First-year international students face significant social challenges, including potential reductions in social support and difficulties in forming friendships. A nuanced examination of these social challenges is important because they may contribute to experiences of loneliness and social isolation, and in turn, have a negative impact on mental health and academic performance. While quantitative research has established the prevalence, predictors, and outcomes of international students’ loneliness, a qualitative approach can provide a richer, more in-depth account of their lived experiences. As such, we interviewed 15 first-year international students enrolled at a Canadian university about their loneliness and social isolation, the development and maintenance of their social networks, and the challenges they experienced when adjusting to a new cultural and academic environment. Key themes included the emotional and psychological toll of loneliness, especially during holidays, the benefits of solitude, difficulties in forming new friendships in Canada, the lack of opportunities to interact with classmates outside the classroom setting, as well as support from family, friends, and romantic partners from Canada or their heritage culture. Some participants reported feeling a lack of belonging after attending social events, realizing they did not share the same values as others. Family support (e.g., through video calling) was an important source of social support despite the lack of in-person interactions. These results may help to inform university initiatives for enhancing social support systems and assisting international students’ transition to life in Canada’s evolving sociopolitical landscape.
The performance and durability of cutting tools are strongly governed by deposition-induced microstructure, residual stress, and surface integrity. This study investigates the effects of substrate bias voltage and pulsed frequency on the microstructure, mechanical properties, and wear behavior of TiAlN coatings deposited by a magnetically assisted cathodic-arc process. Coatings were characterized by SEM, AFM, XRD, nanoindentation, and scratch testing, while cutting forces, wear evolution, and tool life were evaluated during ultra-high-speed turning of SS304 at 500 m/min. Increasing the bias voltage promoted coating densification accompanied by honeycomb-like surface features, elevated compressive residual stress, enhanced adhesion strength, and increased hardness, with the-300 V DC-biased coating achieving the highest hardness of 35.5 GPa and strongest adhesion. Increasing pulsed-bias frequency altered crystallographic texture and increased surface roughness due to intensified droplet-related features. Despite the superior hardness and adhesion of high-bias coatings, the thickest coating deposited at-70 V exhibited the longest tool life, indicating that coating thickness can dominate wear resistance under severe cutting conditions. Under low-frequency pulsed high-bias conditions, dense and uniform microstructures were obtained, leading to improved wear performance compared with coating deposited at higher pulsed frequency. Distinct trends between tangential and feed force components reflected different sensitivity to surface roughness and interfacial stress states. The results demonstrate that bias voltage and pulsed-frequency control effectively tailor the microstructure, surface integrity, and wear performance of TiAlN coatings. These findings can be used to optimize deposition conditions for high-speed machining applications.
Background International guidelines recommend sterile water for endoscopic irrigation despite limited evidence. Single-use plastic sterile water bottles generate substantial costs, greenhouse gas emissions, and microplastics. We sought to estimate whether replacing sterile water with potable tap water for endoscopic irrigation affected clinical outcomes. Methods This quasi-experimental study involved an interrupted time series design at a multi-campus tertiary care academic health system (The Ottawa Hospital, Canada). All adults undergoing esophagogastroduodenoscopy, colonoscopy, flexible sigmoidoscopy, enteroscopy, ileoscopy, or pouchoscopy between February 2024 and August 2025 were included. A system-wide switch to tap water took place on February 10, 2025. The primary outcome was a composite of emergency department visits, unplanned admissions, or deaths within 7 days post-procedure. Infectious complications within 30 days constituted secondary endpoints. Data were obtained from validated institutional databases. Segmented regression modeled the weekly outcome proportion to estimate the step change post-implementation, with a non-inferiority margin of <0.5% absolute risk increase. Findings A total of 29,522 procedures were performed on 29,423 patients (18,863 pre-implementation; 10,560 post-implementation). Patient and procedural characteristics were comparable. The weekly proportion experiencing the primary outcome was 1.6% in both phases. The post-implementation step change was −0.40% (95% confidence interval [CI] −1.16 to 0.35), with the upper boundary of the 95% CI below the pre-specified non-inferiority margin of 0.5%. Results were similar for infectious complications (step change −0.23%, 95% CI −0.75 to 0.29). Interpretation Potable tap water was non-inferior to sterile water for endoscopic irrigation with respect to clinical outcomes. These findings provide an evidence base for revising current guideline recommendations that mandate sterile water for endoscopic irrigation. Funding This project was supported by the 2024 Canadian Society of Colorectal Surgeons Research Operating Grant.
Advances in intensive care have improved survival from acute critical illness, leading to a growing population of patients with chronic critical illness (CCI), many of whom require prolonged organ support. This condition is associated with persistent catabolism, inflammation, and poor long-term outcomes. Understanding the metabolic alterations underlying this failure to recover may help identify targets for monitoring and intervention. This review synthesizes current clinical and translational evidence on metabolic dysregulation in CCI and discusses its implications for monitoring, nutritional strategies, and therapeutic interventions. We performed a narrative review informed by a structured search of PubMed/MEDLINE followed by manual reference screening. Preclinical, translational and clinical studies addressing energy metabolism, mitochondrial function, autophagy, ketone metabolism, inflammation and recovery trajectories in critical illness were qualitatively synthesized. Available evidence suggests that patients with CCI exhibit persistent metabolic inflexibility, impaired transition from catabolism to anabolism, mitochondrial dysfunction, altered autophagy, and disturbances in substrate utilization, including reduced ketogenesis. These alterations, together with epigenetic and immunometabolic reprogramming, may contribute to muscle wasting, immune dysfunction, and delayed functional recovery, features consistent with the persistent inflammation, immunosuppression and catabolism syndrome (PICS). Much of the mechanistic evidence, however, derives from preclinical or acute-phase studies, and validation of these findings in patients with CCI remains limited. CCI may represent a state of impaired metabolic and cellular adaptation associated with persistent inflammation, organ dysfunction, and failed recovery from acute critical illness. Improved understanding of these mechanisms may support more individualized metabolic monitoring, nutritional support, and therapeutic strategies. Further prospective and translational studies are required to determine clinical relevance and guide targeted interventions.
Air cargo transportation plays a key role in global trade, especially for time-sensitive and high-value products. Accurate prediction of air cargo demand is essential for informed decision-making on infrastructure planning, capacity management, and resource allocation across the air transport sector. While previous studies have largely advanced air cargo demand forecasting through model development and performance comparison, this study introduces a framework to explain and compare established predictive models based on data characteristic analysis (DCA). By examining key time series characteristics, such as stationarity, seasonality, and complexity, across statistical, machine learning, and deep learning approaches, this research examines how intrinsic properties of demand data are associated with forecasting performance. A rolling horizon design is also employed to evaluate how dynamic changes in data characteristics influence model performance over time. The findings reveal that statistical models are particularly sensitive to the mutability and complexity of air cargo demand data, whereas machine learning and deep learning models demonstrate stronger adaptability under diverse demand data conditions. Overall, this study shifts the emphasis from developing new forecasting models to explaining model suitability and supporting model selection, offering both theoretical insights and practical guidance for stakeholders by highlighting which models are best suited under specific data conditions.
