Different surface treatments are applied to improve the surface properties of metallic biomaterials. Chemical etching is one of the common techniques used for roughening the surface. In this regard, the effects of different chemical media on the surface roughness, degradation, and ion release of 316L SS steel were investigated in this study. The 316L SS steel was exposed to chemical etching in six different chemical media including HNO3, H2SO4, HCl, HClO4, NaOH and Na2CO, at four different concentrations (1, 2, 4, and 8 Molar), and at three immersion times (10, 60, and 180 min). SEM, EDS, and XRD analyses were used to examine the morphological and chemical impacts of chemical etching on sample surfaces, and roughness was measured on all sample surfaces. The effect of surface treatment on the degradation and ion release of the samples was comparatively examined. Characterization analyses indicated that the HNO3, H2SO4, and HCl media did not cause a significant reaction on the surface of the 316L SS. On the other hand, different phases were observed on the sample surfaces exposed to HClO4, NaOH, and Na2CO3 media. It was determined that the roughness had no linear correlation with the concentration and immersion time, and similarly, the degree of degradation and amount of ion release displayed no change depending on the roughness. Samples etched in H2SO4, HClO4, NaOH and Na2CO3 provided the highest roughness. It was found that H2SO4 caused a significant increase in the amount of ion release.
Background Social media has been a common platform to disseminate health information by government officials during the COVID-19 pandemic. However, little is known about the determinants of public engagement in officials’ posts on social media, especially during lockdown. Objectives This study aims to investigate how the public engages in officials’ posts about COVID-19 on social media and to identify factors influencing the levels of engagement. Methods A total of 511 adults aged 18 or over completed an online questionnaire during lockdown in Iraq. Levels of engagement in officials’ posts on social media, trust in officials and compliance of government instructions were assessed. Results Fear of COVID-19 and trust in officials were positively associated with compliance of government instructions. Trust in officials was also associated with active engagement in officials’ posts on social media, including commenting, posting and sharing of the posts. Conclusions Trust in government has been established during the COVID-19 pandemic. Public engagement in officials’ posts is crucial to reinforce health policies and disseminate health information.
Keywords One of the most important factors in life today is energy and how to get it. Different methods are used to develop low-cost, high-performance materials for electrical devices such as solar cells. In this paper, some properties of three polymer materials are investigated. Through the use of UV-visible spectrum, we have been able to discover several properties that help determine the level of materials in terms of electrical and electronic devices. Based on Gaussian 09 software, and geometries of all the studied polymers compounds were fully optimized and established on density functional theory with functional B3LYP, which has evolved very favored in current decades. Several quantum chemical properties were investigated and compared with other polymer properties, such as stiffness, flexibility, electronegativity, bandgap energy, ionization potential, chemical potential, electron back donation and electron transport Optoelectronic, UV-vis spectrum, HOMO, LUMO, MEP
Monkeypox (MPX) has been declared a public health emergency of international concern by the World Health Organization. As of November 4, 2022, 78,000 verified cases from 109 countries and territories, and 40 deaths have been reported due to MPX. The present article highlights salient hospital-based prevention and control measures to be adopted and their critical role to mitigate the ongoing MPX outbreaks and global public health emergency. K E Y W O R D S hospital facilities, monkeypox, patient management, prevention and control, safety measures, treatment
The goal of this study was to evaluate if the lexical-semantic organization of Group I (high-proficient bilinguals) was comparable to that of Group II (low-proficient bilinguals), as measured by reaction time and name accuracy scores. We can determine if there is a difference in lexical semantic structure between the two groups by evaluating the speed of lexical activation. To explore the present goal of the study, the researcher conducted several comparisons contrasting the study’s various variables. For accurate responses on the picture-naming test, the MRT (in milliseconds) for HPB and LPB groups in L1 was computed. The t-test result of the group comparison shows that there is no significant difference in RTs between the HPB and LPB groups in L1, while there is a significant difference between the groups in L2.These comparisons were made using statistical analysis, with response time and accuracy serving as the foundation for all of these assessments.
Architectural structures’ nodal coordinates are significant to shape appearance; vertical overloading causes displacement of the joints resulting in shape distortion. This research aims to reshape the distorted shape of a double-layer spherical numerical model under vertical loadings; meanwhile, the stress in members is kept within the elastic range. Furthermore, an algorithm is designed using the fmincon function to implement as few possible actuators as possible to alter the length of the most active bars. Fmincon function relies on four optimization algorithms: trust-region reflective, active set, Sequential quadratic progra mming (SQP), and interior-point. The fmincon function is subjected to the adjustment technique to search for the minimum number of actuators and optimum actuation. The algorithm excludes inactive actuators in several iterations. In this research, the 21st iteration gave optimum results, using 802 actuators and a total actuation of 1493 mm.MATLAB analyzes the structure before and after adjustment and finds the optimum actuator set. In addition, the optimal actuation found in MATLAB is applied to the modeled structure in MATLAB and SAP2000 to verify MATLAB results.
This paper investigates the flexural behavior of high-strength RC beams experimentally to assess the effect of Nano-silica (NS) and Macro-Synthetic High Strength Polypropylene Fiber (MPF). Ordinary Portland cement was partially replaced by the NS and MPF with different proportions to produce four concrete mixtures. Tests were conducted on the full-scale high-strength RC beams, including first crack load, failure load, deflection, concrete strain, steel strain, and mode failure, which were examined and compared. In addition, the tests on the mechanical properties of high-strength concrete mixtures were also conducted at the ages of 28 and 56 days. The test results concluded that the addition of NS and MPF significantly improved the first-cracking and failure loads and decreased deflection at levels of cracking and failure loads. Additionally, an increase in NS content resulted in a minor increase in the ultimate strain related to the failure loads. Furthermore, the mix of 3% NS with 0.5% MPF was found to lead to the highest mechanical characteristics of concrete. The improvements were the concrete compressive strength by 33.6%, split tensile strength by up to 54.1%, and flexural strength by up to 28.3% compared with control specimens.
Vocabulary is the kernel for all language skills; it helps learners to interact with the speakers of that language. This study aims to scrutinize the effect of a short story on developing students’ vocabulary. It is an out-product of fieldwork to figure out the useful points for teaching and learning vocabulary through short stories. The test was used to identify the effect of utilising the short story on vocabulary retention. The test was distributed to forty (40) students of Jalna College. The data of the study were collected from students after teaching them the short story for one month- three hours per week. The data was analyzed by using the SPSS software package. The overall results from the test indicated that the participants were very much impressed with the short story. The students successfully recalled most of the words used in the short story context. Some recommendations were suggested for the teachers to include short stories in their lessons and curriculum.
This study investigates how renewable energy markets reacted to the war in Ukraine in 2022 using event study and network connectedness analyses and compares this effect to traditional energy sources. Combining event study with connectedness analysis is of great interest in identifying abnormal returns from the Russia-Ukraine conflict event. The risk-return profiles make clean energy more appealing to investors, and increased investment in clean energy subsectors leads to improved climate change mitigation. Sampled data are wrangled daily from 03 August 2021 to 30 March 2022. The results confirm that renewable energy markets have positive and significant cumulative abnormalities while traditional energy markets are heavily affected during the post-war. Moreover, we find higher pairwise return connectedness after the announcement event than during and before the war in Ukraine. The geothermal and full cell markets are the more robust net information transmitter to other clean energy sub-sectors. Finally, renewable energy appeared more pertinent during and after the Russian invasion of Ukraine, given its properties to serve diversifications and hedging tools.
In agriculture farming, pests and other plant diseases are the most imperative factor that causes significant hindrance to cucumber production and its quality. Farmers around the globe are currently facing difficulty in recognizing various cucumber leaf diseases, which is imperative to preventing leaf diseases effectively. Manual techniques to diagnose cucumber diseases are often time-consuming, subjective, and laborious. To address this issue, this paper proposes a tuned convolutional neural network (CNN) algorithm to recognise five cucumber diseases and healthy leaves that comprises image enhancement, feature extraction, and classification. Data augmentation methods were utilized as a preprocessing step to enlarge the datasets, and it was also to decrease the chance of overfitting. Automatically features are extracted by using CNN layers. Finally, five cucumber leaf diseases and one healthy leaf are classified. Furthermore, to overcome the lack of a public dataset, a new dataset of cucumber leaf diseases has been constructed that includes spider, leaf miner, downy mildew, powdery mildew, one viral disease, and healthy class leaves. The dataset has a total of 4868 cucumber leaf images. In order to prove the authenticity of the proposed CNN, comparative experiments were conducted using pretrained models (AlexNet, Inception-V3, and ResNet-50). The proposed CNN achieves a recognition accuracy of 98.19% with the augmented dataset and 100% with the publicly plant disease dataset. The experimental results confirm that the proposed CNN algorithm was efficient for recognizing the cucumber leaf diseases compared with other algorithms.
Traditional medicine includes all knowledge, expertize, and practices that are drawn from hypotheses, belief systems, perceptions and treatment of physical and mental illness. Previous research on this type of therapy has produced promising outcomes whereas many therapists are still unaware of the specifics and nature of the treatment being provided. This study intends to identify the traditional medications that therapists use to treat women with gynecological diseases and to identify the factors that influence women to receive conventional treatment. It also attempts to establish a link between sociodemographic characteristics and the conventional therapies employed. A qualitative descriptive study was carried out to understand how particular gynecological disorders in the Raparin region are customarily treated where the information was gathered from 50 clients. Using a questionnaire, the information from the female patients was acquired. The findings demonstrated that the majority of patients visited doctors for PCOS disorders. The research also showed that the traditional treatment's ingredients were rich in essential minerals and vitamins. We recommend and follow more investigation on traditional treatments for gynecological issues, notably massage therapy, which seems to have a favor favorable effect in order to make things clearer for everyone.
Ti-Ni-based shape memory alloys (SMAs) are among the alloys used as biomaterials. The degree of biocompatibility can be improved by adding different bio-compatible elements to these alloy families. In this study, the microstructure, phase transformation temperatures, and biocompatibility of Ti-Ni-Nb-Zr SMAs were investigated by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), optical microscopy (OM), differential scanning calorimetry (DSC), and electrochemical potentiodynamic measurements, respectively. The arc melting method was used to manufacture alloys with nominal compositions of Ti-10Zr-(40-x) Ni-xNb (x = 0 , 2 and 4 at.%). The phase transformation of B19′ ↔ B2 was observed in DSC results, which indicated that the alloys have shape memory behavior. Although martensite plates and dendritic structures are noticeable in OM images, XRD and SEM analyses revealed β-Nb, B19′, B2, and some precipitation phases. The corrosion resistance of the alloys was determined by potentiodynamic corrosion analysis. The alloy with 2 at. % Nb instead of Ni showed the best degree of biocompatibility compared to the other alloys.
Corneal diseases are the most common eye disorders. Deep learning techniques are used to perform automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakness of deep learning. In this work, a method for synthesizing medical images using conditional generative adversarial networks (CGANs), is presented. It also illustrates how produced medical images may be utilized to enrich medical data, improve clinical decisions, and boost the performance of the conventional neural network (CNN) for medical image diagnosis. The study includes using corneal topography captured using a Pentacam device from patients with corneal diseases. The dataset contained 3448 different corneal images. Furthermore, it shows how an unbalanced dataset affects the performance of classifiers, where the data are balanced using the resampling approach. Finally, the results obtained from CNN networks trained on the balanced dataset are compared to those obtained from CNN networks trained on the imbalanced dataset. For performance, the system estimated the diagnosis accuracy, precision, and F1-score metrics. Lastly, some generated images were shown to an expert for evaluation and to see how well experts could identify the type of image and its condition. The expert recognized the image as useful for medical diagnosis and for determining the severity class according to the shape and values, by generating images based on real cases that could be used as new different stages of illness between healthy and unhealthy patients.
Identifying the potential of ecotourism sustainability is one of the priorities of many countries, it is a goal for effective and efficient resource use on earth. Analyzing the growth of the economy and conservation methods for sustainable developing countries can be achieved by determining the possibility of ecotourism sustainability. And, it is essential to base the evaluation on sustainable development. This study assesses and maps the potential for sustainable ecotourism development using geospatial multiple approaches. Using 28 casual indicators within the three major groups of criteria of natural attraction, human attraction, and service tourism attraction were determined and integrated according to geospatial multi-criteria decision analysis. The indicators were prepared from different resources and standardization, criteria ranking, weighting, and spatial aggregation were performed to carry out the ecotourism suitability map in the Kurdistan region of Iraq (KRI). The result has produced a map to identify areas with a high potential for ecotourism sustainability in (KRI). The ecotourism sustainability map shows that about (54%) of the study area has a rating of very high, very good, and good suitability. This means the majority of the study area has a high potential for sustainable ecotourism development. It can be concluded that GIS—Multi-criteria decision analysis (MCDA) has a good ability in combining multiple datasets to produce suitability maps. The results could be used as a basis for tourism-related development plans by the Kurdistan region government and private sector.
The uncontrolled discharge of industrial wastes causes the accumulation of high heavy metal concentrations in soil and water, leading to many health issues. In the present study, a Gram-negative Aeromonas sobria was isolated from heavily contaminated soil in the Tanjaro area, southwest of Sulaymaniyah city in the Kurdistan Region of Iraq; then, we assessed its ability to uptake heavy metals. A. sobria was molecularly identified based on the partial amplification of 16S rRNA using novel primers. The sequence was aligned with 33 strains to analyze phylogenetic relationships by maximum likelihood. Based on maximum tolerance concentration (MTC), A. sobria could withstand Zn, Cu, and Ni at concentrations of 5, 6, and 8 mM, respectively. ICP-OES data confirmed that A. sobria reduced 54.89% (0.549 mM) of the Cu, 62.33% (0.623 mM) of the Ni, and 36.41% (0.364 mM) of the Zn after 72 h in the culture medium. Transmission electron microscopy (TEM) showed that A. sobria accumulated both Cu and Ni, whereas biosorption was suggested for the Zn. These findings suggest that metal-resistant A. sobria could be a promising candidate for heavy metal bioremediation in polluted areas. However, more broadly, research is required to assess the feasibility of exploiting A. sobria in situ.
Cigarettes are known as the most popular tobacco in the world. The aim of this study was to evaluate the concentrations of heavy metals in smoked and non-smoked cigarette butts (CBs) from ten cigarette brands (including five Iranian brands) and human health risk assessment associated with inhalation exposure. Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) was used for heavy metals measurement after CBs digestion. The results showed that the highest concentrations of heavy metals in nonsmoked and smoked cigarette butts are related to Pb (0.63 ± 0.21 mg/g) and Ni (0.81 ± 1.48 mg/g), respectively. The concentration of all heavy metals in smoked CBs is significantly higher than in non-smoked. According to the results, Ni and Cd elements in 60% of Iranian brands and 80% of other brands have HI > 1, which indicates a potential non-carcinogenic risk for consumers. Also, the carcinogenic risk of Cr in all brands is higher than 1.00E-4, which indicates the carcinogenic risk of the consumer in case of continuous exposure to cigarette smoke. Heavy metals in CBs can have potential carcinogenic and non-carcinogenic effects on the health of smokers exposed to inhalation. Therefore, continuous monitoring and regulation of the ingredients of domestically produced and imported cigarettes are recommended.
Spherical domes are picturesque structures built in developed countries to attract tourists. Due to horizontal and vertical overloading, the structures’ attractive shapes may be disturbed, and some members' stress may exceed the elastic level. In this paper, the shape and stress of a deformed double-layer spherical numerical model due to simultaneous lateral and vertical loadings are controlled, meanwhile, the number of actuators to alter the length of active members is minimized. The nodal displacements of the outer shape of the numerical model of the double-layer spherical structure are nullified. In addition, the stress of the members of the structure was monitored to stay within the elastic level. Moreover, the number of used actuators was minimized. These objectives are done by subjecting controlling formulations to a function that finds the minimum of constrained nonlinear multivariable which is called fmincon. The defined function in MATLAB uses one of the optimization algorithms (sequential quadratic programming, interior point, trust-region reflective, and active set). The algorithms search for active members that have a significant influence in controlling the targeted joints and members. Furthermore, the algorithms exclude the inactive actuators in several loops. The results obtained from MATLAB program are validated by SAP2000 software.
The purpose of this study is to improve the efficiency of decontamination using BaSO 4 as a piezocatalyst. Three techniques are employed in this study to enhance the piezocatalytic activity of BaSO 4 . The first method involves coupling BaSO 4 with BaTiO 3 . The acid red 151 and acid blue 113 decontamination rates improved from 56.7% and 60.9% to 61.3% and 64.4%, respectively, as a result of this strategy. Additionally, the composite of BaSO 4 and BaTiO 3 was doped with copper, iron, sulfur, and nitrogen. By doping BaTiO 3 , acid red 151 and acid blue 113 achieved 86.7% and 89.2% efficiency, respectively. Finally, the nanostructures were modified with sucrose. These strategies improved degradation efficiency for acid red 151 and acid blue 113 to 92.9% and 93.3%, respectively. The reusability results showed that the piezo-catalytic activity of the m-S–BaSO 4 –BaTiO 3 catalyst did not show a significant loss after five recycles for the degradation of AB113.
Several messenger ribonucleic acid (mRNA) and inactivated COVID-19 vaccines are available to the global population as of 2022. The acceptance of the COVID-19 vaccine will play a key role in combating the worldwide pandemic. Public confidence in this vaccine is largely based on its safety and effectiveness. This study was designed to provide independent evidence of the adverse effects associated with COVID-19 vaccines among healthcare workers in Iraq and to identify the attitudes of healthcare workers who rejected the vaccination. We conducted a cross-sectional study to collect data on the adverse effects of the Pfizer, AstraZeneca, and Sinopharm vaccines. Data were collected between October 2021 and February 2022. A total of 2,202 participants were enrolled in the study: (89.97%) received injections of the COVID-19 vaccines and (10.03%) were hesitant to receive the vaccination. Participants received either the Pfizer vaccine (62.9%), AstraZeneca vaccine (23.5%) or Sinopharm vaccine (13.6%). Most adverse effects were significantly less prevalent in the second dose than in the first dose. Notably, the adverse effects associated with the Pfizer vaccine were significantly more prevalent in females than in males. Following the first dose, the participants experienced more adverse effects with the AstraZeneca vaccine. Following the second dose, more adverse effects were associated with the Pfizer vaccine. Interestingly, the prevalence of COVID-19 infection in participants who received two doses of the Pfizer vaccine was significantly reduced compared to those who received two doses of either the AstraZeneca or Sinopharm vaccines. According to vaccine-hesitated participants, insufficient knowledge (29.9%), expeditious development (27.6%) and lack of trust in the vaccines (27.1%) were the three major reasons for refusing the vaccines. The results of our study indicated that these adverse effects do not present a significant problem and should not prevent successful control of the COVID-19 pandemic.
Both developed and underdeveloped economies worldwide are now more concerned than ever in respect of achieving environmental sustainability. Accordingly, the majority of the global economies have ratified several environment-related pacts to facilitate the tackling of global environment-related problems. Although these problems are assumed to be addressed using diverse mechanisms, limiting the use of fossil fuels has often been recognized as the ultimate enabler of environmental sustainability. Against this backdrop, this study aims to assess the environmental impacts associated with higher renewable energy use, controlling for economic growth and population size, in the context of the G7 and E7 countries using data from 1997 to 2018. Moreover, instead of using the traditional environmental quality proxies, this study tries to proxy environmental degradation with the load capacity factor levels of the countries of concern. The long-run associations among the study’s variables are confirmed by outcomes generated from the cointegration analysis. Besides, regression analysis highlighted that integrating renewable energy into the energy systems while withdrawing from the use of fossil fuels can help to improve environmental quality by increasing the load capacity factor levels. In contrast, economic growth and population size expansion are evidenced to impose environmental quality-dampening impacts by reducing the load capacity factor levels. However, the findings, in the majority of the cases, are seen to differ across the groups of the G7 and E7 countries, especially in terms of the variations in the magnitudes of marginal environmental effects over the short and long run. Lastly, the causality analysis confirms the directions of the causal relationships among the variables of concern. Based on these results, a couple of policy interventions are recommended for improving environmental quality in the G7 and E7 countries.
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