RWIS based road condition prediction using machine learning algorithms

The need for a forecasting model of road conditions is becoming evermore critical, given the effects of ever-increasing severity in weather. Drastic changes in weather, especially cold fronts, often lead to dangerous roads. Consequently, traffic efficiencies are diminished and, even worse, accidents resulting in loss of life and property could increase. Across the nation, states are responsible for anticipating inclement weather and treating roads accordingly. Treatment costs can be reduced with more precise road condition predictions. The development of machine learning capabilities has enhanced the utilization of Big Data Systems throughout various sectors, including road climatology, making weather forecasting much more efficient and reliable. The study reported in this thesis analyzed various road climatology data, including sub-surface temperature at two- and six-inches from Road and Weather Information Systems (RWIS) deployed by Oklahoma Department of Transportation (ODOT) along the I-35 corridor at various road-bridge intersections aimed at producing a reliable and robust forecast model for predicting road surface temperature in the near and distant future. The predicting importance of each factor is analyzed statistically, and then manually, to determine its requirements for the forecast model. The study also determined the best forecast model after comparing a newly developed neural network with common regression techniques previously available through Machine Learning. Results showed that the novel neural network model offered a reliable 12-hour prediction for road surface temperature at a frequency of five minutes, depending on available historical data from RWIS. Two additional classification models provided Road Conditions Classes. The first was based on time series historical data from RWIS, and the second was based on historical and future data from a GFS (Global Forecast System). Together, these models accurately forecast local road surface temperatures 12-hour in advance of inclement weather in five-minutes frequencies at RMSE of ±1.67. They also accurately classified road conditions at a rate of more than 87.984%

Bio-H2 conversion of wastewater via hybrid dark/photo fermentation reactor

Hydrogen energy is a clean source for liveliness betterthan fossil fuel that has hazardous effects on the environmentand atmosphere. Food wastes and organics in the sewage sludgeare a promising sustainable and renewable source for hydrogenproduction where amalgamation of waste treatment and energyproduction would be more than one benefit expressed intreatment of organic pollutants and energy generation.Discovering biohydrogen production from industrialwastewater by dark and photo fermentation was the main aimof this paper. The biogas produced was composed of H2 andCO2, and the maximum H2 content was 25.94%. This ratio wasachieved at batch configuration system and initial pH 6.2 withstarch concentration 15 g/l. Cause of using dark fermentationeffluent (DFE) was used as substrate for A Rhodobactercapsulatus strain and a clostridium culture were cultivated toproduce hydrogen under different light-dark cycles. Acetic andbutyric acids decreased due to first and second photo stages by21.9% and 4.1 % respectively. Maximum hydrogen yield was470.9 ml H2/mol VFAs

Joint morphological-lexical language modeling for processing morphologically rich languages with application to dialectal Arabic

Language modeling for an inflected language such as Arabic poses new challenges for speech recognition and machine translation due to its rich morphology. Rich morphology results in large increases in out-of-vocabulary (OOV) rate and poor language model parameter estimation in the absence of large quantities of data. In this study, we present a joint morphological-lexical language model (JMLLM) that takes advantage of Arabic morphology. JMLLM combines morphological segments with the underlying lexical items and additional available information sources with regards to morphological segments and lexical items in a single joint model. Joint representation and modeling of morphological and lexical items reduces the OOV rate and provides smooth probability estimates while keeping the predictive power of whole words. Speech recognition and machine translation experiments in dialectal-Arabic show improvements over word and morpheme based trigram language models. We also show that as the tightness of integration between different information sources increases, both speech recognition and machine translation performances improve

The Marshall-Olkin Odd Burr III-G Family of Distributions: Theory, Estimation and Applications

We propose a new ‡exible class called the Marshall-Olkin odd Burr III family for generating continuous distributions and derive some of its statistical properties. We provide three special models which accommodate symmetrical, right-skewed and left-skewed shaped densities as well as bathtub, decreasing, increasing, upside-down bathtub and reversed-J shaped hazard rates. The model parameters are estimated by maximum likelihood, least squares and a percentile method. Some Monte Carlo simulations are performed to check the adequacy of these methods. The ‡exibility of a special model is illustrated by means of three applications to real data

Behavioral and Physiological Indicators of Pain During Nociceptive Procedures Among Mechanically Ventilated Patients at a University Hospital in Cairo

Critically ill mechanically ventilated patients experience significant and prolonged exposure to stressors from many sources related to injury or interventions. Pain is one of the stressors that can alter hemodynamics of such critically ill patients  Aim of the study: to assess behavioral and physiological indicators of pain during nociceptive procedures among mechanically ventilated patients. Research Design: A descriptive exploratory design was utilized. Research questions: a):What are different behavioral indicators of pain during nociceptive procedures among mechanically ventilated patients ? b):What are different physiological indicators of pain during nociceptive procedures among mechanically ventilated patients? Setting: selected intensive care units of a university hospital in Cairo. Sample: A convenience sample of 100 critically ill patients connected to mechanical ventilators. Tools of data collection: Three tools were utilized to collect data pertinent to the current study: tool 1:Mechanically ventilated patients' demographic and medical data, tool 2: Critical Care Pain Observation tool, tool 3: Physiological indicators of pain assessment tool Results:. The current study revealed that during nociceptive procedures ,patients displayed manifestations of pain such: facial expression ,body movement, compliance with ventilator and muscle tension in percentage of:(54%,58%,45%,45%) respectively. Concerning physiological indicators the current study revealed increased means of heart rate, systolic blood pressure ,MAP, diastolic blood pressure ,respiratory rate during endotracheal suction procedure (113.69±11.50,134±17.4,95.5±12.25,76.58±10.86,21.7±7.84) respectively as compared to before and after procedure with significant statically differences as(F/P=8.651/.005,2935.012/.000,2694.048/.000,201.993/.000,488.212/.00). Conclusion: Nociceptive procedures is extremely common in ICUs ,observation of critically ill patients' behavior during those procedures is crucial .Recommendations: There is a need to ensure that pain should be recognized in critical care settings for patients with communication difficulties. Ongoing monitoring of  critically ill patients vital signs during nociceptive procedures. Keywords: Mechanical ventilation, Behavioral indicators, Pain , Nociceptive procedure

New ZnO-based glass ceramic sensor for H2 and NO2 detection

In this study, a glass ceramic with a nominal composition 58ZnO:4Bi2O3:4WO3:33.3B2O3 was synthesized by melt quenching technique. A gas sensor was then manufactured using a ZnO sol-gel phase as a permanent binder of the glass–ceramic to an alumina substrate having interdigitated electrodes. The film sensitivity towards humidity, NH3, H2 and NO2 was studied at different temperatures. X-ray diffraction technique (XRD), field emission- scanning electron microscopy (FE-SEM) and differential thermal analysis (DTA) were used to characterize the prepared material. Though the response in the sub-ppm NO2 concentration range was not explored, the observed results are comparable with the latest found in the literature