Assessment of Vegetation Variation on Primarily Creation Zones of the Dust Storms Around the Euphrates Using Remote Sensing Images

Recently, period frequency and effect domain of the dust storms that enter Iran from Iraq have increased. In this study, in addition to detecting the creation zones of the dust storms, the effect of vegetation cover variation on their creation was investigated using remote sensing. Moderate resolution image Spectroradiometer (MODIS) and Landsat Thematic Mapper (TM5) have been utilized to identify the primarily creation zones of the dust storms and to assess the vegetation cover variation, respectively. Vegetation cover variation was studied using Normalized Differences Vegetation Index (NDVI) obtained from band 3 and band 4 of the Landsate satellite. The results showed that the surrounding area of the Euphrates in Syria, the desert in the vicinity of this river in Iraq, including the deserts of Alanbar Province, and the north deserts of Saudi Arabia are the primarily creation zones of the dust storms entering west and south west of Iran. The results of NDVI showed that excluding the deserts in the border of Syria and Iraq, the area with very weak vegetation cover have increased between 2.44% and 20.65% from 1991 to 2009. In the meanwhile, the retention pound surface areas in the south deserts of Syria as well as the deserts in its border with Iraq have decreased 6320 and 4397 hectares, respectively. As it can be concluded from the findings, one of the main environmental parameters initiating these dust storms is the decrease in the vegetation cover in their primarily creation zones

Using machine learning algorithms in determining the stage of breast cancer from pathology reports

Introduction: After a cancer diagnosis, the most important thing is to determine the stage and grade of the cancer. Pathology reports are the main source for cancer staging, but they do not contain all the information needed for the staging. However, the text of these reports is sometimes the only available information. We were interested in knowing whether text mining methods can be used to predict staging only from pathology reports. Material and Methods: A total of 698 pathology reports of breast cancer cases and their TNM staging collected from multiple centers in West Azerbaijan Province, Iran were used for this study. After preparing the semi-structured reports, the texts of the reports were imported into a program written by Python V3. Three machine learning algorithms of Logistic Regression, SVM, and Naïve Bayes and a simple pipeline were used for the purpose of text mining. The performance of the algorithms was evaluated in terms of accuracy, precision, recall, and F1 score. Results: The Naïve Bayes algorithm achieved excellent results and a value rate of higher than 91% in all evaluation criteria (accuracy, precision, recall and F1 score). This means that the Naïve Bayes algorithm could classify the reports with high efficiency and its predictions were more correct than the other two algorithms. Naïve Bayes also outperformed SVM and Logistic Regression in terms of accuracy, recall and F1 score. In addition, Naïve-Bayes showed faster inference due to its simplicity and lower computational and training time. Conclusion: We suggest using the proposed design in this study for predicting breast cancer staging, where there is a need but not all necessary information except pathology reports. This method may not be a useful for clinical management of cancer patients, but it can be safely used for epidemiological estimations.</p

Using machine learning algorithms in determining the stage of breast cancer from pathology reports

Introduction: After a cancer diagnosis, the most important thing is to determine the stage and grade of the cancer. Pathology reports are the main source for cancer staging, but they do not contain all the information needed for the staging. However, the text of these reports is sometimes the only available information. We were interested in knowing whether text mining methods can be used to predict staging only from pathology reports. Material and Methods: A total of 698 pathology reports of breast cancer cases and their TNM staging collected from multiple centers in West Azerbaijan Province, Iran were used for this study. After preparing the semi-structured reports, the texts of the reports were imported into a program written by Python V3. Three machine learning algorithms of Logistic Regression, SVM, and Naïve Bayes and a simple pipeline were used for the purpose of text mining. The performance of the algorithms was evaluated in terms of accuracy, precision, recall, and F1 score. Results: The Naïve Bayes algorithm achieved excellent results and a value rate of higher than 91% in all evaluation criteria (accuracy, precision, recall and F1 score). This means that the Naïve Bayes algorithm could classify the reports with high efficiency and its predictions were more correct than the other two algorithms. Naïve Bayes also outperformed SVM and Logistic Regression in terms of accuracy, recall and F1 score. In addition, Naïve-Bayes showed faster inference due to its simplicity and lower computational and training time. Conclusion: We suggest using the proposed design in this study for predicting breast cancer staging, where there is a need but not all necessary information except pathology reports. This method may not be a useful for clinical management of cancer patients, but it can be safely used for epidemiological estimations.</p

PM10 monitoring using MODIS AOT and GIS, Kuala Lumpur, Malaysia.

Remote sensing has been increasingly used in retrieval Aerosol optical thickness (AOT) to particulate matter pollution monitoring. In this study, Moderate resolution image Spectroradiometer (MODIS) data were utilized in particulate matter pollution monitoring. Daily aerosol optical thickness (AOT) data retrieved from MODIS using Non-Linear Correlation Coefficient (NLCC) with polynomial equation Were compared with the amount of particulate matter PMIO measured at Three ground Air Quality Monitoring Stations (AQMS)-Victoria Kl, Cheras Kl and Gombak- in Kuala lumpur and surrounding area. The PMIO data were imported in geographical information system (GIS) environment to derive the PMIO maps in Kuala Lumpur stations. Results showed that the amounts of PMIO in dry season are higher than those in rainy season in stations. The NLCC between MODIS AOT and PMIO concentration was obtained higher in Victoria Kl compared to Gombak and Cheras Kl. GIS maps were found to show better distribution of PMIO compared to the ground station data. This study reveals AOT data from MODIS and GIS map can be utilized to study the air quality, especially distribution of PMIO in the places where there are ground measurements

New paths for modelling freshwater nature futures

Freshwater ecosystems are exceptionally rich in biodiversity and provide essential benefits to people. Yet they are disproportionately threatened compared to terrestrial and marine systems and remain underrepresented in the scenarios and models used for global environmental assessments. The Nature Futures Framework (NFF) has recently been proposed to advance the contribution of scenarios and models for environmental assessments. This framework places the diverse relationships between people and nature at its core, identifying three value perspectives as points of departure: Nature for Nature, Nature for Society, and Nature as Culture. We explore how the NFF may be implemented for improved assessment of freshwater ecosystems. First, we outline how the NFF and its main value perspectives can be translated to freshwater systems and explore what desirable freshwater futures would look like from each of the above perspectives. Second, we review scenario strategies and current models to examine how freshwater modelling can be linked to the NFF in terms of its aims and outcomes. In doing so, we also identify which aspects of the NFF framework are not yet captured in current freshwater models and suggest possible ways to bridge them. Our analysis provides future directions for a more holistic freshwater model and scenario development and demonstrates how society can benefit from freshwater modelling efforts that are integrated with the value-perspectives of the NFF. Graphical abstract: [Figure not available: see fulltext.]</p

Relation of air pollution with epidemiology of respiratory diseases in isfahan, Iran from 2005 to 2009

Background: National Institute of Environmental Health Sciences (NIEHS) scientists shows that long-term exposure to air pollutants increases the risk of respiratory diseases such as allergies, asthma, chronic obstructive pulmonary disease, and lung cancer. Children and the elderly are particularly vulnerable to the health effects of ozone, fine particles, and other airborne toxicants. Air pollution factors are considered as one of the underlying causes of respiratory diseases. This study aimed to determine the association of respiratory diseases documented in medical records and air pollution (Map distribution) of accumulation in Isfahan province, Iran. By plotting the prevalence and spatial distribution maps, important differences from different points can be observed. Materials and Methods: The geographic information system (GIS), pollutant standards index (PSI) measurements, and remote Sensing (RS) technology were used after entering data in the mapping information table; spatial distribution was mapped and distribution of Geographical Epidemiology of Respiratory Diseases in Isfahan province (Iran) was determined in this case study from 2005 to 2009. Results: Space with tracing the distribution of respiratory diseases was scattered based on the distribution of air pollution in the points is an important part of this type of diseases in Isfahan province where air pollution was more abundant. Conclusion: The findings of this study emphasis on the importance of preventing the exposure to air pollution, and to control air pollution product industries, to improve work environmental health, and to increase the health professionals and public knowledge in this regard

Factors affecting the release of nifedipine from a swellable elementary osmotic pump

Oral osmotic devices including an elementary osmotic pump (EOP) are efficient systems for the delivery of drugs with high/moderately water-solublility. In this study we designed a new type of EOP for the efficient delivery of poorly water-soluble and practically insoluble drugs. In this system, called swellable elementary osmotic pump (SEOP), drug is released from the delivery orifice in the form of a very fine dispersion of drug in gel which is ready for dissolution and absorption. Factors affecting the release of drug from the SEOP containing a poorly water-soluble drug, nifedipine, were explored extensively. To this end, effect of swelling and wetting agents, orifice size, concentration of osmotic agent, and hydrophobic plasticizer were investigated. Interestingly, in the absence or low concentration of a hydrophobic plasticizer (caster oil), the osmotic devices did not retain their integrity in dissolution media. Caster oil in concentration of > 1% was necessary for tablets to retain their integrity during dissolution process. A zero-order release kinetics for nifedipine was achieved following the effective optimization of the concentrations of swelling agent, osmotic agent, wetting agent, and also size of orifice and membrane thickness in SEOP. The zero-order release lasted for 10 hr at pH 6.8 dissolution medium. The designed SEOP is suggested as an efficient controlled delivery system for oral delivery of a poorly water soluble drug such as nifedipine. Copyright © Informa Healthcare USA, Inc

Downscaling Global Land-Use Scenario Data to the National Level: A Case Study for Belgium

Land use change scenarios, and their projected impacts on biodiversity, are highly relevant at local scales but not adequately captured by the coarse spatial resolutions of global land use models. In this study, we used the land use allocation tool of the GLOBIO 4 model to downscale the Land Use Harmonization v2 (LUH2) data from their original spatial resolution (0.25°) to 100 m and 10 m resolutions, using the country of Belgium as an example. Inputs to the tool included: (1) a reference present-day land cover map at the high spatial resolution, (2) regional land demand projections for three future scenarios, Sustainability (SSP1xRCP2.6), Regional Rivalry (SSP3xRCP6.0), and Fossil-fuelled Development (SSP5xRCP8.5), and (3) raster layers representing the suitability of the grid cells for different land use types. We further investigated the impact of using different reference land cover maps (CORINE at 100 m resolution and ESA WorldCover at 100 m and 10 m resolutions) on the downscaling outcomes. Comparison of downscaled current and future land use maps with the original LUH2 dataset showed that the use of ESA WorldCover as a reference map provides better agreement (RSR: 0.11–0.24, overall accuracy: 0.94–0.98, Kappa: 0.91–0.97) than CORINE (RSR: 0.28–0.33, overall accuracy: 0.90–0.93, Kappa: 0.90–0.91). Additionally, the validation of the present-day downscaled maps showed a good agreement with the independent Copernicus Global Land Service dataset. Our findings suggest that the choice of reference land cover map influences the degree of agreement between the downscaled and the original coarse-grain land-use maps. Moreover, the land use maps produced using our downscaling approach can provide valuable insights into the potential impacts of land use change on biodiversity and can guide local decision-making processes for sustainable land management and conservation efforts

Renewable Energy Use in Smallholder Farming Systems: A Case Study in Tafresh Township of Iran

This study was conducted to investigate use of renewable energy and materials in smallholder farming system of the Tafresh township of Iran. The population of the study consisted of 2,400 small farmers working in the smallholder farming systems of the area, in which 133 people were selected as sample using Cochran formula and simple random sampling technique. In order to gather the information, a questionnaire was developed for the study and validated by the judgment of the experts in agricultural development and extension. The reliability of the main scales of the questionnaire was examined by Cronbach Alpha coefficients, which ranged from 0.7 to 0.93, indicating the tool of study is reliable. The findings revealed that the majority of the respondents use renewable energy and materials directly in its traditional forms without enabling technologies, and they lack the access to renewable technologies to improve the efficiency of energy use. They preferred fossil energy for many activities due to its lower cost and ease of access. The overall conclusion is that there are potentials and capacities for using renewable energies and materials in the farming systems of the Tafresh township. The government has to support and encourage the adoption of renewable technologies and abandon fossil fuels wherever possible.renewable energy; sustainable agriculture; smallholder farming; Tafresh