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New venture status is the most prominent feature of entrepreneurial startups, but its performance implications have remained under-studied. This study aims to bridge this knowledge void and offer precise guidelines for startup managers in boosting performance.

The study develops and tests a multi-perspective model on the linkage between new venture status and firm performance by integrating I/O economics, resource-based view and dynamic capability perspective. The arguments from the first two perspectives point to an adverse effect of new venture status, which is contingent, respectively, on business differentiation and resource endowments. The third perspective grounds a positive relationship between new venture status and performance, which is more pronounced for firms with weaker dynamic capabilities.

Quantitative evidence from a sample of new and established firms in China shows that the direct effect of new venture status is negative but insignificant. Neither business differentiation nor dynamic capabilities moderate the relationship. Low resource endowments, however, reinforce the negative influence of new venture status. New venture status, thus, shapes firm performance through resource scarcity from resource-based view rather than competitive vulnerability from I/O economics or strategic flexibility from dynamic capability perspective.

Newness and new venture performance have both been extensively examined in literature. But the relationship between them has remained largely omitted. The multi-perspective model and the findings in this study help clarify the confusion as to whether newness is good or bad in the context of an emerging market and reveals the subtle mechanism the effect of newness unfolds.

Traditional laboratory measurements of soil water diffusivity (D) and soil water retention curve (SWRC) are always time-consuming and labor-intensive. Therefore, this paper aims to present a simple and robust test method for determining D and SWRC without reducing accuracy.

In this study, a D model of unsaturated soil was established based on Gardner–Russo model and then a combination of Gardner–Russo model with one-dimensional horizontal absorption method to obtain n and a parameters of Gardner–Russo model. One-dimensional horizontal absorption experiments on loam, silt loam and sandy clay loam were conducted to obtain the relationships between measured infiltration rate and cumulative infiltration with wetting front distance. Based on the obtained relationships, the measured infiltration data from the one-dimensional horizontal absorption tests were used to calculate n and a parameters and further constructing D and SWRC.

Both the calculated D and SWRC inversed from the infiltration data were in good agreement with the measured ones that obtained from the traditional horizontal absorption method and the centrifuge method, respectively. Error analysis indicated that only the infiltration data are enough to reliably synchronously determine D and SWRC.

A simple and robust method is proposed for synchronous determination of soil water diffusivity and water retention curve.

The purpose of this paper is to seek an efficient method to tackle the energy provision problem for smart grid with sparse constraints and distributed energy and storage devices.

A complex smart grid is first studied, in which sparse constraints and the complex make-up of different energy consumption due to the integration of distributed energy and storage devices and the emergence of multisellers are discussed. Then, a real-time pricing scheme is formulated to tackle the demand response based on sparse bilevel programming. And then, a bilevel genetic algorithm (BGA) is further designed. Finally, simulations are conducted to evaluate the performance of the proposed approach.

The considered situation is widespread in practice, and meanwhile, the other cases including traditional model without the sparse constraints can be seen as its extensions. The BGA based on sparse bilevel programming has advantages of “no need of convexity of the model.” Moreover, it is feasible without the need to disclose the private information to others; therefore, privacies are protected and system scalability is kept. Simulation results validate the proposed approach has good performance in maximizing social welfare and balancing system energy distribution.

In this paper, the authors consider the sparse constraints due to the fact that each user can only choose limited utility companies per time slot. In reality, there exist some other sparse cases, which deserve further study in the future.

To the best of the authors’ knowledge, this is one of the very first studies to address pricing problems for the smart grid with consideration of sparse constraints and integration of distributed energy and storage devices.

The purpose of this paper is to study the effect of current-carrying condition on the electrochemical process and atmospheric corrosion behavior of the commercial aluminum alloys.

Potentiodynamic polarization tests were performed to study the electrochemical process of the aluminum alloys. Salt spray tests and weight loss tests were carried out to study the atmospheric corrosion behavior. The corrosion morphology of the alloys was observed, and the products were analyzed.

The corrosion process of four aluminum alloys was accelerated in the current-carrying condition. Moreover, the acceleration effect on A2024 and A7075 was much stronger than that on A1050 and A5052. The main factors would be the differences in microstructure and corrosion resistance between these alloys. As the carried current increased, the corrosion rate and corrosion current density of the aluminum alloys gradually increased, with the protection of the corrosion product film decreasing linearly.

This is a recent study on the corrosion behavior of conductors under current-carrying condition, which truly understands the corrosion status of power grid materials. Relevant results provide support for the corrosion protection and safe service of aluminum alloy in power systems.

The purpose of this paper is to classify different conditions of the rolling element bearing (REB) using vibration signals acquired from a customized bearing test rig.

An effort has been made to develop health index (HI) based on singular values of the statistical features to classify different conditions of the REB. The vibration signals from the normal bearing (N), bearing with defect on ball (B), bearing with defect on inner race (IR) and bearing with defect on outer race (OR) have been acquired from a customized bearing test rig under variable load and speed conditions. These signals were subjected to “modified kurtosis hybrid thresholding rule” (MKHTR)-based denoising. The denoised signals were decomposed using discrete wavelet transform. A total of 17 statistical features have been extracted from the wavelet coefficients of the decomposed signal.

Singular values of the statistical features can be effectively used for REB classification.

REB are critical components of rotary machinery right across the industrial sectors. It is a well-known fact that critical bearing failures causes major breakdowns resulting in untold and most expensive downtimes that should be avoided at all costs. Hence, intelligently based bearing failure diagnosis and prognosis should be an integral part of the asset maintenance and management activity in any industry using rotary machines.

It is found that singular values of the statistical features exhibit a constant value and accordingly can be assigned to each type of bearing fault and can be used for fault characterization in practical applications. The effectiveness of this index has been established by applying this to data from Case Western Reserve University data base which is a standard bench mark data for this application. HIs minimizes the computation time when compared to fault diagnosis using soft computing techniques.

The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage, lack of effective features, and small sample sizes, resulting in low fault identification accuracy and slow efficiency. The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.

First, to address the impact of background noise on the accuracy of anomaly signals, the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD) method is used to eliminate strong noise in pipeline signals. Secondly, to address the strong data dependency and loss of local features in the Swin Transformer network, a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed. This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities. Thirdly, to address the sparsity and imbalance of anomaly samples, the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.

In the pipeline anomaly audio and environmental datasets such as ESC-50, the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods. Additionally, the model achieved 98.7% accuracy on the preprocessed anomaly audio dataset and 99.0% on the ESC-50 dataset.

This paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model, addressing noise interference and low accuracy issues in pipeline anomaly detection, and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.

For undergraduate and graduate students in universities, social media are playing an important role in their study/research because a large amount of academic information has been accumulated on social media. Indeed, social media are complementing university libraries. Given that intelligent recommender systems have been widely implemented on social media, this paper aims to examine the adoption mechanism of intelligently recommended information by university students in their study/research.

Building upon the updated information system success model and herding theory, this study developed a research model to examine the determinants of recommended information adoption in mobile applications for social media. Data were collected through an online questionnaire and analyzed with partial least squares structural equation modelling.

The results suggest that herding belief is a valid second-order construct, comprising two first-order dimensions of imitating others and discounting their own information. Information quality, system quality and service quality directly impact satisfaction with the intelligent recommender system. Furthermore, satisfaction with the intelligent recommender system and herding belief directly impact recommended information adoption by university students in their study/research.

This study draws on the updated information system success model and incorporates herding belief as an extended component to investigate recommended information adoption, providing a new lens for understanding recommended information adoption by university students in their study/research.

The purpose of this paper is to facilitate understanding of how to convert free players to paid consumers in free-to-play games.

Drawing on the consumption value framework and affordance theory, the present study argues that in-game purchase behaviors are determined by multiple consumption values of in-game items. The perceptions of consumption values were influenced by game affordances. The model was tested, using data from an empirical survey with 2,006 free-to-play game players.

Monetary, enjoyment and social values of in-game items positively predict purchase behaviors in free-to-play games. Game fairness and balance of challenges and skills significantly influence perceived enjoyment value.

The findings of this study provide operable implications to facilitate in-game consumption. The model was tested within the context of free-to-play multiplayer online battle arena (MOBA) games; however, caution is advisable when generalizing the findings to other subgenre of games.

This study extended and thus validated the consumption value framework in the context of free-to-play MOBA games. This study explored the antecedents of consumption values from the perspective of game affordance.

To address the shortcomings of existing academic user information needs identification methods, such as low efficiency and high subjectivity, this study aims to propose an automated method of identifying online academic user information needs.

This study’s method consists of two main parts: the first is the automatic classification of academic user information needs based on the bidirectional encoder representations from transformers (BERT) model. The second is the key content extraction of academic user information needs based on the improved MDERank key phrase extraction (KPE) algorithm. Finally, the applicability and effectiveness of the method are verified by an example of identifying the information needs of academic users in the field of materials science.

Experimental results show that the BERT-based information needs classification model achieved the highest weighted average F1 score of 91.61%. The improved MDERank KPE algorithm achieves the highest F1 score of 61%. The empirical analysis results reveal that the information needs of the categories “methods,” “experimental phenomena” and “experimental materials” are relatively high in the materials science field.

This study provides a solution for automated identification of academic user information needs. It helps online academic resource platforms to better understand their users’ information needs, which in turn facilitates the platform’s academic resource organization and services.

Sustainable development is inseparable from rational and responsible use of resources and promotion of green entrepreneurship. The contemporary green development agenda encompasses climate, economic, technical, social, cultural, and political dimensions. International efforts to greening the global development are conducted by the major economies, including China as the world’s largest consumer of energy and the biggest emitter of greenhouse gases. China is aware of its environmental problems, as well as of its part of the overall responsibility for the accomplishment of the sustainable development goals. By means of the decarbonization efforts, the latter are integrated both into the national development agenda (the concept of ecological civilization) and China’s international initiatives (the greening narrative within the Belt and Road Initiative). Over the past decade, China has made a breakthrough on the way to promoting green entrepreneurship and greening of its development (better quality of air and water, renewable energy, electric vehicles, and organic farming). On the other hand, emissions remain high, agricultural land loses productivity, and freshwater resources degrade due to climate change. In conventional industries (oil, coal mining, and electric and thermal energy), decarbonization faces an array of impediments. In this chapter, the authors summarize fundamental provisions of China’s approach to building an ecological civilization and measures to reduce emissions and achieve the carbon neutrality status within the nearest decades. The analysis of obstacles to the decarbonization of the economy and possible prospects for the development of green entrepreneurship summarizes China’s practices for possible use in other countries.