Modeling of mechanical behavior of cork in compression

The present work consists of a contribution in modeling the mechanical behavior of cork in compression. For this purpose, compression tests are performed in the non-radial direction on high density reproduction cork samples. Cork shows stress-strain curves, typical of cellular materials, characterized by an elastic slope followed by an important plateau corresponding to buckling of cells; and finally hardening due to the densification of the material. Two behavior models are proposed to represent this behavior. A trilinear model in which each slope represents one of the three domains and whose parameters are identified directly from the stress-strain curves. A more nonlinear model corresponding to a third-order polynomial whose parameters are identified by means of a polynomial regression. Test-model comparisons reveal little relevance of the results given by the trilinear model whereas a very good consistency is observed for the results given by the nonlinear model

Explainable adaptation of time series forecasting

A time series is a collection of data points captured over time, commonly found in many fields such as healthcare, manufacturing, and transportation. Accurately predicting the future behavior of a time series is crucial for decision-making, and several Machine Learning (ML) models have been applied to solve this task. However, changes in the time series, known as concept drift, can affect model generalization to future data, requiring thus online adaptive forecasting methods. This thesis aims to extend the State-of-the-Art (SoA) in the ML literature for time series forecasting by developing novel online adaptive methods. The first part focuses on online time series forecasting, including a framework for selecting time series variables and developing ensemble models that are adaptive to changes in time series data and model performance. Empirical results show the usefulness and competitiveness of the developed methods and their contribution to the explainability of both model selection and ensemble pruning processes. Regarding the second part, the thesis contributes to the literature on online ML model-based quality prediction for three Industry 4.0 applications: NC-milling, bolt installation in the automotive industry, and Surface Mount Technology (SMT) in electronics manufacturing. The thesis shows how process simulation can be used to generate additional knowledge and how such knowledge can be integrated efficiently into the ML process. The thesis also presents two applications of explainable model-based quality prediction and their impact on smart industry practices

Explainable Adaptive Tree-based Model Selection for Time Series Forecasting

Tree-based models have been successfully applied to a wide variety of tasks, including time series forecasting. They are increasingly in demand and widely accepted because of their comparatively high level of interpretability. However, many of them suffer from the overfitting problem, which limits their application in real-world decision-making. This problem becomes even more severe in online-forecasting settings where time series observations are incrementally acquired, and the distributions from which they are drawn may keep changing over time. In this context, we propose a novel method for the online selection of tree-based models using the TreeSHAP explainability method in the task of time series forecasting. We start with an arbitrary set of different tree-based models. Then, we outline a performance-based ranking with a coherent design to make TreeSHAP able to specialize the tree-based forecasters across different regions in the input time series. In this framework, adequate model selection is performed online, adaptively following drift detection in the time series. In addition, explainability is supported on three levels, namely online input importance, model selection, and model output explanation. An extensive empirical study on various real-world datasets demonstrates that our method achieves excellent or on-par results in comparison to the state-of-the-art approaches as well as several baselines.Comment: Accepted and presented at ICDM 202

Listening comprehension problems and strategies among Kurdish EFL learners

This quantitative research investigated listening comprehension problems and strategies usage among Kurdish EFL undergraduates. Additionally, it tested the relationship between the learners’ listening problems and strategy usage. More specifically, the listening problems included input, context, process, affect and task problems while the listening strategies consisted of cognitive, meta-cognitive and socio-affective strategies. Through a survey, a questionnaire was used to elicit data from 165 randomly selected undergraduates in Iraqi-Kurdistan universities. The findings showed that the learners suffered from input and context listening comprehension problems. Meta-cognitive strategy was the major listening strategy used. The relationship between listening problems and strategy usage among the learners was significantly negative and negligible, r= -.186, p < .05. The findings generally imply that it is important for instructors of a second language to take note of the different listening problems that exist among listeners so as to enable them to apply the appropriate strategies

The Effect of the Physical Environment on Social Interaction: The Case of Educational Campuses

Social interaction is an essential component of the collegiate experience. Studies presume that the provision of appropriate space for interaction enhances its possibility. This study aims to reveal the effect of physical urban design elements on the quantity of social interaction between students on their university campuses. It takes place on the Faculty of Engineering campus in Alexandria University. Student questionnaires investigate students’ sense of ownership, their satisfaction with their current campus and the features that would make them spend more time on campus. Furthermore, they reveal common movement patterns around campus throughout a standard day, and highlight the common gathering spaces. The on-site observation investigates the urban design components of these spaces. Based on the space syntax theory, which proposes that movement can be a good predictor for social encounters, the results are compared with integration and choice analyses maps along with the physical setting for each gathering space. From this analysis, thephysical elements with the highest influence on social interaction are determined and modifications are recommended to the current campus setting. Students were found to be walking the routes that showed high integration values in the space syntax analysis and that these routes were also used for their gatherings. The result will help design better campuses in the future or alter current designs to enhance social interaction. Further research seeks to validate the results by applying the study on more campuses in Egyptian universities

Enhancing Artificial Intelligence in Advanced Database Systems for Baghdad's Urban Transportation Management

The issue of transportation in Iraqi cities, particularly Baghdad, is multifaceted and intricate, largely due to the horizontal expansion that puts pressure on services and exacerbates traffic congestion and bottlenecks. The ever-growing population and lack of regulation regarding vehicle imports further compound the situation, making urban transportation in Iraq a challenging problem to address. The digital revolution has ushered in a new era of civilization, marked by significant advancements in communication technology and information systems. This transformation has led to the widespread adoption of communication and information technology in various sectors, including transportation management. However, the successful implementation of digital transportation initiatives requires the collection and organization of extensive data, which is then used to develop graphic and visual software technology, create communication networks, and define new functions for visual and audio files. In this digital age, transportation management has evolved into an interdisciplinary field that leverages the insights from various scientific domains to develop and produce digital maps. The primary objective of digital organization is to recreate reality in a virtual environment, enabling the manipulation of images and the seamless integration of locations beyond geographical boundaries

Prevalence of Depression among Sudanese Patients with type-2 Diabetes Mellitus

Background: Both diabetes mellitus and depression are common disorders, and when coexist; they lead to poor glycemic control that may ultimately increase the risk of both micro and macrovascular complications. In Sudan, few data are available regarding prevalence of depression among subjects with type -2 diabetes mellitus.Objectives: The aim of the study is to determine the prevalence of depression among Sudanese patients with type-2 diabetes mellitus.Materials and Methods: A cross-sectional descriptive study, carried out among Sudanese patients with type-2 diabetes mellitus who attended medical insurance clinic in Omdurman. The patients gave consent and HADS questionnaire was filled.Results: The study enrolled 400 patients with type 2 diabetes mellitus, 176 (44%) of them had depression. Among those with depression, 52.3% had mild depression, 29.5% and 18.2% of them, had severe and moderate depression, respectively. Sixty three percent of the study group were female. Their ages range from 30-79 years with a mean of 56.6 ±13.The average duration of diabetes was 10.3 years. 76% of patients were physically inactive and 16 (4%) of them were smokers. Fifty six (14%) had family history of psychiatric disorders, 170 (42.5%) of them showed lack of enjoyment, 77 (19.3%) of them lacked laughing, 81(20.3%) of them lost sensation of happiness, 68(17%) of them lost energy, 238 (59.5%) of them neglected their external appearance, 76 (19%) of them showed no enjoyment, and 62 (15.5%) of them lost enjoying reading or watching television.Conclusion: Depression is common among Sudanese patients with type -2 diabetes mellitus, therefore screening for depression should be part of routine clinical evaluation of these patients.Key words: Sudan, diabetes mellitus, depression

Explainable online ensemble of deep neural network pruning for time series forecasting

Both the complex and evolving nature of time series data make forecasting among one of the most challenging tasks in machine learning. Typical methods for forecasting are designed to model time-evolving dependencies between data observations. However, it is generally accepted that none of them are universally valid for every application. Therefore, methods for learning heterogeneous ensembles by combining a diverse set of forecasters together appears as a promising solution to tackle this task. While several approaches in the context of time series forecasting have focused on how to combine individual models in an ensemble, ranging from simple and enhanced averaging tactics to applying meta-learning methods, few works have tackled the task of ensemble pruning, i.e. individual model selection to take part in the ensemble. In addition, in classical ML literature, ensemble pruning techniques are mostly restricted to operate in a static manner. To deal with changes in the relative performance of models as well as changes in the data distribution, we employ gradient-based saliency maps for online ensemble pruning of deep neural networks. This method consists of generating individual models’ performance saliency maps that are subsequently used to prune the ensemble by taking into account both aspects of accuracy and diversity. In addition, the saliency maps can be exploited to provide suitable explanations for the reason behind selecting specific models to construct an ensemble that plays the role of a forecaster at a certain time interval or instant. An extensive empirical study on many real-world datasets demonstrates that our method achieves excellent or on par results in comparison to the state-of-the-art approaches as well as several baselines. Our code is available on Github (https://github.com/MatthiasJakobs/os-pgsm/tree/ecml_journal_2022)

Dynamic characteristics of post-tensioned prestressed concrete beams with openings.

Dept. of Civil and Environmental Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1987 .C445. Source: Masters Abstracts International, Volume: 40-07, page: . Thesis (M.A.Sc.)--University of Windsor (Canada), 1987