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Australia appears to be encountering a crisis in the protection of certain heritage places, despite its strong reputation in heritage conservation built up since the 1970s. Consequently, this paper examines changes to national cultural heritage management policy over the last few decades to understand more about this crisis.

Indigenous (Aboriginal and Torres Strait Islander heritage) was selected as the key focus. This paper applies a cultural heritage management framework tested first in Hong Kong to measure Australian paradigm change over 45 years.

It found the 1990s shift away from the provision of independent technical advice on national heritage policy has had a major impact. This shift is based on a change in ethos away from the earlier Whitlam/National Estate broader vision of heritage responsibilities towards a narrower more conservative one at the national level. Also, it found that studies and policymaking should allow for Indigenous voices. More Indigenous input in heritage policy formulation at all levels of government would further decolonise Indigenous heritage governance to deal justly with Indigenous Australians and their heritage.

Resources did not allow for comparative studies of the non-Indigenous (historic) and natural heritage as part of the current study.

The study also included a consultation paper and an online conference presentation that have raised questions about the efficacy of current national policy on Indigenous places, on which a national conversation is urgently needed. The recent review of the National Heritage Strategy by the Australian Commonwealth Government based some of its proposed options on those listed in the consultation paper to initiate this conversation in a limited way.

One finding is that attention to heritage policy and protection must be ongoing at all levels of government and inclusive of First People's human rights, particularly those concerning their heritage. In regard to Australia, most Aboriginal and Torres Strait Islander respondents in this study would like to see targeted funding return for more than just iconic Indigenous places and for the creation of a more independent heritage body that allows them more self-determination in the care of their heritage.

The paper's value is that it investigates the Australian Heritage Commission's impact in the development of Australian cultural heritage management and associated national policy. Also, it provides insights for other postcolonial or New World settler societies dealing with the same issues or any decision-makers considering establishing a national independent body to oversee heritage protection and policymaking.

There is growing interest around the world in more effectively linking public payments to the provision of public goods from agriculture. However, published evidence syntheses suggest mixed, weak or uncertain evidence for many agri-environment scheme options. To inform any future “public money for public goods” based policy, further synthesis work is needed to assess the evidence-base for the full range of interventions currently funded under agri-environment schemes. Further empirical research and trials should then focus on interventions for which there is mixed or limited evidence. Furthermore, to ensure the data collected is comparable and can be synthesised effectively, it is necessary to reach agreement on essential variables and methods that can be prioritised by those conducting research and monitoring. Future policy could then prioritise public money for the public goods that can most reliably be delivered, offering better value for taxpayers and improving the provision of ecosystem services from agricultural landscapes.

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization.

Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT).

Methods procedures, process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods.

Results, observations, and conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

In recent decades, the field of robotic mapping has witnessed widespread research and development in light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) techniques. This paper aims to provide a significant reference for researchers and engineers in robotic mapping.

This paper focused on the research state of LiDAR-based SLAM for robotic mapping as well as a literature survey from the perspective of various LiDAR types and configurations.

This paper conducted a comprehensive literature review of the LiDAR-based SLAM system based on three distinct LiDAR forms and configurations. The authors concluded that multi-robot collaborative mapping and multi-source fusion SLAM systems based on 3D LiDAR with deep learning will be new trends in the future.

To the best of the authors’ knowledge, this is the first thorough survey of robotic mapping from the perspective of various LiDAR types and configurations. It can serve as a theoretical and practical guide for the advancement of academic and industrial robot mapping.

Nowadays, in many organizations, products are not delivered instantly. So, the customers should wait to receive their needed products, which will form a queueing-inventory model. Waiting a long time in the queue to receive products may cause dissatisfaction and churn of loyal customers, which can be a significant loss for organizations. Although many studies have been done on queueing-inventory models, more practical models in this area are needed, such as considering customer prioritization. Moreover, in many models, minimizing the total cost for the organization has been overlooked.

This paper will compare several machine learning (ML) algorithms to prioritize customers. Moreover, benefiting from the best ML algorithm, customers will be categorized into different classes based on their value and importance. Finally, a mathematical model will be developed to determine the allocation policy of on-hand products to each group of customers through multi-channel service retailing to minimize the organization’s total costs and increase the loyal customers' satisfaction level.

To investigate the application of the proposed method, a real-life case study on vaccine distribution at Imam Khomeini Hospital in Tehran has been addressed to ensure model validation. The proposed model’s accuracy was assessed as excellent based on the results generated by the ML algorithms, problem modeling and case study.

Prioritizing customers based on their value with the help of ML algorithms and optimizing the waiting queues to reduce customers' waiting time based on a mathematical model could lead to an increase in satisfaction levels among loyal customers and prevent their churn. This study’s uniqueness lies in its focus on determining the policy in which customers receive products based on their value in the queue, which is a relatively rare topic of research in queueing management systems. Additionally, the results obtained from the study provide strong validation for the model’s functionality.

In this article, the authors analyse a complex social process affecting historic public paths in rural areas in southern Spain. Despite the fact that urban populations are demanding the enhancement of this type of natural heritage for tourism, sports and recreational use, some parts of the network have been abandoned or usurped.

The study is multidisciplinary, comprising three interlinked studies. The cartographic study comprises an inventory of public paths in rural areas based on administrative sources. The legal study analyses local, regional and national regulations governing agricultural, environmental, heritage, sports and tourism uses of the infrastructure. The sociological study analyses social discourses on the uses of public paths, and identifies conflicts between farmers, landowners, environmentalists, sportspeople and tourists.

The preliminary results identified an important public paths network in Andalusia, approximately 160,000 km. The legal study found that there are laws regulating use, although local authorities do not monitor compliance or provide solutions to enhance management. The sociological study determined the attribution of environmental, cultural and economic value to public paths, but also the existence of conflicts between rural and urban populations.

Given that this is ongoing research, only state of the art and some preliminary albeit sufficiently consistent results are presented.

The results could help to guide public policy and governance of public paths.

Public paths promote rural development and a green/sustainable economy.

The research results and conclusions are original.

The region of Guelma, located in the northeast of Algeria, is characterised by its important natural and cultural heritage. This heritage is ignored and abandoned, which leads to its degradation and even its disappearance. Thus, it is imperative to seek to know and understand the reasons for this situation. This study aims to reveal the constraints of heritage promotion and its staging through its insertion in the development process.

A hypothetical–deductive approach based on a qualitative method using surveys was carried out on the knowledge of the heritage of Guelma by the actors concerned. The laws and classification procedures were interpreted to identify the heritage's stakes to ensure sustainable local development through tourism.

The results show the lack of recognition of the natural and cultural heritage by the region's population, the cumbersome, long and complex classification procedure. The weakness of the offer in terms of tourist infrastructures constitutes a handicap to the valorisation of the heritage and prevents the enhancement and development of tourism. Systematically, heritage does not contribute to the local and territorial development of the Guelma region.

The article underlines the importance of raising awareness among the various heritage stakeholders and actors, and first, the population, of the value of their natural and cultural heritage to make it profitable and a driving force for local and territorial development.

The wood construction industry has been described as slow in adapting efficiency-increasing activities in its operations and supply chain. The industry is still facing challenges related to digitalization, such as fragmentation, poor traceability and lack of real-time information. This study evaluates the status of digitalization in construction supply chains by thematically analyzing the existing literature and mapping research trends.

A review of the key literature from 2016 to 2021 was performed. The results highlight various technologies and their applications within supply chains and identify research gaps, especially between theoretical frameworks and actual implementation using a scientometric-thematic analysis.

This paper provides a conceptual framework to further aid researchers in exploring the current trends in Supply Chain 4.0 and its applications in the wood construction industry compared to other more advanced industries. Suggested directions for future research in the wood construction Supply Chain 4.0 are outlined.

The existing literature still lacks a comprehensive review of the potential of a digitalized supply chain, especially in the construction industry. This framework is pivotal to continue explaining and observing the best ways to accelerate and implement Supply Chain 4.0 practices for digitalized supply chain management (SCM) while focusing specifically on the wood construction industry. The literature review results will help develop a comprehensive framework for future research direction to create a clearer vision of the current state of digitalization in supply chains and focus on the wood construction supply chain, thus, fully achieving the benefits of Supply Chain 4.0 in the wood construction industry.

As a typical nature-based solution to climate change, forestry carbon sinks are vital to achieving carbon neutrality in China. However, regulations in China are insufficient to promote the development of carbon offset projects in forestry. This study aims to identify the regulatory obstacles impeding the development of forestry offsets under China’s certified emission reduction (CCER) and explore ways to improve the regulatory system.

This study conducts a qualitative analysis using a normative legal research method. This study conducted a synthetic review of national and local regulatory documents to gain insights into the regulatory landscape of forestry offsets in China. The main contents and characteristics of these documents are illustrated. Furthermore, related secondary literature was reviewed to gain further insight into forestry offset regulations and to identify significant gaps in China’s CCER regulation.

Forestry offset regulations under the CCER are characterized by fragmentation and a relatively lower legally binding force. There is no systematic institutional arrangement for forestry offset development, impeding market expectations and increasing transaction costs. The main challenges in China’s regulation of forestry carbon sinks include entitlement ambiguity, complicated rules for registration and verification, a lack of mechanisms for incentives, risk prevention and biodiversity protection.

Forestry carbon sinks’ multiple environmental and social values necessitate their effective development and utilization. This study assessed forestry offset regulations in China and proposed corresponding institutional arrangements to improve forestry carbon sink regulations under the CCER.

The purpose of this study is to develop a model that accurately forecasts hotel room cancelations and further determines the key cancelation drivers.

Predictive modeling, specifically the machine learning methods, is used to forecast room cancelations and identify the main cancelation factors.

By using three different classification algorithms, this study demonstrates that hotel room cancelation can be accurately predicted using XGBoost, as well as the ensemble method involving Support Vector Machine, Random Forest and XGBoost.

This study attempted to forecast hotel room cancelations by applying a relatively new method, machine learning. By implementing predictive modeling, one of the most emerging and innovative research methods, this study ultimately provides prediction suggestions in various aspects and levels for hotel management operations.

本研究旨在开发一个能够准确预测酒店客房取消的模型, 并进一步确定主要的取消因素。

采用预测建模, 具体来说是机器学习方法, 来预测客房取消, 并识别主要的取消因素。

通过使用三种不同的分类算法, 本研究表明使用XGBoost以及涉及支持向量机、随机森林和XGBoost的集成方法可以准确预测酒店客房取消。

本研究尝试通过应用相对较新的方法, 即机器学习, 来预测酒店客房取消。通过实施预测建模, 这是目前新兴和创新的研究方法之一, 本研究最终为酒店管理运营在各个方面和层面提供了预测建议。