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Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

This paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate.

Firstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved.

The method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data.

The agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.

The purpose of this paper is to illuminate the ethical concerns associated with the use of artificial intelligence (AI) in the medical sector and to provide solutions that allow deriving maximum benefits from this technology without compromising ethical principles.

This paper provides a comprehensive overview of AI in medicine, exploring its technical capabilities, practical applications, and ethical implications. Based on our expertise, we offer insights from both technical and practical perspectives.

The study identifies several advantages of AI in medicine, including its ability to improve diagnostic accuracy, enhance surgical outcomes, and optimize healthcare delivery. However, there are pending ethical issues such as algorithmic bias, lack of transparency, data privacy issues, and the potential for AI to deskill healthcare professionals and erode humanistic values in patient care. Therefore, it is important to address these issues as promptly as possible to make sure that we benefit from the AI’s implementation without causing any serious drawbacks.

This paper gains its value from the combined practical experience of Professor Elhassan gained through his practice at top hospitals worldwide, and the theoretical expertise of Dr. Arabi acquired from international institutes. The shared experiences of the authors provide valuable insights that are beneficial for raising awareness and guiding action in addressing the ethical concerns associated with the integration of artificial intelligence in medicine.

The purpose of this research is to achieve multi-task autonomous driving by adjusting the network architecture of the model. Meanwhile, after achieving multi-task autonomous driving, the authors found that the trained neural network model performs poorly in untrained scenarios. Therefore, the authors proposed to improve the transfer efficiency of the model for new scenarios through transfer learning.

First, the authors achieved multi-task autonomous driving by training a model combining convolutional neural network and different structured long short-term memory (LSTM) layers. Second, the authors achieved fast transfer of neural network models in new scenarios by cross-model transfer learning. Finally, the authors combined data collection and data labeling to improve the efficiency of deep learning. Furthermore, the authors verified that the model has good robustness through light and shadow test.

This research achieved road tracking, real-time acceleration–deceleration, obstacle avoidance and left/right sign recognition. The model proposed by the authors (UniBiCLSTM) outperforms the existing models tested with model cars in terms of autonomous driving performance. Furthermore, the CMTL-UniBiCL-RL model trained by the authors through cross-model transfer learning improves the efficiency of model adaptation to new scenarios. Meanwhile, this research proposed an automatic data annotation method, which can save 1/4 of the time for deep learning.

This research provided novel solutions in the achievement of multi-task autonomous driving and neural network model scenario for transfer learning. The experiment was achieved on a single camera with an embedded chip and a scale model car, which is expected to simplify the hardware for autonomous driving.

This study aims to assess the predictive performance of various factors on Bitcoin returns, used for the development of a robust forecasting support decision model using machine learning techniques, before and during the COVID-19 pandemic. More specifically, the authors investigate the impact of the investor's sentiment on forecasting the Bitcoin returns.

This method uses feature selection techniques to assess the predictive performance of the different factors on the Bitcoin returns. Subsequently, the authors developed a forecasting model for the Bitcoin returns by evaluating the accuracy of three machine learning models, namely the one-dimensional convolutional neural network (1D-CNN), the bidirectional deep learning long short-term memory (BLSTM) neural networks and the support vector machine model.

The findings shed light on the importance of the investor's sentiment in enhancing the accuracy of the return forecasts. Furthermore, the investor's sentiment, the economic policy uncertainty (EPU), gold and the financial stress index (FSI) are the top best determinants before the COVID-19 outbreak. However, there was a significant decrease in the importance of financial uncertainty (FSI and EPU) during the COVID-19 pandemic, proving that investors attach much more importance to the sentimental side than to the traditional uncertainty factors. Regarding the forecasting model accuracy, the authors found that the 1D-CNN model showed the lowest prediction error before and during the COVID-19 and outperformed the other models. Therefore, it represents the best-performing algorithm among its tested counterparts, while the BLSTM is the least accurate model.

Moreover, this study contributes to a better understanding relevant for investors and policymakers to better forecast the returns based on a forecasting model, which can be used as a decision-making support tool. Therefore, the obtained results can drive the investors to uncover potential determinants, which forecast the Bitcoin returns. It actually gives more weight to the sentiment rather than financial uncertainties factors during the pandemic crisis.

To the authors’ knowledge, this is the first study to have attempted to construct a novel crypto sentiment measure and use it to develop a Bitcoin forecasting model. In fact, the development of a robust forecasting model, using machine learning techniques, offers a practical value as a decision-making support tool for investment strategies and policy formulation.

This study seeks to explore the impact of “fear of missing out” (FOMO) and “psychological enhancement” (PE) on addiction to social media applications, subsequently influencing users' life satisfaction and continuous usage intention.

This research involved the administration of two sets of questionnaires during distinct periods: December 15 to December 30, 2022 and August 26 to September 2, 2023. The participants were college students from three universities in China, and the data collection utilized the “Questionnaire Star” platform. Only responses deemed valid and consistent were included in the subsequent statistical analysis. A total of 1,108 valid samples were used for the final analysis. Analyses including reliability, validity, path analysis, structural equation modeling, mediation effects and moderation effects were conducted using SPSS and AMOS software.

The study revealed that both FOMO and PE exerted positive influences on users' addiction to social media applications. Furthermore, this addiction was found to have a negative effect on users' life satisfaction while simultaneously contributing positively to their intention to continue using these platforms. The mediating effect of social media application addiction and the moderating impact of self-regulation were also substantiated through the analysis.

Firstly, it is important to note that the research population of this study is limited to college students, which may limit its generalizability and representativeness. Although college students are a group known for their familiarity with and frequent use of smartphones and social media apps, the findings may not fully capture the behaviors of social media app users in other age groups. To enhance the understanding of social media app addiction across different age groups, future studies should consider expanding the research population and conducting multi-group difference analyses. Secondly, while focusing on specific users within a particular region can minimize unexplained variance in model estimation, it may also restrict the broader applicability of the study results. Therefore, future studies should consider testing the research model with diverse groups from different regions and cultural backgrounds. This approach will provide valuable insights into how social media app addiction may vary across various contexts, thereby enriching our understanding of this phenomenon.

Our findings reveal that in the “attention economy” environment shaped by addiction, social media app managers should leverage technology to swiftly and accurately target audiences, attract them to their platforms and cultivate long-term relationships. Encouraging users to develop new beneficial habits through app-specific functions and precise services will foster continuous usage and unlock revenue and marketing opportunities for app companies.

Despite the extensive scholarly discourse on social media application addiction, there is a lack of a well-defined framework delineating how addictive user behaviors can be leveraged in the marketing strategies of social media application platforms. The present study seeks to address these gaps, contributing to a better understanding of the formation mechanisms and knowledge systems related to social media application addiction. By investigating the causes and consequences of such addiction, this research offers valuable insights and recommendations for the innovative development of these apps, given their widespread popularity. Concurrently, the study establishes a theoretical basis for the concept that users can mitigate the negative effects of social media addiction by exercising their own self-regulation.

As the functionalities and features of social media apps converge, their individual uniqueness starts to diminish, intensifying the competition among social media companies. This escalating rivalry places higher demands on these companies. This study aims to aid social media app companies in comprehending and analyzing the diverse psychological needs of users. By enriching their platform features and services, leading users towards addiction and gaining an edge in the “Attention Economy” competition. Understanding and catering to users' needs will be instrumental in thriving within this dynamic and evolving attention economy landscape.

Technology-enabled communication used in workplace settings includes nuanced tools such as emojis, that are interpreted differently by different populations of people. This paper aims to evaluate the use of emojis in work environments, particularly when they are used sarcastically.

This research uses a survey method administered on MTurk. Overall, 200 participants were included in the analysis. Items were contextualized from prior research and offered on a seven-point Likert scale.

Females are better able to understand if an emoji is used sarcastically. Additionally, older employees are more capable of interpreting sarcasm than younger employees. Finally, understanding of emojis has a negative relationship with frustration, indicating that when users understand emojis are being used sarcastically, frustration is reduced.

This research is primarily limited by the survey methodology. Despite this, it provides implications for theory of mind and practical understanding of emoji use in professional settings. This research indicates emojis are often misinterpreted in professional settings.

The use of emojis is becoming commonplace. The authors show the use of emojis in a professional setting creates confusion, and in some instances can lead to frustration. This work can help businesses understand how best to manage employees with changing communication tools.

This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings.

This study foremostly combined building energy efficiency ratings from several data sources and used them to create predictive models using a variety of ML methods. Secondly, to test the hypothesis of ensemble techniques, this study designed a hybrid stacking ensemble approach based on the best performing bagging and boosting ensemble methods generated from its predictive analytics.

Based on performance evaluation metrics scores, the extra trees model was shown to be the best predictive model. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method. Finally, it was discovered that stacking is a superior ensemble approach for analysing building energy efficiency than bagging and boosting.

While the proposed contemporary method of analysis is assumed to be applicable in assessing energy efficiency of buildings within the sector, the unique data transformation used in this study may not, as typical of any data driven model, be transferable to the data from other regions other than the UK.

This study aids in the initial selection of appropriate and high-performing ML algorithms for future analysis. This study also assists building managers, residents, government agencies and other stakeholders in better understanding contributing factors and making better decisions about building energy performance. Furthermore, this study will assist the general public in proactively identifying buildings with high energy demands, potentially lowering energy costs by promoting avoidance behaviour and assisting government agencies in making informed decisions about energy tariffs when this novel model is integrated into an energy monitoring system.

This study fills a gap in the lack of a reason for selecting appropriate ML algorithms for assessing building energy efficiency. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method.

Existing deterministic models that predict the capacity of corroded reinforced concrete (RC) beams have limited applicability because they were based on accelerated tests that induce general corrosion. This research gap was addressed by performing a combined numerical and statistical analysis on RC beams, subjected to natural corrosion, to achieve a much better forecast.

Data of 42 naturally corroded beams were collected from the literature and analyzed numerically. Four constitutive models and their combinations were considered: the elastic-semi-plastic and elastic-perfectly-plastic models for steel, and two tensile models for concrete with and without the post-cracking stresses. Meanwhile, Popovics’ model was used to describe the behavior of concrete under compression. Corrosion coefficients were developed as functions of corrosion degree and beam parameters through linear regression analysis to fit the theoretical moment capacities with test data. The performance of the coefficients derived from different combinations of constitutive laws was then compared and validated.

The results showed that the highest accuracy (R² = 0.90) was achieved when the tensile response of concrete was modeled without the residual stresses after cracking and the steel was analyzed as an elastic-perfectly-plastic material. The proposed procedure and regression model also showed reasonable agreement with experimental data, even performing better than the current models derived from accelerated tests and traditional procedures.

This study presents a simple but reliable approach for quantifying the capacity of RC beams under more realistic conditions than previously reported. This method is simple and requires only a few variables to be employed. Civil engineers can use it to obtain a quick and rough estimate of the structural condition of corroding RC beams.

Stand-up meetings have received attention for their functional effectiveness in the workplace, but they can also cause affective reactions among attendees. These reactions can affect workplace attitudes and alter the way that employees view and perform their work to the benefit or detriment of the organization.

Following the tenets of the job characteristics model (JCM), a study was conducted on relevant stand-up meetings' effects on beliefs about the meaningfulness of one's work and subsequent motivation. Further analysis explored the effects that meeting load (i.e. the number of meetings) has on the outcomes of meetings.

Consistent with hypotheses, stand-up meeting relevance has an indirect effect on work motivation through work meaningfulness. Meeting load moderates both the indirect effect, such that the effect is stronger at higher numbers of meetings, and the direct effect on work meaningfulness in the opposite direction, as the effect is strongest with fewer meetings.

Organizations should ensure that stand-up meetings are relevant to all attendees and hold the meetings at an appropriate regularity for the best outcomes.

This work examined the stand-up meeting. Most prior meetings research has focused on meetings as a whole or other subtypes and examine meeting relevance and contribution to employee motivation through the lens of JCM.