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RESEARCH DIGEST NO.6: Social Housing and Homelessness:EPOCH Practice evidence review
Get PDFThere are four main ways in which social housing can be used to prevent and reduce homelessness in the European Union, which also extends to other countries in Europe: 1) Increase general supply of good quality housing available at genuinely affordable rents and offering good security of tenure (long and lifetime tenancy agreements). This can include new building, renovation and retrofitting of existing housing and improvements to neighbourhoods to ensure the best use is being made of available social housing and the purchase and repurposing of private rented sector housing. 2) Enhancing universal prevention of homelessness by reducing the risk that individuals and families will lose housing because rents and mortgages in the private sector are unaffordable. Reductions in the level of ‘hidden’ homelessness, i.e. staying with relatives and friends which is broadly associated with a lack of affordable housing supply. This can be achieved simply by significant increases in much more affordable social housing supply. 3) Better use of existing social housing for people at risk of homelessness and providing enduring routes out of homelessness for people who have experienced it. This centres on enhancement of allocation processes and improvements to joint working across social protection, public health, the homelessness sector and social housing providers. 4) Providing housing for Housing First programmes, an evidence-backed service model for people experiencing homelessness associated with multiple and complex treatment and support needs. Social housing can be of particular benefit to Housing First because it can offer very affordable homes with good security of tenure at what can be a better standard than is offered by private rented sector markets
Decision-Focused Learning Enhanced by Automated Feature Engineering for Energy Storage Optimisation
Get PDFDecision-making under uncertainty in energy management is complicated by unknown parameters hindering optimal strategies, particularly in Battery Energy Storage System (BESS) operations. Predict-Then-Optimise (PTO) approaches treat forecasting and optimisation as separate processes, allowing prediction errors to cascade into suboptimal decisions as models minimise forecasting errors rather than optimising downstream tasks. The emerging Decision-Focused Learning (DFL) methods overcome this limitation by integrating prediction and optimisation; however, they are relatively new and have been tested primarily on synthetic datasets with limited evidence of their practical viability. Real-world BESS applications present additional challenges, including greater variability and data scarcity due to collection constraints. Because of these challenges, this work leverages Automated Feature Engineering (AFE) to improve the nascent approach of DFL. This AFE–DFL integration automatically extracts decision-relevant features from limited energy data without requiring domain expertise, while ensuring features directly enhance BESS operational decisions rather than merely improving prediction accuracy metrics. We propose an AFE–DFL framework suitable for small datasets that forecasts electricity prices and demand while optimising BESS operations to minimise costs. We validate the framework’s effectiveness on a novel real-world UK property dataset. The evaluation compares DFL methods against PTO, with and without AFE. Results show that DFL yields lower operating costs than PTO, and adding AFE further improves DFL performance by 22.9–56.5 % compared to models without AFE. These findings provide empirical evidence for DFL’s practical viability, demonstrating that AFE-DFL integration reduces reliance on domain expertise while achieving superior economic outcomes for BESS optimisation
AQEval: R code for the analysis of discrete change in air quality time-series
Get PDFAQEval (Air Quality Evaluation) is an R package for the routine investigation of discrete changes
in air quality time-series. The main functions, quantBreakPoints and quantBreakSegments,
use a three-step method to find possible ‘points-of-change’, test these and quantify the most
likely ‘points-of-change’ or ‘regions’ about them. Other key functions build on these to provide
a workflow to measure smaller changes and/or changes in more complex environments
Understanding drivers and biases of simulated CO emissions from the INFERNO fire model over South America
No full textIntegrating fire representation into climate models improves our understanding of ecosystem-fire-climate interactions by including connections between the carbon cycle and atmospheric composition. The Interactive Fires and Emissions algorithm for Natural Environments (INFERNO) is a new component of the UK Earth System Model (UKESM). Here, we evaluate carbon monoxide (CO) emissions from fires in South America as modelled by the INFERNO fire emissions model, which is coupled to the Joint UK Land Environment Simulator (JULES) in an offline configuration. Different satellite-based inventories were used for comparisons. To identify key factors driving simulated CO emissions and model-inventory biases, we use sensitivity experiments and a machine-learning approach. The findings indicate that INFERNO accurately capture the Arc of Deforestation in the southern Amazon as a primary source region of fire CO emissions, but it tends to overestimate these emissions by about 72 %. The simulated emission patterns in this region are largely determined by drought conditions and Plant Functional Type (PFT), particularly tree fractions. Aligned, the experiments show a 100 % increase in CO emissions in the southern Amazon region when using a drier meteorology dataset compared to the ERA5-based control run. In southern South America, INFERNO emissions, and in particular their seasonal cycle is affected by the tree PFT misrepresentation. The machine learning model explains 67 % of the model-inventory biases using only model inputs, highlighting room for improvement and the need to consider additional factors. The machine learning model identified soil moisture and tree PFT as major contributors to the model bias. Future model development should focus on improving the representation of fuel moisture, fuel load, and human activities (e.g., agriculture and deforestation) in the fire model
DNA barcoding and phylogenetic relationship in Pakistani species of Aveneae-type plastid DNA clade (Pooideae, Poaceae) based on nuclear and chloroplast DNA markers
No full textThe Aveneae-type plastid DNA, clade belonging to the tribe Poeae s.l within the subfamily Pooideae (Poaceae), includes economically, nutritionally and ecologically important grasses. However, due to morphological similarity and prevalence of polyploidy, species boundaries and phylogenetic relationship among its taxa remain uncertain. This study aimed to evaluate the species discrimination power of three universal plant DNA barcode loci (ITS, matK and rbcL) in 20 representative species of the Aveneae-type plastid DNA clade. Genetic distances were computed using MEGA-X software, while tree based maximum likelihood (ML) analyses were performed to infer the phylogeny. On the basis of percent discrimination rate at a significance level of p 90%). This study endorses the effectiveness of all the three barcode regions and provide novel DNA sequences of previously unsampled Himalayan grasses for global Poaceae barcoding initiatives and phylogenetic research
Influence of stage at cancer diagnosis on NHS hospital care costs in England: a national, retrospective, population-based cohort study using individual patient-level data
Get PDFBackground
Estimates of the cost of cancer care are crucial for the economic evaluation of screening interventions and other early cancer diagnosis initiatives. However, data on the cost of cancer is scarce. This study estimated National Health Service (NHS) hospital care costs for eight cancer types by stage at diagnosis in England.
Methods
This national, retrospective, population-based cohort study used individual patient-level data collated by the National Disease Registration Service, NHS England. We included patients aged 50–79 years who were diagnosed with a colorectal, head and neck, liver and bile duct, lung, lymphoma, oesophageal, ovarian, or pancreatic cancer in England between Jan 1, 2014, and Dec 31, 2017. For each patient, we obtained linked national health-care records, incorporating all inpatient hospital care, outpatient activity, and accident and emergency department attendances, and costed these using a payer perspective. Patients were excluded if registration was death certificate only, records related only to a secondary metastatic site, sex and cancer type were incompatible, death status or date were uncertain, or there were zero health-care costs from 6 months before diagnosis to end of follow-up. Net, cancer-related, regression-adjusted hospital care costs were reported for each cancer type and stage overall, annually, and by phase of care. Within each annual period and phase, mean monthly costs were also estimated.
Findings
Of 359 106 cancer records registered, 345 629 cancers were available for analysis, and 333 657 cancers were included in the analysis (147 334 [44·2%] occurred in female patients and 186 323 [55·8%] in male patients; 303 227 [90·9%] among participants of White ethnicity, 4452 [1·3%] among participants of mixed or other ethnicity, 7870 [2·3%] among participants of Asian ethnicity, 4179 [1·3%] among participants of Black ethnicity, and 13 929 [4·2%] among participants of unknown ethnicity). Overall costs were higher at later stages for colorectal, head and neck, lymphoma, and ovarian cancers with mean stage IV costs of £37 838, £36 657, £42 667, and £45 871, respectively. Costs for liver and bile duct, lung, oesophageal, and pancreatic cancers were highest for those diagnosed at stage II (£28 356, £29 553, £33 640, and £39 351, respectively), and slightly lower at stages I, III, and IV. Health-care costs were highest in the initial treatment and the end-of-life phases of care. Within each phase, mean cost per month increased with stage for most cancer types studied, though fewer months of follow-up were observed in each phase for liver and bile duct, lung, oesophageal, and pancreatic cancers.
Interpretation
Cancer-related NHS hospital care costs by stage at diagnosis differed between cancer types; this heterogeneous pattern could inform detailed and nuanced economic evaluations of early detection initiatives.
Funding
GRAIL Bio UK
Interpretable machine learning for occupant-specific PM2.5 exposure assessment in higher education buildings
Get PDFOutdoor-origin fine particulate matter (PM2.5) poses significant health risks in Higher Education Institution (HEI) buildings, where occupants spend extended periods across diverse functional spaces. This study develops a scalable framework for indoor PM2.5 exposure assessment by coupling CONTAM–EnergyPlus co-simulation with machine learning metamodels and SHapley Additive exPlanations (SHAP)-based interpretability. An Extreme Gradient Boosting (XGBoost) metamodel trained on 2,729 zones across five UK HEI buildings achieved high predictive accuracy (R ≈ 0.95; R2 > 0.90 on held-out data). SHAP analysis, representing what appears to be the first such application in HEI indoor air quality assessment using a physics-driven metamodel, identified building airtightness (Q50) as the dominant exposure driver, followed by infiltration air change rate (ACHINF) and indoor–outdoor temperature difference (ΔT). Microenvironmental modelling indicated indicative pronounced exposure heterogeneity among occupant groups within the adopted assumption space: offices dominated staff exposure while educational facilities drove student exposure. Improving airtightness from baseline to Q50 = 3 m3/h·m2 reduced population-weighted exposure by up to 32.3%; however, approximately 88% of zones still exceeded the WHO 2021 annual guideline of 5 μg/m3. These findings demonstrate that envelope improvements alone are insufficient for WHO compliance and must be complemented by integrated mechanical ventilation and filtration strategies, alongside urban-scale policies such as Clean Air Zones and emissions control measures that reduce outdoor PM2.5 at source. The framework provides a transparent, physics-grounded basis for screening HEI building stocks and prioritising evidence-based air quality interventions
Colors, characters, locations, and shapes: The capacity of working memory for multiple, dissimilar sets of items
Get PDFWorking memory (WM) often includes heterogenous items, as when one uses it while assembling a desk from sets of boards, knobs, bolts, and washers. Here, we investigate how WM capacity is limited when recalling multiple sets of items, for which performance surpasses the usual limits observed in single-set procedures. We presented participants (N = 181) with up to four sets of items for serial recall, usually of different stimulus types in the same trial (colors, characters, locations, and/or shapes). Conditions differed in the total number of items, the number of sets, and/or item types across sets in a trial. For uniformity in analyses, Set 1 was kept constant at three items of a type and was usually recalled first, free of output interference. In Experiment 1, recall of Set 1 was not only limited by the total number of items but also by the number of sets in a trial. Experiment 2 ruled out interference as an alternative explanation. Experiments 3–4 showed the dependency of the results on clearly grouped presentation of the sets. The results suggest that groups of items are associated as newly formed, often incomplete chunks offloaded from the focus of attention (FoA) to an activated portion of long-term memory (aLTM) for later retrieval. This offloading process would spare capacity but not without cost; a fraction of an item was lost from Set 1 for each subsequent item recalled. We present a dual-stage theory in which pointers held in the capacity-limited FoA allow retrieval of chunks from aLTM
Zircon deformation features reveal sequence of transient high stress, tension and shearing during seismic faulting: A case study from the Ivrea-Verbano Zone, Italy
Get PDFThe mechanisms associated with the propagation of fault ruptures remain debated in terms of sequence of events, processes and magnitude of stresses involved. Microstructures of zircon grains located within and in the immediate vicinity of pseudotachylyte veins reveal a sequence of events transient in time and space and allow recognition of different processes during rupture. The dynamic rupture causes, at its propagating tip, a damage zone of several centimetres thickness. In this damage zone, zircon grains exhibit crystal-plastic deformation signatures ranging from crystal lattice bending continuous throughout whole grains, to distinct planar deformation bands and {112} twin lamellae. Presence of planar deformation bands and {112} twin lamellae suggest locally high stresses, based on similar features reported from meteorite impacts. Absence of well-developed subgrains indicate dominance of low temperature plasticity at the rupture tip. Subsequently, those grains with highest dislocation densities undergo in-situ grain fragmentation. The observed correlation of grains with very high dislocation densities and in-situ grain fragmentation suggests that the effective tensile strength of these grains is sufficiently decreased by the high stored elastic energy to cause their fragmentation when subject to tensile stresses in the wake of the propagating rupture tip. Subsequent displacement along connected damage zone fracture surfaces results in pseudotachylytes formation.
Our data shows that dynamic rupture initiation and propagation results in stresses heterogeneously distributed in space, magnitude and sign causing both ductile and brittle deformation. Our study highlights the value of the accessory mineral zircon in deciphering the nature of rupture zone dynamics
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