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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.

In order to improve the computation efficiency of the four-point rainflow algorithm, a new fast four-point rainflow cycle counting algorithm (FFRA) using a novel loop iteration mode is proposed.

In this new algorithm, the loop iteration mode is simplified by reducing the number of iterations, tests and deletions. The high efficiency of the new algorithm makes it a preferable candidate in fatigue life online estimation of structural health monitoring systems.

The extensive simulation results show that the extracted cycles by the new FFRA are the same as those by the four-point rainflow cycle counting algorithm (FRA) and the three-point rainflow cycle counting algorithm (TRA). Especially, the simulation results indicate that the computation efficiency of the FFRA has improved an average of 12.4 times compared to the FRA and an average of 8.9 times compared to the TRA. Moreover, the equivalence of cycle extraction results between the FFRA and the FRA is proved mathematically by utilizing some fundamental properties of the rainflow algorithm. Theoretical proof of the efficiency improvement of the FFRA in comparison to the FRA is also given.

This merit makes the FFRA preferable in online monitoring systems of structures where fatigue life estimation needs to be accomplished online based on massive measured data. It is noticeable that the high efficiency of the FFRA attributed to the simple loop iteration, which provides beneficial guidance to improve the efficiency of existing algorithms.

Abnormal vibrations often occur in the liquid oxygen kerosene transmission pipelines of rocket engines, which seriously threaten their safety. Improper handling can result in failed rocket launches and significant economic losses. Therefore, this paper aims to examine vibrations in transmission pipelines.

In this study, a three-dimensional high-pressure pipeline model composed of corrugated pipes, multi-section bent pipes, and other auxiliary structures was established. The fluid–solid coupling method was used to analyse vibration characteristics of the pipeline under various external excitations. The simulation results were visualised using MATLAB, and their validity was verified via a thermal test.

In this study, the vibration mechanism of a complex high-pressure pipeline was examined via a visualisation method. The results showed that the low-frequency vibration of the pipe was caused by fluid self-excited pressure pulsation, whereas the vibration of the engine system caused a high-frequency vibration of the pipeline. The excitation of external pressure pulses did not significantly affect the vibrations of the pipelines. The visualisation results indicated that the severe vibration position of the pipeline thermal test is mainly concentrated between the inlet and outlet and between the two bellows.

The results of this study aid in understanding the causes of abnormal vibrations in rocket engine pipelines.

The causes of different vibration frequencies in the complex pipelines of rocket engines and the propagation characteristics of external vibration excitation were obtained.

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.

This paper aims to propose a new method to derive custom dynamic cyber risk metrics based on the well-known Goal, Question, Metric (GQM) approach. A framework that complements it and makes it much easier to use has been proposed too. Both, the method and the framework, have been validated within two challenging application domains: continuous risk assessment within a smart farm and risk-based adaptive security to reconfigure a Web application firewall.

The authors have identified a problem and provided motivation. They have developed their theory and engineered a new method and a framework to complement it. They have demonstrated the proposed method and framework work, validating them in two real use cases.

The GQM method, often applied within the software quality field, is a good basis for proposing a method to define new tailored cyber risk metrics that meet the requirements of current application domains. A comprehensive framework that formalises possible goals and questions translated to potential measurements can greatly facilitate the use of this method.

The proposed method enables the application of the GQM approach to cyber risk measurement. The proposed framework allows new cyber risk metrics to be inferred by choosing between suggested goals and questions and measuring the relevant elements of probability and impact. The authors’ approach demonstrates to be generic and flexible enough to allow very different organisations with heterogeneous requirements to derive tailored metrics useful for their particular risk management processes.

In this paper, we aim to provide an extensive analysis to understand how various factors influence electricity prices in competitive markets, focusing on the day-ahead electricity market in Romania.

Our study period began in January 2019, before the COVID-19 pandemic, and continued for several months after the onset of the war in Ukraine. During this time, we also consider other challenges like reduced market competitiveness, droughts and water scarcity. Our initial dataset comprises diverse variables: prices of essential energy sources (like gas and oil), Danube River water levels (indicating hydrological conditions), economic indicators (such as inflation and interest rates), total energy consumption and production in Romania and a breakdown of energy generation by source (coal, gas, hydro, oil, nuclear and renewable energy sources) from various data sources. Additionally, we included carbon certificate prices and data on electricity import, export and other related variables. This dataset was collected via application programming interface (API) and web scraping, and then synchronized by date and hour.

We discover that the competitiveness significantly affected electricity prices in Romania. Furthermore, our study of electricity price trends and their determinants revealed indicators of economic health in 2019 and 2020. However, from 2021 onwards, signs of a potential economic crisis began to emerge, characterized by changes in the normal relationships between prices and quantities, among other factors. Thus, our analysis suggests that electricity prices could serve as a predictive index for economic crises. Overall, the Granger causality findings from 2019 to 2022 offer valuable insights into the factors driving energy market dynamics in Romania, highlighting the importance of economic policies, fuel costs and environmental regulations in shaping these dynamics.

We combine principal component analysis (PCA) to reduce the dataset’s dimensionality. Following this, we use continuous wavelet transform (CWT) to explore frequency-domain relationships between electricity price and quantity in the day-ahead market (DAM) and the components derived from PCA. Our research also delves into the competitiveness level in the DAM from January 2019 to August 2022, analyzing the Herfindahl-Hirschman index (HHI).

Nanosensors have a wide range of applications because of their high sensitivity, selectivity and specificity. In the past decade, extensive and pervasive research related to nanosensors has led to significant progress in diverse fields, such as biomedicine, environmental monitoring and industrial process control. This led to better and more efficient detection and monitoring of physical and chemical properties at better resolution, opening new horizons in the development of novel technologies and applications for improved human health, environment protection, enhanced industrial processes, etc.

In this paper, the authors discuss the application of citation network analysis in the field of nanosensor research and development. Cluster analysis was carried out using papers published in the field of nanomaterial-based sensor research, and an in-depth analysis was carried out to identify significant clusters. The purpose of this study is to provide researchers to identify a pathway to the emerging areas in the field of nanosensor research. The authors have illustrated the knowledge base, knowledge domain and knowledge progression of nanosensor research using the citation analysis based on 3,636 Science Citation Index papers published during the period 2011 to 2021.

Among these papers, the bibliographic study identified 809 significant research publications, 11 clusters, 556 research sector keywords, 1,296 main authors, 139 referenced authors, 63 nations, 206 organizations and 42 journals. The authors have identified single quantum dot (QD)-based nanosensor for biological applications, carbon dot-based nanosensors, self-powered triboelectric nanogenerator-based nanosensor and genetically encoded nanosensor as the significant research hotspots that came to the fore in recent years. The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

This is a novel bibliometric analysis in the area of “nanomaterial based sensor,” which is carried out in CiteSpace software.

This paper aims to disentangle Chinese-English-rich resources linguistic and speaker timbre features, achieving cross-lingual speaker transfer for Cambodian.

This study introduces a novel approach: the construction of a cross-lingual feature disentangler coupled with the integration of time-frequency attention adaptive normalization to proficiently convert Cambodian speaker timbre into Chinese-English without altering the underlying Cambodian speech content.

Considering the limited availability of multi-speaker corpora in Cambodia, conventional methods have demonstrated subpar performance in Cambodian speaker voice transfer.

The originality of this study lies in the effectiveness of the disentanglement process and precise control over speaker timbre feature transfer.

This study collected the bibliographic data of 2034 journal articles published in 2000–2021 from Web of Science (WoS) core collection database and adopted two bibliometric analysis methods, namely historiography and keyword co-occurrence, to identify the evolution trend of construction risk management (CRM) research topics.

CRM has been a key issue in construction management research, producing a big number of publications. This study aims to undertake a review of the global CRM research published from 2000 to 2021 and identify the evolution of the research topics relating to CRM.

This study found that risk analysis methods have shifted from simply ranking risks in terms of their relative importance or significance toward examining the interrelationships among risks, and that the objects of CRM research have shifted from generic construction projects toward specified types of construction projects (e.g. small projects, underground construction projects, green buildings and prefabricated projects). In addition, researchers tend to pay more attention to an individual risk category (e.g. political risk, safety risk and social risk) and integrate CRM into cost, time, quality, safety and environment management functions with the increasing adoption of various information and communication technologies.

This study focused on the journal articles in English in WoS core collection database only, thus excluding the publications in other languages, not indexed by WoS and conference proceedings. In addition, the historiography focused on the top documents in terms of document strength and thus ignored the role of the documents whose strengths were a little lower than the threshold.

This review study is more inclusive than any prior reviews on CRM and overcomes the drawbacks of mere reliance on either bibliometric analysis results or subjective opinions. Revealing the evolution process of the CRM knowledge domain, this study provides an in-depth understanding of the CRM research and benefits industry practitioners and researchers.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.