Strategic and regional assessments (SAs/RAs) can inform and influence subsequent impact assessments (IAs) in various ways. In Canada, IA systems that include SA/RA provisions often require their downstream consideration, but leave it to IA professionals to determine how, and to what extent, they should be used in that context. While previous research has investigated perspectives about the goals and utility of SAs/RAs in project IAs, there has been no investigation of the basis for, and origins of, these views. Drawing on experiential learning theory, this study undertook statistical analyses of data from recent surveys of IA regulators and practitioners in Canada to investigate whether different forms of IA experience and knowledge help explain these perspectives. Its findings indicate that amount (years) of IA experience was primarily associated with the view that SAs/RAs should improve project IA efficiency through government scoping decisions. Respondents' self-rated knowledge of SAs/RAs was often associated with perspectives regarding their broader role in improving the quality, effectiveness and efficiency of subsequent project IA scoping and conduct. These findings provide initial evidence that experiential learning can represent one mechanism through which IA professionals develop perspectives on the downstream role of these assessments in practice, while also highlighting that broader SA/RA knowledge may be especially influential in that regard.
The continued reliance on fossil fuels for energy production has led to increasing emissions of greenhouse gases (GHGs), particularly carbon dioxide (CO2), contributing significantly to global climate change. KOH-treated activated carbon has emerged as a promising adsorbent for gas separation and CO2 capture owing to its high surface area, well-developed microporosity, enhanced surface functionality, favorable adsorption kinetics, and cost-effectiveness. However, conventional Fick’s Law-based diffusion models generally assume constant diffusivity and may not adequately represent the dynamic transport behavior occurring during adsorption processes.To address this limitation, an extended Fick’s law (EFL) framework is developed in which diffusivity is treated as a function of adsorption rate, time, and spatial position. The resulting partial differential equation is solved using the Crank-Nicolson numerical method to obtain accurate predictions of gas adsorption behavior. In parallel, several machine learning (ML) approaches, including Gradient Boosting Regression (GBR), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Deep Wavelet Neural Networks (DWNN), are employed as complementary predictive tools and benchmark models.The novelty of the proposed framework lies in combining a physics-based diffusion model with advanced data-driven techniques while incorporating dynamic diffusivity effects into the mass-transfer formulation. The developed model is validated against experimental adsorption data for CO2 and CH4 on KOH-treated activated carbon under different temperature and pressure conditions. The results demonstrate excellent agreement between model predictions and experimental observations, confirming the capability of the EFL model to accurately capture adsorption dynamics. The ML models also provide reliable predictions and reveal important nonlinear relationships among operating variables, enabling a comprehensive assessment of adsorption performance.A systematic sensitivity analysis is conducted to investigate the effects of temperature, pressure, and adsorbent type on adsorption behavior. The proposed framework provides valuable insights for adsorbent design, process optimization, and gas separation technologies. These findings contribute to the development of more efficient carbon capture systems and offer broader applications in energy, environmental, and chemical engineering processes requiring selective gas adsorption and separation.
Strategic environmental assessments (SEAs) and regional assessments (RAs) are often seen as more effective means of assessing and managing cumulative effects compared to project-level impact assessments (IAs) alone. Inevitable uncertainty about future development activities during these assessments is, however, sometimes treated as a constraint – rather than an opportunity - which can affect whether, how and the degree to which cumulative effects are being addressed. Drawing on several recent Canadian SEA and RA examples, this article illustrates and investigates these matters, including the at times limited analysis of cumulative effects in these assessments, associated attempts to have these issues pushed to later assessment tiers, and a resulting lack of planning outputs and influence in practice. The cases illustrate that uncertainty about future development and limitations in data, time and resources are often raised as key reasons for this, reflecting a continued emphasis on effects prediction rather than on achieving a type and level of analysis appropriate to the scale and timing of these assessments, and importantly, capable of informing broader planning outcomes. Case studies also suggest that the latter may be constrained by a lack of institutional willingness to develop or use planning outputs that may affect future development based on potential cumulative effects.
Purpose Fifth metacarpal neck fractures (5MCNFs) comprise up to 18.4% of all hand fractures. Clinical decision making is influenced by radiographic parameters, specifically apex dorsal angulation. Although sagittal computed tomography (CT) and lateral radiographs provide the truest views of the sagittal metacarpal, routine CT is not feasible, and metacarpal overlap on lateral radiograph presents a challenge for reliable measurement. Thus, oblique hand radiographs may be a valuable alternative, providing an isolated view of the fifth metacarpal. This study assesses whether oblique hand radiographs are a reliable and valid method of measuring 5MCNF angulation. Methods A retrospective cohort of patients aged ≥18 years treated nonsurgically for 5MCNFs were recruited from three centers in Calgary. All patients had healed malunions. Radiographs and CT scans were obtained at one clinical visit at least 1-year postinjury. Three physicians measured fracture angulation on oblique hand radiographs and sagittal CT. Results In total, 24 patients with malunited 5MCNFs were identified. The average patient age was 40.3 ± 13.3 years and patients were seen on average 2.97 ± 1.70 years postinjury. All patients were treated nonsurgically, and 91.7% were immobilized in a cast or a splint. The average apex dorsal angulation measurement was 42.6° ± 8.55° on oblique hand radiograph and 42.6° ± 8.86° on sagittal CT (P = .825). The interrater intraclass correlation coefficient was 0.795 (95% CI, 0.353–1.000) on oblique hand radiograph and 0.784 (95% CI, 0.414–1.000) on sagittal CT. The mean absolute interobserver difference in angulation measurement was 4.09° ± 2.90° on oblique hand radiograph and 4.66° ± 2.51° on sagittal CT. The intermodal intraclass correlation coefficients was 0.877 (95% CI, 0.659–1.000). The mean absolute angular error was 3.15° ± 1.82°. Bland–Altman analysis demonstrated no proportional bias. Conclusions Oblique hand radiographs demonstrate good reliability and high validity as a modality for the measurement of 5MCNF angulation. This study provides radiographic guidance for the assessment of 5MCNFs. Type of study/level of evidence Retrospective reliability study III.
Triboelectric nanogenerators (TENGs) are versatile energy-harvesting devices that convert mechanical motion (often low-frequency) into electricity through contact-separation, or sliding modes. They are increasingly explored for renewable energy harvesting in wind-, wave-, and other fluid-driven environments. Effective optimization of TENGs necessitates consideration of mechanical motion, electrostatic charge transfer, and, particularly in fluid-driven systems, complex flow behaviors. Numerical simulation is indispensable for device design; however, most of the existing reviews address these domains in isolation and often neglect the relationship among modeling assumptions, coupling strategies, and predictive accuracy. This review presents a unified framework that integrates structural mechanics, electrostatics, computational fluid dynamics (CFD), and fluid–structure interaction (FSI). As the central concept, coupling depth, is introduced to define the degree of dynamic, bidirectional interaction among physical domains, ranging from single-physics to fully coupled multiphysics simulations. This framework facilitates systematic assessment of model fidelity, computational cost, and reproducibility. The review further examines the impact of modeling choices on simulation outcomes, identifies reproducibility gaps arising from incomplete reporting and implicit assumptions, and proposes a modeling roadmap that emphasizes reduced-order models, machine-learning surrogates, and digital twin technologies as prospective research directions.
