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Accurate measurements of the temperature distributions in hot section components are indispensable for the prognostic and health management of gas turbines. Thin film thermocouple (TFTC) sensors, directly fabricated on the surface of a component, add negligible mass and create little or no disturbance to airflow, and therefore, can provide accurate measurements of fast temperature fluctuations of gas turbines. The purpose of this paper is to evaluate TFTC sensors fabricated by combining pulsed laser deposition (PLD) and micromachining techniques (LM).

The “dry” PLD/LM fabrication approach allows for excellent control of the chemical composition and physical characteristics of the constituent layers and their interfaces, thus achieving good adhesion of the layers to the substrate.

The results of thermal cyclic durability testing of the fabricated TFTC sensors demonstrated that the proposed PLD-based approach can be used to fabricate sensors that are fully functional at temperatures up to 750°C. Analyses of the sensor performance during durability testing revealed: the existence of a threshold temperature below which accurate temperature measurements were achieved; an abrupt drop in the sensor output occurring when the sensor temperature exceeded the threshold value, with a fast recovery of the sensor output once the temperature was reduced below the threshold level; and sensor “training” capable of increasing the threshold value of the TFTC through its exposure to above-the-threshold temperatures.

The work is the first time to demonstrate that simple PLD and LM processes can be used to fabricate TFTC that are fully functional at temperatures up to 750°C.

The purpose of this paper is to study the road segmentation problem of cross-modal remote sensing images.

First, the baseline network based on the U-net is trained under a large-scale dataset of remote sensing imagery. Then, the cross-modal training data are used to fine-tune the first two convolutional layers of the pre-trained network to achieve the adaptation to the local features of the cross-modal data. For the cross-modal data of different band, an autoencoder is designed to achieve data conversion and local feature extraction.

The experimental results show the effectiveness and practicability of the proposed method. Compared with the ordinary method, the proposed method gets much better metrics.

The originality is the transfer learning strategy that fine-tunes the low-level layers for the cross-modal data application. The proposed method can achieve satisfied road segmentation with a small amount of cross-modal training data, so that is has a good application value. Still, for the similar application of cross-modal data, the idea provided by this paper is helpful.

The purpose of this paper is to present a novel scheme of high‐dynamic global positioning system (GPS) software receiver in order to improve the capturing speed and trading accuracy of GPS receiver.

First, the beginning of C/A code can be found through the delay and multiply approach. To solve the problems of estimating a certain satellite's Doppler shift from the signals of several visible satellites, the “delay and accumulation unit” is put forward, and besides, performance of inertial navigation system‐assisted tracking loop in high‐dynamic circumstance is analysed by means of mathematical modelling and simulation experiments, whose results verified the validity of the proposed tracking scheme.

In this paper, the two‐dimension searching process in conventional acquisition scheme is transformed into two one‐dimension searching processes, thus improving the capturing speed.

This software receiver has only been verified by means of mathematical simulation, and the validity in hardware receiver is still obscured.

This paper presents a novel high‐dynamic GPS software receiver scheme, which can be seen as a reference of engineering application and simulation research.

This paper aims to reveal how different types of events and top management teams' (TMTs’) cognitive frames affect the generation of breakthrough innovations.

Drawing on the event system theory and upper echelon theory, this study chose a Chinese manufacturing enterprise as the case firm and conducted an exploratory single-case study to unpack how breakthrough innovation generates over time.

By conducting the in-depth case analysis, the study revealed that firms do not produce breakthrough innovation in the catch-up stage and parallel-running stage but achieve it in the leading stage. It also indicated that when facing proactive events in the catch-up stage, TMTs often adopt a contracted lens, being manifested as consistency orientation, less elastic organizational identity and narrower competitive boundaries. In addition, they tend to adopt a contracted lens when facing reactive and proactive events in the parallel-running stage. In the face of reactive and proactive events in the leading stage, they are more inclined to adopt an expanded lens, being manifested as a coexistence orientation, more elastic organizational identity and wider competitive boundaries.

First, by untangling how TMT's cognitive frame functions in breakthrough innovations, this paper provides a micro-foundation for producing breakthrough innovations and deepens the understanding of upper echelon theory by considering the cognitive dimension of TMTs. Second, by teasing out several typical events experienced by the firm, this paper is the first attempt to reveal how events affect the generation of breakthrough innovation. Third, the work extends the application of the event system theory in technological innovation. It also provides insightful implications for promoting breakthrough innovations by considering the role of proactive and reactive events a firm experiences and TMT's perceptions.

Recognizing every worker's working status instead of only describing the existing construction activities in static images or videos as most computer vision-based approaches do; identifying workers and their activities simultaneously; establishing a connection between workers and their behaviors.

Taking a reinforcement processing area as a research case, a new method for recognizing each different worker's activity through the position relationship of objects detected by Faster R-CNN is proposed. Firstly, based on four workers and four kinds of high-frequency activities, a Faster R-CNN model is trained. Then, by inputting the video into the model, with the coordinate of the boxes at each moment, the status of each worker can be judged.

The Faster R-CNN detector shows a satisfying performance with an mAP of 0.9654; with the detected boxes, a connection between the workers and activities is established; Through this connection, the average accuracy of activity recognition reached 0.92; with the proposed method, the labor consumption of each worker can be viewed more intuitively on the visualization graphics.

With this proposed method, the visualization graphics generated will help managers to evaluate the labor consumption of each worker more intuitively. Furthermore, human resources can be allocated more efficiently according to the information obtained. It is especially suitable for some small construction scenarios, in which the recognition model can work for a long time after it is established. This is potentially beneficial for the healthy operation of the entire project, and can also have a positive indirect impact on structural health and safety.

The purpose of this paper is to test the relationship between environmental regulations and green total factor productivity (GTFP) of China's logistics industry. The high-factor input, high-energy consumption, and high-pollution emissions model of the logistics industry developed within China faces challenges from severe resource and environmental constraints. It is generally believed that environmental regulations effectively restrain pollution emissions and help protect the environment.

The authors employ the undesirable slack-based Malmquist Luenberger model to calculate the GTFP across the provincial logistics industry and use the mediation effect model and threshold effect model to explore the effects and mechanics of environmental transmission regulations on the GTFP.

The main results show significant regional differences in the GTFP of logistics industry across China. In the transmission path of the impact of environmental regulations on the GTFP, regional innovation capabilities have mediation effects. Regional innovation capacities have a masking effect on the transmission path of environmental regulations on accumulated technical efficiency changes (AEC) and accumulated technical changes (ATC). The threshold effect test results show a dual-threshold effect between environmental regulations and the GTFP, with environmental regulations as threshold variable. Furthermore, the impact of regional innovation capability on the GTFP has a dual-threshold effect, with environmental regulation as threshold variable.

First, it is advisable to plan the environmental regulation policy system thoroughly and add supporting measures to ensure the efficiency and smooth implementation of the nation's environmental policies. Second, it is important to further understand the critical role of innovation capability in improving the GTFP. Third, there is an urgent need to standardize the operating behavior and market order of the leading players in the logistics market and to improve the operational efficiency of logistics enterprises.

So far, a systematical study researched on effect of environmental regulation on the GTFP in logistics industry was not published. This study can provide experience for the high-quality development of the logistics industry.

This study aims to offer a comprehensive exploration of the potential and challenges associated with sensor fusion-based virtual reality (VR) applications in the context of enhanced physical training. The main objective is to identify key advancements in sensor fusion technology, evaluate its application in VR systems and understand its impact on physical training.

The research initiates by providing context to the physical training environment in today’s technology-driven world, followed by an in-depth overview of VR. This overview includes a concise discussion on the advancements in sensor fusion technology and its application in VR systems for physical training. A systematic review of literature then follows, examining VR’s application in various facets of physical training: from exercise, skill development and technique enhancement to injury prevention, rehabilitation and psychological preparation.

Sensor fusion-based VR presents tangible advantages in the sphere of physical training, offering immersive experiences that could redefine traditional training methodologies. While the advantages are evident in domains such as exercise optimization, skill acquisition and mental preparation, challenges persist. The current research suggests there is a need for further studies to address these limitations to fully harness VR’s potential in physical training.

The integration of sensor fusion technology with VR in the domain of physical training remains a rapidly evolving field. Highlighting the advancements and challenges, this review makes a significant contribution by addressing gaps in knowledge and offering directions for future research.

The study aims to examine the effect of inclusions and inherent microstructure on fatigue behavior of 34Cr2Ni2Mo steel.

Fatigue behavior of 34Cr2Ni2Mo steel was investigated for up to 1E10 cycles.

Results showed that both inclusion and inherent microstructure have an influence on the crack initiation mechanism. Fatigue cracks mostly initiated from inclusions, whereas substrate-induced crack initiations were also observed. Fatigue life of inclusion-induced failures is mostly determined by the location of inclusions rather than the loading stress. The inherent microstructure seems to tolerate inclusions at a lower stress level in very high-cycle regime owing to the absence of internal inclusion-induced failure. For the substrate-induced crack initiations, high-density dislocations are found to be accumulated around the carbide particle-matrix interface, which may be the cause of crack initiation in the inherent structure due to strain localization.

The effect of inclusions and inherent microstructure on fatigue behavior of 34Cr2Ni2Mo steel up to 1E10 cycles.

Highlights

• Fatigue failure occurs even at a lifetime of 5.76E9 cycles.

• Surface inclusion induced premature failures.

• Inherent microstructure tolerates inclusions at lower stress level.

• Internal carbides promote substrate-induced crack initiations.

Fatigue failure occurs even at a lifetime of 5.76E9 cycles.

Surface inclusion induced premature failures.

Inherent microstructure tolerates inclusions at lower stress level.

Internal carbides promote substrate-induced crack initiations.

This paper aims to expand the application area of Inconel 718 alloy in marine environment, the sensitivity of pitting corrosion should be analyzed and discussed, especially the effect of block carbides.

Effect of carbides on the sensitivity of pitting corrosion for Inconel 718 alloy was carried out at 30°C in 3.5% NaCl solution using dynamic electrochemical impedance spectroscopy and anodic polarization techniques. In addition, the initiation of pitting corrosion was investigated by immersion test in 0.05 M HCl + 6% FeCl₃ solution.

As a result, the precipitation of carbides, as the initiation of pitting corrosion, increased pitting corrosion susceptibility, especially the block carbides could lead to deep-spalling. Within that process, temperature and potential acted as the main controlling factors, and the effect of the latter was more distinct.

The initiation of pitting corrosion was revealed by the immersion test. The mechanism of pitting corrosion was analyzed and discussed.

The purpose of this paper is to investigate the continued use behavior (CU) of link sharing tools based on uses and gratifications theory, the theory of planned behavior and expectation confirmation theory. It then builds a conceptual model that is empirically tested.

Data were collected from 343 students (undergraduates, masters, PhD students, and MBAs) from three Chinese universities via a two-phrase survey. The tools SPSS 18.0 and AMOS 18.0 were used to analyse the reliability, validity, model fits and SEM, respectively.

The results indicate that an individual’s CU of link sharing tools was determined by his or her continued use intention directly and subjective norm indirectly. Users’ satisfaction on link sharing tools was the main factor affecting the continuance intention. Individuals’ motivation needs such as cognitive needs, personal integrative needs, and social integrative needs were found to be the significant predictors of his or her satisfaction. Besides, people with high privacy concern tended to have less satisfaction with link sharing tools.

This study explores users’ CU of link sharing tools in social media for the first time. The theoretical model developed shows the predictors behind people’s CU.