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This study aims to propose a method for monitoring bearing health in the time–frequency domain, termed the Lock-in spectrum, to track the evolution of bearing faults over time and frequency.

The Lock-in spectrum uses vibration signals captured by vibration sensors and uses a lock-in process to analyze specified frequency bands. It calculates the distribution of signal amplitudes around fault characteristic frequencies over short time intervals.

Experimental results demonstrate that the Lock-in spectrum effectively captures the degradation process of bearings from fault inception to complete failure. It provides time-varying information on fault frequencies and amplitudes, enabling early detection of fault growth, even in the initial stages when fault signals are weak. Compared to the benchmark short-time Fourier transform method, the Lock-in spectrum exhibits superior expressive ability, allowing for higher-resolution, long-term monitoring of bearing condition.

The proposed Lock-in spectrum offers a novel approach to bearing health monitoring by capturing the dynamic evolution of fault frequencies over time. It surpasses traditional methods by providing enhanced frequency resolution and early fault detection capabilities.

This paper aims to analyze the characteristics of friction vibration signals and identify the vibration excitation source at the start and stop stage of microtextured end face of dry gas seals.

The friction pair consists of a diamond-like carbon (DLC) film microtextured seal ring and a spiral groove seal ring. Friction vibration signal feature extraction method based on harmonic wavelet packet and spectrum analysis was proposed. Signals were collected using acceleration sensor, acquisition card and LabVIEW software. Vibration acceleration signal was decomposed into 32 frequency bands using MATLAB wavelet packet transformation. The 32nd band coefficient was extracted for reconstruction, time-domain and spectral waveforms were obtained and spectra before/after denoising were compared.

The end face of the DLC film microtextured seal ring generates a good dynamic pressure effect, and the friction and vibration reduction effects are obvious. The harmonic wavelet packet can decompose the vibration signal conveniently and precisely. In the case of this experiment, the frequency of vibration of the seal ring is 7500 HZ.

The results show that the method is effective for the processing of friction vibration signal and the identification of vibration excitation source. The findings will provide ideas for the frictional vibration signal processing and basis for further research in the field of tribology of dry gas seal ring.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0084/

Ciliated microelectromechanical system (MEMS) vector hydrophones pick up sound signals through Wheatstone bridge in cross beam-ciliated microstructures to achieve information transmission. This paper aims to overcome the complexity and variability of the marine environment and achieve accurate location of targets. In this paper, a new method for ocean noise denoising based on improved complete ensemble empirical mode decomposition with adaptive noise combined with wavelet threshold processing method (CEEMDAN-WT) is proposed.

Based on the CEEMDAN-WT method, the signal is decomposed into different intrinsic mode functions (IMFs), and relevant parameters are selected to obtain IMF denoised signals through WT method for the noisy mode components with low sample entropy. The final pure signal is obtained by reconstructing the unprocessed mode components and the denoising component, effectively separating the signal from the wave interference.

The three methods of empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and CEEMDAN are compared and analyzed by simulation. The simulation results show that the CEEMDAN method has higher signal-to-noise ratio and smaller reconstruction error than EMD and EEMD. The feasibility and practicability of the combined denoising method are verified by indoor and outdoor experiments, and the underwater acoustic experiment data after processing are combined beams. The problem of blurry left and right sides is solved, and the high precision orientation of the target is realized.

This algorithm provides a theoretical basis for MEMS hydrophones to achieve accurate target positioning in the ocean, and can be applied to the hardware design of sonobuoys, which is widely used in various underwater acoustic work.

This study aims to explore the synthesis and tribological performances of di-n-octyl sebacate (DOS) synthesized with spherical nano-MoS₂/sericite (SMS) and carboxylated SMS (CSMS) as catalysts.

SMS and CSMS were used as esterification catalysts to synthesize DOS from sebacic acid and n-octanol. The two catalysts were in situ dispersed in the synthesized DOS after the reaction to form suspensions. The tribological performances of the two suspensions after 20 days of storage were studied.

CSMS was more stably dispersed in DOS than SMS, and they reduced friction by 55.6% and 22.2% and wear by 51.3% and 56.5%, respectively. Such results were mainly caused by the COOH on CSMS, which was more conducive to improving the dispersion and friction reduction of CSMS than wear resistance. Another possible reason was the difference between the dispersion amounts of CSMS and SMS in DOS. The sericite of SMS was converted into SiO₂ to enhance wear resistance, while that of CSMS only partially generated SiO₂, and the rest still remained on the surface to reduce friction.

This work provides a more effective SMS catalytical way for DOS synthesis than the traditional inorganic acid catalytical method. SMS does not need to be separated after reaction and can be dispersed directly in DOS as a lubricant additive. Replacing SMS with CSMS can produce a more stable suspension and reduce friction significantly. This work combined the advantages of surface carboxylation modification and in situ catalytic dispersion and provided alternatives for the synthesis of DOS and the dispersion of MoS₂-based lubricant additives.

Based on the micro-electro-mechanical system (MEMS) technology, acoustic emission sensors have gained popularity owing to their small size, consistency, affordability and easy integration. This study aims to provide direction for the advancement of MEMS acoustic emission sensors and predict their future potential for structural health detection of microprecision instruments.

This paper summarizes the recent research progress of three MEMS acoustic emission sensors, compares their individual strengths and weaknesses, analyzes their research focus and predicts their development trend in the future.

Piezoresistive, piezoelectric and capacitive MEMS acoustic emission sensors are the three main streams of MEMS acoustic emission sensors, which have their own advantages and disadvantages. The existing research has not been applied in practice, and MEMS acoustic emission sensor still needs further research in the aspects of wide frequency/high sensitivity, good robustness and integration with complementary metal oxide semiconductor. MEMS acoustic emission sensor has great development potential.

In this paper, the existing research achievements of MEMS acoustic emission sensors are described systematically, and the further development direction of MEMS acoustic emission sensors in the future research field is pointed out. It provides an important reference value for the actual weak acoustic emission signal detection in narrow structures.

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

This chapter explores the perspectives of addressing global challenges related to health care, energy, climate, and environment using the instruments of innovation and structural policy. It develops an analytical framework for continuously scanning emerging technologies and scientific developments using the TIM Analytics database of scientific publications and patent applications. The analysis identified the most promising early developments and weak signals among the technological areas with high activeness of publications over the period of 2015–2022. In the field of health care, emerging technologies are focused on providing better ways of diagnostics (liquid biopsy, biosensors, and theranostics), drug delivery (exosomes, graphene-based drug delivery), tissue recovery (organoids), and preventive treatment (personalized medicine). Emerging trends in clean energy include new types of solar energy generation and storage (perovskite thin film solar cells, concentrated solar power, graphene solar panels, and artificial photosynthesis) and self-powered systems (nanogenerators). To address the issues of climate change and environmental deterioration, one should pay close attention to emerging carbon capture technologies (biochar, direct capture, and BECC).

This chapter explores the strategic responsiveness of commodity multinationals operating in developing countries to the uncertainties raised by the emergent European Union (EU) sustainability regulation. The study applies deductive theory triangulation to derive five response propositions, subsequently contrasted with inductive insights from an exploratory single-case study. The research involves in-depth interviews with a mix of senior and middle management and numerous external stakeholders. Empirical findings are discussed through storytelling and retrospective sensemaking and cross-checked against corporate documents, archive material, and online articles for added validation. This chapter concludes that an authentic commitment to corporate social responsibility and creating shared value can enhance the multinational enterprise (MNE)’s resilience and responsiveness to regulatory uncertainty, especially when combined with early signal scanning and real options reasoning. Through varied, first-hand insights, the case study demonstrates the role of reputation, core values, and ethical leadership in support of effective stakeholder engagement capabilities and the MNE’s ability to develop viable collaborative solutions to uncertainties implied by evolving sustainability regulation and stakeholder expectations. Taking an evolutionary view, this chapter introduces a process perspective on sustainability transition, relevant to firms seeking a shift in focus from mere compliance toward strategic responsiveness founded on adaptability and renewal.

This paper aims at shedding light on the entrepreneurs' perception towards crowdfunding as a new mean for raising capital, and their willingness to send appropriate signals to the potential fund providers/backers.

The research strategy is based on three methodological approaches: desk research on online sources, a map of the crowdfunding phenomenon, and a quantitative approach with a survey performed between June and December 2020. The survey covers a sample of 147 Small and Medium enterprises (SMEs) and startups, in addition to semi-structured interviews with 10 entrepreneurs.

The study reveals that between losing their work and losing part of their firms' equity, entrepreneurs are keen on sending positive signals to backers. Moreover, they are willing to adopt a new way of thinking, as their primary goal is to save their firms, their jobs, and their source of income. The research highlights the concern of entrepreneurs of losing reputation, losing intellectual property, losing control, and of becoming only shareholders in their enterprises.

The main limitation in this paper is that no single study in Lebanon adequately covers the topic and thus extensive research has been carried out on crowdfunding across the world and analyzed in the Lebanese context.

Overcoming funding challenges can reduce brain drain, promote a culture of entrepreneurship, serve the economy, combat poverty, achieve more equitable society, increase the levels of expectations, and turn the flywheel. Moreover, the paper presents clear implications for the field of policy-making both in developing and developed countries.

Considering the serious financial disintermediation and liquidity shortage Lebanon faces, the findings of this study show how important changing entrepreneurial culture and behavior is, and the crucial role crowdfunding could play in providing funds for the SMEs that form 95% of the total business sector in Lebanon.

This study investigates the impact of supplier greenwashing on client sustainability, focusing on environmental, social and economic dimensions. It also emphasizes the mediating roles of information sharing and green trust in this relationship. By applying signalling theory, the research aims to deepen our understanding of the repercussions of greenwashing in interfirm relationships and identify potential mitigating or amplifying factors.

A sample of 312 companies were analysed using a structural equation model implemented with Analysis of Moment Structures (AMOS).

The study reveals that greenwashing negatively affects sustainability both directly and indirectly. Trust and information sharing emerge as crucial mediators in this dynamic, shedding light on the intricate interplay between greenwashing and sustainability.

This research contributes novelty by comprehensively examining the effects of supplier greenwashing practices on client sustainability within interfirm relationships. The application of signalling theory provides a nuanced understanding, highlighting the mediating roles of information sharing and green trust. The study adds valuable insights to the discourse on greenwashing, offering practical implications for businesses navigating sustainability challenges.