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Large language models in building energy applications: A survey
Get PDFThe 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
Driving safety excellence: A multifaceted analysis of leading indicators across industries
Get PDFSeveral studies have examined Safety Leading Indicators (SLIs) and their impact on safety performance in diverse industries. However, no study has comprehensively synthesised these findings across industries to offer a holistic perspective. This lack of integration impedes safety professionals from effectively implementing critical SLIs and adopting measures to enhance safety performance universally. To fill this gap, this study employs a comprehensive hybrid methodological approach, including a Systematic Literature Review (SLR), a questionnaire survey, and expert interviews. Using the SLR undertaken, 67 SLIs impacting safety performance across industries were identified. Analysis of the survey data from safety experts revealed that “training and education”, “incident investigation and analysis”, and “safety observation” were seen to be the top three critical SLIs. These results were further validated using the expert interviews. Additionally, findings from the interviews revealed that “safety performance improvement” and “establishing effective measurement methods” were, respectively, the most crucial drivers and barriers to SLI adoption across industries. The outcomes of this study provide safety professionals with the vital areas to focus on, improving the effectiveness of safety performance enhancement in diverse industries
An introductory guide to battery storage for Village Halls
No full textExecutive summaryWe think that you will definitely get the most out of this guide by reading the whole thing to develop your knowledge and understand some of the details and nuance related to this topic. However, the ten key points are provided in this summary for your convenience and three checklists on what to think about before you get a battery and what your battery professional should do are provided at the end of the guide. A roadmap of theprocess is also provided at the end of the guide.1. Batteries store electricity for later use which can have benefits for energy costs and climate change by shifting demand from peak times. Batteries can also have benefits for resilience to power cuts in certain scenarios but require extra systems for this to work.2. LiFePO4 lithium-ion batteries and Sodium-ion batteries are probably the most suitable for village halls. Sodium-ion batteries have less environmental and ethical challenges; only limited sizes are currently available, but more are in development and costs are comparable.3. Batteries have their own terminology which is important to understand when considering a battery system. All batteries require inverters and the most suitable will depend on your hall’s circumstances and type of electricity connection.4. Smart electricity meters are beneficial and allow you to take full advantage of your battery system, it is possible to use a battery without a smart meter, but its functionality will be limited.5. Certain types of batteries can provide emergency electricity during power cuts, but this must be specifically set up and requires additional cost and complexity. The amount of back-up electricity a battery will give you depends on its size and how you use it.6. Getting a battery which is the correct size for your hall is very important and to do this you need to think about how and when electricity is used in your hall currently and any planned future changes such as heat pumps or solar panels.7. The upfront cost of a battery varies but for a village hall is likely to cost between £5-20k. Exact operational energy savings will depend oncircumstances, but a battery could realistically save your hall several hundred pounds a year.8. Batteries should be installed outside in a well-ventilated but secure enclosure. You will need to notify your insurance company and the local fire service and update your risk assessments when you install a battery.9. Batteries come with energy management platforms and can be managed with an app on your phone. While you can just fit and forget your battery, you will benefit from tailoring its settings to how your hall is used.10. Battery professionals should be qualified electricians who are MCS certified and have received training for the specific make of battery. The MCS and many battery manufacturers provide options to find installers.Please read on for more details, explanation and rationale about all of the above
Federated learning in IoT environments: Examining the three-way see-saw for privacy, model-performance, and network-efficiency
Get PDFThis survey paper provides an in-depth exploration of Federated Learning (FL) in Internet of Things (IoT) environments , focusing on privacy-preserving techniques and their influence on model performance and network efficiency. It highlights key challenges and opportunities at the intersection of these technologies by offering a comprehensive review of FL applications in IoT. First, a customized taxonomy is introduced to evaluate the privacy levels, quality of service (QoS) and network efficiency of various Privacy-Preserving FL (PPFL) solutions in IoT configurations. Furthermore, the survey investigates strategies to improve FL accuracy while addressing resource and network constraints, both independently and together with privacy preservation techniques. Our findings underscore the complexity of optimizing resource utilization, learning performance, and privacy resilience, revealing that no single PPFL solution universally applies. The paper further identifies future research directions, including the integration of advanced technologies beyond 5G networks, and discusses standards, protocols, real-world PPFL projects from world-renowned industries for potential IoT applications
Custodian entrepreneurship: An examination of entrepreneurial activities in English country houses
No full textEnglish country houses are unique institutions that form an essential fabric in the country’s landscape. They highlight British history and are a significant element in the country heritage sector. The literature on country houses has examined various facets of them but there is a scarcity of literature about the type of entrepreneurial activities that are being undertaken at the houses. By examining 68 English country houses, this paper explores their entrepreneurial activities and determines that they can be organized according to physical areas, products and services, users, stakeholders and tactics. A typology depicting the entrepreneurial activities of these houses has been developed. This study makes an original contribution to both theory and practice by introducing the innovative concept of “custodian entrepreneurship” and opening discussion about entrepreneurship in this distinctive part of the UK’s heritage sector
Fixed-time L2 attitude control guaranteeing anti-unwinding performance and energy efficiency
Get PDFThis article deals with the challenging problem of fixed-time L2 control for flexible spacecraft attitude system with a focus on energy-efficiency, anti-unwinding property, and reducing conservativeness in estimating the settling time upper bound. To this end, we first introduce a novel fixed-time stable system which is applicable to spacecraft attitude system represented by unit quaternion. It provides a more accurate estimation of the settling time. Based on the proposed fixed-time system, we define a singularity-free, anti-unwinding sliding surface that does not include any piecewise continuous functions to address singularity issues due to the use of fractional power. This sliding surface contains the initial scalar part of the quaternion to guarantee fixed-time convergence for both equilibrium points, removing the unwinding problem. Considering the sliding variable as the performance vector, the proposed control framework guarantees that the fixed-time L2 gain of lumped disturbance attenuation is less than a predetermined value. The performance of the proposed control framework is established through numerical simulations and validated using a Speedgoat real-time experimental setup
Implementation of the Nursing Associate in the NHS: A rapid realist synthesis to understand mechanisms of integration and workforce development
No full textAim(s): To develop theories about how Nursing Associate (NA) roles are implemented and working within NHS practice: What works, for whom, in what contexts and how? Methods: Rapid realist synthesis of: (1) empirical and grey literature; (2) realist interviews with stakeholders. Sources were analysed using a realist approach that explored the data for novel or causal insights to generate initial programme theories. Results: Empirical and grey sources (n = 15) and transcripts from stakeholder interviews (n = 11) were synthesised which identified three theory areas relating to NA implementation: (1) Scope of NA role: Communication and expectations; (2) Variations to the NA model of working; and (3) Career progression: Entry point, stepping stone and career in itself. Conclusion: The NA holds the potential to improve nursing workforce stability by encouraging locally based, non‐registered healthcare staff to transition to an NA. However, the lack of collective understanding of the NA scope of practice can cause staff friction. It is unknown whether this friction will reduce over time or if staff divisions will lead to further deterioration of the workforce. Implications for the Profession and/or Patient Care: Ongoing clear communication regarding NA scope of practice needs to be provided to aid understanding of their supplementary role and its potential contribution to nursing teams. Impact: This work represents a first step to support both researchers and nursing workforce leaders in furthering knowledge of the impact of integrating NAs in diverse healthcare contexts and to unearth the mechanisms underpinning the success or failure of this new role. Reporting Method: Realist and meta‐narrative evidence syntheses: Evolving standards. Community Inclusion and Engagement (CIE): Planning of the research design and interpretation of the results was completed with nurse clinicians with experience in the NA role