University of Regina

While groundwater is commonly perceived as safe, the excessive presence of trace metals, particularly arsenic (As), can pose significant health hazards. This review examines the current scenario of pollutants and their mitigations focusing on As contamination in groundwater across multiple nations, with a specific emphasis on the Indian Peninsula. Arsenic pollution surpasses the WHO limit of 10 ppb in 107 countries, impacting around 230 million people worldwide, with a substantial portion in Asia, including 20 states and four union territories in India. Analysis of the correlation between the aquifer and arsenic poisoning highlights severe contamination in groundwater originating from loose sedimentary aquifer strata, particularly in recently formed mountain ranges with geological sources presumed to contribute over 90% of arsenic pollution, i.e. a big environmental challenge. A myriad of techniques, including chromatographic, electrochemical, biological, spectroscopic, and colorimetric methods among others, are available for the detection and removal of arsenic from groundwater. Removal strategies encompass a wide array of approaches such as bioremediation, adsorption, coagulation/flocculation, ion exchange, biological processes, membrane treatment, and oxidation techniques specifically tailored for affected areas. Constructed wetlands help to eliminate heavy metal impurities such as As, Zn, Cd, Cu, Ni, Fe, and Cr. Their efficiency is influenced by design and environmental factors. Nanotechnology and nanoparticles have recently been studied to remove arsenic and toxic metal ions from water. Cost-effective solutions including community-based mitigation initiatives, alongside policy and regulatory frameworks addressing arsenic contamination, are essential considerations.

This study outlines a comprehensive process design utilising glycerol‐steam reforming for an H2‐enriched gas stream, coupled with carbon dioxide removal via a chemical absorption system, followed by a techno‐economic analysis. The Aspen Plus economic analyser assesses the developed model, incorporating simulation results and literature data. Initially, the CO2 capture unit was planned with a standalone absorber and stripper, later integrated for solvent makeup calculation. Findings reveal that as catalyst loading increased from 5 to 50 kg, glycerol conversion and product molar fraction improved. For a targeted H2 production of 10 t/day, optimal reactor dimensions are 3.2 m diameter and 30 m length, corresponding to a reactant flow of 105 t/day and a 2.52 MW heat duty at stoichiometry conditions. To capture 95% CO2 from the reformed product stream, absorber and stripper packing heights of 12 and 7 m, respectively, with column diameters of 1.25 and 2.71 m are necessary. The production cost of H2 is determined to be $3.8 per kg, as revealed by the techno‐economic analysis. Calculated values for net present value, discounted payback period, and internal rate of return stand at$30 million, 5 years, and 25.0%, respectively. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

This study focused on mitigating wetland flood disasters in Bangladesh through community-led strategies, particularly in land-based minority communities. Wetland ecosystems, integral to the country’s landscape, are increasingly vulnerable to floods exacerbated by climate change. Recognising the intersectionality of environmental challenges and community well-being led to proactively addressing the impacts of wetland floods. This study uses participatory methods to engage minority communities, particularly those in the wetland regions. Focusing on local community-engaged approaches, the research aims to develop community-led adaptive strategies. The study emphasises the active participation of community members in decision-making processes through a community-led approach, enhancing resilience and sustainability. The study also explores the role of women in these community-led initiatives, acknowledging their unique perspectives and contributions to adaptive strategies. Ultimately, the findings aspire to inform policy frameworks and global discourse on disaster resilience, offering insights into how community-led strategies can serve as effective models in mitigating the impact of wetland flood disasters and foster a sense of hope and optimism for the future.

Objective: Although associations between posttraumatic stress disorder (PTSD) and sleep disturbance are well-established, relatively little work has examined mechanisms that may underlie this association. This study aimed to examine the explanatory role of emotion regulation difficulties and intolerance of uncertainty (IU) in associations between PTSD symptoms and sleep disturbance among a sample of war-exposed Iraqi individuals. Method: We used structural equation modeling in a war-exposed nonclinical sample (N = 617; Mage = 27.63; SD = 4.81; 46.03% females) to model indirect effects from PTSD symptoms to the sleep disturbance via emotion regulation difficulties and IU. Participants completed PTSD symptoms, sleep disturbance, difficulties in emotion regulation, and IU scales. Results: Significant correlations were found between PTSD symptoms and sleep disturbance. Those who reported higher levels of PTSD symptoms also reported higher levels of sleep disturbance. The structural model was an excellent fit to the data (χ² = 166.03; degrees of freedom = 32; comparative fit index = .960; goodness-of-fit index = .954; Tucker–Lewis index = .943; root-mean-square error of approximation = .082), and all hypothesized indirect effects were significant (ps < .001). Conclusion: Findings add to the emerging body of literature examining potential mechanisms that may help to explain the maintenance or even escalation of PTSD-related sleep disturbance. Findings have clinical implications in designing specialized treatments for individuals with PTSD and suggest focusing on emotion regulation difficulties and IU as potential therapeutic targets that putatively underlie PTSD-related sleep disturbance.

Background and objectives End-of-life care supports individuals in the last few weeks or months of their life and their caregivers, offering psychosocial support, symptom management and relief, and resources. While some of the first public end-of-life care facilities were established due to HIV/AIDS, the current needs of caregivers for people living with end-stage HIV are not well understood. Caregivers provide two-thirds of the care for people living with HIV, yet their specific support needs and experiences are under-researched. Existing strategies often use a “one-size-fits-all” approach, which may not address the unique challenges faced by these caregivers, such as stigma and lack of social support. This study aims to synthesize the literature on the end-of-life care experiences and needs of caregivers for individuals living with HIV. Research design and methods A scoping review, guided by Arksey and O’Malley’s framework and the Joanna Briggs Institute’s recommendations, will be conducted. An Information Specialist will assist in developing a search strategy to be applied across databases like Medline, Embase, PsycINFO, and PubMed. Search results from each database will be imported into Covidence software for duplicate removal and title and abstract screening. Two researchers will independently screen studies using the ‘Population–Concept–Context’ (PCC) framework, with screening conducted at two levels: title and abstract, and full-text. The inclusion criteria will be piloted on a random sample of articles to ensure inter-rater agreement (kappa statistic >0.61). Disagreements will be resolved through discussion or with the involvement of a content expert if needed. Final selections will be reported using the PRISMA flow diagram, and reasons for exclusion will be documented. Discussion and implications The findings from this scoping review will provide valuable insights into the end-of-life care experiences and support needs of caregivers for individuals living with HIV. By identifying common themes and challenges, such as caregiver fatigue, emotional strain, stigma, and lack of social support, this study will underscore the inadequacy of the current “one-size-fits-all” approach in addressing the unique needs of these caregivers. This research has the potential to influence both clinical practice and policy by advocating for more personalized support strategies within end-of-life care settings.

In the context of sport events, several stakeholders’ reputations could be impacted by critical incidents, including event organizers, athletes, teams, countries represented by athletes, and sponsors. The purposes of this study were to develop an understanding of (a) how an event organizer, media, and the public framed a critical incident in a rhetorical arena and (b) how frames were connected with the reputations of event stakeholders immediately following a critical incident. A three-phase approach was employed that involved collecting and analyzing data from X/Twitter about a bus crash at the 2013 Tour de France. The critical incident was framed in nine different ways, many of which were emergent. Findings demonstrated that critical incidents at a sport event are interpreted and framed in multiple ways and can have an impact on the reputations of the event and other event stakeholders.

Goal models of the i* family have been shown to be suitable for concisely representing and analyzing goal variability. In standard i*, goal variability emerges due to the presence of OR-refinements, which describe alternative ways by which stakeholder goals can be fulfilled. On closer inspection, however, variability exists in goal models beyond what can be represented by OR-refinements. Firstly, given one set of tasks that fulfill the root goals, there are many alternative orderings by which those tasks can be performed. Secondly, tasks can have many alternative outcomes. In the past, we have proposed various approaches for analyzing these two types of variability, each accompanied by an extension to the i* notation that allows translation of the visual goal model into a formal specification suitable for automated identification of goal-fulfilling alternatives. In this chapter, we propose a consolidation of these extensions into iStar T, a unified extension to the current iStar 2.0 modeling language. iStar T proposes a minimum set of added modeling constructs and a core set of rules that allow translation of models into specifications usable by different reasoning systems. We describe the extensions, and, as examples, we sketch the rules to translate into Hierarchical Task Network and Golog specifications and the kinds of automated analysis that can be done with the result.

Malgré les demandes croissantes pour l’élaboration de politiques respectueuses des données probantes, les stratèges et décideurs des gouvernements municipaux se heurtent souvent à des obstacles pour les intégrer à leurs travaux. Ces obstacles peuvent être particulièrement marqués dans les villes de taille petite à moyenne, dont les ressources sont souvent comparativement limitées par rapport à celles des plus grandes villes qui sont au cœur d'une grande partie des publications existantes. Les auteurs ont interviewé 30 acteurs municipaux qui participent à la planification urbaine de la Ville de Regina — le gouvernement municipal représentant la ville moyenne de Regina située dans l'Ouest canadien. L’étude a révélé que les données probantes ont une importance différente en fonction des stratèges et des décideurs. Les interviewés avaient diverses définitions des données probantes, avaient accédé à des sources de données probantes différentes, accordaient une confiance variable à certaines formes de données probantes et affrontaient divers obstacles à l'intégration des données probantes à leur travail quotidien. Le présent article contribue à mieux comprendre le rôle des données probantes chez les planificateurs urbains et contient des leçons importantes pour corriger l’écart entre les stratèges et décideurs en planification urbaine et les producteurs de données probantes.

Using works of T. Ando and L. Gurvits, the well-known theorem of P.R. Halmos concerning the existence of unitary dilations for contractive linear operators acting on Hilbert spaces is recast as a result for d -tuples of contractive Hilbert space operators satisfying a certain matrix-positivity condition. Such operator d -tuples satisfying this matrix-positivity condition are called, herein, Toeplitz-contractive, and a characterisation of the Toeplitz-contractivity condition is presented. The matrix-positivity condition leads to definitions of new distance-measures in several variable operator theory, generalising the notions of norm, numerical radius, and spectral radius to d -tuples of operators (commuting, for the spectral radius) in what appears to be a novel, asymmetric way. Toeplitz contractive operators form a noncommutative convex set, and a scaling constant $c_d$ c d for inclusions of the minimal and maximal matrix convex sets determined by a stretching of the unit circle $S^1$ S 1 across d complex dimensions is shown to exist.

Accurate prediction of solubility of polymers in solvents a priori is highly desirable in practice. To this end, the Flory-Huggins interaction parameter χ is commonly used and molecular dynamics simulation, a powerful computational tool, has been used for such a purpose. To calculate χ, there exist three possible strategies using molecular dynamics simulation. One is through the calculation of Hildebrand solubility parameters of the pure components while the other two are to calculate the enthalpy of solvation and Gibbs free energy of solvation for the solution, respectively. This study evaluated these three strategies using binary solutions containing a hydrophobic or hydrophilic polymer (polyisobutylene, polystyrene, cis and trans polybutadiene, cis and trans polyisoprene, poly(ethylene oxide), and polyacrylamide) and an aliphatic solvent-cyclohexane. We found that χ determined via solubility parameters predicted the solubility trend but deviated significantly from experimental values. On the other hand, the enthalpy of solvation approach provided the most accurate χ values, compared to experiment, at a reasonable computational demand, especially for hydrocarbon polymers, while the Gibbs free energy of solvation approach, though more computationally intensive, did not significantly improve χ from the enthalpy of solvation approach. In particular, the Gibbs free energy of solvation approach overestimated χ for non-polar polymers. A conformational analysis of the solvated polymers revealed that all polymers collapsed in cyclohexane with polyethylene oxide and polyacrylamide collapsed the most as expected. For the two polar polymers used, the collapse was evidenced by abrupt changes in radius of gyration (Rg) and solvent accessible surface area (SASA) in the early stage of molecular dynamics simulation trajectories, and plateauing at much lower final values. Conversely, the hydrocarbon polymers exhibited minimal deviation from the expected Rg and barely any change in SASA with time. Our findings demonstrated that there exist differences in the accuracy and computational resources used when different molecular dynamics simulation strategies are used in the determination of χ.

Achieving the dual goals of improving water quality and reducing carbon emissions requires a systematic study of the combined effects of economic and environmental policies on industrial systems. A CGE-IWCR model is developed to examine the long-term evolution of industrial economic and environmental responses in the Yangtze River Economic Belt (YREB) under varying levels of carbon and water pollution taxes. The CGE-IWCE model offers several advantages: i) under the dual tax (carbon and water pollution tax) interventions, it can effectively forecast industrial CO2 and water pollution emissions driven by both macro and local factors from 2025 to 2060; ii) it quantitatively captures the interactions between various policy interventions, thereby providing guidance for comprehensive regional policy formulation. It is discovered that in the double-tax scenario, various combinations of carbon and water pollution taxes significantly impact direct carbon emissions and direct water pollution emissions, and from local consumption, imports, and exports in industrial sectors. Among them, the dual pressure of high carbon and water pollution taxes, which significantly raise production costs. In addition, increasing the carbon tax from 10 ¥/tonne to 50 ¥/tonne, the water pollution equivalent (WPE) reduction rate rises from 20.79% to 52.67% with the increase in the carbon tax from 2050 to 2060, when the water pollution tax is low. Between 2025 and 2060, the carbon tax plays a significant role in influencing the total industrial output change rate in YREB. from 2025 to 2060, the water pollution tax will play a more significant role in CO2 reduction in YREB’s industrial sector. The results will offer decision-making support for water pollution reduction and carbon mitigation in the YREB’s industrial sector, quantitatively identify the interactions between the dual taxes, and provide new insights for analyzing the impacts of pollution reduction and carbon mitigation policies.

Overconfidence plays a role in a large number of individual decision biases and has been considered a ‘meta-bias’ for this reason. However, since overconfidence is measured behaviorally with respect to particular tasks (in which performance varies across individuals), it is unclear whether people generally vary in terms of their general overconfidence. We investigated this issue using a novel measure: the Generalized Overconfidence Task (GOT). The GOT is a difficult perception test that asks participants to identify objects in fuzzy (‘adversarial’) images. Critically, participants’ estimated performance on the task is not related to their actual performance. Instead, variation in estimated performance, we argue, arises from generalized overconfidence, that is, people indicating a cognitive skill for which they have no basis. In a series of studies (total N = 1,293), the GOT was more predictive when looking at a broad range of behavioral outcomes than two other overestimation tasks (cognitive and numeracy) and did not display substantial overlap with conceptually related measures (Studies 1a and 1b). In Studies 2a and 2b, the GOT showed superior reliability in a test–retest design compared to the other overconfidence measures (i.e., cognitive and numeracy measures), particularly when collecting confidence ratings after each image and an estimated performance score. Finally, the GOT is a strong predictor of a host of behavioral outcomes, including conspiracy beliefs, bullshit receptivity, overclaiming, and the ability to discern news headlines.

The impacts of extreme events are seldom caused by a single climatic variable but rather arise from the interaction of multiple climate drivers. This study employs observational data sets with high spatiotemporal resolution to analyze the risk of occurrence of compound dry‐hot events in China over the past 120 years (i.e., 1901–2020). Simultaneously, attribution analysis based on distribution functions explores whether and to what extent human activities influence the occurrence of compound events. The results indicate that over the historical 120‐year period, the frequency of compound dry‐hot events in China has gradually increased, with the highest frequency observed in the most recent 40 years (i.e., 1981–2020). The frequency of compound dry‐hot events during this period is approximately four times that of 1901–1940 and about twice that of 1941–1980. The analysis of the relative importance of different factors reveals that temperature changes contribute more (56%) to the occurrence of compound events than precipitation (23%), and also exceed the interaction between them (21%). The substantial increase in compound dry‐hot events is largely attributed to the influence of human activities. Across seven sub‐regions, human activities have led to an increase in the probability of compound events occurring, ranging from 7.9% to 31.6%. The findings of this study indicate that human activities have significant implications for explaining the observed increase in compound hot and dry events over the past 40 years.

The synergistic management of energy-water‑carbon (EWC) nexus systems is crucial for achieving the sustainable development goals (SDGs). Therefore, a non-deterministic interval chance-constrained fractional optimization model for EWC nexus system (ICCF-EWC) management has been developed in this study. This model is capable of handling uncertain parameters represented as stochastic probability distributions and interval values, providing an effective approach to addressing dual-objective optimization problems. Meanwhile, this model is expected to investigate the effect of water scarcity/carbon abatement pressure on the overall system, and potential synergistic abatement effects. A case study of Shanxi Province shows that over the next 30 years, the cumulative installed capacity for clean and renewable energy will exceed 65 %. The dominance of coal-fired electricity will be considerably diminished, with wind power overtaking coal/gas-fired power by around 30 %. Moreover, water scarcity and carbon mitigation pressure would promote the development of electricity conversion mode to clean energy rather than the large-scale carbon capture and storage (CCS) technology upgradation of thermal power. The results can help support low-carbon transition of power systems at a province-level in China and financial incentives related policy making to advance water conservation and carbon emission mitigation. The developed model can also be adapted to other energy resource-dependent regional power systems.