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This study aims to achieve superior localization and mapping performance in point cloud degradation scenarios through the effective removal of dynamic obstacles. With the continuous development of various technologies for autonomous vehicles, the LIDAR-based Simultaneous localization and mapping (SLAM) system is becoming increasingly important. However, in SLAM systems, effectively addressing the challenges of point cloud degradation scenarios is essential for accurate localization and mapping, with dynamic obstacle removal being a key component.

This paper proposes a method that combines adaptive feature extraction and loop closure detection algorithms to address this challenge. In the SLAM system, the ground point cloud and non-ground point cloud are separated to reduce the impact of noise. And based on the cylindrical projection image of the point cloud, the intensity features are adaptively extracted, the degradation direction is determined by the degradation factor and the intensity features are matched with the map to correct the degraded pose. Moreover, through the difference in raster distribution of the point clouds before and after two frames in the loop process, the dynamic point clouds are identified and removed, and the map is updated.

Experimental results show that the method has good performance. The absolute displacement accuracy of the laser odometer is improved by 27.1%, the relative displacement accuracy is improved by 33.5% and the relative angle accuracy is improved by 23.8% after using the adaptive intensity feature extraction method. The position error is reduced by 30% after removing the dynamic target.

Compared with LiDAR odometry and mapping algorithm, the method has greater robustness and accuracy in mapping and localization.

The purpose of this study is to propose a model for integrating microgames in teaching primary Education for Sustainable Development (ESD) and illustrate the application of the proposed model in teaching integrated mathematics, arts, technology and language in primary ESD.

The model was based on conceptual mapping from the reviewed literature and reflection from their teaching experience.

The model and the related teaching-learning activities have been proposed.

The proposed model enhances the use of microgames in an interdisciplinary teaching framework.

The lack of a proper register to store, match and display information on the adapted property has led to a waste of resources and prolonged delays in matching the disabled and elderly people with appropriate properties. This paper presents the development of a Housing Adaptations Register with user-matching functionalities for different mobility categories. The developed system accurately captures and documents adapted home information to facilitate the automated matching of disabled/aged applicants needing an adapted home with suitable property using banding, mobility and suitability index.

A theoretical review was conducted to identify parameters and develop adaptations register construct. A survey questionnaire approach to rate the 111 parameters in the register as either moderate, desirable or essential before system development and application. The system development relied on DSS modelling to support data-driven decision-making based on the decision table method to represent property information for implementing the decision process. The system is validated through a workshop, four brainstorming sessions and three focus group exercises.

Development of a choice-based system that enables the housing officers or the Housing Adaptations Register coordinators to know the level of adaptation to properties and match properties quickly with the applicants based on their mobility status. The merits of the automated system include the development of a register to capture in real-time adapted home information to facilitate the automated matching of disabled/aged applicants. A “choice-based” system that can map and suggest a property that can easily be adapted and upgraded from one mobility band to the other.

The development of a housing adaptation register helps social housing landlords to have a real-time register to match, map and upgrade properties for the most vulnerable people in our society. It saves time and money for the housing associations and the local authorities through stable tenancy for adapted homes. Potentially, it will promote the independence of aged and disabled people and can reduce their dependence on social and healthcare services.

This system provides the local authorities with objective and practical tools that may be used to assess, score, prioritise and select qualified people for appropriate accommodation based on their needs and mobility status. It will provide a record of properties adapted with their features and ensure that matching and eligibility decisions are consistent and uniform.

Weak repeatability is observed in handcrafted keypoints, leading to tracking failures in visual simultaneous localization and mapping (SLAM) systems under challenging scenarios such as illumination change, rapid rotation and large angle of view variation. In contrast, learning-based keypoints exhibit higher repetition but entail considerable computational costs. This paper proposes an innovative algorithm for keypoint extraction, aiming to strike an equilibrium between precision and efficiency. This paper aims to attain accurate, robust and versatile visual localization in scenes of formidable complexity.

SiLK-SLAM initially refines the cutting-edge learning-based extractor, SiLK, and introduces an innovative postprocessing algorithm for keypoint homogenization and operational efficiency. Furthermore, SiLK-SLAM devises a reliable relocalization strategy called PCPnP, leveraging progressive and consistent sampling, thereby bolstering its robustness.

Empirical evaluations conducted on TUM, KITTI and EuRoC data sets substantiate SiLK-SLAM’s superior localization accuracy compared to ORB-SLAM3 and other methods. Compared to ORB-SLAM3, SiLK-SLAM demonstrates an enhancement in localization accuracy even by 70.99%, 87.20% and 85.27% across the three data sets. The relocalization experiments demonstrate SiLK-SLAM’s capability in producing precise and repeatable keypoints, showcasing its robustness in challenging environments.

The SiLK-SLAM achieves exceedingly elevated localization accuracy and resilience in formidable scenarios, holding paramount importance in enhancing the autonomy of robots navigating intricate environments. Code is available at https://github.com/Pepper-FlavoredChewingGum/SiLK-SLAM.

The purpose of this paper is to critically examine the multinational oil companies' (MOC) corporate social responsibility (CSR) initiatives in Nigeria. Its special focus is to investigate the impact of the global memorandum of understanding (GMoU) on promoting gender-equitable agricultural value chains in the Niger Delta region.

This paper adopts a quasi-experimental design that used survey research technique, aimed at gathering information from a representative sample of the population, as it is essentially cross-sectional, describing and interpreting the current situation. A total of 760 rural women (380 from the treatment group and another 380 from the control group) were sampled across the Niger Delta region.

The results from the use of a combination of a logit model and propensity score matching indicate a significant relationship between GMoU model and gender-equitable agricultural value chains in the Niger Delta, Nigeria.

This study implies that CSR of MOCs is a critical factor in the need to integrating gender into agricultural value chains, achieving the goal of increasing agricultural growth and expanding the stable food supply.

This research contributes to gender debate in agricultural value chains from a CSR perspective in developing countries and rationale for demands for social projects by host communities. It concludes that business has an obligation to help in solving problems of public concern.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

An increasing number of images are generated daily, and images are gradually becoming a search target. Content-based image retrieval (CBIR) is helpful for users to express their requirements using an image query. Nevertheless, determining whether the retrieval system can provide convenient operation and relevant retrieval results is challenging. A CBIR system based on deep learning features was proposed in this study to effectively search and navigate images in digital articles.

Convolutional neural networks (CNNs) were used as the feature extractors in the author's experiments. Using pretrained parameters, the training time and retrieval time were reduced. Different CNN features were extracted from the constructed image databases consisting of images taken from the National Palace Museum Journals Archive and were compared in the CBIR system.

DenseNet201 achieved the best performance, with a top-10 mAP of 89% and a query time of 0.14 s.

The CBIR homepage displayed image categories showing the content of the database and provided the default query images. After retrieval, the result showed the metadata of the retrieved images and links back to the original pages.

With the interface and retrieval demonstration, a novel image-based reading mode can be established via the CBIR and links to the original images and contextual descriptions.

Recent international business research finds that state-owned multinational enterprises (SOMNEs) invest relatively more in politically risky host countries than do privately-owned multinational enterprises (MNEs). This study aims to investigate theoretically and empirically whether state ownership mitigates the impact of host-country political risk on subsidiary economic risk.

The authors link theoretical arguments on state ownership to arguments from non-market strategy literature to outline mechanisms whereby state ownership can buffer subsidiaries from political risk, weakening the link between host-country political risk and earnings volatility in subsidiaries. Using a data set on Norwegian MNEs’ foreign subsidiaries across almost two decades, the authors test this prediction using both matching methods and panel regressions.

While standard panel regressions provide empirical support only for the infrastructure sector and for the highest political risk contexts, nearest-neighbour matching models – comparing only otherwise similar private- and SOMNE subsidiaries using the full sample – reveal more general support for the political risk mitigation hypothesis.

The study presents the first comprehensive analysis of whether state ownership can mitigate the effect of political risk on subsidiary economic risk.

The purpose of this paper is to develop a conceptual framework that examines the impacts of changing seasons on tourism.

The paper presents a conceptual process model of the impact of seasons on all aspects of in-destination tourist behaviour. The model is developed from the literature and is then tested using Cairns, Australia as a case study.

Seasons influence the actual and perceived range of products/experiences available, which dictate the pull features of a destination, that in turn, influence who comes and why they come. Combined the activity sets and visitor profile define in-destination behaviour and, ultimately, satisfaction.

The study fills a needed research gap in two ways. Firstly, it explains conceptually and then tests empirically how changes in seasons affect the delivery of tourism products and experiences. Secondly, it adds significantly to our understanding of the factors that influence in-destination behaviour.

Managerial implications for destination management organisations are identified.

This paper presents a new conceptual process model for a previously unexamined issue.

本文提出了一个季节影响目的地游客行为的过程的理论模型。该模型基于文献开发而成, 然后以澳大利亚凯恩斯作为案例进行测试。

本文的目的是开发一个研究季节变化影响旅游的理论框架。

研究发现季节会影响实际和感知的产品或体验的范围, 从而决定一个目的地的吸引力特征。他们反过来可以影响谁来旅游以及他们来旅游的原因。 结合活动和旅游者画像来定义其目的地行为。

这项研究从两个方面填补了理论空白。 首先, 它从概念上解释, 然后实证检验了季节的变化如何影响旅游产品和体验的供应。 其次, 它极大地增强了我们对影响目的地行为的因素的理解。

本文指出了对目的地管理的实践意义。

本文针对先前未经考察的问题提出了一种新的理论过程模型。

El artículo presenta un modelo de proceso conceptual del impacto de las estaciones en todos los aspectos del comportamiento en en destino turístico. El modelo se desarrolla a partir de la literatura y luego se pone a prueba usando Cairns, Australia como estudio de caso.

El propósito de este artículo es desarrollar un marco conceptual que examine los impactos de los cambios de estación en el turismo.

Las estaciones influyen en la gama, real y percibida, de productos/experiencias disponibles que condicionan las características de atracción de un destino. Las estaciones, a su vez, influyen en quién viene y por qué viene. Los conjuntos de actividades combinadas y el perfil del visitante definen el comportamiento en el destino.

El estudio llena un vacío de investigación necesario de dos maneras. Primero, explica conceptualmente y luego demuestra empíricamente cómo los cambios en las estaciones afectan la oferta de productos y experiencias turísticas. En segundo lugar, contribuye significativamente a la comprensión de los factores que influyen en el comportamiento en el destino.

Se identifican las implicaciones de gestión para las organizaciones de gestión de destinos.

Este artículo presenta un nuevo modelo de proceso conceptual para un tema no examinado previamente.

By comparing regression models, this study aims to analyze the added home value of green sustainability features and green efficiency characteristics, rather than green certifications, in the city of Austin.

The adoption of home green energy efficiency upgrades has emerged as a new trend in the real estate industry, offering several benefits to builders and home buyers. These include tax reductions, health improvements and energy savings. Previous studies have shown that energy-certified single-family homes command a premium in the marketplace. However, the literature is limited in its analysis of the effects of green upgrades and certification on different types of single-family homes. To address this gap, this research collected data from 21,292 multiple listing services (MLS) closed home-selling listings in Austin, Texas, over a period of 35 months.

The analysis results showed that green efficiency features could generally increase single-family housing prices by 11.9%, whereas green sustainability upgrades can potentially bring a 11.7% higher selling price. Although green housing certification did not have significant effects on most housing groups, it did increase closing prices by 13.2% for single-family residences sold at the medium price range, which is higher than the impacts from simply listing the green features on MLS.

The study contributes to the body of knowledge by examining the market value of broadly defined energy efficiency and sustainability features in the residential housing market. The findings can help policymakers, brokerage firms, home builders and owners adjust their policies and strategies related to single-family home sales and mortgage approvals. The research also highlights the potential benefits of capitalizing on green housing features other than certifications.