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A reliable deep ensemble hybrid model for urban air quality health index forecasting in maritime Canada
No full textAccurate Air Quality Health Index (AQHI) forecasting is crucial for safeguarding public health and informing policy decisions in coastal urban regions of Maritime Canada. This study introduces a graph-enhanced deep ensemble model that integrates Robust Empirical Mode Decomposition (REMD), Deep Ensemble Random Vector Functional Link (DeepERVFL), graph-based feature selection, and Borda Count multi-criteria decision making for multi-weekly AQHI forecasting. Forecast uncertainty is quantified using bootstrap resampling to ensure confidence in the results. Benchmarking against Recursive LSTM and Histogram-Based Gradient Boosting Ensemble (HBGBE) models shows the superior performance of the REMD-DeepERVFL framework, with BORDA scores of 0.940 (T+1) and 1.06 (T+3) in Halifax, 0.797 (T+3) in Charlottetown, and 0.931 (T+3) in St. John's. The framework supports air-quality early warning systems, public health communication, and climate-health monitoring, offering timely and reliable information. This hybrid approach provides a robust, scalable, and uncertainty-aware solution for regional AQHI forecasting in Atlantic Canada
Reflection on Community-Diversified Influence Maximization in Social Networks
No full textTo celebrate the 50th Anniversary of the Information Systems Journal, we are delighted to share our research reflections on the article “Community-diversified influence maximization in social networks” published at Information Systems in 2020. Our reflections will highlight the impact of this article on the authors’ research trajectories, its influence on the broader research community, and its contributions to industry practice
Hydrologic Variability Drives Differential Methane Dynamics in Agricultural Reservoirs of the Northern Great Plains
No full textClimate variability can regulate aquatic methane fluxes as increasing temperatures can elevate microbial metabolic rates, including methanogenesis. It is less well known how climate‐induced variability in seasonal precipitation and runoff might affect methane concentrations and fluxes in aquatic ecosystems. Here, we measured seasonal methane concentrations and calculated diffusive fluxes from 20 agricultural reservoirs in the northern Great Plains in contrasting wet and dry summers. Relative to the dry year, water column depths increased 65% (from 1.7 to 2.6 m) in the wet year and was associated with stronger stratification and increased anoxia at depth. Solute concentrations also declined during the wet year, with sulfate concentrations less than half that observed in the dry year (645 mg SO4 2− L− 1 vs. 1620 mg SO4 2− L− 1). Together, the more profound anoxia combined with lower sulfate concentrations resulted in significantly higher hypolimnetic CH4 concentrations in the wet year (40.3 μM) compared with the dry year (18.1 μM), particularly in August (30‐fold higher). Despite these patterns, surface CH4 concentrations and estimated diffusive emissions did not significantly increase in wet summers (1.13 μM and 2.31 mmol m− 2 yr− 1 ) relative to dry summers (3.78 μM and 5.71 mmol m− 2 yr− 1 ), likely owing to offsetting mechanisms of increased CH4 storage and oxidation through the deeper water column. Climate‐driven changes in precipitation and runoff are expected to modify the physical factors controlling methanogenesis and methanotrophy. Our findings show corresponding minimal effects on diffusive fluxes of methane, but future studies should also address ebullition and seasonal turnover to capture the full CH4 budget of inland waters
Eternal, Natural, Human, Divine: A Theological Perspective on the Types of Law
No full textThomas Aquinas’s distinction between four kinds of law—eternal, natural, human and divine—has become a staple of Western jurisprudence. This chapter explicates the distinction between these categories of law, beginning with and building upon Aquinas’s analysis. In so doing, it emphasises the essential role of theological doctrines in fully grasping the relationships between the categories. Aquinas’s exposition of the four kinds of law assumes a distinction between divine and human perspectives on law, as well as a specific conception of the sources and limits of the human capacity for reliable legal knowledge, and God’s role in making this knowledge possible. These theological dimensions of Aquinas’s understanding of law have often been obscured in more recent jurisprudential discussions, but they are essential for grasping some of the subtle and enduring insights in his taxonomy
Nitrogen Anchoring Effect Triggering Improved Reversible Hydrogen Storage of Mg(BH4)2
No full textThis research uncovered the influence of nitrogen-doped MXene Ti3CN on the reversible hydrogen storage properties of Mg(BH4)2. The Mg(BH4)2-20Ti3CN composite began releasing hydrogen at a temperature of 80°C. In addition, the composite discharged over 8.8 wt% hydrogen at a comparatively lower temperature of 270°C, whereas the pure Mg(BH4)2 emitted merely 5.9 wt% hydrogen under identical circumstances, which exhibited remarkably enhanced dehydrogenation kinetics. Furthermore, upon completion of four cycles, Mg(BH4)2-20Ti3CN retained a reversible hydrogen storage capacity of 4.5 wt%, representing an 80% improvement over the undoped Mg(BH4)2. In the course of the dehydrogenation phase, the boron atoms in Mg(BH4)2 were anchored by nitrogen atoms in Ti3CN to form B-N bonds, which helped suppress the formation of MgB12H12. During the subsequent rehydrogenation process, the B-N bonds broke, and the boron atoms reparticipated in the reversible transformation into [BH4]− clusters. Meanwhile, the formation of Ti0 by the reaction of Mg(BH4)2 with Ti3CN weakened the B-H bond energy, and the layered structure provided an effective way for hydrogen spillover. These factors collectively improved the reversible hydrogen storage capabilities of Mg(BH4)2
Predicting non-mixing river flow using data-driven approaches: A case study in the Menindee region in Australia
No full textThermal stratification is a significant phenomenon driven by complex interconnected factors that reduce mixing between the top and the bottom layers, impacting oxygen and nutrient dispersion and potentially causing fish mortality. This study has assessed the non-mixing occurrence in river flow using a classification approach, developing a novel hybrid data-driven model from key datasets of the Darling River in Menindee, Australia. The influence of various input variables using the proposed model is investigated, including meteorological drivers, hydrological factors, key data generated by the one-dimensional process-based model named LAKEoneD, and Physics-Informed Neural Networks. The study also considers stratification indices based on the Schmidt stability and empirical river mixing criteria. Supervised machine learning methods were used to classify a given day as mixing or non-mixing conditions. The results showed that the proposed hybrid model integrating Support Vector Machines with key data generated by LAKEoneD outperformed benchmarking models. The study also employed explainable artificial intelligence analysis, suggesting that the minimum air temperature and relative humidity, as the model inputs, played a role in predicting non-mixing river flow conditions. Importantly, the maximum air temperature was another potential input that affected the river flow system, particularly near a fish death event. We conclude that the proposed model can be used as a scientific stratagem for future research in predicting fish and other aquatic organism health related to river flow dynamics, which has implications for environmental authorities guiding better water quality management in river systems
Demand for weather index insurance: evidence from Indian cotton farmers
No full textPurpose
Adverse climatic conditions have significantly impacted agricultural production in India. To manage climate risk, weather index insurance (WII) is emerging as a useful tool to transfer risk. However, despite the potential advantages, the adoption of weather-based insurance remains low. Thus, this study aims to examine the factors influencing farmers’ willingness to pay (WTP) for WII.
Design/methodology/approach
A cross-sectional survey was conducted among 350 cotton farmers in the Virudhunagar district, selected through a multistage sampling approach, incorporating simple random sampling. Data were collected through personal interviews using a pre-tested questionnaire. The logit model is used to analyse how socioeconomic variables, risk aversion (determined using the multiple price list experiment) and risk perception (analysed using a risk matrix) affect weather insurance adoption.
Findings
Results show that 65% of the surveyed households are willing to participate in WII. Risk aversion and risk perception are positively and significantly associated with insurance uptake. Education, annual income, access to credit and weather-related yield loss also have a significant positive correlation with WTP. However, gender, family size, and access to irrigation are negatively associated with WTP.
Research limitations/implications
Future studies could be enhanced by including more diverse farm areas and considering other influential factors.
Originality/value
These findings can assist insurance companies and policymakers in understanding farmers’ risk behaviour and promoting the adoption of WII to improve farmers’ climate resilience
Assumptions of the tertiary education sector for teaching First Nations health courses
No full textAboriginal and Torres Strait Islander health courses develop the cultural capacity and capability of undergraduate health professionals; however, the expectations of tertiary education institutions that all First Nations academics can successfully deliver this specialised area of knowledge, and develop these capabilities in undergraduates may be ill-conceived. Aboriginal and Torres Strait Islander health courses study the truths of colonisation; the oppression, the massacres and the governmental policies which resulted in The Stolen Generations as well as the ongoing trauma and health concerns resulting from colonisation practices. The very areas being discussed in these health courses are also the history and reality for the First Nations academics tasked with teaching this content. Recognising and understanding some of the issues of having First Nations academics teaching Aboriginal and Torres Strait Islander health courses is an important first step to ensuring no further trauma is inflicted upon these academics
10th International Conference on Information and Communication Technology for Intelligent Systems (ICTIS 2025)
No full textChest radiography is a technique based on medical imaging that is employed to detect thoracic diseases. In this paper, we designed an intelligent method to diagnose thorax disease from chest X-ray (CXR) images. A novel empirical curvelet transform, coupled with a deep learning model, is proposed. The collected images are analysed using the proposed empirical curvelet transform (ECT) model. Then, the outputs of ECT model are sent to DenseNet. The proposed model is assessed using several statistical metrics. The proposed model achieves an accuracy of 98%. The results demonstrated the ability of the proposed model to detect Thoracic Disease
Effects of work-from-home (WFH)/hybrid work on well-being, work performance, and work engagement in architectural, engineering, and construction industry
No full textPurpose
The effect of work-from-home (WFH) on human working factors in architectural, engineering and construction (AEC) industry has been unexplored. The work aims to address the existing research gaps and analyze the effects of hybrid work patterns on work engagement, work performance and various well-being factors. This study seeks to determine the best current work arrangement while providing valuable insights and trends.
Design/methodology/approach
This study deploys a survey of 220 AEC sample population on both work patterns using anonymous online questionnaires. The questions were established and modified based on International Physical Activity Questionnaire (IPAQ), Depression Anxiety Stress Scale (DASS 21), the Utrecht Work Engagement Scale (UWES-9) and the Individual Work Performance Questionnaire (IWPQ).
Findings
While WFH workers currently work 1.38 days per week, they prefer 2.27 days. Hybrid workers show slightly higher engagement, but increasing WFH for on-site employees reduces performance from 2 to 13%. Work performance remains stable, suggesting that maintaining current arrangement can optimize employees’ productivity.
Research limitations/implications
The findings facilitate organizations in the AEC sector to optimize work patterns for enhancing employees’ productivity, satisfaction and overall workplace effectiveness regarding Sociotechnical Systems Theory. Results can advocate their organizational HR policy and standard establishment.
Originality/value
The novelty of this work is to explore the optimal WFH rate and work pattern impacts in the AEC industry for the first time. The work offers insights into WFH work engagement, performance and well-being