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Abstract The role of BaO in the glassy structured Na2Si3O7 was investigated in the context of gamma radiations shielding parameters in the study. The mass attenuation coefficient, half layer value, and mean free path of the Na2Si3O7/BaO composites were calculated experimentally for the photons with the energies of 81 keV and 356 kev emitted from 133Ba point radioactive source. The same parameters were also calculated by Monte Carlo N-particle simulation (MCNP5) for the gamma photons which are emitted from 133Ba, 241Am, 99mTc, 177Lu, 192Ir, and 137Cs radioactive sources. The effective atomic number and effective electron density were determined by WinXCom software. Additionally, the scattered gamma photon intensity of the composites was realized for the energy of 364 keV and compared with the most utilized radiation shielding material lead. It was concluded that the composite having the highest BaO additive exhibits the best gamma photon absorption ability at all energies investigated.

This dissertation is an investigation of the gendered dynamics of sustainable agriculture practiced by women operating commercial-scale, sustainable farms in Jaffna, Sri Lanka, with a focus on women's agency and freedoms within the agricultural sector. Women operate 26% of farms in Jaffna, yet women are notably absent from agricultural development policies. As climate change threatens the livelihoods and security of farmers in Jaffna, establishing data on the opportunities, barriers, and experiences of women as farmers is crucial for developing an accurate contextual understanding necessary to inform effective policies that ensure sustained food production and livelihood security. Using the capabilities approach as a conceptual framework and 50 individual interviews of with women farm operators in 2021, during COVID, this research examines the socio-cultural, economic, and policy-driven barriers that constrain women's agency and freedoms as primary farm operators. The study identifies a duality in women's experiences: while they demonstrate remarkable agency in sustainable agricultural practices within their farms, they face significant systemic barriers in engaging with external economic systems. A central insight of the research is the contrasting roles played by middle operators, who exploit women’s dependency to access external markets, and cooperatives, which serve as transformative bridges by fostering collective empowerment and expanding women's capabilities across both spheres. The study further highlights the innovative localized strategies women employ to adapt to climate vulnerabilities and systemic constraints. By blending traditional ecological knowledge with modern agricultural technologies, women have developed integrated farm management systems that optimize productivity, enhance biodiversity, and build resilience to environmental shocks. These findings challenge the conventional image of farmers as solely men battling nature and instead position women as central agents of ecological stewardship and sustainability.

Abstract The study presents recent developments in the Turkish power market and introduces an Analytic Hierarchy Process model to evaluate and compare the relative overall attractiveness of power plant options for Turkey. The developed model incorporates technical characteristics, resource availability, socio-economic, environmental, cost, political, legal and organisational aspects, for evaluating and prioritising power plant types (biomass, coal, geothermal, hydro, natural gas, nuclear, petroleum, solar and wind). The study incorporates perspectives of different experts that represent various stakeholders of the Turkish power sector. The study reveals that supply reliability, investment costs and contribution to national economy are perceived as most important factors, whereas waste disposal and decommissioning costs are perceived as least important factors. Considering the overall weights, the most attractive power plant types for the Turkish power market are coal, hydro and natural gas power plants. The study indicates that Turkey should drastically decrease the installed capacity share of traditionally dominant power plants (to 58% from 89% in 2016) and fossil fuel power plants (to 40% from 56% in 2016), and increase the share of renewable power plants (to 52% from 44% in 2016), indigenous resource based power plants (to 67% from 56% in 2016) and nuclear power plants.

The phenomenon of global change is prompting the implementation of measures aimed at mitigating various effects, with humans being primarily impacted. The objective of this investigation is to analyse the potential self-sufficiency in the built environment through the utilisation of energy generated by photovoltaic systems. These systems have the capacity to reduce or even eliminate the reliance on grid electricity and non-renewable energy sources. The study commences with the delineation of eight neighbourhoods within the city of Madrid, Spain, each characterised by a distinct building typology. In order to ascertain the degree of self-sufficiency, this study calculates the energy production from solar photovoltaic systems installed on rooftops, and then proceeds to assess the energy consumption of each building. The results of the analysis demonstrate the capacity of photovoltaic installations to meet the electricity demands of buildings, such as single-family houses, almost completely, and partially in others, such as multi-family dwellings with multiple units per floor. Furthermore, the impact of differences in building height and urban context on self-sufficiency potential is explored.

The presence and accretion of airborne particulates, including ash, sand, dust, and other compounds, in gas turbine engines can adversely affect performance and life of components. Engine experience and experimental work has shown that the thickness of accreted layers of these particulates can become large relative to the engine components on which they form. Numerical simulation to date, using a variety of flow coupling models, has largely ignored the effects of resultant changes in the passage geometry due to the build-up of deposited particles. This paper will focus on updating the boundaries of the flow volume geometry by integrating the deposited volume of particulates on the solid surface. Numerical models of small particulate turbulent motion and stick/bounce models are developed and integrated within commercial software to perform 3D fluid simulations to capture the deposition behaviour. The technique is implemented using a novel, coupled deposition-dynamic mesh morphing approach to the simulation of particulate-laden flows using RANS modelling of the bulk fluid, and Lagrangian-based particulate tracking. On an iterative basis the calculated particle deposition distributions are used to modify the surface topology by altering the locations of surface nodes. The mesh, continuous phase solution, and particle tracking are then recalculated, from which the mesh is again modified. The sensitivity to the modelling time steps employed is explored. This mesh morphing technique is further refined through the application of the particle stick-bounce model of Bons et al. [1] and the Continuous Random Walk model. An impingement geometry case is used to assess the validity of the technique, and a passage with film cooling holes is interrogated. The paper illustrates that for engine realistic levels of internal deposition this can lead to a significant disparity in the local aerodynamic flow field. Modelling of several internal flow fields have been investigated to illustrate the use of the technique. Differences are seen for all of the sticking and solid phase motion models employed. Notably, there are real discrepancies in using commercial and bespoke models. At small solid particle sizes considerable disparity is observed between the discrete and continuous random walk modelling approaches, while the position and level of accretion is altered through the use of a non-isotropic stick and bounce model.

Abstract Background and Aims Anthropogenic disturbances are causing a co-occurring increase in biotic (ungulate herbivory) and abiotic (drought) stressors, threatening plant reproduction in oak-dominated ecosystems. However, could herbivory compensate for the adverse impact of drought by reducing evapotranspiration? Thus, we investigated the isolated and joint effects of herbivory and drought on oak seedlings of two contrasting Mediterranean species that differ in leaf habit and drought resistance. Methods California oak seedlings from the evergreen, and more drought-resistant, Quercus agrifolia and the deciduous Q. lobata (n = 387) were assigned to a fully crossed factorial design with herbivory and drought as stress factors. Seedlings were assigned in a glasshouse to three to four clipping levels simulating herbivory and three to four watering levels, depending on the species. We measured survival, growth and leaf attributes (chlorophyll, secondary metabolites, leaf area and weight) once a month (May–September) and harvested above- and below-ground biomass at the end of the growing season. Key Results For both oak species, simulated herbivory enhanced seedling survival during severe drought or delayed its adverse effects, probably due to reduced transpiration resulting from herbivory-induced leaf area reduction and compensatory root growth. Seedlings from the deciduous, and less drought-resistant species benefited from herbivory at lower levels of water stress, suggesting different response across species. We also found complex interactions between herbivory and drought on their impact on leaf attributes. In contrast to chlorophyll content which was not affected by herbivory, anthocyanins increased with herbivory – although water stress reduced differences in anthocyanins due to herbivory. Conclusions Herbivory seems to allow Mediterranean oak seedlings to withstand summer drought, potentially alleviating a key bottleneck in the oak recruitment process. Our study highlights the need to consider ontogenetic stages and species-specific traits in understanding complex relationships between herbivory and drought stressors for the persistence and restoration of multi-species oak savannas.

Due to increasing volume of measurements in smart grids, surrogate based learning approaches for modeling the power grids are becoming popular. This paper uses regression based models to find the unknown state variables on power systems. Generally, to determine these states, nonlinear systems of power flow equations are solved iteratively. This study considers that the power flow problem can be modeled as an data driven type of a model. Then, the state variables, i.e., voltage magnitudes and phase angles are obtained using machine learning based approaches, namely, Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), and Support Vector Regression (SVR). Several simulations are performed on the IEEE 14 and 30-Bus test systems to validate surrogate based learning based models. Moreover, input data was modified with noise to simulate measurement errors. Numerical results showed that all three models can find state variables reasonably well even with measurement noise.