Near East University

This article studied and compared the two nonprobability sampling techniques namely, Convenience Sampling and Purposive Sampling. Convenience Sampling and Purposive Sampling are Nonprobability Sampling Techniques that a researcher uses to choose a sample of subjects/units from a population. Although, Nonprobability sampling has a lot of limitations due to the subjective nature in choosing the sample and thus it is not good representative of the population, but it is useful especially when randomization is impossible like when the population is very large. It can be useful when the researcher has limited resources, time and workforce. It can also be used when the research does not aim to generate results that will be used to create generalizations pertaining to the entire population. Therefore, there is a need to use nonprobability sampling techniques. The aim of this study is to compare among the two nonrandom sampling techniques in order to know whether one technique is better or useful than the other. Different articles were reviewed to compare between Convenience Sampling and Purposive Sampling and it is concluded that the choice of the techniques (Convenience Sampling and Purposive Sampling) depends on the nature and type of the research.

The World Health Organization has declared Covid-19 as a pandemic that has posed a contemporary threat to humanity. This pandemic has successfully forced global shutdown of several activities, including educational activities, and this has resulted in tremendous crisis-response migration of universities with online learning serving as the educational platform. The crisis-response migration methods of universities, faculty and students, challenges and opportunities were discussed and it is evident that online learning is different from emergency remote teaching, online learning will be more sustainable while instructional activities will become more hybrid provided the challenges experienced during this pandemic are well explored and transformed to opportunities.

Osteoporosis -related to various factors including menopause and aging- is the most common chronic metabolic bone disease, which is characterized by increased bone fragility. Although it is seen in all age groups, gender, and races, it is more common in Caucasians (white race), older people, and women. With an aging population and longer life span, osteoporosis is increasingly becoming a global epidemic. Currently, it has been estimated that more than 200 million people are suffering from osteoporosis. According to recent statistics from the International Osteoporosis Foundation, worldwide, 1 in 3 women over the age of 50 years and 1 in 5 men will experience osteoporotic fractures in their lifetime. Every fracture is a sign of another impending one. Osteoporosis has no clinical manifestations until there is a fracture. Fractures cause important morbidity; in men, in particular, they can cause mortality. Moreover, osteoporosis results in a decreased quality of life, increased disability-adjusted life span, and big financial burden to health insurance systems of countries that are responsible for the care of such patients. With an early diagnosis of this disease before fractures occur and by assessing the bone mineral density and with early treatment, osteoporosis can be prevented. Therefore, increasing awareness among doctors, which, in turn, facilitates increase awareness of the normal populace, will be effective in preventing this epidemic.

This article is on representation of basis and the basis selection of techniques. The representation of this two is performed either by the method of probability random sampling or by the method of non-probability random sampling. The selection of random type is done by probability random sampling while the non-selection type is by non-probability probability random sampling. This selection of techniques is talking about either without control (unrestricted) or with control (restricted) when individually the element of each sample is selected from a given totality, the drawn of sample element goes with unrestricted while all the other types of the sampling is to be considered as a restricted sampling.

Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spread of COVID-19 seems to have a detrimental effect on the global economy and health. A positive chest X-ray of infected patients is a crucial step in the battle against COVID-19. Early results suggest that abnormalities exist in chest X-rays of patients suggestive of COVID-19. This has led to the introduction of a variety of deep learning systems and studies have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic. Deep learning networks like convolutional neural networks (CNNs) need a substantial amount of training data. Because the outbreak is recent, it is difficult to gather a significant number of radiographic images in such a short time. Therefore, in this research, we present a method to generate synthetic chest X-ray (CXR) images by developing an Auxiliary Classifier Generative Adversarial Network (ACGAN) based model called CovidGAN. In addition, we demonstrate that the synthetic images produced from CovidGAN can be utilized to enhance the performance of CNN for COVID-19 detection. Classification using CNN alone yielded 85% accuracy. By adding synthetic images produced by CovidGAN,the accuracy increased to 95%. We hope this method will speed up COVID-19 detection and lead to more robust systems of radiology.

Brain tumor segmentation is an important task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumors for cancer diagnosis, from large amount of MRI images generated in clinical routine, is a difficult and time consuming task. There is a need for automatic brain tumor image segmentation. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. Recently, automatic segmentation using deep learning methods proved popular since these methods achieve the state-of-the-art results and can address this problem better than other methods. Deep learning methods can also enable efficient processing and objective evaluation of the large amounts of MRI-based image data. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. Different than others, in this paper, we focus on the recent trend of deep learning methods in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of deep learning methods are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed.

Abstract The Metaverse has been the centre of attraction for educationists for quite some time. This field got renewed interest with the announcement of social media giant Facebook as it rebranding and positioning it as Meta. While several studies conducted literature reviews to summarize the findings related to the Metaverse in general, no study to the best of our knowledge focused on systematically summarizing the finding related to the Metaverse in education. To cover this gap, this study conducts a systematic literature review of the Metaverse in education. It then applies both content and bibliometric analysis to reveal the research trends, focus, and limitations of this research topic. The obtained findings reveal the research gap in lifelogging applications in educational Metaverse. The findings also show that the design of Metaverse in education has evolved over generations, where generation Z is more targeted with artificial intelligence technologies compared to generation X or Y. In terms of learning scenarios, there have been very few studies focusing on mobile learning, hybrid learning, and micro learning. Additionally, no study focused on using the Metaverse in education for students with disabilities. The findings of this study provide a roadmap of future research directions to be taken into consideration and investigated to enhance the adoption of the Metaverse in education worldwide, as well as to enhance the learning and teaching experiences in the Metaverse.

This work was designed to evaluate whether external application of nitric oxide (NO) in the form of its donor S-nitroso-N-acetylpenicillamine (SNAP) could mitigate the deleterious effects of NaCl stress on chickpea (Cicer arietinum L.) plants. SNAP (50 μM) was applied to chickpea plants grown under non-saline and saline conditions (50 and 100 mM NaCl). Salt stress inhibited growth and biomass yield, leaf relative water content (LRWC) and chlorophyll content of chickpea plants. High salinity increased electrolyte leakage, carotenoid content and the levels of osmolytes (proline, glycine betaine, soluble proteins and soluble sugars), hydrogen peroxide (H2O2) and malondialdehyde (MDA), as well as the activities of antioxidant enzymes, such as superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), and glutathione reductase in chickpea plants. Expression of the representative SOD, CAT and APX genes examined was also up-regulated in chickpea plants by salt stress. On the other hand, exogenous application of NO to salinized plants enhanced the growth parameters, LRWC, photosynthetic pigment production and levels of osmolytes, as well as the activities of examined antioxidant enzymes which is correlated with up-regulation of the examined SOD, CAT and APX genes, in comparison with plants treated with NaCl only. Furthermore, electrolyte leakage, H2O2 and MDA contents showed decline in salt-stressed plants supplemented with NO as compared with those in NaCl-treated plants alone. Thus, the exogenous application of NO protected chickpea plants against salt stress-induced oxidative damage by enhancing the biosyntheses of antioxidant enzymes, thereby improving plant growth under saline stress. Taken together, our results demonstrate that NO has capability to mitigate the adverse effects of high salinity on chickpea plants by improving LRWC, photosynthetic pigment biosyntheses, osmolyte accumulation and antioxidative defense system.

-expressing senescent cells can be eliminated, and senolytic drugs dasatinib and quercetin. We found that obesity results in the accumulation of senescent glial cells in proximity to the lateral ventricle, a region in which adult neurogenesis occurs. Furthermore, senescent glial cells exhibit excessive fat deposits, a phenotype we termed "accumulation of lipids in senescence." Clearing senescent cells from high fat-fed or leptin receptor-deficient obese mice restored neurogenesis and alleviated anxiety-related behavior. Our study provides proof-of-concept evidence that senescent cells are major contributors to obesity-induced anxiety and that senolytics are a potential new therapeutic avenue for treating neuropsychiatric disorders.

The article provides the description and comparison of two non-random samplings which are snowball or chain referral sampling and sequential sampling. Snowball sampling has been widely used in qualitative sociological research, especially in the study of deviant behavior and is used in the place where the population is hard to reach. It also described different form of sampling method. While in sequential sampling, sampling was taken at a given time interval and modification can be made by correcting the research and sampling method to centralize the analysis and make a satisfied decision.

Salinity stress affected crop production of more than 20% of irrigated land globally. In the present study the effect of different concentrations of NaCl (0, 100, and 200 mM) on growth, physio-biochemical attributes, antioxidant enzymes, oil content, etc. in Brassica juncea and the protective role of Trichoderma harzianum (TH) was investigated. Salinity stress deteriorates growth, physio-biochemical attributes, that ultimately leads to decreased biomass yield in mustard seedlings. Higher concentration of NaCl (200 mM) decreased the plant height by 33.7%, root length by 29.7% and plant dry weight (DW) by 34.5%. On the other hand, supplementation of TH to NaCl treated mustard seedlings showed elevation by 13.8, 11.8, and 16.7% in shoot, root length and plant DW respectively as compared to plants treated with NaCl (200 mM) alone. Oil content was drastically affected by NaCl treatment; however, TH added plants showed enhanced oil percentage from 19.4 to 23.4% in the present study. NaCl also degenerate the pigment content and the maximum drop of 52.0% was recorded in Chl. 'a'. Enhanced pigment content was observed by the application of TH to NaCl treated plants. Proline content showed increase by NaCl stress and maximum accumulation of 59.12% was recorded at 200 mM NaCl. Further enhancement to 70.37% in proline content was recorded by supplementation of TH. NaCl stress (200 mM) affirms the increase in H2O2 by 69.5% and MDA by 36.5%, but reduction in the accumulation is recorded by addition of TH to mustard seedlings. 200 mM NaCl elevated SOD, POD, APX, GR, GST, GPX, GSH, and GSSG in the present study. Further enhancement was observed by the application of TH to the NaCl fed seedlings. NaCl stress suppresses the uptake of important elements in both roots and shoots, however, addition of TH restored the elemental uptake in the present study. Mustard seedlings treated with NaCl and TH showed restricted Na uptake as compared to seedlings treated with NaCl alone. In conclusion, TH proved to be very beneficial in imparting resistance to the mustard plants against NaCl stress through improved uptake of essential elements, modulation of osmolytes and antioxidants.

The outbreak of the novel corona virus disease (COVID-19) in December 2019 has led to global crisis around the world. The disease was declared pandemic by World Health Organization (WHO) on 11th of March 2020. Currently, the outbreak has affected more than 200 countries with more than 37 million confirmed cases and more than 1 million death tolls as of 10 October 2020. Reverse-transcription polymerase chain reaction (RT-PCR) is the standard method for detection of COVID-19 disease, but it has many challenges such as false positives, low sensitivity, expensive, and requires experts to conduct the test. As the number of cases continue to grow, there is a high need for developing a rapid screening method that is accurate, fast, and cheap. Chest X-ray (CXR) scan images can be considered as an alternative or a confirmatory approach as they are fast to obtain and easily accessible. Though the literature reports a number of approaches to classify CXR images and detect the COVID-19 infections, the majority of these approaches can only recognize two classes (e.g., COVID-19 vs. normal). However, there is a need for well-developed models that can classify a wider range of CXR images belonging to the COVID-19 class itself such as the bacterial pneumonia, the non-COVID-19 viral pneumonia, and the normal CXR scans. The current work proposes the use of a deep learning approach based on pretrained AlexNet model for the classification of COVID-19, non-COVID-19 viral pneumonia, bacterial pneumonia, and normal CXR scans obtained from different public databases. The model was trained to perform two-way classification (i.e., COVID-19 vs. normal, bacterial pneumonia vs. normal, non-COVID-19 viral pneumonia vs. normal, and COVID-19 vs. bacterial pneumonia), three-way classification (i.e., COVID-19 vs. bacterial pneumonia vs. normal), and four-way classification (i.e., COVID-19 vs. bacterial pneumonia vs. non-COVID-19 viral pneumonia vs. normal). For non-COVID-19 viral pneumonia and normal (healthy) CXR images, the proposed model achieved 94.43% accuracy, 98.19% sensitivity, and 95.78% specificity. For bacterial pneumonia and normal CXR images, the model achieved 91.43% accuracy, 91.94% sensitivity, and 100% specificity. For COVID-19 pneumonia and normal CXR images, the model achieved 99.16% accuracy, 97.44% sensitivity, and 100% specificity. For classification CXR images of COVID-19 pneumonia and non-COVID-19 viral pneumonia, the model achieved 99.62% accuracy, 90.63% sensitivity, and 99.89% specificity. For the three-way classification, the model achieved 94.00% accuracy, 91.30% sensitivity, and 84.78%. Finally, for the four-way classification, the model achieved an accuracy of 93.42%, sensitivity of 89.18%, and specificity of 98.92%.

A novel learning experience that increases student motivation can be created in a learning environment that includes a gamification approach to assess competence. Student views on gamification were surveyed to determine the best application of this method, the environment necessary for its use, and the manner by which the application should proceed. The effect of a gamification approach on student achievement through intra-class competition was assessed using quantitative and qualitative methods. In this study, the Kahoot application was the preferred gamification method used. Participating students included 65 undergraduate students studying at the Department of Preschool Teaching. The findings showed that inclusion of a gamification method increased the interest of students in the class, and increased student ambitions for success. This method was also found to have a positive impact on student motivation. Furthermore, the results of this study indicate that the Kahoot application can be used effectively for gamification of lessons. In conclusion, the gamification method has an impact on students that renders them more ambitious and motivated to study.

Jasmonates (JAs) [Jasmonic acid (JA) and methyl jasmonates (MeJAs)] are known to take part in various physiological processes. Exogenous application of JAs so far tested on different plants under abiotic stresses particularly salinity, drought, and temperature (low/high) conditions have proved effective in improving plant stress tolerance. However, its extent of effectiveness entirely depends on the type of plant species tested or its concentration. The effects of introgression or silencing of different JA- and Me-JA-related genes have been summarized in this review, which have shown a substantial role in improving crop yield and quality in different plants under stress or non-stress conditions. Regulation of JAs synthesis is impaired in stressed as well as unstressed plant cells/tissues, which is believed to be associated with a variety of metabolic events including signal transduction. Although, mitogen activated protein kinases (MAPKs) are important components of JA signaling and biosynthesis pathways, nitric oxide, ROS, calcium, ABA, ethylene, and salicylic acid are also important mediators of plant growth and development during JA signal transduction and synthesis. The exploration of other signaling molecules can be beneficial to examine the details of underlying molecular mechanisms of JA signal transduction. Much work is to be done in near future to find the proper answers of the questions like action of JA related metabolites, and identification of universal JA receptors etc. Complete signaling pathways involving MAPKs, CDPK, TGA, SIPK, WIPK, and WRKY transcription factors are yet to be investigated to understand the complete mechanism of action of JAs.

BACKGROUND: For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. METHODS: The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. FINDINGS: Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). INTERPRETATION: Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. FUNDING: Gates Foundation and Bloomberg Philanthropies.

This study aimed to evaluate the dynamic effects of globalization, renewable energy consumption, non-renewable energy consumption, and economic growth on carbon-dioxide emission levels in Argentina over the 1970–2018 period. The econometric methodology considered in this study involved applications of methods that are robust to handling structural break problems in the data. Among the major findings, the Maki cointegration, with multiple structural breaks, analysis revealed long-run associations between carbon-dioxide emissions, renewable and non-renewable energy consumption, globalization, and economic growth. The elasticity estimates from the Autoregressive Distributed Lag model analysis showed evidence of renewable energy consumption and globalization reducing the emissions while non-renewable energy consumption was found to boost the emissions, both in the short- and long-run. Besides, globalization and renewable energy consumption were found to jointly reduce the emissions while globalization and non-renewable energy consumption were found to jointly boost the emissions in the long-run only. Moreover, the environmental Kuznets curve hypothesis was also verified in this study. Based on these key findings, several critically important policies are recommended.

Seismic evidence has long indicated that the south flank of Kilauea Volcano is mobile.Examination of triangulation, trilateration, and leveling data obtained throughout the 20th century shows that the south flank of Kilauea has been displaced upward and away from the rift zones by as much as several metres.The amount of horizontal displacement approximates the probable amount of dilation that accompanies the intrusion of magma as dikes in the rift zones and is greatest for p~riods of most intense intrusive activity, as evidenced by the frequency of eruptions.Displacement and seismic events on the south flank take place soon after intrusive activity, indicating that the displacement is the result of forceful intrusion in the rift zones, not the cause of relatively passive intrusion.The dikes are thought to be nearly vertical or to dip steeply southward, on the basis of both interpretation of seismic and displacement data for Kilauea itself and comparison with dikes exposed in older, eroded Hawaiian shield volcanoes.In contrast to the south flank, seismic and geodetic data indicate that the north flank is virtually immobile.This contrast is believed to reflect the fact that Kilauea was built on the south slope of Mauna Loa and was consequently influenced by the gravitational stress system of Mauna Loa, which favors displacement away from the volcanic edifice.The north flank of Kilauea is effectively buttressed by Mauna Loa, whereas thr south flank is unbuttressed and free to move away from the edifice when prompted by forceful intrusion of magma.The active part of the east rift zone of Kilauea has apparently migrated several kilometres southward with time.This is shown by the location of recent vents and by the location of the axis of a positive gravity anomaly along the north edge of the active part of the rift zone.The southward migration helps explain several features of the geometry of the east rift zone, particularly its prominent bend near Kilauea Caldera.The southwest rift zone and the caldera also show some evidence of southward migration.The Hilina fault system is considered to be a gravity-controlled system not directly related to the rift zones.Gravitational instability resulting from uplift and seaward displacement is eventually relieved by normal faulting along the seaward part of the south flank.The Hilina faults are thought to bottom at shallow depth without interseeting magma reservoirs, except possibly along part of the lower east rift zone, where the fault system impinges upon the rift zone.Strains have been accumulating within the Hilina system throughout this century, and a high level of instability may have been reached.We anticipate a subsidence event in the not too distant future, possibly similar to the damaging events of 1823 and 1868.(While this paper was in press, such an event occurred on November 29, 1975.)I

Chest diseases are very serious health problems in the life of people. These diseases include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and lung diseases. The timely diagnosis of chest diseases is very important. Many methods have been developed for this purpose. In this paper, we demonstrate the feasibility of classifying the chest pathologies in chest X-rays using conventional and deep learning approaches. In the paper, convolutional neural networks (CNNs) are presented for the diagnosis of chest diseases. The architecture of CNN and its design principle are presented. For comparative purpose, backpropagation neural networks (BPNNs) with supervised learning, competitive neural networks (CpNNs) with unsupervised learning are also constructed for diagnosis chest diseases. All the considered networks CNN, BPNN, and CpNN are trained and tested on the same chest X-ray database, and the performance of each network is discussed. Comparative results in terms of accuracy, error rate, and training time between the networks are presented.

Mobile learning (m-learning) as a kind of learning model allowing learners to obtain learning materials anywhere and anytime using mobile technologies and the Internet. It is necessary that the elements of mobile learning are organized correctly and the interactions between the various elements are combined in an efficient and optimum way so that the mobile learning is successful and the implementation is efficient. In addition, the characteristics of mobile learning should be organized, and the way they are applied to mobile learning activities and the application methods and the duration of the application time should be planned well in advance. Consequently, a deeper insight into theory-based research is required to better understand the underlying motivations that lead academics to adopting mobile learning elements and characteristics. These reasons have motivated authors to carry out this study. Learner, teacher, environment, content and assesstment are basic elements of the complete mobile learning. The core characteristics of mobile learning are ubiquitous, portable size of mobile tools, blended, private, interactive, collaborative, and instant information. They enable learners to be in the right place at the right time, that is, to be where they are able to experience the authentic joy of learning. The aim of this study is to describe the basic elements and characteristic of mobile learning according to new trends in developing technology. The paper might be useful for anyone interested in designing, preparing and implementing a mobile learning.

Despite concerns regarding the environmental impacts of microplastics, knowledge of the incidence and levels of synthetic particles in large marine vertebrates is lacking. Here, we utilize an optimized enzymatic digestion methodology, previously developed for zooplankton, to explore whether synthetic particles could be isolated from marine turtle ingesta. We report the presence of synthetic particles in every turtle subjected to investigation (n = 102) which included individuals from all seven species of marine turtle, sampled from three ocean basins (Atlantic [ATL]: n = 30, four species; Mediterranean (MED): n = 56, two species; Pacific (PAC): n = 16, five species). Most particles (n = 811) were fibres (ATL: 77.1% MED: 85.3% PAC: 64.8%) with blue and black being the dominant colours. In lesser quantities were fragments (ATL: 22.9%: MED: 14.7% PAC: 20.2%) and microbeads (4.8%; PAC only; to our knowledge the first isolation of microbeads from marine megavertebrates). Fourier transform infrared spectroscopy (FT-IR) of a subsample of particles (n = 169) showed a range of synthetic materials such as elastomers (MED: 61.2%; PAC: 3.4%), thermoplastics (ATL: 36.8%: MED: 20.7% PAC: 27.7%) and synthetic regenerated cellulosic fibres (SRCF; ATL: 63.2%: MED: 5.8% PAC: 68.9%). Synthetic particles being isolated from species occupying different trophic levels suggest the possibility of multiple ingestion pathways. These include exposure from polluted seawater and sediments and/or additional trophic transfer from contaminated prey/forage items. We assess the likelihood that microplastic ingestion presents a significant conservation problem at current levels compared to other anthropogenic threats.