A Novel Partner Selection Method in Virtual Enterprise Based on the Ontology and SOA and AHPmodel

Partner selection is a key problem in virtual enterprise. This paper researches on selecting the dynamic, Competitive and compatible partners, which is the key link in the establishment of virtual enterprise. this paper presented a method have two main steps , first step is detecting candidate partner using semantic concept and second step is selecting proper partner using competency and quality of service in AHP method . The advantage of this method scrutiny of partner selection problem , and partner selection in this method occur dynamic and automation

Linear modelling of water potential and supply for decentralized Energy-Water-Food systems - case Study St. Rupert Mayer, Zimbabwe

Limited water accessibility threatens the development of communities, especially where agriculture is the main income source.  The implementation of decentralized Energy-Water-Food systems is a promising approach to improve the situation in these communities, creating synergies and improving the profitability of the system. The model urbs optimizes Energy-Water-Food systems to generate the highest revenues, considering the local conditions and sustainability limits. This work improves the hydrogeological part of urbs in order to model the water potential of a given community, establishing interrelations of the water sector with the energy and food sectors, and maximizing the long-term benefits within the sustainability limits. The proposed method was applied to the rural community of St. Rupert Mayer in Zimbabwe. In order to analyse the impact of data uncertainty on the model results, the sensitivity of the main input parameters is analysed. The results indicate that it is important to implement reliable input data for dimensioning the proper system configuration, as otherwise the whole system would not be sustainable.&nbs

Detecting and predicting the impact of land use changes on groundwater quality, a case study in Northern Kelantan, Malaysia

The conversions of forests and grass land to urban and farmland has exerted significant changes on terrestrial ecosystems. However, quantifying how these changes can affect the quality of water resources is still a challenge for hydrologists. Nitrate concentrations can be applied as an indicator to trace the link between land use changes and groundwater quality due to their solubility and easy transport from their source to the groundwater. In this study, 25 year records (from 1989 to 2014) of nitrate concentrations are applied to show the impact of land use changes on the quality of groundwater in Northern Kelantan, Malaysia, where large scale deforestation in recent decades has occurred. The results from the integration of time series analysis and geospatial modelling revealed that nitrate (NO3-N) concentrations significantly increased with approximately 8.1% and 3.89% annually in agricultural and residential wells, respectively, over 25 years. In 1989 only 1% of the total area had a nitrate value greater than 10 mg/L; and this value increased sharply to 48% by 2014. The significant increase in nitrate was only observed in a shallow aquifer with a 3.74% annual nitrate increase. Based on the result of the Autoregressive Integrated Moving Average (ARIMA) model the nitrate contamination is expected to continue to rise by about 2.64% and 3.9% annually until 2030 in agricultural and residential areas. The present study develops techniques for detecting and predicting the impact of land use changes on environmental parameters as an essential step in land and water resource management strategy development

Assessment of the potential contamination risk of nitrate in groundwater using indicator kriging (in Amol-Babol Plain, Iran)

In arid and semi-arid regions such as Amol–Babol Plain in north Iran, groundwater is a major source of drinking water. Excessive usage of fertilizers in agricultural land, domestic sewage and industrial wastewater may result in nitrate contamination. The main objective of this study is to assess the potential contamination risk of nitrate pollution. The groundwater samples were collected from 100 agriculture wells during wet and dry seasons in 2009 and analyzed for nitrate concentration. Indicator kriging (IK) method is applied to create maps indicating the predicted probability of nitrate concentrations in groundwater exceeding the WHO drinking water standard of 10 mg/L-N. Based on the risk probability maps, some areas on the southern side of Babol City and the north and north-western side of Amol City showed a high probability of nitrate contamination. Seasonal maps indicated that the probability of nitrate contamination increased in the wet season, compared to the dry season in the study area, due to increase runoff from irrigated lands. Indicator kriging with local indicator thresholds is shown to be a reliable method to assess uncertainty in the estimation

Catastrophic Health Expenditure among Iranian Households:Evidence from the COVID-19 Era

Background: Monitoring households’ exposure to catastrophic health expenditure (CHE) based on out-of-pocket (OOP) health payments is a critical tool for evaluating the equitable financial protection status within the health system. The COVID-19 pandemic has brought unprecedented global change and potentially affected the mentioned protection indicators. This study aimed to assess the prevalence of CHE among households in Iran during the COVID-19 period. Methods: The present study employed a retrospective-descriptive design utilizing data derived from two consecutive cross-sectional Annual Household Income and Expenditure Surveys (HIES) undertaken by the Statistical Centre of Iran (SCI) in 2020 and 2021. The average annual OOP health payments and the prevalence of households facing CHE were estimated separately for rural and urban areas, as well as at the national level. Based on the standard method recommended by the World Health Organization (WHO), CHE was identified as situations in which OOP health payments surpass 40% of a household’s capacity to pay (CTP). The intensity of CHE was also calculated using the overshoot measure. All statistical analyses were carried out using Excel-2016 and Stata-14 software. Results: The average OOP health payments increased in 2021, compared to 2020, across rural and urban areas as well as at the national level. Urban residents consistently experienced higher OOP health payments than rural residents and the national level in both years. At the national level, the prevalence of CHE was 2.92% in 2020 and increased to 3.18% in 2021. In addition, rural residents faced a higher prevalence of CHE based on total health services OOP, outpatient services OOP, and inpatient services OOP compared to urban residents and the national level. Regarding the intensity of CHE using overshoot, the results for 2020 and 2021 revealed that the overshoot ranged between 0.60% and 0.65% in rural areas, between 0.30% and 0.33% in urban areas, and between 0.38% and 0.41% at the national level. Conclusion: A considerable percentage of households in Iran still incur CHE. This trend has increased in the second year of COVID-19 compared to the first year, as households received more healthcare services. The situation is even more severe for rural residents. There is an urgent need for targeted interventions in the health system, such as strengthening prepayment mechanisms, to reduce OOP and ensure equitable protection for healthcare recipients

Spatiotemporal variation of groundwater quality using integrated multivariate statistical and geostatistical approaches in Amol–Babol Plain, Iran

In recent years, groundwater quality has become a global concern due to its effect on human life and natural ecosystems. To assess the groundwater quality in the Amol–Babol Plain, a total of 308 water samples were collected during wet and dry seasons in 2009. The samples were analysed for their physico-chemical and biological constituents. Multivariate statistical analysis and geostatistical techniques were applied to assess the spatial and temporal variabilities of groundwater quality and to identify the main factors and sources of contamination. Principal component analysis (PCA) revealed that seven factors explained around 75 % of the total variance, which highlighted salinity, hardness and biological pollution as the dominant factors affecting the groundwater quality in the Plain. Two-way analysis of variance (ANOVA) was conducted on the dataset to evaluate the spatio-temporal variation. The results showed that there were no significant temporal variations between the two seasons, which explained the similarity between six component factors in dry and wet seasons based on the PCA results. There are also significant spatial differences (p > 0.05) of the parameters under study, including salinity, potassium, sulphate and dissolved oxygen in the plain. The least significant difference (LSD) test revealed that groundwater salinity in the eastern region is significantly different to the central and western side of the study area. Finally, multivariate analysis and geostatistical techniques were combined as an effective method for demonstrating the spatial structure of multivariate spatial data. It was concluded that multiple natural processes and anthropogenic activities were the main sources of groundwater salinization, hardness and microbiological contamination of the study area