Capitalist reforms usually drive outmigration of peasants to cities, while elders, children, and women responsible for their care are “left behind” in the countryside. The plight of these “left behind” populations is a major focus of recent agrarian studies in China. However, rural women are not merely passive victims of these transformations. Building on ethnographic research in Guangxi and Henan provinces from 2013 to 2017, and drawing on critical gender studies and feminist political ecology, I show how the food safety (...) crisis in China creates conditions for peasant women to increase control and income from organic food production, often establishing alternative food networks with the support of female scholars and NGO organizers. Thus, I shift focus of scholarship on rural women from “left behind” to leaders in struggles for justice and food sovereignty. (shrink)

Patent strategy is increasingly recognized as a vital contributor in promoting core competitiveness of an enterprise. A top management team has been indicated as one of the key factors driving changes in patent strategy. Based on upper echelons theory, this study examines how TMT characteristics, including, team diversity, emotional intelligence, and safety climate, influence enterprise patent strategic change and, hence, the business outcome. The data from 930 top managers in 228 enterprises showed that the changes in patent strategies are significantly (...) influenced by the characteristics of top managers. These aforementioned internal TMT factors have diverse effects on the speed and scope of the enterprise patent strategic change, which in turn affects firm performance in a positive and negative way, respectively. (shrink)

Aiming at suppressing harmful effect for building structure by surface motion, semiactive nonsmooth control algorithm with Deep Learning is proposed. By finite-time stable theory, the building structure closed-loop system’s stability is discussed under the proposed control algorithm. It is found that the building structure closed-loop system is stable. Then the proposed control algorithm is applied on controlling the building structural vibration. The seismic action is chosen as El Centro seismic wave. Dynamic characteristics have comparative analysis between semiactive nonsmooth control and (...) passive control in two simulation examples. They demonstrate that the designed control algorithm has great robustness and anti-interference. The proposed control algorithm is more effective than passive control in suppressing structural vibration. (shrink)

A set of sentential revision operations can be generated in a select-direct way within a new framework for belief change named descriptor revision firstly introduced in Hansson [8]. In this paper, we adopt another constructive approach to these operations, based on a relation \ on sentences named believability relation. Intuitively, \ means that the subject is at least as prone to believe or accept \ as to believe or accept \. We demonstrate that so called H-believability relations and basic believability (...) relations, the second of which the is axiomatically characterized with a set of weak postulates, are faithful alternative models for two typical select-direct sentential revision operations. Then we investigate additional postulates on believability relations that correlate with properties of the generated revision operations. Finally, we show that traditional AGM revision operations can be reconstructed from a strengthened variant of the basic believability relation and there is a close connection between this relation and the standard epistemic entrenchment relation. (shrink)

Background: Losing one’s only child may lead to post-traumatic stress disorder, of which re-experiencing is the core symptom. However, neuroimaging studies of sex differences in re-experiencing in the context of the trauma of losing one’s only child and PTSD are scarce; comparisons of the functional networks from the hippocampal subfields to the thalamus might clarify the neural basis.Methods: Thirty couples without any psychiatric disorder who lost their only child, 55 patients with PTSD, and 50 normal controls underwent resting-state functional magnetic (...) resonance imaging. The functional connectivity from the hippocampal subregions to the thalamus and the correlations of FC with re-experiencing symptoms were analyzed within and between the sexes.Results: Compared with husbands without PTSD, wives without PTSD had higher re-experiencing symptoms and weaker FC between the right hippocampal cornu ammonis 3 and the right thalamus. Moreover, only the correlation between the RCA3-RT FC and re-experiencing in wives without PTSD was significant. Among the three groups, only the RCA3-RT FC in female subjects was markedly different. Additionally, the RCA3-RT FC in wives without PTSD was remarkably lower relative to female patients with PTSD.Conclusion: Wives without PTSD who lost their only child had worse re-experiencing symptoms relative to their husbands, which was associated with the FC alteration between the hippocampal subregions and the thalamus. Importantly, the low level of the RCA3-RT FC may play a potentially protective role against the development of PTSD in wives who have lost their only child. (shrink)

This study explores the dynamic feature of organizational citizenship behaviors under the condition of challenge stressors, as this has not been addressed by previous research. Combining the cybernetic theory of stress and social exchange theory, this study builds a dynamic computational model regarding the circular causality between challenge stressors and organizational citizenship behaviors. By conducting a series of simulation experiments, we validated and demonstrated important questions regarding organizational citizenship behaviors. Specifically, when both the initial value of challenge stressors and the (...) importance of challenge appraisal are higher, organizational citizenship behaviors tend to show a sharped inverted U shape at the early stage. When both the equilibrium level of job satisfaction and the initial value of challenge stressors are higher, organizational citizenship behaviors will show an inverted N shape over time. The number and frequency of assigned challenge tasks have an interactive effect on the accumulation of organizational citizenship behaviors within a period. Our theory contributes to identifying the dynamic relationship between challenge stressors and organizational citizenship behaviors. Findings from dynamic computational theory can offer suggestions for managers to encourage employees’ engagement in organizational citizenship behaviors. (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)

Multimodal neuroimaging provides a rich source of data for identifying brain regions associated with disease progression and aging. However, present studies still typically analyze modalities separately or aggregate voxel-wise measurements and analyses to the structural level, thus reducing statistical power. As a central example, previous works have used two quantitative MRI parameters—R2* and quantitative susceptibility —to study changes in iron associated with aging in healthy and multiple sclerosis subjects, but failed to simultaneously account for both. In this article, we propose (...) a unified framework that combines information from multiple imaging modalities and regularizes estimates for increased interpretability, generalizability, and stability. Our work focuses on joint region detection problems where overlap between effect supports across modalities is encouraged but not strictly enforced. To achieve this, we combine L1, total variation, and L2 group lasso penalties. While the TV penalty encourages geometric regularization by controlling estimate variability and support boundary geometry, the group lasso penalty accounts for similarities in the support between imaging modalities. We address the computational difficulty in this regularization scheme with an alternating direction method of multipliers optimizer. In a neuroimaging application, we compare our method against independent sparse and joint sparse models using a dataset of R2* and QS maps derived from MRI scans of 113 healthy controls: our method produces clinically-interpretable regions where specific iron changes are associated with healthy aging. Together with results across multiple simulation studies, we conclude that our approach identifies regions that are more strongly associated with the variable of interest, more accurate, and more stable with respect to training data variability. This work makes progress toward a stable and interpretable multimodal imaging analysis framework for studying disease-related changes in brain structure and can be extended for classification and disease prediction tasks. (shrink)

Neurons encode and transmit signals through chemical synaptic or electrical synaptic connections in the actual nervous system. Exploring the biophysical properties of coupling channels is of great significance for further understanding the rhythm transitions of neural network electrical activity patterns and preventing neurological diseases. From the perspective of biophysics, the activation of magnetic field coupling is the result of the continuous release and propagation of intracellular and extracellular ions, which is very similar to the activation of chemical synaptic coupling through (...) the continuous release of neurotransmitters. In this article, an induction coil is used to connect two HR neurons to stimulate the effect of magnetic field coupling. It is inevitable that time delays can affect the coupling process in the transmission of information, and it should be considered in the coupled model. Firstly, the firing characteristics and bifurcation modes of two coupled HR neurons are studied by using one parameter and two parameters bifurcation. With the increase of propagation delay and coupling gain, the chaotic state of neurons disappears and the high-period window decreases due to the influence of energy transfer between neurons. Then, the synchronization patterns of two HR neurons with different stimulation are analyzed by error diagrams and time series diagrams. It is confirmed that the synchronous pattern has certain regularity and is related not only to the neurons with large stimulation current but also to the time delay and coupling gain. The research conclusions of this article will provide the corresponding theoretical basis for medical experiments. (shrink)

Choice revision is a sort of non-prioritized multiple revision, in which the agent partially accepts the new information represented by a set of sentences. We investigate the construction of choice revision based on a new approach to belief change called descriptor revision. We prove that each of two variants of choice revision based on such construction is axiomatically characterized with a set of plausible postulates, assuming that the object language is finite. Furthermore, we introduce an alternative modelling for choice revision, (...) which is based on a type of relation on sets of sentences, named multiple believability relation. We show without assuming a finite language that choice revision constructed from such relations is axiomatically characterized with the same sets of postulates that we proposed for the choice revision based on descriptor revision, whenever the relations satisfy certain rationality conditions. (shrink)

A burgeoning body of research has shown that authoritarian leadership embodies the characteristics of “light” and “dark,” meaning that it does not always have a negative impact on employees’ creative activities. However, studies explaining this potential positive effect are insufficient. To extend the AL and creativity literature, we draw on self-determination theory and event system theory, and elicit discipline-focused AL and appointment event criticality to examine whether, when, and how authoritarian leaders affect employee creativity positively. With time-lagged data collected from (...) 435 employees and their direct leaders in China, we found that discipline-focused AL has an indirect positive effect on employee creativity through creative self-efficacy. Additionally, appointment event criticality strengthens the positive relationship between discipline-focused AL and creative self-efficiency, and the indirect impact of discipline-focused AL on employee creativity through creative self-efficiency. The theoretical and practical implications are discussed. (shrink)

Chronic back pain (CBP) is a leading cause of disability and results in considerable socio-economic burdens worldwide. Although CBP patients are commonly diagnosed and treated with a focus on the ‘end organ dysfunction’ (i.e., peripheral nerve injuries or diseases), the evaluation of CBP remains flawed and problematic with great challenges. Given that the peripheral nerve injuries or diseases are insufficient to define the etiology of CBP in some cases, the evaluation of alterations in the central nervous system becomes particularly necessary (...) and important. With the development of advanced neuroimaging techniques, extensive studies have been carried out to identify the cortical abnormalities in CBP patients. Here, we provide a comprehensive overview on a series of novel findings from these neuroimaging studies to improve our understanding of the cortical abnormalities originated in the disease. First, CBP patients normally exhibit central sensitization to external painful stimuli, which is indexed by increased pain sensitivity and brain activations in pain-related brain regions. Second, long-term suffering from chronic pain leads to emotional disorders, cognitive impairments, and the abnormalities of the relevant brain networks among CBP patients. Third, CBP is associated with massive cortical reorganization, including structural, functional, and metabolic brain changes. Overall, a deep insight into the neural mechanisms underlying the development and outcome of CBP through more sophisticated neuroimaging investigations could not only improve our current understanding of the etiology of CBP but also facilitate the diagnosis and treatment of CBP based on precision medicine. (shrink)

The identification and analysis of the spatiotemporal dynamic traffic patterns in citywide road networks constitute a crucial process for complex traffic management and control. However, city-scale and synchronal traffic data pose challenges for such kind of quantification, especially during peak hours. Traditional studies rely on data from road-based detectors or multiple communication systems, which are limited in not only access but also coverage. To avoid these limitations, we introduce real-time, traffic condition digital maps as our input. The digital maps keep (...) spatiotemporal urban traffic information in nature and are open to access. Their pixel colors represent traffic conditions on corresponding road segments. We propose a stacked convolutional autoencoder-based method to extract a low-dimension feature vector for each input. We compute and analyze the distances between vectors. The statistical results show different traffic patterns during given periods. With the actual data of Chongqing city, we compare the feature extraction performance between our proposed method and histogram. The result shows our proposed method can extract spatiotemporal features better. For the same data set, there is little difference in the number distribution of red pixels found in the statistics result of the histogram, while differences do exist in the results of our proposed method. We find the most fluctuated morning is on Friday; the most fluctuated evening is on Tuesday; and the most stable evening is on Wednesday. The distance captured by our method can represent the evolution of different traffic conditions during the morning and evening peak hours. Our proposed method provides managers with assistance to sense the dynamics of citywide traffic conditions in quantity. (shrink)

As professional football stadiums continue to grow in popularity worldwide, fans are able to watch the game in closer proximity, but the design of professional football stadiums to shorten the distance between fans and the playing field also exacerbates the impact of the home advantage on the referee’s decision to call a penalty. Studies have confirmed the existence of the home advantage and found that experienced referees can reduce the impact of this interference, but the neural mechanisms behind this phenomenon (...) have not been adequately investigated. In this study, we designed a soccer referee decision making task based on a home field effect scenario in a real soccer game, and used event-related potentials to compare the decision making and EEG differences between individuals with different experience levels when faced with foul actions under spectator noise interference. The experiments showed that individuals with different experience levels triggered a significant ERN EEG component when performing the penalty decision task under the home field effect factor, suggesting that the interference of the home field effect may lead referees to correct their previous decision-making behavior patterns in the penalty decision and reduce unfavorable calls against the home team. In contrast, referees with officiating experience elicited smaller ERN amplitudes compared to other subjects, suggesting that experience factors may inhibit this tendency to change behavioral patterns. This study suggests that in response to the increasing trend of professional football stadiums, policy makers should place more emphasis on enhancing the experience level of referees in the training of referees to ensure the fairness of the game. (shrink)

With the service-dominant logic gradually replacing the traditional commodity-dominant reason, co-creating value with consumers has become an essential marketing practice for enterprises. As a critical information carrier in enterprise marketing communication, the co-creation signal impacts co-creation observers. Enterprises are now exploring how to effectively release co-creation signals to attract most observers to participate in value creation activities actively. Based on self-determination theory, this study investigates the influence mechanism of co-creation signals on observers’ willingness to co-create and the moderating role of (...) mental simulation through the experimental method. The conclusions are as follows: The co-creation signal has a significant positive effect on the co-creation intention of observers. Self-designed co-creation signals generate more important co-creation willingness than self-produced co-creation signals; Self-determination and its dimensions mediate the relationship between co-creation signals and the observer’s co-creation willingness; and Mental simulation moderates the relationship between co-creation signal and self-determination. This study has important theoretical significance in enriching co-creation signals and mental simulation research. It also provides management implications for enterprises to release co-creation signals and optimize co-creation marketing practices effectively. (shrink)

Aiming at the problem that the embedded platform cannot meet the real-time detection of multisource images, this paper proposes a lightweight target detection network MNYOLO suitable for embedded platforms using deep separable convolution instead of standard convolution to reduce the number of model parameters and calculations; at the same time, the visible light target detection model is used as the pretraining model of the infrared target detection model and the infrared target data set collected on the spot is fine-tuned to (...) obtain the infrared target detection model. On this basis, a decision-level fusion detection model is obtained to realize the complementary information of infrared and visible light multiband information. The experimental results show that it has a more obvious advantage in detection accuracy than the single-band target detection model while the decision-level fusion target detection model meets the real-time requirements and also verifies the effectiveness of the above algorithm. (shrink)

The objective of this study was to examine the association between resilience and trajectories of college students’ negative and positive affect during the COVID-19 pandemic. A total of 391 college students recruited from China completed a daily online negative and positive affect scale for 1 week, and their resilience was also measured. Profiles of brief trajectories of negative and positive affect over time were identified using the latent growth mixture model, and the effect of resilience on these trajectories was further (...) explored. Two latent profiles of negative affect were found: a constant high negative affect profile and a slowly decreasing low negative affect profile, while three latent profiles of positive affect were identified: a slowly increasing high positive affect profile, a rapidly decreasing medium positive affect profile, and a constant medium positive affect profile. The optimism dimension of resilience predicted the membership in the various profiles significantly, whereas the prediction of tenacity and strength dimensions of resilience was not significant. Activities that promote resilience, especially optimism, should be included to improve the daily emotions of college students during COVID-19. (shrink)

Lithium-ion batteries have been widely used as energy storage systems and in electric vehicles due to their desirable balance of both energy and power densities as well as continual falling price. Accurate estimation of the state-of-charge of a battery pack is important in managing the health and safety of battery packs. This paper proposes a compact radial basis function neural model to estimate the state-of-charge of lithium battery packs. Firstly, a suitable input set strongly correlated with the package SOC is (...) identified from directly measured voltage, current, and temperature signals by a fast recursive algorithm. Secondly, a RBF neural model for battery pack SOC estimation is constructed using the FRA strategy to prune redundant hidden layer neurons. Then, the particle swarm optimization algorithm is used to optimize the kernel parameters. Finally, a conventional RBF neural network model, an improved RBF neural model using the two stage method, and a least squares support vector machine model are also used to estimate the battery SOC as a comparative study. Simulation results show that generalization error of SOC estimation using the novel RBF neural network model is less than half of that using other methods. Furthermore, the model training time is much less than the LSSVM method and the improved RBF neural model using the two-stage method. (shrink)

“Rough set” is a theory put forward by the polish scholar Z. Pawlak, which is a useful mathematics tool for dealing with vague and uncertain information. Rough set theory can achieve a subset of all attribute which preserves the discernible ability of original features, by using the data only with no additional information. As a typical system of multi-agent, the decision-making system of soccer robot has the features of multi-layered, antagonism, and cooperation. On the bases of rough set theory, this (...) paper established a decision making system with complete information for soccer robot, and then reduce the condition and decision attributes and their values, to get the simply decision rules. On the otherwise, considering the situation of information loss, we study decision making of imperfect information system, extract the decision rules and calculate the reliability, so that the rules can assist the agent to make right decision in competition. The simulation result shows that the algorithm is correct and effective. (shrink)