The Recession and its Impact on Foreign Direct Investment Flows into the Food System of Less Developed Countries

This study investigates the effects of the current recession on foreign direct investment (FDI) in the food sectors of developing countries. The study tests the hypothesis that the economic recession adversely affects FDI flows in the food sector. The specific objectives are: to identify determinants that influence FDI inflows; to develop an econometric model to estimate changes in FDI inflows as influenced by factor determinants, including the present recession; and to compare the impact of the recession on FDI in the food system in different developed and developing economies.Recession, FDI, Developing countries, Agricultural and Food Policy, International Development, International Relations/Trade,

A Simulation Framework for Traffic Safety with Connected Vehicles and V2X Technologies

With the advancement in automobile technologies, existing research shows that connected vehicle (CV) technologies can provide better traffic safety through Surrogate Safety Measure (SSM). CV technologies involves two network systems: traffic network and wireless communication network. We found that the research in the wireless communication network for CV did not interact properly with the research in SSM in transportation network, and vice versa. Though various SSM has been proposed in previous studies, a few of them have been tested in simulation software in limited extent. On the other hand, A large body of researchers proposed various communication architecture for CV technologies to improve communication performance. However, none of them tested the advanced SSM in their proposed architecture. Hence, there exists a research gap between these two communities, possibly due to difference in research domain. In this study, we developed a V2X simulation framework using SUMO, OMNeT++ and Veins for the development and testing of various SSM algorithms in run time simulation. Our developed framework has three level of communication ( CV to RSU To TS) system and is applicable for large traffic network that can have mixed traffic system (CV and non-CV), multiple road side unit (RSUs), and traffic server (TS). Moreover, the framework can be used to test SSM algorithms for other traffic networks without doing much modification. Our developed framework will be publicly available for its further development and optimization

Use of Machine Learning and Natural Language Processing to Enhance Traffic Safety Analysis

Despite significant advances in vehicle technologies, safety data collection and analysis, and engineering advancements, tens of thousands of Americans die every year in motor vehicle crashes. Alarmingly, the trend of fatal and serious injury crashes appears to be heading in the wrong direction. In 2021, the actual rate of fatalities exceeded the predicted rate. This worrisome trend prompts and necessitates the development of advanced and holistic approaches to determining the causes of a crash (particularly fatal and major injuries). These approaches range from analyzing problems from multiple perspectives, utilizing available data sources, and employing the most suitable tools and technologies within and outside traffic safety domain.The primary source for traffic safety analysis is the structure (also called tabular) data collected from crash reports. However, structure data may be insufficient because of missing information, incomplete sequence of events, misclassified crash types, among many issues. Crash narratives, a form of free text recorded by police officers to describe the unique aspects and circumstances of a crash, are commonly used by safety professionals to supplement structure data fields. Due to its unstructured nature, engineers have to manually review every crash narrative. Thanks to the rapid development in natural language processing (NLP) and machine learning (ML) techniques, text mining and analytics has become a popular tool to accelerate information extraction and analysis for unstructured text data. The primary objective of this dissertation is to discover and develop necessary tools, techniques, and algorithms to facilitate traffic safety analysis using crash narratives. The objectives are accomplished in three areas: enhancing data quality by recovering missed crashes through text classification, uncovering complex characteristics of collision generation through information extraction and pattern recognition, and facilitating crash narrative analysis by developing a web-based tool. At first, a variety of NoisyOR classifiers were developed to identify and investigate work zone (WZ), distracted (DD), and inattentive (ID) crashes. In addition, various machine learning (ML) models, including multinomial naive bayes (MNB), logistic regression (LGR), support vector machine (SVM), k-nearest neighbor (K-NN), random forest (RF), and gated recurrent unit (GRU), were developed and compared with NoisyOR. The comparison shows that NoisyOR is simple, computationally efficient, theoretically sound, and has one of the best model performances. Furthermore, a novel neural network architecture named Sentence-based Hierarchical Attention Network (SHAN) was developed to classify crashes and its performance exceeds that of NoisyOR, GRU, Hierarchical Attention Network (HAN), and other ML models. SHAN handled noisy or irrelevant parts of narratives effectively and the model results can be visualized by attention weight. Because a crash often comprises a series of actions and events, breaking the chain of events could prevent a crash from reaching its most dangerous stage. With the objectives of creating crash sequences, discovering pattern of crash events, and finding missing events, the Part-of-Speech tagging (PT), Pattern Matching with POS Tagging (PMPT), Dependency Parser (DP), and Hybrid Generalized (HGEN) algorithms were developed and thoroughly tested using crash narratives. The top performer, HGEN, uses predefined events and event-related action words from crash narratives to find new events not captured in the data fields. Besides, the association analysis unravels the complex interrelations between events within a crash. Finally, the crash information extraction, analysis, and classification tool (CIEACT), a simple and flexible online web tool, was developed to analyze crash narratives using text mining techniques. The tool uses a Python-based Django Web Framework, HTML, and a relational database (PostgreSQL) that enables concurrent model development and analysis. The tool has built-in classifiers by default or can train a model in real time given the data. The interface is user friendly and the results can be displayed in a tabular format or on an interactive map. The tool also provides an option for users to download the word with their probability scores and the results in csv files. The advantages and limitations of each proposed methodology were discussed, and several future research directions were outlined. In summary, the methodologies and tools developed as part of the dissertation can assist transportation engineers and safety professionals in extracting valuable information from narratives, recovering missed crashes, classifying a new crash, and expediting their review process on a large scale. Thus, this research can be used by transportation agencies to analyze crash records, identify appropriate safety solutions, and inform policy making to improve highway safety of our transportation system

On relations of anisotropy and linear inhomogeneity using Backus average, 1-D tomography and two-parameter velocity inversion

We divide this thesis into three major parts. In the first part, we study three velocity models and corresponding traveltimes to obtain the inhomogeneity and anisotropy of a medium by comparing them to the field data. We derive an analytical relation that relates the linear inhomogeneity of a layered medium to the anisotropy parameter in an equivalent medium. For the analytic ease, we consider the P and S wave velocity gradients to be equal. We relax this constraint in the third part of the thesis, where the velocity gradients are independent of each other. We find that the obtained value of the anisotropy in the equivalent medium is in the same order of magnitude as the inhomogeneity parameter from the linearly inhomogeneous and elliptically anisotropic medium. This statement encourages us to do further investigation on the more general relationship between the inhomogeneity and anisotropy parameters in an equivalent medium. In the second part, we develop a 1-D traveltime tomography method to calculate the velocity of a medium. We use the results of 1-D tomography to obtain linear inhomogeneity parameters in a specific layer. To get the trustworthiness of the method, we perform several synthetic experiments. We show that the inverted model parameters are reasonably accurate and stable. To examine the results of linear inhomogeneity parameters using a different method, we also develop an inversion method based on a two-parameter velocity model. Finally, we apply both the methods to Vertical Seismic Profile (VSP) data and do a study comparing their results. In the third part, we derive an analytical relationship between the anisotropy, characterized by the Thomsen [1986] parameters, and the linear inhomogeneity parameters, which forms a system of three equations for nine unknowns. To obtain well-posedness, we constrain the problem by considering two seismological methods, 1-D tomography and two-parameter methods, applied to field data. Lastly, we compare the results that come from the application of each method to the analytical relationship, for a particular region of interest, to assess the validity of the theoretical relationship

Modeling of Traceability Information System for Material Flow Control Data.

This paper focuses on data modeling for traceability of material/work flow in information layer of manufacturing control system. The model is able to trace all associated data throughout the product manufacturing from order to final product. Dynamic data processing of Quality and Purchase activities are considered in data modeling as well as Order and Operation base on lots particulars. The modeling consisted of four steps and integrated as one final model. Entity-Relationships Modeling as data modeling methodology is proposed. The model is reengineered with Toad Data Modeler software in physical modeling step. The developed model promises to handle fundamental issues of a traceability system effectively. It supports for customization and real-time control of material in flow in all levels of manufacturing processes. Through enhanced visibility and dynamic store/retrieval of data, all traceability usages and applications is responded. Designed solution is initially applicable as reference data model in identical lot-base traceability system

Noise optimization of amorphous SixGeyO1-x-y uncooled microbolometer

A detailed investigation of reduction of low-frequency noise voltage power spectral density (PSD) of silicon germanium oxide (SixGeyO1-x-y) uncooled infrared (IR) microbolometers has been performed. The experimental methods used to conduct the research are presented. The noise reduction was achieved by passivating SixGeyO1-x-y with Si3N4 layers and by annealing the devices in vacuum at 200 �C, 250 �C, or 300 �C with different time interval from 1 to 5 hours. First, uncooled IR microbolometers with a pixel area of 40�40 �m2 were fabricated (by another research team member) on four wafers with different SixGeyO1-x-y compositions while the other layer thicknesses were fixed. The IR sensitive layer was passivated with Si3N4 thin films for the purpose of reducing the noise. Second, the temperature coefficient of resistance (TCR) and the corresponding resistivity (?) of each devices were measured as a function of temperature between 0 ? 70 oC. The measured TCR and resistivity were -3.518/K and 0.763�103 V2/Hz, -2.590/K and 1.170�103 V2/Hz, -3.864/K and 3.573�103 V2/Hz and - 3.103 and 0.730�103 V2/Hz for devices from W01, W02, W03 and W04, respectively. The voltage noise PSD was then measured using a bias current between 0.07 - 0.6 �A across many devices from each wafer, with each device given a unique number for the purpose of tracking them. Before annealing, the lowest noise voltage PSD measured at the corner frequency of several devices (W01D21, W02D45, W03D36 and W04D33) from the four fabricated wafers were 7.59�10-15 V2/Hz, 1.89�10-14 V2/Hz,1.82�10-14 V2/Hz, and 2.79�10-14 V2/Hz, at 25 Hz, 12 Hz, 190 Hz, and 160 Hz respectively. The corresponding 1/f-noise coefficients, Kf, were 3.65�10-14, 3.01�10-14, 1.97�10-14, and 2.74�10-13 respectively. To optimize and reduce the measured noise, the same measured devices and others from each wafer were annealed in vacuum (4mTorr) with different time interval from 1 to 5 hours at either 200 oC, or 250 oC, or 300 oC. The measurements demonstrated that the voltage noise PSD was reduced as the annealing time interval was increased to a certain time period, after that the voltage noise PSD started to increase again. For example, the lowest measured noise of each device (W01D21, W03D45 and W04D33) from the four wafers at the corner frequency, after 3h or 4 h time interval, was 1.96�10-14 V2/Hz at 12 Hz, 1.5� 10-14 at 77.5 Hz, 2.11 �10-14 at 12 Hz, respectively. However, in wafer 02 (02D45), the voltage noise PSD was 1.13� 10-14 V2/Hz at 23 Hz with 1 h period of annealing. Thus, the results demonstrated that the voltage noise PSD of device W04D33 was significantly lowered after annealing at 300 �C for 4 hours. Annealing devices at higher temperature 300 �C reduced the low frequency voltage noise PSD more than that of 200 �C and 250 �C temperature. The measured Hooge�s parameter of the three devices from W04 after annealing were 2.39�10-13 for W04D43 at 200 �C in 2h period, 2.19�10-16 for W04D11 at 250 �C in 3 h period and 1.36�10-14 for W04D33 at 300 �C in 3 h period. Other devices from W01, W02 and W03 the measured Hooge�s parameters decreased after annealing. For example, before annealing the noise parameters (?, ? and Kf ) of the device W01D22 were 1.26, 2.24 and 1.44�10-12 which are 0.95, 2.00 and 2.02�10-13 after annealing, and for the device W03D45 the noise parameters were 1.59, 3.71 and 1.19�10-12 but which were seen 1.50, 1.88 and 7.23�10-14 after annealing, respectively. However, annealing of devices reduced the noise parameter Kf. This clearly indicates that annealing the device at higher temperature enabled the reduction of 1/f-noise. The possible reasons for the reduction of voltage noise are the dangling bonds, grain boundary and crystal structure were repaired in sensing layer after heating the devices. Trapping-detraping mechanism stated inside the interfacial oxide was also a potential source of increasing 1/f noise