Recently, scholars have begun to shift their focus toward the idea of the marketization of startups and the relationship with entrepreneurial psychology or other factors; however, the establishment of a unified and clear standard of entrepreneurship educational methods remains unfulfilled. Our study investigates 46 representative startups in four industries, including financial technology, biotechnology, education, and cultural tourism areas in Guangdong-Hong Kong-Macao Greater Bay Area and Southeast Asia to observe factors from different backgrounds but matter in common for building entrepreneurship education (...) systems and methods in different countries. We used the fuzzy qualitative comparison method to survey startup entrepreneurs and executives through questionnaires, selecting startup key factors including entrepreneurial psychology, product advantage, market and cultural environment, entrepreneurial policy, and geographical advantage. The survey was conducted on six key variables, namely, geographical advantage, to observe the conditional grouping and paths of factors influencing the establishment of Startups from an overall perspective. This study explores the path combination that plays a key role in the establishment of new enterprises, and further uses specific industry cases to verify the rationality and credibility of the path combination. The main conclusions are as follows: There are five combination paths affecting the establishment of new enterprises, which are “psychology and market,” “psychology, product, and region,” “psychology, culture and policy,” “psychology, market, and culture,” and “market, policy, and region” combination paths; Entrepreneur psychology, market environment, and entrepreneurship policy are the core conditions to improve the effectiveness of the establishment of new enterprises, while the other three variables are non-core variables in different paths; There are different paths of entrepreneurial paths and factor combinations in different regions or industries. (shrink)

Photovoltaic power forecasting can provide strong support for the safe operation of the power system. Existing forecasting methods are ineffective for grid scheduling decisions or risk analysis. The novel multicluster interval prediction method is proposed to consider the volatility and randomness of PV power output. First, this method utilizes the sparse autoencoder and Bayesian regularized NARX network for point forecasting of PV power. Second, density peak clustering improved by kernel Mahalanobis distance is applied to classify the dataset into multiple clusters, (...) including forecasting error and meteorological factors. Finally, the joint probability density is established by multivariate kernel density estimation to accomplish the PV power interval prediction. The proposed hybrid method is applied for the interval prediction of PV power at Yulara, Australia. Comparative research of point forecasting is implemented to evaluate the machine learning and deep learning methods, with the proposed SAE-BRNARX under four different periods. Results shows that the average values of nRMSE, MRE, nMAE, and R2 for the four periods are 4.45%, 0.90%, −0.15%, 3.39%, and 95.93%, respectively. Moreover, the results of interval prediction obtained by the other interval prediction approaches are compared with the proposed KMDDPC-MKDE. It shows that the average values of PICP, PINAW, ACE, and nMPICD for four periods are 93.93%, 9.50%, 3.93%, and 7.10% at 90% confidence level, respectively. Outcomes demonstrate that the proposed method can obtain more accuracy, a higher coverage rate, narrower average bandwidth, and a closer distance between the middle of interval and actual value than other methods. (shrink)

With the rapid development of Internet of Things technology, RFID technology has been widely used in various fields. In order to optimize the RFID system hardware deployment strategy and improve the deployment efficiency, the prediction of the RFID system identification rate has become a new challenge. In this paper, a neighborhood rough set and random forest combination model is proposed to predict the identification rate of an RFID system. Firstly, the initial influencing factors of the RFID system identification rate are (...) reduced using neighborhood rough set theory combined with the principle of heuristic attribute reduction of neighborhood weighted dependency, thus obtaining a kernel factor subset. Secondly, a random forest prediction model is established based on the kernel factor subset, and a confusion matrix is established using out-of-bag data to evaluate the prediction results. The test is conducted under the constructed RFID experimental environment, whose results showed that the model can predict the identification rate of the RFID system in a fast and efficient way, and the classification accuracy can reach 90.5%. It can effectively guide the hardware deployment and communication parameter protocol setting of the system and improve the system performance. Compared with BP neural network and other prediction models, NRS-RF has shorter prediction time and faster calculation speed. Finally, the validity of the proposed model was verified by the RFID intelligent archives management platform. (shrink)

Time preference reversals refers to systematic inconsistencies between preferences and valuations in intertemporal choice. When faced with a pair of intertemporal options, people preferred the smaller-sooner option but assign a higher price to the larger-later one. Different hypotheses postulate that the differences in evaluation scale or output format between the choice and the bid tasks cause the preference reversal. However, these hypotheses have not been distinguished. In the present study, we conducted a hybrid task, which shares the same evaluation scale (...) with the bid task and shares the same output format with the choice task. By comparing these three tasks, we can figure out the key reason for time preference reversal. The eye-tracking measures reflecting attention allocation, cognitive effort and information search pattern were examined. Results showed that participants' time preference and eye-tracking measures in the hybrid task were similar to those in the choice task, but different from those in the bid task. Our findings suggest that the output format is the core reason for time preference reversal and may deepen our understanding of the mechanisms that underlie time preference reversal. (shrink)

Recently, creative deviance has been lauded to be an innovation-enhancing approach with applications in many new and high-tech domains. Previous study on antecedents to creative deviance remains scattered and vague. Our research conceptualizes creative deviance from the perspective of independent innovation and explores its antecedents, mechanisms, as well as conditions. Team authoritarian leadership is conceptualized as a contradictory unity as it mixes advantages and disadvantages. However, it is surprising to find that there are very few researches that have examined its (...) relevant influence mechanisms and boundary conditions for authoritarian leadership. Contributing to an advanced understanding of authoritarian leadership in research and development teams, we investigated whether team authoritarian leadership is positively or negatively related to creative deviance. Drawing on social information processing theory and regulatory focus theory, we supposed that team authoritarian leadership facilitates creative deviance when the degree is low and inhibits it when the degree is high; dual occupational stress and prevention regulatory focus play mediation roles between team authoritarian leadership and creative deviance respectively, both variables play a chain mediation role in that relationship; and the mindfulness characteristic of an individual moderates the inverted-U team authoritarian leadership-creative deviance association, such that this association is weaker with low individual mindfulness. With two-phase questionnaire data collected from 433 members in 82 R&D teams of high-tech enterprises in electronic information technology, new material technology, new medical technology, resource and environment technology and advanced manufacturing technology randomly selected from five provinces in eastern China, these hypotheses are supported empirically. Overall, we find that, our study broadens antecedents and the relevant occurrence mechanisms of creative deviance when studied through a leadership management lens. Moreover, our research enriches the cognate studies on authoritarian leadership by empirically demonstrating that team authoritarian leadership may function as an double-edged sword of creative deviance in the R&D workplace. These above findings offer insightful thoughts to scholars in the field of authoritarian leadership and bring practical suggestions for team superiors who seek to implement best innovation practice. (shrink)

BackgroundAt present, adherence to antipsychotic treatment is often poor, leading to the recurrence of symptoms. This increases the likelihood of the patient experiencing disability and thus increases the disease burden for the patient, their family, and society as a whole. However, to date, there is no clear evidence regarding the effect of medication adherence interventions on outcomes for patients with schizophrenia. Moreover, the traditional intervention methods are limited by manpower and resources in low- and middle-income countries. Recent studies have demonstrated (...) that increasing a patient’s level of self-compassion may improve their treatment adherence. Online mental health care interventions have advantages in terms of feasibility and acceptability for patients with schizophrenia. In this regard, a WeChat-based self-compassion training protocol to improve patient treatment adherence was designed in this study and will be evaluated in the future to determine its impact on patients with schizophrenia.MethodsThe protocol for the randomized controlled trial is based on the SPIRIT 2013 statement. This parallel RCT will aim to recruit 392 patients with schizophrenia who will be randomized at a 1:1 ratio into a 3-week intervention or control group. Both groups will receive routine care. The intervention group will also receive WeChat-based self-compassion training, which requires participants to complete three tasks every day, including a reading task, a meditation task, and a self-compassion journal task. The control group will receive WeChat-based psychological health education, which will only require participants to read positive articles about psychological health every day. Medication adherence, self-compassion, stigma, and social support will be measured at baseline, immediately after the intervention, and 3 weeks after the intervention. Program feasibility will be evaluated throughout the course of the study, and acceptability will be measured immediately after the intervention.Expected results:The intervention described here will address the barriers to accessing mental health care for people with schizophrenia, including patients’ desire for independent management, difficulty accessing providers, and concerns about privacy and stigma. The current study provides guidance for clinical nurses to carry out psychological intervention, with the ultimate aim of addressing the problems associated with a shortage of psychological professionals in low- and middle-income countries. (shrink)

Security issue against different attacks is the core topic of cyberphysical systems. In this paper, optimal control theory, reinforcement learning, and neural networks are integrated to provide a brief overview of optimal robust control strategies for a benchmark power system. First, the benchmark power system models with actuator and sensor attacks are considered. Second, we investigate the optimal control issue for the nominal system and review the state-of-the-art RL methods along with the NN implementation. Third, we propose several robust control (...) strategies for different types of cyberphysical attacks via the optimal control design, and stability proofs are derived through Lyapunov theory. Furthermore, the stability analysis with the NN approximation error, which is rarely discussed in the previous works, is studied in this paper. Finally, two different simulation examples demonstrate the effectiveness of our proposed methods. (shrink)

儒家思想由孔孟荀之發揚而成為中國文化之主流，佛教自東漢明帝時傳入中國，融入中國文化與思想，由小乘佛教蔚為大乘佛學。二千年來經歷歷史之分合，終為中國之民眾、士大夫所接受。其間有梁武帝捨棄王位出家，武則天 以佛教為國教，亦有三武之禍，至今儒佛融合，已成必然之趨勢。 在哲學思維上，不論從形上論、人生論、倫理論、道德論探討，儒家重生，佛教重死，都有超越生命之價值所在，悲天憫人，慈悲喜捨，都提供眾生體認生命之終極價值，若能將儒家仁愛之精神與佛教慈悲之胸懷融合為一，必能 撥去世人眼前之黑霧，迎向燦爛之雲天，則中國之新文化於焉形成，馨香禱祝。.

本志编纂以辩证唯物主义和历史唯物主义为指导, 力求客观、科学地记述孟子及其思想学说, 以及与孟子有密切关系的人、事、物.

本书第一编主要讨论刑法客观主义理论中所蕴含的抽象性、行为事实的客观性问题及其与法治立场的契合性。第二编主要研究刑法客观主义的反对派即刑法主观主义理论对法治立场的动摇。.

周山(1949-),上海社会科学院哲学研究所副研究员、周易研究中心秘书长、中国逻辑史研究会理事、上海市逻辑学会理事.