Search results

Traditional grey models are integer order whitening differential models; these models are relatively effective for the prediction of regular raw data, but the prediction error of irregular series or shock series is large, and the prediction effect is not ideal.

The new model realizes the dynamic expansion and optimization of the grey Bernoulli model. Meanwhile, it also enhances the variability and self-adaptability of the model structure. And nonlinear parameters are computed by the particle swarm optimization (PSO) algorithm.

Establishing a prediction model based on the raw data from the last six years, it is verified that the prediction performance of the new model is far superior to other mainstream grey prediction models, especially for irregular sequences and oscillating sequences. Ultimately, forecasting models are constructed to calculate various energy consumption aspects in Chongqing. The findings of this study offer a valuable reference for the government in shaping energy consumption policies and optimizing the energy structure.

It is imperative to recognize its inherent limitations. Firstly, the fractional differential order of the model is restricted to 0 < a < 2, encompassing only a three-parameter model. Future investigations could delve into the development of a multi-parameter model applicable when a = 2. Secondly, this paper exclusively focuses on the model itself, neglecting the consideration of raw data preprocessing, such as smoothing operators, buffer operators and background values. Incorporating these factors could significantly enhance the model’s effectiveness, particularly in the context of medium-term or long-term predictions.

This contribution plays a constructive role in expanding the model repertoire of the grey prediction model. The utilization of the developed model for predicting total energy consumption, coal consumption, natural gas consumption, oil consumption and other energy sources from 2021 to 2022 validates the efficacy and feasibility of the innovative model.

These findings, in turn, provide valuable guidance and decision-making support for both the Chinese Government and the Chongqing Government in optimizing energy structure and formulating effective energy policies.

This research holds significant importance in enriching the theoretical framework of the grey prediction model.

The highlights of the paper are as follows:

1. A novel grey Bernoulli prediction model is proposed to improve the model’s structure.

2. Fractional derivative, fractional accumulating generation operator and Bernoulli equation are added to the new model.

3. The proposed model can achieve full compatibility with the traditional mainstream grey prediction models.

4. Energy consumption in Chongqing verifies that the performance of the new model is much better than that of the traditional grey models.

5. The research provides a reference basis for the government to formulate energy consumption policies and optimize energy structure.

A novel grey Bernoulli prediction model is proposed to improve the model’s structure.

Fractional derivative, fractional accumulating generation operator and Bernoulli equation are added to the new model.

The proposed model can achieve full compatibility with the traditional mainstream grey prediction models.

Energy consumption in Chongqing verifies that the performance of the new model is much better than that of the traditional grey models.

The research provides a reference basis for the government to formulate energy consumption policies and optimize energy structure.

This paper aims to elucidate the pivotal role of Digital Twin (DT) technology in addressing the adverse impacts of Urban Heat Island (UHI) and consolidate the fragmented knowledge of DT technology in urban environments by identifying applied actions, proposing an approach and revealing challenges for tackling UHI effects.

Using a systematic literature review, 24 materials were retrieved from scholarly databases to provide a comprehensive understanding of DT technology and propose a conceptual framework for mitigating UHI effects.

The results revealed three major study categories within the DT and UHI domains: (1) DT-enabled actions for urban greenery optimisation, (2) DT implementation for enhancing resilience in urban planning and (3) increasing the fidelity level of DT for addressing UHI effects. Additionally, this paper introduces REFLECT, a conceptual DT-enabled framework consisting of seven layers: Retrieve, Establish, Facilitate, Lump, Examine, Cognition and Take. The framework proposes developing a systems-based model with identifiable scopes, strategies and factors through a multilayered platform, specifying model input, process and output towards mitigating UHI effects.

This paper contributes to the discourse on sustainable urban development by highlighting the challenges associated with DT technology in mitigating UHI. It introduces a conceptual framework to demonstrate applications and directions for developing innovative solutions to unlock the full potential of DT technology in mitigating UHI effects.

The theme park industry has attracted wide attention and visitors’ perceptions are key to improving theme park management. Applying the cognitive-affective theory, this study aims to explore various cognitive attributes and affective attitudes and how they affect the overall theme park image.

A mixed research method was used to analyze tourists’ cognitive, affective and overall evaluations of theme parks through text mining and logistic regression and to verify their internal relationships.

Study 1 confirms the impact of six features of theme parks, including food and beverage consumption, merchandising, spatiality, immersive technologies, interactive performances and thematization. Study 2 reveals that finer-grained emotions such as goodness, sadness, disgust, surprise, fear, joy and anger are present in visitor reviews. Study 3 confirms the significant influence of cognitive characteristics and emotions related to theme parks on the overall image through regression analysis. The findings carry meaningful implications for theme park managers, offering guidance on customer needs, perceived negative attributes and how to improve visitor experiences.

This study explores the attribute characteristics of cognitive and affective images of theme parks and their influence on the overall image, thereby enriching the research on the connotations of cognitive-affective theory. In particular, this study introduces and quantitatively analyses the concept of theme parks for the first time through a large-scale data analysis, which empirically reconciles the contradictions of previous reviews of different definitions of theme parks.

主题公园评论：文本挖掘的认知特征和情感如何决定主题公园形象

摘要

主题公园行业备受关注, 而游客的看法是改进主题公园管理的关键所在。本研究运用认知-情感理论, 旨在探索游客评论的认知属性与情感态度, 以及它们如何影响主题公园的整体形象。

采用混合研究方法, 通过文本挖掘和逻辑回归分析游客对主题公园的认知、情感及总体评价, 并验证它们之间的内在关系。

研究1证实了主题公园的六个特征所产生的影响, 包括餐饮消费、商品销售、空间性、沉浸式技术、互动表演和主题化。研究2表明, 游客评价中存在更细化的情感, 如好感、悲伤、厌恶、惊讶、恐惧、喜悦和愤怒。研究3通过回归分析证实了与主题公园相关的认知和情感特征对整体形象具有显著影响。这些研究结果对主题公园管理者具有重要意义, 为了解客户需求、识别负面属性以及如何改善游客体验提供了指导。

本研究对主题公园认知和情感形象的属性特征及其对整体形象的影响进行了探索, 从而丰富了认知-情感理论内涵。特别是本研究首次通过引入大规模数据并量化分析了主题公园的概念, 从实证角度调和了以往针对主题公园不同定义的相关评论中存在的矛盾。

Reseñas de parques temáticos: cómo la minería de textos de las características cognitivas y las emociones pueden determinar la imagen del parque temático

Resumen

El sector de los parques temáticos ha atraído una gran atención y las percepciones de los visitantes son clave para mejorar su gestión. Aplicando la teoría cognitivo-afectiva, este estudio pretende explorar diversos atributos cognitivos y actitudes afectivas, y cómo afectan a la imagen global del parque temático.

Se adoptó un enfoque de investigación de métodos mixtos para analizar las evaluaciones cognitivas, afectivas y globales de los turistas sobre los parques temáticos mediante minería de textos y regresión logística, y para validar la relación intrínseca entre ellas.

El estudio 1 confirmó el impacto de seis características de los parques temáticos, como el consumo de alimentos y bebidas, el merchandising, la espacialidad, las tecnologías inmersivas, los espectáculos interactivos y la tematización. El estudio 2 reveló la presencia de emociones más sutiles en las evaluaciones de los visitantes, como la bondad, la tristeza, el asco, la sorpresa, el miedo, la alegría y la ira. El estudio 3 confirmó, mediante un análisis de regresión, que las características cognitivas y las emociones asociadas a los parques temáticos tienen un efecto significativo en la imagen global. Estas conclusiones tienen importantes implicaciones para los gestores de los parques temáticos, ya que proporcionan orientación para comprender las necesidades de los clientes, identificar los atributos negativos percibidos y saber cómo mejorar las experiencias de los visitantes.

Este estudio explora las características de los atributos de las imágenes cognitivas y afectivas de los parques temáticos y su impacto en la imagen global, enriqueciendo así la investigación sobre las connotaciones de la teoría cognitivo-afectiva. En particular, este estudio introduce y analiza cuantitativamente por primera vez el concepto de parque temático mediante un análisis de datos a gran escala, que concilia empíricamente las contradicciones que existían en revisiones anteriores de las distintas definiciones de parque temático.

After the standstill generated by the COVID-19 outbreak, airlines were forced to adapt their operations and strategies to the new market requirements and constraints both in terms of supply and demand. The initial recovery included many short-term decisions for survival. Yet, as the market recovered, sometimes in new forms, airlines rebuilt their networks for a new long-term future. In this chapter, we compare the pre- and postpandemic network configurations of a selection of European airlines using a combination of network structure, topology, and connectivity indicators, which help understand airlines' network economics. As a general trend, low-cost carriers recovered faster than full-service ones, in many cases already exceeding prepandemic supply volumes. However, our study shows the high heterogeneity of responses and postpandemic network structures across business models. While some airlines kept very similar structures, adapting to the demand change, some opted for a bolder strategy, growing their networks compared to prepandemic levels. Also, in some cases, new regulations or other external events forced the changes in networks. Finally, although in 2022 data still showed long-haul markets a little behind compared to prepandemic figures, signs such as the continued passenger increase and the ramp up of aircraft orders by airlines point toward a full recovery leading back to a sector based on continued traffic growth.

For underwater hydraulic machinery, the unique structure significantly enhances the three-dimensional non-uniformity of turbulence within the flow domain and high Reynolds number turbulence introduces complex effects on the machinery. Therefore, studying the turbulent flow characteristics in underwater hydraulic machinery is crucial for system stability.

This paper conducts a numerical analysis on a specific type of underwater hydraulic machinery. A numerical calculation model is established under stable inflow conditions to analyze the flow trends and pressure changes at different flow speeds. Subsequently, structural modifications are made to the underwater hydraulic machinery, and the characteristics of the velocity field, pressure field and vorticity distribution under different model parameters are analyzed.

The results indicate that changes in internal structure have a certain impact on flow characteristics. When the structural changes are significant, the fluid flow becomes more complex and pressure fluctuations become more intense. The research findings provide a scientific basis and theoretical guidance for the structural design of underwater hydraulic machinery and have significant research implications for controlling fluid-induced noise.

Affected by the inherent structural characteristics of the flow channel structure, the flow direction of the high-speed water flow changes drastically in the flow channel, so it is of great significance to study its flow characteristics for the stability of the system.

This paper provides a thorough examination of the advancements and impacts of artificial intelligence (AI) on construction management (CM) over the past five years, particularly focusing on its role in mitigating prevalent challenges such as inefficiency and ensuring quality. By methodically reviewing and synthesizing the body of research conducted in this period, it underscores key contributions and breakthroughs in the application of AI within construction management (AICM). Additionally, the study aims to shed light on emerging trends and forecast future directions for technological innovation in the construction management sector.

Guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, this research conducts a bibliometric analysis of 176 relevant publications from the past five years. The analysis focuses on the adoption of AICM across three critical areas: construction equipment management, improvement of construction safety and construction cost optimization. Additionally, the study systematically identifies and examines 14 emerging themes within this domain, ensuring a comprehensive exploration aligned with PRISMA guidelines.

This manuscript summarizes recent research from the past five years in three key areas: construction equipment management, construction safety management and construction cost management within the realm of AICM. It identifies key gaps and outlines future research directions, including enhancing AI-driven equipment integration, developing sophisticated AI-based safety systems and optimizing cost management with advanced data analytics. These findings and directions are essential for steering the field toward greater digital innovation and sustainability.

This research provides a detailed analysis of the literature within the AICM domain, thoughtfully compiling significant findings and highlighting the importance of addressing user needs. The insights and recommendations shared aim to be beneficial for both academic researchers and industry professionals, contributing to the ongoing development of AICM as it moves toward a future characterized by digital innovation and sustainability.

Autonomous underwater vehicle (AUV) is widely used in resource prospection and underwater detection due to its excellent performance. This study considers input saturation, nonlinear model uncertainties and external ocean current disturbances. The containment errors can be limited to a small neighborhood of zero in finite time by employing control strategy. The control strategy can keep errors within a certain range between the trajectory followed by AUVs and their intended targets. This can mitigate the issues of collisions and disruptions in communication which may arise from AUVs being in close proximity or excessively distant from each other.

The tracking errors are constrained. Based on the directed communication topology, a cooperative formation control algorithm for multi-AUV systems with constrained errors is designed. By using the saturation function, state observers are designed to estimate the AUV’s velocity in six degrees of freedom. A new virtual control algorithm is designed through combining backstepping technique and the tan-type barrier Lyapunov function. Neural networks are used to estimate and compensate for the nonlinear model uncertainties and external ocean current disturbances. A neural network adaptive law is designed.

The containment errors can be limited to a small neighborhood of zero in finite time so that follower AUVs can arrive at the convex hull consisting of leader AUVs within finite time. The validity of the results is indicated by simulations.

The state observers are designed to approximate the speed of the AUV and improve the accuracy of the control method. The anti-saturation function and neural network adaptive law are designed to deal with input saturation and general disturbances, respectively. It can ensure the safety and reliability of the multiple AUV systems.

This study expanded the model of technology acceptance and investigated how the relationship between usefulness, ease of use, efficiency, personalization, safety and security and behavioural intention differ on Gen Z and silver tourists toward smart hotel. This study further applies multiple group analysis to examine whether there are substantial differences among these two groups of respondents.

Using an online survey, this study was undertaken with Gen Z and silver tourists in mainland China who had stayed in smart hotel over the past 12 months. A total of 474 valid responses were collected. Structural equation modelling and multigroup analysis were employed to test the proposed relationships.

This study revealed that personalization did not affect the behavioural intention among Gen Z tourists, meanwhile, there is no positive relationship between usefulness, efficiency and behavioural intention on silver group. Additionally, the findings revealed that there are no substantial differences among Gen Z (digital natives) and silver customers (digital immigrants) regarding smart hotel behavioural intentions.

This study offers strategic guidance for hotel managers to design and reposition smart hotel based on different customer sectors. Further, important implications for smart devices manufacturers are also provided to improve the functioning of hotel service robots.

This is the first study to compare the drivers and outcomes of behavioural intentions among different age groups of tourists toward smart hotels.

The concept of cost stickiness has emerged as a crucial factor affecting organizational resilience. While prior literature has mainly focused on the short-term performance implications of cost stickiness, this study aims to investigate its influence on organizational resilience over the long term.

Using data from Chinese A-share listed companies spanning from 2010 to 2019, we examine how cost stickiness impacts organizational resilience and explore the underlying mechanisms. This study contributes to the theoretical understanding of cost stickiness and organizational resilience and offers practical insights for businesses aiming to strengthen their resilience amid economic uncertainties.

Our findings suggest that cost stickiness not only promotes sustained performance growth and stabilizes financial outcomes but also enhances organizational resilience. Moreover, we find that macroeconomic policy uncertainty and micro-level corporate governance positively moderate the relationship between cost stickiness and organizational resilience. Additionally, our analysis reveals that organizational slack plays a partial mediating role in this relationship.

It is essential to clarify the concepts of cost and cost stickiness. Enterprises should scientifically implement the policy concept of “cutting overcapacity and reducing costs” while maintaining a reasonable cost stickiness.

Shareholders and institutional investors of enterprises should pay close attention to the behavior of managers to reduce the possibility of “inferior” stickiness due to agency problems. Both external institutional investors and inner shareholders play essential roles in supervising managers.

Enterprises should focus on cultivating organizational resilience. Resilience is vital for an enterprise to adapt quickly to external changes, overcome adversity, and make progress. Enterprises should reduce the excessive emphasis on short-term performance, focus on long-term development, cultivate strategic thinking, and establish a corporate culture.

The results show that cost stickiness can enhance organizational resilience. Economic policy uncertainty, the shareholding ratio of institutional investors, and the shareholding ratio of the largest shareholder can inhibit the opportunistic behavior of management, thereby positively regulating the relationship between cost stickiness and organizational resilience.

Given the popularity of online health communities (OHCs) and medical question-and-answer (Q&A) services, it is increasingly important to understand what constitutes useful answers and user-adopted standards in healthcare domain. However, few studies provide insights into how health information characteristics, provider characteristics and recipient characteristics jointly influence user information adoption decisions. To fill this research gap, this study examines the combined effects of physicians' certainty tone as information characteristics, seniority as provider characteristics and disease severity as recipient characteristics on patients' health information adoption.

Drawing on dual-process theory and information adoption model, an extended information adoption model is established in this study to examine the effect of attitude certainty on patients' health information adoption, and the moderating effects of online seniority and offline seniority, as well as patient motivation level—disease severity. Utilizing logit regression models, the authors empirically tested the hypotheses based on 4,224 Q&A records from a popular Chinese OHC.

The results show that (1) attitude certainty has a significant positive impact on patients' health information adoption, (2) the relationship between attitude certainty and information adoption is negatively moderated by physicians' online seniority, but is positively moderated by offline seniority; (3) there is a negative three-way interaction effect of attitude certainty, online seniority and disease severity on patients' health information adoption.

This study extends the information adoption model to examine the two-way interaction between argument quality and source reliability, as well as the three-way interaction with user motivation level, especially for health information adoption in the healthcare field. These findings also provide direct practical applications for knowledge contributors and OHCs.