Effects of Training and Development on Job Satisfaction

This thesis will focus on the effect training and development in the workplace on employees job satisfaction. Research has stated that with the proper kinds of training and development employees will be more satisfied with their jobs overall. Researchers believe that the kinds of training and development given to employees will also improve the overall quality and productivity of the work environment. With companies trying to move into the global job market training and development has come to the for front of all companies improvement programs. Most of the large companies CEO believe that training and development is the only way in which a company will survive in the business world of today. The purpose of this study is to determine if training and development in three different manufacturing environments effects job satisfaction . The hypothesis of this study is with proper training and development employees will be more satisfied with their jobs. Twenty-five participants from Globe and Donaldson and Nineteen from Hudson Valley Polymer were surveyed. Globe had twenty-three males and two females. Donaldson had twenty males and five females. Hudson Valley Polymer had twelve males and seven females. Each participants took a licket scale survey on training and development. The data was analysis by descriptive, inferential statistics and multivariates statistics. Results of the analysis produced evidence that the employees from Globe, Hudson Valley Polymer and a combined Globe and Hudson Valley Polymer accepted the hypothesis. The employees for these two companies felt the company was doing an acceptable job on training and development and they were satisfied with their jobs. Donaldson on the other hand did not accept the hypothesis but they also did not accept the null hypothesis. Donaldson employees felt that the company was doing a poor job on training and development and they were not satisfied with their jobs

Forecasting and Prediction of Solar Energy Generation using Machine Learning Techniques

The growing demand for renewable energy sources, especially wind and solar power, has increased the requirement for precise forecasts in the energy production process. Using machine learning (ML)techniques offers a revolutionary way to deal with this problem, and this thesis uses machinelearning (ML) to estimate solar energy production with the goal of revolutionizing decision-making processes through the analysis of large datasets and the generation of accurate forecasts.Solar meteorological data is analyzed methodologically using regression, time series analysis, and deep learning algorithms. The study demonstrates how well machine learning-based forecasting works to anticipate future solar energy output. Quantitative evaluations show excellent prediction accuracy and verify the techniques used. For example, the key observations made were that the Multiple Linear Regression methods demonstrates reasonable predictive ability with moderate Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values yet slightly lower R-squared values compared to other methods.The study also provides a reflective analysis of result significance, methodology dependability, and result generalizability, as well as a summary of its limits and recommendations for further study. The conclusion provides implications for broader applications across energy sectors and emphasizes the critical role that ML-based forecasting plays in predicting solar energy generation. By utilizing renewable energy sources like solar power, this approach aims to lessen dependency on non-renewable resources and pave the way for a more sustainable future

Correlative Field and Laboratory Microcosm Approaches in Ascertaining Xenobiotic Effect and Fate in Diverse Aquatic Microenvironments.

The impact of a multitude of toxic chemicals, or xenobiotics, on diverse aquatic environments and the need to consider such factors in adjacent land use and disposal situations has necessitated the development of usable analytical predictive approaches. A microbial and enzymatic assessment protocol for determining the environmental effect and fate of these manufactured chemicals in coastal wetlands was devised. The protocol combined in situ analyses of interrelated soil/sediment microenvironments with statistical and analytical laboratory microcosm approaches in presenting valid predictive models of xenobiotic fate and effect. The general objective of this combined field/laboratory analysis was to provide a better understanding of biotic and abiotic factors that influence toxic chemical breakdown over a range of salinity conditions. An overview of research in the area of microcosm development and design was first outlined followed by a technical description of two aquatic microcosm systems developed in this study. Variations in microbial diversity, enzyme activity, microbial ATP, and substrate uptake in ecologically-diverse, interrelated coastal soil/sediment microenvironments were examined to develop baseline protocols for combined in situ/microcosm fate and effect analyses. Strong correlative linkages were established between control microcosm and in situ measurements. Environmental impact was assessed using these correlative field approaches in a toxicant runoff incident in a freshwater swamp forest habitat. A testing protocol for examining microcosm features and operating parameters was also presented. Sequential and differential introduction of a toxicant into microcosm systems assessed the importance of pre-exposure and adaptation responses in environmental fate and effect estimates. Several industrial source phenols were used in a test case to evaluate overall microcosm performance. Relative biotransformation and biodegradation was dependent upon ecological factors such as salinity, temperature, pH/Eh and water-sediment ratio. In addition, the chemical structure of the phenolic compound itself was a key factor in compound disappearance and in its effect on important food web substrate rates, i.e., chitin degradation, in wetland systems. Biotransformation and biodegradation kinetics of several toxicant classes were examined with microcosm mathematical model estimates comparing closely to chromatography residue data and in situ results. A ranking criteria of relative toxicity for potentially hazardous chemical classes in coastal wetlands was achieved

Deep Learning Approach for Raman Spectroscopy

Raman spectroscopy is a widely used technique for organic and inorganic chemical material identification. Throughout the last century, major improvements in lasers, spectrometers, detectors, and holographic optical components have uplifted Raman spectroscopy as an effective device for a variety of different applications including fundamental chemical and material research, medical diagnostics, bio-science, in-situ process monitoring and planetary investigations. Undoubtedly, mathematical data analysis has been playing a vital role to speed up the migration of Raman spectroscopy to explore different applications. It supports researchers to customize spectral interpretation and overcome the limitations of the physical components in the Raman instrument. However, large, and complex datasets, interferences from instrumentation noise and sample properties which mask the true features of samples still make Raman spectroscopy as a challenging tool. Deep learning is a powerful machine learning strategy to build exploratory and predictive models from large raw datasets and has gained more attention in chemical research over recent years. This chapter demonstrates the application of deep learning techniques for Raman signal-extraction, feature-learning and modelling complex relationships as a support to researchers to overcome the challenges in Raman based chemical analysis

Cost benefit analysis of space communications technology. Volume 2: Final report

For abstract, see preceding accession

Towards smart buildings : Utilizing sensor data and building information model in a multi-purpose environment

Smart buildings are built structures, which utilize information technological solutions to share information between different parts of the building and to automize different actions, such as air conditioning, heating and light conditions. The objective of this master’s thesis is to provide insight on how sensor data and the building information models could be utilized in a multi-purpose environment. Additionally, how sensor data and business information model can be combined practically. A case building is used in order to study multi-purpose environments. The case building is located in one of the Finland’s largest cities and it contains a school, a maternity clinic and a library, for example. The case building has some characteristics of a smart building, such as sensors and automation, but they are not fully utilized. The thesis is performed as a part of 4Apis project, which is funded by Business Finland. The thesis consists of literature review, in which the literature surrounding the combination of sensor data and building information models is studied, and an empirical interview research, in which the staff and other stake holders of the case building are interviewed. Many use-cases for the combination of sensor data and building information model was found from the literature. Literature use-cases were categorized into four different themes: people in 3D space, structural health monitoring, safety and sustainability. From the literature, many ways of combining sensor data and building information model were found. For example exporting the BIM model into a more sophisticated 3D model. Although a majority of them were so-called ad hoc solutions, one model was found from the literature, which was designed for general usage, but there were no signs at all of its use in practice. The case building had sensor data, which can be utilized with data analytics. The data was retrieved in a raw spreadsheet format, which was not utilizable as such, because of the structure of the data. By preprocessing, the data was processed by the study group into a form, from which it was possible to generate easily interpretable charts by the study group. By analyzing the sensor data, many phenomena could be verified. The class rooms of the case building were empty in the evenings, at night and on the weekends. This means that a majority of time, the classrooms were empty. From the temperature sensors it was noticed, that the temperature in a third floor class room rose higher during the days, when compared to a class room which was located in the second flood. A more in depth analysis of the sensor data is necessary during further research. In order to retrieve more qualitative data about the benefits of sensor data and building information models for the building users, three people, the principal of the case building, the energy supervisor of the city and the service coordinator of the case building, were interviewed. The interviews were conducted in a semi-structured fashion, with an emphasis on free discussion. The interviewees’ thoughts about the combination of sensor data and building information model were discussed in the interviews. There were three themes that were recognized from the interviews: efficacy, safety and wellbeing. Especially the emergency operations and energy efficiency were represented in the interviews. In both, the interviews and the literature, highlighted safety, sustainability and efficacy. Because of the small sample size of the interview study, it is recommended that the subject should be studied with a larger sample size. This thesis offers multiple views to the combination of sensor data and building information model, especially to the field of building technology In the future, the subject should be researched for example with a more in-depth data analysis or with empirically testing the solutions

Automated statistical audit system for a government regulatory authority

Governments all over the world are faced with numerous challenges while running their countries on a daily basis. The predominant challenges which arise are those which involve statistical methodologies. Official statistics to South Africa’s infrastructure are very important and because of this it is important that an effort is made to reduce the challenges that occur during the development of official statistics. For official statistics to be developed successfully quality standards need to be built into an organisational framework and form a system of architecture (Statistics New Zealand 2009:1). Therefore, this study seeks to develop a statistical methodology that is appropriate and scientifically correct using an automated statistical system for audits in government regulatory authorities. The study makes use of Mathematica to provide guidelines on how to develop and use an automated statistical audit system. A comprehensive literature study was conducted using existing secondary sources. A quantitative research paradigm was adopted for this study, to empirically assess the demographic characteristics of tenants of Social Housing Estates and their perceptions towards the rental units they inhabit. More specifically a descriptive study was undertaken. Furthermore, a sample size was selected by means of convenience sampling for a case study on SHRA to assess the respondent’s biographical information. From this sample, a pilot study was conducted investigating the general perceptions of the respondents regarding the physical conditions and quality of their units. The technical development of an automated statistical audit system was discussed. This process involved the development and use of a questionnaire design tool, statistical analysis and reporting and how Mathematica software served as a platform for developing the system. The findings of this study provide insights on how government regulatory authorities can best utilise automated statistical audits for regulation purposes and achieved this by developing an automated statistical audit system for government regulatory authorities. It is hoped that the findings of this study will provide government regulatory authorities with practical suggestions or solutions regarding the generating of official statistics for regulatory purposes, and that the suggestions for future research will inspire future researchers to further investigate automated statistical audit systems, statistical analysis, automated questionnaire development, and government regulatory authorities individually

Exploring the faecal microbiome associated with preterm birth

Jacob Westaway investigated the gut microbiome of preterm infants born in North Queensland, Australia. He observed unique associations between the gut microbiome and the unfavourable covariates, as well as acute modulation in association with probiotic prophylaxis. This supports expansion of the probiotic supplementation criteria and highlights the importance of the microbiome in health