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Acceptability of a digital pulmonary rehabilitation app as an adjunct or alternative to usual care for people with chronic lung diseases: A qualitative study of patients’ views and experiences
Get PDFSupplementary Material is available online at: https://journals.sagepub.com/doi/10.1177/14799731251365632#supplementary-materials .Background:
Centre-PR may not be accessible for people living distant from PR centres. Remote digital PR may have equivalent benefits to centre-PR; however, previous trials were potentially biased towards digitally literate patients, and largely excluded participants with a preference for centre-PR. There is limited data on the real-world implementation of, and acceptability for, Digital-PR alone or as an adjunct to other models of PR.
Objectives:
To gather patients’ views about the acceptability of Active+me REMOTE, a digital pulmonary rehabilitation app (Digital-PR).
Methods:
A qualitative exploratory study using semi-structured interviews with a subset (n = 15) of patients in a mixed method, feasibility study of a hybrid pulmonary rehabilitation, blending Digital-PR with other models of PR. Transcribed data were coded descriptively using Braun and Clarkes’ methodology, data interpretation was facilitated through a Miro virtual whiteboard.
Results:
There was appreciation for the concept of Digital-PR, indicated by positive responses in the domains of “friends and family recommendation,” “intention to continue using the app,” and “privacy concerns.” Benefits were reported by two participants who had declined centre-based PR. The app was rated low regarding user-friendliness. Challenges in understanding/using the app and a perception of challenges for others were reported and were associated with poor digital literacy and tech savviness. High digital skills did not predict a favourable assessment of the app as user-friendly.
Discussion:
Whilst there was a general appreciation for the concept of digital PR as an adjunct or alternative to traditional centre-based PR, the app did not appear to be user-friendly, nor acceptable to people with low digital literacy. The findings have implications for the wider routine implementation of Digital-PR.NHS Accelerated Access Collaborative through a Small Business Research Initiative (SBRI) healthcare award
Optimising renewable energy communities for rural and islanded areas
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe market for distributed Renewable Energy Systems has increased considerably in
recent decades, driven by the necessity for a reduction in global carbon emissions in an
effort to combat climate change.
While the focus on decarbonising the energy sector in Europe has been successful in
recent years, it has disproportionately benefited urban population centres. Those who live
in built-up environments will likely have better access to newer, greener technology, with
islanded communities often relying on a weaker grid with fossil fuel reliant infrastructure.
These communities are therefore at high risk of being left behind in the energy transition
towards net-zero emissions.
This study presents a novel solution to the problem of decarbonising remote, islanded
populations by means of Renewable Energy Communities (RECs). The test location,
Formentera, was chosen due to its unique set of challenges and opportunities regarding
energy security and access to clean energy. A generalised, modular model was developed
in Python, allowing the integration of generation (wind and solar), storage (battery and
hydrogen), and real-world data from the test location. The model simulates the dynamic
dispatch of the system over hourly increments to evaluate the annual performance.
The system is optimised using the Non-dominated Sorting Genetic Algorithm (NSGA-II),
which identified an inherent trade-off relationship between cost reduction and decarbonisation
of the REC. Results show that the deployment in the case study location can deliver
improvements in both cost and emissions relative to a grid-only scenario. A comparison
of storage configurations shows a considerable benefit to co-locating batteries and a
regenerative hydrogen storage system due to the latter’s ability to act as a seasonal storage
buffer. Findings suggest that a ’friendly’ local trading policy outperforms a market-based
regime on cost savings, and ensures better energy equity between members.
The analysis incorporates Monte Carlo simulations of estimated assumption ranges and
a variance-based Sobol sensitivity analysis. These methods reveal the range of variability
in the result arising from uncertainty in the input assumptions, including those which
most impact performance, thus identifying high-risk areas for project monitoring and
intervention. These can not only support the design stage of the REC but also contribute
to risk-aware planning and policy development.
The model’s development in Python allows for a scalable foundation on which future
research can be built, and contribute to the commercialisation of an REC-focused planning
tool. The outcome of this work provides a novel, quantitative guide for energy developers,
government entities, and network operators on REC development. The model framework
can be used to trade-off system cost and emissions reduction, design for and navigate
potential future energy policy, assess energy equity, and ensure a clearer route to realising
the net-zero aspirations of rural, islanded communities
Mangroves support an estimated annual abundance of over 700 billion juvenile fish and invertebrates
Get PDFData availability:
The raw fish density data, and species and species group predictions are available on Zenodo (https://doi.org/10.5281/zenodo.14965669).Code availability:
Code used in this study is available on Zenodo (https://doi.org/10.5281/zenodo.14965669).Supplementary information is available online at: https://www.nature.com/articles/s43247-025-02229-w#Sec21 .Mangroves are a critical habitat that provide a suite of ecosystem services and support livelihoods. Here we undertook a global analysis to model the density and abundance of 37 commercially important juvenile fish and juvenile and resident invertebrates that are known to extensively use mangroves, by fitting expert-identified drivers of density to fish and invertebrate density data from published field studies. The numerical model predicted high densities throughout parts of Southeast and South Asia, the northern coast of South America, the Red Sea, and the Caribbean and Central America. Application of our model globally estimates that mangroves support an annual abundance of over 700 billion juvenile fish and invertebrates. While abundance at the early life-history stage does not directly equate to potential economic or biomass gains, this estimate indicates the critical role of mangroves globally in supporting fish and fisheries, and further builds the case for their conservation and restoration.This work forms part of a project supported by the International Climate Initiative (IKI). The German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) supports this initiative on the basis of a decision adopted by the German Bundestag. Initial work on this study was supported by the Lyda Hill Foundation
Micro-Patterns in Solidity Code
Get PDFThis is a preprint version of the conference paper presented to Proceedings at The 29th International Conference on Evaluation and Assessment in Software Engineering (EASE 2025), 17-20 June, Istanbul, Turkey. It is available online at https://arxiv.org/abs/2505.01282.Solidity is the predominant programming language for blockchain-based smart contracts, and its characteristics pose significant challenges for code analysis and maintenance. Traditional software analysis approaches, while effective for conventional programming languages, often fail to address Solidity-specific features such as gas optimization and security constraints.
This paper introduces micro-patterns - recurring, small-scale design structures that capture key behavioral and structural peculiarities specific to a language - for Solidity language and demonstrates their value in understanding smart contract development practices. We identified 18 distinct micro-patterns organized in five categories (Security, Functional, Optimization, Interaction, and Feedback), detailing their characteristics to enable automated detection.
To validate this proposal, we analyzed a dataset of 23258 smart contracts from five popular blockchains (Ethereum, Polygon, Arbitrum, Fantom and Optimism). Our analysis reveals widespread adoption of micro-patterns, with 99% of contracts implementing at least one pattern and an average of 2.76 patterns per contract. The Storage Saver pattern showed the highest adoption (84.62% mean coverage), while security patterns demonstrated platform-specific adoption rates. Statistical analysis revealed significant platform-specific differences in pattern adoption, particularly in Borrower, Implementer, and Storage Optimization patterns
Streamlining Copyright Protection: Leveraging Algorithmic Justice in Administrative and Civil Systems
Get PDFAs social media platforms generate unprecedented volumes of user content, accelerated by the rise of generative artificial intelligence (AI), ensuring efficient and fair copyright enforcement has become a pressing global challenge. This paper explores how algorithmic justice can be leveraged to streamline both administrative and civil systems for copyright protection. In Europe and the US, enforcement follows a tiered approach, ranging from notice and takedown legal procedures to litigation, while China complements civil remedies with a more rapid administrative enforcement process led by the National Copyright Administration. Though faster and less burdensome in evidentiary terms, China’s approach raises questions of consistency and fairness. With AI increasingly deployed in content moderation and dispute resolution, this paper argues that robust data governance is essential to ensure that algorithmic enforcement mechanisms remain transparent, accountable, and interoperable across jurisdictions. Key requirements include model transparency, explainability of decisions, and detailed audit trails to enable oversight and contestation. Through comparative analysis of copyright enforcement regimes in China, Europe, and the US, this paper identifies best practices for integrating algorithmic tools into administrative and civil frameworks, with the goal of streamlining enforcement while safeguarding user rights and legal integrity in the age of AI
Influence of process parameters on powder jet properties in L-DEDp using different nozzle designs
Get PDFIn the Laser Directed Energy Deposition (L-DEDp) process, a laser melts fine metal powder delivered through a carrier gas as a focused powder jet. The geometry and behaviour of this jet, particularly its stand-off distance and focus diameter, are directly influenced by process parameters such as carrier gas flow, shielding gas flow, and powder mass flow. These characteristics affect the interaction between the laser and the material, which influence the deposition quality. In this study, a camera-based monitoring system was employed to capture images of the powder gas jet stream (PGJS), enabling precise measurement of its geometrical features through image processing techniques. Experiments were conducted using two different nozzle designs across a wide range of process parameters to investigate how each parameter influences the jet’s shape and stability. The results show that carrier gas has a dominant effect on particle velocity and jet convergence, while powder mass flow primarily impacts the jet’s focus diameter. Shielding gas was found to affect stand-off distance more significantly at lower carrier gas levels. This work contributes to a better understanding of PGJS behaviour and provides valuable insights for optimising L-DEDp across different nozzle configurations
A Transformer Model-Based Methodology for Person-Independent Human Activity Recognition Using Wi-Fi CSI Data
No full textResumo: Ao capturar e interpretar sinais Wi-Fi em ambientes internos, o CSI pode ser usado para detectar atividade física, quedas ou movimentos diários de um paciente, permitindo que cuidadores e profissionais de saúde monitorem pacientes sem a necessidade de sensores vestíveis ou câmeras invasivas. Portanto, este artigo propõe uma metodologia chamada MPA-CSI para identificar a atividade de uma pessoa em uma sala por meio da análise de dados CSI e um conjunto de dados usado para sua avaliação. O MPA-CSI usa modelos Transformer desenvolvidos para processar dados de séries temporais. O MPA-CSI é capaz de identificar a atividade de pessoas que não participaram da fase de treinamento do modelo. A acurácia da identificação de movimento é de 96,67% usando um conjunto de dados CSI de 59 voluntários.By capturing and interpreting Wi-Fi signals in indoor environments, CSI can be used to detect physical activity, falls, or daily movements of a patient, allowing caregivers and healthcare professionals to monitor patients without the need for wearable sensors or invasive cameras. Therefore, this paper proposes a methodology called MPA-CSI to identify the activity of a person in a room through the analysis of CSI data and a dataset used for its evaluation. MPA-CSI uses Transformer models developed to process time series data featuring a structure that allows capturing temporal dependencies. MPA-CSI is capable of identifying activities of people who did not participate in the training phase. The movement identification accuracy is 96.67% using a dataset with CSI data from 59 volunteers.O presente trabalho foi realizado com apoio da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Código de Financiamento 001, CAPES Print, CNPq, FAPERJ, FINEP, INCT-ICONIoT e INCT-MACC
Aquifer-specific flood forecasting using machine learning: A comparative analysis for three distinct sedimentary aquifers
Get PDFData availability:
Data will be made available on request.Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S0048969725023964?via%3Dihub#s0115 .Accurate flood prediction is critical for avoiding catastrophic impacts, but its difficulty varies by geological location. This study evaluates four machine learning models – TFT, Informer, LSTM, and XGBoost – for multi-horizon flood forecasting (1-4 days), across Limestone, Chalk, and Greensand located in the Thames Basin, UK. Stations were carefully chosen using the UK government flood risk maps, geological mapping, and Environment Agency hydrological data to guarantee a complete portrayal of aquifer-specific groundwater-river interactions. The results show that the model accuracy varies significantly depending on aquifer features. Rapid GWL-river interactions allowed Limestone aquifers to achieve very high precision (R^2 = 0.98–0.99), with transformers and LSTM clearly surpassing XGBoost. The accuracy of Chalk aquifers was moderate (R^2 = 0.77–0.80), indicating delayed reactions and intermediate permeability. Greensand aquifers were difficult to model due to delayed and complex reactions, resulting in low or negative R^2 values. Correlation study confirmed these findings: Limestone showed a significant groundwater-river linkage (r = 0.84), Chalk moderate (r = 0.26), and Greensand had a small negative association (r = −0.14). The novelty of this study highlights the significant impact of subsurface hydrology on predicted reliability, revealing aquifer-specific geological restrictions in ML-based forecasting. This research offers a more physically consistent early warning method by fusing GWL data with developed transformer architectures. The results highlight the significance of adjusting forecasting frameworks to geological environments, which has direct implications for resilience planning and flood risk management at the watershed scale.This study was partially funded by the UKRI project 10063665
Corticospinal Excitability During Explosive Voluntary Contractions and Its Association with Rapid Torque Production
No full textData Availability Statement:
The unidentified data are available from the first author upon request.Supporting Information is available online at: https://onlinelibrary.wiley.com/doi/full/10.1111/ejn.70321#support-information-section .We investigated relationships between rapid torque and corticospinal excitability (denoted by motor-evoked potential; MEP) and inhibition (denoted by silent period duration; SPD) during explosive voluntary contractions, as well as differences in MEP and SPD between different phases of explosive contractions and at maximum voluntary contraction (MVC) plateau. In 14 adults, and across multiple repeated trials, quadriceps muscle MEP and SPD were measured at the early, middle and late phases of knee-extensor isometric explosive contractions, and at the MVC plateau, using transcranial magnetic stimulation (TMS). Torque at equivalent time points was also measured on trials without TMS. Using repeated measures correlation applied to early phase data from TMS trials, we found MEP and torque (measured just prior to MEP) were correlated across trials within participants (r = 0.43, p < 0.001). Using Spearman rho correlations to investigate correlations across participants for each phase, we found MEP (averaged across phases up to the phase of interest) and torque (measured on non-TMS trials) to be significantly correlated for the middle phase only (rho = 0.73, p = 0.004). Linear mixed effects models were used to investigate the effect of phase (three explosive phases and MVC plateau) on MEP and SPD. Absolute MEP, MEP normalised to maximal M-wave and SPD all increased across the phases of explosive contraction and up to MVC plateau (fixed effects of phase, p < 0.025). Our results suggest corticospinal excitability may be an important determinant of rapid torque. Further, corticospinal inhibition and excitability both increase throughout the rising torque-time curve and up to MVC plateau
The insights from the crowd: Drawing inferences from many approaches to key empirical questions in international business
Get PDFData Availability Statement:
Our methods and analyses were pre-registered at https://osf.io/4euaj. The supplementary document of this paper is accessible at https://doi.org/10.1057/s41267-025-00808-9. Further Results and Materials are posted on the Open Science Framework at https://osf.io/ew3vz/?view_only=52ab5518ada34e0ca1046aeab08a5122.Supplementary Information is available online at: https://link.springer.com/article/10.1057/s41267-025-00808-9#Sec30 .Abstract In this crowdsourced initiative, 57 independent analysts used the same longitudinal dataset to address four major empirical questions in international business. For all four research questions, different analysts obtained substantial estimates in opposite directions, meaning that they could have drawn any conclusion at all had they conducted the project alone. Aggregating across the results obtained by different analysts pointed to an overall answer for two of the four research questions, although for one of the two questions, the evidence was more suggestive than conclusive. That said, the variability in results was not simply random, and could in some cases be meaningfully explained. Choices regarding how to operationalize variables played an important role in determining the empirical results, and expert analysts were more likely to report large positive effects. Rather than exhibiting a bias to confirm their pre-existing beliefs, analysts appeared to rationally update their beliefs considering the evidence. Overall, these findings empirically demonstrate the role of subjective researcher choices in shaping results in international business research yet also show that it is still possible to draw meaningful conclusions in science. We advocate for an open science of international business in which the consequences of subjective analytic choices are rendered as transparent as possible.This project was supported by the Multi-Year Research Grant from the University of Macau (Reference No.: MYRG-GRG2024-00140-FBA), awarded to Tianyou Hu, and the National Natural Science Foundation of China (Project No.: 72572121, 72122016), awarded to Nan Zhou. Eric Uhlmann is grateful for funding from the INSEAD R&D committee
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