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Smart farming technologies (SFTs) can increase yields and reduce the environmental impacts of farming by improving the efficient use of inputs. This paper is to estimate farmers' preference and willingness to pay (WTP) for a well-defined SFT, smart drip irrigation (SDI) technology.

This study conducted a discrete choice experiment (DCE) among 1,300 maize farmers in North China to understand their WTP for various functions of SDI using mixed logit (MIXL) models.

The results show that farmers have a strong preference for SDI in general and its specific functions of smart sensing and smart control. However, farmers do not have a preference for the function of region-level agronomic planning. Farmers' preferences for different functions of SDI are heterogeneous. Their preference was significantly associated with their education, experience of being village cadres and using computers, household income and holding of land and machines. Further analysis show that farmers' WTP for functions facilitated by hardware is close to the estimated prices, whereas their WTP for functions wholly or partially facilitated by software is substantially lower than the estimated prices.

Findings from the empirical study lead to policy implications for enhancing the design of SFTs by integrating software and hardware and optimizing agricultural extension strategies for SFTs with digital techniques such as videos.

This study provides initial insights into understanding farmers' preferences and WTP for specific functions of SFTs with a DCE.

The regulations for qualitative genetically modified (GM) food labeling do not effectively eliminate the information asymmetries pertaining to the consumption of GM products. China's GM food labeling law requires the presentation of certain categories of GM products on GMO labels on packages. Such information is invaluable for understanding whether the disclosure of more information on GM foods can help alleviate information asymmetry while reducing consumer fear and risk perceptions of GM foods, and thus cause changes in their behaviors. The purpose of this paper is to explore the heterogeneity of consumer preferences for enhanced GM food labeling, how consumer preferences are influenced by labeling information, and how these preferences vary in different consumers.

Both descriptive statistics and econometric techniques, including the multivariate ordered Probit model, were applied to a sample of 566 urban consumers in the Yangtze River Delta region of China. All respondents were divided into two groups: people who would definitely not buy GM foods before selecting enhanced labeling information (“consumer group 1”, sample size 282) and people who would definitely not object to buying GM foods before selecting enhanced labeling information (“consumer group 2”, sample size 274).

The findings suggest that urban consumers have a preference for different types of enhanced labeling information about GM foods, that such a preference significantly influences their willingness to pay for GM foods, that there exists a large difference in the preferences of different consumer groups, and that enhanced GM food labeling information has a greater impact on those consumers who would not accept GM foods.

This research identifies the GM information disclosures that most affect consumer preferences and how these preferences vary across different segments of consumers, which is a current gap in the literature. The study has demonstrated that enhanced labels of GM foods with diverse information would not alter a consumer's WTP for GM foods who does not perceive the value of information from the enhanced labels, but change positively consumers with uncertainty willingness to pay before disclosure.

Accelerating the green economy’s transition is a practical means of lowering emissions and conserving energy, and its effects on the greenhouse effect merit careful consideration. Growing environmental deterioration has compelled decision-makers to prioritize sustainability alongside economic growth. Policymakers and the business community are interested in green investment (GRE), but its effects on social and environmental sustainability are still unknown. Based on this, this study aims at looking into the time-frequency interplay between GRE and carbon dioxide emissions and assessing the impacts of economic growth, financial globalization and fossil fuel energy (FUE) usage on this nexus in Vietnam across different time and frequency domains.

The authors employ continuous wavelets, cross wavelet transforms, wavelet coherence, Rua’s wavelet correlation and wavelet-based Granger causality tests to capture how the domestic variance and covariance of two-time series co-vary as well as the co-movement interdependence between two variables in the time-frequency domain.

The results shed new light on the fact that GRE will increase the levels of environmental quality in Vietnam in the short and medium run and there is a bidirectional causality between the two indicators across different time and frequencies. In addition, when the authors observe the effect of economic growth, financial globalization and fossil fuel energy consumption on this interplay, the findings suggest that, in different time and frequencies, any joined positive change in these indicators will move the CO₂ emissions-GRE nexus.

Policymakers and governments can greatly benefit from this topic by utilizing the function of economic institutions in capital control of GRE and CO₂ emissions and modifying the impact of GRE on the greenhouse effect by accelerating the green growth of economic industries.

The current work contributes to the current literature on GRE and CO₂ emissions in several dimensions: (1) considering the sustainable development in Vietnam, by employing a new single-country dataset of GRE index, this paper aims to contribute to the growing body of research on the factors that influence CO₂ emissions, as well as to provide a detailed explanation for the relationship between GRE and CO₂ emissions; (2) localized oscillatory components in the time-domain region have been used to evaluate the interplay between GRE and CO₂ emission in the frequency domain, overcoming the limitations of the fundamental time-series analysis; (3) the mediation role of economic growth, financial globalization and FUE in affecting the GRE-CO₂ relationship is empirically explored in the study.

This purpose of this paper is to present a new approach of solving quantum control problem.

State‐transition‐matrix (STM) is used to solute quantum control problem.

The paper obtains the matrix representation for wave function, normalization, and some other quantum operators, and then gives the possible methods to establish the STM model and solve it.

Based on this, many tools of the widely used control theory can be adopted to denote or analyze the quantum system.

The purpose of this paper is to develop a human activity-aware adaptive shared control solution for human–robot interaction in surgical operation. Hands-on control and teleoperation are two main procedures switched frequently in teleoperated minimally invasive surgery (MIS). The detailed human activity in the procedures can be defined and recognized using the sensor information. In this paper, a novel continuous adaptive shared control method is proposed for manipulators with Cartesian impedance control in the surgical scenario.

A human activity-aware shared control solution by adjusting the weight function is introduced to achieve smooth transition among different human activities, including hands-on control and teleoperation. Instead of introducing various controllers and switching among them during the surgical procedures, the proposed solution integrated all the human activity-based controllers into a single controller and the transition among the procedures is smooth and stable. The effectiveness of the proposed control approach was verified in a lab setup environment. The results prove that the robot behavior is stable and smooth. The algorithm is feasible and can achieve a human activity-aware adaptive shared control solution for human–robot interaction in surgical operation.

Based on the experiment, the results confirm that the proposed human activity-aware adaptive shared control solution can switch the device behavior automatically using the real-time sensor information. The transition between different activities is smooth and stable.

For teleoperated surgical applications, the proposed method integrated different controllers for various human activities into a single controller by recognizing the activities using the real-time sensor information and the transition between different procedures is smooth and stable. It eases the surgical work for the surgeon and enhances the safety during the transition of control modes. The presented scheme provides a general solution to address the switching of working procedures in teleoperated MIS.

To the best of the authors’ knowledge, this paper is the first to propose human activity-aware adaptive shared control solution for human–robot interaction in surgical operations.

This study aims to investigate the conditional indirect effect of academic self-efficacy in the interplay between academic motivation and academic satisfaction through academic engagement among university students.

A cross-sectional study was performed on 1,638 Vietnamese university students (31.9% males and 68.1% females) aged 16 to 36 (Mean = 20.06, SD = 1.428). The participants filled out a questionnaire with the Vietnam versions of the General Self-Efficacy Scale, Academic Motivation Scale, Academic Life Satisfaction Scale and Academic Engagement Scale. Model 4 and Model 7 in the PROCESS macro were used for the mediation analysis and the moderated mediation analysis.

Results showed that the indirect effect of academic engagement on the academic motivation-academic satisfaction link was significant. Furthermore, academic self-efficacy moderated this indirect effect. The indirect effect was stronger among students with high academic self-efficacy and weaker among students with low academic self-efficacy.

This study’s findings contribute to educational research on academic satisfaction and can be used by institutions of higher education and educators to enhance academic satisfaction among university students.

Green finance aims to promote sustainable financial activities, environmental conservation and ecological balance. This study examines how renewable energy consumption (REN), technological innovation (TEC) and green finance (GRF) influence CO2 emissions in Vietnam from 2000 to 2022.

We utilize a novel three-stage methodology including quantile-on-quantile regression, wavelet coherence and wavelet-quantile regression to explore the relationship in the structure of intercorrelation in terms of quantile, time and frequency.

The findings show that Vietnam will increase environmental quality for higher green development. Specifically, there is a negative influence of TEC, REN and GRF on CO2 emissions across different quantiles and timescales.

The study recommends policies that support green development and reduce carbon emissions, such as increasing the use of renewable energy and conducting well-planned research to achieve a carbon-free, sustainable environment.

This article looks into the effects of GRF, TEC and REN on CO2 emissions in Vietnam. Some studies argue that green development in underdeveloped nations is insufficient to reduce CO2 emissions, thereby limiting the sample to a few advanced economies. Adopting diverse methodologies demonstrates the varied and intricate nature of understanding CO2 drivers. Additionally, our work makes detailed policy implications for Vietnam to meet its net-zero emission target and achieve sustainable development by 2050.

Emergency service dispatchers perform a vital role in assisting callers by helping them through the emergency they are in and ensuring the delivery of first responders. If an emergency caller prematurely hangs up a call, before the arrival of first responders, it can impact a dispatcher’s ability to provide them with assistance. This paper aims to understand why staged callers, who are attempting to cover up a crime, and authentic callers hang up by identifying which linguistic and behavioural indicators occur prior to the end of a call.

A sample of 40 emergency calls (20 staged; 20 authentic) related to health and welfare emergencies were analysed with a temporal measure known as indicator waves. This provided a means to identify which indicators occurred above the level expected by chance near the end of authentic and staged calls.

The results of the current study show that the indicators that proceeded hanging up in authentic calls reflected the nature of the situation (e.g. nonurgency wherein the victim is deceased or is in a stable condition). Whereas the indicators that proceeded hanging up in staged calls focused on the “staging” aspect of the incident by accounting for forensic evidence that could be incriminating.

The identification of indicators that occur before the end of staged and authentic calls provides further insight into the differences between the two call types and offers opportunities for further research, potentially leading to application.

Stiffened plates have been widely used in civil, marine, aerospace engineering. As a kind of thin-walled structure operating in complex environment, stiffened plates mostly undergo a variety of dynamic loads, which may sometimes result in large-amplitude vibration. Additionally, initial stresses and geometric imperfections are widespread in this type of structure. Furthermore, it is universally known that initial stresses and geometric imperfections may affect mechanical behavior of structures severely, particularly in dynamic analysis. Thus, the purpose of this paper is to study the stress variation rule of a stiffened plate during large-amplitude vibration considering initial stresses and geometric imperfections.

The initial stresses are represented in the form of initial bending moments applying to the stiffened plate, while the initial geometric imperfections are considered by means of trigonometric series, and they are assumed existing in the plate along the z-direction exclusively. Then, the dynamic equilibrium equations of the stiffened plate are established using Lagrange’s equation as well as aforementioned conditions. The nonlinear differential equations of motion are simplified as a two-degree-of-freedom system by considering 1:2 and 1:3 internal resonances, respectively, and the multiscale method is applied to solve the equations.

The influence of initial stresses on the plate, stresses during internal resonance is remarkable, while that is moderate for initial geometric imperfections. (Upon considering the existence of initial stresses or geometric imperfections, the stresses of motivated modes are less than the primary mode for both and internal resonances). The influence of bidirectional initial stresses on the plate’s stresses during internal resonance is more remarkable than that of unidirectional initial stresses. The coupled vibration in 1%3A2 internal resonance is fiercer than that in internal resonance.

Stiffened plates are widely used in engineering structures. However, as a type of thin-walled structure, stiffened plates vibrate with large amplitude in most cases owning to their complicated operation circumstance. In addition, stiffened plates usually contain initial stresses and geometric imperfections, which may result in the variation of their mechanical behavior, especially dynamical behavior. Based on the above consideration, this paper studies the nonlinear dynamical behavior of stiffened plates with initial stresses and geometrical imperfections under different internal resonances, which is the originality of this work. Furthermore, the research findings can provide references for engineering design and application.

Despite the widespread use of univariate empirical mode decomposition (EMD) in financial market forecasting, the application of multivariate empirical mode decomposition (MEMD) has not been fully investigated. The purpose of this study is to forecast the stock price index more accurately, relying on the capability of MEMD in modeling the dependency between relevant variables.

Quantitative and comprehensive assessments were carried out to compare the performance of some selected models. Data for the assessments were collected from three major stock exchanges, namely, the standard and poor 500 index from the USA, the Hang Seng index from Hong Kong and the Shanghai Stock Exchange composite index from China. MEMD-based support vector regression (SVR) was used as the modeling framework, where MEMD was first introduced to simultaneously decompose the relevant covariates, including the opening price, the highest price, the lowest price, the closing price and the trading volume of a stock price index. Then, SVR was used to set up forecasting models for each component decomposed and another SVR model was used to generate the final forecast based on the forecasts of each component. This paper named this the MEMD-SVR-SVR model.

The results show that the MEMD-based modeling framework outperforms other selected competing models. As per the models using MEMD, the MEMD-SVR-SVR model excels in terms of prediction accuracy across the various data sets.

This research extends the literature of EMD-based univariate models by considering the scenario of multiple variables for improving forecasting accuracy and simplifying computability, which contributes to the analytics pool for the financial analysis community.