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The purpose of this paper is to theoretically analyze and experimentally demonstrate the investigation of and present a kind of sensing system for monitoring simultaneous temperature and strain measurements based on highly nonlinear fiber (HNLF) and single mode fiber (SMF).

First, the stimulated Brillouin scattering (SBS) characteristics of the HNLF have been studied, including the Brillouin gain bandwidth, Brillouin gain center frequency and SBS threshold. Second, based on the Brillouin gain center frequency, the Brillouin frequency shift coefficients of strain and temperature in HNLF have been studied. Third, the sensing and signal interrogation scheme for simultaneous monitoring of temperature and strain with high resolution has been presented.

It is found that the HNLF has a wider Brillouin gain bandwidth. The SBS threshold of HNLF is 78 mW, which is much larger than 7.9 mW of SMF. Also, the Brillouin frequency shift coefficients of strain and temperature in HNLF are 0.0308 and 0.413 MHz/°C, respectively.

The larger threshold of SBS is useful to avoid SBS under certain situations that Spontaneous Brillouin Scattering is necessary and should be applied. The technique is based on the fact that the Brillouin frequency shift coefficients of strain and temperature in HNLF are different from those in SMF. Therefore, the two-parameter monitoring can be achieved by producing SBS and obtaining the back-scattering Brillouin signal light simultaneously in HNLF and SMF.

This paper aims to forecast the future development of UIC technology according to the changes of IPC in different time periods. The findings also provide a theoretical basis for the UIC policymakers of energy-saving environmental protection industry.

The present study will take energy-saving technology as an example, through using a two-mode network analysis method to build the four-stage IPC code UIC innovation-technology network, researching UIC innovation network applicant heterogeneity, UIC technology network and technology relevance.

Through the establishment of UIC innovation-technology two-mode network, namely, applicant node CN and IPC node TN, this paper analyzes the important applicants, focal technologies, correlation of focal technologies and the development trends of energy saving technology in the future by using the related theories and methods of two-mode network.

At present, only about 11% of energy-saving enterprises in China have R&D activities. The government needs to implement top-level design, improve industrial policies, strengthen investment in technology R&D and build a platform for technological innovation in energy-saving industries to support the R&D of common and key technologies in energy-saving industries.

To explore the economical and reasonable semi-rigid permeable base layer ratio, solve the problems caused by rainwater washing over the pavement base layer on the slope, improve its drainage function, improve the water stability and service life of the roadbed pavement and promote the application of semi-rigid permeable base layer materials in the construction of asphalt pavement in cold regions.

In this study, three semi-rigid base course materials were designed, the mechanical strength and drainage properties were tested and the effect and correlation of air voids on their performance indexes were analyzed.

It was found that increasing the cement content increased the strength but reduced the air voids and water permeability coefficient. The permeability performance of the sandless material was superior to the dense; the performance of the two sandless materials was basically the same when the cement content was 7%. Overall, the skeleton void (sand-containing) type gradation between the sandless and dense types is more suitable as permeable semi-rigid base material; its gradation is relatively continuous, with cement content? 4.5%, strength? 1.5 MPa, water permeability coefficient? 0.8 cm/s and voids of 18–20%.

The study of permeable semi-rigid base material with large air voids could help to solve the problems of water damage and freeze-thaw damage of the base layer of asphalt pavements in cold regions and ensure the comfort and durability of asphalt pavements while having good economic and social benefits.

This study aims to develop a polymer cement-based waterproof coating with self-healing capability to efficiently and intelligently solve the building leakage caused by cracking of waterproof materials, along with excellent durability to prolong its service life.

Ion chelators are introduced into the composite system based on ethylene vinyl acetate copolymer emulsion and ordinary Portland cement to prepare self-healing polymer cement-based waterproof coating. Hydration, microstructure, wettability, mechanical properties, durability, self-healing performance and self-healing products of polymer cement-based waterproof coating with ion chelator are investigated systematically. Meanwhile, the chemical composition of self-healing products in the crack was examined.

The results showed that ion chelators could motivate the hydration of C₂S and C₃S, as well as the formation of hydration products (C-S-H gel) of the waterproof coating to improve its compactness. Compared with the control group, the waterproof coating with ion chelator had more excellent water resistance, alkali resistance, thermal and UV aging resistance. When the dosage of ion chelator was 2%, after 28 days of curing, cracks with a width of 0.29 mm in waterproof coating could fully heal and cracks with a width of 0.50 mm could achieve a self-healing efficiency of 72%. Furthermore, the results reveal that the self-healing product in the crack was calcite crystalline CaCO₃.

A novel ion chelator was introduced into the composite coating system to endow it with excellent self-healing ability to prolong its service life. It has huge application potential in the field of building waterproofing.

The ongoing paradigm shift in the energy sector holds paramount implications for the realization of the sustainable development goals, encompassing critical domains such as resource optimization, environmental stewardship and workforce opportunities. Concurrently, this transformative trajectory within the power sector possesses a dual-edged nature; it may ameliorate certain challenges while accentuating others. In light of the burgeoning research stream on open innovation, this study aims to examine the intricate dynamics of knowledge-based industry-university-research networking, with an overarching objective to elucidate and calibrate the equilibrium of ambidextrous innovation within power systems.

The authors scrutinize the role of different innovation organizations in three innovation models: ambidextrous, exploitative and exploratory, and use a multiobjective decision analysis method-entropy weight TOPSIS. The research was conducted within the sphere of the power industry, and the authors mined data from the widely used PatSnap database.

Results show that the breadth of knowledge search and the strength of an organization’s direct relationships are crucial for ambidextrous innovation, with research institutions having the highest impact. In contrast, for exploitative innovation, depth of knowledge search, the number of R&D patents and the number of innovative products are paramount, with universities playing the most significant role. For exploratory innovation, the depth of knowledge search and the quality of two-mode network relations are vital, with research institutions yielding the best effect. Regional analysis reveals Beijing as the primary hub for ambidextrous and exploratory innovation organizations, while Jiangsu leads for exploitative innovation.

The study offers valuable implications to cope with the dynamic state of ambidextrous innovation performance of the entire power system. In light of the findings, the dynamic state of ambidextrous innovation performance within the power system can be adeptly managed. By emphasizing a balance between exploratory and exploitative strategies, stakeholders are better positioned to respond to evolving challenges and opportunities. Thus, the study offers pivotal guidance to ensure sustained adaptability and growth in the power sector’s innovation landscape.

The primary originality is to extend and refine the theoretical understanding of ambidextrous innovation within power systems. By integrating several theoretical frameworks, including social network theory, knowledge-based theory and resource-based theory, the authors enrich the theoretical landscape of power system ambidextrous innovation. Also, this inclusive examination of two-mode network structures, including the interplay between knowledge and cooperation networks, unveils the intricate interdependencies between these networks and the ambidextrous innovation of power systems. This approach significantly widens the theoretical parameters of innovation network research.

Mobile health (mHealth) services are considered an important means of relieving the problems of the aging population. The efficiency of mHealth services can be enhanced by engaging more elderly users and guaranteeing their continued use. However, limited attention has been directed toward investigating elderly users' continuance intention regarding mHealth services. The purpose of this paper is to explain elderly users' continuance intention by investigating the contingent role of technology anxiety and health anxiety on affective trust and cognitive trust.

Survey data were collected from 232 elderly users to verify the research model and hypotheses based on structural equation modeling (SEM).

This study revealed that both affective and cognitive trust enhance elderly users' continuance intention regarding their use of mHealth services. Health anxiety strengthens the effect of cognitive trust but weakens the effect of affective trust with regard to continuance intention. Furthermore, technology anxiety strengthens the effect of affective trust but not the effect of cognitive trust with regard to continuance intention.

This study is one of the first to examine elderly users' continuance intention regarding mHealth services use from the perspective of affective and cognitive trust, thus enriching the extant literature on the use of mHealth services. Additionally, this study sheds light on the contingent effects of technology anxiety and health anxiety on affective and cognitive trust, which have been neglected by previous research.

Conventional machining methods for fabricating piezoelectric components such as ultrasound transducer arrays are time-consuming and limited to relatively simple geometries. The purpose of this paper is to develop an additive manufacturing process based on the projection-based stereolithography process for the fabrication of functional piezoelectric devices including ultrasound transducers.

To overcome the challenges in fabricating viscous and low-photosensitive piezocomposite slurry, the authors developed a projection-based stereolithography process by integrating slurry tape-casting and a sliding motion design. Both green-part fabrication and post-processing processes were studied. A prototype system based on the new manufacturing process was developed for the fabrication of green-parts with complex shapes and small features. The challenges in the sintering process to achieve desired functionality were also discussed.

The presented additive manufacturing process can achieve relatively dense piezoelectric components (approximately 95 per cent). The related property testing results, including X-ray diffraction, scanning electron microscope, dielectric and ferroelectric properties as well as pulse-echo testing, show that the fabricated piezo-components have good potentials to be used in ultrasound transducers and other sensors/actuators.

A novel bottom-up projection system integrated with tape casting is presented to address the challenges in the piezo-composite fabrication, including small curing depth and viscous ceramic slurry recoating. Compared with other additive manufacturing processes, this method can achieve a thin recoating layer (as small as 10 μm) of piezo-composite slurry and can fabricate green parts using slurries with significantly higher solid loadings. After post processing, the fabricated piezoelectric components become dense and functional.

With the rapid development of social media, many organizations have begun to attach importance to social media platforms. This research studies the management and the use of social media in academic music libraries, taking the Center for Chinese Music Studies of the Chinese University of Hong Kong (CCMS) as a case study.

We conducted a sentiment analysis of posts on Facebook’s public page to analyze the reaction to the posts with some exploratory analysis, including the communication trend and relevant factors that affect user interaction.

Our results show that the Facebook channel for the library has a good publicity effect and active interaction, but the number of posts and interactions has a downward trend. Therefore, the library needs to pay more attention to the management of the Facebook channel and take adequate measures to improve the quality of posts to increase interaction.

Few studies have analyzed existing data directly collected from social media by programming based on sentiment analysis and natural language processing technology to explore potential methods to promote music libraries, especially in East Asia, and about traditional music.

The study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.

This study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.

The study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.

The model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.

To help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.

To estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.

The proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.

This study aims to investigate the tendency for firms, exhibiting an entrepreneurial spirit in their growth strategies, to engage in misconduct within the context of China's rapidly developing economy. The authors also examine how this relationship is influenced by governance mechanisms, specifically management shareholding and executive functional diversity. Furthermore, the authors explore the mediating roles of organizational complexity and performance pressure in linking entrepreneurial growth to firm misconduct. This research provides a novel perspective for understanding the impact of entrepreneurial growth on corporate ethical risks, and offers practical insights for maintaining ethical standards in firms during their pursuit of growth.

This study focuses on publicly traded, mature companies that exhibit an entrepreneurial inclination in their growth strategies, demonstrating entrepreneurial vigor through activities such as product innovation and market expansion. This exploration incorporates both theoretical and empirical approaches, scrutinizing A-share listed companies in China from 2008 to 2019. To validate the robustness of this study's findings, the authors have applied diverse methodologies such as propensity score matching, classification regression, and alternative indicator analysis.

This study found that the entrepreneurial growth-oriented strategy is positively related to firm misconduct. It also uncovers that governance mechanisms like management shareholding and executive functional diversity moderate this relationship. Moreover, organizational complexity and performance pressure partially mediate the relationship between an entrepreneurial growth strategy and firm misconduct.

For instance, more detailed categorization of corporate misconduct, based on punishment severity, could be explored. Additional characteristics like age, education, gender, and team/board diversity could help further understand the relationship between entrepreneurial growth strategy and misconduct. By addressing these limitations and exploring further avenues for research, the authors can deepen the understanding of this relationship and provide valuable insights for firms seeking to mitigate potential risks.

First, for regulators, shareholders, creditors and investors, knowing and understanding the relationship between growth-oriented strategies and corporate violations is helpful for them to scientifically evaluate the potential risks that may exist in the company, and can also carry out differentiated supervision on the company based on different types of company-oriented strategies. Second, when designing the corporate governance mechanism, listed companies should fully consider the role of management shareholding. Finally, executives should treat cross-functional experience dialectically, especially in growth oriented strategic companies.

This research provides a novel perspective for understanding the impact of entrepreneurial growth on corporate ethical risks, and offers practical insights for maintaining ethical standards in firms during their pursuit of growth.

This study stands out by examining the influence of entrepreneurial growth strategy on firm misconduct, thus enhancing previous studies that primarily centered on entrepreneurial start-ups. The authors offer a nuanced comprehension of the potential risks intrinsic to corporate entrepreneurship and highlight the crucial role of efficient governance structures in curbing corporate misbehavior while fostering entrepreneurial growth.