ANALYZING THE SYSTEM FEATURES, USABILITY, AND PERFORMANCE OF A CONTAINERIZED APPLICATION ON CLOUD COMPUTING SYSTEMS

This study analyzed the system features, usability, and performance of three serverless cloud computing platforms: Google Cloud’s Cloud Run, Amazon Web Service’s App Runner, and Microsoft Azure’s Container Apps. The analysis was conducted on a containerized mobile application designed to track real-time bus locations for San Antonio public buses on specific routes and provide estimated arrival times for selected bus stops. The study evaluated various system-related features, including service configuration, pricing, and memory & CPU capacity, along with performance metrics such as container latency, Distance Matrix API response time, and CPU utilization for each service. Easy-to-use usability was also evaluated by assessing the quality of documentation, a learning curve for be- ginner users, and a scale-to-zero factor. The results of the analysis revealed that Google’s Cloud Run demonstrated better performance and usability when com- pared to AWS’s App Runner and Microsoft Azure’s Container Apps. Cloud Run exhibited lower latency and faster response time for distance matrix queries. These findings provide valuable insights for selecting an appropriate serverless cloud ser- vice for similar containerized web applications

Development of a Non-Reacting LES Solver for Unstructured Grid

The present thesis consists of development of an LES based explicit solver which could simulate non reacting flows. The numerical simulation is carried out using Dynamic k equation subgrid scale model. Along with solving the Navier- Stokes equation a convection diffusion equation for mass fraction is also solved which would correct the equivalent density.The length and time scales for the mesh and simulations are calculated based on the Kolmogorov’s hypothesis and the CFL number is calculated accordingly.An explicit solver is used because of the fact that the calculated CFL number is extremely lower than 1. Two cases were validated using the above developed code, first is the case of an axisymmetric turbulent jet of air entering a quiscent atmosphere and the second one is the case where a variable density fluid(here Helium) entering the same quiscent air. The development of the plumes are captured.The development of the plume structures of both the cases are discussed. The averaged velocity profiles are also discussed. The mean velocity , turbulent fluctuations and Reynolds stresses are plotted. A brief study on the parallelisation technique used in OpenFoam is also done. Finally using the fluctuating data from both simulations the energy spectrum graphs are plotted which ensures that the mesh is suitable for the present study

REDAffectiveLM: Leveraging Affect Enriched Embedding and Transformer-based Neural Language Model for Readers' Emotion Detection

Technological advancements in web platforms allow people to express and share emotions towards textual write-ups written and shared by others. This brings about different interesting domains for analysis; emotion expressed by the writer and emotion elicited from the readers. In this paper, we propose a novel approach for Readers' Emotion Detection from short-text documents using a deep learning model called REDAffectiveLM. Within state-of-the-art NLP tasks, it is well understood that utilizing context-specific representations from transformer-based pre-trained language models helps achieve improved performance. Within this affective computing task, we explore how incorporating affective information can further enhance performance. Towards this, we leverage context-specific and affect enriched representations by using a transformer-based pre-trained language model in tandem with affect enriched Bi-LSTM+Attention. For empirical evaluation, we procure a new dataset REN-20k, besides using RENh-4k and SemEval-2007. We evaluate the performance of our REDAffectiveLM rigorously across these datasets, against a vast set of state-of-the-art baselines, where our model consistently outperforms baselines and obtains statistically significant results. Our results establish that utilizing affect enriched representation along with context-specific representation within a neural architecture can considerably enhance readers' emotion detection. Since the impact of affect enrichment specifically in readers' emotion detection isn't well explored, we conduct a detailed analysis over affect enriched Bi-LSTM+Attention using qualitative and quantitative model behavior evaluation techniques. We observe that compared to conventional semantic embedding, affect enriched embedding increases ability of the network to effectively identify and assign weightage to key terms responsible for readers' emotion detection

MICROCHANNEL OPTIMIZATION FOR HEAT DISSIPATION FROM A SOLID SUBSTRATE

ABSTRACT A computational model was developed to analyze and optimize the convective heat transfer for water flowing through rectangular microchannels fabricated in a silicon substrate. A baseline case was analyzed by solving the nondimensional governing equations. Using a quasi three-dimensional computational model, the velocity and temperature distributions were obtained and the numerical results were then used to determine the overall dimensionless thermal resistance for the convective heat transfer from the substrate to the fluid. To validate the numerical model, the average Nusselt numbers as determined by the numerical model were compared with experimental results available in the literature, for channels with comparable hydraulic diameters. The procedure for arriving at an optimum geometric configuration and arrangement of microchannels on the substrate, subject to given design constraints, so that the thermal resistance is at a minimum, is described and demonstrated using the computational model

Diffusion of hydrogen into and through γ-iron by density functional theory

This study is concerned with the early stages of hydrogen embrittlement on an atomistic scale. We employed density functional theory to investigate hydrogen diffusion through the (100), (110) and (111) surfaces of γ-Fe. The preferred adsorption sites and respective energies for hydrogen adsorption were established for each plane, as well as a minimum energy pathway for diffusion. The H atoms adsorb on the (100), (110) and (111) surfaces with energies of ∼4.06 eV, ∼3.92 eV and ∼4.05 eV, respectively. The barriers for bulk-like diffusion for the (100), (110) and (111) surfaces are ∼0.6 eV, ∼0.5 eV and ∼0.7 eV, respectively. We compared these calculated barriers with previously obtained experimental data in an Arrhenius plot, which indicates good agreement between experimentally measured and theoretically predicted activation energies. Texturing austenitic steels such that the (111) surfaces of grains are preferentially exposed at the cleavage planes may be a possibility to reduce hydrogen embrittlement

Interpretable AI for bio-medical applications

This paper presents the use of two popular explainability tools called Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) to explain the predictions made by a trained deep neural network. The deep neural network used in this work is trained on the UCI Breast Cancer Wisconsin dataset. The neural network is used to classify the masses found in patients as benign or malignant based on 30 features that describe the mass. LIME and SHAP are then used to explain the individual predictions made by the trained neural network model. The explanations provide further insights into the relationship between the input features and the predictions. SHAP methodology additionally provides a more holistic view of the effect of the inputs on the output predictions. The results also present the commonalities between the insights gained using LIME and SHAP. Although this paper focuses on the use of deep neural networks trained on UCI Breast Cancer Wisconsin dataset, the methodology can be applied to other neural networks and architectures trained on other applications. The deep neural network trained in this work provides a high level of accuracy. Analyzing the model using LIME and SHAP adds the much desired benefit of providing explanations for the recommendations made by the trained model

MRI diagnosis in multiligamentous injuries of knee with associated dislocations and neurovasacular sequelae: a retrospective analysis of injury patterns

Background Simultaneous injury of two or more knee ligaments with concurrent tears involving the anterior cruciate and medial collateral ligaments is considered to be associated with femorotibial knee dislocations (KD). The purpose of this study is to characterize multiligamentous knee injury patterns associated with dislocations on MRI and to describe the incidence of their sequelae such as tibial plateau fractures, peroneal nerve injuries, and posterolateral corner (PLC) injuries. Participants and methods After obtaining institutional ethical committee approval, we retrospectively identified 108 multiligamentous knee injuries in 100 patients who met with trauma and were treated at our tertiary care center between April 2014 and December 2018. Descriptive statistics were reported using numbers and percentages for categorical variables in cases of multiligamentous injuries, ipsilateral tibial plateau fractures, ipsilateral femoral fractures, peroneal nerve injury, arterial injury, compartment syndrome, and PLC injuries. Results The most common (39.8%) injury pattern was a combined disruption of the anterior cruciate ligament, posterior cruciate ligament, and PLC. Schenck KD III-M was the most common injury type in KD, constituting 16.7%. Medial-sided injuries were the most common injury patterns seen with KD. There was a significant risk of peroneal nerve injury with lateral-sided injuries. Conclusion KD, though rare, may have devastating clinical sequelae such as compartment syndrome if not recognized and treated. Therefore, it is necessary to recognize imaging findings of femorotibial joint dislocations and associated injuries to the adjacent neurovascular bundles