The guide dog robot (GDR) is a low-speed companion robot that serves visually impaired people and is used to guide blind people to walk steadily, carrying a variety of intelligent technologies and needing to have the ability to guide with optimal energy consumption in specific scenarios. This paper proposes an innovative technique for virtual-real collaborative path planning and navigation of the GDR specific indoor scenarios, and designs an experimental method for virtual-real collaborative path planning of the GDR specific scenarios. The (...) energy consumption integral equation is used to solve for the energy consumption of the GDR with virtual-real synergy, and the difference in energy consumption is compared for three different navigation directions: horizontal, vertical and oblique obstacle avoidance. The results show that the optimized GDR saves 6.91% in rectilinear movement and 10.60% in curved movement. The efficiency of planning and navigating the GDR in specific domestic scenarios is verified by a virtual-real cooperative. The realization of optimal path planning for energy consumption is instrumental in exploring many of the most significant thought in the path planning and navigation of mobile robots in indoor specific scenarios. (shrink)

Understanding the neural basis of schizophrenia (SZ) is important for shedding light on the neurobiological mechanisms underlying this mental disorder. Structural and functional alterations in the anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), hippocampus, and medial prefrontal cortex (MPFC) have been implicated in the neurobiology of SZ. However, the effective connectivity among them in SZ remains unclear. The current study investigated how neuronal pathways involving these regions were affected in first-episode SZ using functional magnetic resonance imaging (fMRI). Forty-nine patients (...) with a first-episode of psychosis and diagnosis of SZ—according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision—were studied. Fifty healthy controls (HCs) were included for comparison. All subjects underwent resting state fMRI. We used spectral dynamic causal modeling (DCM) to estimate directed connections among the bilateral ACC, DLPFC, hippocampus, and MPFC. We characterized the differences using Bayesian parameter averaging (BPA) in addition to classical inference (t-test). In addition to common effective connectivity in these two groups, HCs displayed widespread significant connections predominantly involved in ACC not detected in SZ patients, but SZ showed few connections. Based on BPA results, SZ patients exhibited anterior cingulate cortico-prefrontal-hippocampal hyperconnectivity, as well as ACC-related and hippocampal-dorsolateral prefrontal-medial prefrontal hypoconnectivity. In summary, spectral DCM revealed the pattern of effective connectivity involving ACC in patients with first-episode SZ. This study provides a potential link between SZ and dysfunction of ACC, creating an ideal situation to associate mechanisms behind SZ with aberrant connectivity among these cognition and emotion-related regions. (shrink)

ABSTRACTWushu is widely misunderstood and its essentially combative nature is being challenged in public discussion. Understanding and recovering its essential nature has become a core issue. This...

Consumers play an important role as one of the main actors in food safety social co-governance. To create a pattern of food safety social co-governance, the active and effective participation of consumers is critical. To encourage consumers to participate in food safety social co-governance voluntarily and positively, we attempted to develop and preliminarily validate a multidimensional questionnaire on consumer psychological capital that could be used to measure the degree of consumer participation in food safety social co-governance. The aim of the (...) initial sample and test sample 2 was to investigate the factor structure of a preliminary measure of consumer psychological capital. A 4-factor model with 23 items explained 61.05% of the total variance in item scores. The aim of test sample 3 was to measure the retest reliability. Test sample 4 was randomly allocated to the modeling sample and validation sample to verify questionnaire reliability and validity. Convergent validity, discriminant validity, and the internal inconsistency coefficients of the questionnaire were assessed in the modeling sample. While processing CFA, we deleted 9 items with small standardized factor loadings. The remaining 14 items in the final revised 4-factor model included self-efficacy, resilience, hope, and optimism. The fit indices of the revised four-factor model and second-order factor model in the modeling sample revealed an acceptable model fit. The convergent validity and discriminant validity of the revised model were good and acceptable, respectively. A cross-validation procedure confirmed the appropriateness of the revised four-factor model and second-order factor model in the validation sample. The cross-validation results confirmed that the fit indices of the revised four-factor model fitted the data well and the second-order factor model in the validation sample reached acceptable values. We concluded that the questionnaire developed in this study had good reliability and stable and acceptable construct validity. It could provide a theoretical basis for measuring psychological capital in food safety co-governance. (shrink)

Complex training (CT) is a combination training method that alternates between performing high-load resistance training (RT) and plyometric training within one single session. The study aimed to examine the effects of CT on lower-limb strength and power of elite female modern pentathlon athletes under the new modern pentathlon format and competition rules. Ten female participants (age: 23.55 ± 2.22 years, weight: 60.59 ± 3.87 kg, height: 169.44 ± 4.57 cm, and training experience: 6.90 ± 2.08 years) of the national modern (...) pentathlon team completed 8 weeks of RT as followed by 8 weeks of CT, with 2 weeks of break. Then, the participants conducted 8 weeks of CT, which included RT combined with plyometric training (e.g., drop jump and continuous jump). All stages of training were designed by the linear strength training period theories, requiring participants to train twice for the first 4 weeks and three times for the second 4 weeks. The one-repetition maximum (1RM) of squat, isometric mid-thigh pull (IMTP), counter-movement jump (CMJ), squat jump (SJ), pre-stretch augmentation percentage (PSAP), and reaction strength index (RSI) were assessed before and after both RT and CT training. One-way repeated-measure ANOVA models revealed that the 1RM of squat was significantly improved (p < 0.001) after RT as compared to pre-RT. No significant improvement in IMTP (p = 0.055), CMJ (p = 0.194), SJ (p = 0.692), PSAP (p = 0.087), and RSI (p = 0.238) was not observed. After CT, 1RM of squat (p < 0.001), IMTP (p < 0.035), CMJ (p < 0.001), SJ (p < 0.008), RSI (p < 0.006) were significant improved as compared to pre-RT, post-RT and pre-CT, while significant improvements in PSAP were observed as compared to pre-RT (p = 0.003) and pre-CT (p = 0.027), but not to post-RT (p = 0.156). This pilot study showed the promise of CT following RT to improve lower-limb strength and power in elite female modern pentathlon athletes. The findings are worthwhile to be confirmed in future studies with larger sample size and randomized design. (shrink)

This paper investigates the stabilization and trajectory tracking problem of wheeled mobile robot with a ceiling-mounted camera in complex environment. First, an adaptive visual servoing controller is proposed based on the uncalibrated kinematic model due to the complex operation environment. Then, an adaptive controller is derived to provide a solution of uncertain dynamic control for a wheeled mobile robot subject to parametric uncertainties. Furthermore, the proposed controllers can be applied to a more general situation where the parallelism requirement between the (...) image plane and operation plane is no more needed. The overparameterization of regressor matrices is avoided by exploring the structure of the camera-robot system, and thus, the computational complexity of the controller can be simplified. The Lyapunov method is employed to testify the stability of a closed-loop system. Finally, simulation results are presented to demonstrate the performance of the suggested control. (shrink)

Both corruption and subjective wellbeing are of concern to academics and governments. Although some evidence suggests that corruption deteriorates subjective wellbeing, the relationship between perception of official corruption and subjective wellbeing is still unknown. This study aims to examine the link between perceived official corruption and subjective wellbeing in the context of China and whether satisfaction with government performance has a mediating effect in the process. Based on data from China General Social Survey, a structural equation model was used to (...) test the hypotheses. The results of 3,033 Chinese respondents suggest that perception of official corruption is negatively related to subjective wellbeing, and satisfaction with government performance plays a mediating role in the relationship between perception of official corruption and subjective wellbeing. (shrink)

本书饱蘸着我军军魂所赋予我们的激情,与战友们一起追忆我军以往的辉煌历程,讴歌我军当今的伟大壮举,憧憬我军未来的丰功伟绩,漫议军人的理想、信仰、道德、信念、气节、情操、奉献、价值。.

Ben shu nei rong bao kuo:zhong hua min zu dao de sheng huo zong lun,Yuan gu zhi zhan guo dao de sheng huo de ji ben zhuang kuang,Dao de guan xi ji wu zhong ji ben lun chang de que li,Li yi gui fan he li wen hua de xing qi yu hong yang,De hua tian xia yu dao de jiao yu zhi kai zhan deng.

This study proposes a moderated mediation model based on deontic justice theory to investigate the impact of unethical peer behavior on employee performance. Data were collected in China through two survey studies, with two measurement points in each study. The data in study 1 were obtained from 271 employees of 17 firms, and the data in study 2 were collected from 225 employees of 9 firms. Confirmatory factor analysis was conducted to confirm the factorial validity of the measures employed in (...) the studies. The hypothesized moderated mediation model was tested using hierarchical multiple regression analysis. The results show that employee deontic justice mediates the negative relationship between unethical peer behavior and employee performance, and a stronger ethical climate tends to strengthen this indirect relationship. This study extends previous research by introducing deontic justice theory to explore the unconscious process that relates unethical peer behavior to employee performance. The theoretical and practical implications of this paper are discussed. (shrink)

Individuals vary in perceptual accuracy when categorising facial expressions, yet it is unclear how these individual differences in non-clinical population are related to cognitive processing stages at facial information acquisition and interpretation. We tested 104 healthy adults in a facial expression categorisation task, and correlated their categorisation accuracy with face-viewing gaze allocation and personal traits assessed with Autism Quotient, anxiety inventory and Self-Monitoring Scale. The gaze allocation had limited but emotion-specific impact on categorising expressions. Specifically, longer gaze at the eyes (...) and nose regions were coupled with more accurate categorisation of disgust and sad expressions, respectively. Regarding trait measurements, higher autistic score was coupled with better recognition of sad but worse recognition of anger expressions, and contributed to categorisation bias towards sad expressions; whereas higher anxiety level was associated with greater categorisation accuracy across all expressions and with increased tendency of gazing at the nose region. It seems that both anxiety and autistic-like traits were associated with individual variation in expression categorisation, but this association is not necessarily mediated by variation in gaze allocation at expression-specific local facial regions. The results suggest that both facial information acquisition and interpretation capabilities contribute to individual differences in expression categorisation within non-clinical populations. (shrink)

Previous research has established that people can implicitly learn chunks, which do not require a memory buffer to process. The present study explores the implicit learning of nonlocal dependencies generated by higher than finite-state grammars, specifically, Chinese tonal retrogrades and inversions , which do require buffers . People were asked to listen to and memorize artificial poetry instantiating one of the two grammars; after this training phase, people were informed of the existence of rules and asked to classify new poems, (...) while providing attributions of the basis of their judgments. People acquired unconscious structural knowledge of both tonal retrogrades and inversions. Moreover, inversions were implicitly learnt more easily than retrogrades constraining the nature of the memory buffer in computational models of implicit learning. (shrink)

Previous research has shown that people can implicitly acquire mappings between word forms and literal meanings . We argue, from the metaphor-representation and embodiment perspectives, that people can unconsciously establish mappings between word forms and not only literal but also metaphorical meanings. Using Williams’ paradigm, we found that transfer of form-meaning connections from a concrete domain to an abstract domain was achieved in a metaphor-consistent way without awareness. Our results support the view that unconscious knowledge can be flexibly deployed in (...) an abstract way not previously explored in the implicit learning literature. (shrink)

In recent years, the analysis of legal judgments and the prediction of outcomes based on case factual descriptions have become hot research topics in the field of judiciary. Among them, the task of charge prediction aims to predict the applicable charges of a judicial case based on its factual description, making it an important research area in the intelligent judiciary. While significant progress has been made in machine learning and deep learning, traditional methods are limited to handling data in Euclidean (...) space and cannot effectively capture the semantic information in the text. To overcome the limitations of traditional learning approaches, many studies have started exploring the use of graphs to represent rich relationships between entities in text and employing graph convolutional neural networks to learn text representations. In this paper, we propose a charge prediction method based on graph convolutional neural networks. By constructing a similarity graph between cases and utilizing graph convolutional neural networks to learn case feature representations, we can better capture the relational information between cases and improve the accuracy of charge prediction. Experimental results on multiple benchmark datasets demonstrate that our proposed model outperforms traditional methods in charge prediction tasks. (shrink)

This study examined the correlation between depressive symptoms, interpersonal sensitivity, and social support before and during the COVID-19 pandemic and verified causal relationships among them. The study used Social Support Scale and Symptom Self-Rating Scale to investigate relevant variables. A total of 1,414 employees from company were recruited for this longitudinal study, which a follow up study was conducted on the same group of participants 1 year later. Paired sample t-test results showed that significant differences were only found in social (...) support, not in depressive symptoms or interpersonal sensitivity. The results of correlation analysis showed that social support, depressive symptoms, and interpersonal sensitivity were significantly correlated between wave 1 and wave 2. The cross-lag autoregressive pathway showed that employees’ social support level, depressive symptoms, and interpersonal sensitivity all showed moderate stability. Crossing paths showed that wave 1 social support could significantly predict wave 2 depressive symptoms and wave 2 interpersonal sensitivity. Wave 1 depressive symptoms could significantly predict wave 2 social support, while wave 1 interpersonal sensitivity could not predict wave 2 social support. Social support can be considered as a protective factor against mental health problems. (shrink)

As an essential direction for developing inclusive finance, digital financial inclusion breaks through the time and space constraints of inclusive finance development and has extensive connections between different regions. However, no research has modelled the network connections and the role and position of different digital financial inclusion development regions. This study constructed the spatial association network of China’s digital financial inclusion development and used the network analysis method and the quadratic assignment procedure method to study the structural and locational properties (...) and the influencing factors of the network. We found that although the network had a relatively low density, its connectivity and stability were excellent, and the network structure is not hierarchical; the centrality of some rapidly developing central and western provinces was greater than that of some developed eastern provinces; developed eastern provinces played a net spillover role, driving the development of digital financial inclusion in central and western provinces; and the spatial association was affected by the development level of the PC Internet and economy, the industrial structure, and the spatial adjacency. This study enriches the research on digital financial inclusion and provides a scientific basis for the formulation and implementation of policies to promote the further development of digital financial inclusion. (shrink)

This paper focuses on the diaspora Chinese community in Limerick – an Irish county town in the southwest of the Republic of Ireland – and examines how Chinese parents have responded to the education policy shift resulting from the 2017 Irish foreign language strategy, which added Chinese to the official educational curriculum. A semi-structured group interview was conducted with four Chinese-speaking parents. Analysis of the data revealed that identity preservation and maintaining bonds with extended family are the predominant expressed reasons (...) for parents to cultivate their children’s Chinese proficiency. English and Irish are prioritized over Chinese. Though the parents hold positive attitudes towards the inclusion of Chinese as a school subject in Ireland, they are disempowered from taking advantage of participating in the implementation of this national language shift due to a lack of access to social, cultural, and economic capital. Analysed through a Bourdieuian lens, the findings further expose the limitations or constraints placed on family language policy and the discrepancy between macro- and micro-level language policies. The paper is intended to shed light on education, social justice, and equality, bridging the gap between micro-level family language practices and macro-level language policy. (shrink)

An integrated hog futures price forecasting model based on whale optimization algorithm, LightGBM, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise is proposed to overcome the limitations of a single machine learning model with low prediction accuracy and insufficient model stability. The simulation process begins with a grey correlation analysis of the hog futures price index system in order to identify influencing factors; after that, the WOA-LightGBM model is developed, and the WOA algorithm is used to optimize the LightGBM (...) model parameters; and, finally, the residual sequence is decomposed and corrected by using the CEEMDAN method to build a combined WOA-LightGBM-CEEMDAN model. Furthermore, it is used for comparison experiments to check the validity of the model by using data from CSI 300 stock index futures. Based on all experimental results, the proposed combined model shows the highest prediction accuracy, surpassing the comparative model. The model proposed in this study is accurate enough to meet the forecasting accuracy requirements and provides an effective method for forecasting future prices. (shrink)

In recent years, China has increasingly emphasized art education, particularly dance, for its therapeutic benefits and cultural significance. Dance Movement Therapy (DMT), which originated in the U.S. in the 1960s, integrates emotional and physical health through dance. This interdisciplinary approach combines art, psychology, and exercise, showing effectiveness in treating psychological disorders and enhancing well-being. Integrating DMT into Chinese higher education could improve mental health outcomes for students by offering an engaging, participatory alternative to traditional counseling methods.

Adolescents have gradually become a vital group of interacting with social media recommendation algorithms. Although numerous studies have been conducted to investigate negative reactions that the dark side of recommendation algorithms brings to social media users, little is known about the resistance intention and behavior based on their agency in the daily process of encountering algorithms. Focusing on the concept of algorithm resistance, this study used a two-path model to investigate the algorithmic resistance of rural Chinese adolescents in their daily (...) use of short video apps. The findings revealed that the perceived threat to freedom, algorithmic literacy, and peer influence were positively associated with the resistance willingness and intention; while the independent psychology on algorithmic recommendations significantly weakened resistance willingness and intention. Furthermore, this study verified the mediating role of resistance willingness and intention between the above independent variables and resistance behavior. Additionally, the positive impact of resistance willingness on resistance intention was confirmed. In conclusion, this study offers a comprehensive approach to further understanding adolescents’ algorithmic resistance awareness and behavior by combining psychological factors, personal competency, and interpersonal influences, as well as two types of resistance reactions. (shrink)

There exists a widely accepted opinion in Chan/Zen 禪 studies that Chan enlightenment is a mysterium ineffabile, impenetrable by human intellect. Reviewing the debate between Hu Shi 胡適 and D. T. Suzuki over Chan enlightenment and accounts of testimony by Chan masters and practitioners in history, this essay argues that Chan enlightenment can be understood rationally and intellectually. By analyzing the time-honored Chan practices that have led to enlightenment, it seeks to understand the mystery as an extraordinary mental condition in (...) which an enlightened person temporarily blocks thinking in terms of language, consciousness, and intellect, and experiences a momentary “erasing” of what has been impressed on the psyche since birth. (shrink)

ObjectiveThis study aims to investigate anxiety and its influencing factors in patients with drug-induced liver injury (DILI).Materials and MethodsNinety-four patients with DILI were enrolled and evaluated with a self-rating anxiety scale (SAS). According to the anxiety score, they were divided into four groups: the non-anxiety, mild anxiety, moderate anxiety, or severe anxiety groups, and the scores were analyzed based on demographic and biochemical indicators.ResultsOf the 94 patients with DILI, 63 did not have anxiety and 31 had anxiety (32.9%), of which (...) 27 had mild, 3 had moderate, and 1 had severe anxiety. There were no statistically significant differences in gender, age, occupation, and level of education between the groups (F= 1.42,H= 2.361,H= 6.751,H= 1.796, andP> 0.05); anxiety score and degree of anxiety between the types of drugs that led to the liver injury (H= 0.812,H= 1.712, andP> 0.05); anxiety score between the different degrees of liver injury (H= 2.836,H= 4.957,P> 0.05); or length of hospital stay or prognosis between the degrees of anxiety (F= 1.487,H= 0.761,P> 0.05). However, there were statistically significant differences in the degree of anxiety between different degree and types of liver injury (H= 7.981,H= 8.208,P< 0.05).ConclusionPatients with DILI may have anxiety, especially mild anxiety. The occurrence of anxiety in patients with DILI is not related to gender, age, occupation, or level of education but may be related to the degree and type of liver injury. Anxiety has no impact on the length of stay in hospital or the prognosis of the DILI. These findings may contribute to the development of management strategies for patients with DILI. (shrink)

The development of artificial intelligence technology has brought changes to various industries. Under the concept of green sustainable development, how to use the progress of science and technology to implement low-carbon strategies is a problem that every enterprise should consider. Aiming at the problem of picking up goods in logistics industry, this paper proposed a secondary task assignment theory for multiple unmanned aerial vehicle based on green scheduling. The theory greatly improves the utilization rate of unmanned aerial vehicle and reduces (...) the energy consumption. We analyzed the advantages and disadvantages of local optimal algorithm and global optimal algorithm in time and energy consumption. Through repeated experiments in different ranges, we have well verified the high efficiency and general applicability of this theory, which can provide theoretical and practical implications for logistics enterprises using UAV to achieve low-carbon sustainable development in the future. (shrink)

Physarum polycephalum, a unicellular and multiheaded slime mould, can form highly efficient networks connecting separated food sources during the process of foraging. These adaptive networks exhibit a unique characteristic in that they are optimized without the control of a central consciousness. Inspired by this phenomenon, we present an efficient exploration and navigation strategy for a swarm of robots, which exploits cooperation and self-organisation to overcome the limited abilities of the individual robots. The task faced by the robots consists in the (...) exploration of an unknown environment in order to find a path between two distant target areas. For the proposed algorithm, we experimentally present robustness tests and obstacle tests conducted to analyse the performance of our algorithm and compare the proposed algorithm with other swarm robot foraging algorithms that also focus on the path formation task. This work has certain significance for the research of swarm robots and Physarum polycephalum. For the research of swarm robotics, our algorithm not only can lead multirobot as a whole to overcome the limitations of very simple individual agents but also can offer better performance in terms of search efficiency and success rate. For the research of Physarum polycephalum, this work is the first one combining swarm robots and Physarum polycephalum. It also reveals the potential of the Physarum polycephalum foraging principle in multirobot systems. (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)

Thoughts of Ghazālī on philosophy of politics and its relevance to Malaysia.

Al-Ghazali's political ideas in the context of politics in Malaysia.