Search results for: hybrid adaptive modeling

Authors: Ricardo Andrés Márquez Ortiz

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Objective. The objective of this literature review is to compile updated information from studies that have addressed the association between dental pathologies and diet in prehistoric populations of the equatorial Pacific coast. Materials and method. The research carried out corresponds to a documentary study of ex post facto retrospective, historiographic and bibliometric design. A bibliographic review search was carried out in the libraries of the Colombian Institute of Anthropology and History (ICANH) and the National University of Colombia for books and articles on the archeology of the region. In addition, a search was carried out in databases and the Internet for books and articles on dental anthropology, archeology and dentistry on the relationship between dental pathologies and diet in prehistoric and current populations from different parts of the world. Conclusions. The complex societies (500 BC - 300 AD) of the equatorial Pacific coast used an agricultural system of intensive monoculture of corn (Zea mays). This form of subsistence was reflected in an intensification of dental pathologies such as dental caries, dental abscesses generated by cavities, and enamel hypoplasia associated with a lower frequency of wear. The Upper Formative period (800 A.D. -16th century A.D.) is characterized by the development of polyculture, slash-and-burn agriculture, as an adaptive agricultural strategy to the ecological damage generated by the intensive economic activity of complex societies. This process leads to a more varied diet, which generates better dental health.

Keywords: dental pathologies, nutritional diet, equatorial pacific coast, dental anthropology

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Authors: Adel Hanna, Tahar Ayadat, Mohammad Etezad, Cyrille Cros

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A number of theoretical methods have been developed over the years to calculate the amount settlement of the soil reinforced with group of stone columns. The results deduced from these methods sometimes show large disagreement with the experimental observations. The reason of this divergence might be due to the fact that many of the previous methods assumed the deform shape of the columns which is different with the actual case. A new method to calculate settlement of the ground reinforced with group of stone columns is presented in this paper which overcomes the restrictions made by previous theories. This method is based on results deduced from numerical modeling. Results obtained from the model are validated.

Keywords: stone columns, group, soft soil, settlement, prediction

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Authors: Hela Ayeb Mrabtini, Ghazi Bellakhal, Jamel Chahed

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The presence of the dispersed phase in gas-liquid bubbly flow considerably alters the liquid turbulence. The bubbles induce turbulent fluctuations that enhance the global liquid turbulence level and alter the mechanisms of turbulence. RANS modeling of uniformly sheared flows on an isolated sphere centered in a control volume is performed using first and second order turbulence closures. The sphere is placed in the production-dissipation equilibrium zone where the liquid velocity is set equal to the relative velocity of the bubbles. The void fraction is determined by the ratio between the sphere volume and the control volume. The analysis of the turbulence statistics on the control volume provides numerical results that are interpreted with regard to the effect of the bubbles wakes on the turbulence structure in uniformly sheared bubbly flow. We assumed for this purpose that at low void fraction where there is no hydrodynamic interaction between the bubbles, the single-phase flow simulation on an isolated sphere is representative on statistical average of a sphere network. The numerical simulations were firstly validated against the experimental data of bubbly homogeneous turbulence with constant shear and then extended to produce numerical results for a wide range of shear rates from 0 to 10 s^-1. These results are compared with our turbulence closure proposed for gas-liquid bubbly flows. In this closure, the turbulent stress tensor in the liquid is split into a turbulent dissipative part produced by the gradient of the mean velocity which also contains the turbulence generated in the bubble wakes and a pseudo-turbulent non-dissipative part induced by the bubbles displacements. Each part is determined by a specific transport equation. The simulations of uniformly sheared flows on an isolated sphere reproduce the mechanisms related to the turbulent part, and the numerical results are in perfect accordance with the modeling of the transport equation of the turbulent part. The reduction of second order turbulence closure provides a description of the modification of turbulence structure by the bubbles presence using a dimensionless number expressed in terms of two-time scales characterizing the turbulence induced by the shear and that induced by bubbles displacements. The numerical simulations carried out in the framework of a comprehensive analysis reproduce particularly the attenuation of the turbulent friction showed in the experimental results of bubbly homogeneous turbulence subjected to a constant shear.

Keywords: gas-liquid bubbly flows, homogeneous turbulence, turbulence closure, uniform shear

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Authors: Xinyuan Zou, Qihui Tang, Shujian Wang, Yulin Huang, Jie Gui, Xiangping Liu, Gang Liu, Yanqiang Tao

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Childhood trauma can have a long-lasting influence on individuals and contribute to mental disorders, including depression and anxiety. The current study aimed to explore the symptomatic and developmental patterns of depression, anxiety, and stress among adolescents who have suffered from childhood trauma. A total of 3,598 college students (female = 1,617 (44.94%), Mean Age = 19.68, SD Age = 1.35) in China completed the Childhood Trauma Questionnaire (CTQ) and the Depression, Anxiety, and Stress Scales (DASS-21), and 2,337 participants met the selection standard based on the cut-off scores of the CTQ. The symptomatic network and directed acyclic graph (DAG) network approaches were used. The results revealed that males reported experiencing significantly more physical abuse, physical neglect, emotional neglect, and sexual abuse compared to females. However, females scored significantly higher than males on all items of DASS-21, except for “Worthless”. No significant difference between the two genders was observed in the network structure and global strength. Meanwhile, among all participants, “Down-hearted” and “Agitated” appeared to be the most interconnected symptoms, the bridge symptoms in the symptom network, as well as the most vital symptoms in the DAG network. Apart from that, “No-relax” also served as the most prominent symptom in the DAG network. The results suggested that intervention targeted at assisting adolescents in developing more adaptive coping strategies with stress and regulating emotion could benefit the alleviation of comorbid depression, anxiety, and stress.

Keywords: symptom network, childhood trauma, depression, anxiety, stress

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Authors: Adil Rasheed, Mandar Tabib

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The paper describes the ability of a high-resolution Computational Fluid Dynamics model to predict terrain-induced turbulence and wind shear close to the ground. Various sensitivity studies to choose the optimal simulation setup for modeling the flow characteristics in a complex terrain are presented. The capabilities of the model are demonstrated by applying it to the Sandnessjøen Airport, Stokka in Norway, an airport that is located in a mountainous area. The model is able to forecast turbulence in real time and trigger an alert when atmospheric conditions might result in high wind shear and turbulence.

Keywords: aviation safety, terrain-induced turbulence, atmospheric flow, alert system

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Authors: Djallel Bouamama, Yasser R. Haddadi

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Accurate brain tumor segmentation is crucial for diagnosis and treatment planning, yet it remains a challenging task due to the variability in tumor shapes and intensities. This paper introduces a distinct approach to brain tumor image segmentation by leveraging an advanced architecture known as Mamba-Unet. Building on the well-established U-Net framework, Mamba-Unet incorporates distinct design enhancements to improve segmentation performance. Our proposed method integrates a multi-scale attention mechanism and a hybrid loss function to effectively capture fine-grained details and contextual information in brain MRI scans. We demonstrate that Mamba-Unet significantly enhances segmentation accuracy compared to conventional U-Net models by utilizing a comprehensive dataset of annotated brain MRI scans. Quantitative evaluations reveal that Mamba-Unet surpasses traditional U-Net architectures and other contemporary segmentation models regarding Dice coefficient, sensitivity, and specificity. The improvements are attributed to the method's ability to manage class imbalance better and resolve complex tumor boundaries. This work advances the state-of-the-art in brain tumor segmentation and holds promise for improving clinical workflows and patient outcomes through more precise and reliable tumor detection.

Keywords: brain tumor classification, image segmentation, CNN, U-NET

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Authors: Neetu Manocha

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Image segmentation is a technique where a picture is parted into distinct parts having similar features which have a place with similar items. Various segmentation strategies have been proposed as of late by prominent analysts. But, after ultimate thorough research, the novelists have analyzed that generally, the old methods do not decrease the segmentation error rate. Then author finds the technique HIST-SI to decrease the segmentation error rates. In this technique, cluster-based and threshold-based segmentation techniques are merged together. After then, to improve the result of HIST-SI, the authors added the method of filtering and linking in this technique named Imp_HIST-SI to decrease the segmentation error rates. The goal of this research is to find a new technique to decrease the segmentation error rates and produce much better results than the HIST-SI technique. For testing the proposed technique, a dataset of Bhuvan – a National Geoportal developed and hosted by ISRO (Indian Space Research Organisation) is used. Experiments are conducted using Scikit-image & OpenCV tools of Python, and performance is evaluated and compared over various existing image segmentation techniques for several matrices, i.e., Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR).

Keywords: satellite image, image segmentation, edge detection, error rate, MSE, PSNR, HIST-SI, linking, filtering, imp_HIST-SI

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Authors: Hany Mustafa, Mahmoud Roushdi, Khaled Kheireldin

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Rainfall variability is an important feature of semi-arid climates. Climate change is very likely to increase the frequency, magnitude, and variability of extreme weather events such as droughts, floods, and storms. The Blue Nile Basin is facing extreme climate change-related events such as floods and droughts and its possible impacts on ecosystem, livelihood, agriculture, livestock, and biodiversity are expected. Rainfall variability is a threat to food production in the Blue Nile Basin countries. This study investigates the long-term variations and trends of seasonal and annual precipitation over the Blue Nile Basin for 102-year period (1901-2002). Six statistical trend analysis of precipitation was performed with nonparametric Mann-Kendall test and Sen's slope estimator. On the other hands, four statistical absolute homogeneity tests: Standard Normal Homogeneity Test, Buishand Range test, Pettitt test and the Von Neumann ratio test were applied to test the homogeneity of the rainfall data, using XLSTAT software, which results of p-valueless than alpha=0.05, were significant. The percentages of significant trends obtained for each parameter in the different seasons are presented. The study recommends adaptation strategies to be streamlined to relevant policies, enhancing local farmers’ adaptive capacity for facing future climate change effects.

Keywords: Blue Nile basin, climate change, Mann-Kendall test, trend analysis

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Authors: N. Li, Y. M. Cheng

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In this paper, the classical bearing capacity problem is re-considered from discrete element analysis. In the discrete element approach, the bearing capacity problem is considered from the elastic stage to plastic stage to rupture stage (large displacement). The bearing capacity failure mechanism of a strip footing on soil is investigated, and the influence of micro-parameters on the bearing capacity of soil is also observed. It is found that the distinct element method (DEM) gives very good visualized results, and basically coincides well with that derived by the classical methods.

Keywords: bearing capacity, distinct element method, failure mechanism, large displacement

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Authors: Tertia Delia Nova, Masaji Watanabge

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Infection of bird flu within a poultry farm involves hosts, virus, and medium. Intrusion of bird flu into a poultry farm divides the population into two groups; healthy and susceptible chickens and infected chickens. A healthy and susceptible bird is infected to become an infected bird. Bird flu viruses spread among chickens through medium such as air and droppings, and increase in hosts. A model for an infection process of bird flu within a poultry farm is described, numerical techniques are illustrated, and numerical results are introduced.

Keywords: bird flu, poultry farm, model for an infection process, flu viruses

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Authors: M. Rodionov, Z. Dedovets

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The article describes the theoretical concept of teaching secondary school students proof demonstration skills in mathematics. It describes in detail different levels of mastery of the concept of proof-which correspond to Piaget’s idea of there being three distinct and progressively more complex stages in the development of human reflection. Lessons for each level contain a specific combination of the visual-figurative components and deductive reasoning. It is vital at the transition point between levels to carefully and rigorously recalibrate teaching to reflect the development of more complex reflective understanding. This can apply even within the same age range, since students will develop at different speeds and to different potential. The authors argue that this requires an aware and adaptive approach to lessons to reflect this complexity and variation. The authors also contend that effective teaching which enables students to properly understand the implementation of proof arguments must develop specific competences. These are: understanding of the importance of completeness and generality in making a valid argument; being task focused; having an internalised locus of control and being flexible in approach and evaluation. These criteria must be correlated with the systematic application of corresponding methodologies which are best likely to achieve success. The particular pedagogical decisions which are made to deliver this objective are illustrated by concrete examples from the existing secondary school mathematics courses. The proposed theoretical concept formed the basis of the development of methodological materials which have been tested in 47 secondary schools.

Keywords: education, teaching of mathematics, proof, deductive reasoning, secondary school

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Authors: Madina Hamiane

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Modelling and simulation of an analogy function generator is presented based on a polynomial expansion model. The proposed function generator model is based on a 10th order polynomial approximation of any of the required functions. The polynomial approximations of these functions can then be implemented using basic CMOS circuit blocks. In this paper, a circuit model is proposed that can simultaneously generate many different mathematical functions. The circuit model is designed and simulated with HSPICE and its performance is demonstrated through the simulation of a number of non-linear functions.

Keywords: modelling and simulation, analog function generator, polynomial approximation, CMOS transistors

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Authors: Mohammad Vahed, Ana Sadeghitohidi, Majid Vahed, Hiroki Takahashi

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In the genomics field, the huge amounts of data have produced by the next-generation sequencers (NGS). Data volumes are very rapidly growing, as it is postulated that more than one billion bases will be produced per year in 2020. The growth rate of produced data is much faster than Moore's law in computer technology. This makes it more difficult to deal with genomics data, such as storing data, searching information, and finding the hidden information. It is required to develop the analysis platform for genomics big data. Cloud computing newly developed enables us to deal with big data more efficiently. Hadoop is one of the frameworks distributed computing and relies upon the core of a Big Data as a Service (BDaaS). Although many services have adopted this technology, e.g. amazon, there are a few applications in the biology field. Here, we propose a new algorithm to more efficiently deal with the genomics big data, e.g. sequencing data. Our algorithm consists of two parts: First is that BDaaS is applied for handling the data more efficiently. Second is that the hybrid method of MapReduce and Fuzzy logic is applied for data processing. This step can be parallelized in implementation. Our algorithm has great potential in computational analysis of genomics big data, e.g. de novo genome assembly and sequence similarity search. We will discuss our algorithm and its feasibility.

Keywords: big data, fuzzy logic, MapReduce, Hadoop, cloud computing

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Authors: Amir Rashid

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Cloud Computing frameworks empower organizations to cut expenses by outsourcing computation resources on-request. As of now, customers of Cloud service providers have no methods for confirming the privacy and ownership of their information and data. To address this issue we propose the platform of a trusted cloud computing program (TCCP). TCCP empowers Infrastructure as a Service (IaaS) suppliers, for example, Amazon EC2 to give a shout box execution condition that ensures secret execution of visitor virtual machines. Also, it permits clients to bear witness to the IaaS supplier and decide if the administration is secure before they dispatch their virtual machines. This paper proposes a Trusted Cloud Computing Platform (TCCP) for guaranteeing the privacy and trustworthiness of computed data that are outsourced to IaaS service providers. The TCCP gives the deliberation of a shut box execution condition for a client's VM, ensuring that no cloud supplier's authorized manager can examine or mess up with its data. Furthermore, before launching the VM, the TCCP permits a client to dependably and remotely acknowledge that the provider at backend is running a confided in TCCP. This capacity extends the verification of whole administration, and hence permits a client to confirm the data operation in secure mode.

Keywords: cloud security, IaaS, cloud data privacy and integrity, hybrid cloud

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Authors: Ferah Tesfaye Admasu

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Autism Spectrum Disorder (ASD) often presents significant communication challenges, particularly for non-verbal individuals who struggle to express their needs and emotions effectively. Assistive technologies (AT) have emerged as vital tools in enhancing communication abilities for this population. Recent advancements in artificial intelligence (AI) hold the potential to revolutionize the design and functionality of these technologies. This study explores the application of AI in developing intelligent, adaptive, and user-centered assistive technologies for non-verbal individuals with autism. Through a review of current AI-driven tools, including speech-generating devices, predictive text systems, and emotion-recognition software, this research investigates how AI can bridge communication gaps, improve engagement, and support independence. Machine learning algorithms, natural language processing (NLP), and facial recognition technologies are examined as core components in creating more personalized and responsive communication aids. The study also discusses the challenges and ethical considerations involved in deploying AI-based AT, such as data privacy and the risk of over-reliance on technology. Findings suggest that integrating AI into assistive technologies can significantly enhance the quality of life for non-verbal individuals with autism, providing them with greater opportunities for social interaction and participation in daily activities. However, continued research and development are needed to ensure these technologies are accessible, affordable, and culturally sensitive.

Keywords: artificial intelligence, autism spectrum disorder, non-verbal communication, assistive technology, machine learning

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Authors: Julio Vicente Cateia

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This study aims to analyze the economic impacts of infrastructure investment in Africa, focusing on the Guinea-Bissau economy. Through a dynamic CGE model, we find that the natural resource revenues (or aid)-funded infrastructure investments generate externalities that increase factor returns. The private investment improvements propagate externalities effects on GDP and job opportunities outcomes. Household income and consumption were positively impacted, though the poorer benefited the most. The income inequality has reduced. However, funding by the mix of debt and direct taxes produces opposite effects. We suggest a potential pro-poor growth agenda in Africa.

Keywords: infrastructure investment, poverty alleviation, CGE modeling, African economies, applied economics

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Authors: Nisha Nandlal Sharma, Julaluk Carmai, Saiprasit Koetniyom, Bernd Markert

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Whiplash Injuries are mostly experienced in car accidents. Symptoms of whiplash are commonly reported in studies, neck pain and headaches are two most common symptoms observed. The whiplash Injury mechanism is poorly understood. In present study, hybrid neck muscle model were developed with a combination of solid tetrahedral elements and 1D beam elements. Solid tetrahedral elements represents passive part of the muscle whereas, 1D beam elements represents active part. To simulate the active behavior of the muscle, Hill-type muscle model was applied to beam elements. To simulate non-linear passive properties of muscle, solid elements were modeled with rubber/foam material model. Some important muscles were then inserted into THUMS (Total Human Model for Safety) THUMS was given a boundary conditions similar to experimental tests. The model was exposed to 4g and 7g rear impacts as these load impacts are close to low speed impacts causing whiplash. The effect of muscle activation level on occupant kinematics during whiplash was analyzed.

Keywords: finite element model, muscle activation, THUMS, whiplash injury mechanism

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Authors: Sakshi Ghai

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The word ‘emotion’ has been microscopically studied through psychological, anthropological and biological lenses and have indubitably been one of the most researched concepts as, in all situations and reactions that constitute human life, emotions form the very niche of our mutual existence. While understanding the social aspects of cognition, one can realize that emotions are deeply interwoven with language and thereby are pivotal in inducing human actions and behavior. The society or the outward social structure is the result of the inward psychological structure of our human relationships, for the individual is the result of the total experience, knowledge and conduct of man. The aim of this paper is threefold: first, to establish the relation between mental representations of emotions and its neuropsychological connection with language on a conscious and sub-conscious level; secondly, to describe how innate, basic and higher cognitive emotions affect the constantly changing state of an agent and peruse its assistance in determining the moral compass within all beings. Lastly, in the course of this paper, the concept of the architecture of mind is explored considering how it has developed an ability to display adaptive emotional states and responses, which are in sync with the language of thought. For every response to the social environment is so deeply determined by the very social milieu in which one is situated, language has a fundamental role in constructing emotions and articulating behavior. Being linguistic beings, we tend to associate emotion, feelings and other aspects of inwards mental states intrinsically with the language we use. This paper aims to devise a discursive approach to understand how emotions are fabricated, intertwined with the mental constructs further expressed and communicated through the various units of language.

Keywords: mental representation, emotion, language, psychology

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Authors: Enze Zhang

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Large numbers of inter-basin water transfer (IBWT) projects are constructed or proposed all around the world as solutions to water distribution and supply problems. Nowadays, as climate change warms the atmosphere, alters the hydrologic cycle, and perturbs water availability, large scale IBWTs which are sensitive to these water-related changes may carry significant risk. Given this reality, IBWTs have elicited great controversy and assessments of vulnerability to climate change are urgently needed worldwide. In this paper, we consider the South-to-North Water Transfer Project (SNWTP) in China as a case study, and introduce a bottom-up vulnerability assessment framework. Key hazards and risks related to climate change that threaten future water availability for the SNWTP are firstly identified. Then a performance indicator is presented to quantify the vulnerability of IBWT by taking three main elements (i.e., sensitivity, adaptive capacity, and exposure degree) into account. A probabilistic Budyko model is adapted to estimate water availability responses to a wide range of possibilities for future climate conditions in each region of the study area. After bottom-up quantifying the vulnerability based on the estimated water availability, our findings confirm that SNWTP would greatly alleviate geographical imbalances in water availability under some moderate climate change scenarios but raises questions about whether it is a long-term solution because the donor basin has a high level of vulnerability due to extreme climate change.

Keywords: vulnerability, climate change, inter-basin water transfer, bottom-up

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Authors: Marinko Skare, Malgorzata Porada Rochon

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This paper study the link between energy justice and economic growth. The link between energy justice and growth has not been extensively studied. Here we study the impact and importance of energy justice, as a part of the energy transition process, on economic growth. Our study shows energy justice growth is an important determinant of economic growth and development that should be addressed at the industry and economic levels. We use panel data modeling and causality testing to research the empirical link between energy justice and economic growth. Industry and economy-level policies designed to support energy justice initiatives are beneficial to economic growth. Energy justice is a necessary condition for green growth and sustainability targets.

Keywords: energy justice, economic growth, panel data, energy transition

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Authors: Yu Zhao, Mengqin Sun, Yahui Zhang

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Apparel products (or apparel) with the sense of aesthetics, usability (ergonomics) and function are fundamental and varied in people’s daily life. The numerous apparel thus produced by apparel industry, have been triggered many issues, such as the waste of sources and the environmental pollutions. In this study, a hybrid architecture called modular architecture of apparel (MAA) has been proposed to deal with the variety of apparel, and thus to overcome the aforementioned issues. Generally, the establishment of MAA takes advantage of the modular design of a general product that a product is assembled with many modules through their modular interface connector. The development of MAA is to first analyze the structure of apparel in terms of the necessity to form an apparel and the aesthetics, ergonomics, and function of apparel; then to divide apparel into many segments (or module in product design) based on the structure of apparel; to develop modular interfaces and modular interface connectors in terms of the features of apparel’s modules. It is noted that in the general product design, modules of a product are only about the function and ergonomics, but in MAA, the module of aesthetics is developed. Further, an apparel design with employing the MAA is carried out to validate its usefulness and efficiency. There are three contributions out of this study, the first is to overcome the aforementioned issues (i.e. waste of source and environmental pollutions); the second is the improvement of the modular design for product by considering aesthetics; the third is to add the value in realizing the personalized mass production of apparel in the near future.

Keywords: apparel, architecture, modular design, segment

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Authors: Sidi Ahmed Maouloud, Cheikh Ba

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Much of West Africa’s cultural heritage is written in the Al-Soudani Arabic script, which was widely used in West Africa before the time of European colonization. This Al-Soudani Arabic script is an African version of the Maghrebi script, in particular, the Al-Mebssout script. However, the local African qualities were incorporated into the Al-Soudani script in a way that gave it a unique African diversity and character. Despite the existence of several Arabic datasets in Oriental script, allowing for the analysis, layout, and recognition of texts written in these calligraphies, many Arabic scripts and written traditions remain understudied. In this paper, we present a dataset of words from Al-Soudani calligraphy scripts. This dataset consists of 100 images selected from three different manuscripts written in Al-Soudani Arabic script by different copyists. The primary source for this database was the libraries of Boston University and Cambridge University. This dataset highlights the unique characteristics of the Al-Soudani Arabic script as well as the new challenges it presents in terms of automatic word recognition of Arabic manuscripts. An HTR system based on a hybrid ANN (CRNN-CTC) is also proposed to test this dataset. SAMRA is a dataset of annotated Arabic manuscript words in the Al-Soudani script that can help researchers automatically recognize and analyze manuscript words written in this script.

Keywords: dataset, CRNN-CTC, handwritten words recognition, Al-Soudani Arabic script, HTR, manuscripts

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Authors: Jun Wang, Tingcun Wei

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The digital pulse width modulator (DPWM) is the crucial building block for digitally-controlled DC-DC switching converter, which converts the digital duty ratio signal into its analog counterpart to control the power MOSFET transistors on or off. With the increase of switching frequency of digitally-controlled DC-DC converter, the DPWM with higher time resolution is required. In this paper, a 15-bits DPWM with three-level hybrid structure is presented; the first level is composed of a7-bits counter and a comparator, the second one is a 5-bits delay line, and the third one is a 3-bits digital dither. The presented DPWM is designed and implemented using the PLL megafunction of FPGA (Field Programmable Gate Arrays), and the required frequency of clock signal is 128 times of switching frequency. The simulation results show that, for the switching frequency of 2 MHz, a DPWM which has the time resolution of 15 ps is achieved using a maximum clock frequency of 256MHz. The designed DPWM in this paper is especially useful for high-frequency digitally-controlled DC-DC switching converters.

Keywords: DPWM, digitally-controlled DC-DC switching converter, FPGA, PLL megafunction, time resolution

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Authors: Nop Sopipan

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In this paper, we forecast the volatility of Baht/USDs using Markov Regime Switching GARCH (MRS-GARCH) models. These models allow volatility to have different dynamics according to unobserved regime variables. The main purpose of this paper is to find out whether MRS-GARCH models are an improvement on the GARCH type models in terms of modeling and forecasting Baht/USD volatility. The MRS-GARCH is the best performance model for Baht/USD volatility in short term but the GARCH model is best perform for long term.

Keywords: volatility, Markov Regime Switching, forecasting, Baht/USD

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Authors: Mintu Philip, Renumol V. G.

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An introductory programming course is a major challenge faced in Computing Education. Many of the introductory programming courses fail because student concentrate mainly on writing programs using a programming language rather than involving in problem solving. Computational thinking is a general approach to solve problems. This paper proposes a new preliminary course that aims to develop computational thinking skills in students, which may help them to become good programmers. The proposed course is designed based on the four basic components of computational thinking - abstract thinking, logical thinking, modeling thinking and constructive thinking. In this course, students are engaged in hands-on problem solving activities using a new problem solving model proposed in this paper.

Keywords: computational thinking, computing education, abstraction, constructive thinking, modelling thinking

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Authors: C. Iraklis, G. Evmiridis, A. Iraklis

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Renewable energy sources and distributed power generation units already have an important role in electrical power generation. A mixture of different technologies penetrating the electrical grid, adds complexity in the management of distribution networks. High penetration of distributed power generation units creates node over-voltages, huge power losses, unreliable power management, reverse power flow and congestion. This paper presents an optimization algorithm capable of reducing congestion and power losses, both described as a function of weighted sum. Two factors that describe congestion are being proposed. An upgraded selective particle swarm optimization algorithm (SPSO) is used as a solution tool focusing on the technique of network reconfiguration. The upgraded SPSO algorithm is achieved with the addition of a heuristic algorithm specializing in reduction of power losses, with several scenarios being tested. Results show significant improvement in minimization of losses and congestion while achieving very small calculation times.

Keywords: congestion, distribution networks, loss reduction, particle swarm optimization, smart grid

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Authors: Ibrahim Elshaer, Samar Kamel

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Organizational politics (OP) has received a great deal of attention in the management literature due to its popularity, mystery, and potential advantages for those how can use it. It involves the use of power and social networks within an organization to promote interests and gain potential benefits. Its implication for human resource (HR) management decisions is heretofore one of its least studied aspects, and awaits further investigation. Therefore, it is our intention to investigate certain relations between organizational politics and the validity of HR decisions in addition to the expected dysfunctional consequences. The study is undertaken on two HR management practices- Performance appraisal (measured by the distributive justice scale) and downsizing- depending on data gathered from the hotel industry in Egypt; a developing Non-Western country, in which Political practices of HR management are common in public and private organizations. Data was obtained from a survey of 600 employees in the Egyptian hotel industry. A total of 500 responses were attained. 100 uncompleted questionnaires were excluded leaving 400 usable with response rate of around 80%. Structural equation modeling (SEM) was employed to test the causal relationship between the research variables. The analysis of the current study data reveals that organizational politics is negatively linked to the perception of distributive justice of performance appraisal, additionally, the perception of distributive justice in performance appraisal is positively linked to the perception of validity in the downsizing decisions and finally the perception of OP is negatively linked to the perception of downsizing decisions validity. This study makes three important contributions. First although there have been several studies on OP, the majority of these studies have focused on examining its effect on employees’ attitudes in workplace. This empirical study helps in identifying the influence of OP on the effectiveness and success of HR decisions and accordingly the organizational system. Second, it draws attention to OP as an important phenomenon that influence HR management in hospitality industry, since empirical evidences concerning OP in the hospitality management literature are meager. Third, this study contributes to the existing downsizing literature by examining OP and low distributive justice as challenges of the effectiveness of the downsizing process. Finally, to the best of the authors’ knowledge, no empirical study in the tourism and hospitality management literature has examined the effect of OP and distributive justice on the workplace using data gathered from the hotel industry in Egypt; a developing non-Western setting.

Keywords: organizational politics, performance appraisal, downsizing, structural equation modeling, hotel industry

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Authors: Iván Rivet, Narges Dialami, Miguel Cervera, Michele Chiumenti

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Process parameters in Additive Manufacturing (AM) play a critical role in the mechanical performance of the final component. In order to find the input configuration that guarantees the optimal performance of the printed part, the process-performance relationship must be found. Fused Filament Fabrication (FFF) is the selected demonstrative AM technology due to its great popularity in the industrial manufacturing world. A material model that considers the different printing patterns present in a FFF part is used. A voxelized mesh is built from the manufacturing toolpaths described in the G-Code file. An Adaptive Mesh Refinement (AMR) based on the octree strategy is used in order to reduce the complexity of the mesh while maintaining its accuracy. High-fidelity and cost-efficient Finite Element (FE) simulations are performed and the influence of key process parameters in the mechanical performance of the component is analyzed. A robust optimization process based on appropriate failure criteria is developed to find the printing direction that leads to the optimal mechanical performance of the component. The Tsai-Wu failure criterion is implemented due to the orthotropy and heterogeneity constitutive nature of FFF components and because of the differences between the strengths in tension and compression. The optimization loop implements a modified version of an Anomaly Detection (AD) algorithm and uses the computed metrics to obtain the optimal printing direction. The developed methodology is verified with a case study on an industrial demonstrator.

Keywords: additive manufacturing, optimization, printing direction, mechanical performance, voxelization

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Authors: Ruzanna Muradyan

Abstract:

Medical data mining is a crucial field in contemporary healthcare that offers cutting-edge tactics with enormous potential to transform patient care. This abstract examines how sophisticated data mining techniques could transform the healthcare industry, with a special focus on how they might improve patient outcomes. Healthcare data repositories have dynamically evolved, producing a rich tapestry of different, multi-dimensional information that includes genetic profiles, lifestyle markers, electronic health records, and more. By utilizing data mining techniques inside this vast library, a variety of prospects for precision medicine, predictive analytics, and insight production become visible. Predictive modeling for illness prediction, risk stratification, and therapy efficacy evaluations are important points of focus. Healthcare providers may use this abundance of data to tailor treatment plans, identify high-risk patient populations, and forecast disease trajectories by applying machine learning algorithms and predictive analytics. Better patient outcomes, more efficient use of resources, and early treatments are made possible by this proactive strategy. Furthermore, data mining techniques act as catalysts to reveal complex relationships between apparently unrelated data pieces, providing enhanced insights into the cause of disease, genetic susceptibilities, and environmental factors. Healthcare practitioners can get practical insights that guide disease prevention, customized patient counseling, and focused therapies by analyzing these associations. The abstract explores the problems and ethical issues that come with using data mining techniques in the healthcare industry. In order to properly use these approaches, it is essential to find a balance between data privacy, security issues, and the interpretability of complex models. Finally, this abstract demonstrates the revolutionary power of modern data mining methodologies in transforming the healthcare sector. Healthcare practitioners and researchers can uncover unique insights, enhance clinical decision-making, and ultimately elevate patient care to unprecedented levels of precision and efficacy by employing cutting-edge methodologies.

Keywords: data mining, healthcare, patient care, predictive analytics, precision medicine, electronic health records, machine learning, predictive modeling, disease prognosis, risk stratification, treatment efficacy, genetic profiles, precision health

Procedia PDF Downloads 59

Authors: Danni Cong, Meiping Wu, Hua Mu, Xiaofeng He, Junxiang Lian, Juliang Cao, Shaokun Cai, Hao Qin

Abstract:

Gravity field is of great significance in geoscience, national economy and national security, and gravitational gradient measurement has been extensively studied due to its higher accuracy than gravity measurement. Gravity gradient sensor, being one of core devices of the gravity gradient instrument, plays a key role in measuring accuracy. Therefore, this paper starts from analyzing the working principle of the gravity gradient sensor by Newton’s law, and then considers the relative motion between inertial and non-inertial systems to build a relatively adequate mathematical model, laying a foundation for the measurement error calibration, measurement accuracy improvement.

Keywords: gravity gradient, gravity gradient sensor, accelerometer, single-axis rotation modulation

Procedia PDF Downloads 325