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This study provides a comprehensive overview of the impact of artificial intelligence (AI) applications on oral healthcare, focusing on clinical outcomes.

A systematic approach was used to gather articles from databases such as Scopus, ScienceDirect, PubMed, Web of Science and Google Scholar from 2010 to 2024. The selection criteria included articles published in English, focusing solely on clinical applications of AI in dentistry. Articles such as conference proceedings, editorial material and personal opinions were excluded. The articles were analyzed and visualized using Rayyan software, Microsoft Excel and VOSviewer.

Results indicate that 120 publications were authored by 58 scholars from 92 institutions across 29 countries, with a notable surge since 2018. This analysis showed the significant emphasis on the use of deep learning, demonstrating its high accuracy and performance in oral healthcare, often exceeding that of dentists. It also proved that even though AI is sometimes seen as an auxiliary tool, many studies revealed that AI has a performance near dental professionals’ levels. Findings concluded that the majority of studies indicate that AI is generating better clinical outcomes in oral healthcare.

This study provides dental professionals with insights on integrating AI for better diagnosis and treatment. Policymakers and healthcare institutions can use these findings to inform AI adoption and training strategies.

It presents novel and valuable findings that can benefit various stakeholders by shedding light on the present scenario and potential future paths of AI integration in oral healthcare, contributing to its overall advancement.

In this paper, a new grey Cosine New Structured Grey Model (CNSGM(1,N)) prediction power model is constructed for the small-sample modeling and prediction problem with complex nonlinearity and insignificant volatility.

Firstly, the weight of some relevant factors is determined by the grey comprehensive correlation degree, and the data are preprocessed. Secondly, according to the principle of “new information priority” and the volatility characteristics of the sequence growth rate, the ideas of damping accumulation power index and trigonometric function are integrated into the New Structured Grey Model (NSGM(1,N)) model. Finally, the non-structural parameters are optimized by the genetic algorithm, and the structural parameters are calculated by the least squares method, so a new CNSGM(1,N) predictive power model is constructed.

Under the principle of “new information priority,” through the combination with the genetic algorithm, the traditional first-order accumulation generation is transformed into damping accumulation generation, and the trigonometric function with the idea of integer is introduced to further simulate the phenomenon that the volatility is not obvious in the real system. It is applied to the simulation and prediction of China’s carbon dioxide emissions, and compared with other comparison models; it is found that the model has a better simulation effect and excellent performance.

The main contribution of this paper is to propose a new grey CNSGM(1,N) prediction power model, which can not only be applied to complex nonlinear cases but also reflect the differences between the old and new data and can reflect the volatility characteristics of the characteristic behavior sequence of the system.

Assembly action recognition plays an important role in assembly process monitoring and human-robot collaborative assembly. Previous works overlook the interaction relationship between hands and operated objects and lack the modeling of subtle hand motions, which leads to a decline in accuracy for fine-grained action recognition. This paper aims to model the hand-object interactions and hand movements to realize high-accuracy assembly action recognition.

In this paper, a novel multi-stream hand-object interaction network (MHOINet) is proposed for assembly action recognition. To learn the hand-object interaction relationship in assembly sequence, an interaction modeling network (IMN) comprising both geometric and visual modeling is exploited in the interaction stream. The former captures the spatial location relation of hand and interacted parts/tools according to their detected bounding boxes, and the latter focuses on mining the visual context of hand and object at pixel level through a position attention model. To model the hand movements, a temporal enhancement module (TEM) with multiple convolution kernels is developed in the hand stream, which captures the temporal dependences of hand sequences in short and long ranges. Finally, assembly action prediction is accomplished by merging the outputs of different streams through a weighted score-level fusion. A robotic arm component assembly dataset is created to evaluate the effectiveness of the proposed method.

The method can achieve the recognition accuracy of 97.31% and 95.32% for coarse and fine assembly actions, which outperforms other comparative methods. Experiments on human-robot collaboration prove that our method can be applied to industrial production.

The author proposes a novel framework for assembly action recognition, which simultaneously leverages the features of hands, objects and hand-object interactions. The TEM enhances the representation of dynamics of hands and facilitates the recognition of assembly actions with various time spans. The IMN learns the semantic information from hand-object interactions, which is significant for distinguishing fine assembly actions.

Agriculture is an activity that plays an important role in human life. Similarly, the agricultural sector plays an important role in the national economy. One of the biggest problems of the agricultural sector is the carbon gas it produces during production. Fertilizing activities and tools used in plowing the fields cause this gas to be produced. The release of the said gas into nature causes serious damage to the environment. Therefore, carbon emissions in the agricultural sector are of vital importance. In line with this purpose, it is aimed to determine the most appropriate strategy for carbon emission in this study. As a result of the DEMATEL analysis, it was seen that the most appropriate strategy was effective regulations and auditing.

The aging and deterioration of engineering building structures present significant risks to both life and property. Fiber Bragg grating (FBG) sensors, acclaimed for their outstanding reusability, compact form factor, lightweight construction, heightened sensitivity, immunity to electromagnetic interference and exceptional precision, are increasingly being adopted for structural health monitoring in engineering buildings. This research paper aims to evaluate the current challenges faced by FBG sensors in the engineering building industry. It also anticipates future advancements and trends in their development within this field.

This study centers on five pivotal sectors within the field of structural engineering: bridges, tunnels, pipelines, highways and housing construction. The research delves into the challenges encountered and synthesizes the prospective advancements in each of these areas.

The exceptional performance of FBG sensors provides an ideal solution for comprehensive monitoring of potential structural damages, deformations and settlements in engineering buildings. However, FBG sensors are challenged by issues such as limited monitoring accuracy, underdeveloped packaging techniques, intricate and time-intensive embedding processes, low survival rates and an indeterminate lifespan.

This introduces an entirely novel perspective. Addressing the current limitations of FBG sensors, this paper envisions their future evolution. FBG sensors are anticipated to advance into sophisticated multi-layer fiber optic sensing networks, each layer encompassing numerous channels. Data integration technologies will consolidate the acquired information, while big data analytics will identify intricate correlations within the datasets. Concurrently, the combination of finite element modeling and neural networks will enable a comprehensive simulation of the adaptability and longevity of FBG sensors in their operational environments.

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

This paper aims to develop a framework for the bioaccounting measurement of environmental assets based on natural wealth sustainability. Specifically, this paper proposes a theoretical structure for qualitative and quantitative organization-level assessments of the existence and circulation of water, air, wildlife, flora, soil and subsoil resources.

This research used an inductive method with a qualitative and quantitative approach. The authors postulate a systemic and comprehensive bioaccounting measurement of environmental assets, including heterogeneous and homogeneous methods and quantitative and qualitative valuations of the resources that comprise environmental assets.

The authors describe a theoretical structure for the bioaccounting measurement of environmental assets based on the sustainability of natural wealth through heterogeneous and homogeneous measurement methods and show how to integrate these assets through an homogeneous method.

The development of this general theoretical structure will require the integration of theoretical, conceptual and technical developments from multiple disciplines. The authors hope that the scientific community will evaluate and study this proposal for faster progress towards its practical implementation in organizations.

The authors structured the bioaccounting measurements, which are presented individually for each class of environmental assets. Each of these assets requires subcategories (accounts, subaccounts and resources) and recognition/measurement units. Environmental value units (EVUs) are used to standardize the plurality of measurement units.

This paper aims to evaluate the dynamic performance of hybrid roller bearings under lubricant contamination.

Some steel rollers in traditional cylindrical thrust roller bearings were replaced with ceramic rollers to assemble hybrid roller bearings. Friction experiments were conducted under lubricant contamination using alumina as the contaminant, and simultaneous vibration acceleration signals from the bearings were collected to evaluate their tribological and dynamic performance.

Under lubricant contamination, hybrid roller bearings with a sufficient number of ceramic rollers exhibit greater wear resistance compared to traditional all-steel bearings. There is a noticeable suppression of energy in both tangential and normal frequency bands of the bearings, with more pronounced suppression observed in higher frequency bands.

This study provides valuable insights for the development of hybrid ceramic bearings.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2024-0291/

Language barriers have always been obstacles to traveling abroad, making travelers less interested. This study aims to investigate how ChatGPT, as a translator, affects travelers’ behavioral intentions based on the perceived language barriers using the ChatGPT voice translation app.

A total of 531 responses were collected over a specific survey period using a cross-sectional time frame. This study proposed a research model based on technology acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT), and the proposed hypotheses were investigated using bootstrapping techniques and Smart-PLS analysis.

This study’s results are significant as they reveal that ChatGPT enables translated service to travelers and enhances their willingness to travel abroad. This finding indicates a considerable enhancement in the traveler’s confidence in communicating with residents, thereby emphasizing ChatGPT’s pivotal role in overcoming language barriers in the travel industry.

This research is novel in its approach as it delineates the influence of ChatGPT’s translation services on travelers’ willingness and behavioral intentions regarding international travel. By filling a significant gap in the existing literature, this study provides a fresh perspective on the role of technology in overcoming language barriers in the travel industry.

语言障碍一直是出国旅行的障碍, 使旅行者不太感兴趣。本研究调查了作为翻译工具的ChatGPT, 基于ChatGPT语音翻译应用程序感知到的语言障碍, 如何影响旅行者的行为意图。

使用横断面时间框架共收集了531份调查问卷。本研究提出了一个基于TAM和UTAUT的研究模型, 并使用bootstrapping技术和Smart-PLS分析对提出的假设进行了研究。

该研究结果具有重要意义, 因为它们揭示了ChatGPT可以为旅行者提供翻译服务, 并增强了他们出国旅游的意愿。这一发现表明旅行者与当地居民沟通的信心大大增强, 从而强调了ChatGPT在克服旅游行业语言障碍方面的关键作用。

本研究在方法上是新颖的, 讨论了ChatGPT翻译服务对旅行者国际旅行意愿和行为意图的影响。本研究为了解技术在克服旅行语言障碍方面的作用提供了一个新的视角, 填补了相关研究缺口。

Las barreras lingüísticas han sido siempre un obstáculo para viajar al extranjero, lo que ha restado interés a los viajeros. Este estudio investiga cómo ChatGPT, como traductor, afecta a las intenciones de comportamiento de los viajeros en función de las barreras lingüísticas percibidas utilizando la aplicación de traducción de voz ChatGPT.

Se recogieron un total de 531 respuestas a lo largo de un periodo de encuesta específico utilizando un marco temporal transversal. Este estudio propuso un modelo de investigación basado en Modelo de Aceptación de la Tecnología (TAM) y Modelo Unificado de Aceptación y Uso de Tecnología (UTAUT), y las hipótesis propuestas se investigaron mediante técnicas de bootstrapping y análisis Smart-PLS.

Los resultados de este estudio son significativos, ya que revelan que ChatGPT permite ofrecer un servicio traducido a los viajeros y aumenta su disposición a viajar al extranjero. Este hallazgo indica una mejora considerable de la confianza del viajero a la hora de comunicarse con los residentes, lo que subraya el papel fundamental de ChatGPT para superar las barreras lingüísticas en el sector de los viajes

Esta investigación es novedosa en su planteamiento, ya que describe la influencia de los servicios de traducción de ChatGPT en la disposición y las intenciones de comportamiento de los viajeros respecto a los viajes internacionales. Al llenar una laguna importante en la literatura existente, este estudio aporta una nueva perspectiva sobre el papel de la tecnología en la superación de las barreras lingüísticas en el sector de los viajes.

This study aims to examine the interaction effects of chatbots’ language style and customers’ decision-making journey stage on customer’s service encounter satisfaction and the mediating role of customer perception of emotional support and informational support using the construal level theory and social support theory as conceptual frameworks.

This study used a scenario-based experiment with a 2 (chatbot’s language style: abstract language vs concrete language) × 2 (decision-making journey stage: informational stage vs transactional stage) between-subjects design.

Findings show that during the informational stage, chatbots that use abstract language style exert a strong influence on service encounter satisfaction through emotional support. During the transactional stage, chatbots that use concrete language style exert a strong impact on service encounter satisfaction through informational support.

Findings provide some suggestions for improving customer–chatbot interaction quality during online service encounters.

This study offers a novel perspective on customer interaction experience with chatbots by investigating the chatbot’s language styles at different decision-making journey stages.