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The neo-Kaleckian model follows the ideas of Marx, Keynes and Kalecki, that investment is a key influencing factor in the dynamics of the capitalist mode of production. Through the discussion of different forms of investment decision function, this paper constructs the analysis framework of wage-led and profit-led economic growth regimes.

The model has become an important theoretical paradigm for current Western heterodox economists regarding the research on the impact of functional income distribution on economic growth, and it has a very large impact on both theoretical and empirical research. Starting from Marx's reproduction theory, this article discusses the theoretical shortcomings of the neo-Kaleckian growth regime model.

This paper mainly focuses on three aspects: (1) the ideological legacy of “Smith's Dogma”; (2) neglecting the restrictions on income distribution from the organic composition of capital and the surplus value rate; (3) technological progress and the formation of a new long economic wave.

The authors believe that the neo-Kaleckian model unilaterally emphasizes the demand-side factors in the economy and, unconsciously or not, ignores the role of the supply-side, which makes it encounter certain limitations in explaining long-term growth. Even if some empirical conclusions are employed to bridge functional income distribution and technological progress, there is still a lack of a theoretical basis for accurately describing long-term economic changes using this model. In order to better promote high-quality economic development and accelerate the formation of a new pattern of economic development in which the domestic large-scale cycle is the mainstay and the domestic and international double cycles promote each other, the authors need to adopt a policy combination with the supply-side as the main and the demand-side as the supplement, and to work from both sides.

The purpose of this paper is to propose a new approach to retrieve similar questions in online health communities to improve the efficiency of health information retrieval and sharing.

This paper proposes a hybrid approach to combining domain knowledge similarity and topic similarity to retrieve similar questions in online health communities. The domain knowledge similarity can evaluate the domain distance between different questions. And the topic similarity measures questions’ relationship base on the extracted latent topics.

The experiment results show that the proposed method outperforms the baseline methods.

This method conquers the problem of word mismatch and considers the named entities included in questions, which most of existing studies did not.

The purpose of this paper is to examine the effects of servant leadership on employees' promotive voice behavior and prohibitive voice behavior by focusing on the mediating role of job engagement and the moderating role of proactive personality.

Time-lagged data were collected using a field survey research design. The participants included 216 employees and 23 supervisors in two commercial banks in China.

Perceived servant leadership was positively related to employees' promotive and prohibitive voice behavior, and these relationships were mediated by enhanced job engagement. In addition, employees' proactive personality amplified the relationship between perceived servant leadership and job engagement, and the mediating effect of job engagement on the relationship between perceived servant leadership and voice behavior.

This study enhances understanding of the mechanisms underlying the servant leadership – voice model by identifying the mediating role of job engagement. The results also demonstrate the moderating role of proactive personality in enhancing the effects of servant leadership. However, the survey design was not longitudinal, which limits the study's ability to confirm causality.

The findings reveal that servant leadership, employees' job engagement, and proactive personality can facilitate employees' promotive and prohibitive voice behavior.

This study addresses the unexplored mediating mechanism of the relationship between servant leadership and voice behavior, and offers new directions for servant leadership and voice research.

This review analyzes data from research articles published from 2010 to 2022 related to workplace ostracism which include theoretical or empirical practical implications. The primary motive of this review is to identify main themes of practical implications relevant to workplace ostracism.

In total, 86 research articles published in 56 journals were retrieved from six well-known management science databases, namely, Science Direct, Emerald Online, Springer Link, Taylor and Francis, Wiley and Sage. The affinity diagram was utilized to organize the practical implications of the studies into meaningful themes. Moreover, in order to prioritize the main themes, the Pareto diagram was utilized.

Eleven themes have been used to categorize the practical implications of the reviewed articles, demonstrating various human resource (HR) interventions for avoiding or limiting the feelings of ostracism at work. Specifically, they focus on training and development, culture, formal and informal meetings, interpersonal relationships, task interdependence, monitoring, trust and transparency, proper channel, job autonomy and individual characteristics.

While many systematic and traditional literature reviews have been undertaken in an attempt to thoroughly organize extant literature on various aspects of workplace ostracism, no study has addressed the main themes of practical implications vis-à-vis employees experiencing workplace ostracism. Moreover, the majority of them are apparently out of date (prior to 2019), and there is just one study undertaken up to 2020.

Climatic changes caused by greenhouse gases (GHGs) emissions are an urgent challenge for all regions around the globe while the livestock sector is an important source of GHGs emissions. The adoption of low-carbon manure treatment technology (LMTT) by farmers is emerging as an effective remedy to neutralize the carbon emissions of livestock. This paper aims to incorporate environmental literacy and social norms into the analysis framework, with the aim of exploring the impact of environmental literacy and social norms on farmers' adoption of LMTT and finally reduce GHGs emission and climate effects.

This research survey is conducted in Hebei, Henan and Hubei provinces of China. First, this research measures environmental literacy from environmental cognition, skill and responsibility and describes social norms from descriptive and imperative social norms. Second, this paper explores the influence of environmental literacy and social norms on the adoption of LMTT by farmers using the logit model. Third, Logit model's instrumental approach, i.e. IV-Logit, is applied to address the simultaneous biases between environmental skill and farmers’ LMTT adoption. Finally, the research used a moderating model to analyze feasible paths of environmental literacy and social norms that impact the adoption of LMTT by farmers.

The results showed that environmental literacy and social norms significantly and positively affect the adoption of LMTT by farmers. In particular, the effects of environmental literacy on the adoption of LMTT by farmers are mainly contributed by environmental skill and responsibility. The enhancement of social norms on the adoption of LMTT by farmers is mainly due to the leading role of imperative social norms. Meanwhile, if the endogeneity caused by the reverse effect between environmental skill and farmers’ LMTT adoption is dealt with, the role of environmental skill will be weakened. Additionally, LMTT technologies consist of energy and resource technologies. Compared to energy technology, social norms have a more substantial moderating effect on environmental literacy, affecting the adoption of farmer resource technology.

To the best of the authors’ knowledge, a novel attempt is made to examine the effects of environmental literacy and social norms on the adoption of LMTT by farmers, with the objective of identifying more effective factors to increase the intensity of LMTT adoption by farmers.

This study reviews the extent of application of artificial intelligence (AI) tools in the construction industry.

A thorough literature review (based on 165 articles) was conducted using Elsevier's Scopus due to its simplicity and as it encapsulates an extensive variety of databases to identify the literature related to the scope of the present study.

The following items were extracted: type of AI tools used, the major purpose of application, the geographical location where the study was conducted and the distribution of studies in terms of the journals they are published by. Based on the review results, the disciplines the AI tools have been used for were classified into eight major areas, such as geotechnical engineering, project management, energy, hydrology, environment and transportation, while construction materials and structural engineering. ANN has been a widely used tool, while the researchers have also used other AI tools, which shows efforts of exploring other tools for better modelling abilities. There is also clear evidence of that studies are now growing from applying a single AI tool to applying hybrid ones to create a comparison and showcase which tool provides a better result in an apple-to-apple scenario.

The findings can be used, not only by the researchers interested in the application of AI tools in construction, but also by the industry practitioners, who are keen to further understand and explore the applications of AI tools in the field.

There are no studies to date which serves as the center point to learn about the different AI tools available and their level of application in different fields of AEC. The study sheds light on various studies, which have used AI in hybrid/evolutionary systems to develop effective and accurate predictive models, to offer researchers and model developers more tools to choose from.

The purpose of this paper aims to model interaction relationship of traffic agents for motion prediction, which is critical for autonomous driving. It is obvious that traffic agents’ trajectories are influenced by physical lane rules and agents’ social interactions.

In this paper, the authors propose the social relation and physical lane aggregator for multimodal motion prediction, where the social relations of agents are mainly captured with graph convolutional networks and self-attention mechanism and then fused with the physical lane via the self-attention mechanism.

The proposed methods are evaluated on the Waymo Open Motion Dataset, and the results show the effectiveness of the proposed two feature aggregation modules for trajectory prediction.

This paper proposes a new design method to extract traffic interactions, and the attention mechanism is used in each part of the model to extract and fuse different relational features, which is different from other methods and improves the accuracy of the LSTM-based trajectory prediction method.

The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs’ mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell.

In this paper, the authors use behavioral pattern perception layer, multi-aspect perception layer and memory network enhancement layer to adaptively explore the mind of a digital-self and generate the mental representation of a digital-self from three aspects including external behavior, multi-aspect factors of the mind and memory units. The authors use the mental representations to assist behavioral decision-making.

The evaluation in real-world open data sets shows that the proposed method can model the mind and verify the influence of the mind on the behavioral decisions, and its performance is better than the universal baseline methods for modeling user interest.

In general, the authors use the behaviors of the digital-self to mine and explore its mind, which is used to assist the digital-self to make decisions and promote the transaction in the network of CrowdIntell. This work is one of the early attempts, which uses neural networks to model the mental representation of digital-self.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.