Search Results (55,130)

The transition from non-renewable to renewable energy sources has emerged as a pressing global issue, driven by concerns over climate change, resource depletion, and the need for sustainable development. This study compares Canada, an energy-rich nation, and Bangladesh, an energy-scarce country, to understand the structural, institutional, and market factors driving their respective renewable energy transitions. Using univariate time-series models (ARIMA, ETS, and Prophet) for energy demand forecasting and extensive literature-based policy evaluation, the paper examines trends in energy production, consumption, and trade from 1990 to 2024. Our analysis indicates that Canada’s vast reserves of both renewable and non-renewable energy sources, its diversified energy portfolio, and carbon-pricing framework support a stable decarbonization pathway, with renewables projected to account for more than 20% of total supply by 2030. However, regional disparities and political resistance from the established energy sector continue to delay transition outcomes. On the other hand, Bangladesh has limited renewable and non-renewable energy sources, with its primary energy resource being natural gas reserves. Consequently, its heavy reliance on imports (over 75% of primary energy) and institutional bottlenecks expose its energy system to commodity-price volatility, undermining energy security and slowing renewable investment. Despite these challenges, targeted solar programs and concessional financing have modestly increased the penetration of renewable energy. The analysis highlights that commodity market fluctuations, technological innovations (such as smart grids and energy storage), and market-based policy instruments critically shape each country’s transition trajectory. A coordinated policy linking market stabilization, innovation investment, and social inclusion is essential for achieving a just and secure low-carbon transition in both countries. Full article

The accelerating global energy transition has substantially increased demand for critical minerals such as copper, nickel, and lithium, positioning mining firms as key actors in the decarbonization of energy systems. However, the expansion of mineral extraction raises important sustainability challenges because mining activities remain highly energy- and carbon-intensive. This study investigates whether green innovation can simultaneously improve environmental performance and financial performance in critical mineral mining firms and examines the moderating role of institutional governance. Using a balanced panel of 35 publicly listed mining companies from Australia, Canada, Chile, Brazil, and Indonesia over the period 2015–2024, the analysis applies fixed-effects panel regressions complemented by dynamic specifications and multiple robustness tests, including alternative variable definitions and System Generalized Method of Moments (GMM) estimation. The results show that green innovation significantly reduces carbon intensity, indicating that environmental investments in renewable energy integration, electrification, and process efficiency contribute to improving emissions performance in mining operations. Green innovation also enhances firm financial performance, although the benefits emerge gradually over time, suggesting delayed financial gains followed by long-term efficiency improvements. Furthermore, governance quality strengthens the positive relationship between green innovation and firm performance, highlighting the importance of institutional environments in shaping the economic returns of sustainability strategies. By providing firm-level evidence across major mineral-producing economies, this study contributes to the literature on critical minerals, environmental finance, and the institutional dimensions of the just energy transition. Full article

Background/Objective: In recent years, epidemiological and clinical evidence has suggested an association between the use of second-generation antipsychotics (SGAs) and hyperglycemic complications: notably, diabetic ketoacidosis (DKA) and hyperglycemic hyperosmolar state (HHS). However, the role of first-generation antipsychotics (FGAs) remains less well understood. To conduct a systematic review of evidence established in case reports (CRs) on adverse drug reactions, specifically DKA and HHS, associated with the use of both FGAs and SGAs in order to identify patterns that may inform clinical awareness and future research. Methods: Pertinent bibliographic databases (MEDLINE, EMBASE, PsycINFO and the Cochrane Central Register of Controlled Trials (CENTRAL)) were searched using index phrases and keywords up until 17 October 2025. Eligible CRs discussed exposure to at least one US FDA-approved antipsychotic drug (APD) and assessed either DKA or HHS. Results: A total of 151 CRs were included in the systematic review (DKA, n = 121; HHS, n = 28; both conditions, n = 2). Patients aged 30 to 39 years accounted for the highest number of emergencies (n = 49, 32.5%), which occurred mostly in males (n = 108, 71.5%). The most common mental health diagnosis was schizophrenia (n = 77, 51%), followed by bipolar disorder (n = 26, 17.2%). Olanzapine was associated with the highest number of DKA cases (n = 53, 43.1%), followed by clozapine (n = 24, 19.5%). The average blood glucose at presentation was 842.8 mg/dL for DKA patients and 1252.8 mg/dL for HHS patients. The average hemoglobin A1c levels (HbA1c) were 11.5% and 12%, respectively, for these two conditions. Of the 12 reported fatalities, treatment with olanzapine was noted in four DKA cases and in one HHS case. Conclusion: This analysis provides additional evidence of an association between the use of atypical APDs and DKA or HHS. Clinicians should continue to monitor metabolic risk factors for these conditions, as well as educating patients about the prevention of acute diabetic complications. Full article

Although increasing numbers of young people are trying to quit e-cigarettes, personalized tools to support vaping cessation remain limited. We aimed to build a machine learning model to predict individual probability of short-term relapses and identify person-specific barriers to successful cessation. Data were taken from the “Stop Vaping Challenge” smartphone app. We included past 30-day e-cigarette users aged 15–35 years (n = 311) who completed 387 quit challenges. Feature selection minimized number of predictors while maximizing predictive ability. We built multiple GBM survival models with different sets of predictors to predict time to vaping relapse. The five-feature model yielded the best performance (C-index 0.751), thereby was selected as the final model. These five features were: self-confidence in quitting, intention to quit, average e-liquid used per week, time to first vape and mood trend during challenge. We stratified the challenges by the individual relapse risk by 7 days into low-, medium-, and high probability of quit success. This approach can inform tailored quit plans for vaping cessation. SHAP analysis demonstrated individual-level barriers to cessation, which can guide the development of personalized, predictor-centric, adaptive behavioral interventions. However, future research is needed to implement the model in real-world settings and evaluate its effectiveness and generalizability. Full article

Grasslands globally are threatened by loss and degradation as shifting factors in climate and management put them at risk. These grassland ecosystems support local economies and are a center of biodiversity, which makes understanding the risks that affect them key to effectively protecting them. One major risk to grasslands is woody plant encroachment, and reliable management hinges on understanding its patterns. A major challenge of woody plant encroachment is detecting it at early stages (<20% cover). This study investigated the utility of a combination of environmental features and remotely sensed data for differentiating varying levels of woody plant encroachment in a montane Canadian grassland. The response of woody species to environmental factors including slope and available moisture varied by individual species. As in past studies, it was challenging to separate the early stages of encroachment using base spectral bands or NDVI, even with the use of higher-resolution satellite imagery. Bands in the yellow and red wavelength regions both showed promise for shrub detection, providing more between band separability and key modeling components. The spatial resolution and band combinations used here were able to model woody plant cover levels, helping to facilitate the implementation of effective management in combating woody plant encroachment. Full article

Objectives: The aim of this study was to assess the differences in diagnostic delay and disease progression in people with ALS (PALS) based on site of onset. Methods: A retrospective analysis of prospectively collected data was performed, including all PALS seen in the ALS clinic in the Hadassah Medical Center between January 2009 and March 2022. PALS were divided to three groups based on site of onset (upper limb onset—ULO, lower limb onset—LLO, or bulbar onset—BO). A linear mixed-effects model was constructed with the following variables: diagnostic delay, site of onset, age of onset and time since the initial visit. The model was applied to the ALSFRS-R total score and the bulbar and motor subscales. Results: Data from 1255 visits of 281 PALS were included in the study. PALS with LLO had longer diagnostic delays than PALS in the BO group. Slower decline of total ALSFRS-R score was observed in younger PALS, and in PALS with LLO when compared with PALS with BO or ULO. The slower decline of ALSFRS-R in PALS with LLO was due to a slower decline in the motor subscale. Longer diagnostic delays were associated with lower total ALSFRS-R scores at the initial visit and with slower rates of decline. Conclusions: Comparison among PALS with ULO, LLO and BO revealed differences in the diagnostic delay and in the rate of functional decline, suggesting that differentiating between ULO and LLO ALS may be useful in the stratification of PALS in clinical trials. Full article

Building Information Modeling (BIM) supports information integration and coordination across the construction lifecycle, but benefits depend on collaboration that is shaped by the selected project delivery method (PDM). BIM-PDM evidence is difficult to consolidate due to heterogeneous terminology and fragmented, context-specific studies. This scoping review maps which PDMs are addressed in the BIM-related literature and how adequacy is framed. Following PRISMA-ScR, Web of Science and Scopus were searched and 71 studies met the eligibility criteria. Publications increased markedly after 2018 and were geographically concentrated, with the largest shares associated with author affiliations in China, the United Kingdom, Australia, Canada, Malaysia, and the United States. Integrated Project Delivery (IPD) was the most frequently examined (46 studies), followed by Design–Bid–Build (DBB) (29), Design–Build (DB) (29), Public–Private Partnership (PPP) (17), and Engineering, Procurement, and Construction (EPC) (14), while Alliancing, Lean-oriented delivery approaches, and Construction Management were comparatively underrepresented. A temporal analysis indicates a recent shift toward collaborative delivery methods in BIM research. Case-based studies are predominantly situated in public sector projects, with DBB, DB, EPC, and IPD examined across both infrastructure and building contexts, while PPP is limited to infrastructure. The literature is largely focused on design and construction phases, with limited attention to early project stages and operation and maintenance. Results indicate both traditional and relationship-based PDMs are studied in the existing literature, with research framing PDMs that allow for early contractor involvement as most compatible with BIM. Moreover, IPD, DB, and EPC show the best alignment compared to most used traditional DBB methods primarily due to the early involvement of the contractor in the project. EPC and DB achieve this through the allocation of responsibility to the contractor, whereas IPD relies on the early engagement of key participants and the systematic alignment of their objectives. Collaborative and relationship-based approaches are consistently presented as the most suitable for BIM, while DBB tends to constrain BIM benefits because of its fragmented nature. This study contributes by providing a systematic synthesis of BIM-PDM relationships in the scientific literature, identifying the key mechanisms underlying the suitability of different delivery methods for BIM implementation, and offering recommendations for future research based on the identified gaps. Full article

The growing need for sustainable aviation propulsion has increased interest in hydrogen fuel cell systems as alternatives to combustion engines. This study presents the modeling, simulation, and optimization of a hybrid hydrogen–electric powertrain for the MIMIQ unmanned aerial vehicle (UAV). A 2 kW proton exchange membrane fuel cell is integrated with a 12S lithium-polymer battery via a DC–DC converter, enabling parallel power sharing and in-flight battery recharging. A MATLAB-based dynamic model was developed using mission power profiles derived from flight data and refined using momentum theory. The developed model was benchmarked through a comparative simulation of a combustion-based hybrid-electric powertrain variant of the same platform, demonstrating consistency in electrical and energetic behavior. Multi-objective optimization using NSGA-II was performed to maximize hover endurance and to minimize energy consumption while maximizing payload over a full mission. Results from this computational framework show that endurance is primarily constrained by hydrogen availability rather than battery capacity, with the fuel cell operating near its optimal efficiency region. The findings indicate that hydrogen–electric architectures offer improved endurance, reduced emissions and better scalability compared to combustion-based systems, supporting their suitability for long-endurance UAV applications. Full article

Background/Objectives: Masters athletes are adults aged ≥40 who compete in sport, exhibiting superior physical function and healthier aging than their sedentary peers. However, even highly trained masters athletes experience age-related performance declines. Women masters athletes represent a growing yet understudied population who may face unique physiological challenges. This scoping review synthesizes literature from 1984 to 2024, examining the impact of age and menopause on determinants of endurance performance in women masters athletes. Methods: Following JBI scoping review methodology, six databases were searched (Medline, Embase, Central, CINAHL, SPORTdiscus, Scopus). Studies were evaluated for population characteristics, methodological approaches, and physiological determinants of performance (i.e., aerobic capacity, lactate kinetics, and exercise economy). Results: Twenty-nine studies were included. Most (n = 28) assessed aerobic capacity, reporting declines between 0.36 and 0.84 mL·kg⁻¹·min⁻¹·year⁻¹ (0.5–2.4%·year⁻¹). These reductions were primarily associated with decreased cardiac output followed by changes in body composition. Training volume emerged as a predictor of aerobic capacity, but the effects of menopause were unclear. Findings on lactate kinetics and exercise economy were mixed but preliminary research indicated that lactate threshold relative to VO₂max generally increased, peak lactate remained stable and energy cost increased with age. Fitness and health characteristics among women athletes differed from sedentary populations, emphasizing the need for athlete-specific data to support training and health decisions. Conclusions: Aging is associated with decreased aerobic capacity and variable changes in lactate kinetics and exercise economy. While training volume may attenuate performance decrements, the impact of menopause remains uncertain, underscoring the need for longitudinal research to better support this growing segment of the population. Full article

The Eastern Canadian Gastrointestinal Cancer Consensus Conference convened annually and was held in Fredericton, New Brunswick, from 18 to 20 September 2025. Attendees included experts in medical oncology, radiation oncology, nuclear medicine, and general practitioners in oncology (GPO) from across the eastern Canadian provinces who are engaged in the care and management of patients with gastrointestinal malignancies. The consensus statement resulting from this meeting addresses several key topics, including the management of early-stage gastroesophageal junction cancer, recent developments in molecular biomarkers and colorectal cancer treatments, secondary prevention strategies for colorectal cancer, and treatment of hepatocellular carcinoma. Full article

Accurate classification of physical activity (PA) intensity is essential for exercise prescription, rehabilitation monitoring, and evaluation of guideline adherence. However, widely used wrist-worn accelerometer cut-points may substantially misclassify physiological intensity. This study evaluated absolute accelerometer thresholds during a maximal 2400 m run in military office workers and examined whether individualized cut-points improve agreement with physiological intensity. Seventy-four military office workers completed the test while wearing a wrist-worn ActiGraph GT9X Link and a chest-worn Zephyr BioHarness. Participants achieved near-maximal physiological effort, with peak heart rate averaging 187 ± 11 bpm (95 ± 4.2% age-predicted HRmax). Despite this high intensity, absolute wrist-worn cut-points classified only 34.5% of participants as performing vigorous activity for most of the test. Individualized cut-points, derived from each participant’s individual reference intensity, calculated as the three highest consecutive one-minute epochs during the 2400 m test, substantially improved agreement between accelerometer-derived classifications and physiological intensity. Agreement with %HRmax increased from fair (κ = 0.31), using absolute thresholds, to good (κ = 0.74), using individualized thresholds, and intraclass correlation increased from 0.52 to 0.81. These findings demonstrate that absolute cut-points markedly underestimate high-intensity activity, potentially leading to inaccurate exercise load monitoring and misinterpretation of training intensity. Individualized calibration during a standardized maximal running test provides a feasible strategy to improve the validity of intensity assessment using wearables. Although the study population consisted of military office workers, the approach may be applicable to other active populations. However, further validation in independent samples is needed. Full article

Enhanced rock weathering (ERW) is a promising carbon dioxide removal strategy that accelerates silicate mineral dissolution to generate alkalinity and sequester carbon in soils and aquatic systems. The frequency and severity of fires are increasing globally, and fire-prone regions such as agricultural lands, forests, and grasslands overlap substantially with potential ERW deployment areas. However, fire–ERW interactions remain unexamined. This perspective synthesizes the literature on fire effects on soil properties to develop a conceptual framework for predicting fire impacts on ERW performance. An assessment of the available literature reveals that the effects of fire on soil pH and inorganic carbon are nonlinear with respect to severity, complicating both dissolution kinetics and carbon verification. Base cation pulses from ash are temporary and subject to rapid export. Fire-induced soil water repellency and erosion may dominate chemical effects in controlling ERW material fate, particularly during the first year post-fire. Pyrogenic carbon and thermally altered minerals create novel soil‒rock interactions with unknown consequences for weathering rates. The authors concluded that fire history must be incorporated as a covariate in ERW deployment planning and monitoring, reporting, and verification design. Full article

Digital game-based learning environments (DGBLEs) are increasingly integrated into classrooms as learning tools, yet limited research exists regarding the impact of students’ discrete emotions on digital gameplay performance. This study examined the role of emotions and arousal in predicting performance outcomes during digital gameplay. Thirty-two grade 5 students (M_age = 10.99, 62.5% male) played four digital games (two math; two identically designed non-math). During gameplay, real-time heart rate and affective data were collected and analyzed using an interpretable machine learning approach (XGBoost). Results suggest that students performed better on non-math games, as compared to math games. Real-time anger was associated with lower performance, particularly in games, whereas other emotions and physiological measures were not significant predictors. This pilot investigation suggests that discrete emotions, particularly anger, may play a more important role in performance during math gameplay than in comparable non-math activities. The results highlight the importance of supporting emotional regulation during digital math learning, as unmanaged anger may impact performance. This study contributes to the growing literature on affective dynamics in digital game-based learning. Full article

Understanding how oat (Avena sativa L.) cultivars differ in canopy development and competitive ability is essential for improving yield stability under increasing weed pressure. This study used unmanned aerial vehicle (UAV)-based multispectral imaging to characterize the temporal spectral and structural traits of sixteen oat cultivars grown under weed-free and weedy conditions across two locations for two years. Weedy conditions involved natural weed populations and pseudo-weeds where canola (Brassica napus) seeded as a weed. Weekly drone imaging was carried out using a multispectral sensor, which provided vegetation indices (NDVI, NDRE, ExG) and canopy metrics (ground cover, height, volume). Logistic and Gompertz models were fitted to cultivar traits to describe growth trajectories and obtain dynamic growth parameters. Cultivars showed clear differences in early canopy expansion, maximum NDVI, and canopy volume, with forage types expressing aggressive growth and several grain types combining high early growth rate with high yield potential. Machine-learning models integrating static and dynamic UAV-derived plant traits identified early ground cover and NDRE at three weeks after planting as the strongest predictors of grain yield. Models accurately predicted both weed-free (MAE = 262, R² = 0.90) and weedy yield (MAE = 258, R² = 0.90), demonstrating that early-season UAV traits capture the physiological and structural characteristics associated with competitive ability and grain yield. These findings show that high-throughput UAV phenotyping can reliably identify traits linked to yield formation and weed tolerance, providing a scalable approach for selecting competitive oat cultivars without relying solely on labor-intensive weedy field trials. Full article

The advent of immune checkpoint inhibitors has driven progress in hepatocellular carcinoma (HCC) treatment outcomes and enabled opportunities for combining therapeutic modalities. Growing evidence substantiates the utility of radiotherapy, particularly at ablative doses, in the management of HCC. Given the potential for radiotherapy to induce an immunostimulatory environment and potentiate immune checkpoint inhibitor activity, the expanding HCC treatment landscape compels exploration of the combination of radiotherapy and immunotherapy. This review highlights recent advances in the treatment of HCC using radiotherapy and immunotherapy in combination. Radiation can potentiate an anti-tumor response and tumor microenvironment permissive to immunotherapy. Results from randomized clinical trials and retrospective studies consistently show that combinations of radiotherapy and immunotherapy improved the treatment outcomes of unresectable or advanced HCC—especially HCC with macrovascular invasion. Active research to further improve treatment efficacy and reduce side effects is exemplified by more than 20 ongoing clinical trials combining external beam radiotherapy and immunotherapy to treat HCC. Ongoing research aims at prolonging survival and downstaging advanced or unresectable HCC. Full article