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This chapter is building conceptual background of psychological risk for international tourists. Drawing on Place Attachment Theory, Moral Disengagement Theory, Followership Theory, Job Demands-Resources, Acculturation Theory and Goal Progress Theory of Rumination, this chapter proposes a framework of psychological risks with six psychological risks that tourists could encounter in foreign destination: destination detachment risk, moral disengagement risk, risk of false risk assessment, burnout risk, risk of loneliness and risk of rumination. High destination detachment could lead tourists to behave less environmentally friendly, while high moral disengagement could lead tourists to behave less ethically friendly. Followership to the influencers in social media could lead tourists to engage in risk-taking behaviours and false risk assessment, leading to burnout risk, risk of loneliness and risk of rumination, where negative autobiographical memory is created and forming memory-related distress when they arrive homes. Place detachment and moral disengagement risk local environmental and social health, while burnout, loneliness and rumination pose risks for the tourists' psychological health. Several studies propose suggestions for the destination manager and tourists to manage the risk effectively and adequately, including place attachment and moral engagement campaign, careful travel planning and social support.

The purpose of this paper is to target on designing a short-term load prediction framework that can accurately predict the cooling load of office buildings.

A feature selection scheme and stacking ensemble model to fulfill cooling load prediction task was proposed. Firstly, the abnormal data were identified by the data density estimation algorithm. Secondly, the crucial input features were clarified from three aspects (i.e. historical load information, time information and meteorological information). Thirdly, the stacking ensemble model combined long short-term memory network and light gradient boosting machine was utilized to predict the cooling load. Finally, the proposed framework performances by predicting cooling load of office buildings were verified with indicators.

The identified input features can improve the prediction performance. The prediction accuracy of the proposed model is preferable to the existing ones. The stacking ensemble model is robust to weather forecasting errors.

The stacking ensemble model was used to fulfill cooling load prediction task which can overcome the shortcomings of deep learning models. The input features of the model, which are less focused on in most studies, are taken as an important step in this paper.

Uses data on 1,550 nonlethal assaults recorded by Baltimore County Police Department. Examines factors that are associated with the likelihood of officer injury after an assault. Notes that factors affecting the probability of assault do not necessarily correspond with the factors that affect the likelihood of injury. Analyzes a broader spectrum of contributory factors than those addressed by other research. Finds inter alia that greater officer proficiency in unarmed defensive tactics may reduce their assault‐related injuries, since most incidents do not involve arms; that in‐service training should be biased toward less experienced officers who are at greater risk; that officer height is a significant variable; that many officers suffer multiple attacks; that domestic disturbances do not rank higher than other dangers, but that this may reflect the possibility that officers anticipate potential violence and take better precautions before attending the scene.

The emergence of smart wearables using clothing as a technology platform is a significant milestone with considerable implications for industrial convergence, creating new value for fashion. This paper aimed to present a premeditated prototype to integrate a human activity recognition (HAR) system into outdoor clothing.

For the development of wearable HAR (WHAR) clothing, this paper explored three subject areas: fashion design related to the structural feature of the clothing platform, electronics related to wearable circuits and modules design and graphic user interface design related to smartphone application development.

For WHAR functions in outdoor terrains, the coexistence of accelerometer–gyroscope sensing and distance-sensing could be practical to surpass the technological limitation of activity and posture recognition with gyro sensors highly depending on the changes of acceleration and angles.

Through the vital sign check and physical activity–change recognition function, this study's WHAR system allows users to check their health by themselves and avoid overwork. A quick rescue is possible manually and automatically in a dangerous situation by notifying others. Thus, it can help protect users' health and safety (life).

This study designed the modularization of HAR functions generally installed in indoor medical spaces. Through the approach, smart clothing–embracing WHAR systems optimized for health and safety care for outdoor environments was pursued to diversify expensive roles of clothing for technological applications.

The purpose of this study is to address the critical need for patent value evaluation within patent management, particularly in the context of the digital economy. Recognizing the importance of utilizing historical data, this research aims to uncover effective methodologies that enhance the appraisal of patent value, which is vital for informed decision-making in the management of scientific and technological advancements.

This study introduces a comprehensive evaluation model by analyzing various factors that influence patent value. An index system is constructed that integrates technical, economic and legal aspects to facilitate a nuanced assessment of patents. The methodological core of this research is the development of an XGBoost patent value appraisal model, which incorporates Bayesian optimization to refine the evaluation process. The model’s validity is tested through empirical analysis of patents in the rapidly evolving sector of cloud computing.

The empirical results demonstrate that the XGBoost model, strengthened by Bayesian optimization, outperforms traditional categorization techniques. The proposed model shows superior performance in terms of accuracy, precision, recall rate and operational feasibility. These findings indicate a significant improvement in the precision of patent potential and value assessments, leading to more reliable and actionable insights for patent management.

This study introduces a novel patent evaluation model that combines XGBoost with Bayesian optimization. XGBoost enhances performance by integrating weak learners, ideal for complex, nonlinear problems like patent valuation. Bayesian optimization refines hyperparameters efficiently using prior distributions and known results. Its practical implications for patent management and technology exploration are substantial, offering a new tool for strategic decision-making.

Chinese housing market has been growing fast during the past decade, and price-related forecasting has turned to be an important issue to various market participants, including the people, investors and policy makers. Here, the authors approach this issue by researching neural networks for rent index forecasting from 10 major cities for March 2012 to May 2020. The authors aim at building simple and accurate neural networks to contribute to pure technical forecasting of the Chinese rental housing market.

To facilitate the analysis, the authors examine different model settings over the algorithm, delay, hidden neuron and data spitting ratio.

The authors reach a rather simple neural network with six delays and two hidden neurons, which leads to stable performance of 1.4% average relative root mean square error across the ten cities for the training, validation and testing phases.

The results might be used on a standalone basis or combined with fundamental forecasting to form perspectives of rent price trends and conduct policy analysis.

This study aims to critically evaluate the competitiveness of Transformer-based models in financial forecasting, specifically in the context of stock realized volatility forecasting. It seeks to challenge and extend upon the assertions of Zeng et al. (2023) regarding the purported limitations of these models in handling temporal information in financial time series.

Employing a robust methodological framework, the study systematically compares a range of Transformer models, including first-generation and advanced iterations like Informer, Autoformer, and PatchTST, against benchmark models (HAR, NBEATSx, NHITS, and TimesNet). The evaluation encompasses 80 different stocks, four error metrics, four statistical tests, and three robustness tests designed to reflect diverse market conditions and data availability scenarios.

The research uncovers that while first-generation Transformer models, like TFT, underperform in financial forecasting, second-generation models like Informer, Autoformer, and PatchTST demonstrate remarkable efficacy, especially in scenarios characterized by limited historical data and market volatility. The study also highlights the nuanced performance of these models across different forecasting horizons and error metrics, showcasing their potential as robust tools in financial forecasting, which contradicts the findings of Zeng et al. (2023)

This paper contributes to the financial forecasting literature by providing a comprehensive analysis of the applicability of Transformer-based models in this domain. It offers new insights into the capabilities of these models, especially their adaptability to different market conditions and forecasting requirements, challenging the existing skepticism created by Zeng et al. (2023) about their utility in financial forecasting.

The purpose of this paper is to develop a flexible design-oriented development cost method for commercial aviation aircraft based on small sample and poor information.

To predict the development cost of commercial aviation aircraft accurately, the methodology is based on the collected cost data and actual technical, and then the cost prediction relationships derived from an exhaustive statistical and filtered from regression analysis are incorporated. A series of regression equations with high regression coefficient are yielded after the cost driving factors of the development cost are fixed. Next, several sets of equations with high regression coefficient are selected for final integration. It is a flexible method that can be used efficiently to predict the cost of commercial aviation aircraft.

The development of commercial aviation aircraft has relatively a late start and no cost prediction model has been suitable for small sample, the proposed method is expected and is rather desirable.

By comparing the approach with the ordinary regression model and back propagation (BP) neural network, the scheme in this work is more efficient and convenient.

The results obtained in this paper show that the proposed method not only has a certain degree of versatility, but also can provide a preliminary prediction of the development cost of commercial aviation aircraft.

Social media provides a convenient way to popularise first aid knowledge amongst the general public. So far, little is known about the factors influencing individuals’ adoption of first aid knowledge on social media. Drawing on the information adoption model (IAM), this study investigates the joint effects of cognitive factors (e.g. perceived information usefulness (PIU)), affective factors (e.g. arousal (AR)) and social factors (e.g. descriptive norms (DN)) on first aid knowledge adoption (KA) and examines their antecedent cues from the perspective of information characteristics.

The data were collected from 375 social media users, and the structural equation model was adopted to analyse the results.

The results indicate that PIU, AR and DN all have positive direct effects on first aid KA. Additionally, the study highlights the positive synergistic effect of AR and PIU. Furthermore, the study suggests that AR is determined by message vividness (MV) and emotional tone (ET), whilst DN are determined by peer endorsement (PEE) and expert endorsement (EXE).

Our research is groundbreaking as it delves into the adoption of first aid knowledge through social media, thus pushing the boundaries of existing information adoption literature. Additionally, our study enhances the IAM by incorporating emotional and social elements and provides valuable insights for promoting the spread of first aid knowledge via social media.