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The modular multilevel converter (MMC) is a favored topology in the industry, but its reliability is at risk with an increase in the number of sub-modules (SMs) due to a rise in switching components. The essential need for maintaining capacitor voltage balance in each arm leads to increased complexity and cost, as numerous voltage sensors are required. This study introduces an innovative approach to minimize the number of voltage sensors by employing an enhanced algorithm for open-circuit fault detection in switches. The proposed scheme organizes each arm into groups, each containing two SMs and one voltage sensor, aiming to reduce the overall sensor count. A novel fault detection mechanism is presented, identifying open-circuit faults by comparing group output voltages in healthy and defective conditions. The capacitor voltage estimation algorithm in the sensor reduction scheme is noted for its simplicity compared to other methods. The effectiveness of these methods is validated through simulations and experimental implementations across diverse scenarios, affirming their reliability. [ABSTRACT FROM AUTHOR]

Aims Nurses caring for patients with obesity face difficulties in providing services. The difficulty of care causes inaccurate and incomplete care and sometimes not receiving care, which can lead to many complications for the patient. For this purpose, this study was conducted to explain nurses’ experiences facilitating and impeding factors in providing care to patients with obesity. Participants & Methods In this qualitative conventional content analysis study, 20 nurses and 6 patients with obesity in two large hospitals affiliated with Mashhad University of Medical Sciences were interviewed. The participants were selected by purposeful sampling, and the interviews were conducted in-depth and semi-structured. Data analysis was done using content analysis & MAXQDA software. Findings The facilitating and impeding factors in providing care to patients with obesity in the hospital were divided into four categories; “cooperation versus passivity in the process of selfcare,” “influential level family involvement at the patient’s bedside,” “organizational challenges in care,” and “nurse competence level in care.” Conclusion Organizational changes such as creating collaborative care in difficult situations, considering the patient classification system for patients with obesity, providing large-sized welfare and treatment equipment, increasing the skill level of nurses, and ways to improve interaction with the patient and his family can be practical solutions in providing care to patients with obesity. [ABSTRACT FROM AUTHOR]

After collecting data, in researches, the type of data distribution must be determined; since any analysis requires the distribution of its own data. Soil properties, such as salinity, are also the same case. Due to its direct and indirect effects on plant growth, soil salinity is an important feature that has always been investigated in agriculture and natural resources, leading to a lot of researches. These researches have often focused on the mapping of salinity using different interpolation methods and their accuracy. But the effect of the data distribution on the analysis process has been less considered. Accordingly, the purpose of this study is to investigate the effect of the distribution of soil salinity data on soil salinity mapping using Kriging method. For this purpose, 610 soil samples were taken from 0-50 cm soil depth based on a grid method and their salinity (Electrical Conductivity, EC) was determined in saturated paste extracts. Variography operations for data were performed based on both, the original distribution of the data and the usual data distribution employed for Kriging i.e. normal distribution. Salinity maps were obtained for both data distributions. Estimations were made using cross-validation approach. According to the findings, only the fitness criterion (R2) is not enough to select the optimal variogram model, while other criteria such as the proportion of the spatial structure, residual sum of square (RSS) and the nugget effect should be analyzed as well. The results showed: 1- the accuracy of the estimation based on the original distribution of the data, (i.e. non-normal distribution) which was greater than the accuracy of the estimated data using normal distribution; 2- the predictions and errors from the both, normally and non-normally distributed data did not have the normal distribution and 3 - data transformation had no effect on the normalization of the distribution of the predictions and the errors. Therefore, it is suggested that in the Kriging, in addition to the conventional method, i.e., performing Kriging using normal distribution data, the original data with non-normal distribution should also be analyzed. Finally, the type of data distribution and the optimal variogram model could be selected by comparing the obtained results.

This study was conducted to rate the land characteristics of corn in hot areas based on artificial neural networks and regression models. For this purpose, 63 corn fields were selected in southern Iran. In each farm, a pedon was excavated, described and sampled. A questionnaire was completed for each farm. A stepwise regression model was used to study the relationship between land characteristics and corn yield. A characteristic-function curve was used to rate the land characteristics. Finally, crop requirements were prepared by artificial neural network and regression models and verified by comparing the actual and predicted performance levels. The results of regression analysis showed that soil salinity, exchangeable sodium percentage, sand, clay, phosphorous, gypsum and potassium recorded the highest effect on yield and according to the artificial neural network, the exchangeable sodium percentage, soil salinity, soil texture and cation exchange capacity are the most important. Based on regression and artificial neural network methods, the threshold limit and break even production for soil salinity were 4, 2.5, 12, and 10 dS m-1, respectively, but for exchangeable sodium percentage the values were 18, 14, 35, and 30, respectively. The coefficient of determination (R2) between the actual and predicted yield based on the regression model was 0.88, but it was 0.945 (training data) and 0.837 (testing data) for the artificial neural network. Also, the results of the verification of the prepared crop requirements tables showed that the correlation of determination between the land index and the yield in the regression method was 0.78 but it was 0.81 for the artificial neural network, these results are acceptable in both methods.

The effects of geological conditionwere assessed on density of Soil Organic Carbon (SOC) and Nitrogen (N)in a sequence of hillslope landscape, derived from different lithology i.e. loess deposit, reworked loess, marl with mixed siltstone and shale, reddish brown clay deposits and older loess in the semiarid area of northern Iran. However, other factors can influence SOC and N density such as land use, topography and climate with geology, pasture land use have been selected with a homogeneous climate to study their influence on density SOC and N of different lithology. Total of 108 soil samples were selected from two layers of 0-20 cm (surface) and 20-40 cm (subsurface). Results showed higher amount of SOC and N density, Cation Exchange Capacity (CEC) and silt were in surface layer of loess deposit that is related to vegetation density and root growth in this material than other conditions. On the contrary, the amounts of mentioned parameters were the lowest in marl. However, there was no significant difference between density of SOC and N in subsurface layer, but trend changes was similar with the surface. Overall, results show that there is a correlation between geological conditions and storing SOC and N. In conclusion, protection of surface and subsurface soil is important to increase density of SOC and N. Especially, overgrazing on steep slope of marl must be reduced or prohibited because rate of carbon loss to the atmosphere was significant and it is important in a changing environment from landscape to global scale.

Soil is a vital parameter for planting design in urban green spaces especially in adverse climate conditions. In this study, the utilization of analytic hierarchy process (AHP) in surveying land suitability for urban green space from measured criteria like suitability indices, including physical (i.e. clay, silt, sand, bulk density, penetration resistance), chemical (i.e. electrical conductivity, sodium absorption ratio, organic matter, Olsen phosphorus, water soluble potassium and DTPA-extractable of Fe, Zn, Cu, Mn) and heavy metal (i.e. total Ni, Cd and Pb) properties of soil and topography attributes (i.e. slope degree) data were investigated. The subjective value judgment, in light of questionnaire survey was utilized for pair-wise comparison after standard AHP method in a structure of four criteria (suitability indices) alongside related sub-criteria (19 factors). AHP method and GIS techniques were combined to create suitability map. The outcomes demonstrated that less than 8.86% of the all study region has no restriction and 34.54% has extreme restrictions for green space land use. The southern part of the riverside parks had higher suitability, whereas the northern area had lower suitability. The real limiting variables include soil compaction, soil salinity, and Zn deficiency; albeit direct mild accumulations were for lead in Ahvaz riverside parks. This study delineates the efficacy of AHP and weighted overlay model for the soil suitability investigation of green space in the study territory. In light of the outcomes, bulk density and organic matter are the most vital indices of soil in urban green space.

Among a variety of storage technologies used for energy storage systems, supercapacitors, Pb-Acid Batteries (PABs), and Lithium Batteries (LBs) are widely used for microgrid applications. The supercapacitors with high-power density are suitable for fast power regulations; conversely, the PABs have high-energy density, which makes them suitable for long-term energy management. Since the PABs and supercapacitor can complement each other and overcome mutual defficiencies, their combination as a hybrid energy storage system can be beneficial. However, the LB enjoys both high-energy and high-power densities. Therefore, an LB ESS (LBESS) can similarly function like a Pbacid battery-Supercapacitor Hybrid ESS (PSHESS). However, their topologies, life cycles, and costs are different. This study tends to determine the one which is technically and economically more suitable for applications in islanded microgrids. For this purpose, a frequency control and energy management scheme is proposed. It maintains the balance between demand and supply and also keeps the microgrid frequency within safe operational limits using the least needed sizes for the energy storage systems. Using the simulation results, the sizes and costs of the energy storage systems are determined. For a decade of operation, the PSHESS has incurred almost 49.2% more cost than the LBESS, which makes the LBESS more cost effective. [ABSTRACT FROM AUTHOR]

There are various methods and models for land evaluation. These methods are classified according to the number of used resources. The best resources capabilities or potentials and land use can be found by analyzing one of the main resources, in the lands close relations to ecologic resources exist. Soil has a great potential for introducing the studied region specifications. Hence, it challenges the mistaken belief stating: “soil is only valid for agricultural applications and it is weak in measuring developing and planning domain.” The present study is done with the aim to achieve the best land use according to a single-factor (soil) model for tourism planning. A soil map was obtained using a combination of conventional and digital soil mapping methods. In conventional process soils were mapped using aerial photo interpretation and physiographic methods and in digital process an elevation model and satellite images were used. Based on the field works and laboratory analyses, the soil map included 13 soil units resulting 176 delineations. Tourism region needs more essential requirements rather than recreation area. Thus, an ecological capability evaluation model developed by Bagheri Bodaghabadi (2016) was employed to evaluate all delineations of the soil map using GIS. Finally, the best environmental planning was presented to design the study area for tourism usage. The results also showed there are moderate to high suitable classes for all tourism purposes, resulting the study area has potential to develop a tourism region. The most important limiting factors in the area include soil depth, slope and coarse fragment of soil. It was also suggested to use local models to evaluate the ecological capacity in the small area.