To understand the relationship between kindergarten school-based research and preschool teachers’ teaching ability and identify the mechanism by which SBR affects teachers’ teaching ability, a survey of randomly sampled preschool teachers in Sichuan Province was conducted online using three scales assessing community learning, teaching ability and teaching philosophy. The survey results showed that the level of SBR reached the upper-middle level. The level of SBR positively predicted the teaching beliefs and teaching ability of preschool teachers. SBR influenced teaching ability through (...) direct and indirect effects, with indirect effects accounting for 23.5% of the total effect; however, only constructivist teaching beliefs played a mediating role in the relationship between SBR and teaching ability via an indirect effect. SBR focused on improvement in teaching ability should not only examine the connection between SBR and practical problems to promote improvement in teachers’ teaching ability but also should examine the role of teachers’ beliefs and influence teachers’ teaching abilities by changing teachers’ teaching beliefs. (shrink)

According to the received account of guilt in the philosophical literature, one cannot feel guilt unless one takes oneself to have done something morally wrong. But ordinary people feel guilt in many cases in which they do not take themselves to have done anything morally wrong. In this paper, I focus on one kind of guilt without perceived wrongdoing, guilt about being merely causally responsible for a bad state-of-affairs. I go on to present a novel account of guilt that explains (...) guilt about mere causal responsibility, according to which guilt represents part of the self as bound up with what is bad. (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)

According to the safety account of knowledge, one knows that p only if one's belief could not easily have been false. An important issue for the account is whether we should only examine the belief in the target proposition when evaluating whether a belief is safe or not. In this paper, it is argued that if we only examine the belief in the target proposition, then the account fails to account for why beliefs in necessary truths could fall short of (...) knowledge. But, if we also examine beliefs in other relevant propositions, then the account fails to preserve epistemic closure. Therefore, the safety account cannot find a safe path between epistemic closure and necessary truths. (shrink)

We often feel survivor guilt when the very circumstances that harm others leave us unscathed. Although survivor guilt is both commonplace and intelligible, it raises a puzzle for the standard philosophical account of guilt, according to which people feel guilt only when they take themselves to be morally blameworthy. The standard account implies that survivor guilt is uniformly unfitting, as people are not blameworthy simply for having fared better than others. In this paper, we offer a rival account of guilt, (...) the relational account of guilt, which makes sense of survivor guilt and other forms of guilt without self-blame while preserving the intelligibility of guilt about culpable wrongdoing. According to this account, guilt involves the feeling of being unable to justify ourselves to others, and we lack self-justification when we (however blamelessly) stand on the positive side of an undesirable asymmetry with them. When someone survives something that those around her do not, the disparity in outcome constitutes an asymmetry that is often undesirable, because it arises from luck or violates a requirement of solidarity. Thus, survivor guilt is often fitting. (shrink)

In this study we present a self-organizing connectionist model of early lexical development. We call this model DevLex-II, based on the earlier DevLex model. DevLex-II can simulate a variety of empirical patterns in children's acquisition of words. These include a clear vocabulary spurt, effects of word frequency and length on age of acquisition, and individual differences as a function of phonological short-term memory and associative capacity. Further results from lesioned models indicate developmental plasticity in the network's recovery from damage, in (...) a non-monotonic fashion. We attribute the network's abilities in accounting for lexical development to interactive dynamics in the learning process. In particular, variations displayed by the model in the rate and size of early vocabulary development are modulated by (a) input characteristics, such as word frequency and word length, (b) consolidation of lexical-semantic representation, meaning-form association, and phonological short-term memory, and (c) delayed processes due to interactions among timing, severity, and recoverability of lesion. Together, DevLex and DevLex-II provide an accurate computational account of early lexical development. (shrink)

Based on the person-organization fit theory, this research aims to investigate how socially responsible HRM positively affects employees’ organizational citizenship behaviors toward the environment by increasing person-organization fit. This study also captures the moderating effect of the perceived role of ethics and social responsibility in influencing the indirect effect of SRHRM on OCBE via person-organization fit. Data were collected from 302 employees in a state-owned chain hotel in Shanghai, China. The results indicated that SRHRM indirectly influenced employee’s engagement in OCBE (...) through person-organization fit. The positive relationship between SRHRM and person-organization fit and the indirect effect of SRHRM on OCBE via person-organization fit were more significant when employees hold high rather than low levels of RESR. Research implications and prospects were also explored in this study. (shrink)

It is a commonplace in economics that we should disregard sunk costs. The sunk cost effect might be widespread, goes the conventional wisdom, but we would be better off if we could rid ourselves of it. In this paper, I argue against the orthodoxy by showing that the sunk cost effect is often beneficial. Drawing on discussions of related topics in dynamic choice theory, I show that, in a range of cases, being disposed to honor sunk costs allows an agent (...) to mimic a resolute chooser, someone who adopts the best plan at the outset of a decision problem and sticks with it, even when resoluteness is unfeasible. I discuss several kinds of cases in which honoring sunk costs coincides with resolute choice. (shrink)

Philosophers have in recent years displayed an increasing interest in investigating folk metaethical beliefs using rigorous empirical methods. Taken together, these studies put significant pressure on many philosophical theories that depend on the truth of folk moral objectivism, the view that the folk see morality as objectively grounded. Frequently included among the target of criticism is Mackie’s error theory, or more specifically the conceptual claim thereof. Finding this criticism misplaced, Benjamin Fraser tries to exonerate error theory from such accusation by (...) distinguishing different senses of objectivity. In this paper, I show why Fraser’s strategy is not successful so that the pressure empirically brought upon classical error theory is genuine. However, I also argue that even on the new picture suggested by experimental studies, the folk still entertain erroneous beliefs, which, when coupled with plausible metasemantic assumptions and further empirical support, should warrant an error theory after all, though not in its classical form. (shrink)

Taking the semiotic landscaping of the Occupy Movement in Hong Kong in 2014 as an example, the article explores the semiotic relation between the materialization of signs and the constitution of a collectivity. Juxtaposing the concepts of ‘collective assemblage of enunciation’, ‘landscaping’ and ‘autoglottic space’, the article argues that collective enunciations are not autonomous from human agency. Instead, collective enunciations can be created by deliberate de-subjectification and de-personification in the materialization of signs.

This study aims to help enterprises enhance their innovation capabilities in the environment of knowledge economy globalization and stand out in the fierce industry competition. Firstly, data on existing higher education theories and innovation theories are analyzed. Secondly, two companies in the sample data are selected for detailed analysis. Finally, research conclusion and corresponding talent management strategies are presented. The results show that the cumulative contribution value of employees is 87.496%. The cumulative contribution value of human capital is 70.322%. The (...) contribution value of cumulative innovation performance is 61.658%. The cumulative contribution value of R&D investment is 45.306%. The coefficient for the overall sample size is 0.509. The employee quality coefficient is 0.452. The correlation coefficient for educational attainment is 0.598. The high-tech service industry has the highest correlation coefficient at 0.504. The auto industry has the highest coefficient at 0.669. The experimental research has drawn the following conclusion: Talents positively affect enterprise innovation performance; Research and Development investment has a positive correlation with enterprise innovation performance; R&D investment has a positive correlation with talents. Through experimental research, the education level of employees is measured by academic qualifications, but the essence of academic qualification measurement is the level of knowledge and skills that employees have. In summary, the study can extend the strategic analysis of the cultivation of innovative talents and play a valuable auxiliary role in cultivating innovative talents. (shrink)

Natural selection is the main mechanism that drives the evolution of species, including human societies. Under natural selection, human species responds through genetic and cultural adaptations to internal and external selection pressures for survival and reproductive success. However, this theory is ineffective in explaining human societal evolution in the Holocene and a cultural selection argument has been made to remedy the theory. The present article provides a critique of the cultural selection argument and proposes an alternative conception that treats human (...) self-selection as an emergent mechanism of human societal evolution characterized by a new type of selection pressure and a separate fitness criterion. Specifically, the evolution of human societies is divided into two major periods, each driven by a different mode of selection: natural selection acting on genes and cultures for survival and reproductive success prior to the Neolithic Revolution, and human self-selection acting on cultures – and potentially genes as well – for thrival and prosperous living after the Neolithic Revolution. The conditions for the transition from the first mode of selection to the second and the implications of this transition for social research are also discussed. (shrink)

The business environment is increasingly uncertain due to the rapid development of disruptive information technologies, the changing global economy, and the COVID-19 pandemic. This brings great uncertainties to investors to predict the performance changes and risks of companies. This research proposes a sequential data-based framework that aggregates data from multiple sources including both structured and unstructured data to predict the performance changes. It leverages data generated from the early risk warning system in China stock market to measure and predict organization (...) performance changes based on the risk warning status changes of public companies. Different from the models in existing literature that focus on the prediction of risk warning of companies, our framework predicts a portfolio of organization performance changes, including business decline and recovery, thus helping investors to not only predict public company risks, but also discover investment opportunities. By incorporating sequential data, our framework achieves 92.3% macro-F1 value on real-world data from listed companies in China, outperforming other static models. (shrink)

Ravasio (2023) recently presents an interesting discussion of strategies to deal with lookism. He categorizes strategies into revisionary and redistributive ones and argues for a case against the former. This note is a critical response to both the taxonomy and the evaluation. I argue that the taxonomy needs to be improved and that the case against revisionary strategies cannot be maintained.

The capacitive voltage transformer is a special measuring and protecting device, which is commonly applied in high-voltage power systems. Its measurement accuracy is affected seriously by the stray capacitances of the capacitance voltage divider to ground and other charged parts. In this study, based on the boundary element method, a mathematical model was established firstly to calculate the stray capacitance. Then, the voltage distribution of the CVD was obtained by the CVD’s equivalent circuit model. Next, the effect of stray capacitance (...) on the voltage distribution and the voltage difference ratio of CVD was analysed in detail. We finally designed three types of shield and optimized their structure parameters to reduce VDR. The results indicated that the average deviation rate between calculated and experimental measured voltages is only 0.015%; that is to say, the method has high calculation precision. The stray capacitance of the CVD to ground is far larger than that of the CVD to the high-voltage terminal. It results in the inhomogeneous distribution of voltage and the increase of VDR. For the test CVT, its VDR exceeds the requirement of class 0.2. Among all of the three types of shield, the C type reduced the VDR of the test CVT the most. After optimizing the structure parameters of C-type shield, the VDR is further reduced to 0.08%. It is not only in accord with the requirement of class 0.2 but also has an adequate margin. (shrink)

We propose a fast and reliable corner detector that can detect corners under non-uniform illumination and fuzzy mineshaft images effectively. First, we presented an inner mask that used only four pixels to determine the flat and corner regions of an image, which could eliminate unnecessary computation of flat regions, thus reducing computing cost. Second, we separated the corner regions into background and foreground and computed the separate corner threshold to settle non-uniform illumination. Third, we proposed a fast corner-detection algorithm to (...) compute the nucleus continuous contributive segment based on the corner state. Finally, we proposed two effective methods to remove the false corners. Experimental results showed that our approach has a better detection quality and is less time consuming than three other algorithms on an artificial image, a noisy image, and non-uniform images and could meet the real-time requirement of mineshaft applications. (shrink)

In face recognition systems, highly robust facial feature representation and good classification algorithm performance can affect the effect of face recognition under unrestricted conditions. To explore the anti-interference performance of convolutional neural network reconstructed by deep learning framework in face image feature extraction and recognition, in the paper, first, the inception structure in the GoogleNet network and the residual error in the ResNet network structure are combined to construct a new deep reconstruction network algorithm, with the random gradient descent and (...) triplet loss functions as the model optimizer and classifier, respectively, and it is applied to the face recognition in Labeled Faces in the Wild face database. Then, the portrait pyramid segmentation and local feature point segmentation are applied to extract the features of face images, and the matching of face feature points is achieved using Euclidean distance and joint Bayesian method. Finally, Matlab software is used to simulate the algorithm proposed in this paper and compare it with other algorithms. The results show that the proposed algorithm has the best face recognition effect when the learning rate is 0.0004, the attenuation coefficient is 0.0001, the training method is SGD, and dropout is 0.1, and the algorithm proposed in this paper has the largest mean average precision compared to other CNN algorithms. The correct rate of face feature matching of the algorithm proposed in this paper is 84.72%, which is higher than LetNet-5, VGG-16, and VGG-19 algorithms, the correct rates of which are 6.94%, 2.5%, and 1.11%, respectively, but lower than GoogleNet, AlexNet, and ResNet algorithms. At the same time, the algorithm proposed in this paper has a faster matching time and a higher correct matching rate than the joint Bayesian method, indicating that the deep reconstruction network algorithm proposed in this paper can be used in face image recognition, FE, and matching, and it has strong anti-interference. (shrink)

Based on a national survey of Chinese scientific personnel in 2008, this paper sheds new light on the relationship between social networks and scientific performance. In this study, we used position generator to measure scientists’ ego-centered social networks. The scientists’ performance was measured by multiple indexes, including recognitions from the academic, governmental, and market sectors. The findings show that size and composition of scientists’ social networks have significant effect on their scientific performance. The notions of “information communication mechanism” and “resource (...) acquisition mechanism” are introduced to explain how network composition affects scientific performance along multiple dimensions. The policy implications of the study are also discussed. (shrink)

Objective Diabetes is a global public health concern. Management of diabetes depends on successful implementation of strategies to alleviate decline in executive functions (EFs), a characteristic of diabetes progression. In this review, we describe recent research on the relationship between diabetes and EF, summarize the existing evidence, and put forward future research directions and applications. Methods Herein, we provide an overview of recent studies, to elucidate the relationship between DM and EF. We identified new screening objectives, management tools, and intervention (...) targets for diabetes management. We also discuss the implications for clinical practice. Results In both types 1 and 2 diabetes mellitus (DM), hyperglycemia substantially impairs EF in people of all age groups and ethnicities. Hypoglycemia can similarly impair EF. Interestingly, a decline in EF contributes to DM progression. Glucose dysregulation and EF decline exacerbate each other in a vicious cycle: poor blood glucose control, impaired EF, diabetes management task failure, then back to poor blood glucose control. Many pathophysiological indexes (e.g., obesity, metabolic index, inflammatory and immune factors), neuropsychological indexes (e.g., compliance, eating habits, physical exercise, sleep, and depression), and genetic factors are changed by this pathological interaction between DM and EF. These changes can provide insight into the pathophysiological mechanisms of diabetes-related EF decline. Conclusion Further studies, including large-scale prospective and randomized controlled trials, are needed to elucidate the mechanism of the interaction between diabetes and EF and to develop novel strategies for breaking this cycle. (shrink)

Repeated unethical behaviour by consumers is a serious challenge for participants in business transactions, including consumers, retailers, and those responsible for market supervision. Due to the inherent risk of such behaviours, we examine perceived risk to uncover the psychological mechanism by which consumers consider past behaviour (PAB) when deciding to repeat unethical behaviour. We divide perceived risk into two categories, material risk (MAR) and nonmaterial risk (NMR), based on two kinds of ethical evaluation and explore their mediating effects in the (...) process through which PAB influences consumers' intentions to repeat unethical behaviour (CIRUB). We check the moderating effect of consumers' ethical beliefs on the relationship between PAB and CIRUB. We use survey data from four typical ethical scenarios to test the proposed structural relationships. Direct positive effects of PAB on consumers' intentions are observed in all four scenarios. NMR has stable, positive mediating effects in all scenarios. MAR is found to have negative mediating effects in two scenarios (switching price tags and using an expired coupon) and a positive effect in one scenario (copying software). However, no effect is observed in the benefiting from a cashier's mistake scenario. Finally, ethical beliefs are proven to positively moderate the relationship between PAB and consumers' intentions in the first two scenarios, but not the last two. This study also discusses the implications of the findings and offers suggestions for future research. (shrink)

Greenwashing has become a common way in which many enterprises address expectations related to corporate social responsibility. As an abnormal and fake type of social responsibility behavior, greenwashing causes many problems, thus highlighting the importance of exploring the impact of corporate greenwashing. However, previous studies have mainly examined the impacts of greenwashing on enterprises, industry, and society, and the impact of greenwashing on individual employee behavior, which is important with regard to the sustainability of the enterprise, remains limited. The main (...) purpose of this study is to explore how perceived greenwashing affects employee green behavior (EGB). Based on the stimulus–organism–response theory and social cognitive theory, a two-wave survey was conducted to investigate 232 Chinese enterprise employees. The impact of perceived greenwashing on EGB was analyzed. The mediating role of green organizational identity (GOI) and the moderating role of self-serving leadership in this context were examined. The results reveal that perceived greenwashing is negatively related to EGB. Furthermore, GOI mediates the direct relationship between perceived greenwashing and EGB. Self-serving leadership weakens the positive relationship between GOI and EGB. This study thus develops a theoretical model of greenwashing and EGB. It also provides empirical evidence that can support attempts to promote EGB. (shrink)

Subgraph matching on a large graph has become a popular research topic in the field of graph analysis, which has a wide range of applications including question answering and community detection. However, traditional edge-cutting strategy destroys the structure of indivisible knowledge in a large RDF graph. On the premise of load-balancing on subgraph division, a dominance-partitioned strategy is proposed to divide a large RDF graph without compromising the knowledge structure. Firstly, a dominance-connected pattern graph is extracted from a pattern graph (...) to construct a dominance-partitioned pattern hypergraph, which divides a pattern graph as multiple fish-shaped pattern subgraphs. Secondly, a dominance-driven spectrum clustering strategy is used to gather the pattern subgraphs into multiple clusters. Thirdly, the dominance-partitioned subgraph matching algorithm is designed to conduct all isomorphic subgraphs on a cluster-partitioned RDF graph. Finally, experimental evaluation verifies that our strategy has higher time-efficiency of complex queries, and it has a better scalability on multiple machines and different data scales. (shrink)

As one of the key technologies for accelerating the construction of the ubiquitous Internet of Things, demand response not only guides users to participate in power market operations but also increases the randomness of grid operations and the difficulty of load forecasting. In order to solve the problem of rough feature engineering processing and low prediction accuracy, a short-term load forecasting model of LSTM neural network considering demand response is proposed. First of all, in view of the strong randomness and (...) complexity of input features, the weighted method is used to process multiple input features to strengthen the contribution of effective features and tap the potential value of features. Secondly, an improved genetic algorithm is used to obtain the best LSTM parameters; finally, the special gate structure of the LSTM model is used to selectively control the influence of input variables on the model parameters and perform load forecasting. The experimental results show that the research has high prediction accuracy and application value and provides a new way for the development of power load forecasting. (shrink)

The extraction process of palm seed oil was optimized. Using palm seed as raw material, oil extraction rate was used as an index. The effects of flash extraction, ultrasonic-assisted extraction, supercritical extraction, and aqueous enzymatic extraction on the yield of palm seed oil were investigated. The extraction methods and technological conditions of palm seed oil were optimized by the orthogonal method on the basis of single factor. The seed oil was analyzed and detected. The results showed that the water enzymatic (...) extraction method was the best, and the optimal extraction conditions were as follows: enzymatic hydrolysis time 16 h, enzymatic hydrolysis temperature 50°C, and enzymatic content 2.0%. The oil yield of palm seed was 16.48%. Conclusion. Water enzymatic extraction process of palm seed oil is reasonable, the active ingredients are rich, and the quality of seed oil is better, providing reference for the development and research of palm seed oil. (shrink)

In a complex network, each edge has different functions on controllability of the whole network. A network may be out of control due to failure or attack of some specific edges. Bridges are a kind of key edges whose removal will disconnect a network and increase connected components. Here, we investigate the effects of removing bridges on controllability of network. Various strategies, including random deletion of edges, deletion based on betweenness centrality, and deletion based on degree of source or target (...) nodes, are used to compare with the effect of removing bridges. It is found that the removing bridges strategy is more efficient on reducing controllability than the other strategies of removing edges for ER networks and scale-free networks. In addition, we also found the controllability robustness under edge attack is related to the average degree of complex networks. Therefore, we propose two optimization strategies based on bridges to improve the controllability robustness of complex networks against attacks. The effectiveness of the proposed strategies is demonstrated by simulation results of some model networks. These results are helpful for people to understand and control spreading processes of epidemic across different paths. (shrink)

Zero-shot learning is dedicated to solving the classification problem of unseen categories, while generalized zero-shot learning aims to classify the samples selected from both seen classes and unseen classes, in which “seen” and “unseen” classes indicate whether they can be used in the training process, and if so, they indicate seen classes, and vice versa. Nowadays, with the promotion of deep learning technology, the performance of zero-shot learning has been greatly improved. Generalized zero-shot learning is a challenging topic that has (...) promising prospects in many realistic scenarios. Although the zero-shot learning task has made gratifying progress, there is still a strong deviation between seen classes and unseen classes in the existing methods. Recent methods focus on learning a unified semantic-aligned visual representation to transfer knowledge between two domains, while ignoring the intrinsic characteristics of visual features which are discriminative enough to be classified by itself. To solve the above problems, we propose a novel model that uses the discriminative information of visual features to optimize the generative module, in which the generative module is a dual generation network framework composed of conditional VAE and improved WGAN. Specifically, the model uses the discrimination information of visual features, according to the relevant semantic embedding, synthesizes the visual features of unseen categories by using the learned generator, and then trains the final softmax classifier by using the generated visual features, thus realizing the recognition of unseen categories. In addition, this paper also analyzes the effect of the additional classifiers with different structures on the transmission of discriminative information. We have conducted a lot of experiments on six commonly used benchmark datasets. The experimental results show that our model outperforms several state-of-the-art methods for both traditional as well as generalized zero-shot learning. (shrink)

This study presents an evolutionary game to model interactions among stakeholders with potential conflicts, including the operational enterprise of incineration plant, the local government, and the residents nearby. System dynamics is used to simulate the change of strategic actions corresponding to the three players, in order to seek for the evolutionary stability strategies. A numerical case is proposed to demonstrate the game theory application, in which the impacts of governmental incentive and punishment on the player’s actions are investigated. The results (...) indicated that administrative penalty is effective not only in motivating the enterprises to upgrade treatment facilities for ensuring environmental quality but also in helping the local government and residents to approach dominant strategies. Policy implications are given based on the results to lay out a foundation for the alleviation of the conflicts. (shrink)

Existing image enhancement methods have problems of a slow data transmission and poor conversion effect, resulting in a low image-recognition rate and recognition efficiency. To solve these problems and improve the recognition accuracy and recognition efficiency of image features, this study proposes an edge detail enhancement algorithm for a high-dynamic range image. The original image is transformed by Fourier transform, and the low-frequency and high-frequency images are obtained by the frequency-domain Gaussian filtering and inverse Fourier transform. The low-frequency image is (...) processed by the contrast limited adaptive histogram equalization, and the high-frequency image is obtained by the nonsharpening masking and gray transformation. The low-frequency enhanced and the high-frequency enhanced images are weighted and fused to enhance the edge details of the image. Finally, the experimental results show that the proposed high-dynamic range image edge detail enhancement algorithm maintains the image recognition rate of more than 80% during the practical application, and the recognition time is within 1,200 min, which enhances the image effect, improves the recognition accuracy and recognition efficiency of image characteristics, and fully meets the research requirements. (shrink)

BackgroundThe underrepresentation of scholarly works from low- and middle-income countries (LMICs) in academic literature is a documented concern, attributed partly to editorial biases. This trend, prevalent across various disciplines, has been less explored in the context of medical ethics journals. This study aimed to examine the composition of editorial board members (EBM) in high-impact medical ethics journals and to evaluate the extent of international diversity within these editorial teams.MethodsThis study incorporated an analysis of 16 high-impact medical ethics journals. Information regarding (...) the EBM of these journals was systematically gathered and categorized based on the World Bank’s country income classifications. An in-depth examination of the editorial board compositions was then conducted.ResultsThe study identified 669 EBM across the selected journals. A predominant 89.84% (601) of these members were from high-income countries (HICs), with upper-middle-income countries contributing 7.47% (50) and lower-middle-income countries 2.69% (18). No EBM were associated with low-income countries. A regional breakdown indicated that North America was the most represented area, accounting for 48.88% (327), followed by Europe & Central Asia (27.50%, 184), East Asia & Pacific (13.45%, 90), Latin America & Caribbean (4.63%, 31), Sub-Saharan Africa (4.19%, 28), Middle East & North Africa (0.75%, 5), and South Asia (0.60%, 4). In total, these EBMs hailed from 46 different countries, with the United States representing the largest proportion (43.80%, 293), followed by the United Kingdom (13.15%, 88), Australia (7.92%, 53), Germany (6.73%, 45), and Canada (5.08%, 34).ConclusionsThere is a significant lack of international representation within the EBM of high-impact medical ethics journals. The majority of editors in this field are affiliated with HICs, leading to a severe underrepresentation of LMICs within the editorial boards. (shrink)

This research investigated how interpersonal communication with a large audience can influence communicators’ attitudes. Research on the saying-is-believing effect has shown that when an individual’s attitude is perceived in advance by a communicator, the communicator tunes the message to the person, which biases the communicator’s attitude toward the person’s attitude. In this study, we examined the conditions under which audience tuning and attitude bias can occur with audiences containing more than one individual. We manipulated communicators’ perceived group entity for a (...) large audience and the audience’s prior attitudinal valence and measured the audience’s epistemic trust. The results showed that communicators tuned their messages to the audience’s attitude when they perceived group entitativity and epistemic trust. Furthermore, tuning the message to the audience was found to bias communicators’ subsequent impressions of the topic in a direction closer to the audience’s attitude. These results suggest that perceiving a large audience as a group influences the subsequent impressions of electronic word-of-mouth product or service communicators. (shrink)

The growth of strawberry will be stressed by biological or abiotic factors, which will cause a great threat to the yield and quality of strawberry, in which various strawberry diseased. However, the traditional identification methods have high misjudgment rate and poor real-time performance. In today's era of increasing demand for strawberry yield and quality, it is obvious that the traditional strawberry disease identification methods mainly rely on personal experience and naked eye observation and cannot meet the needs of people for (...) strawberry disease identification and control. Therefore, it is necessary to find a more effective method to identify strawberry diseases efficiently and provide corresponding disease description and control methods. In this paper, based on the deep convolution neural network technology, the recognition of strawberry common diseases was studied, as well as a new method based on deep convolution neural network strawberry disease recognition algorithm, through the normal training of strawberry image feature representation in different scenes, and then through the application of transfer learning method, the strawberry disease image features are added to the training set, and finally the features are classified and recognized to achieve the goal of disease recognition. Moreover, attention mechanism and central damage function are introduced into the classical convolutional neural network to solve the problem that the information loss of key feature areas in the existing classification methods of convolutional neural network affects the classification effect, and further improves the accuracy of convolutional neural network in image classification. (shrink)

Humans can understand thousands of abstract words, even when they do not have clearly perceivable referents. Recent views highlight an important role of social experience in grounding of abstract concepts and sub-kinds of abstract concepts, but empirical work in this area is still in its early stages. In the present study, a picture-word semantic priming paradigm was employed to investigate the contribution effect of social experience that is provided by real-life pictures to social abstract concepts and emotional abstract concepts. Using (...) a lexical decision task, we examined responses to picture-SA word pairs and picture-EA word pairs in social/emotional semantically related and unrelated conditions. All pairs shared either positive or negative valence. The results showed quicker responses to positive SA and EA words that were preceded by related vs. unrelated prime pictures. Specifically, positive SA words were facilitated by the corresponding social scene pictures, whereas positive EA words were facilitated by pictures depict the corresponding facial expressions and gestures. However, such facilitatory effect was not observed in negative picture-SA/EA word conditions. This pattern of results suggests that a facilitatory effect of social experience on abstract concepts varies with different sub-kinds of abstract concepts, that seems to be limited to positive SA concepts. Overall, our findings confirm the crucial role of social experience for abstract concepts and further suggest that not all abstract concepts can benefit from social experience, at least in the semantic priming. (shrink)

Exploring the spiritual cognition of human organ donation registration volunteers, aiming to provide new ideas for promoting the development of organ donation through this perspective. This qualitative research was conducted following the conventional content analysis method. 10 registered volunteers for human organ donation were selected from July to December 2023 for face-to-face semi-structured interviews. Snowball sampling was employed to select the participants. In-depth semi-structured interviews were conducted for data gathering. Theoretical saturation was achieved through 10 interviews. Colaizzi phenomenological 7-step analysis (...) method was used to analyze the interview content. The spirituality of registered volunteers for human organ donation can be summarized into three themes and seven sub themes: ① Spiritual Understanding (Love & Life Extension, Dedication to society, Death elevates life); ② The Supporting Role of Spirituality (Become a spiritual motivation for a better life, self-transcendence ); ③ The Spiritual Emotional Value of Organ Donation (Sense of accomplishment and pride, Sense of complexity and ambivalence). The willingness to donate organs is closely related to spirituality. By understanding the spirituality of this group, it is possible to take an important step in promoting the development of organ donation. (shrink)

This article provides a method for estimating the bounds of transaction costs in horizontal mergers. Consider, for example, a completed monopoly merger in linear Cournot oligopolies with 10 symmetric firms. The method shows that its transaction costs are at most 25% (78%) of total premerger profits if there is zero (100%) excess capacity. Such estimations can be extended in a straightforward manner to other mergers and other oligopoly models. The estimation is based both on the profitability precondition, and on the (...) non-empty core precondition, which postulates that the split of a merger’s profits be in its core. The article shows that the core in linear Cournot oligopolies has a non-empty interior, and indicates that the non-empty core precondition also sheds new lights on understanding important issues such as the stylized fact that mergers are likely to occur in markets plagued by excess capacities; why profitable mergers might not be formed; and why completed mergers might break up in the future. (shrink)

It is well documented that self-control has a positive effect on individuals’ subjective well-being. However, little research has focused on the moderators underlying this relationship. The present research used two studies to examine the moderating role of both trait and state motivation on the relationship between self-control and subjective well-being using psychometric and experimental models, respectively. In Study 1, we explored whether trait motivation moderated the relationship between trait self-control and subjective well-being using a psychometric model. In Study 2, we (...) examined the moderating effects of both trait and state motivation on the effect of state self-control on subjective well-being using an experimental model. Our results indicated that self-control had a positive effect on subjective well-being, with this relationship being primarily moderated by prevention motivation. When state and trait prevention motivations were congruent, self-control had the most obvious impact on subjective well-being. This study suggests that current understandings around the association between self-control and happiness is limited, implying that motivation should be the focus of future research. (shrink)

New energy vehicle is an innovative means of transportation, and its development has been widely concerned all over the world. However, few studies investigate the purchase intention of new energy vehicles from the perspective of combining altruism and cultural factors. Based on the extended norm activation model, this study explores the influencing factors of NEVs’ purchasing intention and the moderating effects of “mianzi” and green peer influence. According to 302 valid questionnaires, the results indicated that the extended NAM model is (...) useful in predicting consumer purchasing behavior with an improved explanatory power in purchase intentions of NEVs from 15 to 26%. The awareness of consequences, the ascription of responsibility, and green self-identity have a positive impact on the personal norm. Personal norm and green self-identity are positively associated with purchase intention. “Mianzi” and green peer influence positively moderate the relationship between green self-identity and intention to purchase. The findings give new insights into the impact of cultural factors on purchasing NEVs and profound suggestions for policymakers and enterprises to promote the development of NEVs. (shrink)

Central to scholarly discussions within the field of feminist epistemology is the question of a researcher’s positionality and the subsequent impact on knowledge production. In particular, Donna Haraway’s elaboration of ‘situated knowledge’ has been highly influential. As an epistemological principle, this concept emphasizes the researcher’s embodied location within the research context. Yet the question remains, how does one apply this principle within the concrete practices of knowledge production? In a research project based on Norwegian transnational adoptees’ identity work, the author, (...) being an immigrant, was situated as an ‘outsider within’. How does this positionality influence knowledge production? The concept of ‘outsider within’ comes from standpoint epistemology and has been taken up within black feminist thought to authorize knowledge about dominant society produced from a position of marginality. However as a concrete situatedness, can an ‘outsider within’ position also be used to think about the production of situated knowledge? This article offers an exploration of these questions. The author argues that one way to put feminist situated knowledge to use within an empirical study is through the conceptualization of ‘outsider within’ as a situated interactive space instead of a fixed standpoint. (shrink)

本书内容包括深圳人敬业精神的内涵、深圳人敬业的环境、深圳人的敬业理念、深圳人的敬业激励机制、深圳各行业敬业度分析、深圳人的敬业心理透视、深圳敬业激励机制典型案例等.