The dissertation clearly outlines the concept of Controlling, analyses and reviews the state of controlling system in the company agriKomp Bohemia s. r. o. Based on analysis evaluation, it suggests options of improving the quality of the current system, together with schemes to implement a system of plans.

That companies need to respond to the issue of climate change is no longer in question and with multiple carbon management activity options to choose from, companies need to select the most appropriate carbon management strategy to meet the challenges of a carbon constrained future. Because of South Africa’s vulnerability to the impacts of climate change as a developing country and because of business’ pivotal role in addressing this urgent issue, it is important to characterise the corporate responses to climate change. The contextual factors that influence carbon management strategy decisions need to be understood so that appropriate policy decisions are taken to encourage innovation related to climate change opportunities.To this end, secondary data in the form of qualitative responses from 70 large South African listed companies to the Carbon Disclosure Project 2011 questionnaire were analysed for this study during September and October 2012. The detailed responses were first mined using a text-mining statistical program to identify the five carbon management activities currently practised by the companies. A cluster analysis of these activities revealed four general response strategies to climate change and carbon emission reduction pressures.The companies were found to have a strong focus on saving energy with less focus on higher-order sustainability activities. While market capitalisation, turnover, sector and carbon commitment were shown to correlate and indeed predict the carbon management strategy chosen by companies, no significant link was found between carbon management strategy and corporate financial performance. / Dissertation (MBA)--University of Pretoria, 2012. / Gordon Institute of Business Science (GIBS) / unrestricted

En este trabajo se exponen investigaciones relevantes que discuten los efectos que las Fusiones y Adquisiciones (F&A) tienen sobre las variables empresariales, con particular énfasis sobre la construcción y destrucción de valor. Para este fin, se analizan las variables de estrategia gerencial, creación de valor, función comercial, capital humano, aspectos operativos y tecnológicos y gobierno corporativo. Finalmente, a la luz de la información bibliográfica revisada, y con la finalidad de que pueda servir de apoyo a las empresas para la toma de decisiones en relación a los procesos de F&A, se recomienda llevar a cabo un estudio empírico que permita cuantificar los efectos sobre las variables empresariales e identificar cuáles son las condiciones que favorecen el éxito de las F&A. / This paper presents relevant research that discusses the effects that Mergers and Acquisitions (M&A) have on business variables, with particular emphasis on the construction and destruction of value. For this purpose, the variables of management strategy, value creation, commercial function, human capital, operational and technological aspects, and corporate governance are analyzed. Finally, in light of the bibliographic information reviewed, and with the objective that it can serve to companies as a support for decision making in relation to M & A processes, it is recommended to carry out an empirical study to quantify the effects on the business variables and identify which are the conditions that favor the success of the M & A. / Trabajo de Suficiencia Profesional

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis proposes an Adaptive Rule-Based Energy Management Strategy (ARBS EMS) for a parallel hybrid electric vehicle (P-HEV). The strategy can effciently be deployed online without the need for complete knowledge of the entire duty cycle in order to optimize fuel consumption. ARBS improves upon the established Preliminary Rule-Based Strategy (PRBS) which has been adopted in commercial vehicles. When compared to PRBS, the aim of ARBS is to maintain the battery State of Charge (SOC) which ensures the availability of the battery over extended distances. The proposed strategy prevents the engine from operating in highly ineffcient regions and reduces the total equivalent fuel consumption of the vehicle. Using an HEV model developed in Simulink, both the proposed ARBS and the established PRBS strategies are compared across eight short duty cycles and one long duty cycle with urban and highway characteristics. Compared to PRBS, the results show that, on average, a 1.19% improvement in the miles per gallon equivalent (MPGe) is obtained with ARBS when the battery initial SOC is 63% for short duty cycles. However, as opposed to PRBS, ARBS has the advantage of not requiring any prior knowledge of the engine effciency maps in order to achieve optimal performance. This characteristics can help in the systematic aftermarket hybridization of heavy duty vehicles.

The purpose of this thesis is to increase an understanding about management strategies in transitional economies from the perspective of creativity/entrepreneurship, control, trust and social/cultural changes. The study is carried out through a case study of management strategies in a transition economy, Kazakhstan. Transitional economies are not a new interest for management research. However not many studies have been made in Kazakhstan about management strategies, compared to the countries that are situated closer to Europe, such as East European countries.. There are not so many studies done about the Central Asian transition economies like Kazakhstan. Therefore a country like Kazakhstan can contribute to the literature. Another important distinction in research of transitional economies is whether it concerns local experiences or cross- cultural experiences. The study shows the perspective of Kazakhstani managers on creativity/entrepreneurship, control, trust and social/cultural change and their perceptions of western management strategies. The study shows that there are two phases in a transition economy. The first phase is in the beginning of a transition economy, that time is characterized as chaotic and unpredictable. The second phase is the time when the economy becomes more stable and people get values back based on socialism and used them in business. In the present time, the values are formed through a mixture socialism and capitalism. The process of transition influences the four variables: creativity/entrepreneurship, control, trust and social/cultural changes. It also influenced the way of management in transitional economy. At the end of the thesis there will be given some theoretical and managerial implications of the thesis, the limitations of the thesis and the suggestions for future research.

This thesis focuses on developing an intelligent energy management strategy for eco-driving in Connected and Autonomous Hybrid Electric Vehicles (CA-HEV's), which can be implemented in real-time. The strategy is divided into two layers, i.e. the upper level controller and the lower level controller. The upper level controller can be executed on the remote server. It is responsible for extracting the information from the driver about the trip and the vehicle information using the communication capabilities of the CA-HEV. The gathered information is then utilized by dynamic programming (DP), which is implemented in a bi-layer fashion to reduce the computation burden on the server. The outer layer of the DP algorithm and the optimal velocity trajectory and the inner layer optimizes the power distribution in the powertrain to minimize fuel consumption alongside maintaining charge balance conditions. These global optimal results are evaluated for an ideal environment without any traffic information. The lower level controller is responsible for real-time implementation on vehicles in the real world environment and is based on a well-accredited reinforcement learning (RL) strategy, i.e., Q-learning. The RL-based controller optimally distributes the power in a CA-HEV and maintains charge balance conditions. Furthermore, the RL-based controller is also trained on the remote server based on global optimal results obtained from the DP algorithm. The optimal parameter information is then resent to the vehicle's embedded controller for real-time implementation. Simulations are performed for Toyata Prius (2010) on MATLAB and Simulink, and road information is gathered from SUMO. Simulation results provide a comparative study between the global optimal and the RL-based controller. To validate the adaptiveness of the RL-based controller, it is also tested on two approximate real-world drivecycles and its performance is compared against global optimal results evaluated using DP. / Thesis / Master of Applied Science (MASc)

I samband med den globala uppvärmningen ökar efterfrågan för rena och förnybara bränslen alltmer i dagens samhälle. Eftersom flygindustrin idag är ansvarig för samma mängd växthusgaser som all motortrafik i Sverige, skulle ett byte till en avgasfri energikälla för flygfarkoster vara ett stort framsteg. Därför har projektet genom modellering framtagit ett hybridsystem av ett batteri och en bränslecell och undersökt hur kombinationen av olika storlekar på dem presterar i en driftcykel. Då batterier har hög specifik effekt men är tunga, kompletteras de med fördel av bränsleceller, som är lättviktiga och bidrar med uthållig strömförsörjning. På så sätt blir hybriden optimal för flygfarkoster. Kandidatarbetet är en del av projektet Green Raven, ett tvärvetenskapligt samarbete mellan instutitionerna Tillämpad Elektrokemi, Mekatronik och Teknisk Mekanik på Kungliga Tekniska Högskolan. Driftcykelmodelleringen gjordes i Simulink, och flera antaganden gjordes beträffande effektprofilen, samt bränslecellens mätvärden och effekt. Tre olika energihushållningsscheman skapades, vilka bestämde bränslecellseffekten beroende på vätgasnivån och batteriets laddningstillstånd. Skillnaden på systemen var vilka intervall av laddningstillstånd hos batteriet som genererade olika effekt hos bränslecellen.  Det bästa alternativet visade sig vara 0/100-systemet, eftersom det var det enda som inte orsakede någon degradering av bränslecellens kapacitet. / In today’s society, with several environmental challenges such as global warming, the demand for cleanand renewable fuels is ever increasing. Since the aviation industry in Sweden is responsible for the sameamount of greenhouse gas emissions as the motor traffic, a change to a non-polluting energy source forflying vehicles would be considerable progress. Therefore, this project has designed a hybrid system of abattery and a fuel cell and investigated how different combinations of battery and fuel cell sizes perform ina drive cycle, through computer modelling. As batteries possess a high specific power but are heavy, thefuel cells with high specific energy complement them with a sustained and lightweight power supply,which makes the hybrid perfect for aviation. The bachelor thesis is a part of Project Green Raven, aninterdisciplinary collaboration with the institutions of Applied Electrochemistry, Mechatronics andEngineering Mechanics at KTH Royal Institute of Techology. The drive cycle simulations were done inSimulink, and several assumptions regarding the power profile, fuel cell measurements and power weremade. Three different energy management strategies were set up, determining the fuel cell powerdepending on hydrogen availability and state of charge of the battery. The strategies were called 35/65,20/80 and 0/100, and the difference between them was at which state of charge intervals the fuel cellchanged its power output. The best strategy proved to be 0/100, since it was the only option which causedno degradation of the fuel cell whatsoever.

Introduction: Prolonged neonatal jaundice is defined as persistent jaundice at two-three weeksof age. Prolonged neonatal jaundice is usually harmless but one in 2500 newborns have jaundicedue to cholestasis, why further investigation must be made. Region Örebro County introduced anew referral routine for prolonged neonatal jaundice 2021-02-12 that allows for follow up inLindesberg or Karlskoga instead of Örebro alone, and only three variables need to be mentionedfor the referrals to be considered complete, contrary to previous six. Aim: To compare Region Örebro County’s current and previous referral routine for prolongedneonatal jaundice in regard to compliance and complete referrals. Methods: A chart review was performed of all children born in Region Örebro County between2021-02-12 and 2022-02-01 with either sampled bilirubin and/or diagnosis code p.55, p.57-59. Results: A statistically significant difference was observed between the routines regarding stoolcolour (p=0.004), general condition (p<0.001), complete referrals (p<0.001) and length ofinvestigation (p<0.001). Significantly fewer patients were lost during investigation (p<0.002) orhad no feedback on their test results (p<0.011). Two cases of cholestasis were found. The meanvalue of conjugated bilirubin was higher in patients who saw a doctor. Few children werereferred from Lindesberg or Karlskoga. Conclusion: The current routine had more complete referrals, shorter investigation times andless absence of feedback as well as fewer patients lost during investigation. Sick patients wereidentified before getting critically ill. Shifting the entire investigation to primary care andimplementing stool charts could possibly improve the routine further.

Energy-efficient powertrain components and advanced vehicle control strategies are two effective methods to promote the potential of hybrid electric vehicles (HEVs). Aiming at hybrid system efficiency improvement, this thesis presents a comprehensive review of energy-efficient hybrid powertrain specific engines and proposes three improved energy management strategies (EMSs), from a basic non-adaptive real-time approach to a state-of-the-art learning-based intelligent approach. To evaluate the potential of energy-efficient powertrain components in HEV efficiency improvement, a detailed discussion of hybrid powertrain specific engines is presented. Four technological solutions, i.e., over-expansion cycle, low temperature combustion mode, alternative fuels, and waste heat recovery techniques, are reviewed thoroughly and explicitly. Benefits and challenges of each application are identified, followed by specific recommendations for future work. Opportunities to simplify hybrid-optimized engines based on cost-effective trade-offs are also investigated. To improve the practicality of HEV EMS, a real-time equivalent consumption minimization strategy (ECMS)-based HEV control scheme is proposed by incorporating powertrain inertial dynamics. Compared to the baseline ECMS without such considerations, the proposed control strategy improves the vehicle drivability and provides a more accurate prediction of fuel economy. As an improvement of the baseline ECMS, the proposed dynamic ECMS offers a more convincing and better optimal solution for practical HEV control. To address the online implementation difficulty faced by ECMS due to the equivalence factor (EF) tuning, a predictive adaptive ECMS (A-ECMS) with online EF calculation and instantaneous power distribution is proposed. With a real-time self-updating EF profile, control dependency on drive cycles is reduced, and the requirement for manual tuning is also eliminated. The proposed A-ECMS exhibits great charge sustaining capabilities on all studied drive cycles with only slight increases in fuel consumption compared to the basic non-adaptive ECMS, presenting great improvement in real-time applicability and adaptability. To take advantage of machine learning techniques for HEV EMS improvement, a deep reinforcement learning (DRL)-based intelligent EMS featuring the state-of-the-art asynchronous advantage actor-critic (A3C) algorithm is proposed. After introducing the fundamentals of reinforcement learning, formulation of the A3C-based EMS is explained in detail. The proposed algorithm is trained successfully with reasonable convergence. Training results indicate the great learning ability of the proposed strategy with excellent charge sustenance and good fuel optimality. A generalization test is also conducted to test its adaptability, and results are compared with an A-ECMS. By showing better charge sustaining performance and fuel economy, the proposed A3C-based EMS proves its potential in real-time HEV control. / Thesis / Doctor of Philosophy (PhD)

The automotive industry is inevitably experiencing a paradigm shift from fossil fuels to electric powertrain with significant technological breakthroughs in vehicle electrification. Emerging hybrid electric vehicles were one of the first steps towards cleaner and greener vehicles with a higher fuel economy and lower emission levels. The energy management strategy in hybrid electric vehicles determines the power flow pattern and significantly affects vehicle performance. Therefore, in this thesis, a learning-based strategy is proposed to address the energy management problem of a hybrid electric vehicle in various driving conditions. The idea of a deep recurrent neural network-based energy management strategy is proposed, developed, and evaluated. Initially, a hybrid electric vehicle model with a rule-based supervisory controller is constructed for this case study to obtain training data for the deep recurrent neural network and to evaluate the performance of the proposed energy management strategy. Secondly, due to its capabilities to remember historical data, a long short-term memory recurrent neural network is designed and trained to estimate the powertrain control variables from vehicle parameters. Extensive simulations are conducted to improve the model accuracy and ensure its generalization capability. Also, several hyper-parameters and structures are specifically tuned and debugged for this purpose. The novel proposed energy management strategy takes sequential data as input to capture the characteristics of both driver and controller behaviors and improve the estimation/prediction accuracy. The energy management controller is defined as a time-series problem, and a network predictor module is implemented in the system-level controller of the hybrid electric vehicle model. According to the simulation results, the proposed strategy and prediction model demonstrated lower fuel consumption and higher accuracy compared to other learning-based energy management strategies. / Thesis / Master of Applied Science (MASc)