Xin Xu

© 2016 American Chemical Society.Layered transition metal chalcogenides with large spin orbit coupling have recently sparked much interest due to their potential applications for electronic, optoelectronic, spintronics, and valleytronics. However, most current understanding of the electronic structure near band valleys in momentum space is based on either theoretical investigations or optical measurements, leaving the detailed band structure elusive. For example, the exact position of the conduction band valley of bulk MoS2 remains controversial. Here, using angle-resolved photoemission spectroscopy with submicron spatial resolution, we systematically imaged the conduction/valence band structure evolution across representative chalcogenides MoS2, WS2, and WSe2, as well as the thickness dependent electronic structure from bulk to the monolayer limit. These results establish a solid basis to understand the underlying valley physics of these materials, and also provide a link between chalcogenide electronic band structure and their physical properties for potential valleytronics applications.

© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, WeinheimAll transparent conducting materials of technological practicality are n-type; the inferior conductivity of p-type TCMs has limited their adoption. In addition, many relatively high-performing p-type TCMs require synthesis temperatures >400 °C. Here, room-temperature pulsed laser deposition of copper-alloyed zinc sulfide thin films is reported. For 0.09 ≤ x ≤ 0.35, CuxZn1-xS has high p-type conductivity, up to 42 S cm−1 at x = 0.30, with an optical band gap tunable from ≈3.0–3.3 eV and transparency, averaged over the visible, of 50%–71% for 200–250 nm thick films. In this range, synchrotron X-ray and electron diffraction reveal a nanocrystalline ZnS structure. Secondary crystalline CuyS phases are not observed, and at higher Cu concentrations, x > 0.45, films are amorphous and poorly conducting. Within the TCM regime, the conductivity is temperature independent, indicating degenerate hole conduction. A decrease in lattice parameter with Cu content suggests that the hole conduction is due to substitutional incorporation of Cu onto Zn sites. This hole-conducting phase is embedded in a less conducting amorphous CuyS, which dominates at higher Cu concentrations. The combination of high hole conductivity and optical transparency for the peak conductivity CuxZn1-xS films is among the best reported to date for a room temperature deposited p-type TCM.

ABSTRACT: Magnetic fusion is a long-term solution for producing electrical power for the world, and the large thermonuclear international device being constructed will produce net energy and a path to fusion energy provided the computer modeling is accurate. To effectively address the requirements of the high-end fusion simulation community, application developers, algorithm designers, and hardware architects must have reliable simulation data gathered at scale for scientifically valid configurations. This paper presents detailed benchmarking results for a set of magnetic fusion applications with a wide variety of underlying mathematical models including both particle-in-cell and Eulerian codes using both implicit and explicit numerical solvers. Our evaluation on a petascale Cray XE6 platform focuses on profiling these simulations at scale identifying critical performance characteristics, including scalability, memory/network bandwidth limitations, and communication overhead. Overall results are a key in improving fusion code design, and are a critical first step towards exascale hardware-software co-design — a process that tightly couples applications, algorithms, imple- mentation, and computer architecture.

Based on the institutional theory, this article attempts to examine two consecutive questions regarding the impact of various factors on corporate decision in environmental information disclosure (EID): (1) whether or not to disclose; and (2) the level of disclosure. The relevance of these factors is empirically tested using data collected from publicly listed manufacturing companies from 2006 to 2008 in China. Some interesting findings appear. We find that firms that are state-owned, those that operate in environmentally sensitive industries, those having more industrial peers engaged in EID, and those with better reputation are more likely to disclose environmental information. When it comes to the content of EID, variables that attempt to capture external institutional pressures exhibit either no or weak explanatory power. Only the variable of organizational image and reputation is demonstrated to have a significant impact on both the act and the content of EID. This study provides a snapshot of the dialogues between constituencies in the organizational field and EID development.

The stock market’s reaction to information disclosure of environmental violation events (EVEs) is investigated multi-dimensionally for Chinese listed companies, including variables such as pollution types, information disclosure sources, information disclosure levels, modernization levels of the region where the company locates, ultimate ownership of the company, and ownership held by the largest shareholder. Using the method of event study, daily abnormal return (AR) and accumulative abnormal return (CAR) are calculated under different event window for examining the extent to which the stock market responds to the EVEs. Furthermore, statistical significance of the difference in stock market reaction is compared between event firms with different characteristics. The relationship between CAR and its impact factors is examined by multivariate analysis. The findings reveal that the average reduction in market value is estimated to be much lower than the estimated changes in market value for similar events in other countries, demonstrating that the negative environmental events of Chinese listed companies currently have weak impact on the stock market

We have empirically examined the relationship between top executives’ turnover and the corporate environmental responsibility by identifying the influence of ten specific turnover reasons resulting in the chairman’s departure and two important types of chairman’s succession. Using a sample of 782 manufacturing listed companies across 3 years in China, we find that the corporate environmental responsibility is negatively associated with the involuntary and negative turnover (i.e., dismissal, health and death, and forced resignation) and positively associated with improving corporate governance, and not associated with the normal turnover (i.e., retirement and contract expiration) and the types of chairman’s succession (i.e., independence, and internal or external promotion). Our study significantly contributes to research in environmental disclosure by revealing the relationship between chairman’s turnover and the corporate environmental responsibility. A feasible way is suggested to regulators and other stakeholders in monitoring or assessing the possible abnormality of environmental responsibility when firms experience involuntary and negative chairman’s turnover in the emerging economies

This study focuses on a traditional business ethics question and aims to use advanced techniques to improve the performance of corporate fraud prediction. Based on the GONE framework, we adopt the machine learning model to predict the occurrence of corporate fraud in China. We first identify a comprehensive set of fraud-related variables and organize them into each category (i.e., Greed, Opportunity, Need, and Exposure) of the GONE framework. Among the six machine learning models tested, the Random Forest (RF) model outperforms the other five models in corporate fraud prediction. Based on the RF model, we show that Exposure variables play a more important role in predicting corporate fraud than other input variables. These results highlight the importance of Exposure variables in corporate fraud prediction and promote the practical use of the machine learning model in solving business ethics questions.

Machine learning methods have been used in multifactor stock strategy for years. This paper uses three machine learning methods and linear regression method to find the most appropriate approach. First, a framework is established and 10 style factors and 30 industry factors are chosen. Second, four methods are used to forecast portfolio returns and compared by predicting returns, successful rate, and Sharpe ratio. Finally, this paper draws conclusion. The main findings are as follows: the support vector regression has the most stable successful rate for predicting, while ridge regression and linear regression have the most unstable successful rate with more extreme cases; algorithm of support vector regression fitting higher-degree polynomials in Chinese A-share market is optimized, compared with the traditional linear regression both in terms of stock return and retracement control; the results of support vector regression significantly outperforming the CSI 500 index prove further.

The way students are treated by their teachers have been proven to play a paramount role in students’ performance. Either teachers’ academic buoyancy or academic motivation may change students’ mindset about learning a language, leading to facilitating this process or making it even more demanding than it seems. Considering that, it can be taken into account how important teachers’ academic buoyancy would be and its mediators such as academic motivation should draw precise attention to themselves. However, a few studies have been conducted about this pivotal role so far. To fill this gap, this review paper provides a glimpse at the underlying roles of academic buoyancy and academic motivation and interest. Furthermore, it presents the definitions and applications of each construct. Finally, some implications and future recommendations are put forward to enthusiastic scholars.

Structural equation model is a multivariate statistical analysis method. It can not only test some unpredictable abstract ideas, but also design parameters for the causal connection model between independent variables and dependent variables. Among them, the analysis of various latent variables is based on the verification factor analysis technology. The research first collects various relevant data, derives the latent variables and measurement variables, then composes the measurement model, and then verifies the adaptability of the measurement model structure mode through actual data collection. When such suitable factors are determined, a causal model based on path analysis technology for latent variables, that is, a structural model, can be used for parameter design. This article uses the structural equation model as an analysis tool, starting from the three aspects of teachers, cases, and students to evaluate the application effect of the case teaching method in the classroom teaching of intellectual property law course and construct a structural equation model of the influencing factors of the case teaching effect. To understand students’ satisfaction with case teaching effects and related influencing factors, the research results show that the effect of case teaching is jointly affected by the level of teachers, case selection, and student response. Among them, teacher factors have the greatest impact on case teaching effects, followed by case factors, and students have the least.

Objective: Losing the only child is considered as the most severe kind of bereavement. It can trigger intense grief symptoms along with loss of psychosocial resources, but meanwhile, it can also lead to posttraumatic growth (PTG). The current study aimed to examine (a) whether a curvilinear relationship exists between grief and PTG, and (b) the moderating role of resources-loss among Chinese bereaved parents who lost their only child (shidu parents). Methods: 199 shidu parents from five provinces completed the assessment packet covering prolonged grief symptoms and PTG, as well as resource loss. Hierarchical regression analyses were computed to examine the curvilinear relationship and the moderating effect of interpersonal loss and socio-economic loss. Results: There was no significant curvilinear relationship between grief and PTG in shidu parents. Under the high levels of interpersonal loss, shidu parents’ PTG scores decreased as the severity of grief increased. However, the socio-economic loss did not show a significant moderating effect. Conclusion: The results of the current study did not show a significant curvilinear relationship between grief and PTG in shidu parents. High levels of grief coupled with high levels of interpersonal loss may interfere with their ability to achieve PTG. Therefore, evaluating degrees of interpersonal loss seems to be an important part of the treatment process when working with shidu parents. To facilitate their adaptation from a positive perspective, helping them maintain existing or develop new interpersonal relationships may be helpful.