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In order to improve the ride comfort of vehicle suspension, this paper first proposed a shock absorber with four-stage adjustable damping forces. The purpose of this paper is to validate its modeling and characteristics, indicator diagrams and velocity diagrams, which are the main research points.

In order to validate the fluid flow modeling, a series of mathematical modeling is established and solved by using Matlab/Simulink. An experiment rig based on electro-hydraulic loading servo system is designed to test the prototype. Finally, indicator diagram and velocity diagram are obtained and compared both in simulation and experiments.

Results indicate that at the same damping position, damping force will increase with the rise of rod’s velocity: if the rod’s velocity is fixed, the damping force changes apparently by altering the damping position. The shock absorber is softest at damping position 1, and it is hardest at damping position 4; although there is no any badly empty stroke and skewness in indicator diagram by simulation, a temporary empty stroke happens at maximum displacement of piston rob, both in rebound and compression strokes.

Compared with results of the simulation and experiments, the design of a four-stage damping adjustable shock absorber (FDASA) is validated correctly in application, and may improve the overall dynamic performance of vehicle.

This paper is mainly focused on the design and testing of an FDASA, which may obtain four-stages damping characteristics, that totally has a vital importance to improve the performance of vehicle suspension.

The purpose of this paper is to solve the problem existing in the forecast of impact disturbance grey system.

Under the axiomatic system of buffer operator in grey system theory, a novel kind of buffer operators with variable weight λ based on the principle of average tempo of time sequence and using new information is proposed. The optimization solution for variable weight λ is obtained by using genetic algorithm. It is proved that the new buffer operators are effective.

The results show that the new buffer operators accord with the buffer operator's three axioms and the monotonicity non‐variable axiom. It is proved theoretically and in practice that the new buffer operators are more useful than other buffer operators in grey modeling for sequence with impact disturbance.

The novel buffer operators can reduce the randomness of grey sequence distorted by impact factors, and the forecast accuracy of a model which is built through the process with a novel buffer operator is significantly increased.

The paper succeeds in constructing two novel buffer operators with variable weight and the properties of novel operators are studied. The method to solve optimization value of weight is proposed. The method widens the scope of grey model application.

By market capitalisation, Bitcoin, which debuted in 2009, is the biggest cryptocurrency globally. A decentralised ledger system called blockchain is used in the creation, distribution, trading, and storage of Bitcoin, with the original goal being to address the shortcomings of fiat currency. This chapter highlights potential dangers and legal concerns when Bitcoin interacts with the actual economy and the traditional financial system. Besides, the details also discuss the platform’s design principles and attributes for a non-technical readership. When assessing its transactional potential, some recognise its potential for speculation, while others are doubtful of its admirable intent. The write-up also explores the potential of the adoption of cryptocurrencies in Southeast Asia due to the vast adoption of Bitcoins in countries such as Vietnam and the Philippines following the establishment of cryptocurrency technology and e-commerce. In addition, rankings of the cryptocurrency and legal stance from each country in Southeast Asia were exhibited as the solid foundation of cryptocurrencies existent for transaction purposes. The rise of central bank digital currencies (CBDC) and the future directions of Bitcoins were also highlighted in this write-up to spur the debate on whether cryptocurrency remains a fad of sensation or is legalised as the medium of exchange in an ever-growing digital world of commerce.

Aero-engine casings commonly use composite cylindrical shell structures with excellent properties such as corrosion resistance and fatigue resistance. Still, their vibration behavior is relatively complex and may cause fatigue vibration damage, so it is essential to analyze the vibration characteristics of composite cylindrical shells. The purpose of this paper is to analyze the vibration characteristics of multilayer composite cylindrical shells subjected to external pressures and having different interlayer thickness ratios and provide some theoretical basis for the fatigue damage prediction of cylindrical shell casing to ensure the safety and stability of the engine during flight.

Firstly, the vibration differential equation with external pressure is established based on Soedel theory considering nonlinear effects, while four symmetric boundary conditions are chosen to constrain the cylindrical shell. Then the Rayleigh–Ritz method, which is more efficient and accurate in calculating large structural systems, is applied to solve the problem, and the theoretical model of three-layer cylindrical shell under external pressure is established. The accuracy of the model is verified by comparing the data with the specialized literature. Subsequently, the effects of different external pressures and different thickness-to-diameter ratios, different length-to-diameter ratios and different interlayer thickness percentages on the natural frequency of multilayer composite cylindrical shells were investigated by control variable analysis.

The conclusions obtained show that the external pressure increases the natural frequency of the cylindrical shell and that the frequency characteristics of the cylindrical shell vary for different boundary conditions. The effect of length-to-diameter ratio, thickness-to-diameter ratio and the percentage of the thickness of the intermediate layer on the natural frequency of the cylindrical shell are significantly increased under external pressure. Because the presence of external pressure increases the frequency of the cylindrical shell by about 70%, it has almost no effect on the frequency at the minimum number of circumferential waves, and the effect on the frequency at the maximum number of circumferential waves is reduced to about 50%. The frequencies in the SL-SL boundary condition are all in perfect agreement with the S-S boundary condition under the influence of different influencing factors.

In this paper, the effect of external pressure and the natural properties of the cylindrical shell under external pressure on the cylindrical shell’s frequency is considered, emphasizing the effect of different layer thickness ratios on the frequency. This paper aims to summarize the changing law between the natural frequency of the cylindrical shell itself and different design parameters during the flight pressure process. Reliable theoretical predictions are provided for analyzing the vibrational behavior of shells subjected to external pressures in aerospace, as well as a database for the practical production of cylindrical shells.

User-generated content (UGC) refers to semantic and behavioral traces created by users on various social media platforms. While several waves of platforms have come and gone, the long-term sustainability of UGC activities has become a critical question that bears significance for theoretical understanding and social media practices.

Based on a large and lengthy dataset of both blogging and microblogging activities of the same set of users, a multistate survival analysis was applied to explore the patterns of users' staying, switching and multiplatforming behaviors, as well as the underlying driving factors.

UGC activities are generally unsustainable in the long run, and natural attrition is the primary reason, rather than competitive switching to new platforms. The availability of leisure time, expected gratification and previous experiences drive users' sustainability.

The authors adopted actual behavioral data from two generations of platforms instead of survey data on users' switching intentions. Four types of users are defined: loyal, switcher, multiplatformer and dropout. As measured by the transitions among the four states, the different sustainability behaviors are thereby studied via an integrated framework. These two originalities bridge gaps in the literature and offer new insights into exploring user sustainability in social media.

To achieve the dual goals of decarbonization and food security, this paper examines China's carbon footprint reduction in 2050 based on current mitigation strategies.

Considering publications as featured evidence, this study develops an investigation of agricultural decarbonization in China. First, the authors summarize the mitigation strategies for agricultural greenhouse gas (GHG) emissions in the existing literature. Second, the authors demonstrate the domestic food production target in 2050 and the projection target's projected life-cycle-based GHG emissions at the commodity level. Lastly, the authors forecast China's emission removal in the agri-food sector in 2050 concerning current mitigation strategies and commodity productions. The authors highlight the extent to which each mitigation strategy contributes to decarbonization in China.

Practices promoting sustainable development in the agri-food sector significantly contribute to GHG emission removal. The authors find mitigation strategies inhibiting future GHG emissions in the agri-food sector comprise improving nitrogen use efficiency in fertilizers, changing food consumption structure, manure management, cover crops, food waste reduction, dietary change of livestock and covered manure. A 10% improvement in nitrogen use efficiency contributes to 5.03% of GHG emission removal in the agri-food sector by 2050. Reducing food waste and food processing from 30% to 20% would inhibit 1.59% of the total GHG emissions in the agri-food sector.

This study contributes to policy discussions by accounting for agricultural direct and indirect emission components and assessing the dynamic changes in those related components. This study also extends existing research by forecasting to which extent the decarbonization effects implemented by current mitigation strategies can be achieved while meeting 2050 food security in China.

Construction is a risky industry. Therefore, organizations are seeking ways towards improving their safety performance. Among these, the integration of technology into health and safety leads to enhanced safety performance. Considering the benefits observed in using technology in safety, this study aims to explore digital technologies' use and potential benefits in construction health and safety.

An extensive bibliometrics analysis was conducted to reveal which technologies are at the forefront of others and how these technologies are used in safety operations. The study used two different databases, Web of Science (WoS) and Scopus, to scan the literature in a systemic way.

The systemic analysis of several studies showed that the digital technologies use in construction are still a niche theme and need more assessment. The study provided that sensors and wireless technology are of utmost importance in terms of construction safety. Moreover, the study revealed that artificial intelligence, machine learning, building information modeling (BIM), sensors and wireless technologies are trending technologies compared to unmanned aerial vehicles, serious games and the Internet of things. On the other hand, the study provided that the technologies are even more effective with integrated use like in the case of BIM and sensors or unmanned aerial vehicles. It was observed that the use of these technologies varies with respect to studies conducted in different countries. The study further revealed that the studies conducted on this topic are mostly published in some selected journals and international collaboration efforts in terms of researching the topic have been observed.

This study provides an extensive analysis of WoS and Scopus databases and an in-depth review of the use of digital technologies in construction safety. The review consists of the most recent studies showing the benefits of using such technologies and showing the usage on a systemic level from which both scientists and practitioners can benefit to devise new strategies in technology usage.

Work-related low back disorders (LBDs) are prevalent among rebar workers although their causes remain uncertain. The purpose of this study is to examine the self-reported discomfort and spinal biomechanics (muscle activity and spinal kinematics) experienced by rebar workers.

In all, 20 healthy male participants performed simulated repetitive rebar lifting tasks with three different lifting weights, using either a stoop (n = 10) or a squat (n = 10) lifting posture, until subjective fatigue was reached. During these tasks, trunk muscle activity and spinal kinematics were recorded using surface electromyography and motion sensors, respectively.

A mixed-model, repeated measures analysis of variance revealed that an increase in lifting weight significantly increased lower back muscle activity at L3 level but decreased fatigue and time to fatigue (endurance time) (p < 0.05). Lifting postures had no significant effect on spinal biomechanics (p < 0.05). Test results revealed that lifting different weights causes disproportional loading upon muscles, which shortens the time to reach working endurance and increases the risk of developing LBDs among rebar workers.

Future research is required to: broaden the research scope to include other trades; investigate the effects of using assistive lifting devices to reduce manual handling risks posed; and develop automated human condition-based solutions to monitor trunk muscle activity and spinal kinematics.

This study fulfils an identified need to study laboratory-based simulated task conducted to investigate the risk of developing LBDs among rebar workers primarily caused by repetitive rebar lifting.

With the fierce competition in the cold chain logistics market, achieving and maintaining excellent customer satisfaction is the key to an enterprise's ability to stand out. This research aims to determine the factors that affect customer satisfaction in cold chain logistics, which helps cold chain logistics enterprises identify the main aspects of the problem. Further, the suggestions are provided for cold chain logistics enterprises to improve customer satisfaction.

This research uses the text mining approach, including topic modeling and sentiment analysis, to analyze the information implicit in customer-generated reviews. First, latent Dirichlet allocation (LDA) model is used to identify the topics that customers focus on. Furthermore, to explore the sentiment polarity of different topics, bi-directional long short-term memory (Bi-LSTM), a type of deep learning model, is adopted to quantify the sentiment score. Last, regression analysis is performed to identify the significant factors that affect positive, neutral and negative sentiment.

The results show that eight topics that customer focus are determined, namely, speed, price, cold chain transportation, package, quality, error handling, service staff and logistics information. Among them, speed, price, transportation and product quality significantly affect customer positive sentiment, and error handling and service staff are significant factors affecting customer neutral and negative sentiment, respectively.

The data of the customer-generated reviews in this research are in Chinese. In the future, multi-lingual research can be conducted to obtain more comprehensive insights.

Prior studies on customer satisfaction in cold chain logistics predominantly used questionnaire method, and the disadvantage of which is that interviewees may fill out the questionnaire arbitrarily, which leads to inaccurate data. For this reason, it is more scientific to discover customer satisfaction from real behavioral data. In response, customer-generated reviews that reflect true emotions are used as the data source for this research.

This study aimed to identify factors influencing AI/chatbot usage in education and research, and to evaluate the extent of the impact of these factors.

This study used a mixed approach of qualitative and quantitative methods. It is based on both primary and secondary data. The primary data were collected through an online survey. In total, 177 responses from teachers were included in this study. The collected data were analyzed using a statistical package for the social sciences.

The study revealed that the significant factors influencing the perception of the academic and research community toward the adoption of AI/interactive tools, such as Chatbots/ChatGpt for education and research, are challenges, benefits, awareness, opportunities, risks, sustainability and ethical considerations.

This study highlighted the importance of resolving challenges and enhancing awareness and benefits while carefully mitigating risks and ethical concerns in the integration of technology within the educational and research environment. These insights can assist policymakers in making decisions and developing strategies for the efficient adoption of AI/interactive tools in academia and research to enhance the overall quality of learning experiences.

The present study adds value to the existing literature on AI/interactive tool adoption in academia and research by offering a quantitative analysis of the factors impacting teachers' perception of the usage of such tools. Furthermore, it also indirectly helps achieve various UNSDGs, such as 4, 9, 10 and 17.