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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 |
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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 |