Search results for: adaptive facade

Authors: Amal A. Hassan, Faten H. Fahmy, Abd El-Shafy A. Nafeh, Hosam K. M. Youssef

Abstract:

Due to the growing penetration of wind energy into the power grid, it is very important to study its interactions with the power system and to provide good control technique in order to deliver high quality power. In this paper, an intelligent control methodology is proposed for optimizing the controllers’ parameters of doubly fed induction generator (DFIG) based wind turbine generation system (WTGS). The genetic algorithm (GA) and particle swarm optimization (PSO) are employed and compared for the parameters adaptive tuning of the proposed proportional integral (PI) multiple controllers of the back to back converters of the DFIG based WTGS. For this purpose, the dynamic model of WTGS with DFIG and its associated controllers is presented. Furthermore, the simulation of the system is performed using MATLAB/SIMULINK and SIMPOWERSYSTEM toolbox to illustrate the performance of the optimized controllers. Finally, this work is validated to 33-bus test radial system to show the interaction between wind distributed generation (DG) systems and the distribution network.

Keywords: DFIG wind turine, intelligent control, distributed generation, particle swarm optimization, genetic algorithm

Procedia PDF Downloads 267

Authors: Omer Abdalla Ishag, Magdi Baker Amien

Abstract:

Physicians used the stethoscope for listening patient heart sounds in order to make a diagnosis. However, the determination of heart conditions by acoustic stethoscope is a difficult task so it requires special training of medical staff. This study developed an accurate model for analyzing the phonocardiograph signal based on PC and DSP processor. The system has been realized into two phases; offline and real time phase. In offline phase, 30 cases of heart sounds files were collected from medical students and doctor's world website. For experimental phase (real time), an electronic stethoscope has been designed, implemented and recorded signals from 30 volunteers, 17 were normal cases and 13 were various pathologies cases, these acquired 30 signals were preprocessed using an adaptive filter to remove lung sounds. The background noise has been removed from both offline and real data, using wavelet transform, then graphical and statistics features vector elements were extracted, finally a look-up table was used for classification heart sounds cases. The obtained results of the implemented system showed accuracy of 90%, 80% and sensitivity of 87.5%, 82.4% for offline data, and real data respectively. The whole system has been designed on TMS320VC5509a DSP Platform.

Keywords: code composer studio, heart sounds, phonocardiograph, wavelet transform

Procedia PDF Downloads 446

Authors: Pankaj Kumar

Abstract:

Rapid global changes resulted in unfavorable hydrological, ecological, and environmental changes and cumulatively affected natural resources. As a result, the local communities become vulnerable to water stress, poor hygiene, the spread of diseases, food security, etc.. However, the central point for this vulnerability revolves around water resources and the way people interrelate with the hydrological system. Also, most of the efforts to minimize the adverse effect of global changes are centered on the mitigation side. Hence, countries with poor adaptive capacities and poor governance suffer most in case of disasters. However, several transdisciplinary numerical tools are well designed and are capable of answering “what-if questions” through scenario analysis using a system approach. This study has predicted the future water environment in Marikina River in the National Capital Region, Metro Manila of Philippines, using Water Evaluation and Planning (WEAP), an integrated water resource management tool. Obtained results can answer possible adaptation measures along with their associated uncertainties. It also highlighted various challenges for the policy planners to design adaptation countermeasures as well as to track the progress of achieving SDG 6.0.

Keywords: water quality, Philippines, climate change adaptation, hydrological simulation, wastewater management, weap

Procedia PDF Downloads 105

Authors: Maria Ezziani, Rabie Zine, Amine Amar, Ilhame Kissani

Abstract:

This paper introduces a predictive model for urban transportation engineering, which is vital for efficient traffic management. Utilizing comprehensive datasets and advanced statistical techniques, the model accurately forecasts travel times by considering temporal variations and traffic dynamics. Machine learning algorithms, including regression trees and neural networks, are employed to capture sequential dependencies. Results indicate significant improvements in predictive accuracy, particularly during peak hours and holidays, with the incorporation of traffic flow and speed variables. Future enhancements may integrate weather conditions and traffic incidents. The model's applications range from adaptive traffic management systems to route optimization algorithms, facilitating congestion reduction and enhancing journey reliability. Overall, this research extends beyond travel time estimation, offering insights into broader transportation planning and policy-making realms, empowering stakeholders to optimize infrastructure utilization and improve network efficiency.

Keywords: predictive analytics, traffic flow, travel time estimation, urban transportation, machine learning, traffic management

Procedia PDF Downloads 84

Authors: A. Derrouazin, N. Mekkakia-M, R. Taleb, M. Helaimi, A. Benbouali

Abstract:

Nowadays the use of different sources of renewable energy for the production of electricity is the concern of everyone, as, even impersonal domestic use of the electricity in isolated sites or in town. As the conventional sources of energy are shrinking, a need has arisen to look for alternative sources of energy with more emphasis on its optimal use. This paper presents design of a sustainable Hybrid Power System (PV-Wind-Storage) assisted by grid as supplementary sources applied to case study residential house, to meet its entire energy demand. A Fuzzy control system model has been developed to optimize and control flow of power from these sources. This energy requirement is mainly fulfilled from PV and Wind energy stored in batteries module for critical load of a residential house and supplemented by grid for base and peak load. The system has been developed for maximum daily households load energy of 3kWh and can be scaled to any higher value as per requirement of individual /community house ranging from 3kWh/day to 10kWh/day, as per the requirement. The simulation work, using intelligent energy management, has resulted in an optimal yield leading to average reduction in cost of electricity by 50% per day.

Keywords: photovoltaic (PV), wind turbine, battery, microcontroller, fuzzy control (FC), Matlab

Procedia PDF Downloads 648

Authors: Arthur D. Wiedemann, Christopher Fuller, Kyle A. Pascioni

Abstract:

With the introduction of electric motors, small unmanned aerial vehicle designers have to consider trade-offs between acoustic noise and thrust generated. Currently, there are few low-computational tools available for predicting acoustic noise emitted by a propeller into the far-field. Artificial neural networks offer a highly non-linear and adaptive model for predicting isolated and interactive tonal noise. But neural networks require large data sets, exceeding practical considerations in modeling experimental results. A methodology known as physics guided machine learning has been applied in this study to reduce the required data set to train the network. After building and evaluating several neural networks, the best model is investigated to determine how the network successfully predicts the acoustic waveform. Lastly, a post-network transfer function is developed to remove discontinuity from the predicted waveform. Overall, methodologies from physics guided machine learning show a notable improvement in prediction performance, but additional loss functions are necessary for constructing predictive networks on small datasets.

Keywords: aeroacoustics, machine learning, propeller, rotor, neural network, physics guided machine learning

Procedia PDF Downloads 228

Authors: Ritu Chaturvedi, Manoj Paul

Abstract:

A pot experiment was conducted to investigate the effect of Arbuscular mycorrhizal fungus (AMF) on metal(loid) uptake and accumulation efficiency of Pisum sativum along with physiological and biochemical response. Plants were grown in soil spiked with 50 and 100 mg kg-1 Pb, 25 and 50 mg kg-1 Cd, 50 and 100 mg kg-1 As and a combination of all three metal(loid)s. A parallel set was maintained and inoculated with arbuscular mycorrhizal fungus for comparison. After 60 days, plants were harvested and analysed for metal(loid) content. A steady increase in metal(loid) accumulation was observed on increment of metal(loid) dose and also on AMF inoculation. Plant height, biomass, chlorophyll, carotenoid and carbohydrate content reduced upon metal(loid) exposure. Increase in enzymatic (CAT, SOD and APX) and nonenzymatic (Proline) defence proteins was observed on metal(loid) exposure. AMF inoculation leads to an increase in plant height, biomass, chlorophyll, carotenoids, carbohydrate and enzymatic defence proteins (p≤0.001) under study; whereas proline content was reduced. Considering the accumulation efficiency and adaptive response of plants and alleviation of stress by AMF, this symbiosis can be applied for on-site remediation of Pb and Cd contaminated soil.

Keywords: heavy metal, mycorrhiza, pea, phyroremediation

Procedia PDF Downloads 234

Authors: Christina Adly, Meena Abdelmeseeh, Tamer Basha

Abstract:

This paper proposes a system for hand movement recognition using multichannel surface EMG(sEMG) signals obtained from 40 subjects using 40 different exercises, which are available on the Ninapro(Non-Invasive Adaptive Prosthetics) database. First, we applied processing methods to the raw sEMG signals to convert them to their amplitudes. Second, we used deep learning methods to solve our problem by passing the preprocessed signals to Fully connected neural networks(FCNN) and recurrent neural networks(RNN) with Long Short Term Memory(LSTM). Using intrasubject evaluation, The accuracy using the FCNN is 72%, with a processing time for training around 76 minutes, and for RNN's accuracy is 79.9%, with 8 minutes and 22 seconds processing time. Third, we applied some postprocessing methods to improve the accuracy, like majority voting(MV) and Movement Error Rate(MER). The accuracy after applying MV is 75% and 86% for FCNN and RNN, respectively. The MER value has an inverse relationship with the prediction delay while varying the window length for measuring the MV. The different part uses the RNN with the intersubject evaluation. The experimental results showed that to get a good accuracy for testing with reasonable processing time, we should use around 20 subjects.

Keywords: hand movement recognition, recurrent neural network, movement error rate, intrasubject evaluation, intersubject evaluation

Procedia PDF Downloads 142

Authors: Muhammad Bilal Shaikh

Abstract:

Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

Procedia PDF Downloads 68

Authors: Ariful Islam, Showkat Ahmad Lone

Abstract:

Statistical distributions have long been employed in the assessment of semiconductor devices and product reliability. The power function-distribution is one of the most important distributions in the modern reliability practice and can be frequently preferred over mathematically more complex distributions, such as the Weibull and the lognormal, because of its simplicity. Moreover, it may exhibit a better fit for failure data and provide more appropriate information about reliability and hazard rates in some circumstances. This study deals with estimating information about failure times of items under step-stress partially accelerated life tests for competing risk based on adoptive type-I progressive hybrid censoring criteria. The life data of the units under test is assumed to follow Mukherjee-Islam distribution. The point and interval maximum-likelihood estimations are obtained for distribution parameters and tampering coefficient. The performances of the resulting estimators of the developed model parameters are evaluated and investigated by using a simulation algorithm.

Keywords: adoptive progressive hybrid censoring, competing risk, mukherjee-islam distribution, partially accelerated life testing, simulation study

Procedia PDF Downloads 347

Authors: Stuti Modi, Aditi Kambli

Abstract:

This paper addresses the need for intelligent water management and distribution system in smart cities to ensure optimal consumption and distribution of water for drinking and sanitation purposes. Water being a limited resource in cities require an effective system for collection, storage and distribution. In this paper, applications of two mostly widely used particular types of data-driven models, namely artificial neural networks (ANN) and fuzzy logic-based models, to modelling in the water resources management field are considered. The objective of this paper is to review the principles of various types and architectures of neural network and fuzzy adaptive systems and their applications to integrated water resources management. Final goal of the review is to expose and formulate progressive direction of their applicability and further research of the AI-related and data-driven techniques application and to demonstrate applicability of the neural networks, fuzzy systems and other machine learning techniques in the practical issues of the regional water management. Apart from this the paper will deal with water storage, using ANN to find optimum reservoir level and predicting peak daily demands.

Keywords: artificial neural networks, fuzzy systems, peak daily demand prediction, water management and distribution

Procedia PDF Downloads 186

Authors: Mahdi Motahari, Mojtaba Farzaneh, Ebrahim Sepidbar

Abstract:

Tracking of moving and flying targets is one of the most important issues in image processing topic. Estimating of trajectory of desired object in short-term and long-term scale is more important than tracking of moving and flying targets. In this paper, a new way of identifying and estimating of future trajectory of a moving ball in long-term scale is estimated by using synthesis and interaction of image processing algorithms including noise removal and image segmentation, Kalman filter algorithm in order to estimating of trajectory of ball in football game in short-term scale and intelligent adaptive neuro-fuzzy algorithm based on time series of traverse distance. The proposed system attain more than 96% identify accuracy by using aforesaid methods and relaying on aforesaid algorithms and data base video in format of synthesis and interaction. Although the present method has high precision, it is time consuming. By comparing this method with other methods we realize the accuracy and efficiency of that.

Keywords: tracking, signal processing, moving targets and flying, artificial intelligent systems, estimating of trajectory, Kalman filter

Procedia PDF Downloads 459

Authors: Gautam Kumar Saharia, Sagardeep Talukdar, Riki Dutta, Sudipta Nandy

Abstract:

The physics informed neural network (PINN) method opens up an approach for numerically solving nonlinear partial differential equations leveraging fast calculating speed and high precession of modern computing systems. We construct the PINN based on strong universal approximation theorem and apply the initial-boundary value data and residual collocation points to weekly impose initial and boundary condition to the neural network and choose the optimization algorithms adaptive moment estimation (ADAM) and Limited-memory Broyden-Fletcher-Golfard-Shanno (L-BFGS) algorithm to optimize learnable parameter of the neural network. Next, we improve the PINN with a weighted loss function to obtain both the bright and dark soliton solutions of Fokas-Lenells equation (FLE). We find the proposed scheme of adjustable weight coefficients into PINN has a better convergence rate and generalizability than the basic PINN algorithm. We believe that the PINN approach to solve the partial differential equation appearing in nonlinear optics would be useful to study various optical phenomena.

Keywords: deep learning, optical Soliton, neural network, partial differential equation

Procedia PDF Downloads 126

Authors: Kaushik Sathupadi, Sandesh Achar

Abstract:

Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, machine learning

Procedia PDF Downloads 36

Authors: Zhenming Wang, Jun Zhu, Yuchen Yang, Ning Zhao

Abstract:

In the present paper, an alternative framework is proposed to construct a class of finite difference multi-resolution nested weighted essentially non-oscillatory (WENO) schemes with an increasingly higher order of accuracy for solving inviscid Euler equations. These WENO schemes firstly obtain a set of reconstruction polynomials by a hierarchy of nested central spatial stencils, and then recursively achieve a higher order approximation through the lower-order precision WENO schemes. The linear weights of such WENO schemes can be set as any positive numbers with a requirement that their sum equals one and they will not pollute the optimal order of accuracy in smooth regions and could simultaneously suppress spurious oscillations near discontinuities. Numerical results obtained indicate that these alternative finite-difference multi-resolution nested WENO schemes with different accuracies are very robust with low dissipation and use as few reconstruction stencils as possible while maintaining the same efficiency, achieving the high-resolution property without any equivalent multi-resolution representation. Besides, its finite volume form is easier to implement in unstructured grids.

Keywords: finite-difference, WENO schemes, high order, inviscid Euler equations, multi-resolution

Procedia PDF Downloads 145

Authors: Jay N. Vyas, Sachin Daxini

Abstract:

Numerical simulation techniques are widely used now in product development and testing instead of expensive, time-consuming and sometimes dangerous laboratory experiments. Numerous numerical methods are available for performing simulation of physical problems of different engineering fields. Grid based methods, like Finite Element Method, are extensively used in performing various kinds of static, dynamic, structural and non-structural analysis during product development phase. Drawbacks of grid based methods in terms of discontinuous secondary field variable, dealing fracture mechanics and large deformation problems led to development of a relatively a new class of numerical simulation techniques in last few years, which are popular as Meshless methods or Meshfree Methods. Meshless Methods are expected to be more adaptive and flexible than Finite Element Method because domain descretization in Meshless Method requires only nodes. Present paper introduces Meshless Methods and differentiates it with Finite Element Method in terms of following aspects: Shape functions used, role of weight function, techniques to impose essential boundary conditions, integration techniques for discrete system equations, convergence rate, accuracy of solution and computational effort. Capabilities, benefits and limitations of Meshless Methods are discussed and concluded at the end of paper.

Keywords: numerical simulation, Grid-based methods, Finite Element Method, Meshless Methods

Procedia PDF Downloads 389

Authors: Minjung Maing, Sibei Liu

Abstract:

This research discusses the results of a study of window opening behavior of large housing developments in the high-density megacity of Hong Kong. The methods used for the study involved field observations using photo documentation of the four cardinal elevations (north, south-east, and west) of two large housing developments in a very dense urban area of approx. 46,000 persons per square meter within the city of Hong Kong. The targeted housing developments (A and B) are large public housing with a population of about 13,000 in each development of lower income. However, the mean income level in development A is about 40% higher than development B and home ownership is 60% in development A and 0% in development B. Mapping of the surrounding amenities and layout of the developments were also studied to understand the available activities to the residents. The photo documentation of the elevations was taken from November 2016 to February 2018 to gather a full spectrum of different seasons and both in the morning and afternoon (am/pm) times. From the photograph, the window opening behavior was measured by counting the amount of windows opened as a percentage of all the windows on that façade. For each date of survey data collected, weather data was recorded from weather stations located in the same region to collect temperature, humidity and wind speed. To further understand the behavior, simulation studies of microclimate conditions of the housing development was conducted using the software ENVI-met, a widely used simulation tool by researchers studying urban climate. Four major conclusions can be drawn from the data analysis and simulation results. Firstly, there is little change in the amount of window opening during the different seasons within a temperature range of 10 to 35 degrees Celsius. This means that people who tend to open their windows have consistent window opening behavior throughout the year and high tolerance of indoor thermal conditions. Secondly, for all four elevations the lower-income development B opened more windows (almost two times more units) than higher-income development A meaning window opening behavior had strong correlations with income level. Thirdly, there is a lack of correlation between outdoor horizontal wind speed and window opening behavior, as the changes of wind speed do not seem to affect the action of opening windows in most conditions. Similar to the low correlation between horizontal wind speed and window opening percentage, it is found that vertical wind speed also cannot explain the window opening behavior of occupants. Fourthly, there is a slightly higher average of window opening on the south elevation than the north elevation, which may be due to the south elevation being well shaded from high angle sun during the summer and allowing heat into units from lower angle sun during the winter season. These findings are important to providing insight into how to better design urban environments and indoor thermal environments for a liveable high density city.

Keywords: high-density housing, subtropical climate, urban behavior, window opening

Procedia PDF Downloads 125

Authors: Yana Gorina, Olga Lopatina, Elina Tsigeman, Larisa Mararitsa

Abstract:

The ability to act in concert with others, the so-called sensorimotor synchronisation, is a fundamental human ability that underlies successful interpersonal coordination. The manifestation of accuracy and plasticity in synchronisation is an adaptive aspect of interaction with the environment, as well as the ability to predict upcoming actions and behaviour of others. The ability to temporarily coordinate one’s actions with a predictable external event is manifested in such types of social behaviour as a synchronised group dance to music played live by an orchestra, group sports (rowing, swimming, etc.), synchronised actions of surgeons during an operation, applause from an admiring audience, walking rhythms, etc. Both our body and mind are involved in achieving the synchronisation during social interactions. However, it has not yet been well described how the brain determine the external rhythm and what neuropeptides coordinate and synchronise actions. Over the past few decades, there has been an increased interest among neuroscientists and neurophysiologists regarding the neuropeptide oxytocin in the context of its complex, diverse and sometimes polar effects manifested in the emotional and social aspects of behaviour (attachment, trust, empathy, emotion recognition, stress response, anxiety and depression, etc.). Presumable, oxytocin might also be involved in social synchronisation processes. The aim of our study is to test the hypothesis that oxytocin is linked to interpersonal synchronisation in a pair of strangers.

Keywords: behavior, movement, oxytocin, synchronization

Procedia PDF Downloads 62

Authors: Sumeyra Karatas, Veysel Karatas, Reyhan Safak, Gamze Bulut-Ozturk, Ozgul Kartal

Abstract:

Productive struggle entails a student's cognitive exertion to comprehend mathematical concepts and uncover solutions not immediately apparent. The significance of productive struggle in learning mathematics is accentuated by influential educational theorists, emphasizing its necessity for learning mathematics with understanding. Consequently, supporting productive struggle in learning mathematics is recognized as a high-leverage and effective mathematics teaching practice. In this study, the investigation into the role of productive struggle in learning mathematics led to the development of a comprehensive rubric for productive struggle pedagogy through an exhaustive literature review. The rubric consists of eight primary criteria and 37 sub-criteria, providing a detailed description of teacher actions and pedagogical choices that foster students' productive struggles. These criteria encompass various pedagogical aspects, including task design, tool implementation, allowing time for struggle, posing questions, scaffolding, handling mistakes, acknowledging efforts, and facilitating discussion/feedback. Utilizing this rubric, a team of researchers and teachers designed eight 90-minute lesson plans, employing a productive struggle pedagogy, for a two-week unit on solving systems of linear equations. Simultaneously, another set of eight lesson plans on the same topic, featuring identical content and problems but employing a traditional lecture-and-practice model, was designed by the same team. The objective was to assess the impact of supporting productive struggle on students' mathematics learning, defined by the strands of mathematical proficiency. This quasi-experimental study compares the control group, which received traditional lecture- and practice instruction, with the treatment group, which experienced a productive struggle in pedagogy. Sixty-six 10th and 11th-grade students from two algebra classes, taught by the same teacher at a high school, underwent either the productive struggle pedagogy or lecture-and-practice approach over two-week eight 90-minute class sessions. To measure students' learning, an assessment was created and validated by a team of researchers and teachers. It comprised seven open-response problems assessing the strands of mathematical proficiency: procedural and conceptual understanding, strategic competence, and adaptive reasoning on the topic. The test was administered at the beginning and end of the two weeks as pre-and post-test. Students' solutions underwent scoring using an established rubric, subjected to expert validation and an inter-rater reliability process involving multiple criteria for each problem based on their steps and procedures. An analysis of covariance (ANCOVA) was conducted to examine the differences between the control group, which received traditional pedagogy, and the treatment group, exposed to the productive struggle pedagogy, on the post-test scores while controlling for the pre-test. The results indicated a significant effect of treatment on post-test scores for procedural understanding (F(2, 63) = 10.47, p < .001), strategic competence (F(2, 63) = 9.92, p < .001), adaptive reasoning (F(2, 63) = 10.69, p < .001), and conceptual understanding (F(2, 63) = 10.06, p < .001), controlling for pre-test scores. This demonstrates the positive impact of supporting productive struggle in learning mathematics. In conclusion, the results revealed the significance of the role of productive struggle in learning mathematics. The study further explored the practical application of productive struggle through the development of a comprehensive rubric describing the pedagogy of supporting productive struggle.

Keywords: effective mathematics teaching practice, high school algebra, learning mathematics, productive struggle

Procedia PDF Downloads 51

Authors: Aleksandra Majewska

Abstract:

The article analyses the impact of Western sanctions on Russian rhythmic gymnastics since the outbreak of war in Ukraine. The chronological presentation of events shows how international political tensions and economic sanctions have affected the organisation of competitions, training and the careers of athletes. The article outlines the key moments and decisions that have changed the landscape of Russian sport, including the decision to change the citizenship made by some gymnasts in order to continue competing in international competitions. Russia strongly opposes participation in competitions without its flag and anthem while maintaining the view that Russian gymnasts are crucial to the prestige of rhythmic gymnastics in the world. In response to the sanctions, Russia created its own rules for rhythmic gymnastics, according to which they now compete domestically. Furthermore, this sport in Russia is strongly linked to politics, which further emphasises its importance in the national and international context. The information collected derives from numerous interviews with Russian athletes, coaches and other people, which are available only in the Russian language. The findings highlight the significant difficulties Russian athletes have faced due to their isolation in the international arena and the adaptive strategies adopted by Russia in the face of these challenges. The article makes an important contribution to understanding the consequences of global politics on the world of sport and the fate of individual athletes.

Keywords: sport, gymnastics, war in Ukraine, sanctions

Procedia PDF Downloads 39

Authors: Shyam Sunder Kawan

Abstract:

Museums are enclosed buildings encompassing and displaying creative artworks, artefacts, and discoveries for people’s knowledge and observation. In the context of Nepal, museums and exhibition areas are either adaptive to small gallery spaces in residences or ‘neo-classical palatial complexes’ that evolved during the 19th century. This study accepts the sparse occurrence of a diverse range of artworks and expressions in the country's complex cultural manifestations within vivid ethnic groups. This study explores the immense potential of one such prevalence beyond the delimitation of physical boundaries. Taking Bhaktapur World Heritage Site as a case, the study perpetuates its investigation into real-time life activities that this city and its cultural landscapes ensemble. Seeking the ‘musealization’ as an urban process to induce museums into the city precinct, this study anticipates art space into urban spaces to offer a limitless experience for this contemporary world. Unveiling art as an experiential component, this study aims to conceptualize a living heritage as an infinite resource for museum interpretation beyond just educational institute purposes.

Keywords: living museum, site museum, museulization, contemporary arts, cultural heritage, historic cities

Procedia PDF Downloads 103

Authors: Emine Umran Topcu

Abstract:

In both urban design and architecture, the primary goal is considered to be looking for ways in which people feel and think about space and place. Humans, in general, see a place as security and space as freedom and feel attached to place and long for space. Contemporary urban design manifests itself by addressing basic physical and psychological human needs. Not much attention is paid to transcendence. There seems to be a gap in the hierarchy of human needs. Usually, social aspects of public space are addressed through urban design. More personal and intimately scaled needs of an individual are neglected. How does built form contribute to an individual’s growth, contemplation, and exploration? In other words, a greater meaning in the immediate environment. Architects love to talk about meaning, poetics, attachment and other ethereal aspects of space that are not visible attributes of places. This paper aims at describing spirituality through built form with a personal experience of Kamppi Chapel of Helsinki. Experience covers various modes through which a person unfolds or constructs reality. Perception, sensation, emotion, and thought can be counted as for these modes. To experience is to get to know. What can be known is a construct of experience. Feelings and thoughts about space and place are very complex in human beings. They grow out of life experiences. The author had the chance of visiting Kamppi Chapel in April 2017, out of which the experience grew. The Kamppi Chapel is located on the South side of the busy Narinnka Square in central Helsinki. It offers a place to quiet down and compose oneself in a most lively urban space. With its curved wooden facade, the small building looks more like a museum than a chapel. It can be called a museum for contemplation. With its gently shaped interior, it embraces visitors and shields them from the hustle bustle of the city outside. Places of worship in all faiths signify sacred power. The author, having origins in a part of the world where domes and minarets dominate the cityscape, was impressed by the size and the architectural visibility of the Chapel. Anyone born and trained in such a tradition shares the inherent values and psychological mechanisms of spirituality, sacredness and the modest realities of their environment. Spirituality in all cultural traditions has not been analyzed and reinterpreted in new conceptual frameworks. Fundamentalists may reject this positivist attitude, but Kamppi Chapel as it stands does not look like it has a say like “I’m a model to be followed”. It just faces the task of representing a religious facility in an urban setting largely shaped by modern urban planning, which seems to the author as looking for a new definition of individual status. The quest between the established and the new is the demand for modern efficiency versus dogmatic rigidity. The architecture here has played a very promising and rewarding role for spirituality. The designers have been the translators for human desire for better life and aesthetic environment for an optimal satisfaction of local citizens and the visitors alike.

Keywords: architecture, Kamppi Chapel, spirituality, urban

Procedia PDF Downloads 182

Authors: J. Lino Alves, L. Lopes

Abstract:

It is not new that industrial design projects are a result of cooperative work from different areas of knowledge. However, in the academic teaching of Industrial Design and Mechanical Engineering courses, it is not recurrent that those competences are mixed before the professional life arrives. This abstract intends to describe two semester experiences carried out by two professors - a mechanical engineer and an industrial designer - in the last two academic years, for which they created mixed teams of Industrial Design and Mechanical Engineering (UPorto University). The two experiences differ in several factors; the main one is related to the challenges of online education, a constraint that affected the second experience. In the first year, even before foreseeing the effects that the pandemic would reconfigure the education system, a partnership with the Education Service of Águas do Porto was established. The purpose of the exercise was the project development of a game that could be an interaction element oriented to potentiate a positive experience and as an educational contribution to the children. In the second year, already foreseeing that the teaching experience would be carried out online, it was decided to design an open briefing, which allowed the groups to choose among three themes: a hand scale game using additive manufacturing; a modular system for ventilated facade using a parametric design basis; or, a modular system for vertical gardens. In methodological terms, besides the weekly follow-up, with the simultaneous support of the two professors, a group self-evaluation was requested; and a form to be filled individually to evaluate other groups. One of the first conclusions is related to the briefing format. Industrial Design students seem comfortable working on an open briefing that allows them to draw the project on a conceptual basis created for that purpose; on the other hand, Mechanical Engineering students were uncomfortable and insecure in the initial phase due to the absence of concrete, closed "order." In other words, it is not recurrent for Mechanical Engineering students that the creative component is stimulated, seemingly leaving them reserved to the technical solution and execution, depriving them of the co-creation phase during the conceptual construction of the project's own brief. Another fact that was registered is related to the leadership positions in the groups, which alternated according to the state of development of the project: design students took the lead during the ideation/concept phase, while mechanical engineering ones took a greater lead during the intermediate development process, namely in the definition of constructive solutions, mass/volume calculations, manufacturing, and material resistance. Designers' competences were again more evident and assumed in the final phase, especially in communication skills, as well as in simulations in the context of use. However, at some moments, it was visible the capacity for quite balanced leadership between engineering and design, in a constant debate centered on the human factor of the project - evidenced in the final solution, in the compromise and balance between technical constraints, functionality, usability, and aesthetics.

Keywords: education, industrial design, mechanical engineering, teaching ethodologies

Procedia PDF Downloads 174

Authors: Akrem Sellami, Imed Riadh Farah, Basel Solaiman

Abstract:

With the development of HyperSpectral Imagery (HSI) technology, the spectral resolution of HSI became denser, which resulted in large number of spectral bands, high correlation between neighboring, and high data redundancy. However, the semantic interpretation is a challenging task for HSI analysis due to the high dimensionality and the high correlation of the different spectral bands. In fact, this work presents a dimensionality reduction approach that allows to overcome the different issues improving the semantic interpretation of HSI. Therefore, in order to preserve the spatial information, the Tensor Locality Preserving Projection (TLPP) has been applied to transform the original HSI. In the second step, knowledge has been extracted based on the adjacency graph to describe the different pixels. Based on the transformation matrix using TLPP, a weighted matrix has been constructed to rank the different spectral bands based on their contribution score. Thus, the relevant bands have been adaptively selected based on the weighted matrix. The performance of the presented approach has been validated by implementing several experiments, and the obtained results demonstrate the efficiency of this approach compared to various existing dimensionality reduction techniques. Also, according to the experimental results, we can conclude that this approach can adaptively select the relevant spectral improving the semantic interpretation of HSI.

Keywords: band selection, dimensionality reduction, feature extraction, hyperspectral imagery, semantic interpretation

Procedia PDF Downloads 354

Authors: B. Siva Kumar Reddy, B. Lakshmi

Abstract:

WiMAX is a high speed broadband wireless access technology that adopted OFDM/OFDMA techniques to supply higher data rates with high spectral efficiency. However, OFDM suffers in view of high Peak to Average Power Ratio (PAPR) and high affect to synchronization errors. In this paper, the high PAPR problem is solved by using phase modulation to get Constant Envelop Orthogonal Frequency Division Multiplexing (CE-OFDM). The synchronization failures are brought down by employing a frequency lock loop, Poly phase clock synchronizer, Costas loop and blind equalizers such as Constant Modulus Algorithm (CMA) equalizer and Sign Kurtosis Maximization Adaptive Algorithm (SKMAA) equalizers. The WiMAX physical layer is executed on Software Defined Radio (SDR) prototype by utilizing USRP N210 as hardware and GNU Radio as software plat-forms. A SNR estimation is performed on the signal received through USRP N210. To empathize wireless propagation in specific environments, a sliding correlator wireless channel sounding system is designed by using SDR testbed.

Keywords: BER, CMA equalizer, Kurtosis equalizer, GNU Radio, OFDM/OFDMA, USRP N210

Procedia PDF Downloads 349

Authors: Shivam Yadav, Neelam Atri

Abstract:

Cyanobacteria, the photosynthetic prokaryotes are indispensable components of paddy soil contribute substantially to the nitrogen economy however often appended with metal load. They are well known to play crucial roles in maintenance of soil fertility and rice productivity. Nickel is one such metal that plays a vital role in the cellular physiology, however at higher concentrations it exerts adverse effects. Arsenic is another toxic metalloid that negatively affects the cyanobacterial proliferation. However species-specific comparative responses under As and Ni is largely unknown. The present study focuses on the comparative effects of nickel (Ni2+) and arsenite (As(III)) on two diazotrophic cyanobacterial species (Anabaena doliolum and Anabaena sp. PCC7120) in terms of antioxidative aspects. Oxidative damage measured in terms of lipid peroxidation and peroxide content was significantly higher after As(III) than Ni treatment as compared to control. Similarly, all the studied enzymatic and non-enzymatic parameters of antioxidative defense system except glutathione reductase (GR) showed greater induction against As(III) than Ni. Moreover, integrating comparative analysis of all studied parameters also demonstrated interspecies variation in terms of stress adaptive strategies reflected through higher sensitivity of Anabaena doliolum over Anabaena PCC7120.

Keywords: antioxidative system, arsenic, cyanobacteria, nickel

Procedia PDF Downloads 154

Authors: Annamarija Paula

Abstract:

Modern psychology is divided into three distinct domains: cognition, affect, and conation. Historically, psychology devalued the importance of studying the effect in order to explain human behavior as it supposedly lacked both rational thought and a scientific foundation. As a result, affect remained the least studied domain for years to come. However, the last 30 years have marked a significant change in perspective, claiming that not only is affect highly adaptive, but it also plays a crucial role in cognitive processes. Affective states have a crucial impact on human behavior, which led to fundamental advances in the study of affective states on perception and judgment. Positive affect and negative affect are distinct entities and have different effects on social information processing. In addition, emotions of the same valence are manifested in distinct and unique physiological reactions indicating that not all forms of positive or negative affect are the same or serve the same purpose. The effect plays a vital role in perception and judgments, which impacts the validity and reliability of memory retrieval. The research paper analyzes key findings from the past three decades of observational and empirical research on affective states and cognition. The paper also addresses the limitations connected to the findings and proposes suggestions for possible future research.

Keywords: memory, affect, perception, judgement, mood congruency effect

Procedia PDF Downloads 130

Authors: Homa Parmoon, Narges Hamzeh

Abstract:

Since today's urban spaces are disposed towards behavioral disorders and lack of security, both qualitative and quantitative aspects of security especially social and physical security are considered as basic necessities in urban planning. This research focused on the variable of place of living, examined social security in the old and new textures, and investigated the amount of residents’ social security in Shiraz including safety, financial, emotional and moral security. To this end, two neighborhoods in region 1 of Shiraz- Qasr-Al-Dasht (old texture) and Moalem (new texture)- were examined through a comparative study of 60 samples lived in two neighborhoods. Data were gathered through two-way ANOVA between the variables of residential context and internal and external security. This analysis represents the significance or insignificance of the model as well as the individual effects of each independent variable on the dependent variable. It was tested by ANCOVA and F-test. Research findings indicated place of living has a significant effect on families’ social security. The safety, financial, emotional, and moral security also represented a great impact on social security. As a result, it can be concluded that social security changes with the changing in place of living.

Keywords: social security, damaged area, two-way ANOVA, Shiraz

Procedia PDF Downloads 139

Authors: Zabeehullah, Fahim Arif, Yawar Abbas

Abstract:

Software Defined Network (SDN) is a next genera-tion networking model which simplifies the traditional network complexities and improve the utilization of constrained resources. Currently, most of the SDN based Internet of Things(IoT) environments use traditional network routing strategies which work on the basis of max or min metric value. However, IoT network heterogeneity, dynamic traffic flow and complexity demands intelligent and self-adaptive routing algorithms because traditional routing algorithms lack the self-adaptions, intelligence and efficient utilization of resources. To some extent, SDN, due its flexibility, and centralized control has managed the IoT complexity and heterogeneity but still Software Defined IoT (SDIoT) lacks intelligence. To address this challenge, we proposed a model called Deep Routing Strategy (DRS) which uses Deep Learning algorithm to perform routing in SDIoT intelligently and efficiently. Our model uses real-time traffic for training and learning. Results demonstrate that proposed model has achieved high accuracy and low packet loss rate during path selection. Proposed model has also outperformed benchmark routing algorithm (OSPF). Moreover, proposed model provided encouraging results during high dynamic traffic flow.

Keywords: SDN, IoT, DL, ML, DRS

Procedia PDF Downloads 110

Authors: Mawloud Mosbah, Bachir Boucheham

Abstract:

We present here the results for a comparative study of some techniques, available in the literature, related to the relevance feedback mechanism in the case of a short-term learning. Only one method among those considered here is belonging to the data mining field which is the K-Nearest Neighbours Algorithm (KNN) while the rest of the methods is related purely to the information retrieval field and they fall under the purview of the following three major axes: Shifting query, Feature Weighting and the optimization of the parameters of similarity metric. As a contribution, and in addition to the comparative purpose, we propose a new version of the KNN algorithm referred to as an incremental KNN which is distinct from the original version in the sense that besides the influence of the seeds, the rate of the actual target image is influenced also by the images already rated. The results presented here have been obtained after experiments conducted on the Wang database for one iteration and utilizing colour moments on the RGB space. This compact descriptor, Colour Moments, is adequate for the efficiency purposes needed in the case of interactive systems. The results obtained allow us to claim that the proposed algorithm proves good results; it even outperforms a wide range of techniques available in the literature.

Keywords: CBIR, category search, relevance feedback, query point movement, standard Rocchio’s formula, adaptive shifting query, feature weighting, original KNN, incremental KNN

Procedia PDF Downloads 280