Thermodynamic analysis of a dual-loop organic Rankine cycle (ORC) for waste heat recovery of a petrol engine

Huge amounts of low-grade heat energy are discharged to the environment by vehicular engines. Considering the large number of vehicles in the world, such waste energy has a great impact on our environment globally. The Organic Rankine Cycle (ORC), which uses an organic fluid with a low boiling point as the working medium, is considered to be the most promising technology to recover energy from low-grade waste heat. In this study, a dual-loop ORC is presented to simultaneously recover energy from both the exhaust gases and the coolant of a petrol engine. A high-temperature (HT) ORC loop is used to recover heat from the exhaust gases, while a low-temperature (LT) ORC loop is used to recover heat from the coolant and the condensation heat of the HT loop. Figure 1 shows the schematic of the dual-loop ORC. Differing from previous research, two more environmentally friendly working fluids are used, and the corresponding optimisation is conducted. First, the system structure and operating principle are described. Then, a mathematical model of the designed dual-loop ORC is established. Next, the performance of the dual-loop cycle is analysed over the entire engine operating region. Furthermore, the states of each point along the cycle and the heat load of each component are compared with the results of previous research. The results show that the dual-loop ORC can effectively recover the waste heat from the petrol engine, and that the effective thermal efficiency can be improved by about 20 ~ 24%, 14~20%, and 30% in the high-speed, medium-speed, and low-speed operation regions, respectively. The designed dual-loop ORC can achieve a higher system efficiency than previous ORCs of this structure. Therefore, it is a good choice for waste heat recovery from vehicle engines

Impact Assessment of New Energy Characteristics on Regional Power Grid Considering Multiple Time Scales

[Introduction] With the development of new energy, the influence of new energy uncertainty and time characteristics on power grid is increasing day by day. Traditional new energy indexes are difficult to describe the interaction between power grid and new energy. It is necessary to establish evaluation system and index to quantify the impact of new energy on power grid. [Method] Construct the evaluation system from multi-dimensional and multi-scale and establish new energy output characteristic index, electric quantity characteristic index, peak regulation characteristic index and flexibility demand index to analyze the new energy output characteristics, the relationship between new energy output and electric quantity, the influence of new energy on peak regulation and the influence of new energy fluctuation on power grid at critical moments. Typical scene features were mined by applying indexes from different time scales such as year, season, month, day and hour. [Result] All kinds of indexes of the evaluation system has been calculated by taking the actual wind power, PV power and load in a certain area as an example. The results show quantitatively the influence of regional new energy on power grid and its distribution characteristics at different time scales. The engineering practicability of the proposed index system is verified. [Conclusion] The proposed index calculation method is quick and simple and the physical meaning of indexes is clear and intuitive and helpful to guide the planning and dispatching of new energy

Based on Network Pharmacology and Molecular Docking to Discuss the Mechanism of Antitussive and Expectorant Action of Ruanerli

The antitussive and expectorant effects of Ruanerli and its mechanism were investigated by methods of network pharmacology. The outcomes predicted were verified by molecular docking and animal experiments. The components and targets of Ruanerli were obtained by literature investigation and TCMSP database screen. Mapping with two groups of genes related to "cough" and "sputum" from GeneCards database, the target genes of antitussive and expectorant effects of Ruanerli were obtained. GO and KEGG enrichment analysis of the target genes was performed by Metascape platform. The PPI network among the target genes was constructed through STRING data platform. Cytoscape plugin CytoHubba was used to screen the Top10 genes related to antitussive and expectorant effects of Ruanerli, and KEGG pathway enrichment was performed on the Top10 genes through Metascape data platform to predict the possible signal pathways involved in antitussive and expectorant effects of Ruanerli. Autodock Vina was used for molecular docking between the predicted Top10 gene proteins and the Top 3 active ingredients of Ruanerli. Finally, the predicted results were verified by ammonia induced cough test and phenol red excretion test. According to the analysis of multiple databases, 51 chemical components and 282 corresponding targets have been reported, eighty of them were related to the antitussive and expectorant effects of Ruanerli. The Top10 genes selected by Degree value were mainly concentrated in infection and immune-related pathways. Molecular docking test showed that the Top10 genes had strong binding activity with the Top3 chemical components (Caffeic acid, Rutin and Valeraldehyde) in PPI network. Animal experiments showed that the cough induced by ammonia was significantly inhibited when treated with Ruanerli in mice. The levels of IL-6 and IL-13 in serum were reduced and the excretion of phenol red in mice trachea was increased. PCR and WB detection showed that the mRNA levels and protein expressions of inflammatory genes IL6, IL1B, VEGFA, PTGS2 and MAPK3 were decreased, suggesting that the antitussive and expectorant effects of Ruanerli might be related to decreasing the expression of inflammatory genes and the release of inflammatory factors

Fine mapping and candidate gene analysis of proportion of four-seed pods by soybean CSSLs

Soybean yield, as one of the most important and consistent breeding goals, can be greatly affected by the proportion of four-seed pods (PoFSP). In this study, QTL mapping was performed by PoFSP data and BLUE (Best Linear Unbiased Estimator) value of the chromosome segment substitution line population (CSSLs) constructed previously by the laboratory from 2016 to 2018, and phenotype-based bulked segregant analysis (BSA) was performed using the plant lines with PoFSP extreme phenotype. Totally, 5 ICIM QTLs were repeatedly detected, and 6 BSA QTLs were identified in CSSLs. For QTL (qPoFSP13-1) repeated in ICIM and BSA results, the secondary segregation populations were constructed for fine mapping and the interval was reduced to 100Kb. The mapping results showed that the QTL had an additive effect of gain from wild parents. A total of 14 genes were annotated in the delimited interval by fine mapping. Sequence analysis showed that all 14 genes had genetic variation in promoter region or CDS region. The qRT−PCR results showed that a total of 5 candidate genes were differentially expressed between the plant lines having antagonistic extreme phenotype (High PoFSP > 35.92%, low PoFSP< 17.56%). The results of haplotype analysis showed that all five genes had two or more major haplotypes in the resource population. Significant analysis of phenotypic differences between major haplotypes showed all five candidate genes had haplotype differences. And the genotypes of the major haplotypes with relatively high PoFSP of each gene were similar to those of wild soybean. The results of this study were of great significance to the study of candidate genes affecting soybean PoFSP, and provided a basis for the study of molecular marker-assisted selection (MAS) breeding and four-seed pods domestication

A role for the IgH intronic enhancer Eμ in enforcing allelic exclusion

The intronic enhancer (Eμ) of the immunoglobulin heavy chain (IgH) locus is critical for V region gene assembly. To determine Eμ's subsequent functions, we created an Igh allele with assembled VH gene but with Eμ removed. In mice homozygous for this Eμ-deficient allele, B cell development was normal and indistinguishable from that of mice with the same VH knockin and Eμ intact. In mice heterozygous for the Eμ-deficient allele, however, allelic exclusion was severely compromised. Surprisingly, this was not a result of reduced suppression of V-DJ assembly on the second allele. Rather, the striking breakdown in allelic exclusion took place at the pre-B to immature B cell transition. These findings reveal both an important role for Eμ in influencing the fate of newly arising B cells and a second checkpoint for allelic exclusion

Nudel and FAK as Antagonizing Strength Modulators of Nascent Adhesions through Paxillin

Competition for binding to the cellular protein paxillin by the proteins Nudel and focal adhesion kinase is important for the proper regulation of cell adhesion and migration

Digital-Twin-Based System for Foam Cleaning Robots in Spent Fuel Pools

This paper introduces a digital-twin-based system for foam cleaning robots in spent fuel pools, aiming to efficiently clean foam in spent fuel pools. The system adopts a four-layer architecture, including the physical entity layer, twin data layer, twin model layer, and application service layer. Initially, the robot was modeled in two dimensions, encompassing physical and kinematic aspects. Subsequently, data collection and fusion were carried out using laser radar and depth cameras, establishing a virtual model of the working scenario and mapping the physical entity to the digital twin model. Building upon this foundation, improvements were made in applying the full-coverage path planning algorithm by integrating a pure tracking algorithm, thereby enhancing the cleaning efficiency. Obstacle detection and localization were conducted using infrared and depth cameras positioned above the four corners of the spent fuel pool, with the digital twin platform transmitting coordinates to the robot for obstacle avoidance operations. Finally, comparative experiments were conducted on the robot’s full-coverage algorithm, along with simulation experiments on the robot’s position and motion direction. The experimental results indicated that this approach reduced the robot’s overall cleaning time and energy consumption. Furthermore, it enabled motion data detection for the digital twin robot, reducing the risk of collisions during the cleaning process and providing insights and directions for the intelligent development of foam cleaning robots

CAiTST: Conv-Attentional Image Time Sequence Transformer for Ionospheric TEC Maps Forecast

In recent years, transformer has been widely used in natural language processing (NLP) and computer vision (CV). Comparatively, forecasting image time sequences using transformer has received less attention. In this paper, we propose the conv-attentional image time sequence transformer (CAiTST), a transformer-based image time sequences prediction model equipped with convolutional networks and an attentional mechanism. Specifically, we employ CAiTST to forecast the International GNSS Service (IGS) global total electron content (TEC) maps. The IGS TEC maps from 2005 to 2017 (except 2014) are divided into the training dataset (90% of total) and validation dataset (10% of total), and TEC maps in 2014 (high solar activity year) and 2018 (low solar activity year) are used to test the performance of CAiTST. The input of CAiTST is presented as one day’s 12 TEC maps (time resolution is 2 h), and the output is the next day’s 12 TEC maps. We compare the results of CAiTST with those of the 1-day Center for Orbit Determination in Europe (CODE) prediction model. The root mean square errors (RMSEs) from CAiTST with respect to the IGS TEC maps are 4.29 and 1.41 TECU in 2014 and 2018, respectively, while the RMSEs of the 1-day CODE prediction model are 4.71 and 1.57 TECU. The results illustrate CAiTST performs better than the 1-day CODE prediction model both in high and low solar activity years. The CAiTST model has less accuracy in the equatorial ionization anomaly (EIA) region but can roughly predict the features and locations of EIA. Additionally, due to the input only including past TEC maps, CAiTST performs poorly during magnetic storms. Our study shows that the transformer model and its unique attention mechanism are very suitable for images of a time sequence forecast, such as the prediction of ionospheric TEC map sequences