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Security breaches have been arising issues that cast a large amount of financial losses and social problems to society and people. Little is known about how social media could be used a surveillance tool to track messages related to security breaches. This paper aims to fill the gap by proposing a framework in studying the social media surveillance on security breaches along with an empirical study to shed light on public attitudes and concerns.

In this study, the authors propose a framework for real-time monitoring of public perception to security breach events using social media metadata. Then, an empirical study was conducted on a sample of 1,13,340 related tweets collected in August 2015 on Twitter. By text mining a large number of unstructured, real-time information, the authors extracted topics, opinions and knowledge about security breaches from the general public. The time series analysis suggests significant trends for multiple topics and the results from sentiment analysis show a significant difference among topics.

The study confirms that social media monitoring provides a supplementary tool for the traditional surveys which are costly and time-consuming to track security breaches. Sentiment score and impact factors are good predictors of real-time public opinions and attitudes to security breaches. Unusual patterns/events of security breaches can be detected in the early stage, which could prevent further destruction by raising public awareness.

The sample data were collected from a short period of time on Twitter. Future study could extend the research to a longer period of time or expand key words search to observe the sentiment trend, especially before and after large security breaches, and to track various topics across time.

The findings could be useful to inform public policy and guide companies responding to consumer security breaches in shaping public perception.

This study is the first of its kind to undertake the analysis of social media (Twitter) content and sentiment on public perception to security breaches.

Uncertain level discount (ULD) is a type of promotion combining regular discount (RD) with uncertainty. The purpose of this paper is to explore the impact of ULD on consumers’ perceived quality compared with RD and to identify the relevant influencing mechanism and boundary for the effectiveness of ULD.

Three online experiments were conducted with 445 participants from China. First, experiment 1 compares the attractiveness of ULD and RD. Second, experiment 2 evaluates the impacts of ULD and RD on consumers’ perceived quality and clarifies the mechanism in this process. Finally, experiment 3 examines the moderating effect of product knowledge.

ULD has the same level of attractiveness as RD with equivalent expected discount value for consumers. Besides, consumers in ULD give higher ratings to product quality compared with those in RD, and the lower diagnosticity of price cues in ULD underlies the differential effects of ULD vs RD. Furthermore, product knowledge moderates the relationship between the two promotions and perceived quality.

The findings provide valuable guidance for managers to conduct promotional campaigns. ULD is an effective promotion to attract consumers to purchase with keeping consumers’ perceived quality high, and such effectiveness will rise for products that consumers are unfamiliar with. Managers can make rational use of ULD to achieve positive promotion results in both the short and long term.

Few studies pay attention to the long-term effects of the uncertain promotion. This research profoundly investigates the impact of ULD on perceived quality, which complements existing studies from a more integrated perspective that combines short- and long-term effects. Also, this research identifies the mechanism based on the cue diagnosticity theory and puts forward a new explanation for positive uncertainty in uncertain promotions. Finally, this research applies the impact of product knowledge on information process strategies into the uncertain promotion, which clarifies the utility boundary of ULD from a new perspective and offers a more comprehensive understanding for this promotion.

This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.

In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.

The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.

In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

This paper aims to improve the tribological properties of lithium complex greases using nanoparticles to investigate the tribological behavior of single additives (nano-TiO₂ or nano-CeO₂) and composite additives (nano-TiO₂–CeO₂) in lithium complex greases and to analyze the mechanism of their influence using a variety of characterization tools.

The morphology and microstructure of the nanoparticles were characterized by scanning electron microscopy and an X-ray diffractometer. The tribological properties of different nanoparticles, as well as compounded nanoparticles as greases, were evaluated. Average friction coefficients and wear diameters were analyzed. Scanning electron microscopy and three-dimensional topography were used to analyze the surface topography of worn steel balls. The elements present on the worn steel balls’ surface were analyzed using energy-dispersive spectroscopy and X-ray photoelectron spectroscopy.

The results showed that the coefficient of friction (COF) of grease with all three nanoparticles added was low. The grease-containing composite nanoparticles exhibited a lower COF and superior anti-wear properties. The sample displayed its optimal tribological performance when the ratio of TiO₂ to CeO₂ was 6:4, resulting in a 30.5% reduction in the COF and a 29.2% decrease in wear spot diameter compared to the original grease. Additionally, the roughness of the worn spot surface and the maximum depth of the wear mark were significantly reduced.

The main innovation of this study is the first mixing of nano-TiO₂ and nano-CeO₂ with different sizes and properties as compound lithium grease additives to significantly enhance the anti-wear and friction reduction properties of this grease. The results of friction experiments with a single additive are used as a basis to explore the synergistic lubrication mechanism of the compounded nanoparticles. This innovative approach provides a new reference and direction for future research and development of grease additives.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2023-0291/