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نتیجه جستجو - Predictive tools

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 Oocyte and embryo evaluation by AI and multi-spectral autofluorescence imaging: Livestock embryology needs to catch-up to clinical practice
ارزیابی تخمک و جنین توسط هوش مصنوعی و تصویربرداری خودکار فلورسانس چند طیفی: جنین شناسی دام باید به مراحل بالینی برسد-2020
A highly accurate ‘non-invasive quantitative embryo assessment for pregnancy’ (NQEAP) technique that determines embryo quality has been an elusive goal. If developed, NQEAP would transform the selection of embryos from both Multiple Ovulation and Embryo Transfer (MOET), and even more so, in vitro produced (IVP) embryos for livestock breeding. The area where this concept is already having impact is in the field of clinical embryology, where great strides have been taken in the application of morphokinetics and artificial intelligence (AI); while both are already in practice, rigorous and robust evidence of efficacy is still required. Even the translation of advances in the qualitative scoring of human IVF embryos have yet to be translated to the livestock IVP industry, which remains dependent on the MOET-standardised 3- point scoring system. Furthermore, there are new ways to interrogate the biochemistry of individual embryonic cells by using new, light-based methodologies, such as FLIM and hyperspectral microscopy. Combinations of these technologies, in particular combining new imaging systems with AI, will lead to very accurate NQEAP predictive tools, improving embryo selection and recipient pregnancy success.
Keywords: Embryo selection | Machine learning | Pregnancy establishment | Embryo metabolism | Morphokinetics
مقاله انگلیسی
2 Integrated reservoir/wellbore production model for oil field asphaltene deposition management
مخزن یکپارچه / مدل تولید چاه برای مدیریت رسوب آسفالتین در میدان نفت-2020
Asphaltene deposition prevention, mitigation, and management remains a major challenge to the oil industry due to its complexity, poor understanding, and inadequate predictive tools. A literature review study on asphaltene deposition revealed a lack of integrative models that link reservoir, wellbore, and surface facility to predict asphaltene deposition taking into account the effect of their interaction on asphaltene deposition. In addition, most of the existing studies are focused on either modeling the thermodynamics aspects of asphaltene precipitation, or single-phase asphaltene deposition. Therefore, it is critical to model asphaltene deposition under multiphase flow conditions to, accurately, develop prevention, mitigation, and management strategies, which depends on not only asphaltene thermodynamics, but also multiphase flow hydrodynamics and behavior. The objective of this study is to develop a robust systematic approach for predicting asphaltene deposition in production system through coupling reservoir and wellbore production models, which provides a cost-effective optimal mitigation and management strategies. The proposed work in this study integrates five models, namely reservoir asphaltene deposition model, equation-of-state (EOS) model, asphaltene thermodynamics precipitation model, mechanistic multiphase flow model, and asphaltene deposition transport model. The abovementioned models are integrated using developed workflow platform, which enables compositional tracking throughout the entire production system. Furthermore, experimental fluid characterization data was used to tune the EOS model to ensure accurate phase behavior and volumetric calculations, and to tune the thermodynamic asphaltene precipitation model. A field case input data is used to evaluate the proposed integrated model, which indicates severe asphaltene depositions in production tubing. The proposed model predicted the location and growth of asphaltene deposition thickness with time and space in the inner production-tubing wall. The model results also show that local asphaltene deposition reduced tubing cross-sectional area, increasing in-situ superficial oil and gas velocities, thus increasing pressure drop and decreasing flowrate. Sensitivity analyses to investigate several parameters such as depletion drive mechanism, asphaltene particle size, and injection of CO2 rich gas on asphaltene deposition show excellent results that are aligned with the physical and theoretical understanding of asphaltene deposition. These results are critical in selecting, optimizing, and implementi.
Keywords: Integrated modeling | Asphaltene precipitation and deposition | Micellization model | Equation-of-state | Transport mechanism
مقاله انگلیسی
3 Improving breast cancer care coordination and symptom management by using AI driven predictive toolkits
بهبود هماهنگی مراقبت از سرطان پستان و مدیریت علائم با استفاده از ابزارهای پیش بینی کننده هوش مصنوعی-2020
Integrated breast cancer care is complex, marked by multiple hand-offs between primary care and specialists over an extensive period of time. Communication is essential for treatment compliance, lowering error and complication risk, as well as handling co-morbidity. The director role of care, however, becomes often unclear, and patients remain lost across departments. Digital tools can add significant value to care communication but need clarity about the directives to perform in the care team. In effective breast cancer care, multidisciplinary team meetings can drive care planning, create directives and structured data collection. Subsequently, nurse navigators can take the director’s role and become a pivotal determinant for patient care continuity. In the complexity of care, automated AI driven planning can facilitate their tasks, however, human intervention stays needed for psychosocial support and tackling unexpected urgency. Care allocation of patients across centres, is often still done by hand and phone demanding time due to overbooked agenda’s and discontinuous system solutions limited by privacy rules and moreover, competition among providers. Collection of complete outcome information is limited to specific collaborative networks today. With data continuity over time, AI tools can facilitate both care allocation and risk prediction which may unveil non-compliance due to local scarce resources, distance and costs. Applied research is needed to bring AI modelling into clinical practice Keywords: Care coordination | Symptom management | Predictive tools | Care allocation | Nurse navigator | Multidisciplinary discussion
مقاله انگلیسی
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