Introduction: Biochemistry laboratories provide important information about the health status of people for clinical interventions and decisions. The purpose of this study was to evaluate the data elements of biochemistry laboratory information systems, measure their values, and determine the Local Minimum dataset (MDS) for designing these systems.Methods: A descriptive-analytical survey was used in this study. The statistical population of this research was hospital and non-hospital biochemistry laboratories in Isfahan city, Iran, and sampling was carried out using the convenient sampling method. The research tools were a checklist and a questionnaire with confirmed validity. Data were analyzed using descriptive statistics and one-sample t, independent t, Friedman and Leven tests via SPSS21 software.Results: The average utilization of the standard data elements in non-hospital and hospital biochemistry laboratory information systems were 61.7% and 61.2, respectively. The Minimum utilization of the standard elements was in the identity information section, physicians and referring centers information sections; and the maximum utilization of the standards was in the accounting section. Some data elements had high value, but had not been considered in the laboratory information systems and some of the current data elements used in the laboratory information systems had low values.Conclusion: Despite the widespread use of the biochemistry laboratory information systems in Iran, the required Minimum dataset and international standards have not been considered in their design. Because of the different applications of these systems, using a Local Minimum dataset for designing these systems is very important.

In this paper, a smile and laugh facial expression is presented based on dimension reduction and description process of the key points. The paper has two main objectives; the first is to extract the Local critical points in terms of their apparent features, and the second is to reduce the system’ s dependence on training inputs. To achieve these objectives, three different scenarios on extracting the features are proposed. First of all, the discrete parts of a face are detected by Local binary pattern method that is used to extract a set of global feature vectors for texture classification considering various regions of an input-image face. Then, in the first scenario and with respect to the correlation changes of adjacent pixels on the texture of a mouth area, a set of Local key points are extracted using the Harris corner detector. In the second scenario, the dimension reduction of the extracted points of first scenario provided by principal component analysis algorithm leading to reduction in computational costs and overall complexity without loss of performance and flexibility; and in the final scenario, a set of critical points is extracted through comparing the extracted points’ coordinates of the first scenario and the BRISK Descriptor, which is utilized a neighborhood sampling strategy of directions for a key-point. In the following, without training the system, facial expressions are detected by comparing the shape and the geometric distance of the extracted Local points of the mouth area. The well-known standard Cohn-Kaonde, CAFÉ , JAFFE and Yale benchmark dataset are applied to evaluate the proposed approach. The results shows an overall enhancement of 6. 33% and 16. 46% for second scenario compared with first scenario and third scenario compared with second scenario. The experimental results indicate the power efficiency of the proposed approach in recognizing images more than 90 % across all the datasets.

The traditional method for studying non-stationary signals is spectrogram based on the short-time Fourier transform (STFT). The well known limitation of the STFT is the inherent trade-off between time and frequency resolution. The Wigner-Ville (WV) distribution has the best time-frequency resolution, but its draw back is generating cross-terms. The matching pursuit (MP) distribution based on using the Gaussian atom is always positive, does not include crossterm, and has convenient resolution. In this paper, we have shown in addition to the known properties, the MP distribution can also remove the additive noise inherently. On the other words, we are able to remove the noise just by limiting the algorithm iterations and without paying any additional cost. Although the MP distribution based on using the Gaussian atoms is always positive and it has convenient resolution, according to the MP the time marginal and the frequency marginal will not be obtained accurately. In this paper, it has been shown that by implementing the Minimum cross entropy (MCE) technique according to the MP distribution as a priory positive distribution, the new extracted distribution has the most similarity to the MP distribution and it also satisfies the correct time and frequency marginal.

The improvement of multi-resistance properties of the bacterial pathogen has recently been discussed as an emerging issue. In this regard, iron oxide nanoparticles have attracted the researchers’,attention due to their wide application in the realm of medicine. Iron oxide nanoparticles have a high specific surface area that enables them to interact with the bacterial surface structure and has considerable antibacterial activity. The current study aimed to synthesize a novel antimicrobial agent from iron oxide nanoparticles and determine its Minimum inhibitory concentration (MIC) on different gram-positive and negative variant bacterial strains isolated and characterized from the infected urinary tract of Iraqi elderly patients. This study was conducted from September 2020 to December 2020 on 75 urine samples collected from the infected urinary tract of elderly patients in the ages range of 60-75 years admitted to Al-Yarmouk Medical Hospital, Baghdad, Iraq. Isolation of bacterial isolates was carried out using differential and selective media. Afterward, they were characterized and confirmed using different biochemical tests and VITEK 2 system, respectively. Magnetic nanoparticles were fabricated by co-precipitation of ferric ions (Fe3+) and ferrous ions (Fe2+) in presence of ammonium hydroxide solution (25%). The characterization of synthesized nanoparticles was performed subsequently using UV-VIS spectroscopy analysis, Scanning Electron Microscope (SEM), Fourier transform infrared spectroscopy analysis, X-ray Diffraction analysis (XRD), and Energy-dispersive X-ray spectrum (EDX). The MIC of synthesized sonicated Fe3O4NP against different bacterial strains was determined using the broth culture dilution method through making serial dilutions of 50, 100, 200, 400, 500, 600, 800, 900 μ, g/ml from a 5mg/ml nanoparticle stock solution. Afterward, the lowest concentration of nanoparticles required to arrest the growth of bacteria was determined through the colony-forming unit of each treated bacteria on brain heart infusion agar. In total, 17bacterial isolates were identified from the infected urinary tract, five bacterial isolates (E. coli, Pseudomanas aeruginosa, Staphylococcus aureus, Enterococcus faecalis, and Micrococcus luteus). In addition, two Proteus mirabilis strains were identified separately and were tested against synthesized Fe3O4NPto determine the MIC. The novel synthesized antibacterial agent showed excellent bioactivity, compared with controls (consisting of bacterial suspension without ferrous oxide nanoparticles), and the synthesized antibacterial agent was considered significantly active against all the bacterial strains at a p-value less than 0. 05. The Fe3O4NP were active against gram-negative more than gram-positive bacteria. The MIC of synthesized and characterized Fe3O4NPwasapplied on seven gram-positive and negative bacterial isolates using bacteria-Fe3O4NP complex. Significant effects were observed on all strains, compared with controls, and this complex could significantly inhibit gram-negative more than gram-positive bacteria.

Identifying the constraining factors of production and yield gap is very important. Therefore; this research was performed to identify the production constraining factors of Local rice cultivars. All management practices from nursery preparation to harvesting stages for 100 paddy fields of Local rice cultivars were recorded through field studies, in Sari, from 2015-2016. In the CPA, the actual and calculated potential yield were 4495 and 5703 kg/ha, respectively and the gap was 1221 kg/ha. The yield gap caused by number of top-dressing variables was 324 kg/ha, equal to 27% of the total yield gap. The yield gap related to previous year of legumes cultivation was 218 kg ha-1, equal to 18% of the total yield variation. Among the 10 variables entered in the CPA model, the effects of top-dress fertilizer application and its application frequency and foliar application were remarkable, which could compensate a significant part of the yield gap (444 kg/ha, 37% of total) in the farmers’ fields by managing these variables. According to boundary line analysis (BLA) finding, actual yield mean on the basis of optimal limit related to 12 variables under study was 5369 kg/ha, with 881 kg/ha yield gap . Mean relative yield and relative yield gap for 12 variables (transplanting date, seedling age, number of seedlings per hill, planting density, nitrogen and phosphorous per hectare, nitrogen before transplanting, harvesting date, lodging problem, pest problem, diseases problem and weeds problem) were 83.64 and 16.35 kg/ha, respectively. Based on the finding, it can be stated that the model precision is appropriate and can be applied for both estimation of the quantity of yield gap and determining the portion of each restricting yield variables.

The central issue of the present paper is the study of the causes of the mother tongue decline in the Sahne city in Kermanshah province. The issue of language is beyond the dialect of people and is refer to the form of life and the matter of macroeconomic and political relations. From this perspective, the form of life and the language associated with it explain the praxis of the members of the society. The appropriate method of examining the problem should be able to analyze the justification of agents to their situation, that is why Grounded Theory is chosen as the method of this research and data analysis is taken from abduction reasoning. The results of the analysis introduced the categories of globalizing, modernizing, de-use of language, economic necessity and the government as the most important factors of Local language fading. In this essay the meaning of globalization is the globalizing of special type of economic relation that explain other categories role in the process of fading of languages.

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel Local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the Local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a Local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.