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This chapter explores how the development of football fandom for the Chinese national team and local football clubs is strongly associated with societal changes. Although the performances of Chinese football teams, especially the national team, have failed to impress the world, football remains the most popular because of millions of supporters with loyalty and passion. Most studies related to fans mainly focus on the economic and political implications of spectatorship along with the rise of China. Nevertheless, few articles are available to answer the fundamental questions, such as ‘When did these supporters come out?’ and ‘What were the factors of the development of fandom?’. By going through archival records and published documents over the last decades, this chapter offers a comprehensive and historical analysis of the development of football fandom in the People's Republic of China (PRC) and deals with these unanswered questions. As such, this chapter does not intend to be the most authoritative one but is one of the rare sources to lay down the foundation for research on Chinese football fandom. Furthermore, this chapter also proves that studies on football fandom can be a useful window for observing Chinese society.

This study aims to assess carbon emissions in urban aged residential buildings in Qingdao, Shandong Province, constructed prior to 2000, and to evaluate retrofitting and rebuilding strategies for potential carbon reduction.

Field investigations and literature reviews were conducted to identify key factors influencing carbon emissions, such as shape coefficient, window-to-wall ratio and envelope structure. A combination of generalization and mathematical statistical methods was used to classify buildings based on construction year, form, structural type and energy-saving goals. Cluster analysis was employed to extract six typical building models.

Results demonstrate that building form complexity positively correlates with carbon emissions per unit area, while longer lifespans reduce emission intensity. Retrofitting exhibits shorter carbon payback periods (1.62–3.92 years) than rebuilding (18.7–49.94 years), indicating superior environmental performance. Pre-1986 buildings are advised for demolition/rebuilding due to limited retrofit benefits. For 1986–1995 buildings, retrofitting is recommended if structurally viable. Post-1996 buildings favor retrofitting over new construction for its shorter payback and lower emissions, enhancing long-term carbon reduction.

This study contributes to the understanding of carbon emissions in urban aged residential buildings by considering various factors and providing specific recommendations for retrofitting and rebuilding strategies tailored to different construction periods. Additionally, it highlights the importance of building form complexity and remaining lifespan in determining carbon emissions, offering insights for sustainable urban development and carbon reduction initiatives.

Thin cracks on the surface, such as those found in nuclear power plant concrete structures, are difficult to identify because they tend to be thin. This paper aims to design a novel segmentation network, called U-shaped contextual aggregation network (UCAN), for better recognition of weak cracks.

UCAN uses dilated convolutional layers with exponentially changing dilation rates to extract additional contextual features of thin cracks while preserving resolution. Furthermore, this paper has developed a topology-based loss function, called ℓ_cl Dice, which enhances the crack segmentation’s connectivity.

This paper generated five data sets with varying crack widths to evaluate the performance of multiple algorithms. The results show that the UCAN network proposed in this study achieves the highest F1-Score on thinner cracks. Additionally, training the UCAN network with the ℓ_cl Dice improves the F1-Scores compared to using the cross-entropy function alone. These findings demonstrate the effectiveness of the UCAN network and the value of incorporating the ℓ_cl Dice in crack segmentation tasks.

In this paper, an exponentially dilated convolutional layer is constructed to replace the commonly used pooling layer to improve the model receptive field. To address the challenge of preserving fracture connectivity segmentation, this paper introduces ℓ_cl Dice. This design enables UCAN to extract more contextual features while maintaining resolution, thus improving the crack segmentation performance. The proposed method is evaluated using extensive experiments where the results demonstrate the effectiveness of the algorithm.

Digital technology (DT) is a massive and robust tool for organizational success. This paper aims to examine the roles of digitalization and digital innovation (DI) in developing the capability of a digital economy.

The authors used a cross-sectional study to collect the data from the managers of Egyptian SME manufacturing firms. This study utilized 322 samples.

From applying the structural equation model (SEM), this study’s findings show that digital capability (DC) and digital orientation (DO) exert a positive effect on the firm’s digital economy capability (DEC). In addition, DC has a positive impact on DI. In contrast, digital technology self-efficacy (DTSE) negatively predicts DEC. This study’s results also confirm DO’s negative effect on DI. The DTSE is a positive enabler of DI that has also positively affected the DEC. The mediating results demonstrate that DI reinforces the positive connection between DO and DEC. On the other hand, DI does not mediate the connection between DO and DEC and between DTSE and DEC.

This study’s outcomes support policymakers and manufacturing organizations in employing DT to improve DEC and, thereby, develop firm performance and success. The study’s findings also encourage organizations to invest in bringing about a digital culture within them. Finally, by developing DT and DI, firms can nurture a conducive culture of creativity and forward-thinking.

This study directly overcomes the need for an integrated framework of all DI, DTSE, DO, DC and DEC. Furthermore, DI’s mediating contribution between DC and DEC, between DO and DEC and between DTSE and DEC adds fresh insights to the existing literature.

Early prediction of any type of cancer is important for the treatment of this type of disease, therefore, our target to evaluate whether monitoring early changes in plasma human epidermal growth factor receptor 2 (HER2) levels (using EIS), could help in the treatment of breast cancer or not? Human epidermal growth factor receptor 2 (HER2) overexpression is an important biomarker for treatment selection in earlier stages of cancers. The combined detection of the HER2 gene in plasma for blood cancer provides an important reference index for the prognosis of metastasis to other tissues. For this purpose, the authors fabricated and characterized a model wireless biosensor-based electrochemical impedance spectroscopy (EIS) for detecting HER2 plasma as therapeutics.

Most sensors generally are fabricated based on a connection between component of the sensors and the external circuits through wires. Although these types of sensors provide suitable sensitivities and also quick responses, the connection wires can be limited to the sensing ability in various devices approximately. Therefore, the authors designed a wireless sensor, which can provide the advantages of in vivo sensing and also long-distance sensing, quickly.

The biosensor structure was designed for detection of HER2, HER3 and HER-4 from lab-on-chip approach with six units of screen-printed electrode (SPE), which is built of an electrochemical device of gold/silver, silver/silver or carbon electrodes. The results exhibited that the biosensor is completely selective at low concentrations of the plasma and HER2 detection via the standard addition approach has a linearity plot, therefore, by using this type of biosensors HER2 in plasma can be detected.

This is then followed by detecting HER2 in real plasma using standard way which proved to have great linearity (R² = 0.991) proving that this technique can be used to detect HER2 solution in real patients.