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This paper aims to consider the potential implications of the layering of regulation in relation to hydraulic fracturing (fracking) at the borders between the nations of the UK.

This paper uses a qualitative research method grounded in particular in legal geography to examine the existing approaches to regulating hydraulic fracturing and identify the places and their features that are constructed as a result of their intersection at the borders of the nations comprising the UK.

The current regulatory framework concerning hydraulic fracturing risks restricts the places in which the practice can occur in such a manner as to potentially cause greater environmental harm should the process be used. The regulations governing the process are not aligned in relation to the surface and subsurface aspects of the process to enable their management, once operational, as a singularly constructed place of extraction. Strong regulation at the surface can have the effect of influencing placement of the site only in relation to the place at which the resource sought reaches the surface, whilst having little to no impact on the environmental harms, which will result at the subsurface or relative to other potential surface site positions, and potentially even increasing them.

This paper is limited by uncertainty as to the future use of hydraulic fracturing to extract oil and gas within the UK. The issues raised within it would also be applicable to other extractive industries where a surface site might be placed within a radius of the subsurface point of extraction, rather than having to be located at a fixed point relative to that in the subsurface. This paper therefore raises concerns that might be explored more generally in relation to the regulation of the place of resource extraction, particularly at legal borders between jurisdictions, and the impact of regulation, which does not account for the misalignment of regulation of spaces above and below the surface that form a single place at which extraction occurs.

This paper considers the potential impacts of misaligned positions held by nations in the UK in relation to environmentally harmful practices undertaken by extractive industries, which are highlighted by an analysis of the extant regulatory framework for hydraulic fracturing.

Whilst the potential for cross internal border extraction of gas within the UK via hydraulic fracturing and the regulatory consequences of this has been highlighted in academic literature, this paper examines the implications of regulation for the least environmentally harmful placement of the process.

This research paper takes a narrow approach to examining the apparent link between poverty and the resource extraction industries. However, it acknowledges that much more is to be explored about this relationship. Many complexities influence the occurrence and degree of poverty in a particular country, region, or community.

The literature review identified proposed and operational poverty reduction actions and processes categorized under the broad concept of community development projects. The surveyed cases describe how various corporate strategies, work processes, labor requirements and efficient management and governance solutions are purported to improve poverty-reduction efforts potentially.

No causal linkages between poverty and hypothetically valuable and successful community development projects were found. No poverty monitoring evaluations and learning data (MEL) for these projects were proposed and published in most of the literature. However, associations were observed between some business practices implemented in these resource extraction community development projects and observations of indicators of lower poverty levels.

The research improves our understanding of the requirements and opportunities for successful community development projects by highlighting processes, company strategy, human resource programs and enlightened governance that can contribute to reducing poverty.

The paper identifies the characteristics of community development projects that appear to span natural resource extraction industries and countries. Effective management strategies and representative and formally designated organisational governance boards are essential for these projects.

Learning from past construction accident reports is critical to reducing their occurrence. Digital technology provides feasibility for extracting risk factors from unstructured reports, but there are few related studies, and there is a limitation that textual contextual information cannot be considered during extraction, which tends to miss some important factors. Meanwhile, further analysis, assessment and control for the extracted factors are lacking. This paper aims to explore an integrated model that combines the advantages of multiple digital technologies to effectively solve the above problems.

A total of 1000 construction accident reports from Chinese government websites were used as the dataset of this paper. After text pre-processing, the risk factors related to accident causes were extracted using KeyBERT, and the accident texts were encoded into structured data. Tree-augmented naive (TAN) Bayes was used to learn the data and construct a visualized risk analysis network for construction accidents.

The use of KeyBERT successfully considered the textual contextual information, prompting the extracted risk factors to be more complete. The integrated TAN successfully further explored construction risk factors from multiple perspectives, including the identification of key risk factors, the coupling analysis of risk factors and the troubleshooting method of accident risk source. The area under curve (AUC) value of the model reaches up to 0.938 after 10-fold cross-validation, indicating good performance.

This paper presents a new machine-assisted integrated model for accident report mining and risk factor analysis, and the research findings can provide theoretical and practical support for accident safety management.

The study examines the effect of natural resources (NRs) and the control of corruption, voice and accountability and regulatory quality on carbon emissions in Africa. Aside from their individual effects, the moderation effect of institutional quality is assessed.

Data from 32 African countries from 2002 to 2021 and the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) regression methods were used for the investigation.

In the long term, the NRs effect is sensitive to the estimation technique employed. However, quality regulatory framework, robust corruption control and voice and accountability abate any positive effect of NRs on carbon emissions. Institutional quality can be argued to moderate the CO2-emitting potentials of resource extraction in the selected African countries.

Enhancing regulation quality, enforcing corruption control and empowering citizens towards greater participation in governance and demanding accountability are essential catalyst to effectively mitigate CO2 emissions resulting from NRs.

The moderation effect of control of corruption, voice and accountability and regulatory quality on the NR–carbon emission nexus is examined.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image. The authors used three feature extraction methods: handcrafted features, convolutional features and autoencoder features. Subsequently, these features were mapped to contact locations through a contact location regression network. Finally, the network performance was evaluated using spherical fittings of three different radii to further determine the optimal feature extraction method.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image.

This research indicates that data collected by probes can be used for contact localization. Introducing a batch normalization layer after the feature extraction stage significantly enhances the model’s generalization performance. Through qualitative and quantitative analyses, the authors conclude that convolutional methods can more accurately estimate contact locations.

The paper provides both qualitative and quantitative analyses of the performance of three contact localization methods across different datasets. To address the challenge of obtaining accurate contact locations in quantitative analysis, an indirect measurement metric is proposed.

This study aims to explore the integration of machine learning (ML) in tribology to optimize lubrication interval decisions, aiming to enhance equipment lifespan and operational efficiency through wear image analysis.

Using a data set of scanning electron microscopy images from an internal combustion engine, the authors used AlexNet as the feature extraction algorithm and the J48 decision tree algorithm for feature selection and compared 15 ML classifiers from the lazy-, Bayes and tree-based families.

From the analyzed ML classifiers, instance-based k-nearest neighbor emerged as the optimal algorithm with a 95% classification accuracy against testing data. This surpassed individually trained convolutional neural networks’ (CNNs) and closely approached ensemble deep learning (DL) techniques’ accuracy.

The proposed approach simplifies the process, enhances efficiency and improves interpretability compared to more complex CNNs and ensemble DL techniques.

The purpose of this paper is to consider the ethical and environmental implications of allowing space resource extraction to disrupt existing fuel economies, including how companies can be held accountable for ensuring the responsible use of their space assets. It will also briefly consider how such assets should be taxed, and the cost/benefit analyses required to justify the considerable expense of supporting this emerging space industry.

This paper adopts theoretical bioethics methodologies to explore issues of normative ethics and the formulation of moral rules to govern individual, collective and institutional behaviour. Specifically, it considers social justice and social contract theory, consequentialist and deontological accounts of ethical evaluation. It also draws on sociological and organisational literature to discuss Dowling and Pfeffer’s (1975) and Suchman’s (1995) theories of pragmatic, cognitive and moral legitimacy as they may be applied to off-world mining regulations and the handling of space assets.

The findings of this conceptual paper indicate there is both a growing appetite for tighter resource extraction regulations to address climate change and wealth concentration globally, and an opportunity to establish and legitimise new ethical norms for commercial activity in space that can avoid some of the challenges currently facing fossil fuel divestment movements on Earth.

By adopting methodologies from theoretical bioethics, sociology and business studies, including applying a legitimacy lens to the issue of off-world mining, this paper synthesises existing knowledges from these fields and brings them to the new context of the future space resource economy.

The purpose of this study is to establish a method for accurately extracting torch and seam features. This will improve the quality of narrow gap welding. An adaptive deflection correction system based on passive light vision sensors was designed using the Halcon software from MVtec Germany as a platform.

This paper proposes an adaptive correction system for welding guns and seams divided into image calibration and feature extraction. In the image calibration method, the field of view distortion because of the position of the camera is resolved using image calibration techniques. In the feature extraction method, clear features of the weld gun and weld seam are accurately extracted after processing using algorithms such as impact filtering, subpixel (XLD), Gaussian Laplacian and sense region for the weld gun and weld seam. The gun and weld seam centers are accurately fitted using least squares. After calculating the deviation values, the error values are monitored, and error correction is achieved by programmable logic controller (PLC) control. Finally, experimental verification and analysis of the tracking errors are carried out.

The results show that the system achieves great results in dealing with camera aberrations. Weld gun features can be effectively and accurately identified. The difference between a scratch and a weld is effectively distinguished. The system accurately detects the center features of the torch and weld and controls the correction error to within 0.3mm.

An adaptive correction system based on a passive light vision sensor is designed which corrects the field-of-view distortion caused by the camera’s position deviation. Differences in features between scratches and welds are distinguished, and image features are effectively extracted. The final system weld error is controlled to 0.3 mm.

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.

This study aims to characterize the properties of natural cellulose fiber from the pseudo-stems of the curcuma zedoaria plant.

The fiber was extracted using the biological retting process (cold-water retting). The intrinsic fiber properties obtained were used to evaluate the possibility of using fiber for textile applications.

The average length of a curcuma zedoaria fiber was 34.77 cm with a fineness value of 6.72 Tex. A bundle of curcuma zedoaria fibers was comprised of many elementary fibers. Curcuma zedoaria had an irregular cross-section, with the lumen having a varied oval shape. Curcuma zedoaria fibers had tenacity and elongation value of 3.32 gf/denier and 6.95%, respectively. Curcuma zedoaria fibers had a coefficient of friction value of 0.46. Curcuma zedoaria fibers belong to a hygroscopic fiber type with a moisture regain value of 10.29%.

Extraction and Characterization of Curcuma zedoaria Pseudo-stems Fibers for Textile Application.