scholarly journals An Overall Evaluation of Water Transparency in Lake Malawi from MERIS Data

2019 ◽  
Vol 11 (3) ◽  
pp. 279 ◽  
Author(s):  
Augusto Vundo ◽  
Bunkei Matsushita ◽  
Dalin Jiang ◽  
Mangaliso Gondwe ◽  
Rossi Hamzah ◽  
...  
Keyword(s):  
Water Quality ◽  
Satellite Data ◽  
Lake Malawi ◽  
Water Transparency ◽  
Estimation Accuracy ◽  
Imaging Spectrometer ◽  
Routine Monitoring ◽  
Level 3 ◽  
Level 1 ◽  
Highly Correlated

Lake Malawi is an important water resource in Africa. However, there is no routine monitoring of water quality in the lake due to financial and institutional constraints in the surrounding countries. A combination of satellite data and a semi-analytical algorithm can provide an alternative for routine monitoring of water quality, especially in developing countries. In this study, we first compared the performance of two semi-analytical algorithms, Doron11 and Lee15, which can estimate Secchi disk depth (SD) from satellite data in Lake Malawi. Our results showed that even though the SD estimations from the two algorithms were very highly correlated, the Lee15 outperformed the Doron11 in Lake Malawi with high estimation accuracy (RMSE = 1.17 m, MAPE = 18.7%, R = 0.66, p < 0.05). We then evaluated water transparency in Lake Malawi using the SD values estimated from nine years of Medium Resolution Imaging Spectrometer (MERIS) data (2003–2011) with the Lee15 algorithm. Results showed that Lake Malawi maintained four water transparency levels throughout the study period (i.e., level 1: SD > 12 m; level 2: SD between 6–12 m; level 3: SD between 3–6 m; level 4: SD between 1.5–3 m). The level 1 and 2 water areas tended to shift or trade places depending on year or season. In contrast, level 3 and 4 water areas were relatively stable and constantly distributed along the southwestern and southern lakeshores. In general, Lake Malawi is dominated by waters with SD values larger than 6 m (>95%). This study represents the first overall and comprehensive analysis of water transparency status and spatiotemporal variation in Lake Malawi.

Anwendung des 10-Gruppenklassifikationssystems nach Robson für den Vergleich eines Perinatalzentrums Level 1 mit einem Level-3-Zentrum

2020 ◽  
Author(s):  
C Pulvermacher ◽  
P van de Vondel ◽  
L Gerzen ◽  
U Gembruch ◽  
W Merz
Keyword(s):  
Level 3 ◽  
Level 1

EVALUASI PELATIHAN DI PT. PUPUK KUJANG CIKAMPEK

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Lania Muharsih ◽  
Ratih Saraswati
Keyword(s):  
Training Evaluation ◽  
Work Productivity ◽  
Learning Behavior ◽  
Level 3 ◽  
Level 1 ◽  
Four Levels ◽  
A Company ◽  
Level 2 ◽  
Evaluation Sheet ◽  
Assessment Factor

This study aims to determine the training evaluation at PT. Kujang Fertilizer. PT. Pupuk Kujang is a company engaged in the field of petrochemicals. Evaluation sheet of PT. Fertilizer Kujang is made based on Kirkpatrick's theory which consists of four levels of evaluation, namely reaction, learning, behavior, and results. At level 1, namely reaction, in the evaluation sheet is in accordance with the theory of Kirkpatrick, at level 2 that is learning should be held pretest and posttest but only made scale. At level 3, behavior, according to theory, but on assessment factor number 3, quantity and work productivity should not need to be included because they are included in level 4. At level 4, that is the result, here is still lacking to get a picture of the results of the training that has been carried out because only based on answers from superiors without evidence of any documents.   Keywords: Training Evaluation, Kirkpatrick Theory.    Penelitian ini bertujuan mengetahui evaluasi training di PT. Pupuk Kujang. PT. Pupuk Kujang merupakan perusahaan yang bergerak di bidang petrokimia. Lembar evaluasi PT. Pupuk Kujang dibuat berdasarkan teori Kirkpatrick yang terdiri dari empat level evaluasi, yaitu reaksi, learning, behavior, dan hasil. Pada level 1 yaitu reaksi, di lembar evaluasi tersebut sudah sesuai dengan teori dari Kirkpatrick, pada level 2 yaitu learning seharusnya diadakan pretest dan posttest namun hanya dibuatkan skala. Pada level 3 yaitu behavior, sudah sesuai teori namun pada faktor penilaian nomor 3 kuantitas dan produktivitas kerja semestinya tidak perlu dimasukkan karena sudah termasuk ke dalam level 4. Pada level 4 yaitu hasil, disini masih sangat kurang untuk mendapatkan gambaran hasil dari pelatihan yang sudah dilaksanakan karena hanya berdasarkan dari jawaban atasan tanpa bukti dokumen apapun.   Kata kunci: Evaluasi Pelatihan, Teori Kirkpatrick.

Monitoring effluent quality in hypertrophic wastewater reservoirs using remote sensing

1996 ◽  
Vol 33 (8) ◽  
pp. 23-29 ◽  
Author(s):  
I. Dor ◽  
N. Ben-Yosef
Keyword(s):  
Water Quality ◽  
Theoretical Model ◽  
Principal Component ◽  
Cost Effective ◽  
Operational Parameters ◽  
Effluent Quality ◽  
Optical Images ◽  
Routine Monitoring ◽  
Cost Effective Method ◽  
Wastewater Quality

About one hundred and fifty wastewater reservoirs store effluents for irrigation in Israel. Effluent qualities differ according to the inflowing wastewater quality, the degree of pretreatment and the operational parameters. Certain aspects of water quality like concentration of organic matter, suspended solids and chlorophyll are significantly correlated with the water column transparency and colour. Accordingly optical images of the reservoirs obtained from the SPOT satellite demonstrate pronounced differences correlated with the water quality. The analysis of satellite multispectral images is based on a theoretical model. The model calculates, using the radiation transfer equation, the volume reflectance of the water body. Satellite images of 99 reservoirs were analyzed in the chromacity space in order to classify them according to water quality. Principal Component Analysis backed by the theoretical model increases the method sensitivity. Further elaboration of this approach will lead to the establishment of a time and cost effective method for the routine monitoring of these hypertrophic wastewater reservoirs.

scholarly journals 905 How to prioritise patients and safely resume elective surgery during the Covid-19 pandemic

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
H Subbiah Ponniah ◽  
M Ahmed ◽  
T Edwards ◽  
J Cobb ◽  
E Dean ◽  
...  
Keyword(s):  
Patient Safety ◽  
High Risk ◽  
Orthopaedic Surgery ◽  
Elective Surgery ◽  
Paediatric Patients ◽  
Level 3 ◽  
Level 1 ◽  
Surgical Unit

Abstract Introduction There are now over 2.5 million NHS patients awaiting elective surgery, with the most in orthopaedics. We present an algorithm and results for safely and equitably restarting surgery at COVID-light sites. Method An MDT applied the COVID-19 Algorithm for Resuming Elective Surgery (CARES) on 1169 patients awaiting elective orthopaedic surgery. It assessed safety, procedural efficacy, and biopsychosocial factors, to prioritise patients. They were assigned to five categories and underwent surgery at one of three COVID-light sites (1. access to HDU/ITU/Paediatrics/specialist equipment, 2. an NHS elective surgical unit and 3. a private elective surgical unit). Results 21 ‘Urgent’ patients received expedited care; 118 were Level 1/2; 222 were Level 3; 808 were Level 4. In 6 weeks, 355 surgeries were performed, with Urgent and Level 1/2 cases performed soonest (mean 18 days, p &lt; 0.001). 33 high-risk/complex/paediatric patients had surgery at Site 1 and the rest at Sites 2 and 3. No patients contracted COVID-19 within 2 weeks of surgery. Conclusions We validated a widely generalisable model to facilitate resumption of elective surgery in COVID-light sites. It enabled surgery for patients in most suffering, undergoing the most efficacious procedures and/or at highest risk of deterioration, without compromising patient-safety.

scholarly journals An Atmospheric Visibility Grading Method Based on Ensemble Learning and Stochastic Weight Average

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 869
Author(s):  
Xiuguo Zou ◽  
Jiahong Wu ◽  
Zhibin Cao ◽  
Yan Qian ◽  
Shixiu Zhang ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Traffic Accidents ◽  
Feature Sets ◽  
Atmospheric Visibility ◽  
Average Accuracy ◽  
Level 3 ◽  
Visual Characteristics ◽  
Level 1 ◽  
Grading Model ◽  
Level 2

In order to adequately characterize the visual characteristics of atmospheric visibility and overcome the disadvantages of the traditional atmospheric visibility measurement method with significant dependence on preset reference objects, high cost, and complicated steps, this paper proposed an ensemble learning method for atmospheric visibility grading based on deep neural network and stochastic weight averaging. An experiment was conducted using the scene of an expressway, and three visibility levels were set, i.e., Level 1, Level 2, and Level 3. Firstly, the EfficientNet was transferred to extract the abstract features of the images. Then, training and grading were performed on the feature sets through the SoftMax regression model. Subsequently, the feature sets were ensembled using the method of stochastic weight averaging to obtain the atmospheric visibility grading model. The obtained datasets were input into the grading model and tested. The grading model classified the results into three categories, with the grading accuracy being 95.00%, 89.45%, and 90.91%, respectively, and the average accuracy of 91.79%. The results obtained by the proposed method were compared with those obtained by the existing methods, and the proposed method showed better performance than those of other methods. This method can be used to classify the atmospheric visibility of traffic and reduce the incidence of traffic accidents caused by atmospheric visibility.

The Magical Number Two, Plus or Minus One: Some Limits on our Capacity for Processing Musical Information

2002 ◽  
Vol 6 (2) ◽  
pp. 185-219 ◽  
Author(s):  
Adam Ockelford
Keyword(s):  
Cognitive Processing ◽  
General Principle ◽  
Empirical Work ◽  
Theoretical Framework ◽  
Musical Structure ◽  
Magical Number ◽  
Level 3 ◽  
Level 1 ◽  
Level 2

Like all fields of human artistic endeavour, music is constrained by our cognitive processing requirements and limitations (Swain, 1986; Lerdahl, 1988; Huron, 2001). This articleconsiders particular forms of constraint pertaining to the relationships that the structure-seeking mind (subconsciously) fabricates between perceived musical events. It is proposed that 2±1 may be a universal limitation pertaining to the level of relationships so ideated. That is, in terms of Lewin'S (1987) theoretical framework in which “intervals” can be intuited between the “elements” of musical “spaces”, it is posited that the cognition of musical structure occurs either through intervals (level 1), through intervals between these (level 2), or — in some circumstances — through intervals between these(level 3). This proposition is explored through the psychomusicological model developed by Ockelford (1991, 1993, 1999), which too analyses musical structure in terms of the relationships that may be cognised between its discrete perceptual components. In particular, the model identifies a type of cognitive link through which events (at any level) arefelt to imply others the same or similar — through so-called “zygonic” relationships. This theory suggests a further general principle: that the highest level of relationship inoperation at any given point must be zygonic if the music is to be structurally coherent. Evidence for this, and for the limit on the level of relationships of 2±1, is offeredthrough a series of musical examples, which illustrate a variety of musical organisation in action. Finally, empirical work is suggested to explore further the theoretical ideas that arepresented here.

Systems biology markup language: Level 2 and beyond

2003 ◽  
Vol 31 (6) ◽  
pp. 1472-1473 ◽  
Author(s):  
A. Finney ◽  
M. Hucka
Keyword(s):  
Systems Biology ◽  
Computational Models ◽  
Chemical Species ◽  
Biochemical Networks ◽  
Markup Language ◽  
Exchange Format ◽  
Systems Biology Markup Language ◽  
Level 3 ◽  
Level 1 ◽  
Level 2

The SBML (systems biology markup language) is a standard exchange format for computational models of biochemical networks. We continue developing SBML collaboratively with the modelling community to meet their evolving needs. The recently introduced SBML Level 2 includes several enhancements to the original Level 1, and features under development for SBML Level 3 include model composition, multistate chemical species and diagrams.

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