Search results for: spatial transformer network

Authors: David Nantes, George Gilbert

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There are a lot of risks that might crop up as a tunneling project develops, and it's crucial to be aware of them. Due to the unexpected nature of tunneling projects and the interconnectedness of risk occurrences, the risk assessment approach presents a significant challenge. The purpose of this study is to provide a hybrid FDEMATEL-ANP model to help prioritize risks during tunnel construction projects. The ambiguity in expert judgments and the relative severity of interdependencies across risk occurrences are both taken into consideration by this model, thanks to the Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL). The Analytic Network Process (ANP) method is used to rank priorities and assess project risks. The authors provide a case study of a subway tunneling construction project to back up the validity of their methodology. The results showed that the proposed method successfully isolated key risk factors and elucidated their interplay in the case study. The proposed method has the potential to become a helpful resource for evaluating dangers associated with tunnel construction projects.

Keywords: risk, prioritization, FDEMATEL, ANP, tunneling construction projects

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Authors: Nguyen Chi Quang, Nguyen Duong Tri Nguyen

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This article is the first step to research and outline the structure of the geotechnical database in the geological survey of a power project; in the context of this report creating the database that has been carried out for the Bac Ai pumped storage hydropower project. For the purpose of providing a method of organizing and storing geological and topographic survey data and experimental results in a spatial database, the RockWorks software is used to bring optimal efficiency in the process of exploiting, using, and analyzing data in service of the design work in the power engineering consulting. Three-dimensional (3D) geotechnical models are created from the survey data: such as stratigraphy, lithology, porosity, etc. The results of the 3D geotechnical model in the case of Bac Ai pumped storage hydropower project include six closely stacked stratigraphic formations by Horizons method, whereas modeling of engineering geological parameters is performed by geostatistical methods. The accuracy and reliability assessments are tested through error statistics, empirical evaluation, and expert methods. The three-dimensional model analysis allows better visualization of volumetric calculations, excavation and backfilling of the lake area, tunneling of power pipelines, and calculation of on-site construction material reserves. In general, the application of engineering geological modeling makes the design work more intuitive and comprehensive, helping construction designers better identify and offer the most optimal design solutions for the project. The database always ensures the update and synchronization, as well as enables 3D modeling of geological and topographic data to integrate with the designed data according to the building information modeling. This is also the base platform for BIM & GIS integration.

Keywords: database, engineering geology, 3D Model, RockWorks, Bac Ai pumped storage hydropower project

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Authors: N. Ploysook, R. Rugsaj, C. Suvanjumrat

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The characteristic requirement for producing rectangular shape bottles was a uniform thickness of the plastic bottle wall. Die shaping was a good technique which controlled the wall thickness of bottles. An advance technology which was the finite element method (FEM) for blowing parison to be a rectangular shape bottle was conducted to reduce waste plastic from a trial and error method of a die shaping and parison control method. The artificial intelligent (AI) comprised of artificial neural network and genetic algorithm was selected to optimize the die gap shape from the FEM results. The application of AI technique could optimize the suitable die gap shape for the parison blow molding which did not depend on the parison control method to produce rectangular bottles with the uniform wall. Particularly, this application can be used with cheap blow molding machines without a parison controller therefore it will reduce cost of production in the bottle blow molding process.

Keywords: AI, bottle, die shaping, FEM

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Authors: Sumayya S.

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Making cities adaptive and inclusive is one among the inherent goal and challenge for contemporary cities. This is a serious concern when the urban transformations occur in varying magnitude due to visible and invisible factors. One type of visibly invisible factor is ecommerce and its expanding operation that is understood to cause changes to the conventional spatial structure positively and negatively. With the continued growth in e-commerce activities and its future potential, market analysts, media, and even retailers have questioned the importance of a future presence of traditional Brick-and-mortar stores in cities as a critical element, with some even referring to the repeated announcement of the closure of some store chains as the end of the online shopping era. Essentially this raises the question of how adaptive and inclusive the cities are to the dynamics of transformative changes that are often unseen. People have become more comfortable with seating inside and door delivery systems, and this increased change in usage of public spaces, especially the commercial corridors. Through this research helped in presetting a new approach for planning and designing commercial activities centers and also presents the impact of ecommerce on the urban fabric, such as division and fragmentation of space, showroom syndrome, reconceptualization of space, etc., in a critical way. The changes are understood by analyzing the e-commerce logistic process. Based on the inferences reach at the conclusion for the need of an integrated approach in the field of planning and designing of public spaces for the sustainable omnichannel retailing. This study was carried out with the following objectives Monitoring the impact of e commerce on the traditional shopping space. Explore the new challenges and opportunities faced by the urban form. Explore how adaptive and inclusive our cities are to the dynamics of transformative changes caused by ecommerce.

Keywords: E-commerce, shopping streets, online environment, offline environment, shopping factors

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Authors: N. V. Klassen, A. A. Vasin, A. M. Likhter, K. A. Voronin, A. V. Mariasevskaya, I. M. Shmit’ko

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Regular patterning in mixtures of bio-polymers (chitosan and collagen) and nanoparticles in water suspensions has been found by means of optical microscopy. The patterning was created either by external electrical field of moderate amplitude (200–1000 v/cm) or spontaneously. Simultaneously with the patterning pushing out of water drops mixed with nanoparticles to the external regions was observed. These phenomena are explained by interactions of charged bio-polymers and nanoparticles with external and internal electrical fields as well as with the regions of decreased dielectrical permittivity surrounding nano-objects in water which possesses anomalously high dielectrical permittivity. Electrical charges of opposite signs of the nano-objects induce their mutual attraction whereas dipole moments created around these nano-objects by the electrical fields are pushing these particles to the regions with lower fields. Due to this reason, non-homogeneities of dielectrical permittivity around nano-objects immersed into water suspension induces mutual repulsion of the objects. This spatial decrease of this repulsion with the inter-particle distances is more sharp than that of the Coulomb attraction. So, at longer distances, the attractions are stronger whereas at shorter distances the repulsion prevails. At a certain distance these two forces compensate each other creating the equilibrium state of the mixture of nano-objects with opposite charges. When the groups of positive and negative nano-objects consist from identical particles, quasi-periodical pattern of the suspension is observed like mesoscopic two-dimensional super-crystal. These results can clarify the mechanisms of healing of internal organs with direct or alternative electrical fields.

Keywords: bio-polymers, chitosan, collagen, nanoparticles, Coulomb attraction, polarization repulsion, periodical patterning, electrical low frequency resonances

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Authors: Nihal Murali, Kunal Gupta, Surekha Bhanot

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Training of artificial neural networks (ANNs) using reinforcement learning (RL) techniques is being widely discussed in the robot learning literature. The high model complexity of ANNs along with the model-free nature of RL algorithms provides a desirable combination for many robotics applications. There is a huge need for algorithms that generalize using raw sensory inputs, such as vision, without any hand-engineered features or domain heuristics. In this paper, the standard control problem of line following robot was used as a test-bed, and an ANN controller for the robot was trained on images from a live video feed using Q-learning. A virtual agent was first trained in simulation environment and then deployed onto a robot’s hardware. The robot successfully learns to traverse a wide range of curves and displays excellent generalization ability. Qualitative analysis of the evolution of policies, performance and weights of the network provide insights into the nature and convergence of the learning algorithm.

Keywords: artificial neural networks, q-learning, reinforcement learning, robot learning

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Authors: Nindyo Cahyo Kresnanto, Muhamad Willdan, Wika Harisa Putri

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Gunungkidul is one of the emerging tourism industry areas in Yogyakarta Province, Indonesia. This article describes how GIS can predict the development of tourism potential in Gunungkidul. The tourism sector in Gunungkidul Regency contributes 3.34% of the total gross regional domestic product and is the economic sector with the highest growth with a percentage of 18.37% in the post-Covid-19 period. This contribution makes researchers consider that several tourist sites need to be explored more to increase regional economic development gradually. This research starts by collecting spatial data from tourist locations tourists want to visit in Gunungkidul Regency based on survey data from 571 respondents. Then the data is visualized with ArcGIS software. This research shows an overview of tourist destinations interested in travellers depicted from the lowest to the highest from the data visualization. Based on the data visualization results, specific tourist locations potentially developed to influence the surrounding economy positively. The visualization of the data displayed is also in the form of a desire line map that shows tourist travel patterns from the origin of the tourist to the destination of the tourist location of interest. From the desire line, the prediction of the path of tourist sites with a high frequency of transportation activity can figure out. Predictions regarding specific tourist location routes that high transportation activities can burden can consider which routes will be chosen. The route also needs to be improved in terms of capacity and quality. The goal is to provide a sense of security and comfort for tourists who drive and positively impact the tourist sites traversed by the route.

Keywords: tourism development, GIS and survey, transportation, potential desire line

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Authors: Md. Ahsan Ayub, Kazi A. Kalpoma, Humaira Tasnim Proma, Syed Mehrab Kabir, Rakib Ibna Hamid Chowdhury

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Constraint Satisfaction Problem (CSP) is observed in various applications, i.e., scheduling problems, timetabling problems, assignment problems, etc. Researchers adopt a CSP technique to tackle a certain problem; however, each technique follows different approaches and ways to solve a problem network. In our exhaustive study, it has been possible to visualize the processes of essential CSP algorithms from a very concrete constraint satisfaction example, NQueens Problem, in order to possess a deep understanding about how a particular constraint satisfaction problem will be dealt with by our studied and implemented techniques. Besides, benchmark results - time vs. value of N in N-Queens - have been generated from our implemented approaches, which help understand at what factor each algorithm produces solutions; especially, in N-Queens puzzle. Thus, extended decisions can be made to instantiate a real life problem within CSP’s framework.

Keywords: arc consistency (AC), backjumping algorithm (BJ), backtracking algorithm (BT), constraint satisfaction problem (CSP), forward checking (FC), least constrained values (LCV), maintaining arc consistency (MAC), minimum remaining values (MRV), N-Queens problem

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Authors: Abhimanyu Sharma

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India is known for its cultural heritage. As the country is rich in diversity along its length and breadth, the state of Jammu & Kashmir is world famous for the beautiful tourist destinations in the Kashmir region of the state. However, equally destined destinations are also located in Jammu region of the said state. For most of the time in last 50-60 years, the prime focus of development was centered around Kashmir region. But now due to an ever increase in globalization, the focus is decentralizing throughout the country. Pertinently, the potential of Jammu Region needs to be incorporated into the world tourist map in particular. One such spot in the Jammu region of the state is a place called ‘Mubarak Mandi’ – the palace with the royal residence of the Maharaja of Jammu & Kashmir from the Dogra Dynasty, is located in the heart of Jammu city (the winter capital of the state). Since the place is destined with a heritage importance but yet lack the supporting infrastructure to attract the national tourist in general and worldwide tourist at large. For such places, conservation and restoration of the existing structures are the potential tools to overcome the present limiting nature of the place. The rejuvenation of this place through potential and dynamic conservation techniques is targeted through this paper. This paper deals with developing and restoring the areas within the whole campus with appropriate building materials, conservation techniques, etc. to promote a great number of visitors by developing it into a prioritised tourist attraction point. Major thrust shall be on studying the criteria’s for developing the place considering the psychological effect needed to create a socially interactive environment. Additionally, thrust shall be on the spatial elements that will aid in creating a common platform for all kinds of tourists. Accordingly, different conservation guidelines (or model) shall be targeted through this paper so that this Jammu region shall also be an equally contributor to the tourist graph of the country as the Kashmir part is.

Keywords: conservation, heritage architecture, rejuvenating, restoration

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Authors: The Danh Phan

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House price forecasting is a main topic in the real estate market research. Effective house price prediction models could not only allow home buyers and real estate agents to make better data-driven decisions but may also be beneficial for the property policymaking process. This study investigates the housing market by using machine learning techniques to analyze real historical house sale transactions in Australia. It seeks useful models which could be deployed as an application for house buyers and sellers. Data analytics show a high discrepancy between the house price in the most expensive suburbs and the most affordable suburbs in the city of Melbourne. In addition, experiments demonstrate that the combination of Stepwise and Support Vector Machine (SVM), based on the Mean Squared Error (MSE) measurement, consistently outperforms other models in terms of prediction accuracy.

Keywords: house price prediction, regression trees, neural network, support vector machine, stepwise

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Authors: Pavan K. Rallabandi, Kailash C. Patidar

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In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence or pattern recognition/ classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.

Keywords: hybrid systems, hidden markov models, recurrent neural networks, deterministic finite state automata

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Authors: Ainouna Bouziane

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The ability of Electron Tomography to recover the 3D structure of catalysts, with spatial resolution in the subnanometer scale, has been widely explored and reviewed in the last decades. A variety of experimental techniques, based either on Transmission Electron Microscopy (TEM) or Scanning Transmission Electron Microscopy (STEM) have been used to reveal different features of nanostructured catalysts in 3D, but High Angle Annular Dark Field imaging in STEM mode (HAADF-STEM) stands out as the most frequently used, given its chemical sensitivity and avoidance of imaging artifacts related to diffraction phenomena when dealing with crystalline materials. In this regard, our group has developed a methodology that combines image denoising by undecimated wavelet transforms (UWT) with automated, advanced segmentation procedures and parameter selection methods using CS-TVM (Compressed Sensing-total variation minimization) algorithms to reveal more reliable quantitative information out of the 3D characterization studies. However, evaluating the accuracy of the magnitudes estimated from the segmented volumes is also an important issue that has not been properly addressed yet, because a perfectly known reference is needed. The problem particularly complicates in the case of multicomponent material systems. To tackle this key question, we have developed a methodology that incorporates volume reconstruction/segmentation methods. In particular, we have established an approach to evaluate, in quantitative terms, the accuracy of TVM reconstructions, which considers the influence of relevant experimental parameters like the range of tilt angles, image noise level or object orientation. The approach is based on the analysis of material-realistic, 3D phantoms, which include the most relevant features of the system under analysis.

Keywords: electron tomography, supported catalysts, nanometrology, error assessment

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Authors: Peng Guo

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Objective To investigate the clinical characteristics and changes of video-polysomnography (v-PSG) in Parkinson disease (PD) patients accompanied with cognitive impairment. Methods Three hundred and ninety-four patients with PD were enrolled in Beijing Tiantan Hospital, according to CI level, the patients were divided into PD without cognitive impairment (PD-NCI), PD with mild cognitive impairment (PD-MCI), and PD with dementia (PDD) group. Collect patient's demographic data, including gender, onset age, education level and duration. The cognitive function of PD patients was evaluated by Montreal cognitive assessment (MoCA) scale, and the overall cognitive function and cognitive domains of the three groups were compared.Using v-PSG to assess the sleep status of patients. Correlation analysis of MoCA Scale and v-PSG results in PD-CI group. Results 1. In 394 cases of PD, 94 cases (23.86%) in PD-NCI group , 177 cases(44.92%) in PD-MCI group , 123 cases (31.22%) in PDD group. 2.There was no significant difference in gender, age of onset, education level and duration in PD-NCI group, PD-MCI group and PDD group （P＞0.05）. 3. The total score of MoCA scale in PD-NCI group, PD-MCI group and PDD group decreased one by one. In PD-NCI group, PD-MCI group and PDD group, the scores of each cognitive domain in MoCA scale decreased significantly（P<0.05）. 4.Compared with the PD-MCI group, PDD group had lower total sleep time, lower sleep efficiency （P<0.05）. Compared with PD-NCI group, PDD group had lower total sleep time and lower sleep efficiency （P<0.05）.5. The sleep efficiency of PD-CI patients is positively correlated with the total score of MoCA scale, visual spatial function, executive function, delayed recall and attention score（P<0.05). Conclusions The incidence of CI in PD patients was high; The cognitive function and cognitive domains of PD-CI patients were significantly impaired; In patients with PD-CI, total sleep time decreased, sleep efficiency decreased, and it was related to overall cognitive function and partial cognitive impairment.

Keywords: Parkinson disease, cognitive impairment, clinical characteristics, sleep disorders, video-polysomnography

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Authors: Jingsi Li, Neil S. Ferguson

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Time pressure could have an influence on the productivity, quality of decision making, and the efficiency of problem-solving. This has been mostly stemmed from cognitive research or psychological literature. However, a salient scarce discussion has been held for transport adjacent fields. It is conceivable that in many activity-travel contexts, time pressure is a potentially important factor since an excessive amount of decision time may incur the risk of late arrival to the next activity. The activity-travel rescheduling behavior is commonly explained by costs and benefits of factors such as activity engagements, personal intentions, social requirements, etc. This paper hypothesizes that an additional factor of perceived time pressure could affect travelers’ rescheduling behavior, thus leading to an impact on travel demand management. Time pressure may arise from different ways and is assumed here to be essentially incurred due to travelers planning their schedules without an expectation of unforeseen elements, e.g., transport disruption. In addition to a linear-additive utility-maximization model, the less computationally compensatory heuristic models are considered as an alternative to simulate travelers’ responses. The paper will contribute to travel behavior modeling research by investigating the following questions: how to measure the time pressure properly in an activity-travel day plan context? How do travelers reschedule their plans to cope with the time pressure? How would the importance of the activity affect travelers’ rescheduling behavior? What will the behavioral model be identified to describe the process of making activity-travel rescheduling decisions? How do these identified coping strategies affect the transport network? In this paper, a Mixed Heuristic Model (MHM) is employed to identify the presence of different choice heuristics through a latent class approach. The data about travelers’ activity-travel rescheduling behavior is collected via a web-based interactive survey where a fictitious scenario is created comprising multiple uncertain events on the activity or travel. The experiments are conducted in order to gain a real picture of activity-travel reschedule, considering the factor of time pressure. The identified behavioral models are then integrated into a multi-agent transport simulation model to investigate the effect of the rescheduling strategy on the transport network. The results show that an increased proportion of travelers use simpler, non-compensatory choice strategies instead of compensatory methods to cope with time pressure. Specifically, satisfying - one of the heuristic decision-making strategies - is adopted commonly since travelers tend to abandon the less important activities and keep the important ones. Furthermore, the importance of the activity is found to increase the weight of negative information when making trip-related decisions, especially route choices. When incorporating the identified non-compensatory decision-making heuristic models into the agent-based transport model, the simulation results imply that neglecting the effect of perceived time pressure may result in an inaccurate forecast of choice probability and overestimate the affectability to the policy changes.

Keywords: activity-travel rescheduling, decision making under uncertainty, mixed heuristic model, perceived time pressure, travel demand management

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Authors: Walid A. Attia, Taher M. Bazan, Fawzy Eltohamy, Mahmoud Fathy

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System performance evaluation is an essential stage in the design of high-resolution satellite telescopes prior to the development process. In this paper, a system performance evaluation of a very high-resolution satellite telescope is investigated. The evaluated system has a Korsch optical scheme design. This design has been discussed in another paper with respect to three-mirror anastigmat (TMA) scheme design and the former configuration showed better results. The investigated system is based on the Korsch optical design integrated with a time-delay and integration charge coupled device (TDI-CCD) sensor to achieve a ground sampling distance (GSD) of 25 cm. The key performance metrics considered are the spatial resolution, the signal to noise ratio (SNR) and the total modulation transfer function (MTF) of the system. In addition, the national image interpretability rating scale (NIIRS) metric is assessed to predict the image quality according to the modified general image quality equation (GIQE). Based on the orbital, optical and detector parameters, the estimated GSD is found to be 25 cm. The SNR has been analyzed at different illumination conditions of target albedos, sun and sensor angles. The system MTF has been computed including diffraction, aberration, optical manufacturing, smear and detector sampling as the main contributors for evaluation the MTF. Finally, the system performance evaluation results show that the computed MTF value is found to be around 0.08 at the Nyquist frequency, the SNR value was found to be 130 at albedo 0.2 with a nadir viewing angles and the predicted NIIRS is in the order of 6.5 which implies a very good system image quality.

Keywords: modulation transfer function, national image interpretability rating scale, signal to noise ratio, satellite telescope performance evaluation

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Authors: Katleho Daniel Makatjane, Diteboho Lawrence Xaba, Ntebogang Dinah Moroke

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The current study investigates the behaviour of time-varying parameters that are based on the score function of the predictive model density at time t. The mechanism to update the parameters over time is the scaled score of the likelihood function. The results revealed that there is high persistence of time-varying, as the location parameter is higher and the skewness parameter implied the departure of scale parameter from the normality with the unconditional parameter as 1.5. The results also revealed that there is a perseverance of the leptokurtic behaviour in stock returns which implies the returns are heavily tailed. Prior to model estimation, the White Neural Network test exposed that the stock price can be modelled by a GAS model. Finally, we proposed further researches specifically to model the existence of time-varying parameters with a more detailed model that encounters the heavy tail distribution of the series and computes the risk measure associated with the returns.

Keywords: generalized autoregressive score model, South Africa, stock returns, time-varying

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Authors: Toka Aly

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Historically, marketplaces were the most important nodes and focal points of cities, where different activities took place. Commercial streets offer more than just spaces for shopping; they also offer choices for social activities and cultural exchange. They are considered the backbone of the city’s vibrancy and vitality. Despite that, the public life in Cairo’s commercial streets has deteriorated, where the shopping activities became reliant mainly on 'planned formal places', mainly in privatized or indoor spaces like shopping malls. The main aim of this paper is to explore the key elements and tools of assessing the successfulness of commercial streets in Cairo. The methodology followed in this paper is based on a case study methodology (multiple cases) that is based on assessing and analyzing the physical and social elements in historical and contemporary commercial streets in El Muiz Street and Baghdad Street in Cairo. The data collection is based on personal observations, photographs, maps and street sections. Findings indicate that the key factors of analyzing commercial streets are factors affecting the sensory experience, factors affecting the social behavior, and general aspects that attract people. Findings also indicate that urban features have clear influence on shopping pedestrian activities in both streets. Moreover, in order for a commercial street to be successful, shopping patterns must provide people with a quality public space that can provide easy navigation and accessibility, good visual continuity, and well-designed urban features and social gathering. Outcomes of this study will be a significant endeavor in providing a good background for urban designers on analyzing and assessing successfulness of commercial streets. The study will also help in understanding the different physical and social pattern of vending activities taking place in Cairo.

Keywords: activities, commercial street, marketplace, successful, vending

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Authors: Hasti Ebrahimi

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This study sheds light on the challenges and difficulties caused by the Iranian clerical system’s intervention in the country’s school education during the COVID-19 pandemic, when schools remained closed for almost two years. The pandemic brought Iranian elementary school education to a standstill for almost 6 months before the country developed a nationwide learning platform – a customized television network. While the initiative seemed to have been welcomed by the majority of Iranian parents, it resented some of the more traditional strata of the society, including the influential Friday Prayer Leaders who found the televised version of the elementary education ‘less spiritual’ and ‘more ‘material’ or science-based. That prompted the Iranian Channel of Education, the specialized television network that had been chosen to serve as a nationally televised school during the pandemic, to try to redefine much of its online elementary school educational content within the religious ideology of the Islamic Republic of Iran. As a result, young clergies appeared on the television screen as preachers of Islamic morality, religious themes and even sociology, history, and arts. The present research delves into the consequences of such an intervention, how it might have impacted the infrastructure of Iranian elementary education and whether or not the new ideology-infused curricula would withstand the opposition of students and mainstream teachers. The main methodology used in this study is Critical Discourse Analysis with a cognitive approach. It systematically finds and analyzes the alternative ideological structures of discourse in the Iranian Channel of Education from September 2021 to July 2022, when the clergy ‘teachers’ replaced ‘regular’ history and arts teachers on the television screen for the first time. It has aimed to assess how the various uses of the alternative ideological discourse in elementary school content have influenced the processes of learning: the acquisition of knowledge, beliefs, opinions, attitudes, abilities, and other cognitive and emotional changes, which are the goals of institutional education. This study has been an effort aimed at understanding and perhaps clarifying the relationships between the traditional textual structures and processing on the one hand and socio-cultural contexts created by the clergy teachers on the other. This analysis shows how the clerical portion of elementary education on the Channel of Education that seemed to have dominated the entire televised teaching and learning process faded away as the pandemic was contained and mainstream classes were restored. It nevertheless reflects the deep ideological rifts between the clerical approach to school education and the mainstream teaching process in Iranian schools. The semantic macrostructures of social content in the current Iranian elementary school education, this study suggests, have remained intact despite the temporary ideological intervention of the ruling clerical elite in their formulation and presentation. Finally, using thematic and schematic frameworks, the essay suggests that the ‘clerical’ social content taught on the Channel of Education during the pandemic cannot have been accepted cognitively by the channel’s target audience, including students and mainstream teachers.

Keywords: televised elementary school learning, Covid 19, critical discourse analysis, Iranian clerical ideology

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Authors: G. C. Joshi, M. Paul, B. K. Kalita, V. Ranga, J. S. Rawat, P. S. Rawat

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Being a habitat of the global village, every place has established connection through the strength and power of social media piercing through the political boundaries. Social media is a digital platform, where people across the world can interact as it has advantages of being universal, anonymous, easily accessible, indirect interaction, gathering and sharing information. The power of social media lies in the intensity of sharing extreme opinions or feelings, in contrast to the personal interactions which can be easily mapped in the form of Sentiment Mapping. The easy access to social networking sites such as Facebook, Twitter and blogs made unprecedented opportunities for citizens to voice their opinions loaded with dynamics of emotions. These further influence human thoughts where social media plays a very active role. A recent incident of public importance was selected as a case study to map the sentiments of people through Twitter. Understanding those dynamics through the eye of an ordinary people can be challenging. With the help of R-programming language and by the aid of GIS techniques sentiment maps has been produced. The emotions flowing worldwide in the form of tweets were extracted and analyzed. The number of tweets had diminished by 91 % from 25/08/2017 to 31/08/2017. A boom of sentiments emerged near the origin of the case, i.e., Delhi, Haryana and Punjab and the capital showed maximum influence resulting in spillover effect near Delhi. The trend of sentiments was prevailing more as neutral (45.37%), negative (28.6%) and positive (21.6%) after calculating the sentiment scores of the tweets. The result can be used to know the spatial distribution of digital penetration in India, where highest concentration lies in Mumbai and lowest in North East India and Jammu and Kashmir.

Keywords: sentiment mapping, digital literacy, GIS, R statistical language, spatio-temporal

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Authors: Misha Alexander, S. B. Waykar

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Visual Cryptography is a special unbreakable encryption technique that transforms the secret image into random noisy pixels. These shares are transmitted over the network and because of its noisy texture it attracts the hackers. To address this issue a Natural Visual Secret Sharing Scheme (NVSS) was introduced that uses natural shares either in digital or printed form to generate the noisy secret share. This scheme greatly reduces the transmission risk but causes distortion in the retrieved secret image through variation in settings and properties of digital devices used to capture the natural image during encryption / decryption phase. This paper proposes a new NVSS scheme that extracts the secret key from randomly selected unaltered multiple natural images. To further improve the security of the shares data hiding techniques such as Steganography and Alpha channel watermarking are proposed.

Keywords: decryption, encryption, natural visual secret sharing, natural images, noisy share, pixel swapping

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Authors: Luqman Ali Shah, Murtaza Sayed, Mohammad Siddiq

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Three-dimensional cross-linked polymer microgels with temperature responsive N-isopropyl acrylamide (NIPAM) and pH-sensitive methacrylic acid (MAA) were successfully synthesized by free radical emulsion polymerization with different amount of MAA. Silver and gold nanoparticles with size of 6.5 and 3.5 nm (±0.5 nm) respectively were homogeneously reduced inside these materials by chemical reduction method at pH 2.78 and 8.36 for the preparation of hybrid materials. The samples were characterized by FTIR, DLS and TEM techniques. The catalytic activity of the hybrid materials was investigated for the reduction of 4-nitrophenol (4- NP) using NaBH4 as reducing agent by UV-visible spectroscopy. The hybrid polymer network synthesized at pH 8.36 shows enhanced catalytic efficiency compared to catalysts synthesized at pH 2.78. In this study, it has been explored that catalyst activity strongly depends on amount of MAA, synthesis pH and type of metal nanoparticles entrapped.

Keywords: cross-linked polymer microgels, free radical polymerization, metal nanoparticles, catalytic activity, comparative study

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Authors: Idris Lawal

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Electronic payment system is simply a payment or monetary transaction made over the internet or a network of computers. This study was carried out in order to assess how electronic payment system has impacted on banks service delivery, to examine the efficiency of electronic payment system in Nigeria and to determine the level of customer's satisfaction as a direct result of the deployment of electronic payment systems. It is an empirical study conducted using structured questionnaire distributed to officials and customers of Access Bank plc. Chi-square(x2) was adopted for the purpose of data analysis. The result of the study showed that the development of electronic payment system offer great benefit to bank customers including improved services, reduced turn-around time, ease of banking transaction, significant cost saving etc. The study recommends that customer protection laws should be properly put in place to safeguard the interest of end users of e-payment instruments.

Keywords: bank, electronic payment systems, service delivery, customer's satisfaction

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Authors: Frederic Jumelle, Kelvin So, Didan Deng

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A method and system for neuropsychological performance test, comprising a mobile terminal, used to interact with a cloud server which stores user information and is logged into by the user through the terminal device; the user information is directly accessed through the terminal device and is processed by artificial neural network, and the user information comprises user facial emotions information, performance test answers information and user chronometrics. This assessment is used to evaluate the cognitive performance and emotional response of the subject to a series of dichotomous questions describing various situations of daily life and challenging the users' knowledge, values, ethics, and principles. In industrial applications, the timing of this assessment will depend on the users' need to obtain a service from a provider, such as opening a bank account, getting a mortgage or an insurance policy, authenticating clearance at work, or securing online payments.

Keywords: artificial intelligence, neurofinance, neuropsychology, risk management

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Authors: Anas H. Aljemely, Jianping Xuan

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Rolling bearing fault diagnosis plays a pivotal issue in the rotating machinery of modern manufacturing. In this research, a raw vibration signal and improved deep learning method for bearing fault diagnosis are proposed. The multi-dimensional scales of raw vibration signals are selected for evaluation condition monitoring system, and the deep learning process has shown its effectiveness in fault diagnosis. In the proposed method, employing an Exponential linear unit (ELU) layer in a convolutional neural network (CNN) that conducts the identical function on positive data, an exponential nonlinearity on negative inputs, and a particular convolutional operation to extract valuable features. The identification results show the improved method has achieved the highest accuracy with a 100-dimensional scale and increase the training and testing speed.

Keywords: bearing fault prognostics, developed CNN model, multiple-scale evaluation, deep learning features

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Authors: Andrew Kanzunguze, Lusayo Mwabumba, Jason K. Gilbertson, Dominic B. Gondwe, George Z. Nxumayo

Abstract:

Knowledge about the spatio-temporal distribution of invasive plants in protected areas provides a base from which hypotheses explaining proliferation of plant invasions can be made alongside development of relevant invasive plant monitoring programs. The aim of this study was to investigate the spatio-temporal distribution of bracken fern on the grassland plateau of Nyika National Park over the past 30 years (1986-2016) as well as to determine the current extent of the invasion. Remote sensing, machine learning, and statistical modelling techniques (object-based image analysis, image classification and linear regression analysis) in geographical information systems were used to determine both the spatial and temporal distribution of bracken fern in the study area. Results have revealed that bracken fern has been increasing coverage on the Nyika plateau at an estimated annual rate of 87.3 hectares since 1986. This translates to an estimated net increase of 2,573.1 hectares, which was recorded from 1,788.1 hectares (1986) to 4,361.9 hectares (2016). As of 2017 bracken fern covered 20,940.7 hectares, approximately 14.3% of the entire grassland plateau. Additionally, it was observed that the fern was distributed most densely around Chelinda camp (on the central plateau) as well as in forest verges and roadsides across the plateau. Based on these results it is recommended that Ecological Niche Modelling approaches be employed to (i) isolate the most important factors influencing bracken fern proliferation as well as (ii) identify and prioritize areas requiring immediate control interventions so as to minimize bracken fern proliferation in Nyika National Park.

Keywords: bracken fern, image classification, Landsat-8, Nyika National Park, spatio-temporal distribution

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Authors: Rodrigo S. Fonseca, Antônio C. P. Veiga

Abstract:

Aiming at helping people with some movement limitation that makes typing and communication difficult, there is a need to customize an assistive tool with a learning environment that helps the user in order to optimize text input, identifying the error and providing the correction and possibilities of choice in the Portuguese language. The work presents an Orthographic and Grammatical System that can be incorporated into writing environments, improving and facilitating the use of an alphanumeric keyboard, using a prototype built using a genetic algorithm in addition to carrying out the prediction, which can occur based on the quantity and position of the inserted letters and even placement in the sentence, ensuring the sequence of ideas using a Long Short Term Memory (LSTM) neural network. The prototype optimizes data entry, being a component of assistive technology for the textual formulation, detecting errors, seeking solutions and informing the user of accurate predictions quickly and effectively through machine learning.

Keywords: genetic algorithm, neural networks, word prediction, machine learning

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Authors: Gaurav Sancheti

Abstract:

This study presents an impacting approach of Artificial Neural Networks (ANNs) in determining the quantity of High Tensile Steel (HTS) strands required in post-tensioned (PT) slabs. Various PT slab configurations were generated by varying the span and depth of the slab. For each of these slab configurations, quantity of required HTS strands were recorded. ANNs with backpropagation algorithm and varying architectures were developed and their performance was evaluated in terms of Mean Square Error (MSE). The recorded data for the quantity of HTS strands was used as a feeder database for training the developed ANNs. The networks were validated using various validation techniques. The results show that the proposed ANNs have a great potential with good prediction and generalization capability.

Keywords: artificial neural networks, back propagation, conceptual design, high tensile steel strands, post tensioned slabs, validation techniques

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Authors: Girish Chandran, Milu Tigi

Abstract:

Brian O'Doherty's seminal essay, Inside the white Cube theorized and named the dominant mode of display and exhibition of Modern Art museums. Ever since the advent of Biennales and other site-specific public art projects we have seen a departure from the white cube mode of exhibition. The physicality, materiality and context within which an artwork is framed has a role in the production of meaning of public art. Equally, artworks contribute to the meaning and identity of a place. This to and fro relationship between the site and artwork and its influence on the sense of place and production of meaning is being explored in this paper in the context of Kochi Muziris Biennale (KMB). Known as the Peoples biennale with over 5 lakh visitors, it is India's first Biennale and its largest art exhibition of contemporary art. The paper employs place theory and contemporary curatorial theories to present the case. The KMB has an interesting mix of exhibition spaces which includes existing galleries and halls, site-specific projects in public spaces, infill developments and adaptive reuse of heritage and other unused architecture. The biennale was envisioned as an event connecting to the history, socio-political peculiarities of the cultural landscape of Kerala and more specifically Kochi. The paper explains the role of spatial elements in forming a curatorial narrative connected to the above mentioned ambitions.The site-specific nature of exhibition and its use of unused architecture helps in the formation of exhibition spaces unique in type and materiality. The paper argues how this helps in the creation of an 'archeology of the place'. The research elucidates how a composite nature of experience helps connect with the thematic ambitions of the Biennale and how it brings about an aesthetics distinct to KMB.

Keywords: public art, curatorial practice, architecture, place, contemporary art, site specificity

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Authors: Jessemyn Modiini, Timothy Lynar, Elena Sitnikova

Abstract:

We present a novel enhanced compartmental model for malware spread analysis in cyber security. This paper applies cyber security data features to epidemiological compartmental models to model the infectious potential of malware. Compartmental models are most efficient for calculating the infectious potential of a disease. In this paper, we discuss and profile epidemiologically relevant data features from a Domain Name System (DNS) dataset. We then apply these features to epidemiological compartmental models to network traffic features. This paper demonstrates how epidemiological principles can be applied to the novel analysis of key cybersecurity behaviours and trends and provides insight into threat modelling above that of kill-chain analysis. In applying deterministic compartmental models to a cyber security use case, the authors analyse the deficiencies and provide an enhanced stochastic model for cyber epidemiology. This enhanced compartmental model (SUEICRN model) is contrasted with the traditional SEIR model to demonstrate its efficacy.

Keywords: cybersecurity, epidemiology, cyber epidemiology, malware

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Authors: Lan Nguyen, Kien Le Tan, Bao Nguyen Pham Gia

Abstract:

Due to the high cost, specialized dynamic measurement devices for industrial lands are difficult for many colleges to equip for hands-on teaching. This study connects a dynamic measurement sensor and receiver utilizing an inexpensive Raspberry Pi 4 board, some 24-bit ADC circuits, a geophone vibration sensor, and embedded Python open-source programming. Gather and analyze signals for dynamic measuring, ground vibration monitoring, and structure vibration monitoring. The system may wirelessly communicate data to the computer and is set up as a communication node network, enabling real-time monitoring of background vibrations at various locations. The device can be utilized for a variety of dynamic measurement and monitoring tasks, including monitoring earthquake vibrations, ground vibrations from construction operations, traffic, and vibrations of building structures.

Keywords: sensors, FFT, signal processing, real-time data monitoring, ground propagation wave, python, raspberry Pi 4

Procedia PDF Downloads 89