Evaluating the price policy of raw milk is of great significance to the sustainable development of an industry supply chain. In this context, our study used the multi-period difference-in-difference method to systematically examine the impact of the policy implementation on product price and profit distribution in the supply chain. The results showed the following: the price of raw milk in the implementation area of the price support policy is 13.54% higher than that of the unimplemented area; the effect of price (...) increase in the western region is higher than that in the eastern region, and the central region ; furthermore, the purchase price guidance policy of raw milk drives price increase or price suppression in the links of the supply chain to promote a balanced distribution of profits among the participants in the chain. These conclusions all have good stability and have reference significance for further improving and adjusting the price support policy of raw milk to realize the sustainable development of the Chinese dairy industry. This will enhance the production confidence of Chinese raw milk producers and improve Chinese consumers’ expectations and consumer psychology regarding domestic dairy products. (shrink)

Chu tu wen xian yu ru dao guan xi " nei rong jian jie: ru dao guan xi shi yi xiang gui mo hong da de yan jiu ke ti, ta she ji daozheng ge Zhongguo si xiang shi de nei zai jie gou he fa zhan guo cheng, she ji dao Zhongguo wen hua jin hou de zou xiang, yi yi zhong da. Er shi shi ji yi hou, yi huo de le shu liang ke guan (...) de yan jiu cheng guo. Zui jin chu tu wen xian de da liang yong xian you wei zhe ge wen ti de tao lun ti gong le xu duo xin cai liao, xin shi ye, xin guian dian."Chu tu wen xian yu ru dao guan xi " shou ji le 30 duo pian you dai biao xing de lun wen, tong guo "Kong Lao Meng Zhuang zhi bian", "Ru dao hu bu yu ying xiang", "Chu tu wen xian xin shi ye" san ge ban kuai jia yi cheng xian, xi wang du zhe jie ci dui yi wang ru dao guan xi yan jiu de fa zhan mai luo yu kuang jia you zong ti de ba wo, bing you ci kai chu xin de yan jiu tian di. (shrink)

Ben shu xi tong jie shi le zhi jue lun si xiang zai zhong guo xian dai zhe xue fa zhan yan bian de gui ji, Fen xi le liang shu ming, Xiong shi li, Feng you lan, He lin, Mou zong san, Fang dong mei, Tang jun yi deng ren yun yong zhi jue fang fa lai jian gou ge zi zhe xue si xiang ti xi zhi de yu shi, Ji you zhuan ti yan jiu (...) de shen du, You jian gu le shi de guang du, Xian ming gou le chu zhi jue lun zai zhong guo xian dai zhe xue yan jiu zhong de du te lun kuo. (shrink)

This study conducted a social network analysis of the evolutionary characteristics of the world dairy trade network based on the overall trade pattern. In addition, the evolution of trade blocs and the co-opetition relationships involving dairy products in major countries were analyzed in terms of supply and demand. The results show that continuous and complex changes have taken place in the world’s dairy trade network since 2001. The number of trade entities in dairy products has stabilized since 2012. At present, (...) approximately 94% of countries (regions) are involved in dairy product trade, such that the world dairy trade network exhibits the small-world effect and scale-free property. The world import pattern for dairy products has changed. While export centers have not changed, import centers have shifted from Europe, America, and East Asia to North America, East Asia, and the Middle East. The world dairy trade network consists of the EU trade bloc headed by Germany, the former Soviet Union–Brazil trade bloc, and the Asia–Australia–America trade bloc. The trade blocs have evolved due to geographical positions, historical cultures, and political relations. In a trade bloc, the diversification of import sources is more prominent in demand countries. European and Asian markets have become the main markets of the major exporters. In this study, the evolutionary characteristics of the world dairy trade network and the co-opetition relationships were analyzed to provide scientific support to inform the development of dairy trade policies. The results can provide technical and psychological support to policy-makers in various countries in their dairy trade decision-making. (shrink)

Traditional multiple-group confirmatory factor analysis is usually criticized for having too restrictive model assumption, namely the scalar measurement invariance. The new multiple-group analysis methodology, alignment, has become an effective alternative. The alignment evaluates measurement invariance and more importantly, permits factor mean comparisons without requiring scalar invariance which is usually required in traditional multiple-group CFA. Some simulation studies and empirical studies have investigated the applicability of alignment under different conditions, but some areas remain unexplored. Based on the simulation studies of Asparouhov (...) and Muthén and of Flake and McCoach, this current simulation study is broken into two sections. The first study investigates the minimal group sizes required for alignment in three-factor models. The second study compares the performance of multiple-group CFA, multiple-group exploratory structural equation model, and alignment by including different proportions and magnitudes of cross-loadings in the models. Study 1 shows that when the model has no noninvariant parameters, the alignment requires relatively lower group sizes. Explicitly, the minimal group size required for alignment was 250 when the amount of groups was three, the minimal group size was 150 when the amount of groups was nine, and 200 when the amount of groups was 15. When there are noninvariant parameters in the model and the amount of groups is low, a group size of 350 is a safe rule of thumb. When there are noninvariant parameters in the model and the amount of groups is high, a group size of 250 is required for trustworthy results. The magnitude of noninvariance and the noninvariance rate do not affect the minimal group size required for alignment. Study 2 shows that multiple-group CFA provides accurate factor mean estimates when each factor had 20% factor loading with small-sized cross-loading. Multiple-group ESEM provides accurate factor mean estimates when the magnitude of cross-loading is small or when each factor had 20% factor loading with medium-sized cross-loading. Alignment provides accurate factor mean estimates when there are only small-sized cross-loadings in the model. The parameter estimates, coverage rates and ratios of average standard error to standard deviation for each methodology are not influenced by the amount of groups. Recommendations are concluded for using multiple-group CFA, multiple-group ESEM, traditional alignment and aligned ESEM based on the results. Multiple-group CFA is more suitable for use when scalar invariance is established. Multiple-group ESEM works best when there are small-sized or only a few medium-sized cross-loadings in the model. Traditional alignment allows for small-sized cross-loadings and a few noninvariant parameters in the model. AESEM integrates the advantages of alignment and ESEM, can provide accurate estimates when noninvariant parameters and cross-loadings both exist in the model. Compared to multiple-group CFA, multiple-group ESEM, the alignment methodology performs well in more situations. (shrink)

Scholars have paid so much academic attention to UPB in the past decade. However, there is lacking a quantitative review to uncover the relationship between UPB and its antecedents. To address this, we make a meta-analytic review about UPB. Specifically, we propose a theoretical framework of antecedents of UPB and test it using meta-analysis technology (k = 67, n = 20,957). We found moral disengagement, organizational identification, identification with supervisors, leader UPB, ethical judgments, psychological entitlement, transformational leadership, and job satisfaction (...) are positively related to UPB. Moral identity and taking responsibility are negatively related to UPB. Besides, ethical leadership, abusive supervision, and authoritarian leadership have double-edged sword effects on UPB. For ethical leadership, it increases UPB via LMX, on the other hand, it decreases UPB via moral disengagement. For abusive supervision and authoritarian leadership, they increase UPB via moral disengagement and decrease UPB via LMX meanwhile. We discuss the results of the current meta-analysis and put some future research directions. (shrink)

Product innovation integrates technology, knowledge, management practices, and market innovation, making it essential to gain a competitive advantage. Effective management of dynamic knowledge, which is the foundation of and driving force for product innovation, is a powerful tool that allows a firm to successfully innovate, adapt to environmental changes, and improve its competitiveness. In the “nanosecond age,” unlearning and learning in an organization is crucial to a firm’s ability to promptly update its organizational knowledge and maintain innovation vitality. Based on (...) the dynamic knowledge management perspective, this study integrates and constructs a theoretical model with environmental dynamism as the moderating variable, discusses the impact of organizational unlearning on product innovation performance, and empirically analyzes 208 valid questionnaires in the Yangtze River Delta using the multiple regression method. The results show that organizational unlearning shares a positive relationship with dynamic capabilities and product innovation performance. Dynamic capability is positively related to product innovation performance and has a partial mediating effect on the relationship between organizational unlearning and product innovation performance. Environmental dynamism shares a positive moderating effect on the relationship between organizational unlearning and product innovation performance. This study deepens the existing research on the factors that influence product innovation performance, which may help firms improve their dynamic knowledge management and product innovation performance. (shrink)

Background: Burnout is a stress-induced syndrome considered to be closely related to work. Although social support could relief burnout syndrome, its effect on learning burnout in medical students remains unclear. The objectives of the study are to evaluate the association between learning burnout and social support in Chinese medical students.Methods: A cross-sectional online survey was distributed to students who participated in online learning in a medical college in Wuhan during the COVID-19 epidemic. We used the Lian version of the Maslach (...) Burnout Inventory to assess learning burnout and the Social Support Rating Scale to assess social support. Chi-square tests were used to analyze factors associated with burnout. Independent t-test and multiple logistic regression were explored to analyze the relationship between social support and burnout.Results: A total of 684 students have completed the survey, of which 315 met standard criteria for learning burnout. Multiple logistic regression analysis has revealed that seniors, low family income and low social support were significant predictors of learning burnout. After adjusting for the grade and family income, there was a significant and relevant association between social support and learning burnout.Conclusions: Learning burnout was highly prevalent in medical students at our college. Senior students and low family income might be risk factors for learning burnout. Social support, especially subjective support and utilization of support might play a protective role in reducing the risk of learning burnout. (shrink)

Through a literature analysis, this study proposes that the difference between scientific innovation and technological innovation has been ignored in the current research on the level of scientific and technological innovation and its influencing factors. Combined with multidimensional proximity and knowledge type of current research, a theoretical induction has been carried on their corresponding relation with scientific innovation and technological innovation, research hypotheses were proposed the multidimensional proximity effect on the mode and degree of scientific innovation and technological innovation, five (...) theoretical factors, which are the economic development level, regional economic structure, the level of opening to the outside world, science and technology input and education input, are proposed to affect the level of scientific innovation and technological innovation. In this study, the Yangtze River Delta region of China from 2001 to 2018 is selected as the research sample, and the two hypotheses proposed are tested through a mixed method of exploratory spatial data analysis and spatial panel econometric model. The main conclusions are as follows: i) As an exogenous variable, geographical proximity has a small impact on the level of scientific innovation, but a large impact on the level of technological innovation; ii) As endogenous variables, theoretical influencing factors may not play a significant role in the actual environment due to the complex influence of multidimensional proximity; iii) Based on the idea of improving multidimensional proximity and the actual situation of the region and the city, we can formulate policies conducive to improving the regional and urban innovation environment. (shrink)

Adaptive dynamic programming, which belongs to the field of computational intelligence, is a powerful tool to address optimal control problems. To overcome the bottleneck of solving Hamilton–Jacobi–Bellman equations, several state-of-the-art ADP approaches are reviewed in this paper. First, two model-based offline iterative ADP methods including policy iteration and value iteration are given, and their respective advantages and shortcomings are discussed in detail. Second, the multistep heuristic dynamic programming method is introduced, which avoids the requirement of initial admissible control and achieves (...) fast convergence. This method successfully utilizes the advantages of PI and VI and overcomes their drawbacks at the same time. Finally, the discrete-time optimal control strategy is tested on a power system. (shrink)

IntroductionTranscranial direct current stimulation is a noninvasive brain stimulation technique that applied to modulate brain activity and enhance motor recovery. However, the neurobiological substrates underlying the effects of tDCS on brain function remain poorly understood. This study aimed to investigate the central mechanisms of tDCS on improving the athletic performance of male rowing athletes.MethodsTwelve right-handed male professional rowing athletes received tDCS over the left primary motor cortex while undergoing regular training. The resting-state functional magnetic resonance imaging data were acquired before (...) and after tDCS. Measures of amplitude of low-frequency fluctuation and regional homogeneity were calculated and compared between baseline and follow-up, as well as topological measures including global and local efficiency of functional brain networks constructed by graph theoretical analysis.ResultsMale rowing athletes showed increased isokinetic muscle strength of the left knee and left shoulder after tDCS. Increased ALFF values were found in the right precentral gyrus of male rowing athletes after tDCS when compared with those before tDCS. In addition, male rowing athletes showed increased ReHo values in the left paracentral lobule following tDCS. Moreover, increased nodal global efficiency was identified in the left inferior frontal gyrus of male rowing athletes after tDCS.ConclusionThe findings suggested that simultaneous tDCS-induced excitation over the primary motor cortex might potentially improve the overall athletic performance in male rowing athletes through the right precentral gyrus and left paracentral lobule, as well as left inferior frontal gyrus. (shrink)

The safety sign is important in our daily life and workplace to prevent potential safety issues. However, it remains undetermined whether the safety signs would influence the cognitive control ability of the people, which serves to guide the behaviors in a goal-directed manner. Therefore, this study aimed to examine the effect of safety signs on cognitive control by uncovering the behavioral performance and neural manifestations underlying the monitoring of conflict and error. The participants performed a flanker task after watching low- (...) and high-hazard safety signs with the electroencephalogram data recorded continually. The behavioral results indicated a classic congruency effect with higher accuracy rate and faster response time under a congruent condition compared to an incongruent condition. However, no hazard effect on behavioral performances was observed. The results of event-related potentials demonstrated a more negative N2 elicited by the incongruent trials and an increased ERN difference between the error and correct responses in the high-hazard condition compared to those in the low-hazard condition, implying that the monitoring of the conflict and error were both enhanced after watching the high-hazard safety signs. This study contributes to the understanding of the relationship between safety signs and cognitive control, and further expand the measurements that can be applied to assess the effectiveness of safety signs design. (shrink)

IntroductionDuring the COVID-19 outbreak, many citizens were asked to stay at home in self-quarantine, which can pose a significant challenge with respect to remaining physically active and maintaining mental health. This study aimed to evaluate the prevalence of inadequate physical activity, anxiety, and depression and to explore the relationship of physical activity with anxiety and depression symptoms among Chinese college students during quarantine.MethodUsing a web-based cross-sectional survey, we collected data from 1,396 Chinese college students. Anxiety and depression were assessed with (...) the Self-Rating Anxiety Scale and the Self-Rating Depression Scale, respectively. The data on physical activity were collected by types of physical activity and the International Physical Activity Questionnaire.ResultsDuring the COVID-19 outbreak, about 52.3% of Chinese college students had inadequate physical activity. The rates of anxiety and depression symptoms were 31.0 and 41.8%, respectively. A high level of physical activity was significantly closely associated with low anxiety, while a moderate, or high level of physical activity was significantly closely associated with reduced depression after adjusting confounding demographic factors. Moreover, specific types of physical activity, such as stretching and resistance training, were negatively correlated with both anxiety and depression; doing household chores was negatively correlated with depression.ConclusionOur findings highlight specific levels and types of home-based physical activities that need to be taken into consideration to protect the mental health of college students during the COVID-19 epidemic. (shrink)

The extant literature has largely conceptualized abusive supervision as a hot and impulsive form of aggression. In this paper, we offer a cold and strategic perspective on how abusive supervision might be used strategically to achieve goals. Drawing on the Machiavellian literature and social interaction theory of aggression, we develop a moderated serial mediation model, in which leader Machiavellianism predicts their strategic use of abusive supervision on subordinates via the mediating role of leaders’ guanxi with direct supervisor. We further theorize (...) that this mediation effect is more pronounced when guanxi among team members (team member guanxi, TMG) is stronger, because Mach leaders are more likely to perceive high TMG as a threat to their power. Finally, we propose that Mach leaders’ strategic use of abusive supervision has negative implications for team outcomes, manifested in low levels of team voice and team organizational citizenship behaviors toward fellow team members (OCBI). Analyses of two studies—Study 1 using multi-wave data (355 leaders) from a US sample and Study 2 using a multi-wave, multi-source, and multilevel data (1252 subordinates and 273 leaders) from a Chinese sample—corroborated our model. This study provides a comprehensive examination of who, how, and when strategic abuse unfolds in the workplace and its negative implication for team outcomes. (shrink)

Given the importance of data safety for psychology, the present study investigated the influence of data leaking scandal on campus customers’ financial consumption behaviors at intelligent tourism platforms in China, and explored the roles that individual characteristics play in this process by focusing on a set of participants from colleges. Data were collected through sending out an online questionnaire, where respondents were asked to finish a series of questions about their background information, their trust, future consuming intention, and defensive behaviors (...) toward intelligent platforms. After they finished these questions, a short description about an online tourism platform leaking customers’ personal information was presented to the respondents, following which they were asked to report about their future consuming intentions and defensive behaviors again. In total, 236 participants of college students and teachers were recruited. Paired samples mean comparison showed that after the stimulus was presented, the respondents had a significant decrease in future financial consumption intention, and a significant increase in defensive behaviors toward online tourism platforms due to risks perceived. Multiple regression analysis was conducted subsequently to investigate individual characteristics that may account for part of the decrease in consuming intention. Results showed that, customers with higher level of trust and monthly income, as well as older customers, tend to experience higher level of decrease in consuming intention, and increase in defensive behaviors. These findings highlighted the importance of online tourism platforms guaranteeing data security of their customers. (shrink)

Implicit morality theories refer to people’s beliefs about whether individuals’ moral character is fixed or malleable. Drawing on the social cognitive theory of morality, we examine the relationship between employees’ implicit morality theories and their organizational citizenship behaviors toward coworkers (OCBC) and coworker-directed deviance (CDD) through a moral self-regulatory mechanism. A laboratory experiment (Study 1), an online experiment (Study 2), and a multi-wave, multi-source field survey (Study 3) found that the more employees held a fixed belief about morality, the lower (...) their sense of moral control, especially when their moral identity was lower. This perceived lack of moral control, in turn, predicted decreased OCBC, particularly when the workgroup ethical climate was weak. However, this relationship did not hold for CDD. Overall, our research highlights implicit morality theories as a novel antecedent of employees’ workplace behaviors, and identifies the underlying moral self-regulatory process, along with individual and situational boundary conditions. (shrink)

Responding to recent concerns about the reliability of the published literature in psychology and other disciplines, we formed the X-Phi Replicability Project to estimate the reproducibility of experimental philosophy. Drawing on a representative sample of 40 x-phi studies published between 2003 and 2015, we enlisted 20 research teams across 8 countries to conduct a high-quality replication of each study in order to compare the results to the original published findings. We found that x-phi studies – as represented in our sample (...) – successfully replicated about 70% of the time. We discuss possible reasons for this relatively high replication rate in the field of experimental philosophy and offer suggestions for best research practices going forward. (shrink)

A pathogenic connection between autoreactive T cells, fungal infection, and carcinogenesis has been demonstrated in studies of human autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy as well as in a mouse model in which kinase-dead Ikkα knock-in mice develop impaired central tolerance, autoreactive T cell–mediated autoimmunity, chronic fungal infection, and esophageal squamous cell carcinoma, which recapitulates APECED. IκB kinase α is one subunit of the IKK complex required for NF-κB activation. IKK/NF-κB is essential for central tolerance establishment by regulating the development of medullary thymic (...) epithelial cells that facilitate the deletion of autoreactive T cells in the thymus. In this review, we extensively discuss the pathogenic roles of inborn errors in the IKK/NF-κB loci in the phenotypically related diseases APECED, immune deficiency syndrome, and severe combined immunodeficiency; differentiate how IKK/NF-κB components, through mTEC, T cells/leukocytes, or epithelial cells, contribute to the pathogenesis of infectious diseases, autoimmunity, and cancer; and highlight the medical significance of IKK/NF-κB in these diseases. IKK/NF-κB regulates the expression of many genes that encode proteins involved in many crucial biological functions, such as immunity, tissue homeostasis, and fungal/bacterial/viral infections. Also, IKKα plays anti-tumor activities in many organs independently of NF-κB pathways. Thus, an inborn error in one of these gene loci can cause severe human diseases through these complicated mechanisms. (shrink)

Nowadays, depression is the world’s major health concern and economic burden worldwide. However, due to the limitations of current methods for depression diagnosis, a pervasive and objective approach is essential. In the present study, a psychophysiological database, containing 213 subjects, was constructed. The electroencephalogram signals of all participants under resting state and sound stimulation were collected using a pervasive prefrontal-lobe three-electrode EEG system at Fp1, Fp2, and Fpz electrode sites. After denoising using the Finite Impulse Response filter combining the Kalman (...) derivation formula, Discrete Wavelet Transformation, and an Adaptive Predictor Filter, a total of 270 linear and nonlinear features were extracted. Then, the minimal-redundancy-maximal-relevance feature selection technique reduced the dimensionality of the feature space. Four classification methods distinguished the depressed participants from normal controls. The classifiers’ performances were evaluated using 10-fold cross-validation. The results showed thatK-Nearest Neighbor had the highest accuracy of 79.27%. The result also suggested that the absolute power of the theta wave might be a valid characteristic for discriminating depression. This study proves the feasibility of a pervasive three-electrode EEG acquisition system for depression diagnosis. (shrink)

Residual useful lifetime prediction plays a key role of failure prediction and health management in equipment. Aiming at the problems of residual life prediction without comprehensively considering multistage and individual differences in equipment performance degradation at present, we explore a prediction model that can fit the multistage random performance degradation. Degradation modeling is based on the random Wiener process. Moreover, according to the degradation monitoring data of the same batch of equipment, we apply the expectation maximization algorithm to estimate the (...) prior distribution of the model. The real-time remaining life distribution of the equipment is acquired by merging prior information of real-time degradation data and historical degradation monitoring data. The accuracy of the proposed model is demonstrated by analyzing a practical case of metalized film capacitors, and the result shows that a better RUL estimation accuracy can be provided by our model compared with existing models. (shrink)

Yang, Guorong 楊國榮: The Self-maturating and the Maturating of Things: The Becoming of the World of Meaning 成己與成物—意義世界的生成. Content Type Journal Article Pages 269-271 DOI 10.1007/s11712-011-9214-5 Authors Feng Xiang, Department of Philosophy, East China Normal University, 500 Dongchuan Road, Shanghai, 200241 People’s Republic of China Journal Dao Online ISSN 1569-7274 Print ISSN 1540-3009 Journal Volume Volume 10 Journal Issue Volume 10, Number 2.

Appendix 1 was incomplete in the initial online publication. The original article has been corrected.

Deep brain stimulation is a clinical intervention for the treatment of movement disorders. It has also been applied to the treatment of psychiatric disorders such as depression, anorexia nervosa, obsessive-compulsive disorder, and schizophrenia. Psychiatric disorders including schizophrenia, bipolar disorder, and major depression can lead to psychosis, which can cause patients to lose touch with reality. The ventral tegmental area, located near the midline of the midbrain, is an important region involved in psychosis. However, the clinical application of electrical stimulation of (...) the VTA to treat psychotic diseases has been limited, and related mechanisms have not been thoroughly studied. In the present study, hyperlocomotion and stereotyped behaviors of the mice were employed to mimic and evaluate the positive-psychotic-like behaviors. We attempted to treat positive psychotic-like behaviors by electrically stimulating the VTA in mice and exploring the neural mechanisms behind behavioral effects. Local field potential recording and in vivo fiber photometry to observe the behavioral effects and changes in neural activities caused by DBS in the VTA of mice. Optogenetic techniques were used to verify the neural mechanisms underlying the behavioral effects induced by DBS. Our results showed that electrical stimulation of the VTA activates local gamma-aminobutyric acid neurons, and dopamine neurons, reduces hyperlocomotion, and relieves stereotyped behaviors induced by MK-801 injection. The results of optogenetic manipulation showed that the activation of the VTA GABA neurons, but not DA neurons, is involved in the alleviation of hyperlocomotion and stereotyped behaviors. We visualized changes in the activity of specific types in specific brain areas induced by DBS, and explored the neural mechanism of DBS in alleviating positive psychotic-like behaviors. This preclinical study not only proposes new technical means of exploring the mechanism of DBS, but also provides experimental justification for the clinical treatment of psychotic diseases by electrical stimulation of the VTA. (shrink)

Compare with other professions, teachers are reported to have a higher risk of poor mental health. This study examined the relationships between emotional intelligence, perceived organizational justice, and mental health among Chinese high school teachers. Three hundred and eighty-one high school teachers, with their age range between 21 and 50 years, were administered the Emotional Intelligence Scale, Perceived Organizational Justice Scale, and Mental Health Scale. The result found that emotional intelligence and perceived organizational justice directly influence the mental health of (...) high school teachers. In addition, perceived organizational justice mediated the association between emotional intelligence and mental health. Moreover, the present study analyzes the different role of subtypes of perceived organizational justice on the relationships between emotional intelligence and mental health, and the results showed that the mediating effects of perceived distributive justice and interactive justice on emotional intelligence and mental health are not significant, only the perceived procedural justice mediated the relationships between emotional intelligence and teachers’ mental health. The results are discussed in a conceptual context. (shrink)

This study used the Social Cognitive Theory and Broaden-and-Build Theory to propose and validate a chain mediation model. In total, 417 Chinese college students were studied to explore the effects of parent–child relationships on their academic performance. In addition, we investigated the chain-mediating roles of gratitude and psychological capital. The results showed that the parent–child relationship significantly and positively affected the academic performance of college students; gratitude partially mediated the parent–child relationship and the academic performance of college students; psychological capital (...) partially mediated the parent-child relationship and the academic performance of college students; and gratitude and psychological capital exerted a chain-mediating effect between parent–child relationships and the academic performance of college students. Based on the results of the study, we conclude that the parent–child relationship not only directly affects the academic performance of college students but also indirectly affects it through the chain mediation of gratitude and psychological capital. Moreover, we proposed reasonable suggestions on how colleges and universities can guide students to deal with parent-child relationships, strengthen gratitude education, and improve psychological capital. (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)

Lentinus edodes sticks are susceptible to mold infection during the culture process, and manual identification of infected sticks is heavy, untimely, and inaccurate. Aiming to solve this problem, this paper proposes a method for identifying infected Lentinus edodes sticks based on improved ResNeXt-50 deep transfer learning. First, a dataset of Lentinus edodes stick diseases was constructed. Second, based on the ResNeXt-50 model and the pretraining weight of the ImageNet dataset, the influence of pretraining weight parameters on recognition accuracy was studied. (...) Finally, six fine-tuning strategies of the fully connected layer were designed to modify the fully connected layer of ResNeXt-50. The experimental results show that the recognition accuracy of the method proposed in this paper can reach 94.27%, which is higher than the Vgg16, GoogLeNet, ResNet50, and MobileNet v2 models by 8.47%, 6.49%, 4.68%, and 9.38%, respectively, and the F1-score can reach 0.9422. The improved method proposed in this paper can reduce the calculation pressure and overfitting problem of the model, improve the accuracy of the model in the identification of Lentinus edodes stick mold diseases, and provide an effective solution for the selection of diseased sticks. (shrink)

In this paper, a seven-parameter BRDF model with double-peak characteristic, which could fit double-peak data, was adopted to fit the BRDF of Mars simulation soil. At the same time, the three-dimensional figure of the original data of the sample and the three-dimensional curve of the fitted curve were given. The results proved that the model worked well for this type of data. In addition, the experimental data of four kinds of earth sand samples with different roughness were also fitted and (...) analyzed. It was found that the model still had a good fitting effect on such data. At last, the Mars simulation soil and the four kinds of earth sand samples with different roughness were compared horizontally and vertically. Moreover, the double-peak characteristics and other properties of the Mars simulation soil and the earth sand were analyzed under the same and different roughness. (shrink)

The innovation of the deep learning modeling scheme plays an important role in promoting the research of complex problems handled with artificial intelligence in smart cities and the development of the next generation of information technology. With the widespread use of smart interactive devices and systems, the exponential growth of data volume and the complex modeling requirements increase the difficulty of deep learning modeling, and the classical centralized deep learning modeling scheme has encountered bottlenecks in the improvement of model performance (...) and the diversification of smart application scenarios. The parallel processing system in deep learning links the virtual information space with the physical world, although the distributed deep learning research has become a crucial concern with its unique advantages in training efficiency, and improving the availability of trained models and preventing privacy disclosure are still the main challenges faced by related research. To address these above issues in distributed deep learning, this research developed a clonal selective optimization system based on the federated learning framework for the model training process involving large-scale data. This system adopts the heuristic clonal selective strategy in local model optimization and optimizes the effect of federated training. First of all, this process enhances the adaptability and robustness of the federated learning scheme and improves the modeling performance and training efficiency. Furthermore, this research attempts to improve the privacy security defense capability of the federated learning scheme for big data through differential privacy preprocessing. The simulation results show that the proposed clonal selection optimization system based on federated learning has significant optimization ability on model basic performance, stability, and privacy. (shrink)