Pedestrian Injury Severity Analysis in Motor Vehicle Crashes in Ohio

According to the National Highway Traffic Safety Administration, 116 pedestrians were killed in motor vehicle crashes in Ohio in 2015. However, no study to date has analyzed crashes in Ohio in order to explore the factors contributing to the pedestrian injury severity resulting from motor vehicle crashes. This study fills this gap by investigating the crashes involving pedestrians exclusively in Ohio. This study uses the crash data from the Highway Safety Information System, from 2009 to 2013. The explanatory factors include the pedestrian, driver, vehicle, crash, and roadway characteristics. Both fixed- and random-parameters ordered probit models of injury severity (where possible outcomes are major, minor, and possible/no injury) were estimated. The model results indicate that older pedestrian (65 and over), younger driver (less than 24), driving under influence (DUI), struck by truck, dark-unlighted roadways, six-lane roadways, and speed limits of 40 mph and 50 mph were all factors associated with more severe injuries to the pedestrians. Conversely, older driver (65 and over), passenger car, crash occurring in urban locations, daytime traffic off-peak (10 a.m. to 3:59 p.m.), weekdays, and daylight condition were all factors associated with less severe injuries. This study provides specific safety recommendations so that effective countermeasures can be developed and implemented by policy makers, which in turn will improve overall highway safety

Predicting Educational Relevance For an Efficient Classification of Talent

This research work utilizes machine learning approach to build a predictive model for the prediction of the students and the job seekers’ to quantify their fitness's for the courses and jobs they plan to pursue, respectively. Some of the existing research utilizes GPA for academic prediction and use personality prediction and computing in social domains for various industrial goals. On the other hand, this research work advances the state of the art to correlate and blend the personality features with the academic attributes to identify and classify the relevant talent of the individuals for the academic and real world success with improved predictive modeling. This work incorporates three algorithms to quantify a talent in the relevance, and then predict good fit students and good fit candidates, based on supervised learning, stochastic probability distribution and classification rules, etc. This work opens many opportunities for future research towards Genomics data mining to mine individuals for various areas

We Are What We Generate - Understanding Ourselves Through Our Data

AbstractWe have tendency to exhibit ourselves through the data we share about ourselves including, liking, friendship, follows, disliking, pictures, audio, videos, causes, blogs and sites. Such data about us have already been used by big data companies to create customized ads and marketing tactics. However, while such data being in unstructured and noisy format, utilization and research is at its early stages. In this paper, we elaborate on the idea of understanding individuals through lens of data they produce in context of our main research work for Predicting Educational Relevance For an Efficient Classification of Talent (PERFECT) algorithm engine. We illustrate some of research problems in relevance of such data and identify research problem as ground for this paper. We present sub set of our framework including algorithm and math constructs, for the problem we identify. We conclude that such analytics and cognitive research can help to improve education, healthcare, Job economy, crime control, etc. Thus we coin the phrase “we are what we generate”, with our work in this paper. We suggest future work and opportunities in relevant directions

Assignment of Freight Traffic in a Large-Scale Intermodal Network under Uncertainty

This paper presents a methodology for freight traffic assignment in a large-scale road-rail intermodal network under uncertainty. Network uncertainties caused by natural disasters have dramatically increased in recent years. Several of these disasters (e.g., Hurricane Sandy, Mississippi River Flooding, Hurricane Harvey) severely disrupted the U.S. freight transport network, and consequently, the supply chain. To account for these network uncertainties, a stochastic freight traffic assignment model is formulated. An algorithmic framework, involving the sample average approximation and gradient projection algorithm, is proposed to solve this challenging problem. The developed methodology is tested on the U.S. intermodal network with freight flow data from the Freight Analysis Framework. The experiments consider four types of natural disasters that have different risks and impacts on the transportation network: earthquake, hurricane, tornado, and flood. The results demonstrate the feasibility of the model and algorithmic framework to obtain freight flows for a realistic-sized network in reasonable time (between 417 and 716 minutes). It is found that for all disaster scenarios the freight ton-miles are higher compared to the base case without uncertainty. The increase in freight ton-miles is the highest under the flooding scenario; this is due to the fact that there are more states in the flood-risk areas and they are scattered throughout the U.S

Enhanced Machine Learning Engine Engineering Using Innovative Blending, Tuning, and Feature Optimization

Investigated into and motivated by Ensemble Machine Learning (ML) techniques, this thesis contributes to addressing performance, consistency, and integrity issues such as overfitting, underfitting, predictive errors, accuracy paradox, and poor generalization for the ML models. Ensemble ML methods have shown promising outcome when a single algorithm failed to approximate the true prediction function. Using meta-learning, a super learner is engineered by combining weak learners. Generally, several methods in Supervised Learning (SL) are evaluated to find the best fit to the underlying data and predictive analytics (i.e., “No Free Lunch” Theorem relevance). This thesis addresses three main challenges/problems, i) determining the optimum blend of algorithms/methods for enhanced SL ensemble models, ii) engineering the selection and grouping of features that aggregate to the highest possible predictive and non-redundant value in the training data set, and iii) addressing the performance integrity issues such as accuracy paradox. Therefore, an enhanced Machine Learning Engine Engineering (eMLEE) is inimitably constructed via built-in parallel processing and specially designed novel constructs for error and gain functions to optimally score the classifier elements for improved training experience and validation procedures. eMLEE, as based on stochastic thinking, is built on; i) one centralized unit as Logical Table unit (LT), ii) two explicit units as enhanced Algorithm Blend and Tuning (eABT) and enhanced Feature Engineering and Selection (eFES), and two implicit constructs as enhanced Weighted Performance Metric(eWPM) and enhanced Cross Validation and Split (eCVS). Hence, it proposes an enhancement to the internals of the SL ensemble approaches. Motivated by nature inspired metaheuristics algorithms (such as GA, PSO, ACO, etc.), feedback mechanisms are improved by introducing a specialized function as Learning from the Mistakes (LFM) to mimic the human learning experience. LFM has shown significant improvement towards refining the predictive accuracy on the testing data by utilizing the computational processing of wrong predictions to increase the weighting scoring of the weak classifiers and features. LFM further ensures the training layer experiences maximum mistakes (i.e., errors) for optimum tuning. With this designed in the engine, stochastic modeling/thinking is implicitly implemented. Motivated by OOP paradigm in the high-level programming, eMLEE provides interface infrastructure using LT objects for the main units (i.e., Unit A and Unit B) to use the functions on demand during the classifier learning process. This approach also assists the utilization of eMLEE API by the outer real-world usage for predictive modeling to further customize the classifier learning process and tuning elements trade-off, subject to the data type and end model in goal. Motivated by higher dimensional processing and Analysis (i.e., 3D) for improved analytics and learning mechanics, eMLEE incorporates 3D Modeling of fitness metrics such as x for overfit, y for underfit, and z for optimum fit, and then creates logical cubes using LT handles to locate the optimum space during ensemble process. This approach ensures the fine tuning of ensemble learning process with improved accuracy metric. To support the built and implementation of the proposed scheme, mathematical models (i.e., Definitions, Lemmas, Rules, and Procedures) along with the governing algorithms’ definitions (and pseudo-code), and necessary illustrations (to assist in elaborating the concepts) are provided. Diverse sets of data are used to improve the generalization of the engine and tune the underlying constructs during development-testing phases. To show the practicality and stability of the proposed scheme, several results are presented with a comprehensive analysis of the outcomes for the metrics (i.e., via integrity, corroboration, and quantification) of the engine. Two approaches are followed to corroborate the engine, i) testing inner layers (i.e., internal constructs) of the engine (i.e., Unit-A, Unit-B and C-Unit) to stabilize and test the fundamentals, and ii) testing outer layer (i.e., engine as a black box) for standard measuring metrics for the real-world endorsement. Comparison with various existing techniques in the state of the art are also reported. In conclusion of the extensive literature review, research undertaken, investigative approach, engine construction and tuning, validation approach, experimental study, and results visualization, the eMLEE is found to be outperforming the existing techniques most of the time, in terms of the classifier learning, generalization, metrics trade-off, optimum-fitness, feature engineering, and validation

Visibility as Muslim, Perceived Discrimination and Psychological Distress among Muslim Students in the UK

Perceived discrimination, a subjective appraisal of disadvantageous treatment on the grounds of identity, is negatively associated with wellbeing. We explored this association among British Muslim students, sampled online, by questions about perceived and experienced discrimination, visibility as a Muslim, symptoms of depression and anxiety, and positivity. Results from 457 respondents showed greater discrimination was experienced by those with more visible signs of Muslim faith, with a small but statistically significant positive correlation between perceived discrimination and psychological distress. Many participants gave examples of discrimination experienced. Implications for educational institutes, policy makers, clinicians, and the wider Muslim community are discussed

Effect of strong electrolytes on edible oils part III: viscosity of canola oil in 1,4-dioxane in the presence of HCl, NaOH and NaCl at different temperatures

Effect of strong electrolytes on the viscosity of canola oil in 1,4 dioxane was undertaken. The viscosity of oil in 1,4 dioxane was found to increase with the concentration of oil and decrease with rise in temperature. Strong electrolytes reduce the rate of flow of oil in 1,4 dioxane. It was noted that amongst these electrolytes, NaOH is more efficient reducing electrolyte than HCl and NaCl. The study was also extended in terms of ion-ion and ion-solvent interactions. The values of Jones-Dole coefficients (A and B) were evaluated graphically. The increase in negative values of A-cefficient with temperature is due to agitation of the molecules at higher temperature, dissociation and partial association of electrolytes in 1,4 dioxane. The positive values of B-cefficient show that these electrolytes behave as structure breaker in 1,4 dioxane. Distortion of the solvent structure is not appreciable (small), which resulted in the positive values of B-coefficient. Fluidity parameters were also evaluated and the change in these values with temperature and concentration of oil shows that the electrolytes behave as structure breaker. The energy of activation, latent heat of vaporization and molar volume of oil were also evaluated and discussed. Journal of Applied Sciences and Environmental Management Vol. 10(1) 2006: 47-5

Effect of strong electrolytes on edible oils part II: vViscosity of maize oil in 1,4-dioxane in the presence of HCl, NaOH and NaCl at different temperatures

The effects of strong electrolytes like HCl, NaOH and NaCl on the viscosity of maize oil at various temperatures (298 – 323 K) with the difference of 5 K using 1,4 dioxane as solvent were determined. The viscosity of oil was found to be increased with the increasing concentration of oil and decreases with the rise of temperature. The addition of electrolytes decreases the viscosity of oil although very little which shows that the electrolytes increase the distance between oil molecules and cause the enhancement of rate of flow and the increment of temperature drops the rate of flow of the solutions. Furthermore the concentration of electrolytes increases the viscosity of oil solutions. It is due to the presence of unsaturated ingredients present in the oil and thermal effect. The electrolytes behave as structure breaker. The effect of temperature was also determined in terms of fluidity parameters, energy of activation, latent heat of vaporization, molar volume of oil and free energy change of activation for viscous flow. Journal of Applied Sciences and Environmental Management Vol. 10 (3) 2006: 67-7

Estimation of Pb from metal and electroplating industrial waste by zeolite-3A

The concentration of lead in sediment and liquid waste samples of selected metal electroplating industries was measured by atomic absorption spectrophotometer. The data obtained revealed that lead content in liquid wastes varies in the range of 0.582-14.97 mg L-1 and 1.300- 757.8 mg Kg -1 in sediments. Removal of lead in the sediments and liquid waste was then carried out using zeolite-3A. Result shows the quantitative removal of lead. Factors that effect the lead removal include the adsorbent concentration, pH and temperature. The applicability of Freundlich, Langmuir and Dubinin - Radushkevich equations for the present system has been tested and the values of distribution coefficient were also evaluated. Thermodynamic parameters like ΔG°, ΔH° and ΔS° were also calculated. The results of such studies suggested that about (99.9%) removal was obtained by using zeolite-3A. It show that zeolite- 3A could be utilized as a potential decontaminate for the removal of lead from metal electroplating industries waste before discharging into hydrosphere. @JASE

Utilizing Big Data Analytics to Improve Education

Analytics can be defined as the process of determining, assessing, and interpreting meaning from volumes of data. It has been categorized in three different categories - descriptive, predictive and prescriptive. Predictive analysis can serve many segments of society as it can reveal hidden relationship which may not be apparent with descriptive modeling. Analytics advancement plays an important role in higher education planning. It answers several questions such as -which students will enroll in particular course, what courses are on trending or obsolete, what is the level of student satisfaction in the current education system, effectiveness of online study environment, how to design a better curriculum, likelihood of students transfer, drop out or failure to complete the course. Not only, data analytics helps in analyzing above points but also can be helpful in predictive modeling for faculty, administrative and students groups who are looking out for genuine results about the university rankings, based on which they make their decisions. Using the dataset “Academic Ranking of World Universities, 2003-2014”, we studied and analyzed to forecast how university’s management and faculty could adapt to changes to improve their education and thereby the ranking of their universities in the upcoming years. Microsoft SQL Server Data Mining Add-ins Excel 2008 was employed as a software mining tool for predicting the trending university ranking. This research paper concentrates upon predictive analysis of university ranking using forecasting based on data mining technique