Computational Algorithms Research Articles

Exploring Zig-Zag Triangles: H-Coverings and Practical Applications

Graph theory is widely used to analyze the structure models in chemistry, biology, computer science, operations research and sociology. Molecular bonds, species movement between regions, development of computer algorithms, shortest spanning tree in a weighted graphs, aircraft scheduling and exploration of diffusion mechanism are some of these structure models. A computing system C can execute an algorithm A if A is graph-isomorphic to a subgraph of CG. This is equivalent of defining a graph isomorphism between A and subgraph of CG. This research article gives a small description of graph theory applications and one of graph model (zig-zag triangle) to define a suitable computer algorithm. Let Γ be a connected, simple graph with finite vertices v and edges e. A family {Γ1, Γ2, . . . , Γp} ⊂ Γ of subgraphs such that for all e ∈ E, e ∈ Γl, for some l, l = 1, 2, . . . , p is an edge-covering of Γ. If Γl ∼= Γ′, ∀l, then Γ has an Γ′-covering. Graph Γ with Γ′-covering is an (ad, d)-Γ′-antimagic if f : V ∪ E → {1, 2, . . . , |V | + |E|} a bijection exists and the sum over all vertex-weights and edge-weights of Γ′ form a set {ad, ad + d, . . . , ad + (p − 1)d}. The labeling ξ is super for ξ(VΓ) = {1, 2, 3, . . . , |VΓ|} and graph Γ is Γ′-supermagic for d = 0. This manuscript investigates super (ad, d)-Γ′-antimagic labelings of zig-zag triangles for differences d = 1, 2, . . . , 8.

Application of artificial intelligence in music generation: a systematic review

Our analysis explores the benefits of artificial intelligence (AI) in music generation, showcasing progress in electronic music, automatic music generation, evolution in music, contributions to music-related disciplines, specific studies, contributions to the renewal of western music, and hardware development and educational applications. The identified methods encompass neural networks, automation and simulation, neuroscience techniques, optimization algorithms, data analysis, and Bayesian models, computational algorithms, and music processing and audio analysis. These approaches signify the complexity and versatility of AI in music creation. The interdisciplinary impact is evident, extending into sound engineering, music therapy, and cognitive neuroscience. Robust frameworks for evaluation include Bayesian models, fractal metrics, and the statistical creator-evaluator. The global reach of this research underscores AI's transformative role in contemporary music, opening avenues for future interdisciplinary exploration and algorithmic enhancements.

Distributed algorithm for parallel computation of the n queens solutions

The n-queen problem represents a classic challenge in artificial intelligence (AI) research. It involves the placement of n queens on an n x n chessboard, with the objective of ensuring that no queen threatens another. This problem has long been a source of fascination for mathematicians and computer scientists alike due to its inherent complexity. It is a well-known fact that as the value of ‘n’ increases, the problem becomes more challenging and falls into the NP problem class. Given the computational demands of the problem, parallel methods are of critical importance. It is noteworthy that scalable and parallel approaches for the n-queen problem remain to be developed. The majority of existing methods working on graphs attempt to parallelize a recursive sequential algorithm. However, the unpredictable nature of these algorithms makes it challenging to parallelize them on modern computer architectures. Consequently, we have selected an iterative algorithm from the literature in order to facilitate parallelization. This paper presents an innovative approach to parallelization that differs from traditional matrix-based strategies. The n-queen graph is distributed among a network of nodes, ensuring effective load balancing through dynamic partitioning and real-time computation. Our bespoke distributed algorithm, designed for the maximum clique problem on the n-queen graph, operates with true concurrency, thus obviating the necessity for resource or data sharing. The results of our assessment demonstrate that the parallel algorithm outperforms a cutting-edge sequential algorithm in terms of task completion time. The findings demonstrate that the speedups are almost perfect and that the workloads are distributed evenly across the network nodes. Furthermore, the results demonstrate high scalability, with task completion times decreasing as the number of nodes increases.

Validation of reference genes for cardiac RT-qPCR studies spanning the fetal to adult period

Many genes used as internal controls for mRNA expression studies are unstable (change) over development. This study determined an approach to validate reference genes for mRNA studies spanning the fetal period to adulthood in sheep hearts.•We determined the mRNA expression of 12 candidate reference genes (ACTB, GAPDH, H3-3A, HYAL2, PPIA, RNA18S1, RPL32, RPL37A, RPL41, RPLP0, RPS15, and YWHAZ) via RT-qPCR. Per RefFinder, which incorporates computational algorithms by BestKeeper, comparative delta Ct, GeNorm, and NormFinder, RPL32, RPL37A, HYAL2, ACTB and GAPDH were the most stable reference genes, although none were unchanged across all ages.•Systematical calculation of the geometric means of 3 reference genes revealed the combination of HYAL2, RPL32, and RPL37A was unchanged across the 5 fetal, neonatal, and adult ages.•We determined the most stable combination of reference genes for cardiac gene expression studies in sheep from fetus to newborn to adult; these steps are applicable to determine internal controls for mRNA studies in other organs, other species, and periods in which reference gene instability is high.

Is 26S proteasome non-ATPase regulatory subunit 6 a potential molecular target for intrahepatic cholangiocarcinoma?

In this editorial we comment on the article by Tang et al published in the recent issue of World Journal of Hepatology . Drug therapy of intrahepatic cholangiocarcinoma (iCCA) poses an enormous challenge since only a small proportion of patients demonstrate beneficial responses to therapeutic agents. Thus, there has been a sustained search for novel molecular targets for iCCA. The study by Tang et al evaluated the role of 26S proteasome non-ATPase regulatory subunit 6 (PSMD6), a 19S regulatory subunit of the proteasome, in human iCCA cells and specimens. The authors employed clustered regularly interspaced short palindromic repeat (CRISPR) knockout screening technology integrated with the computational CERES algorithm, and analyzed the human protein atlas (THPA) database and tissue microarrays. The results show that PSMD6 is a gene essential for the proliferation of 17 iCCA cell lines, and PSMD6 protein was overexpressed in iCCA tissues without a significant correlation with the clinicopathological parameters. The authors conclude that PSMD6 may play a promoting role in iCCA. The major limitations and defects of this study are the lack of detailed information of CRISPR knockout screening, in vivo experiments, and a discussion of plausible mechanistic cues, which, therefore, dampen the significance of the results. Further studies are required to verify PSMD6 as a molecular target for developing novel therapeutics for iCCA. In addition, the editorial article summarizes the latest advances in molecular targeted drugs and recently emerging immunotherapy in the clinical management of iCCA, development of proteasome inhibitors for cancer therapy, and advantages of CRISPR screening technology, computational methods, and THPA database as experimental tools for fighting cancer. We hope that these comments may provide some clues for those engaged in the field of basic and clinical research into iCCA.

Abstract B013: Hallmarks of progression in early neoantigen-expressing lung adenocarcinoma

Abstract Lung adenocarcinoma (LUAD) is the leading cause of cancer-related mortality, largely due to its high prevalence rate and limited therapeutic options. Immunotherapy remains one of the most promising therapeutic avenues, yet further research is necessary to enhance therapy effectiveness for a broader range of patients. Central to the mechanism of immunotherapy is the detection of tumor neoantigens by cytotoxic T cells, which trigger the elimination of tumor cells. However, it is largely unknown how the early interaction between T cells and neoantigen-expressing tumor cells affects the acquisition of cancer hallmarks and tumor progression. To study this, our lab has developed the iNversion-INduced Joined neoAntigen (NINJA) genetically engineered mouse model in which we can initiate expression of a neoantigen in Kras-mutant, Trp53-null, tdTomato-expressing lung adenocarcinoma cells (KPT-NINJA). This system enables us to analyze tumor initiation, progression, and advanced LUAD development over 20 weeks in both neoantigen-expressing and non-expressing tumors. We defined cancer hallmark acquisition and tumor progression in our model by using a combination of multiplex immunofluorescence and computational algorithms to quantify tumor size, immune infiltration, metastasis, and expression of cancer molecular markers p-ERK and Ki67. We isolated tumor cell and immune cell subsets using neural networks to segment cells and tumor structures and used the colocalization of molecular markers to confirm the identity of various cell types. Our initial data validated that within immunogenic tumors there is an increase of T cell infiltration in tumors and have found immunogenic tumors are associated with overall smaller tumors at early time points. Interestingly, these tumors are associated with a decrease in p-ERK expression, suggesting immune infiltration is negatively correlated with p-ERK expression and tumor size. Together, these data indicate that increased immunogenic pressure may be disrupting early tumor proliferation and hallmark progression, shaping the trajectory of development at a very early stage. We are also correlating other molecular markers of tumor progression with immune infiltration, such as Ki67 expression, which we will colocalize with the other findings to construct a model of how tumors acquire hallmark capabilities from an immunogenic perspective. By isolating the mechanism of early tumor progression in immunogenic contexts, we can begin to predict disease trajectory. This will contribute to a better understanding of the mechanisms behind immune evasion and may uncover crucial insights to broaden immune therapy efficacy. Citation Format: Ishan Bansal, Jennifer Loza, Brian G. Hunt, Kelli Connolly, Srividhya Venkatesan, Nikhil Joshi. Hallmarks of progression in early neoantigen-expressing lung adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr B013.

Metasurfaces‐Enabled Advanced Multidimensional Imaging: Principle and Applications

AbstractThe efficient acquisition of multidimensional information—spatial, polarization, frequency, and phase of optical fields, is a key research focus. Metasurfaces, which are emerging as planar arrays of artificial nanostructures, offer significant advantages in advanced imaging due to their exceptional ability to manipulate electromagnetic waves through subwavelength optical resonators. Meanwhile, the rapidly developing computational imaging algorithms have also greatly enhanced the performance of metasurfaces in multi‐dimensional imaging. In this review, we focus on the research progress in metasurface‐empowered multidimensional imaging, including advanced spatial imaging, compact polarization imaging, high‐resolution spectral imaging, single‐shot phase imaging, and edge imaging. Their basic principles and imaging results will also be presented. Furthermore, a summary of several current challenges and anticipation in various future directions within this field will also be provided.

A Sequential Calibration Approach to Address Challenges of Repeated Calibration of a COVID-19 Model.

Mathematical models served a critical role in COVID-19 decision making throughout the pandemic. Model calibration is an essential, but often computationally burdensome, step in model development that provides estimates for difficult-to-measure parameters and establishes an up-to-date modeling platform for scenario analysis. In the evolving COVID-19 pandemic, frequent recalibration was necessary to provide ongoing support to decision makers. In this study, we address the computational challenges of frequent recalibration with a new calibration approach. We calibrated and recalibrated an age-stratified dynamic compartmental model of COVID-19 in Minnesota to statewide COVID-19 cumulative mortality and prevalent age-specific hospitalizations from March 22, 2020 through August 20, 2021. This period was divided into 10 calibration periods, reflecting significant changes in policies, messaging, and/or epidemiological conditions in Minnesota. When recalibrating the model from one period to the next, we employed a sequential calibration approach that leveraged calibration results from previous periods and adjusted only parameters most relevant to the calibration target data of the new calibration period to improve computational efficiency. We compared computational burden and performance of the sequential calibration approach to a more traditional calibration method, in which all parameters were readjusted with each recalibration. Both calibration methods identified parameter sets closely reproducing prevalent hospitalizations and cumulative deaths over time. By the last calibration period, both approaches converged to similar parameter values. However, the sequential calibration approach identified parameter sets that more tightly fit calibration targets and required substantially less computation time than traditional calibration. Sequential calibration is an efficient approach to maintaining up-to-date models with evolving, time-varying parameters and potentially identifies better-fitting parameter sets than traditional calibration. This study used a sequential calibration approach, which takes advantage of previous calibration results to reduce the number of parameters to be estimated in each round of calibration, improving computational efficiency and algorithm convergence to best-fitting parameter values.Both sequential and traditional calibration approaches were able to identify parameter sets that closely reproduced calibration targets. However, the sequential calibration approach generated parameter sets that yielded tighter fits and was less computationally burdensome.Sequential calibration is an efficient approach to maintaining up-to-date models with evolving, time-varying parameters.

Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review.

To accurately capture an individual's food intake, dietitians are often required to ask clients about their food frequencies and portions, and they have to rely on the client's memory, which can be burdensome. While taking food photos alongside food records can alleviate user burden and reduce errors in self-reporting, this method still requires trained staff to translate food photos into dietary intake data. Image-assisted dietary assessment (IADA) is an innovative approach that uses computer algorithms to mimic human performance in estimating dietary information from food images. This field has seen continuous improvement through advancements in computer science, particularly in artificial intelligence (AI). However, the technical nature of this field can make it challenging for those without a technical background to understand it completely. This review aims to fill the gap by providing a current overview of AI's integration into dietary assessment using food images. The content is organized chronologically and presented in an accessible manner for those unfamiliar with AI terminology. In addition, we discuss the systems' strengths and weaknesses and propose enhancements to improve IADA's accuracy and adoption in the nutrition community. This scoping review used PubMed and Google Scholar databases to identify relevant studies. The review focused on computational techniques used in IADA, specifically AI models, devices, and sensors, or digital methods for food recognition and food volume estimation published between 2008 and 2021. A total of 522 articles were initially identified. On the basis of a rigorous selection process, 84 (16.1%) articles were ultimately included in this review. The selected articles reveal that early systems, developed before 2015, relied on handcrafted machine learning algorithms to manage traditional sequential processes, such as segmentation, food identification, portion estimation, and nutrient calculations. Since 2015, these handcrafted algorithms have been largely replaced by deep learning algorithms for handling the same tasks. More recently, the traditional sequential process has been superseded by advanced algorithms, including multitask convolutional neural networks and generative adversarial networks. Most of the systems were validated for macronutrient and energy estimation, while only a few were capable of estimating micronutrients, such as sodium. Notably, significant advancements have been made in the field of IADA, with efforts focused on replicating humanlike performance. This review highlights the progress made by IADA, particularly in the areas of food identification and portion estimation. Advancements in AI techniques have shown great potential to improve the accuracy and efficiency of this field. However, it is crucial to involve dietitians and nutritionists in the development of these systems to ensure they meet the requirements and trust of professionals in the field.

Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm.

Face detection is a multidisciplinary research subject that employs fundamental computer algorithms, image processing, and patterning. Neural networks, on the other hand, have been widely developed to solve challenges in the domains of feature extraction, pattern detection, and the like in general. The presented study investigates the DNN (deep neural networks) use in the creation of facial detection operating systems. In this study, a novel optimized deep network has been presented to face detection. In this paper, after using some preprocessing stages for contrast enhancement and increasing the data number for the next deep tool, they fed to a bidirectional recurrent neural network (BRNN). The network is optimized via a novel enhanced version of Ebola optimization algorithm to provide far greater accuracy. The suggested procedure is examined on GTFD (Georgia Tech Face Database) and the results indicate that the proposed technique significantly outperforms other comparative methods, attaining an accuracy of 94.3%, a precision of 93.51%, a recall of 94.53%, and an F1-score of 92.47%. Furthermore, the method exhibits resilience against various challenges, achieving an accuracy of 95.6% under occlusions, 96.3% under lighting variations, 94.8% under pose variations, and 92.4% under low resolution conditions. Simulation results depict that the suggested technique gives far greater accuracy in comparison with the other comparative approaches.

Mathematical Optimization of Electric Motor Designs

When designing and improving the performance of electric motors, mathematical optimization is very important. The main goals are usually to make the motors more efficient, lower their costs, and work within certain limits. The main topic of this study is how to improve electric motor designs using advanced optimization methods, such as multi-objective optimization. The study looks at how to use different types of computer algorithms together, like gradient-based methods, genetic algorithms, and particle swarm optimization, to solve difficult design problems. Some of these problems are reducing energy loss, making the best use of materials, and finding the right balance between different performance measures such as speed, power density, and temperature management. Building mathematical models of the motor's physical and functional features is the first step in this method. Then, optimization methods are applied to these models. Finite element analysis (FEA) is used to correctly model the motor's electric behavior. This makes sure that the optimization process considers physical limits and nonlinearities that happen in the real world. The study also looks into how different design factors, like the shape of the motor's core, the way the windings are set up, and the materials used, affect its total performance. The study also looks at the fact that motor design has more than one goal by using Pareto front analysis to find the best ways to balance different goals. This lets people come up with motor designs that are good for speed, efficiency, and cost-effectiveness. Case studies of the design of different kinds of electric motors, such as induction motors, permanent magnet synchronous motors (PMSMs), and brushless DC motors (BLDCs), show that the proposed optimization method works well. The results show that mathematical optimization has the ability to make the planning process a lot better. This could lead to motors that work better, cost less, and are better suited to certain uses. The study ends with a talk of how optimization methods could be used in the future to improve the design of electric motors, especially for new technologies like electric cars and green energy systems.

Abstract 4137570: Atherosclerotic Cardiovascular Disease Risks in Children: An electronic health record data analytics approach

Background and Aims: There is little data on atherosclerotic cardiovascular disease (ASCVD) risk conditions, factors, and tiers in the pediatric population. This study aimed to develop and validate electronic health record (EHR) data analytic algorithms to attain the prevalence of 24 risk conditions, 7 factors, and 4 tiers in children in a rural community. Methods: This study included all the 495,902 subjects from 0 to 18 years at the Geisinger Medical Center from 2004 to 2022. The demographic and clinical data were collected using ICD-10 codes, clinical and laboratory data, and a dual-coded computational algorithm was developed based on the AHA guideline to derive the ASCVD risk conditions, factors, and tiers. To validate the EHR algorithms, the algorithm (rater 1) and a clinician (rater 2) extracted and analyzed the data from 40 randomly selected subjects from this cohort. Kendall’s coefficient of concordance (W) and Gwet’s agreement coefficient were used to evaluate algorithm and clinician agreement within the ASCVD tiers, conditions and factors, respectively. Results: High concordance was observed between algorithmic and clinician raters for risk tiers (W=80%), conditions (80-97%), and factors (49% for blood pressure, and 92% to 97% for the rest of the risk factors). In this cohort, the median age was 3.9 years, 51.0% were male, 80.6% were Non-Hispanic/Latino, and 88.1% were White. Overall, 15.5% (N = 76,945) were at-risk, 7.0% (N = 34,530) at moderate risk, and 4.2% (N = 20,761) at high risk for ASCVD, respectively. The most prevalent risk conditions were obesity (9.1%, N = 45,074), chronic inflammatory conditions (6.8%, N = 33,685), severe obesity (2.7%, N = 13,442), insulin resistance with comorbidities (2.7%, N = 13,397), and hypertension (2.1%, N = 10,436). The most prevalent risk factors were elevated BMI (27.4%, N = 135,904), blood pressure (12.5%, N = 62,015), and fasting lipid profile (8.7%, N = 43,100). Conclusion: Our EHR data analytics algorithm is in high agreement with the clinician rater. The high, moderate, and at-risk ASCVD risk tiers are prevalent in a large contemporary pediatric cohort at Geisinger. Both ASCVD risk conditions and factors are also prevalent, especially obesity, diabetes, hypertension, and hyperlipidemia. These findings suggest the further application and refinement of these algorithms to design innovative approaches for ASCVD risk reduction and prevention in children.

A computational algorithm for optimal design of a bioartificial organ scaffold architecture.

We develop a computational algorithm based on a diffuse interface approach to study the design of bioartificial organ scaffold architectures. These scaffolds, composed of poroelastic hydrogels housing transplanted cells, are linked to the patient's blood circulation via an anastomosis graft. Before entering the scaffold, the blood flow passes through a filter, and the resulting filtered blood plasma transports oxygen and nutrients to sustain the viability of transplanted cells over the long term. A key issue in maintaining cell viability is the design of ultrafiltrate channels within the hydrogel scaffold to facilitate advection-enhanced oxygen supply ensuring oxygen levels remain above a critical threshold to prevent hypoxia. In this manuscript, we develop a computational algorithm to analyze the plasma flow and oxygen concentration within hydrogels featuring various channel geometries. Our objective is to identify the optimal hydrogel channel architecture that sustains oxygen concentration throughout the scaffold above the critical hypoxic threshold. The computational algorithm we introduce here employs a diffuse interface approach to solve a multi-physics problem. The corresponding model couples the time-dependent Stokes equations, governing blood plasma flow through the channel network, with the time-dependent Biot equations, characterizing Darcy velocity, pressure, and displacement within the poroelastic hydrogel containing the transplanted cells. Subsequently, the calculated plasma velocity is utilized to determine oxygen concentration within the scaffold using a diffuse interface advection-reaction-diffusion model. Our investigation yields a scaffold architecture featuring a hexagonal network geometry that meets the desired oxygen concentration criteria. Unlike classical sharp interface approaches, the diffuse interface approach we employ is particularly adept at addressing problems with intricate interface geometries, such as those encountered in bioartificial organ scaffold design. This study is significant because recent developments in hydrogel fabrication make it now possible to control hydrogel rheology and utilize computational results to generate optimized scaffold architectures.

AI solutions for overcoming delays in telesurgery and telementoring to enhance surgical practice and education.

Artificial intelligence (AI) has emerged as a transformative tool in surgery, particularly in telesurgery and telementoring. However, its potential to enhance data transmission efficiency and reliability in these fields remains unclear. While previous reviews have explored the general applications of telesurgery and telementoring in specific surgical contexts, this review uniquely focuses on AI models designed to optimise data transmission and mitigate delays. We conducted a comprehensive literature search on PubMed and IEEE Xplore for studies published in English between 2010 and 2023, focusing on AI-driven, surgery-related, telemedicine, and delay-related research. This review includes methodologies from journals, conferences, and symposiums. Our analysis identified a total of twelve AI studies that focus on optimising network resources, enhancing edge computing, and developing delay-robust predictive applications. Specifically, three studies addressed wireless network resource optimisation, two proposed low-latency control and transfer learning algorithms for edge computing, and seven developed delay-robust applications, five of which focused on motion data, with the remaining two addressing visual and haptic data. These advancements lay the foundation for a truly holistic and context-aware telesurgical experience, significantly transforming remote surgical practice and education. By mapping the current role of AI in addressing delay-related challenges, this review highlights the pressing need for collaborative research to drive the evolution of telesurgery and telementoring in modern robotic surgery.

CRISPRoffT: comprehensive database of CRISPR/Cas off-targets.

The CRISPR (clustered regularly interspaced short palindromic repeats)/Cas (CRISPR-associated protein) programmable nuclease system continues to evolve, with in vivo therapeutic gene editing increasingly applied in clinical settings. However, off-target effects remain a significant challenge, hindering its broader clinical application. To enhance the development of gene-editing therapies and the accuracy of prediction algorithms, we developed CRISPRoffT (https://ccsm.uth.edu/CRISPRoffT/). Users can access a comprehensive repository of off-target regions predicted and validated by a diverse range of technologies across various cell lines, Cas enzyme variants, engineered sgRNAs (single guide RNAs) and CRISPR editing systems. CRISPRoffT integrates results of off-target analysis from 74 studies, encompassing 29 experimental prediction techniques, 368 guide sequences, 226164 potential guide and off-target pairs and 8840 validated off-targets. CRISPRoffT features off-target data from different CRISPR approaches (knockout, base editing and prime editing) applied under diverse experimental conditions, including 85 different Cas/guide RNA (gRNA) combinations used across 34 different human and mouse cell lines. CRISPRoffT provides results of comparative analyses for individual guide sequences, genes, cell types, techniques and Cas/gRNA combinations under different conditions. CRISPRoffT is a unique resource providing valuable insights that facilitate the safety-driven design of CRISPR-based therapeutics, inform experimental design, advance the development of computational off-target prediction algorithms and guide RNA design algorithms.

Exploration and Development of Quantum Computing Algorithms for Optimization, Cryptography, and Machine Learning Applications

Quantum computing, with its potential to revolutionize computation, relies fundamentally on the development of efficient algorithms to leverage its unparalleled processing capabilities. This research delves into the creation, exploration, and refinement of quantum computing algorithms, focusing on their applications in optimization, cryptography, and machine learning. Quantum algorithms such as Shor's and Grover's have demonstrated remarkable advantages in factorization and search problems, while more recent innovations are tackling complex challenges in global optimization and data analysis. By integrating principles of quantum mechanics—such as superposition, entanglement, and interference—these algorithms promise exponential speed-ups over classical counterparts in specific domains. This study critically analyzes existing quantum algorithms, proposes advancements in hybrid quantum-classical frameworks, and explores their implications for practical problem-solving. Through simulations and theoretical evaluations, it aims to bridge the gap between quantum theory and real-world applications, contributing to the evolution of quantum computing as a transformative technology.

TMOD-11. ADVANCED MRI ANALYSIS TO ASSESS CHEMO-RADIATION RESPONSE IN PEDIATRIC EPENDYMOMA MODELS

Abstract Pediatric Ependymoma (EPN) is an aggressive brain tumor for which the benefits of chemo-radiation (CR) therapy have yet to be established. Our team has previously reported behavior patterns of EPN, including high tumor cellularity, cytological anaplasia, high mitotic index, tumor necrosis, and the presence of inflammatory cells such as M2-type macrophages. Recently, we have established advanced MRI protocols and computational algorithms for cell- and vessel-size imaging. The goal of this study is to develop advanced MRI analysis for CR response biomarkers (including tumor size, cell- and vessel-size correlates, and texture analysis) in mice with patient-derived xenografts (PDX) of pediatric EPN. Female severely immune deficient (SCID) mice were used for intracranial orthotopical inoculation of disaggregated tumors from pediatric EPN patients. All multi-parametric MRI sequences (fast spin echo, FLAIR, diffusion weighted, quantitative T2- and r2*-maps) were acquired on a Bruker 9.4 Tesla MRI scanner prior to CR, followed by 2 days and 2 weeks after CR treatment. All radiation treatment was performed using an animal image-guided precision XRAD irradiator (2Gyx5days), using MRI and CT guided EPN localization with added 30 mg/kg 5-fluoracil. All MRI analysis was performed using Bruker ParaVision software and MATLAB based modeling. The sensitivity of T2w-MRI scans was 0.2 mm for the smallest tumor detected; the median tumor volume at baseline was 2.5 mm3. Untreated mice showed rapid progression of tumor growth from small to intermediate to large EPN. The increased tumor sizes in untreated EPN were characterized by low ADC, highly elevated blood vessel density and large tumor cell size. A significant decrease in vessel size density and an increase in inflammatory cells were seen as soon as 2 days after CR therapy. The late response (2 weeks post CR) is characterized by increased ADC values and decreased cell size, resulting in significantly decreased tumor volumes.

Cyber Hacking Breaches Detection Using Machine Learning

Cyber hacking breach prediction is one of the emerging technologies and detecting and predicting breaches through computer algorithms has become a very challenging task To make malware detection more effective, scalable and more efficient than traditional system calls human involvement Machine learning to be used for breach detection and prediction The main goal is a series of cyber hacking attacks each of which will harm the person’s information and financial reputation. Government and non-profit organizations’ data, such as user and company information,, can be compromised, posing a risk to their finances and reputation if they collect information from websites and social networks it can trigger a cyber attack. Organizations such as the healthcare sector are capable of holding sensitive information that must be handled discreetly and securely. Data breaches can lead to identity theft, fraud, and other losses. The findings show that 70% of breaches affect a wide range of organisations, including healthcare providers. The investigation indicates a possible data breach. Due to the heavy usage of computer programs and security on the host and network, there is a risk of data breach. Machine learning can be used to detect these attacks. Research uses machine learning models to protect against web security flaws. The data set is available from the Privacy Rights Clearing House. Teaching employees how to use modern security measures can reduce data breaches. This can help to understand attack detection and data security. Machine learning models such as Random Forest, Decision Tree, k-means and Multi- layer Perceptron are used to predict data violations

IGA Approximations of Elastic Interfaces and their Defects in an Elastic Medium with Couple Stress

AbstractThe objective of this work is to develop and implement a computational algorithm for calculating stress and couple-stress fields induced by bulk and interfacial line defects such as dislocations and generalized disclinations within phase/grain boundaries. The thermodynamic driving forces on line and planar defects, responsible for coupled plasticity and interface motion, fully coupled to stress, couple-stress and applied boundary conditions are also computed. A continuum approach for small deformations is considered following the formulation outlined in Acharya and Fressengeas (Continuum mechanics of the interaction of phase boundaries and dislocations in solids. In: Differential Geometry and Continuum Mechanics, pages 123–165, 2015), extended herein in the thermodynamics to accommodate physically necessary ingredients that arose in the modeling in Zhang and Acharya (J Mech Phys Solids 119:188–223, 2018), Zhang et al. (J Mech Phys Solids 114:258–302, 2018). Constitutive relations are derived from kinematics, balance laws and from the use of the second law of thermodynamics in global form. One of the challenges presented by this approach is the inclusion of couple stresses, Toupin (Arch Rational Mech Anal 17(2):85–112, 1964), and the consequent treatment of 4th order systems arising from the equations of balances of linear and angular momentum. In order to deal with these equations, the classical FEM approach is replaced by iso-geometric analysis (IGA), as proposed by Hughes et al. (Comput Methods Appl Mech Eng 194(39):4135–4195, 2005). Results on stress and couple stress fields of the various defects involved are computed. Further, the coupling of the dislocation density with the eigenwall allows for the capturing of the shear parallel to the grain boundary, which has been observed experimentally and through molecular dynamics.

Emulating Multiparticle Emitters with Pair-Sources: Digital Discovery of a Quantum Optics Building Block

Abstract Linear quantum optics is advancing quickly, driven by sources of correlated photon pairs. Multi-photon sources beyond pairs would be a powerful resource, but are a difficult technology to implement. We have discovered a way in which we can combine multiple pair-sources to act analogous to sources of four, six or even eight correlated photons for the creation of highly entangled quantum states and other quantum information tasks. The existence of such setups is interesting from a conceptual perspective, but also offers a useful abstraction for the construction of more complicated photonic experiments, ranging from state generation to complex quantum networks. We show that even just going from probabilistic two-photon sources to effective four-photon sources allows conceptually new experiments for which no other building principles were known before. The setups which inspired the formulation of these abstract building blocks were discovered by a computer algorithm that can efficiently design quantum optics experiments. Our manuscript demonstrates how artificial intelligence can act as a source of inspiration for the scientific discoveries of new ideas and concepts in physics.
intelligence can act as a source of inspiration for the scientific discoveries of new ideas and concepts in physics.