Objectives The genetic polymorphisms of the endothelial nitric oxide synthase (eNOS) gene are strongly associated with several cardiovascular diseases (CVDs) in various populations. The current study aimed to investigate the association of the eNOS rs1800779 (A/G) polymorphism with the progress of myocardial infarction (MI). Methods Eighty-five healthy subjects and 80 patients with MI admitted to the Erbil Cardiac Centre in the Kurdistan Region of Iraq were enrolled in the study. All participants were Kurdish from the same ethnic group. The amplification refractory mutation system polymerase chain reaction (ARMS-PCR) was used to determine the rs1800779 (A/G) polymorphism of eNOS, and the nitric oxide (NO) serum level was detected by spectrophotometer. Results The genotypic frequencies of the eNOS rs1800779 AA (wild type), AG, and GG were 58.75%, 33.75%, and 7.50%, respectively, in the MI patients, and 49.41%, 43.53%, and 7.06%, respectively, for the control group. The frequencies of the A and the G alleles were 75.6% and 24.4%, respectively, in the MI group, and 71.2% and 28.8%, respectively, in the control subjects. The results revealed a lack of association of the rs1800779 genotype distribution with the level of NO serum and increased risk of MI. Conclusion The study concluded that there is a lack of association between the genotypes and alleles of the rs1800779 eNOS and susceptibility to MI in the studied population.
The main objective of this research is to determine the environmental health concerns associated with radon gas accumulation and compare quantities in various kinds of construction factories in Erbil city. For this purpose, 52 dosimeters were deployed throughout 13 industries by using CR-39 solid-state nuclear track detectors (SSNTD). These tests were conducted out in factories over a 60-day exposure period. The results show that the radon concentration values ranged from (21 to 190) Bq.m−3, with an average of (86.36 ± 9) Bq.m−3, different from the studied samples, red brick samples show radon concentration over the Environmental Protection Agency’s recommended value of 148 Bq.m−3. The mean value of the annual effective dose was found to be (2.179 ± 1) mSv.y−1 below and within the range of the reference level of (3–10) mSv.y−1 of ICRP. The average annual lung cancer rate per 10⁶ people was shown to be (94.125). The dose rate to lungs (DLungs) varied from (1.07 to 7.11) nGy.h−1, with an average value of (3.454) nGy.h−1. The annual effective dose equivalent for lung AEDElung to tracheobronchial AEDET−B and pulmonary/ pulmonary lymph region AEDEP+PL, varies from (1.62 to 10.76), with an average value of (5.23) mSv.y−1 and from (0.81 to 5.38) mSv.y−1, with an average value of (2.615) mSv.y−1, respectively. The present results revealed the need for radon monitoring measurements because worker exposure in some factories can exceed permitted levels. Finally, owners should improve the factories ventilation systems to avoid the accumulation of ²²²Rn and its progeny.
Economic Load Dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to Economic Load Dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness Dependent Optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness Dependent Optimizer (FDO) examines the search spaces based on the searching approach of Particle Swarm Optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have carried out Fitness Dependent Optimizer to solve the Economic Load Dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of Fitness Dependent Optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for Fitness Dependent Optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multi-dimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. Empirical results obtained using the enhanced variant of the Fitness Dependent Optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional Fitness Dependent Optimizer. The experimental study obtained 7.94E-12.
Abstract In this study, three empirical formulae for calculating the (n, p) reaction cross section were derived from the EXFOR experimental data conducted over neutron energy range of 14–15 MeV. By incorporating the proton separation energy and binding energy into the principal formula, we obtained empirical formulae for estimating the (n, p) reaction cross sections for 128 nuclei that were classified into even–even, even–odd and odd–even sets of nuclei in the mass number range of 9 ≤ A ≤ 205. The TALYS 1.95 and EMPIRE 3.2 codes were used to evaluate the (n, p) cross sections for the selected nuclei within the specified neutron energy. The comparison of calculated cross sections from the proposed formulae with those from codes and previous systematics indicates the empirical formulae developed in this study are more accurate for experimental data predictions.
Achieving an accurate and reliable estimation of tunnel boring machine (TBM) performance can diminish the hazards related to extreme capital costs and planning tunnel construction. Here, a hybrid long short-term memory (LSTM) model enhanced by grey wolf optimization (GWO) is developed for predicting TBM-penetration rate (TBM-PR). 1125 datasets were considered including six input parameters. To vanish overfitting, the dropout technique was used. The effect of input time series length on the model performance was studied. The TBM-PR results of the LSTM-GWO model were compared to some other machine learning (ML) models such as LSTM. The results were evaluated using root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (R²). Finally, the LSTM-GWO model produced the most accurate results (test: R²=0.9795; RMSE=0.004; MAPE=0.009%). The mutual information test revealed that input parameters of rock fracture class and uniaxial compressive strength have the most and least impact on the TBM-PR, respectively.
New empirical formulae for calculating the (n, p) reaction cross-sections were obtained by using the EXFOR experimental data for the neutron energy 14–15 MeV. Considering the average binding energy per nucleon and dependences of cross-section in the principal formula, we get empirical formulae for estimating the (n, p) reaction cross-sections for 122 nuclei in the mass number range of 18 ≤ A ≤ 205. The EMPIRE 3.2 and TALYS 1.95 codes have been used to evaluate the (n, p) cross-sections for the selected nuclei within the specified neutron energy. The comparison of calculated cross-sections from the present proposed formulae with those from codes and previous systematics reveals more accurate predictions of the experimental data.
Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research - from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programs. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5,163 species distributed across 1,453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology - from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.
Deep sequential (DS) models are extensively employed for forecasting time series data since the dawn of the deep learning era, and they provide forecasts for the values required in subsequent time steps. DS models, unlike other traditional statistical models for forecasting time series data, can learn hidden patterns in temporal sequences and have the memorising data from prior time points. Given the widespread usage of deep sequential models in several domains, a comprehensive study describing their applications is necessary. This work presents a comprehensive review of contemporary deep learning time series models, their performance in diverse domains, and an investigation of the models that were employed in various applications. Tree deep sequential models, namely, artificial neural network (ANN), long short-term memory (LSTM), and temporal-conventional neural network (TCNN) along with their applications for forecasting time series data, are elaborated. We showed a comprehensive comparison between such models in terms of application fields, model structure and activation functions, optimisers, and implementation, with a goal of learning more about the optimal model used. Furthermore, the challenges and perspectives of future development of deep sequential models are presented and discussed. We conclude that the LSTM model is widely employed, particularly in the form of a hybrid model, in which the most accurate predictions are made when the shape of hybrids is used as the model.
The present study proposes a multipurpose cropping pattern optimization to minimize the environmental impacts of water and energy use on agriculture through the income–energy–river ecosystem nexus approach in different hydrological conditions. The following three main purposes are considered in the optimization model: (1) mitigating greenhouse gas emissions due to farming by considering minimization of agricultural energy consumption; (2) mitigating environmental impacts on the river ecosystem by considering it as the main source for supplying irrigation demand in the case study and (3) maximizing farmers’ income. Field studies are carried out in the case study for recording energy inputs to assess average energy use, irrigation demand, production yield and other required parameters for the selected crops. The fuzzy physical habitat simulation is utilized to develop an environmental impact function in the river ecosystem. Based on the results of the case study, the optimization model is able to balance energy use, impacts on the river ecosystem and farmers’ income. However, its performance is not best in terms of all the defined purposes. The results indicate that more than 50% of the initial income is provided, while energy use is mitigated by more than 70% on average. Furthermore, the river ecosystem is protected properly. HIGHLIGHTS A multipurpose cropping pattern optimization model is developed.; The environmental impacts of water and energy on agriculture are minimized simultaneously.; River habitat simulation is used for minimizing the impact of water use.; Greenhouse gas emissions are minimized.; An optimal cropping pattern is proposed.;
The Facebook application is used as a resource for collecting the comments of this dataset, The dataset consists of 6756 comments to create a Medical Kurdish Dataset (MKD). The samples are comments of users, which are gathered from different posts of pages (Medical, News, Economy, Education, and Sport). Six steps as a preprocessing technique are performed on the raw dataset to clean and remove noise in the comments by replacing characters. The comments (short text) are labeled for positive class (medical comment) and negative class (non-medical comment) as text classification. The percentage ratio of the negative class is 55% while the positive class is 45%.
To collect the handwritten format of separate Kurdish characters, each character has been printed on a grid of 14 × 9 of A4 paper. Each paper is filled with only one printed character so that the volunteers know what character should be written in each paper. Then each paper has been scanned, spliced, and cropped with a macro in photoshop to make sure the same process is applied for all characters. The grids of the characters have been filled mainly by volunteers of students from multiple universities in Erbil.
Algal studies in Iraq and Kurdistan may go back to the beginning of this century (1910), it did not flourish until establishing the two universities (Basra and Sulaimaniyah) in south and the north of the country, respectively. A review of algal study shows the presence of more than 2700 identified algal taxon in Iraq. This investigation reveals the need for rechecking the identification and nomenclature of most of the taxon, particularly the diatoms. The existing gap of knowledge of algae in this part of the world have already been reduced; however, still there is a much more need to be carried out by scientists in order to fulfill or at least reduce the gap of knowledge in this respect in this part of the world. However, physiological and biochemical studies on this sort of flora are quite rear in Iraq; therefore, one may regard this country to be a virgin yard for such investigation in parallel with more accurate and detail ecological and phycological studies. Quite many other references, particularly M.Sc. Thesis on such subjects, have not been included in this review, only attention was given to the most significant publication, whereas others have been excluded in this study.
Uropathogenic E. coli (UPEC) is problematic and still the leading cause of urinary tract infections worldwide. It is developed resistance against most antibiotics. The investigation, surveillance system, and efficient strategy will facilitate selecting an appropriate treatment that could control the bacterial distribution. The present study aims to investigate the epidemiology and associated risk factors of uropathogenic E. coli and to study their antibiotic resistance patterns. 1585 midstream urine specimens were collected from symptomatic urinary tract infections (UTI) patients (225 males and 1360 females) admitted to Zakho emergency hospital, Zakho, Kurdistan Region, Iraq from January 2016 until the end of December 2018. Specimens were inoculated on blood and MacConkey plates and incubated at 37оC for 24 hours. Uropathogenic E. coli was diagnosed based on gram staining, colony characteristics, and standard biochemical tests in accordance with local standards and guidelines. All isolates were screened for their antibiogram pattern using the disk diffusion method based on the Clinical and Laboratory Standards Institute guidelines. The results showed that out of 1585 urine specimens, 1026 (64.7%) were UTIs positive with a statistically higher rate in 2016 (83.6%) (P< 0.0001). The UTIs frequency in females was significantly higher than males (P< 0.0001). Generally, the uropathogenic E. coli represented 21.1% with the highest level in 2016 (22.9%). The uropathogenic E. coli rate was higher, statistically not significant, in females (21.4%) than males (18.5%) (P=0.4946). Additionally, through the three years of study, uropathogenic E. coli (UPEC) was in high frequency in February and May 2016. The female’s age group from 20 to 39 years was the most vulnerable (46%) form total infected females, while those from 70-74 years (1%) were the least susceptible in males and females. A high percentage (80.56 %) of multidrug resistance E. coli isolates was observed with high resistance against 𝛽-lactamase and macrolides antibiotics. However, higher sensitivity was towards imipenem and meropenem. In conclusion, the wrong and overuse of antibiotics will ncrease the resistance rate of E. coli. For this reason, proper use of available antibiotics is necessary. Also, the educational programs and periodic monitoring of antimicrobial susceptibility are essential for reducing the antibiotic resistance rate.
Background and objectives The World Health Organization (WHO) announced the appearance of a new coronavirus disease in Hubei province, China, to be a public health emergency of international concern. The objectives of this study can be highlighted through classifying the information sources for identifying protective practices, death probability, gender–death associations probability and education level. Methodology This is a descriptive design study conducted among the Kurdistan Region/Iraq population via an online application between 1 March and 1 May 2020. Three hundred twenty people participated in this questionnaire study. The data were collected through an online form, relying upon a self-report questionnaire. The questionnaire had three main parts. The first part is related to the socio-demographic characteristics of the sample, including gender, age, family status, address status and education level. The second part involves the items related to precautionary measures using none, sometimes, and always. The last part contains items related to death probability owing to other causes and this includes five categories: extremely low, low, intermediate, high and extremely high. The validity and reliability of this questionnaire were revised by the panel of experts before the data collection. Results The outcomes of the study revealed that the majority, ca. 73%, of the Kurdistan Region/Iraq population depended on TV to obtain information about COVID-19. Also, this investigation showed that there is a substantial association between participants with infection prevention and control practices relevant to COVID-19. Moreover, according to this study, there is a significant relationship between the death probability and COVID-19. Concurrently, there is not any significant association between other causes, namely cancer, heart diseases, diabetes and road traffic accidents, and the death probability. Conclusion This study showed that for the majority of the Kurdistan Region/Iraq population the most reliable source of information for any COVID-19 related updates is the TV broadcast. This study also indicated that there is strong association for the majority of individuals regarding their practices for prevention from COVID-19 and death probability with COVID-19. However, there is not any substantial association between the epidemic and the other deadly calamities and the death probability.
This paper discusses the role of private sector development in overcoming the challenges of the resource curse. It identifies the developmental factors in the private sector in natural resource dependent countries by adapting a dynamic flexible adjustment model. Its empirical results are based on panel data from 110 natural resource producers in developed, developing, and emerging countries during the period 1990–2017. The findings show that natural resource rents can foster private sector development, and the speed of adjustment towards the target level of development is faster in oil and gas exporting countries.
[This corrects the article DOI: 10.1155/2013/957853.].
The construction phase of oil and gas projects (OGPs) is a risky process and project managers face numerous challenges during this particular period. A proper risk analysis and management during the construction phase of the OGPs not only will affect the timely and successful operation of the project as a whole, it can also affect occurrence of risks in subsequent phases and overall economic viability of the project. As a result, via using extensive literature review, this study tries to answer the question of what are main risks involved in construction phase of OGPs and which methods are used for identifying them? The outcome of this research would likely be a valuable source for construction professionals to improve project performance while managing existing risks. It is also useful to avoid common problems that befall many project managers and will assist them to have a better understanding of risk management as part of a project plan.
This paper aims at examining the role of job autonomy in organizations and its relation with employee performance. This will be achieved by providing a critical review of the subject matter in existing management literature. In recent years, the concept of job autonomy has gained an increasing importance in practice of Human Resource Management. Even some studies claimed that job autonomy directly affects job performance and some of its indicators including job satisfaction, motivation, job engagement and job commitment. As a result, current paper aims at studying the effect of job autonomy on employee performance by critically reviewing existing work of human resource scholars. Main research questions approached by authors include: Is there any meaningful relationship between job autonomy and employee’s job performance distinguished in existing literature? If yes, what impact can be expected from job autonomy on employee’s job performance?
In order to investigate the degree of contamination of heavy metals (As, Cd, Cr, Cu, Fe, Pb, and Ni) in the Aqyazi River in Iran, sediment samples were collected from the river receiving wastewater from an iron-manufacturing plant. For this study, contamination indices, geoaccumulation index (Igeo), contamination factor (CF), and pollution load index (PLI), were used to assess contamination by the heavy metals. The results of the Igeo indicated that the sediments were moderately contaminated by Cu and strongly to extremely contaminated by Cd. Based on spatial distribution of concentrations and the Igeo, mining activity was the source of Cu and Cd in the Aqyazi River. Furthermore, the elevated Igeo of Cd at upmost northern station was not influenced by the mining activity, suggesting that there may be another upstream anthropogenic source of Cd. The CF values indicated the same trend as the Igeo. The PLI was calculated using all the metals analyzed in this study, and displayed that the sediments were not polluted. However, the PLI was re-calculated using only Cu and Cd and indicated that the sediments were polluted. Our results suggest further studies to trace another source of Cd upstream of the Aqyazi River and to investigate influence of the river waters on accumulation of heavy metals in soils and vegetables downstream.
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