Düzce Üniversitesi

The purpose of this study is to investigate the impact of estimation techniques and sample sizes on model fit indices in structural equation models constructed according to the number of exogenous latent variables under multivariate normality. The performances of fit indices are compared by considering effects of related factors. The Ratio Chi-square Test Statistic to Degree of Freedom, Root Mean Square Error of Approximation, and Comparative Fit Index are the least affected indices by estimation technique and sample size under multivariate normality, especially with large sample size.

Recent studies suggest that users’ preferences of social media use differ according to their individual differences and use motives, and that these factors can lead to problematic social media use (PSMU) among a minority of users. The objectives of the present study were to investigate the influences of (i) demographics and Big Five personality dimensions on social media use motives; (ii) demographics and use motives on social media site preferences; and (iii) demographics, personality, popular social media sites, and social media use motives on PSMU. The sample comprised 1008 undergraduate students, aged between 17 and 32 years (M = 20.49, SD = 1.73; 60.5% women). The participants completed a questionnaire comprising the Social Media Use Questionnaire, Social Media Usage Aims Scale, and Ten-Item Personality Inventory. Multiple linear and hierarchical regression analyses showed that social media use motives of (i) meeting new people and socializing, (ii) expressing or presenting a more popular self, and (iii) passing time and entertainment were associated with problematic social media use. Moreover, participants that preferred Instagram, Snapchat, and Facebook reported higher scores of problematic social media use. Finally, being female, introverted, conscientious, agreeable, and neurotic were associated with PSMU. The findings offer empirical evidence for uses and gratifications theory because the findings demonstrated that (i) different personality traits predict different motives, (ii) different motives predict preference of different platforms, and (iii) different individual differences such as personality, preference of platform, and specific use motives predict PSMU.

A total of 444 naturally distributed taxa belonging to 62 families are used in the traditional medicine in the East Anatolian region of Turkey. These mainly belong to the families like Asteraceae (93 taxa), Lamiaceae (52 taxa), Rosaceae (30 taxa), Fabacaee (27 taxa), Boraginaceae (20 taxa), Apiaceae (17 taxa), Brassicaceae (16 taxa), Ranunculaceae (16 taxa), Malvaceae (12 taxa), Liliaceae (11 taxa), Polygonaceae (10 taxa), Euphorbiaceae (8 taxa), Scrophulariaceae (7 taxa), Solanaceae (6 taxa), Plantaginaceae (5 taxa), Crassulaceae (5 taxa) and Chenopodiaceae (5 taxa). The dominating genera are Achillea (11 taxa), Centaurea (11 taxa), Scorzonera (9 taxa), Alcea (8 taxa), Euphorbia (8 taxa), Salvia (8 taxa), Anthemis (7 taxa), Taraxacum (7 taxa), Tragopogon (7 taxa), Allium (7 taxa), Artemisia (6 taxa), Crataegus (6 taxa), Ranunculus (6 taxa), Rubus (6 taxa), Rumex (6 taxa), Thymus (6 taxa), Anchusa (5 taxa), Plantago (5 taxa), Rosa (5 taxa), Stachys (5 taxa), Tanacetum (5 taxa) and Verbascum (5 taxa). Although this region shows the highest ratio of endemism (25%) in Turkey, this ratio for medicinal plants lies around 8 percent. Out of 444 taxa evaluated medicinally 82 were observed to be poisonous. Local people in the region generally use herbal remedies for the treatment of gastro-intestinal disorders, respiratory system disorders, rheumatic pain, kidney stones, hemorrhoids and skin troubles such as cut, wounds, burns, and abscess. In this paper an attempt has been made to present the information on the medicinal plants of the region for its availability to the researchers in different fields related to herbal drugs.

Abstract The European Vegetation Archive ( EVA ) is a centralized database of European vegetation plots developed by the IAVS Working Group European Vegetation Survey. It has been in development since 2012 and first made available for use in research projects in 2014. It stores copies of national and regional vegetation‐ plot databases on a single software platform. Data storage in EVA does not affect on‐going independent development of the contributing databases, which remain the property of the data contributors. EVA uses a prototype of the database management software TURBOVEG 3 developed for joint management of multiple databases that use different species lists. This is facilitated by the SynBioSys Taxon Database, a system of taxon names and concepts used in the individual European databases and their corresponding names on a unified list of European flora. TURBOVEG 3 also includes procedures for handling data requests, selections and provisions according to the approved EVA Data Property and Governance Rules. By 30 June 2015, 61 databases from all European regions have joined EVA , contributing in total 1 027 376 vegetation plots, 82% of them with geographic coordinates, from 57 countries. EVA provides a unique data source for large‐scale analyses of European vegetation diversity both for fundamental research and nature conservation applications. Updated information on EVA is available online at http://euroveg.org/eva-database .

This study analyzes the impact of organizational culture and empowerment on innovation capability, and examines the peculiarities of these effects. The study's hypotheses are tested by applying both individual and firm-level analyses to survey data collected from 743 employees from 93 small and medium-sized firms located in Turkey. For medium-sized enterprises on both the individual and firm level of analysis, results suggest that collectivism and uncertainty avoidance are positively associated with empowerment, whereas power distance is negatively related to empowerment. Assertiveness focus has no relations with empowerment and innovation capability, yet among cultural dimensions, only uncertainty avoidance is related to innovation capability. For small-sized enterprises, findings suggest that both power distance and uncertainty avoidance are linked to both empowerment and innovation capability on the individual level, whereas two new paths between collectivism and innovation capability and between assertiveness focus and empowerment are found on the firm level. Also, empowerment is found to be positively related to innovation capability for both small and medium-sized enterprises (SMEs) on both the individual and firm level. In terms of managerial practice, our study helps clarify the key role played by cultural dimensions in the process of shaping an empowering and innovative work environment. Findings also reveal that managers should focus on participative managerial practices (e.g., empowerment) to promote innovation capability of SMEs.

Parkinson's Disease (PD) is a progressive neurodegenerative disease with multiple motor and non-motor characteristics. PD patients commonly face vocal impairments during the early stages of the disease. So, diagnosis systems based on vocal disorders are at the forefront on recent PD detection studies. Our study proposes two frameworks based on Convolutional Neural Networks to classify Parkinson's Disease (PD) using sets of vocal (speech) features. Although, both frameworks are employed for the combination of various feature sets, they have difference in terms of combining feature sets. While the first framework combines different feature sets before given to 9-layered CNN as inputs, whereas the second framework passes feature sets to the parallel input layers which are directly connected to convolution layers. Thus, deep features from each parallel branch are extracted simultaneously before combining in the merge layer. Proposed models are trained with dataset taken from UCI Machine Learning repository and their performances are validated with Leave-One-Person-Out Cross Validation (LOPO CV). Due to imbalanced class distribution in our data, F-Measure and Matthews Correlation Coefficient metrics are used for the assessment along with accuracy. Experimental results show that the second framework seems to be very promising, since it is able to learn deep features from each feature set via parallel convolution layers. Extracted deep features are not only successful at distinguishing PD patients from healthy individuals but also effective in boosting up the discriminative power of the classifiers.

The interest in using natural antimicrobials instead of chemical preservatives in food products has been increasing in recent years. In regard to this, essential oils-natural and liquid secondary plant metabolites-are gaining importance for their use in the protection of foods, since they are accepted as safe and healthy. Although research studies indicate that the antibacterial and antioxidant activities of essential oils (EOs) are more common compared to other biological activities, specific concerns have led scientists to investigate the areas that are still in need of research. To the best of our knowledge, there is no review paper in which antifungal and especially antimycotoxigenic effects are compiled. Further, the low stability of essential oils under environmental conditions such as temperature and light has forced scientists to develop and use recent approaches such as encapsulation, coating, use in edible films, etc. This review provides an overview of the current literature on essential oils mainly on antifungal and antimycotoxigenic but also their antibacterial and antioxidant activities. Additionally, the recent applications of EOs including encapsulation, edible coatings, and active packaging are outlined.

Background and aims Recent research has suggested that social networking site use can be addictive. Although extensive research has been carried out on potential addiction to social networking sites, such as Facebook, Twitter, YouTube, and Tinder, only one very small study has previously examined potential addiction to Instagram. Consequently, the objectives of this study were to examine the relationships between personality, self-liking, daily Internet use, and Instagram addiction, as well as exploring the mediating role of self-liking between personality and Instagram addiction using path analysis. Methods A total of 752 university students completed a self-report survey, including the Instagram Addiction Scale (IAS), the Big Five Inventory (BFI), and the Self-Liking Scale. Results Results indicated that agreeableness, conscientiousness, and self-liking were negatively associated with Instagram addiction, whereas daily Internet use was positively associated with Instagram addiction. The results also showed that self-liking partially mediated the relationship of Instagram addiction with agreeableness and fully mediated the relationship between Instagram addiction with conscientiousness. Discussion and conclusions This study contributes to the small body of literature that has examined the relationship between personality and social networking site addiction and is one of only two studies to examine the addictive use of Instagram and the underlying factors related to it.

The rapid increase in network traffic has recently led to the importance of flow-based intrusion detection systems processing a small amount of traffic data. Furthermore, anomaly-based methods, which can identify unknown attacks are also integrated into these systems. In this study, the focus is concentrated on the detection of anomalous network traffic (or intrusions) from flow-based data using unsupervised deep learning methods with semi-supervised learning approach. More specifically, Autoencoder and Variational Autoencoder methods were employed to identify unknown attacks using flow features. In the experiments carried out, the flow-based features extracted out of network traffic data, including typical and different types of attacks, were used. The Receiver Operating Characteristics (ROC) and the area under ROC curve, resulting from these methods were calculated and compared with One-Class Support Vector Machine. The ROC curves were examined in detail to analyze the performance of the methods in various threshold values. The experimental results show that Variational Autoencoder performs, for the most part, better than Autoencoder and One-Class Support Vector Machine.

Although nickel-based aerospace superalloys such as alloy 625 have superior properties including high-tensile and fatigue strength, corrosion resistance and good weldability, etc., its machinability is a difficult task which can be solved with alternative cooling/lubrication strategies. It is also important that these solution methods are sustainable. In order to facilitate the machinability of alloy 625 with sustainable techniques, we investigated the effect of minimum quantity lubrication (MQL), cryogenic cooling with liquid nitrogen (LN2) and hybrid-CryoMQL methods on tool wear behavior, cutting temperature, surface roughness/topography and chip morphology in a turning operation. The experiments were performed at three cutting speeds (50, 75 and 100 m/min), fixed cutting depth (0.5 mm) and feed rate (0.12 mm/rev). As a result, CryoMQL improved surface roughness (1.42 µm) by 24.82% compared to cryogenic cooling. The medium level of cutting speed (75 m/min) can be preferred for the lowest roughness value and lowest peak-to-valley height when turning of alloy 625. Further, tool wear is decreased by 50.67% and 79.60% by the use of MQL and CryoMQL compared with cryogenic machining. An interesting result that MQL is more effective than cryogenic machining in reducing cutting tool wear.

Abstract Proper use and efficient management of natural resources are critical to shaping a sustainable future in many resource‐rich countries in Africa. It is also well‐known that globalization creates a great awareness for sustainable resource extraction and provides cleaner production technology transfers to underdeveloped countries and enables them to establish a sustainable development pattern. However, evidence on the role of globalization in reducing the environmental impacts of natural resources in resource‐based economies is relatively scant. This study investigates sustainable future strategies by examining the role of natural resources, globalization, human capital, and urbanization in shaping the ecological footprint that is a broader indicator of environmental sustainability. To this end, Sub‐Saharan African countries—endowed with a rich natural resource base ranging from arable land, forest, freshwater, marine resources, oil, natural gas, minerals, and wildlife—are analyzed through advanced estimation techniques. Empirical results show that both resource dependence and abundance complicate to design a sustainable future by increasing the pressure on the environment. Similarly, urbanization deteriorates ecological conditions in Sub‐Saharan African countries. However, globalization and human capital seem the main sources of a cleaner and sustainable environment. The findings of the study shed new light on the main role of globalization in providing cleaner practices to reverse the negative influence of natural resource dependence and/or abundance on environmental quality.

This study has been designed to boost the inhibition efficiency and stability of carboxymethyl cellulose (CMC) and this objective has been achieved by incorporating silver nanoparticles (AgNPs) generated in situ by reduction of AgNO3 using natural honey into CMC matrix. Characterization of CMC/AgNPs composite was done using transmission electron microscope (TEM), Fourier transform infrared (FTIR) spectroscopy, ultraviolet–visible spectroscopy (UV–vis), scanning electron microscope (SEM), and energy dispersive X-ray spectroscopy (EDS). Weight loss, electrochemical (dynamic electrochemical impedance spectroscopy, electrochemical impedance spectroscopy, and potentiodynamic polarization) supported by surface assessment (SEM, atomic force microscope, and FTIR) techniques are deployed for the anticorrosion studies of CMC/AgNPs on St37 specimen in 15% H2SO4 medium. CMC/AgNPs performs better than CMC. At 25 °C, optimum inhibition efficiency of 93.94% is afforded by 1000 ppm of CMC/AgNPs from DEIS method. Inhibition efficiency of 96.37% has been achieved from weight loss method at 60 °C. CMC/AgNPs is found to retard both the anodic and cathodic reactions and the adsorption is explained using Langmuir adsorption isotherm. AFM and SEM graphics reveal smoother surface for St37 sample in the acid solution containing inhibitor than inthe solution without the inhibiting agent. FTIR and EDS results show that CMC/AgNPs molecules were adsorbed on the metal surface.

One of the relatively new negative consequences of smartphone use is “phubbing” (snubbing someone while an individual checks their smartphone in the middle of a real-life conversation). The purpose of the present study was to investigate the direct and indirect associations of neuroticism, trait anxiety, and trait fear of missing out with phubbing via state fear of missing out and problematic Instagram use. A total of 423 adolescents and emerging adults aged between 14 and 21 years (53% female) participated in the study. Findings indicated that females had significantly higher scores of phubbing, fear of missing out, problematic Instagram use, trait anxiety, and neuroticism. Path analysis showed that trait fear of missing out and neuroticism were indirectly associated with phubbing via state fear of missing out and problematic Instagram use. State fear of missing out was directly and indirectly associated with phubbing via problematic Instagram use. The present study is the first to demonstrate empirical evidence for the relationship between different dimensions of fear of missing out, problematic Instagram use, and phubbing.

Abstract Aims Vegetation‐plot records provide information on the presence and cover or abundance of plants co‐occurring in the same community. Vegetation‐plot data are spread across research groups, environmental agencies and biodiversity research centers and, thus, are rarely accessible at continental or global scales. Here we present the sPlot database, which collates vegetation plots worldwide to allow for the exploration of global patterns in taxonomic, functional and phylogenetic diversity at the plant community level. Results sPlot version 2.1 contains records from 1,121,244 vegetation plots, which comprise 23,586,216 records of plant species and their relative cover or abundance in plots collected worldwide between 1885 and 2015. We complemented the information for each plot by retrieving climate and soil conditions and the biogeographic context (e.g., biomes) from external sources, and by calculating community‐weighted means and variances of traits using gap‐filled data from the global plant trait database TRY. Moreover, we created a phylogenetic tree for 50,167 out of the 54,519 species identified in the plots. We present the first maps of global patterns of community richness and community‐weighted means of key traits. Conclusions The availability of vegetation plot data in sPlot offers new avenues for vegetation analysis at the global scale.

The corrosion inhibition of low carbon steel in 1 M HCl solution with different concentrations of Schinopsis lorentzii extract was studied using Tafel extrapolation, linear polarization, and electrochemical impedance spectroscopy (EIS). It was found that Schinopsis lorentzii extract acted as slightly cathodic inhibitor and inhibition efficiencies increased with the increase of extract concentration. The adsorption of the molecules of the extract on the low carbon steel surface was in accordance with the Temkin adsorption isotherm. The results showed that Schinopsis lorentzii extract could serve as a corrosion inhibitor of the low carbon steel in hydrochloric acid environment.

In this article, the Gravitational Search Algorithm (GSA) has been proposed to find the optimal solution for Combined Economic and Emission Dispatch (CEED) problems. It is aimed, in the CEED problem, that scheduling of generators should operate with both minimum fuel costs and emission levels, simultaneously, while satisfying the load demand and operational constraints. In this paper, the CEED problem is formulated as a multi-objective problem by considering the fuel cost and emission objectives of generating units. The bi-objective optimization problem is converted into a single objective function using a price penalty factor in order to solve it with GSA. The proposed algorithm has been implemented on four different test cases, having a valve point effect with transmission loss and having no valve point effect without transmission loss. In order to see the effectiveness of the proposed algorithm, it has been compared with other algorithms in the literature. Results show that the GSA is more powerful than other algorithms.

The present study was designed to investigate the interaction between platelet indices, inflammatory markers and disease activity in rheumatoid arthritis (RA) subjects. The effects of anti-TNF-alpha therapy and conventional treatment on platelet indices were also compared. We studied 97 patients with RA (19 men, 78 women: mean age 51 years) and 33 age and sex-matched healthy subjects as a control group. All RA patients were administered conventional therapy. After 3 months of therapy, 35 subjects who had high disease activity score (DAS28 > 5.1) were grouped as non-responders and were administered infliximab as a TNF-alpha blocker at the standard intravenous dose. Responders to the conventional therapy and non-responders were also compared. At baseline white blood cell (WBC), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), platelet count and mean platelet volume (MPV) were significantly higher in patients with RA. Mean platelet volume was positively correlated with DAS28 score (r = 0.27; p = 0.007). These markers of inflammation and platelet indices were substantially decreased after therapy. The reductions were similar in responders to conventional therapy and non-responders (TNF alpha group). In conclusion, we found that MPV was correlated with inflammatory markers and disease activity in patients with RA. Both anti-TNF-alpha and conventional therapy decreases markers of inflammation and platelet indices. MPV can reflect both disease activity and response to treatment.

In this paper superalloys, their processing and application areas have been researched. The superalloys are widely used in the industrial production elds such as aircraft, nucleer, space industry and so on due to superior properties at high temperature and resistance to metallurgical and structural variations. The most important groups of the superalloys is Ni, Fe and Co-based superalloys. Also processing of the superalloys are investigated and another goal of the present paper is to investigate microstructure and mechanical properties of IN718 subjected to strength ening heat treatment.

BACKGROUND: Determination of serum uric acid concentrations and role in risk of metabolic syndrome (MS) were investigated in 1877 participants in a cross-sectional population-based study including a brief follow-up. METHODS: The MS was identified by modified criteria of the Adult Treatment Panel III, and coronary heart disease (CHD) by clinical findings and Minnesota coding of resting electrocardiograms. Uric acid concentrations were measured by the uricase method. RESULTS: Metabolic syndrome was present in 39.1% of the cohort. Linear regression analysis of uric acid levels in a model comprising 13 variables identified gender, waist girth, total cholesterol (TC), alcohol usage, triglycerides, log C-reactive protein (CRP), and log gamma-glutamyl transferase (GGT), and in women diuretic use and elevated blood pressure (BP), as significant independent covariates whereby the largest contribution (1.6 mg/dL) was generated by waist girth. Logistic regression analysis of serum uric acid for MS disclosed for the top versus the bottom tertile an odds ratio (OR) of 1.89 (95% confidence interval [CI]: 1.45-2.46) in men and women combined, after adjustment for sex, age, TC, log CRP, log GGT, alcohol, and diuretic drug use, presence of diabetes/impaired fasting glucose, elevated BP, and smoking status. This corresponded to an increase by 35% in MS likelihood for each 1 SD uric acid increment. This rate declined to a significant 15% by inclusion of waist girth into the model. The OR of uric acid concentrations for prevalent and incident CHD, adjusted for age, MS, smoking, and diuretic use, was not significant among women and only tended toward significance in men. CONCLUSIONS: Abdominal obesity is the main determinant of uric acid variance. An increment of 1 SD in serum uric acid levels are associated in both sexes with a 35% higher MS likelihood, independent of 10 risk factors related to MS. After adjustment for waist girth, a more modest but significant likelihood persists, which suggests that serum uric acid is a determinant of MS.

We report a facile synthesis of monodisperse NiPd alloy nanoparticles (NPs) and their assembly on graphene (G) to catalyze the tandem dehydrogenation of ammonia borane (AB) and hydrogenation of R-NO2 and/or R-CN to R-NH2 in aqueous methanol solutions at room temperature. The 3.4 nm NiPd alloy NPs were prepared by coreduction of nickel(II) acetate and palladium(II) acetlyacetonate by borane-tert-butylamine in oleylamine and deposition on G via a solution phase self-assembly process. G-NiPd showed composition-dependent catalysis on the tandem reaction with G-Ni30Pd70 being the most active. A variety of R-NO2 and/or R-CN derivatives were reduced selectively into R-NH2 via G-Ni30Pd70 catalyzed tandem reaction in 5–30 min reaction time with the conversion yields reaching up to 100%. Our study demonstrates a new approach to G-NiPd-catalyzed dehydrogenation of AB and hydrogenation of R-NO2 and R-CN. The G-NiPd NP catalyst is efficient and reusable, and the reaction can be performed in an environment-friendly process with short reaction times and high yields.