Search Results (6)

Batch crystallization processes are prone to batch-to-batch inconsistencies arising from operational uncertainties and equipment-induced noise. This study presents a Robustness-Aware Genetic Algorithm (RAGA) integrated with a Long Short-Term Memory (LSTM) digital twin for the design of robust crystallization procedures. The RAGA employs a hierarchical fitness function that strictly enforces a target median crystal size D₅₀ as the primary constraint while maximizing process yield as a secondary objective. Robustness is incorporated directly into the optimization by requiring candidate trajectories to satisfy the D₅₀ specification across five independent stochastic realizations with perturbed operating conditions. A candidate is promoted in the evolutionary search only if all five evaluations produce a predicted D₅₀ within ±2 µm of the target. The framework was applied to seeded cooling crystallization of creatine monohydrate across three target crystal sizes of 115, 125, and 135 µm. Robustness of optimal crystallization procedures was independently verified through 100-run Monte Carlo simulations under ±10% parameter perturbations with success defined as D₅₀ within ±5 µm of target. Experimental validation at laboratory scale confirmed that optimized procedures translate to practice, with two of three target sizes achieved within the ±5 µm specification and the third deviating due to the combined effect of LSTM prediction uncertainty and thermal lag. Despite having no embedded mechanistic knowledge, the optimizer successfully converged on physically coherent crystallization strategies. Its variations in seed loading, batch time, and cooling trajectory parameters remained entirely consistent with established principles of supersaturation management. The results demonstrate that embedding robustness directly within the evolutionary optimization loop enables consistent crystal size control using data-driven models. Full article

Power documentation serves as the core guideline for the safe operation of power systems, and its precise retrieval is crucial for ensuring grid stability and safety. In this context, Retrieval-Augmented Generation (RAG) frameworks emerge as an effective technique by combining LLMs with natural language understanding capabilities and a retrieval-based model with traceability. However, existing Retrieval-Augmented Generation (RAG) frameworks face several main challenges for power-system documents: semantic drift caused by non-standardized industry terminology, increased semantic noise due to fixed-window segmentation, and knowledge conflicts in the multi-source retrieval context. To address these challenges, we propose a multi-path adaptive fusion retrieval framework based on small language models (SLMs). To map queries to standard terminology, our framework first constructs a common terminology repository and section-structure-aware index for the power industry while fully preserving the physical hierarchical logic from related documents. Subsequently, the SLM in our framework assigns prior weights based on query features and retrieved context, which contributes to adaptive fusion of retrieval paths through confidence assessment and consistency verification. With the help of the fusion process, our method effectively filters retrieval noise and resolves knowledge conflicts. Experimental results on real-world power-document datasets covering dispatch, energy storage and emergency response show that our framework achieves an average recall of 91%, outperforming DENSE and BM25 by 21% and 28% respectively. Compared with other methods, it yields the optimal BERTScore F1 (0.7798) and Rouge-1/2/L F1 (0.2430, 0.1588, 0.2098) and achieves the best results in the RAGAS framework evaluation, which significantly enhances the rigor and reliability of the question-answering system in the power engineering domain. Full article

In response to escalating urban waterlogging crises exacerbated by global warming and accelerated urbanization, an innovative waterlogging risk assessment framework was advanced in this study to bolster urban resilience and promote sustainable urban development. Current methodologies often suffer from subjective bias in weight assignments for evaluation indicators. To overcome this limitation, the projection pursuit (PP) technique was integrated with a real-coded accelerated genetic algorithm (RAGA) to derive objective indicator weights. Focusing on the built-up area of Xiushan County in Chongqing, the InfoWorks ICM was employed to develop a 1D-2D coupled hydrodynamic model for simulating the dynamic spatiotemporal evolution of waterlogging events. Based on three dimensions namely hazard, sensitivity, and vulnerability, an urban waterlogging risk assessment model was developed and ArcGIS was utilized to precisely generate risk distribution maps under rainfall scenarios with return periods of 20 years and 100 years. Additionally, to enhance flood mitigation capabilities in identified high-risk zones, this study proposed implementing stormwater storage tank systems. Simulation results demonstrated that these measures achieve a 50.88% reduction in overflow volumes in critical areas, effectively lowering peak waterlogging depth from 0.74 m to 0.53 m. Key findings revealed that high-risk areas exhibit significant spatial clustering in low-elevation districts characterized by high population density and economic development intensity, where extreme rainfall events amplify water accumulation vulnerabilities, highlighting the importance of sustainable land use planning and climate adaptation strategies. The proposed assessment methodology not only enables objective quantification of urban waterlogging risks but also facilitates evidence-based formulation of targeted mitigation strategies, facilitating the goals of urban sustainability and long-term environmental resilience. Full article

The Three Gorges Reservoir Area in China presents a critical conflict between industrial development and ecological conservation. It functions as a key hub for water management, energy production, and shipping, while also serving as a vital zone for ecological and environmental protection. Focusing on Zigui County, this study developed a 16-indicator evaluation system integrating geological, ecological, and socioeconomic factors. It utilized the Analytic Hierarchy Process (AHP), coefficient of variation (CV), and the Real-Coded Accelerating Genetic Algorithm-Projection Pursuit (RAGA-PP) model for evaluation, the latter of which optimizes the projection direction and utilizes PP to transform high-dimensional data into a low-dimensional space, thereby obtaining the values of the projection indices. The findings indicate the following: (1) The RAGA-PP model outperforms conventional AHP-CV methods in assessing Zigui County’s eco-geological environment, showing superior accuracy (higher Moran’s I) and spatial consistency. (2) Hotspot analysis confirms these results, revealing distinct spatial patterns. (3) From 2000 to 2020, “bad” quality areas decreased from 17.31% to 12.33%, while “moderate” or “better” zones expanded. (4) This improvement reflects favorable natural conditions and reduced human impacts. These trends underscore the effectiveness of China’s ecological civilization policies, which have prioritized sustainable development through targeted environmental governance, afforestation initiatives, and stringent regulations on industrial activities. Full article

This study utilized MAC-T cells cultured in vitro as a model to investigate the effects of varying concentrations of valine on α-casein synthesis and its underlying regulatory mechanisms. In this experiment, MAC-T cells were subjected to a 12 h starvation period, followed by the addition of valine in a range of concentrations (a total of seven concentrations: 0.000, 1.596, 3.192, 6.384, 12.768, 25.536, and 51.072 mM, as well as in 10% Fetal Bovine Serum). The suitable range of valine concentrations was determined using enzyme-linked immunosorbent assays (ELISAs). Real-time fluorescent quantitative PCR (RT-qPCR) and Western blot analyses were employed to evaluate the expression levels and phosphorylation states of the casein alpha s1 gene (CSN1S1), casein alpha s2 gene (CSN1S2) and mTOR signaling pathway-related genes. The functionality of the mTOR signaling pathway was further validated through rapamycin (100.000 nM) inhibition experiments. Results indicated that 1× Val (6.384 mM), 2× Val (12.768 mM), 4× Val (25.536 mM), and 8× Val (51.072 mM) significantly enhanced α-casein synthesis (p < 0.01). Within this concentration range, valine significantly upregulated the expression of CSN1S1, CSN1S2, and mTOR signaling pathway-related genes including the RagA gene (RRAGA), RagB gene (RRAGB), RagC gene (RRAGC), RagD gene (RRAGD), mTOR, raptor gene (RPTOR), and 4EBP1 gene (EIF4EBP1), eukaryotic initiation factor 4E (EIF4E), and S6 Kinase 1 (S6K1) (p < 0.01). Notably, the expression of the eukaryotic elongation factor 2 (EEF2) gene peaked at 1× Val (6.384 mM), while the expression of other genes reached their maximum at 4× Val (25.536 mM). Additionally, valine significantly increased the phosphorylation levels of mTOR, S6K1, 4E-binding protein-1 (4EBP1), ribosomal protein S6 (RPS6), and eEF2 (p < 0.01), with the highest phosphorylation levels of mTOR, S6K1, and RPS6 observed at 4× Val (25.536 mM). Rapamycin treatment significantly inhibited mTOR phosphorylation and α-casein synthesis (p < 0.01); however, the addition of 4× Val (25.536 mM) partially mitigated this inhibitory effect. In conclusion, valine promotes α-casein synthesis by activating the mTOR signaling pathway, with an optimal concentration of 4× Val (25.536 mM). Full article

Under long-term water flow, the physical and chemical properties of hydraulic tunnel linings are more likely to deteriorate than those of road tunnels, thus affecting the normal operation of tunnels. Evaluating the durability of hydraulic tunnel linings can help to grasp the durability of tunnels in a timely and accurate manner and provide a basis for the routine maintenance of tunnels. This paper proposes new methods for evaluating the durability of hydraulic tunnel linings. Firstly, the types of tunnel defects are divided, the durability indices corresponding to the defects are selected scientifically, and a hydraulic tunnel durability evaluation index system is established. Then, the G2 method is modified by the entropy value method to make it a subjective and objective weighting method, which can make the weights fit the reality while the calculation is easy, and the TOPSIS method is modified by the gray correlation degree to optimize the judgment criteria between the evaluation scheme and the ideal solution. Finally, the practicality and accuracy of this method are verified by the calculation of the five sections of a tunnel with lining durability grades of A, B, C, B, and C, respectively, which matched the calculation results of the RAGA-PP method in the related literature. Full article