Many engineering problems are naturally posed as optimal control problems. It may involve moving between two points in the fastest possible way, or to put a satellite into orbit with minimum energy consumption. Many optimal control problems are too difficult to be solved analytically and therefore require the use of numerical methods. The numerical methods that are the most widespread are the so-called direct methods. However, there is one major drawback with these. If the problem is non-convex, the solution is not guaranteed globally optimal, that is, the absolute best, instead it is guaranteed locally optimal, that is the best in its vicinity. To compensate for this, the problem should be solved several times, under different conditions, in order to investigate whether the solution is a good candidate for the global optimum. CasADi is a software specifically designed for dynamic optimization. It has gained wide spread in recent years because it provides all the necessary building blocks for dynamic optimization. This has given individual engineers and scientists the ability to independently formulate and solve all sorts of optimal control problems. However, this requires good theoretical knowledge of the necessary numerical methods. The advantage of a toolbox, which solves general optimal control problems, is that the underlying numerical methods have been tested and shown to function on optimal control problems with known solutions. This means that the user does not need exhaustive knowledge of the numerical methods involved, but can focus on formulating and solving optimal control problems. The main contribution of this thesis is an optimal control toolbox for MATLAB based on CasADi. The toolbox does not require expert knowledge of the numerical methods, but provides an alternative lower level abstraction that allows for more complex problem formulations. The toolbox implements two direct methods, direct multiple shooting and direct collocation. This allows a problem formulation with many degrees of freedom. The most important property of the toolbox is that the discretization can be changed, without the problem formulation needing to be altered. This way the user can easily change the conditions for his/her problem. The thesis describes how the two implemented direct methods work, and the design choices made. It also describes what remains to test and evaluate, and the problems that have been used as a reference during the development process.

Two novel materials have been developed: TiO2/poly(dimethylsiloxane) (PDMS) beads as buoyant photocatalyst materials for water remediation, and copper rhodamine‑B silane (Cu‑RBS) as an n ‑type organic/inorganic hybrid for thermoelectric applications. The approach to incorporate TiO2 into low‑density PDMS beads addresses many of the challenges traditionally encountered when creating buoyant photocatalysts, an area which is crucial for wide‑spread remediation of water resources, including natural bodies of water. The performance and reusability of the buoyant photocatalyst materials, demonstrated by using methylene blue as a model degradation target, is strong enough for environmental application. The use of a kinetic model and the introduction of a parameter to allow comparison of buoyant photocatalysts is also included as part of the analysis. The performance of Cu‑RBS was investigated as a low‑temperature thermoelectric material. Clear improvements in the electrical conductivity and Seebeck coefficient are observed for RBS upon coordination to Cu2+. Evidence explaining this improvement is provided by computational analysis and by concentration‑dependent optical absorption and fluorescent emission measurements, all of which indicate that a metal‑to‑ligand charge transfer occurs from Cu2+ to RBS. Although the power factor of Cu‑RBS is low compared to other materials reported in the literature, these results provide a promising approach to increasing both the Seebeck coefficient and electrical conductivity of n‑type small molecule organic systems.

In the continental United States, mean surface air temperature is expected to increase by up to 5°C within 100 years. With hotter temperatures, leaf budbreak is expected to occur earlier in forests, and leaf area is expected to increase in locations where temperature is limiting. The response of plant photosynthesis to hotter temperatures is less certain; plant productivity could increase or decrease. Past studies have found intraspecific variation in the responses of forest tree productivity, phenology, canopy leaf area, and leaf isoprene emission to warming, which all influence carbon uptake and yield for agricultural tree species; it is therefore important to understand not only how hot climates affect carbon uptake and biomass production between different tree species, but also in different genotypes of the same species. We conducted a common garden study at the Biosphere 2 research center near Oracle, AZ, USA. We created a hybrid poplar plantation of 168 trees, which were planted as cuttings in January 2013. The trees used in this study are comprised of 5 distinct genotypes of Populus deltoides × trichocarpa from a range of average annual air temperatures. We measured photosynthetic capacity, leaf phenological timing, canopy leaf area and aboveground woody biomass in 2014 growing season, and leaf isoprene emission in the 2015 growing season. We observed a strong effect of genotype on aboveground woody biomass increment, implying strong local adaptation to the home range and limited phenotypic plasticity in terms of physiological and biometric responses to high temperature environments. Our study suggests that genotypes from hotter home ranges are able to maintain photosynthetic capacity and canopy leaf area late into the growing season, despite high temperatures, and thus produce more aboveground woody biomass. This study may have implications for agricultural management—as temperatures warm where managers currently grow hybrid poplar for agricultural or other purposes, the genotypes from those home ranges would likely have reduced yield; managers could investigate the use of genotypes from home ranges with higher average temperatures to replace the vulnerable local varieties.

A problem that has become apparently growing in the deployment of renewable energy systems is the power grids inability to accept the forecasted growth in renewable energy generation integration. To support forecasted growth in renewable generation integration, it is now recognised that Energy Storage Technologies (EST) must be utilised. Recent advances in Hydrogen Energy Storage Technologies (HEST) have unlocked their potential for use with constrained renewable generation. HEST combines Hydrogen production, storage and end use technologies with renewable generation in either a directly connected configuration, or indirectly via existing power networks. A levelised cost (LC) model has been developed within this thesis to identify the financial competitiveness of the different HEST application scenarios when used with grid constrained renewable energy. Five HEST scenarios have been investigated to demonstrate the most financially competitive configuration and the benefit that the by-product oxygen from renewable electrolysis can have on financial competitiveness. Furthermore, to address the lack in commercial software tools available to size an energy system incorporating HEST with limited data, a deterministic modelling approach has been developed to enable the initial automatic sizing of a hybrid renewable hydrogen energy system (HRHES) for a specified consumer demand. Within this approach, a worst-case scenario from the financial competitiveness analysis has been used to demonstrate that initial sizing of a HRHES can be achieved with only two input data, namely – the available renewable resource and the load profile. The effect of the electrolyser thermal transients at start-up on the overall quantity of hydrogen produced (and accordingly the energy stored), when operated in conjunction with an intermittent renewable generation source, has also been modelled. Finally, a mass-transfer simulation model has been developed to investigate the suitability of constrained renewable generation in creating hydrogen for a hydrogen refuelling station.

Singapore är ett fascinerande land. På bara några få decennier har landet gått från att vara en liten instabil stadsstat utan större naturresurser till att bli ett ekonomiskt världscentrum. Mur hur fungerar egentligen Singapores politiska styrelseskick? Är landet verkligen en demokrati eller har det i själva verket diktatoriska drag? Denna uppsats syftar till att steg för steg undersöka landets politiska situation för att slutligen landa i en definition om vad Singapore egentligen står politiskt. Dem styrande hävdar att dem levererar en effektiv och okorrumperad regering till medborgarna medan vissa oppositionspolitiker menar att regeringen styr med auktoritära medel. Hur kan man definiera den politiska situationen i Singapore idag och framför allt vilken regimtyp kan landet klassas som? Detta arbete är en fallstudie av teorikonsumerande art där olika fakta kommer att analyseras och sedan sammanfattas i en slutlig analys där jag skall fastställa regimtypen.

UA Open Access Publishing Fund / We examined a secondary contact zone between two species of desert tortoise, Gopherus agassizii and G. morafkai. The taxa were isolated from a common ancestor during the formation of the Colorado River (4–8 mya) and are a classic example of allopatric speciation. However, an anomalous population of G. agassizii comes into secondary contact with G. morafkai east of the Colorado River in the Black Mountains of Arizona and provides an opportunity to examine reinforcement of species’ boundaries under natural conditions. We sampled 234 tortoises representing G. agassizii in California (n = 103), G. morafkai in Arizona (n = 78), and 53 individuals of undetermined assignment in the contact zone including and surrounding the Black Mountains. We genotyped individuals for 25 STR loci and determined maternal lineage using mtDNA sequence data. We performed multilocus genetic clustering analyses and used multiple statistical methods to detect levels of hybridization. We tested hypotheses about habitat use between G. agassizii and G. morafkai in the region where they co-occur using habitat suitability models. Gopherus agassizii and G. morafkai maintain independent taxonomic identities likely due to ecological niche partitioning, and the maintenance of the hybrid zone is best described by a geographical selection gradient model.

Transaction cost economics (TCE) theorists traditionally have classified transactions between firms as governed by either market or hierarchy. By assessing characteristics of the transaction - asset specificity, uncertainty, and frequency - firms choose the governance form which minimizes transaction costs, the costs of administering the business deal. During the 1980s, however, TCE has found itself unable to explain the proliferation of strategic alliances. These hybrid relationships seek the benefits of both markets and hierarchies, including quasi-integration, the control of assets without actual ownership. Further, hybrids tend to prefer trust-based relational contracting. TCE's acknowledgment of hybrids, however, raises other questions surrounding the behavioral assumptions which supposedly influence the transaction characteristic governance linkage. Various dissenting researchers have theorized that (1) trust is more dominant in business than opportunism (2) the behavioral assumptions actually function as variables in different contexts, and (3) trust offers an integration mechanism for behavioral variables.

Despite the increasing popularity of hybrid-electric vehicles (HEVs), few studies have quantified their real-world particle emissions from internal combustion engine (ICE) re-ignition events (RIEVs). RIEVs have been known to occur under unstable combustion conditions which frequently result in particle number emission rates (PNERs) that exceed stabilized engine operation. Tailpipe total PN (5 to 560 nm diameter) emission rates (#/s) from a conventional vehicle (CV) and hybrid electric vehicle (HEV) 2010 Toyota Camry were quantified on a 50 km (32 mi) route over a variety of roadways in Chittenden County, Vermont using the Total On-board Tailpipe Emissions Measurement System (TOTEMS). While HEVs are known to have significant fuel conserving benefits compared to conventional vehicles, less is known about the relative emissions performance of HEVs. This study is the first to characterize RIEVs under a range of real-world driving conditions and to directly compare HEV and CV PNER during driving on different road sections. A total of 28 CV and 33 HEV sampling runs were conducted over an 18-month period under ambient temperatures ranging between -4 and 35 °C. A road classification based upon speed and intersection density divided the route into four different road sections: Freeway, Rural, Urban I and Urban II. Due to the distinct on-off cycling of the HEV ICE, a new operational mode framework (ICE OpMode) was developed to characterize shutdown, off, re-ignition and stabilized HEV ICE operation. Road section was found to affect overall ICE OpMode distribution, with HEV engine-off operation averaging 57%, 36% and 5% of total operation for combined Urban, Rural and Freeway road sections, respectively. Re-ignition frequency was found to range between 11 and 133 events per hour, with spatial density ranging between 0.1 and 5.6 events per kilometer of roadway. A total of 3212 re-ignition events were observed and recorded, and mean HEV PNER during RIEVs, on average, ranged between 2.4 and 4.4 times greater than that of HEV Stabilized operation. Approximately 65% of all re-ignition events resulted in a peak PNER exceeding the 95% percentile for all ICE-on activity in both vehicles (9.3 x 1011 #/s), known as a High Emission Event Record (HEER). RIEV operation made up only 7.4% of total ICE-on operation for both vehicles but accounted for 35.4% of all HEERs. Overall, total particles emitted during HEV operation associated with re-ignition events ranged from 5% for Freeway driving to 60% for Urban I driving. Comparisons between vehicles found an average of 37% and 7% fuel conserving benefits of the HEV during Urban I and Freeway driving, respectively. However, a different effect was found for PN emissions. During Urban I driving, where RIEVs were most frequent, on average HEV PNER was 2.3 times greater than overall mean CV PNER. For Freeway driving, where the HEV operated similar to a conventional vehicle, mean CV PNER was 2.4 times greater than mean HEV PNER. PNER from partial re-ignition events following an incomplete ICE shutdown (no period of prior engine off operation) were on average 1.65 times greater than those occurring when the ICE shutdown for at least one second. The typical fuel consumption benefits of HEVs in urban driving are associated with a tradeoff in PN emissions. The HEV ICE operating behavior has implications for the spatial distribution of PN hot-spots as well as the associated micro-scale modeling of alternative vehicle technology emissions. It is likely that building a model of HEV behavior based upon CV activity will be appropriate, with consideration of a hybridization factor and, as a result of these analyses, a re-ignition factor.

The uncontrolled usage of hashtags in social media makes them vary a lot in the quality of semantics and the frequency of usage. Such variations pose a challenge to the current approaches which capitalize on either the lexical semantics of a hashtag by using metadata or the contextual semantics of a hashtag by using the texts associated with a hashtag. This thesis presents a hybrid approach to clustering hashtags based on their semantics, designed in two phases. The first phase is a sense-level metadata-based semantic clustering algorithm that has the ability to differentiate among distinct senses of a hashtag as opposed to the hashtag word itself. The gold standard test demonstrates that sense-level clusters are significantly more accurate than word-level clusters. The second phase is a hybrid semantic clustering algorithm using a consensus clustering approach which finds the consensus between metadata-based sense-level semantic clusters and text-based semantic clusters. The gold standard test shows that the hybrid algorithm outperforms both the text-based algorithm and the metadata-based algorithm for a majority of ground truths tested and that it never underperforms both baseline algorithms. In addition, a larger-scale performance study, conducted with a focus on disagreements in cluster assignments between algorithms, shows that the hybrid algorithm makes the correct cluster assignment in a majority of disagreement cases.

This paper presents an analysis of the feasibility of utilizing a hybrid renewable energy system to supply the energy demand of a milk and meat farm in Cuba. The study performs simulation and optimization to obtain a system design of a hybrid renewable energy system for application on the farm Desembarco del Granma in the Villa Clara province in the central part of Cuba, for three different cases of biomass availability. The energy resources considered are solar PV, biogas, and wind. A field study is carried out to evaluate the energy load and the biomass resource available for biogas production of the farm Desembarco del Granma, and the feasibility of biogas electrification is evaluated for the three different scenarios of biomass availability. The field study methodology includes semi structured interviews and participant observation for information collection. The farm Desembrero del Granma is estimated to have a scaled annual average electrical load of 264 kWh/day with peak load 26.34 kW, while the scaled annual average deferrable load of the farm was estimated to be 76 kWh/day with a peak load 16 kW. The thermal load was find to consist primarily of energy for water heating and cooking. The thermal demand for cooking was estimate to be 4.5 kWh per day, while the thermal load for water heating was not estimated. The thermal energy need for water heating is assumed to be provided for by solar thermal energy, and is not included in the energy system models of this study. For the modeling, the thermal demand for cooking is assumed to be provided by combustion of biogas. System simulation and optimization in regard to energy efficiency, economic viability and environmental impact is carried out by applying the Hybrid Optimization Model for Electric Renewables (HOMER) simulation and optimization software tool. For two of the biomass scenarios, the optimized energy systems received in HOMER were identical; hence only two biomass scenarios were analyzed. The first one represents the current biomass collected and the biogas production capacity of the farm (including the one not yet utilized), and the second one represents the amount of biomass available if the animals would be gathered in the same place all of the time. A PV-wind hybrid energy system with 100 kW PV installed capacity, 30 kW wind power installed capacity consisting of 10 wind turbines of the size 3 kW, a battery bank of 100 batteries (83.4 Ah/24 V), and a 100 kW inverter is considered the most feasible solution for the current biomass scenario. For the increased biomass scenario, a PV-biogas hybrid energy system configuration of 5 kW PV installed capacity, a 60 kW biogas generator, and an inverter of the size 10 kW is considered the most feasible option. Biogas electrification is shown to not be economically feasible for the current biomass scenario during the conditions modeled in this study, but for the increased biomass scenario biogas electrification was shown to be a feasible option. If the farm would build more biodigestors, biogas electrification could thereby be effective from a financial point of view.