Search results for: resilient architecture

Authors: Yuhang Wang, Jorg Schluter, Sergiy Shelyag

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The high cost associated with high-resolution computational fluid dynamics (CFD) is one of the main challenges that inhibit the design, development, and optimisation of new combustion systems adapted for renewable fuels. In this study, we propose a physics-guided CNN-based model to predict turbulence evolution and mixing without requiring a traditional CFD solver. The model architecture is built upon U-Net and the inception module, while a physics-guided loss function is designed by introducing two additional physical constraints to allow for the conservation of both mass and pressure over the entire predicted flow fields. Then, the model is trained on the Large Eddy Simulation (LES) results of a natural turbulent mixing layer with two different Reynolds number cases (Re = 3000 and 30000). As a result, the model prediction shows an excellent agreement with the corresponding CFD solutions in terms of both spatial distributions and temporal evolution of turbulent mixing. Such promising model prediction performance opens up the possibilities of doing accurate high-resolution manifold-based combustion simulations at a low computational cost for accelerating the iterative design process of new combustion systems.

Keywords: computational fluid dynamics, turbulence, machine learning, combustion modelling

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Authors: Aymen Arfaoui, Abdelkader Soumaya, Noureddine Ben Ayed

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The Rouas-Ruissateis a part of TunisianAtlas system. Analyze of the collected field data allowed us to propose a new interpretation for the main structural features of thisregion. Tectonic inversions along NE-SW trending fault of Zaghouan and holokinetic movements are the main factors controlling the architecture and geometry of the Jebel Rouas-Ruissate. The presence of breccias, Slumps, and synsedimentaryfaults along NW-SE and N-S trending major faults show that they were active during the Mesozoicextensionalepisodes. During Cenozoic inversion period, this structurewas shaped as imbricatefansformed byNE-SW trending thrust faults. The angularunconformitybetweenupperEocene- Oligocene, and Cretaceousdeposits reveals a compressive Eocene tectonic phase (called Pyrenean phase)occurred duringPaleocene-lower Eocene.The Triassicsaltsacted as a decollementlevel in the NE-SW trendingfault propagation fold model of the Rouas-Ruissate.The inversion of fault-slip data along the main regional fault zones reveals a coexistence of strike-slip and reverse fault stress regimes with NW-SE maximum horizontal stress(SHmax) characterizing the Alpine compressive phase (Upper Tortonian).

Keywords: tunisia, imbricate fans, triassic decollement level, fault propagation fold

Procedia PDF Downloads 147

Authors: Mohsin Ali Shaikh, Song Weiguo, Muhammad Kashan Surahio, Usman Shahid, Rehmat Karim

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When the area of a structure that is threatened by a disaster affects personal safety, the effectiveness of disaster prevention, evacuation, and rescue operations can be summarized by three assessment indicators: personal safety, property preservation, and attribution of responsibility. These indicators are applicable regardless of the disaster that affects the building. People need to get out of the hazardous area and to a safe place as soon as possible because there's no other way to respond. The results of the tragedy are thus closely related to how quickly people are advised to evacuate and how quickly they are rescued. This study considers present fire prevention systems to address catastrophes and improve building safety. It proposes the methods of Prevention Level for Deployment in Advance and Spatial Transformation by Human-Machine Collaboration. We present and prototype a real-time fire protection system architecture for building disaster prevention, evacuation, and rescue operations. The design encourages the use of simulations to check the efficacy of evacuation, rescue, and disaster prevention procedures throughout the planning and design phase of the structure.

Keywords: prevention level, building information modeling, quality management system, simulated reality

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Authors: Muhammad Zubair Khan, Yugyung Lee

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Deep learning has recently achieved enormous response in semantic image segmentation. The previously developed U-Net inspired architectures operate with continuous stride and pooling operations, leading to spatial data loss. Also, the methods lack establishing long-term pixels connection to preserve context knowledge and reduce spatial loss in prediction. This article developed encoder-decoder architecture with bi-directional LSTM embedded in long skip-connections and densely connected convolution blocks. The network non-linearly combines the feature maps across encoder-decoder paths for finding dependency and correlation between image pixels. Additionally, the densely connected convolutional blocks are kept in the final encoding layer to reuse features and prevent redundant data sharing. The method applied batch-normalization for reducing internal covariate shift in data distributions. The empirical evidence shows a promising response to our method compared with other semantic segmentation techniques.

Keywords: deep learning, semantic segmentation, image analysis, pixels connection, convolution neural network

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Authors: Apoorva Vinod, Archana Mathur, Snehanshu Saha

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Though there exists a plethora of Activation Functions (AFs) used in single and multiple hidden layer Neural Networks (NN), their behavior always raised curiosity, whether used in combination or singly. The popular AFs –Sigmoid, ReLU, and Tanh–have performed prominently well for shallow and deep architectures. Most of the time, AFs are used singly in multi-layered NN, and, to the best of our knowledge, their performance is never studied and analyzed deeply when used in combination. In this manuscript, we experiment with multi-layered NN architecture (both on shallow and deep architectures; Convolutional NN and VGG16) and investigate how well the network responds to using two different AFs (Sigmoid-Tanh, Tanh-ReLU, ReLU-Sigmoid) used alternately against a traditional, single (Sigmoid-Sigmoid, Tanh-Tanh, ReLUReLU) combination. Our results show that using two different AFs, the network achieves better accuracy, substantially lower loss, and faster convergence on 4 computer vision (CV) and 15 Non-CV (NCV) datasets. When using different AFs, not only was the accuracy greater by 6-7%, but we also accomplished convergence twice as fast. We present a case study to investigate the probability of networks suffering vanishing and exploding gradients when using two different AFs. Additionally, we theoretically showed that a composition of two or more AFs satisfies Universal Approximation Theorem (UAT).

Keywords: activation function, universal approximation function, neural networks, convergence

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Authors: Wang Haining, Zhang Hong

Abstract:

In China, rapid urbanization needs more and more buildings constructed for the growing population in cities. With the help of the methodology which contains investigation, contrastive analysis, design based on component with BIM and experiment before real construction, this research based on mobile light structure system, trying to the sustainable problems partly in present China by systematic study. The system cannot replace the permanent heavy structure completely. So the goal is the improvement of the whole building system by the addition of light structure. This house system uses modularized envelopes and standardized connections, which are pre-fabricated and assembled in factories and transported like containers. Aluminum is used as the structural material in this system, and inorganic thermal insulation material used in the envelope, which have high fireproof properties. The relationship between manufactory and construction of the system is progressive hierarchy. They exist as First Industrial, Second Industrial, Third Industrial and Site Assembly Stage. It could maximize the land usage capacity by fully exploit the area where normal permanent architecture can't take advantage of. Not only the building system itself especially the thermal isolated materials used and active solar photovoltaic system equipped can save energy, but also the way of product development is sustainable.

Keywords: aluminum house, light Structure, rapid assembly, repeat construction

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Authors: F. Amirarfaei, K. Khorasani

Abstract:

In this paper, reconfigurable consensus achievement of a team of agents with marginally stable linear dynamics and single input channel has been considered. The control algorithm is based on a first order linear protocol. After occurrence of a LOE fault in one of the actuators, using the imperfect information of the effectiveness of the actuators from fault detection and identification module, the control gain is redesigned in a way to still reach consensus. The idea is based on the modeling of change in effectiveness as change of Laplacian matrix. Then as special cases of this class of systems, a team of single integrators as well as double integrators are considered and their behavior subject to a LOE fault is considered. The well-known relative measurements consensus protocol is applied to a leaderless team of single integrator as well as double integrator systems, and Gersgorin disk theorem is employed to determine whether fault occurrence has an effect on system stability and team consensus achievement or not. The analyses show that loss of effectiveness fault in actuator(s) of integrator systems affects neither system stability nor consensus achievement.

Keywords: multi-agent system, actuator fault, stability analysis, consensus achievement

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Authors: Scarlet Alejandra Romano, Joni Kareco

Abstract:

The three pillars of sustainability (social, economic and environmental) are relevant concepts to the Architecture, Engineering, and Construction (AEC) industry because of the increase of international agreements and guidelines related to this topic during the last years. Considering these three pillars, the AEC industry faces important challenges, for instance, to decrease the carbon emissions (environmental challenge), design sustainable spaces for people (social challenge), and improve the technology of this field to reduce costs and environmental problems (economic and environmental challenge). One alternative to overcome these challenges is Building Information Modelling program (BIM) because according to several authors, this technology improves the performance of the sustainable buildings in all their lifecycle phases. The main objective of this paper is to explore and analyse the current advantages and disadvantages of the BIM implementation in the life-cycle of sustainable buildings considering the three pillars of sustainability as analysis parameters. The methodology established to achieve this objective is exploratory-descriptive with the literature review technique. The partial results illustrate that despite the BIM disadvantages and the lack of information about its social sustainability advantages, this software represents a significant opportunity to improve the three sustainable pillars of the sustainable buildings.

Keywords: building information modelling, building lifecycle analysis, sustainability, sustainable buildings

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Authors: Telesphore Tiendrebeogo, Oumarou Sié

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Cloud computing technologies have attracted considerable interest in recent years. Thus, these latters have become more important for many existing database applications. It provides a new mode of use and of offer of IT resources in general. Such resources can be used “on demand” by anybody who has access to the internet. Particularly, the Cloud platform provides an ease to use interface between providers and users, allow providers to develop and provide software and databases for users over locations. Currently, there are many Cloud platform providers support large scale database services. However, most of these only support simple keyword-based queries and can’t response complex query efficiently due to lack of efficient in multi-attribute index techniques. Existing Cloud platform providers seek to improve performance of indexing techniques for complex queries. In this paper, we define a new cloud computing architecture based on a Distributed Hash Table (DHT) and design a prototype system. Next, we perform and evaluate our cloud computing indexing structure based on a hyperbolic tree using virtual coordinates taken in the hyperbolic plane. We show through our experimental results that we compare with others clouds systems to show our solution ensures consistence and scalability for Cloud platform.

Keywords: virtual coordinates, cloud, hyperbolic plane, storage, scalability, consistency

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Authors: Lina Psarra, Manogj Sundaresan, Purjeet Sutar

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In response to the global imperative for sustainable solutions, this article proposes the development of a comprehensive circularity index applicable to a wide range of products across various industries. The absence of a consensus on using a universal metric to assess circularity performance presents a significant challenge in prioritizing and effectively managing sustainable initiatives. This circularity index serves as a quantitative measure to evaluate the adherence of products, processes, and systems to the principles of a circular economy. Unlike traditional distinct metrics such as recycling rates or material efficiency, this index considers the entire lifecycle of a product in one single metric, also incorporating additional factors such as reusability, scarcity of materials, reparability, and recyclability. Through a systematic approach and by reviewing existing metrics and past methodologies, this work aims to address this gap by formulating a circularity index that can be applied to diverse product portfolio and assist in comparing the circularity of products on a scale of 0%-100%. Project objectives include developing a formula, designing and implementing a pilot tool based on the developed Product Circularity Index (PCI), evaluating the effectiveness of the formula and tool using real product data, and assessing the feasibility of integration into various sustainability initiatives. The research methodology involves an iterative process of comprehensive research, analysis, and refinement where key steps include defining circularity parameters, collecting relevant product data, applying the developed formula, and testing the tool in a pilot phase to gather insights and make necessary adjustments. Major findings of the study indicate that the PCI provides a robust framework for evaluating product circularity across various dimensions. The Excel-based pilot tool demonstrated high accuracy and reliability in measuring circularity, and the database proved instrumental in supporting comprehensive assessments. The PCI facilitated the identification of key areas for improvement, enabling more informed decision-making towards circularity and benchmarking across different products, essentially assisting towards better resource management. In conclusion, the development of the Product Circularity Index represents a significant advancement in global sustainability efforts. By providing a standardized metric, the PCI empowers companies and stakeholders to systematically assess product circularity, track progress, identify improvement areas, and make informed decisions about resource management. This project contributes to the broader discourse on sustainable development by offering a practical approach to enhance circularity within industrial systems, thus paving the way towards a more resilient and sustainable future.

Keywords: circular economy, circular metrics, circularity assessment, circularity tool, sustainable product design, product circularity index

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Authors: Raweyah Al-Sedairawi

Abstract:

While high-rise housing detained massive negative connotations on several societies and well-being, this typology did deliver housing demand efficiently. Despite its adverse reference due to declining precedents, high-rise housing is still in global demand. Yet the suitability of this typology is still questioned. In this research, the suitability of high-rise housing as a socio-culturally sustainable solution to meet housing demands will be examined. By questioning what is the potential of high-rise housing as a socio-culturally sustainable solution for housing demands, the research will examine some high-rise housing practices. Through reviewing the literature on the origins of high-rise housing, how and why they were developed, some unsuccessful cases, and some successful cases, with the identification of factors for successful high-rise living. Thus, the research groundings will materialize from existing patterns of housing demands. Whilst most of the literature covers the housing market from an economic, real estate, and political perspective, there is less amount that discloses occupants’ reactions towards this typology and its appropriateness for the reason that income controls individuals’ choices. To bridge the gap, the prospect of implementing the study would be effective. This will be applied through a mixture of a qualitative and a quantitative methodology by conducting questionnaires and focus groups on existing cases of high-net-worth residential towers.

Keywords: architecture, behaviors, high-rise, socio-cultural, sustainability

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Authors: Pule P. Mpopetsi, Warren M. Potts, Nicola James, Amber Childs

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Increased CO₂ production and the consequent ocean acidification (OA) have been identified as one of the greatest threats to both calcifying and non-calcifying marine organisms. Traditionally, marine fishes, as non-calcifying organisms, were considered to have a higher tolerance to near-future OA conditions owing to their well-developed ion regulatory mechanisms. However, recent studies provide evidence to suggest that they may not be as resilient to near-future OA conditions as previously thought. In addition, earlier life stages of marine fishes are thought to be less tolerant than juveniles and adults of the same species as they lack well-developed ion regulatory mechanisms for maintaining homeostasis. This study focused on the effects of near-future OA on larval Argyrosomus japonicus, an estuarine-dependent marine fish species, in order to identify the tipping point of tolerance for the larvae of this species. Larval A. japonicus in the present study were reared from the egg up to 22 days after hatching (DAH) under three treatments. The three treatments, (pCO₂ 353 µatm; pH 8.03), (pCO₂ 451 µatm; pH 7.93) and (pCO₂ 602 µatm; pH 7.83) corresponded to levels predicted to occur in year 2050, 2068 and 2090 respectively under the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (IPCC RCP) 8.5 model. Size-at-hatch, growth, development, and metabolic responses (standard and active metabolic rates and metabolic scope) were assessed and compared between the three treatments throughout the rearing period. Five earlier larval life stages (hatchling – flexion/post-flexion) were identified by the end of the experiment. There were no significant differences in size-at-hatch (p > 0.05), development or the active metabolic (p > 0.05) or metabolic scope (p > 0.05) of fish in the three treatments throughout the study. However, the standard metabolic rate was significantly higher in the year 2068 treatment but only at the flexion/post-flexion stage which could be attributed to differences in developmental rates (including the development of the gills) between the 2068 and the other two treatments. Overall, the metabolic scope was narrowest in the 2090 treatment but varied according to life stage. Although not significantly different, metabolic scope in the 2090 treatment was noticeably lower at the flexion stage compared to the other two treatments, and the development appeared slower, suggesting that this could be the stage most prone to OA. The study concluded that, in isolation, OA levels predicted to occur between 2050 and 2090 will not negatively affect size-at-hatch, growth, development, and metabolic responses of larval A. japonicus up to 22 DAH (flexion/post-flexion stage). The present study also identified the tipping point of tolerance (where negative impacts will begin) in larvae of the species to be between the years 2090 and 2100.

Keywords: climate change, ecology, marine, ocean acidification

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Authors: Timothy Akinwande, Eddie Hui, Karien Dekker

Abstract:

Housing the urban poor remains a persistent challenge, despite evident research attention over many years. It is, therefore, pertinent to investigate affordable housing provision challenges with novel approaches. For innovative solutions to affordable housing constraints, it is apposite to thoroughly examine housing solutions vis a vis the key elements of the housing supply value chain (HSVC), which are housing finance, housing construction and land acquisition. A pragmatic analysis will examine affordable housing solutions from demand and supply perspectives to arrive at consolidated solutions from bilateral viewpoints. This study thoroughly examined informal housing finance strategies of the urban poor and diligently investigated expert opinion on affordable housing finance solutions. The research questions were: (1) What mutual grounds exist between informal housing finance solutions of the urban poor and housing expert solutions to affordable housing finance constraints in developing economies? (2) What are effective approaches to affordable housing finance in developing economies from an integrated demand - supply perspective? Semi-structured interviews were conducted in the 5 largest slums of Lagos, Nigeria, with 40 informal settlers for demand-oriented solutions, while focus group discussion and in-depth interviews were conducted with 12 housing experts in Nigeria for supply-oriented solutions. Following a rigorous thematic, content and descriptive analyses of data using NVivo and Excel, findings ascertained mutual solutions from both demand and supply standpoints that can be consolidated into more effective affordable housing finance solutions in Nigeria. Deliberate finance models that recognise and include the finance realities of the urban poor was found to be the most significant supply-side housing finance solution, representing 25.4% of total expert responses. Findings also show that 100% of sampled urban poor engage in vocations where they earn little irregular income or zero income, limiting their housing finance capacities and creditworthiness. Survey revealed that the urban poor are involved in community savings and employ microfinance institutions within the informal settlements to tackle their housing finance predicaments. These are informal finance models of the urban poor, revealing common grounds between demand and supply solutions for affordable housing financing. Effective, affordable housing approach will be to modify, institutionalise and incorporate the informal finance strategies of the urban poor into deliberate government policies. This consolidation of solutions from demand and supply perspectives can eliminate the persistent misalliance between affordable housing demand and affordable housing supply. This study provides insights into mutual housing solutions from demand and supply perspectives, and findings are informative for effective, affordable housing provision approaches in developing countries. This study is novel in consolidating affordable housing solutions from demand and supply viewpoints, especially in relation to housing finance as a key component of HSVC. The framework for effective, affordable housing finance in developing economies from a consolidated viewpoint generated in this study is significant for the achievement of sustainable development goals, especially goal 11 for sustainable, resilient and inclusive cities. Findings are vital for future housing studies.

Keywords: affordable housing, affordable housing finance, developing economies, effective affordable housing, housing policy, urban poor, sustainable development goal, sustainable affordable housing

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Authors: Hsin-Hung Lin, Janet Chang, Te-Yi Chang, You-Sheng Huang

Abstract:

The natural and men-made disasters have become huge challenges for tourism destinations as well as emphasizing the fragility of the industry. Hot springs, among all destinations, are prone to disasters due to their dependence on natural resources and locations. After the COVID-19 outbreak, hot spring destinations have experienced not only the loss of businesses but also the psychological trauma. However, evidence has also shown that the impacts may not necessarily reduce the resilience for people but may be converted into posttraumatic growth. In Taiwan, a large proportion of hot springs are located in rural or indigenous areas. As a result, hot spring resources are associated with community cohesion for local residents. Yet prior research on hot spring destinations has mainly focused on visitors, whereas residents have been overlooked. More specifically, the relationship between hot springs resources and resident resilience in the face of the COVID-19 impacts remains unclear. To fulfill this knowledge gap, this paper aims to explore the COVID-19 impacts on residents’ hot spring experiences as well as individual and community resilience from the perspective of posttraumatic growth. A total of 315 residents of 13 hot spring destinations that are most popular in Taiwan were recruited. Online questionnaires were distributed over travel forums and social networks after the COVID-19. This paper subsequently used Partial Least Squares Structural Equation Modeling for data analysis as the technique offers significant advantages in addressing nonnormal data and small sample sizes. A preliminary test was conducted, and the results showed acceptable internal consistency and no serious common method variance. The path analysis demonstrated that the COVID-19 impacts strengthened residents’ perceptions of hot spring resources and experiences, implying that the pandemic had propelled the residents to visit hot springs for the healing benefits. In addition, the COVID-19 impacts significantly enhanced residents’ individual and community resilience, which indicates that the residents at hot springs are more resilient thanks to their awareness of external risks. Thirdly, residents’ individual resilience was positively associated with hot spring experiences, while community resilience was not affected by hot spring experiences. Such findings may suggest that hot spring experiences are more related to individual-level experiences and, consequently, have insignificant influence on community resilience. Finally, individual resilience was proved to be the most relevant factor that help foster community resilience. To conclude, the authorities may consider exploiting the hot spring resources so as to increase individual resilience for local residents. Such implications can be used as a reference for other post-disaster tourist destinations as well. As for future research, longitudinal studies with qualitative methods are suggested to better understand how the hot spring experiences have changed individuals and communities over the long term. It should be noted that the main subjects of this paper were focused on the hot spring communities in Taiwan. Therefore, the results cannot be generalized for all types of tourism destinations. That is, more diverse tourism destinations may be investigated to provide a broader perspective of post-disaster recovery.

Keywords: community resilience, hot spring destinations, individual resilience, posttraumatic growth

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Authors: Jamshaid Ul Rahman, Faiza Makhdoom, Dianchen Lu

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Activation functions play a decisive role in determining the capacity of Deep Neural Networks (DNNs) as they enable neural networks to capture inherent nonlinearities present in data fed to them. The prior research on activation functions primarily focused on the utility of monotonic or non-oscillatory functions, until Growing Cosine Unit (GCU) broke the taboo for a number of applications. In this paper, a Convolutional Neural Network (CNN) model named as ASU-CNN is proposed which utilizes recently designed activation function ASU across its layers. The effect of this non-monotonic and oscillatory function is inspected through feature map visualizations from different convolutional layers. The optimization of proposed network is offered by Adam with a fine-tuned adjustment of learning rate. The network achieved promising results on both training and testing data for the classification of CIFAR-10. The experimental results affirm the computational feasibility and efficacy of the proposed model for performing tasks related to the field of computer vision.

Keywords: amplifying sine unit, activation function, convolutional neural networks, oscillatory activation, image classification, CIFAR-10

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Authors: Sonia Perez-Gamboa, Qingquan Sun, Yan Zhang

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Non-intrusive sensor-based human activity recognition (HAR) is utilized in a spectrum of applications, including fitness tracking devices, gaming, health care monitoring, and smartphone applications. Deep learning models such as convolutional neural networks (CNNs) and long short term memory (LSTM) recurrent neural networks (RNNs) provide a way to achieve HAR accurately and effectively. In this paper, we design a multi-layer hybrid architecture with CNN and LSTM and explore a variety of multi-layer combinations. Based on the exploration, we present a lightweight, hybrid, and multi-layer model, which can improve the recognition performance by integrating local features and scale-invariant with dependencies of activities. The experimental results demonstrate the efficacy of the proposed model, which can achieve a 94.7% activity recognition rate on a benchmark human activity dataset. This model outperforms traditional machine learning and other deep learning methods. Additionally, our implementation achieves a balance between recognition rate and training time consumption.

Keywords: deep learning, LSTM, CNN, human activity recognition, inertial sensor

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Authors: Nicholas Anderson, Steven Petersen, Tom Bates, Val Anderson

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Under the multiple-use management regime established in the United States for federally owned lands, government agencies have come under pressure from commercial apiaries to grant permits for the summer pasturing of honeybees on government lands. Federal agencies have struggled to integrate honeybees into their management plans and have little information to make regulations that resolve how many colonies should be allowed in a single location and at what distance sets of hives should be placed. Many conservation groups have voiced their concerns regarding the introduction of honeybees to these natural lands, as they may outcompete and displace native pollinating species. Assessing the quality of an area in regard to its floral resources, pollen, and nectar can be important when attempting to create regulations for the integration of commercial honeybee operations into a native ecosystem. Areas with greater floral resources may be able to support larger numbers of honeybee colonies, while poorer resource areas may be less resilient to introduced disturbances. Attempts are made in this study to determine flower cover using high resolution drone imagery to help assess the floral resource availability to pollinators in high elevation, tall forb communities. This knowledge will help in determining the potential that different areas may have for honeybee pasturing and honey production. Roughly 700 images were captured at 23m above ground level using a drone equipped with a Sony QX1 RGB 20-megapixel camera. These images were stitched together using Pix4D, resulting in a 60m diameter high-resolution mosaic of a tall forb meadow. Using the program ENVI, a supervised maximum likelihood classification was conducted to calculate the percentage of total flower cover and flower cover by color (blue, white, and yellow). A complete vegetation inventory was taken on site, and the major flowers contributing to each color class were noted. An accuracy assessment was performed on the classification yielding an 89% overall accuracy and a Kappa Statistic of 0.855. With this level of accuracy, drones provide an affordable and time efficient method for the assessment of floral cover in large areas. The proximal step of this project will now be to determine the average pollen and nectar loads carried by each flower species. The addition of this knowledge will result in a quantifiable method of measuring pollen and nectar resources of entire landscapes. This information will not only help land managers determine stocking rates for honeybees on public lands but also has applications in the agricultural setting, aiding producers in the determination of the number of honeybee colonies necessary for proper pollination of fruit and nut crops.

Keywords: honeybee, flower, pollinator, remote sensing

Procedia PDF Downloads 133

Authors: Hsin-Hung Lin, Janet Chang, Te-Yi Chang, You-Sheng Huang

Abstract:

The natural and men-made disasters have become huge challenges for tourism destinations as well as emphasizing the fragility of the industry. Hot springs, among all destinations, are prone to disasters due to their dependence on natural resources and locations. After the COVID-19 outbreak, hot spring destinations have experienced not only the loss of businesses but also the psychological trauma. However, evidence has also shown that the impacts may not necessarily reduce the resilience for people but may be converted into posttraumatic growth. In Taiwan, a large proportion of hot springs are located in rural or indigenous areas. As a result, hot spring resources are associated with community cohesion for local residents. Yet prior research on hot spring destinations has mainly focused on visitors, whereas residents have been overlooked. More specifically, the relationship between hot springs resources and resident resilience in the face of the COVID-19 impacts remains unclear. To fulfill this knowledge gap, this paper aims to explore the COVID-19 impacts on residents’ hot spring experiences as well as individual and community resilience from the perspective of posttraumatic growth. A total of 315 residents of 13 hot spring destinations that are most popular in Taiwan were recruited. Online questionnaires were distributed over travel forums and social networks after the COVID-19. This paper subsequently used Partial Least Squares Structural Equation Modeling for data analysis as the technique offers significant advantages in addressing nonnormal data and small sample sizes. A preliminary test was conducted, and the results showed acceptable internal consistency and no serious common method variance. The path analysis demonstrated that the COVID-19 impacts strengthened residents’ perceptions of hot spring resources and experiences, implying that the pandemic had propelled the residents to visit hot springs for the healing benefits. In addition, the COVID-19 impacts significantly enhanced residents’ individual and community resilience, which indicates that the residents at hot springs are more resilient thanks to their awareness of external risks. Thirdly, residents’ individual resilience was positively associated with hot spring experiences, while community resilience was not affected by hot spring experiences. Such findings may suggest that hot spring experiences are more related to individual-level experiences and, consequently, have insignificant influence on community resilience. Finally, individual resilience was proved to be the most relevant factor that help foster community resilience. To conclude, the authorities may consider exploiting the hot spring resources so as to increase individual resilience for local residents. Such implications can be used as a reference for other post-disaster tourist destinations as well.As for future research, longitudinal studies with qualitative methods are suggested to better understand how the hot spring experiences have changed individuals and communities over the long term. It should be noted that the main subjects of this paper were focused on the hot spring communities in Taiwan. Therefore, the results cannot be generalized for all types of tourism destinations. That is, more diverse tourism destinations may be investigated to provide a broader perspective of post-disaster recovery.

Keywords: community resilience, hot spring destinations, individual resilience, posttraumatic growth (PTG)

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Authors: Chia-Chen Wei, Pack Hsieh, Jeffrey Chen

Abstract:

Surface Mount Technology (SMT) is widely used in the area of the electronic assembly in which the electronic components are mounted to the surface of the printed circuit board (PCB). Most of the defects in the SMT process are mainly related to the quality of solder paste printing. These defects lead to considerable manufacturing costs in the electronics assembly industry. Therefore, the solder paste inspection (SPI) machine for controlling and monitoring the amount of solder paste printing has become an important part of the production process. So far, the setting of the SPI threshold is based on statistical analysis and experts’ experiences to determine the appropriate threshold settings. Because the production data are not normal distribution and there are various variations in the production processes, defects related to solder paste printing still occur. In order to solve this problem, this paper proposes an online machine learning algorithm, called the automatic threshold adjustment (ATA) algorithm, and closed-loop architecture in the SMT process to determine the best threshold settings. Simulation experiments prove that our proposed threshold settings improve the accuracy from 99.85% to 100%.

Keywords: big data analytics, Industry 4.0, SPI threshold setting, surface mount technology

Procedia PDF Downloads 111

Authors: Olivia Bory Devisme, Denis Genon-Catalot, Frederic Alicalapa, Pierre-Olivier Lucas De Peslouan, Jean-Pierre Chabriat

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In the context of climate change, it is essential that island territories gain energy autonomy. Currently mostly dependent on fossil fuels, the island of Reunion lo- cated in the Indian Ocean nevertheless has a high potential for solar energy. As the market for photovoltaic panels has been growing in recent years, the issues of energy losses linked to the multiple conversions from direct current to alternating current are emerging. In order to quantify these advantages and disadvantages by a comparative study, this document present the measurements carried out on a direct current test bench, particularly for lighting, ventilation, air condi- tioning and office equipment for the tertiary sector. All equipment is supplied with DC power from energy produced by photovoltaic panels. A weather sta- tion, environmental indoor sensors, and drivers are also used to control energy. Self-consumption is encouraged in order to manage different priorities between user consumption and energy storage in a lithium iron phosphate battery. The measurements are compared to a conventional electrical architecture (DC-AC- DC) for energy consumption, equipment overheating, cost, and life cycle analysis.

Keywords: DC microgrids, solar energy, smart buildings, storage

Procedia PDF Downloads 157

Authors: Salha Abdullah Ali Al-Shamrani, Maha Muhammad Dhaher Aljuhani, Eman Ali Ahmed Aldhaheri

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With the proliferation of Internet of Things (IoT) devices in various industries, there has been a concurrent rise in security vulnerabilities, particularly spoofing attacks. This study explores the potential of blockchain technology in enhancing the security of IoT systems and mitigating these attacks. Blockchain's decentralized and immutable ledger offers significant promise for improving data integrity, transaction transparency, and tamper-proofing. This research develops and implements a blockchain-based IoT architecture and a reference network to simulate real-world scenarios and evaluate a blockchain-integrated intrusion detection system. Performance measures including time delay, security, and resource utilization are used to assess the system's effectiveness, comparing it to conventional IoT networks without blockchain. The results provide valuable insights into the practicality and efficacy of employing blockchain as a security mechanism, shedding light on the trade-offs between speed and security in blockchain deployment for IoT. The study concludes that despite minor increases in time consumption, the security benefits of incorporating blockchain technology into IoT systems outweigh potential drawbacks, demonstrating a significant potential for blockchain in bolstering IoT security.

Keywords: internet of things, spoofing, IoT, access control, blockchain, raspberry pi

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Authors: Sifan Guo, Xuesen Zheng

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Visitors as the vital research group have been mentioned in high frequency in the field of museum studies. With the rise of the New Museology Movement, new challenges in the museum appeared, ranging from how to eliminate the cliché class prejudices in museums to how to make visitor-oriented museums more welcome. In line with this new situation, to create a successful visiting experience is the focus of museums in today. National museums as tourist attractions always attract flooded attention, however the local museums may have the different situations. The residents could be one of the main visitors to the local museums how to attract them returned should be considered here. There are various people with different cultural, education and religion backgrounds, it is necessary to keep the balance of the education and entertainment to reach visitors’ expectations. Regarding these questions, a mixed methods research approach has been adopted: observations, tracking and questionnaires. Based on analysing some museums’ cases in the UK, it can be argued that: 1) Audiences’ accessibility support their options and judgments during the visiting. 2) Highly inclusive architecture and narrative expressions could encourage the visitors to proceed deeply understanding and alleviate conflicts. In addition, the main characteristics of the local museums and the interlinks between museums and urban renaissance will be clarified. The conclusion informs not only practical suggestions for reachable characteristic design, but also potential future research subjects.

Keywords: accessibility, challenging visitors, new museology movement, visiting experience

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Authors: Violeta Damjanovic-Behrendt

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This paper presents an approach for optimal cyber security decisions to protect instances of a federated Internet of Things (IoT) platform in the cloud. The presented solution implements the repeated Stackelberg Security Game (SSG) and a model called Stochastic Human behaviour model with AttRactiveness and Probability weighting (SHARP). SHARP employs the Subjective Utility Quantal Response (SUQR) for formulating a subjective utility function, which is based on the evaluations of alternative solutions during decision-making. We augment the repeated SSG (including SHARP and SUQR) with a reinforced learning algorithm called Naïve Q-Learning. Naïve Q-Learning belongs to the category of active and model-free Machine Learning (ML) techniques in which the agent (either the defender or the attacker) attempts to find an optimal security solution. In this way, we combine GT and ML algorithms for discovering optimal cyber security policies. The proposed security optimization components will be validated in a collaborative cloud platform that is based on the Industrial Internet Reference Architecture (IIRA) and its recently published security model.

Keywords: security, internet of things, cloud computing, stackelberg game, machine learning, naive q-learning

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Authors: Mazharuddin Syed Ahmed

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This research presents a framework used to develop The Ara Polytechnic College of Architecture Studies building “Kahukura” which is Green Building certified. This framework integrates the development of a smart building digital twin by utilizing Building Information Modelling (BIM) and its BIM maturity levels, including Levels of Development (LOD), eight dimensions of BIM, Heritage-BIM (H-BIM) and Facility Management BIM (FM BIM). The research also outlines a structured approach to building performance analysis and integration with the circular economy, encapsulated within a five-level digital twin framework. Starting with Level 1, the Descriptive Twin provides a live, editable visual replica of the built asset, allowing for specific data inclusion and extraction. Advancing to Level 2, the Informative Twin integrates operational and sensory data, enhancing data verification and system integration. At Level 3, the Predictive Twin utilizes operational data to generate insights and proactive management suggestions. Progressing to Level 4, the Comprehensive Twin simulates future scenarios, enabling robust “what-if” analyses. Finally, Level 5, the Autonomous Twin, represents the pinnacle of digital twin evolution, capable of learning and autonomously acting on behalf of users.

Keywords: building information modelling, circular economy integration, digital twin, predictive analytics

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Authors: Muhammad Atif, Cang Yan

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The enhancement of low-light images is a significant area of study aimed at enhancing the quality of captured images in challenging lighting environments. Recently, methods based on convolutional neural networks (CNN) have gained prominence as they offer state-of-the-art performance. However, many approaches based on CNN rely on increasing the size and complexity of the neural network. In this study, we propose an alternative method for improving low-light images using an autoencoder-based multiscale knowledge transfer model. Our method leverages the power of three autoencoders, where the encoders of the first two autoencoders are directly connected to the decoder of the third autoencoder. Additionally, the decoder of the first two autoencoders is connected to the encoder of the third autoencoder. This architecture enables effective knowledge transfer, allowing the third autoencoder to learn and benefit from the enhanced knowledge extracted by the first two autoencoders. We further integrate the proposed model into the PIX to PIX GAN framework. By integrating our proposed model as the generator in the GAN framework, we aim to produce enhanced images that not only exhibit improved visual quality but also possess a more authentic and realistic appearance. These experimental results, both qualitative and quantitative, show that our method is better than the state-of-the-art methodologies.

Keywords: low light image enhancement, deep learning, convolutional neural network, image processing

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Authors: Caroline McDonald-Harker, Julie Drolet

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Abstract— In the last six years Canada In the last six years Canada has experienced two major and catastrophic environmental disasters– the 2013 Southern Alberta flood and the 2016 Fort McMurray, Alberta wildfire. These two disasters resulted in damages exceeding 12 billion dollars, the costliest disasters in Canadian history. In the aftermath of these disasters, many families faced the loss of homes, places of employment, schools, recreational facilities, and also experienced social, emotional, and psychological difficulties. Children and youth are among the most vulnerable to the devastating effects of disasters due to the physical, cognitive, and social factors related to their developmental life stage. Yet children and youth also have the capacity to be resilient and act as powerful catalyst for change in their own lives and wider communities following disaster. Little is known, particularly from a sociological perspective, about the specific factors that contribute to resilience in children and youth, and effective ways to support their overall health and well-being. This paper focuses on the voices and experiences of children and youth residing in these two disaster-affected communities in Alberta, Canada and specifically examines: 1) How children and youth’s lives are impacted by the tragedy, devastation, and upheaval of disaster; 2) Ways that children and youth demonstrate resilience when directly faced with the adversarial circumstances of disaster; and 3) The cumulative internal and external factors that contribute to bolstering and supporting resilience among children and youth post-disaster. This paper discusses the characteristics associated with high levels of resilience in 183 children and youth ages 5 to 17 based on quantitative and qualitative data obtained through a mix methods approach. Child and youth participants were administered the Children and Youth Resilience Measure (CYRM-28) in order to examine factors that influence resilience processes including: individual, caregiver, and context factors. The CYRM-28 was then supplemented with qualitative interviews with children and youth to contextualize the CYRM-28 resiliency factors and provide further insight into their overall disaster experience. Findings reveal that high levels of resilience among child and youth participants is associated with both individual factors and caregiver factors, specifically positive outlook, effective communication, peer support, and physical and psychological caregiving. Individual and caregiver factors helped mitigate the negative effects of disaster, thus bolstering resilience in children and youth. This paper discusses the implications that these findings have for understanding the specific mechanisms that support the resiliency processes and overall recovery of children and youth following disaster; the importance of bridging the gap between children and youth’s needs and the services and supports provided to them post-disaster; and the need to develop resiliency processes and practices that empower children and youth as active agents of change in their own lives following disaster. These findings contribute to furthering knowledge about pragmatic and representative changes to resources, programs, and policies surrounding disaster response, recovery, and mitigation.

Keywords: children and youth, disaster, environment, resilience

Procedia PDF Downloads 119

Authors: S. Khosravi, X. Chelius, A. Unger, D. Rieger, J. Frickel, T. Sachsenheimer, C. Luechtenborg, R. Schieweck, B. Bruegger, B. Westermann, T. Klecker, W. Neupert, M. E. Harner

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The use of Saccharomyces cerevisiae as a model organism to study eukaryotic cell functions has been used successfully for decades. Like virtually all eukaryotic cells, they contain mitochondria as essential organelles performing various functions, including participation in lipid metabolism. They are separated from the cytosol by a double membrane system consisting of the mitochondrial inner membrane (MIM) and the mitochondrial outer membrane (MOM). This physical separation of the mitochondria requires an exchange of metabolites, proteins, and lipids. Proteinaceous contact sites are thought to be important for this communication. Recently, it was found that Cqd1, in cooperation with Cqd2, controls the distribution of Coenzyme Q within the cell. In this study, a contact site is described, formed by the MOM protein complex Por1-Om14 and the UbiB protein kinase-like MIM protein Cqd1. The present results suggest the additional involvement of Cqd1 in the homeostasis of phospholipids. Moreover, we show that overexpression of the UbiB family proteins also causes tethering of the mitochondria to the endoplasmatic reticulum. Due to the conservation of the subunits of this contact site to higher eukaryotes, its identification in S. cerevisiae might provide promising avenues for further research in other organisms.

Keywords: contact sites, mitochondrial architecture, mitochondrial proteins, yeast mitochondria

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Authors: Minsung Kim, Hyunkoo Kang, Jinkoo Kim

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In this study, a hybrid energy dissipation device is developed by combining a steel slit plate and friction pads to be used for seismic retrofit of structures, and its effectiveness is investigated by comparing the life cycle costs of the structure before and after the retrofit. The seismic energy dissipation capability of the dampers is confirmed by cyclic loading tests. The probabilities of reaching various damage states are obtained by fragility analysis, and the life cycle costs of the model structures are computed using the PACT (Performance Assessment Calculation Tool) program based on FEMA P-58 methodology. The fragility analysis shows that the probabilities of reaching limit states are minimized by the seismic retrofit with hybrid dampers and increasing column size. The seismic retrofit with increasing column size and hybrid dampers results in the lowest repair cost and shortest repair time. This research was supported by a grant (13AUDP-B066083-01) from Architecture & Urban Development Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

Keywords: FEMA P-58, friction dampers, life cycle cost, seismic retrofit

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Authors: Muhamad Nugraha

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Economic growth always correlate positively with the magnitude of the unemployment rate. This is caused by labor which one of important variable to keep growth in the real sector of the region. Meanwhile, the economic structure in districts of Jember showed an increase of economic activity began to shift towards the industrial sector and some other economic sectors, so they have an affects to considerations for policy makers to increase economic growth in Jember as an autonomous region in East Java Province. At the fact, SMEs is among the factors driving economic growth in the region. This is shown by the high amount of SMEs. However, employment in the sector grew slightly slowed. It is caused by a lack of productivity in SMEs. Through the analysis of the transformation of economic structure theory, and the theory of Triple Helix using descriptive analytical method Location Quotient and Shift - Share, found that the results of the economic structure in Jember slowly shifting from the agricultural sector to the industrial sector, because it is dominated by trade sector, hotel and restaurant sector. In addition, SMEs is the potential sector of economic growth in Jember. While to maximizing role and functions of the institution's Research and Development Center of SMEs, there are three points to be known, that are Business Landscape, Business Architecture and Value Added.

Keywords: economic growth, SMEs, labor, Research and Development Center of SMEs

Procedia PDF Downloads 436

Authors: Lata Shakya, Toshio Otsuki, Saori Imoto, Bijaya Krishna Shrestha, Umesh Bahadur Malla

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2015 Gorkha Nepal earthquake have damaged the human settlements in 14 districts of Nepal. Historic core areas of three principal cities namely Kathmandu, Lalitpur and Bhaktapur including numerous traditional ‘newari’ settlements in the peripheral areas have been either collapsed or severely damaged. Despite Government of Nepal and (international) non-government organisations’ attempt towards disaster risk management through the preparation of policies and guidelines and implementation of community-based activities, the recent ‘Gorkha’ earthquake has demonstrated the inadequate preparedness, poor implementation of a legal instrument, resource constraints, and managerial weakness. However, the social capital through community based institutions, self-help attitude, and community bond has helped a lot not only in rescue and relief operation but also in a post-disaster temporary shelter living thereby exhibiting the resilient power of the local community. Conducting a detailed case study of ‘Kaminani’ community with 42 houses at ward no. 16 of Bhaktapur municipality, this paper analyses the local community’s response and activities on the Gorkha earthquake in rescue and relief operation as well as in post disaster work. Leadership, the existence of internal/external aid, physical and human support are also analyzed. Social resource and networking are also explained through critical review of the existing community organisation and their activities. The research methodology includes literature review, field survey, and interview with community leaders and residents based on a semi-structured questionnaire. The study reveals that community carried their recovery process in four different phases: (i) management of emergency evacuation, (ii) constructing community owed temporary shelter for individuals, (iii) demolishing upper floors of the damaged houses, and (iv) planning for collaborative housing reconstruction. As territorial based organization, religion based agency and aim based institution exist in the survey area from pre-disaster time, it can be assumed that the community activists including leaders are well experienced to create aim-based group and manage teamwork to deal with various issues and problems collaboratively. Physical and human support including partial financial aid from external source as a result of community leader’s personal networking is extended to the community members. Thus, human/social resource and personal/social network play a crucial role in the recovery process. And to build such social capital, community should have potential from pre-disaster time.

Keywords: Gorkha Nepal earthquake, local community, recovery process, social resource, social network

Procedia PDF Downloads 247