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Background: Hyperkalemia is a potentially life-threatening electrolyte disturbance that if not diagnosed on time may lead to devastating conditions and sudden cardiac death. Blood sampling for potassium level checks is time-consuming and can delay the treatment of severe hyperkalemia on time. So, we propose a non-invasive method for correct and rapid hyperkalemia detection. Methods: The cardiac signal of patients referred to the Pediatrics Emergency room of Shahid Rejaee Hospital was measured by a 12-lead Philips electrocardiogram (ECG) device. Immediately, the blood samples of the patients were sent to the laboratory for potassium serum level determination. We defined 16 features for each cardiac signal at lead 2 and extracted them automatically using the algorithm developed. With the help of the principal component analysis (PCA) algorithm, the dimension reduction operation was performed. The algorithms of decision tree (DT), random forest (RF), logistic regression, and support vector machine (SVM) were used to classify serum potassium levels. Finally, we used the receiver operation characteristic (ROC) curve to display the results Results: In the period of 5 months, 126 patients with a serum level above 4.5 (hyperkalemia) and 152 patients with a serum potassium level below 4.5 (normal potassium) were included in the study. Classification with the help of a RF algorithm has the best result. Accuracy, Precision, Recall, F1, and area under the curve (AUC) of this algorithm are 0.71, 0.87, 0.53, 0.66, and 0.69, respectively Conclusions: A lead2-based RF classification model may help clinicians to rapidly detect severe dyskalemias as a non-invasive method and prevent life-threatening cardiac conditions due to hyperkalemia.

Background: An abnormal variation in blood electrolytes, such as potassium, contributes to mortality in children admitted to intensive care units. Continuous and real-time monitoring of potassium serum levels can prevent fatal arrhythmias, but this is not currently practical. The study aims to use machine learning to estimate blood potassium levels with accuracy in real time noninvasively. Methods: Hospitalized patients in the Pediatric Department of the Rajaie Cardiology and Medical Research Center and Tehran Heart Center were recruited from December 2021 to June 2022. The electrocardiographic (ECG) features of patients were evaluated. We defined 16 features for each signal and extracted them automatically. The dimension reduction operation was performed with the assistance of the correlation matrix. Linear regression, polynomials, decision trees, random forests, and support vector machine algorithms have been used to find the relationship between characteristics and serum potassium levels. Finally, we used a scatter plot and mean square error (MSE) to display the results. Results: Of 463 patients (mean age: 8 ± 1 year; 56% boys) hospitalized, 428 patients met the inclusion criteria, with 35 patients having a high noise of ECG were excluded. After the dimension reduction step, 11 features were selected from each cardiac signal. The random forest regression algorithm showed the best performance with an MSE of 0.3. Conclusion: The accurate estimation of serum potassium levels based on ECG signals is possible using machine learning algorithms. This can be potentially useful in predicting serum potassium levels in specific clinical scenarios.

Land use change is a critical factor affecting the structure and functioning of biological communities, particularly forest birds. The Hyrcanian forests in northern Iran are undergoing significant transformations due to increasing agricultural expansion and the growth of orchards encroaching upon forest edges. While research often suggests that the edge effect can enhance species diversity in heterogeneous agricultural-forest landscapes, the effects of different agricultural practices have received less attention. In this study, we integrated remote sensing, landscape ecology, and statistical analysis, and served bird communities as ecological indicators aiming to identify the impact of various land use modification on biological communities of an agricultural-forest landscape. We used Sentinel-2 images to generate a land use map of the region based on the random forest classification method. Explanatory variables extracted from the derived land use were measured in 75 cell grids (1200 × 1200 m) and bird information in 400 × 400-meter units. We calculated 15 landscape metrics for each 1200-meter landscape unit cell and combined them with residential area and road length data for statistical analysis. The Redundancy Analysis (RDA) method was employed to examine the effects of environmental variables on bird community composition. We identified a total of 38 breeding bird species belonging to 21 families in the region. The RDA variance partitioning accounted for 0.554 as the constrained fraction, indicating that 54 % of the bird community variance was explained by the explanatory variables. Generalist and urban-dwelling birds were mainly dispersed across negative values of canonical axis 1 (RDA1). Conversely, forest specialist birds were scattered along positive values of RDA1. Less specialized understory birds, depending on more open woods with higher species diversity, were mostly illustrated with shorter projections across RDA2 extremes, correlated with orchards. Our findings demonstrate that the marginal habitats of Hyrcanian forests, predominantly consisting of orchard plantations, exhibit greater bird community diversity compared to dense forests or croplands. Although species diversity has been observed to increase initially, the ongoing trend of orchard encroachment on forest patches may result in heightened edge effects, an increased presence of generalist species, and a decline in specialized forest species. These results can help guide land use planning to achieve biodiversity goals in heterogeneous agricultural-forest landscapes.

Despite past research linking HLF mutations to cancer development, no pan-cancer analyses of HLF have been published. As a result, we utilized multiple databases to illustrate the potential roles of HLF in diverse types of cancers. Several databases were used to assess HLF expression in the TCGA cancer samples. Additional assessments were undertaken to investigate the relationship between HLF and overall survival, immune cell infiltration, genetic alterations, promoter methylation, and protein-protein interaction. HLF's putative roles and the relationship between HLF expression and drug reactivity were investigated. HLF expression was shown to be lower in tumor tissues from a variety of malignancies when compared to normal tissues. There was a substantial link found between HLF expression and patient survival, genetic mutations, and immunological infiltration. HLF influenced the pathways of apoptosis, cell cycle, EMT, and PI3K/AKT signaling. Abnormal expression of HLF lowered sensitivity to numerous anti-tumor drugs and small compounds. According to our findings, reduced HLF expression drives cancer growth, and it has the potential to be identified as a vital biomarker for use in prognosis, immunotherapy, and targeted treatment of a range of malignancies.

Introduction: Deliberate Self-Harm (DSH) is the intentional destruction of healthy body tissue without the intent to commit suicide. Given the rapidly increasing prevalence of non-suicidal self-injury (NSSI) among adolescents, it is essential to conduct a systematic analysis of the risk and protective factors associated with these behaviors using tools that demonstrate validity and reliability. Therefore, the current study was conducted to analyze the psychometric properties of the abbreviated version of the Self-Harm Screening Inventory for adolescents.Method: In this survey study, 300 gifted adolescents were selected using the convenience sampling method. They completed the short version of the Self-Harm Screening Inventory for Adolescents (Kim et al., 2022), the Depression, Anxiety and Stress Scale (Lovibond & Lovibond, 1995), and the Satisfaction with Life Scale (Diener, Emmons, Larsen, & Griffin, 1985).Results: A confirmatory factor analysis supported the hypothesized one-factor structure. The one-factor model had a good fit. The SHSI also demonstrated good internal consistency (Cronbach's alpha = 0.89). Additionally, the Subjective Happiness Scale Inventory (SHSI) was positively correlated with the Depression, Anxiety, and Stress Scale, and negatively correlated with the Satisfaction with Life Scale. These correlations support the criterion validity of the abbreviated version of the Self-Harm Screening Inventory for adolescents.Discussion and Conclusion: The findings suggest that the SHSI is a reliable and valid tool for screening self-harm behaviors among adolescents.

Taxanes are a class of potent anticancer agents used in different cancers. Their low water solubility is the major challenge in formulation development that leads to the use of surfactants and organic solvents which causes severe hypersensitivity reactions. In this study, paclitaxel (PTX) was conjugated to a water-soluble and biocompatible polymer, dextran, to enhance solubility. Levothyroxine was also conjugated to dextran to provide targeted delivery. The cytotoxicity was studied on HepG2 and A375 cell lines before and after treating cell lines with levothyroxine. Results showed approximately 1250-fold water solubility enhancement by dextran conjugation. Drug conjugates presented higher cytotoxicity on A375 cell lines than its free drug counterparts at 10 and 50 nM concentrations after 24 hours. Pre-treatment with levothyroxine decreased the cytotoxicity on A375 as an integrin receptor rich cell line but did not show any significant effect on HepG2 cells which is low in expressing integrin receptor. In conclusion, preparation of levothyroxine targeted dextran conjugate might be an effective strategy for PTX delivery to different cell lines.

Background and Objective: Heart failure is one of the most common chronic, progressive, and debilitating heart disorders, exerting a destructive effect on self-care and quality of life in these patients. The present study aimed to compare the effects of face-to-face and virtual self-care training methods on self‐care and quality of life among patients with heart failure. Materials and Methods: This three-group clinical trial study was conducted on 120 patients with heart failure admitted to Farshchian Hospital in Hamadan. The samples were selected via the available sampling method and assigned to three groups (control, virtual, and face-to-face education) using permuted block randomization. Demographic characteristics form, European Heart Failure Self-care Behaviour Scale (EHFScBs), and Minnesota Living with Heart Failure Questionnaire (MLHFQ) were completed by self-report before the intervention. The control group received only the routine training of the clinic, while the face-to-face training group received self-care training in four one-hour group sessions, one day apart. The virtual group received the same amount of education in the form of educational clips, and two months after the intervention, the questionnaires were completed. Chi-square tests, Fisher's exact test, paired t-test, and one-way analysis of variance were used to analyze the data. Results: The results pointed out that before the intervention, there was no statistically significant difference between the demographic variables, self-care, and quality of life of patients in the three groups (P> 0.05). After the intervention, no statistically significant difference was observed between the virtual and face-to-face training groups in terms of self-care and quality of life (P>0.05). Nonetheless, self-care and quality of life were improved in these two groups compared to the control group (P˂0.05). Conclusion: Both virtual and face-to-face training methods had the same effect on self-care and quality of life. Virtual training is a great alternative to face-to-face training in the absence of necessary conditions.

Abstract 2D transition metal carbides and nitrides (MXenes) suggest an uncommonly broad combination of important functionalities amongst 2D materials. Nevertheless, MXene suffers from facile oxidation and colloidal instability upon conventional water‐based processing, thus limiting applicability. By experiments and theory, It is suggested that for stability and dispersibility, it is critical to select uncommonly high permittivity solvents such as N‐methylformamide (NMF) and formamide (FA) (εr = 171, 109), unlike the classical solvents characterized by high dipole moment and polarity index. They also allow high MXene stacking order within thin films on carbon nanotube (CNT) substrates, showing very high Terahertz (THz) shielding effectiveness (SE) of 40–60 dB at 0.3–1.6 THz in spite of the film thinness

Alzheimer's disease is the most prevalent form of dementia, which is a gradual condition that begins with mild memory loss and progresses to difficulties communicating and responding to the environment. Recent advancements in neuroimaging techniques have resulted in large-scale multimodal neuroimaging data, leading to an increased interest in using deep learning for the early diagnosis and automated classification of Alzheimer's disease. This study uses machine learning (ML) methods to determine the severity level of Alzheimer's disease using MRI images, where the dataset consists of four levels of severity. A hybrid of 12 feature extraction methods is used to diagnose Alzheimer's disease severity, and six traditional machine learning methods are applied, including decision tree, K-nearest neighbor, linear discrimination analysis, Naïve Bayes, support vector machine, and ensemble learning methods. During training, optimization is performed to obtain the best solution for each classifier. Additionally, a CNN model is trained using a machine learning system algorithm to identify specific patterns. The accuracy of the Naïve Bayes, Support Vector Machines, K-nearest neighbor, Linear discrimination classifier, Decision tree, Ensembled learning, and presented CNN architecture are 67.5%, 72.3%, 74.5%, 65.6%, 62.4%, 73.8% and, 95.3%, respectively. Based on the results, the presented CNN approach outperforms other traditional machine learning methods to find Alzheimer severity.

Understanding the relationships between species and their pattern of coexistence is essential in conservation planning. Rüppell's fox and sand cat are two poorly known species of desert areas that coexist in most parts of the desert belt of Africa and the Middle East. However, their habitat selection in many parts of their distribution are unknown. In this research, using the data collected from a three-year monitoring plan, we investigated the relationship between the den sites of Rüppell's fox and sand cat and habitat variables based on the generalized linear mixed model (GLMM) method. The results showed that for Rüppell's foxes, only the density of rodent burrows had a significant and positive effect. In the sand cat model, in addition to prey, vegetation density (P-value < 0.05) and vegetation height (P-value < 0.10) had significantly positive effects and elevation had a significantly negative effect (P-value < 0.10). We found that the sand cat is more specialized in habitat selection and depends more on the habitat cover. While the Rüppell's fox chose a more diverse range of soil and vegetation classes, the sand cat selected its den sites exclusively in sandy loam soils with the predominant cover of Haloxylon spp. and Caligonum spp. Acting as a generalist species, Rüppell's fox displayed a more pronounced response to human presence and selected dens with a broader view of their surroundings. These findings enhance our understanding of mesocarnivore coexistence in desert regions and provide valuable insights for the conservation planning of these species.

The global decline of amphibian populations, driven by anthropogenic activities, is a pressing conservation issue, with salamanders being of particular concern, as these species serve as ecological indicators vulnerable to environmental change. Mountain newts of the Neurergus genus, which are endemic to the Zagros Mountain chain from southeastern Turkey to northern Iraq and southwestern Iran, face a multitude of threats. Among these threats, climate change and land use alterations have been identified as major contributors to the decline of these species. Given the varying spatiotemporal scales at which these factors operate, in this study we aimed to assess the impacts of climate and land use/land cover (LULC) changes (LULCC) on the distribution of the Neurergus genus. We employed MaxEnt model to predict their habitat suitability under current climatic conditions. We projected the predicted model to the future, i.e., 2050, under two climate change scenarios. We then proceeded to map the LULC patterns of the identified suitable habitats for each species using Landsat satellite images, and conducted a hindcast of LULCC within these habitats for three time-slices 1988, 2005, and 2020. Finally, we evaluated the efficiency of current network of protected areas (PAs) and key biodiversity areas (KBAs) in Iran, Iraq, and Turkey to cover suitable habitats of the species. Our results revealed that climate changes would negatively influence all Neurergus species, with southern species in Iran and Iraq, i.e., N. derjugini, N. kaiseri, and N. crocatus exhibiting the greatest range loss. Conversely, LULC change detection indicated that northern species in Turkey, i.e., N. strauchii and N. barani, are more exposed to cropland developments and have experienced greatest habitat changes over the past 30 years. Ultimately, our findings underscore the insufficiency of extant conservation areas in protecting Neurergus habitats and urge the need for comprehensive conservation measures. We recommend promoting less strictly conserved areas, e.g., KBAs, implementing trans-boundary conservation plans, and designating new reserves to ensure long-term preservation of amphibians in the regions.

Introduction: The existence of intelligent decision-making in educational managers increases their recognition skills and they find the ability to identify the decision process, check possibilities, make correct judgments, and plan carefully. Therefore, the present study was conducted with the aim of studying the mediating role of intellectual ethics in the relationship between digital leadership and smart decision-making among elementary school principals in Kermanshah province. Material and Methods: The research is a correlational research based on structural equation modeling of the correlation-covariance matrix. The statistical population includes all the elementary principals of Kermanshah province in the academic year 2021-2022 (N=1902). The sample size was estimated based on proportional stratified random sampling and Cochran's formula (n=320). The tools included intelligent leadership, intellectual ethics and smart decision-making questionnaires, whose content validity was confirmed by experts and professors, and their construct validity was confirmed by factor analysis. The reliability of the questionnaires was calculated through Cronbach's alpha as 0.85, 0.93 and 0.95 respectively. Pearson's correlation test and structural equation modeling were used for data analysis, and SPSS and LISREL software were used. Results: The relationship between digital leadership and intellectual ethics, digital leadership and intelligent decision-making, and the relationship between intellectual ethics and smart decision-making were significant. Finally, it was found that intellectual ethics can play a mediating role in the relationship between digital leadership and smart decision making. Conclusion: The results of this study show that educational managers can make smart decisions through the digital leadership style and, of course, considering intellectual ethics, and in this way, take steps in the direction of advancing educational goals and missions.

The purpose of the present study was to investigate the role of digital leadership in intelligent decision making with the mediation of team reception. The method was descriptive and of the type of correlational studies. The research population of primary school principals in Kermanshah province in 2022 was 1902 people, from this population, based on the Cochran formula, a sample of 320 people was selected using stratified random sampling according to the population of the research classes such as the type of region (privileged, semi-privileged and deprived), city/district (24 cases) and gender ( male and female) was selected. Collecting data from questionnaires; Digital leadership and intelligent decision-making developed by the researcher were used in addition to team reception (Sultani, 2007). Data analysis was done using Pearson's correlation coefficient and structural equation modeling. The findings showed that digital leadership had a direct and significant effect on team reception and intelligent decision-making in elementary school principals. Also, team reception had a direct and significant effect on intelligent decision-making in elementary school principals (p≤0.01). In addition, digital leadership through team reception had an indirect and significant effect on intelligent decision-making in elementary school principals (p≤0.01). It seems that team reception among elementary school principals has a mediating role in the relationship between digital leadership and intelligent decision making.

Background Liver transplantation is a proven management method for end-stage cirrhosis and is estimated to have increased life expectancy by 15 years. The COVID-19 pandemic posed a challenge to patients who were candid for a solid-organ transplant. It has been suggested that the outcomes of liver transplants could be adversely affected by the infection, as immunosuppression makes liver transplant candidates more susceptible to adverse effects while predisposing them to higher thrombotic events.Material and methods In this retrospective study, the cases who received liver transplants from January 2018 to March 2022 were assessed regarding early postoperative mortality rate and hepatic artery thrombosis (HAT) with COVID-19 infection. This study included 614 cases, of which 48 patients were infected.Results This study shows that the early COVID-19-related early postoperative mortality rates substantially increased in the elective setting (OR: 2.697), but the results for the acute liver failure were insignificant. The average model for end-stage liver disease score increased significantly during the pandemic due to new regulations. Although mortality rates increased during the pandemic, the data for the vaccination period show that mortality rates have equalised with the prepandemic era. Meanwhile, COVID-19 infection is assumed to have increased HAT by 1.6 times in the elective setting.Conclusion This study shows that COVID-19 infection in an acute liver failure poses comparatively little risk; hence transplantation should be considered in such cases. Meanwhile, the hypercoagulative state induced by the infection predisposes this group of patients to higher HAT rates.

Industrial wastewater recycling projects are mainly used for alleviation of both water scarcity and contamination of freshwater bodies. These projects mainly address major challenges related to technological and economic aspects rather than stakeholders’ responsibility. Hence, little is known for the role of responsible stakeholders as a major part of planning policy, which requires recognition of their crucial role and integration into associated procedures. This paper presents a new decision support framework to identify responsible stakeholders and reveals the role of their motivations. The approach integrates qualitative and frequency analysis methods into a comprehensive framework to identify the problems over the project lifetime from visible to their roots and link them together with stakeholders through deep mapping. The planning policy framework is applied to a real-world case study of industrial parks in Iran. The results of the case study show that visible economic, social, and technological problems are caused by responsible stakeholders with no direct role in those projects. Additionally, deep mapping analysis shows various deep roots caused by the government and industry are linked to visible problems across all project phases that are related to the role of stakeholders, their behaviour, and deep beliefs. HIGHLIGHTS A new planning policy framework is developed for industrial wastewater reuse projects.; Visible problems, responsible stakeholders, and their beliefs are identified within a project.; Visible problems, stakeholders, and beliefs are interconnected through deep mapping.;

Abstract Species distribution models (SDMs) are practical tools to assess the habitat suitability of species with numerous applications in environmental management and conservation planning. The manipulation of the input data to deal with their spatial bias is one of the advantageous methods to enhance the performance of SDMs. However, the development of a model parameterization approach covering different SDMs to achieve well‐performing models has rarely been implemented. We integrated input data manipulation and model tuning for four commonly‐used SDMs: generalized linear model (GLM), gradient boosted model (GBM), random forest (RF), and maximum entropy (MaxEnt), and compared their predictive performance to model geographically imbalanced‐biased data of a rare species complex of mountain vipers. Models were tuned up based on a range of model‐specific parameters considering two background selection methods: random and background weighting schemes. The performance of the fine‐tuned models was assessed based on recently identified localities of the species. The results indicated that although the fine‐tuned version of all models shows great performance in predicting training data (AUC > 0.9 and TSS > 0.5), they produce different results in classifying out‐of‐bag data. The GBM and RF with higher sensitivity of training data showed more different performances. The GLM, despite having high predictive performance for test data, showed lower specificity. It was only the MaxEnt model that showed high predictive performance and comparable results for identifying test data in both random and background weighting procedures. Our results highlight that while GBM and RF are prone to overfitting training data and GLM over‐predict nonsampled areas MaxEnt is capable of producing results that are both predictable (extrapolative) and complex (interpolative). We discuss the assumptions of each model and conclude that MaxEnt could be considered as a practical method to cope with imbalanced‐biased data in species distribution modeling approaches.

Abstract Effective appointment scheduling (EAS) is essential for the quality and patient satisfaction in hospital management. Healthcare schedulers typically refer patients to a suitable period of service before the admission call closes. The appointment date can no longer be adjusted. This research presents the whale optimization algorithm (WOA) based on the Pareto archive and NSGA-II algorithm to solve the appointment scheduling model by considering the simulation approach. Based on these two algorithms, this paper has addressed the multi-criteria method in appointment scheduling. This paper computes WOA and NSGA with various hypotheses to meet the analysis and different factors related to patients in the hospital. In the last part of the model, this paper has analyzed NSGA and WOA with three cases. Fairness policy first come first serve (FCFS) considers the most priority factor to obtain from figure to strategies optimized solution for best satisfaction results. In the proposed NSGA, the FCFS approach and the WOA approach are contrasted. Numerical results indicate that both the FCFS and WOA approaches outperform the strategy optimized by the proposed algorithm.

Hypoxanthine phosphoribosyltransferase (HPRT1), as a salvage pathway enzyme, plays a crucial role in modulating the cell cycle and has been reported to be overexpressed in multiple cancers. Nevertheless, the relationship between the HPRT1 gene and head and neck squamous cell carcinomas (HNSCCs) has not been investigated so far. In this study, we first evaluated the expression and clinical value of HPRT1 mRNA and protein in tumor and healthy control tissues. Then, we examined mutations of the HPRT1 gene and their association with survival outcomes of patients with HNSCC. We also performed functional analyses of HPRT1 coexpressed genes and examined the association between HPRT1 expression and drug sensitivity. Both HPRT1 mRNA and protein were significantly higher in HNSCC compared with normal tissues, and up‐regulation of HPRT1 was also correlated with age, sex, pathological stage and histological grades of patients with HNSCC. Moreover, HPRT1 and its associated genes were observed to be enriched for several cancer‐related pathways, including DNA replication and cell cycle. Finally, patients exhibiting overexpression of the HPRT1 gene may be resistant to abiraterone and sensitive to several drugs, including tozasertib and teniposide. This study demonstrated that the elevated expression of HPRT1 gene is correlated with the progression of HNSCC; thus, this gene may serve as a useful indicator for the early detection, risk stratification and targeted therapy of patients with HNSCC.

Cold physical plasma is a partially ionized gas operated at body temperature and utilized for heat-sensitive technical and medical purposes. Physical plasma is a multi-component system consisting of, e.g., reactive species, ions and electrons, electric fields, and UV light. Therefore, cold plasma technology is an interesting tool for introducing biomolecule oxidative modifications. This concept can be extended to anticancer drugs, including prodrugs, which could be activated in situ to enhance local anticancer effects. To this end, we performed a proof-of-concept study on the oxidative prodrug activation of a tailor-made boronic pinacol ester fenretinide treated with the atmospheric pressure argon plasma jet kINPen operated with either argon, argon–hydrogen, or argon–oxygen feed gas. Fenretinide release from the prodrug was triggered via Baeyer–Villiger-type oxidation of the boron–carbon bond based on hydrogen peroxide and peroxynitrite, which were generated by plasma processes and chemical addition using mass spectrometry. Fenretinide activation led to additive cytotoxic effects in three epithelial cell lines in vitro compared to the effects of cold plasma treatment alone regarding metabolic activity reduction and an increase in terminal cell death, suggesting that cold physical plasma-mediated prodrug activation is a new direction for combination cancer treatment studies.

Abstract Background The orogeny of the eastern Mediterranean region has substantially affected ecological speciation patterns, particularly of mountain-dwelling species. Mountain vipers of the genus Montivipera are among the paramount examples of Mediterranean neo-endemism, with restricted ranges in the mountains of Anatolia, the Levant, Caucasus, Alborz, and Zagros. Here we explore the phylogenetic and ecological diversification of Montivipera to reconstruct its ecological niche evolution and biogeographic history. Using 177 sequences of three mitochondrial genes, a dated molecular phylogeny of mountain vipers was reconstructed. Based on 320 occurrence points within the entire range of the genus and six climatic variables, ecological niches were modelled and used to infer ancestral niche occupancy. In addition, the biogeographic history and ancestral states of the species were reconstructed across climate gradients. Results Dated phylogenetic reconstruction revealed that the ancestor of mountain vipers split into two major clades at around 12.18 Mya followed by multiple vicariance events due to rapid orogeny. Montivipera colonised coastal regions from a mountain-dwelling ancestor. We detected a highly complex ecological niche evolution of mountain vipers to temperature seasonality, a variable that also showed a strong phylogenetic signal and high contribution in niche occupation. Conclusion Raising mountain belts in the Eastern Mediterranean region and subsequent remarkable changes in temperature seasonality have led to the formation of important centres of diversification and endemism in this biodiversity hotspot. High rates of niche conservatism, low genetic diversity, and segregation of ranges into the endemic distribution negatively influenced the adaptive capacity of mountain vipers. We suggest that these species should be considered as evolutionary significant units and priority species for conservation in Mediterranean mountain ecosystems.

Multiple sclerosis (MS) is a central nervous disorder caused by several factors. Studies have recently shown that non-coding RNA such as miRNA could participate in MS initiation, progression, and active lesion. This study aims to theoretically analyze the potential impact of single-nucleotide polymorphisms (SNPs) on mir-21 and mir-146a/b, which has been previously demonstrated as MS microRNA signature. To fulfill this purpose, the SNPs were investigated for functionality through several online tools, including miRNA-SNP, SNP2-TFBS, RBP-Var, and RNAfold. Furthermore, SNPs of miR-21 and miR-146a/b that exist in pre-miRNA, mature miRNA, and promoter area were extracted; moreover, miRNA and RNA-binding protein interactions were analyzed. This article presented a list of validated SNPs that could affect the expression or function of miR-21 and miR-146a/b for the future practical study of MS and active lesions.

As the world continues to be a globalized society, there have been variations in environmental quality, but studies including trade globalization into the environmental policy framework remain inconclusive. Therefore, employing the time series dataset of Uruguay over the period between 1980 and 2018, the main objective of this current study is to investigate the effect of trade globalization, natural resources rents, economic growth, and financial development on carbon emissions. By employing the bounds testing procedures in combination with the critical approximation p-values of Kripfganz and Schneider (2018), the Autoregressive Distributed Lag estimator, and spectral causality test to achieve the goal of this research. The outcomes of the bounds test confirm a long-run connection between carbon emissions and these determinants. Moreover, from the outcome of the Autoregressive Distributed Lag estimator, we observed that trade liberalization is found to exert CO2 emissions in the long and short run. The economic expansion in Uruguay imposes significant pressure on the quality of the environment in the long and short run. The abundance of natural resources significantly increases environmental deterioration in the long and short run. Furthermore, we uncover that financial development does not impact environmental deterioration in Uruguay. Finally, the outcome of the spectral causality test detected that trade globalization, economic growth, and natural resources forecast carbon emissions with the exclusion of financial development. Based on the outcome, this study suggests that policies should be tailored towards international trade must be reassessed, and the restrictions placed on the exportation of polluting-intensive commodities must be reinforced.

The current empirical literature ignores the possible influence of oil prices on environmental degradation through fiscal policy instruments. Contributing to the literature, this study explores the influence of oil price on the environmental degradation in Turkey through fiscal policy instruments, using a novel methodology of the bootstrap ARDL approach. The FMOLS, CCR, DOLS, and ARDL models are used to examine the long-run linkage among the tested variables. The findings from estimating models demonstrated that government expenditures positively affected environmental degradation in Turkey. In contrast, the taxation revenues negatively affected the environmental degradation. Furthermore, the empirical outcomes affirm that oil prices have a powerful effect on the levels of Turkey’s environmental pollution through taxation revenues, energy, and GDP factors. Therefore, the study suggests that the Turkish policymakers should design policies to avoid any undesirable impacts of the spillover effects of the oil price on the environment using fiscal policy channels. In this sense, the government in Turkey should design a framework that includes financial incentives such as low taxation rates on green energy investment. In addition, the policy markets in Turkey should start to use the carbon tax policy, which is one of the most efficient tools to reduce environmental pollution.

Abstract The phylogeny and species boundaries of Gazella subgutturosa and G. marica have been long debated. The achievements of past conservation efforts have been compromised by a lack of knowledge about the phylogeny and taxonomic status of different populations. We integrated the recent genetic findings by previous studies with morphometric analyses and ecological niche modeling (ENM) to assess discreteness among populations of these gazelle species in Asia. Taxonomic diversity of gazelles was investigated by using principal components analysis (PCA) based on 14 cranial measures of male skulls. Ecological niche divergence was examined based on a PCA on climatic factors and a species distribution modeling (SDM) with environmental variables. Morphometric results indicated substantial differentiation in size between skulls of the western Zagros Mountains including west and south‐western Iran and Arabian Peninsula from all other samples east of the Zagros Mountains from Iran to China. ENM also revealed that gazelles in the east and west of Zagros Mountains occupy distinct niches and that there are apparent areas of disconnection across the goitered gazelle suitable range. A complete divergent niche occupation was also observed between goitered gazelles of northern Mongolia and other populations of the species, except those in China. Taking the inferences from ENM and morphology together with previous genetics results, we conclude that gazelles in the west and south‐west of Iran may represent G. marica. Also, our combined analyses revealed divergence among gazelles of Iran, Central Asia, and Mongolia/China. These results may pave the way for future studies and have conservation implications particularly for reintroduction/supplementation programs.

Background and Objective: Depression is one of the risk factors for cardiovascular disease leading to re-admission, reduced quality of life, and increased risk of mortality. On the other hand, some people often tolerate and adapt to life problems and pressures more than others. Therefore, the purpose of the present study was to use cognitive-behavioral therapy to reduce depression and increase psychological hardiness in order to reduce the problems of patients with cardiovascular disease. Materials and Methods: In this study, a semi-experimental design with pre-test, post-test and control group was performed. The study population included all patients with heart disease men and women over twenty years and was down sixty years that were hospitalized in Farshchian hospital in Hamedan. Of this numbers, thirty people were selected by convenience sampling method and randomly divided into two groups of 15 participants and control. In order to evaluate depression from Beck Depression Inventory and measure the hardiness of personal opinion survey test were used. Then, using 10 sessions of cognitive behavioral therapy, changes in the post test were measured. Research data using multivariate analysis of covariance were analyzed. Results: The findings showed that teaching cognitive-behavioral therapy, reducing the rate of depression among heart patients. Also, this treatment increases the psychological hardiness. Conclusion: The findings of the present study cognition and conceptualization of the variables presented, it is possible to use cognitive-behavioral therapy in reducing depression and increasing hardiness benefit heart patients.

Nowadays considering the growth of the population and increasing the need for housing, construction activities have been booming more remarkably than the past. So, the volume of construction waste has increased which has caused many environmental problems. These derbies have potation to reuse in construction activities. Recycling is one of the best ways to manage this debris. Because it reduces the volume of waste. It also confirms to reusing, and it reduces the need to extract and uses natural resources. Considering the importance of this, this waste can be used as replacement of aggregates in concrete. In this study, the effect of different percentages of replacement of construction and demolition waste as coarse aggregate on mechanical properties of concrete has been investigated, and the effect of using these debris on compressive strength, tensile strength and flexural strength concrete was studied. The percentages of replacement are 0%, 10%, 20%, 30%, and 50%, and the water to cement ratio is 0.49. Tests were done for two curing time 7 and 28 days. Also, in order to analysing the results, statistical analysis of one-way ANOVA was carried out. The results show that with increasing the replacement rate, compressive strength, tensile strength and flexural strength are decreased, but these decreases aren’t significant

BackgroundAlzheimer’s disease (AD) is a degenerative condition characterized by progressive cognitive impairment and dementia. Findings have revolutionized current knowledge of miRNA in the neurological conditions. Two regulatory mechanisms determine the level of mature miRNA expression; one is miRNA precursor processing, and the other is gene expression regulation by transcription factors. This study is allocated to the in-silico investigation of miRNA’s SNPs and their effect on other cell mechanisms.MethodsWe used databases which annotate the functional effect of SNPs on mRNA-miRNA and miRNA-RBP interaction. Also, we investigated SNPs which are located on the promoter or UTR region.ResultsmiRNA SNP3.0 database indicated several SNPs in miR-339 and miR-34a in the upstream and downstream of pre-miRNA and mature miRNAs. While, for some miRNAs miR-124, and miR-125, no polymorphism was observed, and also miR-101 with ΔG -3.1 and mir-328 with ΔG 5.8 had the highest and lowest potencies to produce mature microRNA. SNP2TFBS web-server presented several SNPs which altered the Transcription Factor Binding Sites (TFBS) or generated novel TFBS in the promoter regions of related miRNA. At last, RBP-Var database provided a list of SNPs which alter miRNA-RBP interaction pattern and can also influence other miRNAs’ expression.DiscussionThe results indicated that SNPs microRNA affects both miRNA function and miRNA expression. Our study expands molecular insight into how SNPs in different parts of miRNA, including the regulatory (promoter), the precursor (pre-miRNA), functional regions (seed region of mature miRNA), and RBP-binding motifs, which theoretically may be correlated to the Alzheimer’s disease.

Traffic prediction is critical to expanding a smart city and country because it improves urban planning and traffic management. This prediction is very challenging due to the multifactorial and random nature of traffic. This study presented a method based on ensemble learning to predict urban traffic congestion based on weather criteria. We used the NAS algorithm, which in the output based on heuristic methods creates an optimal model concerning input data. We had 400 data, which included the characteristics of the day’s weather, including six features: absolute humidity, dew point, visibility, wind speed, cloud height, and temperature, which in the final column is the urban traffic congestion target. We have analyzed linear regression with the results obtained in the project; this method was more efficient than other regression models. This method had an error of 0.00002 in terms of MSE criteria and SVR, random forest, and MLP methods, which have error values of 0.01033, 0.00003, and 0.0011, respectively. According to the MAE criterion, this method has a value of 0.0039. The other methods have obtained values of 0.0850, 0.0045, and 0.027, respectively, which show that our proposed model has a minor error than other methods and has been able to outpace the other models.

As a new field of oxidative stress-based therapy, cold physical plasma is a promising tool for several biomedical applications due to its potential to create a broad diversity of reactive oxygen and nitrogen species (RONS). Although proposed, the impact of plasma-derived RONS on the cell membrane lipids and properties is not fully understood. For this purpose, the changes in the lipid bilayer functionality under oxidative stress generated by an argon plasma jet (kINPen) were investigated by electrochemical techniques. In addition, liquid chromatography-tandem mass spectrometry was employed to analyze the plasma-induced modifications on the model lipids. Various asymmetric bilayers mimicking the structure and properties of the erythrocyte cell membrane were transferred onto a gold electrode surface by Langmuir-Blodgett/Langmuir-Schaefer deposition techniques. A strong impact of cholesterol on membrane permeabilization by plasma-derived species was revealed. Moreover, the maintenance of the barrier properties is influenced by the chemical composition of the head group. Mainly the head group size and its hydrogen bonding capacities are relevant, and phosphatidylcholines are significantly more susceptible than phosphatidylserines and other lipid classes, underlining the high relevance of this lipid class in membrane dynamics and cell physiology.

Energy has been one of the most important topics of political and social discussion in recent decades. A significant proportion of the country’s revenues is derived from energy resources, making it one of the most important and strategic macro policy and sustainable development areas. Energy demand modeling is one of the essential strategies for better managing the energy sector and developing appropriate policies to increase productivity. With the increasing global demand for energy, it is necessary to develop intelligent forecasting methods and algorithms. Different economic and non-economic indicators can be used to estimate the energy demand, including linear and non-linear statistical methods, mathematics, and simulation models. This non-linear relationship between these indicators and energy demand has led researchers to search for intelligent solutions, such as artificial neural networks for non-linear modeling and prediction. The purpose of this study was to use a deep neural network with fuzzy wavelets to predict energy demand in Iran. For the training of the presented components, a hybrid training method incorporating both an inline PSO and a gradient-based algorithm is presented. The provided technique predicts energy consumption in Tehran, Mashhad, Ahvaz, and Urmia from 2010 to 2021. This study shows that the presented method provides high-performance prediction at a lower level of complexity.

Effect of sandblasting of the copper electrode structures before deposition of gold thin film for micro electrical impedance tomography (EIT) system has been studied experimentally. The comparison has been performed on the unmodified copper electrodes and the sandblasted electrodes before deposition of gold layer, using structural analysis while their performance in EIT system has been measured and analyzed. The results of scanning electron microscopy and atomic force microscopy show that the sandblasting of the electrodes results in the deposition of gold film with smaller grain size and uniformly, comparing to the unmodified structure. The measurement of impedance shows that the sandblasting will increase the double layer capacitance of electrode structure which improves the impedance measurement accordingly.

Climate change and the expected increase in frequency of dry summers are likely to affect the growth of important tree species. We investigated relationships between morphological and chemical leaf traits of European beech (Fagus sylvatica L.) and environmental factors along a water availability gradient consisting of 12 sites located throughout Switzerland. We found that leaf dry mass and leaf area tended to decrease with increasing long-term mean annual precipitation (MAP) and actual to potential transpiration (AT/PT), two correlated variables that form the water availability gradient. These results contrast with those of several other studies, and might be explained by favorable temperature and humidity conditions during leaf formation in spring at the dry sites. Although the relationship was not linear over the whole gradient, the drier sites were characterized by beech with a lower specific leaf area, along with higher foliar potassium (K) and lower foliar nitrogen (N) concentrations. These patterns likely reflect strategies developed as an adaptation to reduced water availability, but they also result from variation in the availability of nutrients in soil across our sites. In the case of N, there are indications that atmospheric deposition plays a role in foliar concentrations.

Background and Objective: With the onset of adulthood, the rate of high-risk behaviors in individuals rises. Research has shown that preventive training schedules reduces the incidence of high-risk behaviors. The purpose of this study was to investigate Effectiveness of Drug Use and Acquired Immune Deficiency Syndrome (AIDS) Prevention Package on Drug Use and High Risk Behaviors Reduction among College Students of Islamic Azad University, Roudehen Branch. Methods: The study sample was composed of all students in different grades. The sampling method of research had two stages. First, 384 students were selected according to Krejcie and Morgan chart, using stratified random sampling method; they answered to Risky Behaviors questionnaire. Then 15 participants were randomly assigned to the experimental group and 15 participants to the control group. Using risky behavior questionnaire a pre-test was performed for both groups. Then the experimental group participated in 12 session (each session 90 minutes) of drug use and Acquired Immune Deficiency Syndrome prevention workshop; the control group remained on a waiting list. At the end of training sessions and one month follow up, both groups were tested again. Data was analyzed using repeated measures design. Results: The results showed that the variable scores of tendency to drug use, sexual risk behaviors, anti-social behavior and suicidal thoughts in the post-test and follow-up in experimental group was significantly lower than the control group. So we can conclude that the training package of drug use and AIDS prevention and prevention is effective in drug use and risky behavior reduction. Conclusion: It can be concluded that the drug use and acquired immune deficiency syndrome Prevention Package can be effective in reducing drug use and high-risk behaviors.

The application of plant hormones has been long considered in the production of the plants, but the use of a whole new set of plant growth regulators including brassinosteroids and human hormones including progesterone and estradiol has been an interesting field of study for plant researchers in recent decades. The present study aimed to explore the effect of plant and animal steroid growth regulators on growth and development of Petunia, Tagetes and Calendula. The first factor was devoted to steroid growth regulators including: No hormone, 1 mg L−1 progesterone, 1 mg L−1 estradiol, 1 mg L−1 24-epibrassinolide, and 1 mg L−1 homobrassinolide. The second factor included three ornamental plant species including Petunia hybrida, Tagetes erecta, and Calendula officinalis. The recorded traits included morphological and biochemical compounds such as catalase, peroxidase, and superoxide dismutase. It was found that the highest leaf area, chlorophyll content, and peroxidase level were obtained from the plants treated with estradiol. The application of homobrassinolide had the highest effect of carotenoid and chlorophyll contents. Among the interactions, “24-epibrassinolide × Petunia” resulted in the highest leaf area, “estradiol × Calendula” resulted in highest superoxide dismutase activity, and “estradiol × Petunia” resulted in the highest peroxidase activity. We observed the desirable impact of steroids on the studied traits. Estradiol, homobrassinolide and 24-epibrassinolide improved some traits, but no specific effect was observed for the application of progesterone. According to the results, it is recommended to apply steroid growth regulators to improve the quality and yield of the plants.

Introduction: Childhood cansers are the second leading cause of death among children. Since the incidence and prevalence of malignancies are different in different regions and there are several cultural and environmental factors involved, the aim of this study was investigating epidemiology of common malignancies in children in Hamedan Province during the years of 1386-1395. Methods: In this cross-sectional study, the population study was the children with malignancies referred to Besat's Hospital in Hamadan Province. The collected data were analyzed using SPSS 20 software. Descriptive indices and Chi-square test were used in this study. Results: All available records showed that the malignancy was more prevalent in boys in freqency (57/7%). Most acute lymphoblastic leukemia (ALL) patients were 1-9 years, most acute myeloid leukemia (AML) patients were older than 9 years and most other malignancies were 1-4 years and over 9 years. The number of blood platelets (PLT) and the white blood cells (WBC) of the majority of patients in other malignancies, respectively, were greater than 100,000 and less than 50,000. For acute lymphoblastic leukemia and acute myeloid leukemia patients, the number of PLT and WBC were less than 100,000 and less than 50,000, respectively. The most clinical symptoms for acute lymphoblastic leukemia patients were fever and pale, fever and bleeding for acute myeloid leukemia AML patients, and finally masses and fever for patients with other malignancies. Conclusion: Although the results of the family cancer were consistent with expected results concluded of similar studies, but the incidence of other malignancies in females was different (51/1%) with the results of some other studies.

A MoIV mono-oxido bis-dithiolene complex, [MoO(mohdt)2]2− (mohdt = 1-methoxy-1-oxo-4-hydroxy-but-2-ene-2,3-bis-thiolate) was synthesized as a structural and functional model for molybdenum oxidoreductase enzymes of the DMSO reductase family. It was comprehensively characterized by inter alia various spectroscopic methods and employed as an oxygen atom transfer (OAT) catalyst. The ligand precursor of mohdt was readily prepared by a three-step synthesis starting from dimethyl-but-2-ynedioate. Crystallographic and 13C-NMR data support the rationale that by asymmetric substitution the electronic structure of the ene-dithio moiety can be fine-tuned. The MoIVO bis-dithiolene complex was obtained by in situ reaction of the de-protected ligand with the metal precursor complex trans-[MoO2(CN)4]4−. The catalytic oxygen atom transfer mediated by the complex was investigated by the model OAT reaction from DMSO to triphenylphosphine with the substrate transformation being monitored by 31P NMR spectroscopy. [MoO(mohdt)2]2− was found to be catalytically active reaching 93% conversion, albeit with a rather low reaction rate (reaction time 56 h). The observed overall catalytic activity is comparable to those of related complexes with aromatic dithiolene ligands despite the novel ligand being aliphatic in nature and originally perceived to perform more swiftly. The respective results are rationalized with respect to a potential intermolecular interaction between the hydroxyl and ester functions together with the electron-withdrawing functional groups of the dithiolene ligands of the molybdenum mono-oxido complex and equilibrium between the active monomeric MoIVO and MoVIO2 and the unreactive dimeric Mo2VO3 species.

It is necessary to optimize the use of water resources in arid and semi-arid of world because of limited water resources, low performance efficiency, increasing population and demand for water. Suitable cropping patterns are very effective and efficient method for use of water. Due to limited water resources in Birjand plain, this research was conducted to optimization of crop pattern in the plain. For this purpose, crop pattern in Birjand plain (A) and 8 common crop patterns were studied. The method of multi-criteria decision analytic hierarchy process and LINGO programming model used to determine the best cropping pattern. Economic and social measures used to maximize net income, employment and labor and minimizing water consumption, cost and virtual water in both model. Results showed that profit and water is factor that is more important other than to determine crop pattern. Results show that saffron, cumin, barley, wheat, summer crops, vegetables, sugar beet, forage plant and cotton are the highest priority for cultivation. AHP results showed that 8, 2 and 4 models were determined the best cropping patterns so that in these models the forage plant (21.12%), wheat (36.74%) and wheat (46.76%) had the highest cultivation area, respectively. Model 8 due to had a low coefficient of determination (R2) in LINGO so this model is inappropriate. The cultivated wheat and vegetable in models 2 and 4 increased than cropping patterns prevalent in the region. Cucurbit plants, saffron, cotton and barley in Model 2 increased compared to the current model, while Model 4 decreased. Cumin decreased in model 2 but increased in model 4. Cultivation of forage plant and sugar beet in both models reduced compared to the current crop pattern.

This article is focused on developing an Energy Service Company (ESCO) risk assessment model for use by energy savings certificates (ESC) market regulators. This model enables market regulators to determine the appropriate point in time for ESCOs to sell their certificates with the aim of minimizing risk as well as maximizing economic gain yet remain motivated for reducing the cost of energy efficiency technologies. To this end, the interactions between an ESCO and other parties (such as suppliers) in the market in addition to the principles of the energy efficiency performance contract are taken into consideration. Then, appropriate probability distributions have been fitted to the stochastic variables to be applied in the Net Present Value (NPV) function, based on sampled company data. A case study considers a one MW Organic Rankine Cycle (ORC) implementation in Iran’s petrochemical industry. The finding of this study shows if the ESCO is allowed to sell the certificates during the first seven years as well reduce 30% of the investment cost, the expected Net Present Value over Investment Cost (NPV/I) savings will cover more than one cycle.

In this study, by employing a common synthetic protocol, an unusual and unexpected tetra-nuclear nickel dithiolene complex was obtained. The synthesis of the [Ni4(ecpdt)6]2− dianion (ecpdt = (Z)-3-ethoxy-3-oxo-1-phenylprop-1-ene-1,2-bis-thiolate) with two K+ as counter ions was then intentionally reproduced. The formation of this specific complex is attributed to the distinct dithiolene precursor used and the combination with the then coordinated counter ion in the molecular solid-state structure, as determined by X-ray diffraction. K2[Ni4(ecpdt)6] was further characterized by ESI-MS, FT-IR, UV-Vis, and cyclic voltammetry. The tetra-nuclear complex was found to have an uncommon geometry arising from the combination of four nickel centers and six dithiolene ligands. In the center of the arrangement, suspiciously long Ni–S distances were found, suggesting that the tetrameric structure can be easily split into two identical dimeric fragments or two distinct groups of monomeric fragments, for instance, upon dissolving. A proposed variable magnetism in the solid-state and in solution due to the postulated dissociation was confirmed. The Ni–S bonds of the “inner” and “outer” nickel centers differed concurrently with their coordination geometries. This observation also correlates with the fact that the complex bears two anionic charges requiring the four nickel centers to be present in two distinct oxidation states (2 × +2 and 2 × +3), i.e., to be hetero-valent. The different coordination geometries observed, together with the magnetic investigation, allowed the square planar “outer” geometry to be assigned to d8 centers, i.e., Ni2+, while the Ni3+ centers (d7) were in a square pyramidal geometry with longer Ni–S distances due to the increased number of donor atoms and interactions.

INTRODUCTION: Effective communication is one of the most important factors in advancing the goals of library services and the identifying factors affecting it and having a single model for such factors are of utmost importance. The aim of this study was to develop a structural model of self-compassion measured based on spiritual intelligence (SQ) as factors affecting communication in medical library and information science students. MATERIALS AND METHODS: This was a correlational study done by structural equation modeling method. A total of 290 undergraduate and graduate students of Medical Library and Information Sciences of Iran's Universities of Medical Sciences were selected by simple random sampling. In this study, the data were collected by the Neff's Self-Compassion Scale and the King's Spiritual Intelligence Self-report Inventory. Self-compassion measured based on SQ was tested by goodness-of-fit indices. Statistical analysis was performed using SmartPLS software. RESULTS: The results showed that there was a significant relationship between SQ and self-compassion. Furthermore, the positive values of predictive indices could confirm the self-compassion measured based on SQ. Composite reliability indicated the high reliability of SQ and self-compassion. CONCLUSION: According to the fitting indices of the model, it can be concluded that the self-compassion model has an acceptable fit on the basis of SQ.

This paper presents a comprehensive framework to manage the main risk events of highway construction pro­jects within three stages: (1) identification of potential risks; (2) assessment and prioritisation of identified risks based on fuzzy FMEA; (3) identification of appropriate response. The main criteria analysed for prioritising potential risk events are cost, time and quality which are quantified and combined using fuzzy AHP. A new expert system is suggested for identifying an appropriate risk response strategy for a risk event based on risk factor, control number and risk alloca­tion. The best response action for a risk event is then identified with respect to the same criteria using “scope expected deviation” (SED) index. The proposed methodology is demonstrated for management of risk events in a construction project of Bijar-Zanjan highway in Iran. For the risk event of “increase in tar price”, deviation from the target values of the criteria is analysed for business-as-usual state plus two risk response actions using SED index. The results show that the response action of “changing paving construction technology from asphalt pavement to RCC pavement” can success­fully cope with the risk event of “increase in tar price” and have the minimum deviation. First published online: 01 Jul 2016

Objective(s): We examined the effects of endurance exercise in the presence of Bunium persicum extract administration on lipid profile and cardiorespiratory capacity in hypercholesterolemic male mice. Materials and Methods: Forty male hypercholesterolemic mice were divided into four groups: Vehicle, Endurance exercise (EE), Bunium persicum extract (BPE), and EE + BPE. The exercise protocol was performed at a speed of 18 m/min, 40 min/day, and 5 days/week for 6 weeks. The BPE was administered orally by a dose of 20 mg/Kg/day. Results: The results indicated that the 6-week endurance training accompanied by Bunium Persicum extract administration increased cardiorespiratory capacity significantly (601±39 vs. 293±20 meters, p

Background: Although it seem that metacognitive processing system have a remarkable impact on general anxiety disorder (GAD), but studies about the effectiveness of metacognitive interventions in reducing the GAD symptoms are not frequent. The purpose of this study was comparing the effectiveness of metacognitive (MCT) and cognitive-behavioral therapy (CBT) on reducing GAD symptoms and metacognitive beliefs. Methods: In this clinical trial study, 45 patients with GAD who met the diagnostic criteria by psychiatrist or psychologist, were selected randomly from the patients referred to psychiatric and psychological clinics and centers of Hamadan City, Iran, in 2012. They were randomly assigned into three groups of cognitive-behavioral intervention, metacognitive intervention and control. Data were gathered before and after the interventions via general anxiety disorder scale (GADS) and metacognitive beliefs questionnaire (MCQ). Data analysis was done using Multivariate ANOVA and LSD post hoc statistical tests. Findings: There was a significant difference between three group scores at post-test (P < 0.01). The mean scores of metacognitive therapy group in GAD symptoms and metacognitive beliefs were lower than other two groups and as well as cognitive-behavioral group rather than control group. Conclusion: The metacognitive and cognitive-behavioral interventions are effective in reducing GAD symptoms and metacognitive beliefs but first one is more effective.

Objective: the aim of this study was investigation the effectiveness of life skills training on reducing the risk factor and enhancement of protective factors in city of Hamadan reformatory place for delinquency children. Method: in this semi-experimental research among 400 persons of Hamadan reformatory place for delinquency children, 36 individual (experimental group 17 persons, control group 16 persons) were selected via random sampling method. The experimental group received 8 training sessions. The main instrument of this research was, drug consuming risk and protective factors questionnaire. Findings: the results indicated that the scores of attitude toward drug, depression and sensation seeking of experimental group are lower than control group and in assertiveness and self-control factors were higher. Conclusion: the life skills training reduced risk factors and enhanced protective factors regarding to drug consuming in delinquent adolescence.

Background : Multiple factors such as retirement, work disability, social rejection, physical illness and etc., have an impact on general health of the elders. One factor among others is the role of psychological variables. The study is intended to assess the effect of positive and negative perfectionism and type-D personality (distressed) on general health of the elders. Methods : In this descriptive-correlation study, 80 people (47 male and 33 female) were selected from residents of Nursing Home in Hamadan and Malayer using non-probability (accessible) sampling method. They responded to the questionnaires of type-D Personality, Goldberg and Hillier′s General Health and Positive and Negative Perfectionism of Terry-Short et al. Results : Positive perfectionism ( r = 0.30) and type-D personality ( r = 0.32) had significant correlation with general health. Multiple regression analysis revealed that positive perfectionism and type-D personality could explain at least 49% of the variance in general health. Concerning the variables of negative affectivity, social inhibition and social function, the females′ mean was higher than that of males and considering the variables of positive perfectionism, and social functioning, the males′ mean was higher than that of females. Conclusions: Positive perfectionism decreases mental disorder of the elders by creating optimistic attitudes and enhancing social functions. On the other hand, type-D personality, unlike positive perfectionism, makes elders susceptible to physical illness and mental disorder.

Aim: The purpose of this study was to comprise of psychological defense mechanism styles and personality characteristics in addicts and healthy individuals. Method: In this causal-comparative study, 70 addicts person (with an average age of 37.29±9.81and the age range 23 to 58 years) were selected via accessible sampling method of clinics and Hamadan’s addicted self-representing center during the Autumn of 2011, The number of 70 relatives of these people that demographic variables were matched as possible with the comparison group were selected. Both groups were asked to respond to the defense mechanism style and Eysenk personality Questionnaires. Results: The result of this study showed that the scores mean of addicts were higher than healthy people on immature defense mechanism style, neourotism, and neurotic and extraversion personality characteristics and lower than in mature defense style variables. Conclusion: Based on the result of this study there was a significant difference between addict individuals and healthy people in defense mechanism and personality characteristics.

We developed an automated approach to differentiate between different types of arrhythmic episodes in electrocardiogram (ECG) signals, because, in real-life scenarios, a software application does not know in advance the type of arrhythmia a patient experiences. Our approach has four main stages: (1) Classification of ventricular fibrillation (VF) versus non-VF segments—including atrial fibrillation (AF), ventricular tachycardia (VT), normal sinus rhythm (NSR), and sinus arrhythmias, such as bigeminy, trigeminy, quadrigeminy, couplet, triplet—using four image-based phase plot features, one frequency domain feature, and the Shannon entropy index. (2) Classification of AF versus non-AF segments. (3) Premature ventricular contraction (PVC) detection on every non-AF segment, using a time domain feature, a frequency domain feature, and two features that characterize the nonlinearity of the data. (4) Determination of the PVC patterns, if present, to categorize distinct types of sinus arrhythmias and NSR. We used the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, Creighton University’s VT arrhythmia database, the MIT-BIH atrial fibrillation database, and the MIT-BIH malignant ventricular arrhythmia database to test our algorithm. Binary decision tree (BDT) and support vector machine (SVM) classifiers were used in both stage 1 and stage 3. We also compared our proposed algorithm’s performance to other published algorithms. Our VF detection algorithm was accurate, as in balanced datasets (and unbalanced, in parentheses) it provided an accuracy of 95.1% (97.1%), sensitivity of 94.5% (91.1%), and specificity of 94.2% (98.2%). The AF detection was accurate, as the sensitivity and specificity in balanced datasets (and unbalanced, in parentheses) were found to be 97.8% (98.6%) and 97.21% (97.1%), respectively. Our PVC detection algorithm was also robust, as the accuracy, sensitivity, and specificity were found to be 99% (98.1%), 98.0% (96.2%), and 98.4% (99.4%), respectively, for balanced and (unbalanced) datasets.