Heriot-Watt University

Traditional methods for proportioning of high-performance concrete (HPC) have certain shortcomings, such as high costs, usage constraints, and nonlinear relationships. Implementing a strategy to optimize the mixtures of HPC can minimize design expenses, time spent, and material wastage in the construction sector. Due to HPC's exceptional qualities, such as high strength (HS), fluidity and resilience, it has been broadly used in construction projects. In this study, we employed Generalized Regression Neural Network (GRNN), Nonlinear AutoRegressive with exogenous inputs (NARX neural network), and Random Forest (RF) models to estimate the Compressive Strength (CS) of HPC in the first scenario. In contrast, the second scenario involved the development of an ensemble model using the Radial Basis Function Neural Network (RBFNN) to detect inferior performance of standalone model combinations. The output variable was the 28 Days CS in MPa, while the input variables included slump (S), water-binder ratio (W/B) %, water content (W) kg/m ³ , fine aggregate ratio (S/a) %, silica fume (SF)%, and superplasticizer (SP) kg/m ³ . An RF model was developed by using R Studio; GRNN and NARX-NN models were developed by using the MATLAB 2019a toolkit; and the pre- and post-processing of data was carried out by using E-Views 12.0. The results indicate that in the first scenario, the Combination M1 of the RF model outperformed other models, with greater prediction accuracy, yielding a PCC of 0.854 and MAPE of 4.349 during the calibration phase. In the second scenario, the ensemble of RF models surpassed all other models, achieving a PCC of 0.961 and MAPE of 0.952 during the calibration phase. Overall, the proposed models demonstrate significant value in predicting the CS of HPC.

Artificial Intelligence (AI) shows promising applications for the perception and planning tasks in autonomous driving (AD) due to its superior performance compared to conventional methods. However, highly complex AI systems exacerbate the existing challenge of safety assurance of AD. One way to mitigate this challenge is to utilize explainable AI (XAI) techniques. To this end, we present the first comprehensive systematic literature review of explainable methods for safe and trustworthy AD. We begin by analyzing the requirements for AI in the context of AD, focusing on three key aspects: data, model, and agency. We find that XAI is fundamental to meeting these requirements. Based on this, we explain the sources of explanations in AI and describe a taxonomy of XAI. We then identify five key contributions of XAI for safe and trustworthy AI in AD, which are interpretable design, interpretable surrogate models, interpretable monitoring, auxiliary explanations, and interpretable validation. Finally, we propose a conceptual modular framework called SafeX to integrate the reviewed methods, enabling explanation delivery to users while simultaneously ensuring the safety of AI models.

Accurately predicting methane adsorption capacity in coal is crucial for assessing coalbed methane resources and ensuring safe extraction. Conventional methane isotherm adsorption experiments often suffer from human error and experimental artefacts, leading to inaccurate and poorly reproducible outcomes. Furthermore, they are time-consuming to conduct, requiring specific and well calibrated experimental equipment. In this paper, a Random Forest (RF) algorithm is introduced to improve the accuracy and reliability of methane adsorption capacity prediction. Approximately 200 sets of experimental data, including parameters such as experimental temperature, equilibrium pressure, moisture, ash content, and volatile matter of coal samples, were collected and analyzed to establish a prediction model based on the RF algorithm. The robustness and reliability of the model were validated using K-Fold cross-validation (KF) and hyperparameter optimization. The results indicate that the Random Forest algorithm performs exceptionally well in predicting methane adsorption capacity, with optimal values for Mean Squared Error (MSE) and the coefficient of determination (R²), demonstrating a high correlation between predicted and actual values. Machine learning algorithms are innovatively combined with traditional experimental methods in this study. By training the model using large datasets, issues of error and reproducibility in traditional experiments are addressed, improving experimental efficiency and providing a more reliable method for evaluating coalbed methane resources. To some extent, the method can replace traditional methane isotherm adsorption experiments in coal, improving prediction accuracy and efficiency, and demonstrating promising prospects for wide application.

We introduce and provide evidence to support the Proportionality Hypothesis which states that Covid-19 infection fatality rates are approximately proportional to all-cause death rates by age and subgroup (e.g., socio-economic class). We also show that vaccination played a very significant role in preventing people infected with Covid-19 from needing to be hospitalised, since it reduced the average severity of an infection. Death rates involving Covid-19 were very significantly lower for people in the fully vaccinated group compared to the unvaccinated group. During the pandemic, death rates from other causes were in some cases reduced (e.g., flu and pneumonia), in some cases unchanged (e.g., lung cancer) and in some cases elevated (e.g., heart disease). We discuss the implications of our findings both for potential adjustments to extrapolative mortality models which allow for future pandemics in a way that is consistent with the Proportionality Hypothesis and for insurance companies in terms of both modelling extreme scenarios and the design of mortality catastrophe bonds.

Background Sociolinguistic research on workplace mental health stigma is scarce and consequently, there are a lack of relevant conceptual models. Drawing on Goffman’s notion of stigma as a ‘language of relationships’, and Heller’s concept of ‘discursive space’, this paper offers a conceptual model of how stigma is produced and reinforced in workplace settings. Specifically, the model maps the complex discursive processes of mental health stigmatization through workplace discursive practices. Methods The model is empirically grounded and draws on 23 in-depth participant interviews with professional services employees in Hong Kong. Through a meta-discursive analysis of the employees’ experience in the workplace, the paper investigates how mental health stigma is produced in the workplace. Results Conceiving the workplace as a discursive space, the model demonstrates that mental health stigma unfolds across three discursive layers, namely immediate encounters, organizational practices, and societal ideologies. Mediated by discursive practices, such as identity management, stigma is both produced and perpetuated across the three layers. Conclusions The paper provides a model for analyzing the production of mental health stigma through dynamic discursive activities in the workplace. By doing so, it offers a way to systematically map how stigma, brought about through discourse in organizational settings, can regulate both interpersonal relationships and resource allocation (such as career prospects).

A basic income (BI) is defined by its characteristics, in contrast to a means-tested benefit, or more accurately here, an income-tested benefit (ITB). Both are tax-exempt. However, the ITB recipients’ gross incomes are taxed in two stages. An ITB is defined by the mechanism (a taper) used to ensure that a recipient does not profit unduly from the benefit. The special case of an ITB that is ‘a periodic cash payment unconditionally delivered to all on an individual basis’ would still not be categorised as a BI when gross income is zero, because ITBs are means-tested. The BI’s essential characteristic ‘without means test’ is identified as ‘it is separate from, and paid prior to, any taxation of incomes or wealth’. The consequences of this essential difference are summarised here. The effects of the characteristics of a BI and the structural features of a typical ITB system are also explored.

A series of styrene–acrylonitrile (SAN) copolymer nanoparticles were prepared by grafting styrene–acrylonitrile from both aggregated silica and colloidally dispersed silica nanoparticles using atom-transfer radical polymerisation (ATRP). Cross-linking and macroscopic gelation were minimised by using a miniemulsion system. The thermal and mechanical behavior of composites were made from PSAN aggregated silica nanoparticles or colloidally dispersed silica has been examined by Differential scanning calorimetry (DSC) and Dynamic mechanical thermal analysis (DMTA). The filler particles increased the rubbery modulus above the T g of PSAN considerably and led to a temperature-independent plateau of the modulus between 130 and 240 °C similar to that normally observed for crosslinked amorphous polymers. Covalent attachment of PSAN to the silica nanoparticles, by grafting the polymer from the surface of the silica using atom-transfer radical polymerization (ATRP), gave rise to hybrid materials with a comparable elastic plateau. While neat PSAN started to flow and deform irreversibly above 120 °C, the new silica nanoparticle–polymer hybrid materials proved stable up to 240 °C, which was more than 120 °C above the T g of the polymer. Aggregated silica nanoparticles displayed more affect compared to colloidally dispersed silica.

Wind farm flow control (WFFC) is an emerging technology involving coordinated operation of wind turbines within a wind farm to achieve collective goals. To design and evaluate controllers, wind farm flow models are used that capture the key aerodynamics of the system whilst remaining computationally efficient for iterative controller design. This review article reveals considerable heterogeneity in the potential wind farm flow models to study WFFC. Lack of consensus is problematic as differences in results from separate studies are attributable to both controller and model effects, making it hard to draw comparative conclusions. Hence, an expert elicitation is completed surveying WFFC practitioners. Two key contributions are presented. First, a guide to available software for WFFC, which, combined with results from an expert elicitation on flow model requirements, facilitates selection of suitable software for investigating WFFC problems. Secondly, critical future research areas are identified. Research into high fidelity wind direction models (particularly transient effects) and wake meandering models for fatigue load investigations are identified as critical to the field. A lack of consensus regarding the importance of atmospheric boundary layer effects, wake induced turbulence, and lateral wind correlation identifies the requirement of sensitivity studies in these areas.

The Southern Ocean, a region highly vulnerable to climate change, plays a vital role in regulating global nutrient cycles and atmospheric CO2 via the biological carbon pump. Diatoms, photosynthetically active plankton with dense opal skeletons, are key to this process as their exoskeletons are thought to enhance the transfer of particulate organic carbon to depth, positioning them as major vectors of carbon storage. Yet conflicting observations obscure the mechanistic link between diatoms, opal and particulate organic carbon fluxes, especially in the twilight zone where greatest flux losses occur. Here we present direct springtime flux measurements from different sectors of the subpolar Southern Ocean, demonstrating that across large areas of the subpolar twilight zone, carbon is efficiently transferred to depth, albeit not by diatoms. Rather, opal is retained near the surface ocean, indicating that processes such as diatom buoyancy regulation and grazer repackaging can negate ballast effects of diatoms’ skeletons. Our results highlight that the presence of diatoms in surface waters of the Southern Ocean’s largest biome does not guarantee their importance as vectors for efficient carbon transfer through the subpolar twilight zone. Climate change-driven shifts in phytoplankton community composition may affect biologically sequestered carbon pools less than currently predicted.

The roughskin dogfish Centroscymnus owstonii, a deep‐sea shark, has a patchy global distribution, with most knowledge stemming from incidentally captured specimens. Using a deep‐sea remote lander video system, we observed multiple C. owstonii individuals alive on the footage at 1054 m off Little Cayman, Cayman Islands, Western Atlantic Ocean, marking, to our knowledge, the first record of the species in the Greater Antilles, central Caribbean Sea, while also adding a new species locality record for the Cayman Islands. This study expands our knowledge of the distribution of the roughskin dogfish in the region, and highlights the utility of video lander systems for enhancing and expanding our understanding of the biology and diversity of deep‐sea sharks.

Engineering 3D tissue-like constructs for applications such as regenerative medicine remains a major challenge in biomedical research. Recently, self-healing, viscoplastic fluids have been introduced as suspension media to allow lower viscosity, water-rich bioinks to be printed within them for the fabrication of more biomimetic structures. Here, we present gellan gum granular gels produced through the application of shear during gelation, as a candidate suspension medium. We demonstrate that these granular gels exhibit viscoplasticity over a wide range of temperatures, permitting their use for 3D bioprinting of filaments and droplets at low (4°C) as well as physiological temperatures. These granular gels exhibit very low yield stresses (down to 0.4 Pa) which facilitated printing at print speeds up to 60 mm.s⁻¹. Furthermore, we demonstrate the printing of cell-laden droplets maintained over 7 days to show the potential for multiple days of cell culture, as well as the fabrication of hydrogel features within a crosslinkable version of the suspension medium containing granular gellan gum and gelatine-methacryloyl. The combination of ease of preparation, high printing speed, wide temperature tolerance, and crosslinkability makes this gellan gum sheared through cooling-induced gelation an attractive candidate for suspended biofabrication.

The Southern North Sea (SNS) gas basin is a key area for CO 2 storage projects in the UK. Many of the now-depleted Permian (Rotliegend) Leman Sandstone Formation fields are being re-evaluated as carbon stores. However, the reservoir is known to be highly faulted, often leading to field compartmentalization. This has historically impacted field development and production, and will challenge suitability for CO 2 storage by limiting site capacity, requiring a high number of injector wells, and increasing capital costs. It is necessary to understand the nature of these pressure compartments - and whether any individual culmination can house sufficient capacity - before devising a carbon storage development programme. The highly compartmentalized Indefatigable (Inde) Field was evaluated as a case study. A static model of the field was constructed using 3D seismic and well log data, and subsequently used to calculate the CO 2 capacity of each of the 12 compartments. Five compartments were found to have capacities larger than 10 Mt, with the large Main Horst found to host 51% of total CO 2 capacity. A sequential filling-and-sealing storage site development plan is suggested based on the evaluation and ranking of these compartments.

Purpose Exercise is known to acutely affect T-lymphocyte populations in the peripheral blood, which is intensity- and duration-dependent. However, effects of longer duration endurance exercise (>5 h) on T-cells in the days following are unknown. The aim of this study was to investigate the circulating T-cell changes that occur in response to an ultra-endurance event, which may provide insight into the inflammatory response to ultra-endurance exercise. Methods Ten individuals (m = 7, f = 3) completing an Ironman 70.3 event volunteered for the study. Peripheral blood samples were taken 1–2 days pre-race (PRE-RACE), and 1 day (RACE + 1) and 2 days (RACE + 2) post-race, with circulating T-cells enumerated by flow cytometry (total CD3+, CD4+ and CD8+ T-cells, regulatory T-cells [CD4+CD25+CD127−; TREG], naïve [CD27+CD45RA+; NA], central memory [CD27+CD45RA−; CM], effector memory [CD27−CD45RA−; EM], and effector memory CD45RA+ [CD27−CD45RA+; EMRA]). Results There were no changes in total CD3+, CD4+ and CD8+ T-cells. TREG RACE + 1 was significantly higher compared to PRE-RACE, as were the proportion of CD4+ NA cells and CD8+ CM cells at RACE + 2; CD8+ EM cells fell at RACE + 2 (absolute counts and proportion). Conclusion In conclusion, the ultra-endurance event evoked T-cell changes over the 48 h recovery period, with an increase in T-cells that regulate the immune response, and a reduction in circulating EM T-cells, most likely trafficked to sites of tissue damage and inflammation.

Buffer exchange is a common process in manufacturing protocols for a wide range of bioprocessing applications, with a variety of technologies available to manipulate biological materials for culture medium exchange, cell washing and buffer removal. Microfluidics is an emerging field for buffer exchange and has shown promising results with both prototype research and commercialised devices which are inexpensive, highly customisable and often have the capacity for scalability to substantially increase throughput. Microfluidic devices are capable of processing biological materials and exchanging solutions without the need for conventional processing techniques like centrifugation, which are time-consuming, unsuitable for large volumes and may be damaging to cells. The use of microfluidic separation devices for cell therapy manufacturing has been under-explored despite some device designs successfully being used for diagnostic enrichment of rare circulating tumour cells from peripheral blood. This mini-review aims to review the current state of microfluidic devices for buffer exchange, provide an insight into the advantages microfluidics offers for buffer exchange and identify future developments key to exploiting the technology for this application.

Oceanic gateways play a crucial role in controlling global ocean circulation. However, gateway effects on low-latitude deep-water circulation are poorly understood. The South China Sea, located in the western Pacific, was influenced by changes in the equatorial and low-latitude gateways, which recorded significant oceanographic variations since the Oligocene. Here, we identify contourite features in the deep South China Sea from seismic data and drill cores from Ocean Drilling Program Leg 184 and International Ocean Discovery Program Expeditions 349 and 367/368, as evidence for the influence of Circumpolar Deep Water originating from the eastern Indian Ocean until ca. 10 Ma. Final closure of the deep Indonesian Gateway at ca. 10 Ma caused disruption of the deep-water connection between the Pacific and Indian Oceans and a reorganization of global deep-water circulation. These changes in gateway tectonics may significantly contribute to the Middle to Late Miocene global climate and oceanic conditions.

Aims: Current evidence of the impact of acute exercise on insulin levels in individuals with type 1 diabetes remains controversial. Therefore, we conducted a systematic review and meta-analysis to explore exercise-induced changes in insulin levels. Materials and methods: We conducted a systematic review (until 05 November 2023) and meta-analysis exploring the effect of exercise on insulin concentration in individuals with type 1 diabetes. We included randomised cross-over studies for rapid-acting insulin and pre- and post-studies for long-acting insulin in individuals with type 1 diabetes performing any type of acute exercise and had a control condition. The exercise-induced change in insulin levels was the outcome of interest. When possible, the mean differences (MDs) in insulin levels were pooled using the DerSimonian and Laird random effect method. Risk of bias was assessed for each included study. Results: Seventeen trials, encompassing 186 participants with type 1 diabetes, were included in the systematic review. Twelve out of 17 studies included participants on rapid-acting insulin regimens and used a cross-over design, whereas five out of 17 single-arm studies included participants on (ultra)long-acting insulin. Seven out of 12 studies on rapid-acting insulins and all the single-arm studies were at high risk of bias. Results suggest a statistically significant, small-to-moderate increase of rapid-acting insulin after 30 min of exercise (MD of 18.44 [95% CI 0.02; 36.86; I2 0%] pmol/L); meanwhile, findings on (ultra)long-acting insulin were inconclusive. Conclusions: A small-to-moderate increase of insulin levels in studies including rapid-acting insulin was found after a bout of physical exercise in individuals with type 1 diabetes. However, current gaps in high-quality evidence challenge our understanding of insulin kinetics around exercise.

Coloniality is strongly shaped by aspects of social foraging behaviour. For example, colonies may be important sources of information, while food competition may increase foraging efforts and limit colony size. Understanding foraging ecology considering these apparent trade‐offs is required to develop a better understanding of colonial living. We combined animal‐borne GPS, cameras and dive recorders to study social foraging in breeding adult northern gannets Morus bassanus—a wide‐ranging colonial seabird. We first tested for indirect evidence of prey depletion around the colony by estimating dive location, depth and duration. Next, we tested for sociality during different behaviours (commuting, foraging and resting) and distance from the colony. Finally, we quantified flocks of inbound and outbound birds to compare social foraging between outbound and inbound legs of the commute. Dive probability and depth (n = 46 individuals; n = 1590 dives) increased with distance from the colony, creating dive clusters at ~100 and 180 km consistent with conspecific prey depletion. Camera stills (n = 8 individuals; n = 7495 images) show gannets are highly social, but this varied among behaviours. Sociality was highest during foraging and commuting; especially inbound and social foraging was more likely far from the colony. Gannets were equally likely to be solitary or social when leaving the colony but returning birds were more likely in larger flocks. In summary, despite experiencing intraspecific competition for food, gannets engage in dynamic, context‐dependent social foraging associations. Conspecifics aggregated far from the colony possibly because of a prey depletion halo closer to home, but this provided potential benefits via local enhancement and by returning to the colony in flocks. Our results therefore illustrate how competition may, paradoxically, facilitate some aspects of group foraging in colonial animals.