Search results for: vulnerability intelligence

Authors: Xiaojiao Duan, Zhaoxia Yi, Maria Assumpta Komugabe, Munirpallam A. Venkataramanan

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This study explores stakeholders' perceptions and use of AI in university admissions using a conceptual model. The model suggests that AI expertise mediates the relationship between various factors (positions, experience, perceived benefits, concerns) and the desire to adopt AI. By reviewing existing research, the study identifies variables, correlations, and research gaps. The findings highlight the influence of institutional positions, AI expertise, knowledge, perceived advantages, and concerns on attitudes and intentions toward AI implementation. The review provides a framework for future research, emphasizes ethical AI use, and offers practical insights for admissions stakeholders.

Keywords: artificial intelligence, college admissions, ethical considerations, technology adoption, perceptions of AI

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Authors: Silvia Zanazzi

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Artificial intelligence (AI) has become an integral part of our world, transitioning from scientific exploration to practical applications that impact daily life. The emergence of generative AI is reshaping education, prompting new questions about the role of teachers, the nature of learning, and the overall purpose of schooling. While AI offers the potential for optimizing teaching and learning processes, concerns about discrimination and bias arising from training data and algorithmic decisions persist. There is a risk of a disconnect between the rapid development of AI and the goals of building inclusive educational environments. The prevailing discourse on AI in education often prioritizes efficiency and individual skill acquisition. This narrow focus can undermine the importance of collaborative learning and shared experiences. A growing body of research challenges this perspective, advocating for AI that enhances, rather than replaces, human interaction in education. This study aims to examine the relationship between AI and education critically. Reviewing existing research will identify both AI implementation’s potential benefits and risks. The goal is to develop a framework that supports the ethical and effective integration of AI into education, ensuring it serves the needs of all learners. The theoretical reflection will be developed based on a review of national and international scientific literature on artificial intelligence in education. The primary objective is to curate a selection of critical contributions from diverse disciplinary perspectives and/or an inter- and transdisciplinary viewpoint, providing a state-of-the-art overview and a critical analysis of potential future developments. Subsequently, the thematic analysis of these contributions will enable the creation of a framework for understanding and critically analyzing the role of artificial intelligence in schools and education, highlighting promising directions and potential pitfalls. The expected results are (1) a classification of the cognitive biases present in representations of AI in education and the associated risks and (2) a categorization of potentially beneficial interactions between AI applications and teaching and learning processes, including those already in use or under development. While not exhaustive, the proposed framework will serve as a guide for critically exploring the complexity of AI in education. It will help to reframe dystopian visions often associated with technology and facilitate discussions on fostering synergies that balance the ‘dream’ of quality education for all with the realities of AI implementation. The discourse on artificial intelligence in education, highlighting reductionist models rooted in fragmented and utilitarian views of knowledge, has the merit of stimulating the construction of alternative perspectives that can ‘return’ teaching and learning to education, human growth, and the well-being of individuals and communities.

Keywords: education, artificial intelligence, teaching, learning

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Authors: Parisa Mansour

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Introduction: Early diagnosis of coronavirus disease (COVID-19) is extremely important to isolate and treat patients in time, thus preventing the spread of the disease, improving prognosis and reducing mortality. High-resolution computed tomography (HRCT) chest imaging and artificial intelligence (AI)-based analysis of HRCT chest images can play a central role in the treatment of patients with COVID-19. Objective: To investigate different chest HRCT findings in different stages of COVID-19 pneumonia and to evaluate the potential role of artificial intelligence in the quantitative assessment of lung parenchymal involvement in COVID-19 pneumonia. Materials and Methods: This retrospective observational study was conducted between May 1, 2020 and August 13, 2020. The study included 2169 patients with COVID-19 who underwent chest HRCT. HRCT images showed the presence and distribution of lesions such as: ground glass opacity (GGO), compaction, and any special patterns such as septal thickening, inverted halo, mark, etc. HRCT findings of the breast at different stages of the disease (early: andlt) 5 days, intermediate: 6-10 days and late stage: >10 days). A CT severity score (CTSS) was calculated based on the extent of lung involvement on HRCT, which was then correlated with clinical disease severity. Use of artificial intelligence; Analysis of CT pneumonia and quot; An algorithm was used to quantify the extent of pulmonary involvement by calculating the percentage of pulmonary opacity (PO) and gross opacity (PHO). Depending on the type of variables, statistically significant tests such as chi-square, analysis of variance (ANOVA) and post hoc tests were applied when appropriate. Results: Radiological findings were observed in HRCT chest in 1438 patients. A typical pattern of COVID-19 pneumonia, i.e., bilateral peripheral GGO with or without consolidation, was observed in 846 patients. About 294 asymptomatic patients were radiologically positive. Chest HRCT in the early stages of the disease mostly showed GGO. The late stage was indicated by such features as retinal enlargement, thickening and the presence of fibrous bands. Approximately 91.3% of cases with a CTSS = 7 were asymptomatic or clinically mild, while 81.2% of cases with a score = 15 were clinically severe. Mean PO and PHO (30.1 ± 28.0 and 8.4 ± 10.4, respectively) were significantly higher in the clinically severe categories. Conclusion: Because COVID-19 pneumonia progresses rapidly, radiologists and physicians should become familiar with typical TC chest findings to treat patients early, ultimately improving prognosis and reducing mortality. Artificial intelligence can be a valuable tool in treating patients with COVID-19.

Keywords: chest, HRCT, covid-19, artificial intelligence, chest HRCT

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Authors: Chandan Hegde, K. Ashwini

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Commonsense reasoning is the simulation of human ability to make decisions during the situations that we encounter every day. It has been several decades since the introduction of this subfield of artificial intelligence, but it has barely made some significant progress. The modern computing aids also have remained impotent in this regard due to the absence of a strong methodology towards commonsense reasoning development. Among several accountable reasons for the lack of progress, drawing inference out of commonsense knowledge-base stands out. This review paper emphasizes on a detailed analysis of representation of reasoning uncertainties and feasible prospects of programming aids for drawing inferences. Also, the difficulties in deducing and systematizing commonsense reasoning and the substantial progress made in reasoning that influences the study have been discussed. Additionally, the paper discusses the possible impacts of an effective inference technique in commonsense reasoning.

Keywords: artificial intelligence, commonsense reasoning, knowledge base, uncertainty in reasoning

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Authors: Kazi Ashief Mahmood

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Geographically Bangladesh is located in a natural hazard prone area. Compared to the rest of the areas, the coastal sub-districts are more vulnerable to climate variability and change. However, the hydro-geophysical reality of the sub-districts predominantly determines their contexts of vulnerability and its nature differs accordingly. Intriguingly enough, the poorest of the areas appear to be the most cornered among the different vulnerable sectors. Among of these deprived segments; however, the women, the persons with disability and the minorities are generally more vulnerable and they face a high risk of marginalized. The most threatening hydro-geophysical climate vulnerability have been created by prolonged dry season as observed at Koyra Upazilla in Khulna districts and Shyamnagar in Satkhira districts. The prolonged dry season creates severe surface salinity by which farmers cannot produce or use their to cultivate. The absence of land-based production and employment in the area has led to severe food insecurity. As a result, farmers tend to change their livelihood option and many of them are forced to migrate to the other areas of the country in search of livelihood. Besides salinity intrusion, water logging, drought and different climate change induced hazards are endangering safe drinking water sources and putting small-holders out of agriculture-based livelihoods in the Koyra and Shyamnagar Upazilla. A sizeable fraction of small-holders are still trying to hold on to their small scale shrimp production, despite being under pressure to sell off their cultivating lands to their influential shrimp merchants. While their desperate effort to take advantage of the increasing salinity is somewhat successful, their families still face a greater risk of health hazards owing to the lack of safe drinking water. Unless the issues of salinity in drinking water cannot be redressed, the state of the affected people will be in great jeopardy. Most of the inhabitants of oKyra and Shyamnagar Upazilla are living under the poverty line. Thus, poverty is a major factor that intensifies the vulnerability caused by hydro-geophysical climatic conditions. The government and different NGOs are trying to improve the present scenario by implementing different disaster risk reduction projects along with poverty reduction for community empowerment.

Keywords: assessment, climate change, climate induced hazards, coastal zone

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Authors: Fiyinfoluwa Giwa, Nicholas Ngepah

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This paper investigates the relationship between the Fourth Industrial Revolution and food security in South Africa. The Ordinary Least Square was adopted from 2012 Q1 to 2021 Q4. The study used artificial intelligence investment and the food production index as the measure for the fourth industrial revolution and food security, respectively. Findings reveal a significant and positive coefficient of 0.2887, signifying a robust statistical relationship between AI adoption and the food production index. As a policy recommendation, this paper recommends the introduction of incentives for farmers and agricultural enterprises to adopt AI technologies -and the expansion of digital connectivity and access to technology in rural areas.

Keywords: Fourth Industrial Revolution, food security, artificial intelligence investment, food production index, ordinary least square

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Authors: Naci Büyükkaracığan

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Artificial neural networks (ANN) is an electrical model based on the human brain nervous system and working principle. Artificial neural networks have been the subject of an active field of research that has matured greatly over the past 55 years. ANN now is used in many fields. But, it has been viewed that artificial neural networks give better results in particular optimization and control systems. There are requirements of optimization and control system in many of the area forming the subject of civil engineering applications. In this study, the first artificial intelligence systems are widely used in the solution of civil engineering systems were examined with the basic principles and technical aspects. Finally, the literature reviews for applications in the field of civil engineering were conducted and also artificial intelligence techniques were informed about the study and its results.

Keywords: artificial neural networks, civil engineering, Fuzzy logic, statistics

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Authors: Nozar Kishi, Babak Kamrani, Filmon Habte

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Natural hazards such as earthquakes and tropical storms, are very frequent and highly destructive in Japan. Japan experiences, every year on average, more than 10 tropical cyclones that come within damaging reach, and earthquakes of moment magnitude 6 or greater. We have developed stochastic catastrophe models to address the risk associated with the entire suite of damaging events in Japan, for use by insurance, reinsurance, NGOs and governmental institutions. KCC’s (Karen Clark and Company) catastrophe models are procedures constituted of four modular segments: 1) stochastic events sets that would represent the statistics of the past events, hazard attenuation functions that could model the local intensity, vulnerability functions that would address the repair need for local buildings exposed to the hazard, and financial module addressing policy conditions that could estimates the losses incurring as result of. The events module is comprised of events (faults or tracks) with different intensities with corresponding probabilities. They are based on the same statistics as observed through the historical catalog. The hazard module delivers the hazard intensity (ground motion or wind speed) at location of each building. The vulnerability module provides library of damage functions that would relate the hazard intensity to repair need as percentage of the replacement value. The financial module reports the expected loss, given the payoff policies and regulations. We have divided Japan into regions with similar typhoon climatology, and earthquake micro-zones, within each the characteristics of events are similar enough for stochastic modeling. For each region, then, a set of stochastic events is developed that results in events with intensities corresponding to annual occurrence probabilities that are of interest to financial communities; such as 0.01, 0.004, etc. The intensities, corresponding to these probabilities (called CE, Characteristics Events) are selected through a superstratified sampling approach that is based on the primary uncertainty. Region specific hazard intensity attenuation functions followed by vulnerability models leads to estimation of repair costs. Extensive economic exposure model addresses all local construction and occupancy types, such as post-linter Shinand Okabe wood, as well as concrete confined in steel, SRC (Steel-Reinforced Concrete), high-rise.

Keywords: typhoon, earthquake, Japan, catastrophe modelling, stochastic modeling, stratified sampling, loss model, ERM

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Authors: M. Caramihai, Irina Severin

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This paper deals with the design of an intelligent control structure for a bioprocess of Hansenula polymorpha yeast cultivation. The objective of the process control is to produce biomass in a desired physiological state. The work demonstrates that the designed Hybrid Control Techniques (HCT) are able to recognize specific evolution bioprocess trajectories using neural networks trained specifically for this purpose, in order to estimate the model parameters and to adjust the overall bioprocess evolution through an expert system and a fuzzy structure. The design of the control algorithm as well as its tuning through realistic simulations is presented. Taking into consideration the synergism of different paradigms like fuzzy logic, neural network, and symbolic artificial intelligence (AI), in this paper we present a real and fulfilled intelligent control architecture with application in bioprocess control.

Keywords: bioprocess, intelligent control, neural nets, fuzzy structure, hybrid techniques

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Authors: Arwa Alnowaiser, Hala Shoukri

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The ‘FreeAITherapist’ is an artificial intelligence application that uses the power of AI to offer advice and mental health counseling to its users through its chatbot services. The AI therapist is designed to understand users' issues, concerns, and problems and respond appropriately; it provides empathy and guidance and uses evidence-based therapeutic techniques. With its user-friendly platform, it ensures accessibility for individuals in need, regardless of their geographical location. This website was created in direct response to the growing demand for mental health support, aiming to provide a cost-effective and confidential solution. Through promising confidentiality, it considers user privacy and data security. The ‘FreeAITherapist’ strives to bridge the gap in mental health services, offering a reliable resource for individuals seeking guidance and counseling to improve their overall well-being.

Keywords: artificial intelligence, mental health, AI therapist, website, counseling

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Authors: Roy Y. C. Huang, J. S. Kuang, Hamdolah Behnam

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Beam-column connections play an important role in the reinforced concrete moment resisting frame (RCMRF), which is one of the most commonly used structural systems around the world. The premature failure of such connections would severely limit the seismic performance and increase the vulnerability of RCMRF. In the past decades, researchers primarily focused on investigating the structural behaviour and failure mechanisms of conventional beam-column joints, the beam width of which is either smaller than or equal to the column width, while studies in wide beam-column joints were scarce. This paper presents the preliminary experimental results of two full-scale exterior wide beam-column connections, which are mainly designed and detailed according to ACI 318-14 and ACI 352R-02, under reversed cyclic loading. The ratios of the design shear force to the nominal shear strength of these specimens are 1.0 and 1.7, respectively, so as to probe into differences of the joint shear strength between experimental results and predictions by design codes of practice. Flexural failure dominated in the specimen with ratio of 1.0 in which full-width plastic hinges were observed, while both beam hinges and post-peak joint shear failure occurred for the other specimen. No sign of premature joint shear failure was found which is inconsistent with ACI codes’ prediction. Finally, a modification of current codes of practice is provided to accurately predict the joint shear strength in wide beam-column joint.

Keywords: joint shear strength, reversed cyclic loading, seismic vulnerability, wide beam-column joints

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Authors: Shryas Bhurat, Aryan Vashistha, Sampreet Dinakar Nayak, Ayush Gupta

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As the technology is advancing more towards high computational resources, there is a paradigm shift in the usage of these resources to optimize the design process. This paper discusses the usage of ‘Generative Design using Artificial Intelligence’ to build better models that adapt the operations like selection, mutation, and crossover to generate results. The human mind thinks of the simplest approach while designing an object, but the intelligence learns from the past & designs the complex optimized CAD Models. Generative Design takes the boundary conditions and comes up with multiple solutions with iterations to come up with a sturdy design with the most optimal parameter that is given, saving huge amounts of time & resources. The new production techniques that are at our disposal allow us to use additive manufacturing, 3D printing, and other innovative manufacturing techniques to save resources and design artistically engineered CAD Models. Also, this paper discusses the Genetic Algorithm, the Non-Domination technique to choose the right results using biomimicry that has evolved for current habitation for millions of years. The computer uses parametric models to generate newer models using an iterative approach & uses cloud computing to store these iterative designs. The later part of the paper compares the topology optimization technology with Generative Design that is previously being used to generate CAD Models. Finally, this paper shows the performance of algorithms and how these algorithms help in designing resource-efficient models.

Keywords: genetic algorithm, bio mimicry, generative modeling, non-dominant techniques

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Authors: Jurijs Salijevs, Katrina Bolocko

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The integration of artificial intelligence and neural networks has significantly changed full-body imaging and high-resolution 3D mapping systems, and this paper reviews research in these areas. With an emphasis on their use in the early identification of melanoma and other disorders, the goal is to give a wide perspective on the current status and potential future of these medical imaging technologies. Authors also examine methodologies such as machine learning and deep learning, seeking to identify efficient procedures that enhance diagnostic capabilities through the analysis of 3D body scans. This work aims to encourage further research and technological development to harness the full potential of AI in disease diagnosis.

Keywords: artificial intelligence, neural networks, 3D scan, body scan, 3D mapping system, healthcare

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Authors: Raghunandan Gurumurthy, Aricson Pereira, Sandeep Patil

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The escalating adoption of electric vehicles (EVs) across the globe has underscored the critical importance of advancing battery system technologies. This has catalyzed a shift towards the design and development of battery systems that not only exhibit higher energy efficiency but also boast enhanced thermal performance and sophisticated multi-material enclosures. A significant leap in this domain has been the incorporation of simulation-based design optimization for battery packs and Battery Management Systems (BMS), a move further enriched by integrating artificial intelligence/machine learning (AI/ML) approaches. These strategies are pivotal in refining the design, manufacturing, and operational processes for electric vehicles and energy storage systems. By leveraging AI/ML, stakeholders can now predict battery performance metrics—such as State of Health, State of Charge, and State of Power—with unprecedented accuracy. Furthermore, as Li-ion batteries (LIBs) become more prevalent in urban settings, the imperative for bolstering thermal and fire resilience has intensified. This has propelled Battery Thermal Management Systems (BTMs) to the forefront of energy storage research, highlighting the role of machine learning and AI not just as tools for enhanced safety management through accurate temperature forecasts and diagnostics but also as indispensable allies in the early detection and warning of potential battery fires.

Keywords: electric vehicles, battery thermal management, industrial engineering, machine learning, artificial intelligence, manufacturing

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Authors: Abdelkader Hachemi, Boualem Remini

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Seawater intrusion is a significant challenge faced by coastal aquifers in the Mediterranean basin. This study aims to determine the position of the sharp interface between seawater and freshwater in the aquifer of Wadi Nador, located in the Wilaya of Tipaza, Algeria. A numerical areal sharp interface model using the finite element method is developed to investigate the spatial and temporal behavior of seawater intrusion. The aquifer is assumed to be homogeneous and isotropic. The simulation results are compared with geophysical prospection data obtained through electrical methods in 2011 to validate the model. The simulation results demonstrate a good agreement with the geophysical prospection data, confirming the accuracy of the sharp interface model. The position of the sharp interface in the aquifer is found to be approximately 1617 meters from the sea. Two scenarios are proposed to predict the interface position for the year 2024: one without pumping and the other with pumping. The results indicate a noticeable retreat of the sharp interface position in the first scenario, while a slight decline is observed in the second scenario. The findings of this study provide valuable insights into the dynamics of seawater intrusion in the Wadi Nador aquifer. The predicted changes in the sharp interface position highlight the potential impact of pumping activities on the aquifer's vulnerability to seawater intrusion. This study emphasizes the importance of implementing measures to manage and mitigate seawater intrusion in coastal aquifers. The sharp interface model developed in this research can serve as a valuable tool for assessing and monitoring the vulnerability of aquifers to seawater intrusion.

Keywords: seawater intrusion, sharp interface, coastal aquifer, algeria

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Authors: Shuai Zhao, Shalendra D. Sharma, Jin Xu

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This research uses advanced machine learning models to learn a number of hypotheses regarding political executives. Specifically, it analyses the impact these powerful leaders have on economic growth by using leaders’ data from the Archigos database from 1835 to the end of 2015. The data is processed by the AutoGluon, which was developed by Amazon. Automated Machine Learning (AutoML) and AutoGluon can automatically extract features from the data and then use multiple classifiers to train the data. Use a linear regression model and classification model to establish the relationship between leaders and economic growth (GDP per capita growth), and to clarify the relationship between their characteristics and economic growth from a machine learning perspective. Our work may show as a model or signal for collaboration between the fields of statistics and artificial intelligence (AI) that can light up the way for political researchers and economists.

Keywords: comparative politics, political executives, leaders’ characteristics, artificial intelligence

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Authors: Sultan Ghaleb Aldaihani

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- The environment in which educational leadership takes place is becoming increasingly complex due to factors like globalization and rapid technological change. - This is creating a "leadership gap" where the complexity of the environment outpaces the ability of leaders to effectively respond. - Educational leadership involves guiding teachers and the broader school system towards improved student learning and achievement. 2. Implications of Artificial Intelligence (AI) in Educational Leadership: - AI has great potential to enhance education, such as through intelligent tutoring systems and automating routine tasks to free up teachers. - AI can also have significant implications for educational leadership by providing better information and data-driven decision-making capabilities. - Computer-adaptive testing can provide detailed, individualized data on student learning that leaders can use for instructional decisions and accountability. 3. Enhancing Decision-Making Processes: - Statistical models and data mining techniques can help identify at-risk students earlier, allowing for targeted interventions. - Probability-based models can diagnose students likely to drop out, enabling proactive support. - These data-driven approaches can make resource allocation and decision-making more effective. 4. Improving Efficiency and Productivity: - AI systems can automate tasks and change processes to improve the efficiency of educational leadership and administration. - Integrating AI can free up leaders to focus more on their role's human, interactive elements.

Keywords: Education, Leadership, Technology, Artificial Intelligence

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Authors: Joud Tayeb, Dunia Moussa, Rafal Al-Khawlani, Huda Elyas

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This research delves into the intricate AI features of Jenni, an AI-powered chatbot designed to offer personalized and engaging conversations. We explore the fundamental technologies driving Jenni's capabilities, including natural language processing (NLP), machine learning, and deep learning. Through a meticulous analysis of these technologies, we aim to unravel how Jenni effectively processes and understands user queries, generates contextually relevant responses, and continuously learns from interactions. To gain deeper insights into user experiences and satisfaction, a comprehensive survey was conducted. By analyzing the collected data, we determine that consumers mostly like Jenni AI and reported that it has improved their essay writing process, yet the algorithm needs to improve certain aspects, such as accuracy.

Keywords: natural language processing, machine learning, deep learning, artificial intelligence, Jenni

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Authors: Rodrigo Aguiar, Adelino Ferreira

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Road traffic accidents are the leading cause of unnatural death and injuries worldwide, representing a significant problem of road safety. In this context, the use of artificial intelligence with advanced machine learning techniques has gained prominence as a promising approach to predict traffic accidents. This article investigates the application of machine learning algorithms to develop traffic accident frequency prediction models. Models are evaluated based on performance metrics, making it possible to do a comparative analysis with traditional prediction approaches. The results suggest that machine learning can provide a powerful tool for accident prediction, which will contribute to making more informed decisions regarding road safety.

Keywords: machine learning, artificial intelligence, frequency of accidents, road safety

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Authors: Olga Leontjeva

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In the age of Artificial Intelligence (AI), some organization management approaches need to be adapted or changed. Human Resource Management (HRM) is a part of organization management that is under the managers' focus nowadays, because AI integration into organization activities brings some HRM-connected challenges. The topic became more significant during the crises of many organizations in the world caused by the coronavirus pandemic (COVID-19). The paper presents an approach, which will be used for the study that is going to be focused on the various case analysis. The author of the future study will analyze the cases of the organizations from Latvia and Spain that are grouped by the size, type of activity and area of business. The information for the cases will be collected through structured interviews and online surveys. The main result presented is the questionnaire developed that will be used for the study as well as the definition and description of sampling. The first round of the survey will be based on convenience sampling that is the main limitation of the study. To conclude, the approach developed will help to collect valid data if the organizations participating in the survey are ready to share their cases in depth, so the researchers could draw the right conclusions and generalize compared organizations’ cases. The questionnaire developed for the survey is applicable for both written online data collection as well as for the interviews. The case analysis will help to identify some HRM challenges that are connected to AI integration into organization activities such as management of different generation employees and their training peculiarities.

Keywords: age of artificial intelligence, case analysis, generation Y and Z employees, human resource management

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Authors: Jihan Zakarriya

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This paper aims at examining the concepts of gender vulnerability, militarized spaces and environmental degradation in Egyptian novelist Radwa Ashour’s Specters (1999) and Iraqi novelist Betool Khadiri’s Absent (2005). Although the socio-economic, environmental and political conditions in the 1990s- Egypt and Iraq are different, this paper argues that Ashour’s Specters and Khadiri’s Absent show the two societies as sharing a concern with the politics of public participation, individual freedom and political violence. For example, while Specters exposes the planned processes of economic-political and cultural violence towards Egyptian environment and people that undermine concepts of justice, equality and democracy, Absent shows the destructive effects of the systematic, successive waves of (international) militarized interferences and socio-economic sanctions imposed on Iraq following the Gulf War that hinder efforts of social development and kindle ethnic-religious violence and polarization in the country. This paper investigates and relates issues of gender, environmental and political violence and repression in Ashour’s Specters and Khadiri’s Absent in relation to the concepts of public space and security in the two countries. The paper argues that the selected novels articulate a particular awareness of the political-international conflicts and difficulties in the 1990s-Egypt and Iraq, with the aim both to emphasize the issue of gender as a tool of oppression and power hierarchy worldwide and to figure out new notions of public participation and change.

Keywords: gender violence, public space, environment, change

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Authors: Samira Ghasempourkazemi

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In terms of the COVID-19 pandemic, lots of mutations in the urban system, which have systemic impacts, have clearly appeared. COVID-19 not only had a direct impact on health but also caused significant losses to other departments, including the economy, education, tourism, environment and the construction industry. Therefore, the pandemic caused a disruption in the whole urban system. Particularly, today’s large urban areas are not designed in order to be compatible during a pandemic. Hence, cities are more vulnerable to infectious disease threats according to the population density, built environment and socioeconomic aspects. Considering the direct relationship between population and rate of infection, higher rates are given to those individuals located in areas with high-density populations. Population density can be a factor that seems to have a strong impact on the spread of infectious diseases. Thus, the preliminary hypothesis can be related to a densely populated areas which become hotspots for the rapid spread of the pandemic due to high levels of interaction. In addition, some other indicators can be effective in this condition, such as age range, education and socio-economy. To figure out the measure of infectious disease risk in densely populated areas in Istanbul is an objective of this study. Besides, this study intends to figure out Vulnerability Index in the case of COVID-19. In order to achieve the proper result, the considered method can be Analytic Hierarchy Process (AHP) by involving the mentioned variables. In the end, the study represents the COVID Vulnerability of densely populated areas in a metro city and the gaps that need to be identified and plugged for the pandemic-resilience city of tomorrow.

Keywords: infectious disease, COVID-19, urban system, densely populated area

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Authors: Laura Micheli, Majd Hijazi, Mahmoud Faytarouni

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The damage reported by oil and gas industrial facilities revealed the utmost vulnerability of steel liquid storage tanks to seismic events. The failure of steel storage tanks may yield devastating and long-lasting consequences on built and natural environments, including the release of hazardous substances, uncontrolled fires, and soil contamination with hazardous materials. It is, therefore, fundamental to reliably predict the damage that steel liquid storage tanks will likely experience under future seismic hazard events. The seismic performance of steel liquid storage tanks is usually assessed using vulnerability curves obtained from the numerical simulation of a tank under different hazard scenarios. However, the computational demand of high-fidelity numerical simulation models, such as finite element models, makes the vulnerability assessment of liquid storage tanks time-consuming and often impractical. As a solution, this paper presents a surrogate model-based strategy for predicting seismic-induced damage in steel liquid storage tanks. In the proposed strategy, the surrogate model is leveraged to reduce the computational demand of time-consuming numerical simulations. To create the data set for training the surrogate model, field damage data from past earthquakes reconnaissance surveys and reports are collected. Features representative of steel liquid storage tank characteristics (e.g., diameter, height, liquid level, yielding stress) and seismic excitation parameters (e.g., peak ground acceleration, magnitude) are extracted from the field damage data. The collected data are then utilized to train a surrogate model that maps the relationship between tank characteristics, seismic hazard parameters, and seismic-induced damage via a data-driven surrogate model. Different types of surrogate algorithms, including naïve Bayes, k-nearest neighbors, decision tree, and random forest, are investigated, and results in terms of accuracy are reported. The model that yields the most accurate predictions is employed to predict future damage as a function of tank characteristics and seismic hazard intensity level. Results show that the proposed approach can be used to estimate the extent of damage in steel liquid storage tanks, where the use of data-driven surrogates represents a viable alternative to computationally expensive numerical simulation models.

Keywords: damage prediction , data-driven model, seismic performance, steel liquid storage tanks, surrogate model

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Authors: Kshama Sharma

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Education is one of the most powerful tools to transform the world into a just, sustainable, and more peaceful place for existing lives across the globe. However, its recent objective approach focused on materialistic, factual, and existing knowledge, has a constraint of human experiences that is limited to certain dimensions only. And leads to a materialistic world which is deprived of spiritual approaches and makes it less compassionate, and more grades oriented. To make it more comprehensive, education should explore the subjective approaches towards spiritualism to connect lives with the greater self and consciousness of cosmic intelligence. This approach will bring a major shift in the orientation of pedagogical processes, assessment strategies, and administrative management of the present education system. Spirituality often related to the religious aspect of human civilization and development, however, when universal consciousness /cosmic intelligence (which is often claimed as dark energy) and the human mind competencies works in coherence and coordination then the efficiency of human mind reaches to a different dimension and achieve extraordinary level of human understanding. Quantitative analysis of the existing secondary data from the different agencies working in the field of meditation had been analyzed to conclude its implications on human mind and further how it can effectively use in education to bring the desired and expected results. Any kind of meditation practice affects the cognitive, mental, physical, emotional, and conscious state of mind. If aligned with the teaching and learning methodology will lead to conscious learner and peaceful world.

Keywords: spirituality, cosmic intelligence, consciousness, mind competencies

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Authors: Ramoshweu Melvin Sethobya, Emmanuel Chirenje, Mihlali Hobo, Simon Sebothoma

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The recent surge in residential infrastructure development around the metropolitan areas of South Africa has necessitated conditions for thorough geotechnical assessments to be conducted prior to site developments to ensure human and infrastructure safety. This paper appraises the success in the application of multi-method geophysical techniques for the delineation of sinkhole vulnerability in a residential landscape. Geophysical techniques ERT, MASW, VES, Magnetics and gravity surveys were conducted to assist in mapping sinkhole vulnerability, using an existing sinkhole as a constraint at Venterspost town, West of Johannesburg city. A combination of different geophysical techniques and results integration from those proved to be useful in the delineation of the lithologic succession around sinkhole locality, and determining the geotechnical characteristics of each layer for its contribution to the development of sinkholes, subsidence and cavities at the vicinity of the site. Study results have also assisted in the determination of the possible depth extension of the currently existing sinkhole and the location of sites where other similar karstic features and sinkholes could form. Results of the ERT, VES and MASW surveys have uncovered dolomitic bedrock at varying depths around the sites, which exhibits high resistivity values in the range 2500-8000ohm.m and corresponding high velocities in the range 1000-2400 m/s. The dolomite layer was found to be overlain by a weathered chert-poor dolomite layer, which has resistivities between the range 250-2400ohm.m, and velocities ranging from 500-600m/s, from which the large sinkhole has been found to collapse/ cave in. A compiled 2.5D high resolution Shear Wave Velocity (Vs) map of the study area was created using 2D profiles of MASW data, offering insights into the prevailing lithological setup conducive for formation various types of karstic features around the site. 3D magnetic models of the site highlighted the regions of possible subsurface interconnections between the currently existing large sinkhole and the other subsidence feature at the site. A number of depth slices were used to detail the conditions near the sinkhole as depth increases. Gravity surveys results mapped the possible formational pathways for development of new karstic features around the site. Combination and correlation of different geophysical techniques proved useful in delineation of the site geotechnical characteristics and mapping the possible depth extend of the currently existing sinkhole.

Keywords: resistivity, magnetics, sinkhole, gravity, karst, delineation, VES

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Authors: M. Ben Slimene, M.-S. Ouali

Abstract:

Asset management of urban infrastructures faces a multitude of challenges that need to be overcome to obtain a reliable measurement of performances. Predicting the performance of slowly aging systems is one of those challenges, which helps the asset manager to investigate specific failure modes and to undertake the appropriate maintenance and rehabilitation interventions to avoid catastrophic failures as well as to optimize the maintenance costs. This article presents a methodology for modeling the deterioration of slowly degrading assets based on an operating history. It consists of extracting degradation profiles by grouping together assets that exhibit similar degradation sequences using an unsupervised classification technique derived from artificial intelligence. The obtained clusters are used to build the performance prediction models. This methodology is applied to a sample of a stormwater drainage culvert dataset.

Keywords: artificial Intelligence, clustering, culvert, regression model, slow degradation

Procedia PDF Downloads 112

Authors: Reggie Chandra

Abstract:

Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

Procedia PDF Downloads 172

Authors: Ahmed Atef, Mohamed Alhasheem, Eman Beshr

Abstract:

In this paper, an enhanced DC microgrid design is proposed using the DAB converter as a conversion unit in order to harvest the maximum power from the PV array. Each connected DAB converter is controlled with an enhanced control strategy. The controller is based on the artificial intelligence (AI) technique to regulate the terminal PV voltage through the phase shift angle of each DAB converter. In this manner, no need for a Maximum Power Point Tracking (MPPT) unit to set the reference of the PV terminal voltage. This strategy overcomes the stability issues of the DC microgrid as the response of converters is superior compared to the conventional strategies. The proposed PV interface system is modelled and simulated using MATLAB/SIMULINK. The simulation results reveal an accurate and fast response of the proposed design in case of irradiance changes.

Keywords: DC microgrid, DAB converter, parallel operation, artificial intelligence, fast response

Procedia PDF Downloads 791

Authors: Balamurugan L.

Abstract:

Artificial intelligence (AI) and its applications in Intellectual Property Rights (IPR) has been significantly growing in recent years. In last couple of years, AI tools for Patent Research and Patent Analytics have been well-stabilized in terms of accuracy of references and representation of identified patent insights. However, AI tools for Patent Prosecution and Patent Litigation are still in the nascent stage and there may be a significant potential if such market is explored further. Our research is primarily focused on identifying potential whitespaces and schematic algorithms to automate the Patent Prosecution and Patent Litigation Process of the Intellectual Property. The schematic algorithms may assist leading AI tool developers, to explore such opportunities in the field of Intellectual Property. Our research is also focused on identification of pitfalls of the AI. For example, Information Security and its impact in IPR, and Potential remediations to sustain the IPR in the digital environment.

Keywords: artificial intelligence, patent analytics, patent drafting, patent litigation, patent prosecution, patent research

Procedia PDF Downloads 67

Authors: Hae-Won Seo, Yong-Gu Lee

Abstract:

Recent progress in the next generation of automobile technology is geared towards incorporating information technology into cars. Collectively called smart cars are bringing intelligence to cars that provides comfort, convenience and safety. A branch of smart cars is connected-car system. The key concept in connected-cars is the sharing of driving information among cars through decentralized manner enabling collective intelligence. This paper proposes a foundation of the information model that is necessary to define the driving information for smart-cars. Road conditions are modeled through a unique data structure that unambiguously represent the time variant traffics in the streets. Additionally, the modeled data structure is exemplified in a navigational scenario and usage using UML. Optimal driving route searching is also discussed using the proposed data structure in a dynamically changing road conditions.

Keywords: connected-car, data modeling, route planning, navigation system

Procedia PDF Downloads 375