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The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

Autonomous vehicles (AVs) have the potential to transform the infrastructure, mobility and social well-being paradigms in New Zealand (NZ) amid its unprecedented population and road safety challenges. But, public acceptance, co-evolution of regulations and AV technology based on interpersonal and institutional trust perspectives pose significant challenges. Previous theories and models need to be more comprehensive to address trust influencing autonomous driving (AD) factors in natural settings. Therefore, this study aims to find key AD factors corresponding to the chain of human-machine interaction (HMI) events happening in real time and formulate a guiding framework for the successful deployment of AVs in NZ.

This study utilized a comprehensive literature review complemented by an AV users’ study with 15 participants. AV driving sprints were conducted on low, medium and high-density roads in Auckland, followed by 15 ideation workshops to gather data about the users’ observations, feelings and attitudes towards the AVs during HMI.

This research study determined nine essential trust-influencing AD determinants in HMI and legal readiness domains. These AD determinants were analyzed, corresponding to eight AV events in three phases. Subsequently, a guiding framework was developed based on these factors, i.e. human-machine interaction autonomous driving events relationship identification framework (HMI-ADERIF) for the deployment of AVs in New Zealand.

This study was conducted only in specific Auckland areas.

This study is significant for advanced design research and provides valuable insights, guidelines and deployment pathways for designers, practitioners and regulators when developing HMI Systems for AD vehicles.

This study is the first-ever AV user study in New Zealand in live traffic conditions. This user study also claimed its novelty due to AV trials in congested and fast-moving traffic on the four-lane motorway in New Zealand. Previously, none of the studies conducted AV user study on SUV BMW vehicle and motorway in real-time traffic conditions; all operations were completely autonomous without any input from the driver. Thus, it explored the essential autonomous driving (AD) trust influencing variables in human factors and legal readiness domains. This research is also unique in identifying critical AD determinants that affect the user trust, acceptance and adoption of AVs in New Zealand by bridging the socio-technical gap with futuristic research insights.

The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.

This study aims to explore the interplay between regulatory, normative and cultural-cognitive factors in the context of sustainability-driven organisational change in Mexican small and medium-sized enterprises (SMEs).

Using qualitative research methods, data were collected through interviews with key stakeholders from eight SMEs in Mexico. Thematic analysis was conducted to explore how regulatory, normative and cultural-cognitive forces influence sustainability initiatives within these organisations.

The findings reveal a dynamic relationship between regulatory frameworks and broader societal norms, with SMEs strategically aligning with evolving expectations to drive sustainability. Influenced by consumer preferences, normative forces play a pivotal role in shaping internal and external organisational norms. Cultural-cognitive forces, including organisational values and leadership practices, further reinforce this interplay, highlighting the importance of organisational culture in driving sustainable practices.

This study contributes to understanding institutional dynamics driving sustainability initiatives among SMEs, particularly in the Mexican context. Explaining the complex interactions among regulatory, normative and cultural-cognitive forces offers a holistic framework for comprehending the complexities of sustainability-driven organisational change.

The findings provide practical insights for SMEs seeking to navigate sustainability initiatives. Emphasising the proactive role of regulatory compliance, fostering a culture of sustainability and leveraging collaborative opportunities within industries are recommended strategies for driving meaningful organisational change towards sustainability.

This study’s value lies in its development of a conceptual framework illustrating the complex interactions among regulatory, normative and cultural-cognitive forces driving sustainability-driven organisational change in Mexican SMEs. Elucidating these dynamics provides a nuanced understanding of how these forces intertwine, offering valuable insights for navigating sustainability initiatives for SMEs in Mexico.

Este estudio explora la interacción entre factores regulatorios, normativos y cultural-cognitivos en el contexto del cambio organizacional impulsado por la sostenibilidad en las pequeñas y medianas empresas mexicanas.

Utilizando métodos de investigación cualitativa, se recopilaron datos a través de entrevistas con partes interesadas clave de ocho PYMES en México. Se llevó a cabo un análisis temático para explorar cómo las fuerzas regulatorias, normativas y cultural-cognitivas influyen en las iniciativas de sostenibilidad dentro de estas organizaciones.

Los resultados revelan una relación dinámica entre los marcos regulatorios y las normas sociales más amplias, con las PYMES alineándose estratégicamente con las expectativas cambiantes para impulsar la sostenibilidad. Influenciadas por las preferencias de los consumidores, las fuerzas normativas desempeñan un papel fundamental en la formación de normas organizacionales internas y externas. Las fuerzas cultural-cognitivas, incluidas los valores organizacionales y las prácticas de liderazgo, refuerzan aún más esta interacción, destacando la importancia de la cultura organizacional en el impulso de prácticas sostenibles.

Este estudio contribuye a la comprensión de las dinámicas institucionales que impulsan las iniciativas de sostenibilidad entre las PYMES, particularmente en el contexto mexicano. Explicar las complejas interacciones entre fuerzas regulatorias, normativas y cultural-cognitivas ofrece un marco holístico para comprender las complejidades del cambio organizacional impulsado por la sostenibilidad.

El valor de este estudio radica en el desarrollo de un marco conceptual que ilustra las complejas interacciones entre fuerzas regulatorias, normativas y cultural-cognitivas que impulsan el cambio organizacional impulsado por la sostenibilidad en las PYMES mexicanas. Elucidar estas dinámicas proporciona una comprensión matizada de cómo estas fuerzas se entrelazan, ofreciendo valiosas ideas para navegar iniciativas de sostenibilidad para las PYMES en México.

Los hallazgos proporcionan ideas prácticas para las PYMES que buscan navegar las iniciativas de sostenibilidad. Se recomiendan estrategias como enfatizar el papel proactivo del cumplimiento regulatorio, fomentar una cultura de sostenibilidad y aprovechar las oportunidades de colaboración dentro de las industrias para impulsar un cambio organizacional significativo hacia la sostenibilidad.

Este estudo explora a interação entre fatores regulatórios, normativos e cultural-cognitivos no contexto da mudança organizacional impulsionada pela sustentabilidade em pequenas e médias empresas mexicanas.

Utilizando métodos de pesquisa qualitativa, os dados foram coletados por meio de entrevistas com partes interessadas de oito PMEs no México. Foi realizada uma análise temática para explorar como as forças regulatórias, normativas e cultural-cognitivas influenciam as iniciativas de sustentabilidade dentro dessas organizações.

Os resultados revelam uma relação dinâmica entre as estruturas regulatórias e as normas sociais mais amplas, com as PMEs alinhando-se estrategicamente às expectativas em evolução para impulsionar a sustentabilidade. Influenciadas pelas preferências dos consumidores, as forças normativas desempenham um papel crucial na formação de normas organizacionais internas e externas. As forças cultural-cognitivas, incluindo valores organizacionais e práticas de liderança, reforçam ainda mais essa interação, destacando a importância da cultura organizacional na promoção de práticas sustentáveis.

Este estudo contribui para a compreensão das dinâmicas institucionais que impulsionam iniciativas de sustentabilidade entre as PMEs, particularmente no contexto mexicano. Explicar as complexas interações entre forças regulatórias, normativas e cultural-cognitivas oferece uma estrutura holística para compreender as complexidades da mudança organizacional impulsionada pela sustentabilidade.

Os resultados fornecem insights práticos para PMEs que buscam navegar em iniciativas de sustentabilidade. Recomenda-se enfatizar o papel proativo do cumprimento regulatório, fomentar uma cultura de sustentabilidade e aproveitar as oportunidades de colaboração dentro das indústrias como estratégias para impulsionar uma mudança organizacional significativa em direção à sustentabilidade.

O valor deste estudo reside no desenvolvimento de um quadro conceitual que ilustra as complexas interações entre forças regulatórias, normativas e cultural-cognitivas que impulsionam a mudança organizacional impulsionada pela sustentabilidade nas PMEs mexicanas. Elucidar essas dinâmicas fornece uma compreensão diferenciada de como essas forças se entrelaçam, oferecendo insights valiosos para PMEs no México.

Harmonic drives are used widely in aviation, robotics and instrumentation due to their benefits including high transmission ratio, compact structure and zero backlash. One of the common faults of a harmonic drive is the axial movement of the input shaft. In such a case, its input shaft moves in the axial direction relative to the body of the harmonic drive. The purpose of this study is to propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives.

In the two proposed fault diagnosis methods, the wavelet threshold algorithm is firstly used for filtering noises of the motor current signal. Then, the feature of the denoised current signal is extracted by the empirical mode decomposition (EMD) method and the wavelet packet energy-entropy (WPEE) theory, respectively, obtaining two kinds of feature sets. After a deep learning model based on the deep belief network (DBN) is constructed and trained by using these feature sets, we finally identify the normal harmonic drives and the ones with the axial movement fault.

In contrast to the traditional back propagation (BP) neural network model and support vector machine (SVM) model, the fault diagnosis methods based on the combination of the EMD (as well as the WPEE) and the DBN model can obtain higher accuracy rates of fault diagnosis for axial movement of harmonic drives, which can be greater than or equal to 97% based on the data of the performed experiment.

The authors propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives, which are verified by the experiment. The presented study may be beneficial for the development of self-diagnosis and self-repair systems of different robots and precision machines using harmonic drives.

The purpose of this study is to address the significant impact AVs will have on public services and the ability of first responders to conduct their jobs safely and effectively. Autonomous vehicles (AVs) are expected to drastically change the transportation industry, and it is vital that first responders be equipped to integrate them into their occupational responsibilities.

A systematic literature review was conducted, and following a multistep exclusion process, 161 articles were selected for detailed review. The impacts of AVs on first responders were identified, classified and categorized into lists of challenges and opportunities. Based on the findings of the literature review, a SWOT (strengths, weaknesses, opportunities and threats) analysis was conducted, and stakeholder management strategies were designed.

Through the examination of the impacts of AVs on first responders, 17 identified challenges and opportunities were classified into the following categories: AV-related emergency response and training, perceptions and acceptance of AVs, technology development and laws and regulations. The study revealed that the optimal benefits of AVs would require stakeholders to focus more on how they interact with first responders; thus, 14 stakeholder management strategies were identified. First responders, AV manufacturers, legislators and future research paths will all benefit from this study, as it can facilitate smooth interactions between AVs and first responders.

A range of studies have been published on the safety of AVs and the public’s perceptions of this new technology; however, the integration of AVs and their interactions with first responders has been neglected. The goal of this study was to fill that research gap by providing a thorough synthesis of autonomous driving systems in the context of their interactions with first responders.

This study aims to address the issues of limited pipe diameter adaptability and low inspection efficiency of current pipeline inspection robots, a new type of pipeline inspection robot capable of adapting to various pipe diameters was designed.

The diameter-changing mechanism uses a multilink elastic telescopic structure consisting of telescopic rods, connecting rods and wheel frames, driven by a single motor with a helical drive scheme. A geometric model of the position relationships of the hinge points was established based on the two extreme positions of the diameter-changing mechanism.

A pipeline inspection robot was designed using a simple linkage agency, which significantly reduced the weight of the robot and enhanced its adaptive pipe diameter ability. The analysis determined that the robot could accommodate pipe diameters ranging from 332 mm to 438 mm. A static equilibrium equation was established for the robot in the hovering state, and the minimum pressing force of the wheels against the pipe wall was determined to be 36.68 N. After experimental testing, the robots could successfully pass a height of 15 mm, demonstrating the good obstacle capacity of the robot.

This paper explores and proposes a new type of multilink elastic telescopic variable diameter pipeline inspection robot, which has the characteristics of strong adaptability and flexible operation, which makes it more competitive in the field of pipeline inspection robots and has great potential market value.

The robot is characterized by the innovative design of a multilink elastic telescopic structure and the use of a single motor to drive the wheel for spiral motion. On the basis of reducing the weight of the robot, it has good pipeline adaptability, climbing ability and obstacle-crossing ability.

The objective of this study is to systematically identify, categorize and assess the driving factors and interdependencies associated with various types of healthcare waste. The study specifically focuses on waste that has been managed or is recommended for treatment through the application of Lean Six Sigma (LSS) methodologies.

To accomplish the study’s objectives, interpretive structural modeling (ISM) was utilized. This analytical tool aided in quantifying the driving power and dependencies of each form of healthcare waste, referred to as “enablers,” as well as their related variables. As a result, these enablers were classified into four distinct categories: autonomous, dependent, linkage and drivers or independents.

In the healthcare sector, the “high cost” (HC) emerges as an autonomous variable, operating with substantial independence. Conversely, variables such as skill wastage, poor service quality and low patient satisfaction are identified as dependent variables. These are distinguished by their low driving power and high dependency. On the flip side, variables related to transportation, production, processing and defect waste manifest strong driving forces and minimal dependencies, categorizing them as independent factors. Notably, inventory waste (IW) is highlighted as a salient issue within the healthcare domain, given its propensity to engender additional forms of waste.

Employing the ISM model, along with comprehensive case study analyses, provides a detailed framework for examining the complex hierarchies of waste existing within the healthcare sector. This methodological approach equips healthcare leaders with the tools to accurately pinpoint and eliminate unnecessary expenditures, thereby optimizing operational efficiency and enhancing patient satisfaction. Of particular significance, the study calls attention to the key role of IW, which often acts as a trigger for other forms of waste in the sector, thus identifying a crucial area requiring focused intervention and improvement.

This research reveals new insights into how waste variables are structured in healthcare, offering a useful guide for managers looking to make their waste-reduction strategies more efficient. These insights are highly relevant not just for healthcare providers but also for the administrators and researchers who are helping to shape the industry. Using the classification and ranking model developed in this study, healthcare organizations can more easily spot and address common types of waste. In addition, the model serves as a useful tool for practitioners, helping them gain a deeper, more detailed understanding of how different factors are connected in efforts to reduce waste.

This study aims to solve the problem that both the speed and the required driving power of electric vehicles (EVs) will change during the dynamic wireless charging (DWC) process, making it difficult to charge EVs with a constant power considering the overall efficiency of DWC system, the numbers of EVs and the power supply capacity. Therefore, this paper proposes the power control and efficiency optimization strategies for multiple EVs.

The wireless power charging system for multiple loads with a structure of double-sided LCC compensation topology is established. The expressions of optimal transmission efficiency and optimal equivalent impedance are derived. Taking the Tesla Model 3 as an example, a method to determine the number of EVs allowed by one transmitter coil and the overall charging power is proposed considering EV speed, power supply capacity, safe braking distance and overall efficiency. Then, the power control strategy, which can adapt to the changes of EV speed and the efficiency optimization strategy under different numbers of EVs are proposed.

In this paper, a method to determine the numbers of EVs allowed by one transmitter coil and the overall charging power is proposed considering EVs speed, power supply capacity, safe braking distance and overall efficiency. The accuracy of the charging power is good enough and the overall efficiency reaches a maximum of 91.79% when the load resistance changes from 5Ω to 20Ω.

In this paper, the power control and efficiency optimization strategy of DWC system for multiple EVs are proposed. Specifically, a method of designing the number of EVs and charging power allowed by one transmitter coil considering the factors of EV speed, power supply capacity, safe braking distance and overall efficiency is designed. The overall efficiency of the experiment reaches a maximum of 91.79% after adopting the optimization strategy.

The sensitivity and fragility of the construction industry’s economic system make the economic resilience of the construction industry (ERCI) a key concern for stakeholders and decision-makers. This study aims to measure the ERCI, identify the heterogeneity and spatial differences in ERCI, and provide scientific guidance and improvement paths for the industry. It provides a foundation for the implementation of resilience policies in the construction industry of developing countries in the future.

The comprehensive index method, Theil index method, standard deviation ellipse method and geographic detector model are used to investigate the spatial differences, spatiotemporal evolution characteristics and the influencing factors of the ERCI from 2005 to 2020 in China.

The ERCI was “high in the east and low in the west”, and Jiangsu has the highest value with 0.64. The Theil index of ERCI shows a wave downward pattern, with significant spatial heterogeneity. The overall difference in ERCI is mainly caused by regional differences, with the contribution rates being higher by more than 70%. Besides, the difference between different regions is increasing. The ERCI was centered in Henan Province, showing a clustering trend in the “northeast-southwest” direction, with weakened spatial polarization and a shrinking distribution range. The market size, input level of construction industry factors, industrial scale and economic scale are the main factors influencing economic resilience. The interaction between each influencing factor exhibits an enhanced relationship, including non-linear enhancement and dual-factor enhancement, with no weakening or independent relationship.

Exploring the spatial differences and driving factors of the ERCI in China, which can provide crucial insights and references for stakeholders, authorities and decision-makers in similar construction economic growth leading to the economic growth of the national economy context areas and countries.

The construction industry development is the main engine for the national economy growth of most developing countries. This study establishes a comprehensive evaluation index on the resilience measurement and analyzes the spatial effects, regional heterogeneity and driving factors on ERCI in the largest developing country from a dynamic perspective. Moreover, it explores the multi-factor interaction mechanism in the formation process of ERCI, provides a theoretical basis and empirical support for promoting the healthy development of the construction industry economy and optimizes ways to enhance and improve the level of ERCI.