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This paper aims to disentangle Chinese-English-rich resources linguistic and speaker timbre features, achieving cross-lingual speaker transfer for Cambodian.

This study introduces a novel approach: the construction of a cross-lingual feature disentangler coupled with the integration of time-frequency attention adaptive normalization to proficiently convert Cambodian speaker timbre into Chinese-English without altering the underlying Cambodian speech content.

Considering the limited availability of multi-speaker corpora in Cambodia, conventional methods have demonstrated subpar performance in Cambodian speaker voice transfer.

The originality of this study lies in the effectiveness of the disentanglement process and precise control over speaker timbre feature transfer.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.

Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being.

A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation.

Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them.

The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.

This performance autoethnography may or may not be interpreted as a continuation of a conversation regarding the experiences of those with devalued statuses in academic settings. The authors rely on “strange accounting” to consider their experiences in the academy from various standpoints: before and after promotion, before and after leaving academia. While reflecting on our past experiences, we introduce the concept of “everyday precariousness” as a way of explaining the normalization of instability, insecurity, and negative affect that is part of everyday life for those with devalued statuses in academic settings and beyond. Everyday precariousness is an embodied experience for those in vulnerable positions. Normalized exposure to risks, such as discrimination, harassment, bullying, or structural instability, produces an undercurrent of threat that permeates academic culture. Our stories of everyday precariousness span race, ethnicity, class, academic roles, and gender boundaries (among many others). Analyzing these experiences furthers previous work on the uses of strange accounting as well as the dynamics of status silencing. In the final analysis, unresisted and unabated, everyday precariousness and status silencing can lead to institutional failure and resonance disasters.

This chapter examines the connections between race and class divisions and examines how they shape racial inequities in the distribution of resources, power and privilege. Throughout history, racial identity has been a key factor in determining a person's position in modern capitalist societies. As such, issues of race and class have preoccupied sociologists and other scholars with diverse ideological orientations. This is highlighted in debates around the nexus of race and class in the production of racial structures, laws and institutions that legitimate and perpetuate the normalisation and centrality of whiteness. This chapter summarises some of the historical and ongoing debates, providing a synthesis of how race and class divisions continue to shape contemporary intergroup relations and social policy. It delves into racial capitalism and how race intersects with other social identities to determine socio-economic hierarchy in many western countries.

The normalization of COVID-19 disease prevention has presented a major opportunity for the transformation and upgrade of cultural and tourism products in Taiwan. This paper aims to explore the attractiveness of Taiwanese military dependents’ villages, known as juancun, two studies were conducted to test the path relationships of juancun experiencescape, authenticity, memorability and revisit intention.

This research conducted two studies by using a mixed sampling of snowball sampling and purposive sampling for the questionnaire survey. Based on the 397 valid questionnaires retained in Study 1, a cultural heritage site experiencescape was developed and validated; in Study 2, 393 valid questionnaires were collected, and structural equation modeling was conducted to test hypotheses that were developed drawing on the stimulus–organism–response model and theory of buyer behavior.

It is revealed that cultural heritage site experiencescape include natural and cultural, social, sensory and functional factors and that the experiencescape affects authenticity positively; “existential authenticity–memorability” mediates the relationship of experiencescape and revisit intention, whereas co-creation behavior has the positive moderating effects on the relationship of experiencescape and authenticity.

The research developed an experiencescape scale for cultural heritage sites and described the managerial implications for destination management organizations regarding product development and service design.

COVID-19疫情防控的常态化为台湾的文化和旅游产品升级转型提供了巨大的契机。为了明确台湾眷村的旅游吸引力, 本研究操作两个研究设计以检定眷村体验景观、真实性、难忘性及重游意图之间的路径关系。

本研究操作的两个研究设计皆采用滚雪球抽样和目的性抽样的混合抽样方法进行问卷调查。基于研究设计一回收的397份有效问卷, 发展和验证了眷村文化遗产地体验景观量表; 运用研究设计二回收的393份有效问卷, 操作结构方程模型分析以检定基于刺激-机体-反应理论和买方行为理论发展的假设。

本研究揭示了文化遗产地体验景观包含自然与文化、社会、感官、功能性因素; 体验景观正向影响真实性; “存在主义真实性-难忘性”中介调节体验景观和重游意图的关系; 共创行为正向调节体验景观与真实性的关系。

本研究发展了文化遗产地体验景观量表, 并就产品发展和服务设计为旅游目的地管理组织提供了实务建议。

Esta investigación llevó a cabo dos estudios mediante un muestreo mixto de bola de nieve y muestreo intencionado para la encuesta por cuestionario. A partir de los 397 cuestionarios válidos retenidos en el Estudio 1, se desarrolló y validó el experiencescape del patrimonio cultural; en el Estudio 2, se recogieron 393 cuestionarios válidos y se realizó un modelado de ecuaciones estructurales para probar las hipótesis que se desarrollaron a partir del modelo estímulo-organismo-respuesta y la teoría del comportamiento del comprador.

La normalización de la prevención de la enfermedad COVID-19 ha supuesto una gran oportunidad para la transformación y mejora de los productos culturales y turísticos de Taiwán. Para explorar el atractivo de las aldeas de los familiares de militares en Taiwan, conocidos como juancun, se realizaron dos estudios para probar las relaciones de trayectoria del experiencescape del juancun, la autenticidad, la memorabilidad y la intención de volver a visitarlo.

Se revela que el experiencescape del patrimonio cultural de la aldea militar incluye factores naturales y culturales, sociales, sensoriales y funcionales, y que el experiencescape afecta positivamente a la autenticidad; la “autenticidad existencial-memorabilidad” media en la relación del experiencescape y la intención de volver a visitarlo, mientras que el comportamiento de cocreación tiene los efectos moderadores positivos en la relación del experiencescape y la autenticidad.

La investigación desarrolló una escala de experiencescape para el patrimonio cultural del sitio y describió las implicaciones gerenciales para las organizaciones de gestión de destinos en relación con el desarrollo de productos y el diseño de servicios.

This study aims to explore the impact of board size, board gender diversity and federation age on the likelihood of having a female chair in National Sports Federations (NSF).

A quantitative methodology compares 300 sports boards in five countries (Italy, Portugal, Spain, Turkey and the UK), using data collected from NSF’s websites.

The board size and federation age have no significant impact on having a female board chair when the countries and the percentage of female directors are included in the model. When the number of women is measured in absolute value rather than in relative terms, the only variable that predicts a woman chair is the country. When the model does not include country differences, the percentage of female directors is key in predicting a chairwoman, and when the number of women is used as a variable instead of the percentage, a board’s smaller size increases the odds of having a chairwoman.

There are some limitations to this study which we believe provide useful directions for future research. Firstly, the authors have not considered the role of gender typing in sports activities which explains the extent that women participate in specific sports (Sobal and Milgrim, 2019) and the related perception of such sports in society. The social representation of sports activities classified as masculine, feminine or gender-neutral can hypothetically influence women’s access to that specific federations’s leadership. The authors included the country factor only partially, as a control variable, as the social representation of sports usually goes beyond national boundaries.

This study has implications for sport policymakers and stakeholders, and for institutions such as the IOC or the European Union that implement equality policies. If the aim is to increase female presence in the highest position of a sports board and to achieve gender equality more generally, other policies need to be implemented alongside gender quotas for the sports boards, namely, those specifically related to the recruitment and selection of the sports board chairs (Mikkonen et al., 2021). For example, given the implications of critical mass and its ability to increase more female’s engagement then the role of existing chairs acting as mentors and taking initiative in this objective may be warranted. Furthermore, attention should be paid to the existing gender portfolio of each board and its subsequent influence on recruiting a female chair, regardless of the organization’s age. Knoppers et al. (2021) concluded that resistance to gender balance by board members is often related to discriminatory discourses against women. The normalization of the discourses of meritocracy, neoliberalism, silence/passivity about the responsibility of structures and an artificial defence of diversity emphasise that equality should not only be determined by women (Knoppers et al., 2021).

When countries are included in the model, the results suggest that the social representation of a female board member is different from that of a female board chair.

The originality of the study is that it shows the factors that constrain women taking up a chair position on NSFs. Theoretically, it contributes to existing literature by demonstrating how a critical mass of females on boards may also extend to the higher and most powerful position of chair.

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.

Focusing on micro-level indicators, we investigate financial inclusion levels in rural China, examining its determinants and impact on household welfare. We construct a financial inclusion index of four essential financial services: savings, digital payments, credit and insurance. We identify factors influencing financial inclusion among Chinese rural households and assess the effects of financial inclusion on household welfare.

With the entropy method, we use data from the 2019 China Household Finance Survey to assess financial inclusion levels in rural China. Determinants and their impact on welfare are analyzed through probit and ordinary least squares models, respectively. Propensity scoring matching is applied to address potential endogeneity.

We reveal that rural households exhibit limited usage of formal financial services, with notable regional disparities. The eastern region enjoys the highest financial inclusion and the central region lags behind. Household characteristics such as family size, education level of the household head, income, employment status and financial literacy significantly influence financial inclusion. Financial inclusion positively impacts household welfare as indicated by household consumption expenditure. The use of different types of financial services is crucial with varying but significant effects on household welfare.

This study offers valuable insights into China’s rural financial inclusion progress, highlighting potential barriers and guiding government actions.

Accurately evaluating fluid flow behaviors and determining permeability for deforming porous media is time-consuming and remains challenging. This paper aims to propose a novel machine-learning method for the rapid estimation of permeability of porous media at different deformation stages constrained by hydro-mechanical coupling analysis.

A convolutional neural network (CNN) is proposed in this paper, which is guided by the results of finite element coupling analysis of equilibrium equation for mechanical deformation and Boltzmann equation for fluid dynamics during the hydro-mechanical coupling process [denoted as Finite element lattice Boltzmann model (FELBM) in this paper]. The FELBM ensures the Lattice Boltzmann analysis of coupled fluid flow with an unstructured mesh, which varies with the corresponding nodal displacement resulting from mechanical deformation. It provides reliable label data for permeability estimation at different stages using CNN.

The proposed CNN can rapidly and accurately estimate the permeability of deformable porous media, significantly reducing processing time. The application studies demonstrate high accuracy in predicting the permeability of deformable porous media for both the test and validation sets. The corresponding correlation coefficients (R²) is 0.93 for the validation set, and the R² for the test set A and test set B are 0.93 and 0.94, respectively.

This study proposes an innovative approach with the CNN to rapidly estimate permeability in porous media under dynamic deformations, guided by FELBM coupling analysis. The fast and accurate performance of CNN underscores its promising potential for future applications.