Search results for: co-citation networks; keyword co-occurrence network

Authors: Wiem Ben Khalifa, Dalila Souilem, Mahmoud Neji

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The evolutions of Social Web and Semantic Web lead us to ask ourselves about the way of supporting the personalization of learning by means of intelligent filtering of educational resources published in the digital networks. We recommend personalized courses of learning articulated around a first educational course defined upstream. Resuming the context and the stakes in the personalization, we also suggest anchoring the personalization of learning in a community of interest within a group of learners enrolled in the same training. This reflection is supported by the display of an active and semantic system of learning dedicated to the constitution of personalized to measure courses and in the due time.

Keywords: Semantic Web, semantic system, ontology, evaluation, e-learning

Procedia PDF Downloads 327

Authors: Osman Turkoglu, Alvin Estilo, Ritu Gupta, Liliam Pineda-Salgado, Rajesh Pandey

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Background: Hepatotoxicity can be linked to a variety of clinical symptoms and histopathological signs, posing a great challenge in the surveillance of suspected drug-induced liver injury (DILI) cases in the safety database. Additionally, the majority of such cases are rare, idiosyncratic, highly unpredictable, and tend to demonstrate unique individual susceptibility; these qualities, in turn, lend to a pharmacovigilance monitoring process that is often tedious and time-consuming. Objective: Develop a multifactorial algorithm to assist pharmacovigilance physicians in identifying high-risk hepatotoxicity cases associated with DILI from the sponsor’s safety database (Argus). Methods: Multifactorial selection criteria were established using Structured Query Language (SQL) and the TIBCO Spotfire® visualization tool, via a combination of word fragments, wildcard strings, and mathematical constructs, based on Hy’s law criteria and pattern of injury (R-value). These criteria excluded non-eligible cases from monthly line listings mined from the Argus safety database. The capabilities and limitations of these criteria were verified by comparing a manual review of all monthly cases with system-generated monthly listings over six months. Results: On an average, over a period of six months, the algorithm accurately identified 92% of DILI cases meeting established criteria. The automated process easily compared liver enzyme elevations with baseline values, reducing the screening time to under 15 minutes as opposed to multiple hours exhausted using a cognitively laborious, manual process. Limitations of the algorithm include its inability to identify cases associated with non-standard laboratory tests, naming conventions, and/or incomplete/incorrectly entered laboratory values. Conclusions: The newly developed multifactorial algorithm proved to be extremely useful in detecting potential DILI cases, while heightening the vigilance of the drug safety department. Additionally, the application of this algorithm may be useful in identifying a potential signal for DILI in drugs not yet known to cause liver injury (e.g., drugs in the initial phases of development). This algorithm also carries the potential for universal application, due to its product-agnostic data and keyword mining features. Plans for the tool include improving it into a fully automated application, thereby completely eliminating a manual screening process.

Keywords: automation, drug-induced liver injury, pharmacovigilance, post-marketing

Procedia PDF Downloads 146

Authors: Olalekan Lawrence Ojo

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Designing microwave terrestrial propagation networks requires a thorough evaluation of the severity of multipath fading, especially at frequencies below 10 GHz. In nations like Nigeria, without a large enough databases to support the existing empirical models, the mistakes in the prediction technique intended for the evaluation may be severe. The need for higher bandwidth for various satellite applications makes the investigation of the effects of radio refractivity, fading due to multipath, and Geoclimatic factors on satellite propagation links more important. One of the key elements to take into account for the best functioning of microwave frequencies is the clear air effects. This work has taken into account the geographical distribution of radio refractivity and fades depth over a number of stations in Nigeria. Data from five locations in Nigeria—Akure, Enugu, Jos, Minna, and Sokoto—based on five-year (2017–2021) measurement methods of atmospheric pressure, relative, and humidity temperature—at two levels (ground surface and 100 m heights)—are studied to deduced their effects on signals propagated through a µwave communication links. The assessments included considerations for µwave communication systems as well as the impacts of the dry and wet components of radio refractivity, the effects of the fade depth at various frequencies, and a 20 km link distance. The results demonstrate that the percentage occurrence of the dry terms dominated the radio refractivity constituent at the surface level, contributing a minimum of about 78% and a maximum of about 92%, while at heights of 100 meters, the percentage occurrence of the dry terms dominated the radio refractivity constituent, contributing a minimum of about 79% and a maximum of about 92%. The spatial distribution reveals that, regardless of height, the country's tropical rainforest (TRF) and freshwater swampy mangrove (FWSM) regions reported the greatest values of radio refractivity. The statistical estimate shows that fading values can differ by as much as 1.5 dB, especially near the TRF and FWSM coastlines, even during clear air conditions. The current findings will be helpful for budgeting Earth-space microwave links, particularly for the rollout of Nigeria's 5G and 6G projected microcellular networks.

Keywords: fade depth, geoclimatic factor, refractivity, refractivity gradient

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Authors: Safwan Mawlood Hussien

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Internet of Things (IoT) is a promising technology has received considerable attention in different fields such as health, industry, defence, and agro, etc. Due to the limitation capacity of computing, storage, and communication, IoT objects are more vulnerable to attacks. Many solutions have been proposed to solve security issues, such as key management using symmetric-key ciphers. This study provides a scalable group distribution key management based on ECcryptography; with less transmitted messages The method has been validated through simulations in OMNeT++.

Keywords: elliptic curves, Diffie–Hellman, discrete logarithm problem, secure key exchange, WSN security, IoT security, smart-agro

Procedia PDF Downloads 117

Authors: Somayeh Komeylian

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The direction of arrival (DoA) estimation is the crucial aspect of the radar technologies for detecting and dividing several signal sources. In this scenario, the antenna array output modeling involves numerous parameters including noise samples, signal waveform, signal directions, signal number, and signal to noise ratio (SNR), and thereby the methods of the DoA estimation rely heavily on the generalization characteristic for establishing a large number of the training data sets. Hence, we have analogously represented the two different optimization models of the DoA estimation; (1) the implementation of the decision directed acyclic graph (DDAG) for the multiclass least-squares support vector machine (LS-SVM), and (2) the optimization method of the deep neural network (DNN) radial basis function (RBF). We have rigorously verified that the LS-SVM DDAG algorithm is capable of accurately classifying DoAs for the three classes. However, the accuracy and robustness of the DoA estimation are still highly sensitive to technological imperfections of the antenna arrays such as non-ideal array design and manufacture, array implementation, mutual coupling effect, and background radiation and thereby the method may fail in representing high precision for the DoA estimation. Therefore, this work has a further contribution on developing the DNN-RBF model for the DoA estimation for overcoming the limitations of the non-parametric and data-driven methods in terms of array imperfection and generalization. The numerical results of implementing the DNN-RBF model have confirmed the better performance of the DoA estimation compared with the LS-SVM algorithm. Consequently, we have analogously evaluated the performance of utilizing the two aforementioned optimization methods for the DoA estimation using the concept of the mean squared error (MSE).

Keywords: DoA estimation, Adaptive antenna array, Deep Neural Network, LS-SVM optimization model, Radial basis function, and MSE

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Authors: Kréhi Serge Agbli, Samuel Portebos, Michaël Salomon

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Here a methodology is considered aiming at evaluating the economic benefit of the provision of a primary frequency control unit using a Battery Energy Storage System (BESS). In this methodology, two control types (basic and hysteresis) are implemented and the corresponding minimum energy storage system power allowing to maintain the frequency drop inside a given threshold under a given contingency is identified and compared using DigSilent’s PowerFactory software. Following this step, the corresponding energy storage capacity (in MWh) is calculated. As PowerFactory is dedicated to dynamic simulation for transient analysis, a first order model related to the IEEE 9 bus grid used for the analysis under PowerFactory is characterized and implemented on MATLAB-Simulink. Primary frequency control is simulated using the two control types over one-month grid's frequency deviation data on this Simulink model. This simulation results in the energy throughput both basic and hysteresis BESSs. It emerges that the 15 minutes operation band of the battery capacity allocated to frequency control is sufficient under the considered disturbances. A sensitivity analysis on the width of the control deadband is then performed for the two control types. The deadband width variation leads to an identical sizing with the hysteresis control showing a better frequency control at the cost of a higher delivered throughput compared to the basic control. An economic analysis comparing the cost of the sized BESS to the potential revenues is then performed.

Keywords: battery energy storage system, electrical network frequency stability, frequency control unit, PowerFactor

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Authors: Ricky Saputra, Rashmi Walvekar, Mohammad Khalid, Kaveh Shahbaz, Suganti Ramarad

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This particular study of interest aims to study the effect of coupling ultrasonic treatment with eutectic solvents in devulcanization process of waste rubber tyre. Specifically, three different types of Deep Eutectic Solvents (DES) were utilized, namely ChCl:Urea (1:2), ChCl:ZnCl₂ (1:2) and ZnCl₂:urea (2:7) in which their physicochemical properties were analysed and proven to have permissible water content that is less than 3.0 wt%, degradation temperature below 200ᵒC and freezing point below 60ᵒC. The mass ratio of rubber to DES was varied from 1:20-1:40, sonicated for 1 hour at 37 kHz and heated at variable time of 5-30 min at 180ᵒC. Energy dispersive x-rays (EDX) results revealed that the first two DESs give the highest degree of sulphur removal at 74.44 and 76.69% respectively with optimum heating time at 15 minutes whereby if prolonged, reformation of crosslink network would be experienced. Such is supported by the evidence shown by both FTIR and FESEM results where di-sulfide peak reappears at 30 minutes and morphological structures from 15 to 30 minutes change from smooth with high voidage to rigid with low voidage respectively. Furthermore, TGA curve reveals similar phenomena whereby at 15 minutes thermal decomposition temperature is at the lowest due to the decrease of molecular weight as a result of sulphur removal but increases back at 30 minutes. Type of bond change was also analysed whereby it was found that only di-sulphide bond was cleaved and which indicates partial-devulcanization. Overall, the results show that DES has a great potential to be used as devulcanizing solvent.

Keywords: crosslink network, devulcanization, eutectic solvents, reformation, ultrasonic

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Authors: Ahmad Fauzi Makarim, Okkie Puspitorini, Hani'ah Mahmudah, Nur Adi Siswandari, Ari Wijayanti

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As a maritime country, Indonesia needs a solution in maritime connectivity which can assist the maritime communication system which including communication from harbor to the ship or ship to ship. The needs of many application services for maritime communication, whether for safety reasons until voyage service to help the process of voyage activity needs connection with a high bandwith. To support the government efforts in handling that kind of problem, a research is conducted in maritime communication issue by applying the new developed technology in Indonesia, namely IEEE 802.11. In this research, 3 outdoor WiFi devices are used in which have a frequency of 5.8 GHz. Maritime of Tanjung Perak harbor in Surabaya until Karang Jamuang Island are used as the location of the research with defining permission of ship node spreading by Navigation District Class 1. That maritime area formed by state 1 and state 2 areas which are the narrow area with average wave height of 0.7 meter based on the data from BMKG S urabaya. After that, wave height used as one of the parameters which are used in analyzing characteristic of signal propagation at sea surface, so it can be determined on the coverage area of transmitter system. In this research has been used three samples of outdoor wifi, there is the coverage of device A can be determined about 2256 meter, device B 4000 meter, and device C 1174 meter. Then to analyze of network connectivity for the ship to ship is used AODV routing algorithm system based on the value of the power transmit was smallest of all nodes within the transmitter coverage.

Keywords: maritime of Indonesia, maritime communications, outdoor wifi, coverage, AODV

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Authors: Nivedhitha Venkatakrishnan

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Flooding in urban areas in India has become a usual ritual phenomenon and a nightmare to most cities, which is a consequence of man-made disruption resulting in disaster. The City planning in India falls short of withstanding hydro generated disasters. This has become a barrier and challenge in the process of development put forth by urbanization, high population density, expanding informal settlements, environment degradation from uncollected and untreated waste that flows into natural drains and water bodies, this has disrupted the natural mechanism of hazard protection such as drainage channels, wetlands and floodplains. The magnitude and the impact of the mishap was high because of the failure of development policies, strategies, plans that the city had adopted. In the current scenario, cities are becoming the home for future, with economic diversification bringing in more investment into cities especially in domains of Urban infrastructure, planning and design. The uncertainty of the Urban futures in these low elevated coastal zones faces an unprecedented risk and threat. The study on focuses on three major pillars of resilience such as Recover, Resist and Restore. This process of getting ready to handle the situation bridges the gap between disaster response management and risk reduction requires a shift in paradigm. The study involved a qualitative research and a system design approach (framework). The initial stages involved mapping out of the urban water morphology with respect to the spatial growth gave an insight of the water bodies that have gone missing over the years during the process of urbanization. The major finding of the study was missing links between traditional water harvesting network was a major reason resulting in a manmade disaster. The research conceptualized the ideology of a sponge city framework which would guide the growth through institutional frameworks at different levels. The next stage was on understanding the implementation process at various stage to ensure the shift in paradigm. Demonstration of the concepts at a neighborhood level where, how, what are the functions and benefits of each component. Quantifying the design decision with rainwater harvest, surface runoff and how much water is collected and how it could be collected, stored and reused. The study came with further recommendation for Water Mitigation Spaces that will revive the traditional harvesting network.

Keywords: flooding, man made disaster, resilient city, traditional harvesting network, waterbodies

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Authors: Mohamed Gaafar, Evan Davies

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Many municipalities within Canada and abroad use chloramination to disinfect drinking water so as to avert the production of the disinfection by-products (DBPs) that result from conventional chlorination processes and their consequential public health risks. However, the long-lasting monochloramine disinfectant (NH2Cl) can pose a significant risk to the environment. As, it can be introduced into stormwater sewers, from different water uses, and thus freshwater sources. Little research has been undertaken to monitor and characterize the decay of NH2Cl and to study the parameters affecting its decomposition in stormwater networks. Therefore, the current study was intended to investigate this decay starting by building a stormwater model and validating its hydraulic and hydrologic computations, and then modelling water quality in the storm sewers and examining the effects of different parameters on chloramine decay. The presented work here is only the first stage of this study. The 30th Avenue basin in Southern Edmonton was chosen as a case study, because the well-developed basin has various land-use types including commercial, industrial, residential, parks and recreational. The City of Edmonton has already built a MIKE-URBAN stormwater model for modelling floods. Nevertheless, this model was built to the trunk level which means that only the main drainage features were presented. Additionally, this model was not calibrated and known to consistently compute pipe flows higher than the observed values; not to the benefit of studying water quality. So the first goal was to complete modelling and updating all stormwater network components. Then, available GIS Data was used to calculate different catchment properties such as slope, length and imperviousness. In order to calibrate and validate this model, data of two temporary pipe flow monitoring stations, collected during last summer, was used along with records of two other permanent stations available for eight consecutive summer seasons. The effect of various hydrological parameters on model results was investigated. It was found that model results were affected by the ratio of impervious areas. The catchment length was tested, however calculated, because it is approximate representation of the catchment shape. Surface roughness coefficients were calibrated using. Consequently, computed flows at the two temporary locations had correlation coefficients of values 0.846 and 0.815, where the lower value pertained to the larger attached catchment area. Other statistical measures, such as peak error of 0.65%, volume error of 5.6%, maximum positive and negative differences of 2.17 and -1.63 respectively, were all found in acceptable ranges.

Keywords: stormwater, urban drainage, simulation, validation, MIKE URBAN

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Authors: Hasnun Wara Khondker, M. Tarek Morad

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Bangladesh is one of the most populous countries of the world in terms of density. Dhaka, the capital of Bangladesh currently has a population of approximately 15-16 million of which around 9 million people are accommodated in Dhaka South City Corporation (DSCC) within around 109 square kilometer area. Despite having various urban issues, country is at its pick of economic progress and Dhaka is the core of this economic growth. To ensure the proper economic development and citizens wellbeing, city needs an ingenious, congestion-free public transportation network. Bus stop/bus bay is an essential infrastructure for ensuring efficient public transportation flow within the city along with enhancing accessibility, user comfort, and safety through public amenities. At present, there is no established Mass Rapid Transit or Bus Rapid Transit network within the city and therefore these private owned buses are the only major mode of mass transportation of Dhaka city. DSCC has undertaken a project to re-design several bus stops and bus bays according to the universal standard for better urban mobility and user satisfaction. This paper will analyze the design approach of the bus stop/bay infrastructure within Dhaka South, putting the research lens on sustainable urban mobility with case studies of similar kind of urban context. The paper will also study the design process with setting several parameters, i.e., accessibility, passenger safety, comfort, sustainability, etc. Moreover, this research will recommend a guideline for designing a bus stop based on the analysis of the design methods.

Keywords: bus stop, Dhaka, public transportation, sustainable urban mobility, universal accessibility, user safety

Procedia PDF Downloads 373

Authors: Bo-wen Zhu, Bin Wu, Feng Chen

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It has become a common measure among governments to support green development of enterprises through Green Credit policies. In China, the Central Bank of China and other authorities even put forward corresponding assessment requirements for proportion of green credit in commercial banks. Policy changes might raise concerns about commercial banks turning a blind eye to greenwashing behavior by enterprises. The lack of effective regulation may lead to a diffusion of such behavior, and eventually result in the phenomenon of “bad money driving out good money”, which could dampen the incentive effect of Green Credit policies. This paper employs a complex network model based on an evolutionary game analysis framework involving enterprises, banks, and regulatory authorities to investigate inhibitory effect of the Green Credit regulation on enterprises’ greenwashing behavior, banks’ opportunistic and collusive behaviors. The findings are as follows: (1) Banking opportunism rises with Green Credit evaluation criteria and requirements for the proportion of credit balance. Restrictive regulation against violating banks is necessary as there is an increasing trend of banks adopting opportunistic strategy. (2) Raising penalties and probability of regulatory inspections can effectively suppress banks’ opportunistic behavior, however, it cannot entirely eradicate the opportunistic behavior on the bank side. (3) Although maintaining a certain inspection probability can inhibit enterprises from adopting greenwashing behavior, enterprises choose a catering production strategy instead. (4) One-time rewards from local government have limited effects on the equilibrium state and diffusion trend of bank regulatory decision-making.

Keywords: green credit, greenwashing behavior, regulation, diffusion effect

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Authors: Mohsen Shirani, Micaela Demichela

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Food supply chain is one of the most complex supply chain networks due to its perishable nature and customer oriented products, and food safety is the major concern for this industry. IT system could help to minimize the production and consumption of unsafe food by controlling and monitoring the entire system. However, there have been many issues in adoption of IT system in this industry specifically within SMEs sector. With this regard, this study presents a novel approach to use IT and tractability systems in the food supply chain, using application of RFID and central database.

Keywords: food supply chain, IT system, safety, SME

Procedia PDF Downloads 471

Authors: Tunde Ope-Davies

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Transmediality and platformization as online content-sharing protocols have continued to accentuate the growing impact of the unprecedented digital revolution across the world. The rapid transformation across all sectors as a result of this revolution has continued to spotlight the increasing importance of new media technologies in redefining and reshaping the rhythm and dynamics of our private and public discursive practices. Equally, social and political activities are being impacted daily through the creation and transmission of political discourse content through multi-channel platforms such as mobile telephone communication, social media networks and the internet. It has been observed that digital platforms have become central to the production, processing, and distribution of multimodal social data and cultural content. The platformization paradigm thus underpins our understanding of how digital platforms enhance the production and heterogenous distribution of media and cultural content through these platforms and how this process facilitates socioeconomic and political activities. The use of multiple digital platforms to share and transmit political discourse material synchronously and asynchronously has gained some exciting momentum in the last few years. Nigeria’s 2023 general elections amplified the usage of social media and other online platforms as tools for electioneering campaigns, socio-political mobilizations and civic engagement. The study, therefore, focuses on transmedia and platformed political discourse as a new strategy to promote political candidates and their manifesto in order to mobilize support and woo voters. This innovative transmedia digital discourse model involves a constellation of online texts and images transmitted through different online platforms almost simultaneously. The data for the study was extracted from the 2023 general elections campaigns in Nigeria between January- March 2023 through media monitoring, manual download and the use of software to harvest the online electioneering campaign material. I adopted a discursive-analytic qualitative technique with toolkits drawn from a computer-mediated multimodal discourse paradigm. The study maps the progressive development of digital political discourse in this young democracy. The findings also demonstrate the inevitable transformation of modern democratic practice through platform-dependent and transmedia political discourse. Political actors and media practitioners now deploy layers of social media network platforms to convey messages and mobilize supporters in order to aggregate and maximize the impact of their media campaign projects and audience reach.

Keywords: social media, digital humanities, political discourse, platformized discourse, multimodal discourse

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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: 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: Kristin Prentice, Carri Hand

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As the Canadian population ages and chronic health conditions continue to escalate, older adults will require various types of housing, such as long term care or retirement homes. Moving to a new home may require a change in leisure activities and social networks, which could be challenging to maintain identity and create a sense of home. Leisure has been known to help older adults maintain or increase their quality of life and life satisfaction and may help older adults in moving to new homes. Sense of home and identity within older adults' transitions to new homes are concepts that may also relate to leisure engagement. Literature is scant regarding the role of leisure in older adults moving to new homes and how the sense of home and identity inter-relate. This study aims to explore how leisure may play a role in older adults' transitioning to new homes, including how sense of home and identity inter-relate. An ethnographic approach will be used to understand the culture of older adults transitioning to new homes. This study will involve older adults who have recently relocated to a mid-sized city in Ontario, Canada. The study will focus on the older adult’s interactions with and connections to their home environment through leisure. Data collection will take place via video-conferencing and will include a narrative interview and two other interviews to discuss an activity diary of leisure engagement pre and post move and mental maps to capture spaces where participants engaged in leisure. Participants will be encouraged to share photographs of leisure engagement taken inside and outside their home to help understand the social spaces the participants refer to in their activity diaries and mental maps. Older adults attempt to adjust to their new homes by maintaining their identity, developing a sense of home through creating attachment to place, and maintaining social networks, all of which have been linked to engaging in leisure. This research will provide insight into the role of leisure in this transition process and the extent that the home and community can contribute to aiding their transition to the new home. This research will contribute to existing literature on the inter-relationships of leisure, sense of home, and identity and how they relate to older adults moving to new homes. This research also has potential for influencing policy and practice for meeting the housing needs of older adults.

Keywords: leisure, older adults, transition, identity

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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: Roberto Cabezas H

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The current work shows the methodology developed to create narrative lighting spaces for the multimedia performance piece 'cluster: the vanished paradise.' This empirical research is focused on exploring unconventional roles for machines in subjective creative processes, by delving into the semantics of data and machine intelligence algorithms in hybrid technological, creative contexts to expand epistemic domains trough human-machine cooperation. The creative process in scenic and performing arts is guided mostly by intuition; from that idea, we developed an approach to embed collective intuition in computational creative systems, by joining the properties of Generative Adversarial Networks (GAN’s) and Fuzzy Clustering based on a semi-supervised data creation and analysis pipeline. The model makes use of GAN’s to learn from phenomenological data (data generated from experience with lighting scenography) and algorithmic design data (augmented data by procedural design methods), fuzzy logic clustering is then applied to artificially created data from GAN’s to define narrative transitions built on membership index; this process allowed for the creation of simple and complex spaces with expressive capabilities based on position and light intensity as the parameters to guide the narrative. Hybridization comes not only from the human-machine symbiosis but also on the integration of different techniques for the implementation of the aided design system. Machine intelligence tools as proposed in this work are well suited to redefine collaborative creation by learning to express and expand a conglomerate of ideas and a wide range of opinions for the creation of sensory experiences. We found in GAN’s and Fuzzy Logic an ideal tool to develop new computational models based on interaction, learning, emotion and imagination to expand the traditional algorithmic model of computation.

Keywords: fuzzy clustering, generative adversarial networks, human-machine cooperation, hybrid collective data, multimedia performance

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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: 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. it 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: Jingwen Ou

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Writing for academic purposes in a second or foreign language is one of the most important and the most demanding skills to be mastered by non-native speakers. Traditionally, the EAP writing instruction at the tertiary level encompasses the teaching of academic genre knowledge, more specifically, the disciplinary writing conventions, the rhetorical functions, and specific linguistic features. However, one of the main sources of challenges in English academic writing for L2 students at the tertiary level can still be found in proficiency in academic discourse, especially vocabulary, academic register, and organization. Data-Driven Learning (DDL) is defined as “a pedagogical approach featuring direct learner engagement with corpus data”. In the past two decades, the rising popularity of the application of the data-driven learning (DDL) approach in the field of EAP writing teaching has been noticed. Such a combination has not only transformed traditional pedagogy aided by published DDL guidebooks in classroom use but also triggered global research on corpus use in EAP classrooms. This study endeavors to delineate a systematic review of research in the intersection of DDL and EAP writing instruction by conducting a systematic literature review on both indirect and direct DDL practice in EAP writing instructional settings in China. Furthermore, the review provides a synthesis of significant discoveries emanating from prior research investigations concerning Chinese university students’ perception of Data-Driven Learning (DDL) and the subsequent impact on their academic writing performance following corpus-based training. Research papers were selected from Scopus-indexed journals and core journals from two main Chinese academic databases (CNKI and Wanfang) published in both English and Chinese over the last ten years based on keyword searches. Results indicated an insufficiency of empirical DDL research despite a noticeable upward trend in corpus research on discourse analysis and indirect corpus applications for material design by language teachers. Research on the direct use of corpora and corpus tools in DDL, particularly in combination with genre-based EAP teaching, remains a relatively small fraction of the whole body of research in Chinese higher education settings. Such scarcity is highly related to the prevailing absence of systematic training in English academic writing registers within most Chinese universities' EAP syllabi due to the Chinese English Medium Instruction policy, where only English major students are mandated to submit English dissertations. Findings also revealed that Chinese learners still held mixed attitudes towards corpus tools influenced by learner differences, limited access to language corpora, and insufficient pre-training on corpus theoretical concepts, despite their improvements in final academic writing performance.

Keywords: corpus linguistics, data-driven learning, EAP, tertiary education in China

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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: 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: Despoina Mouratidis, Katia Lida Kermanidis

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Machine translation (MT) is a very important task in Natural Language Processing (NLP). MT evaluation is crucial in MT development, as it constitutes the means to assess the success of an MT system, and also helps improve its performance. Several methods have been proposed for the evaluation of (MT) systems. Some of the most popular ones in automatic MT evaluation are score-based, such as the BLEU score, and others are based on lexical similarity or syntactic similarity between the MT outputs and the reference involving higher-level information like part of speech tagging (POS). This paper presents a language-independent machine learning framework for classifying pairwise translations. This framework uses vector representations of two machine-produced translations, one from a statistical machine translation model (SMT) and one from a neural machine translation model (NMT). The vector representations consist of automatically extracted word embeddings and string-like language-independent features. These vector representations used as an input to a multi-layer neural network (NN) that models the similarity between each MT output and the reference, as well as between the two MT outputs. To evaluate the proposed approach, a professional translation and a "ground-truth" annotation are used. The parallel corpora used are English-Greek (EN-GR) and English-Italian (EN-IT), in the educational domain and of informal genres (video lecture subtitles, course forum text, etc.) that are difficult to be reliably translated. They have tested three basic deep learning (DL) architectures to this schema: (i) fully-connected dense, (ii) Convolutional Neural Network (CNN), and (iii) Long Short-Term Memory (LSTM). Experiments show that all tested architectures achieved better results when compared against those of some of the well-known basic approaches, such as Random Forest (RF) and Support Vector Machine (SVM). Better accuracy results are obtained when LSTM layers are used in our schema. In terms of a balance between the results, better accuracy results are obtained when dense layers are used. The reason for this is that the model correctly classifies more sentences of the minority class (SMT). For a more integrated analysis of the accuracy results, a qualitative linguistic analysis is carried out. In this context, problems have been identified about some figures of speech, as the metaphors, or about certain linguistic phenomena, such as per etymology: paronyms. It is quite interesting to find out why all the classifiers led to worse accuracy results in Italian as compared to Greek, taking into account that the linguistic features employed are language independent.

Keywords: machine learning, machine translation evaluation, neural network architecture, pairwise classification

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Authors: S. Mehrab Amiri, Nasser Talebbeydokhti

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Modeling sediment transport processes by means of numerical approach often poses severe challenges. In this way, a number of techniques have been suggested to solve flow and sediment equations in decoupled, semi-coupled or fully coupled forms. Furthermore, in order to capture flow discontinuities, a number of techniques, like artificial viscosity and shock fitting, have been proposed for solving these equations which are mostly required careful calibration processes. In this research, a numerical scheme for solving shallow water and Exner equations in fully coupled form is presented. First-Order Centered scheme is applied for producing required numerical fluxes and the reconstruction process is carried out toward using Monotonic Upstream Scheme for Conservation Laws to achieve a high order scheme.  In order to satisfy C-property of the scheme in presence of bed topography, Surface Gradient Method is proposed. Combining the presented scheme with fourth order Runge-Kutta algorithm for time integration yields a competent numerical scheme. In addition, to handle non-prismatic channels problems, Cartesian Cut Cell Method is employed. A trained Multi-Layer Perceptron Artificial Neural Network which is of Feed Forward Back Propagation (FFBP) type estimates sediment flow discharge in the model rather than usual empirical formulas. Hydrodynamic part of the model is tested for showing its capability in simulation of flow discontinuities, transcritical flows, wetting/drying conditions and non-prismatic channel flows. In this end, dam-break flow onto a locally non-prismatic converging-diverging channel with initially dry bed conditions is modeled. The morphodynamic part of the model is verified simulating dam break on a dry movable bed and bed level variations in an alluvial junction. The results show that the model is capable in capturing the flow discontinuities, solving wetting/drying problems even in non-prismatic channels and presenting proper results for movable bed situations. It can also be deducted that applying Artificial Neural Network, instead of common empirical formulas for estimating sediment flow discharge, leads to more accurate results.

Keywords: artificial neural network, morphodynamic model, sediment continuity equation, shallow water equations

Procedia PDF Downloads 184