Investigation on Assessment of the Karnali Employment Program by Using System Dynamics Approach

Karnali Employment Program (KEP) is employment led poverty reduction program initiated by the Government of Nepal in 2006 through its budget speech with an initial amount of NRS. 180 million. It aimed to provide safety net to ultra-poor household through short-term employment against seasonality and other shocks. The KEP aimed to providing 100 days of guaranteed wage employment to at least one unemployed family member in every household. The program failed miserably and KEP was only able to achieve 15 to 10 days of employment to the poor household of Karnali Region since its inception. Since, this intervention program impacts the labor market and socio-economic condition of Karnali Zone, it is necessary to evaluate the challenges that lead to the failure of implementing KEP. Therefore, to evaluate the failure of this program, system dynamics approaches was used to analyze the shortcomings of KEP program and to draw lessons for future policies implementation. To analyze the failure of the program five models: the population chain, labor demand, labor supply, government budget and agriculture were modelled explicitly to explain the dynamic problem of the study. The behavior of these models strongly indicated that failure to hire skilled employees and failure to recognize the targeted ultra-poor household led to underperformance of the program. To address this dynamic problem, policies such as hiring skilled employees according to the requirement of project and providing employment to the percentage of people who are identified as ultra-poor are induced in the model. The outcome of this policy showed an improvement in number of employment days provided by KEP. The employment days increased from 15 to 98 days which is quite close to the original target of 100 days. Therefore, to properly implement KEP, Government of Nepal should exert its resources in properly identifying and targeting the ultra-poor employees in order to implement programs like KEP successfully.Master's Thesis in System DynamicsGEO-SD35

Accuracy of point of care ultrasound in the diagnosis of long bone fractures in the emergency department.

Introduction: Long bone fractures account for a significant portion of injuries in the emergency department (ED). This study aimed to determine the accuracy of point of care ultrasound (POCUS) compared to x-ray in the diagnosis of long bone fractures in the ED. Method: This cross-sectional study assessed 147 patients presenting to the ED of Patan Academy of Health Sciences (PAHS), with suspected long bone fractures, from Oct 2021   through Jun 2022. In all patients, POCUS examination was done by emergency fellows and then standard plain x-ray was performed. Data were analyzed by SPSS 28.0 to determine sensitivity and specificity. Result: A total of 147 patients were included in the study. Compared with x-ray, sensitivity, specificity, PPV and NPV of POCUS in determining fractures was found to be 86%, 98.96%, 97.72% and 93.2%, respectively. Based on bone injured, the highest sensitivity and specificity were obtained with forearm fractures, which was equal to 97.22% and 100%, respectively. Based on age categorization, the highest sensitivity (100%) and specificity (100%) were obtained in pediatric age group i.e. up to 16 years. Conclusion: This study demonstrated that POCUS has high sensitivity and specificity in the diagnosis of long bone fractures, compared to x-ray

Statistical leakage estimation in 32nm CMOS considering cells correlations

International audienceIn this paper a method to estimate the leakage power consumption of CMOS digital circuits taking into account input states and process variations is proposed. The statistical leakage estimation is based on a pre-characterization of library cells considering correlations (ρ) between cells leakages. A method to create cells leakage correlation matrix is introduced. The maximum relative error achieved in the correlation matrix is 0.4% with respect to the correlations obtained by Monte Carlo simulations. Next the total circuit leakage is calculated from this matrix and cells leakage means and variances. The accuracy and efficiency of the approach is demonstrated on a C3540 (8 bit ALU) ISCAS85 Benchmark circuit

An exonuclease I-sensitive DNA repair pathway in Deinococcus radiodurans: a major determinant of radiation resistance

Deinococcus radiodurans R1 recovering from acute dose of γ radiation shows a biphasic mechanism of DNA double-strand break repair. The possible involvement of microsequence homology-dependent, or non-homologous end joining type mechanisms during initial period followed by RecA-dependent homologous recombination pathways has been suggested for the reconstruction of complete genomes in this microbe. We have exploited the known roles of exonuclease I in DNA recombination to elucidate the nature of recombination involved in DNA double-strand break repair during post-irradiation recovery of D. radiodurans. Transgenic Deinococcus cells expressing exonuclease I functions of Escherichia coli showed significant reduction in γ radiation radioresistance, while the resistance to far-UV and hydrogen peroxide remained unaffected. The overexpression of E. coli exonuclease I in Deinococcus inhibited DNA double-strand break repair. Such cells exhibited normal post-irradiation expression kinetics of RecA, PprA and single-stranded DNA-binding proteins but lacked the divalent cation manganese [(Mn(II)]-dependent protection from γ radiation. The results strongly suggest that 3' (ρ) 5' single-stranded DNA ends constitute an important component in recombination pathway involved in DNA double-strand break repair and that absence of sbcB from deinococcal genome may significantly aid its extreme radioresistance phenotype

CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation

Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. While a large variety of DA techniques has been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed single-class problems. To tackle these limitations, the Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). CrossMoDA is the first large and multi-class benchmark for unsupervised cross-modality DA. The challenge's goal is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are performed using contrast-enhanced T1 (ceT1) MRI. However, there is growing interest in using non-contrast sequences such as high-resolution T2 (hrT2) MRI. Therefore, we created an unsupervised cross-modality segmentation benchmark. The training set provides annotated ceT1 (N=105) and unpaired non-annotated hrT2 (N=105). The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137). A total of 16 teams submitted their algorithm for the evaluation phase. The level of performance reached by the top-performing teams is strikingly high (best median Dice - VS:88.4%; Cochleas:85.7%) and close to full supervision (median Dice - VS:92.5%; Cochleas:87.7%). All top-performing methods made use of an image-to-image translation approach to transform the source-domain images into pseudo-target-domain images. A segmentation network was then trained using these generated images and the manual annotations provided for the source image.Comment: Submitted to Medical Image Analysi

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI