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The ongoing adverse effects of climate change have led scientific think tanks to aim towards achieving net-zero greenhouse gas (GHG) emissions targets with affordable and clean energy (Sustainable Development Goal 7). One of the significant contributors to the escalating emissions rate is the use of conventional vehicles. The uptake of electric vehicles (EVs) is a promising solution for a cleaner economy. However, increased penetration poses various challenges to the power system. There is a need to explore alternatives, such as hydrogen fuel cell vehicles (HFCVs), to use the advantages of both electric and conventional vehicles and bridge the gap between them. However, the transition to hydrogen-based transport requires intensive study of its key benefits and issues, the actions that need to be taken to achieve a changeover concerning light and heavy vehicles and whether such kind of transformation is likely or even possible. This chapter highlights the brief history and mechanics of HFCVs. It further analyses the various benefits and challenges which the technology poses. Additionally, it addresses critical questions regarding the feasibility of the shift towards hydrogen fuel to satisfy the world's rapidly growing energy needs and meet net-zero targets based on real-life applications. This chapter will be a valuable resource for further research, development and education efforts in HCFVs to assist in the rapidly growing transportation needs for automobiles and other vehicles.

The purpose of this paper is to introduce the simple synthesis process of thermal-insulation coating by using three different nanoparticles, namely, nano-zinc oxide (ZnO), nano-tin dioxide (SnO₂) and nano-titanium dioxide (TiO₂), which can reduce the temperature of solar cells.

The thermal-insulation coating is designed using sol-gel process. The aminopropyltriethoxysilane/methyltrimethoxysilane binder system improves the cross-linking between the hydroxyl groups, -OH of nanoparticles. The isopropyl alcohol is used as a solvent medium. The fabrication method is a dip-coating method.

The prepared S1B1 coating (20 Wt.% of SnO₂) exhibits high transparency and great thermal insulation property where the surface temperature of solar cells has been reduced by 13°C under 1,000 W/m² irradiation after 1 h. Meanwhile, the Z1B2 coating (20 Wt.% of ZnO) reduced the temperature of solar cells by 7°C. On the other hand, the embedded nanoparticles have improved the fill factor of solar cells by 0.2 or 33.33%.

Findings provide a significant method for the development of thermal-insulation coating by a simple synthesis process and low-cost materials.

The thermal-insulation coating is proposed to prevent exterior heat energy to the inside solar panel glass. At the same time, it can prevent excessive heating on the solar cell’s surface, later improves the efficiency of solar cell.

This study presents a the novel method to develop and compare the thermal-insulation coating by using various nanoparticles, namely, nano-TiO₂, nano-SnO₂ and nano-ZnO at different weight percentage.

The safety of high-speed rail operation environments is an important guarantee for the safe operation of high-speed rail. The operating environment of the high-speed rail is complex, and the main factors affecting the safety of high-speed rail operating environment include meteorological disasters, perimeter intrusion and external environmental hazards. The purpose of the paper is to elaborate on the current research status and team research progress on the perception of safety situation in high-speed rail operation environment and to propose directions for further research in the future.

In terms of the mechanism and spatio-temporal evolution law of the main influencing factors on the safety of high-speed rail operation environments, the research status is elaborated, and the latest research progress and achievements of the team are introduced. This paper elaborates on the research status and introduces the latest research progress and achievements of the team in terms of meteorological, perimeter and external environmental situation perception methods for high-speed rail operation.

Based on the technical route of “situational awareness evaluation warning active control,” a technical system for monitoring the safety of high-speed train operation environments has been formed. Relevant theoretical and technical research and application have been carried out around the impact of meteorological disasters, perimeter intrusion and the external environment on high-speed rail safety. These works strongly support the improvement of China’s railway environmental safety guarantee technology.

With the operation of CR450 high-speed trains with a speed of 400 km per hour and the application of high-speed train autonomous driving technology in the future, new and higher requirements have been put forward for the safety of high-speed rail operation environments. The following five aspects of work are urgently needed: (1) Research the single factor disaster mechanism of wind, rain, snow, lightning, etc. for high-speed railways with a speed of 400 kms per hour, and based on this, study the evolution characteristics of multiple safety factors and the correlation between the high-speed driving safety environment, revealing the coupling disaster mechanism of multiple influencing factors; (2) Research covers multi-source data fusion methods and associated features such as disaster monitoring data, meteorological information, route characteristics and terrain and landforms, studying the spatio-temporal evolution laws of meteorological disasters, perimeter intrusions and external environmental hazards; (3) In terms of meteorological disaster situation awareness, research high-precision prediction methods for meteorological information time series along high-speed rail lines and study the realization of small-scale real-time dynamic and accurate prediction of meteorological disasters along high-speed rail lines; (4) In terms of perimeter intrusion, research a multi-modal fusion perception method for typical scenarios of high-speed rail operation in all time, all weather and all coverage and combine artificial intelligence technology to achieve comprehensive and accurate perception of perimeter security risks along the high-speed rail line and (5) In terms of external environment, based on the existing general network framework for change detection, we will carry out research on change detection and algorithms in the surrounding environment of high-speed rail.

This study aims to fabricate a novel electromagnetic interference (EMI) shielding composite aerogel with both thermal insulation and high temperature warning functions.

An emerging bio-based polypyrrole (PPy) gel/Fe₃O₄/calcium alginate (PFC) EMI shielding composite aerogel was prepared by freeze-drying and in situ polymerization method. First, Fe₃O₄/calcium alginate (CA) aerogel was obtained by freeze-drying the Fe₃O₄/CA mixture. Then, PPy/Fe₃O₄/CA was obtained by synthesizing PPy on the surface of CA/Fe₃O₄ aerogel through in situ polymerization. Finally, PPy/Fe₃O₄/CA was immersed in porphyrin solution (cross-linking agent) to get the final PFC EMI shielding composite aerogel.

Due to the matched impedance between Fe₃O₄ and PPy, the EMI shielding performance of PFC composite aerogel can reach up to −8 dB. In addition, the PFC EMI shielding composite aerogel also shows excellent self-extinguishing and thermal insulation properties. After leaving the flame, the burning PFC aerogel is quickly extinguished. When the PFC aerogel is placed on the heating plate at 230 °C, the temperature on the side of the aerogel away from the heating plate is only 90.3 °C after 5 min of heating. The electrical resistance of the PFC composite aerogel can be reduced from 3.62 × 104 O to 5 × 102 O to trigger the warning light after 3 s of exposure to the alcohol lamp flame. This reversible thermal resistance response characteristic can be used to give an early warning signal when the PFC encounters high temperature or flame.

This work provides a novel strategy for designing a multifunctional EMI shielding composite aerogel with repeatable high temperature warning performance. This PFC composite aerogel shows potential applications in the prevention of material combustion in high temperature electromagnetic environments.

This study aims to improve detection efficiency of fluorescence biosensor or a graphene field-effect transistor biosensor. Graphene field-effect transistor biosensing and fluorescent biosensing were integrated and combined with magnetic nanoparticles to construct a multi-sensor integrated microfluidic biochip for detecting single-stranded DNA. Multi-sensor integrated biochip demonstrated higher detection reliability for a single target and could simultaneously detect different targets.

In this study, the authors integrated graphene field-effect transistor biosensing and fluorescent biosensing, combined with magnetic nanoparticles, to fabricate a multi-sensor integrated microfluidic biochip for the detection of single-stranded deoxyribonucleic acid (DNA). Graphene films synthesized through chemical vapor deposition were transferred onto a glass substrate featuring two indium tin oxide electrodes, thus establishing conductive channels for the graphene field-effect transistor. Using π-π stacking, 1-pyrenebutanoic acid succinimidyl ester was immobilized onto the graphene film to serve as a medium for anchoring the probe aptamer. The fluorophore-labeled target DNA subsequently underwent hybridization with the probe aptamer, thereby forming a fluorescence detection channel.

This paper presents a novel approach using three channels of light, electricity and magnetism for the detection of single-stranded DNA, accompanied by the design of a microfluidic detection platform integrating biosensor chips. Remarkably, the detection limit achieved is 10 pm, with an impressively low relative standard deviation of 1.007%.

By detecting target DNA, the photo-electro-magnetic multi-sensor graphene field-effect transistor biosensor not only enhances the reliability and efficiency of detection but also exhibits additional advantages such as compact size, affordability, portability and straightforward automation. Real-time display of detection outcomes on the host facilitates a deeper comprehension of biochemical reaction dynamics. Moreover, besides detecting the same target, the sensor can also identify diverse targets, primarily leveraging the penetrative and noninvasive nature of light.

This study aims to explore a novel framework for housing price bubbles in the Turkish economy during the pandemic. It examines the probability of housing bubble formation relative to the pre-pandemic period and identifies possible determinants of housing bubbles in the Turkish economy.

In this study, a two-stage novel estimation method is applied. In the first stage, bubble periods are identified through the right-tailed supremum augmented Dickey–Fuller test. In the second stage, the determinants of these bubbles are identified, and the housing bubble determinants during the COVID-19 pandemic are compared to the pre-pandemic period.

The findings indicate that there is an asset price bubble in the housing market during the pandemic period. Furthermore, mortgage credit expansion, mortgage credit rates and the depreciation of the Turkish Lira against the USD could increase housing bubble formation. However, housing sector sales to foreign investors do not contribute to housing bubble formation during the pandemic in the Turkish housing market.

To the best of the author’s knowledge, this is the first study to address the relative determinants of housing bubbles in an emerging market context during the pandemic.

This study aims to investigate the influence of customer satisfaction and brand loyalty on hotel guests’ repurchase intentions when they experience consumer confusion.

A quantitative research method was used in the study and the data were collected through a survey. A survey was used to collect data from 406 hotel guests staying at four and five-star hotels. The structural equation model was used to test the influence of consumer confusion on hotel guests’ repurchase intentions.

The findings of the study show that while consumer confusion has a negative effect on hotel guests’ repurchase intentions, customer satisfaction and brand loyalty have a positive influence on their repurchase intentions. Customer satisfaction and brand loyalty tend to have a partial mediating role in the relationship between consumer confusion and repurchase intention.

The findings show the need for the hotel management to provide simple, concise, yet sufficient information enabling tourists to differentiate their offerings to reduce confusion.

Previous research has largely neglected the role of guests’ loyalty and satisfaction with the hotel brand. This research shows that guests’ loyalty and satisfaction with the hotel brand play an important role in terms of the repurchase intention and in reducing confusion.

The purpose of this article is to assess the extent to which environmental economic practices are recognized and implemented in developing countries, with a specific emphasis on the Nigerian construction sector.

To examine the extent of awareness and usage, a quantitative research approach was adopted with close-ended questionnaires disseminated to construction professionals in Nigeria. The collected data were subjected to analysis using various statistical techniques such as percentages, frequencies and mean scores. Furthermore, to investigate any significant differences in the respondents' opinions, the Kruskal–Wallis H-test was applied.

The study's findings indicate that the level of awareness and implementation of environmental economic practices among construction professionals in Nigeria is limited. Based on the findings of this study, a roadmap of recommendations has been proposed to promote the adoption of environmental economic practices in the Nigerian construction industry. The implementation of these recommendations can help reduce the negative impact of construction activities on the environment and public health and contribute to the development of sustainable cities, communities and societies.

The outcomes from this study can inform policy and decision-making by providing insights into the level of knowledge and awareness of environmental economic practices among construction professionals in Nigeria.

The findings from this study provide a baseline for assessing the level of knowledge and awareness of sustainable practices among construction professionals in Nigeria, which can inform future studies on the effectiveness of training and awareness programs.

Production and transportation of precast components, as two continuous service stages of a precast plant, play an important role in meeting customer needs and controlling costs. However, there is still a lack of production and transportation scheduling methods that comprehensively consider delivery timeliness and transportation economy. This article aims to study the integrated scheduling optimization problem of in-plant flowshop production and off-plant transportation under the consideration of practical constraints of customer order delivery time window, and seek an optimal scheduling method that balances delivery timeliness and transportation economy.

In this study, an integrated scheduling optimization model of flowshop production and transportation for precast components with delivery time windows is established, which describes the relationship between production and transportation and handles transportation constraints under the premise of balancing delivery timeliness and transportation economy. Then a genetic algorithm is designed to solve this model. It realizes the integrated scheduling of production and transportation through double-layer chromosome coding. A program is designed to realize the solution process. Finally, the validity of the model is proved by the calculation of actual enterprise data.

The optimized scheduling scheme can not only meet the on-time delivery, but also improve the truck loading rate and reduce the total cost, composed of early cost in plant, delivery penalty cost and transportation cost. In the model validation, the optimal scheduling scheme uses one less truck than the traditional EDD scheme (saving 20% of the transportation cost), and the total cost can be saved by 17.22%.

This study clarifies the relationship between the production and transportation of precast components and establishes the integrated scheduling optimization model and its solution algorithm. Different from previous studies, the proposed optimization model can balance the timeliness and economy of production and transportation for precast components.

Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.