Explore the vast range of titles available.

The topic of indoor air pollution has yet to receive the same level of attention as ambient pollution. We spend considerable time indoors, and poorer indoor air quality affects most of us, particularly people with respiratory and other health conditions. There is a pressing need for methodological case studies focusing on informing households about the causes and harms of indoor air pollution and supporting changes in behaviour around different indoor activities that cause it. The use of indoor air quality (IAQ) sensor data to support behaviour change is the focus of our research in this paper. We have conducted two studies—first, to evaluate the effectiveness of the IAQ data visualisation as a trigger for the natural reflection capability of human beings to raise awareness. This study was performed without the scaffolding of a formal behaviour change model. In the second study, we showcase how a behaviour psychology model, COM-B (Capability, Opportunity, and Motivation-Behaviour), can be operationalised as a means of digital intervention to support behaviour change. We have developed four digital interventions manifested through a digital platform. We have demonstrated that it is possible to change behaviour concerning indoor activities using the COM-B model. We have also observed a measurable change in indoor air quality. In addition, qualitative analysis has shown that the awareness level among occupants has improved due to our approach of utilising IoT sensor data with COM-B-based digital interventions.

With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental results showed that calibration with AH has better performance as compared with RH. The experimental results showed the selection and calibration techniques that can be used in designing similar LCS based monitoring systems.

In smart cities, relief items distribution is a complex task due to the factors such as incomplete information, unpredictable exact demand, lack of resources, and causality levels, to name a few. With the development of Internet of Things (IoT) technologies, dynamic data update provides the scope of distribution schedule to adopt changes with updates. Therefore, the dynamic relief items distribution schedule becomes a need to generate humanitarian supply chain schedules as a smart city application. To address the disaster data updates in different time periods, a dynamic optimised model with a sliding time window is proposed that defines the distribution schedule of relief items from multiple supply points to different disaster regions. The proposed model not only considers the details of available resources dynamically but also introduces disaster region priority along with transportation routes information updates for each scheduling time slot. Such an integrated optimised model delivers an effective distribution schedule to start with and updates it for each time slot. A set of numerical case studies is formulated to evaluate the performance of the optimised scheduling. The dynamic updates on the relief item demands’ travel path, causality level and available resources parameters have been included as performance measures for optimising the distributing schedule. The models have been evaluated based on performance measures to reflect disaster scenarios. Evaluation of the proposed models in comparison to the other perspective static and dynamic relief items distribution models shows that adopting dynamic updates in the distribution model cover most of the major aspects of the relief items distribution task in a more realistic way for post-disaster relief management. The analysis has also shown that the proposed model has the adaptability to address the changing demand and resources availability along with disaster conditions. In addition, this model will also help the decision-makers to plan the post-disaster relief operations in more effective ways by covering the updates on disaster data in each time period.

Electricity price prediction has an imperative role in the UK energy market among energy trading organisations. The price prediction directly impacts organisational policy for profitable electricity trading, better bidding plans, and the optimisation of energy storage devices for any surplus energy. Business organisations always look for the use of price-prediction models with higher accuracy to help them maximise benefits. With the enhancement of Internet of Things (IoT) technology, data availability on energy demand, and hence the associated price prediction modelling has become more effective tools than before. However, price prediction has been a challenging task because of the uncertainty in the demand and supply and other external factors such as weather, and gas prices as these factors can influence the fluctuation of electricity prices. In this regard, the selection of an appropriate prediction model is crucial for business organisations. In this paper, an analytical study has been presented to predict short-term electricity prices in the UK market as a use case for a UK-based energy trading company. ARIMA, Prophet, XGBoost as well as Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long-Short Term Memory (LSTM) algorithms have been analysed. In this study, UK Market Index Data (MID) from Elexon API data has been used that represent half-hourly electricity prices. In addition, gas prices, and initial demand out-turn data, as external factors, are introduced into the models for improving the accuracy and performance of these models. The comparative analysis shows that the ARIMA can handle only one external factor in its prediction model, while the Prophet and XGBoost can incorporate multiple external regressors in their models. Also, the models based on deep learning algorithms can deal with univariate and multivariate time series. The comparative analysis also revealed that the XGBoost model has better performance than the ARIMA and Prophet models, as has been found in earlier studies. With the extended analysis, it has been found that deep learning models outperform ARIMA, Prophet, and XGBoost models in terms of prediction accuracy. This extended comparative analysis gives the flexibility to choose the appropriate model selection for any organisation working in analogous business scenarios as of the business use case of this study.

A key aspect of the development of Smart Cities involves the efficient and effective management of resources to improve liveability. Achieving this requires large volumes of sensors strategically deployed across urban areas. In many cases, however, it is not feasible to install devices in remote and inaccessible areas, resulting in incomplete data coverage. In such situations, citizens can often play a crucial role in filling this data collection gap. A popular complimentary science to traditional sensor-based data collection is to design Citizen Science (CS) activities in collaboration with citizens and local communities. Such activities are also designed with a feedback loop where the Citizens benefit from their participation by gaining a greater sense of awareness of their local issues while also influencing how the activities can align best with their local contexts. The participation and engagement of citizens are vital and yet often a real challenge in ensuring the long-term continuity of CS projects. In this paper, we explore engagement factors, factors that help keeping engagement high, in technology-centric CS projects where technology is a key enabler to support CS activities. We outline a literature review of exploring and understanding various motivational and engagement factors that influence the participation of citizens in technology-driven CS activities. Based on this literature, we present a mobile-based flood monitoring citizen science application aimed at supporting data collection activities in a real-world CS project as part of an EU project. We discuss the results of a user evaluation of this app, and finally discuss our findings within the context of citizens’ engagement.

Background: Leigh’s Disease is a rare mitochondrial disorder primarily affecting the central nervous system, with frequent secondary cardiac manifestations such as hypertrophic and dilated cardiomyopathies. Early detection of cardiac complications is crucial for patient management, but manual interpretation of cardiac MRI is labour-intensive and subject to inter-observer variability. Methodology: We propose an integrated deep learning framework using cardiac MRI to automate the detection of cardiac abnormalities associated with Leigh’s Disease. Four CNN architectures—Inceptionv3, a custom 3-layer CNN, DenseNet169, and EfficientNetB2—were trained on preprocessed MRI data (224 × 224 pixels), including left ventricular segmentation, contrast enhancement, and gamma correction. Morphological features (area, aspect ratio, and extent) were also extracted to aid interpretability. Results: EfficientNetB2 achieved the highest test accuracy (99.2%) and generalization performance, followed by DenseNet169 (98.4%), 3-layer CNN (95.6%), and InceptionV3 (94.2%). Statistical morphological analysis revealed significant differences in cardiac structure between Leigh’s and non-Leigh’s cases, particularly in area (212,097 vs. 2247 pixels) and extent (0.995 vs. 0.183). The framework was validated using ROC (AUC = 1.00), Brier Score (0.000), and cross-validation (mean sensitivity = 1.000, std = 0.000). Feature embedding visualisation using PCA, t-SNE, and UMAP confirmed class separability. Grad-CAM heatmaps localised relevant myocardial regions, supporting model interpretability. Conclusions: Our deep learning-based framework demonstrated high diagnostic accuracy and interpretability in detecting Leigh’s disease-related cardiac complications. Integrating morphological analysis and explainable AI provides a robust and scalable tool for early-stage detection and clinical decision support in rare diseases.

Climate change is a global phenomenon having wide-ranging social, economic, ecological, and environmental sustainability implications. This study assesses climate change awareness, understanding, causes, mitigation measures, and practices among residents of the Kathmandu Valley through a mixed-method approach. Quantitative surveys with 433 respondents and four Focus Group Discussions (FGDs) are conducted with diverse demographics. Descriptive statistics is used to summarize quantitative data, and the chi-square (χ2) test is used to measure the associations between awareness, understanding, causes, mitigation measures, and practices among various demographics. The analysis shows that respondents frequently link climate change to extreme weather events, particularly flooding, severe hot and cold waves, and changes in rain precipitation patterns. Furthermore, the respondents identify deforestation, industrialization, and fossil fuels as the primary causes, with mitigation strategies such as afforestation, recycling waste, and use of renewable energies for long-term environmental sustainability. Similarly, the survey analysis also revealed that greenhouse gases like carbon dioxide and methane are major drivers of climate change; individuals, industries, and governments are held accountable for climate change with industries as key polluters. Furthermore, individuals are self-aware to adopt sustainable practices, and the government can play a vital role through policies promoting renewable energy, afforestation, and waste management, alongside raising awareness. Other highlights of the analysis have been raising voices of collective action at all levels, which is crucial to mitigate the impact of climate change. The study also addresses the gaps in comprehensive climate literacy and underscores the need for targeted educational initiatives to foster informed climate actions within the community. Likewise, the study brings the findings that policymakers should prioritize inclusive engagement strategies, ensuring that climate policies and adaptation programs are accessible, particularly to those who are less represented in environmental discourse, such as older adults and unschooled individuals.

A theoretical study on the mechanism of the thermal decomposition of CF 3 C(O)OCH 2 O radical is presented for the first time. Geometry optimization and frequency calculations were performed at the MPWB1K/6–31 + G(d, p) level of theory and energetic information further refined by calculating the energy of the species using G2(MP2) theory. Three plausible decomposition pathways including α-ester rearrangement, reaction with O 2 and thermal decomposition (C–O bond scission) were considered in detail. Our results reveal that reaction with O 2 is the dominant path for the decomposition of CF 3 C(O)OCH 2 O radical in the atmosphere, involving the lowest energy barrier, which is in accord with experimental findings. Our theoretical results also suggest that α-ester rearrangement leading to the formation of trifluoroacetic acid TFA makes a negligible contribution to decomposition of the title alkoxy radical. The thermal rate constants for the above decomposition pathways were evaluated using canonical transition state theory (CTST) at 298 K.

A theoretical study on the mechanism of the thermal decomposition of CF(3)C(O)OCH(2)O radical is presented for the first time. Geometry optimization and frequency calculations were performed at the MPWB1K/6-31 + G(d, p) level of theory and energetic information further refined by calculating the energy of the species using G2(MP2) theory. Three plausible decomposition pathways including α-ester rearrangement, reaction with O(2) and thermal decomposition (C-O bond scission) were considered in detail. Our results reveal that reaction with O(2) is the dominant path for the decomposition of CF(3)C(O)OCH(2)O radical in the atmosphere, involving the lowest energy barrier, which is in accord with experimental findings. Our theoretical results also suggest that α-ester rearrangement leading to the formation of trifluoroacetic acid TFA makes a negligible contribution to decomposition of the title alkoxy radical. The thermal rate constants for the above decomposition pathways were evaluated using canonical transition state theory (CTST) at 298 K.

To understand the atmospheric chemistry of hydrofluoroethers, we have studied the oxidation of a highly fluorinated compound n-C 2 F 5 CF(OCH 3 )CF(CF 3 ) 2 (HFE-7300) by OH/Cl oxidants. Here, we have employed M06-2X functional along with a 6-31 + G(d,p) basis set to obtain the optimized structures, various forms of energies, and different modes of frequencies for all species. We have characterized energies of all species on the potential energy surface, and it indicates that H-abstraction from n-C 2 F 5 CF(OCH 3 )CF(CF 3 ) 2 by Cl atom is kinetically more dominant than the H-abstraction reaction initiated by OH radical. In contrast, the calculated energy change (Δ r H° 298 and Δ r G° 298 ) results govern that OH-initiated H-abstraction reaction is highly exothermic and spontaneous compared to the Cl-initiated H-abstraction reaction. Rate constants are estimated using transition state theory as well as canonical variation transition state theory at the temperature range 200–1000 K and 1 atm pressure. The calculated rate constants of the H-abstraction channels are found to be in good agreement with the reported experimental rate constant at 298 K. Moreover, we have estimated the atmospheric lifetimes of HFE-7300 for the reaction with OH radical and Cl atom and are found to be 1.75 and 153.93 years, respectively. Additionally, the global warming potentials for HFE-7300 molecule are also estimated for 20-, 100-, and 500-year time horizons. Further, subsequent aerial oxidation of product radical (n-C 2 F 5 CF(OCH 2 )CF(CF 3 ) 2 ) in the presence of NO radical is performed, and it produced alkoxy radical via formation of peroxy radical. This alkoxy radical undergoes unimolecular decompositions via two different ways and formed n-C 2 F 5 CF(OCHO)CF(CF 3 ) 2 and n-C 2 F 5 CF(OH) CF(CF 3 ) 2 products.