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This study aimed to identify a specific taxonomy of entry modes for international construction contractors and to develop a decision-making mechanism based on case-based reasoning (CBR) to facilitate the selection of the most suitable entry modes.

According to the experience orientation of the construction industry, a CBR entry mode decision model was established, and based on successful historical cases, a two-step refinement process was carried out to identify similar situations. Then the validity of the model is proved by case analysis.

This study identified an entry mode taxonomy for international construction contractors (ICCs) and explored their decision-making mechanisms. First, a two-dimension model of entry mode for ICCs was constructed from ownership and value chain dimensions; seven common ICC entry modes were identified and ranked according to market commitment. Secondly, this study reveals the impact mechanism of the ICC entry mode from two aspects: the external environment and enterprise characteristics. Accordingly, an entry mode decision model is established.

Firstly, sorting out the categories of entry mode in the construction field, which provide an entry mode list for ICCs to select. Secondly, revealing the impact mechanism of ICC entry mode, which proposes a systematic decision-making system for the selection of ICC entry mode. Thirdly, constructing a CBR entry mode decision-making model from an empirical perspective, which offers tool support and reduces transaction costs in the decision-making process.

The study on entry modes for ICCs is still in the preliminary exploratory stage. The authors investigate the entry mode categories and decision-making mechanisms for ICCs based on Uppsala internationalization process theory. It widens the applied scope of Uppsala and promotes cross-disciplinary integration. In addition, the authors creatively propose a two-stage retrieval mechanism in the CBR model, which considers the order of decision variables. It refines the influence path of the decision variables on ICCs' entry mode.

Global climate change speeds up ice melting and increases flooding incidents. China launched a sponge city policy as a holistic nature-based solution combined with urban planning and development to address flooding due to climate change. Using Weibo analytics, this paper aims to study public perceptions of sponge city.

This study collected 53,586 sponge city contents from Sina Weibo via Python. Various artificial intelligence tools, such as CX Data Science of Simply Sentiment, KH Coder and Tableau, were applied in the study.

76.8% of public opinion on sponge city were positive, confirming its positive contribution to flooding management and city branding. 17 out of 31 pilot sponge cities recorded the largest number of sponge cities related posts. Other cities with more Weibo posts suffered from rainwater and flooding hazards, such as Xi'an and Zhengzhou.

To the best of the authors’ knowledge, this study is the first to explore the public perception of sponge city in Sina Weibo.

Night lighting reflects the prosperous development of economic and the increasingly rich and colorful cultural life. Currently, various technical standards, protocols and management specifications have been developed to build a safe, comfortable and economical lighting environment. However, prevailing evaluation systems focus on objective indexes of illumination and have ignored environmental characteristics and subjective feelings and lacked consideration of regional culture, economic benefit, management and maintenance. In this context, a lighting evaluation system combining subjective and objective is proposed for the first time in this study to explore approaches to guide the development of a healthy and comfortable urban night-time environment.

Existing research and relevant lighting standards are analyzed and an evaluation model with a logical hierarchy is constructed by combining with the evaluation theory that is set based on people and the environment. The index weights were scientifically determined on the basis of the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method. The rationality and validity of the proposed evaluation system is verified in accordance with field projects and case studies.

Taking into account traditional and cultural factors, the evaluation model established has an acceptable accuracy. Evaluation based on subjective-objective combination can provide a scientific basis for the management and optimization of night lighting.

The proposed evaluation system can serve as a guiding reference for other areas of cultural identity and esthetic perspective.

The paper seeks to report data on cigarette smoking, anti‐smoking practices, physicians' receipt of anti‐smoking training, and the association between receipt of the training and anti‐smoking practice among physicians in Wuhan, China.

Participants were selected through the stratified random sampling method. The questionnaires were completed by the sampled physicians and the response rate of the survey was 98.1 percent.

Among the total sample, 11 percent were current smokers. Significantly more male physicians than female physicians were current smokers (31.6 vs 0.9 percent, p<0.001). In total, 41 percent of physicians always or often asked patients about smoking habits, and 61 percent of them often advised patients to quit. Receiving anti‐tobacco training significantly increased the likelihood for physicians to ask patients about smoking (odd ratio=2.55, p<0.001) and to advise patients against smoking (odd ratio=4.05, p<0.001) with and without controlling gender, age, education, type of hospital and medical services specialty.

More effort should be devoted to training for physicians with focus on anti‐smoking practice and smoking cessation counseling in addition to assist physicians themselves to quit smoking.

The findings of this study update the data regarding cigarette smoking among physicians in Wuhan, China, and their practice of anti‐tobacco counseling. It indicates that it is very important to provide the training regarding anti‐smoking counseling among physicians.

The construction of smart cities holds the potential to drive digital innovation in the construction industry through various means, such as enhancing supply and demand. This study echoes the urgent need for the construction industry to overcome development challenges. Hence, it is necessary to study the extent and ways in which smart city policies promote digital innovation in the construction industry.

This study treats China’s smart city policies as quasi-natural experiments. Using a dataset of Chinese prefecture-level cities from 2007 to 2021 and a difference-in-differences model, the study scrutinizes the impact of smart city policies on digital innovation within the construction industry.

The study reveals a substantial positive influence of smart city policies on digital innovation in the construction industry. In addition, the study explains these results by analysing supply-side and demand-side mechanisms. Moreover, the effect of smart city pilot policies on promoting digital innovation within the construction industry displays noteworthy heterogeneity across cities at different regional and political levels.

By exploring the impact and mechanisms of smart city policies on digital innovation in the construction industry, this research contributes to a more comprehensive and profound comprehension of the role of policies in facilitating the digital transformation of the construction sector. It is a valuable reference for policymakers and industry practitioners aiming to advance digital development.

The abnormal behaviors of staff at petroleum stations pose significant safety hazards. Addressing the challenges of high parameter counts, lengthy training periods and low recognition rates in existing 3D ResNet behavior recognition models, this paper proposes GTB-ResNet, a network designed to detect abnormal behaviors in petroleum station staff.

Firstly, to mitigate the issues of excessive parameters and computational complexity in 3D ResNet, a lightweight residual convolution module called the Ghost residual module (GhostNet) is introduced in the feature extraction network. Ghost convolution replaces standard convolution, reducing model parameters while preserving multi-scale feature extraction capabilities. Secondly, to enhance the model's focus on salient features amidst wide surveillance ranges and small target objects, the triplet attention mechanism module is integrated to facilitate spatial and channel information interaction. Lastly, to address the challenge of short time-series features leading to misjudgments in similar actions, a bidirectional gated recurrent network is added to the feature extraction backbone network. This ensures the extraction of key long time-series features, thereby improving feature extraction accuracy.

The experimental setup encompasses four behavior types: illegal phone answering, smoking, falling (abnormal) and touching the face (normal), comprising a total of 892 videos. Experimental results showcase GTB-ResNet achieving a recognition accuracy of 96.7% with a model parameter count of 4.46 M and a computational complexity of 3.898 G. This represents a 4.4% improvement over 3D ResNet, with reductions of 90.4% in parameters and 61.5% in computational complexity.

Specifically designed for edge devices in oil stations, the 3D ResNet network is tailored for real-time action prediction. To address the challenges posed by the large number of parameters in 3D ResNet networks and the difficulties in deployment on edge devices, a lightweight residual module based on ghost convolution is developed. Additionally, to tackle the issue of low detection accuracy of behaviors amidst the noisy environment of petroleum stations, a triple attention mechanism is introduced during feature extraction to enhance focus on salient features. Moreover, to overcome the potential for misjudgments arising from the similarity of actions, a Bi-GRU model is introduced to enhance the extraction of key long-term features.

The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage, lack of effective features, and small sample sizes, resulting in low fault identification accuracy and slow efficiency. The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.

First, to address the impact of background noise on the accuracy of anomaly signals, the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD) method is used to eliminate strong noise in pipeline signals. Secondly, to address the strong data dependency and loss of local features in the Swin Transformer network, a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed. This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities. Thirdly, to address the sparsity and imbalance of anomaly samples, the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.

In the pipeline anomaly audio and environmental datasets such as ESC-50, the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods. Additionally, the model achieved 98.7% accuracy on the preprocessed anomaly audio dataset and 99.0% on the ESC-50 dataset.

This paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model, addressing noise interference and low accuracy issues in pipeline anomaly detection, and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.

The purpose of this paper is to improve the tribological properties of aluminum cylinder liner. Higher martensite contents were closely related to the higher hardness and excellent wear resistance of Fe-based coatings. Furthermore, the grain size of the Fe-based coating was approximately 40 nm, which provides an excellent fine grain strengthening effect.

To improve the tribological properties of aluminum cylinder liners, a Fe-based martensite coating was prepared by internal plasma spraying technology, whose microstructure and tribological properties were then investigated.

Sprayed Fe-based coating possessed a low contact angle and strong adhesion with lubricating oil. In a simulated engine condition, Fe-based coating exhibited a decreased friction coefficient and increased wear resistance under oil lubrication, which was dominated by a stronger adhesive force with lubricating oil, higher martensite contents on the worn surface, higher hardness and higher H/E value than those of the reference HT 200 and Al-19Si cylinder material.

Nanostructure Fe-based martensite coating was sprayed on an aluminum cylinder liner, which demonstrated remarkable advantages over the reference cylinder material.

What kind of robotic service do customers prefer when they dine out alone? This study aims to investigate how robotic service type affects solo diners’ attitude toward robotic service and restaurant revisit intention, through the mediation of rapport. It also examines the moderating effects of the need to belong and restaurant type.

Three experiments were conducted. Study 1 used a one-factor between-subjects design to test the effect of robotic service type on rapport and solo diners’ responses. Study 2 conducted a 2 (robotic service type: service-delivery vs entertainment) × need to belong quasi-experimental design to examine the moderation of need to belong. Study 3 used a 2 (robotic service type: service-delivery vs entertainment) × 2 (restaurant type: traditional restaurant vs solo-friendly restaurant) factorial between-subjects design to test the moderation of restaurant type. A qualitative study (Study 4) complements the experimental results based on semistructured interviews.

Entertainment (vs service-delivery) robotic service has a stronger effect on solo diners’ responses, with rapport serving as a mediator. Additionally, solo diners with a heightened need to belong demonstrate an intensified rapport effect when receiving entertainment-oriented robotic service. Furthermore, restaurant type plays a moderating role between robotic service type and consumer responses. For traditional restaurants, solo diners who receive entertainment (vs service-delivery) robotic service tend to form stronger rapport and favorable responses. The results of the qualitative study elucidate and support the hypothesized relationships of the experimental studies.

Restaurant operators could consider offering entertainment-based smart devices that allow solo diners to indulge themselves during the dining encounter. Restaurants could also design environmental cues that can signify a sense of comfort, such as redesigning tables with individual seats for solo diners to enhance their perceptions of shared characteristics among other solo diners in the same space.

This research advances the literature on solo dining and robotic service, by investigating how human–robot interaction can fulfill solo diners’ relatedness goals, as self-determination theory suggests. This inquiry also represents an early attempt in the hospitality literature to empirically examine the influence of robotic service type on consumer responses through the mediation of rapport.

This paper aims to investigate whether electroencephalography (EEG) technology is effective in qualifying the tactile sensation evoked by non-steady cutaneous electrical stimulation. EEG is a novel method for electrotactile analysis and has demonstrated the discrimination ability for electrotactile sensation under steady contact conditions in recent years. However, in non-steady contact conditions, it is necessary to test its effectiveness. This study aims to explore an objective analysis method in comparison to psychophysical approach and to provide a methodology for non-steady electrotactile research.

With EEG experimentation on 13 volunteers, the authors collected evoked potentials by the predesigned “1” and “0” stimulation events. In addition, with a series of data preprocessing including artifact elimination, band-pass filtering, baseline normalization, data superposition and fast Fourier transform transformation, the authors got the power spectrum of alpha, beta and gamma rhythms. Furthermore, statistics analysis and ANOVA test were adopted for exploring the discrepancy of the spectrum characterizations for different non-steady electrostimulation events.

The EEG power spectrum of the central cortical brain is valuable in discriminating the two types of stimulation events. The power of alpha rhythm especially in the central cortical brain evoked by event “1,” whose current level is equal to the threshold, was significantly lower than that evoked by event “0,” whose level is less than the threshold (p < 0.05). Then, the power of the beta rhythm presented counter-change (p < 0.05). This study suggests that EEG may have the potential to qualify non-steady electrotactile sensation for engineering applications.

Limiting factors of non-steady electrotactile stimulation were considered in this study. Different tapping frequency and contact time should be investigated in future studies.

This paper fulfills a challenge in qualifying the tactile sensations evoked by non-steady electrical stimulation with EEG characteristics.