BP (Canada)

BackgroundKnowledge translation (KT) interventions are essential for implementing evidence-based practices in healthcare. However, despite their proven effectiveness in addressing global health challenges, KT strategies in surgery remain challenging to apply. This scoping review examines KT strategies in surgery, their effectiveness, and key barriers and enablers to their implementation.MethodsThis scoping review followed the Arksey and O’Malley and Levac et al. frameworks, integrating the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) and the PRISM (Practical Robust Implementation Sustainability Model) models to evaluate the effectiveness of knowledge translation interventions in surgical practice change and associated contextual barriers and facilitators. A systematic search was conducted across MEDLINE (PubMed, OVID), CINAHL (EBSCO), and PsycINFO (ProQuest). Articles were screened using predefined selection criteria, emphasizing experimental and quasi-experimental studies. Data extraction categorized KT interventions: knowledge diffusion, dissemination, and implementation approaches.ResultsA total of 34 studies met the inclusion criteria. Most were hospital-based (88%) and focused on guideline adherence. The review identified three primary KT strategies: (i) educational materials and educational outreach, (ii) reminders and prompts, and (iii) audit and feedback systems. The most effective KT strategies used a combination of these interventions to maximize impact. Barriers included physician resistance, limited leadership support, financial constraints, and workflow disruptions, while enablers included institutional leadership, structured training programs, financial incentives, and interdisciplinary collaboration. A notable finding was the lack of standardized validation processes for adopting changes in the surgical setting, which often burdens individual surgeons and their institutions, thereby constraining both capacity and motivation for practice change.ConclusionsFindings suggest that layered, interdisciplinary KT strategies are the most effective for driving surgical practice change and overcoming institutional barriers. The integrated application of RE-AIM and PRISM frameworks proved valuable in assessing the interventions’ sustainability and real-world effectiveness. This comprehensive analysis contributes to the growing body of knowledge on effective implementation strategies in surgical settings and provides a foundation for future practice improvement initiatives. Future research should focus on refining KT methodologies, expanding implementation frameworks, and addressing barriers to sustainability across diverse surgical settings.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12913-025-13369-2.

International audience

Abstract In North America shale gas projects, operators aim to extend the horizontal sections to boost single well production and reduce drilling and completion costs. However, as the horizontal section length increases, especially when the laterals exceed 3500 meters, numerous complex drilling challenges arise. These include issues with weight transfer during slide drilling, high downhole friction and torque, mud losses caused by high mud weight and friction loss in long horizontal section, short service lives and low efficiency of downhole tools, limited capability to track thin strata due to restricted logging equipment, and difficulties in deploying production casing, including risks such as differential sticking and prolonged running times. To address these challenges, this paper presents an optimized well design strategy for the drilling of unconventional extended-reach unconventional horizontal wells. Firstly, a slim hole design is adopted to cut costs related to casing, cement, and drilling fluids. Secondly, the upper drilling string is switched from 4″ to 4.5″ drill pipes to provide enough torque and hydraulic power, and the Bottom Hole Assembly (BHA) utilizes even-wall stator positive displacement motors (PDM) and motorized rotary steerable systems (RSS) to enhance drilling efficiency in the intermediate and horizontal sections. Third, the oil-based mud (OBM) system is optimized for stability, low friction, and employs clay-free invert emulsion, complemented by managed pressure drilling (MPD) to control ECD. Then, downhole vibration sensors integrated into RSS enable predictive maintenance against tool fatigue. Next, drilling bit optimization based on formation strength analysis increases rate of penetration and extends bit life. Next, dual-telemetry xBolt (MWD) technology, combined with azimuthal gamma ray logging (LWD), which precisely reveals bed crossings and boundaries, optimizes wellbore placement. Finally, a specialized casing running tool and robust casing connection ensure successful deployment of 5.5-inchcasings in long laterals. These optimizations delivered outstanding outcomes. Several records were broken, such as the longest 4,875-meter horizontal slim-hole section drilled in a single bit run, with the average sweet zone drilling ratio of 96%, the longest single-run deployment of 5.5-inch and 4.5-inch hybrid casing at a depth of 8,157-meter, and a rapid 17-day drilling cycle without incidents. In recent years, numerous horizontal wells have extended beyond 4,000m in lateral length, achieving an average horizontal rate of penetration (ROP) exceeding 50m/h and an average drilling duration of within 20 days. Finally, These drilling optimizations deployed in the Duvernay shale project have notably reduced per-meter costs and enhanced drilling speed, setting a new benchmark within the Western Canadian unconventional project. These practices has set the stage for continued drilling of extended reach horizontal wells in the development of similar unconventional reservoirs.

Abstract A Physics-Informed Neural Network (PINN) was developed to solve the complex Navier-Stokes (N-S) equations for multiphase flow measurement including flowrates of individual phases in the multiphase mixture, and phase ratios. The results were compared against a mathematical based multiphase flow measurement approach and validated through a controlled loop test using oil, water, and gas. The PINN embeds fundamental fluid dynamics principles into its learning process, ensuring that the neural network adheres to the governing physical laws of motion, continuity, and conservation. Unlike traditional neural networks that rely solely on data-driven learning, PINNs incorporate fluid dynamics driven differential equations, to enhance model accuracy and reliability. This study evaluates the accuracy, efficiency, and repeatability of PINNs by comparing their results against traditional mathematical-based multiphase flow measurement (MPFM) technology and controlled test cases. The developed PINN successfully solved the Navier- Stokes (N-S) equations in their closest approximation to the original form, with minimal assumptions and simplifications, to estimate multiphase flow rates, integrating fundamental fluid dynamics principles into its learning process. The results demonstrated that the PINN-based approach achieved accuracy comparable to traditional mathematical multiphase flow measurement (MPFM) technologies, while significantly improving computational efficiency. During controlled loop tests with oil, water, and gas, the PINN model consistently captured flow dynamics with high precision, validating its effectiveness under real-world conditions. Moreover, the PINN method exhibited strong generalization capabilities, adapting to various flow regimes without the need for extensive empirical calibration. Compared to conventional MPFM technologies, PINN reduced reliance on predefined correlations and offered greater adaptability to changing fluid properties. The repeatability of results confirmed the model's reliability, positioning PINNs as a promising alternative for complex multiphase flow measurement applications. These findings highlight the potential of PINNs as an innovative, physics-guided, and data-efficient solution, effectively bridging the gap between traditional physics-based methods and modern machine learning techniques. This paper presents an innovative application of Physics-Informed Neural Networks (PINNs) for multiphase flow measurement, offering a data-driven yet physics-guided alternative to traditional mathematical MPFM technologies. By embedding fundamental physical laws into the neural network training process, the proposed approach enhances accuracy, computational efficiency, and adaptability, while minimizing reliance on extensive empirical calibration. This advancement significantly improves real-time flow monitoring, equipping practicing engineers with a more reliable, data-efficient, and scalable solution for complex multiphase flow measurement in industrial applications.

Abstract Understanding whether a species’ distribution results from recent and/or anthropogenic events or ancient vicariant factors is critical for conservation planning. The Endangered Sanje mangabey (Cercocebus sanjei), endemic to Tanzania’s Udzungwa Mountains, is currently divided into two populations approximately 100 km apart. These represent distinct evolutionary lineages that diverged around 0.71 million years ago. We aimed to investigate i) the phylogeographic history and recent changes in size and range of the two the Sanje mangabey populations, and ii) whether lack of suitable habitat between populations and consequent inability to disperse may have influenced the 0.71 MY divergence time. We used 64 mitochondrial control region sequences collected non-invasively. The probability of suitable habitat across Tanzania and the Udzungwa Mountains was modelled at three time points: Mid-Holocene (6,000 YA), Last Glacial Maximum (22,000 YA), and the Last Interglacial period (120,000-140,000 YA). We found six haplotypes, clustered into two haplogroups. Significant differentiation was estimated between populations, which show no evidence for recent range expansion or contraction. The ecological niche modelling revealed fluctuating extents of suitable habitat across southern Tanzania. Large genetic differentiation between populations may have been influenced by a general trend in aridification in East Africa across the last 40,000 years, resulting in a shift of montane forests to gradually higher elevations. Intermediate populations may have become extinct as suitable habitat retracted, leaving relict populations with relatively stable demographic histories ancestral to the present-day populations. This study supports their preliminary designation as separate evolutionary significant units, a conclusion with conservation management implications.

Children's storied play, or in other words, their fictive world-building, has much to teach us with regard to understanding story and how story pedagogy might be otherwise shaped in curricular spaces. In this article, we explore this proposition by drawing on interconnected and entangled moments of children's outdoor storied play in a Canadian first-grade class. By mapping the children's movements within three storied-play encounters, we animate how what could appear as the most ordinary and mundane ways of composing story in outdoor, open-ended and make-believe play, offers a lively form of protestation to the predominant Western narrative paradigm of beginning-middle-end story structure. Bringing the three moments into conversation with literary theorist Marielle Macé's concept of narrative stylization, we attend to ways story lives and grows within children's storied and playful worldbuilding. Ultimately, we argue that limiting instructional methods to that which is centred around the objectification and rationalization of storying (i.e., typical beginning-middle-end story structure) can displace other pedagogical directions that recognize and extend the myriad ways in which stories are made, learned, and understood. We conclude the article with an exploration of how educators might promote and build upon the narrative stylizations - fiction-in-the-doing - that children are always already engaging in their storied play.