Investigating the Spatial and Temporal Variability of Precipitation using Entropy Theory

Abstract This study uses entropy theory to develop a novel application of the apportionment entropy disorder index (AEDI) to capture both spatial and temporal variability in monthly precipitation for various types of hydrologic modeling. In total, 41 Environment Canada stations across Ontario with long term (1955 to 2005) records and a very low percentage of missing data were selected. It was found that the fall and summer seasons are the major contributors to annual precipitation variability. Spatial variability of annual precipitation was observed to be increasing from southern to northern Ontario. The AEDI index map of Ontario, developed in this study, has been successfully integrated into several hydrologic models

Role of AYUSH Therapies in Modern Medicine: A Qualitative Study to Explore the Awareness and Attitudes of Doctors Towards the Utilization of Alternate System of Medicine for Diabetes Mellitus.

OBJECTIVE: To understand doctors' attitude to and awareness of AYUSH therapies for the treatment of diabetes mellitus (DM). METHODS: This qualitative study, using a usage-and-attitude survey, was conducted in secondary centers across Mumbai, India. The study surveyed 77 physicians, including those specializing in diabetes. RESULTS: The majority of doctors were aware of Ayurveda (69%) and Homeopathy (52%). Some doctors were aware of Unani (34%) and Siddha (32%). Most doctors (60%) thought that Ayurveda was effective in some way. Almost all doctors (97%) thought that allopathic medicine was effective for DM. The majority of doctors (68%) had not recommended AYUSH therapies as an adjunct to modern medicines. Approximately half of the doctors (52%) believed that AYUSH therapies posed a safety concern for patients and 46% thought that AYUSH therapies could not be used to manage any form of DM. A large group of doctors thought that the main barrier preventing AYUSH therapies from being integrated into current allopathic management of DM was the lack of strong scientific evidence and clinical trials. CONCLUSION: The majority of doctors are aware to some degree of Ayurveda and homeopathic forms of treatment. The majority believe that AYUSH therapies pose a safety concern for patients and have no role in treatment for any form of DM. The most common barrier preventing AYUSH therapies from becoming a mainstream treatment option for DM is the lack of scientific evidence. From this sample, it seems that greater efforts are required to conduct research into the efficacy and safety of AYUSH therapies to ensure that doctors are able to provide holistic care for patients with DM

A RP-HPLC METHOD FOR SIMULTANEOUS DETERMINATION OF HALOPERIDOL AND TRIHEXYPHENIDYL HYDROCHLORIDE IN TABLET DOSAGE FORM

A simple, fast and precise reversed phase-high performance liquid chromatographic (RP-HPLC) method is proposed for simultaneous determination of the binary mixture of haloperidol and tri- hexyphenidyl hydrochloride in pharmaceutical formulations. This method uses a mobile phase consisting of methanol:acetonitrile:water (50:40:10 v/v/v), Zodiac C18 column in isocratic mode, detection wavelength of 221 nm and a flow rate of 1.2 ml/minutes. The measured retention times for haloperidol and trihexyphenidyl hydrochloride are 6.12±0.02 and 8.06±0.02 minutes, respectively. The resolution of the two chromatographic peaks is 8.23. The validation of the method showed good linearity in the range 5-50 μg/ml for haloperidol and in the range 2-20 μg/ml for trihexyphenidyl hydrochloride. Further, satisfactory results are also established in terms of mean percent- age recovery (99.01-99.77% for haloperidol and 99.08-100.33% for trihexyphenidyl hydrochloride), intra-day and inter-day precision (<2%) and robustness. The advantages of this method are good resolution with sharper peaks and sufficient precision. This could be used for the determination of the above drugs in dosage forms in combination or individually and in bulk

Chronic within-hive video recordings detect altered nursing behaviour and retarded larval development of neonicotinoid treated honey bees

Risk evaluations for agricultural chemicals are necessary to preserve healthy populations of honey bee colonies. Field studies on whole colonies are limited in behavioural research, while results from lab studies allow only restricted conclusions on whole colony impacts. Methods for automated long-term investigations of behaviours within comb cells, such as brood care, were hitherto missing. In the present study, we demonstrate an innovative video method that enables within-cell analysis in honey bee (Apis mellifera) observation hives to detect chronic sublethal neonicotinoid effects of clothianidin (1 and 10 ppb) and thiacloprid (200 ppb) on worker behaviour and development. In May and June, colonies which were fed 10 ppb clothianidin and 200 ppb thiacloprid in syrup over three weeks showed reduced feeding visits and duration throughout various larval development days (LDDs). On LDD 6 (capping day) total feeding duration did not differ between treatments. Behavioural adaptation was exhibited by nurses in the treatment groups in response to retarded larval development by increasing the overall feeding timespan. Using our machine learning algorithm, we demonstrate a novel method for detecting behaviours in an intact hive that can be applied in a versatile manner to conduct impact analyses of chemicals, pests and other stressors

Machine Learning Techniques for Gully Erosion Susceptibility Mapping: A Review

Gully erosion susceptibility mapping (GESM) through predicting the spatial distribution of areas prone to gully erosion is required to plan gully erosion control strategies relevant to soil conservation. Recently, machine learning (ML) models have received increasing attention for GESM due to their vast capabilities. In this context, this paper sought to review the modeling procedure of GESM using ML models, including the required datasets and model development and validation. The results showed that elevation, slope, plan curvature, rainfall and land use/cover were the most important factors for GESM. It is also concluded that although ML models predict the locations of zones prone to gullying reasonably well, performance ranking of such methods is difficult because they yield different results based on the quality of the training dataset, the structure of the models, and the performance indicators. Among the ML techniques, random forest (RF) and support vector machine (SVM) are the most widely used models for GESM, which show promising results. Overall, to improve the prediction performance of ML models, the use of data-mining techniques to improve the quality of the dataset and of an ensemble estimation approach is recommended. Furthermore, evaluation of ML models for the prediction of other types of gully erosion, such as rill–interill and ephemeral gully should be the subject of more studies in the future. The employment of a combination of topographic indices and ML models is recommended for the accurate extraction of gully trajectories that are the main input of some process-based models

Machine Learning Techniques for Gully Erosion Susceptibility Mapping: A Review

Gully erosion susceptibility mapping (GESM) through predicting the spatial distribution of areas prone to gully erosion is required to plan gully erosion control strategies relevant to soil conservation. Recently, machine learning (ML) models have received increasing attention for GESM due to their vast capabilities. In this context, this paper sought to review the modeling procedure of GESM using ML models, including the required datasets and model development and validation. The results showed that elevation, slope, plan curvature, rainfall and land use/cover were the most important factors for GESM. It is also concluded that although ML models predict the locations of zones prone to gullying reasonably well, performance ranking of such methods is difficult because they yield different results based on the quality of the training dataset, the structure of the models, and the performance indicators. Among the ML techniques, random forest (RF) and support vector machine (SVM) are the most widely used models for GESM, which show promising results. Overall, to improve the prediction performance of ML models, the use of data-mining techniques to improve the quality of the dataset and of an ensemble estimation approach is recommended. Furthermore, evaluation of ML models for the prediction of other types of gully erosion, such as rill–interill and ephemeral gully should be the subject of more studies in the future. The employment of a combination of topographic indices and ML models is recommended for the accurate extraction of gully trajectories that are the main input of some process-based models

Evaluating Three Hydrological Distributed Watershed Models: MIKE-SHE, APEX, SWAT

Selecting the right model to simulate a specific watershed has always been a challenge, and field testing of watersheds could help researchers to use the proper model for their purposes. The performance of three popular Geographic Information System (GIS)-based watershed simulation models (European Hydrological System Model (MIKE SHE), Agricultural Policy/Environmental Extender (APEX) and Soil and Water Assessment Tool (SWAT)) were evaluated for their ability to simulate the hydrology of the 52.6 km2 Canagagigue Watershed located in the Grand River Basin in southern Ontario, Canada. All three models were calibrated for a four-year period and then validated using an independent four-year period by comparing simulated and observed daily, monthly and annual streamflow. The simulated flows generated by the three models are quite similar and closely match the observed flow, particularly for the calibration results. The mean daily/monthly flow at the outlet of the Canagagigue Watershed simulated by MIKE SHE was more accurate than that simulated by either the SWAT or the APEX model, during both the calibration and validation periods. Moreover, for the validation period, MIKE SHE predicted the overall variation of streamflow slightly better than either SWAT or APEX