The effect of a continuous low temperature on the operation of waste stabilization lagoons

This study was undertaken to determine the effect of a continuous low temperature on the operation of a waste stabilization lagoon and to provide information of value to the sewage treatment field. The investigation was conducted using two experimental lagoons subjected to a continuous illumination of 550 to 600 foot candles emitted by two fluorescent light fixtures. Of these units, the Cold Room Lagoon was maintained at a controlled temperature of 5° C. and the Warm Room Lagoon was operated at room temperature in the range of 16 to 32° C. Dissolved oxygen and biochemical oxygen demand determinations were the primary parameters used in this study. In addition pH, suspended solids, and coliform bacteria determinations provided additional information of value. Several different loadings of raw sewage were applied during the early phases of the investigation to establish the appropriate loadings for this study and to enable the acclimatization of the units. During the main portion of the study 16 fluid ounces of raw sewage were added daily to the Cold Room Lagoon resulting in a BOD loading of 11.3 pounds of BOD per acre per day and 32 ounces were added to the Warm Room Lagoon providing a loading of 22.3 pounds of BOD per acre per day. It was found that waste stabilization lagoons operated effectively under a continuous low temperature but had a decreased efficiency and required reduced organic loadings --Abstract

Optimal solutions for complex design problems: Using isoperformance software for human factors trade offs

A major application of isoperformance is as a trade-off methodology of the three major drivers of system design; equipment, training variables, and user characteristics. The flexibility of isoperformance allows each of these three components to be nearly any rational variation. For example, aptitude may be military Armed Forces Qualification Testing (AFQT) categories, cutoff scores within a selection procedure, or simply dichotomizing high and low scorers (pass/fail). Equipment may be new versus old, 'smart' versus dumb, high versus low resolution, etc. Training may be short versus long or varieties of media types (lecture versus CAI/CBI versus self-paced workbooks). In its final computerized form isoperformance lets the user set an operational level of performance (e.g., a jet pilot in a simulated emergency must take prescribed corrective action and clear the plane in several seconds, pilot astronauts will check out all shuttle flight systems within 30 minutes, or Mission Specialists must handle sucdessfully a required number of job elements). At this point the computer program guides the user through any requested trade-offs of the three components while maintaining the specified operational level of performance through isoperformance curves. A demonstration of the computer program is currently available

Globe tourism

This is a journal article.For many years the Globe has been often derided and occasionally praised as a tourist destination. Why? And does it matter? Dennis Kennedy and Gabriel Egan dispute the issue

A menu of self-administered microcomputer-based neurotoxicology tests

This study examined the feasibility of repeated self-administration of a newly developed battery of mental acuity tests. Researchers developed this battery to be used to screen the fitness for duty of persons in at-risk occupations (astronauts, race car drivers), or those who may be exposed to environmental stress, toxic agents, or disease. The menu under study contained cognitive and motor tests implemented on a portable microcomputer including: a five-test core battery, lasting six minutes, which had demonstrable reliabilities and stability from several previous repeated-measures studies, and also 13 new tests, lasting 42 minutes, which had appeared in other batteries but had not yet been evaluated for repeated-measures implementation in this medium. Sixteen subjects self-administered the battery over 10 repeated sessions. The hardware performed well throughout the study and the tests appeared to be easily self-administered. Stabilities and reliabilities of the test from the core battery were comparable to those obtained previously under more controlled experimental conditions. Analyses of metric properties of the remaining 13 tests produced eight additional tests with satisfactory properties. Although the average retest reliability was high, cross-correlations between tests were low, indicating factorial richness. The menu can be used to form batteries of flexible total testing time which are likely to tap different mental processes and functions

Feature Selection Techniques and Classification Accuracy of Supervised Machine Learning in Text Mining

Text mining is a special case of data mining which explore unstructured or semi-structured text documents, to establish valuable patterns and rules that indicate trends and significant features about specific topics. Text mining has been in pattern recognition, predictive studies, sentiment analysis and statistical theories in many areas of research, medicine, financial analysis, social life analysis, and business intelligence. Text mining uses concept of natural language processing and machine learning. Machine learning algorithms have been used and reported to give great results, but their performance of machine learning algorithms is affected by factors such as dataset domain, number of classes, length of the corpus, and feature selection techniques used. Redundant attribute affects the performance of the classification algorithm, but this can be reduced by using different feature selection techniques and dimensionality reduction techniques.  Feature selection is a data preprocessing step that chooses a subset of input variable while eliminating features with little or no predictive information. Feature selection techniques are Information gain, Term Frequency, Term Frequency-Inverse document frequency, Mutual Information, and Chi-Square, which can use a filters, wrappers, or embedded approaches. To get the most value from machine learning, pairing the best algorithms with the right tools and processes is necessary. Little research has been done on the effect of feature selection techniques on classification accuracy for pairing of these algorithms with the best feature selection techniques for optimal results. In this research, a text classification experiment was conducted using incident management dataset, where incidents were classified into their resolver groups. Support vector machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB) and Decision tree (DT) machine learning algorithms were examined. Filtering approach was used on the feature selection techniques, with different ranking indices applied for optimal feature set and classification accuracy results analyzed. The classification accuracy results obtained using TF were, 88% for SVM, 70% for NB, 79% for Decision tree, and KNN had 55%, while Boolean registered 90%, 83%, 82% and 75%, for SVM, NB, DT, and KNN respectively. TF-IDF, had 91%, 83%, 76%, and 56% for SVM, NB, DT, and KNN respectively. The results showed that algorithm performance is affected by feature selection technique applied. SVM performed best, followed by DT, KNN and finally NB. In conclusion, presence of noisy data leads to poor learning performance and increases the computational time. The classifiers performed differently depending on the feature selection technique applied. For optimal results, the classifier that performed best together with the feature selection technique with the best feature subset should be applied for all types of data for accurate classification performance. Keywords: Text Classification, Supervised Machine Learning, Feature Selection DOI: 10.7176/JIEA/9-3-06 Publication date:May 31st 201