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From biology to bureaucracy? Impact of the Supreme Court's Equality Act ruling on sex classification.
No full textOn 16 April 2025, the UK Supreme Court issued a landmark Equality Act ruling that cements a biologically grounded definition of "sex" in antidiscrimination law. While the decision ostensibly upholds the Act's commitment to equal treatment for LGBTQ+ individuals, it pivots the dispute away from questions of identity and social recognition and toward the mechanics of legal classification and administrative enforcement. By favouring chromosomal or birth-assigned criteria over self-identification, the Court reshapes both the conceptual foundations of sex-based protections and the day-to-day operation of workplace, educational, and public-accommodation policies. This shift is driven by an unequivocal textualist methodology: by enforcing a uniform, biology-based reading of "sex," the Court transforms what was previously a flexible interplay of identity and rights into a rigid framework of bureaucratic compliance. This article traces the journey "from biology to bureaucracy" by examining how a seemingly technical definitional choice will reshape regulatory procedures, agency guidance, and judicial review for years to come. First, the legislative history of the Equality Act 2010 and its recognition of "sex" as a protected characteristic is outlined. Next, the Supreme Court’s majority and dissenting opinions in For Women Scotland Ltd v Scottish Ministers are analysed. Finally, the ruling's implications for future rulemaking, enforcement, and the broader pursuit of substantive equality are assessed
Adversarial multi-source domain generalization approach for power prediction in unknown photovoltaic systems.
No full textAccurate forecasting of power output for previously unseen photovoltaic installations is of critical importance to the reliability and efficiency of renewable-energy management systems. Existing data-driven PV prediction techniques rely primarily on historical measurements from familiar systems, which constrains their applicability to new sites without prior observations. To address this limitation, we introduce a Generative Adversarial Domain enhanced Prediction Network (GADPN). GADPN employs an adversarial generator to synthesize diverse pseudo domain samples that mitigate distributional discrepancies between source and target domains. Through an alternating optimization regime, the framework enforces both semantic consistency and manifold regularization constraints to align synthesized and empirical feature representations, while a Transformer-based predictor captures local and global temporal dynamics. We evaluate the proposed approach on nine geographically and capacity-diverse PV systems (ranging from 2.16 kW to 45.78 kW) under a zero-sample setting. Experimental results demonstrate that GADPN achieves coefficients of determination exceeding 0.97 in eight of the nine cases and attains a peak coefficient of determination of 0.9993, outperforming state-of-the-art baselines. These findings confirm GADPN's effectiveness for robust, zero-sample generalization in PV power forecasting
Leveraging artificial intelligence for water optimisation in upstream oil and gas energy operations.
Get PDFWater scarcity and climate change are significant challenges for sustainable water management worldwide. Factors such as population growth, industrial development, and unsustainable practices are increasing water demand. The upstream oil and gas energy industry faces water management challenges, including sourcing, treating, transporting, and disposing of water while meeting Environmental, Social, and Governance (ESG) requirements. This study introduces the Water Usage Efficiency Index (WUEI) using artificial intelligence in Python, a novel quantitative framework aligned with UN Sustainable Development Goals. The WUEI assesses water management in upstream energy operations by analysing water intensity, source sustainability, and temporal variability. Data from the Alberta Energy Regulator and oil sands operators are used to evaluate operational efficiency and water recycling rates from 2013 to 2022. WUEI scores range from 0.624 to 2.130, highlighting areas for improvement and guiding water management strategies. This standardised approach supports ESG objectives and promotes industry best practices. The research offers a practical, AI-enhanced method for evaluating water efficiency in the oil and gas sector, contributing to sustainable water management and ESG goals. Collaboration among academia, industry, and policymakers is essential for the widespread adoption of the WUEI framework
An exploration into the relationship between information needs satisfaction and creativity of visual artists in Greece.
Get PDFThere is a current research gap based on exploring the information behaviour of visual artists (professional, semi-professional, and amateur) and, particularly, the impact of their information needs satisfaction on their creativity. This research aims to address this gap, providing a comprehensive analysis of the complex relationship between information needs and creativity in Greek visual artists involved in both fine and applied arts. Based on Wilson's 1981 information behaviour model, a questionnaire survey was administered to a total of 777 visual artists in Greece, capturing their information-seeking behaviour, their information needs, the resources they use, and the primary obstacles they encounter when searching for information, exploring the influence of demographic characteristics. The survey also examined the perceived importance visual artists place on the impact of information needs satisfaction on different visual art creative outcomes, focusing on diverse activities, such as painting, sculpture, printmaking, photography, digital art, and decoration. This research highlights the critical role that both digital and traditional information access plays in fulfilling visual artists' information needs and fostering artistic creativity and imagination, as well as the importance of informal networks, professional development, and digital information literacy. As visual artists create within an ever-evolving information environment, support systems that, not only provide key information, but also assist in navigating and managing the increasing complexity of the artist’s information world are needed. Interaction with global art, both contemporary and historical, enhances personal growth and aesthetic principles. Artists benefit from a constant flow of accessible information through digital platforms, social media, and personal contacts. However, challenges like information overload, burnout, and digital exclusion exist. Education in digital literacy and intellectual property is vital for effectively using information and respecting creative sources. This study specifically investigates the information-seeking behaviour and needs of visual artists in Greece, a context that has not been thoroughly explored in the research literature. It offers unique insights into the cultural and professional environment of Greek visual artists and explores how information needs satisfaction plays a crucial role in artistic creation by enhancing their imagination, fostering their creativity, and providing new perspectives and outlets for their creative endeavours
Development and implementation of an applied professional development module for final year undergraduate students.
No full textAcademics and practitioners in sport and exercise science higher education face challenges in producing graduates proficient in real-world employment skills and ensuring students have the necessary academic and scientific rigor. With a broader range of qualifications entering university courses, universities must ensure continuity and address students lacking study skills, which negatively impact their learning potential
Preferentially-orientated gradient precipitates enable unique strength-ductility synergy in Mg-Sn binary alloys. [Video]
No full textConventional manufacturing approaches, including casting, thermal deformation and annealing, have faced great challenges in achieving both exceptional strength and ductility for Mg alloys. Herein, we report an effective strategy for simultaneously enhancing the tensile yield strength (YS = 341 ± 9.6 MPa) and elongation (EL = 15% ± 1%) of a Mg-4Sn (at.%) binary alloy at room temperature, which has been prepared by an ultrahigh-pressure treatment followed by Joule-heat treatment (UPJT). More attractively, the aging condition (80 μs, 500 Hz, 600 s) is the most time-efficient mode for aged Mg alloys. The reason is mainly associated with the presence of a unique preferentially-orientated gradient precipitate structure, as confirmed by transmission electron microscopy observations, density functional theory calculations and molecular dynamics simulations. Both experimental and theoretical results demonstrate that twin boundary-induced precipitation followed by precipitate-assisted twin boundary migration accounts for the formation of gradient precipitate structures. The fine Mg2Sn particles can effectively pin dislocation movement to enhance its strength. Comparatively, the coarse Mg2Sn particles can undergo plastic deformation and shear deformation, contributing to its high ductility. The strategy of preferentially orientated gradient structure provides a budding perspective for designing new Mg alloys with superior mechanical properties. This output file contains a video comparing the deformation behavior of three different structures under the same strain condition
DevSecOps implementation for continuous security in financial trading software application development.
Get PDFDevSecOps incorporates security into the DevOps workflow, ensuring robust protection throughout the software development lifecycle. This research addresses the security gaps in financial trading applications, where traditional methods often prioritize speed over security. Using the Design Science Research Methodology (DSRM), the study examines secure coding practices, regulatory compliance, and incident response strategies. Findings highlight the benefits of embedding automated security testing and continuous monitoring to enhance resilience against evolving threats. Tailored developer training addresses knowledge gaps specific to trading platforms, ensuring compliance with regulatory demands and safeguarding sensitive financial data. By accelerating deployment timelines while strengthening security and compliance, this study demonstrates the critical role of a DevSecOps model in creating scalable, secure, and resilient trading applications
Antimicrobial stewardship curricula for undergraduate healthcare education applicable to the UK: a full review of online resources.
Get PDFBackground: Antimicrobials are the cornerstone of modern medicine, used to treat millions of people worldwide. Antimicrobial stewardship (AMS) is included as part of healthcare professional (HCP) undergraduate (UG) curricula; however, these have not been compared with WHO guidance and it is not known if there are education resources to help embed these principles into UG curricula and whether content may vary. Aims: To identify published UG curricula or competency frameworks (CFs) for AMS relevant to medical, nursing, pharmacy, dental and allied HCPs in the UK. Also, to assess whether these curricula meet WHO recommendations and identify any gaps compared with the UK Health Security Agency (UKHSA) antimicrobial prescribing and stewardship competency framework. Methods: A search for online education resources was carried out to identify UG curricula or CFs for HCPs for AMS or antimicrobial resistance applicable to the UK. Results: Seven curricula or CFs were identified and reviewed. One AMS CF for all UG health workers, three for pharmacy UG students, one nursing AMS CF, one medical AMS CF, and the pathology curriculum. Domains varied between them but significant overlap was identified, and the majority of knowledge objectives in the WHO modules were covered in all UG curricula/CFs (except those objectives listed under the role of laboratory staff, which were only in the pathology curriculum). Gaps were identified between the most recently published UG curricula for pharmacists and the UKHSA antimicrobial prescribing and stewardship CF. Conclusions: There is significant correlation between the UG curricula/CFs for HCPs in the UK, the WHO curricula guide, and UKHSA antimicrobial prescribing and stewardship CF; however, there are some identified gaps. Despite the availability of UG curricula and/or CFs for the majority of HCPs, these are not consistently embedded in UG training
Integrated non-destructive testing for assessing manufacturing defects in melt-fusion bonded thermoplastic composite pipes.
Get PDFThe thermoplastic composite pipe (TCP) manufacturing process introduces defects that impact their performance, such as voids, misalignment, and delamination. Consequently, there is an increasing demand for effective non-destructive testing (NDT) techniques to assess the influence of these manufacturing defects on TCP. The objective is to identify and quantify internal defects at a microscale, thereby improving quality control. A combination of methods, including NDT, has been employed to achieve this goal. The density method is used to determine the void volume fraction. Microscopy and void analysis are performed on pristine samples using optical micrography and scanning electron microscopy (SEM), while advanced techniques like X-ray computer tomography (XCT) and ultrasonic inspections are also applied. The interlayer between the reinforced and inner layers showed good consolidation, though a discontinuity was noted. Microscopy results confirmed solid wall construction, with SEM aligning with the XY axis slice, showing predominant fibre orientation around ±45° and ±90°, and deducing the placement orientation to be ±60°. Comparing immersion, 2D microscopy, and XCT methods provided a comparative approach, even though they could not yield precise void content values. The analysis revealed a void content range of 0-2.2%, with good agreement between microscopy and Archimedes' methods. Based on XCT and microscopy results, an increase in void diameter at constant volume increases elongation and reduces sphericity. Both methods also indicated that most voids constitute a minority of the total void fraction. To mitigate manufacturing defects, understanding the material's processing window is essential, which can be achieved through comprehensive material characterization of TCP materials
Unsupervised domain adaptation for VHR urban scene segmentation via prompted foundation model-based hybrid training joint-optimized network.
Get PDFUnsupervised Domain Adaptation for Remote Sensing Semantic Segmentation (UDA-RSSeg) is to adapt a model trained on the source domain data to the target domain samples, thereby minimizing the need for annotated data across diverse remote sensing scenes. In urban planning and monitoring, the task of UDA-RSSeg on Very-High-Resolution (VHR) images has garnered significant research interest. While recent deep learning techniques have demonstrated huge success in tackling the UDA-RSSeg task for VHR urban scenes, a persistent challenge in addressing the domain shift issue remains. Specifically, there are two primary problems: (1) severe inconsistencies in feature representation across diverse domains, characterized by notably differing data distributions, and (2) the domain gap problem due to the representation bias of the source domain patterns when translating features to predictive logits. To solve these problems, we propose a prompted foundation model based hybrid training joint-optimized network (PFM-JONet) for UDA-RSSeg on VHR urban scene. Our approach integrates the notable "Segment Anything Model" (SAM) as prompted foundation model to leverage its robust generalized representation capabilities, thereby alleviating feature inconsistencies. Based on the feature extracted by SAM-Encoder, we introduce a mapping decoder designed to convert SAM-Encoder features into predictive logits. Additionally, a prompted segmentor is employed to generate class-agnostic maps, which guide the mapping decoder’s feature representations. To efficiently optimize the entire network in an end-to-end manner, we design a hybrid training scheme that integrates feature-level and logits-level adversarial training strategies alongside a self-training mechanism. This scheme enhances the model from diverse, compatible perspectives. To evaluate the performance of our proposed PFM-JONet, we conduct extensive experiments on urban scene benchmark datasets, including ISPRS (Potsdam/Vaihingen) and CITY-OSM (Paris/Chicago). On ISPRS dataset, PFM-JONet surpasses previous SOTA methods by 1.60% in mean IoU value across four adaptation tasks. For CITY-OSM's adaptation task, it outperforms SOTA by 4.84% in mean IoU value. These results demonstrate the effectiveness of our method. Furthermore, visualization and analysis reinforce the method's interpretability. The code of this paper is available at https://github.com/CV-ShuchangLyu/PFM-JONet
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