The thesis is divided into two main parts. The first part is focusing to theoretical bases of work, it is a brief introduction to business performance and utilization of modern indicators to measure it with an emphasis on benchmarking. In second part, we deal with the practical application of information acquired and that a specific enterprise. It is an analysis of the current situation with companies in the same field of business. With the help of financial analysis, SWOT matrix identify the strengths and weaknesses of the company. The most important part is devoted to benchmarking based on publicly available data from the financial statements. For these purposes, I especially enjoyed the information from the available statement. Based on the information they are designed adequate opportunities in improving and recommendations for evaluating enterprise.

Facial recognition technology has become a ubiquitous tool in security and personal identification. However, the rise of this technology has been accompanied by concerns over inherent biases, particularly regarding ethnic and gender. This thesis examines the extent of these biases by focusing on the influence of dataset imbalances in facial recognition algorithms. We employ a structured methodological approach that integrates AI-generated images to enhance dataset diversity, with the intent to balance representation across ethnics and genders. Using the ResNet and Vgg model, we conducted a series of controlled experiments that compare the performance impacts of balanced versus imbalanced datasets. Our analysis includes the use of confusion matrices and accuracy, precision, recall and F1-score metrics to critically assess the model’s performance. The results demonstrate how tailored augmentation of training datasets can mitigate bias, leading to more equitable outcomes in facial recognition technology. We present our findings with the aim of contributing to the ongoing dialogue regarding AI fairness and propose a framework for future research in the field.

The Middle Fork John Day Basin in Northeastern Oregon is prime habitat for spring Chinook salmon and Steelhead trout. In 2008, a major tributary supporting rearing habitat, Big Boulder Creek, was restored to its historic mid-valley channel along a 1 km stretch of stream 800 m upstream of the mouth. Reduction of peak summer stream temperatures was among the goals of the restoration. Using Distributed Temperature Sensing (DTS) Fiber Optic Technology, stream temperature was monitored prior to restoration in June 2008, and after restoration in September 2008, July 2009, and August 2009. Data gathered was used to determine locations of groundwater and hyporheic inflow and to form a stream temperature model of the system. The model was used both to develop an evaluation method to interpret components of model performance, and to better understand the physical processes important to the study reach. A very clear decreasing trend in surface temperature was seen throughout each of the DTS stream temperature datasets in the downstream 500 m of the study reach. Observed reduction in temperature was 0.5°C (±0.10) in June 2008, 0.3°C (±0.37) in September 2008, 0.6°C (±0.25) in July 2009, and 0.2°C (±0.08) in August 2009. Groundwater inflow was calculated to be 3% of the streamflow for July 2009 and 1% during the August 2009 installation. Statistically significant locations of groundwater and hyporheic inflow were also determined. July 2009 data was used to model stream temperature of the 1 km (RMSE 0.28°C). The developed model performance evaluation method measures timelag, offset, and amplitude at a downstream observed or simulated point compared with the boundary condition, rather than evaluating the model based on error. These measures are particularly relevant to small scale models in which error may not be a true reflection of the ability of a model to correctly predict temperature. Breaking down model performance into these three predictive measures was a simple and graphic method to show the model's predictive capability without sorting through large amounts of data. To better understand the model and the stream system, a sensitivity analysis was conducted showing high sensitivity to streamflow, air temperature, groundwater inflow, and relative humidity. Somewhat surprisingly, solar radiation was among the lowest sensitivity. Furthermore, three model scenarios were run: a 25% reduction in water velocity, a 5°C increase in air temperature, and no groundwater inflow. Simulations of removal of groundwater inflows resulted in a 0.5°C increase in average temperature over the modeled time period at the downstream end, further illustrating the importance of groundwater in this stream system to reduce temperatures. / Graduation date: 2012