Parametric optimization and heat transfer analysis of a dual loop ORC (organic Rankine cycle) system for CNG engine waste heat recovery

In this study, a dual loop ORC (organic Rankine cycle) system is adopted to recover exhaust energy, waste heat from the coolant system, and intercooler heat rejection of a six-cylinder CNG (compressed natural gas) engine. The thermodynamic, heat transfer, and optimization models for the dual loop ORC system are established. On the basis of the waste heat characteristics of the CNG engine over the whole operating range, a GA (genetic algorithm) is used to solve the Pareto solution for the thermodynamic and heat transfer performances to maximize net power output and minimize heat transfer area. Combined with optimization results, the optimal parameter regions of the dual loop ORC system are determined under various operating conditions. Then, the variation in the heat transfer area with the operating conditions of the CNG engine is analyzed. The results show that the optimal evaporation pressure and superheat degree of the HT (high temperature) cycle are mainly influenced by the operating conditions of the CNG engine. The optimal evaporation pressure and superheat degree of the HT cycle over the whole operating range are within 2.5–2.9 MPa and 0.43–12.35 K, respectively. The optimal condensation temperature of the HT cycle, evaporation and condensation temperatures of the LT (low temperature) cycle, and exhaust temperature at the outlet of evaporator 1 are kept nearly constant under various operating conditions of the CNG engine. The thermal efficiency of the dual loop ORC system is within the range of 8.79%–10.17%. The dual loop ORC system achieves the maximum net power output of 23.62 kW under the engine rated condition. In addition, the operating conditions of the CNG engine and the operating parameters of the dual loop ORC system significantly influence the heat transfer areas for each heat exchanger

Energy e-commerce user portrait and multi-agent cooperative game price mechanism design

With the development of big data, our lives have gradually entered the information age, which has changed and reshaped the behavior of enterprises and consumers. In this paper, a user portrait clustering model based on big data is proposed to implement business model design for specific groups after clustering, target potential user groups for active marketing, and promote actual purchase behavior. In this paper, cost, risk, and contribution factors are introduced to improve the basic cooperative game allocation method. The improved model considers the operating cost of the main body, the level of risk, and the contribution proportion of the actual energy supply. In order to verify the effectiveness and applicability of the benefit distribution strategy based on the cooperative game proposed by the project, the research results provide a certain reference for precision marketing in relevant industries and enterprises

Facilitating Multi-Role and Multi-Behavior Collaboration of Large Language Models for Online Job Seeking and Recruiting

The emergence of online recruitment services has revolutionized the traditional landscape of job seeking and recruitment, necessitating the development of high-quality industrial applications to improve person-job fitting. Existing methods generally rely on modeling the latent semantics of resumes and job descriptions and learning a matching function between them. Inspired by the powerful role-playing capabilities of Large Language Models (LLMs), we propose to introduce a mock interview process between LLM-played interviewers and candidates. The mock interview conversations can provide additional evidence for candidate evaluation, thereby augmenting traditional person-job fitting based solely on resumes and job descriptions. However, characterizing these two roles in online recruitment still presents several challenges, such as developing the skills to raise interview questions, formulating appropriate answers, and evaluating two-sided fitness. To this end, we propose MockLLM, a novel applicable framework that divides the person-job matching process into two modules: mock interview generation and two-sided evaluation in handshake protocol, jointly enhancing their performance through collaborative behaviors between interviewers and candidates. We design a role-playing framework as a multi-role and multi-behavior paradigm to enable a single LLM agent to effectively behave with multiple functions for both parties. Moreover, we propose reflection memory generation and dynamic prompt modification techniques to refine the behaviors of both sides, enabling continuous optimization of the augmented additional evidence. Extensive experimental results show that MockLLM can achieve the best performance on person-job matching accompanied by high mock interview quality, envisioning its emerging application in real online recruitment in the future

Evaluation of cartilage degeneration using multiparametric quantitative ultrashort echo time-based MRI: an ex vivo study

BackgroundThe quantitative MR techniques developed rapidly, vary MR-biomarkers have shown the ability to assess the quality of articular cartilage. This study aimed to investigate the diagnostic efficacy of multi-parametric quantitative ultrashort echo time (UTE)-based MRI for evaluating human cartilage degeneration.MethodsTwenty fresh anterolateral femoral condyle samples were obtained from 20 patients (age, 58.8±6.6 years; 6 females) who underwent total knee arthroplasty due to primary osteoarthritis (OA). The samples were imaged using UTE-based magnetization transfer (UTE-MT), UTE-based adiabatic T1ρ (UTE-AdiabT1ρ), UTE-based T2* (UTE-T2*), and CubeQuant-T2 sequences. Cartilage degeneration was classified based on the OA Research Society International grade and polarized light microscopy (PLM) collagen organization score. Spearman's correlation analysis was used to determine the relationships between quantitative MRI biomarkers [UTE-MT ratio (UTE-MTR), UTE-AdiabT1ρ, UTE-T2*, and CubeQuant-T2], OA Research Society International grade, and PLM collagen organization score. The diagnostic efficacy of each MRI biomarker for the detection of mild cartilage degeneration was assessed using the area under the receiver operating characteristic (ROC) curve (AUC).ResultsOf the quantitative MRI biomarkers, UTE-MTR had the strongest correlation with both OA Research Society International grade (r=-0.709, P<0.001) and PLM collagen organization score (r=0.579, P<0.001). The UTE-MTR and UTE-AdiabT1ρ values showed significant differences between the normal group and the mild degeneration group (P=0.047 and 0.015, respectively), while UTE-T2* and CubeQuant-T2 did not. The UTE-MTR values were 15.90%±1.06% and 14.59%±1.35% for normal and mildly degenerated cartilage, respectively. The UTE-AdiabT1ρ values were 40.19±2.87 and 42.6±2.26 ms for normal and mildly degenerated cartilage, respectively. ROC analysis showed that UTE-MTR (AUC =0.805, P=0.001, sensitivity =73.7%, specificity =89.5%) had the highest diagnostic efficacy for mild cartilage degeneration, while UTE-AdiabT1ρ (AUC =0.727, P=0.017) and CubeQuant-T2 (AUC =0.712, P=0.026) showed lower diagnostic efficacy.ConclusionsQuantitative UTE-MT and UTE-AdiabT1ρ biomarkers may potentially be used in the evaluation of early cartilage degeneration

Improving Bird's Eye View Semantic Segmentation by Task Decomposition

Semantic segmentation in bird's eye view (BEV) plays a crucial role in autonomous driving. Previous methods usually follow an end-to-end pipeline, directly predicting the BEV segmentation map from monocular RGB inputs. However, the challenge arises when the RGB inputs and BEV targets from distinct perspectives, making the direct point-to-point predicting hard to optimize. In this paper, we decompose the original BEV segmentation task into two stages, namely BEV map reconstruction and RGB-BEV feature alignment. In the first stage, we train a BEV autoencoder to reconstruct the BEV segmentation maps given corrupted noisy latent representation, which urges the decoder to learn fundamental knowledge of typical BEV patterns. The second stage involves mapping RGB input images into the BEV latent space of the first stage, directly optimizing the correlations between the two views at the feature level. Our approach simplifies the complexity of combining perception and generation into distinct steps, equipping the model to handle intricate and challenging scenes effectively. Besides, we propose to transform the BEV segmentation map from the Cartesian to the polar coordinate system to establish the column-wise correspondence between RGB images and BEV maps. Moreover, our method requires neither multi-scale features nor camera intrinsic parameters for depth estimation and saves computational overhead. Extensive experiments on nuScenes and Argoverse show the effectiveness and efficiency of our method. Code is available at https://github.com/happytianhao/TaDe.Comment: Accepted by CVPR 202

Author Correction:3D-printed liquid metal polymer composites as NIR-responsive 4D printing soft robot

Correction to: Nature Communications https://doi.org/10.1038/s41467-023-43667-4, published online 28 November 2023

Lithospheric electrical structure across the Bangong-Nujiang Suture in northern tibet revealed by magnetotelluric

Competing hypotheses have been proposed to explain the subduction polarity of the Bangong-Nujiang Tethyan Ocean and the formation of the high-conductivity anomaly beneath the Qiangtang terrane. However, the lithospheric architecture of the northern Tibetan Plateau is still poorly understood due to inhospitable environments and topography. Therefore, in the winter of 2021, a 440 km long, SN-trending broadband magnetotelluric (MT) profile was recorded in northern Tibet to detect its regional lithospheric structure. The nonlinear conjugate gradients algorithm is conducted to invert the individual TM mode data. A reliable 2D electrical model was obtained by ablation processing and analysis of broadband magnetotelluric data to test the lithospheric electrical structure and dynamics between the northern Lhasa and Qiangtang terranes. The inversion results reveal the lithospheric structure at a depth of 100 km in northern Tibet, which synthesizes geological, geochemical and deep seismic reflection evidence and firmly identifies that the trace of the south-dipping conductor mainly resulted from the southward subduction of the Bangong-Nujiang Tethyan Ocean under the Lhasa terrane and the trace of the north-dipping conductor likely due to the northward subduction of the Bangong-Nujiang Tethyan Ocean under the Qiangtang terrane. In addition, the magnetotelluric profile also images a high-conductivity lithospheric-scale anticline beneath the central Qiangtang terrane, which may correspond to the upwelling of postcollisional magmatism triggered by northward subduction of the Bangong-Nujiang Tethyan Ocean under the Qiangtang terrane, aqueous fluid and/or partial melting

The study of biosafety risk identification and analysis for facilities in biosafety level 3 laboratories

ObjectiveThe aim of this study was to identify and analyze biosafety risk points in biosafety level 3 (BSL-3) laboratory facilities to bring awareness to the attention of administrative staff, reduce the biosafety risks, and improve the risk management.MethodsThe biosafety risk points in BSL-3 facilities were identified by literature searches and field research methods, and the identified biosafety risk points subsequently analyzed using the fault analysis event tree method.Results & conclusionThroughout the comprehensive screening and identification of biosafety risk points in BSL-3 laboratory facilities, risk assessments were performed to rank their seriousness. This will help effectively reduce the biosafety risk level of BSL-3 laboratory facilities

Topological optimization of an offshore-wind-farm power collection system based on a hybrid optimization methodology

This paper proposes a hybrid optimization method to optimize the topological structure of an offshore-wind-farm power collection system, in which the cable connection, cable selection and substation location are optimally designed. Firstly, the optimization model was formulated, which integrates cable investment, energy loss and line construction. Then, the Prim algorithm was used to initialize the population. A novel hybrid optimization, named PSAO, based on the merits of the particle swarm optimization (PSO) and aquila optimization (AO) algorithms, was presented for topological structure optimization, in which the searching characteristics between PSO and AO are exploited to intensify the searching capability. Lastly, the proposed PSAO method was validated with a real case. The results showed that compared with GA, AO and PSO algorithms, the PSAO algorithm reduced the total cost by 4.8%, 3.3% and 2.6%, respectively, while achieving better optimization efficiency.Web of Science112art. no. 27