Search results for: ordinal response models

Authors: Valentina Zhang

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While facial expression is a complex and individualized behavior, all facial emotion recognition (FER) systems known to us rely on a single facial representation and are trained on universal data. We conjecture that: (i) different facial representations can provide different, sometimes complementing views of emotions; (ii) when employed collectively in a discussion group setting, they enable more accurate emotion reading which is highly desirable in autism care and other applications context sensitive to errors. In this paper, we first study FER using pixel-based DL vs semantics-based DL in the context of deepfake videos. Our experiment indicates that while the semantics-trained model performs better with articulated facial feature changes, the pixel-trained model outperforms on subtle or rare facial expressions. Armed with these findings, we have constructed an adaptive FER system learning from both types of models for dyadic or small interacting groups and further leveraging the synthesized group emotions as the ground truth for individualized FER training. Using a collection of group conversation videos, we demonstrate that FER accuracy and personalization can benefit from such an approach.

Keywords: neurodivergence care, facial emotion recognition, deep learning, ground truth for supervised learning

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Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

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Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

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Authors: Aizreena Azaman, Mai Ishibashi, Masanori Ishizawa, Shin-Ichiroh Yamamoto

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Sensory input plays an important role to human posture control system to initiate strategy in order to counterpart any unbalance condition and thus, prevent fall. In previous study, joint stiffness was observed able to describe certain issues regarding to movement performance. But, correlation between balance ability and joint stiffness is still remains unknown. In this study, joint stiffening strategy at ankle and hip were observed under different sensory manipulations and its correlation with conventional clinical test (Functional Reach Test) for balance ability was investigated. In order to create unstable condition, two different surface perturbations (tilt up-tilt (TT) down and forward-backward (FB)) at four different frequencies (0.2, 0.4, 0.6 and 0.8 Hz) were introduced. Furthermore, four different sensory manipulation conditions (include vision and vestibular system) were applied to the subject and they were asked to maintain their position as possible. The results suggested that joint stiffness were high during difficult balance situation. Less balance people generated high average joint stiffness compared to balance people. Besides, adaptation of posture control system under repetitive external perturbation also suggested less during sensory limited condition. Overall, analysis of joint stiffening response possible to predict unbalance situation faced by human.

Keywords: balance ability, joint stiffness, sensory, adaptation, dynamic

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Authors: Taoufik Bettaieb, Sihem Soufi

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As a bioactive metalloid, silicon (Si) is an essential element for plant growth and development. It also plays a crucial role in enhancing plants’ resilience to different abiotic and biotic stresses. The morpho-physiological, biochemical, and molecular background of Si-mediated stress tolerance in plants were unraveled. Cold stress is a severe abiotic stress response to the decrease of plant growth and yield by affecting various physiological activities in plants. Several approaches have been used to alleviate the adverse effects generated from cold stress exposure, but the cost-effective, environmentally friendly, and defensible approach is the supply of silicon. Silicon has the ability to neutralize the harmful impacts of cold stress. Therefore, based on these hypotheses, this study was designed in order to investigate the morphological and physiological background of silicon effects applied at different concentrations on cold stress mitigation during early growth of a turfgrass, namely Paspalum vaginatum Sw. Results show that silicon applied at different concentrations improved the morphological development of Paspalum subjected to cold stress. It is also effective on the photosynthetic apparatus by maintaining stability the photochemical efficiency. As the primary component of cellular membranes, lipids play a critical function in maintaining the structural integrity of plant cells. Silicon application decreased membrane lipid peroxidation and kept on membrane frontline barrier relatively stable under cold stress.

Keywords: crops, cold stress, silicon, abiotic stress

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Authors: Emad Matar Alotaibi

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In line with other Arabic countries, Saudi Arabia is currently undergoing large scale school reform in response to key factors brought about by globalization. While there is a growing body of research exploring these systemic changes in urban environments, there is very little published research regarding rural schools. In fact, rural schools are still under-examined globally comparing to their urban and suburban counterparts over a range of reform dimensions. In Saudi Arabia, there are around 1128 rural areas that contain about 3200 schools. Several challenges face rural schools, especially in relation to recruitment, retention, and professional development opportunities for teachers and school leaders. However, there is very little in depth research which explores these issues “on the ground”. The aim of this research is fill this knowledge gap and explore teachers’ and leaders’ perceptions and experiences of working in rural schools in KSA. In Saudi Arabia, there is a growing body of research into school leadership. However, there is very little published research specifically exploring rural schools. By using an in-depth case study approach and adopting an analytical framework based on the interlinking concepts of leadership practices, culture, and CPD, this study offers and significant and original contribution to knowledge in this area. This study also will adopt a qualitative multiple case studies, which is going to employ semi-structured interviews, focus groups, and documentary analysis.

Keywords: leadership practice, school culture, continuing professional development, rural school

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Authors: Samira Hamiditehrani

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Automated vehicles (AVs) are emerging technologies that could potentially offer a wide range of opportunities and challenges for the transportation sector. The advent of AV technology has also resulted in new business models in shared mobility services where many ride hailing and car sharing companies are developing on-demand AVs including shared automated vehicles (SAVs) and pooled automated vehicles (Pooled AVs). SAVs and Pooled AVs could provide alternative shared mobility services which encourage sustainable transport systems, mitigate traffic congestion, and reduce automobile dependency. However, the success of on-demand AVs in addressing major transportation policy issues depends on whether and how the public adopts them as regular travel modes. To identify conditions under which individuals may adopt on-demand AVs, previous studies have applied human behavior and technology acceptance theories, where Theory of Planned Behavior (TPB) has been validated and is among the most tested in on-demand AV research. In this respect, this study has three objectives: (a) to propose and validate a theoretical model for behavioral intention to use SAVs and Pooled AVs by extending the original TPB model; (b) to identify the characteristics of early adopters of SAVs, who prefer to have a shorter and private ride, versus prospective users of Pooled AVs, who choose more affordable but longer and shared trips; and (c) to investigate Canadians’ intentions to adopt on-demand AVs for regular trips. Toward this end, this study uses data from an online survey (n = 3,622) of workers or adult students (18 to 75 years old) conducted in October and November 2021 for six major Canadian metropolitan areas: Toronto, Vancouver, Ottawa, Montreal, Calgary, and Hamilton. To accomplish the goals of this study, a base bivariate ordered probit model, in which both SAV and Pooled AV adoptions are estimated as ordered dependent variables, alongside a full structural equation modeling (SEM) system are estimated. The findings of this study indicate that affective motivations such as attitude towards AV technology, perceived privacy, and subjective norms, matter more than sociodemographic and travel behavior characteristic in adopting on-demand AVs. Also, the results of second objective provide evidence that although there are a few affective motivations, such as subjective norms and having ample knowledge, that are common between early adopters of SAVs and PooledAVs, many examined motivations differ among SAV and Pooled AV adoption factors. In other words, motivations influencing intention to use on-demand AVs differ among the service types. Likewise, depending on the types of on-demand AVs, the sociodemographic characteristics of early adopters differ significantly. In general, findings paint a complex picture with respect to the application of constructs from common technology adoption models to the study of on-demand AVs. Findings from the final objective suggest that policymakers, planners, the vehicle and technology industries, and the public at large should moderate their expectations that on-demand AVs may suddenly transform the entire transportation sector. Instead, this study suggests that SAVs and Pooled AVs (when they entire the Canadian market) are likely to be adopted as supplementary mobility tools rather than substitutions for current travel modes

Keywords: automated vehicles, Canadian perception, theory of planned behavior, on-demand AVs

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Authors: Sagri Sharma

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Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms and statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data.

Keywords: artificial intelligence, biomarker, gene expression datasets, hepatocellular carcinoma, machine learning, supervised learning algorithms, support vector machine

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Authors: Anjali Bansal

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Reproductive preferences mainly deal with two questions: when a couple wants children and how many they want. Questions related to these desires are often included in the fertility surveys as they can provide relevant information on the subsequent behavior. The aim of the study is to observe whether respondent’s response to these questions changed over time or not. We also tried to identify socio- economic and demographic factors associated with the stability (or instability) of fertility preferences. For this purpose, we used IHDS1 (2004-05) and follow up survey IHDS2 (2011-12) data and applied bivariate, multivariate and multilevel repeated measure analysis to it to find the consistency between responses. From the analysis, we found that preferences of women changes over the course of time as from the bivariate analysis we have found that 52% of women are not consistent in their desired family size and huge inconsistency are found in desire to continue childbearing. To get a better overlook of these inconsistencies, we have computed Intra Class Correlation (ICC) which tries to explain the consistency between individuals on their fertility responses at two time periods. We also explored that husband’s desire for additional child specifically male offspring contribute to these variations. Our findings lead us to a cessation that in India, individuals fertility preferences changed over a seven-year time period as the Intra Class correlation comes out to be very small which explains the variations among individuals. Concerted efforts should be made, therefore, to educate people, and conduct motivational programs to promote family planning for family welfare.

Keywords: change, consistency, preferences, over time

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Authors: Gabriele Borg, Alexei Debono, Charlie Abela

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There on a constant rise in the availability of high volumes of data gathered from multiple sources, resulting in an abundance of unprocessed information that can be used to monitor patterns and trends in user behaviour. Similarly, year after year, Malta is also constantly experiencing ongoing population growth and an increase in mobilization demand. This research takes advantage of data which is continuously being sourced and converting it into useful information related to the traffic problem on the Maltese roads. The scope of this paper is to provide a methodology to create a custom dataset (MalTra - Malta Traffic) compiled from multiple participants from various locations across the island to identify the most common routes taken to expose the main areas of activity. This use of big data is seen being used in various technologies and is referred to as ITSs (Intelligent Transportation Systems), which has been concluded that there is significant potential in utilising such sources of data on a nationwide scale. Furthermore, a series of traffic prediction graph neural network models are conducted to compare MalTra to large-scale traffic datasets.

Keywords: graph neural networks, traffic management, big data, mobile data patterns

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Authors: Jaber H. Almutairi, Hosny Z. Abou-Ziyan, Issa F. Almesri, Mosab A. Alrahmani

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The present work investigates the behavior of fluid flow inside tunnel kilns using 3D-CFD (Computational Fluid Dynamics) simulations. The CFD simulations are carried out with the FLUENT software and validated against experimental results on fluid flow and heat transfer in tunnel kilns. A grid dependency study is conducted in the current work to improve the accuracy of the results. Three turbulence models k–ω, standard k–ε, and RNG k–ε are tested where k–ω model gives the best results in comparison with the experiment. The numerical results reveal an intriguing phenomenon where a long flow separation zone behind the setting is observed under different geometric and operation conditions. It was found that the uniformity of flow distribution can be substantially improved by rearranging the geometrical parameters of brick setting relative to kiln/setting. This improvement of flow distribution plays a critical role to enhance the quality and quantity of the production. It can be concluded that a better design and operation of tunnel kilns in terms of productivity and energy consumption can be obtained by taking into consideration the flow uniformity inside the tunnel kilns using CFD modelling.

Keywords: tunnel kilns, flow separation, flow uniformity, computational fluid dynamics

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Authors: Adeline Meriaux, Claire Gaiani, Jennifer Burgain, Frantz Fournier, Lionel Muniglia, Jérémy Petit

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Spray-drying process is widely used for the production of dairy powders for food and pharmaceuticals industries. It involves the atomization of a liquid feed into fine droplets, which are subsequently dried through contact with a hot air flow. The resulting powders permit transportation cost reduction and shelf life increase but can also exhibit various interesting functionalities (flowability, solubility, protein modification or acid gelation), depending on operating conditions and milk composition. Indeed, particles porosity, surface composition, lactose crystallization, protein denaturation, protein association or crust formation may change. Links between spray-drying conditions and physicochemical and functional properties of powders were investigated by a design of experiment methodology and analyzed by principal component analysis. Quadratic models were developed, and multicriteria optimization was carried out by the use of genetic algorithm. At the time of abstract submission, verification spray-drying trials are ongoing. To perform experiments, milk from dairy farm was collected, skimmed, froze and spray-dried at different air pressure (between 1 and 3 bars) and outlet temperature (between 75 and 95 °C). Dry matter, minerals content and proteins content were determined by standard method. Solubility index, absorption index and hygroscopicity were determined by method found in literature. Particle size distribution were obtained by laser diffraction granulometry. Location of the powder color in the Cielab color space and water activity were characterized by a colorimeter and an aw-value meter, respectively. Flow properties were characterized with FT4 powder rheometer; in particular compressibility and shearing test were performed. Air pressure and outlet temperature are key factors that directly impact the drying kinetics and powder characteristics during spray-drying process. It was shown that the air pressure affects the particle size distribution by impacting the size of droplet exiting the nozzle. Moreover, small particles lead to more cohesive powder and less saturated color of powders. Higher outlet temperature results in lower moisture level particles which are less sticky and can explain a spray-drying yield increase and the higher cohesiveness; it also leads to particle with low water activity because of the intense evaporation rate. However, it induces a high hygroscopicity, thus, powders tend to get wet rapidly if they are not well stored. On the other hand, high temperature provokes a decrease of native serum proteins which is positively correlated to gelation properties (gel point and firmness). Partial denaturation of serum proteins can improve functional properties of powder. The control of air pressure and outlet temperature during the spray-drying process significantly affects the physicochemical and functional properties of powder. This study permitted to better understand the links between physicochemical and functional properties of powder, to identify correlations between air pressure and outlet temperature. Therefore, mathematical models have been developed and the use of genetic algorithm will allow the optimization of powder functionalities.

Keywords: dairy powders, spray-drying, powders functionalities, design of experiment

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Authors: Gu Dongxing, Li Yuxuan, Nong Tengxiao, Burra Venkata Durga Kumar

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Since computers are widely used in business models, more and more companies choose to store important information in computers to improve productivity. However, this information can be compromised in many cases, such as when it is stored locally on the company's computers or when it is transferred between servers and clients. Of these important information leaks, source code leaks are probably the most costly. Because the source code often represents the core technology of the company, especially for the Internet companies, source code leakage may even lead to the company's core products lose market competitiveness, and then lead to the bankruptcy of the company. In recent years, such as Microsoft, AMD and other large companies have occurred source code leakage events, suffered a huge loss. This reveals to us the importance and necessity of preventing source code leakage. This paper aims to find ways to prevent source code leakage based on the direction of operating system, and based on the fact that most companies use Linux or Linux-like system to realize the interconnection between server and client, to discuss how to reduce the possibility of source code leakage during data transmission.

Keywords: data transmission, Linux, source code, operating system

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Authors: Serena Clark, Katriona O'Sullivan, Kevin Marshall, Mac MacLachlan

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This paper explores the United Nation's sustainable development goals (SDGs) alongside digital poverty and proposes that digital poverty should be a new SDG. The SDGs concentrate on 17 key areas, including economic growth, reducing inequalities, climate action, ending poverty, gender equality, and quality education. Many of the plans to fulfill these goals involve the creation and adaptation of new technologies. As we have seen with COVID-19, access to these technologies has determined communities and societies' ability to respond to these challenges in both developed and developing nations. For example, the transition to online education due to the lockdowns had a detrimental effect on children who did not have access to technology to provide continuity in their educational development. Digitalization and emerging technologies, especially information and communication technologies (ICTs), can help address each goal. Digital poverty and exclusion exacerbate the gap between rich and poor within our societies and internationally, and COVID-19 has further highlighted these issues. Closing this gap can support achieving the SDGs. If access to digital technologies measure society's response and resilience in addressing the challenges the SDGs seek to resolve, should reducing digital poverty be an SDG of its own? This paper will explore this question, arguing that digital poverty should be an independent SDG working alongside and supporting the achievement of the other 17 SDGs.

Keywords: digital poverty, digital exclusion, United Nations sustainable development goals, information and communication technologies

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Authors: Akram Boubakri, Fethi Choubeni, Tan Hoa Vuong, Jacques David

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The control of electromagnetic waves is a key aim of several researches over the past decade. In this regard, Metamaterials have shown a strong ability to manipulate the electromagnetic waves on a subwavelength scales thanks to its unconventional properties that are not available in natural materials such as negative refraction index, super imaging and invisibility cloaking. Metalenses were used to avoid some drawbacks presented by conventional lenses since focusing with conventional lenses suffered from the limited resolution because they were only able to focus the propagating wave component. Nevertheless, Metalenses were able to go beyond the diffraction limit and enhance the resolution not only by collecting the propagating waves but also by restoring the amplitude of evanescent waves that decay rapidly when going far from the source and that contains the finest details of the image. Metasurfaces have many mechanical advantages over three-dimensional metamaterial structures especially the ease of fabrication and a smaller required volume. Those structures have been widely used for antenna performance improvement and to build flat metalenses. In this work, we showed that a well-designed metasurface lens operating at the frequency of 5.9GHz, has efficiently enhanced the radiation characteristics of a patch antenna and can be used for WLAN applications (IEEE 802.11 a). The proposed metasurface lens is built with a geometrically modified unit cells which lead to a change in the response of the lens at different position and allow the control of the wavefront beam of the incident wave thanks to the gradient refractive index.

Keywords: focusing, gradient index, metasurface, metalens, WLAN Applications

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Authors: Dirk Zapf, Bastian Leuger

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For several years, natural gas and crude oil have been stored in salt caverns in Germany and also worldwide. The dimensioning concepts have been continuously developed from a rock mechanics point of view. In addition to the possibilities of realizing large numerical calculation models based on real survey data nowadays, especially the consideration of mechanical processes such as damage and healing played a role in the development of adequate material laws. In addition, thermodynamic aspects have had to be considered for some years in the operation of a gas storage cavern since temperature changes have a significant influence on the stress states in the vicinity of a storage cavern. The possibility of thermally induced fracturing processes is also investigated in the context of rock mechanics dimensioning. In recent years, the energy crisis and the finite nature of fossil fuel use have led to increased discussion of the use of salt caverns for hydrogen storage. In this paper, state of the art is presented, the current research work is described, and an outlook is given as to which questions still need to be answered from a rock mechanics point of view in connection with large-scale storage of hydrogen in salt caverns.

Keywords: cavern design, hydrogen, rock salt, thermomechanical coupled calculations

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Authors: M. Javanmard

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This study was aimed at analyzing the effects of packaging (MAP) and preservation conditions on the packaged fresh walnut kernel quality. The central composite plan was used for evaluating the effect of oxygen (0–10%), carbon dioxide (0-10%), and temperature (4-26 °C) on qualitative characteristics of walnut kernels. Also, the response level technique was used to find the optimal conditions for interactive effects of factors, as well as estimating the best conditions of process using least amount of testing. Measured qualitative parameters were: peroxide index, color, decreased weight, mould and yeast counting test, and sensory evaluation. The results showed that the defined model for peroxide index, color, weight loss, and sensory evaluation is significant (p < 0.001), so that increase of temperature causes the peroxide value, color variation, and weight loss to increase and it reduces the overall acceptability of walnut kernels. An increase in oxygen percentage caused the color variation level and peroxide value to increase and resulted in lower overall acceptability of the walnuts. An increase in CO₂ percentage caused the peroxide value to decrease, but did not significantly affect other indices (p ≥ 0.05). Mould and yeast were not found in any samples. Optimal packaging conditions to achieve maximum quality of walnuts include: 1.46% oxygen, 10% carbon dioxide, and temperature of 4 °C.

Keywords: shelled walnut, MAP, quality, storage temperature

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Authors: Aristeidis Samitas, Evangelos Vasileiou

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The aim of this study is to examine the month and the trading month effect under changing financial trends. We choose the Greek stock market to implement our assumption because there are clear and long term periods of financial growth and recession. Daily financial data from Athens Exchange General Index for the period 2002-2012 are considered. The paper employs several linear and non-linear models, although the TGARCH asymmetry model best fits in this sample and for this reason we mainly present the TGARCH results. Empirical results show that changing economic and financial conditions influences the calendar effects. Especially, the trading month effect totally changes in each fortnight according to the financial trend. On the other hand, in Greece the January effect exists during the growth periods, although it does not exist when the financial trend changes. The findings are helpful to anybody who invest and deals with the Greek stock market. Moreover, they may pave the way for an alternative calendar anomalies research approach, so it may be useful to investors who take into account these anomalies when they draw their investment strategy.

Keywords: month effect, trading month effect, economic cycles, crisis

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Authors: S. Perera, T. R. Walsh, M. Solvang

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The structure-property relationships of calcium aluminosilicate (CAS) glass surfaces are of scientific and technological interest regarding dissolution phenomena. Molecular dynamics (MD) simulations can provide atomic-scale insights into the structure and properties of the CAS interfaces in vacuo as the first step to conducting computational dissolution studies on CAS surfaces. However, one limitation to date is that although the bulk properties of CAS glasses have been well studied by MD simulation, corresponding efforts on CAS surface properties are relatively few in number (both theoretical and experimental). Here, a systematic computational protocol to create CAS surfaces in vacuo is developed by evaluating the sensitivity of the resultant surface structure with respect to different factors. Factors such as the relative thickness of the surface layer, the relative thickness of the bulk region, the cooling rate, and the annealing schedule (time and temperature) are explored. Structural features such as ring size distribution, defect concentrations (five-coordinated aluminium (AlV), non-bridging oxygen (NBO), and tri-cluster oxygen (TBO)), and linkage distribution are identified as significant features in dissolution studies.

Keywords: MD simulation, CAS glasses, surface structure, structure-property, CAS interface

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Authors: Ororho Maureen Ekpelezie

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This study investigates language educators' perceptions and experiences regarding AI-driven personalized teacher training modules in Awka South, Anambra State, Nigeria. Utilizing a stratified random sampling technique, 25 schools across various educational levels were selected to ensure a representative sample. A total of 1000 questionnaires were distributed among language teachers in these schools, focusing on assessing their perceptions and experiences related to AI-driven personalized teacher training. With an impressive response rate of 99.1%, the study garnered valuable insights into language teachers' attitudes towards AI-driven personalized teacher training and its effectiveness in enhancing language teaching competence. The quantitative analysis revealed predominantly positive perceptions towards AI-driven personalized training modules, indicating their efficacy in addressing individual learning needs. However, challenges were identified in the long-term retention and transfer of AI-enhanced skills, underscoring the necessity for further refinement of personalized training approaches. Recommendations stemming from these findings emphasize the need for continued refinement of training methodologies and the development of tailored professional development programs to alleviate educators' concerns. Overall, this research enriches discussions on the integration of AI technology in teacher training and professional development, with the aim of bolstering language teaching competence and effectiveness in educational settings.

Keywords: language teacher training, AI-driven personalized learning, professional development, language teaching competence, personalized teacher training

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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Authors: Marc Arthur Bien Aimé, Julio da Silveira Moreira

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This study examines the political speech in Haiti over the course of the decade 2011-2021, focusing on the speeches of the presidents Michel J. Martelly and Jovenel Moïse and their impacts on their awareness collective. In using a qualitative approach, we have analyzed the speech of the president pronounced in response to the political instability of countries, as well as interviews with a group of 20 Haitians living in Port- Au-Prince. Our results put in evidence their complex relationship between politics, awareness collective, and the influence of the powers imperialists. We show that the situation in Haiti's disastrous social and political situation is driven by personal political interests and the absence of a state political project. Moreover, the speeches of the president’s analysis are meaningless, transforming concepts such as social progress and justice in simple words. This political rhetoric contributes to the domination symbolic of the population of Haitian. This study is also linked to the theme “Constitutions, processes democratic and critical of the state in Latin America,” emphasizing the importance of analysis of political speech to understand the complexities of the democratic process and criticism of the State in their Latin American region. We suggest future research to deepen our understanding of these political dynamics and their impact on public policies and developments of the constitutions throughout Latin America.

Keywords: political discourse, conscience collective, inequality social, democratic processes, constitutions, Haiti

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Authors: Daniel James Pitchforth, Timothy James Rogers, Ulf Tyge Tygesen, Elizabeth Jane Cross

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Wave loading is a primary cause of fatigue within offshore structures and its quantification presents a challenging and important subtask within the SHM framework. The accurate representation of physics in such environments is difficult, however, driving the development of data-driven techniques in recent years. Within many industrial applications, empirical laws remain the preferred method of wave loading prediction due to their low computational cost and ease of implementation. This paper aims to develop an approach that combines data-driven Gaussian process models with physical empirical solutions for wave loading, including Morison’s Equation. The aim here is to incorporate physics directly into the covariance function (kernel) of the Gaussian process, enforcing derived behaviors whilst still allowing enough flexibility to account for phenomena such as vortex shedding, which may not be represented within the empirical laws. The combined approach has a number of advantages, including improved performance over either component used independently and interpretable hyperparameters.

Keywords: offshore structures, Gaussian processes, Physics informed machine learning, Kernel design

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Authors: Yasmeen A. S. Essawy, Khaled Nassar

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With the rapid increase of complexity in the building industry, professionals in the A/E/C industry were forced to adopt Building Information Modeling (BIM) in order to enhance the communication between the different project stakeholders throughout the project life cycle and create a semantic object-oriented building model that can support geometric-topological analysis of building elements during design and construction. This paper presents a model that extracts topological relationships and geometrical properties of building elements from an existing fully designed BIM, and maps this information into a directed acyclic Elemental Graph Data Model (EGDM). The model incorporates BIM-based search algorithms for automatic deduction of geometrical data and topological relationships for each building element type. Using graph search algorithms, such as Depth First Search (DFS) and topological sortings, all possible construction sequences can be generated and compared against production and construction rules to generate an optimized construction sequence and its associated schedule. The model is implemented in a C# platform.

Keywords: building information modeling (BIM), elemental graph data model (EGDM), geometric and topological data models, graph theory

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Authors: Muhammad Iqbal, Edi Istiyono

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Misconceptions are one of the problems in physics learning where students' understanding is not in line with scientific theory. The aim of this research is to find diagnostic tools to identify misconceptions and how to remediate physics misconceptions. In this research, the articles that will be reviewed come from the Scopus database related to physics misconceptions from 2013-2023. The articles obtained from the Scopus database were then selected according to the Prisma model, so 29 articles were obtained that focused on discussing physics misconceptions, especially regarding diagnostic tools and remediation methods. Currently, the most widely used diagnostic tool is the four-tier test, which is able to measure students' misconceptions in depth by knowing whether students are guessing or not and from then on, there is also a trend toward five-tier diagnostic tests with additional sources of information obtained. So that the origin of students' misconceptions is known. There are several ways to remediate student misconceptions, namely 11 ways and one of the methods used is digital practicum so that abstract things can be visualized into real ones. This research is limited to knowing what tools are used to diagnose and remediate misconceptions, so it is not yet known how big the effect of remediation methods is on misconceptions. The researcher recommends that in the future further research can be carried out to find out the most appropriate remediation method for remediating student misconceptions.

Keywords: misconception, remediation, systematic review, tools

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Authors: Monjurul Hasan, Golam Kibria, Abdus Salam

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Pervious concrete is a form of lightweight porous concrete, obtained by eliminating the fine aggregate from the normal concrete mix. The advantages of this type of concrete are lower density, lower cost due to lower cement content, lower thermal conductivity, relatively low drying shrinkage, no segregation and capillary movement of water. In this paper an investigation is made on the mechanical response of the pervious concrete at zero fine level (zero fine concrete) made with local brick aggregate. Effect of aggregate size variation on the strength, void ratio and permeability of the zero fine concrete is studied. Finally, a comparison is also presented between the stone aggregate made pervious concrete and brick aggregate made pervious concrete. In total 75 concrete cylinder were tested for compressive strength, 15 cylinder were tested for void ratio and 15 cylinder were tested for permeability test. Mix proportion (cement: Coarse aggregate) was kept fixed at 1:6 (by weights), where water cement ratio was valued 0.35 for preparing the sample specimens. The brick aggregate size varied among 25mm, 19mm, 12mm. It has been found that the compressive strength decreased with the increment of aggregate size but permeability increases and concrete made with 19mm maximum aggregate size yields the optimum value. No significant differences on the strength and permeability test are observed between the brick aggregate made zero fine concrete and stone aggregate made zero fine concrete.

Keywords: pervious concrete, brick aggregate concrete, zero fine concrete, permeability, porosity

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Authors: Samia Rabet, Rachida Chemini, Gerhard Schäfer, Farid Aiouache

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The textile industry generates large quantities of wastewater, which poses significant environmental problems due to its complex composition and high levels of pollutants loaded principally with heavy metals, large amounts of COD, and dye. Separation treatment methods are often known for their effectiveness in removing contaminants whereas membrane separation techniques are a promising process for the treatment of textile effluent due to their versatility, efficiency, and low energy requirements. This study focuses on the modeling and simulation of membrane separation technologies with a cross-flow filtration process for textile effluent treatment. It aims to explore the application of mathematical models and computational simulations using ASPEN Plus Software in the prediction of a complex and real effluent separation. The results demonstrate the effectiveness of modeling and simulation techniques in predicting pollutant removal efficiencies with a global deviation percentage of 1.83% between experimental and simulated results; membrane fouling behavior, and overall process performance (hydraulic resistance, membrane porosity) were also estimated and indicating that the membrane losses 10% of its efficiency after 40 min of working.

Keywords: membrane separation, ultrafiltration, textile effluent, modeling, simulation

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Authors: Nafiseh Mollaei, Reihaneh Kardehi Moghaddam

Abstract:

In case of Diabetes Mellitus the controlling of insulin is very difficult. This illness is an incurable disease affecting millions of people worldwide. Glucose is a sugar which provides energy to the cells. Insulin is a hormone which supports the absorption of glucose. Fuzzy control strategy is attractive for glucose control because it mimics the first and second phase responses that the pancreas beta cells use to control glucose. We propose two control algorithms a type-1 fuzzy controller and an interval type-2 fuzzy method for the insulin infusion. The closed loop system has been simulated for different patients with different parameters, in present of the food intake disturbance and it has been shown that the blood glucose concentrations at a normoglycemic level of 110 mg/dl in the reasonable amount of time. This paper deals with type 1 diabetes as a nonlinear model, which has been simulated in MATLAB-SIMULINK environment. The novel model, termed the Augmented Minimal Model is used in the simulations. There are some uncertainties in this model due to factors such as blood glucose, daily meals or sudden stress. In addition to eliminate the effects of uncertainty, different control methods may be utilized. In this article, fuzzy controller performance were assessed in terms of its ability to track a normoglycemic set point (110 mg/dl) in response to a [0-10] g meal disturbance. Finally, the development reported in this paper is supposed to simplify the insulin delivery, so increasing the quality of life of the patient.

Keywords: interval type-2, fuzzy controller, minimal augmented model, uncertainty

Procedia PDF Downloads 423

Authors: Ensieh Hajeb, Hefzollah Mohammadiyan, Mohamad Baqer Heidari

Abstract:

In this paper layout plans and determine the best place to install a heat recovery boilers , gas turbines , and simulation models built to evaluate the performance of the design and operating conditions, heat recovery boiler design using model built on the basis of operating conditions , the effect of various parameters on the performance of the designed heat recovery boiler , heat recovery boiler installation was designed to evaluate the technical and economic impact on performance would be Turbo generator. Given the importance of this issue, that is the main goal of economic efficiency and reduces costs; this project has been implemented similar plans in which the target is implementation specific patterns. The project will also help us in the process of gas refineries and the actual efficiency of the process after adding a system to analyze the turbine and predict potential problems and how to fix them and appropriate measures according to the results of simulation analysis and results of the process gain. The results of modeling and the effect of different parameters on this line, the software has been ThermoFlow.

Keywords: boiler, gas turbine, turbo generator, power flow

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Authors: Nagasamy Venkatesh Dhandapani

Abstract:

The objective of the present study was to develop sustained release oral matrix tablets of anti epileptic drug levetiracetam. The sustained release matrix tablets of levetiracetam were prepared using hydrophilic matrix hydroxypropyl methylcellulose (HPMC) as a release retarding polymer by wet granulation method. Prior to compression, FTIR studies were performed to understand the compatibility between the drug and excipients. The study revealed that there was no chemical interaction between drug and excipients used in the study. The tablets were characterized by physical and chemical parameters and results were found in acceptable limits. In vitro release study was carried out for the tablets using 0.1 N HCl for 2 hours and in phosphate buffer pH 7.4 for remaining time up to 12 hours. The effect of polymer concentration was studied. Different dissolution models were applied to drug release data in order to evaluate release mechanisms and kinetics. The drug release data fit well to zero order kinetics. Drug release mechanism was found as a complex mixture of diffusion, swelling and erosion.

Keywords: levetiracetam, sustained-release, hydrophilic matrix tablet, HPMC grade K 100 MCR, wet granulation, zero order release kinetics

Procedia PDF Downloads 312

Authors: Rueyhung Weng, Chia-En Huang, Jung-Chi-Liao

Abstract:

The transition zone (TZ) serves as a diffusion barrier to regulate the ins and outs of the proteins recruited to the primary cilia. TCTN2 is one of the TZ proteins and its mutation causes Joubert syndrome, a serious multi-organ disease. Despite its important medical relevance, the functions of TCTN2 remain elusive. Here we created a TCTN2 gene deleted retinal pigment epithelial cells (RPE1) using CRISPR/Cas9-based genome editing technique and used this knockout line to reveal roles of TCTN2. TCTN2 knockout RPE1 cells displayed a significantly reduced ciliogenesis or a shortened primary cilium length in the cilium-remaining population. Intraflagellar transport protein IFT88 aberrantly accumulated at the tip of TCTN2 deficient cells. Guanine nucleotide exchange factor Arl13B was mostly absent from the ciliary compartment, with a small population localizing at the ciliary tip. The deficient TZ was corroborated with the mislocalization of two other TZ proteins TMEM67 and MKS1. In addition, TCTN2 deficiency induced TZ impairment led to the suppression of Sonic hedgehog signaling in response to Smoothened (Smo) agonist. Together, depletion of TCTN2 destabilizes other TZ proteins and considerably alters the localization of key transport and signaling-associated proteins, including IFT88, Arl13B, and Smo.

Keywords: CRISPR/Cas9, primary cilia, Sonic hedgehog signaling, transition zone

Procedia PDF Downloads 341