A research study into beginning German students\u27 individual and group processing of written texts

This work examines the effect of rereading a text as a group effort and individually on beginning German students. The study included eleven participants, all in their second semester of learning German. All participants read a text written in German. The text selected is from the textbook (Kontakte-4th edition) used at the University of Tennessee, Knoxville. After reading the text once and writing an individual recall, the participants were divided into two main groups: One group individually reread the text while the other group was divided into two subgroups where the text was discussed instead of rereading it individually. All participants then had to write a second recallBoth recalls were scored. The scores were then normalized, since the study focused on determining the percentages of improvement in the students\u27 performance. The scores were then analyzed by running a t-test on the normalized scores of the two main groups. The t-test indicated that statistically there was no significant difference between the individual rereading group and the discussion group. The results showed however that rereading, both in groups and individually improved the students\u27 reading comprehension

Nature-Inspired Topology Optimization of Recurrent Neural Networks

Hand-crafting effective and efficient structures for recurrent neural networks (RNNs) is a difficult, expensive, and time-consuming process. To address this challenge, this work presents three nature-inspired (NI) algorithms for neural architecture search (NAS), introducing the subfield of nature-inspired neural architecture search (NI-NAS). These algorithms, based on ant colony optimization (ACO), progress from memory cell structure optimization, to bounded discrete-space architecture optimization, and finally to unbounded continuous-space architecture optimization. These methods were applied to real-world data sets representing challenging engineering problems, such as data from a coal-fired power plant, wind-turbine power generators, and aircraft flight data recorder (FDR) data. Initial work utilized ACO to select optimal connections inside recurrent long short-term memory (LSTM) cell structures. Viewing each LSTM cell as a graph, ants would choose potential input and output connections based on the pheromones previously laid down over those connections as done in a standard ACO search. However, this approach did not optimize the overall network of the RNN, particularly its synaptic parameters. I addressed this issue by introducing the Ant-based Neural Topology Search (ANTS) algorithm to directly optimize the entire RNN topology. ANTS utilizes a discrete-space superstructure representing a completely connected RNN where each node is connected to every other node, forming an extremely dense mesh of edges and recurrent edges. ANTS can select from a library of modern RNN memory cells. ACO agents (ants), in this thesis, build RNNs from the superstructure determined by pheromones laid out on the superstructure\u27s connections. Backpropagation is then used to train the generated RNNs in an asynchronous parallel computing design to accelerate the optimization process. The pheromone update depends on the evaluation of the tested RNN against a population of best performing RNNs. Several variations of the core algorithm was investigated to test several designed heuristics for ANTS and evaluate their efficacy in the formation of sparser synaptic connectivity patterns. This was done primarily by formulating different functions that drive the underlying pheromone simulation process as well as by introducing ant agents with 3 specialized roles (inspired by real-world ants) to construct the RNN structure. This characterization of the agents enables ants to focus on specific structure building roles. ``Communal intelligence\u27\u27 was also incorporated, where the best set of weights was across locally-trained RNN candidates for weight initialization, reducing the number of backpropagation epochs required to train each candidate RNN and speeding up the overall search process. However, the growth of the superstructure increased by an order of magnitude, as more input and deeper structures are utilized, proving to be one limitation of the proposed procedure. The limitation of ANTS motivated the development of the continuous ANTS algorithm (CANTS), which works with a continuous search space for any fixed network topology. In this process, ants moving within a (temporally-arranged) set of continuous/real-valued planes based on proximity and density of pheromone placements. The motion of the ants over these continuous planes, in a sense, more closely mimicks how actual ants move in the real world. Ants traverse a 3-dimensional space from the inputs to the outputs and across time lags. This continuous search space frees the ant agents from the limitations imposed by ANTS\u27 discrete massively connected superstructure, making the structural options unbounded when mapping the movements of ants through the 3D continuous space to a neural architecture graph. In addition, CANTS has fewer hyperparameters to tune than ANTS, which had five potential heuristic components that each had their own unique set of hyperparameters, as well as requiring the user to define the maximum recurrent depth, number of layers and nodes within each layer. CANTS only requires specifying the number ants and their pheromone sensing radius. The three applied strategies yielded three important successes. Applying ACO on optimizing LSTMs yielded a 1.34\% performance enhancement and more than 55% sparser structures (which is useful for speeding up inference). ANTS outperformed the NAS benchmark, NEAT, and the NAS state-of-the-art algorithm, EXAMM. CANTS showed competitive results to EXAMM and competed with ANTS while offering sparser structures, offering a promising path forward for optimizing (temporal) neural models with nature-inspired metaheuristics based the metaphor of ants

Evaluation Of Concrete Degradation Using Acoustic Emission: Data Filtering And Damage Detection

The prevalence of aging and deteriorating infrastructure in the U.S. has raised concerns regarding its level of serviceability, reliability, and vulnerability to natural disasters. This issue has gained attention recently and efforts are being conducted to accelerate the delivery of enhanced nondestructive testing (NDT) and structural health monitoring (SHM) methods. Acoustic emission (AE) is a strong candidate for these applications due to its high sensitivity and potential for damage detection in different materials. However, several challenges associated with the technique hinder the development of automated, reliable, real-time SHM using AE. This study aims to advance the use of AE for condition assessment of concrete structures by addressing two main challenges. The first is AE data filtering to exclude irrelevant noise and wave reflections. Effective filtering and data reduction enhances the quality of the data and lowers the cost of its transfer and analysis; ultimately increasing the reliability of the method. The second issue is detecting slow rate material degradation mechanisms in concrete. For example, alkali-silica reaction (ASR) affects civil infrastructure around the nation, and available condition assessment methods for this type of damage are either invasive or not feasible for field conditions. Despite the awareness of ASR concrete deterioration; there is lack of research investigating the ability of AE to detect and assess it. In addition, recent laboratory investigations have shown promising results in detecting and evaluating damage related to corrosion of steel in concrete using AE. However, the results have not been extended to field applications. This dissertation includes three studies that address the aforementioned issues. In the first study, wavelet analysis was used to study the distribution of energy in AE signals in the time-frequency domain. Criteria to differentiate between AE signals from artificial sources (pencil lead breaks) and wave reflections were developed. The results were tested and validated by applying the developed filters on data collected from actual cracking during load testing of a prestressed concrete beam. The second study presents a laboratory test conducted to assess the feasibility of using AE to detect ASR damage in concrete. Accelerated ASR testing was undertaken with a total of fifteen specimens tested; twelve ASR and three control specimens. The results of this study showed that AE has the potential to detect and classify ASR damage. Relatively good agreement was obtained with standard ASR measurements of length change and petrographic examination. The third study discusses a field application for long-term, remote monitoring of damage due to corrosion of reinforcing steel and potential thermal cracking in a decommissioned nuclear facility. The structure was monitored for approximately one year and AE damage detection and classification methods were successfully applied to assess the damage at the monitored regions. This study also included an accelerated corrosion test conducted on a concrete block cut from a representative structure. The studies included in this dissertation provide: 1) an innovative approach for filtering AE data collected during cracking of concrete, 2) a proof of concept study on detecting ASR damage using AE, and 3) field application on AE monitoring of corrosion damage in aging structure. The outcomes of this research demonstrate the ability of AE for condition assessment, structural health monitoring, and damage prognosis for in-service structures

The Impact of Public Debt on Economic Growth in Palestine (2005-2019)

This study aimed at examining the impact of public debt and domestic investment on economic growth in Palestine for the period 2005-2019. The study used multiple linear regression to examine the study hypotheses. The study  found diverse and, in some cases, inconsistent evidence on the relative impact of public debt on economic growth. The results show that there is a positive long-run relationship between public debt and economic growth. The study concluded that public debt is positively correlated with domestic investment. With the stability of other factors, the increase domestic investment is positive and strongly significant. In fact, a 1% variation of physical capital leads to an increase of 0.33% of economic growth in Palestine.  The effect of public debt on economic growth is also positive, may be for two reasons: either because the palestenian  public-debt-to-GDP ratio did not reach  a threshold beyond which public debt significantly lowers economic growth or because most of palestenian public debt is domestic debt. Keywords: gross domestic product, economic growth, public debt, domestic investment, Palestine. DOI: 10.7176/JESD/13-20-07 Publication date:October 31st 202

The Impact of Foreign Direct Investment on Economic Growth in Palestine

Foreign Direct Investment (FDI) is usually considered an important factor for economic growth in developing countries. FDI plays an important role in transferring technology from developed to developing economies. It also stimulates domestic investments and enhances human as well as physical capital in the host countries. This study aimed at identifying the effect of FDI and some other variables on the Palestinian economy.  In the light of data analysis, time series data for FDI inflows, gross capital formation (GCF) and labor force (LF) were gathered for Palestine over the period 2005-2019.The study found that  an increase in foreign direct investment by 1% leads to an increase in GDP by 0.149%, and this satisfies the assumption that increasing foreign direct investment leads to a high rate of economic growth in Palestine. Keywords:foreign direct investments, economic growth, developing countries,  gross domestic product, the Palestinian economy. DOI: 10.7176/JESD/13-22-06 Publication date: November 30th 202

Patch-based 3D reconstruction of deforming objects from monocular grey-scale videos

Abstract. The ability to reconstruct the spatio-temporal depth map of a non-rigid object surface deforming over time has many applications in many different domains. However, it is a challenging problem in Computer Vision. The reconstruction is ambiguous and not unique as many structures can have the same projection in the camera sensor. Given the recent advances and success of Deep Learning, it seems promising to use and train a Deep Convolutional Neural Network to recover the spatio-temporal depth map of deforming objects. However, training such networks requires a large-scale dataset. This problem can be tackled by artificially generating a dataset and using it in training the network. In this thesis, a network architecture is proposed to estimate the spatio-temporal structure of the deforming object from small local patches of a video sequence. An algorithm is presented to combine the spatio-temporal structure of these small patches into a global reconstruction of the scene. We artificially generated a database and used it to train the network. The performance of our proposed solution was tested on both synthetic and real Kinect data. Our method outperformed other conventional non-rigid structure-from-motion methods