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This work aims to demonstrate the feasibility of fully preserving the historical heritage at the same time reordering the cities and their traffic.

This paper describes the sustainable solution designed for the landscape change required and to maintain the bridge integrity by excavating under the pier with the maintenance of traffic during its execution.

It is concluded that the elimination of urban motorways on the surface often leads to the excavation of tunnels under the existing buildings, with little coverage in most of them. This complicates the implementation of burials in cities with an important historical heritage, which must be given conservation priority in the choice of technical solutions.

The Segovia Bridge over the Manzanares River, the oldest bridge in Madrid, was built in the 16th century. With the burial of the M-30 motorway, it has been necessary to build a tunnel immediately under one of the bridge piers, practically without lining between the foundations and the upper slab of the tunnel.

Previous research has analyzed the consequence of network stability; however, little is known about how partner type diversity influence network stability in R&D network. Based on knowledge-based view and social network theory, the purpose of this paper is to unravel the internal mechanisms between partner type diversity and network stability through the mediating role of knowledge recombination in R&D network.

The authors collected an unbalanced panel patent data set from information communication technology industry for the period 1994–2016. Then, the authors tested the different dimensions of partner type variety and its relevance in the R&D network and the mediating role of knowledge recombination through adopting the multiple linear regression.

Results indicate an inverted U-shaped relationship between partner type diversity (variety and relevance) and network stability, whereas knowledge recombination partially mediate these relationships.

From the perspective of R&D networks, this paper explores that there are the under-researched phenomena the antecedent of network stability through nodal attributes (i.e. partner type variety and partner type relevance). Moreover, this paper empirically examined the mediating role of knowledge recombination in the partner type diversity–network stability relationships. The novel perspective allows focal firm to recognize importance of nodal attributes, which are critical to fully excavate the potential capabilities of cooperating partners in R&D network.

This study aims to excavate the external and internal driving factors of enterprise digital innovation and further condense the function mechanisms and response actions of the innovation process as well as the final innovation outcomes to propose the theoretical framework of enterprise digital innovation with the logic thread of “innovation motivation–innovation process–innovation result”.

Based on the digital background and integrating previous case studies on enterprise digital innovation, this study uses qualitative meta-analysis to explore the motivations of enterprise digital innovation, the function mechanisms and response actions of innovation process as well as the innovation outcomes.

This study finds that digitally transformed enterprises driven by external environmental factors and internal organizational factors follow the logic of adaptive and open innovation, and finally accomplish the digital innovation results of products, services, processes, business models, technology and social responsibility through the response actions of adaptation and interaction mechanisms.

In the digital economy era, digital innovation has become the key strategic choice for enterprise transformation, upgrading and innovation. Why and how enterprises realize digital innovation with the help of emerging digital technology remain to be explored. This study has important theoretical significance and practical value for the digital innovation and transformation of enterprises under the digital background.

This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.

Based on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.

It has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.

Previous studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.

This paper aims to examine the management status quo of archaeological heritage in Vietnam seen in the case of Vuon Chuoi, a complex of Bronze Age sites located in Central Hanoi, which has been believed to be Hanoi’s first human settlement. Like other archaeological sites located in urban areas, this site has been under threat of destruction caused by land encroachment pressure. Although researchers have long waged a campaign for preservation, the dissensus among key stakeholders and the dispute over responsibility have left this site at the heart of an interminable polemic over legislation.

This research utilises a qualitative approach, and the primary data were collected throughout multiple field trips in 2019 and 2020. Several open-ended interviews were conducted with various state and nonstate actors involved in the Vuon Chuoi Complex’s management process. The discussion was also supported by analysing related legal documents retrieved from national archives and official online directories.

This paper dissects the current legislative and administrative framework applied in governing heritage in general and archaeological sites in Vietnam, in particular. The results indicate that existing flaws in Vietnam’s legal system are detectable, and the unsystematic organisation has led to deferment of the decision-making processes. Also, there is an apparent difference found in the attitude of the bodies in charge toward the treatment of listed and unlisted sites.

This research outlines that in the wake of urbanisation and industrialisation in Vietnam, a consensus among key stakeholders and an inclusive legal system are required to help preserve archaeological sites in urgent need of attention. Although several Vietnamese laws and regulations have been put into practice, they have shown critical barriers and gaps in conserving Vietnamese cultural heritage.

The purpose is to provide decision support for tourists recommending scenic spots and corresponding suggestions for the management of scenic spots.

Based on the Baidu index data generated, this paper analyzes the temporal and spatial characteristics of network attention of 5A scenic spots in Sichuan Province. The online comment data are used to build the assessment model of scenic spots based on network attention, and the comment information of tourists is mined and analyzed through statistical analysis. At the same time, the key attributes of scenic spots from the perspective of network attention are evaluated and analyzed by using the probabilistic linguistic term set. Finally, this paper further constructs a recommendation model based on the key attribute set of scenic spots.

This paper uses different types of tourism network information, integrates multi-types of data and methods, fully excavates the value information of tourism network information, constructs the research framework of “scenic spot assessment + scenic spot recommendation” from the perspective of network attention, analyzes the network attention characteristics of scenic spots, evaluates the performance of scenic spots, and implements scenic spot recommendation.

This paper integrates multi-source data and multidisciplinary theoretical methods to form a scenic spot research framework of “assessment + recommendation” from the perspective of network attention.

This study aims to document the hypogea churches in Lay Gayint Woreda, South Gondar, to provide information for future tourism development practices and serve as insurance against loss of value due to unmanaged deteriorative factors.

The study followed a descriptive research design with qualitative research approach. Data were collected through field observations, interviews and written sources examination.

The study explored the lesser-known hypogea churches, which have significant tourist attraction values, including environmental, historical and architectural significance. However, the use of these potential cultural resources for tourism development is not yet attempted, and their conservation status is found to be critical. This documentation work is significant both for the sake of future tourism development plans and as insurance against looming cultural losses.

This study did not record the ancient treasures of the churches because of the current political instabilities that hindered the access of data. However, it has implication for the need of an extensive documentation activity to trace the cultural resources in the remote areas of the country for future tourism development and conservation practices.

This paper documented the remote hypogea churches not only for the purpose of future tourism development plan but also as an insurance of their values against unmanaged destructive factors.

By reconsidering the concept of the historic environment, the aim of this study is to better understand how heritage is expressed by examining the networks within which the cultural performances of the historic environment take place. The goal is to move beyond a purely material expression and seek the expansion of the cultural dimension of the historic environment.

Conceptually, the historic environment is considered a valuable resource for heritage expression and exploration. The databases and records that house historic environment data are venerated and frequented entities for archeologists, but arguably less so for non-specialist users. In inventorying the historic environment, databases fulfill a major role in the planning process and asset management that is often considered to be more than just perfunctory. This paper approaches historic environment records (HERs) from an actor network perspective, particularizing the social foundation and relationships within the networks governing the historic environment and the environment's associated records.

The paper concludes that the performance of HERs from an actor-network perspective is a hegemonic process that is biased toward the supply and input to and from professional users. Furthermore, the paper provides a schematic for how many of the flaws in heritage transmission have come about.

The relevance here is largely belied by the fact that HERs as both public digital resources and as heritage networks were awaiting to be addressed in depth from a theoretical point of view.

In 2020, mining activity by Rio Tinto destroyed rock caves in Western Australia's Juukan Gorge that are considered sacred sites by the First Nation Peoples of that area, the Puutu Kunti Kurrama and Pinikura (PKKP) Peoples. This paper examines the public response to the damage caused at this culturally sensitive site and identifies cultural heritage protection strategies that emerged in the aftermath of this catastrophic event.

This research applies a qualitative case study method and analysis of open-sourced official policy documents, media reports and published institutional statements.

The research identified specific cultural heritage protection strategies, including stakeholder-driven advocacy and shared values approach to business practices to help foster a greater appreciation of the connections between people, objects and lands. Whilst the mining activities were considered lawful, significant gaps in the legislation to protect heritage sites were also exposed.

Using a recent case that occurred in 2020, this paper unpacks how the motivations for accessing minerals can override cultural sensibilities and legal/ethical frameworks established to protect cultural heritage. This paper brings to light the liabilities associated with the mining industry when operating in a culturally significant environment where appropriate due diligence to manage cultural heritage is not thoroughly applied. The paper highlights the role the community can play in demanding improved corporate social responsibility which can, in turn, act as a strategy for cultural heritage protection.

The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.

In this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.

Numerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.

Compared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.