Evaluation Of Architecture Research Articles

Evaluation of streamflow predictions from LSTM models in water- and energy-limited regions in the United States

The application of Long Short-Term Memory (LSTM) models for streamflow predictions has been an area of rapid development, supported by advancements in computing technology, increasing availability of spatiotemporal data, and availability of historical data that allows for training data-driven LSTM models. Several studies have focused on improving the performance of LSTM models; however, few studies have assessed the applicability of these LSTM models across different hydroclimate regions. This study investigated the single-basin trained local (one model for each basin), multi-basin trained regional (one model for one region), and grand (one model for several regions) models for predicting daily streamflow in water-limited Great Basin (18 basins) and energy-limited New England (27 basins) regions in the United States using the CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) data set. The results show a general pattern of higher accuracy in daily streamflow predictions from the regional model when compared to local or grand models for most basins in the New England region. For the Great Basin region, local models provided smaller errors for most basins and substantially lower for those basins with relatively larger errors from the regional and grand models. The evaluation of one-layer and three-layer LSTM network architectures trained with 1-day lag information indicates that the addition of model complexity by increasing the number of layers may not necessarily increase the model skill for improving streamflow predictions. Findings from our study highlight the strengths and limitations of LSTM models across contrasting hydroclimate regions in the United States, which could be useful for local and regional scale decisions using standalone or potential integration of data-driven LSTM models with physics-based hydrological models.

THE ROLE OF BIG DATA IN SYSTEMS ENGINEERING: A REVIEW

This research review explores the transformative intersection of Big Data and Systems Engineering (SE), a convergence that leverages the vast capabilities of Big Data analytics to enhance the design, analysis, and management of complex systems. As the digital era ushers in unprecedented volumes of data, traditional systems engineering approaches are challenged to adapt, necessitating a paradigm shift towards integrating Big Data technologies. This review draws on recent scholarly contributions to highlight the implications, challenges, and innovative solutions emerging at this intersection. The integration of Big Data into Systems Engineering introduces significant opportunities for innovation. By processing and analyzing vast datasets, systems engineers can uncover hidden patterns and insights, leading to more efficient, reliable, and adaptable systems. However, this integration is not without its challenges. Information security emerges as a paramount concern, with the risk of insider attacks necessitating the development of new architectural solutions. Furthermore, the benchmarking and evaluation of Big Data systems present unique challenges due to the diversity of data and workloads involved. To address these challenges, this review examines several key contributions to the field. It discusses a formal framework for designing information systems that handle heterogeneous data, emphasizing the role of ontological models in separating system architecture from its implementation. The review also highlights the importance of security in Big Data systems, proposing a novel architecture for detecting insider attacks through data replication. Additionally, it explores the development of BigDataBench, a benchmark suite that facilitates the comprehensive evaluation of Big Data systems and architectures. Moreover, the review delves into the design of Big Data analytics architectures, focusing on goal-oriented modeling and the resolution of obstacles to quality goal achievement. It also introduces datar, a unified framework for Big Data Management Systems, showcasing a solution that manages Big Data in a pluggable, automatic, and intelligent manner. In conclusion, the intersection of Big Data and Systems Engineering heralds a new era of system design and management. By addressing the inherent challenges and leveraging innovative solutions, this convergence holds the potential to significantly enhance the capabilities of Systems Engineering, driving forward the development of complex, data-driven systems.
 Keywords: Big Data, Systems Engineering, Information Security, Data Management Systems, Benchmarking Big Data Systems.

Automotive intelligence: Unleashing the potential of AI beyond advance driver assisting system, a comprehensive review

Big data, AI, and machine learning have ushered in a transformative era in computer processing and application. AI's capacity to emulate human cognition spans a spectrum of industries, from rudimentary tasks to intricate decision-making processes. Projections indicate that within a short span, AI technology will bestow computers with human-like capabilities, rendering it indispensable. This advancement has substantially bolstered industry autonomy, fortifying security and safety through the integration of electronics, internet connectivity, advanced algorithms, and cutting-edge processing chips.In this context, Automated Guided Vehicles (AGVs) leverage AI-powered systems and sensors to optimize performance and proactively thwart potential failures. Going beyond Advanced Driver Assistance Systems (ADAS), this review scrutinizes the broader spectrum of AI applications. Specifically, it synthesizes pertinent studies encompassing AI's impact on car emissions, predictive maintenance, security, and the realm of connected vehicles.While AGVs can utilize AI algorithms to chart emission-efficient routes and engineer energy-conservative vehicles, AI, through diverse algorithms, proficiently anticipates component failures, substantially enhancing the cost-effectiveness of predictive maintenance and bolstering vehicle reliability. Moreover, AI integrated with sensor technology safeguards vehicles against a spectrum of threats. Neural networks and machine learning further automate security measures for both cargo and passengers. Concurrently, AI and machine learning fortify traffic control and enhance vehicle communication within connected vehicle frameworks, albeit necessitating fortifications in data security, encryption, and system efficiency.This survey paper comprehensively covers the expanse of automotive AI and machine learning applications, extending beyond ADAS. It underscores that the evolving mobility landscape necessitates a thorough evaluation of network architecture, connectivity, and performance metrics, encompassing AGV technology, networking protocols, and swarm dynamics. In this vein, the paper advocates for experts to address existing literature gaps and proactively enhance automotive systems.

Deep Learning Model for Colon Cancer Classification using InceptionV3

Colon cancer is a significant global public health concern, necessitating an accurate and timely diagnosis for effective treatment. Leveraging advancements in deep learning, this study proposes a novel approach to colon cancer classification using InceptionV3 convolutional neural network architecture. A dataset comprising 1600 colonoscopy images divided into colon_aca (adenocarcinoma) and colon_n (normal) classes was utilized. The model demonstrated promising performance, achieving a training accuracy of 98.86% and a validation accuracy of 99.74% after 100 epochs. This success was accomplished by employing a Stochastic Gradient Descent (SGD) optimizer with a learning rate of 0.0001 and momentum of 0.9, along with categorical cross-entropy loss. Our findings underscore the importance of deep learning models, specifically InceptionV3, in facilitating the precise classification of colon cancer, thus offering a valuable tool for assisting clinicians in early detection and treatment decision-making. Future research may explore the integration of additional clinical data and the evaluation of alternative deep learning architectures to further enhance diagnostic accuracy.

Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model

The process of evaluating the effectiveness, speed, and general capabilities of a computer system that is embedded inside a larger device or system and created for a particular purpose is known as embedded computer performance evaluation. Specialized computing systems called embedded systems are built into a wide range of gadgets, including medical, industrial, , automotive and consumer electronics. In this manuscript, Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model(MEGNN-EC-PEM) is proposed. Initially, the input data is obtained from real-time sensor measurements and system metrics for model training and testing. The input image is pre-processed using Orthogonal Master-slave Adaptive Notch Filter (OMANF) and it removes the noise from the collected data. Then, the pre- processed data are fed to embedded computer performance using Meta-path extracted graph neural network (MEGNN). In general, MEGNN does not express adapting optimization techniques to determine optimal parameters to assure precise embedded computer performance evaluation model. Hence, the Hunter–prey optimization algorithm (HPOA) is used to optimize Meta-path extracted graph neural network which accurately categorized embedded computer performance. Then, the proposed MEGNN-EC-PEM is implemented and the performance metrics like Modelling Accuracy, latency, Throughput, memory cost and Energy Consumption are analyzed. The performance of the MEGNN-EC-PEM approach attains 19.41%, 20.08% and 32.57% higher modelling accuracy, 22.41%, 23.08% and 24.57% lower latency and 23.01%, 23.08% and 24.07% higher throughput when analyzed through existing techniques like a state-based modelling approach for effective performance evaluation of embedded system architectures at transaction level (SMA-EPE-ESAT), Evaluating the performance of pre-trained convolutional neural network for audio classification on embedded schemes for anomaly detection in smart cities (PCNN-ACES-ADSC) and Animal behaviour classification via deep learning on embedded systems (ABC-VDL-ES) methods respectively.

Comparative Evaluation of Antibacterial Efficacy, Molecular Docking of Ethanolic Extract of Blackseed, Seaweed and Calcium Hydroxide Intracanal Medicament with Enterococcus Faecalis Antigens

ABSTRACT Aim: To evaluate the inhibitory effect of ethanolic extract blackseed, seaweed, and calcium hydroxide intracanal medicament with Enterococcus faecalis biofilm. To study the binding interaction between the active components of blackseed and seaweed against the enterococcal surface protein of (E. faecalis) by molecular docking. Materials and Methods: The ethanolic extracts of blackseed and seaweed were prepared using the Soxhlet apparatus. They were divided into three groups, namely, |Group I: Calcium hydroxide, Group II: Blackseed, and Group III: Seaweed. The antibacterial activity of the three groups was detected employing various concentrations ranging from 250, 125, and 62.5 μg/ml and based on the zone of inhibition. The inhibitory potential of medicaments to inhibit E. faecalis growth at various stages and kinetics plate were assessed following biofilm architecture evaluation by crystal violet biofilm assay. With the Swissdock suite, the molecular docking procedure was carried out. PyMOL version 4.1.5 was the program used for visualization. Since enterococcal surface protein (Esp) is primarily involved in the formation of biofilms, it was chosen as the target protein of E. faecalis. Based on their chromatographic investigations, Group II Thymoquinone (TQ) and Group III Ledenoxide were chosen as ligands. Results: The percentage of inhibition of E. faecalis biofilm was analyzed as statistically significant observed within groups. On post-hoc analysis, significant differences were present between the groups (P < 0.05). Molecular docking reveals binding energies of thymoquinone (Group II) and ledenoxide (Group III) against the enterococcal surface protein of E. faecalis were −6.90 Kcal/mol and −6.44 Kcal/mol, respectively. Conclusion: Compared to seaweed, black seed extract exhibited higher antibacterial activity against the E. faecalis biofilm in microbial inhibition and molecular interaction.

Rice Grains Detection, Classification, and Quality Prediction Using Deep Learning

Abstract: Rice, as the most consumed food worldwide, faces a continual demand, necessitating rigorous quality inspection for both local consumption and international trade. Manual quality assessment methods are fraught with issues such as time consumption, high costs, and error susceptibility. This research paper introduces an innovative solution employing Deep Convolutional Neural Networks (CNNs) to automate rice grain detection, classification, and quality prediction from scanned images. The methodology integrates comprehensive image pre-processing and quality assessment techniques, encompassing image acquisition, pre-processing, CNN architecture construction, model training, and evaluation. Notably, it includes noise removal and segmentation to optimize input images. The grain samples undergo image processing using MATLAB, where an algorithm is applied to analyze their features. Classification is then performed based on the color, shape, and size characteristics of the grains. Various image features are extracted such as centroids, mean intensity, chalkiness, and perimeter further aid in quality assessment. Performance evaluation of the trained model is conducted through confusion matrix analysis and computation of classification metrics like accuracy, specificity, sensitivity, precision, recall, and F1-score. This methodology offers a robust framework for automated rice grain classification and quality prediction, thereby significantly enhancing efficiency and reliability in the global rice industry

A practical evaluation of online self-assisted previewing architecture on rain classroom for biochemistry lab courses

In the aftermath of the COVID pandemic, we have been promoting online learning as a new learning tool. This study investigated the design of the optimal structure of online self-assisting coursework for laboratory courses that can assist students to better prepare for hands-on experiments. Undergraduate students from Liaoning University, who took the “Biochemistry Experiments” laboratory class, were asked to evaluate a self-learning segment offered on the Rain Classroom, an online platform that was provided to students before each in-person lab session as a preparation. Survey questions consisting of both multiple-choice and free-response questions were carefully designed to help evaluate students' sentiment toward the online previewing platform. Cramér's V correlation was used to determine the correlation between multiple choice answers, WordCloud and topic modeling analysis were conducted after textmining to analyze the emotions students express through the free response questions, and an analysis of variance (ANOVA) was conducted to evaluate the relationship between students' usage of the online interface and overall performance in class. The majority of students believe the previewing of the content on Rain Classroom to be helpful for their upcoming hands-on practice in the in-person laboratory course. At the same time, helpful insights were drawn from students' feedback through sentiment analysis from their entries in free-response questions in the survey. We showed that having online tools to pre-expose students to the laboratory-related material is helpful in preparing the students for hands-on laboratory courses. At the same time, we also offer a few suggestions that may guide the design of future online resources for laboratory classes such as involving multi-modality media to improve engagement and perfecting the interactive feature to increase its usage by students.

Predicting Flight Delays with Machine Learning: A Case Study from Saudi Arabian Airlines

Flight delays are a major concern for both travelers and airlines, with significant financial and reputational consequences. Accurately predicting flight delays is crucial for enhancing customer satisfaction and airline revenues. In this paper, we leverage the power of artificial intelligence and machine learning techniques to build a framework for accurately predicting flight delays. To achieve this, we collected flight information from September 2017 to April 2023, along with weather data, and performed extensive feature engineering to extract informative features to train our model. We conduct a comparative analysis of various popular machine learning architectures with distinctive characteristics, aiming to determine their efficacy in achieving optimal accuracy on our newly proposed dataset. Based on our evaluation of various architectures, our findings demonstrate that CatBoost outperformed the others by achieving the highest test accuracy and the lowest error rate in the challenging use case of Saudi Arabia. Moreover, to simulate real-world scenarios, our framework evaluates the best-performing model that has been selected for deployment in a web application, which provides users with the ability to accurately forecast flight delays and offers a user-friendly dashboard with valuable insights and analysis capabilities.

Polysomnographic Characteristics of Sleep Architecture in Children With Obstructive Sleep Apnea.

The conventional measure of sleep fragmentation is via polysomnographic evaluation of sleep architecture. Adults with OSA have disruption in their sleep cycles and spend less time in deep sleep stages. However, there is no available evidence to suggest that this is also true for children and published results have been inconclusive. To determine polysomnographic characteristics of sleep architecture in children with OSA and investigate effects relative to OSA severity. Overnight polysomnograms (PSG) of children referred for suspected OSA were reviewed. Subjects were classified by apnea hypopnea index (AHI). PSG parameters of sleep architecture were recorded and analyzed according to OSA severity. Two hundred and eleven children were studied (median age of 7.0 years, range 4-10 years) Stage N1 sleep was longer while stage N2 sleep and REM sleep was reduced in the OSA group when compared to those without OSA (6.10 vs 2.9, P < .001; 42.0 vs 49.7, P < .001; 14.0 vs 15.9, P = .05). The arousal index was also higher in the OSA group (12.9 vs 8.2, P < .001). There was a reduction in sleep efficiency and total sleep time and an increase in wake after sleep onset noted in the OSA group (83.90 vs 89.40, P = .003; 368.50 vs 387.25, P = .001; 40.1 ± 35.59 vs 28.66 ± 24.14, P = .007; 29.00 vs 20.50; P = .011). No significant difference was found in N3 sleep stage (33.60 vs 30.60, P = .14). We found evidence that children with OSA have a disturbance in their sleep architecture. The changes indicate greater sleep fragmentation and more time spent in lighter stages of sleep. Future research is needed and should focus on more effective methods to measure alterations in sleep architecture.

Optimized Deep Learning Algorithm for Predicting Pulmonary Nodules in CT Images

Lung cancer remains a significant global health challenge, demanding early detection for improved patient outcomes. In recent years, deep learning, notably Convolutional Neural Networks (CNNs), has emerged as a potent tool for lung cancer detection and diagnosis from medical imaging data. This research offers an extensive review of CNN-based approaches for lung cancer detection, highlighting their strengths, limitations, and potential clinical impact. The study discusses the methodology, covering data collection, preprocessing, model architecture selection, training, evaluation, and validation, alongside future directions and clinical implications. CNNs offer researchers and healthcare professionals avenues to augment early detection, personalized treatment planning, and ultimately, enhance patient care in lung cancer management. Through rigorous development and evaluation, CNN models trained on diverse datasets of chest X-rays or CT scans have demonstrated remarkable accuracy in identifying suspicious lung lesions indicative of cancer, often outperforming conventional methods. The proposed study utilizes the GoogleNet (Inception v1) CNN model to detect lung cancer. The performance of GoogleNet improved the accuracy of detection by approximately 4.29% compared to existing methods.

Advanced approach for Moroccan administrative documents digitization using pre-trained models CNN-based: character recognition

In the digital age, efficient digitization of administrative documents is a real challenge, particularly for languages with complex scripts such as those used in Moroccan documents. The subject matter of this article is the digitization of Moroccan administrative documents using pre-trained convolutional neural networks (CNNs) for advanced character recognition. This research aims to address the unique challenges of accurately digitizing various Moroccan scripts and layouts, which are crucial in the digital transformation of administrative processes. Our goal was to develop an efficient and highly accurate character recognition system specifically tailored for Moroccan administrative texts. The tasks involved comprehensive analysis and customization of pre-trained CNN models and rigorous performance testing against a diverse dataset of Moroccan administrative documents. The methodology entailed a detailed evaluation of different CNN architectures trained on a dataset representative of various types of characters used in Moroccan administrative documents. This ensured the adaptability of the models to real-world scenarios, with a focus on accuracy and efficiency in character recognition. The results were remarkable. DenseNet121 achieved a 95.78% accuracy rate on the Alphabet dataset, whereas VGG16 recorded a 99.24% accuracy on the Digits dataset. DenseNet169 demonstrated 94.00% accuracy on the Arabic dataset, 99.9% accuracy on the Tifinagh dataset, and 96.24% accuracy on the French Special Characters dataset. Furthermore, DenseNet169 attained 99.14% accuracy on the Symbols dataset. In addition, ResNet50 achieved 99.90% accuracy on the Character Type dataset, enabling accurate determination of the dataset to which a character belongs. In conclusion, this study signifies a substantial advancement in the field of Moroccan administrative document digitization. The CNN-based approach showcased in this study significantly outperforms traditional character recognition methods. These findings not only contribute to the digital processing and management of documents but also open new avenues for future research in adapting this technology to other languages and document types.

LPMSAEF: Lightweight process mining-based software architecture evaluation framework for security and performance analysis

The article discusses the need for a lightweight software architecture evaluation framework that can address practitioners' concerns. Specifically, the proposed framework uses process mining and Petri nets to analyze security and performance in software development's early and late stages. Moreover, the framework has been implemented in six case studies, and the results show that it is a feasible and effective solution that can detect security and performance issues in complex and heterogeneous architecture with less time and effort. Furthermore, the article provides a detailed explanation of the framework's features, factors, and evaluation criteria. Additionally, this article discusses the challenges associated with traditional software architecture documentation methods using Unified Modeling Language diagrams and the limitations of code alone for creating comprehensive Software Architecture models. Various methods have been developed to extract implicit Software Architecture from code artifacts, but they tend to produce code-oriented diagrams instead of Software Architecture diagrams. Therefore, to bridge the model-code gap, the article proposes a framework that considers existing Software Architecture in the source code as architectural components and focuses on Software Architecture behaviors for analyzing performance and security. The proposed framework also suggests comparing Software Architecture extracted by different Process Mining algorithms to achieve consensus on architecture descriptions, using visualizations to understand differences and similarities. Finally, the article suggests that analyzing the previous version of a system's Software Architecture can lead to improvements and deviations from planned Software Architecture can be detected using traceability approaches to aid software architects in detecting inconsistencies.

A Distributed Cloud Architecture Based on General De Bruijn Overlay Network

Distributed cloud systems enable the distribution of computing resources across various geographical locations. While offering benefits like accelerated content delivery, the scalability and coherence maintenance of these systems pose significant challenges. Recent studies reveal shortcomings in existing distributed system schemes to meet modern cloud application demands and maintain coherence among heterogeneous system elements. This paper proposes a service-oriented network architecture for distributed cloud computing networks. Using a De Bruijn network as a software-defined overlay network, the architecture ensures scalability and coherence. Through service-based addressing, requests are issued to designated service address bands, streamlining service discovery. The architecture's evaluation through extensive simulations showcases sustainable scalability and inherent load-balancing properties. The paper concludes with insights into future research directions, emphasizing the extension of the proposed architecture to emerging distributed cloud use cases and decentralized security.

Low-cost architecture performance evaluation strategy based on pixel difference degree contrast measurement

The time and effort required to manually design deep neural architectures is extremely high, which has led to the development of neural architecture search technology as an automatic architecture design method. However, the neural architecture search convergence process is slow and expensive, and the process requires training a large number of candidate architectures to get the final result. If the final accuracy of an architecture can be predicted from its initial state, this problem can be greatly alleviated. Therefore, this paper proposes a low-cost architecture performance evaluation strategy based on pixel difference degree contrast measurement, which takes 1) the difference matrix value between the feature map generated in the untrained architecture and the original image, and 2) the predicted accuracy of the neural network as evaluation indices. A new multi-index weight comprehensive measurement strategy was introduced to comprehensively score the multi-index, the real architecture performance can be approximately represented by score, which greatly reduces the cost of architecture evaluation. The experimental show that the proposed scoring strategy is highly correlated with real architecture accuracy. In the practical engineering application research, this strategy can search a high-performance architecture with an accuracy of 96.2% within 343.3 s, which proves that the proposed strategy can significantly improve the search efficiency in practical applications, reduce the subjectivity of artificial architecture design, and promote the application of practical time-consuming projects.

Comprehensive Survey of Cutting-Edge Methods for Software Architecture Evaluation

The crucial responsibility of assessing software architecture is of utmost importance in ensuring that a software system conforms to superior qualities. It is a crucial tool for cutting expenses and labor during the course of the software development lifecycle. The main goal of software architecture evaluation is to provide reliable methods for determining and improving the quality characteristics that are innate in software. This study is deeply devoted to investigating the wide range of techniques used in software architecture assessment. One of the main focuses of these evaluation techniques is scenario-based assessments, which offer a comprehensive picture of the software's behavior under different circumstances. Within the scope of this study, a thorough analysis is carried out on eighteen different methods. Thirteen of these fall into the category of early evaluation techniques, which are positioned to detect and address architectural problems at the outset of development. The other five strategies are classified as late assessment techniques and focus on validating and optimizing the software architecture in the latter stages of the development process.

Exploring biometric domain adaptation in human action recognition models for unconstrained environments

AbstractIn conventional machine learning (ML), a fundamental assumption is that the training and test sets share identical feature distributions, a reasonable premise drawn from the same dataset. However, real-world scenarios often defy this assumption, as data may originate from diverse sources, causing disparities between training and test data distributions. This leads to a domain shift, where variations emerge between the source and target domains. This study delves into human action recognition (HAR) models within an unconstrained, real-world setting, scrutinizing the impact of input data variations related to contextual information and video encoding. The objective is to highlight the intricacies of model performance and interpretability in this context. Additionally, the study explores the domain adaptability of HAR models, specifically focusing on their potential for re-identifying individuals within uncontrolled environments. The experiments involve seven pre-trained backbone models and introduce a novel analytical approach by linking domain-related (HAR) and domain-unrelated (re-identification (re-ID)) tasks. Two key analyses addressing contextual information and encoding strategies reveal that maintaining the same encoding approach during training results in high task correlation while incorporating richer contextual information enhances performance. A notable outcome of this study is the comprehensive evaluation of a novel transformer-based architecture driven by a HAR backbone, which achieves a robust re-ID performance superior to state-of-the-art (SOTA). However, it faces challenges when other encoding schemes are applied, highlighting the role of the HAR classifier in performance variations.

Sixth Generation 6G to the Waying Seventh 7G Wireless Communication Visions and Standards, Challenges, Applications

The increasing need for next-generation wireless networks becomes apparent as the Internet of Everything (IoE) gains prominence in smart services, projecting widespread popularity in the future. While sixth generation (6G) networks have the capability to support a diverse range of IoE services, their potential limitations in meeting the demands of innovative applications prompt consideration for seventh generation (7G) wireless systems. This article seeks to compare the characteristics of 6G and the planned 7G wireless systems. The commercial development of fifth generation (5G) mobile communication systems is currently underway, introducing new services and enhancing user experiences. Despite these advancements, 5G encounters challenges that require on-going improvements. With the International Telecommunication Union Radio communication Sector (ITU-R) actively envisioning 6G and anticipating a consensus on 7G by October 2024, numerous unresolved questions persist in global discussions. This paper delivers a comprehensive overview of the current understanding of 7G, exploring the vision, technical requirements, and application possibilities. It presents a critical evaluation of the 7G network architecture and essential technologies. Furthermore, the article provides an in-depth examination of advanced 7G verification platforms and existing test beds, unveiling these aspects for the first time. Future research directions and lingering issues are emphasized to contribute to the on-going global discourse on 7G networks. Discussions on lessons learned from 7G networks culminate in the suggestion that 7G systems signify the pinnacle of mobile communication technology. The ongoing research on 7G, an intelligent cellular technology, holds promise for the future of wireless communication.

Evaluation of sleep habits and sleep architecture in children referred to the sleep ward of Qazvin children's hospital, Qazvin, Iran during 2016-2022

Evaluation of sleep habits and sleep architecture in children referred to the sleep ward of Qazvin children's hospital, Qazvin, Iran during 2016-2022

Abstract PR016: Topology of the tumor microenvironment: Integrating imaging, modeling, and topological data analysis

Abstract Cancer evolution involves multiple stochastic processes across many scales. In addition to genomic and transcriptomic changes, hallmarks of cancer within solid tumors include the malignant co-evolution of the tumor microenvironment. Although the evaluation of tissue architecture plays a key role in pathologist staging, the automated extraction of additional structural features remains far from translation to the clinic. However, one particularly effective novel methodology for extracting patterns from these clinical images is topological data analysis(TDA). TDA is a field that studies the shape of data and its connectedness using concepts from geometric and algebraic topology. Recent work using topological data analysis has allowed the extraction of image features. Such features have been associated with clinical outcomes in both the analysis of H&amp;E images in prostate cancer and CT images in lung cancer. We set out to question how the topological features of tissues, including tumor and stromal composition, evolve during treatment and between original sites and metastasis. To analyze this we use a combination of tissue sections, imaging, and cell-automata models and persistent homology and cubical persistence techniques from TDA. We apply certain topological methods for the first time to tissue images (H&amp;E) and to the output of spatial simulations. We focus specifically on analyzing the evolution of topological features in ovarian and lung cancer and compare our features to 2D and 3D simulations (HAL) with varying game theoretical interactions as well as comparing features across tissues from matched primary and metastatic sites. We find that several topological features are conserved between primary and metastatic sites and some feature evolution is replicated in simulation. Citation Format: Rowan Barker-Clarke, Jason M. Gray, Jacob G. Scott. Topology of the tumor microenvironment: Integrating imaging, modeling, and topological data analysis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr PR016.