The state-of-the-art progress in cloud detection, identification, and tracking approaches: a systematic review

A cloud is a mass of water vapor floating in the atmosphere. It is visible from the ground and can remain at a variable height for some time. Clouds are very important because their interaction with the rest of the atmosphere has a decisive influence on weather, for instance by sunlight occlusion or by bringing rain. Weather denotes atmosphere behavior and is determinant in several human activities, such as agriculture or energy capture. Therefore, cloud detection is an important process about which several methods have been investigated and published in the literature. The aim of this paper is to review some of such proposals and the papers that have been analyzed and discussed can be, in general, classified into three types. The first one is devoted to the analysis and explanation of clouds and their types, and about existing imaging systems. Regarding cloud detection, dealt with in a second part, diverse methods have been analyzed, i.e., those based on the analysis of satellite images and those based on the analysis of images from cameras located on Earth. The last part is devoted to cloud forecast and tracking. Cloud detection from both systems rely on thresholding techniques and a few machine-learning algorithms. To compute the cloud motion vectors for cloud tracking, correlation-based methods are commonly used. A few machine-learning methods are also available in the literature for cloud tracking, and have been discussed in this paper too

Granisetron versus Granisetron-Dexamethasone for Prevention of Postoperative Nausea and Vomiting in Pediatric Strabismus Surgery: A Randomized Double-Blind Trial

Aim. Efficacy of granisetron and combination of granisetron and dexamethasone was evaluated for prevention of postoperative nausea and vomiting (PONV) in children undergoing elective strabismus surgery. Methods. A total of 136 children (1–15 years) were included. Children received either granisetron (40 mcg/kg) [group G] or combination of granisetron (40 mcg/kg) and dexamethasone (150 mcg/kg) [group GD]. Intraoperative fentanyl requirement and incidence and severity of oculocardiac reflex were assessed. PONV severity was assessed for first 24 hours and if score was >2, it was treated with metoclopramide. Postoperative analgesia was administered with intravenous fentanyl and ibuprofen. Results. The demographic profile, muscles operated, and fentanyl requirement were comparable. Complete response to PONV in first 24 hours was observed in 75% (51/68) of children in group G and 76.9% (50/65) of children in group GD, which was comparable statistically (p=0.96, Fisher exact test; OR 1.11, 95% CI 0.50, 2.46). Incidence of PONV between 0 and 24 hours was comparable. One child in group G required rescue antiemetic in first 24 hours and none of the children had severe PONV in group GD. There was no significant difference in incidence or severity of oculocardiac reflex. Conclusion. Dexamethasone did not increase efficacy of granisetron for prevention of PONV in elective pediatric strabismus surgery. Registration number of clinical trial was CTRI/2009/091/001000

Natural Time Series Parameters Forecasting: Validation of the Pattern-Sequence-Based Forecasting (PSF) Algorithm; A New Python Package

Climate change has contributed substantially to the weather and land characteristic phenomena. Accurate time series forecasting for climate and land parameters is highly essential in the modern era for climatologists. This paper provides a brief introduction to the algorithm and its implementation in Python. The pattern-sequence-based forecasting (PSF) algorithm aims to forecast future values of a univariate time series. The algorithm is divided into two major processes: the clustering of data and prediction. The clustering part includes the selection of an optimum value for the number of clusters and labeling the time series data. The prediction part consists of the selection of a window size and the prediction of future values with reference to past patterns. The package aims to ease the use and implementation of PSF for python users. It provides results similar to the PSF package available in R. Finally, the results of the proposed Python package are compared with results of the PSF and ARIMA methods in R. One of the issues with PSF is that the performance of forecasting result degrades if the time series has positive or negative trends. To overcome this problem difference pattern-sequence-based forecasting (DPSF) was proposed. The Python package also implements the DPSF method. In this method, the time series data are first differenced. Then, the PSF algorithm is applied to this differenced time series. Finally, the original and predicted values are restored by applying the reverse method of the differencing process. The proposed methodology is tested on several complex climate and land processes and its potential is evidenced

Natural Time Series Parameters Forecasting: Validation of the Pattern-Sequence-Based Forecasting (PSF) Algorithm; A New Python Package

Climate change has contributed substantially to the weather and land characteristic phenomena. Accurate time series forecasting for climate and land parameters is highly essential in the modern era for climatologists. This paper provides a brief introduction to the algorithm and its implementation in Python. The pattern-sequence-based forecasting (PSF) algorithm aims to forecast future values of a univariate time series. The algorithm is divided into two major processes: the clustering of data and prediction. The clustering part includes the selection of an optimum value for the number of clusters and labeling the time series data. The prediction part consists of the selection of a window size and the prediction of future values with reference to past patterns. The package aims to ease the use and implementation of PSF for python users. It provides results similar to the PSF package available in R. Finally, the results of the proposed Python package are compared with results of the PSF and ARIMA methods in R. One of the issues with PSF is that the performance of forecasting result degrades if the time series has positive or negative trends. To overcome this problem difference pattern-sequence-based forecasting (DPSF) was proposed. The Python package also implements the DPSF method. In this method, the time series data are first differenced. Then, the PSF algorithm is applied to this differenced time series. Finally, the original and predicted values are restored by applying the reverse method of the differencing process. The proposed methodology is tested on several complex climate and land processes and its potential is evidenced

Typing of Neisseria gonorrhoeae isolates by phenotypic and genotypic techniques in New Delhi, India

BACKGROUND: The objective of this study is to investigate gonococcal isolates using phenotypic and genotypic methods. METHODOLOGY: Sixty gonococcal isolates obtained were examined. Strains were divided into 9 resistant phenotypes: Chromosomally mediated penicillin-resistant Neisseria gonorrhoeae (CMRNGP), penicillinase-producing NG (PPNG), chromosomally mediated tetracycline-resistant NG (CMRNGT), TRNG, PPNG and TRNG, CMRNGPT, quinolone resistant NG (QRNG), Azithro R, and decreased susceptibility (DS) to ceftriaxone. These isolates were also subjected to auxotyping and NG-multi-antigen sequence typing (MAST). RESULTS: Of 60 isolates, 32 (53.33%) PPNG and only one was CMRNGP; 16 (26.66%) were CMRNGT, while 18 (30%) were TRNG. Both PPNG and TRNG found in 13 (21.66%) and none were CMRNGPT. QRNG was seen in 93.33%, 5% Azithromycin R, and 6.66% were DS to ceftriaxone. Based on auxotyping, 24 (40%) nonrequiring, 16 (26.66%) were proline requiring, 13 (21.66%) arginine requiring while 7 (11.66%) belonged to others. The most common ST was 6058 (32.5%). The discriminatory indices of antibiogram, auxotyping and NG-MAST were 0.77, 0.72, and 0.95, respectively. CONCLUSIONS: NG-MAST is the method of choice for epidemiological studies