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In this study, an improved multigroup (MG) cross section library correction system for the DeCART2D transport lattice code was developed by introducing group-wise correction factors for absorption, fission reactions, and the scattering matrix. In this system, corrected cross sections are generated by the product of the correction factor and the cross section, and the library corrections are performed through multiple iterations using the fixed-point iteration method. To examine the performance of the improved correction system, APR-1400 benchmark analyses were performed using an optimized DeCART2D MG cross section library generated by the correction system. Notably, the library produced by the improved library correction system showed better accuracy in terms of reactivity and power distribution compared to a library generated by the existing library correction system and an uncorrected library. In the near future, the newly improved MG cross section library correction system will be applied not only to the 47-group library for pressurized water reactor systems but also to MG libraries with a larger number of energy groups for Gen-IV systems.

This pioneering work presents a novel approach for manufacturing B-doped ZrO2, creating in-situ ZrB2O5 within the ZrO2 matrix. A cost-effective hydrothermal synthesis using zircon mineral yields a material with exceptional radiation shielding properties. Incorporating B into the ZrO2 lattice combines ZrO2's gamma attenuation with B's neutron shielding against ionizing radiation. XRD, SEM, and EDX characterization revealed structural properties, phase compositions, and elemental distributions. Monte Carlo simulation and XCOM theoretical modeling investigated B atoms' role in shielding parameters across 0.0332–2.506 MeV γ-ray energies. Results show increased B concentration slightly decreases shielding properties while enhancing mechanical properties including crystallite size, lattice distortions, and dislocation density. B concentration increases from 2.1 to 5.4 wt% decrease LAC values to 65.959–58.613 cm-1 (0.0332 MeV), 0.464–0.461 cm-1 (0.511 MeV), and 0.208–0.270 cm-1 (2.506 MeV). B concentration up to 5.4 wt% enhances mechanical properties without significantly affecting radiation shielding, especially at intermediate energies.

ABSTRACTThe multi‐field coupling relationship and temperature evolution mechanism of gas‐containing coal in areas affected by geological structures were investigated, focusing specifically on the engineering aspects of a reverse fault in the No. 3 coal seam at the Xinjing Coal Mine. An analysis was conducted to examine the thermal‐fluid‐solid coupling behavior of gas‐containing coal. A thermal‐fluid‐solid coupling model for gas‐containing coal, accounting for the effects of damage, was developed to simulate the incubation process of coal and gas outbursts within the fault zone during the advancement of the working face. The study has indicated that faults not only degrade the mechanical properties of the surrounding coal‐rock mass, but also disrupt the continuity of coal seam stress. Gas tends to accumulate near fault zones, resulting in differences in the gas pressure and content on either side of the fault, thereby substantially increasing the likelihood of coal and gas outbursts. The primary factors influencing temperature variations include deformation energy, energy from gas expansion, thermal convection, thermal conduction, and the thermal effects associated with adsorption and desorption. Among these factors, the endothermic effect associated with adsorption and desorption significantly influences the temperature fluctuations in coal. The results of this study provide a theoretical foundation for exploring the mechanisms underlying coal and gas outbursts, improving the interdisciplinary coupling theory for coal and gas systems and employing temperature metrics to predict such outbursts.

Startupföretag inom avancerade material står inför unika utmaningar utöver de som traditionella startups möter, såsom långa utvecklingscykler, hög osäkerhet kring implementering av ny teknik och svårigheter att definiera en hållbar affärsmodell. För att hantera dessa utmaningar krävs en djup förståelse för vad kunder uppfattar som värdefullt, både vad gäller produktens funktion och hur företaget passar in organisatoriskt. Denna process är tidskrävande, resursintensiv och kan påverka företagets överlevnad. Syftet med detta examensarbete var att undersöka vad kunder värdesätter och efterfrågar för att inleda ett samarbete med en AMS. Målet med examensarbetet var att förstå hur ett sådant startup kan hantera sina utmaningar, utveckla en hållbar tillväxtstrategi samt identifiera generella möjligheter och utmaningar för startups inom denna kategori. En kvalitativ studie genomfördes genom nio semistrukturerade intervjuer med kunder, en branschexpert och representanter från case-företaget. Det insamlade materialet analyserades sedan med hjälp av tematisk analys. Fyra centrala teman identifierades: samskapande/utveckling, förståelse för kundbehov, validering och riskhantering samt hållbarhet och trender. Dessa faktorer visade sig påverka hur kunderna utvärderar företaget och dess erbjudande. Resultaten diskuterades i relation till befintlig teori och ledde till både praktiska och teoretiska implikationer. Studien betonade särskilt vikten av att identifiera rätt tidiga användare, hantera resurseffektivitet för att minska kostnaderna och därigenom skapa en stabil grund för uppskalning av företaget. Advanced material startups (AMSs) face unique challenges beyond those of typical startups, such as long development cycles, high uncertainty regarding technology adoption, and difficulties in defining a viable business model. To overcome these, they must understand what their customers perceive as valuable, both in terms of product functionality and organizational fit. This process is time-consuming, resource-intensive, and may affect the survival of the company. The purpose of this thesis was to explore what customers value and require in order to engage with an AMS. Therefore, this study provides an understanding of how this kind of startup can address challenges faced in the scaling phase, develop a sustainable scaling strategy, and to understand the general challenges and opportunities a startup within this category encounters. A qualitative study was conducted through nine semi-structured interviews with customers, an industry expert, and the case company. The data was then analyzed using thematic analysis. Four key themes emerged: co-innovation/development, understanding customer needs, validation and risk management, and sustainability and trends. These aspects were found to influence how customers evaluate the startup and its offering. The findings were discussed in relation to existing theory and resulted in both managerial and theoretical implications, emphasizing the importance of identifying the right early adopters and managing resource efficiency to reduce burn rate and that way build a groundwork for scaling.

Accurately estimating the remaining useful life (RUL) of lithium-ion batteries in energy storage systems is critical for ensuring both the safety and reliability of the power grid. To address the complex nonlinear degradation behavior associated with battery aging, this study proposes a novel RUL prediction framework that integrates ensemble empirical mode decomposition (EEMD) with an ensemble learning algorithm. The approach first applies EEMD to decompose aging data into a residual component and several intrinsic mode functions (IMFs). The residual component is then modeled using a long short-term memory (LSTM) network, while the Kolmogorov–Arnold network (KAN) focuses on learning from the IMF components. These individual predictions are subsequently combined to reconstruct the overall capacity degradation trajectory. Experimental validation on real lithium-ion battery aging datasets demonstrates that the proposed method provides highly accurate RUL predictions, exhibits strong robustness, and effectively captures nonlinear characteristics under varying operating conditions. Specifically, the method achieves R2 above 0.96 with absolute RUL errors within 2–3 cycles on NASA datasets, and maintains R2 values above 0.91 with errors within 7–15 cycles on CALCE datasets. Furthermore, the optimal KAN hyperparameters for different IMF components are identified, offering valuable insights for multi-scale modeling and future model optimization.

Underwater wireless sensor networks (UWSNs) widely used for maritime object detection or for monitoring of oceanic parameters that plays vital role prediction of tsunami to life-cycle of marine species by deploying sensor nodes at random locations. However, the dynamic and unpredictable underwater environment poses significant challenges in communication, including interference, collisions, and energy inefficiency. In changing underwater environment to make routing possible among nodes or/and base station (BS) an adaptive receiver-initiated deep adaptive with power control and collision avoidance MAC (DAWPC-MAC) protocol is proposed to address the challenges of interference, collisions, and energy inefficiency. The proposed framework is based on Deep Q-Learning (DQN) to optimize network performance by enhancing collision avoidance in a varying sensor locations, conserving energy in changing path loss with respect to time and depth and reducing number of relaying nodes to make communication reliable and ensuring synchronization. The dynamic and unpredictable underwater environment, shaped by variations in environmental parameters such as temperature (T) with respect to latitude, longitude, and depth, is carefully considered in the design of the proposed MAC protocol. Sensor nodes are enabled to adaptively schedule wake-up times and efficiently control transmission power to communicate with other sensor nodes and/or courier node plays vital role in routing for data collection and forwarding. DAWPC-MAC ensures energy-efficient and reliable time-sensitive data transmission, improving the packet delivery rati (PDR) by 14%, throughput by over 70%, and utility by more than 60% compared to existing methods like TDTSPC-MAC, DC-MAC, and ALOHA MAC. These enhancements significantly contribute to network longevity and operational efficiency in time-critical underwater applications.

Thermal simulations of buildings play a critical role in optimizing energy efficiency, thermal comfort, and heating, ventilation and air conditioning (HVAC) systems design. Accurate weather data is essential for reliable simulations, as local weather and climate have a significant impact on energy requirements for space heating and cooling and thermal comfort. This study conducted a literature review regarding the sources, types, and uncertainties of weather data used for thermal simulations of buildings, including typical meteorological years (TMYs) and extreme weather files under current and future climates. Additionally, this paper evaluates methods for weather data processing, including interpolation, downscaling, and synthetic generation, to improve simulation accuracy. Finally, approaches are proposed for constructing weather files for the future and extreme conditions under a changing climate. This review aims to provide a guide for researchers and practitioners to enhance the reliability of thermal modeling through informed construction, selection, and application of weather data.

ABSTRACTSpecies interactions can be altered by climate change but can also mediate its effects. The gyrfalcon (Falco rusticolus) and the ptarmigan (Lagopus spp.) form a predator–prey couple that reflects the dynamics of boreal, tundra, and alpine ecosystems. To determine how climate change may impact the alpine food web, we investigated how ptarmigan abundance and local weather impact gyrfalcon diet and feeding behaviour, nest occupancy, and reproductive success. Using wildlife cameras, we monitored gyrfalcon nests throughout the nestling period to collect data on diet and feeding behaviour. We quantified the gyrfalcon's functional response by describing how ptarmigan kill rates relate to ptarmigan density. Additionally, we quantified the gyrfalcon's numerical demographic and aggregative response by describing how gyrfalcon reproductive success and nest occupancy, respectively, were related to ptarmigan density, using data from large‐scale monitoring projects. Ptarmigan were the dominant prey species, representing 98% of the diet. The proportion of ptarmigan in the gyrfalcon diet and gyrfalcon breeding success increased in springs with more snow, but breeding success decreased with more snow during the nestling period. Gyrfalcon reproductive success was positively related to ptarmigan density, but gyrfalcon nest occupancy and the ptarmigan kill rate were not related to ptarmigan density. These results indicate that the effect of climate change is not straightforward, and investigating how (a)biotic factors impact both prey and predator is relevant in predicting how a predator will respond to climate change. Following current climate predictions, spring will occur earlier, which will change the food‐web structure through prey availability and diversity and through interactions with other species. This requires adaptations from gyrfalcons and other predators. We emphasise that the impact of climate change on predators and other species can be more accurately evaluated on a multi‐species level rather than individually.