (ability to detect and isolate faults) analysis and formal generation of robust fault indicators with respect to parameter uncertainties.The developed theory applied to a nonlinear FC system has proved its efficiency.

Keywords: bond graph, fuel cell, fault detection and isolation (FDI), robust diagnosis, structural analysis

Procedia PDF Downloads 369

Authors: S. N. Deepa, A. D. Srinivasan, K. T. Veeramanju, R. Sandeep Kumar, Ashwini Mathapati

Abstract:

Power transformer is a vital element in a power system as it continuously regulates power flow, maintaining good voltage regulation. The working of transformer much depends on the oil insulation, the oil insulation also decides the aging of transformer and hence its reliability. The mineral oil based liquid insulation is globally accepted for power transformer insulation; however it is potentially hazardous due to its non-biodegradability. In this work efficient alternative biodegradable insulating fluid is presented as a replacement to conventional mineral oil. Dielectric tests are performed as distinct alternating fluid to evaluate the suitability for transformer insulation. The selection of the distinct natural esters for an insulation system is carried out by the laboratory investigation of Breakdown voltage, Oxidation stability, Dissipation factor, Permittivity, Viscosity, Flash and Fire point. It is proposed to study and characterize the properties of natural esters to be used in power transformer. Therefore for the investigation of the dielectric behavior rice bran oil, sesame oil, and sunflower oil are considered for the study. The investigated results have been compared with the mineral oil to validate the dielectric behavior of natural esters.

Keywords: alternative insulating fluid, dielectric properties, natural esters, power transformers

Procedia PDF Downloads 149

Authors: M. Saiful Islam, M. Mohandes, S. Rehman, S. Badran

Abstract:

Increasing necessity of wind power is directing us to have precise knowledge on wind resources. Methodical investigation of potential locations is required for wind power deployment. High penetration of wind energy to the grid is leading multi megawatt installations with huge investment cost. This fact appeals to determine appropriate places for wind farm operation. For accurate assessment, detailed examination of wind speed profile, relative humidity, temperature and other geological or atmospheric parameters are required. Among all of these uncertainty factors influencing wind power estimation, vertical extrapolation of wind speed is perhaps the most difficult and critical one. Different approaches have been used for the extrapolation of wind speed to hub height which are mainly based on Log law, Power law and various modifications of the two. This paper proposes a Artificial Neural Network (ANN) and Genetic Algorithm (GA) based hybrid model, namely GA-NN for vertical extrapolation of wind speed. This model is very simple in a sense that it does not require any parametric estimations like wind shear coefficient, roughness length or atmospheric stability and also reliable compared to other methods. This model uses available measured wind speeds at 10m, 20m and 30m heights to estimate wind speeds up to 100m. A good comparison is found between measured and estimated wind speeds at 30m and 40m with approximately 3% mean absolute percentage error. Comparisons with ANN and power law, further prove the feasibility of the proposed method.

Keywords: wind profile, vertical extrapolation of wind, genetic algorithm, artificial neural network, hybrid machine learning

Procedia PDF Downloads 494

Authors: Marie-Elise Beydon, Daniel Sacristan, Isabel Ruppen

Abstract:

MB02 (Alymsys®) is a candidate biosimilar to bevacizumab, which was developed against the reference product (RP) Avastin® sourced from both the European Union (EU) and United States (US). MB02 has been extensively characterized comparatively to Avastin® at a physicochemical and biological level using sensitive orthogonal state-of-the-art analytical methods. MB02 has been demonstrated similar to the RP with regard to its primary and higher-order structure, post- and co-translational profiles such as glycosylation, charge, and size variants. Specific focus has been put on the characterization of Fab-related activities, such as binding to VEGF A 165, which directly reflect the bevacizumab mechanism of action. Fc-related functionality was also investigated, including binding to FcRn, which is indicative of antibodies' half-life. The data generated during the analytical similarity assessment demonstrate the high analytical similarity of MB02 to its RP.

Keywords: analytical similarity, bevacizumab, biosimilar, MB02

Procedia PDF Downloads 294

Authors: George Eduful, Yuanyuan Fan, Ahmed Abu-Siada

Abstract:

Ferroresonance poses a substantial threat to the quality and reliability of power distribution systems due to its inherent characteristics of sustained overvoltages and currents. This paper aims to enhance the understanding and reduce the ferroresonance threat by investigating the susceptibility of different transformer configurations using MATLAB/Simulink simulations. To achieve this, four 200 kVA transformers with different vector groups (D-Yn, Yg-Yg, Yn-Yn, and Y-D11) and core types (3-limb, 5-limb, single-phase) were systematically exposed to controlled ferroresonance conditions. The impact of varying the length of the 11 kV cable connected to the transformers was also examined. Through comprehensive voltage, current, and total harmonic distortion analyses, the performance of each configuration was evaluated and compared. The results of the study indicate that transformers with Y-D11 and Yg-Yg configurations exhibited lower susceptibility to ferroresonance, in comparison to those with D-Y11 and Yg-Yg configurations. This implies that the Y-D11 and Yg-Yg transformers are better suited for applications with high risks of ferroresonance. The insights provided by this study are of significant value for the strategic selection and deployment of transformers in power systems, particularly in settings prone to ferroresonance. By identifying and recommending transformer configurations that demonstrate better resilience, this paper contributes to enhancing the overall robustness and reliability of power grid infrastructure.

Keywords: about cable-connected, core type, ferroresonance, over voltages, power transformer, vector group

Procedia PDF Downloads 51