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With the rapid development of autonomous driving technology in daily life scenarios and logistics, some potential start-ups have emerged on such tracks. Therefore, research on this industry in the field of business research cannot be ignored. This article analyzes the investment value of pony.ai, one of the startups in the autonomous driving industry. Firstly, the macro market where pony.ai is located is analyzed, concluding that this industry is expected to develop rapidly and flourish in the next few years. Following the background information and projected financial position of pony.ai, which provides a basic understanding of the company. Additionally, showing the investment value of pony.ai by presenting its current situation through four dimensions, also known as SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis. The results show that pony.ai can be a worthwhile investment choice, whereas specific improvements are needed. The results also have certain reference significance for analyzing startups of the same type.

An in situ artificial LiF/Li–Sn solid electrolyte interface film possesses high Li+ conductivity and good electrical insulation, endowing the Li//LiFePO4 battery with 86.3% capacity after 1000 cycles.

The lithium (Li) ion and electron diffusion behaviors across the actual solid electrolyte interphase (SEI) play a critical role in regulating the Li nucleation and growth and improving the performance of lithium-sulfur (Li-S) batteries. To date, a number of researchers have pursued an SEI with high Li-ion conductivity while ignoring the Li dendrite growth caused by electron tunneling in the SEI. Herein, an artificial anti-electron tunneling layer with enriched lithium fluoride (LiF) and sodium fluoride (NaF) nanocrystals is constructed using a facile solution-soaking method. As evidenced theoretically and experimentally, the LiF/NaF artificial SEI exhibits an outstanding electron-blocking capability that can reduce electron tunneling, resulting in dendrite-free and dense Li deposition beneath the SEI, even with an ultrahigh areal capacity. In addition, the artificial anti-electron tunneling layer exhibits improved ionic conductivity and mechanical strength, compared to those of routine SEI. The symmetric cells with protected Li electrodes achieve a stable cycling of 1500 h. The LiF/NaF artificial SEI endows the Li-S full cells with long-term cyclability under conditions of high sulfur loading, lean electrolyte, and limited Li excess. This study provides a perspective on the design of the SEI for highly safe and practical Li-S batteries.

Lithium-sulfur (Li-S) batteries are widely considered to be next-generation storage technologies due to their high energy density, low cost and non-toxicity. However, the soluble lithium polysulfides (LiPS) migrating to the anode side inevitably causes side reactions with the Li anode, resulting in severe corrosion of the Li anode, loss of active materials, and rapid battery failure. Therefore, it is necessary to develop effective strategies to avoid LiPS exposure to Li anodes. Herein, a stable UiO-66-ClO

As one of the most commonly used clustering methods, the Fuzzy C-means (FCM) clustering strategy extends the notion of hard clustering to associate each pattern with every cluster using a membership function. Although a lot of efforts have been made by the clustering community, it is still unclear how to evaluate the fuzziness of different versions of FCM. To fill this theoretical blank, by observing a family of objective functions, a definition of fuzzy degree is provided to quantify the fuzziness of different versions of FCM according to their dedicated objective functions. Then a general Fuzzy C-Means (GFCM) clustering algorithm is proposed to solve the clustering problem of using FCM under different distance metrics and fuzzy degrees. From the perspective of fuzzy attribute, the using of this fuzzy degree can be used to reveal the essential difference among a group of FCM-based clustering algorithms, and the proposed GFCM achieves the aim of using objective functions to control the fuzziness of clustering results. Additionally, some properties and relations of these FCM-based clustering algorithms under different fuzzy degrees are discussed and the convergence and stability of the proposed GFCM are proved. Finally, extensive experiments have been performed to demonstrate that by comparison with the effect caused by distance metric, the choices of fuzzy degree have a more significant effect and improvement on the performances of a FCM-based clustering algorithm.

Li‒S batteries have shown great potential as secondary energy batteries. However, the side reaction between Li anodes and polysulfides seriously limited their practical application. Herein, the artificial protective film, which is consisted of Li-Nafion and TiO2, was designed and successfully prepared to achieve a corrosion-resistant Li anode in Li-S battery. In the composite protective film, the Li-Nafion could efficiently prevent the contact between Li anodes and polysulfides, and the incorporation of TiO2 nanoparticles into the Nafion could significantly increase the ionic conductivity and mechanical strength of the protective film. Li-Li symmetric cells with an optimal artificial protective film exhibited an extended cycle-life of 750 h at a current density of 1 mA/cm2 in Li2S8 electrolyte. Moreover, the Li‒S full battery with an optimal protective Li anode exhibited higher capacity retention of 777.4 mAh/g after 100 cycles at 0.1 C as well as better rate performance than the cell with a pure Li anode. This work provides alternative insights to suppress the side reaction for Li‒S batteries with high capacity retention.

Rapid climate warming speeds up the solid-liquid water cycle and reduces the solid water storage in cold regions of the Earth. Snowfall is the most crucial input for the cryosphere. However, the potential snowfall phenology (PSP) variability has not been systematically and comprehensively studied. For this reason, we initially proposed three indicators, i.e., the start of potential snowfall season (SPSS), the end of potential snowfall season (EPSS), and the length of potential snowfall season (LPSS), to describe the characteristics of the PSP, then we explored the spatial-temporal variation of those three PSP indicators past, present, and future across the Chinese Tianshan mountainous region (CTMR) based on the observed daily air temperature from 26 meteorological stations during 1961–2017/2020 combined with 14 models data from CMIP6 (the Phase 6 of the Coupled Model Intercomparison Project) under four different scenarios (SSP126, SSP245, SSP370, and SSP585) during 2021–2100. It proved that the SPSS, EPSS, and LPSS could reproduce features of the PSP well across the study area. In the past and present, the potential snowfall season started on October 29th, ended on March 20th, and lasted for about four months and 23 days across the CTMR on average. The rate of advancing EPSS (−1.6 days/10a) was faster than that of postponing SPSS (1.1 days/10a) during 1961–2017/2020. It also found significant delaying by 2–13 days in the starting time, and advancing for 1–13 days in the ending time, respectively, which reduced 1–27 days for the LPSS. The potential snowfall season started later, ended later, and lasted longer in the north and center than in the south. Similar to the past and present, the SPSS, EPSS, and LPSS will vary under four emission scenarios during 2021–2100. Potential snowfall season will start later, end earlier, and last fewer days under the higher emission scenario. Under the highest emission scenario, SSP585, the starting time will be postponed by 41 days in 2100, while the ending time will be up to 23 days in advance in 2100, implying that it will cut down the length by 63 days (about two months), and the length of the potential snowfall season will only last two and a half months in 2100 under the SSP585 scenario. Spatially, the length of the potential snowfall season in the west and southwest of the CTMR will be compressed by more days because of the more delayed starting time and advanced ending time under all four scenarios. The results indicate that annual total snowfall will decrease, including amount and frequency, then reduce snow cover or mass, which finally feedback to the atmosphere in the form of more rapid warming for the lower reflectivity to solar radiation. Our research provides a new direction to capture the potential snowfall phenology in the alpine region and can be easily expanded to other snow-dominated areas worldwide.

Lithium metal is considered to be the best candidate for rechargeable batteries due to its unique advantages. But the instability and the uncontrollable dendrites of the lithium metal anode greatly limit its commercialization. In recent years, in order to obtain stable Li metal anodes, various three-dimensional (3D) current collectors have been proposed. However, for traditional 3D current collectors, its advantages in structure still need to be improved. Therefore, the 3D hierarchical Cu@Ag nanostructure consisting of Ag-decorated Cu nanowires grown on Cu foam as a current collector (denoted as 3D HCu@Ag) is well designed and successfully prepared. Cu nanowires were

The lithium sulfur (Li-S) battery is an active research area in the field of energy storage systems, but the shuttle effect is a serious obstacle hindering its application. Herein, a CoSe

On the premise of ensuring profits, how to give a relatively dispersed portfolio selection result reasonably and rapidly is a challenging problem in both theory and practice. Although the use of optimization models to make decision has been shown to be an essential approach towards portfolio selection, there still has an acute need for developing a knowledge-based expert model for portfolio selection so that this model can achieve better performance in reliability and real time, especially in leading more distributed investments. In this paper, a knowledge-based expert model is proposed for portfolio selection with the aid of analytic hierarchy process (AHP) and fuzzy sets. In the proposed model, the expert knowledge which can reflect the investment attitude and experience of different investors is mainly integrated into the criterion layer and represented by a reciprocal matrix, and the scheme layer is abstracted to a strictly consistent matrix by comparing and analyzing the state characteristics of investment objects. In order to characterize the state characteristics of investment objects under fuzzy environment, their corresponding time series data are quantified as fuzzy variables in advance. Experiments involving synthetic and real-world data demonstrate that the proposed model produces better performance than other typical portfolio selection models and gives more distributed investments.

Obesity is a worldwide epidemic leading to non-alcoholic fatty liver disease. Alterations in the liver fat fraction (LFF) assessed by MRI following bariatric surgery is a promising feature; however, few studies have been fully elucidated. To determine the alterations in the LFF features following surgery using MRI, to determine the correlation with the clinical non-alcoholic steatohepatitis score (C-NASH score), and to identify the predictive factors for postoperative score changes. Patients (n = 69) underwent MRI to measure the LFF at baseline and 3 months postoperatively. Paired sample t tests were applied to investigate the alterations in the major parameters. Univariate analyses were performed to evaluate the factors predicting C-NASH score changes after surgery. Compared with the baseline levels, the LFF significantly decreased 3 months postoperatively (P

The efficiency of hydrogen production through water splitting is limited by the slow kinetics of the oxygen evolution reaction (OER). Although transition bimetal catalysts have advantages for OER, which of the metal plays a dominant role is still not clearly identified. Here, we synthesized a series of crystal (NixCo1−x)2B catalysts via a simple ball-milling method. Among these catalysts, (Ni0.75Co0.25)2B exhibits the best OER activity with a low overpotential of 224 mV at 10 mA cm−2 in a 1 M KOH solution as well as robust stability, which is superior to that of the benchmark IrO2 catalyst. More importantly, the chemical origins of these catalysts have been successfully identified via CV cycling on hydrophobic and hydrophilic electrodes. We found that Co sites served as both the adsorption centers of OH* intermediates and active centers for OER, while Ni sites facilitated the key conversion of Co2+ to Co3+ during OER through CV cycling. The findings are well supported by Raman spectroscopy and X-ray photoelectron spectroscopy (XPS) data. Our results open a reasonable strategy for high-performance OER catalysts.

The binary transition metal sulfides are highly desirable electrode materials for high-performance supercapacitors, thanks for their excellent electrochemical activity and electrical conductivity. Herein, the hierarchical FeCo2S4 arrays are designed and constructed on Ni foam, which consists of microsheets anchored nanosheets. The FeCo2S4 hierarchical structure provides high conductivity and abundant electroactive sites as well as facial and short ion transport paths. Therefore, the FeCo2S4 electrode achieves an excellent specific capacitance of 2488.50 F g−1 (7.66 F cm−2) at 5 mA cm−2, and a high level of cycle stability of 95.67% retention after 5000 cycles. The FeCo2S4//activated carbon (AC) device delivers the maximal energy density of 44.89 W h kg−1 and the maximal power density of 4015.56 W kg−1. The impressive electrochemical properties of hierarchical FeCo2S4 arrays make it a promising high-performance electrode for hybrid supercapacitors.

Aiming at a large number of ambiguous, imprecise and incomplete data in the real world, fuzzy time series has come into being and developed into an effective forecasting approach. In the process of modeling and forecasting of fuzzy time series, the prediction performance of fuzzy time series can be effectively improved by partitioning the universe of discourse into different lengths. In this paper, a forecasting approach for fuzzy time series, which introduces the granularity mechanism into interval division and employs differential data for incremental forecasting, is proposed to solve the problem of time series forecasting with high forecasting precision. In the proposed approach, in order to describe the fuzzy logic relationship and fuzzy trend of historical data, we first do differential processing on the historical samples. Then, Fuzzy C-means (FCM) clustering algorithm is used to generate several partition intervals tentatively. In the sequel, we use the principle of justifiable granularity to constantly adjust the width of all the intervals, so that these information granules associated with corresponding intervals become the most "informative" information granules. Finally, the boundary of information granules is used as the basis of interval division to complete the forecasting task. An illustrative example is provided to demonstrate the essence of the proposed approach. The comparative experiment with other representative approaches shows that the proposed approach can significantly improve the prediction accuracy of time series.

To solve the problem of automatically analyzing and evaluating the teaching content and effect of teachers in massive open online courses (MOOC) environment, an automatic teaching evaluation system is proposed in this paper to evaluate the sentiment of teacher and content of “online classes”. Firstly, the multimodal sentiment analysis model based on voice and text is built, which can determine the degree of “positive” and “negative” sentiments of teachers. Then, the textbook and Baidu Encyclopedia are used as two kinds of syllabus. The “3D matching degree decision model” is built to compare the differences between the teaching content and the syllabus, then the matching degree of teaching content is given. According to the results of the sentiment analysis and matching with syllabus, the teaching quality can be effectively judged. Finally, experiments in are conducted in MOOC environment. The results of the automatic analysis and evaluation system used for teaching quality perform well.

The rapid development of the portable and wearable electronics creates a high demand for better performance energy storage devices with high areal capacity as well as excellent flexibility. Herein, a free-standing Ti3C2/FeOOH quantum dots (QDs) hybrid film has been designed and synthesized by a simple electrostatic self-assembly, where amorphous FeOOH QDs not only act as pillars to prevent restacking of the Ti3C2 nanosheets but also provide considerable capacitance as active materials. The Ti3C2/FeOOH QDs hybrid film exhibits 2.3 times higher areal capacitance (485 mF cm−2) than the pristine Ti3C2 film and good cycle stability (94.8% retention over 5000 cycles) in neutral electrolyte. Moreover, an asymmetric device has been prepared by combining the hybrid film as negative electrode and MnO2 on carbon cloth as positive electrode, which operates at an output potential difference of 1.6 V. The device delivers the maximum energy density of 40 μW h cm−2 and the maximum power density of 8.2 mW cm−2 with 82% capacitance retention after 3000 cycles. The facile preparation process and excellent electrochemical properties of the free-standing Ti3C2/FeOOH QDs hybrid film make it a promising candidate for constructing high-performance flexible energy storage devices.