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Activity of Metalaxyl-M plus mancozeb, Fosetyl-Al, and Phosphorous Acid against Phytophthora Crown and Root Rot of Apricot and Cherry Caused by Phytophthora palmivora
(CZECH ACADEMY AGRICULTURAL SCIENCES, 2017) Derviş, Sibel; Derviş, Sibel
Crown and root rot, caused by the Oomycete pathogen Phytophthora palmivora, has become a destructive disease of apricot and cherry in eastern Turkey. There are no currently registered fungicides labeled for its control. In greenhouse experiments conducted in 2012 and 2013, 1-year-old potted apricot rootstock Zerdali and cherry rootstock Mahaleb plants were treated either with foliar spray of fosetyl-Al (140, 160, and 180 g a.i./100 l) or phosphorous acid (187.5, 200, and 215 g a.i./100 l) or soil drench of 100 ml of metalaxyl-M (= mefenoxam)+mancozeb (12+192, 16+256, and 20+320 g a.i./100 l) one day after wound inoculation of crowns and roots. In both years, phosphorous acid at 200 and 215 g a.i./100 l, fosetyl-Al at 160 and 180 g a.i./100 l, and metalaxyl-M+mancozeb at 20+320 g a.i./100 l significantly reduced the root rot severity on Zerdali by 70.68-80.00% and crown rot severity on both Zerdali and Mahaleb, by 68.32-91.96 and 74.21-82.60%, respectively, compared with phosphorous acid at 187.5 g a.i./100 l, fosetyl-Al at 140 g a.i./100 l, metalaxyl-M+mancozeb at 12+192 and 16+256 g a.i./100 l and control. Moreover, fosetyl-Al at 180 g a.i./100 l and metalaxyl-M+mancozeb at 20+320 g a.i./100 l significantly reduced the root rot severity on Mahaleb compared to fosetyl-Al at 140 and 160 g a.i./100 l, metalaxyl-M+mancozeb at 12+192 and 16+256 g a.i./100 l, phosphorous acid treatments and control in 2012, providing the best control of the disease by 88.00-90.68%. Two/three phosphorous acid foliar applications at 200 g a.i./100 l suppressed symptom development when field applications were made on a curative basis in 2014 and 2015.
Allelic variations of glutenin subunits and their association with quality traits in bread wheat genotypes
(Turkish Journal of Agriculture and Forestry, 2017) Aktaş, Hüsnü; Baloch, Faheem Shehzad
The present study was conducted to evaluate the genotype × environment interaction of the yield and quality traits for five bread wheat varieties commonly grown in the Southeastern Anatolia Region of Turkey and 20 advanced lines developed within the framework of the International Winter Wheat Improvement Project. We also determined the allelic pattern of the Glu-1 and Glu-3 loci of these genotypes and examined whether these loci had an effect on the quality traits. There was a significant variation among the genotypes and environments in terms of grain yield, protein content, sedimentation volume (SV), and the extensograph dough energy value (EDEV). The results of the study indicated that genotypic effect was more influential on SV and EDEV than environmental effect; thus, both traits could be used in breeding programs to develop elite cultivars with better quality. Twelve different high-molecularweight (HMW) glutenin alleles were identified at the Glu-A1, Glu-B1, and Glu-D1 loci, resulting in 14 allelic combinations, and 17 different alleles were observed in 19 combinations for low-molecular-weight (LMW) subunits. Furthermore, among all the genotypes, 13 + 16 and 13 + 19 alleles at Glu-B1 and 5 + 12 at Glu-D1 were observed to have the lowest frequency. Our study indicated that the combinations of HMW glutenin alleles with 2* at Glu-A1, 17 + 18 and 13 + 16 at Glu-B1, and 5 + 10 at Glu-D1, as well as the combinations of LMW alleles with subunits c and d at Glu-A3; subunits d, b, c, and g at Glu-B3; and subunits a and b at Glu-D3 had positive effects on the quality traits.
AMMI Model to Assess Durum Wheat Genotypes in Multi-Environment Trials
(JOURNAL OF AGRICULTURAL SCIENCE AND TECHNOLOGY, 2018) Tekdal, S; Kendal, E.
The goal of this research was to assess the stability and yield performance of 150 durum wheat genotypes in multi-environment trials in two locations (Diyarbakir and Kiziltepe), in 2011-2012, and 2012-2013 growing seasons. The trials were designed by Lattice Experimental Design with two replications (incomplete block design). The AMMI (Additive Main Effects and Multiplicative Interaction) and GEI (GenotypexEnvironment Interaction) analysis were used in the study to estimate GEI effects on grain yield, because of plant breeders' great interest in these models for breeding programs. AMMI evaluation indicated that genotypes made the most important contributions to treatments Sum of Squares (59.8%), environments (3.5%), and GEI (36.7%), respectively, suggesting that grain yield had been affected by environment. IPCA 1 and IPCA 2 axes (Principal Component) were significant as P< 0.01 and explained 63.8 and 36.2%, respectively. Results showed that Kiziltepe 2013 was more stable and high yielding, meanwhile Diyarbakir 2012 and Diyarbakir 2013 environments were unstable and low yielding. According to stability variance, usually the province lines were more productive and stable than some old cultivars and many landraces/genotypes. Moreover, genotype G24 was more effective in all environments. The GEI model according to AMMI analysis suggested that this genotype can be considered as a candidate, due to extensive adaptability and high performances in all environments.
Antioxidant Properties of Cultured Mycelia from Four Pleurotus Species Produced in Submerged Medium
(TAYLOR & FRANCIS INC, 2013) Dundar, Abdurrahman; Okumus, Veysi; Ozdemir, Sadin; Yildiz, Abdunnasir
The ethanolic extracts of dried cultured mycelia of Pleurotus ostreatus, Pleurotus eryngii, Pleurotus florida, and Pleurotus sajor-caju were analyzed for antioxidant activity in different systems. Tests used are as follows: reducing power, free radical scavenging, superoxide anion radical scavenging, total antioxidant activity, metal chelating activitiy, etc.; total phenolic content was determined. The percentage inhibition of P. ostreatus, P. eryngii, P. florida, and P. sajor-caju at 20 mg/mL concentration on peroxidation in a -carotenelinoleic acid system was 57.19, 60.68, 62.12, and 58.81%, respectively. The reducing power of P. eryngii was higher than the other samples, and its value was 0.86 at 10 mg/mL concentration. P. ostreatus and P. sajor-caju proved to be better at scavenging superoxide anion radicals than the P. eryngii and P. florida. In the scavenging effect of DPPH radical test, P. ostreatus showed the highest activity potential and P. sajor-caju showed the strongest metal chelating capacity.
Association with Leptin Gene c.-2548 G > A Polymorphism, Serum Leptin Levels, and Body Mass Index in Turkish Obese Patients
(HUMANA PRESS INC, 2013) Demir, Cemil; Say Şahin, Deniz; Demir, Cemil; Çelik, M. Murat; Çelik, Mustafa; Uçar, Edip; Güneşaçar, Ramazan
Leptin is a protein hormone which plays a critical role in the regulation of both body-weight through reducing food intake and stimulating energy expenditure. Several polymorphisms in leptin gene (LEP), which encodes for leptin, have been described. However, its association with obesity is still controversial. Therefore, in the present study, we aimed to investigate whether LEP c.-2548 G > A polymorphism was associated with serum leptin levels, lipid parameters, and body mass index in Turkish obese patients. Forty-seven obese patients and 48 healthy individuals were included in the study. Blood samples were collected for DNA extraction. LEP c.-2548 G > A polymorphism were detected using polymerase chain reaction-restriction fragment length polymorphism technique. Serum leptin levels and lipid parameters were measured by ELISA and enzyme colorimetric assay techniques, respectively. GA or AA genotypes and A allele carrier frequencies of the c.-2548 G > A polymorphism in the LEP were higher in obese (38.3, 34.0 and 72.3 %) when compared with controls (14.6, 12.5, and 27.1 %; p = 0.011, 0.016, and 0.002, respectively). On the other hand, AA or AG genotypes were also related to increased serum leptin levels (p < 0.001) and body mass index (p < 0.0001). All these consequences showed that LEP -2548 AA or AG genotypes are important predictors for increased levels of leptin and BMI in Turkish obese patients and it may be a useful marker for obesity risk in our population.
Automatic Detection of Brain Tumors With the Aid of Ensemble Deep Learning Architectures and Class Activation Map Indicators by Employing Magnetic Resonance Images
(Elsevier, 2024) Turk, Omer; Ozhan, Davut; Acar, Emrullah; Akinci, Tahir Cetin; Yilmaz, Musa; Türk, Ömer
Today, as in every life-threatening disease, early diagnosis of brain tumors plays a life-saving role. The brain tumor is formed by the transformation of brain cells from their normal structures into abnormal cell structures. These formed abnormal cells begin to form in masses in the brain regions. Nowadays, many different techniques are employed to detect these tumor masses, and the most common of these techniques is Magnetic Resonance Imaging (MRI). In this study, it is aimed to automatically detect brain tumors with the help of ensemble deep learning architectures (ResNet50, VGG19, InceptionV3 and MobileNet) and Class Activation Maps (CAMs) indicators by employing MRI images. The proposed system was implemented in three stages. In the first stage, it was determined whether there was a tumor in the MR images Tumor) were detected from MR images (Multi-class Approach). In the last stage, CAMs of each tumor group were created as an alternative tool to facilitate the work of specialists in tumor detection. The results showed that the overall accuracy of the binary approach was calculated as 100% on the ResNet50, InceptionV3 and MobileNet architectures, and 99.71% on the VGG19 architecture. Moreover, the accuracy values of 96.45% with ResNet50, 93.40% with VGG19, 85.03% with InceptionV3 and 89.34% with MobileNet architectures were obtained in the multi-class approach.
Biosorption of chlorophenoxy acid herbicides from aqueous solution by using low-cost agricultural wastes
(TAYLOR & FRANCIS INC, 2015) Okumus, Veysi; Celik, K. Serdar; Ozdemir, Sadin; Dundar, Abdurrahman; Kilinc, Ersin
The potential biosorption abilities of apple shell (AS), orange peel (OP), banana peel (BP), and millet waste (MW) as a bio-waste material to remove 2,4-dichlorophenoxy acetic acid (2,4-D), 2,4-dichlorophenoxy propanoic acid (2,4-DP), and 2,4-dichlorophenoxy butyric acid (2,4-DB) from aqueous solution were investigated in batch condition. Optimum biosorption conditions were determined as a function of contact time, pH of the solution, amount of biomass, and initial pesticide concentrations. The concentrations of the pesticides in the remaining solutions were simultaneously determined by high performance thin layer chromatography. Langmuir and Freundlich models were applied to describe the biosorption isotherm of the pesticides by agricultural wastes. The experimental adsorption data were fitted to Langmuir adsorption models (r(2)>0.99). It was found that adsorption of 2,4-DP was higher for all biosorbent. The highest maximum adsorption capacities of 2,4-DP were found as 40.08, 22.71, 33.26, and 45.45mg/g, respectively, for AS, OP, BP, and MW. Maximum adsorption capacity was obtained for 2,4-D as OP>MW>BP>AS, 2,4-DP as MW>AS>BP>OP, and 2,4-DB as OP>AS>BP>MW. From the results, it can be said that MW was an effective biosorbent for removal of 2,4-DP and OP was an effective biosorbent for removal of 2,4-D and 2,4-DB through the studied pesticides. Optimum biosorption conditions were determined as 60min of contact time, 100mg of biomass, AS at pH 6.0, OP at pH 6.0, MW at pH 7.0, and BP at pH 7.0. FT-IR was employed to understand the surface properties of biosorbents. According to the results, agricultural wastes have high adsorption capacity.
A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data
(Sage Journals, 2022) Uyulan, Caglar; Erguzel, Turker Tekin; Türk, Ömer; Farhad, Shams; Metin, Bariş; Tarhan, Nevzat; Türk, Ömer
Automatic detection of Attention Deficit Hyperactivity Disorder (ADHD) based on the functional Magnetic Resonance Imaging (fMRI) through Deep Learning (DL) is becoming a quite useful methodology due to the curse of-dimensionality problem of the data is solved. Also, this method proposes an invasive and robust solution to the variances in data acquisition and class distribution imbalances. In this paper, a transfer learning approach, specifically ResNet-50 type pre-trained 2D-Convolutional Neural Network (CNN) was used to automatically classify ADHD and healthy children. The results demonstrated that ResNet-50 architecture with 10-k cross-validation (CV) achieves an overall classification accuracy of 93.45%. The interpretation of the results was done via the Class Activation Map (CAM) analysis which showed that children with ADHD differed from controls in a wide range of brain areas including frontal, parietal and temporal lobes.
Classification of electroencephalogram records related to cursor movements with a hybrid method based on deep learning
(Wiley Online Library, 2021) Türk, Ömer; Türk, Ömer
In brain computer interface (BCI), many transformation methods are used whenprocessing electroencephalogram (EEG) signals. Thus, the EEG can be represen-ted in different domains. However, designing an EEG-based BCI system withoutany transformation technique is a challenge. For this purpose, in this study, aBCI model is proposed without any transformation. The classification of cursordown and cursor up movements using the EEG signals received from the brain isaimed at in the proposed model. The EEG patterns were classified using twomethods. Firstly, EEG signals were classified by classic convolutional neural net-work (CNN). Secondly, proposed hybrid structure obtained the EEG features,which were classified by k-NN and SVM, using CNN. Classification with CNNarchitecture gave a result of 68.15% while the hybrid method using k-NN andSVM classifiers yielded 97.55% and 97.61% respectively. The hybrid proposedmethod were more successful than the studies in the literature.
Classification of Epilepsy Types from Electroencephalogram Time Series Using Continuous Wavelet Transform Scalogram-Based Convolutional Neural Network
(ASTM International, 2020) Türk, Ömer; Akpolat, Veysi; Varol, Sefer; Aluçlu, Mehmet Ufuk; Özerdem, Mehmet Siraç; Türk, Ömer
During the supervisory activities of the brain, the electrical activities of nerve cell clusters produce oscillations. These complex biopotential oscillations are called electroencephalogram (EEG) signals. Certain diseases, such as epilepsy, can be detected by measuring these signals. Epilepsy is a disease that manifests itself as seizures. These seizures manifest themselves in different characteristics. These different characteristics divide epilepsy seizure types into two main groups: generalized and partial epilepsy. This study aimed to classify different types of epilepsy from EEG signals. For this purpose, a scalogram-based, deep learning approach has been developed. The utilized classification process had the following main steps: the scalogram images were obtained by using the continuous wavelet transform (CWT) method. So, a one-dimension EEG time series was converted to a two-dimensional time-frequency data set in order to extract more features. Then, the increased dimension data set (CWT scalogram images) was applied to the convolutional neural network (CNN) as input patterns for classifying the images. The EEG signals were taken from Dicle University, Neurology Clinic of Medical School. This data consisted of four classes: healthy brain waves, generalized preseizure, generalized seizure, and partial epilepsy brain waves. With the proposed method, the average accuracy performance of three of the EEG records' classes (healthy, generalized preseizure, and generalized seizure), and that of all four classes of EEG records were 90.16 % (± 0.20) and 84.66 % (± 0.48). According to these results, regarding the specific accuracy ratings of the recordings, the healthy EEG records scored 91.29 %, generalized epileptic seizure records were at 96.50 %, partial seizure EEG records scored 89.63 %, and the preseizure EEG records had a 90.44 % rating. The results of the proposed method were compared to the results of both similar studies and conventional methods. As a result, the performance of the proposed method was found to be acceptable.
Comparing durum wheat cultivars by genotype x yield x trait and genotype x trait biplot method
(CHILEAN JOURNAL OF AGRICULTURAL RESEARCH, 2019) Kendal, Enver
The specification of the most convenient cultivars based on multiple trait indices is a new approach in durum wheat (Triticum durum Desf.) adaptation and stability studies. This approach helps to define the best cultivar based on multiple traits and multiple locations because cultivars are affected by unpredictable climatic conditions. Some traits (ears per square meter, spike length, number of grains per spike, spike yield, and leaf chlorophyll content among others) can be produced for primary breeding purposes because they are influenced by environmental factors and indirectly affect grain yield and quality. Therefore, in the present study, the new genotype × yield trait (GYT) biplot approach was used to identify the best cultivar among 10 durum wheat cultivars based on multiple environments (8) and multiple traits (18). Cultivar ranking was examined by a superiority index that combined yield and other target traits with the GYT biplot. The general adaptability of each cultivar in terms of all the traits indicated differences based on environment means, and significant differences were found between varieties for the GYT biplot. In the GYT biplot, yield-trait combinations clearly indicated the most stable cultivars, whereas in the genotype × trait (GT) biplot, the best cultivars were not defined for all traits. ‘Sariçanak’ was ranked as the best combination of physio-morphological traits with grain yield, ‘Zühre’ was the best for more quality traits, and ‘Güneyyildizi’ was the best for both physio-morphological and quality traits in the GYT biplot. The GYT biplot combines traits with yield and can help the visual identification of the best cultivars; it is better than the GT biplot method.
The convolutional neural network approach from electroencephalogram signals in emotional detection
(Concurrency Computation, 2021) Türk, Ömer; Özerdem, Mehmet Siraç; Türk, Ömer
Although brain-computer interfaces (BCI) progress rapidly, the desired success has not been achieved yet. One of these BCI is to detect emotional states in humans. An emotional state is a brain activity consisting of hormonal and mental reasons in the face of events. Emotions can be detected by electroencephalogram (EEG) signals due to these activities. Being able to detect the emotional state from EEG signals is important in terms of both time and cost. In this study, a method is proposed for the detection of the emotional state by using EEG signals. In the proposed method, we aim to classify EEG signals without any transform (Fourier transform, wavelet transform, etc.) or feature extraction method as a pre-processing. For this purpose, convolutional neural networks (CNNs) are used as classifiers, together with SEED EEG dataset containing three different emotional (positive, negative, and neutral) states. The records used in the study were taken from 15 participants in three sessions. In the proposed method, raw channel-time EEG recordings are converted into 28 × 28 size pattern segments without pre-processing. The obtained patterns are then classified in the CNN. As a result of the classification, three emotion performance averages of all participants are found to be 88.84%. Based on the participants, the highest classification performance is 93.91%, while the lowest classification performance is 77.70%. Also, the average f-score is found to be 0.88 for positive emotion, 0.87 for negative emotion, and 0.89 for neutral emotion. Likewise, the average kappa value is 0.82 for positive emotion, 0.81 for negative emotion, and 0.83 for neutral emotion. The results of the method proposed in the study are compared with the results of similar studies in the literature. We conclude that the proposed method has an acceptable level of performance.
Covalent immobilization of benzoylformate decarboxylase from Pseudomonas putida on magnetic epoxy support and its carboligation reactivity
(Elsevier, 2014) Tural; Tarhan; Tural, Bilsen; Tuba; Servet
Epoxy attached magnetic nanoparticles were prepared and used as solid support for covalent immobi-lization and stabilization of benzoylformate decarboxylase (BFD, E.C. 4.1.1.7) fromPseudomonas putida.A three-step immobilization/stabilization procedure is applied. The enzyme is firstly covalently immobi-lized under mild experimental conditions (e.g. pH 7.0, no added MgSO4and 20◦C). Secondly, the enzymeis immobilized under more drastic conditions (higher pH values, higher ionic strengths, etc.) to facili-tate an increase in effective concentration of the enzyme on the support near the epoxide reactive sites.Thirdly, the remaining epoxy groups are blocked to stop any additional interaction between the enzymeand the support. With more drastic conditions, the loading of enzyme can be increased from 1.25 to6.70 mg enzyme per gram of support. The covalently bounded enzyme was characterized in terms ofits activity and stability for the formation of (S)-2-hydroxypropiophenone (2-HPP). The activity of theimmobilized BFD was determined to be 53.0% related to the activity of the free enzyme. The immobilizedbiocatalyst retained 95% of its original activity after five reaction cycles.
Cyclophosphamide induced oxidative stress, lipid per oxidation, apoptosis and histopathological changes in rats: Protective role of boron
(Elsevier, 2020) Cengiz, Mustafa; Şahintürk, Varol; Cetik Yildiz, Songul; Kurcanay Şahin, İlknur; Bilici, Namık; Onur Yaman, Suzan; Altuner, Yılmaz; Appak-Baskoy, Sıla; Ayhanci, Adnan
Background Cyclophosphamide (CP) is an alkylating chemotherapeutic drug used in the treatment of many types of cancer. However, as with other chemotherapeutic drugs, the use of CP is limited by the damage to healthy tissues such as testes, bladder and liver as well as cancerous tissue. Boron (B) is a trace element with many biological properties such as antioxidant, anti-apoptotic and anti-lipid per oxidation. Methods This current study aims to determine protective effects of B on CP induced testicular toxicity. The rats were divided into 4 groups (control, CP, B and B plus CP groups). The testes of experimental animals were taken for histological, apoptotic markers and biochemical analysis. Results The damage to some seminifer tubules, loss of typical appearance, thinning of seminifer epithelium and relative enlargement of the tubule lumen were watched in testis of the group that administrated CP. Moreover, Bcl-2, TAC and GSH levels decreased while TOC, OSI, MDA, Bax and Caspase-3 levels increased. On the other hand, pretreatment limited to B in the B plus CP group, testicular tissue improved. In addition, Bcl-2, GSH, TAC levels increased, Bax, MDA, TOC, OSI and caspase-3 levels decreased. Conclusion B significantly reduced testicular lipid per-oxidation and strengthened antioxidant defenses. Our results showed that pre-treatment B can protect rat testis against CP-induced testicular damage owing to its anti-lipid per oxidation, anti-oxidant and anti-apoptotic properties.
Determination of hydroxymethylfurfural in Turkish honeys, pekmez (grape molasses), and jam samples by high-performance liquid chromatography with diode array detection
(Wiley Online Library, 2022) Umaz, Adil; Aydin, Firat; Umaz, Adil; Kılınc, Ersin; Arslan, Yasin; aydin, Isil
Hydroxymethylfurfural (HMF) is a chemical contaminant that is not naturally present in carbohydrate-containing food samples. The HMF amounts were changed between 4.8 and 214.1 mg/kg in honey; between 7.8 and 204.0 mg/kg in pekmez (grape molasses); between 5.9 and 22.4 mg/kg in strawberry jam; between 6.0 and 65.4 mg/kg in apricot jam; and between 9.2 and 55.9 mg/kg in cherry found by HPLC. The linear calibration plot of HMF was ranged from 0.05 to 10.0 mu g/ml. The correlation coefficient was found to be 0.99997. LOD and LOQ values were found to be 12.0 and 40.0 ng/ml, respectively. According to the experimental results, HMF amounts of four honey samples, three pekmez (grape molasses) samples, one apricot jam, and one cherry jam for investigated samples were found to be higher than the permission amount of Turkish Food Codex. Novelty impact statement Based on experimental results in this study, if the honey is in a transparent package such as a glass, it should be stored in a dark environment. It would be healthier to prefer molasses produced by reliable brands at low temperatures under vacuum instead of molasses made with traditional methods in open boilers. Generally, since jams with high HMF amount have an overcooked or even burnt flavor, this is an extremely negative situation for consumers.
Determination of zinc in dried nuts by slotted quartz tube-flame atomic absorption spectrometry (SQT-FAAS) with citric acid-coated magnetic nanoparticle (CAMNP) based solid phase extraction (SPE)
(Taylor & Francis inc, 2025) Umaz, Adil; Umaz, Adil; Aydin, Isil; Aydin, Firat
This study reports a sensitive method for trace zinc using slotted quartz tube-flame atomic absorption spectrometry (SQT-FAAS) with citric acid-coated magnetic nanoparticle-solid phase extraction (CAMNP-SPE). To increase the efficiency, we univariately optimized the pH, buffer volume, CAMNP mass, eluent concentration and volume, mixing type and time, temperature, and interaction time. The study identified the limits of detection and quantification values for CAMNP-SPE-SQT-FAAS under the specified optimum conditions to be 12.30 and 41.00 mu g L-1, respectively. The newly developed method improved the detection power of traditional FAAS by 13.25 times. The findings of this study report a sensitive and accurate method to determine trace zinc in complex matrices.
Development of Armillae mellea immobilized nanodiamond for the preconcentrations of Cr(III), Hg(II) and Zn(II)
(Analytical Biochemistry, 2021) Kılınç, Ersin; Ozdemir, Sadin; Şen, Fatih; Soylak, Mustafa
In this study, we present an environmental friend and easy procedure for simultaneous preconcentration of Cr(III), Hg(II) and Zn(II) by solid-phase extraction before their determination by inductively coupled plasma optical emission spectrometry. Armillae mellea immobilized nanodiamond was used as sorbent. During the study, critical parameters influencing the extraction performance were investigated in detail. The best parameters were found as pH 5.0, 2.0 mL min−1 of flow rate, 200 mg of Armillae mellea, 300 mL of sample volume. LOD values were found as 0.025, 0.13 and 0.038 ng mL−1, respectively for Cr(III), Hg(II) and Zn(II). By applying the developed procedure, sensitivities of ICP-OES were improved for 60 fold for Cr(III), Hg(II) and Zn(II). Their concentrations in different food samples were measured after microwave digestion and solid-phase extraction.
Development software program for extraction of photovoltaic cell equivalent circuit model parameters based on the Newton–Raphson method
(SpringerLink, 2022) Adak, Süleyman; Cangi, Hasan; Yılmaz, Ahmet Serdar; Arifoğlu, Uğur
Finding the equivalent circuit parameters for photovoltaic (PV) cells is crucial as they are used in the modeling and analysis of PV arrays. PV cells are made of silicon. These materials have a nonlinear characteristic. This distorts the sinusoidal waveform of the current and voltage. As a result, harmonic components are formed in the system. The PV cell is the smallest building block of the PV system and produces voltages between 0.5 V and 0.7 V. It serves as a source of current. The amount of radiation hitting the cell determines how much current it produces. In an ideal case, a diode and a parallel current source make up the equivalent circuit of the PV cell. In practice, the addition of a series and parallel resistor is made to the ideal equivalent circuit. There are many equivalent circuits in the literature on modeling the equivalent circuit of a PV cell. The PV cell single–diode model is the most used model due to its ease of analysis. In this study, the iterative method by Newton–Raphson was used to find the equivalent circuit parameters of a PV cell. This method is one of the most widely used methods for determining the roots of nonlinear equations in numerical analysis. In this study, five unknown parameters (Iph, Io, Rs, Rsh and m) of the PV cell equivalent circuit were quickly discovered with the software program prepared based on the Newton–Raphson method in MATLAB
Ecofriendly/Rapid Synthesis of Silver Nanoparticles Using Extract of Waste Parts of Artichoke (Cynara scolymus L.) and Evaluation of their Cytotoxic and Antibacterial Activities
(Hindawi, 2021) Keskin, Cumali; Baran, Ayşe; Baran, Mehmet Fırat; Irtegun Kandemir, Sevgi; Valiyeva, Mahbuba; Mehraliyeva, Sevil; Khalilov, Rovshan; Eftekhari, Aziz
Recycling wastes and providing their use in useful fields attract attention every day. In our study, with the extract prepared from the parts of the Cynara scolymus L. (artichoke) plant that is not suitable for human consumption, silver nanoparticles were easily synthesized in an ec-friendly, energy-free way. Characterization of the obtained nanoparticles was done with a UV-visible spectrophotometer (UV-Vis.), fourier transform infrared spectroscopy (FTIR), X-ray diffraction diffractometer (XRD), scanning electron microscope (SEM), transmission electron microscopy (TEM), and zeta potential analysis data. In these data, it was determined that AgNPs have a maximum absorbance at 458.8 nm wavelength, a crystal nanosize of 28.78 nm, and a spherical appearance. The zeta potential of (-) 16.9 mV indicates that silver nanoparticles exhibit a stable structure. Particles show antimicrobial effects on pathogenic species at concentrations of 0.03-0.25 μg/ml, and it was determined by using the minimum inhibition concentration (MIC) microdilution method. By examining their cytotoxic effects on U118, CaCo-2, and Skov-3 cancer cell lines and healthy HDF cell lines by the MTT method, concentrations of inhibitive effects on survival were determined.
Economic fast synthesis of olive leaf extract and silver nanoparticles and biomedical applications
(Taylor & Francis Online, 2021) Keskin, Cumali; Aktepe, Necmettin; Baran, Ayşe; Baran, Mehmet Fırat; Aktepe, Necmettin; Yavuz, Ömer; İrtegun Kandemir, Sevgi
In this study, silver nanoparticles (AgNPs) were synthesized economically and simply using an environmentally friendly method with the extract obtained from agricultural waste olive leaves. AgNPs synthesized according to the analysis data were determined to have maximum absorbance at 433.5 nm wavelength, spherical appearance, 7.2 nm crystal nano size and -19.9 mV zeta potential. It was determined by the microdilution method with Minimum Inhibition Concentration (MIC) that AgNPs exert a suppressive effect on the growth of pathogen gram-negative, positive bacteria and yeast at very low concentrations. The cytotoxic effects of the particles were investigated on healthy cell lines (HDF) and cancerous cell lines (U118, CaCo-2, Skov-3). AgNPs showed up to 70% suppression in cancer cell lines.

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