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This review aims to give a critical view of the wheel/rail high frequency vibration-induced vibration fatigue in railway bogie.

Vibration fatigue of railway bogie arising from the wheel/rail high frequency vibration has become the main concern of railway operators. Previous reviews usually focused on the formation mechanism of wheel/rail high frequency vibration. This paper thus gives a critical review of the vibration fatigue of railway bogie owing to the short-pitch irregularities-induced high frequency vibration, including a brief introduction of short-pitch irregularities, associated high frequency vibration in railway bogie, typical vibration fatigue failure cases of railway bogie and methodologies used for the assessment of vibration fatigue and research gaps.

The results showed that the resulting excitation frequencies of short-pitch irregularity vary substantially due to different track types and formation mechanisms. The axle box-mounted components are much more vulnerable to vibration fatigue compared with other components. The wheel polygonal wear and rail corrugation-induced high frequency vibration is the main driving force of fatigue failure, and the fatigue crack usually initiates from the defect of the weld seam. Vibration spectrum for attachments of railway bogie defined in the standard underestimates the vibration level arising from the short-pitch irregularities. The current investigations on vibration fatigue mainly focus on the methods to improve the accuracy of fatigue damage assessment, and a systematical design method for vibration fatigue remains a huge gap to improve the survival probability when the rail vehicle is subjected to vibration fatigue.

The research can facilitate the development of a new methodology to improve the fatigue life of railway vehicles when subjected to wheel/rail high frequency vibration.

With the recognition that generating economic growth is not the same as sustaining it, the challenge to catch-up and growth literature is discerning between these processes. Recent research suggests that the decline in the frequency of “shrinking” episodes is more important for long-term development than higher growth rates. By using a framework centred around social capabilities, this study aims to investigate the effects of income inequality and poverty on economic shrinking frequency, as opposed to previous literature that has exclusively had a growth focus. The aim is to investigate how and why some societies might be more resilient to economic shrinking.

The research is a quantitative study, and the authors build a longitudinal data set including 23 developing countries throughout 42 years to test the paper’s purpose. This study uses country and period fixed-effects specifications as well as cross-sectional graphical representations to investigate the relationship between proxies of economic inclusivity and the frequency of shrinking episodes.

The authors demonstrate that while inclusive societies are more resilient to shrinking overall, it is changes in poverty levels, but not changes in income inequality, that appear to be correlated with economic shrinking frequency. Inequality, while still an important element to explain countries’ growth potential as an initial condition, does not seem to make the sample more resilient to shrinking. The authors conclude that the mechanisms in which poverty and inequality are correlated with the catch-up process must run through different channels. Ultimately, processes that explain growth may intersect but not always overlap with the ones that explain resilience to shrinking.

The need for inclusive growth in long-term development has been championed for decades, yet inclusion has seldom been explored from the shrinking perspective. Though poverty reduction is already an important mainstream political objective, this paper differentiates itself by providing an alternate viewpoint of why this is important. Income inequality could have more of an economic growth limiting effect, while poverty reduction could be required to build resilience to economic shrinking. Developing countries will need both growth and resilience to shrinking, to catch-up with higher-income economies, which policymakers might need to balance carefully.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

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 study seeks to examine the relationship between macroeconomic shock indicators, namely geopolitical risk (GPR), global economic policy uncertainty (GEPU) and financial stress (FS), and returns as well as volatilities on seven carefully selected stock markets in Africa. Specifically, the study intends to unravel the co-movement and interdependence between the respective macroeconomic shock indicators and each of the stock markets under consideration across time and frequency.

This study employed wavelet coherence approach to examine the strength and stability of the relationships across different time scales and frequency components, thereby providing valuable insights into specific periods and frequency ranges where the relationships are particularly pronounced.

The study found that GEPU, Financial Stress (FS) and GPR failed to induce significant influence on African stock market returns in the short term (0–4 months band), but tend to intensify in the long-term band (after 6th month). On the contrary, stock market volatilities exhibited strong coherence and interdependence with GEPU, FSI and GPR in the short-term band.

This study happens to be the first of its kind to comprehensively consider how the aforementioned macro-economic shock indicators impact stock markets returns and volatilities over time and frequency. Further, none of the earlier studies has attempted to examine the relationship between macro-economic shocks, stock returns and volatilities in different crisis periods. This study is the first of its kind in to employ data spanning from May 2007 to April 2023, thereby covering notable crisis periods such as global financial crisis (GFC) and the COVID-19 pandemic episodes.

Financial stability has become a focal point for central banks since the global financial crisis. However, the optimal mix between monetary and financial stability policies remains unclear. In this study, the “soft” approach to such policy mix was tested – how often monetary policy (in inflation reports) analyses financial stability issues. This paper aims to discuss the aforementioned objective.

A total of 648 inflation reports published by 11 central banks from post-communist countries in 1998-2019 were reviewed using a text-mining method.

Results show that financial stability topics (mainly cyclical aspects of systemic risk) on average account for only 2%of inflation reports’ content. Although this share has grown somewhat since the global financial crisis (in CZ, HU and PL), it still remains at a low level. Thus, not enough evidence was found on the use of a “soft” policy mix in post-communist countries.

Given the strong interactions between price and financial stability, this paper emphasizes the need to increase the attention of monetary policymakers to financial stability issues.

The study combines two research areas, i.e. monetary policy and modern text mining techniques on a sample of post-communist countries, something which to the best of the authors’ knowledge has not been sufficiently explored in the literature before.

In a dynamic, continuously evolving sports landscape, social media have become an indispensable tool for sports organizations to cultivate meaningful connections with fans. The rapid pace of technological advancements has elevated these digital platforms from a supplementary role to a pivotal position within strategic management frameworks. The existing literature explores how football clubs can utilize social media, but analyzing social media strategies within the context of football leagues is lacking. The absence of comparative studies benchmarking clubs across different geographical regions while simultaneously analyzing multiple social media platforms is especially noteworthy. In this study, a comprehensive analysis of social media engagement is undertaken within esteemed football leagues spanning Europe, South America and North America.

Drawing on relationship marketing and employing content analysis as a methodological tool, the study examined 10,772 posts from the official accounts of eight football leagues on Facebook, Twitter and Instagram.

Across the leagues, the findings reveal that content quality drives engagement more than frequency. In addition, several format combinations were identified that facilitate engagement and Instagram emerged as the top social media platform for generating fan engagement.

This is one of the first empirical studies focusing on optimizing the use of social media to amplify fan engagement across various geographies and social media accounts and formats simultaneously.

This paper aims to overcome the limitations of low efficiency, low power density and strong electromagnetic interference (EMI) of the existing pulsed melt inert gas (MIG) welding power supply. So a novel and simplified implementation of digital high-power pulsed MIG welding power supply with LLC resonant converter is proposed in this work.

A simple parallel full-bridge LLC resonant converter structure is used to design the digital power supply with high welding current, low arc voltage, high open-circuit voltage and a wide range of arc loads, by effectively exploiting the variable load and high-power applications of LLC resonant converter.

The efficiency of each converter can reach up to 92.3%, under the rated operating condition. Notably, with proposed scheme, a short-circuit current mutation of 300 A can stabilize at 60 A within 8 ms. Furthermore, the pulsed MIG welding test shows that a stable welding process with 280 A peak current can be realized and a well-formed weld bead can be obtained, thereby verifying the feasibility of LLC resonant converter for pulsed MIG welding power supply.

The high efficiency, high power density and weak EMI of LLC resonant converter are conducive to the further optimization of pulsed MIG welding power supply. Consequently, a high performance welding power supply is implemented by taking adequate advantages of LLC resonant converter, which can provide equipment support for exploring better pulsed MIG welding processes.

The purpose of this paper is to provide a guiding framework for studying the travel patterns of PT users. The combination of public transit (PT) users’ travel data and user profiling (UP) technology to draw a portrait of PT users can effectively understand users’ travel patterns, which is important to help optimize the scheduling of PT operations and planning of the network.

To achieve the purpose, the paper presents a three-level classification method to construct the labeling framework. A station area attribute mining method based on the term frequency-inverse document frequency weighting algorithm is proposed to determine the point of interest attributes of user travel stations, and the spatial correlation patterns of user travel stations are calculated by Moran’s Index. User travel feature labels are extracted from travel data containing Beijing PT data for one consecutive week.

In this paper, a universal PT user labeling system is obtained and some related methods are conducted including four categories of user-preferred travel area patterns mining and a station area attribute mining method. In the application of the Beijing case, a precise exploration of the spatiotemporal characteristics of PT users is conducted, resulting in the final Beijing PTUP system.

This paper combines UP technology with big data analysis techniques to study the travel patterns of PT users. A user profile label framework is constructed, and data visualization, statistical analysis and K-means clustering are applied to extract specific labels instructed by this system framework. Through these analytical processes, the user labeling system is improved, and its applicability is validated through the analysis of a Beijing PT case.

This study assessed two technology systems aimed at enabling a man with intellectual disability, blindness, deafness and motor and tactile discrimination problems to make verbal requests through simple one-hand signs.

The study was conducted according to an ABAB design. During the B (intervention) phases, the man used the two systems, which included (1) nine mini recording devices fixed on the man’s clothes or wheelchair (i.e. in positions the man touched with his sign movements) and (2) nine tags with radio frequency identification codes (fixed at approximately the same positions as the mini recording devices) and a dedicated tag reader, respectively. Making a sign (i.e. touching a recording device or reaching a tag) led to the verbalization of the request related to that sign.

During baseline, the mean frequency of signs/requests made was below 2 per session, and only some of those requests were identified/satisfied. During the intervention, the mean frequency of requests made and satisfied was about 10 per session with each of the systems.

The results, which are to be taken with caution given the preliminary nature of the study, seem to suggest that the systems can help translate simple signs into verbal requests.