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Early fault detection of bearing plays an increasingly important role in the operation of rotating machinery. Based on the properties of early fault signal of bearing, this paper aims to describe a novel hybrid early fault detection method of bearings.

In adaptive variational mode decomposition (AVMD), an adaptive strategy is proposed to select the optimal decomposition level K of variational mode decomposition. Then, a criterion based on envelope entropy is applied to select the optimal intrinsic mode functions (OIMF), which contains most useful fault information. Afterwards, local tangent space alignment (LTSA) is used to denoising of OIMF. The envelope spectrum of the OIMF is used to analyze the fault frequency, thereby detecting the fault. Experiments are conducted in a simulated signal and two experimental vibration signals of bearings to verify the effect of the new method.

The results show that the proposed method yields a good capability of detecting bearing fault at an early stage. The new method can extract more useful information and can reduce noise, which can provide better detection accuracy compared with the other two methods.

An adaptive strategy based on center frequency is proposed to select the optimal decomposition level of variational mode decomposition. Envelope entropy is used to fault feature selection. Combining the advantage of the AVMD-envelope entropy and LTSA, which suits the nature of the early fault signal. So, the proposed method has better detection accuracy, which provides a good alternative for early fault detection of bearings.

The purpose of this paper is to explore the antecedents of media attention in the context of early-stage startups. While many studies have examined the implications of media attention on firm outcomes, few have investigated the antecedents especially in the context of early-stage startups who significantly lack organizational legitimacy. This study attempts to answer an important and yet unanswered question: What type of startups are more likely to be covered by the media?

Using Poisson regression, the authors analyze all media articles written about 315 early-stage ventures in the USA.

The authors found that startups with a prestigious investor or a patent have more media attention and startups with a female founder or prior entrepreneurial experience have less. The results suggest that entrepreneurial signals do play a role in media attention, but that the signal–signaler relationship may be more complicated than that in the investment literature.

Entrepreneurs may benefit from signaling less noisy and unambiguous signals that the media pays more attention to, such as getting an endorsement from reputable third parties early on, which might activate noisy signals.

The contribution of this paper is to extend the current literature on media attention and entrepreneurship by shedding light on attributes of startups that may help or hurt the volume of media attention in an uncertain and noisy environment.

The purpose of this paper is to present an overview of the concept of early warning signs in projects and explain how a performance measurement system can be utilized as a source of data for an early warning approach signaling that a project is about to experience problems at some stage in the future.

Combination of action research and semi‐structured interviews and document analysis supplemented by a post‐mortem analysis after project close‐out.

Detection of early warning signals in projects can be better enabled through the application of a performance measurement system with properly defined key performance indicators. Utilization of this tool can positively affect the overall success of the project.

The case study involved only one project from the oil and gas industry.

The empirical case study was developed to illustrate the usefulness of exploiting a performance measurement system in a project. A procedure was demonstrated for developing and implementing an early warning system based on performance measurement, and specific performance indicators have been described for other projects to copy.

This paper highlights the gap in the literature concerning the link between early warning and project management and the link between early warning and performance measurement. It offers a new idea on how performance measurement can be used as an effective early warning system and is intended to be primarily of use to project management practitioners and practically‐oriented academics who are interested in developing fresh insights into new approaches for better management of projects.

The purpose of this paper is to propose a framework to detect adverse drug reactions (ADRs) using internet user search data, so that ADR events can be identified early. Empirical investigation of Avandia, a type II diabetes treatment, is conducted to illustrate how to implement the proposed framework.

Typical ADR identification measures and time series processing techniques are used in the proposed framework. Google Trends Data are employed to represent user searches. The baseline model is a disproportionality analysis using official drug reaction reporting data from the US Food and Drug Administration’s Adverse Event Reporting System.

Results show that Google Trends series of Avandia side effects search reveal a significant early warning signal for the side effect emergence of Avandia. The proposed approach of using user search data to detect ADRs is proved to have a longer leading time than traditional drug reaction discovery methods. Three more drugs with known adverse reactions are investigated using the selected approach, and two are successfully identified.

Validation of Google Trends data’s representativeness of user search is yet to be explored. In future research, user search in other search engines and in healthcare web forums can be incorporated to obtain a more comprehensive ADR early warning mechanism.

Using internet data in drug safety management with a proper early warning mechanism may serve as an earlier signal than traditional drug adverse reaction. This has great potential in public health emergency management.

The research work proposes a novel framework of using user search data in ADR identification. User search is a voluntary drug adverse reaction exploration behavior. Furthermore, user search data series are more concise and accurate than text mining in forums. The proposed methods as well as the empirical results will shed some light on incorporating user search data as a new source in pharmacovigilance.

With the intensification of market competition, there is a growing demand for weak signal identification and evolutionary analysis for enterprise foresight. For decades, many scholars have conducted relevant research. However, the existing research only cuts in from a single angle and lacks a systematic and comprehensive overview. In this paper, the authors summarize the articles related to weak signal recognition and evolutionary analysis, in an attempt to make contributions to relevant research.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. Framework comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

The research results show that it is necessary to improve the automation level in the process of weak signal recognition and analysis and transfer valuable human resources to the decision-making stage. In addition, it is necessary to coordinate multiple types of data sources, expand research subfields and optimize weak signal recognition and interpretation methods, with a view to expanding weak signal future research, making theoretical and practical contributions to enterprise foresight, and providing reference for the government to establish weak signal technology monitoring, evaluation and early warning mechanisms.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. It comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

Outlines previous research on company failure prediction and discusses some of the methodological issues involved. Extends an earlier study (Tan 1997) using artificial neural networks (ANN) to predict financial distress in Australian credit unions by extending the forecast period of the models, presents the results and compares them with probit model results. Finds the ANN models generally at least as good as the probit, although both types improved their accuracy rates (for Type I and Type II errors) when early warning signals were included. Believes ANN “is a promising technique” although more research is required, and suggests some avenues for this.

The purpose of this paper is threefold: to classify wildcards into three particular types sharing similar characteristics; use the Fuzzy TOPSIS as a new method in foresight to turn qualitative ideas into quantitative ones; and apply a combination of Fuzzy TOPSIS and a panel of experts to prioritize weak signals.

In this paper, the authors classify wildcards into three particular types which share similar character: natural wildcards, artificial wildcards (Degree 1) and artificial wildcards (Degree 2). Wildcards point to unexpected and surprising events including important results that can form watershed in the development of a specific trend. In addition, the authors present a Fuzzy TOPSIS model which can be used in various cases to prioritize a number of weak signals and put them in order, so that the most important ones are likely to yield the wildcard in the future

The authors presented a classification of wildcards with the same characteristics being natural wildcards, artificial wildcards (Degree 1) and artificial wildcards (Degree 2). The authors also prioritized the weak signals to deal with the most important ones and take appropriate action in advance so as to minimize possible damages and maximize the benefits of potential wildcards in an uncertain environment.

In this paper, the authors report on the prioritizing of weak signals by applying Fuzzy TOPSIS and classify wildcards. This is significant because, by identifying the most important weak signals, appropriate actions can be taken in the future if necessary. The paper should be of interest to readers in the area of participatory foresight.

This paper aims to describe the approach taken in National Health Service Scotland to sharing information between health and care oversight bodies and the development of an analytical framework to monitor and identify early signals of serious problems in the quality and safety of health and care services.

A review of the reports from UK public inquiries into serious failures in health and social care services identified the prominent themes that appear repeatedly as the causes of failure. These themes were used to develop an analytical framework setting out the seven primary causes of failures in the quality and safety of health and care services and the triggers or signals for each of these primary causes.

In Scotland, the Sharing Intelligence for Health and Care Group uses the analytical framework to collate their combined intelligence and shapes their discussions around the known signs of systemic failure and their early warning signs.

Research into the nature of organisational failure in the health and care sector is limited. This paper provides a practical framework for regulators and providers to target their attention to the known signs of systemic failure and ensure that the early warning signs are routinely surfaced, understood and addressed.

International air express carriers, frequently referred to as international courier companies, operate integrated global networks consisting of aircraft, hubs, vehicles, data systems and tens of thousands of employees spread across all continents. The inherently global nature of such systems – the leading firm operates in 228 countries – makes them sensitive to a foray of risks and threats. This paper, which was presented at the Seventh International Public Relations Research Symposium (Lake Bled, Slovenia, 7th‐8th July, 2000), reports on an ongoing crisis management research project that started in 1997 and which investigates early markers of crises experienced by DHL Worldwide Express. After a concise literature overview, which maps the relationship between organisational crises and early warning signals, two research outputs are discussed. First, the production of a crisis register is described. Computer‐aided content analysis of an internal service bulletin identified 103 service crises which the company managed in 68 countries during a 15‐month period. Secondly, detailed case investigation generated a model of crisis gestation. Prior to conflagration, exacerbating conditions and precipitating events interact to produce a number of dynamic effects. Five types of gestation‐advancing effects are illustrated. The paper concludes by reflecting on some of the consequences globally dispersed operations can have on corporate crisis management processes.

The continuous failure of construction projects notwithstanding appreciable increase in project management knowledge has necessitated a proactive approach of assessing early warning signs (EWS) of building projects failure. Building projects are expected to show warning signs before experiencing crises, comparable to a patient displaying symptoms of a disease. Thus, this study aims to examine the EWS that predisposed building projects to failure in Nigeria to provide empirical data for enhancing projects delivery.

Primary data were used for the study. Structured questionnaire was administered to consultants and contractors’ personnel within Lagos State, Nigeria. A total of 180 copies of questionnaire were administered and 134 copies (combined response rate of 74.44 per cent) were retrieved. Frequency distribution, percentages, mean item score and Mann–Whitney test were used to analyse the data.

Most construction professionals applied the EWS approach from project planning and early construction phase. The most significant EWS predisposing building projects to failure were “Management inability and incompetence to proactively detect and manage problems at early project stages”, “Actual expenditure is constantly shooting beyond cost estimates” and “Incurred costs already getting higher than the anticipated benefits”. Project/construction management-related symptoms are most significant to predisposing building projects to failure.

The study provided implications for effective project management of building projects through proactive approach which is very paramount to improving the delivery of building projects in Nigeria.

The study provides implications for proactive management of building projects, thereby enhancing the delivery of building projects.