In recent years, perovskite solar cells (PSCs) have attracted great attention in the photovoltaic research field, because of their high-efficiency (certified 22.1%) and low-cost. In this review ...paper, we briefly introduce the history of efficiency development for PSCs, and discuss some of the major problems for large-area (≥1 cm
2
) PSC devices. In addition, we summarize the recent progress in the aspects of fabrication methods for large-area perovskite films, and improving the efficiency and stability of the large-area PSC devices. Finally, we give a short summary and outlook of large-area PSC devices. This article is mainly organized into three parts. The first part focuses on the main fabricating technologies for large-area perovskite films. The second section discusses some methods that are used to improve the efficiency of PSCs. In the last part, different approaches are used to improve the stability of PSCs.
In this review, we summarize the recent progress in the aspects of the fabrication methods for large-area perovskite films, improving the efficiency and stability of the large-area PSC devices.
In this paper, we argue that obtaining government R&D subsidies has a certification effect and is used by innovative entrepreneurial firms as a legitimation strategy to access bank finance. We extend ...the extant literature on the certification effect by combining legitimacy theory with information asymmetry to build our theoretical framework. We test our theoretical model under China’s unique institutional setting, in particular, the weak intellectual property rights (IPR) protection. Using 549 listed and 192 unlisted Chinese innovative entrepreneurial firms from 2009 to 2013, we find a positive certification effect on the acquisition of bank loans for all those sample firms. This positive effect is more profound in unlisted firms in our sample than the listed ones. We further find that regional variation of IPR protection has a moderating effect on the effectiveness of the certification. The certification effect is more significant in those regions where IPR protection is weaker.
Simultaneous localization and mapping (SLAM), as one of the core prerequisite technologies for intelligent mobile robots, has attracted much attention in recent years. However, the traditional SLAM ...systems rely on the static environment assumption, which becomes unstable for the dynamic environment and further limits the real-world practical applications. To deal with the problem, this paper presents a dynamic-environment-robust visual SLAM system named YOLO-SLAM. In YOLO-SLAM, a lightweight object detection network named Darknet19-YOLOv3 is designed, which adopts a low-latency backbone to accelerate and generate essential semantic information for the SLAM system. Then, a new geometric constraint method is proposed to filter dynamic features in the detecting areas, where dynamic features can be distinguished by utilizing the depth difference with Random Sample Consensus (RANSAC). YOLO-SLAM composes the object detection approach and the geometric constraint method in a tightly coupled manner, which is able to effectively reduce the impact of dynamic objects. Experiments are conducted on the challenging dynamic sequences of TUM dataset and Bonn dataset to evaluate the performance of YOLO-SLAM. The results demonstrate that the RMSE index of absolute trajectory error can be significantly reduced to 98.13% compared with ORB-SLAM2 and 51.28% compared with DS-SLAM, indicating that YOLO-SLAM is able to effectively improve stability and accuracy in the highly dynamic environment.
Abstract
Background
Colon cancer is the foremost reason of cancer-related mortality worldwide. Colon adenocarcinoma constitutes 90% of colon cancer, and most patients with colon adenocarcinoma (COAD) ...are identified until advanced stage. With the emergence of an increasing number of novel pathogenic mechanisms and treatments, the role of mitochondria in the development of cancer, has been studied and reported with increasing frequency.
Methods
We systematically analyzed the effect of mitochondria-related genes in COAD utilizing RNA sequencing dataset from The Cancer Genome Atlas database and 1613 mitochondrial function-related genes from MitoMiner database. Our approach consisted of differentially expressed gene, gene set enrichment analysis, gene ontology terminology, Kyoto Encyclopedia of Genes and Genomes, independent prognostic analysis, univariate and multivariate analysis, Kaplan–Meier survival analysis, immune microenvironment correlation analysis, and Cox regression analysis.
Results
Consequently, 8 genes were identified to construct 8 mitochondrial-related gene model by applying Cox regression analysis, CDC25C, KCNJ11, NOL3, P4HA1, QSOX2, Trap1, DNAJC28, and ATCAY. Meanwhile, we assessed the connection between this model and clinical parameters or immune microenvironment. Risk score was an independent predictor for COAD patients’ survival with an AUC of 0.687, 0.752 and 0.762 at 1-, 3- and 5-year in nomogram, respectively. The group with the highest risk score had the lowest survival rate and the worst clinical stages. Additionally, its predictive capacity was validated in GSE39582 cohort.
Conclusion
In summary, we established a prognostic pattern of mitochondrial-related genes, which can predict overall survival in COAD, which may enable a more optimized approach for the clinical treatment and scientific study of COAD. This gene signature model has the potential to improve prognosis and treatment for COAD patients in the future, and to be widely implemented in clinical settings. The utilization of this mitochondrial-related gene signature model may be benefit in the treatments and medical decision-making of COAD.
The influences of different axes on the accuracy of a machine tool vary due to their positions in the structure of the machine tool and their local errors. In this paper, geometric error contribution ...modeling and a sensitivity evaluation of the axes of a machine tool are proposed to obtain the influences of each axis and determine the crucial axes of the machine tool. First, the error vector components of the position-independent errors are obtained by product of exponential (POE) theory. Second, the error contributions of all axes are established based on transforming differential changes between coordinate frames by using the POE formula of the tool relative to each axis and the error vectors of the axes. Third, an error sensitivity matrix of each axis is established according to the formula of error contribution about the error vector of the axis. Fourth, two methods are proposed for an error sensitivity evaluation of the axes to determine the crucial axes: one method employs the weights of the error contributions of the axes, and the other method employs the error sensitivity coefficients of the axes. Finally, simulations and real cutting experiments are carried out with the SmartCNC500_DRTD five-axis machine tool to verify the effectiveness of the error contribution modeling and error sensitivity evaluation of the axis.
•The influences and error vector of position-independent errors are developed.•Error contribution of each axis on the integrated errors of the machine tool are obtained.•Error sensitivity matrix of all axes are established including tool position and tool orientation.•Error sensitivity evaluation of the axis is proposed with two methods.•The crucial axes of the machine tool are obtained.
We built a livestreaming impulsive buying model based on stimulus-organism-response (SOR) theory, and we explored the impact of atmospheric cues (ACELS) and sales promotion (SPELS) on impulsive ...buying (IBI) based on emotions (EOC) and Zhong Yong tendency (ZYT) of online consumers. Combined with holistic orientation, perspective integration, and harmony maintenance, ZYT is a cognitive process involving individual events. We gathered 478 samples using a questionnaire to test the proposed research model. The empirical findings show that as the stimuli in the livestreaming environment, ACELS and SPELS during livestreaming greatly boost EOC while significantly constraining consumers' ZYT. Among online consumers, positive EOC promotes IBI, whereas ZYT dampens it. In addition, EOC and ZYT mediate the relationship between stimulus factors and response factors in parallel, resulting in four model mediation paths. By incorporating the SOR model, this study provides theoretical underpinnings for the role of cognitive processing in impulsive purchases, as well as useful guidance for e-commerce platforms and streamers to effectively understand Chinese consumers' purchase behavior, which benefits the development of effective promotion strategies and the creation of powerful marketing tools.
In order to accurately monitor the tool wear process, it is usually necessary to collect a variety of sensor signals during the cutting process. Different sensor signals can provide complementary ...information in the feature space. In addition, monitoring signals are time series data, which also contains a wealth of time dimension tool degradation information. However, how to fuse multi-sensor information in time and space dimensions is a key issue that needs to be solved. In this paper, a new time–space attention mechanism driven multi-feature fusion method is proposed for tool wear monitoring and residual useful life (RUL) prediction. A time–space attention mechanism is innovatively introduced into the tool wear monitoring model, and features are weighted from two dimensions of space and time. It can more accurately capture the complex spatio-temporal relationship between tool wear values and features, so that the model can accurately predict wear values even if it gives up cutting force signals with good trends. The experimental results show that the correlation of the predicted wear and the actual wear is greater than 0.95, and the relative accuracy of the RUL predicted by the predicted wear combined with the particle filter can also be around 0.78. Compared with other feature fusion models, the proposed method realizes the tool wear monitoring more accurately and has better stability.
Perovskite solar cells (PSCs) have gained tremendous research interest because of their tolerance of defects, low cost, and facile processing. In PSC devices, PbI2 has been utilized to passivate ...defects at perovskite film surfaces and GBs; however, a systematic mechanism of PbI2 in situ passivation for enhancing the solar cells efficiency has not been fully explored. Here, this work, we systematically studies the effect of the precise PbI2 ratio and the PbI2 in situ passivation mechanism based on trap density, carrier lifetime, Fermi level, and so forth. This study finds the appropriate ratio of I/Pb to be around 2.57:1 using energy-dispersive spectroscopy. After the moderate excess PbI2 in situ passivation, the trap density is reduced from 6.12 × 1016 to 3.38 × 1016 cm–3, and the carrier lifetime is extended from 168.35 to 368.77 ps by using fs-TA spectroscopy. This result indicates that the moderate excess PbI2 in situ passivation can reduce the trap density and suppress the nonradiative recombination. The efficiency of solar cell has shown a nearly 11.3% improvement of 19.55% for an I/Pb ratio of 2.57:1 compared with 2.69:1. It also demonstrates that the efficiency of PSCs can be enhanced effectively by PbI2 in situ passivation.
Thermal errors are one of the main error sources affecting the machining accuracy of the machine tool. Thermal error modeling is a prerequisite for thermal error compensation to reduce the thermal ...error and improve machining accuracy. In this paper, a chicken swarm optimization algorithm-based radial basic function (CSO-RBF) neural network is applied to integrated thermal error modeling. At first, correlation analysis-based
K
-Means clustering and radial basis function neural network (KC-RBF) approach is proposed to screen optimal temperature-sensitive point combination. The correlation analysis-based
K
-Means clustering is used to obtain temperature-sensitive point combinations corresponding to different
K
values. The mean value of residual and root mean square error are established to evaluate the results of RBF model to filter the optimal temperature-sensitive point combination. Secondly, one CSO-RBF neural network is proposed to handle the nonlinear relationship between temperature variables and thermal errors. RBF model-based fitness function is proposed for CSO to obtain the optimal initial structure parameters of RBF. The optimal thermal error model is established by training RBF with the optimal initial structure parameters and the measured data. At last, different experiments are carried out on VMC850 machining center: training and testing of thermal error models at a fixed speed for Y-direction thermal drift error; verification of thermal error models for different speeds of different error parameters. It is worth mentioning that the model trained with one thermal error parameter measured at a certain speed is also applied for different thermal error parameters at different speeds. Results show that the proposed CSO-RBF model has high accuracy and strong robustness.
Graphene has attracted tremendous interest due to its unique physical and chemical properties. The atomic thickness, high carrier mobility and transparency make graphene an ideal electrode material ...which can be applied to various optoelectronic devices such as solar cells, light-emitting diodes and photodetectors. In recent years, there has been a growing interest in developing graphene/silicon Schottky junction solar cells and the power conversion efficiency has reached up to 15.8% with an incredible speed. In this review, we introduce the structure and mechanism of graphene/silicon solar cells briefly, and then summarize several key strategies to improve the performance of the cells. Finally, the challenges and prospects of graphene/silicon solar cells are discussed in the development of the devices in detail.
The structure and mechanism of graphene/silicon solar cells, and several key strategies to improve the performance of the cells.
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