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This research explores the differential effects of pixel-level and object-level visual complexity in firm-generated content (FGC) on consumer engagement in terms of the number of likes and shares, and further investigates the moderating role of image brightness.

Drawing on a deep learning analysis of 85,975 images on a social media platform in China, this study investigates visual complexity in FGC.

The results indicate that pixel-level complexity increases both the number of likes and shares. Object-level complexity has a U-shaped relationship with the number of likes, while it has an inverted U-shaped relationship with the number of shares. Moreover, image brightness mitigates the effect of pixel-level complexity on likes but amplifies the effect on shares; contrarily, it amplifies the effect of object-level complexity on likes, while mitigating its effect on shares.

Although images play a critical role in FGC, visual data analytics has rarely been used in social media research. This study identified two types of visual complexity and investigated their differential effects. We discuss how the processing of information embedded in visual content influences consumer engagement. The findings enrich the literature on social media and visual marketing.

The purpose of this paper is to study the cavitation erosion stages of AA5083 by electrochemical noise (EN).

EN technology including noise resistance and fast Fourier transform were used to characterize the electrochemical process during the cavitation erosion process.

AA5083 suffers from uniform corrosion during the cavitation erosion process. The whole cavitation erosion process can be divided into three stages: incubation stage, acceleration stage and steady-state stage. EN signals showed obvious differences in different stages of cavitation erosion.

EN technique is a suitable method that can be used to study cavitation erosion mechanism and identify cavitation erosion stages.

The aim of this paper is to construct a device that simulated the seawater splash zone, dynamic waterline zone (splash zone) and full immersion zone. Localized corrosion of 2A12 aluminum alloy long-scale specimen was studied.

Corrosion morphologies before and after the corrosion product removal were used to identify corrosion intensity at full seawater immersion zone, dynamic waterline zone (splash zone) and atmospheric zone. The average depth and diameter of corrosion pits in the three zones were evaluated by three-dimensional optical microscopy. The impact of wetting time of the atmospheric zone on the localized corrosion was investigated.

Corrosion pits were observed on the surface on day 4 for the wetted atmospheric zone (Case 1), and on the surface on day 8 for the alternant wet/dry atmospheric zone (Case 2). The corrosion product layer on the surface for Case 1 was partially broken down while the layer on the surface for Case 2 was intact. Average pitting depth and pitting diameters for Case 1 were more serious than that for Case 2.

The above findings revealed that the humidity of the atmospheric zone had great impact on the localized corrosion of aluminum alloy at the seawater splash zone.

This study aims to investigate the safety and liability of autonomous vehicles (AVs), and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs.

The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations. The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs, respectively, as well as accommodating the heterogeneity issue simultaneously.

The findings show that day, location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability.

The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability.

This paper aims to characterize the surface film formed on Alloys 800 and 690 in chloride and thiosulfate-containing solution at 300°C.

Alloy 800 and 690 were immersed in chloride and thiosulfate-containing solution at 300°C up to five days, and then the surface film was analyzed by scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM) and energy dispersive X-ray spectrometers (EDX).

Through static immersion experiments in a high-temperature and high-pressure water environment, the alloy samples covered by surface film after five days of immersion were obtained. The morphology of the surface film was characterized at both horizontal and cross-sectional scales using SEM and focused ion beam-TEM techniques. It was observed that due to the influence of the quartz lining, the surface film primarily exhibited a bilayered structure. The first layer contained a significant amount of SiO₂, with a higher content of metal hydroxides compared to metal oxides. The second layer was predominantly composed of Fe, Ni and Cr, with a higher content of metal oxides compared to metal hydroxides.

The results showed that the materials of the lining of the autoclave could significantly influence the film composition of the tested material, which should be paid attention when analyzing the corrosion mechanism at high temperature.

The purpose of this paper is to study the corrosion behavior of 316L stainless steel under cavitation condition in simulated seawater.

Electrochemical impedance spectroscopy and electrochemical noise analyses are used to characterize the electrochemical process during the cavitation erosion process.

Because of good corrosion resistance of this material, mechanical damage is the main cause of cavitation erosion. The alloy surface is in active dissolution state during the cavitation erosion process, and the corrosion rate is accelerated as time prolongs which is verified by electrochemical results.

Electrochemical noise is an effective way to study the corrosion under cavitation erosion process.

Individuals’ driving behavior data are becoming available widely through Global Positioning System devices and on-board diagnostic systems. The incoming data can be sampled at rates ranging from one Hertz (or even lower) to hundreds of Hertz. Failing to capture substantial changes in vehicle movements over time by “undersampling” can cause loss of information and misinterpretations of the data, but “oversampling” can waste storage and processing resources. The purpose of this study is to empirically explore how micro-driving decisions to maintain speed, accelerate or decelerate, can be best captured, without substantial loss of information.

This study creates a set of indicators to quantify the magnitude of information loss (MIL). Each indicator is calculated as a percentage to index the extent of information loss (EIL) in different situations. An overall information loss index named EIL is created to combine the MIL indicators. Data from a driving simulator study collected at 20 Hertz are analyzed (N = 718,481 data points from 35,924 s of driving tests). The study quantifies the relationship between information loss indicators and sampling rates.

The results show that marginally more information is lost as data are sampled down from 20 to 0.5 Hz, but the relationship is not linear. With four indicators of MILs, the overall EIL is 3.85 per cent for 1-Hz sampling rate driving behavior data. If sampling rates are higher than 2 Hz, all MILs are under 5 per cent for importation loss.

This study contributes by developing a framework for quantifying the relationship between sampling rates, and information loss and depending on the objective of their study, researchers can choose the appropriate sampling rate necessary to get the right amount of accuracy.

The rapid development of Intelligent and Connected Vehicles (ICVs) has boomed a new round of global technological and industrial revolution in recent decades. The Technology Roadmap of Intelligent and Connected Vehicles (2020) comprehensively analyzes the technical architecture, research status and future trends of ICVs. The methodology that supports the roadmap should get studied.

This paper interprets the roadmap from the aspects of strategic significance, technical content and characteristics of the roadmap, and evaluates the impact of the roadmap on researchers, industries and international strategies.

The technical architecture of ICVs as the “three rows and two columns” structure is studied, the methodology that supported the roadmap is explained with a case study and the influence of key technologies with proposed development routes is analyzed.

This paper could help researchers understand both thoughts and methodologies behind the technology roadmap of ICVs.

This article examines the influence of Daoist nothingness on leadership in growing Chinese small and medium enterprises (SMEs). Daoism is based on a “letting-go” approach through maintaining inherent openness, which challenges goal-oriented and hierarchical approaches typical of Western and Confucian leadership theories. This facilitates the cross-fertilization of ideas related to the effective management of smaller firms.

This study focuses on SME leaders in a group of 12 growing SMEs in the Shanghai logistics industry in China. Narrative and semi-structured interviews explored emerging aspects beyond the established model of leadership associated with reputation-building. This led to in-depth, thick descriptions, broadening our understanding of leadership and reputation-building.

SME leaders follow nothingness by continuously adopting a letting-go approach which spontaneously fosters reputation-building. By maintaining inherent openness, nothingness functions as an enabling principle that mobilizes multi-beings leading to reputation-building in unintended ways.

A greater plurality of empirical and methodological contexts in Western and non-Western countries helps to understand the dynamics and intersection of Daoist nothingness, leadership and reputation-building.

SME leaders recounted how they discursively practised nothingness for extended periods in their everyday practice. The study shows the significance of nothingness for SME leaders who aspire to grow their businesses by reputation-building among salient stakeholders.

Daoist nothingness provides insights into the distinctive approach of Chinese SME leaders and their relationships with local and distant stakeholders. By engaging in active non-action they relax pre-determined intentions and immerse themselves in the process of leading, where the connections between goals and processes are automatically animated. Such an approach differs from the top-down and goal-oriented approach to leadership adopted in many Western SMEs.

This paper makes two theoretical contributions. First, it indicates the powerful influence of Daoist nothingness on leadership by drawing on the broader context of entrepreneurship in Chinese SMEs. Secondly, it enriches existing concepts such as reputation by endowment and reputation borrowing by demonstrating how Daoist nothingness silently fosters both local reputation and generalized reputation.

The purpose of this work is to bridge FL and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. FL is an emerging collaborative machine learning technique that trains a model across multiple devices or servers holding private data samples without exchanging their data. The locally trained results are aggregated by a centralized server in a privacy-preserving way. However, there is an assumption where the centralized server is trustworthy, which is impractical. Fortunately, blockchain technology has opened a new era of data exchange among trustless strangers because of its decentralized architecture and cryptography-supported techniques.

In this study, the author proposes a novel design of a smart agent inspired by the smart contract concept. Specifically, based on the proposed smart agent, a fully decentralized, privacy-preserving and fair deep learning blockchain-FL framework is designed, where the agent network is consistent with the blockchain network and each smart agent is a participant in the FL task. During the whole training process, both the data and the model are not at the risk of leakage.

A demonstration of the proposed architecture is designed to train a neural network. Finally, the implementation of the proposed architecture is conducted in the Ethereum development, showing the effectiveness and applicability of the design.

The author aims to investigate the feasibility and practicality of linking the three areas together, namely, multi-agent system, FL and blockchain. A blockchain-FL framework, which is based on a smart agent system, has been proposed. The author has made several contributions to the state-of-the-art. First of all, a concrete design of a smart agent model is proposed, inspired by the smart contract concept in blockchain. The smart agent is autonomous and is able to disseminate, verify the information and execute the supported protocols. Based on the proposed smart agent model, a new architecture composed by these agents is formed, which is a blockchain network. Then, a fully decentralized, privacy-preserving and smart agent blockchain-FL framework has been proposed, where a smart agent acts as both a peer in a blockchain network and a participant in a FL task at the same time. Finally, a demonstration to train an artificial neural network is implemented to prove the effectiveness of the proposed framework.