Why is the United Kingdom fuel poor?

Tackling fuel poverty and decarbonising heating are two of the biggest challenges before the UK economy. Using Structural Equation Modelling (SEM) this research evaluates the main causes of fuel poverty within the UK, drawing parameters from various definitions and it assesses the immediate impacts of fuel poverty. A series of fuel poverty determinants have been drawn from the literature to develop a SEM model, using fuel poverty as a latent construct. Fuel Poverty has then been divided further into three latent factors, Household, Macroeconomic factors and Renewable energy. A time series data of 13 years (2010–2022) has been collated from a variety of published databases to arrive at the most significant indicators of the latent constructs. The paper also evaluates if the decarbonisation target can be achieved along with reducing the fuel poverty by including the indicators of renewable energy. A robust interrogation has been done of the most recent policy measures and its relevance based on the results obtained by the longitudinal data analysis. The research presents strong evidence that there has been limited investments in housing efficiency and green energy projects, there is an immediate need to control inflation and household incomes. Additionally, policies related to the dependence on oil and gas as primary energy source, winter fuel payments and gas and electricity disconnections need attention

Tailoring durability and tribological stability of PECVD-derived diamond-like carbon films through nitrogen doping .

Structural integrity (SI), durability, and tribological stability (TS) of diamond-like carbon (DLC) films deposited on silicon substrates were tailored through nitrogen doping during deposition via radio-frequency plasma-enhanced chemical vapor deposition (RF-PECVD). This approach specifically addresses the challenges of poor adhesion, short lifetime, surface irregularities, and limited wear resistance encountered by DLC coatings in dry sliding environments. Nitrogen flow rates ranging from 0 to 10 sccm were introduced during deposition to modulate bonding structure and surface morphology. At 10 sccm, the nitrogen-doped DLC (N-DLC) films exhibited a uniform, droplet-free surface and suppressed voids achieving 42 % reduction in maximum surface height (Sz), reaching 27 nm and stable average roughness (Ra) of 1 nm. The deposition rate increased significantly from 0.5 to 13 nm/min, resulting in film thicknesses up to 2.3 μm due to enhanced adatom surface mobility. XPS analysis indicated a reduction in sp3 content from 43 % to 28 %, along with an increase in the ID/IG ratio, reflecting a shift toward graphitic sp2 bonding associated with improved self-lubricating behaviour. Although hardness decreased from 28 to 21 GPa, the adhesion strength improved by 73 % from 30 N to 52 N due to interfacial stress relaxation and formation of a silicon carbide interlayer. Under dry sliding conditions, the N-DLC coatings demonstrated negligible wear and ultra-low friction performance with ultra-low coefficient of friction of 0.05. These results demonstrate that N-doping effectively tailors bonding structures and interfacial properties, enhancing N-DLC coatings for MEMS and dry-contact applications

On the impact of greyscale imagenet pre-training for chest x-ray model transferability.

Standard ImageNet pre-training relies on RGB natural images, introducing visual features such as colour and texture that may misalign with medical imaging modalities, such as chest X-rays (CXRs). In this work, we conduct a systematic analysis of greyscale ImageNet pre-training, using both three-channel and single-channel model variants, evaluating their perturbation stability, attribution alignment, and transferability. First, we train and benchmark three ResNet-50 backbones (RGB, 3c-Grey, 1c-Grey) on ImageNet-1K, using the model-vs-human framework, and find that greyscale variants improve top-1 accuracy under parametric and binary image perturbations by up to 10.9%, with average gains of 4.23–4.43% over RGB. Then, using these backbones, we transfer-learn to a CXR nodule classification task, and evaluate model generalisation across four public datasets. Greyscale variants, particularly the single-channel model, achieve up to 3.5% higher F1 scores, with average gains of 1–3% over RGB. Finally, we perform a quantified attribution analysis that reveals that greyscale models produce saliency maps with stronger alignment to expert-annotated nodules, yielding 5% higher nodule coverage and 1.7% higher IoU on average. We release our greyscale pre-trained weights to support further work on generalisable and shortcut-resistant medical imaging. https://github.com/sophie-haynes/greyscale-imagenet-for-cxr

Financial challenges of students and early-career professionals working in the healthcare sector: a scoping review.

The global healthcare workforce is facing a substantial shortage and an uneven distribution of qualified professionals, which restricts access to essential healthcare services. This shortage could be mitigated through more effective support of healthcare workers in training. Therefore, an overview of existing economic barriers for this demographic is necessary. To review the existing literature on financial challenges of students and early-career professionals in the healthcare sector. Following the PRISMA-ScR-guidelines, publications published between January 2008 and February 2024 were identified using PubMed and Scopus. 17,268 articles were screened by reviewing their titles and abstracts followed by a detailed review of full texts with cross-validation. Themes were identified, clustered, and analyzed. This scoping review included 167 articles focusing on the themes debt (36.5%, n=61) and loans (10.2 %, n=17) and their influence on career pathways, the role of employment for career satisfaction, summarizing findings concerning salary (29.9%, n=50), finances (25.1%, n=42), funding (10.8%, n=18), and savings (10.2%, n=17), and obstacles toward a sustainable lifestyle, which included results considering career choice (34.1%, n=57), migration (7.2%, n=12), gender disparity (6.0%, n=10) and working conditions (2.4 %, n=4). Efforts to close the healthcare workforce gap require greater investment in training, compensation, and support of junior healthcare workers. Students and early-career professionals need particular attention to build a sustainable, resilient, and reliable healthcare workforce

Evaluating cross-domain sentiment analysis using convolutional neural network for Amazon dataset.

Sentiment Analysis (SA) has garnered extensive research attention over the past decades as a means to comprehend users' attitudes and opinions in various domains. With the proliferation of online communities and the rapid generation of social media content, understanding sentiments has become crucial for decision-makers and stakeholders. Cross-Domain Sentiment Analysis (CSDA) is the process of analysing and interpreting sentiments in text data across different subject areas or contexts, accounting for the varying nuances and contextual differences in sentiment expression. The problem of CDSA poses a significant challenge in the field of Natural Language Processing (NLP), as the sentiment polarity of words and expressions can vary drastically across different domains. For instance, a word like "unpredictable" can convey positive sentiment in the context of a movie review but may signify negative sentiment when referring to the performance of a computer system. Deep Learning (DL), a subfield of machine learning, has shown promising results in various domains since its emergence in 2006, especially in complex problem-solving involving vast datasets. This paper aims to evaluate CDSA performance using Convolutional Neural Network (CNN) on the Amazon dataset. The study builds upon our previous research that highlighted the limitations of classical Machine Learning (ML) approaches for CDSA. The result demonstrates that the DL model is the state-of-the-art in machine learning classification tasks even though with a limited features engineering task. In conclusion, understanding people's opinions across different subjects on the internet is crucial but complex and using advanced Deep Learning methods like the Convolutional Neural Network can help address these challenges effectively

Modified CBAM: sub-block pooling for improved channel and spatial attention.

The Convolutional Block Attention Module (CBAM) has emerged as a widely adopted attention mechanism, as it seamlessly integrates into the Convolutional Neural Network (CNN) architecture with minimal computational overhead. However, its reliance on global average and maximum pooling in the channel and spatial attention modules leads to information loss, particularly in scenarios demanding fine-grained feature analysis, such as medical imaging. In this paper, we propose the Modified CBAM (MCBAM) to address this critical limitation. This novel framework eliminates the dependence on global pooling by introducing a sub-block pooling strategy that captures nuanced feature relationships, preserving critical spatial and channel-wise information. MCBAM iteratively computes attention maps along channel and spatial dimensions, adaptively refining features for superior representational power. Comprehensive evaluations on diverse datasets, including C-NMC (acute lymphoblastic leukemia), PCB (peripheral blood cells), and COVID-19 (Chest X-ray), demonstrate the efficacy of MCBAM. Additionally, we evaluate MCBAM against similar alternatives, such as the Bottleneck Attention Module (BAM), Normalisation-Based Attention Module (NAM), and Triplet Attention Module (TAM), demonstrating that MCBAM consistently outperforms these advanced attention mechanisms across all datasets and metrics. Furthermore, results reveal that MCBAM surpasses the standard CBAM and establishes itself as a robust and effective enhancement for attention mechanisms, with notable improvements in medical imaging tasks, offering critical advantages in complex scenarios

Social interaction and dark tourism in prison museums.

This study explores how prison tourism experiences are co-constructed through situated visitor interactions. Penal heritage sites are decommissioned prisons, transformed into immersive educational attractions, drawing upon multiple interpretative practices to engage visitors with historic and contemporary issues of crime and punishment. Using ethnomethodological conversation analysis (EMCA), this research examines how gestures, talk, gaze and bodily position influence how visitors see, interpret, and emotionally negotiate difficult heritage. Findings reveal that visitors co-produce dark tourism experiences and negotiate the perceived darkness of sites through embodied practice. Situating visitors as active social agents, this study provides insights into the co-construction of dark tourism experiences, emphasising interpretation as an emergent process shaped by interaction rather than predetermined by site design or individual motivation

XAI disagreement in neonatal pain classification.

Artificial Intelligence (AI) offers a promising approach to automating neonatal pain assessment, improving consistency and objectivity in clinical decision-making. However, differences between how humans and AI models perceive and explain pain-related features present challenges for adoption. In this study, we introduce a region-based explanation framework that improves interpretability and agreement between XAI methods and human assessments. Alongside this, we present a multi-metric evaluation protocol that jointly considers robustness, faithfulness, and agreement to support informed explainer selection. Applied to neonatal pain classification, our approach reveals several key insights: region-based explanations are more intuitive and stable than pixel-based methods — leading to higher consensus amongst explainer ensembles; both humans and machines focus on central facial features, such as the nose, mouth, and eyes; agreement is higher in "pain" cases than "no-pain" cases likely due to clearer visual cues; and robustness positively correlates with agreement, while higher faithfulness can reduce pixel-level consensus. Our findings highlight the value of region-based evaluation and multi-perspective analysis for improving the transparency and reliability of AI systems in clinical settings. We hope that this framework can support clinicians in better understanding model decisions, enabling more informed trust and integration of AI support in neonatal care

Yugoslav venation: skeletal traces of the past in the practice of the present.

This chapter critically examines practices that are engaged with the material and political cultures of the former Yugoslavia in its successor states and the remnants of that common space. Taking the standpoint that the haunting of the present by Yugoslav pasts began after the 1974 constitutional redraft and the growing economic crises in the last fifteen years of the former state, different forms of hauntology are presented through the means of case studies. Following Mark Fisher's assertion that haunting entails not just the spectral presence of unfulfilled visions of the future from the past in our present, but also our awareness of a more collective and just future society yet unrealized, it considers five different contemporary artistic practices and the variations of the hauntological that they offer. While Borko Lazeski's monumental National Liberation War fresco can be double-coded as a mourning for a lost work and a summation of achievements at the end of a career, Adela Jušić's powerful When I Die, You Can Do What You Want focuses on the embodiment of personal trauma and loss in conflict. Selma Selman's destructive performance in Rijeka connotes the "haunting" of post-Yugoslav societies by marginalized Roma communities, as a means of not only challenging that marginalization but turning its force against itself. The Montenegrin artist Irena Lagator Pejović offers a critical reflection on the parasitic relationship of neoliberal capitalism to the remains of socialism. Elena Chemerska's durational project on the Monument to Freedom, by her father Gligor Chemerski (1980/1981) in Kochani, North Macedonia, is not only an example of the persistence of a Yugoslav-informed contemporary art practice but also opens up the possibility of a collective discussion on what freedom may mean and how a different kind of society may be worked toward collectively

Mitigating class imbalance in multiclass educational data: a hybrid one-vs-one and density-based resampling approach.

Class imbalance remains a critical challenge in educational data classification, particularly under multiclass settings where multiple majority and minority categories coexist. These issues are especially detrimental when predicting student performance, as standard machine learning models tend to exhibit bias toward dominant classes, resulting in poor detection of underrepresented, yet academically vulnerable, student groups. This paper presents a novel hybrid resampling framework tailored for multiclass educational datasets characterized by severe imbalance and blurred decision regions. The proposed method integrates one-vs-one decomposition to reduce multiclass complexity, a density-aware undersampling strategy to selectively reduce majority instances within high-density regions, and a targeted minority oversampling using Borderline-SMOTE to enhance decision boundary precision. Empirical evaluations conducted on a proprietary dataset of 3,094 undergraduate health sciences students and four benchmark educational datasets demonstrate the method’s superiority over state-of-the-art resampling techniques. The results validate the framework’s efficacy in improving the generalization and fairness of student performance prediction models in imbalanced multiclass educational settings