Karabük University

Fiber reinforced polymer (FRP) composite materials are heterogeneous and anisotropic materials that do not exhibit plastic deformation. They have been used in a wide range of contemporary applications particularly in space and aviation, automotive, maritime and manufacturing of sports equipment. Carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP) composite materials, among other fiber reinforced materials, have been increasingly replacing conventional materials with their excellent strength and low specific weight properties. Their manufacturability in varying combinations with customized strength properties, also their high fatigue, toughness and high temperature wear and oxidation resistance capabilities render these materials an excellent choice in engineering applications. In the present review study, a literature survey was conducted on the machinability properties and related approaches for CFRP and GFRP composite materials. As in the machining of all anisotropic and heterogeneous materials, failure mechanisms were also reported in the machining of CFRP and GFRP materials with both conventional and modern manufacturing methods and the results of these studies were obtained by use of variance analysis (ANOVA), artificial neural networks (ANN) model, fuzzy inference system (FIS), harmony search (HS) algorithm, genetic algorithm (GA), Taguchi's optimization technique, multi-criteria optimization, analytical modeling, stress analysis, finite elements method (FEM), data analysis, and linear regression technique. Failure mechanisms and surface quality is discussed with the help of optical and scanning electron microscopy, and profilometry. ANOVA, GA, FEM, etc. are used to analyze and generate predictive models.

Abstract Aim The EUNIS Habitat Classification is a widely used reference framework for European habitat types (habitats), but it lacks formal definitions of individual habitats that would enable their unequivocal identification. Our goal was to develop a tool for assigning vegetation‐plot records to the habitats of the EUNIS system, use it to classify a European vegetation‐plot database, and compile statistically‐derived characteristic species combinations and distribution maps for these habitats. Location Europe. Methods We developed the classification expert system EUNIS‐ESy, which contains definitions of individual EUNIS habitats based on their species composition and geographic location. Each habitat was formally defined as a formula in a computer language combining algebraic and set‐theoretic concepts with formal logical operators. We applied this expert system to classify 1,261,373 vegetation plots from the European Vegetation Archive (EVA) and other databases. Then we determined diagnostic, constant and dominant species for each habitat by calculating species‐to‐habitat fidelity and constancy (occurrence frequency) in the classified data set. Finally, we mapped the plot locations for each habitat. Results Formal definitions were developed for 199 habitats at Level 3 of the EUNIS hierarchy, including 25 coastal, 18 wetland, 55 grassland, 43 shrubland, 46 forest and 12 man‐made habitats. The expert system classified 1,125,121 vegetation plots to these habitat groups and 73,188 to other habitats, while 63,064 plots remained unclassified or were classified to more than one habitat. Data on each habitat were summarized in factsheets containing habitat description, distribution map, corresponding syntaxa and characteristic species combination. Conclusions EUNIS habitats were characterized for the first time in terms of their species composition and distribution, based on a classification of a European database of vegetation plots using the newly developed electronic expert system EUNIS‐ESy. The data provided and the expert system have considerable potential for future use in European nature conservation planning, monitoring and assessment.

Fossil fuels must be replaced with renewable energy resources to ensure sustainable development, reduce the dependence on fossil fuels, address environmental challenges including climate change.

As an antioxidant, lycopene has acquired importance as it prevents autoxidation of fats and related products. Tomatoes are an important agricultural product that is a great source of lycopene. It contains many vitamins and minerals, fiber, and carbohydrates and is associated with various positive effects on health. The antioxidant potential of tomatoes is substantially explained with lycopene compounds. Diet is a major risk factor for heart diseases which is shown as the most important cause of death in the world. It has been observed that the lycopene taken in the diet has positive effects in many stages of atherosclerosis. The serum lipid levels, endothelial dysfunction, inflammation, blood pressure, and antioxidative potential are mainly affected by lycopene. These natural antioxidants, which can also enhance the nutritional value of foods, may lead to new ways if used in food preservation. In this review study, the antioxidant potential and cardiovascular protection mechanism of lycopene are discussed.

Importance: Antisocial behavior (ASB) places a large burden on perpetrators, survivors, and society. Twin studies indicate that half of the variation in this trait is genetic. Specific causal genetic variants have, however, not been identified. Objectives: To estimate the single-nucleotide polymorphism-based heritability of ASB; to identify novel genetic risk variants, genes, or biological pathways; to test for pleiotropic associations with other psychiatric traits; and to reevaluate the candidate gene era data through the Broad Antisocial Behavior Consortium. Design, Setting, and Participants: Genome-wide association data from 5 large population-based cohorts and 3 target samples with genome-wide genotype and ASB data were used for meta-analysis from March 1, 2014, to May 1, 2016. All data sets used quantitative phenotypes, except for the Finnish Crime Study, which applied a case-control design (370 patients and 5850 control individuals). Main Outcome and Measures: This study adopted relatively broad inclusion criteria to achieve a quantitative measure of ASB derived from multiple measures, maximizing the sample size over different age ranges. Results: The discovery samples comprised 16 400 individuals, whereas the target samples consisted of 9381 individuals (all individuals were of European descent), including child and adult samples (mean age range, 6.7-56.1 years). Three promising loci with sex-discordant associations were found (8535 female individuals, chromosome 1: rs2764450, chromosome 11: rs11215217; 7772 male individuals, chromosome X, rs41456347). Polygenic risk score analyses showed prognostication of antisocial phenotypes in an independent Finnish Crime Study (2536 male individuals and 3684 female individuals) and shared genetic origin with conduct problems in a population-based sample (394 male individuals and 431 female individuals) but not with conduct disorder in a substance-dependent sample (950 male individuals and 1386 female individuals) (R2 = 0.0017 in the most optimal model, P = 0.03). Significant inverse genetic correlation of ASB with educational attainment (r = -0.52, P = .005) was detected. Conclusions and Relevance: The Broad Antisocial Behavior Consortium entails the largest collaboration to date on the genetic architecture of ASB, and the first results suggest that ASB may be highly polygenic and has potential heterogeneous genetic effects across sex.

Glioblastoma is the most common form of primary brain tumor in adults and is essentially incurable. Despite aggressive treatment regimens centered on radiotherapy, tumor recurrence is inevitable and is thought to be driven by glioblastoma stem-like cells (GSC) that are highly radioresistant. DNA damage response pathways are key determinants of radiosensitivity but the extent to which these overlapping and parallel signaling components contribute to GSC radioresistance is unclear. Using a panel of primary patient-derived glioblastoma cell lines, we confirmed by clonogenic survival assays that GSCs were significantly more radioresistant than paired tumor bulk populations. DNA damage response targets ATM, ATR, CHK1, and PARP1 were upregulated in GSCs, and CHK1 was preferentially activated following irradiation. Consequently, GSCs exhibit rapid G2-M cell-cycle checkpoint activation and enhanced DNA repair. Inhibition of CHK1 or ATR successfully abrogated G2-M checkpoint function, leading to increased mitotic catastrophe and a modest increase in radiation sensitivity. Inhibition of ATM had dual effects on cell-cycle checkpoint regulation and DNA repair that were associated with greater radiosensitizing effects on GSCs than inhibition of CHK1, ATR, or PARP alone. Combined inhibition of PARP and ATR resulted in a profound radiosensitization of GSCs, which was of greater magnitude than in bulk populations and also exceeded the effect of ATM inhibition. These data demonstrate that multiple, parallel DNA damage signaling pathways contribute to GSC radioresistance and that combined inhibition of cell-cycle checkpoint and DNA repair targets provides the most effective means to overcome radioresistance of GSC.

Deep Learning (DL) is the most efficient technique to handle a wide range of challenging problems such as data analytics, diagnosing diseases, detecting anomalies, etc. The development of DL has raised some privacy, justice, and national security issues. Deepfake is a DL-based application that has been very popular in recent years and is one of the reasons for these problems. Deepfake technology can create fake images and videos that are difficult for humans to recognize as real or not. Therefore, it needs to be proposed some automated methods for devices to detect and evaluate threats. In another word, digital and visual media must maintain their integrity. A set of rules used for Deepfake and some methods to detect the content created by Deepfake have been proposed in the literature. This paper summarizes what we have in the critical discussion about the problems, opportunities, and prospects of Deepfake technology. We aim for this work to be an alternative guide to getting knowledge of Deepfake detection methods. First, we cover Deepfake history and Deepfake techniques. Then, we present how a better and more robust Deepfake detection method can be designed to deal with fake content.

Synthetic grafting needs improvements to eliminate secondary surgeries for the removal of implants after healing of the defected tissues. Tissue scaffolds are engineered to serve as temporary templates, which support the affected tissue and gradually degrade through the healing period. Beside mechanical function to withstand the anatomic loading conditions, scaffolds should also provide a decent biological function for the diffusion of nutrients and oxygen to the cells, and excretion of the wastes from the cells to promote the new tissue growth and vascularization. Moreover, the degradation byproducts of the scaffolds should be safe to the human body. Development of such multifunctional scaffolds requires selection of the right material, design, and manufacturing method. Mg has been recognized as the prominent biodegradable metal with regards to its mechanical properties matching to that of human bone, degradability in the body fluid, and its ability to stimulate new tissue growth. Scaffolds with intricate porous structures can be designed according to the patient-specific anatomic data using computer aided designs. Additive manufacturing (AM) is the right method to materialize these models rapidly with reasonably acceptable range of dimensional accuracy. Thus, the recent research trend is to develop ideal scaffolds using biodegradable Mg through AM methods. This review compiles and discusses the available literature on the AM of biodegradable Mg parts from the viewpoints of material compositions, process conditions, formation quality, dimensional accuracy, microstructure, biodegradation, and mechanical properties. The current achievements are summarized together, and future research directions are identified to promote clinical applications of biodegradable Mg through the advancement of AM.

Growing energy demand, environmental impact, energy security issues, and rural economic development have encouraged the development of sustainable renewable fuels. Nonfood lignocellulosic biomass is a suitable source for sustainable energy because the biomass feedstocks are low cost, abundant, and carbon neutral. Recent thermochemical conversion studies are frequently directed at converting biomass into high-quality liquid fuel precursors or chemicals in a single step. Supercritical ethanol has been selected as a promising solvent medium to deconstruct lignocellulosic biomass because ethanol has extraordinary solubility towards lignocellulosic biomass and can be resourced from cellulosic ethanol facilities. This review provides a critical insight into both catalytic and noncatalytic strategies of lignocellulose deconstruction. In this context, the supercritical ethanol deconstruction pathways are thoroughly reviewed; GC-MS, 1D and 2D NMR spectroscopy, and elemental analysis strategies towards liquid biomass deconstruction products are also critically presented. This review aims to provide readers a broad and accurate roadmap of novel biomass to biofuel conversion techniques.

Handedness has been extensively studied because of its relationship with language and the over-representation of left-handers in some neurodevelopmental disorders. Using data from the UK Biobank, 23andMe and the International Handedness Consortium, we conducted a genome-wide association meta-analysis of handedness (N = 1,766,671). We found 41 loci associated (P < 5 × 10−8) with left-handedness and 7 associated with ambidexterity. Tissue-enrichment analysis implicated the CNS in the aetiology of handedness. Pathways including regulation of microtubules and brain morphology were also highlighted. We found suggestive positive genetic correlations between left-handedness and neuropsychiatric traits, including schizophrenia and bipolar disorder. Furthermore, the genetic correlation between left-handedness and ambidexterity is low (rG = 0.26), which implies that these traits are largely influenced by different genetic mechanisms. Our findings suggest that handedness is highly polygenic and that the genetic variants that predispose to left-handedness may underlie part of the association with some psychiatric disorders. A genome-wide association study of 1.7 million individuals identified 41 genetic variants associated with left-handedness and 7 associated with ambidexterity. The genetic correlation between the traits was low, thereby implying different aetiologies.

Purpose The advent of social media brought a new perspective for guerrilla marketing since it allows ads to reach more people through the internet. The purpose of this paper is to investigate the influence of guerrilla marketing in social media on brand image. Design/methodology/approach A conceptual model was developed based on the information acceptance model (IACM). The research model was validated through structural equation modelling based on the surveys of 385 university students. Findings The results support the proposed model and confirm that guerrilla marketing in social media has a positive effect on both functional and symbolic brand image. Research limitations/implications This study was conducted with university students. This sample was deemed appropriate since the study had to be conducted with people who use social media. However, although the age group of university students constitutes the majority of social media users, they may not fully represent the whole population. Also, this study showed four guerrilla marketing examples to participants before they commenced filling in the questionnaire. Although the authors selected the most generic guerrilla advertisements during the pilot tests and eliminated the ones which were difficult to understand, this can still be considered as limitations of the study. Practical implications This study has both theoretical and managerial implications. First, most of the guerrilla marketing studies focus on consumers and neglect possible impacts on brands. In order to fulfil this gap in the literature, this study investigates the influence of guerrilla marketing in brand image. Besides, this study contributes to IACM by expanding its scope through testing its determinants on “brand image”. It proves that IACM is valid for use in different contexts. On the managerial side, this study provides marketers with a frame of reference to understand the information adoption process of guerrilla marketing on social media. Originality/value Current studies regarding the influence of guerrilla marketing mostly focus on consumers, where the possible impacts on brands have been relatively neglected. This study attempts to fill this gap by focussing on the brand image.

Pharmaceutical contamination threatens both humans and the environment, and several technologies have been adapted for the removal of pharmaceuticals. The coagulation-flocculation process demonstrates a feasible solution for pharmaceutical removal. However, the chemical coagulation process has its drawbacks, such as excessive and toxic sludge production and high production cost. To overcome these shortcomings, the feasibility of natural-based coagulants, due to their biodegradability, safety, and availability, has been investigated by several researchers. This review presented the recent advances of using natural coagulants for pharmaceutical compound removal from aqueous solutions. The main mechanisms of natural coagulants for pharmaceutical removal from water and wastewater are charge neutralization and polymer bridges. Natural coagulants extracted from plants are more commonly investigated than those extracted from animals due to their affordability. Natural coagulants are competitive in terms of their performance and environmental sustainability. Developing a reliable extraction method is required, and therefore further investigation is essential to obtain a complete insight regarding the performance and the effect of environmental factors during pharmaceutical removal by natural coagulants. Finally, the indirect application of natural coagulants is an essential step for implementing green water and wastewater treatment technologies.

In the present study, the performance and exhaust emissions of a single-cylinder, direct-injection and air-cooled diesel engine using diethyl ether (DEE)-diesel fuel mixtures were estimated by artificial neural networks (ANN). The test engine was run with pure diesel and diesel-DEE blends at different engine speeds and loads to obtain the test and training data required to build the ANN model. In the designed ANN model, brake specific fuel consumption (BSFC), exhaust gas temperature (EGT), brake thermal efficiency (BTE), nitrogen oxides (NO x ), hydrocarbons (HC), carbon monoxides (CO) and smoke were selected as the output layer while engine load, engine speed and fuel blending ratio were selected as input layer. An ANN model was developed using 75% of the experimental results for training. The performance of the ANN model was measured by comparing the test data generated from the unused part of the training. According to the obtained data, ANN model predicts exhaust emissions and engine performance with a regression coefficient (R2) at 0.964–0.9878 interval. At the same time, mean relative error (MRE) values ranged from 0.51% to 4.8%. These results show that the ANN model is able to use for estimating low-power diesel engine emissions and performance.

Abstract Motivation Assessing biodiversity status and trends in plant communities is critical for understanding, quantifying and predicting the effects of global change on ecosystems. Vegetation plots record the occurrence or abundance of all plant species co‐occurring within delimited local areas. This allows species absences to be inferred, information seldom provided by existing global plant datasets. Although many vegetation plots have been recorded, most are not available to the global research community. A recent initiative, called ‘sPlot’, compiled the first global vegetation plot database, and continues to grow and curate it. The sPlot database, however, is extremely unbalanced spatially and environmentally, and is not open‐access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata and (b) securing permission from data holders of 105 local‐to‐regional datasets to openly release data. We thus present sPlotOpen, the largest open‐access dataset of vegetation plots ever released. sPlotOpen can be used to explore global diversity at the plant community level, as ground truth data in remote sensing applications, or as a baseline for biodiversity monitoring. Main types of variable contained Vegetation plots ( n = 95,104) recording cover or abundance of naturally co‐occurring vascular plant species within delimited areas. sPlotOpen contains three partially overlapping resampled datasets ( c . 50,000 plots each), to be used as replicates in global analyses. Besides geographical location, date, plot size, biome, elevation, slope, aspect, vegetation type, naturalness, coverage of various vegetation layers, and source dataset, plot‐level data also include community‐weighted means and variances of 18 plant functional traits from the TRY Plant Trait Database. Spatial location and grain Global, 0.01–40,000 m². Time period and grain 1888–2015, recording dates. Major taxa and level of measurement 42,677 vascular plant taxa, plot‐level records. Software format Three main matrices (.csv), relationally linked.

Bu çalışmada öncelikle farklı kurum ve kuruluşlar tarafından çerçevesi çizilen ve bireylerin edinmeleri gerekli 21.Yüzyıl becerileri ortaya konulmuştur. Ardından 21.Yüzyıl becerilerinin eğitim sistemi içinde öğrencilere nasıl kazandırılabileceği incelenmiştir. Bu bağlamda incelenen farklı rapor, doküman ve araştırmalara göre 21.Yüzyıl becerilerinin temel beceriler ve okuryazarlık becerileri boyutlarında güçlü biçimde tartışıldığı görülmüştür. Temel beceriler eleştirel düşünme, problem çözme, yenilikçilik ve yeni fikirler üretme, iş birliği ve iletişim, liderlik, kültürler arası ve küresel farkındalık niteliklerine vurgu yapmaktadır. Okuryazarlık becerileri ise Bilgi ve iletişim teknolojileri (BİT) okuryazarlığı, Bilgi okuryazarlığı, Temel içerik bilgisine ilişkin okuryazarlık (Ana dil, sanat, ekonomi, coğrafya, vatandaşlık, Matematik ve fen okuryazarlığı), sağlık, çevre ve finansal okuryazarlık niteliklerine vurgu yapmaktadır. Bu becerilerin eğitim sistemi içinde kazandırılabilmesine yönelik zihniyet dönüşümü, farkındalık oluşturma, öğretmen niteliklerini geliştirme ve eğitim programlarında düzenlemeler gibi önerilerde bulunulmuştur.

The current study investigates a mediated-effects model of distributed leadership and teacher professional learning. The model proposes that teacher trust in school principal and teacher work motivation act as mediators of distributed leadership effects on teacher professional learning. The study is based on survey data collected from 327 teachers employed in public primary schools in Turkey. The results show that distributed leadership has positive indirect effects on teacher professional learning. Both teacher trust in school principal and teacher work motivation mediate the effects of distributed leadership on teacher professional learning. This study contributes to a growing body of research evidencing a positive link between distributed leadership and teacher professional learning.

Nanotechnology has been widely used in many fields including in soil and groundwater remediation. Nanoremediation has emerged as an effective, rapid, and efficient technology for soil and groundwater contaminated with petroleum pollutants and heavy metals. This review provides an overview of the application of nanomaterials for environmental cleanup, such as soil and groundwater remediation. Four types of nanomaterials, namely nanoscale zero-valent iron (nZVI), carbon nanotubes (CNTs), and metallic and magnetic nanoparticles (MNPs), are presented and discussed. In addition, the potential environmental risks of the nanomaterial application in soil remediation are highlighted. Moreover, this review provides insight into the combination of nanoremediation with other remediation technologies. The study demonstrates that nZVI had been widely studied for high-efficiency environmental remediation due to its high reactivity and excellent contaminant immobilization capability. CNTs have received more attention for remediation of organic and inorganic contaminants because of their unique adsorption characteristics. Environmental remediations using metal and MNPs are also favorable due to their facile magnetic separation and unique metal-ion adsorption. The modified nZVI showed less toxicity towards soil bacteria than bare nZVI; thus, modifying or coating nZVI could reduce its ecotoxicity. The combination of nanoremediation with other remediation technology is shown to be a valuable soil remediation technique as the synergetic effects may increase the sustainability of the applied process towards green technology for soil remediation.

The wide range of aluminium variants (alloys and composites) has made it an important material for aviation, automotive components, auto-transmission locomotive section units, S.C.U.B.A. tanks, ship, vessels, submarines fabrication and design etc. regardless of the fact that the aluminium alloys were being utilized in myriads of sectors owing to its exceptional superior and versatile functional characteristics, the property such as wear-resistant ought to be enhanced in order to further prolong diverse spectrum of applications. An aluminium alloy having lower hardness and tensile strength has been incorporated with silicon carbide that drastically strengthens the properties. This study involves fabrication of aluminium silicon carbide with muscovite/hydrated aluminium potassium silicate/aluminosilicate in stir casting method to obtain a hybrid metal matrix composite. Maintaining a constant amount of aluminium and silicon carbide, muscovite or hydrated aluminium potassium silicate is varied to obtain three distinctive compositions of (Al/SiC/muscovite) composites. The mechanical characteristics like tensile-strength, flexural-strength, toughness, hardness, scratch adhesion, percent-porosity and density were studied. The dispersion of muscovite and silicon carbide particles were observed by viewing the microstructure photographs obtained using optical microscopy and Scanning Electron Microscope (SEM). EDAX analysis affirms the presence of reinforcing constituents in Al–Mg–Si–T6 alloy matrix. A drum type wear apparatus was utilized to evaluate the percentage of wear-loss in different compositions using different loads and it was found that the wear-loss decreases linearly as the muscovite percentage was increased.

Abstract Cigarette smoking is the leading cause of preventable morbidity and mortality. Genetic variation contributes to initiation, regular smoking, nicotine dependence, and cessation. We present a Fagerström Test for Nicotine Dependence (FTND)-based genome-wide association study in 58,000 European or African ancestry smokers. We observe five genome-wide significant loci, including previously unreported loci MAGI2/GNAI1 (rs2714700) and TENM2 (rs1862416), and extend loci reported for other smoking traits to nicotine dependence. Using the heaviness of smoking index from UK Biobank ( N = 33,791), rs2714700 is consistently associated; rs1862416 is not associated, likely reflecting nicotine dependence features not captured by the heaviness of smoking index. Both variants influence nearby gene expression (rs2714700/ MAGI2-AS3 in hippocampus; rs1862416/ TENM2 in lung), and expression of genes spanning nicotine dependence-associated variants is enriched in cerebellum. Nicotine dependence (SNP-based heritability = 8.6%) is genetically correlated with 18 other smoking traits ( r g = 0.40–1.09) and co-morbidities. Our results highlight nicotine dependence-specific loci, emphasizing the FTND as a composite phenotype that expands genetic knowledge of smoking.

Abstract This study presents a more efficient and innovative prototype of a hydrogen generation system using proton exchange membrane (PEM) electrolyzer. The aim of this study is to generate hydrogen gas energy that conducts the chemical reaction by electrolytic movements as well as to design a system that generates energy with H 2 through new technology. The Cr‐C coated SS304 bipolar plates were used in the electrolysis cells and the septic mixture (urea, ammonia, methyl alcohol) was used in the electrolyzer as a chemical solution to make the hydrogen production more efficient and cost effective. The super strong magnets were also mounted on the outer surface of the electrolysis cells to improve the performance and efficiency. The performance of the electrolyzer was determined by operating the current and voltage parameters. The results were collected through experiments and the optimization of the different parameters. In this prototype study, the production of hydrogen gas in the system (1 MW) through the presented system was found to be as 6 m 3 h −1 and the simple payback period (SPP) was calculated as 2.32 y. These results indicate that this system can produce hydrogen more efficiently and economically.