In the present study, the topic under scrutiny is how Philanthropic Corporate Social Responsibility plays a role within the workplace. This study is being presented due to the fact that CSR is often researched with the frame of reference of marketing, whereas the managerial perspective is not often considered. The research question addressed will take a managerial perspective on CSR and examine if implementing Philanthropic CSR only affects extrinsic factors, or if it also has an intrinsic effect on employee motivation. In this research, the questions undergoing discussion to support the study are 1) To what extent does CSR motivate employees? 2) What involvement level of CSR is required for employee motivation? 3) What level does CSR fit into on Maslow’s Hierarchy of Needs? This thesis focuses mainly on connecting Philanthropic CSR to Maslow’s Hierarchy of Needs. Other supporting theories that will be used include: Herzberg’s Motivation Hygiene Theory, McGregor’s X and Y Theory, Self-Determination Theory, and Motivational Crowding Theory. To conduct the research needed, the authors have conducted a deductive study through a comparative design, obtaining qualitative data by the means of semi structured skype interviews. These interviews focus on American for-profit companies that have CSR in their strategy but focus on profit. The data collected has then been analyzed through a thematic approach. The information obtained suggests that Philanthropic CSR plays a role within the workplace, however, it is not seen as an incentive by employees. Moreover, an employee must be personally involved within the company’s CSR program to fully feel the effects that the program has to offer. This is due to the emotional investments made while participating in the said philanthropy. The conclusion found from this study is that Philanthropic CSR does have an intrinsic effect on employees because of the ‘feel good feeling’ they benefit from when they do participate in the Philanthropic CSR. However, to fully understand the effects of the motivational aspects, further research must be conducted.

Syfte: Mot bakgrund kring en ökad turism i europeiska storstäder (Statista, u.å.) har syftet med studien varit att studera Barcelona som exempel för att bidra med en djupare förståelse kring hur turister och lokalbefolkning uppfattar varandra i stadsmiljöer med högt turistantal. Samt undersöka hur turismen har förändrat stadens stadskärna och miljö (Duran, 2005). Metod: I studien har ett kvalitativt tillvägagångssätt applicerats i form av semistrukturerade intervjuer och insamling av vetenskapliga artiklar till empirin. Tolv personer som har intervjuats som har koppling till Barcelona och det sammanfattas i empirin, tillsammans med de vetenskapliga artiklarna. Slutsatser: Den här studie har undersökt Barcelona som exempel för att belysa massturism som är ett omfattande problem som även går att finna i andra europeiska storstäder. Genom de insamlade intervjuerna och de vetenskapliga artiklar påvisades det att Barcelona har fått både positiva och negativa effekter av den massiva turism som finns i staden. Utifrån de insamlade intervjusvaren kunde slutsatsen dras att den sociala aspekten har störst negativ inverkan på lokalbefolkningen i staden. / Purpose: Against the background of increased tourism in European metropolitan areas (Statista, u.o.), the purpose of the study has been to study Barcelona as an example to contribute a deeper understanding of how tourists and locals perceive each other in urban environments with a high tourist number. As well as investigating how tourism has changed the city's city center and environment (Duran, 2005). Methodology: In this study, a qualitative approach has been applied in the form of semi-structured interviews and the gathering of scientific articles of empirical data. Twelve people interviewed who are connected to Barcelona and this is summarized in empirical section, along with the scientific articles. Conclusions: This study has explored Barcelona as an example to illustrate mass tourism, which is a major problem that can be found in other European cities. Through the collected interviews and the scientific articles, it was demonstrated that Barcelona has had both positive and negative effects of the massive tourism that exists in the city. Based on the collected interview answers, it could be concluded that the social aspect has the most negative impact on local people in the city.

In this thesis we show how reactive processes can be affected by the presence of different types of spatial constraints, so much so that their nonlinear dynamics can be qualitatively altered or that new and unexpected behaviors can be produced. To understand how this interplay can occur in general terms, we theoretically investigate four very different examples of this situation. <p><p>The first system we study is a reversible trimolecular chemical reaction which is taking place in closed one-dimensional lattices. We show that the low dimensionality may or may not prevent the reaction from reaching its equilibrium state, depending on the microscopic properties of the molecular reactive mechanism. <p><p>The second reactive process we consider is a network of biological interactions between pigment cells on the skin of zebrafish. We show that the combination of short-range and long-range contact-mediated feedbacks can promote a Turing instability which gives rise to stationary patterns in space with intrinsic wavelength, without the need of any kind of motion.<p><p>Then we investigate the behavior of a typical chemical oscillator (the Brusselator) when it is constrained in a finite space. We show that molecular crowding can in such cases promote new nonlinear dynamical behaviors, affect the usual ones or even destroy them. <p><p>Finally we look at the situation where the constraint is given by the presence of a solid porous matrix that can react with a perfect gas in an exothermic way. We show on one hand that the interplay between reaction, heat flux and mass transport can give rise to the propagation of adsorption waves, and on the other hand that the coupling between the chemical reaction and the changes in the structural properties of the matrix can produce sustained chemomechanical oscillations. <p><p>These results show that spatial constraints can affect the kinetics of reactions, and are able to produce otherwise absent nonlinear dynamical behaviors. As a consequence of this, the usual understanding of the nonlinear dynamics of reactive systems can be put into question or even disproved. In order to have a better understanding of these systems we must acknowledge that mechanical and structural feedbacks can be important components of many reactive systems, and that they can be the very source of complex and fascinating phenomena.<p> / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

My thesis is divided into five chapters, starting with a general introduction in first chapter and sample preparation and protein-NMR assignment techniques in second chapter. The remaining three chapters focus on three different areas/projects that I have worked on. Chapter 1: Introduction to nanomaterials and all the experimental techniques This chapter reviews different kinds of nanomaterials and their application utilized for protein-nanomaterial interaction in our study, along with the introduction to different spectroscopy and microscopy techniques used for the interaction studies. Starting with introduction of nanomaterials and all the experimental techniques, which constitute the arsenal for structural studies of the protein-nanomaterial interaction, different steps enroute to structural and dynamic interaction are outlined in detail. Chapter 2: Preparation and Characterization of Proteins used for nanomaterial interaction studies Proteins are generally of three kinds- globular (structured), intrinsically disordered and membrane bound. These proteins have different functions in living organisms and play a major role to maintain metabolism and other important factors. To probe protein-nanomaterial interactions, we have chosen different protein/peptides. This chapter describes the protocol/procedure used for purifying the proteins. For studying a globular protein, ubiquitin was chosen. Nanomaterial-IDP interaction was investigated using the intrinsically disordered central linker domain of human insulin like growth factor binding protein-2 (L-hIGFBP2). The hydrophobic membrane interacting part of the prion protein was chosen as a representative membrane protein. The characterization of the proteins by NMR spectroscopy is also described. Chapter 3: A nanomaterial based novel macromolecular crowding agent Carbon quantum dots (CQD) are nanomaterials with size less than 10 nm, first obtained in 2004 during purification of single-walled carbon-nanotubes. Since then CQDs have been used in a wide range of applications due to their low cost of preparation and favorable properties such as chemical inertness, biocompatibility, non-toxicity and solubility in aqueous medium. One of the applications of CQDs has been their use for imaging and tracking proteins inside cells, based on their intrinsic fluorescence. Further, quantum dots exhibit concentration dependent aggregation while retaining their solubility. Fluorescent carbon quantum dots (CQD) induce macromolecular crowding making them suitable for probing the structure, function and dynamics of both hydrophilic and hydrophobic peptides/ proteins under near in-cell conditions. We have prepared hydrophilic and hydrophobic quantum dots to see the crowding effect. After characterization of CQD, we tested the property of proteins with CQD and found that CQD behaves as a macromolecular crowding agent by mimicking near in-cell conditions. In our study, we have chosen a globular protein, an intrinsically disordered protein (IDP) and one hydrophobic membrane peptide. We have also compared the crowding property of CQD with ficoll which is widely used commercial crowding agent. The overall study tells that the CQD acts like crowding agent and can be used for the study of macromolecular crowding effect. This makes them suitable for structural and functional studies of proteins in near in-cell conditions. Chapter 4: Ubiquitin-Graphene oxide interactions Described here is the interaction of human ubiquitin with GO using NMR spectroscopy and other techniques such as Fluorescence spectroscopy, isothermal titration calorimetry (ITC), UV-Visible spectroscopy, dynamic light scattering (DLS), zeta potential measurements and transmission electron microscopy (TEM). The globular protein ubiquitin interacts with GO and undergoes a dynamic and reversible association-dissociation in a fast exchange regimen as revealed by NMR spectroscopy. The conformation of the protein is not affected and the primary interaction is seen to be electrostatic in nature due to the polar functional groups present on the protein and GO sheet surface. For the first time we have shown that the interaction between ubiquitin and GO is dynamic in nature with fast and reversible adsorption/desorption of protein from the surface of GO. This insight will help in understanding the mechanistic aspects of interaction of GO with cellular proteins and will help in designing appropriate functionalized graphene oxide for its biological application. Chapter 5: Section A: Interaction of an intrinsically disordered protein (L-HIGFBP2) with graphene oxide The interaction between intrinsically disordered linker domain of human insulin-like growth factor binding protein-2 (L-hIGFBP2) with GO was studied using NMR spectroscopy and other techniques such as isothermal titration calorimetry (ITC), dynamic light scattering (DLS), zeta-potential measurements. The study revealed that the disordered protein L-hIGFBP2 interacts with GO through electrostatic interaction and undergoes a dynamic and reversible association-dissociation in a fast exchange regime. The conformation of the protein is not affected. Section B: Stability of an Intrinsically disordered protein through weak interaction with Silver nanoparticles Using NMR spectroscopy and other techniques we probed the mechanism of L-hIGFBP2–AgNP interactions which render the IDP stable. The study reveals a mechanism which involves a relatively fast and reversible association–dissociation of L-hIGFBP2 (dynamic exchange) from the surface of AgNP. The AgNP–L-hIGFBP2 complex remains stable for more than a month. The techniques employed in addition to NMR include UV-Visible spectroscopy, dynamic light scattering (DLS), zeta potential measurements and transmission electron microscopy (TEM) to probe the protein-AgNP interaction here in this section.

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One of the biggest challenges in healthcare is Emergency Department (ED)crowding which creates high constraints on the whole healthcare system aswell as the resources within and can be the cause of many adverse events.Is is a well known problem were a lot of research has been done and a lotof solutions has been proposed, yet the problem still stands unsolved. Byanalysing Real-World Data (RWD), complex problems like ED crowding couldbe better understood. Currently very few applications of survival analysis hasbeen adopted for the use of production data in order to analyze the complexityof logistical problems. The aims for this thesis was to apply survival analysisthrough advanced Machine Learning (ML) models to RWD collected at aSwedish hospital too see how the Length Of Stay (LOS) until admission ordischarge were affected by different factors. This was done by formulating thecrowding in the ED for survival analysis through the use of the LOS as thetime and the decision regarding admission or discharge as the event in order tounfold the clinical complexity of the system and help impact clinical practiceand decision making.By formulating the research as time-to-event in combination with ML, thecomplexity and non linearity of the logistics in the ED is viewed from a timeperspective with the LOS acting as a Key Performance Indicator (KPI). Thisenables the researcher to look at the problem from a system perspective andshows how different features affect the time that the patient are processedin the ED, highlighting eventual problems and can therefore be useful forimproving clinical decision making. Five models: Cox Proportional Hazards(CPH), Random Survival Forests (RSF), Gradient Boosting (GB), ExtremeGradient Boosting (XGB) and DeepSurv were used and evaluated using theConcordance index (C-index) were GB were the best performing model witha C-index of 0.7825 showing that the ML models can perform better than thecommonly used CPH model. The models were then explained using SHapleyAdaptive exPlanations (SHAP) values were the importance of the featureswere shown together with how the different features impacted the LOS. TheSHAP also showed how the GB handled the non linearity of the features betterthan the CPH model. The five most important features impacting the LOS wereif the patient received a scan at the ED, if the visited and emergency room,age, triage level and the label indicating what type of medical team seemsmost fit for the patient. This is clinical information that could be implementedto reduce the crowding through correct decision making. These results show that ML based survival analysis models can be used for further investigationregarding the logistic challenges that healthcare faces and could be furtherused for data analysis with production data in similar cases. The ML survivalanalysis pipeline can also be used for further analysis and can act as a first stepin order to pinpoint important information in the data that could be interestingfor deeper data analysis, making the process more efficient. / En av de största utmaningarna inom vården är trängsel på akuten som skaparstora ansträngninar inom vårdsystemet samt på dess resurser och kan varaorsaken till många negativa händelser. Det är ett välkänt problem där mycketforskning har gjorts och många lösningar har föreslagits men problemetär fortfarande olöst. Genom att analysera verklig data så kan komplexaproblem som trängsel på akuten bli bättre förklarade. För närvarande harfå tillämpningar av överlevnadsanalys applicerats på produktionsdata för attanalysera komplexiteten av logistiska problem. Syftet med denna avhandlingvar att tillämpa överlevnadsanalys genom avancerade maskininlärningsmetoderpå verklig data insamlat på ett svenskt sjukhust för att se hur vistelsens längdför patienten fram till inläggning påverkades av olika faktorer. Detta gjordesgenom att applicera överlevnadsnanalys på trängsel på akuten genom attanvända vistelsens längd som tid och beslutet om intagning eller utskrivningsom händelsen. Detta för att kunna analysera systemets kliniska komplexitetoch bidra till att påverka klinisk praxis och beslutsfattande.Genom att formulera forskningsfrågan som en överlevnadsanalys i kombinationmed maskininlärning kan den komplexitet och icke-linjäritet som logistikenpå akuten innebär studeras genom ett tidsperspektiv där vistelsens längdfungerar som ett nyckeltal. Detta gör det möjligt för forskaren att ävenstudera problemet från ett systemperspektiv och visar hur olika egenskaperoch situationer påverkar den tid som patienten bearbetas på akuten. Detta uppmärksammar eventuella problem och kan därför vara användbart för attförbättra det kliniska beslutsfattandet. Fem olika modeller: CPH, RSF, GB,XGB och DeepSurv användes och utvärderades med hjälp av C-index där GBvar den bäst presterande modellen med ett C-index på 0.7825 vilket visar attmaskininlärningsmetoderna kan prestera bättre än den klassiska och vanligtförekommande CPH modellen. Modellerna förklarades sedan med hjälp utavSHAP värden där vikten utav de olika variablerna visades tillsammmans med deras påverkan. SHAP visade även att GB modellen hanterade icke-linjäriteten bättre än CPH modellen. De fem viktigaste variablerna som påverkade vistelsens längd till intagning var om patienten blev scannad påakutmottagningen, om de blev mottagna i ett akutrum, ålder, triagenivå ochvilket medicinskt team som ansågs bäst lämpat för patienten. Detta är kliniskinformation som skulle kunna implementeras genom beslutsfattande för attminska trängseln på akuten. Dessa resultat visar att maskininlärningsmetoderför överlevnadsanalys kan användas för vidare undersökning angående de logistiska utmaningar som sjukvården står inför och kan även användas ytterligareför datanalys med produktionsdata i liknande fall. Processen med överlevnadsanalys och ML kan även användas för vidare analys och kan agera som ett förstasteg för att framhäva viktig information i datan som skulle vara intressant fördjupare data analys. Detta skulle kunna göra processen mer effektiv.

The study has empirically examined the relative impact of public and private investment on economic growth and has also tested the crowding in or crowding out effect of public investment on private investment in four Southern African economies – Malawi, South Africa, Zambia and Zimbabwe. The analysis used annual time-series data covering the period from 1970 to 2014. The study provides new evidence to contribute firstly to the current debate regarding the relative importance of public and private investment in economic growth processes and secondly, on whether public investment crowds in or crowds out private investment in the selected countries. For this purpose, the study employed two empirical models using the recently developed Autoregressive Distributed Lag (ARDL)-bounds testing approach to cointegration. Model 1 examines the relative impact of public and private investment on economic growth while Model 2 investigates the crowding in or crowding out effect of gross public investment and its subcomponents (infrastructural and non-infrastructural) on private investment. The results of Model 1 largely supported the private investment-led economic growth strategy. In all the study countries, private investment had a positive impact on economic growth. Also, public investment positively contributed to economic growth in Zimbabwe, but in the remaining study countries, public investment had a negative relationship with economic growth. Results from Model 2 reveal that: (i) the crowding out effect of gross public investment on private investment predominates in the study countries; (ii) infrastructural public investment crowds in private investment in South Africa and Zimbabwe in the long run while it crowds out private investment in Malawi and Zambia in the short run; and (iii) non-infrastructural public investment crowds out private investment in South Africa and Zambia. On balance, the results from Model 2 show that public investment tends to crowd out private investment in the selected countries and this further underscore the importance of the private sector-led economic growth processes in the study countries. / Economics / D. Phil. (Economics)

The <u>first essay</u> [“Twin Deficits and Free Lunches: Macroeconomic Outcomes In Anticipation of Foreign Aid”] concerns itself with situations in which private agents anticipate a future windfall (free lunch) that will help service the debt resulting from a present fiscal expansion (implemented via a temporary tax cut). Such expectations of a windfall can arise in the context of natural resource discoveries or, more interestingly, due to perceptions by agents in “too important to fail” countries that will be bailed out through higher foreign aid or debt relief. We employ an overlapping generations model featuring credit constraints to study the real effects of such free lunch expectations in a small open economy, drawing contrasts with the standard tax and money finance closure rules. The model is solved analytically and shows that anticipated aid is equivalent to current aid when agents have perfect foresight, so that a temporary tax cut is seen as permanent. Accordingly, agents raise their consumption and indebtedness (at the expense of future generations) by an amount that is an increasing function of their “impatience” (subjective rates of time preference plus probability of death). A worsening of the current account obtains (twin deficits) across a range of plausible closure rules, including those featuring money finance. The introduction of credit constrained households (we study the variant where myopic agents spend their current disposable incomes) does not alter the basic result in the case of full aid finance, but does matter for mixed tax-aid regimes, in more complex settings where agent expectations and donor promises on aid diverge, and when governments face borrowing constraints so that the timing of aid delivery matters. The <u>second essay</u> [“The Role of Domestic Debt in Economic Growth: An Empirical Investigation For Developing Economies”] focuses on the remaining source of government financing, i.e. domestic debt, and the role it can play in mobilizing private savings, facilitating credit intermediation in higher risk settings (i.e. serving a “collateral” function on bank balance sheets), developing financial markets and supporting economic growth in general. To investigate this question empirically, we set up a new domestic debt database covering about 100 developing economies, going back three decades to 1975; explore Granger causality links between domestic debt and key macroeconomic and institutional variables; and estimate the growth impact of domestic debt using panel regressions, allowing for non-linear effects. Domestic debt, as a share of GDP is found to exert a significant positive impact on economic growth, with potential channels including domestic savings mobilization, provision of risk-insurance on banks’ balance sheets; and greater institutional accountability of the state to its citizens. Although this result countervails more established arguments against domestic debt (i.e. that it leads to crowding out and banks to become lazy), there is some evidence that above a ratio of 35 percent of bank deposits, domestic debt does begin to undermine economic growth. The growth payoff also depends on debt quality, with higher payoffs observed for positive interest-rate bearing marketable debt issued to nonbank sectors. The <u>third and final essay</u> [“Why Do Banks in Developing Economies Hold Domestic Government Securities?”] explores demand-side determinants of domestic debt, by focusing on commercial bank holdings of government paper, discriminating carefully between voluntary factors (such as mean-variance portfolio optimization) and statutory ones (cash reserve and capital adequacy requirements). The analysis is made possible by the construction of a dataset on government and private returns (real and nominal) for almost 600 banks from 70 emerging and low-income economies, spanning the (pre-Basel II) period 1995-2005. A battery of structural cross-section regressions indicates that banks’ portfolio decisions are at least as significantly influenced by mean-variance considerations as regulatory factors: the actual portfolio share of government securities (λ) responds intuitively, and sizably, to variations in the moments of the distributions for government and private returns as well as in the minimum-variance portfolio share (λ*). Higher cash reserve requirements tilt portfolios away from government securities toward riskier private lending, while higher capital adequacy requirements work the other way. The association between actual portfolios and the identified determinants is noticeably weaker at lower ends of the λ distribution, suggesting the domination of non-CAPM factors in those contexts.

General lighting with InGaN light emitting diodes (LEDs) as light sources is of particular interest in terms of energy savings and related environmental benefits due to high lighting efficiency, long lifetime, and Hg-free nature. Incandescent and fluorescent light sources are used for general lighting almost everywhere. But their lighting efficiency is very limited: only 20-30 lm/W for incandescent lighting bulb, approximately 100 lm/W for fluorescent lighting. State-of-the-art InGaN LEDs with a luminous efficacy of over 200 lm/W at room temperature have been reported. However, the goal of replacing the incandescent and fluorescent lights with InGaN LEDs is still elusive since their lighting efficiency decreases substantially when the injection current increases beyond certain values (typically 10-50 Acm-2). In order to improve the electroluminescence (EL) performance at high currents for InGaN LEDs, two approaches have been undertaken in this thesis. First, we explored the preparation and characterization of non-polar and semi-polar GaN substrates (including a-plane, m-plane and semi-polar planes). These substrates serve as promising alternatives to the commonly used c-plane, with the benefit of a reduced polarization-induced electric field and therefore higher quantum efficiency. It is demonstrated that LEDs on m-plane GaN substrates have inherently higher EL quantum efficiency and better efficiency retention ability at high injection currents than their c-plane counterparts. Secondly, from a device structure level, we explored the possible origins of the EL efficiency degradation at high currents in InGaN LEDs and investigated the effect of hot electrons on EL of LEDs by varying the barrier height of electron blocking layer. A first-order theoretical model is proposed to explain the effect of electron overflow caused by hot electron transport across the LED active region on LED EL performance. The calculation results are in agreement with experimental observations. Furthermore, a novel structure called a “staircase electron injector” (SEI) is demonstrated to effectively thermalize hot electrons, thereby reducing the reduction of EL efficiency due to electron overflow. The SEI features several InyGa1-yN layers, with their In fraction (y) increasing in a stepwise manner, starting with a low value at the first step near the junction with n-GaN.