Engineering Sciences Research Research Articles

Incidence of diabetes after SARS-CoV-2 infection in England and the implications of COVID-19 vaccination: a retrospective cohort study of 16 million people

Some studies have shown that the incidence of type 2 diabetes increases after a diagnosis of COVID-19, although the evidence is not conclusive. However, the effects of the COVID-19 vaccine on this association, or the effect on other diabetes subtypes, are not clear. We aimed to investigate the association between COVID-19 and incidence of type 2, type 1, gestational and non-specific diabetes, and the effect of COVID- 19 vaccination, up to 52 weeks after diagnosis. In this retrospective cohort study, we investigated the diagnoses of incident diabetes following COVID-19 diagnosis in England in a pre-vaccination, vaccinated, and unvaccinated cohort using linked electronic health records. People alive and aged between 18 years and 110 years, registered with a general practitioner for at least 6 months before baseline, and with available data for sex, region, and area deprivation were included. Those with a previous COVID-19 diagnosis were excluded. We estimated adjusted hazard ratios (aHRs) comparing diabetes incidence after COVID-19 diagnosis with diabetes incidence before or in the absence of COVID-19 up to 102 weeks after diagnosis. Results were stratified by COVID-19 severity (categorised as hospitalised or non-hospitalised) and diabetes type. 16 669 943 people were included in the pre-vaccination cohort (Jan 1, 2020-Dec 14, 2021), 12 279 669 in the vaccinated cohort, and 3 076 953 in the unvaccinated cohort (both June 1-Dec 14, 2021). In the pre-vaccination cohort, aHRs for the incidence of type 2 diabetes after COVID-19 (compared with before or in the absence of diagnosis) declined from 4·30 (95% CI 4·06-4·55) in weeks 1-4 to 1·24 (1·14-1.35) in weeks 53-102. aHRs were higher in unvaccinated people (8·76 [7·49-10·25]) than in vaccinated people (1·66 [1·50-1·84]) in weeks 1-4 and in patients hospitalised with COVID-19 (pre-vaccination cohort 28·3 [26·2-30·5]) in weeks 1-4 declining to 2·04 [1·72-2·42] in weeks 53-102) than in those who were not hospitalised (1·95 [1·78-2·13] in weeks 1-4 declining to 1·11 [1·01-1·22] in weeks 53-102). Type 2 diabetes persisted for 4 months after COVID-19 in around 60% of those diagnosed. Patterns were similar for type 1 diabetes, although excess incidence did not persist beyond 1 year after a COVID-19 diagnosis. Elevated incidence of type 2 diabetes after COVID-19 is greater, and persists for longer, in people who were hospitalised with COVID-19 than in those who were not, and is markedly less apparent in people who have been vaccinated against COVID-19. Testing for type 2 diabetes after severe COVID-19 and the promotion of vaccination are important tools in addressing this public health problem. UK National Institute for Health and Care Research, UK Research and Innovation (UKRI) Medical Research Council, UKRI Engineering and Physical Sciences Research Council, Health Data Research UK, Diabetes UK, British Heart Foundation, and the Stroke Association.

"The impact of the COVID-19 pandemic on research activities: A survey of the largest Italian academic community".

The aim of the present work is to explore the impact of the COVID-19 pandemic on research activities in a vast multidisciplinary academic community to identify the most critical issues. To this purpose we planned a survey addressed to the entire academic research staff at "Sapienza" University of Rome, which represents the largest Italian academic community. A questionnaire consisting of both open and closed-ended questions was delivered to 4118 individuals in April 2021. A total of 544 responses were collected. All academic roles were sufficiently represented in the study cohort. The median number of critical issues experienced by academic research staff was three. Among these, the three most frequently reported were related to: "Access to libraries / laboratories / research sites" (21.9%), "Limitation to stay abroad / study / research periods" (17.6%), "Progress of experimental work" (14.7%), with variable prevalence according to academic position and gender. Older subjects reported issues with "Projects' financial reporting" and "Expiration of acquired consumable material more frequently". The most common critical aspects reported in relation to the economic burden were: being "Unable to allocate funds" (31.4%), a "Reduction in clinical and scientific activity" (26.3%) and experiencing "Increased expenses (comprising private costs)" (21.2%) with no differences between genders. Researchers in Applied Sciences and Natural Sciences reported a higher frequency of problems in clinical and scientific activities, whereas increased expenses were reported also by researchers operating in the Humanities field. As a possible solution aimed at improving these issues, most subjects, especially those aged >45 years, indicated "Economic aid" (22.6%), "Reduction in bureaucracy" (19.9%) or "Enhancement of the scientific and clinical activities", whereas those aged ≤45 years felt that an increased duration and better access to PhD programs were to be prioritized. Our findings highlight the most critical issues related to research activities during the COVID-19 pandemic in a large academic community. The information achieved may be useful to identify researchers' needs and to design appropriate policies aimed at preparing research institutions for unexpected catastrophic events and limiting the negative impact on academic research activities.

Impact of Big Data and Knowledge Management on Customer Interactions and Consumption Patterns: Applied Science Research Perspective

Impact of Big Data and Knowledge Management on Customer Interactions and Consumption Patterns: Applied Science Research Perspective

Markov Decision Processes with Observation Costs: Framework and Computation with a Penalty Scheme

We consider Markov decision processes where the state of the chain is only given at chosen observation times and of a cost. Optimal strategies involve the optimization of observation times as well as the subsequent action values. We consider the finite horizon and discounted infinite horizon problems as well as an extension with parameter uncertainty. By including the time elapsed from observations as part of the augmented Markov system, the value function satisfies a system of quasivariational inequalities (QVIs). Such a class of QVIs can be seen as an extension to the interconnected obstacle problem. We prove a comparison principle for this class of QVIs, which implies the uniqueness of solutions to our proposed problem. Penalty methods are then utilized to obtain arbitrarily accurate solutions. Finally, we perform numerical experiments on three applications that illustrate our framework. Funding: J. Tam is supported by the Engineering and Physical Sciences Research Council [Grant 2269738].

Correlated Equilibria for Mean Field Games with Progressive Strategies

In a discrete space and time framework, we study the mean field game limit for a class of symmetric N-player games based on the notion of correlated equilibrium. We give a definition of correlated solution that allows us to construct approximate N-player correlated equilibria that are robust with respect to progressive deviations. We illustrate our definition by way of an example with explicit solutions. Funding: O. Bonesini acknowledges financial support from Engineering and Physical Sciences Research Council [Grant EP/T032146/1]. M. Fischer acknowledges partial support through the University of Padua [Research Project BIRD229791 “Stochastic mean field control and the Schrödinger problem”].

Neural embeddings of urban big data reveal spatial structures in cities

Over decades, many cities have been expanded and functionally diversified by population activities, socio-demographics and attributes of the built environment. Urban expansion and development have led to the emergence of spatial structures of cities. Uncovering cities’ spatial structures is critical to understanding various urban phenomena such as segregation, equity of access, and sustainability. In this study, we propose using a neural embedding model—graph neural network (GNN)—that leverages the heterogeneous features of urban areas and their interactions captured by human mobility networks to obtain vector representations of these areas. Using large-scale high-resolution mobility data sets from millions of aggregated and anonymized mobile phone users in 16 metropolitan counties in the United States, we demonstrate that our embeddings encode complex relationships among features related to urban components (such as distribution of facilities) and population attributes and activities. The clustered representations of urban areas show the shared characteristics among urban areas in the same cluster. We show that embeddings generated by a model trained on a different county can capture 50% to 60% of the spatial structure in another county, allowing us to make cross-county comparisons and inferences. The findings reveal complex relationships among urban components in cities. Since the identified multifaceted spatial structures capture the combined effects of various mechanisms, such as segregation, disparate facility distribution, and human mobility, the findings could help identify the limitations of the current city structure to inform planning decisions and policies. Also, the model and findings set the stage for a variety of research in urban planning, engineering and social science through an integrated understanding of how the complex interactions between urban components and population activities and attributes shape the spatial structures in cities.

Public--Private Cooperation Funding Juxtaposed of University--Community Engagement in Europe: Systematic Review

This article explores the utilization of public–private funding models in European universities and its implications for community empowerment initiatives, particularly University–Community Engagement (UCE) projects. A systematic literature review (SLR) was conducted using the Scopus database, screening 50 articles relating to public–private funding models in European universities. The study reveals that while public–private funding models have gained traction in the academic sphere, they primarily focus on supporting applied science research projects. In contrast, the funding of projects solely oriented toward community empowerment remains limited, with only one article addressing this potential. The findings highlight the challenges associated with employing public–private funding for UCE projects with a strong emphasis on community empowerment. Public–private funding, typically sourced from industry partners, tends to prioritize product innovations and productivity enhancements, posing a mismatch with the social justice-oriented nature of UCE. This discrepancy underscores a significant research gap in UCE and calls for innovative approaches to develop supportive funding frameworks that can effectively sustain UCE initiatives.
 Keywords: public–private funding, university–community engagement, European University

Submodular Functions and Perfect Graphs

We give a combinatorial polynomial-time algorithm to find a maximum weight independent set in perfect graphs of bounded degree that do not contain a prism or a hole of length four as an induced subgraph. An even pair in a graph is a pair of vertices all induced paths between which are even. An even set is a set of vertices every two of which are an even pair. We show that every perfect graph that does not contain a prism or a hole of length four as an induced subgraph has a balanced separator which is the union of a bounded number of even sets, where the bound depends only on the maximum degree of the graph. This allows us to solve the maximum weight independent set problem using the well-known submodular function minimization algorithm. Funding: This work was supported by the Engineering and Physical Sciences Research Council [Grant EP/V002813/1]; the National Science Foundation [Grants DMS-1763817, DMS-2120644, and DMS-2303251]; and Alexander von Humboldt-Stiftung.

A New Simheuristic Approach for Stochastic Runway Scheduling

We consider a stochastic, dynamic runway scheduling problem involving aircraft landings on a single runway. Sequencing decisions are made with knowledge of the estimated arrival times (ETAs) of all aircraft due to arrive at the airport, and these ETAs vary according to continuous-time stochastic processes. Time separations between consecutive runway landings are modeled via sequence-dependent Erlang distributions and are affected by weather conditions, which also evolve continuously over time. The resulting multistage optimization problem is intractable using exact methods, and we propose a novel simheuristic approach based on the application of methods analogous to variable neighborhood search in a high-dimensional stochastic environment. Our model is calibrated using flight tracking data for over 98,000 arrivals at Heathrow Airport. Results from numerical experiments indicate that our proposed simheuristic algorithm outperforms an alternative based on deterministic forecasts under a wide range of parameter values, with the largest benefits seen when the underlying stochastic processes become more volatile and also when the on-time requirements of individual flights are given greater weight in the objective function. Funding: This work was supported by the Engineering and Physical Sciences Research Council [Grant EP/M020258/1]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/trsc.2022.0400 .

Evaluation of drying performance of sports bras

Introduction
 Fabrics used for manufacturing sports bras have to be lightweight, breathable, have fast drying behaviour, and moisture management properties (Uttam, 2013). Therefore, thermo-physiological comfort is an important quality criterion for sports bras (Carneiro et al., 2017). In addition, sports bras have to provide support to stabilize breasts during various physical activities (Ancutiene et al., 2017). These requirements lead to challenges in finding the best sports bra fabric, as bra support (Norris et al., 2021) and thermo-physiological comfort require contrasting fabric properties (Carneiro et al., 2017).
 This work aims to compare drying characteristics of different sports bras types (compression, encapsulation and combination) and fabrics used. A newly developed methodology to investigate the drying behaviour of sports bras in the lab has been taken for their examination.
 Methods
 The measurement set-up was built up in a climatic chamber (at 23°C and 50% RH). It consists of the upper body part of a female manikin, the air channel with fans and a balance to measure the weight change of the sports bra over time (indicative of the amount of moisture evaporated). The Infrared (IR) camera was used to record local surface temperature. Based on these measurements, the local and general drying performance of the sports bras were assessed based on dry fabric weight, moisture uptake, moisture absorption, drying time and drying rate.
 Results
 The results show that type of sports bra and material composition can have influence on better drying performance studied. The range of drying time for the different sport bra types was observed between 13 and 66 min for compression bras, 25 and 42 min for encapsulation bras, and between 35 and 43 min for combination bras. The moisture uptake was detected in a range between 6% and 15% for compression bras, 13% and 15% only for encapsulation bras, while between 7% and 19% for combination bras.
 Discussion/Conclusion 
 We were able to show that there is a significant difference in drying performance between sports bras. In combination with additional data about sensorial comfort and breast support, these findings provide a scientific basis for developing sports bras meeting specific requirements for various sports.
 References
 Ancutiene, K., Koldinska, M., & Havelka, A. (2017). Investigation of tensile resilience properties of stretch denim fabrics. Indian Journal of Fibre & Textile Research, 42, 175-182.
 Carneiro, L. P., Miranda, T. M. R., & Catarino, A. A. (2017). Comparative thermophysiological study in sport bras for running. IOP Conference Series: Materials Science and Engineering, 254(7), Article 072006. https://doi.org/10.1088/1757-899X/254/7/072006
 Norris, M., Blackmore, T., Horler, B., & Wakefield-Scurr, J. (2021). How the characteristics of sports bras affect their performance. Ergonomics, 64(3), 410-425. https://doi.org/10.1080/00140139.2020.1829090
 Uttam, D. (2013) Active sportswear fabrics. International Journal of IT, Engineering and Applied Sciences Research, 2(1), 34-40.

Anveshana's International Journal of Research in Engineering and Applied Sciences Anveshana's International Journal of Research in Engineering and Applied Sciences

Anveshana's International Journal of Research in Engineering and Applied Sciences Anveshana's International Journal of Research in Engineering and Applied Sciences

Turn on the lights! Fundamental science leads to scientific progress: A perspective from developmental biology

Many of the ways in which we interact with the world around us have been shaped by the dual efforts of fundamental and applied sciences. Generally speaking, fundamental science generates knowledge about how things work at a fundamental level, and applied science employs this body of knowledge to create a new product or overcome an existing challenge. Developmental biology is a classic example of fundamental science that drives several avenues of applied science. For example, understanding how cells, tissues, and organs develop, and are coordinated within a functioning organism can form the basis for diagnosing medical conditions and exploring treatments. As a developmental biology lab researching bone and cartilage in birds and fish, we are acutely aware of the disparity in financial support between fundamental and applied science research in Canada. Funding cutting-edge applied research with immediate impacts on society is attractive and more easily justifiable to tax payers. However, funding grassroots fundamental science research is equally important but receives significantly less attention and support because the impacts are harder to predict and are longer-term. This commentary addresses this inequity in science funding and high-lights the dire need to improve supports for early career scientists.

QUANTIFICATION OF THE ACCURACY OF IN VIVO TIBIOFEMORAL KINEMATICS CALCULATED USING MAGNETIC RESONANCE IMAGING AND BIPLANE VIDEO X-RAY

AbstractObjectivesInvestigate Magnetic Resonance Imaging (MRI) as an alternative to Computerised Tomography (CT) when calculating kinematics using Biplane Video X-ray (BVX) by quantifying the accuracy of a combined MRI-BVX methodology by comparing with results from a gold-standard bead-based method.MethodsWritten informed consent was given by one participant who had four tantalum beads implanted into their distal femur and proximal tibia from a previous study. Three-dimensional (3D) models of the femur and tibia were segmented (Simpleware Scan IP, Synopsis) from an MRI scan (Magnetom 3T Prisma, Siemens). Anatomical Coordinate Systems (ACS) were applied to the bone models using automated algorithms1. The beads were segmented from a previous CT and co-registered with the MRI bone models to calculate their positions. BVX (60 FPS, 1.25 ms pulse width) was recorded whilst the participant performed a lunge. The beads were tracked, and the ACS position of the femur and tibia were calculated at each frame (DSX Suite, C-Motion Inc.). The beads were digitally removed from the X-rays (MATLAB, MathWorks) allowing for blinded image-registration of the MRI models to the radiographs. The mean difference and standard deviation (STD) between bead-generated and image-registered bone poses were calculated for all degrees of freedom (DOF) for both bones. Using the principles defined by Grood and Suntay2, 6 DOF kinematics of the tibiofemoral joint were calculated (MATLAB, MathWorks). The mean difference and STD between these two sets of kinematics were calculated.ResultsThe absolute mean femur and tibia ACS position differences (Table 1) between the bead and image-registered poses were found to be within 0.75mm for XYZ, with all STD within ±0.5mm. Mean rotation differences for both bones were found to be within 0.2º for XYZ (Table 1). The absolute mean tibiofemoral joint translations (Table 1) were found to be within ±0.7mm for all DOF, with the smallest absolute mean in compression-distraction. The absolute mean tibiofemoral rotations were found to be within 0.25º for all DOF (Table 1), with the smallest mean was found in abduction-adduction. The largest mean and STD were found in internal-external rotation due to the angle of the X-rays relative to the joint movement, increasing the difficulty of manual image registration in that plane.ConclusionThe combined MRI-BVX method produced bone pose and tibiofemoral kinematics accuracy similar to previous CT results3. This allows for confidence in future results, especially in clinical applications where high accuracy is needed to understand the effects of disease and the efficacy of surgical interventions. Acknowledgements: This research was supported by the Engineering and Physical Sciences Research Council (EPSRC) doctoral training grant (EP/T517951/1).Declaration of Interest(b) declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported:I declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project.

PACMMOD Volume 1, Issue 3: Editorial

We are excited to introduce this new issue of PACMMOD (Proceedings of the ACM on Management of Data). PACMMOD is a new journal, concerned with the principles, algorithms, techniques, systems, and applications of database management systems, data management technology, and science and engineering of data. It includes articles reporting cutting-edge data management, data engineering, and data science research. Articles published at PACMMOD address data challenges at various stages of the data lifecycle, from modeling, acquisition, cleaning, integration, indexing, querying, analysis, exploration, visualization, interpretation, and explanation. They focus on dataintensive components of data pipelines, and solve problems in areas of interest to our community (e.g., data curation, optimization, performance, storage, systems), operating within accuracy, privacy, fairness, and diversity constraints. Articles reporting deployed systems and solutions to data science pipelines and/or fundamental experiences and insights from evaluating real-world data engineering problems are especially encouraged.

In the Lab: Heterogeneous Catalysis Mediated Interconversion between NAD(P)+ and NAD(P)H Accompanied by Consumption and Generation of Hydrogen

Dr. Wang is a Senior Lecturer in Chemical Engineering at Lancaster University. Prior to this, he was a Lecturer in Chemical Engineering at the University of Aberdeen, Postdoctoral Research Associate at Heriot-Watt University, where he also obtained his PhD (2014). He completed both MSc and BEng studies at Tianjin University. Wang has been the author of over 50 peer-reviewed publications, an editorial board member of Chinese Chemical Letters and his research has mainly been funded by the Engineering and Physical Sciences Research Council (EPSRC), Royal Society, UK Catalysis Hub and industry. Wang’s research interest ranges from reaction engineering, green energy and materials to chemicals, where heterogeneous catalysis is the core discipline. His recent work has focused on the innovative use of heterogeneous catalysts (e.g., supported metals) in enzymatic transformations via cofactor regeneration,1,2 paving the way to a potential new regeneration technology.

Embedding Software Engineering in Mixed Methods Research: Computationally Enhanced Risk Communication

Mixed methods research ameliorates many convergent research challenges within the contemporary sociotechnical landscape. We suggest the integration of software engineering in mixed methods research is a critical step to address some of the remaining and persistent challenges. One such research challenge where computational science is particularly well suited is in hazard preparedness—in particular, the creation of risk communication messages to mitigate or to prevent harm. Computationally enhanced risk communication is convergent research that integrates software engineering and social science research for the benefit of protecting humans and infrastructure. To this end, we developed a mixed methods framework for the efficient construction of risk communication messages. We call this the Domain Agnostic Risk Communication (DARC) framework and present it here. The DARC framework formalizes connections between computational science and social science methods. It incorporates the best available science in risk communication research and a cadre of natural language processing techniques to impart validity, reliability, and precision into resultant messages. The DARC framework is highly modular owing to the incorporation of the software engineering principles of abstraction, extensibility, and encapsulation. Although the focus of this position article is on risk communication, we encourage the incorporation of software engineering into mixed methods research and the incorporation of mixed methods more broadly into software engineering experimentation.

The Role of Gas Path Diagnostics in the Changing Gas Turbine After-market

Aero gas turbine maintenance strategies are likely to see profound changes in the future. The remarkable size of the aftermarket for both military and civil aero gas turbines has already changed the nature of the business case. The aftermarket is large, more lucrative and not subject to the cyclical downturns of the original equipment sales. The competition which this considerably profitable after market creates will spur advances in diagnostic techniques. Of particular importance is advanced gas path diagnostics where, in recent years, engineering science research has opened pathways using somewhat surprising technologies. The paper will present work covering linear and non-linear gas path analysis, genetic algorithms, neural networks, fuzzy logic and hybrid systems. Much of the costs associated with the gas turbine after market relates to the gas path and these techniques are seen to play an important role

Preface

International Conference on Environment, Earth Science and Sustainability (ICES) Proceedings of International Conference on Environment, Earth Science and Sustainability (ICES 2022) Following six successful events of the Annual Workshop, the 7th Annual IIES Science and Policy Workshop was jointly organized with the International Conference on Environment, Earth Science and Sustainability (ICES) from 24 Oct to 28 Oct 2022 in Ho Chi Minh City, Vietnam. The ICES 2022 - a multidisciplinary conference - initiated under the auspices of Ho Chi Minh City University of Technology (HCMUT), Trent University (Canada) and the IIES Network, and was taken place in the occasion of the 65th anniversary of HCMUT.This year, the ICES Conference with the theme of “Integrated Multidisciplinary Sciences towards Sustainable Development” was held in Ho Chi Minh City (24 - 26 Oct 2022) and Phu Quoc, Vietnam (27 - 28 Oct 2022). The Conference was successfully organized by a synergy of the consortium of Vietnam National University Ho Chi Minh City (VNU-HCM), Ho Chi Minh City University of Technology, International Institute for Environmental Studies, Trent University, Nan Jing University, Kunsan National University (KSNU), Dalhousie University, and Institute for Circular Economy Development - VNU-HCM.The ICES 2022 covers five main topics of (i) Civil Engineering, (ii) Environmental Sciences and Management, (iii) Environmental Engineering and Technologies, (iv) Earth Sciences/Natural Resources, and (v) Circular Economy/Sustainability. Participants gained insights into current research in civil engineering, earth and environmental science, policy and planning, and attained a worthy opportunity to meet colleagues from several institutions, universities, and agencies. For students, this is a chance to share their research outcomes and to build-up potential collaborative relationships at an international level. The Conference drew substantial attention from leading scientists, managers, lecturers, and merit students from universities, institutes, and research centers as well as officials from relevant government departments, agencies, companies, and enterprises.Overall, the Conference has attracted more than 216 papers from scientists, researchers, government managers, and graduate students. Among them, 79 high-quality papers were recommended to submit for peer-reviewing. Then, the double-blind review process was carried out for the 67 papers, in which each paper has been reviewed for its merit and novelty by at least two reviewers and one editor by matching the content areas. As a result, a total of 37 papers have been finally selected for this proceedings book. We believe that this proceedings book provides a broad overview of recent advanced studies in the fields of Civil Engineering, Material, Circular Economy, Environment, Resources and Earth Sciences for readers.Most importantly, we would like to express our sincere thanks to (i) Division of Science and Technology – Vietnam National University Ho Chi Minh City (Prof. Lam Quang Vinh); (ii) Rector of HCMUT (Prof. Mai Thanh Phong) for their help and other supports. We would like to thank all the reviewers for their timely and rigorous reviews of the papers and all authors for their interests in the ICES 2022.List of Editors are available in the pdf.

Urban–rural disparity in global estimation of PM2·5 household air pollution and its attributable health burden

Polluting fuels and inefficient stove technologies are still a leading cause of premature deaths worldwide, particularly in low-income and middle-income countries. Previous studies of global household air pollution (HAP) have neither considered the estimation of PM2·5 at national level nor the corresponding attributable mortality burden. Additionally, the effects of climate and ambient air pollution on the global estimation of HAP-PM2·5 exposure for different urban and rural settings remain largely unknown. In this study, we include climatic effects to estimate the HAP-PM2·5 exposure from different fuel types and stove technologies in rural and urban settings separately and the related attributable global mortality burden. Bayesian hierarchical models were developed to estimate an annual average HAP-PM2·5 personal exposure and HAP-PM2·5 indoor concentration (including both outdoor and indoor sources). Model variables were selected from sample data in 282 peer-reviewed studies drawn and updated from the WHO Global HAP dataset. The PM2·5 exposure coefficients from the developed model were applied to the external datasets to predict the HAP-PM2·5 exposure globally (personal exposure in 62 countries and indoor concentration in 69 countries). Attributable mortality rate was estimated using a comparative risk assessment approach. Using weighted averages, the national level 24 h average HAP-PM2·5 exposure due to polluting and clean fuels and related death rate per 100 000 population were estimated. In 2020, household use of polluting solid fuels for cooking and heating led to a national-level average personal exposure of 151 μg/m3 (95% CI 133-169), with rural households having an average of 171 μg/m3 (153-189) and urban households an average of 92 μg/m3 (77-106). Use of clean fuels gave rise to a national-level average personal exposure of 69 μg/m3 (62-76), with a rural average of 76 μg/m3 (69-83) and an urban average of 49 μg/m3 (46-53). Personal exposure-attributable premature mortality (per 100 000 population) from the use of polluting solid fuels at national level was on average 78 (95% CI 69-87), with a rural average of 82 (73-90) and an urban average of 66 (57-75). The average attributable premature mortality (per 100 000 population) from the use of clean fuels at the national level is 62 (54-70), with a rural average of 66 (58-74) and an urban average of 52 (47-57). The estimated HAP-PM2·5 indoor concentration shows that the use of polluting solid fuels resulted in a national-level average of 412 μg/m3 (95% CI 353-471), with a rural average of 514 μg/m3 (446-582) and an urban average of 149 μg/m3 (126-173). The use of clean fuels (gas and electricity) led to an average PM2·5 indoor concentration of 135 μg/m3 (117-153), with a rural average of 174 μg/m3 (154-195) and an urban average of 71 μg/m3 (63-80). Using time-weighted HAP-PM2·5 indoor concentrations, the attributable premature death rate (per 100 000 population) from the use of polluting solid fuels at the national level is on average 78 (95% CI 72-84), the rural average being 84 (78-91) and the urban average 60 (54-66). From the use of clean fuels, the average attributable premature death rate (per 100 000 population) at the national level is 59 (53-64), the rural average being 68 (62-74) and the urban average 45 (41-50). A shift from polluting to clean fuels can reduce the average PM2·5 personal exposure by 53% and thereby lower the death rate. For all fuel types, the estimated average HAP-PM2·5 personal exposure and indoor concentrations exceed the WHO's Interim Target-1 average annual threshold. Policy interventions are urgently needed to greatly increase the use of clean fuels and stove technologies by 2030 to achieve the goal of affordable clean energy access, as set by the UN in 2015, and address health inequities in urban-rural settings. Wellcome Trust, The Lancet Countdown, the Engineering and Physical Sciences Research Council, and the Natural Environment Research Council.

Evaluating 28-Days Performance of Rice Husk Ash Green Concrete under Compression Gleaned from Neural Networks

Cement manufacturing and utilization is one of the majorly responsible factors for global CO2 emissions. In light of sustainability and climate change concerns, it is essential to find alternative solutions to reduce the carbon footprint of cement. Secondary cementitious materials (SCMs) are helpful in reducing carbon emissions from concrete. One such solution is the use of agricultural waste as SCMs to reduce carbon emissions from concrete. Especially rice husk ash (RHA) is a silica-rich, globally available agricultural waste material. The compressive strength (CS) of concrete is important and is used to evaluate the material’s strength and durability. Predicting CS using a laboratory method is a costly, time-consuming, and complex process. ML-based prediction models are the modern solution to these problems. In this study, a total of 407 datasets are used to develop an ML-based model by using the ANN algorithm to predict the CS of concrete containing RHA. Cement, coarse aggregates, fine aggregates, water, rice husk ash, superplasticizer, and type of sample are used as input parameters to predict CS at 28 days. Various statistical parameters including correlation coefficient (R), root means square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash-Sutcliffe (NS), and the a20-index have been used to assess the performance of the developed ANN model. The R and RMSE values of training, validation, and testing samples are 0.9928, 0.9864, and 0.9545, and 1.6471 MPa, 2.7149 MPa, and 4.4334 MPa, respectively. The results obtained from this study have been found to be promising and enrich the available literature. This work will nudge civil engineering and material science researchers toward opting for sustainable computing techniques. However, the study’s limitations include the need for additional research into the material’s long-term behaviour as well as the consideration of other characteristics that may affect its strength, such as environmental conditions like temperature and humidity.