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From 2016 to 2017, an experiment was conducted at Razi University Organic Farming to identify the best organic fertilizers for organic sesame production. The research was carried out in the split-plot design based on a randomized complete block design with three replications. Comparison of soil nitrate in the post-planting stage of sesame with soil before the experiment shows that organic nitrate storage with an application of treatments of 10 and 20 t ha-1 of animal manure is 36 and 63% and green manure of fenugreek, berseem clover, and hairy vetch is 63, 54, and 23%, respectively. The reason for improving grain yield is the positive role of animal manure and green manure in the fertility and balance of soil elements. The plant doesn’t face a lack of nutrients and increases the concentration of essential growth elements in the leaves, which increases grain yield. Fenugreek and berseem clover were higher than hairy vetch due to higher nitrogen yield. In the post-harvesting stage of sesame, soil experiments revealed that approximately 24% organic carbon, 58% phosphorus, 16% nitrogen, 63% nitrate, and 50% ammonium were stored in the soil. Potassium content was 13% lower than in soil before the experiment. In general, this study showed that the application of animal and green manure by providing physical and chemical properties of soil in organic field conditions leads to improved traits associated with sesame growth and, ultimately, grain yield. [ABSTRACT FROM AUTHOR]

Abstract Background The current debate about medical futility is mostly driven by theoretical and personal perspectives and there is a lack of empirical data to document experts and public attitudes towards medical futility. Methods To examine the attitudes of the Japanese experts in the fields relevant to medical futility a questionnaire survey was conducted among the members of the Japan Association for Bioethics. A total number of 108 questionnaires returned filled in, giving a response rate of 50.9%. Among the respondents 62% were healthcare professionals (HCPs) and 37% were non-healthcare professionals (Non-HCPs). Results The majority of respondents (67.6 %) believed that a physician's refusal to provide or continue a treatment on the ground of futility judgment could never be morally justified but 22.2% approved such refusal with conditions. In the case of physiologically futile care, three-quarters believed that a physician should inform the patient/family of his futility judgment and it would be the patient who could decide what should be done next, based on his/her value judgment. However more than 10% said that a physician should ask about a patient's value and goals, but the final decision was left to the doctor not the patient. There was no statistically significant difference between HCPs and Non-HCPs (p = 0.676). Of respondents 67.6% believed that practical guidelines set up by the health authority would be helpful in futility judgment. Conclusion The results show that there is no support for the physicians' unilateral decision- making on futile care. This survey highlights medical futility as an emerging issue in Japanese healthcare and emphasizes on the need for public discussion and policy development.

To investigate the effects of agronomic practices and farmer demographics on levels of weed infestation in chickpea fields, a survey was conducted during 2015 in Sanjabi district, Kermanshah, Iran. Two sets of data were collected: (a) weed sampling from the selected chickpea fields, and (b) questionnaire recording of farmer demographics and agronomic management. The farmers were asked about their residence status (resident or migrant), occupation (farming only, farming plus a second job), experience and education levels, as well as seedbed preparation, sowing date and method, source of seed supply, crop cultivar, sowing seed rate, weed control operation and crop rotation history. The results showed that weed species composition was similar across the studied fields. The use of a row crop planter, a sowing rate of 45 kg ha‐1, and weed control resulted in 30.2, 23.03 and 34.2% reductions in weed infestation compared with hand sowing, a sowing rate of 30 kg ha‐1 and lack of weed control, respectively. Weed density decreased with increasing farmer experience and decreased by 33.0% and 23.5% in chickpea fields of resident farmers and owners whose sole occupation was agriculture. We found that 89.5% of the most experienced farmers adopted weed control operations and most resident landowners (77.4%) opted for crop planter (77.4%) and weed control (90.3%) compared to migrant farmers (50 and 68.5%, respectively). More landowners who were exclusively farmers performed weed control (90.6%) than owners who had a second job (67.9%). [ABSTRACT FROM AUTHOR]

Purpose: Acetazolamide is utilized as a treatment which falls effective in treating some type of CSA. Hence, it might be effective as far as opium addicts who suffer from CSA are concerned. Materials and method: The current study was a double-blind, placebo-controlled, cross-over study (clinicalTrials.gov ID: NCT02371473). The whole procedures were identical for both placebo and acetazolamide phases of clinical research. There were 14 CSA more than 5/h and more than 50% of apnea-hypopnea index (AHI). Out of these 14 patients, 10 volunteered to participate in the study. Fast Fourier transformation was used to separate heart rate variability (HRV) into its component VLF (very low frequency band), LF (low frequency band), and HF (high frequency band) rhythms that operate within different frequency ranges. Result: There are significant results in terms of decreased mix apnea and central apnea together due to acetazolamide compared with placebo (P < 0.023). Time of SatO2 < 90% is decreased as well (P < 0.1). There is also decrease of SDNN and NN50 after treatment with acetazolamide respectively (P < 0.001). Regarding fast Fourier transformation, there is increase of pHF and decrease of pLF after acetazolamide treatment (P < 0.001). Conclusion: Acetazolamide seems to be effective in improving oxygenation and a decrease of mixed and central apnea events together. In HRV analysis section, LF power has decreased significantly, which may more likely improve prognosis of the patients. [ABSTRACT FROM AUTHOR]

This study was conducted to predict the yield and biomass of lentil (Lens culinaris L.) affected by weeds using artificial neural network and multiple regression models. Systematic sampling was done at 184 sampling points at the 8-leaf to early-flowering and at lentil maturity. The weed density and height as well as canopy cover of the weeds and lentil were measured in the first sampling stage. In addition, weed species richness, diversity and evenness were calculated. The measured variables in the first sampling stage were considered as predictive variables. In the second sampling stage, lentil yield and biomass dry weight were recorded at the same sampling points as the first sampling stage. The lentil yield and biomass were considered as dependent variables. The model input data included the total raw and standardized variables of the first sampling stage, as well as the raw and standardized variables with a significant relationship to the lentil yield and biomass extracted from stepwise regression and correlation methods. The results showed that neural network prediction accuracy was significantly more than multiple regression. The best network in predicting yield of lentil was the principal component analysis network (PCA), made from total standardized data, with a correlation coefficient of 80% and normalized root mean square error of 5.85%. These values in the best network (a PCA neural network made from standardized data with significant relationship to lentil biomass) were 79% and 11.36% for lentil biomass prediction, respectively. Our results generally showed that the neural network approach could be used effectively in lentil yield prediction under weed interference conditions. [ABSTRACT FROM AUTHOR]

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In complex networks, especially social networks, networks could be divided into disjoint partitions. However, nodes could be partitioned such that the number of internal edges (the edges between the vertices within the same partition) to the number of outer edges (edges between two vertices of different partitions) is high. Generally, these partitions are called communities. Detecting community structure helps data scientists to extract meaningful information from networks and analyze them. In the last decades, various algorithms have been proposed to detect communities in graphs, and each one has examined this issue from a different perspective. Yet, most of these algorithms have a significant time complexity and costly calculations that make them unsuitable to detect communities in large graphs with millions of edges and nodes. Label propagation algorithm (LPA), a fast random-based algorithm, can easily detect communities in big graphs, but its accuracy compared to other algorithms is low. In this paper, we have improved LPA accuracy, and to achieve this goal, we propagate label based on the Local Edge Betweenness score. The proposed algorithm, named Weighted Label Propagation based on Local Edge Betweenness, is able to identify distinct communities in both the real-world and artificial networks. Also, the proposed algorithm could detect communities in weighted graphs. Empirical experiments show that the accuracy and speed of the proposed algorithm are acceptable; additionally, the proposed algorithm is scalable. [ABSTRACT FROM AUTHOR]