Search

Nowadays, with the expansion of various methods of obtaining information from different systems and digital cameras, the use of image processing has been widely used. Due to the increase in the huge amount of data in the industry and the need to control and monitor this amount of data, the use of image processing methods has been widely used in various industries. The analysis of these data at different levels, including the control of production line processes, is one of the most up-to-date methods in the quality control system of the statistical process of products. Therefore, using new approaches for data monitoring and quality control of manufactured products can be considered as a fruitful method. The aim of the current research is to provide a model to control the quality of manufactured products based on the image processing technique approach and using fuzzy transformations for image compression and image data processing. In order to validate the results of this research, MATLAB software was used and the developed model was compared with previous studies. The simulation results show that the proposed GLR control chart is effective in reducing the error and increasing the detection accuracy. So that in the first image after applying the changes in brightness, the warning showed that this represents the proper performance of the proposed control chart against the changes.

The paper provides analysis of findings from research on "pupil voice" collected from 88 Iranian pupils within five primary classrooms. No previous research has been carried out in Iran on "pupil voice" emphasising children's right to a say about different matters in their school lives. Two methods of data collection were applied: (a) participant observation where the observer facilitated workshops within each classroom enabling children to reflect on issues that matter to them; (b) individual interviews with pupils, teachers and headteachers aimed at gathering information regarding school participants' insights into their experiences in schools, including the role of teachers, school regulations and pupil voice activities. Our data reports on the preoccupation teachers and children had with our workshop as an alternative pedagogic form in their schools. While both pupils and teachers saw the advantages of a participatory approach to classroom interactions, national, educational and pedagogic cultural factors provided substantial challenges.

Abstract Background Despite significant achievements in the prevention and control of NCDs in Iran, these conditions are still the biggest challenges to Iran's healthcare system and are estimated to account for 78.1 percent of all deaths. Therefore, this study aimed to reflect on the potential challenges standing in the way to implement the relevant policies, empower the dimensions of governance, and react in an effective and timely manner by Iran's healthcare system to NCDs. Methods This study was conducted with a qualitative approach using the conventional content analysis method. A total of 46 senior managers involved in the prevention and control of NCDs at the medical sciences universities across Iran were recruited through the purposive sampling method and were interviewed via semi-structured interviews. Graneheim and Lundman's approach was utilized to analyze the data. Results According to the analysis of the senior managers' viewpoints, current challenges to implement the program for the prevention and control of NCDs in Iran could be placed into six main categories, including financing, human resources, infrastructure and inputs, legal, executive, administrative, as well as inter-sectoral collaboration, and management and policy-making challenges with their own sub-categories. Conclusion The results revealed that financing was the biggest challenge to successfully implementing the program for the prevention and control of NCDs in Iran. However, strengthening Iran's healthcare system in the field of the prevention of NCDs demanded more innovative measures and strategies, such as the empowerment of human resources, the effective use of intra- and inter-sectoral collaboration, and non-governmental organizations and charities, along with the exploitation of evidence-based studies during policy-making and decision-making processes, with no need for financial resources.

Abstract Electrical vehicles (EVs) are among the fastest‐growing electrical loads that change both temporally and spatially at distribution networks. Moreover, the existence of uncertain parameters, such as EVs as well as domestic loads in power networks, poses serious operational challenges for them. Accordingly, stochastic studies of system performance are a must. Against this background, this paper aims to present a stochastic multi‐objective method for the problem of simultaneous active and reactive power management as well as harmonic compensation in distribution networks in the presence of EVs and non‐linear devices (NLDs). This method minimizes costs associated with power generation and losses. Besides, it improves the total harmonic distortion of voltage (THDv) at network buses subject to network and EV constraints. In the proposed method, to strike a balance between exploration and exploitation abilities, a hybrid technique named the “PSO‐GA optimization algorithm” was used to take advantage of both the genetic algorithm (GA) and the particle swarm optimization (PSO) method. Accordingly, the effectiveness of the proposed method was examined on a standard IEEE 33‐bus distribution network populated with EVs equipped with on‐board bidirectional chargers. The results obtained showed that the proposed model improved network power quality indices as well as economic and technical issues of EVs in parking lots.

Abstract Background Despite the recommendation of the current guidelines, intracoronary administration of nitroglycerine during coronary angiography is often neglected. We investigated the effect of intra-coronary nitroglycerin on the relief of coronary artery stenosis in the candidates for percutaneous coronary intervention (PCI). Methods We included patients with angina pectoris or myocardial infarction who were candidates for PCI. In the coronary angiography, the culprit vessel involved was evaluated, and bolus nitroglycerin at a dose of 25–200 mcg was injected into the affected coronary artery. A significant change in the percentage of coronary artery stenosis was considered a positive response, and these patients were then compared with patients who did not have a substantial change in the percentage of stenosis at the same time. Univariate analysis and then multivariate logistic regression analysis was performed to determine the predictors of response to intracoronary nitroglycerin. Results Among 360 patients, 27 (7.5%) responded to nitroglycerine, and 333 (92.5%) were non-responsive. The mean age of patients was 60.2 ± 11.6 years, ranging from 23 to 93 years, and 265 (73.6%) were men. The study groups were not significantly different in the baseline demographic characteristics. The presence of multivessel disease (Odds ratio (OR) = 16.26, 95% confidence interval (CI):2.07–127.6; P = 0.008) and stenosis in the left circumflex artery (OR = 3.62, 95% CI: 1.03–12.70; P = 0.044) were the independent predictors for nonresponse to nitroglycerine, leading to PCI. Conclusion In some cases, especially those without multivessel diseases, intracoronary nitroglycerine administration can efficiently relieve coronary stenosis and prevent unnecessary PCI.

Background and Aims: Chronic diseases affect daily life activities. Naturally, as people age, their activity level drops, increasing the risk of diseases. Therefore, physical activity and aging are in a vicious cycle. The present study aimed to investigate the relationship between chronic diseases and physical activity in the elderly in Birjand, Iran. Materials and Methods: This cross-sectional study was part of a cohort study entitled "Comprehensive Plan for the Elderly in Birjand" conducted on 1,418 participants aged 60 years and older in 2018. Before data collection, informed consent was obtained, and physical activity was measured using LASA Questionnaire. The standard medical history questionnaire assessed the participant's chronic disease history. Results: Out of 1,418 elderly participants, 683 (48.1%) were male, and 735 (51.8%) were female, 1,063 (74.9%) were in the age range of 60-74. Around 351 (24.8%) of the participants had diabetes, 606 (42.7%) had high blood pressure, 457 (32.2%) suffered from dyslipidemia, and 79 (5.6%) had experienced a heart attack. The results showed that 50% of the geriatrics in the current study had moderate physical activity (more than 600 minutes per week), 37% had no physical activity (less than 300 minutes per week), and 13% had little physical activity (between 300 and 600 minutes per week). Diseases such as arthritis (P=0.03), cancer (P=0.02), hypertension (P=0.001), stroke (P=0.001), myocardial infarction (P=0.04), and heart failure (P=0.03) had a significant relationship with physical activity. Conclusion: As there was a relationship between chronic diseases and physical activity, the results of this study help policymakers in the field of geriatrics' health and proper planning for health and treatment services.

The combat capability of the air force and the flight of fighter and transport aircraft are highly dependent on the presence of ground support systems. The resence of a large number of ground support systems in aircraft repair centers and overhaul centers for heavy repairs indicates the existence of deficiencies in maintenance and repair methods. Aircraft ground support equipment has always faced the problem of increasing maintenance and repair costs, and the statistics of ground-based aircraft support systems have an upward trend, which is now looking for a solution to control the current situation. the Purpose of this study is to explain how to optimize the maintenance method of aircraft ground support systems in nahaja. and the main research question: optimizing the maintenance of aircraft ground support systems in nahaja how should it be? which is based now the type of applied research and the method of doing it are descriptive and intergrated with the approach. The statistica population of the study includes all expert commanders and officials of the aircraft ground support equipment department in nahaja units, which is a factor of 120 people. at a significance level of five percent, the accuracy of the research hypotheses is constrained. the innovation of this research is counting and extracting instrutions for using aircraft ground support systems, improving the scheduling process of using equipment.

Abstract Background Due to the more stability and a better homogenecity in immune response, the use of thermoresistant vaccines in different chicken types has been increased. Objective This study aimed to evaluate the efficacy of a newly developed Newcastle disease vaccine (ND.TR.IR) originating from I‐2 strain in specific pathogen‐free (SPF) and native and broiler chickens. Methods Following determination of pathogenicity indices on the candidate seed, three efficacy examinations were conducted. In the first experiment, 120 1‐day‐old SPF chickens were randomly allocated to six groups and either vaccinated with ND.TR.IR via eye drop at 1, 7, and 21 days of age (V1, V7, and V21), or considered as non‐vaccinated control groups (C1, C7, and C21). At 20th post‐vaccination day, sera hemagglutination inhibition (HI) antibody titres against ND virus (NDV) were measured and then the chickens were challenged by virulent NDV (vNDV). In the second and third experiments, the efficacy of ND.TR.IR vaccine was compared to routine vaccination program (B1 and LaSota) in native and broiler chickens that were vaccinated at 10 and 20 days of age, respectively. The HI antibody titres were measured on 10, 20, 30, and 40 days of age, and also challenge efficacy test with vNDV was conducted on 30 days of age. Results The studied virus, as a vaccinal seed, complied with the pathogenicity indices of avirulent NDV and molecular identity of I‐2 strain. In the efficacy evaluation trials, the vaccinated chickens had higher HI antibody titres against NDV compared with their corresponding control chickens (p

Introduction: Empathy reduces the inherent anxiety of dental care procedures and can increase the patient's satisfaction with the services provided. On the other hand, The absence of positive empathic attitude in dental practitioners is one of the most important issues in dental care services The aim of this study was to determine the attitude of undergraduate dental students and interns toward empathy and association factors in Islamic Azad University of Tehran - dental branch in 2020. Methods: In this cross-sectional study, 274 people of dental undergraduate students and interns of Islamic Azad University of Tehran - dental branch were chosen. For data collection, demographic and the Persian translation of Jefferson empathy questionnaire were used. SPSS version 16, one-way ANOVA and correlation coefficients were used for data analyses. Results: 255 approved questionnaires were returned. Among the factors studied, three factors (age, gender, previous clinical activity) had statistically significant impact on empathy. Age was inversely related to empathy score (P = 0.02) and empathy score was higher in women than men and this difference was statistically significant (P = 0.034). Students with more previous clinical activities scored higher empathy numbers (P=0/004). Other factor's impact on empathy were not statistically significant (P>0/05). Conclusion: This study found that The average score obtained by students and professional assistants seems to indicate that they have acceptable levels of ethical commitment. Emphasis is placed on ethical policies to increase empathy.

Chemical ionization mass spectrometry (CIMS) is widely used in atmospheric chemistry studies. However, due to the complex interactions between reagent ions and target compounds, chemical understanding remains limited and compound identification difficult. In this study, we apply machine learning to a reference dataset of pesticides in two standard solutions to build a model that can provide insights from CIMS analyses in atmospheric science. The CIMS measurements were performed with an Orbitrap mass spectrometer coupled to a thermal desorption multi-scheme chemical ionization inlet unit (TD-MION-MS) with both negative and positive ionization modes utilizing Br- , O2- , H3O+ and (CH3)2COH+ (AceH+) as reagent ions. We then trained two machine learning methods on these data: (1) random forest (RF) for classifying if a pesticide can be detected with CIMS and (2) kernel ridge regression (KRR) for predicting the expected CIMS signals. We compared their performance on five different representations of the molecular structure: the topological fingerprint (TopFP), the molecular access system keys (MACCS), a custom descriptor based on standard molecular properties (RDKitPROP), the Coulomb matrix (CM) and the many-body tensor representation (MBTR). The results indicate that MACCS outperforms the other descriptors. Our best classification model reaches a prediction accuracy of 0.85 ± 0.02 and a receiver operating characteristic curve area of 0.91 ± 0.01. Our best regression model reaches an accuracy of 0.44 ± 0.03 logarithmic units of the signal intensity. Subsequent feature importance analysis of the classifiers reveals that the most important sub-structures are NH and OH for the negative ionization schemes and nitrogen-containing groups for the positive ionization schemes. [ABSTRACT FROM AUTHOR]

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online.