scholarly journals Are We Learning Enough Pathology in Medical School to Prepare Us for Postgraduate Training and Examinations?

2013 ◽  
Vol 2013 ◽  
pp. 1-3 ◽  
Author(s):  
Emma Marsdin ◽  
Seema Biswas
Keyword(s):  
Deep Learning ◽  
Postgraduate Training ◽  
Clinical Work ◽  
Undergraduate Teaching ◽  
Junior Doctors ◽  
Prescribing Errors ◽  
Basic Principles ◽  
Exam Preparation ◽  
Publishing Houses

Medical schools responded to the first publication of Tomorrow’s Doctors with an abbreviated syllabus and a reduction in didactic teaching hours. Prescribing errors, however, have increased, and there is a perception amongst clinicians that junior doctors know less about the pathological basis of disease. We asked junior doctors how useful they thought their undergraduate teaching in pathology had been in their postgraduate training. We had 70 questionnaire responses from junior doctors within a single deanery and found that although almost every doctor, (96%), thought that pathology formed a major component of their postgraduate exams, most, (67%), thought that their undergraduate teaching left them unprepared for their postgraduate careers, and they had to learn basic principles, as they revised for postgraduate exams. Few used a pathology text for learning, most doctors, (91%), relying on question and answer revision resources for exam preparation. Perhaps, as revision materials are used so widely, they might be adapted for long-term deep learning, alongside clinical work. This presents an opportunity for pathologists, deaneries, royal colleges, and publishing houses to work together in the preparation of quality written and online material readily accessible to junior doctors in their workplace.

Basic principles for assessing the safety of fish products from the ponds exposed to the influence of nuclear fuel cycle enterprises

2019 ◽  
pp. 106-114
Author(s):  
A. V. Trapeznikov ◽  
V. N. Trapeznikova ◽  
A. V. Korzhavin ◽  
V. N. Nikolkin ◽  
A. P. Plataev
Keyword(s):  
Fuel Cycle ◽  
Specific Activity ◽  
River System ◽  
Food Product ◽  
Human Consumption ◽  
Basic Principles ◽  
Fish Products ◽  
Permissible Levels ◽  
Irtysh River

Relevance. In connection with the development of nuclear energy, many aquatic ecosystems have been exposed to radioactive substances. Fish, as an element of biota, is capable to accumulate radionuclides. However, fish is a traditional food. The control of the technogenic radionuclides accumulation level in the ichthyofauna is an important link in ensuring human security.Intention. To analyze the long-term data on the content of long-lived technogenic radionuclides 90Sr and 137Cs in the ichthyofauna of the Ob-Irtysh river system for the period from 2004 to 2016 and Beloyarsky pond for the period from 1977 to 2018.Methodology. Fish as a food product was assessed according to two criteria: a) permissible levels of specific activity of radionuclides (SanPiN 2.3.2.1078-01); b) using the indicator of conformity B and the uncertainty of its definition В (GOST 32161-2013 and GOST 32163-2013).Results and Discussion. Fish of all species that live in the river Tetcha, is not suitable for food use according to the criteria of SanPiN 2.3.2.1078-01, GOST 32161-2013 and GOST 32163-2013. The fish of the Ob-Irtysh river system, the habitat of which is located outside the Tetcha, meets the requirements for fish products. In the period from 1977 to 1989 in the Beloyarsk pond the accumulations of technogenic radionuclides in fish in quantities exceeding sanitary and hygienic standards were possible. Currently, the fish of the Beloyarsky pond fully complies with the sanitary and hygienic requirements for the radiation factor and is safe for human consumption.Conclusion. In the ponds exposed to the atomic energy enterprises, it is necessary to continuously monitor the content of long-lived technogenic radionuclides in fish and assess their amount in accordance with the requirements of SanPiN 2.3.2.1078-01 and using the conformity indicator В and the uncertainty of its determination В. 

BAI’ BITHAMAN AJIL, ALTERNATIF PEMBIAYAAN PERUMAHAN PADA PERBANKAN SYARIAH

2017 ◽  
Vol 13 (1) ◽  
pp. 19-26
Author(s):  
Lily Rahmawati Harahap
Keyword(s):  
National Economy ◽  
Profit Sharing ◽  
Significant Enhancement ◽  
Basic Principles ◽  
Stable Economy

During this recent two decades, national economy sharia continiue growth.Though it just still smaller compare to the existing of conventional finance, itconscioused the growth of sharia finance currently has a significant enhancement. Thepositive and stable economy performance has given good opportunities to thedevelopment of sharia economy in Indonesia. The existence of syariah economy inIndonesia, who has a muslim mayority, is a new point of national economy history.Sharia banking is one of the tools for developing national economy sharia. Manyproducts offered by national sharia banking, which includes basic principles ofimplementation with profit sharing. One of them is Bai’ Bithaman Ajil, which is the partof trading (Baiah). Bai’ Bithaman Ajil product is expected as an alternative for society tochoose sharia banking as a mediator in transaction, specially to fulfill production needsas well as consumption needs, whose transactions are done on a long-term credit.

scholarly journals Domain randomization-enhanced deep learning models for bird detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Mao ◽  
Jun Kang Chow ◽  
Pin Siang Tan ◽  
Kuan-fu Liu ◽  
Jimmy Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Continuous Monitoring ◽  
Bird Species ◽  
Training Data ◽  
Learning Models ◽  
Fine Grained ◽  
Bird Detection ◽  
Relationship Of ◽  
The Relationship

AbstractAutomatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals The Application and Development of Deep Learning in Radiotherapy: A Systematic Review

2021 ◽  
Vol 20 ◽  
pp. 153303382110163
Author(s):  
Danju Huang ◽  
Han Bai ◽  
Li Wang ◽  
Yu Hou ◽  
Lan Li ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Neural Networks ◽  
Decision Making ◽  
Deep Learning ◽  
Radiation Oncology ◽  
Clinical Work ◽  
Radiation Oncologists ◽  
Basic Concepts ◽  
Further Development

With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation oncologists with many promising tools that can simplify the complex radiotherapy process in the clinical work of radiation oncology, improve the accuracy and objectivity of diagnosis, and reduce the workload, thus enabling clinicians to spend more time on advanced decision-making tasks. As the development of DL gets closer to clinical practice, radiation oncologists will need to be more familiar with its principles to properly evaluate and use this powerful tool. In this paper, we explain the development and basic concepts of AI and discuss its application in radiation oncology based on different task categories of DL algorithms. This work clarifies the possibility of further development of DL in radiation oncology.

PROBLEMS OF FORECASTING INDICATORS OF THE DEVELOPMENT OF THE EDUCATIONAL PROCESS IN THE REGION

2021 ◽  
Vol 1 (2) ◽  
pp. 174-182
Author(s):  
A. D. BURYKIN ◽  
◽  
V. Ya. SERBA ◽  
R. A. GASYMOV ◽  
◽  
...  
Keyword(s):  
Local Governments ◽  
Educational Process ◽  
Innovative Development ◽  
Education Development ◽  
Basic Principles ◽  
Technological Forecasting ◽  
Forecasting Methods ◽  
Educational Processes ◽  
Methodological Aspects

The article deals with the theoretical and methodological aspects of the development of methods for assessing the effectiveness of the educational process in the region. Indicators of the development of educational processes are analyzed from the perspective of long-term scientific and technical forecasting. Based on the analysis of the strategic documents, the problems existing in this area of strategic planning and the factors that hinder the innovative development of the education system in the region are identified. The basic principles and approaches to scientific and technological forecasting are defined. The main groups of scientific forecasts and the main directions of education development in Russia are identified. The considered forecasting methods are recommended for use in the work of state authorities and local governments, depending on the complexity of the implementation and the adequacy of the method, the requirements for the information base of the project.

How do junior doctors spend their time in an Acute Medical Unit?

2015 ◽  
Vol 14 (2) ◽  
pp. 57-60
Author(s):  
Eirini V Kasfiki ◽  
◽  
Mamoon Yusaf ◽  
Jivendra Gosai ◽  
Makani Purva ◽  
...  
Keyword(s):  
General Practice ◽  
Hospital Admission ◽  
Postgraduate Training ◽  
Junior Doctors ◽  
Bed Occupancy ◽  
Medical Unit ◽  
Admission Rates ◽  
The Past ◽  
Acute Medical Unit ◽  
The Uk

In the UK, postgraduate training for doctors has undergone significant changes over the past decade general practice, etc. During this period, hospital admission rates and bed occupancy have also increased.

Spoken content retrieval and understanding using deep learning

Impact ◽  
2021 ◽  
Vol 2021 (1) ◽  
pp. 9-11
Author(s):  
Lin-shan Lee
Keyword(s):  
Deep Learning ◽  
Scientific Community ◽  
Online Courses ◽  
The Internet ◽  
Human Voice ◽  
Content Retrieval ◽  
Youtube Videos ◽  
Access Information ◽  
Teaching Machines

Spoken content refers to all content over the Internet which includes human voice, essentially those in multimedia, such As YouTube videos and online courses. Today such content is retrieved via Google primarily based on human-generated text labels, because Google can only retrieve text over the Internet. The goal of this project is to produce technologies to retrieve accurately and efficiently such spoken content directly based on the included audio sounds instead of text labels, because machines today can listen to human voice just as they can read the text. The long term goal is to create a spoken version of Google, which may revolutionize the ways in which humans access information and improve their knowledge. Professor Lin-shan Lee at National Taiwan University is leading this project. He has been a distinguished leader in the global scientific community for the area of teaching machines to speak and listen to human voice for many years.

Sign in / Sign up

Export Citation Format

Share Document