Discrimination Face Female Faculty During the Recruitment & Selection and Training Time in The Academic Sector

2021 ◽  
Vol 27 (3) ◽  
Keyword(s):  
Female Faculty ◽  
Training Time ◽  
And Training ◽  
Academic Sector

scholarly journals P38 COVID-19 impact on Surgical Training And Recovery Planning (COVID-STAR) Survey

BJS Open ◽  
2021 ◽  
Vol 5 (Supplement_1) ◽  
Author(s):  
◽  
Joshua Clements
Keyword(s):  
Surgical Training ◽  
Negative Impact ◽  
Healthcare Delivery ◽  
Cross Sectional Study ◽  
Surgical Trainee ◽  
Surgical Specialty ◽  
Cross Sectional ◽  
Training Time ◽  
Surgical Trainees ◽  
And Training

Abstract Background The COVID-19 pandemic has resulted in dynamic changes to healthcare delivery. Surgery as a specialty has been significantly affected and with that the delivery of surgical training. Method This national, collaborative, cross sectional study comprising 13 surgical trainee associations distributed a pan surgical specialty survey on the COVID-19 impact on surgical training over a 4-week period (11th May - 8th June 2020). The survey was voluntary and open to medical students and surgical trainees of all specialties and training grades. All aspects of training were qualitatively assessed. This study was reported according to STROBE guidelines. Results 810 completed responses were analysed. (M401: F 390) with representation from all deaneries and training grades. 41% of respondents (n = 301) were redeployed with 74% (n = 223) redeployed > 4 weeks. Complete loss of training was reported in elective operating (69.5% n = 474), outpatient activity (67.3%, n = 457), Elective endoscopy (69.5% n = 246) with > 50% reduction in training time reported in emergency operating (48%, n = 326) and completion of work-based assessments (WBA) (46%, n = 309). 81% (n = 551) reported course cancellations and departmental and regional teaching programmes were cancelled without rescheduling in 58% and 60% of cases respectively. A perceived lack of Elective operative exposure and completions of WBA’s were the primary reported factor affecting potential training progression. Overall, > 50% of trainees (n = 377) felt they would not meet the competencies required for that training period. Conclusion This study has demonstrated a perceived negative impact on numerous aspects of surgical training affecting all training specialties and grades.

scholarly journals Training Tips for the Transformer Model

2018 ◽  
Vol 110 (1) ◽  
pp. 43-70 ◽  
Author(s):  
Martin Popel ◽  
Ondřej Bojar
Keyword(s):  
Sentence Length ◽  
Batch Size ◽  
Critical Parameters ◽  
Neural Machine Translation ◽  
Training Time ◽  
Multiple Gpus ◽  
Translation Quality ◽  
Transformer Model ◽  
Data Constraints ◽  
And Training

Abstract This article describes our experiments in neural machine translation using the recent Tensor2Tensor framework and the Transformer sequence-to-sequence model (Vaswani et al., 2017). We examine some of the critical parameters that affect the final translation quality, memory usage, training stability and training time, concluding each experiment with a set of recommendations for fellow researchers. In addition to confirming the general mantra “more data and larger models”, we address scaling to multiple GPUs and provide practical tips for improved training regarding batch size, learning rate, warmup steps, maximum sentence length and checkpoint averaging. We hope that our observations will allow others to get better results given their particular hardware and data constraints.

scholarly journals Mosquito Larvae Detection using Deep Learning

2019 ◽  
Vol 8 (12) ◽  
pp. 804-809
Keyword(s):  
Deep Learning ◽  
Mobile Application ◽  
Web Application ◽  
Individual Performance ◽  
Mosquito Larvae ◽  
Accuracy Score ◽  
Performance Category ◽  
Training Time ◽  
The Cost ◽  
And Training

Dengue cases has become endemic in Malaysia. The cost of operation to exterminate mosquito habitats are also high. To do effective operation, information from community are crucial. But, without knowing the characteristic of Aedes larvae it is hard to recognize the larvae without guide from the expert. The use of deep learning in image classification and recognition is crucial to tackle this problem. The purpose of this project is to conduct a study of characteristics of Aedes larvae and determine the best convolutional neural network model in classifying the mosquito larvae. 3 performance evaluation vector which is accuracy, log-loss and AUC-ROC will be used to measure the model’s individual performance. Then performance category which consist of Accuracy Score, Loss Score, File Size Score and Training Time Score will be used to evaluate which model is the best to be implemented into web application or mobile application. From the score collected for each model, ResNet50 has proved to be the best model in classifying the mosquito larvae species.

Human Performance Assessment to Support Rapid Prototyping and Iterative Design

1989 ◽  
Vol 33 (16) ◽  
pp. 1089-1093 ◽  
Author(s):  
James W. Broyles
Keyword(s):  
Human Performance ◽  
Operational Experience ◽  
System Operation ◽  
Training Time ◽  
Iterative Design ◽  
Performance System ◽  
Navy Personnel ◽  
The Impact ◽  
And Training ◽  
A Current

Fourteen U.S. Navy personnel with Aegis Combat System, Naval Tactical Data System (NTDS), and Non-NTDS operational experience participated in an experiment designed to investigate the impact of proposed workstation designs on operator performance, system usability, and training. Human performance data were collected on a sample of operational procedures typically performed in a Combat Information Center (CIC) for a current Navy Combat System and a prototype workstation. The prototype was developed using specific human factors design principles with the goal of reducing training time, improving operator retention of skills for system operation, reducing errors in system operation, improving operator efficiency (e.g., speed & accuracy of performance), and improving user's satisfaction with the user-computer interface. This paper reports only the preliminary results for data collected from seven subjects who performed procedures using the prototype workstation.

scholarly journals Leveraging Stack4Things for Federated Learning in Intelligent Cyber Physical Systems

2020 ◽  
Vol 9 (4) ◽  
pp. 59
Author(s):  
Fabrizio De Vita ◽  
Dario Bruneo
Keyword(s):  
Intelligent Systems ◽  
Physical World ◽  
Cyber Physical Systems ◽  
Training Data ◽  
Cloud Platform ◽  
Model Accuracy ◽  
Training Time ◽  
Physical Systems ◽  
The Internet Of Things ◽  
And Training

During the last decade, the Internet of Things acted as catalyst for the big data phenomenon. As result, modern edge devices can access a huge amount of data that can be exploited to build useful services. In such a context, artificial intelligence has a key role to develop intelligent systems (e.g., intelligent cyber physical systems) that create a connecting bridge with the physical world. However, as time goes by, machine and deep learning applications are becoming more complex, requiring increasing amounts of data and training time, which makes the use of centralized approaches unsuitable. Federated learning is an emerging paradigm which enables the cooperation of edge devices to learn a shared model (while keeping private their training data), thereby abating the training time. Although federated learning is a promising technique, its implementation is difficult and brings a lot of challenges. In this paper, we present an extension of Stack4Things, a cloud platform developed in our department; leveraging its functionalities, we enabled the deployment of federated learning on edge devices without caring their heterogeneity. Experimental results show a comparison with a centralized approach and demonstrate the effectiveness of the proposed approach in terms of both training time and model accuracy.

Evaluation of a discrete-sample computer-directed clinical analyzer.

1976 ◽  
Vol 22 (2) ◽  
pp. 211-216 ◽  
Author(s):  
R L Forrester ◽  
W Collinge ◽  
P Hashimoto ◽  
J Worrall
Keyword(s):  
Alkaline Phosphatase ◽  
Total Bilirubin ◽  
Correlation Coefficients ◽  
Training Time ◽  
Discrete Sample ◽  
Operational Characteristics ◽  
Manual Methods ◽  
Carry Over ◽  
Instrument Reliability ◽  
And Training

Abstract The operational characteristics of the Gilford System 3500 were evaluated for six months as to accuracy, precision, carry-over, reliability, and ease of operation. Accuracy was evaluated by comparison to manual methods of established accuracy. The tests evaluated and their respective correlation coefficients are as follows: glucose (0.99), blood urea nitrogen (0.99), calcium (0.97), total bilirubin (0.99), aspartate aminotransferase (0.97), alkaline phosphatase (0.98), albumin (0.96), and total protein (0.96). Within-run precision (CV) for three commercial calibration sera of differing analyte concentrations (low, intermediate, and high) were respectively: 0.69, 1.02, 1.18; 5.4, 1.2, 1.09; 0.83, 0.77, 0.86; 5.9, 1.0, 0.86; 6.4, 5.2, 2.1; 3.7, 1.5, 1.3; 0.0, 1.4, 0.97; and 1.2, 1.3, 0.75. Day-to-day precision, similarly evaluated during 101-164 days, met accepted criteria for clinically acceptable precision. Carry-over for each of the eight tests was less than 1%. Instrument reliability has been excellent, and training time is short. In summary, we have found the Gilford System 3500 to be sufficiently precise and fast, easy to operate, highly accurate, and flexible.

scholarly journals Variations of Workload Indices Prior to Injuries: A Study in Trail Runners

2020 ◽  
Vol 17 (11) ◽  
pp. 4037 ◽  
Author(s):  
Sérgio Matos ◽  
Filipe Manuel Clemente ◽  
Rui Silva ◽  
José María Cancela Carral
Keyword(s):  
Body Mass ◽  
External Load ◽  
Injury Risk ◽  
Perceived Exertion ◽  
Training Time ◽  
Positioning Systems ◽  
Total Distance ◽  
Injury Occurrence ◽  
Global Positioning ◽  
And Training

The purpose of this study was to compare the variations of weekly workload indices of internal and external load measures across the three weeks prior to injury occurrences in trail runners. Twenty-five trail runners (age: 36.23 ± 8.30 years old; body mass: 67.24 ± 5.97 kg; height: 172.12 ± 5.12 cm) were monitored daily for 52 weeks using global positioning systems (GPSs) to determine the total distance covered. Additionally, a rate of perceived exertion (RPE) scale was applied to determine session-RPE (sRPE: RPE multiplied by training time). The accumulated load (AL), acute: chronic workload ratio (ACWR), training monotony (TM), and training strain (TS) indices were calculated weekly for each runner. During the period of analysis, the injury occurrences were recorded. The differences were observed in AL and ACWR for sRPE and training time were significantly greater during the injury week when compared to the previous weeks. Similar evidence was found in TM and TS indices for sRPE, training time, and total distance. Furthermore, no meaningful differences were observed in AL and ACWR for total distance in the weeks prior to injury occurrence. Nevertheless, significant between-subjects variability was found, and this should be carefully considered. For that reason, an individualized analysis of the workload dynamics is recommended, avoiding greater spikes in load by aiming to keep a progressive increment of load without consequences for injury risk.

Heart-Rate Variability and Training-Intensity Distribution in Elite Rowers

2014 ◽  
Vol 9 (6) ◽  
pp. 1026-1032 ◽  
Author(s):  
Daniel J. Plews ◽  
Paul B. Laursen ◽  
Andrew E. Kilding ◽  
Martin Buchheit
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Intensity Distribution ◽  
Lactate Threshold ◽  
Training Intensity ◽  
Training Time ◽  
Low Intensity ◽  
Elite Rowers ◽  
And Training ◽  
Intensity Training

Purpose:Elite endurance athletes may train in a polarized fashion, such that their training-intensity distribution preserves autonomic balance. However, field data supporting this are limited.Methods:The authors examined the relationship between heart-rate variability and training-intensity distribution in 9 elite rowers during the 26-wk build-up to the 2012 Olympic Games (2 won gold and 2 won bronze medals). Weekly averaged log-transformed square root of the mean sum of the squared differences between R-R intervals (Ln rMSSD) was examined, with respect to changes in total training time (TTT) and training time below the first lactate threshold (>LT1), above the second lactate threshold (LT2), and between LT1 and LT2 (LT1–LT2).Results:After substantial increases in training time in a particular training zone or load, standardized changes in Ln rMSSD were +0.13 (unclear) for TTT, +0.20 (51% chance increase) for time >LT1, –0.02 (trivial) for time LT1–LT2, and –0.20 (53% chance decrease) for time >LT2. Correlations (±90% confidence limits) for Ln rMSSD were small vs TTT (r = .37 ± .80), moderate vs time >LT1 (r = .43 ± .10), unclear vs LT1–LT2 (r = .01 ± .17), and small vs >LT2 (r = –.22 ± .50).Conclusion:These data provide supportive rationale for the polarized model of training, showing that training phases with increased time spent at high intensity suppress parasympathetic activity, while low-intensity training preserves and increases it. As such, periodized low-intensity training may be beneficial for optimal training programming.

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