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Lignin, an abundant biopolymer derived from plants, is a green binder for stabilizing soil. This study investigates the thermal conductivity of lignin [e.g., sodium ignosulfonate (SL), calcium lignosulfonate (CL), and lignin fiber (LF)] and lignin-stabilized loess. The effects of the source of loess samples, lignin content, curing time, water content, and dry density on the thermal conductivity of the stabilized soils were experimentally evaluated. Furthermore, mineralogy and microstructure of the stabilized loess were investigated using X-ray diffraction, scanning electron microscopy, and mercury intrusion porosimetry tests to provide insights into the mechanisms of lignin-based soil stabilization. The test results showed that lignin had a lower thermal conductivity than water and soil minerals. The addition of lignin reduced the thermal conductivity of loess, with CL and SL causing a slightly greater reduction than LF. Mixing lignin with loess did not generate new crystalline material. The incorporation of CL and SL altered the microstructure of loess, resulting in a densely packed structure with distinct particle bonds and intra-aggregate pores. In contrast, LF provide microscale reinforcement for the soil particles, presenting a loose structure with dominant interaggregate pores. [ABSTRACT FROM AUTHOR]

With the development of the underwater acoustic (UWA) adaptive communication system, energy-efficient transmission has become a critical topic in underwater acoustic (UWA) communications. Due to the unique characteristics of the underwater environment, the transmitter node will almost always have outdated channel state information (CSI), which results in low energy efficiency. In this paper, we take full advantage of bidirectional links and propose an adaptive modulation and coding (AMC) scheme that aims to maximize the long-term energy efficiency of a single link by jointly scheduling the coding rate, modulation order, and transmission power. Considering the complexity characteristics of UWA channels, we proposed a bit error ratio (BER) estimation method based on deep neural networks (DNN). The proposed network could realize channel estimation, feature extraction, and BER estimation by using a fixed pilot of the feedback link. Then, we design a channel classification method based on the estimated BERs of the modulation and coding scheme (MCS) and further model the UWA channels as a finite-state Markov chain (FSMC) with an unknown transition probability. Thus, we formulate the AMC problem as a Markov Decision Process (MDP) and solve it through a reinforcement learning framework. Considering the large state-action pairs, a double deep Q-network (DDQN) based scheme is proposed. Simulation results demonstrate that the proposed AMC scheme outperforms the fixed MCS with a perfect channel information state, and achieves near-optimal energy efficiency. [ABSTRACT FROM AUTHOR]

Equipped with a distributed electric propulsion system, eVTOL is the primary vehicle for future urban air mobility. The electrical machine determines the weight of the distributed electric propulsion system, and the cooling capability determines the electrical machine's weight. The electrical machine with air cooling is preferred for eVTOL due to its simplicity and light weight. This paper presents a new air-cooling system for an electrical machine in an electric ducted fan propulsion system. The novel cooling system consists of a cooling guide vane with a heat pipe (CGVHP), which is located downstream of the ducted fan rotating blade. The heated end of the heat pipe is inserted into the stator end windings and the cold end is inserted into the cooling guide vane. The heat generated in the windings is transmitted to the cooling guide vane by the heat pipe and dissipated by the forced convection using low-temperature air passing through the ducted fan. The cooling performance of the cooling guide vane with the heat pipe was numerically simulated, considering several critical design factors. The simulation results showed that the proposed CGVHP can be helpful for reducing the winding temperature of the electrical machine by over 25.5 °C at the peak power condition of 15.7 kW. [ABSTRACT FROM AUTHOR]

Background. Triglycerides and 25(OH)D had been reported as correlates of IR, but the results suggest substantial heterogeneity across races. In addition, little research reported on whether different 25(OH)D levels affect triglycerides and IR. Therefore, a similar study on the US population would be a great addition to the current one. This study investigated the association between triglycerides and IR at different 25(OH)D levels. Methods. A total of 19,926 participants were included, each containing specific indicators for the study project. IR was estimated as a HOMA-IR index ≥2.73. Four multivariate logistic regression models were developed to analyze the association between TG and IR and whether different 25(OH)D levels influenced this association. Smoothed fitting curves were plotted. Results. Triglyceride was significantly associated with IR (OR: 1.3, 95 CI %), while this association received different 25(OH)D levels (P for interaction <0.001). The effect value OR was 1.33 with the high levels, and its effect value OR was 1.28 with the low levels. Conclusion. This study demonstrates that triglyceride levels are significantly associated with insulin in the US adult population and can be used as a predictor of IR. This correlation was compromised at different 25 (OH)D levels, so future studies need to be explored in more ethnically diverse contexts. [ABSTRACT FROM AUTHOR]