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AI-Blockchain Synergy for Secure Health Monitoring: A Pathway Towards Achievement of SDG 3
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Adaptive electricity consumption forecasting approach for universal environments
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The datasets generated and analysed during the current study are available in the https://data.london.gov.uk/blog/electricity-consumption-in-a-sample-of-london-households/, https://cm.asu.edu, and https://www.iso-ne.com.The development of an accurate electricity consumption forecast model is crucial for stable operation and intelligent management of power systems. Traditional methods often overlook user heterogeneity and lack measures to address concept drift caused by distribution changes in electricity data over time. We propose an adaptive electricity consumption probability forecasting method tailored to universal environments. The method includes a nonmonotonic correlation elimination-based recursive feature selection that adaptively determines the optimal feature combination. Our model incorporates a joint loss function combining point and probability forecasting evaluations to accurately quantify online batch errors. It also features a buffer to store batch data showing pattern changes and dynamically adjusts weights to counteract concept drift. We validated our method, adaptive electricity consumption forecast for universal environments (AECF-UC), against some mainstream methods using a multi-environment dataset. Comparative and ablation experiments show that AECF-UC outperforms others, achieving average RMSE, pinball loss and CRPS of 0.3041, 0.0567 and 0.1683 respectively, with the joint loss method improving prediction accuracy by about 6% over the single-loss method. These results indicate that the proposed method exhibits certain advantages in universality and adaptability.This work was supported in part by the National Natural Science Foundation of China under Grants (62206062 and 62401326), the China Postdoctoral Science Foundation under Grants 2024T170463 and 2024M751676, and the Shuimu Tsinghua Scholar program under Grant 2023SM035
Reconstructing Cross-Cultural Meanings of Addiction Among Women from Three Countries
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Data is unavailable due to privacy restrictions.The gender gap in drug use is narrowing in regions where access to criminalized substances, such as opioids, is increasing. While research shows that substance use is gendered, less is known about the cultural norms and values shaping women’s drug use, as most studies focus on men. Cross-national comparisons of cultural models of addiction are needed to better understand how addiction is perceived and to inform culturally responsive treatment approaches for women. This study examined cultural models of addiction among reproductive-aged women receiving treatment for substance misuse in London, Toronto, and Delhi. Participants completed a semi-structured questionnaire with open-ended and free-list prompts. Findings revealed shared cultural models attributing drug use to psychological factors, such as self-medicating to manage negative emotions or enhance positive ones, as well as relational, developmental, and biological influences. In conclusion, the study highlights the importance of incorporating cultural models into research and treatment. By using an inductive approach to explore meanings surrounding drug use among people in recovery, researchers can better understand how interventions are received and interpreted through existing internal frameworks.This study was funded by the Fulbright Global Scholar award and Ball State University’s ASPIRE grant
Something in the way they move: characteristics of identity present in faces, voices, body movements, and actions
Get PDFThe recognition of familiar individuals relies not only on static features of the person but also on dynamic characteristics unique to each person’s movements. This mini review synthesizes current research on the role of motion in identity recognition, examining how characteristic dynamic cues from the face, voice, and body may contribute to perceivers’ ability to recognize familiar individuals. We highlight corresponding dynamic covariances that may be present across different aspects of an individual’s motion, such as those linking facial and vocal motion. We evaluate the extent to which dynamic patterns might form a coherent ‘dynamic fingerprint.’ Finally, we consider how variability, distinctiveness, and perceiver-related factors (e.g., individual differences and neural mechanisms) shape the recognition of identity through motion. We outline open questions and propose new directions for understanding the integration of dynamic information in person perception.The author(s) declare that no financial support was received for the research and/or publication of this article
Three essays on financial development macroeconomic volatility and monetary policy
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis thesis consists of three studies that cover topics in the increasingly in uential eld of nancial
development and monetary policy. Chapters 2 and 3 explore the case of Brazil by (i) investigating whether
(and how di¤erently) deposits in public and in private banks a¤ect economic growth over extremely long-
time horizons using an uncommon econometric framework and (ii) revisiting the growth- nance nexus
using a new econometric approach and a new and unique data set. More speci cally in Chapter 2 utilizes a
PARCH framework and data for Brazil from 1870 to 2018 we nd that the main explanatory factors, solely
in terms of their negative lagged indirect/direct (short-run) e¤ects on economic growth in Brazil, turn
out to be the domestic nancial development indicators. Further, we nd robust evidence that the U.S.
interest rate a¤ects growth positively both indirectly (via its volatility) and directly (both in the short-
and long-run). Our results are robust to the inclusion of other economic variables i.e. trade openness
and public de cit. We also argue that domestic nancial development in uences growth negatively in
the short-run but positively in the long-run, whereas the impact of international nancial integration is
positive in both cases. Furthermore, the impact of private and public ownership on economic growth
tends to be both direct and indirect. However, our parameter estimations highlight the signi cantly
higher (in absolute magnitude) negative indirect and direct short-run e¤ects of public banks (compared
to those of private banks) on growth. Finally, trade openness and public de cit in uence output growth
negatively in the short-run. Our results are robust to the inclusion of population, in ation, and authority
score as well as dummy variables.
Chapter 3 uses the smooth transition framework and annual time series data for Brazil (i.e. annual
growth rate of gross domestic product (gdp), nancial development, trade openness and a set of political
instability indicators) covering the period from a very long time window, from 1890 to 2003. The new
data we use in this chapter is for political instability. Our research contributes further to the literature
by extending the track of political instability back to the year of 1890. More speci cally, we constructed
our own informal and formal political instability series from 1890 to 1919 (a period with high political
uncertainty in Brazil).
Our main ndings are that (a) nancial development has a mixed (positive and negative) time-varying
impact on economic growth (which signi cantly depends on jointly estimated trade openness thresholds);
(b) trade openness has a positive e¤ect, whereas (c) the e¤ect of political instability, both formal and
informal, on growth is unambiguously negative.
Finally, Chapter 4 continues the investigation on the empirical magnitude of the scal multipliers and
its determinants in the U.S.. We estimate the e¤ects of unanticipated government spending shocks on
output using quarterly U.S. data, 1986-2017. Our contribution is to estimate time-varying scal multi-
pliers conditional on di¤erent states of the business cycle by smooth-transition estimation, characterising
multipliers by the sign of the spending shocks
Party finance: Labour exploits its advantage
No full textThe 2024 General Election took place in the context of important legislative change related to party finance, together with Labour’s growing popularity ultimately being reflected in significant growth in its income as the election approached. The combination of increased campaign spending limits and Labour’s relative success in fundraising in the months leading up to the election meant that Labour was better able to exploit its ability to raise income and spend accordingly in the election campaign. By way of contrast, both the Conservatives and Liberal Democrats were financially far worse off than they had been in 2019
Optimization of Hybrid Composite–Metal Joints: Single Pin
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The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.Deepening the understanding of composite and metal joint methodologies applied in the aerospace industry is crucial for minimizing operational expenditures. Current investigations are focusing on innovative joining techniques that incorporate additive manufactured rivet pins. This research aims to analyze the mechanical strength of these joints for the effective optimization of pin profiles. Through extensive study of the impact of pin geometry on joint performance, we derived the optimal pin design, considering various initial parameters with the objective of minimizing stress concentration in the pin structure. The joint configurations of metal to composite interfaces were systematically examined using finite element analysis and lap shear testing, which included a singular pin and an adhesive-bonding layer. Numerical simulations reveal that the maximum shear stress in the pin is located at the junction between the base of the pin and the metal plate. By optimizing the shape and dimensions of the pin, both the shear and axial stresses can be significantly mitigated. Following the numerical optimization process, a series of enhanced pins have been produced via additive manufacturing techniques to facilitate mechanical testing. The experimental data obtained align closely with the simulation results, thereby reinforcing the validity of the optimization. The optimal configuration for a single pin, involving a 60° angle and a total height of 3.43 mm, achieves the minimum shear stress. Based on these findings, further investigations are underway to explore optimized designs utilizing multiple pins. This paper presents the results of the single pin study, whereas the findings pertaining to the ongoing investigation on the multi-pin configuration will be disseminated in subsequent publications.This research was funded by the collaborative Ph.D. program of Zhongyuan University of Technology (Discipline construction funds, 4600-12120009). The study is also partially supported by the Royal Society grant IEC\NSFC\233524
Carbonated Aggregates and Basalt Fiber-Reinforced Polymers: Advancing Sustainable Concrete for Structural Use
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The original contributions presented in this study are included in the article. Further inquiries can be directed at the corresponding author.In the transition towards a circular economy, redesigning construction materials for enhanced sustainability becomes crucial. To contribute to this goal, this paper investigates the integration of carbonated aggregates (CAs) and basalt fibre-reinforced polymers (BFRPs) in concrete infrastructures as an alternative to natural sand (NS) and steel reinforcement. CA is manufactured using accelerated carbonation that utilizes CO2 to turn industrial byproducts into mineralised products. The structural performance of CA and BFRP-reinforced concrete simply supported slab was investigated through conducting a series of experimental tests to assess the key structural parameters, including bond strength, bearing capacity, failure behavior, and cracking bbehaviour. Carbon footprint analysis (CFA) was conducted to understand the environmental impact of incorporating BFRP and CA. The results indicate that CA exhibits a higher water absorption rate compared to NS. As the CA ratio increased, the ultrasonic pulse velocity (UPV), compressive, tensile, and flexural strength decreased, and the absorption capacity of concrete increased. Furthermore, incorporating 25% CA in concrete has no significant effect on the bond strength of BFRP. However, the load capacity decreased with an increasing CA replacement ratio. Finally, integrating BFRP and 50% of CA into concrete slabs reduced the slab’s CFA by 9.7% when compared with steel-reinforced concrete (RC) slabs.This research received no external funding
Biogenic CO₂ Emissions in the EU Biofuel and Bioenergy Sector: Mapping Sources, Regional Trends, and Pathways for Capture and Utilisation
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The datasets generated during and/or analysed during the current study are available in the OpenAIRE Zenodo repository, https://doi.org/10.5281/zenodo.14651075.The European biofuel and bioenergy industry faces increasing challenges in achieving sustainable energy production while meeting carbon neutrality targets. This study provides a detailed analysis of biogenic emissions from biofuel and bioenergy production, with a focus on key sectors such as biogas, biomethane, bioethanol, syngas, biomass combustion, and biomass pyrolysis. Over 18,000 facilities were examined, including their feedstocks, production processes, and associated greenhouse gas emissions. The results highlight forestry residues as the predominant feedstock and expose significant disparities in infrastructure and technology adoption across EU Member States. While countries like Sweden and Germany lead in emissions management and carbon capture through bioenergy production with carbon capture and storage systems (BECCS), other regions face deficiencies in bioenergy infrastructure. The findings underscore the potential of BECCS and similar carbon management technologies to achieve negative emissions and support the European Green Deal’s climate neutrality goals. This work serves as a resource for policymakers, industry leaders, and researchers, fostering informed strategies for the sustainable advancement of the biofuels sector.This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 101084405 (CRONUS)
Prediction of Diabetes Using Statistical and Machine Learning Modelling Techniques
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The data can be shared upon request.Statistical and machine learning modelling techniques have been effectively used in the healthcare domain and the prediction of epidemiological chronic diseases such as diabetes, which is classified as an epidemic due to its high rates of global prevalence. These techniques are useful for the processes of description, prediction, and evaluation of various diseases, including diabetes. This paper models diabetes disease in Saudi Arabia using the most relevant risk factors, namely smoking, obesity, and physical inactivity for adults aged ≥25 years. The aim of this study is based on developing statistical and machine learning models for the purpose of studying the trends in incidence rates of diabetes over 15 years (1999–2013) and to obtain predictions for future levels of the disease up to 2025, to support health policy planning and resource allocation for controlling diabetes. Different models were developed, namely Multiple Linear Regression (MLR), Support Vector Regression (SVR), Bayesian Linear Regression (BLM), Adaptive Neuro-Fuzzy Inference model (ANFIS), and Artificial Neural Network (ANN). The performance of the developed models is evaluated using four statistical metrices: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and coefficient of determination R-squared. Based on the results, it can be observed that the overall performance for all proposed models was reasonably good; however, the best results were achieved by the ANFIS model with RMSE = 0.04 and R2 = 0.99 for men’s training data, and RMSE = 0.02 and R2 = 0.99 for women’s training data.This research received no external funding
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