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In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.

This paper examines the time-varying efficiency of nine major Middle East and North Africa (MENA) stock markets namely Egypt, Bahrain, UAE, Jordan, Saudi Arabia, Oman, Qatar, Morocco and Tunisia during times of COVID-19 pandemic outbreak and vaccines.

The authors use two econometric approaches: (1) autocorrelation tests including the wild bootstrap automatic variance ratio test, the automatic portmanteau test and the Generalized spectral test, and (2) a non-Bayesian generalized least squares-based time-varying model with statistical inferences.

The results show that the degree of stock market efficiency of Egyptian, Bahraini, Saudi, Moroccan and Tunisian stock markets is influenced by the COVID-19 pandemic crisis. Furthermore, the authors find a tendency toward efficiency in most of the MENA markets after the announcement of the COVID-19's vaccine approval. Finally, the Jordanian, Omani, Qatari and UAE stock markets remain globally efficient during the three sub-periods of the COVID-19 pandemic outbreak.

The results have important implications for asset allocations and financial risk management. Portfolio managers may maximize the benefit of arbitrage opportunities by taking strategic long and short positions in these markets during downward trend periods. Policymakers should implement the action plans and reforms to protect the stock markets from global shocks and ensure the stability of the stock markets.

The purpose of this study is to investigate the effects of nanoadditive lubricants on the vibration and noise characteristics of helical gears compared with conventional lubricants. The experiment aims to analyze whether nanoadditive lubricants can effectively reduce gear vibration and noise under different speeds and loads. It also analyzes the sensitivity of the vibration reduction to load and speed changes. In addition, it compares the axial and radial vibration reduction effects. The goal is to explore the application of nanolubricants for vibration damping and noise reduction in gear transmissions. The results provide a basis for further research on nanolubricant effects under high-speed conditions.

Helical gears of 20CrMnTi were lubricated with conventional oil and nanoadditive oils. An open helical gearbox with spray lubrication was tested under different speeds (200–500 rpm) and loads (20–100 N·m). Gear noise was measured by a sound level meter. Axial and radial vibrations were detected using an M+P VibRunner system and fast Fourier transform analysis. Vibration spectrums under conventional and nanolubrication were compared. Gear tooth surfaces were observed after testing. The experiment aimed to analyze the noise and vibration reduction effects of nanoadditive lubricants on helical gears and the sensitivity to load and speed.

The key findings are that nanoadditive lubricants significantly reduce the axial and radial vibrations of helical gears under low-speed conditions compared with conventional lubricants, with a more pronounced effect on axial vibrations. The vibration reduction is more sensitive to rotational speed than load. At the same load and speed, nanolubrication reduces noise by 2%–5% versus conventional lubrication. Nanoparticles change the friction from sliding to rolling and compensate for meshing errors, leading to smoother vibrations. The nanolubricants alter the gear tooth surfaces and optimize the microtopography. The results provide a basis for exploring nanolubricant effects under high speeds.

The originality and value of this work is the experimental analysis of the effects of nanoadditive lubricants on the vibration and noise characteristics of hard tooth surface helical gears, which has rarely been studied before. The comparative results under different speeds and loads provide new insights into the vibration damping capabilities of nanolubricants in gear transmissions. The findings reveal the higher sensitivity to rotational speed versus load and the differences in axial and radial vibration reduction. The exploration of nanolubricant effects on gear tribological performance and surface interactions provides a valuable reference for further research, especially under higher speed conditions closer to real applications.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2023-0220/

Hydro-viscous drive (HVD) clutches are widely used in equipment requiring soft start, such as fans and pumps, to transmit torque and adjust speed by changing the gap distance between friction pairs. This paper aims to propose a novel two-parameter evaluation method for HVD during the mixed lubrication stage. The objective is to develop an effective model that establishes the relationship between these parameters and the actual surface topography.

In the presented methods, the fractal features of the real manufacturing surface are calculated based on the power spectrum function by the ultra-depth three-dimensional microscope. After that, the hybrid friction model of the friction plate is established based on mixed elasto-hydrodynamic lubrication theory, boundary friction model and fractal theory. Then the torque and load bearing characteristics of the clutch are obtained, and the influences of the surface fractal features are investigated and discussed. Finally, the Weierstrass–Mandelbrot function is adopted for the surface topography characterization and evaluation.

The results indicate that the proposed method exhibits good accuracy, while the speed difference between the friction pair exceeds 2,500 rpm. It is concluded that this paper proposed a way to evaluate the torque and loading capacity of HVD considering the real manufacturing surface topography and is helpful for surface optimization.

The originality and value of this study lie in its development of a novel torque and load bearing capacity evaluation method for HVD in mixed lubrication stage, considering manufacturing surface topography and describing the real manufacturing surface.

This study aims to investigate the design and optimization of landing gear buffers to improve the landing-phase comfort of civil aircraft.

The vibration comfort during the landing and taxiing phases is calculated and evaluated based on the flight-testing data for a type of civil aircraft. The calculation and evaluation are under the guidance of the vibration comfort standard of GB/T13441.1-2007 and related files. The authors establish here a rigid-flexible coupled multibody dynamics finite element model of one full-size aircraft. Furthermore, the authors also implement a dynamic simulation for the landing and taxiing processes. Also, an analysis of how the main parameters of the buffers affect the vibration comfort is presented. Finally, the optimization of the single-chamber and double-chamber buffers in the landing gear is performed considering vibration comfort.

The double-chamber buffer with optimized parameters in landing gear can improve the vibration comfort of the aircraft during the landing and taxiing phases. Moreover, the comfort index can be increased by 25.6% more than that of a single-chamber type.

To the best of the authors’ knowledge, this study first investigates the evaluation methods and evaluation indexes on the aircraft vibration comfort, then further conducts the optimization of the parameters of landing gear buffer with different structures, so as to improve the comfort of aircraft passengers during landing process. Most of the current studies on aircraft landing gear have focused on the strength and safety of the landing gear, with very limited research on cabin vibration comfort during landing and subsequent taxiing because of the coupling of runway surface unevenness and airframe vibration.

This paper aims to prepare new modified alkyd resins and use it as an antimicrobial binder for surface coating applications.

Various modified alkyd resins were prepared by partial replacement of 3,6-dichloro benzo[b]thiophene-2-carbonyl bis-(2-hydroxy ethyl)-amide as a source of polyol with glycerol and confirmed by acid value, FT-IR, 1H-NMR. The modified alkyd resins were covering a wide range of oil lengths and hydroxyl content (0%, 10%, 20% and 30% excess-OH). The antimicrobial activity of the prepared alkyds was also investigated. The coatings of 60 ± 5 µm thickness were applied to the surface of glass panels and mild steel strips by means of a brush. Physico-mechanical tests, chemical resistance and antimicrobial activities were investigated.

The obtained results illustrate that the introduction of benzo[b]thiophene derivative as a modifier polyol within the resin structure improved the film performance and enhanced the physico-mechanical characteristics, chemical resistance and the antimicrobial activities.

The modified alkyd resins can be employed as antimicrobial binders in paint compositions for a variety of surfaces, particularly those that are susceptible to a high number of bacteria.

Modified alkyd resins based on antimicrobial heterocyclic compounds have the potential to be promising in the manufacturing of antimicrobial coatings and development of paints, allowing them to function to prevent the spread of microbial infection, which is exactly what the world requires at this time. Also, they can be applied in different substrates for industrial applications.

This paper aims to examine the return and volatility transmission among economic policy uncertainty (EPU), geopolitical risk (GPR), their interaction (EPGR) and five tradable precious metals: gold, silver, platinum, palladium and rhodium.

Applying time-varying parameter vector autoregression (TVP-VAR) frequency-based connectedness approach to a data set spanning from January 1997 to February 2023, the study analyzes return and volatility connectedness separately, providing insights into how the data, in return and volatility forms, differ across time and frequency.

The results of the return connectedness show that gold, palladium and silver are affected more by EPU in the short term, while all precious metals are influenced by GPR in the short term. EPGR exhibits strong contributions to the system due to its elevated levels of policy uncertainty and extreme global risks. Palladium shows the highest reaction to EPGR, while silver shows the lowest. Return spillovers are generally time-varying and spike during critical global events. The volatility connectedness is long-term driven, suggesting that uncertainty and risk factors influence market participants’ long-term expectations. Notable peaks in total connectedness occurred during the Global Financial Crisis and the COVID-19 pandemic, with the latter being the highest.

Using the recently updated news-based uncertainty indicators, the study examines the time and frequency connectedness between key uncertainty measures and precious metals in their returns and volatility forms using the TVP-VAR frequency-based connectedness approach.

This paper aims to examine potential diversification benefits between Eurozone (i.e. EURO STOXX 50) and key Asia markets: HSI (Hong Kong), KOSPI (South Korea), NIKKEI 225 (Japan) and TSEC (Taiwan). The sample covers the period from 04-01-2008 to 19-10-2023 in daily frequency.

The empirical investigation is based on the wavelet coherence analysis, which is a localized correlation coefficient in the time and frequency domain.

The results provide evidence that long-term diversification benefits exist between EURO STOXX and NIKKEI, EURO STOXX and KOSPI (after 2015) and there are signs for the pair and EURO STOXX-TSEC (after 2014). During the short term, there are signs of diversification benefits during the sample period. However, during the medium term, the diversification benefits seem to diminish.

These results have crucial implications for investors regarding the benefits of international portfolio diversification.

Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.

A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.

The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.

This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.

This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.

This study aims to empirically explore and compare the dynamic dependency between health-care sector and Islamic industries before, during and after the COVID-19 pandemic.

Time-varying student-t copula is used for before, during and after COVID-19 periods. The data used are the daily frequency price series of the selected markets from February 2017 to October 2023.

Empirical results found strong evidence of significant impact of the COVID-19 pandemic on the dependence structure of the studied indexes: Co-movements between various sectors are certain. The authors assist also in the birth of new dependence structure with the health-care industry in response to the COVID-19 crisis. This reflects the contagion occurrence from the health-care sector to other sectors.

By specifically examining the Islamic industry, this study sheds light on the resilience, challenges and opportunities within this sector, contributing novel perspectives to the broader discourse on pandemic-related impacts on economies and industries. Also, this paper conducts a comprehensive temporal analysis, examining the dynamics before, during and after the COVID-19 lockdown. Such approach enables an understanding of how the relationship between the health-care sector and the Islamic industry evolves over time, accounting for both short-term disruptions and long-term effects. By considering the pre-pandemic context, the paper adopts a longitudinal perspective, enabling a deeper understanding of how historical trends, structural factors and institutional frameworks shape the interplay between the health-care sector and the Islamic industry.