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Navigating the system: The experiences of young black men in prison
No full textThis article questions whether the UK can address the unjust treatment of young Black males, give the lack of clear analysis and data
Militant animal rights activity: Terrorism, extremism or something else?
Full text linkSince the early 1970s, the United Kingdom (U.K.) has experienced political violence undertaken by militant animal rights actors. This violence has included the use of car bombs and incendiary devices, which are more akin to the tactics of a terrorist campaign. Similar acts in the United States have been described as “eco-terrorism” yet this label has not gained traction in the U.K. This article is concerned with the labeling of militant animal rights actions in the U.K. and explores the labels that have been applied by the print media, notably The Guardian to the actions of those animal rights actors who have utilized or espoused illegal and violent tactics in the pursuit of their cause. Moreover, the article takes a more in-depth look at the labeling of the group Stop Huntingdon Animal Cruelty (SHAC) in its campaign against Huntingdon Life Sciences and its business partners. How actions are labeled can have repercussions in shaping the public debate and policy implications
Choice of lipid supplementation for in vitro erythroid cell culture impacts reticulocyte yield and characteristics
Full text linkLipids, particularly cholesterol, are critical components of red blood cell (RBC) membranes, influencing protein function, cell stability, and deformability. Reticulocytes (young RBC) derived from in vitro erythroid cultures have been reported to possess less cholesterol than their native counterparts, compromising their functional integrity and lifespan. However, variability in starting materials and culture protocols between studies has hindered definitive conclusions regarding the nature and consequences of this lipid deficiency. Here, we evaluated the influence of lipid sources on reticulocyte quality using a well-established CD34⁺ erythroid culture system. We compared the use of human AB serum and Octaplas (solvent/detergent (S/D)-extracted pooled plasma) as lipid sources. Our results reveal that S/D-extracted plasma leads to cholesterol-deficient reticulocytes with impaired characteristics, including reduced filtration yield, heightened osmotic fragility, and altered PIEZO1 activity. In contrast, AB serum supported the generation of functionally stable reticulocytes, with cholesterol supplementation required to rescue the majority of defects observed with culturing erythroid cells with plasma alone. Importantly, this study provides the first integrated lipidomic, metabolomic, and proteomic characterisation of in vitro-derived reticulocytes cultured under distinct lipid conditions. These multi-omic datasets offer new insights into the consequences of reduced lipid availability during erythroid culture and offer new insights into how culture media affects the development and functionality of lab grown blood
Interrogating marine plastics pollution regulations: The intended roles of the Global Plastics Treaty
No full textMarine plastic pollution is a growing global issue that severely affects our ecosystems, biodiversity, and human well-being. Both national and international organisations have established frameworks and regulations to address the increase of plastic pollution in marine environments, covering everything from rules on the disposal of hazardous materials and chemicals used in plastic manufacturing to initiatives that promote recycling. Despite these efforts, there remains a need for a more effective legal instrument to govern marine plastic pollution. Current conventions lack a comprehensive life-cycle approach or strong enforcement mechanisms. A Global Plastics Treaty could offer a potential solution and fill the gaps present in existing regulations. This article critically examines the current landscape of marine plastics regulation while analysing the intended contributions and challenges that a global plastics law might face in regulating marine plastic pollution
Grape must as a bioelectrochemical processor
Full text linkWe explore spontaneous voltage oscillations in grape must (mustalevria) fermentation systems. This study uses multichannel differential electrode arrays. Seven platinum−iridium (Pt/Ir) electrode pairs tracked bioelectrochemical changes for 200,000 s. They showed complex patterns over time and space. Frequencies varied from 0.00044 to 0.00215 Hz. Power spectral density analysis showed brown noise traits. The spectral slopes ranged from −2.01 to −3.28. This indicates strong temporal integration and memory effects during fermentation. Environmental correlation analysis showed temperature as the primary modulator (r = 0.245−0.558), while humidity exhibited negative correlations (−0.052 to −0.245). Binary state analysis showed that the system uses natural Boolean logic. XOR gates had the highest entropy at 0.93 bits. This suggests that there is significant temporal asynchrony across different spatial areas. Principal component analysis found activation patterns without a single strong mode. It needed 3−4 components to capture 77.6% of the system's variance. The fermentation medium showed uneven metabolic activity across different areas. Also, the electrode locations were statistically independent, with mutual information below 0.206 bits. These findings show that traditional food fermentation systems work like self-organizing bioelectrochemical processors. They can also perform distributed computation through local metabolic interactions. Brown noise scaling and memory effects can impact fermentation monitoring and control. This means short-term measurements may not accurately predict long-term behavior. This work shows that grape must fermentation can be a model system. It helps us study new computational properties in biological electrochemical systems
Residual strength and load redistribution in multi-bolted single lap joints with simulated environmentally assisted cracks
Full text linkEnvironmentally assisted cracking (EAC) in bolted joints poses a serious threat to the structural integrity of aerospace components. Despite widespread industry awareness, the mechanical consequences of spanwise EAC on load sharing in multi-bolt configurations remain insufficiently characterised. This study presents the first systematic experimental investigation into the residual strength and internal load redistribution of three-bolt single-lap shear joints containing predefined, EAC-like cracks. To isolate geometric effects from material-specific corrosion behaviour, 1050 aluminium was employed as a model material, and artificial cracks ranging from 15 to 30 mm were introduced at the central fastener hole. Quasi-static tensile testing, supported by strain-gauge instrumentation, a validated three-dimensional finite element model, and a simplified analytical model, was used to evaluate joint performance. All cracked specimens retained peak load capacities comparable to uncracked controls, indicating significant structural redundancy. However, this apparent resilience masked a critical shift in internal force flow: the fastener adjacent to the crack experienced up to 60 % load reduction, with adjacent bolts compensating for the loss. Importantly, all joints failed by abrupt net-section fracture at the outermost bolt, with no evidence of progressive bearing failure. These results challenge conventional assumptions of damage tolerance by revealing that preserved load capacity can coexist with unpredictable and brittle failure modes. The findings provide experimentally validated benchmarks for stiffness degradation and load sharing in damaged joints, offering guidance for the design, analysis, and maintenance of EAC-prone aerospace structures
Large language models in building energy applications: A survey
Full text linkThe use of large language models (LLMs) in building energy applications (BEAs) is driving intelligent and sustainable solutions. Research in this area has expanded across multiple subfields, highlighting the need for a comprehensive understanding of LLM adoption, key applications, and emerging trends. Existing surveys often focus on narrow technical use cases, overlooking the broader context of LLM integration in building energy (BE) systems. This survey reviews 76 peer-reviewed articles published between 2021 and July 2025 at the intersection of LLMs and BEAs. A multi-scale analysis is presented, including keyword analysis, conceptual linkages via co-occurrence networks, topic modelling across six domains, and temporal assessment of study and method distributions. This approach provides a structured synthesis without delving into model-specific technical details. Key findings indicate that LLMs are transitioning from experimental tools to core infrastructure: they serve as semantic connectors between modelling, automation, and human-centred feedback; foundational methods dominate topic share; and methodological maturity has accelerated since 2023. Practical applications include semantic data integration, automated occupant surveys, decision support for retrofits, and energy-aware control. The survey offers a roadmap for scalable, interoperable, and human-aware BEAs, informing both research and practice
Fast structural analysis of concrete thin-shells using deep learning
No full textThe present paper investigates the use of deep learning models as fast structural analysis tools for the design of concrete thin-shells. A dataset of 20,000 thin-shells with various geometric and material properties is generated. The buckling factor and the stress fields of each thin-shell under design loads are determined using Finite Element analysis. Three different types of deep learning models – Multilayer Perceptron (MLP), Convolutional Neural Network (CNN) and Graph Neural Network (GNN) – are then trained for buckling and stress prediction. For both prediction tasks, the MLP and the CNN are found to be the best performing models, reaching errors below 0.31 % for buckling prediction, and below 0.51 % for peak stress prediction. These results demonstrate the ability of such models to act as fast structural analysis tools for concrete thin-shells. Deep learning models could therefore enable faster and wider design space exploration during the shape optimisation of concrete thin-shells
A hierarchical approach for assessing the vulnerability of tree-based classification models to membership inference attack
No full textMachine learning models can inadvertently expose confidential properties of their training data, making them vulnerable to membership inference attacks (MIA). While numerous evaluation methods exist, many require computationally expensive processes, such as training multiple shadow models. This article presents two new complementary approaches for efficiently identifying vulnerable tree-based models: an ante-hoc analysis of hyperparameter choices and a post-hoc examination of trained model structure. While these new methods cannot certify whether a model is safe from MIA, they provide practitioners with a means to significantly reduce the number of models that need to undergo expensive MIA assessment through a hierarchical filtering approach.More specifically, it is shown that the rank order of disclosure risk for different hyperparameter combinations remains consistent across datasets, enabling the development of simple, human-interpretable rules for identifying relatively high-risk models before training. While this ante-hoc analysis cannot determine absolute safety since this also depends on the specific dataset, it allows the elimination of unnecessarily risky configurations during hyperparameter tuning. Additionally, computationally inexpensive structural metrics serve as indicators of MIA vulnerability, providing a second filtering stage to identify risky models after training but before conducting expensive attacks. Empirical results show that hyperparameter-based risk prediction rules can achieve high accuracy in predicting the most at risk combinations of hyperparameters across different tree-based model types, while requiring no model training. Moreover, target model accuracy is not seen to correlate with privacy risk, suggesting opportunities to optimise model configurations for both performance and privacy
Unlocking social sustainability and inclusivity of digitalized urban public facilities: A causal model across global case studies
No full textThe rapid digital transformation of urban environments is reshaping how citizens interact with public infrastructure. One emerging innovation is Digitalized Urban Public Facilities (DUPFs). While DUPFs are widely recognized for their operational and technological benefits, their social implications, particularly regarding inclusivity and social sustainability, remain underexplored. This study addresses this gap by examining how DUPF characteristics, user experiences, and socio-demographic profiles interact to shape perceptions of inclusivity and social sustainability. Adopting a multi-method quantitative research design, the study combines descriptive analysis and inferential modeling techniques. Drawing from a comprehensive literature review, a causal model is developed and validated using survey data collected from users across four global case studies. Through structural equation modeling (SEM) and moderation analysis, the findings reveal that DUPFs significantly enhance social sustainability, especially among marginalized and older users, who benefit most from improved accessibility, usability, and service responsiveness. The results further highlight that higher levels of digitalization and accessible information correlate strongly with perceived inclusivity. Moderation effects show that age and marginalization status amplify the positive impacts of DUPFs, while gender and income have minimal moderating influence. This study contributes novel insights into the social value of digital public services and provides actionable guidance for designing inclusive, user-centered DUPFs that advance equity and urban sustainability across diverse communities