Search results for: approximation of analytic functions

Authors: Mohamed S. Sasi, Adel M. Mlitan, Abdulfattah M. Alkherraz

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This research project aims to investigate difference in relative rates concerning phosphoryl transfer relevant to biological catalysis of DNA and RNA in the pH-independent reactions. Activated Models of DNA and RNA for alkyl-aryl phosphate diesters (with 4-nitrophenyl as a good leaving group) have successfully been prepared to gather kinetic parameters. Eyring plots for the pH–independent hydrolysis of 1 and 2 were established at different temperatures in the range 100–160 °C. These measurements have been used to provide a better estimate for the difference in relative rates between the reactivity of DNA and RNA cleavage. Eyring plot gave an extrapolated rate of kH2O = 1 × 10-10 s -1 for 1 (RNA model) and 2 (DNA model) at 25°C. Comparing the reactivity of RNA model and DNA model shows that the difference in relative rates in the pH-independent reactions is surprisingly very similar at 25°. This allows us to obtain chemical insights into how biological catalysts such as enzymes may have evolved to perform their current functions.

Keywords: DNA and RNA models, relative rates, reactivity, phosphoryl transfe

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Authors: Diego Tancara, Raul Coto, Ariel Norambuena, Hoseein T. Dinani, Felipe Fanchini

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Quantum machine learning is a growing research field that aims to perform machine learning tasks assisted by a quantum computer. Kernel-based quantum machine learning models are paradigmatic examples where the kernel involves quantum states, and the Gram matrix is calculated from the overlapping between these states. With the kernel at hand, a regular machine learning model is used for the learning process. In this paper we investigate the quantum support vector machine and quantum kernel ridge models to predict the degree of non-Markovianity of a quantum system. We perform digital quantum simulation of amplitude damping and phase damping channels to create our quantum dataset. We elaborate on different kernel functions to map the data and kernel circuits to compute the overlapping between quantum states. We observe a good performance of the models.

Keywords: quantum, machine learning, kernel, non-markovianity

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Authors: Chizzoniti Domenico, Beggiora Klizia, Cattani Letizia, Moscatelli Monica

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This paper concerns the study of sustainable construction materials applied on the "Health Post", a prototype for the primary health care situated in alienated areas of the world. It's suitable for social and climatic Sub-Saharan context; however, it could be moved in other countries of the world with similar urgent needs. The idea is to create a Health Post with local construction materials that have a low environmental impact and promote the local workforce allowing reuse of traditional building techniques lowering production costs and transport. The aim of Primary Health Care Centre is to be a flexible and expandable structure identifying a modular form that can be repeated several times to expand its existing functions. In this way it could be not only a health care centre but also a socio-cultural facility.

Keywords: low costs building, sustainable construction materials, green construction system, prototype, health care, emergency

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Authors: Yulin Liang

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Under the background of protecting ecology, natural education is an effective way for people to understand nature. At the same time, it is a new means of sustainable development of eco-tourism, which can improve the functions of China's protected areas and develop new business formats for the development of national parks. This study takes national park gateway communities as the research object and uses literature review, inductive reasoning and other research methods to sort out the development process of natural education in China and the research progress of natural education design in national park gateway communities. Finally, we discuss how gateway communities can use natural education to transform their development methods and provide a theoretical and practical basis for the development of gateway communities in national parks.

Keywords: natural education, gateway communities, national parks, sustainable development

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Authors: Sergio V Davalos, Alison L. Watkins

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User generated content (UGC) such as website post has data associated with it: time of the post, gender, location, type of device, and number of words. The text entered in user generated content (UGC) can provide a valuable dimension for analysis. In this research, each user post is treated as a collection of terms (words). In addition to the number of words per post, the frequency of each term is determined by post and by the sum of occurrences in all posts. This research focuses on one specific aspect of UGC: sentiment. Sentiment analysis (SA) was applied to the content (user posts) of two sets of political forums related to the US presidential elections for 2012 and 2016. Sentiment analysis results in deriving data from the text. This enables the subsequent application of data analytic methods. The SASA (SAIL/SAI Sentiment Analyzer) model was used for sentiment analysis. The application of SASA resulted with a sentiment score for each post. Based on the sentiment scores for the posts there are significant differences between the content and sentiment of the two sets for the 2012 and 2016 presidential election forums. In the 2012 forums, 38% of the forums started with positive sentiment and 16% with negative sentiment. In the 2016 forums, 29% started with positive sentiment and 15% with negative sentiment. There also were changes in sentiment over time. For both elections as the election got closer, the cumulative sentiment score became negative. The candidate who won each election was in the more posts than the losing candidates. In the case of Trump, there were more negative posts than Clinton’s highest number of posts which were positive. KNIME topic modeling was used to derive topics from the posts. There were also changes in topics and keyword emphasis over time. Initially, the political parties were the most referenced and as the election got closer the emphasis changed to the candidates. The performance of the SASA method proved to predict sentiment better than four other methods in Sentibench. The research resulted in deriving sentiment data from text. In combination with other data, the sentiment data provided insight and discovery about user sentiment in the US presidential elections for 2012 and 2016.

Keywords: sentiment analysis, text mining, user generated content, US presidential elections

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Authors: Viet-Hung Truong, Seung-Eock Kim

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This paper presents a robust software package for practical advanced analysis of space steel framed structures. The pre- and post-processors of the presented software package are coded in the C++ programming language while the solver is written by using the FORTRAN programming language. A user-friendly graphical interface of the presented software is developed to facilitate the modeling process and result interpretation of the problem. The solver employs the stability functions for capturing the second-order effects to minimize modeling and computational time. Both the plastic-hinge and fiber-hinge beam-column elements are available in the presented software. The generalized displacement control method is adopted to solve the nonlinear equilibrium equations.

Keywords: advanced analysis, beam-column, fiber-hinge, plastic hinge, steel frame

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Authors: Shweta Dhindhwal

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Dopamine is an essential neurotransmitter in the central nervous system of all vertebrates and plays an important role in many processes such as motor function, learning and behavior, and sensory activity. One of the important functions of dopamine is release of pituitary hormones. It is synthesized from the amino acid tyrosine. Two types of dopamine receptors, D1-like and D2-like, have been reported in fish. The dopamine containing neurons are located in the olfactory bulbs, the ventral regions of the pre-optic area and tuberal hypothalamus. Distribution of the dopaminergic system has not been studied in the murrel, Channa punctatus. The present study deals with identification of D2 receptors in the brain of murrel. A phylogenetic tree has been constructed using partial sequence of D2 receptor. Distribution of putative dopaminergic neurons in the brain has been investigated. Also, formalin induced hypertrophy of neurosecretory cells in murrel has been studied.

Keywords: dopamine, fish, pre-optic area, murrel

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Authors: Veena Easvaradoss, Ebenezer Joseph, Sumathi Chandrasekaran, Sweta Jain, Aparna Anna Mathai, Senta Christy

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Working memory refers to a cognitive processing space where information is received, managed, transformed, and briefly stored. It is an operational process of transforming information for the execution of cognitive tasks in different and new ways. Many class room activities require children to remember information and mentally manipulate it. While the impact of chess training on intelligence and academic performance has been unequivocally established, its impact on working memory needs to be studied. This study, funded by the Cognitive Science Research Initiative, Department of Science & Technology, Government of India, analyzed the effect of one-year chess training on the working memory of children. A pretest–posttest with control group design was used, with 52 children in the experimental group and 50 children in the control group. The sample was selected from children studying in school (grades 3 to 9), which included both the genders. The experimental group underwent weekly chess training for one year, while the control group was involved in extracurricular activities. Working memory was measured by two subtests of WISC-IV INDIA. The Digit Span Subtest involves recalling a list of numbers of increasing length presented orally in forward and in reverse order, and the Letter–Number Sequencing Subtest involves rearranging jumbled alphabets and numbers presented orally following a given rule. Both tasks require the child to receive and briefly store information, manipulate it, and present it in a changed format. The Children were trained using Winning Moves curriculum, audio- visual learning method, hands-on- chess training and recording the games using score sheets, analyze their mistakes, thereby increasing their Meta-Analytical abilities. They were also trained in Opening theory, Checkmating techniques, End-game theory and Tactical principles. Pre equivalence of means was established. Analysis revealed that the experimental group had significant gains in working memory compared to the control group. The present study clearly establishes a link between chess training and working memory. The transfer of chess training to the improvement of working memory could be attributed to the fact that while playing chess, children evaluate positions, visualize new positions in their mind, analyze the pros and cons of each move, and choose moves based on the information stored in their mind. If working-memory’s capacity could be expanded or made to function more efficiently, it could result in the improvement of executive functions as well as the scholastic performance of the child.

Keywords: chess training, cognitive development, executive functions, school children, working memory

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Authors: Dominic McGann

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Applying Kuhn’s model of paradigm shifts in science to cases of doctrinal change in religion has been a common area of study in recent years. Few authors, however, have sought an explanation for the ease with which this model of theory change in science can be applied to cases of religious change. In order to provide such an explanation of this analytic phenomenon, this paper aims to answer one central question: Why is it that a theory that was intended to be used in an analysis of the history of science can be applied to something as disparate as the doctrinal history of religion with little to no modification? By way of answering this question, this paper begins with an explanation of Kuhn’s model and its applications in the field of religious studies. Following this, Massa’s recently proposed explanation for this phenomenon, and its notable flaws will be explained by way of framing the central proposal of this article, that the operative parts of scientific and religious changes function on the same fundamental concept of changes in understanding. Focusing its argument on this key concept, this paper seeks to illustrate its operation in cases of religious conversion and in Kuhn’s notion of the incommensurability of different scientific paradigms. The conjecture of this paper is that just as a Pagan-turned-Christian ceases to hear Thor’s hammer when they hear a clap of thunder, so too does a Ptolemaic-turned-Copernican-astronomer cease to see the Sun orbiting the Earth when they view a sunrise. In both cases, the agent in question has undergone a similar change in universal understanding, which provides us with a fundamental connection between changes in religion and changes in science. Following an exploration of this connection, this paper will consider the implications that such a connection has for the concept of the division between religion and science. This will, in turn, lead to the conclusion that religion and science are more alike than they are opposed with regards to the fundamental notion of understanding, thereby providing an answer to our central question. The major finding of this paper is that Kuhn’s model can be applied to religious cases so easily because changes in science and changes in religion operate on the same type of change in understanding. Therefore, in summary, science and religion share a crucial similarity and are not as disparate as they first appear.

Keywords: Thomas Kuhn, science and religion, paradigm shifts, incommensurability, insight and understanding, philosophy of science, philosophy of religion

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Authors: Elzbieta Babula, Juhyun Park

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Tversky and Kahneman’s cumulative prospect theory assumes symmetric probability cumulation with regard to the reference point within decision weights. Theoretically, this model should be invariant under the change of the direction of probability cumulation. In the present study, this phenomenon is being investigated by creating a reference model that allows verifying the parameter interactions in the cumulative prospect theory specifications. The simultaneous parametric fitting of utility and weighting functions is applied to binary choice data from the experiment. The results show that the flexibility of the probability weighting function is a crucial characteristic allowing to prevent parameter interactions while estimating cumulative prospect theory.

Keywords: binary choice experiment, cumulative prospect theory, decision weights, parameter interactions

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Authors: Belloufi Mohammed, Sellami Badreddine

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Conjugate gradient methods have played a special role for solving large scale optimization problems due to the simplicity of their iteration, convergence properties and their low memory requirements. In this work, we propose a new class of conjugate gradient methods which ensures sufficient descent. Moreover, we propose a new search direction with the Wolfe line search technique for solving unconstrained optimization problems, a global convergence result for general functions is established provided that the line search satisfies the Wolfe conditions. Our numerical experiments indicate that our proposed methods are preferable and in general superior to the classical conjugate gradient methods in terms of efficiency and robustness.

Keywords: unconstrained optimization, conjugate gradient method, sufficient descent property, numerical comparisons

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Authors: Panagiotis Tsarchopoulos, Nicos Komninos, Christina Kakderi

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Smart cities are high on the political agenda around the globe. However, planning smart cities and deploying applications dealing with the complex problems of the urban environment is a very challenging task that is difficult to be undertaken solely by the cities. We argue that the uptake of smart city strategies is facilitated, first, through the development of smart city application repositories allowing re-use of already developed and tested software, and, second, through cloud computing which disengages city authorities from any resource constraints, technical or financial, and has a higher impact and greater effect at the city level The combination of these two solutions allows city governments and municipalities to select and deploy a large number of applications dedicated to different city functions, which collectively could create a multiplier effect with a greater impact on the urban environment.

Keywords: smart cities, applications, cloud computing, migration to the cloud, application repositories

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Authors: Kedwadee Sombultawee, Sakun Boon-Itt

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This research demonstrates a systematic literature review of 62 peer-reviewed articles published in academic journals from 2000-2016 focusing on the operation and marketing interface area. The findings show the three major clusters of recent research domains, which is a review of the alignment between operations and marketing, identification of variables that impact the company and analysis of the effect of interface. Moreover, the Main Path Analysis (MPA) is mapped to show the knowledge structure of the operation and marketing interface issue. Most of the empirical research focused on company performance and new product development then analyzed the data by the structural equation model or regression. Whereas, some scholars studied the conflict of these two functions and proposed the requirement or step for alignment. Finally, the gaps in the literature are provided for future research directions.

Keywords: operations management, marketing, interface, systematic literature review

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Authors: Khaled Salah

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Model order reduction has been one of the most challenging topics in the past years. In this paper, a hybrid solution of genetic algorithm (GA) and simulated annealing algorithm (SA) are used to approximate high-order transfer functions (TFs) to lower-order TFs. In this approach, hybrid algorithm is applied to model order reduction putting in consideration improving accuracy and preserving the properties of the original model which are two important issues for improving the performance of simulation and computation and maintaining the behavior of the original complex models being reduced. Compared to conventional mathematical methods that have been used to obtain a reduced order model of high order complex models, our proposed method provides better results in terms of reducing run-time. Thus, the proposed technique could be used in electronic design automation (EDA) tools.

Keywords: genetic algorithm, simulated annealing, model reduction, transfer function

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Authors: Yulin Liang

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Under the background of protecting ecology, natural education is an effective way for people to understand nature. At the same time, it is a new means of sustainable development of eco-tourism, which can improve the functions of China 's protected areas and develop new business formats for the development of national parks. This study takes national park gateway communities as the research object and uses literature review, inductive reasoning and other research methods to sort out the development process of natural education in China and the research progress of natural education design in national park gateway communities. Finally, it discuss how gateway communities can use natural education to transform their development methods and provide theoretical and practical basis for the development of gateway communities in national parks.

Keywords: nature education, gateway communities, national park, sustainable development

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Authors: Ahmet Koyuncu, Abdullah Berkan Erdogmus, Orkun Dogu, Sinan Uygur

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Thermoelectric modules are integrated to electronic components to keep their temperature in specific values in electronic cooling applications. They can be used in different ambient temperatures. The cold side temperatures of thermoelectric modules depend on their hot side temperatures, operation currents, and heat loads. Performance curves of thermoelectric modules are given at most two different hot surface temperatures in product catalogs. Characteristic properties are required to select appropriate thermoelectric modules in thermal design phase of projects. Generally, manufacturers do not provide characteristic material property values of thermoelectric modules to customers for confidentiality. Common commercial software applied like ANSYS ICEPAK, FloEFD, etc., include thermoelectric modules in their libraries. Therefore, they can be easily used to predict the effect of thermoelectric usage in thermal design. Some software requires only the performance values in different temperatures. However, others like ICEPAK require three temperature-dependent equations for material properties (Seebeck coefficient (α), electrical resistivity (β), and thermal conductivity (γ)). Since the number and the variety of thermoelectric modules are limited in this software, definitions of characteristic material properties of thermoelectric modules could be required. In this manuscript, the method of derivation of characteristic material properties from the datasheet of thermoelectric modules is presented. Material characteristics were estimated from two different performance curves by experimentally and numerically in this study. Numerical calculations are accomplished in ICEPAK by using a thermoelectric module exists in the ICEPAK library. A new experimental setup was established to perform experimental study. Because of similar results of numerical and experimental studies, it can be said that proposed equations are approved. This approximation can be suggested for the analysis includes different type or brand of TEC modules.

Keywords: electrical resistivity, material characteristics, thermal conductivity, thermoelectric coolers, seebeck coefficient

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Authors: Yuzhi Cai

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This talk presents a Bayesian approach to quantile autoregressive (QAR) time series model estimation and forecasting. We establish that the joint posterior distribution of the model parameters and future values is well defined. The associated MCMC algorithm for parameter estimation and forecasting converges to the posterior distribution quickly. We also present a combining forecasts technique to produce more accurate out-of-sample forecasts by using a weighted sequence of fitted QAR models. A moving window method to check the quality of the estimated conditional quantiles is developed. We verify our methodology using simulation studies and then apply it to currency exchange rate data. An application of the method to the USD to GBP daily currency exchange rates will also be discussed. The results obtained show that an unequally weighted combining method performs better than other forecasting methodology.

Keywords: combining forecasts, MCMC, quantile modelling, quantile forecasting, predictive density functions

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Authors: Shishay Kidanu, Abdullah Alhaj

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Sinkholes in the karst terrain of Greene County, Missouri, pose significant geohazards, imposing challenges on construction and infrastructure development, with potential threats to lives and property. To address these issues, understanding the influencing factors and modeling sinkhole susceptibility is crucial for effective mitigation through strategic changes in land use planning and practices. This study utilizes geographic information system (GIS) software to collect and process diverse data, including topographic, geologic, hydrogeologic, and anthropogenic information. Nine key sinkhole influencing factors, ranging from slope characteristics to proximity to geological structures, were carefully analyzed. The Frequency Ratio method establishes relationships between attribute classes of these factors and sinkhole events, deriving class weights to indicate their relative importance. Weighted integration of these factors is accomplished using the Analytic Hierarchy Process (AHP) and the Weighted Linear Combination (WLC) method in a GIS environment, resulting in a comprehensive sinkhole susceptibility index (SSI) model for the study area. Employing Jenk's natural break classifier method, the SSI values are categorized into five distinct sinkhole susceptibility zones: very low, low, moderate, high, and very high. Validation of the model, conducted through the Area Under Curve (AUC) and Sinkhole Density Index (SDI) methods, demonstrates a robust correlation with sinkhole inventory data. The prediction rate curve yields an AUC value of 74%, indicating a 74% validation accuracy. The SDI result further supports the success of the sinkhole susceptibility model. This model offers reliable predictions for the future distribution of sinkholes, providing valuable insights for planners and engineers in the formulation of development plans and land-use strategies. Its application extends to enhancing preparedness and minimizing the impact of sinkhole-related geohazards on both infrastructure and the community.

Keywords: sinkhole, GIS, analytical hierarchy process, frequency ratio, susceptibility, Missouri

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Authors: Zahra Yazdani Paraii, Zohreh Yazdani Paraei

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Many urban designers and planners have been involved in the design of environmentally friendly or nature adaptable urban development models due to increase in urban populations in the recent century, limitation on natural resources, climate change, and lack of enough water and food. Ecological city is one of the latest models proposed to accomplish the latter goal. In this work, the existing establishing indicators of the ecological city are used regarding energy, water, land use and transportation issues. The model is used to compare the function of traditional settlements of Iran. The result of investigation shows that the specifications and functions of the traditional settlements of Iran fit well into the ecological city model. It is found that the inhabitants of the old cities and villages in Iran had founded ecological cities based on their knowledge of the environment and its natural opportunities and limitations.

Keywords: ecological city, traditional city, urban design, environment

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Authors: Zhaoming Yang, Qi Xiang, Qian He, Michael Havbro Faber, Enrico Zio, Huai Su, Jinjun Zhang

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Resilience is one of the key factors for system safety assessment and optimization, and resilience studies of natural gas pipeline systems (NGPS), especially in terms of process descriptions, are still being explored. Based on the three main stages, which are function loss process, recovery process, and waiting process, the paper has built functions and models which are according to the practical characteristics of NGPS and mainly analyzes the characteristics of deterministic interruptions. The resilience of NGPS also considers the threshold of the system function or users' satisfaction. The outcomes, which quantify the resilience of NGPS in different evaluation views, can be combined with the max flow and shortest path methods, help with the optimization of extra gas supplies and gas routes as well as pipeline maintenance strategies, the quick analysis of disturbance effects and the improvement of NGPS resilience evaluation accuracy.

Keywords: natural gas pipeline system, resilience, process modeling, deterministic disturbance

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Authors: Cristina Costescu, Carmen David, Adrian Roșan

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Executive functions (EF) and emotion regulation strategies are processes that allow individuals to function in an adaptative way and to be goal-oriented, which is essential for success in daily living activities, at school, or in social contexts. The Emotion Regulation and Executive Functioning Scale for Children and Adolescents (REMEX) represents an empirically based tool (based on the model of EF developed by Diamond) for evaluating significant dimensions of child and adolescent EFs and emotion regulation strategies, mainly in school contexts. The instrument measures the following dimensions: working memory, inhibition, cognitive flexibility, executive attention, planning, emotional control, and emotion regulation strategies. Building the instrument involved not only a top-down process, as we selected the content in accordance with prominent models of FE, but also a bottom-up one, as we were able to identify valid contexts in which FE and ER are put to use. For the construction of the instrument, we implemented three focus groups with teachers and other professionals since the aim was to develop an accurate, objective, and ecological instrument. We used the focus group method in order to address each dimension and to yield a bank of items to be further tested. Each dimension is addressed through a task that the examiner will apply and through several items derived from the main task. For the validation of the instrument, we plan to use item response theory (IRT), also known as the latent response theory, that attempts to explain the relationship between latent traits (unobservable cognitive processes) and their manifestations (i.e., observed outcomes, responses, or performance). REMEX represents an ecological scale that integrates a current scientific understanding of emotion regulation and EF and is directly applicable to school contexts, and it can be very useful for developing intervention protocols. We plan to test his convergent validity with the Childhood Executive Functioning Inventory (CHEXI) and Emotion Dysregulation Inventory (EDI) and divergent validity between a group of typically developing children and children with neurodevelopmental disorders, aged between 6 and 9 years old. In a previous pilot study, we enrolled a sample of 40 children with autism spectrum disorders and attention-deficit/hyperactivity disorder aged 6 to 12 years old, and we applied the above-mentioned scales (CHEXI and EDI). Our results showed that deficits in planning, bebavior regulation, inhibition, and working memory predict high levels of emotional reactivity, leading to emotional and behavioural problems. Considering previous results, we expect our findings to provide support for the validity and reliability of the REMEX version as an ecological instrument for assessing emotion regulation and EF in children and for key features of its uses in intervention protocols.

Keywords: executive functions, emotion regulation, children, item response theory, focus group

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Authors: Anubhav Shrivastava, Lakshya Bhat, Shivarudraswamy

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Distributed Generation is the need of the hour. With current advancements in the DG technology, there are some major issues that need to be tackled in order to make this method of generation of energy more efficient and feasible. Among other problems, the control in voltage is the major issue that needs to be addressed. This paper focuses on control of voltage using reactive power control of DGs with the help of fuzzy logic. The membership functions have been defined accordingly and the control of the system is achieved. Finally, with the help of simulation results in Matlab, the control of voltage within the tolerance limit set (+/- 5%) is achieved. The voltage waveform graphs for the IEEE 14 bus system are obtained by using simple algorithm with MATLAB and then with fuzzy logic for 14 bus system. The goal of this project was to control the voltage within limits by controlling the reactive power of the DG using fuzzy logic.

Keywords: distributed generation, fuzzy logic, matlab, newton raphson, IEEE 14 bus, voltage regulation, radial network

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Authors: Lindsey Giunta

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This review examines the current literature regarding the impact of COVID-19 on academic functioning and anxiety in children and adolescents. The objective was to determine the ways in which the pandemic affected youth mental health and academics, in addition to the extent that these factors were transformed as a result of the worldwide state of affairs. Twenty papers were selected and reviewed, and data showed long term consequences in youth mental health resulting from the current pandemic. The COVID-19 pandemic and its associated lockdowns led to disrupted childhood education, and data showed that the growth of cognitive executive functions was impacted to varying degrees dependent upon geographic location. The literature recommends supplemental education on the national level, as well as mental health promotion within communities and schools.

Keywords: pandemic, children, adolescents, anxiety, academic functioning

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Authors: Ali Heydarimoghim

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In this paper, the optimal placement of charging and discharging stations is done to determine the location and capacity of the stations, reducing the cost of electric vehicle owners' losses, reducing the cost of distribution system losses, and reducing the costs associated with the stations. Also, observing the permissible limits of the bus voltage and the capacity of the stations and their distance are considered as constraints of the problem. Given the traffic situation in different areas of a city, we estimate the amount of energy required to charge and the amount of energy provided to discharge electric vehicles in each area. We then introduce the electricity distribution system of the city in question. Following are the scenarios for introducing the problem and introducing the objective and constraint functions. Finally, the simulation results for different scenarios are compared.

Keywords: charging & discharging stations, hybrid vehicles, optimization, replacement

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Authors: Darwin Castillo Huamaní, Francisco A. M. Gomes

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The aim of the multi-material topology optimization (MTO) is to obtain the optimal topology of structures composed by many materials, according to a given set of constraints and cost criteria. In this work, we seek the optimal distribution of materials in a domain, such that the flexibility of the structure is minimized, under certain boundary conditions and the intervention of external forces. In the case we have only one material, each point of the discretized domain is represented by two values from a function, where the value of the function is 1 if the element belongs to the structure or 0 if the element is empty. A common way to avoid the high computational cost of solving integer variable optimization problems is to adopt the Solid Isotropic Material with Penalization (SIMP) method. This method relies on the continuous interpolation function, power function, where the base variable represents a pseudo density at each point of domain. For proper exponent values, the SIMP method reduces intermediate densities, since values other than 0 or 1 usually does not have a physical meaning for the problem. Several extension of the SIMP method were proposed for the multi-material case. The one that we explore here is the ordered SIMP method, that has the advantage of not being based on the addition of variables to represent material selection, so the computational cost is independent of the number of materials considered. Although the number of variables is not increased by this algorithm, the optimization subproblems that are generated at each iteration cannot be solved by methods that rely on second derivatives, due to the cost of calculating the second derivatives. To overcome this, we apply a globally convergent version of the sequential linear programming method, which solves a linear approximation sequence of optimization problems.

Keywords: globally convergence, multi-material design ordered simp, sequential linear programming, topology optimization

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Authors: A. Suparmi, C. Cari, M. Yunianto, B. N. Pratiwi

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D-dimensional Dirac equations of q-deformed shape invariant potentials were solved using supersymmetric quantum mechanics (SUSY QM) in the case of exact spin symmetry. The D dimensional radial Dirac equation for shape invariant potential reduces to one-dimensional Schrodinger type equation by an appropriate variable and parameter change. The relativistic energy spectra were analyzed by using SUSY QM and shape invariant properties from radial D dimensional Dirac equation that have reduced to one dimensional Schrodinger type equation. The SUSY operator was used to generate the D dimensional relativistic radial wave functions, the relativistic energy equation reduced to the non-relativistic energy in the non-relativistic limit.

Keywords: D-dimensional dirac equation, non-central potential, SUSY QM, radial wave function

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Authors: Nasser Mohamed Ramli, Mohamad Syafiq Mohamad

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Many types of controllers were applied on the continuous stirred tank reactor (CSTR) unit to control the temperature. In this research paper, Proportional-Integral-Derivative (PID) controller are compared with Fuzzy Logic controller for temperature control of CSTR. The control system for temperature non-isothermal of a CSTR will produce a stable response curve to its set point temperature. A mathematical model of a CSTR using the most general operating condition was developed through a set of differential equations into S-function using MATLAB. The reactor model and S-function are developed using m.file. After developing the S-function of CSTR model, User-Defined functions are used to link to SIMULINK file. Results that are obtained from simulation and temperature control were better when using Fuzzy logic control compared to PID control.

Keywords: CSTR, temperature, PID, fuzzy logic

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Authors: Fajar Ajie Setiawan, Ratu Azima Mayangsari, Bunga Aprilia

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The emergence of creative hubs around the world, including in Bandung, was primarily driven by the needs of collaborative-innovative spaces for creative industry activities such as the Maker Movement and the Coworking Movement. These activities pose challenges for identification and formulation of sets of indicators for modeling creative hubs in Bandung to help stakeholders in formulating strategies. This study intends to identify their characteristics. This research was conducted using a qualitative approach comparing three concepts of creative hub categorization and integrating them into a single instrument to analyze 12 selected creative hubs. Our results showed three new functions of creative hubs in Bandung: (1) cultural, (2) retail business, and (3) community network. Results also suggest that creative hubs in Bandung are commonly established for networking and community activities. Another result shows that there was a shifting pattern of creative hubs before the 2000s and after the 2000s, which also creates a hybrid group of creative hubs.

Keywords: creative industry, creative hubs, Ngariung, Bandung

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Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami

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Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feed-forward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on self organizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.

Keywords: classification, SOFM algorithm, neural network, neighborhood, RGB image

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Authors: Bin Liu

Abstract:

Prokaryotic promoter, consisted of two short DNA sequences located at in -35 and -10 positions, is responsible for controlling the initiation and expression of gene expression. Different types of promoters have different functions, and their consensus sequences are similar. In addition, their consensus sequences may be different for the same type of promoter, which poses difficulties for promoter identification. Unfortunately, all existing computational methods treat promoter identification as a binary classification task and can only identify whether a query sequence belongs to a specific promoter type. It is desired to develop computational methods for effectively identifying promoters and their types. Here, a two-layer predictor is proposed to try to deal with the problem. The first layer is designed to predict whether a given sequence is a promoter and the second layer predicts the type of promoter that is judged as a promoter. Meanwhile, we also analyze the importance of feature and sequence conversation in two aspects: promoter identification and promoter type identification. To the best knowledge of ours, it is the first computational predictor to detect promoters and their types.

Keywords: promoter, promoter type, random forest, sequence information

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