High Degree Of Matching Research Articles

Local Permeability of a Metasurface in the MHz and GHz Ranges

The work is dedicated to the study of the magnetic response of a metasurface made of split ring resonators (meta-atoms) in the MHz and GHz ranges. A new method for calculating the local permeability of a metasurface in the MHz frequency range is proposed, taking into account the finite sizes of the elements, and for the first time, a result of high degree of accuracy matching with experimental values has been obtained. In the GHz range, additional factors such as the retardation effect, non-uniform current distribution in the meta-atom, and the complexity of the nature of meta-atoms’ interaction compared to the MHz range are also considered, in particular, the emergence of electrical interaction between them.

Urban Spatial Image Acquisition and Examination Based on Geographic Big Data

This study proposes a two-dimensional analytical framework based on urban spatial form and spatial service perspectives, utilizing data on buildings and points of interest (POIs). It integrates fishnet analysis, kernel density analysis, the categorization of POI functionalities, and mixture calculations to enhance our understanding of urban spatial form and function. Taking the main urban area of Zhengzhou as an example, this study identifies image elements that can describe urban spatial characteristics through the results of two-dimensional analysis and enriches the city image in the form of a portrait. The experimental findings demonstrate that the elements of the annular layer, functional landmarks, ring line boundaries, and special districts can fully convey the spatial picture of Zhengzhou City. The performance of the four types of image elements has a high degree of matching with the content of the urban spatial planning of Zhengzhou City, which can effectively identify the urban multi-center structure and development pattern. This paper explores and tests the development status of the city from a new perspective, which can provide an effective reference for the future planning and sustainable development of the city.

Investigation on axial compression performance of self-compacting fly ash concrete filled square steel tube column: Tests and numerical simulation

Self-compacting concrete has the advantages of high fluidity, high uniform density, and low porosity. To promote the engineering application of self-compacting concrete, this article studies the axial compression test of ten square steel tube self-compacting concrete columns. The effects of design parameters such as different grades of concrete strength and internal structures of circular steel tubes, square steel tubes, and H-shaped steel on the mechanical properties of self-compacting fly ash concrete filled square steel tube columns are studied. The failure mode, characteristic load and characteristic displacement, ductility, and energy dissipation capacity of each specimen are analyzed. On the basis of the test, the finite element analysis model of steel tube self-compacting concrete column is established, and the test results and simulation results are compared and analyzed. The results show that: the failure mode of the square steel tube specimen is the multilayer middle and upper ring buckling failure. Internal structural measures of square steel tube and H-shaped steel can improve the bearing capacity, ductility, and energy dissipation capacity of the specimen. It is not recommended to use the specimen with an outer square and an inner circle design. The results of the finite element simulation have a high degree of matching with the test results, which intuitively reflects the changes in force of each component.

Research on Bidirectional Multi-Span Feature Pyramid and Key Feature Capture Object Detection Network

UAV remote sensing (RS) image object detection is a very valuable and challenging technology. This article discusses the importance of key features and proposes an object detection network (URSNet) based on a bidirectional multi-span feature pyramid and key feature capture mechanism. Firstly, a bidirectional multi-span feature pyramid (BMSFPN) is constructed. In the process of bidirectional sampling, bicubic interpolation and cross layer fusion are used to filter out image noise and enhance the details of object features. Secondly, the designed feature polarization module (FPM) uses the internal polarization attention mechanism to build a powerful feature representation for classification and regression tasks, making it easier for the network to capture the key object features with more semantic discrimination. In addition, the anchor rotation alignment module (ARAM) further refines the preset anchor frame based on the key regression features extracted by FPM to obtain high-quality rotation anchors with a high matching degree and rich positioning visual information. Finally, the dynamic anchor optimization module (DAOM) is used to improve the ability of feature alignment and positive and negative sample discrimination of the model so that the model can dynamically select the candidate anchor to capture the key regression features so as to further eliminate the deviation between the classification and regression. URSNet has conducted comprehensive ablation and SOTA comparative experiments on challenging RS datasets such as DOTA-V2.0, DIOR and RSOD. The optimal experimental results (87.19% mAP, 108.2 FPS) show that URSNet has efficient and reliable detection performance.

A maneuver indicator and ensemble learning-based risky driver recognition approach for highway merging areas

Abstract Due to the complex traffic characteristics in highway merging areas, drivers tend to exhibit high-risk driving behaviors. To address the characteristics of driving behavior in highway merging areas, we have developed a real-time identification model for risky drivers by combining a driver risk level labeling method with load balancing-ensemble learning (LB-EL). In this paper, we explore four types of maneuver indicator indexes (MIIs)—acute direction, stomp pedal, dangerous following, and dangerous lane changing—that can describe the negative behaviors of both individual vehicles and vehicle platoons in highway merging areas. To quantize the label driver risk level, we use the interquartile range (IQR) method and Criteria Importance Though Intercriteria Correlation (CRITIC), while we evaluate the reliability of the MII using spatial analysis. Furthermore, we balance the dataset using three load balancing (LB) algorithms and create nine ensemble strategies by pairing adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LGBM) with the three LB algorithms. Finally, we validate the proposed model using trajectory data extracted from UAV videos. The results indicate that the distribution laws of risky driving behaviors in the acute direction and stomp pedal show a high degree of similarity and good matching with the distribution laws of traffic conflict points in existing research. Moreover, the SMOTE-LGBM ensemble model achieves the best performance, reaching an accuracy rate of 93.4% and a recall rate of 92.1%, which demonstrates the validity of our proposed model. This model can be widely applied to recognize risky drivers in video-based surveillance systems.

Enhanced Performance in Cesium Tellurium Chlorine by Hafnium Alloying for X‐Ray Computed Tomography Imaging

AbstractThe term “light yield” (LY) refers to the ability of scintillator materials to convert high‐energy radiation into visible light. A higher LY corresponds to improved energy and spatial resolution in detectors. The enhancement of scintillator material performance is a crucial aspect of their development. In this study, the Cs2TeCl6 (CTC) double perovskite microcrystals is synthesized, and the broadband yellow emission shows a high degree of matching with the Charge‐coupled Device (CCD). However, the LY of CTC is poor for practical applications in X‐ray imaging. The scintillation performance is significantly enhanced through hafnium alloying. The LY increased from ≈4167 to 38 523 photons/MeV, and the detection limit decreased from 948 to 258 nGy s−1. The underlying mechanism of Te4+ ions emission is systematically explored through density function theory (DFT) analysis, along with investigations into pressure‐dependent photoluminescence, temperature‐dependent photoluminescence, and low‐temperature thermoluminescence. Furthermore, a flexible X‐ray scintillator screen with an outstanding high spatial resolution of 15.9 lp mm−1 is successfully fabricated. This work not only represents a substantial advancement in the scintillation performance of Te4+ ions emission double perovskite microcrystals but also provides an effective strategy for the development of the next generation of halide scintillators.

In situ preparation of oriented microbial consortium-based compound enzyme strengthens food waste disintegration and anaerobic digestion: Performance, mechanism, microbial communities and global metabolic pathways

This study used food waste (FW) as the substrate, under the co-culture system, innovatively developed the oriented and high matching degree microbial consortium-based compound enzyme (MCE) used for promoting FW hydrolysis and anaerobic digestion (AD). It was found that the highest hydrolysis efficiency and substrate biodegradability could be obtained after 1:100 MCE (i.e. the inoculation ratio of Aspergillus oryzae CICC 40214 as protease generator and Aspergillus niger CICC 40294 as amylase generator was 1:100) pretreatment, thereby harvesting the highest biomethane yield of 522.61 mL/g VS. The dissection of FTIR results indicated that the structure of main components of starch, protein and lipid in FW was destructed after MCE pretreatment, of which the protein was more susceptible to MCE and disintegrated first, while its structure also became looser. XPS C1s spectra and N1s spectra showed that the C-(C/H) and protein-N of the enzyme pretreated solid were respectively decreased from 77.82 % to 69.19 % and 69.43 % to 40.07 %, which further implied that more carbohydrates and proteins were released from solid phase to liquid phase after MCE pretreatment. The microbial community and metagenome analysis illustrated that MCE pretreatment regulated the microbial communities lead to a shift in the favorable direction of biomethane production. While based on the changes in gene abundance, the deciphering of global metabolic pathways revealed that the 80 %, 60 %, 75 % and 86 % of the functional genes related to glycolysis, amino acid metabolism, TCA cycle and methane metabolism were respectively intensified after 1:100 MCE pretreatment, thereby achieving the maximized biomethane yield.

Research on the Optimization of English Teaching Content in Colleges and Universities by Combining Decision Tree and Big Data Technology

Abstract English teaching in colleges and universities is facing the growth of personalized teaching demand, and the traditional education model is challenging to meet the diverse learning needs of students. Big data and decision tree technology provide a new way for teaching content optimization, which can achieve a high degree of matching between educational resources and students’ needs, and improve teaching effectiveness and learning efficiency. This study explores the application of decision trees and big data technology in optimizing English teaching content in colleges and universities. By constructing an ontology tree model of learning resources and adopting similarity calculation and personalized recommendation algorithms, the study achieves personalized learning resource recommendations for students. The study utilizes comparative experimental methods to analyze achievement changes between the experimental and control classes after customized learning. The personalized teaching program’s effectiveness can be confirmed by the fact that the average final grade of students in the experimental class is 1.5 points higher than that of the control class. The Analysis of CET-4 scores found that personalized teaching improved students’ English listening, reading, and writing skills. This paper combines decision trees and big data technology to optimize teaching content, effectively enhancing students’ English learning outcomes and providing new teaching strategies for English education in colleges and universities.

Protein folding and mental health: The impact of protein misfolding on neurological disorders

Major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are psychiatric disorders that significantly impact an individual's ability to function effectively within society. MDD is characterized by persistent low mood, leading to symptoms such as anhedonia, feelings of worthlessness, and cognitive impairments. BD, another mood disorder, is typified by recurrent episodes of depression and mania or hypomania. The disorder is associated with increased risk of suicide, comorbid psychiatric conditions, and impaired functioning across various domains. SCZ, a severe psychiatric disorder primarily characterized by hallucinations, delusions, and disorganized thinking, affects approximately 1% of the global population. The cognitive, social, and occupational impairments associated with SCZ impose a substantial burden on affected individuals and their families. Even though it sounds like three completely different mental disorders, because of the high degree of matching of symptoms, we firmly believe that there is a relationship between these mental disorders. Protein folding is crucial for proper protein function, and misfolding can lead to neurodegenerative disorders. Through recent research findings, we found that the changes in A plaques and tau tangles appeared in the typical psychiatric diseases mentioned above. This paper explored the impact of protein misfolding, changes in A plaques, and tau tangles on neurological disorders, with a focus on depression, bipolar disorder, and schizophrenia, also examines the role of molecular chaperones in preventing misfolding and the impact of protein misfolding.

Radiation Force Modeling for a Wave Energy Converter Array

The motivation and focus of this work is to generate passive transfer function matrices that model the radiation forces for an array of WECs. Multivariable control design is often based on linear time-invariant (LTI) systems such as state-space or transfer function matrix models. The intended use is for designing real-time control strategies where knowledge of the model’s poles and zeros is helpful. This work presents a passivity-based approach to estimate radiation force transfer functions that accurately replace the convolution operation in the Cummins equation while preserving the physical properties of the radiation function. A two-stage numerical optimization approach is used, the first stage uses readily available algorithms for fitting a radiation damping transfer function matrix to the system’s radiation frequency response. The second stage enforces additional constraints on the form of the transfer function matrix to increase its passivity index. After introducing the passivity-based algorithm to estimate radiation force transfer functions for a single WEC, the algorithm was extended to a WEC array. The proposed approach ensures a high degree of match with the radiation function without degrading its passivity characteristics. The figures of merit that will be assessed are (i) the accuracy of the LTI systems in approximating the radiation function, as measured by the normalized root mean squared error (NRMSE), and (ii) the stability of the overall system, quantified by the input passivity index, ν, of the radiation force transfer function matrix.

On the Hybrid Modelling of Rigid Sonic Crystal Embedded in Plenum Chamber at Low Frequencies Using the Effective Medium Approach and Finite Element Method

This study explores the application of homogenization schemes in modelling acoustic wave propagation in a finite array of circular scatterers embedded into a 2D cavity. The cavity is integrated into the 2D waveguide attached to the cavity inlet and outlet. Square-lattice sonic crystal (SC), composed of rigid and radially symmetric circular cylinders, is used to represent the finite array in this study. The model is aimed at informing the novel designs of plenum windows with superior soundproofing performance. The sound transmission performance is analysed with hybrid numerical simulation using the Finite Element Method (FEM) and homogenization scheme. A theoretical homogenization method based on the Effective Medium Approach (EMA) is reviewed and implemented on the modelling of the SC array inside the cavity at low frequencies and then implemented in FEM. Comparison of the transmission loss (TL) reveals a high degree of matching between settings with and without homogenization at low-frequency range. It is observed that performance of the hybrid model maintains a high degree of matching provided the cavity modes are dominated by a single characteristic scale in the low frequency. This includes the case of a narrow plenum chamber with only a single cylinder placed inside the cavity with a width of one lattice constant. This homogenization method is proven to be more computationally efficient in FEM compared to the modelling of exact geometry of the problem.

Haematopoietic Stem Cell Transplantation for Hepatitis-Associated Aplastic Anaemia: Clinical Characteristics, Outcomes and Risk Factors

Introduction: Hepatitis-associated aplastic anaemia (HAAA) is a severe and life-threatening condition that requires effective treatment approaches. Hematopoietic stem cell transplantation (HSCT) has emerged as a promising therapeutic option for HAAA patients. In this retrospective study, we aimed to investigate the clinical characteristics, outcomes, and prognostic factors associated with HSCT in HAAA patients at a prominent Chinese blood disease hospital between 2008 and 2022. Methods: A comprehensive review of 35 HAAA patients who underwent HSCT was conducted, and clinical data were analyzed to determine the treatment's efficacy and safety. The patients were primarily categorized into those with severe AA (28.6%) and very severe AA (65.7%), with a predominant male representation (68.6%) and a median onset age of 23 years (range, 9-44). Both haploidentical and matched sibling donors were utilized in comparable proportions. Results: The study revealed a promising 5-year overall survival (OS) rate of 74.0% after HSCT, underscoring the effectiveness of this treatment approach in HAAA patients. However, certain factors significantly impacted survival rates and treatment outcomes. Prolonged intervals from diagnosis to HSCT (>=75 days), acute graft-versus-host disease (GVHD), and post-HSCT liver events, such as hepatic GVHD and a three-fold increase in aminotransferase or bilirubin, were found to worsen 5-year OS. Interestingly, higher doses of mononuclear cell count, CD34+, CD3+, CD4+, and CD8+ cells in allografts were associated with poorer OS or graft failure-free survival (GFFS), suggesting that pursuit of higher cell counts in certain cases may be counterproductive. Furthermore, a close examination of the multivariate models revealed that recipients who received sex-matched grafts experienced better OS, while those with younger male donors had a lower incidence of grade II-IV acute GVHD. Moreover, a higher HLA matching degree (HLA >=7/10) between the recipient and donor emerged as an independent prognostic factor associated with better OS and GFFS. Conclusion: This study highlights the safety and efficacy of HSCT as a treatment modality for HAAA patients. Early transplantation, careful donor selection, and efforts to improve post-transplant liver events were identified as crucial factors to optimize treatment outcomes. While the use of certain cells in higher doses may have a negative impact on survival, the importance of achieving a high HLA matching degree cannot be overstated. Moving forward, these findings can serve as valuable insights in guiding the management and treatment decisions for HAAA patients undergoing HSCT. Further research and continuous improvement in transplantation protocols will undoubtedly advance the success rates and long-term prognosis for individuals afflicted with this challenging condition.

Improving Zn metal anode performance with powdery graphene as a deposition modulator

The inadequate cycling stability of Zn anodes, caused by dendritic growth and side reactions, impedes the practical advancement of aqueous Zn metal batteries. Graphene (Gr) has garnered considerable attention in recent years owing to the high degree of lattice constant match to Zn(002). However, the inherent zincophobicity of Gr and its hindrance to Zn2+ transport have necessitated intricate optimization strategies. In this work, we propose a straightforward solution by fabricating a protective layer for Zn anodes using Gr powder (p-Gr), which could effectively modulate Zn deposition behavior. The homogenized interfacial electric field and abundant edges of p-Gr nanosheets facilitate the planar deposition of Zn while concurrently exhibiting excellent capabilities in restricting side reactions. As a result, symmetric cells demonstrate long-lasting lifespan over 2500 h under 5.0 mA cm−2. Furthermore, when paired with a ZnxV2O5 cathode, full cells showcase outstanding capacity retention and cycling stability. Our research imparts valuable insight into the design of high-performance Zn metal anode based on graphene materials.

Effect of Zirconium modified Y zeolite via in situ synthesis and its regulation on the formation of excellent NiW catalyst for ultra-deep hydrodesulfurization of 4,6-DMDBT

Alternative support materials, such as Y zeolite, have been introduced to overcome the challenges associated with industrial alumina-supported hydrodesulfurization (HDS) catalysts. However, to improve catalytic activity while avoiding excessive hydrocarbon cracking, the properties of Y zeolite, including acidity, should be modulated. Herein, Zr-modified Y zeolite was prepared via in-situ synthesis, and the corresponding NiW catalyst was obtained by impregnating hybrid zeolite-alumina supports with metal precursors. The catalytic performance was tested using 4,6-dimethyldibenzothiophene (4,6-DMDBT) as an HDS probe. The position of the Zr species in the zeolites depended on the modification methods. The ZrHY zeolite prepared by impregnation mainly formed non-framework ZrO2 on the surface of zeolite, whereas the HZrY zeolite via in-situ synthesis exhibited Zr species dispersed in the zeolitic framework. Zr species in the zeolitic framework not only enhanced the acidities of the zeolite and hybrid-support (HZrY-Al2O3), but also regulated the interaction of active metals and hybrid support, which significantly enhancing the catalytic activity of the catalyst. Among the four investigated catalysts, the turnover frequency (TOF) and reaction rate constant (k) of 4,6-DMDBT HDS over NiW/HZrYA catalyst were higher efficient than those of other catalysts, and their isomerization selectivity was improved, reducing hydrogen consumption. This enhanced efficiency was attributed to the NiW/HZrYA catalyst having more suitable acid sites, a greater degree of active metals sulfidation, more NiWS phase, superior isomerization performance, and a higher matching degree between hydrogenation and hydrolysis activity. These findings are great meaning for design the ultra-deep low hydrogen consumption HDS catalysts.

Towards Improved Remedial Outcomes in Categorical Aquifers with an Iterative Ensemble Smoother.

Categorical parameter distributions consisting of geologic facies with distinct properties, for example, high-permeability channels embedded in a low-permeability matrix, are common at contaminated sites. At these sites, low-permeability facies store solute mass, acting as secondary sources to higher-permeability facies, sustaining concentrations for decades while increasing risk and cleanup costs. Parameter estimation is difficult in such systems because the discontinuities in the parameter space hinder the inverse problem. This paper presents a novel approach based on Traveling Pilot Points (TRIPS) and an iterative ensemble smoother (IES) to solve the categorical inverse problem. Groundwater flow and solute transport in a hypothetical aquifer with a categorical parameter distribution are simulated using MODFLOW 6. Heads and concentrations are recorded at multiple monitoring locations. IES is used to generate posterior ensembles assuming a TRIPS prior and an approximate multi-Gaussian prior. The ensembles are used to predict solute concentrations and mass into the future. The evaluation also includes an assessment of how the number of measurements and the choice of the geological prior determine the characteristics of the posterior ensemble and the resulting predictions. The results indicate that IES was able to efficiently sample the posterior distribution and showed that even with an approximate geological prior, a high degree of parameterization and history matching could lead to parameter ensembles that can be useful for making certain types of predictions (heads, concentrations). However, the approximate geological prior was insufficient for predicting mass. The analysis demonstrates how decision-makers can quantify uncertainty and make informed decisions with an ensemble-based approach.

Supramolecular Chiral Nanofibers Activated Pro‐Survival Autophagy: An Extracellular Strategy for Hypoxic Cell Protection

AbstractHypoxic injury poses a severe threat to cell survival. Activation of pro‐survival autophagy via autophagy‐inducing nanomaterials (AINMs) has emerged as one of the most promising therapeutic strategies. However, internalized AINMs easily bind with intracellular lysosomes, resulting in disruptive effects on autophagic flux and limited pro‐survival effects. Herein, inspired by the naturally existing chiral structures in actin membrane protein, two nanofibers based on achiral pyridine‐substituted coumarin derivatives (PyC) with only difference in supramolecular chirality are constructed by the method of chirality memory to activate autophagy extracellularly. The high degree of conformational chirality matching between right‐handed PyC‐r nanofibers and actin ensures an enhanced stereoselective interaction between the pyridine groups on PyC and the hydroxyl groups on serine‐rich Girdin protein (an actin‐binding protein involved in cell survival). This stereoselective interaction can activate the autophagy‐related Akt/mTOR pathway via mechanical signal transmission and phosphorylation of Akt, which avoids the disruption of autophagic flux and significantly mitigates cellular hypoxic injury. In a rat myocardial ischemia model, scar formation and functional deterioration are particularly alleviated by injection of PyC‐r nanofibers into murine infarcted myocardium. The findings pioneeringly establish a direct link between chiral biomedical materials and cellular autophagy, offering novel insights for cellular hypoxic protection.

Insights into amine-resin matching strategy for CO2 capture: Adsorption performance tests and Mechanistic investigation

The solid amine adsorbents are potential candidates as materials for post-combustion capture technologies. The type of organic amine and porous material are the key factors influencing the amine-based solid adsorbent performance. Furthermore, there has been relatively little research on the matching mechanism between organic amines and porous materials. This study aims to investigate the effect of the degree of matching between different organic amines and porous materials (X-5 resin) on the performance of CO2 capture. The results indicated that 30 wt% TETA was the highest matching degree with X-5 and had the maximum CO2 adsorption of 205.48 mg/g at 30 ℃. The matching degree between the four amines and the X-5 resin followed TETA > TEPA > PEHA > PEI. Since TETA has the simplest molecular structure, it can be conveniently dispersed in the X-5 pores, which not only significantly improves the utilization of active sites but also prevents the phenomena of agglomeration or sintering. Meanwhile, X-5-30TETA has the highest adsorption energy Eads (−85.22 kJ/mol) and the smallest spatial potential resistance, decreasing the mass transfer resistance effectively, thus further increasing the adsorption capacity. Furthermore, the adsorption kinetics results showed that the Avrami model can relatively accurately predict the experimental results of X-5-TPHI CO2 adsorption. Finally, the in situ DRIFTS indicated that the CO2 adsorption reaction over X-5-30TETA followed predominantly an amphipathic centipede mechanism. This work provides a new idea for the investigation of the matching mechanism between organic amines and porous materials.

Strategy for the zeotropic organic rankine cycle operation to match the heat sink variation

The zeotropic organic Ranking cycle (ORC) is considered a high-efficiency utilization of low-grade energy, caused by its nature of temperature slide, which could reduce the irreversibility loss of evaporation and condensation, and make the ORC more adaptable to the heat source and heat sink. Regarding this advantage, the ‘fH’ and ‘fL’ are defined in this work to describe the system matching degree with the heat source and heat sink, respectively. By considering ORCs employed by different types of mixtures, it is found that when the fL is equal to 1, the relationship between ORC and heat sink is considered the highest matching degree, and the system would exert the best performance. Thus, a system operation strategy is presented for system optimization with the guidance of fL by adjusting the condensation pressure under varied heat sinks. Considering a 0.5R1233zd(E)/0.5R1234yf driven ORC system, the monthly condensation pressures are evaluated according to the supply cooling water. Compared to the referred system, the annual net output and thermal efficiency of the optimized system under the heat source of 120 °C could improve from 785.55 MWh to 905.66 MWh and 8.27 %–9.13 %, respectively. The presented operation strategy contributes to the zeotropic ORC optimization with varied heat sinks.

The impact mechanism of entrepreneurial competency and entrepreneurial model matching on entrepreneurial performance

ABSTRACT From the perspective of matching, this paper explores the impact mechanism of entrepreneurial competency and entrepreneurial model matching on entrepreneurial performance. Then this paper uses cluster analysis and independent sample t-test to carry out the empirical analysis. The research results show the following relationship. (1) Entrepreneurs whose risk aversion psychology, entrepreneurial learning cognition, organizational management functions, and genetic network application capabilities are matched with survival-driven entrepreneurial models can improve the survival performance of new ventures. (2) Entrepreneurs whose risk propensity psychology, knowledge conversion cognition, strategic decision-making functions, and industrial network application capabilities are matched with the opportunity-driven entrepreneurial model, which can improve the survival and growth performance of new ventures. (3) Entrepreneurs’ self-potency psychology, technology transformation cognition, R&D innovation function, and network management capabilities are matched with the innovation-driven entrepreneurial model, which can improve the innovation performance of new ventures. Through empirical analysis, it was found that there were 22 matching samples, accounting for 61.11% of samples. There were 14 mismatched samples, accounting for 38.89% of samples, indicating a higher matching degree between entrepreneurial capability cluster and innovation-driven entrepreneurial model.

How can blockchain be integrated into renewable energy? --A bibliometric-based analysis

Blockchain technology and renewable energy links can realize the fusion of energy flow and information flow. The combination of the two has a high degree of matching, and the related research is also increasing. Although experts have expanded their research on renewable energy and blockchain, there are still restrictions and gaps in the existing research. In this paper, 920 articles from 2016 to 2023 were chosen and visualized using bibliometrics and CiteSpace software to analyze the research field's development base, collaborative groups, hot topics and evolutionary trends. Based on the foregoing research, we summarized the pertinent literature in the area of “Renewable energy & Blockchain” and thoroughly analyzed the current hot issues in depth to determine the future development direction. The findings show that, first, related research in the field increased rapidly in 2016–2023, the research topics and hotspots of “Renewable energy & Blockchain” focus on energy system optimization, clean energy technologies, circular development and renewable energy trading. Second, all subjects have a specific strength and influence contribution in the field, from the micro to the macro level. Among them, China, the United States and their universities have relatively mature research experience and research ability. Third, research gaps in blockchain-based renewable energy application, technology, and policy, as well as potential trends in energy internet, energy management, energy systems, green certificate trading, and green power trading. This study provides researchers with a theoretical foundation for gaining a thorough understanding of current research efforts and future research directions in this field.