Search results for: embedded computing

Authors: Mario Tauzene Afonso Matangue, Ivan Andres Sanchez Ortiz

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The Metropolitan area of Maputo (Mozambique Capital City) is located in semi-arid zone (800 mm annual rainfall) with 1101170 million inhabitants. On the west side, there are the flatlands of Infulene where the Mulauze River flows towards to the Indian Ocean, receiving at this site, the storm water contaminated with sanitary sewage from Maputo, transported through a concrete open channel. In Infulene, local communities grow salads crops such as tomato, onion, garlic, lettuce, and cabbage, which are then commercialized and consumed in several markets in Maputo City. Lettuce is the most daily consumed salad crop in different meals, generally in fast-foods, breakfasts, lunches, and dinners. However, the risk of infection by several pathogens due to the consumption of lettuce, using the Quantitative Microbial Risk Assessment (QMRA) tools, is still unknown since there are few studies or publications concerning to this matter in Mozambique. This work is aimed at determining the annual risk arising from the consumption of lettuce grown in Infulene valley, in Maputo, using QMRA tools. The exposure model was constructed upon the volume of contaminated water remaining in the lettuce leaves, the empirical relations between the number of pathogens and the indicator of microorganisms (E. coli), the consumption of lettuce (g) and reduction of pathogens (days). The reference pathogens were Vibrio cholerae, Cryptosporidium, norovirus, and Ascaris. The water quality samples (E. coli) were collected in the storm water channel from January 2016 to December 2018, comprising 65 samples, and the urban lettuce consumption data were collected through inquiry in Maputo Metropolis covering 350 persons. A non-parametric bootstrap was performed involving 10,000 iterations over the collected dataset, namely, water quality (E. coli) and lettuce consumption. The dose-response models were: Exponential for Cryptosporidium, Kummer Confluent hypergeomtric function (1F1) for Vibrio and Ascaris Gaussian hypergeometric function (2F1-(a,b;c;z) for norovirus. The annual infection risk estimates were performed using R 3.6.0 (CoreTeam) software by Monte Carlo (Latin hypercubes), a sampling technique involving 10,000 iterations. The annual infection risks values expressed by Median and the 95th percentile, per person per year (pppy) arising from the consumption of lettuce are as follows: Vibrio cholerae (1.00, 1.00), Cryptosporidium (3.91x10⁻³, 9.72x 10⁻³), nororvirus (5.22x10⁻¹, 9.99x10⁻¹) and Ascaris (2.59x10⁻¹, 9.65x10⁻¹). Thus, the consumption of the lettuce would result in greater risks than the tolerable levels ( < 10⁻³ pppy or 10⁻⁶ DALY) for all pathogens, and the Vibrio cholerae is the most virulent pathogens, according to the hit-single models followed by the Ascaris lumbricoides and norovirus. The sensitivity analysis carried out in this work pointed out that in the whole QMRA, the most important input variable was the reduction of pathogens (Spearman rank value was 0.69) between harvest and consumption followed by water quality (Spearman rank value was 0.69). The decision-makers (Mozambique Government) must strengthen the prevention measures related to pathogens reduction in lettuce (i.e., washing) and engage in wastewater treatment engineering.

Keywords: annual infections risk, lettuce, non-parametric bootstrapping, quantitative microbial risk assessment tools

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Authors: Bidzina Matsaberidze, Anna Sikharulidze, Gia Sirbiladze, Bezhan Ghvaberidze

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In the modern world emergency management decision support systems are actively used by state organizations, which are interested in extreme and abnormal processes and provide optimal and safe management of supply needed for the civil and military facilities in geographical areas, affected by disasters, earthquakes, fires and other accidents, weapons of mass destruction, terrorist attacks, etc. Obviously, these kinds of extreme events cause significant losses and damages to the infrastructure. In such cases, usage of intelligent support technologies is very important for quick and optimal location-transportation of emergency service in order to avoid new losses caused by these events. Timely servicing from emergency service centers to the affected disaster regions (response phase) is a key task of the emergency management system. Scientific research of this field takes the important place in decision-making problems. Our goal was to create an expert knowledge-based intelligent support system, which will serve as an assistant tool to provide optimal solutions for the above-mentioned problem. The inputs to the mathematical model of the system are objective data, as well as expert evaluations. The outputs of the system are solutions for Fuzzy Multi-Objective Emergency Location-Transportation Problem (FMOELTP) for disasters’ regions. The development and testing of the Intelligent Support System were done on the example of an experimental disaster region (for some geographical zone of Georgia) which was generated using a simulation modeling. Four objectives are considered in our model. The first objective is to minimize an expectation of total transportation duration of needed products. The second objective is to minimize the total selection unreliability index of opened humanitarian aid distribution centers (HADCs). The third objective minimizes the number of agents needed to operate the opened HADCs. The fourth objective minimizes the non-covered demand for all demand points. Possibility chance constraints and objective constraints were constructed based on objective-subjective data. The FMOELTP was constructed in a static and fuzzy environment since the decisions to be made are taken immediately after the disaster (during few hours) with the information available at that moment. It is assumed that the requests for products are estimated by homeland security organizations, or their experts, based upon their experience and their evaluation of the disaster’s seriousness. Estimated transportation times are considered to take into account routing access difficulty of the region and the infrastructure conditions. We propose an epsilon-constraint method for finding the exact solutions for the problem. It is proved that this approach generates the exact Pareto front of the multi-objective location-transportation problem addressed. Sometimes for large dimensions of the problem, the exact method requires long computing times. Thus, we propose an approximate method that imposes a number of stopping criteria on the exact method. For large dimensions of the FMOELTP the Estimation of Distribution Algorithm’s (EDA) approach is developed.

Keywords: epsilon-constraint method, estimation of distribution algorithm, fuzzy multi-objective combinatorial programming problem, fuzzy multi-objective emergency location/transportation problem

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Authors: A. Peyvan, D. Li, J. Komperda, F. Mashayek

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Turbulence can be observed in a variety fluid motions in nature and industrial applications. Recent investment in high-speed aircraft and propulsion systems has revitalized fundamental research on turbulent flows. In these systems, capturing chaotic fluid structures with different length and time scales is accomplished through the Direct Numerical Simulation (DNS) approach since it accurately simulates flows down to smallest dissipative scales, i.e., Kolmogorov’s scales. The discontinuous spectral element method (DSEM) is a high-order technique that uses spectral functions for approximating the solution. The DSEM code has been developed by our research group over the course of more than two decades. Recently, the code has been improved to run large cases in the order of billions of solution points. Running big simulations requires a considerable amount of RAM. Therefore, the DSEM code must be highly parallelized and able to start on multiple computational nodes on an HPC cluster with distributed memory. However, some pre-processing procedures, such as determining global element information, creating a global face list, and assigning global partitioning and element connection information of the domain for communication, must be done sequentially with a single processing core. A separate code has been written to perform the pre-processing procedures on a local machine. It stores the minimum amount of information that is required for the DSEM code to start in parallel, extracted from the mesh file, into text files (pre-files). It packs integer type information with a Stream Binary format in pre-files that are portable between machines. The files are generated to ensure fast read performance on different file-systems, such as Lustre and General Parallel File System (GPFS). A new subroutine has been added to the DSEM code to read the startup files using parallel MPI I/O, for Lustre, in a way that each MPI rank acquires its information from the file in parallel. In case of GPFS, in each computational node, a single MPI rank reads data from the file, which is specifically generated for the computational node, and send them to other ranks on the node using point to point non-blocking MPI communication. This way, communication takes place locally on each node and signals do not cross the switches of the cluster. The read subroutine has been tested on Argonne National Laboratory’s Mira (GPFS), National Center for Supercomputing Application’s Blue Waters (Lustre), San Diego Supercomputer Center’s Comet (Lustre), and UIC’s Extreme (Lustre). The tests showed that one file per node is suited for GPFS and parallel MPI I/O is the best choice for Lustre file system. The DSEM code relies on heavily optimized linear algebra operation such as matrix-matrix and matrix-vector products for calculation of the solution in every time-step. For this, the code can either make use of its matrix math library, BLAS, Intel MKL, or ATLAS. This fact and the discontinuous nature of the method makes the DSEM code run efficiently in parallel. The results of weak scaling tests performed on Blue Waters showed a scalable and efficient performance of the code in parallel computing.

Keywords: computational fluid dynamics, direct numerical simulation, spectral element, turbulent flow

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Authors: Lassi Lehto, Jaakko Kahkonen, Juha Oksanen, Tapani Sarjakoski

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The use of raster-formatted data sets in geospatial analysis is increasing rapidly. At the same time, geographic data are being introduced into disciplines outside the traditional domain of geoinformatics, like climate change, intelligent transport, and immigration studies. These developments call for better methods to deliver raster geodata in an efficient and easy-to-use manner. Data cube technologies have traditionally been used in the geospatial domain for managing Earth Observation data sets that have strict requirements for effective handling of time series. The same approach and methodologies can also be applied in managing other types of geospatial data sets. A cloud service-based geodata cube, called GeoCubes Finland, has been developed to support online delivery and analysis of most important geospatial data sets with national coverage. The main target group of the service is the academic research institutes in the country. The most significant aspects of the GeoCubes data repository include the use of multiple resolution levels, cloud-optimized file structure, and a customized, flexible content access API. Input data sets are pre-processed while being ingested into the repository to bring them into a harmonized form in aspects like georeferencing, sampling resolutions, spatial subdivision, and value encoding. All the resolution levels are created using an appropriate generalization method, selected depending on the nature of the source data set. Multiple pre-processed resolutions enable new kinds of online analysis approaches to be introduced. Analysis processes based on interactive visual exploration can be effectively carried out, as the level of resolution most close to the visual scale can always be used. In the same way, statistical analysis can be carried out on resolution levels that best reflect the scale of the phenomenon being studied. Access times remain close to constant, independent of the scale applied in the application. The cloud service-based approach, applied in the GeoCubes Finland repository, enables analysis operations to be performed on the server platform, thus making high-performance computing facilities easily accessible. The developed GeoCubes API supports this kind of approach for online analysis. The use of cloud-optimized file structures in data storage enables the fast extraction of subareas. The access API allows for the use of vector-formatted administrative areas and user-defined polygons as definitions of subareas for data retrieval. Administrative areas of the country in four levels are available readily from the GeoCubes platform. In addition to direct delivery of raster data, the service also supports the so-called virtual file format, in which only a small text file is first downloaded. The text file contains links to the raster content on the service platform. The actual raster data is downloaded on demand, from the spatial area and resolution level required in each stage of the application. By the geodata cube approach, pre-harmonized geospatial data sets are made accessible to new categories of inexperienced users in an easy-to-use manner. At the same time, the multiresolution nature of the GeoCubes repository facilitates expert users to introduce new kinds of interactive online analysis operations.

Keywords: cloud service, geodata cube, multiresolution, raster geodata

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Authors: Jatziri Y. Moreno-Martínez, Arturo Galván, Israel Enrique Herrera Díaz, José Ramón Gasca Tirado

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In recent years, for the elevated viaducts in Mexico City, a construction system based on precast/pre-stressed concrete elements has been used, in which the bridge girders are divided in two parts by imposing a hinged support in sections where the bending moments that are originated by the gravity loads in a continuous beam are minimal. Precast concrete girders with dapped ends are a representative sample of a behavior that has complex configurations of stresses that make them more vulnerable to cracking due to flexure–shear interaction. The design procedures for ends of the dapped girders are well established and are based primarily on experimental tests performed for different configurations of reinforcement. The critical failure modes that can govern the design have been identified, and for each of them, the methods for computing the reinforcing steel that is needed to achieve adequate safety against failure have been proposed. Nevertheless, the design recommendations do not include procedures for controlling diagonal cracking at the entrant corner under service loading. These cracks could cause water penetration and degradation because of the corrosion of the steel reinforcement. The lack of visual access to the area makes it difficult to detect this damage and take timely corrective actions. Three-dimensional non-linear numerical models based on Finite Element Method to study the cracking at the entrant corner of dapped-end beams were performed using the software package ANSYS v. 11.0. The cracking was numerically simulated by using the smeared crack approach. The concrete structure was modeled using three-dimensional solid elements SOLID65 capable of cracking in tension and crushing in compression. Drucker-Prager yield surface was used to include the plastic deformations. The longitudinal post-tension was modeled using LINK8 elements with multilinear isotropic hardening behavior using von Misses plasticity. The reinforcement was introduced with smeared approach. The numerical models were calibrated using experimental tests carried out in “Instituto de Ingeniería, Universidad Nacional Autónoma de México”. In these numerical models the characteristics of the specimens were considered: typical solution based on vertical stirrups (hangers) and on vertical and horizontal hoops with a post-tensioned steel which contributed to a 74% of the flexural resistance. The post-tension is given by four steel wires with a 5/8’’ (16 mm) diameter. Each wire was tensioned to 147 kN and induced an average compressive stress of 4.90 MPa on the concrete section of the dapped end. The loading protocol consisted on applying symmetrical loading to reach the service load (180 kN). Due to the good correlation between experimental and numerical models some additional numerical models were proposed by considering different percentages of post-tension in order to find out how much it influences in the appearance of the cracking in the reentrant corner of the dapped-end beams. It was concluded that the increasing of percentage of post-tension decreases the displacements and the cracking in the reentrant corner takes longer to appear. The authors acknowledge at “Universidad de Guanajuato, Campus Celaya-Salvatierra” and the financial support of PRODEP-SEP (UGTO-PTC-460) of the Mexican government. The first author acknowledges at “Instituto de Ingeniería, Universidad Nacional Autónoma de México”.

Keywords: concrete dapped-end beams, cracking control, finite element analysis, postension

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Authors: Sam Weber, Deepak Mishra, Susan Wilde, Elizabeth Kramer

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The frequency and severity of cyanobacteria harmful blooms (CyanoHABs) have been increasing over time, with point and non-point source eutrophication and shifting climate paradigms being blamed as the primary culprits. Excessive nutrients, warm temperatures, quiescent water, and heavy and less regular rainfall create more conducive environments for CyanoHABs. CyanoHABs have the potential to produce a spectrum of toxins that cause gastrointestinal stress, organ failure, and even death in humans and animals. To promote enhanced, proactive CyanoHAB management, risk modeling using geospatial tools can act as predictive mechanisms to supplement current CyanoHAB monitoring, management and mitigation efforts. The risk maps would empower water managers to focus their efforts on high risk water bodies in an attempt to prevent CyanoHABs before they occur, and/or more diligently observe those waterbodies. For this research, exploratory spatial data analysis techniques were used to identify the strongest predicators for CyanoHAB blooms based on remote sensing-derived cyanobacteria cell density values for 771 waterbodies in the Georgia Piedmont and landscape characteristics of their watersheds. In-situ datasets for cyanobacteria cell density, nutrients, temperature, and rainfall patterns are not widely available, so free gridded geospatial datasets were used as proxy variables for assessing CyanoHAB risk. For example, the percent of a watershed that is agriculture was used as a proxy for nutrient loading, and the summer precipitation within a watershed was used as a proxy for water quiescence. Cyanobacteria cell density values were calculated using atmospherically corrected images from the European Space Agency’s Sentinel-2A satellite and multispectral instrument sensor at a 10-meter ground resolution. Seventeen explanatory variables were calculated for each watershed utilizing the multi-petabyte geospatial catalogs available within the Google Earth Engine cloud computing interface. The seventeen variables were then used in a multiple linear regression model, and the strongest predictors of cyanobacteria cell density were selected for the final regression model. The seventeen explanatory variables included land cover composition, winter and summer temperature and precipitation data, topographic derivatives, vegetation index anomalies, and soil characteristics. Watershed maximum summer temperature, percent agriculture, percent forest, percent impervious, and waterbody area emerged as the strongest predictors of cyanobacteria cell density with an adjusted R-squared value of 0.31 and a p-value ~ 0. The final regression equation was used to make a normalized cyanobacteria cell density index, and a Jenks Natural Break classification was used to assign waterbodies designations of low, medium, or high risk. Of the 771 waterbodies, 24.38% were low risk, 37.35% were medium risk, and 38.26% were high risk. This study showed that there are significant relationships between free geospatial datasets representing summer maximum temperatures, nutrient loading associated with land use and land cover, and the area of a waterbody with cyanobacteria cell density. This data analytics approach to CyanoHAB risk assessment corroborated the literature-established environmental triggers for CyanoHABs, and presents a novel approach for CyanoHAB risk mapping in waterbodies across the greater southeastern United States.

Keywords: cyanobacteria, land use/land cover, remote sensing, risk mapping

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Authors: Agboka Komi Mensah, John Odindi, Elfatih M. Abdel-Rahman, Onisimo Mutanga, Henri Ez Tonnang

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Ecosystems are networks of organisms and populations that form a community of various species interacting within their habitats. Such habitats are defined by abiotic and biotic conditions that establish the initial limits to a population's growth, development, and reproduction. The habitat’s conditions explain the context in which species interact to access resources such as food, water, space, shelter, and mates, allowing for feeding, dispersal, and reproduction. Dispersal is an essential life-history strategy that affects gene flow, resource competition, population dynamics, and species distributions. Despite the importance of dispersal in population dynamics and survival, understanding the mechanism underpinning the dispersal of organisms remains challenging. For instance, when an organism moves into an ecosystem for survival and resource competition, its progression is highly influenced by extrinsic factors such as its physiological state, climatic variables and ability to evade predation. Therefore, greater spatial detail is necessary to understand organism dispersal dynamics. Understanding organisms dispersal can be addressed using empirical and mechanistic modelling approaches, with the adopted approach depending on the study's purpose Cellular automata (CA) is an example of these approaches that have been successfully used in biological studies to analyze the dispersal of living organisms. Cellular automata can be briefly described as occupied cells by an individual that evolves based on proper decisions based on a set of neighbours' rules. However, in the ambit of modelling individual organisms dispersal at the landscape scale, we lack user friendly tools that do not require expertise in mathematical models and computing ability; such as a visual analytics framework for tracking and forecasting the dispersal behaviour of organisms. The term "visual analytics" (VA) describes a semiautomated approach to electronic data processing that is guided by users who can interact with data via an interface. Essentially, VA converts large amounts of quantitative or qualitative data into graphical formats that can be customized based on the operator's needs. Additionally, this approach can be used to enhance the ability of users from various backgrounds to understand data, communicate results, and disseminate information across a wide range of disciplines. To support effective analysis of the dispersal of organisms at the landscape scale, we therefore designed Pydisp which is a free visual data analytics tool for spatiotemporal dispersal modeling built in Python. Its user interface allows users to perform a quick and interactive spatiotemporal analysis of species dispersal using bioecological and climatic data. Pydisp enables reuse and upgrade through the use of simple principles such as Fuzzy cellular automata algorithms. The potential of dispersal modeling is demonstrated in a case study by predicting the dispersal of Fopius arisanus (Sonan), endoparasitoids to control Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) in Kenya. The results obtained from our example clearly illustrate the parasitoid's dispersal process at the landscape level and confirm that dynamic processes in an agroecosystem are better understood when designed using mechanistic modelling approaches. Furthermore, as demonstrated in the example, the built software is highly effective in portraying the dispersal of organisms despite the unavailability of detailed data on the species dispersal mechanisms.

Keywords: cellular automata, fuzzy logic, landscape, spatiotemporal

Procedia PDF Downloads 49

Authors: Brennan Lodge

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Cybersecurity professionals have long been embroiled in a digital arms race, confronting increasingly sophisticated threats with innovative solutions. The field of cybersecurity is in an unending race against malicious adversaries. As threats evolve in complexity, the tools used to defend against them need to advance even faster. Burdened with a vast arsenal of tools and an expansive scope of threat intelligence, analysts frequently navigate a complex web, trying to discern patterns amidst information overload. Herein lies the potential of Retrieval Augmented Generation (RAG). By combining the capabilities of Large Language Models (LLMs) with a generative AI facet, RAG brings to the table an unparalleled ability for real-time cross-referencing, bridging the gap between raw data and actionable insights. Imagine an analyst named Sarah working at a global Fortune 500 company. Every day, Sarah navigates a maze of diverse knowledge bases, real-time threat intelligence, and her company's vast proprietary data, from network specifics to intricate technical blueprints. One day, she's challenged by a potential breach through a personal device due to the company's global "Bring Your Own Device" policy. With the clock ticking, Sarah has mere minutes to trace the malware's origin, all while considering complex regional regulations. As she races against the benchmark of Mean Time To Resolution (MTTR), she wonders: Could "Cozy Bear" with its notorious malware tactic, HAMMERTOSS, be behind this? Balancing policy intricacies, global network considerations, and ever-emerging cyber threats, Sarah's role epitomizes the intense challenges faced by today's cybersecurity analysts. While analysts grapple with this array of intricate, time-sensitive challenges, the necessity for precision and efficiency is key. RAG technology—a cutting-edge advancement in Gen AI—is a promising solution. Designed to assimilate diverse data sources such as cyber advisory notices, phishing email sentiment, secure and insecure code examples, information security policy documentation, and the MITRE ATT&CK framework, RAG equips analysts with real-time querying capabilities through a vector database and a cross referenced concise response from a Gen AI model. Traditional relational databases often necessitate a tedious process of filtering through numerous entries. Now, with the synergy of vector databases and Gen AI models, analysts can rapidly access both contextually or semantically akin data points. This augmented approach equips analysts with a comprehensive understanding of the prevailing cyber threats, elevating the robustness of cybersecurity defenses and upskilling the analyst and team, too. Vector databases underpin the knowledge translation in Gen AI. They bridge the gap between raw data and translation into meaningful insights, ensuring that analysts are equipped with comprehensive and relevant information. This superior capability of the RAG framework, with its impressive depth and precision, finds application across a broad spectrum of cybersecurity challenges. Let's delve into some use cases where its potential becomes particularly evident: Phishing Email Sentiment Analysis: Phishing remains a predominant vector for cybersecurity breaches. Leveraging RAG's capabilities, analysts can not only assess the potential malevolence of an email but can also understand the context behind it. By cross-referencing patterns from varied data sources in real-time, the detection process evolves from a mere content evaluation to a holistic understanding of attacker tactics, behaviors, and evolving profiles. This allows for the identification of nuanced phishing strategies that might otherwise go undetected. Insecure Code Analysis: Software vulnerabilities form a critical entry point for cyber adversaries. With RAG, the process of code evaluation undergoes a transformation. Instead of manual code reviews, the system pulls insights from vector databases and historical code snippets marked as insecure, enabling detection of vulnerabilities based on historical patterns, emerging threat vectors, and even predictive threat modeling. This ensures that even the most obfuscated or embedded vulnerabilities are identified, and corrective measures can be promptly implemented. Vulnerability and Upskill Advisory: In the fast-paced world of cybersecurity, staying updated is paramount. Through RAG's capabilities, analysts are not only made aware of real-time vulnerabilities but are also guided on the necessary skills and tools needed to combat them. By dynamically sourcing data through vulnerability advisories, news on advanced persistent threats, and tactics to defend, RAG ensures that analysts are not only reactive to threats but are also proactively upskilled, thereby bolstering their defense mechanisms. Information Security Policies for Compliance Teams: Compliance remains at the heart of many organizational cybersecurity strategies. However, with ever-shifting regulatory landscapes, staying compliant becomes a moving target. RAG's ability to source real-time data ensures that compliance teams always have access to the latest policy changes, guidelines, and best practices. This not only facilitates adherence to current standards but also anticipates future shifts, assists with audits, and ensures that organizations remain ahead of the compliance curve. Fusing a RAG architecture with platforms like Slack amplifies its practical utility. Slack, known for its real-time communication prowess, seamlessly evolves into more than just a messaging platform in this context. Cybersecurity analysts can pose intricate queries within Slack and, almost instantaneously, receive comprehensive feedback powered by the harmonious interplay of RAG and Gen AI. This integration effectively transforms Slack into an AI-augmented chatbot-like assistant for cybersecurity professionals, always ready to provide informed insights on-demand, making it an indispensable ally in the ever-evolving cyber battlefield. Navigating the vast landscape of cybersecurity, analysts often encounter unfamiliar terminologies and techniques., analysts require tools that not only detect or inform them of threats, like CISA (U.S Cybersecurity Infrastructure Security Agency) Advisories, but also interpret and communicate them effectively. Consider a junior cybersecurity analyst named Alex, who comes across the term "Kerberoasting" while reviewing a network log. Unfamiliar with its intricacies, Alex turns to Slack to pose a query: "chat explain is Kerberoasting, using CISA." Almost instantaneously, Slack, powered by the harmonious interplay of RAG and Gen AI, provides a detailed response, cross-referencing a recent cyber advisory on the technique. It explains how attackers can exploit the Kerberos Ticket Granting Service to decipher service account passwords, potentially compromising a network. In this dynamic realm of cybersecurity, the blend of RAG and Generative AI represents more than just a technological leap. It embodies a paradigm shift, promising a future where human expertise and AI-driven precision join forces. As cyber threats continue their relentless advance, this synergy ensures that defenders are equipped with an arsenal that's not just reactive, but also profoundly insightful. No longer should analysts be submerged in a deluge of data without direction. Instead, they should be empowered, to discern, act, and preempt with unparalleled clarity and confidence. By harmoniously intertwining human discernment with AI capabilities, we should chart a path towards a future where cybersecurity is not just about defense, but about achieving a strategic advantage, paving the way for a safer, informed and a more secure digital horizon.

Keywords: cybersecurity, gen AI, retrieval augmented generation, cybersecurity defense strategies

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Authors: H. M. El-Askary, S. A. Abd El-Mawla, M. Allali, M. M. El-Hattab, M. El-Raey, A. M. Farahat, M. Kafatos, S. Nickovic, S. K. Park, A. K. Prasad, C. Rakovski, W. Sprigg, D. Struppa, A. Vukovic

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A clear distinction between weather and climate is a necessity because while they are closely related, there are still important differences. Climate change is identified when we compute the statistics of the observed changes in weather over space and time. In this work we will show how the changing climate contribute to the frequency, magnitude and extent of different extreme events using a multi sensor approach with some synergistic modeling activities. We are exploring satellite observations of dust over North Africa, Gulf Region and the Indo Gangetic basin as well as dust versus anthropogenic pollution events over the Delta region in Egypt and Seoul through remote sensing and utilize the behavior of the dust and haze on the aerosol optical properties. Dust impact on the retreat of the glaciers in the Himalayas is also presented. In this study we also focus on the identification and monitoring of a massive dust plume that blew off the western coast of Africa towards the Atlantic on October 8th, 2012 right before the development of Hurricane Sandy. There is evidence that dust aerosols played a non-trivial role in the cyclogenesis process of Sandy. Moreover, a special dust event "An American Haboob" in Arizona is discussed as it was predicted hours in advance because of the great improvement we have in numerical, land–atmosphere modeling, computing power and remote sensing of dust events. Therefore we performed a full numerical simulation to that event using the coupled atmospheric-dust model NMME–DREAM after generating a mask of the potentially dust productive regions using land cover and vegetation data obtained from satellites. Climate change also contributes to the deterioration of different marine habitats. In that regard we are also presenting some work dealing with change detection analysis of Marine Habitats over the city of Hurghada, Red Sea, Egypt. The motivation for this work came from the fact that coral reefs at Hurghada have undergone significant decline. They are damaged, displaced, polluted, stepped on, and blasted off, in addition to the effects of climate change on the reefs. One of the most pressing issues affecting reef health is mass coral bleaching that result from an interaction between human activities and climatic changes. Over another location, namely California, we have observed that it exhibits highly-variable amounts of precipitation across many timescales, from the hourly to the climate timescale. Frequently, heavy precipitation occurs, causing damage to property and life (floods, landslides, etc.). These extreme events, variability, and the lack of good, medium to long-range predictability of precipitation are already a challenge to those who manage wetlands, coastal infrastructure, agriculture and fresh water supply. Adding on to the current challenges for long-range planning is climate change issue. It is known that La Niña and El Niño affect precipitation patterns, which in turn are entwined with global climate patterns. We have studied ENSO impact on precipitation variability over different climate divisions in California. On the other hand the Nile Delta has experienced lately an increase in the underground water table as well as water logging, bogging and soil salinization. Those impacts would pose a major threat to the Delta region inheritance and existing communities. There has been an undergoing effort to address those vulnerabilities by looking into many adaptation strategies.

Keywords: remote sensing, modeling, long range transport, dust storms, North Africa, Gulf Region, India, California, climate extremes, sea level rise, coral reefs

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Authors: Lauren B. Birney

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The envisioned long-term outcome of this three-year research, and implementation plan is for 1) teachers and students to design and build their own computational models of real-world environmental-human health phenomena occurring within the context of the “Human Harbor” and 2) project researchers to evaluate the degree to which these integrated Computer Science (CS) education experiences in New York City (NYC) public school classrooms (PreK-12) impact students’ computational-technical skill development, job readiness, career motivations, and measurable abilities to understand, articulate, and solve the underlying phenomena at the center of their models. This effort builds on the partnership’s successes over the past eight years in developing a benchmark Model of restoration-based Science, Technology, Engineering, and Math (STEM) education for urban public schools and achieving relatively broad-based implementation in the nation’s largest public school system. The Billion Oyster Project Curriculum and Community Enterprise for Restoration Science (BOP-CCERS STEM + Computing) curriculum, teacher professional developments, and community engagement programs have reached more than 200 educators and 11,000 students at 124 schools, with 84 waterfront locations and Out of School of Time (OST) programs. The BOP-CCERS Partnership is poised to develop a more refined focus on integrating computer science across the STEM domains; teaching industry-aligned computational methods and tools; and explicitly preparing students from the city’s most under-resourced and underrepresented communities for upwardly mobile careers in NYC’s ever-expanding “digital economy,” in which jobs require computational thinking and an increasing percentage require discreet computer science technical skills. Project Objectives include the following: 1. Computational Thinking (CT) Integration: Integrate computational thinking core practices across existing middle/high school BOP-CCERS STEM curriculum as a means of scaffolding toward long term computer science and computational modeling outcomes. 2. Data Science and Data Analytics: Enabling Researchers to perform interviews with Teachers, students, community members, partners, stakeholders, and Science, Technology, Engineering, and Mathematics (STEM) industry Professionals. Collaborative analysis and data collection were also performed. As a centerpiece, the BOP-CCERS partnership will expand to include a dedicated computer science education partner. New York City Department of Education (NYCDOE), Computer Science for All (CS4ALL) NYC will serve as the dedicated Computer Science (CS) lead, advising the consortium on integration and curriculum development, working in tandem. The BOP-CCERS Model™ also validates that with appropriate application of technical infrastructure, intensive teacher professional developments, and curricular scaffolding, socially connected science learning can be mainstreamed in the nation’s largest urban public school system. This is evidenced and substantiated in the initial phases of BOP-CCERS™. The BOP-CCERS™ student curriculum and teacher professional development have been implemented in approximately 24% of NYC public middle schools, reaching more than 250 educators and 11,000 students directly. BOP-CCERS™ is a fully scalable and transferable educational model, adaptable to all American school districts. In all settings of the proposed Phase IV initiative, the primary beneficiary group will be underrepresented NYC public school students who live in high-poverty neighborhoods and are traditionally underrepresented in the STEM fields, including African Americans, Latinos, English language learners, and children from economically disadvantaged households. In particular, BOP-CCERS Phase IV will explicitly prepare underrepresented students for skilled positions within New York City’s expanding digital economy, computer science, computational information systems, and innovative technology sectors.

Keywords: computer science, data science, equity, diversity and inclusion, STEM education

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Authors: Zhi-Zhong Chen, Jun-Hao Lu, Jr., Shih-Ying Chueh

Abstract:

Based on Unified Theory of Acceptance and Use of Technology (UTAUT), the study investigates people’s intention on using smart phones. The study additionally incorporates two new variables: 'self-efficacy' and 'attitude toward using'. Samples are collected by questionnaire survey, in which 240 are valid. After Correlation Analysis, Reliability Test, ANOVA, t-test and Multiple Regression Analysis, the study finds that social impact and self-efficacy have positive effect on use intentions, and the use intentions also have positive effect on use behavior.

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