Machine Learning Models for Prediction of Soil Properties in the Riparian Forests

Spatial variability of soil properties is a critical factor for the planning, management, and exploitation of soil resources. Thus, the use of different digital soil mapping models to provide accuracy plays a crucial role in providing soil physicochemical properties maps. Soil spatial variability in forest stands is not well-known in Iran. Meanwhile, riparian buffers are important for several services such as providing high water quality, nutrient recycling, and buffering agricultural production. Accordingly, in this research, 103 soil samples were taken using the Latin hypercubic method in the Maroon riparian forest of Behbahan and agricultural lands in the vicinity of the forest to evaluate the spatial variability of soil nitrogen, potassium, organic carbon, C:N ratio, pH, calcium carbonate, sand, silt, clay, and bulk density. Different machine learning models, including artificial neural networks, random forest, cubist regression tree, and k-nearest neighbor were used to compare the estimation of soil properties. Moreover, three main sources of spatial information including remote sensing images, digital elevation model, and climate parameters were used as ancillary data. Our results indicated that the random forest model has the best results in estimating soil pH, nitrogen, potassium, and bulk density. In contrast, the cubist regression tree indicated the best estimation for organic carbon, C:N ratio, phosphorous, and clay. Further, artificial neural networks showed the best estimation for calcium carbonate, sand, and silt contents. Our results revealed that geospatial information such as terrain parameters, climate parameters, and satellite images could be well used as ancillary data for the spatial mapping of soil physiochemical properties in riparian forests and agricultural lands. In conclusion, a specific machine learning model needs to be used for each soil property to provide highly accurate maps with less error

Polyethylene eye-cover versus artificial teardrops in the prevention of ocular surface diseases in comatose patients: A prospective multicenter randomized triple-blinded three-arm clinical trial.

BackgroundPolyethylene covers are claimed to be useful in preventing ocular surface diseases (OSD); however, evidence of their clinical efficacy is limited. This clinical trial aimed to compare the use of polyethylene eye covers and artificial teardrops versus normal saline on the incidence and severity of OSD in comatose patients.MethodsOf 90 eligible patients randomly assigned to three treatment groups, 79 patients completed the study, In group A, patients (n = 25) received artificial teardrops for left and normal saline for right eyes, in group B (n = 29) polyethylene covers for left and normal saline for right eyes, and in group C (n = 25) polyethylene covers for left and artificial teardrops for right eyes. As the patients were comatose, their blinding did not applicable, and a blinded observer evaluated the patients' eyes based on the Corneal Fluorescein Staining Pattern. The blinded analyzer analyzed collected data by SPSS-16 software at a 95% confidential level.ResultsThe OSDs were observed in 65 (41.14%) out of 158 eyes examined. The artificial teardrop was more effective than the normal saline in group A, polyethylene eye cover was more useful than the normal saline in group B, and polyethylene eye cover was more effective than the artificial teardrop in group C in reducing the incidence of OSD (pConclusionsPolyethylene eye covers significantly reduced the incidence and severity of OSD. Using polyethylene cover is suggested as a safe, effective, and accessible eye care intervention for preventing OSD in comatose patients.Trial registration(IRCT201609129014N115), Iranian Registry of Clinical Trials

The Iranian blood pressure measurement campaign, 2019: study protocol and preliminary results

Purpose Hypertension is one of the most important risk factors for premature mortality and morbidity in Iran. The objective of the Iranian blood pressure (BP) measurement campaign was to identify individuals with raised blood pressure and providing appropriate care and increase the awareness among the public and policymakers of the importance of tackling hypertension. Methods The campaign was conducted in two phases. The first (communication) phase started on May 17th (International Hypertension Day). The second phase started on June 8th, 2019, and lasted up to July 7th during which, blood pressures were measured. The target population was Iranians aged >= 30 years. Participants voluntarily referred to health houses in rural and health posts and comprehensive health centers in urban areas in the setting of the Primary Health Care network. Additionally, over 13,700 temporary stations were set up in highly visited places in urban areas. Volunteer healthcare staff interviewed the participants, measured their BP, and provided them with lifestyle advice and knowledge of the risks and consequences of high blood pressure. They referred participants to physicians in case their BP was high. Participants immediately received a text message containing the relevant advice based on their measured BP and their past history. Results Blood pressure was measured for a total of 26,678,394 participants in the campaign. A total of 13,722,148 participants (51.4%) were female. The mean age was 46 +/- 14.1 years. Among total participants, 15,012,693 adults (56.3%) with no past history of hypertension had normal BP, 7,959,288 participants had BP in the prehypertension range (29.8%), and finally, 3,706,413 participants (13.9%) had either past medical history of hypertension, used medications, or had high BP measured in the campaign. Conclusion The campaign was feasible with the objective to increase the awareness among the public and policymakers of the importance of tackling hypertension in Iran