• Energy Research
• 2025-2025close

This paper proposes a new concept of a passive safety system for small modular reactors as a heat pipe type double-containment vessel. The proposed system configures an additional vessel outside the existing one and functions as a heat pipe that utilizes phase change heat transfer to reduce the requirement of a large pool design. The double-containment vessel features a design consisting solely of the evaporation and condensation sections for heat pipe function with compactness. The ultimate heat sink only serves as a condenser part for the double-containment vessel, therefore significantly reducing the volume of stored fluid. The study numerically evaluates several postulated accidental cases to compare the performance of the double-containment vessel using the thermal hydraulic system code, MARS-KS. Even for serious conditions such as multiple failure accidents without safety systems operation, the double-containment can secure extra golden time to core damage by more than 2,090 sec compared to the original design. This study highlights the feasibility and potential of the heat pipe-based double-containment vessel as a passive safety system, enhancing the safety and reliability of small modular reactors. This kind of design can provide the advantages of safety and economics in the construction of small modular reactors.

The conventional wind forecasting methods often struggle to handle the non-stationary and inconsistent wind patterns. This paper presents a hybrid method of Empirical Wavelet Transform (EWT) and Deep Reinforcement Learning (DRL) for wind speed modeling to overcome the forecasting challenges. The EWT method transforms the original wind speed series into several independent modes and a residual series. In addition, the DRL method is utilised to optimise the weights associated with three distinct supervised deep learning models, i.e., Long Short-Term Memory (LSTM), Convolutional Neural Networks with LSTM (CNN-LSTM), and CNN with Gated Recurrent Units (CNN-GRU). The performance of the proposed EWT-DRL is evaluated against deep learning models, including LSTM, CNN-LSTM, CNN-GRU, and their coupling with EWT. The combination of EWT and the DRL (EWT-DRL) method achieves a Mean Absolute Error (MAE) of 0.151, a Mean Squared Error (MSE) of 0.060, a Root Mean Squared Error (RMSE) of 0.192, and a correlation coefficient (R) of 0.9913. These results indicate the effectiveness of EWT-DRL in improving accuracy for wind speed modeling.

IntroductionHospitals produce and waste large amounts of food. When disposed in landfill it creates greenhouse gases (GHGs) from the decomposition process. While various food waste management strategies exist that divert hospital food waste to an alternative end of life pathway to landfill, it is not clear which can decrease GHG emissions the most. This study aimed to (a) compare the differences in GHG emissions associated with hospital foodservice food waste before and after adopting a food waste management strategy, and (b) identify which waste management strategy can prevent the most GHGs in 1 year.Materials and methodsA secondary analysis of data from a systematic review reporting on food and food-related waste diversion strategies in hospital foodservice was conducted. The online “ReFED Impact Calculator” was used to calculate GHG emissions from food waste in the original scenario (e.g., landfill), and the alternative scenario after a food waste management strategy that reused, recycled or recovered resources was implemented. The net change of GHGs was calculated, and the GHGs emissions avoided in paired samples and between food waste management scenarios was analyzed statistically.ResultsFifty-five food waste management strategies (surplus food donation, feeding animals, anaerobic digestion or industrial uses, and composting) were eligible for analysis and were grouped into eight scenarios. The median GHGs generated decreased after adopting the alternative strategy in all scenarios. There was a statistically significant median reduction in GHGs when changing from landfill to donations (−11.54, p < 0.001), landfill to industrial uses (−25.92, p < 0.001), and landfill to composting (−15.24, p < 0.001). Percentage change in GHGs generated in these 3 scenarios demonstrated a significant difference (p < 0.001), with landfill to donations displaying the greatest reduction in GHGs (−92.02%), followed by composting (−8.69%) and industrial uses (−7.75%).ConclusionVarious food waste diversion strategies can handle types and volumes of hospital food waste, yet each strategy displays a reduction in GHG emissions compared to a lower prioritized strategy. Donating waste shows the greatest reduction in GHG emissions and if food waste cannot be avoided, it may be the preferred end of life pathway for food waste.

The Space Radiobiological Exposure Facility (SREF) is a general experimental facility at the China Space Station for scientific research in the fields of space radiation protection, space radiation biology, biotechnology, and the origin of life. The facility provides an environment with controllable temperatures for experiments with organic molecules and model organisms such as small animals, plant seeds, and microorganisms. The cultivation of small animals can be achieved in the facility with the use of microfluidic chips and images and videos of such experiments can be captured by microscopy. SREF also includes a linear energy transfer (LET) detector, neutron detectors, and a solar ultraviolet (UV) detector to measure the LET spectrum of the charged particles, energy spectrum and dose equivalent of neutrons, and fluence of solar UV radiation, respectively. The facility is reusable, and the model organisms from the first exposure experiment were recovered in orbit and returned to the ground for further study.

Purpose Due to new regulations, changing customer preferences and strategic reorientation, the role of responsible decision-making has become increasingly complex for both small and large firms. In relation to this, this article contends that companies are turning into political actors with political responsibility for sustainability-related issues in their supply chain and beyond. Therefore, this article aims to investigate (1) how companies have become political in their supply chain and (2) which mechanisms contribute to this development. Design/methodology/approach To investigate the political nature of companies in their supply chain, this research draws on a critical realist case study inspired by process tracing. A total of 30 interviews were conducted with actors from within global coffee supply chains to get a wide overview of the different perspectives on how coffee companies deal with political complexity. Findings It is found that companies in the coffee supply chain are increasingly acting like traditional political actors. Their engagement in political activities emphasizes three contextualized causal mechanisms driving this transformation: (1) customer pressure, (2) attention to trust and transparency and (3) personal motivation. Research limitations/implications The results point to the political nature of companies’ supply chain management (SCM)-related activities, indicating they should recognize that their role in the supply chain gives them the capacity to act as state-like actors. Originality/value Political SCM is introduced as a new conceptual element in the SCM discourse by defining the term “political” and linking political theory and sustainable SCM literature. This provides a better understanding of SCM and offers guidance on how SCM can be understood as a set of political activities.