Search results for: attention-based fully convolutional network

Authors: Zuhal Eltayeb Awad

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Alryad neighborhood is located in Khartoum town– the administrative center of the Capital of Sudan. The neighborhood is one of the high-income residential areas with villa type development of low-density. It was planned and developed in 1972 with large plots (600-875m²), wide crossing roads and balanced environment. Recently the area transformed into more compact urban form of high density, mixed-use integrated development with more intensive use of land; multi-storied apartments. The most important socio-economic process in the neighborhood has been the commercialization and deinitialization of the area in connect with the displacement of the residential function. This transformation affected the quality of the neighborhood and the inter-related features of the built environment. A case study approach was chosen to gather the necessary qualitative and quantitative data. A detailed survey on existing development pattern was carried out over the whole area of Alryad. Data on the built and social environment of the neighborhoods were collected through observations, interviews and secondary data sources. The paper reflected a theoretical and empirical interest in the particular characteristics of compact neighborhood with high density, and mixed land uses and their effect on social wellbeing of the residents all in the context of the sustainable development. The research problem is focused on the challenges of transformation that associated with compact neighborhood that created multiple urban problems, e.g., stress of essential services (water supply, electricity, and drainage), congestion of streets and demand for parking. The main objective of the study is to analyze the transformation of this area from residential use to commercial and administrative use. The study analyzed the current situation of the neighborhood compared to the five principles of sustainable neighborhood prepared by UN Habitat. The study found that the neighborhood is experienced changes that occur to inner-city residential areas and the process of change of the neighborhood was originated by external forces due to the declining economic situation of the whole country. It is evident that non-residential uses have taken place uncontrolled, unregulated and haphazardly that led to damage the residential environment and deficiency in infrastructure. The quality of urban life and in particular on levels of privacy was reduced, the neighborhood changed gradually to be a central business district that provides services to the whole Khartoum town. The change of house type may be attributed to a demand-led housing market and absence of policy. The results showed that Alryad is not fully sustainable and self-contained, street network characteristics and mixed land-uses development are compatible with the principles of sustainability. The area of streets represents 27.4% of the total area of the neighborhood. Residential density is 4,620 people/ km², that is lower than the recommendations, and the limited block land-use specialization is higher than 10% of the blocks. Most inhabitants have a high income so that there is no social mix in the neighborhood. The study recommended revision of the current zoning regulations in order to control and regulate undesirable development in the neighborhood and provide new solutions which allow promoting the neighborhood sustainable development.

Keywords: compact neighborhood, land uses, mixed use, residential area, transformation

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Authors: Kwok W. Mui, Ling T. Wong, Chi T. Cheung

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Cooling energy consumption in the residential sector, different from shopping mall, office or commercial buildings, is significantly subject to occupant decisions where in-depth investigations are found limited. It shows that energy consumptions could be associated with housing types. Surveys have been conducted in existing Hong Kong public housings to understand the housing characteristics, apartment electricity demands, occupant’s thermal expectations, and air–conditioning usage patterns for further cooling energy-saving assessments. The aim of this study is to develop a hybrid cooling energy prediction model, which integrated by EnergyPlus (EP) and artificial neural network (ANN) to estimate cooling energy consumption in public residential sector. Sensitivity tests are conducted to find out the energy impacts with changing building parameters regarding to external wall and window material selection, window size reduction, shading extension, building orientation and apartment size control respectively. Assessments are performed to investigate the relationships between cooling demands and occupant behavior on thermal environment criteria and air-conditioning operation patterns. The results are summarized into a cooling energy calculator for layman use to enhance the cooling energy saving awareness in their own living environment. The findings can be used as a directory framework for future cooling energy evaluation in residential buildings, especially focus on the occupant behavioral air–conditioning operation and criteria of energy-saving incentives.

Keywords: artificial neural network, cooling energy, occupant behavior, residential buildings, thermal environment

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Authors: Daniel Pastor, Hyo-Sang Shin

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This paper presents a novel method to use optical flow navigation for long-term navigation. Unlike standard SLAM approaches for augmented reality, OFLAAM is designed for Micro Air Vehicles (MAV). It uses an optical flow camera pointing downwards, an IMU and a monocular camera pointing frontwards. That configuration avoids the expensive mapping and tracking of the 3D features. It only maps these features in a vocabulary list by a localization module to tackle the loss of the navigation estimation. That module, based on the well-established algorithm DBoW2, will be also used to close the loop and allow long-term navigation in confined areas. That combination of high-speed optical flow navigation with a low rate localization algorithm allows fully autonomous navigation for MAV, at the same time it reduces the overall computational load. This framework is implemented in ROS (Robot Operating System) and tested attached to a laptop. A representative scenarios is used to analyse the performance of the system.

Keywords: vision, UAV, navigation, SLAM

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Authors: Luojie Tang, Libin Ouyang, Yihang Gao

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The natural landscape and urban-rural structure, with their attractiveness in the Dali area around Erhai Lake, exhibit striking similarities with Howard's Garden City. With the emergence of the unique phenomenon of the first large-scale gathering of digital nomads in China in Dali, an analysis of Dali's natural, economic, and cultural representations and structures reveals that the Garden City model can no longer fully explain the current overall human living environment in Dali. By interpreting the bottom-up local construction process in Dali based on landscape identity, the transformation of production and lifestyle under new technologies such as ICT(Information and Communication Technology), and the values and lifestyle reshaping embodied in the "reverse urbanization" phenomenon of the middle class in Dali, it is believed that Dali has moved towards a "contemporary garden city influenced by new technology." The article summarizes the characteristics and connotations of this Garden City and provides corresponding strategies for its continued healthy development.

Keywords: dali, ICT, rural-urban relationship, garden city model

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Authors: Rauf R. Hussein, Devon M. Ramey

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Integrating Ground Penetrating Radar (GPR) with Geographic Information Systems (GIS) can provide valuable insights for various applications, such as archaeology, transportation, and utility locating. Although there has been progress toward automating the integration of GPR data with GIS, fully automatic integration has not been achieved yet. Additionally, manually integrating GPR data with GIS can be a time-consuming and error-prone process. In this study, actual, real-world GPR applications are presented, and a software named GPR-GIS 10 is created to interactively extract subsurface targets from GPR radargrams and automatically integrate them into GIS. With this software, it is possible to quickly and reliably integrate the two techniques to create informative subsurface maps. The results indicated that automatic integration of GPR with GIS can be an efficient tool to map and view any subsurface targets in their appropriate location in a 3D space with the needed precision. The findings of this study could help GPR-GIS integrators save time and reduce errors in many GPR-GIS applications.

Keywords: GPR, GIS, GPR-GIS 10, drone technology, automation

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Authors: Damanhur Munardi, Muhammad Hafiz, Dina Nurmalita Sari, Syarifah Ridani Alifa

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Purpose: This research aims to analyze the Benefits, Opportunity, Cost, and Risk aspects of applying the Green Finance concept and to obtain the right Green Finance financing strategy to be implemented within a long-term and short-term strategic framework.Methodology: This research method uses a qualitative-descriptive analysis approach. The analysis technique uses Analytical Network Process (ANP) with a BOCR network structure approach.Findings: The research results show that the most priority long-term strategic alternative based on the long-term BOCR analysis is increasing awareness among the public and industry by 52% and the importance of coordination between related institutions by 50%. Meanwhile, the most priority short-term strategic alternatives are the importance of coordination between related institutions 29%, increasing awareness among the public and industry 28%, the banking industry proactively funding environmentally friendly companies and technology 23%, the existence of Green Finance POS (Standard Operating Procedures) 20%.Implications: This research can be used as a reference for regulators and policymakers in making strategic decisions that can increase green finance financing. The novelty of this research is identifying problems that occur in green finance financing in Aceh province by analyzing opinions from experts in related fields and financial regulators in Aceh to create a strategy that can be implemented to increase green finance financing in Aceh province through BPD in Aceh, namely Bank Aceh.

Keywords: green financing, banking, sharia, islamic

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Authors: Tayo P. Ogundunmade, Adedayo A. Adepoju

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Terrorist attacks in liberal democracies bring about a few pessimistic results, for example, sabotaged public support in the governments they target, disturbing the peace of a protected environment underwritten by the state, and a limitation of individuals from adding to the advancement of the country, among others. Hence, seeking for techniques to understand the different factors involved in terrorism and how to deal with those factors in order to completely stop or reduce terrorist activities is the topmost priority of the government in every country. This research aim is to develop an efficient deep learning-based predictive model for the prediction of future terrorist activities in Nigeria, addressing low-quality prediction accuracy problems associated with the existing solution methods. The proposed predictive AI-based model as a counterterrorism tool will be useful by governments and law enforcement agencies to protect the lives of individuals in society and to improve the quality of life in general. A Heterogeneous Bayesian Neural Network (HETBNN) model was derived with Gaussian error normal distribution. Three primary transfer functions (HOTTFs), as well as two derived transfer functions (HETTFs) arising from the convolution of the HOTTFs, are namely; Symmetric Saturated Linear transfer function (SATLINS ), Hyperbolic Tangent transfer function (TANH), Hyperbolic Tangent sigmoid transfer function (TANSIG), Symmetric Saturated Linear and Hyperbolic Tangent transfer function (SATLINS-TANH) and Symmetric Saturated Linear and Hyperbolic Tangent Sigmoid transfer function (SATLINS-TANSIG). Data on the Terrorist activities in Nigeria gathered through questionnaires for the purpose of this study were used. Mean Square Error (MSE), Mean Absolute Error (MAE) and Test Error are the forecast prediction criteria. The results showed that the HETFs performed better in terms of prediction and factors associated with terrorist activities in Nigeria were determined. The proposed predictive deep learning-based model will be useful to governments and law enforcement agencies as an effective counterterrorism mechanism to understand the parameters of terrorism and to design strategies to deal with terrorism before an incident actually happens and potentially causes the loss of precious lives. The proposed predictive AI-based model will reduce the chances of terrorist activities and is particularly helpful for security agencies to predict future terrorist activities.

Keywords: activation functions, Bayesian neural network, mean square error, test error, terrorism

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Authors: Hassan Ibrahim

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The prevalence of hyperacusis, an auditory condition characterized by heightened sensitivity to sounds, continues to rise, posing challenges for effective diagnosis and intervention. It is believed that this work deepens will deepens the understanding of hyperacusis etiology by employing graph theory as a novel analytical framework. We constructed a comprehensive graph wherein nodes represent various factors associated with hyperacusis, including aging, head or neck trauma, infection/virus, depression, migraines, ear infection, anxiety, and other potential contributors. Relationships between factors are modeled as edges, allowing us to visualize and quantify the interactions within the etiological landscape of hyperacusis. it employ the concept of the metric dimension of a connected graph to identify key nodes (landmarks) that serve as critical influencers in the interconnected web of hyperacusis causes. This approach offers a unique perspective on the relative importance and centrality of different factors, shedding light on the complex interplay between physiological, psychological, and environmental determinants. Visualization techniques were also employed to enhance the interpretation and facilitate the identification of the central nodes. This research contributes to the growing body of knowledge surrounding hyperacusis by offering a network-centric perspective on its multifaceted causes. The outcomes hold the potential to inform clinical practices, guiding healthcare professionals in prioritizing interventions and personalized treatment plans based on the identified landmarks within the etiological network. Through the integration of graph theory into hyperacusis research, the complexity of this auditory condition was unraveled and pave the way for more effective approaches to its management.

Keywords: auditory condition, connected graph, hyperacusis, metric dimension

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Authors: Robert J. Sutton, Jon Elvins, Sean Casey, Eifion Jewell, Justin R. Searle

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Thermochemical storage utilises chemical salts which store and release energy a fully reversible endo/exothermic chemical reaction. Highly porous vermiculite impregnated with CaCl2, LiNO3 and MgSO4 (SIMs – Salt In Matrices) are proposed as potential materials for long-term thermochemical storage. The behavior of these materials during typical hydration and dehydration cycles is investigated. A simple moisture experiment represents the hydration, whilst thermogravimetric analysis (TGA) represents the dehydration. Further experiments to approximate the energy density and to determine the peak output temperatures of the SIMs are conducted. The CaCl2 SIM is deemed the best performing SIM across most experiments, whilst the results of MgSO4 SIM indicate difficulty associated with energy recovery.

Keywords: hydrated states, inter-seasonal heat storage, moisture sorption, salt in matrix

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Authors: Liu Yi-Hui, Chiu Fan-Yun, Lin Yu Fang, Jhang Yu Cih, He You Jing

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The demand for care support has been rising with the aging of society and the advancement of medical science and technology. To meet rising demand, the Taiwanese government promoted the “Long Term Care Ten-Year Plan 2.0” in 2017. However, this policy and its related services failed to be fully implemented because of the ignorance of the public, and their lack of desire, fear, or discomfort in using them, which is a major obstacle to the promotion of long-term care services. Given the above context, this research objectives included the following: (1) to understand the current situation and predicament of family caregivers; (2) to reveal the actual use and assistance of government’s long-term care resources for family caregivers; and (3) to explore the support and impact of social capital on family caregivers. A semi-structured in-depth interview with five family caregivers to understand long-term care networks and social capital for family caregivers.

Keywords: family caregivers, long-term care, social capital

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Authors: Noriyuki Suyama

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The progress and spread of digitalization have led to the provision of a variety of new services. The recent progress in digitization can be attributed to rapid developments in science and technology. First, the research and diffusion of artificial intelligence (AI) has made dramatic progress. Around 2000, the third wave of AI research, which had been underway for about 50 years, arrived. Specifically, machine learning and deep learning were made possible in AI, and the ability of AI to acquire knowledge, define the knowledge, and update its own knowledge in a quantitative manner made the use of big data practical even for commercial PCs. On the other hand, with the spread of social media, information exchange has become more common in our daily lives, and the lending and borrowing of goods and services, in other words, the sharing economy, has become widespread. The scope of this trend is not limited to any industry, and its momentum is growing as the SDGs take root. In addition, the Social Network Service (SNS), a part of social media, has brought about the evolution of the retail business. In the past few years, social network services (SNS) involving users or companies have especially flourished. The People's Republic of China (hereinafter referred to as "China") is a country that is stimulating enormous consumption through its own unique SNS, which is different from the SNS used in developed countries around the world. This paper focuses on the effectiveness and challenges of influencer marketing by focusing on the influence of influencers on users' behavior and satisfaction with Chinese SNSs. Specifically, Conducted was the quantitative survey of Tik Tok users living in China, with the aim of gaining new insights from the analysis and discussions. As a result, we found several important findings and knowledge.

Keywords: customer satisfaction, social networking services, influencer marketing, Chinese consumers’ behavior

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Authors: Milad Abbasi

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Carbon fiber-reinforced polymer (CFRP) composite structures are increasingly being utilized in the automotive industry due to their lightweight and specific energy absorption capabilities. Although it is impossible to predict composite mechanical properties directly using theoretical methods, various research has been conducted so far in the literature for accurate simulation of CFRP structures' energy-absorbing behavior. In this research, axial compression experiments were carried out on hand lay-up unidirectional CFRP composite tubes. The fabrication method allowed the authors to extract the material properties of the CFRPs using ASTM D3039, D3410, and D3518 standards. A neural network machine learning algorithm was then utilized to build a robust prediction model to forecast the axial compressive properties of CFRP tubes while reducing high-cost experimental efforts. The predicted results have been compared with the experimental outcomes in terms of load-carrying capacity and energy absorption capability. The results showed high accuracy and precision in the prediction of the energy-absorption capacity of the CFRP tubes. This research also demonstrates the effectiveness and challenges of machine learning techniques in the robust simulation of composites' energy-absorption behavior. Interestingly, the proposed method considerably condensed numerical and experimental efforts in the simulation and calibration of CFRP composite tubes subjected to compressive loading.

Keywords: CFRP composite tubes, energy absorption, crushing behavior, machine learning, neural network

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Authors: Zheng Yi

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Among the attempts made worldwide to foster urban and transport sustainability, transit-oriented development certainly is one of the most successful. Residents' travel behavior is a concern in the researches about the impacts of transit-oriented development. The study takes 620 English journal papers in the core collection database of Web of Science as the study objects; the paper tries to map out the scientific knowledge mapping in the field and draw the basic conditions by co-citation analysis, co-word analysis, a total of citation network analysis and visualization techniques. This study teases out the research hotspots and evolution of the relationship between urban rail transit and resident's travel behavior from 1999 to 2019. According to the results of the analysis of the time-zone view and burst-detection, the paper discusses the trend of the next stage of international study. The results show that in the past 20 years, the research focuses on these keywords: land use, behavior, model, built environment, impact, travel behavior, walking, physical activity, smart card, big data, simulation, perception. According to different research contents, the key literature is further divided into these topics: the attributes of the built environment, land use, transportation network, transportation policies. The results of this paper can help to understand the related researches and achievements systematically. These results can also provide a reference for identifying the main challenges that relevant researches need to address in the future.

Keywords: urban rail transit, travel behavior, knowledge map, evolution of researches

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Authors: Maulik L. Sisodia, Tirth K. Raval, Aarsh S. Mistry

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This paper summarizes the ongoing researches about the defects in both flexible and rigid pavement and the maintenance in both flexible and rigid pavements. Various defects in pavements have been identified since the existence of both flexible and rigid pavement. Flexible Pavement failure is defined in terms of decreasing serviceability caused by the development of cracks, ruts, potholes etc. Flexible Pavement structure can be destroyed in a single season due to water penetration. Defects in flexible pavements is a problem of multiple dimensions, phenomenal growth of vehicular traffic (in terms of no. of axle loading of commercial vehicles), the rapid expansion in the road network, non-availability of suitable technology, material, equipment, skilled labor and poor funds allocation have all added complexities to the problem of flexible pavements. In rigid pavements due to different type of destress the failure like joint spalling, faulting, shrinkage cracking, punch out, corner break etc. Application of correction in the existing surface will enhance the life of maintenance works as well as that of strengthening layer. Maintenance of a road network involves a variety of operations, i.e., identification of deficiencies and planning, programming and scheduling for actual implementation in the field and monitoring. The essential objective should be to keep the road surface and appurtenances in good condition and to extend the life of the road assets to its design life. The paper describes lessons learnt from pavement failures and problems experienced during the last few years on a number of projects in India. Broadly, the activities include identification of defects and the possible cause there off, determination of appropriate remedial measures; implement these in the field and monitoring of the results.

Keywords: Flexible Pavements, Rigid Pavements, Defects, Maintenance

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Authors: Nellie Nosisi Feza

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Developing young students' number sense is a priority if the aim is to build a rich mathematical foundation for successful schooling and future innovative careers. Capturing students’ interests is crucial while mediating counting concepts. This paper reports South African young children number concepts demonstrated before entering the reception class. It indicates the diverse knowledge attained in different settings before entering formal schooling. The findings indicate that their start is uneven with fully and partly attained number concepts. The findings suggest pre-schooling stimulation that provides rich mathematical experiences and purposeful play towards the attainment of core foundational concepts. Literature directs practice on important core concepts that are foundational in developing number sense.

Keywords: numeracy, learning trajectories, innate abilities, counting, Grade R/reception class

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Authors: Felicia Wong Yen Myan, James Walker

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Development of micro topographies usually begins with the fabrication of a master stamp. Fabrication of such small structures can be technically challenging and expensive. These techniques are often used for applications where patterns only cover a small surface area (e.g. semiconductors, microfluidic channels). This research investigated the use of benchtop lithography to fabricate patterns with average widths of 50 and 100 microns on silicon wafer substrates. Further development of this method will attempt to layer patterns to create hierarchical structures. Photomasks consisted of patterns printed onto transparency films with a high resolution printer and a fully patterned 10cm by 10cm area has been successfully developed. UV exposure was carried out with a self-made array of ultraviolet LEDs that was positioned a distance above a glass diffuser. Observations under a light microscope and SEM showed that developed patterns exhibit an adequate degree of fidelity with patterns from the master stamp.

Keywords: lithography, antifouling, marine, microtopography

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Authors: Sergio Saponara, Luca Fanucci, Riccardo Cassettari, Ruggero Piernicola, Marco Righetto

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The paper presents an innovative networked radar system for detection of obstacles in a railway level crossing scenario. This Monitoring System (MS) is able to detect moving or still obstacles within the railway level crossing area automatically, avoiding the need of human presence for surveillance. The MS is also connected to the National Railway Information and Signaling System to communicate in real-time the level crossing status. The architecture is compliant with the highest Safety Integrity Level (SIL4) of the CENELEC standard. The number of radar sensors used is configurable at set-up time and depends on how large the level crossing area can be. At least two sensors are expected and up four can be used for larger areas. The whole processing chain that elaborates the output sensor signals, as well as the communication interface, is fully-digital, was designed in VHDL code and implemented onto a Xilinx Virtex 6.

Keywords: radar for safe mobility, railroad crossing, railway, transport safety

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Authors: Maléne Maria Magdalena Campbell, Jeremiah Mholo

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Low-income households earning less than 3,500 ZAR (about 175 GBP) per month can apply to the South African government, through the National Housing Subsidy, for fully subsidised houses. An objective of this subsidy is to enable low-income households’ participation in the formal housing market; however, the beneficiaries received houses without title deeds. As such, if the beneficiaries did not have a secured tenure at the time of their death then surviving family may face possible eviction. Therefore, an aim of this research was to determine how these beneficiaries interpret tenure security. The research focused on government subsidised housing in the Dithlake settlement of a rural hamlet named Koffiefontein, in the Letsemeng Local Municipality of South Africa. Quantitative data on the beneficiaries were collected from the local municipality, while qualitative data were collected from a sample of 45 beneficiaries.

Keywords: low-income families, subsidised housing, titling, housing market

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Authors: Paula García-Llamas, Polonia Díez González, Angela Taboada

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In an era marked by unprecedented anthropogenic alterations to landscapes and biodiversity, the consequential loss of fauna, flora, and habitats poses a grave concern. It is imperative to evaluate our capacity to manage and mitigate such changes effectively. This study aims to scrutinize the efficacy of the Natura 2000 Network (NN2000) in landscape conservation within the autonomous community of Castile and Leon (Spain), spanning from 1990 to 2018. Leveraging land use change maps from the European Corine Land Cover database across four subperiods (1990-2000, 2000-2006, 2006-2012, and 2012-2018), we quantified alterations occurring both within NN2000 protected sites and within a 5km buffer zone. Additionally, we spatially assess land use/land cover patterns of change considering fluxes of various habitat types defined within NN2000. Our findings reveal that the protected areas under NN2000 were particularly susceptible to change, with the most significant transformations observed during the 1990-2000 period. Predominant change processes include secondary succession and scrubland formation due to land use cessation, deforestation, and agricultural intensification. While NN2000 demonstrates efficacy in curtailing urbanization and industrialization within buffer zones, its management measures have proven insufficient in safeguarding landscapes against the dynamic changes witnessed between 1990 and 2018, especially in relation to rural abandonment.

Keywords: Corine land cover, land cover changes, site of community importance, special protection area

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Authors: Navid Khayat

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Drained shear strength of saturated soils is fully understood. Shear strength of unsaturated soils is usually expressed in terms of soil suction. Evaluation of shear strength of compacted mixtures of sand-bentonite at optimum water content is main purpose of this research. To prepare the required samples, first, bentonite and sand are mixed in 10, 30, 50 and 70 percent by dry weight and then compacted at the proper optimum water content according to the standard proctor test. The samples were sheared in direct shear machine. Stress-strain relationship of samples indicated a ductile behavior. Most of the samples showed a dilatancy behavior during the shear and the tendency for dilatancy increased with the increase in sand proportion. The results show that with the increase in percentage of sand a decrease in cohesion intercept c' for mixtures and an increase in the angle of internal friction Φ’is observed.

Keywords: bentonite, sand, drained shear strength, cohesion intercept

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Authors: Alisher Ibragimov, Sofya Senotrusova, Aleksandra Beliaeva, Egor Ushakov, Yuri Markin

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Mammography plays a critical role in screening for breast cancer in women, and artificial intelligence has enabled the automatic detection of diseases in medical images. Many of the current techniques used for mammogram analysis focus on a single view (mediolateral or craniocaudal view), while in clinical practice, radiologists consider multiple views of mammograms from both breasts to make a correct decision. Consequently, computer-aided diagnosis (CAD) systems could benefit from incorporating information gathered from multiple views. In this study, the introduce a method based on a Siamese neural network (SNN) model that simultaneously analyzes mammographic images from tri-view: bilateral and ipsilateral. In this way, when a decision is made on a single image of one breast, attention is also paid to two other images – a view of the same breast in a different projection and an image of the other breast as well. Consequently, the algorithm closely mimics the radiologist's practice of paying attention to the entire examination of a patient rather than to a single image. Additionally, to the best of our knowledge, this research represents the first experiments conducted using the recently released Vietnamese dataset of digital mammography (VinDr-Mammo). On an independent test set of images from this dataset, the best model achieved an AUC of 0.87 per image. Therefore, this suggests that there is a valuable automated second opinion in the interpretation of mammograms and breast cancer diagnosis, which in the future may help to alleviate the burden on radiologists and serve as an additional layer of verification.

Keywords: breast cancer, computer-aided diagnosis, deep learning, multi-view mammogram, siamese neural network

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Authors: M. Taheri Tehrani, H. Ajorloo

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In this paper, an intelligent multi-agent framework is developed for each router in which agents have two vital functionalities, traffic shaping and buffer allocation and are positioned in the ports of the routers. With traffic shaping functionality agents shape the traffic forward by dynamic and real time allocation of the rate of generation of tokens in a Token Bucket algorithm and with buffer allocation functionality agents share their buffer capacity between each other based on their need and the conditions of the network. This dynamic and intelligent framework gives this opportunity to some ports to work better under burst and more busy conditions. These agents work intelligently based on Reinforcement Learning (RL) algorithm and will consider effective parameters in their decision process. As RL have limitation considering much parameter in its decision process due to the volume of calculations, we utilize our novel method which invokes Principle Component Analysis (PCA) on the RL and gives a high dimensional ability to this algorithm to consider as much as needed parameters in its decision process. This implementation when is compared to our previous work where traffic shaping was done without any sharing and dynamic allocation of buffer size for each port, the lower packet drop in the whole network specifically in the source routers can be seen. These methods are implemented in our previous proposed intelligent simulation environment to be able to compare better the performance metrics. The results obtained from this simulation environment show an efficient and dynamic utilization of resources in terms of bandwidth and buffer capacities pre allocated to each port.

Keywords: principal component analysis, reinforcement learning, buffer allocation, multi- agent systems

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Authors: Armando Luis Vieira, Carlos Costa, Arthur Araújo

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The tourism system is viewable as a network of relationships amongst business partners where the success of each actor will ultimately be determined by the success of the whole network. Especially since the publication of Gümmesson’s (1996) ‘theory of imaginary organisations’, which suggests that organisational effectiveness largely depends on managing relationships and sharing resources and activities, relationship quality (RQ) has been increasingly recognised as a main source of value creation and competitive advantage. However, there is still ambiguity around this topic, and managers and researchers have been recurrently reporting the need to better understand and capitalise on the quality of interactions with business partners. This research aims at testing an RQ model from a relational, imaginary organisation’s approach. Two mail surveys provide the perceptions of 725 hotel representatives about their business relationships with tour operators, and 1,224 corporate client representatives about their business relationships with hotels (21.9 % and 38.8 % response rate, respectively). The analysis contributes to enhance our understanding on the linkages between RQ and its determinants, and identifies the role of their dimensions. Structural equation modelling results highlight trust as the dominant dimension, the crucial role of commitment and satisfaction, and suggest customer orientation as complementary building block. Findings also emphasise problem solving behaviour and selling orientation as the most relevant dimensions of customer orientation. The comparison of the two ‘dyads’ deepens the discussion and enriches the suggested theoretical and managerial guidelines concerning the contribution of quality relationships to business performance.

Keywords: corporate clients, destination competitiveness, hotels, relationship quality, structural equations modelling, tour operators

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Authors: Akram Alfantazi, Tirdad Nickchi

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Magnesium galvanic corrosion plays an important role in the commercialization of Mg alloys in the automobile industry. This study aims at visualizing the electrochemical activity of the magnesium surface being coupled with pure iron in sulfate-chloride solutions. Scanning electrochemical microscopy was used to monitor the chemical activity of the surface and the data was compared with the conventional corrosion results such as potentiodynamic polarization, linear polarization, and immersion tests. The SECM results showed that the chemical reactivity of Mg is higher than phosphate-permanganate-coated Mg. Regions in the vicinity of the galvanic couple boundary are very active in the magnesium phase and fully protected in the iron phase. Scanning electrochemical microscopy results showed that the conversion coating provided good corrosion resistance for magnesium in the short-term but fails at long-term testing.

Keywords: corrosion, galvanic corrosion, magnesium, scanning electrochemical microscopy

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Authors: Abdulrahman S. Alturki, John S. Loomis

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This paper discusses a corner detection algorithm for camera calibration. Calibration is a necessary step in many computer vision and image processing applications. Robust corner detection for an image of a checkerboard is required to determine intrinsic and extrinsic parameters. In this paper, an algorithm for fully automatic and robust X-corner detection is presented. Checkerboard corner points are automatically found in each image without user interaction or any prior information regarding the number of rows or columns. The approach represents each X-corner with a quadratic fitting function. Using the fact that the X-corners are saddle points, the coefficients in the fitting function are used to identify each corner location. The automation of this process greatly simplifies calibration. Our method is robust against noise and different camera orientations. Experimental analysis shows the accuracy of our method using actual images acquired at different camera locations and orientations.

Keywords: camera calibration, corner detector, edge detector, saddle points

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Authors: Ruvaitha Banu, Amaladhithyan Krishnamoorthy

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IoT networks today solve various consumer problems, from home automation systems to aiding in driving autonomous vehicles with the exploration of multiple devices. For example, in an autonomous vehicle environment, multiple sensors are available on roads to monitor weather and road conditions and interact with each other to aid the vehicle in reaching its destination safely and timely. IoT systems are predominantly dependent on the cloud environment for data storage, and computing needs that result in latency problems. With the advent of Fog networks, some of this storage and computing is pushed to the edge/fog nodes, saving the network bandwidth and reducing the latency proportionally. Managing the data stored in these fog nodes becomes crucial as it might also store sensitive information required for a certain application. Data management in fog nodes is strenuous because Fog networks are dynamic in terms of their availability and hardware capability. It becomes more challenging when the nodes in the network also live a short span, detaching and joining frequently. When an end-user or Fog Node wants to access, read, or write data stored in another Fog Node, then a new protocol becomes necessary to access/manage the data stored in the fog devices as a conventional static way of managing the data doesn’t work in Fog Networks. The proposed solution discusses a protocol that acts by defining sensitivity levels for the data being written and read. Additionally, a distinct data distribution and replication model among the Fog nodes is established to decentralize the access mechanism. In this paper, the proposed model implements stewardship towards the data stored in the Fog node using the application of Reinforcement Learning so that access to the data is determined dynamically based on the requests.

Keywords: IoT, fog networks, data stewardship, dynamic access policy

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Authors: Kawalpreet Kaur, Panagiotis Mitropoulos

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The division of the project scope into work packages is the most important step in the preconstruction phase of construction projects. The work division determines the scope and complexity of each bid package, resulting in dependencies between project participants performing these work packages. The coordination between project participants is necessary because of these dependencies. Excessive dependencies between the bid packages create coordination difficulties, leading to delays, added costs, and contractual friction among project participants. However, the literature on construction provides limited knowledge regarding work structuring approaches, issues, and challenges. Manufacturing industry literature provides a systematic approach to defining the project scope into work packages, and the implementation of social network analysis (SNA) in manufacturing is an effective approach to defining and analyzing the divided scope of work at the dependencies level. This paper presents a case study of implementing a similar approach using SNA in construction bid packages. The study uses SNA to analyze the scope of bid packages and determine the dependency between scope elements. The method successfully identifies the bid package with the maximum interaction with other trade contractors and the scope elements that are crucial for project performance. The analysis provided graphical and quantitative information on bid package dependencies. The study can be helpful in performing an analysis to determine the dependencies between bid packages and their scope elements and how these scope elements are critical for project performance. The study illustrates the potential use of SNA as a systematic approach to analyzing bid package dependencies in construction projects, which can guide the division of crucial scope elements to minimize negative impacts on project performance.

Keywords: work structuring, bid packages, work breakdown, project participants

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Authors: Miroslava Cuperlovic-Culf, Lipu Wang, Ketty Boyle, Nadine Makley, Ian Burton, Anissa Belkaid, Mohamed Touaibia, Marc E. Surrette

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Metabolic changes are one of the major factors in the development of a variety of diseases in various species. Metabolism of agricultural plants is altered the following infection with pathogens sometimes contributing to resistance. At the same time, pathogens use metabolites for infection and progression. In humans, metabolism is a hallmark of cancer development for example. Quantified metabolomics data combined with other omics or clinical data and analyzed using various unsupervised and supervised methods can lead to better diagnosis and prognosis. It can also provide information about resistance as well as contribute knowledge of compounds significant for disease progression or prevention. In this work, different methods for metabolomics quantification and analysis from Nuclear Magnetic Resonance (NMR) measurements that are used for investigation of disease development in wheat and human cells will be presented. One-dimensional 1H NMR spectra are used extensively for metabolic profiling due to their high reliability, wide range of applicability, speed, trivial sample preparation and low cost. This presentation will describe a new method for metabolite quantification from NMR data that combines alignment of spectra of standards to sample spectra followed by multivariate linear regression optimization of spectra of assigned metabolites to samples’ spectra. Several different alignment methods were tested and multivariate linear regression result has been compared with other quantification methods. Quantified metabolomics data can be analyzed in the variety of ways and we will present different clustering methods used for phenotype determination, network analysis providing knowledge about the relationships between metabolites through metabolic network as well as biomarker selection providing novel markers. These analysis methods have been utilized for the investigation of fusarium head blight resistance in wheat cultivars as well as analysis of the effect of estrogen receptor and carbonic anhydrase activation and inhibition on breast cancer cell metabolism. Metabolic changes in spikelet’s of wheat cultivars FL62R1, Stettler, MuchMore and Sumai3 following fusarium graminearum infection were explored. Extensive 1D 1H and 2D NMR measurements provided information for detailed metabolite assignment and quantification leading to possible metabolic markers discriminating resistance level in wheat subtypes. Quantification data is compared to results obtained using other published methods. Fusarium infection induced metabolic changes in different wheat varieties are discussed in the context of metabolic network and resistance. Quantitative metabolomics has been used for the investigation of the effect of targeted enzyme inhibition in cancer. In this work, the effect of 17 β -estradiol and ferulic acid on metabolism of ER+ breast cancer cells has been compared to their effect on ER- control cells. The effect of the inhibitors of carbonic anhydrase on the observed metabolic changes resulting from ER activation has also been determined. Metabolic profiles were studied using 1D and 2D metabolomic NMR experiments, combined with the identification and quantification of metabolites, and the annotation of the results is provided in the context of biochemical pathways.

Keywords: metabolic biomarkers, metabolic network, metabolomics, multivariate linear regression, NMR quantification, quantified metabolomics, spectral alignment

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Authors: C. Benoudjafer, M. N. Tandjaoui, C. Benachaiba

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The use of kinetic energy of the wind is in full rise in the world and it starts to be known in our country but timidly. One or more aero generators can be installed to produce for example electricity on isolated places or not connected to the electrical supply network. To use the wind as energy source, it is necessary to know first the energy needs for the population and study the wind intensity, speed, frequency and direction.

Keywords: Algeria, renewable energies, wind, wind power, aero-generators, wind energetic potential

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Authors: Amani Tahat, Serdal Kirmizialtin

Abstract:

Trajectory fragment methods such as Markov State Models (MSM), Milestoning (MS) and Transition Path sampling are the prime choice of extending the timescale of all atom Molecular Dynamics simulations. In these approaches, a set of structures that covers the accessible phase space has to be chosen a priori using cluster analysis. Structural clustering serves to partition the conformational state into natural subgroups based on their similarity, an essential statistical methodology that is used for analyzing numerous sets of empirical data produced by Molecular Dynamics (MD) simulations. Local transition kernel among these clusters later used to connect the metastable states using a Markovian kinetic model in MSM and a non-Markovian model in MS. The choice of clustering approach in constructing such kernel is crucial since the high dimensionality of the biomolecular structures might easily confuse the identification of clusters when using the traditional hierarchical clustering methodology. Of particular interest, in the case of MS where the milestones are very close to each other, accurate determination of the milestone identity of the trajectory becomes a challenging issue. Throughout this work we present two cluster analysis methods applied to the cis–trans isomerism of dinucleotide AA. The choice of nucleic acids to commonly used proteins to study the cluster analysis is two fold: i) the energy landscape is rugged; hence transitions are more complex, enabling a more realistic model to study conformational transitions, ii) Nucleic acids conformational space is high dimensional. A diverse set of internal coordinates is necessary to describe the metastable states in nucleic acids, posing a challenge in studying the conformational transitions. Herein, we need improved clustering methods that accurately identify the AA structure in its metastable states in a robust way for a wide range of confused data conditions. The single linkage approach of the hierarchical clustering available in GROMACS MD-package is the first clustering methodology applied to our data. Self Organizing Map (SOM) neural network, that also known as a Kohonen network, is the second data clustering methodology. The performance comparison of the neural network as well as hierarchical clustering method is studied by means of computing the mean first passage times for the cis-trans conformational rates. Our hope is that this study provides insight into the complexities and need in determining the appropriate clustering algorithm for kinetic analysis. Our results can improve the effectiveness of decisions based on clustering confused empirical data in studying conformational transitions in biomolecules.

Keywords: milestoning, self organizing map, single linkage, structure clustering

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