Texas A&M University-Kingsville: AKM Digital Repository
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An analysis of expense accounts of vocational agriculture teachers of Area X in 1947-48
No full textThe Legislature of 1947 passed the School Equalization Bill which appropriated funds to match Federal Vocational Funds, and provided for travel funds for Vocational Agriculture Teachers. The Expenses Funds to be paid for the Equalization Bill, and/or to be paid by local schools matched from State and or Federal Funds, is limited to $800.00 per year per school. The policy to be followed in the use of Expense Funds is set by the State Board for Vocational Education and follows in pattern the use of expense funds by other State employees
Los pastores, Spanish American play of the nativity preserved in the vicinity of Corpus Christi, Texas
No full text1.ORIGIN OF MEDIEVAL RELIGIOUS DRAMA
It is not surprising that religious folk drama still exists in the heart of Mexico, a county with a literary ancestry of religious plays, autos sacramentales, and symbolic drama. But it is more incredible and a matter of greater interest to the student of folklore to find on American soil and in the twentieth century the popular representation of a religious drama which had its beginning in the Middle Ages. Yet it is true that traditional religious drama s a medium of popular devotion and entertainment has persisted in the region north of the Rio Grande. The performances are in the Spanish language with some forms now archaic, but which were in common use in the sixteenth and seventeenth centuries, and the actors are usually Mexican laborers
History of public education in Bishop
No full textThe purpose of this thesis is twofold. In the first place, it is to serve as a reference to those interested in securing data on the Bishop School. In the second place, it is to serve as a basis for further research.
The scope of this study includes all public educational work carried on in the Bishop School District from 1910 through 1938. The problem is divided into six chapters, according to the tenure of the Superintendents. Trustees, teachers, janitors, bus drivers, pupils, finance, buildings, curriculum, activities, and graduates are discussed in each chapter. All of the data is arranged in chronological order. Throughout the thesis the author has tried to call the reader’s attention to interesting questions which arise from studying the given data, but has made no attempt to answer them
Earned Value Management Merit as project management system for highway construction
No full textEarned Value Management (EVM) is a project management system that integrates scope, schedule, and cost through various metrics to create a clearer picture of the health of a project. First implemented in federal government projects in the 1960’s, EVM has only recently begun to see wider use outside of the federal government. The ”top-down” nature of the system depends on a constant flow of timely and accurate field data. The difficulty in obtaining this data has often been cited as the primary reason for the slow adoption of the system by the private sector and smaller government agencies. The Texas Department of Transportation (TxDOT) may be uniquely positioned, with its army of inspectors, to collect the critical data necessary to most effectively implement EVM. The focus of this study is to demonstrate the potential merit of the EVM system to state agencies such as the Texas Department of Transportation. The study intends to accomplish this through the application of EVM analysis to the raw data for the $75 million-dollar US Highway 281 Premont Bypass (CSJ 0255-15-005). The selected project is ideal for EVM analysis as it is a high dollar, mature project which will be approximately 80 percent complete at the time of the conclusion of this study. The data consists primarily of schedules, invoices, payment estimates, change orders, and the daily work diaries of the inspectors assigned to the project. The information will be broken down into specific work packages and analyzed via a more rigorous EVM examination than is customarily done by TxDOT at this time. Additionally, various methods of calculating EVM will be compared to determine which method would be best for TxDOT use. The goal was to determine if more rigorous EVM analysis would reveal early warning signs that would have reduced or eliminated project schedule delays or cost overruns sooner, possibly proving EVM’s value to future TxDOT highway projects
A study on improving circuit reliability in semiconductor VLSI chip design
No full textSemi-conductor devices are very sensitive and thus prone to impurities, particles, and minor defects in their manufacturing process. The quality of the finished product depends on the relationship between the several layers of interacting substances in the semiconductor device. Due to the constant advancement in technology, new materials and processes are being used to develop newer devices; devices are being used for longer and in different applications which adds more stress to the circuit and its components. Designers have been struggling with this and there is an enormous need to achieve accurate and efficient circuit-level reliability to overcome the increasing challenges of circuit faults. As the minimum feature size gets smaller especially in sub-micron CMOS circuits with an increased focus on voltage and size scaling, defects become increasingly difficult to prevent and can change the behavior of a logic circuit resulting in a fault.
To ensure the goal of profitability is met, semi-conductor chips and devices are manufactured in high volume and with little possibility of repair after the manufacturing process is completed. As a result, the incorporation of reliability into the design stage and reducing variance in the manufacturing stage has become critical. Design factors affecting semiconductor reliability include soft errors; latch-up; power and current derating; electromigration; logic timing margins; hot carrier injection; temperature derating; and process control
An improved heuristic algorithm for multiple vehicle routing problem
No full textRoad transportation represents one of the most frequently used modes of commercial transportation within supply chain networks. A large amount of freight is moved across roads via trucks, contributing significantly to the economic development of the United States of America. However, the costs in the trucking industry have been on a steady up climb with costs associated with labor and fuel being the major contributors. An effective way to reduce this cost is to optimize routes for freight delivery. This approach reduces the total distance travelled to deliver goods to customers and would in turn reduce fueling cost and labor hours. Optimal routing of vehicles can be carried out by using either exact algorithms or heuristic algorithms. Due to the inefficiencies associated with exact algorithms, heuristics are preferred and has been proven to be practically applicable due to its reduced computational time. However, because heuristics at best only produce near optimal solutions, opportunities exist to improve the effectiveness of existing algorithms to provide better solutions. The main objective of this research is to improve the effectiveness of a heuristic algorithm for the large size vehicle routing problems developed by Shivani Patil in 2019. The heuristic algorithm is a hybrid of a novel grouping technique and a modified minimal spanning tree. The grouping technique optimally divides the total number of customers into groups while the modified minimal spanning tree technique optimizes the routes within each group. To validate the effectiveness of the modified algorithm, its output was compared with the output of the original algorithm and a hybrid of k-means clustering & genetic algorithm using the same data set
Prediction of the apply rate of the postings based on the job characteristics
No full textJob portals and job listings such as Glassdoor, Indeed, and LinkedIn use different data mining techniques and machine learning algorithms to provide the best job recommendations based on a candidate’s preferences. Job recommendations are not only based on the preferences set by candidates. Rather, there are other parameters that need to be considered as well, such as skills required by the job, relocation being provided or not, searched keywords, visa sponsorship, etc. The job recommendations provided by these job recommendation systems play a significant role in the company’s growth and the “Apply Rate” or “Click Through Rate” (CTR) for a particular job posting. CTR is a metric used to measure the success of an online advertisement campaign and how it impacts advertisement rank and quality score. In this research, the apply rate is considered as the measure of whether a user will apply to that particular job or posting based on different characteristics such as job title, job description, popularity of job, job listing matches, location, time period of job listing, etc. The results of this research can be analysed in order to improve future job searches, click rates, and job matches in order to attract more users in an online advertisement system. Amongst the data mining techniques used in this work for prediction of CTR (apply rate), the random forest classifier accuracy was 94.39%, which was higher than the accuracy values of 93.97% for the decision tree classifier and 93.15% for the K-Nearest Neighbour (KNN) classifier
Teacher attrition in rural hispanic schools in South Texas: what will make them stay?
No full textOver the past 15 years, the U.S. educational system has faced the problem of teacher attrition. This is evident as teachers leave the profession at alarming rates with 50% of new teachers leaving the profession within the first three years. Rural schools also face the problem of filling vacant teaching positions. This research considered the factors contributing to teachers leaving the profession. The study considered the relationship between deprofessionalization of teaching, state testing accountability, and teacher autonomy with teacher attrition in rural schools with at least a 65% Hispanic student population. This study was conducted surveying general education teachers from rural school districts in South Texas who varied in age and years of teaching experience. The Pre-Kindergarten through grade 12 teachers were from districts with an enrollment of 120 to 800 with at least a 65% Hispanic population. The results of the overall study were not significant, F(4, 85) = 1.75, p = 0.146. This indicates that when assessed collectively, the combination of predictor variables does not significantly predict teacher attrition. However, examination of the individual predictor values indicates that, despite collective nonsignificance, deprofessionalization (B = 0.22, p = 0.036) and curriculum autonomy (B = 0.25, p = 0.020) significantly predict teacher attrition. For every one unit increase in deprofessionalization scores, teacher attrition was predicted to increase by 0.22 units. For every one-unit increase in curriculum autonomy, teacher attrition was predicted to increase by 0.25 units
Errors in the use of a spectrometer
No full textThe purpose of any experimental work is to obtain certain numerical results; these results depend in general on a number of factors, each of which is known only within certain limits. The accuracy of the final results are, of course, questions of prime importance.
Probably the method which is most often used to determine the accuracy of a given result is that of significant figures. The result is simply assumed to be correct assumed to be correct to the number of significant figures to which the most questionable factor is known. A very elementary analytical study of this method will reveal its weakness
Machine learning based signal processing using physiological signals for stress detection
No full textStress can be defined as the body's attempt to control itself in response to changes in the environment such as through mental, physical, or emotional responses. As a result, work performance may suffer, and the risk of neurological issues such as hypertension and psychological illnesses such as anxiety disorder may rise in the long run. In today's world, an increasing number of people are experiencing some form of stress. As a result, comprehension of stress cognition is required, along with the capacity to build systems with stress cognition characteristics. A methodology of signal processing based on machine learning techniques is presented in this thesis. Physiological information collected were used while driving from multiple healthy participants in various situations and locations, such as Respiration, Galvanic Skin Response Hand which measures the sweat gland activity on the skin of hands, Heart Rate, and Electromyogram which measures muscle response or electrical activity in response to a nerve’s stimulation of the muscle. The data is then segmented for various time intervals such as 100, 200, and 300 seconds, depending on the levels of stress. Statistical features were retrieved and made available to the classifiers namely Support Vector Machine and K-Nearest Neighbor algorithm, resulting in the highest accuracy of 96.77% for 100 and 200-second, and 98.20% for 300-second