• Energy Research
• 2021-2025close
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Abstract Wolfiporia cocos, a versatile fungus acclaimed for its nutritional and therapeutic benefits in Traditional Chinese Medicine, holds immense potential for pharmaceutical and industrial applications. In this study, we aimed to optimize liquid fermentation techniques and culture medium composition to maximize mycelial biomass (MB) yield, pachymic acid (PA) concentration, and overall PA production. Additionally, we investigated the molecular basis of our findings by quantifying the expression levels of genes associated with PA and MB biosynthesis using quantitative real-time polymerase chain reaction. Under the optimized fermentation conditions, significant results were achieved, with maximum MB reaching 6.68 g l−1, PA content peaking at 1.25 mg g−1, and a total PA yield of 4.76 g l−1. Notably, among the four examined genes, squalene monooxygenase, exhibited enhanced expression at 0.06 ratio under the optimized conditions. Furthermore, within the realm of carbohydrate-active enzymes, the glycoside hydrolases 16 family displayed elevated expression levels at 21 ratios, particularly during MB production. This study enhances understanding of genetic mechanism governing MB and PA production in W. cocos, highlighting the roles of squalene monooxygenase and glycoside hydrolases 16 carbohydrate-active enzymes.

Railway is currently envisioned as the most promising transportation system for both people and freight to reduce atmospheric emission and combat climate change. In this context, ensuring the energy efficiency of the railway systems is paramount in order to sustain their future expandability with minimum carbon footprint. Recent advancements in computing and communication technologies are expected to play a significant role to enable novel integrated control and management strategies in which heterogeneous data is exploited to noticeably increase energy efficiency. In this paper we focus on exploiting the convergence of heterogeneous information to improve energy efficiency of railway systems, in particular on the heating system for the railroad switches, one of the major energy intensive components. To this aim, we define new policies to efficiently manage the heating of these switches exploiting also external information such as weather and forecast data. In order to assess the performance of each strategy, a stochastic model representing the structure and operation of the railroad switch heating system and environmental conditions (both weather profiles and specific failure events) has been developed and exercised in a variety of representative scenarios. The obtained results allow to understand both strengths and limitations of each energy management policy, and serves as a useful support to make the choice of the best technique to employ to save on energy consumption, given the system conditions at hand.