Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Acharya, U. Rajendra(1)
- Arslan Topal, E. Işıl(1)
- Aslan, N.(1)
- Barua, Prabal Datta(1)
- Baygin, Mehmet(1)
- Baygin, Nursena(1)
- Can, U.(1)
- Cheong, Kang Hao(1)
- Dogan, Sengul(1)
- Dogan, Yusuf(1)
- Karaaslan Ayhan, N.(1)
- Kaya, Nihat(1)
- Koç, M.M.(1)
- Kurnaz Yetim, N.(1)
- Muezzinoglu, Taha(1)
- Palmer, Elizabeth Emma(1)
- Seker, S.(1)
- Topal, Murat(1)
- Tuncer, Ilknur(1)
- Tuncer, Turker(1)
- Utku, A.(1)
- Özcan, C.(1)
Yayınlar
- MakaleAçık Erişim2023Scopus SJRQ1Investigation of Critical Raw Materials in Sludge of Municipal Wastewater Treatment Plant
Topal, Murat; Arslan Topal, E. Işıl
In the present research, lanthanum, vanadium, tungsten, scandium, bismuth, antimony, gallium, phosphorus, magnesium, and cobalt from critical raw materials (CRMs) were monitored for one year in the sewage sludges from an advanced biological wastewater treatment plant with the aim of presentation of possibility of using sewage sludge as a secondary source of critical raw materials. The amounts of the critical raw materials in sewage sludge were calculated annually, and economic values were revealed. Maximum concentrations of lanthanum, scandium, tungsten, bismuth, antimony, gallium, vanadium, cobalt, magnesium, and phosphorus were 4.3, 1.6, 0.5, 1.51, 1.27, 1.7, 23, 5.6, 6500, and 16,170 mg/kg, respectively. In the sewage sludge, the amount of CRMs, which have been formed annually, have been listed as tungsten < scandium < antimony < bismuth < gallium < lanthanum < cobalt < vanadium < magnesium < phosphorus. In autumn, summer, spring, and summer season, the mean highest concentration was for P, while the minimum concentration was for W. In sewage sludge, P and W elements were the highest and the least of the CRMs, respectively. With the recovery of CRMs in sewage sludge, the highest theoric achievable income is for Mg, while the lowest income is for Sb. It is determined that a total of 26,083.8 USD/year could be obtained by recovering the CRMs mentioned here from the sewage sludge.
AtıfScopus1WoS1OpenAlex3
İncele - MakaleYalnız künye2023Scopus SJRQ1An efficient hybrid weather prediction model based on deep learning
Utku, A.; Can, U.
Weather events directly affect human activities. In particular, extreme weather events with global warming, forest fires, and high air temperatures that cause drought make human life difficult. Effective and accurate weather prediction models are needed to take precautions against such climatic events. Therefore, it is essential to develop models that make precise weather predictions. Technological developments contributed significantly to developing successful deep learning-based weather prediction models. With a high success rate, this study proposed a hybrid weather prediction model based on Convolutional Neural Networks and Recurrent Neural Networks models. The proposed hybrid model was applied to the Jena dataset, which contains 14-parameter, large-scale meteorological data that were never utilized for weather prediction. The experimental results were compared with popular deep learning, machine learning, and statistical methods such as Auto-Regressive Integrated Moving Average, Convolution Neural Networks, Long-Short Term Memory, Multilayer Perceptron, Random Forest, Recurrent Neural Networks, and Support Vector Machine. As a result of these comparisons, the proposed hybrid model obtained the best prediction result for all metrics. For example, according to the weather prediction results for Jena, Germany, the proposed hybrid model got the results of Mean Squared Error: 0.035, Root-Mean-Squared Error: 0.189, Mean Absolute Error: 0.126, and R-Squared: 0.987.
AtıfScopus15WoS11OpenAlex11İncele - MakaleYalnız künye2023Scopus SJRQ1Adsorption potential of neodymium/alginate beads for removal of Congo red and Brilliant blue G from aqueous solution
Seker, S.; Karaaslan Ayhan, N.
In this study, neodymium/alginate (Nd/Alg) beads were prepared to removal of Congo Red (CR) and Brilliant Blue G (BBG) dyes from aqueous solution and the adsorption potentials of these dyes were investigated. For this, Nd/Alg beads prepared using crosslinking were characterized by FTIR, SEM–EDS and TGA. For the adsorption of CR and BBG dyes, the effects of various parameters including the amount of Nd in adsorbent (bead), pH, contact time, adsorbent amount, initial dye concentration and temperature were investigated. The optimum conditions for the effective removal of CR and BBG dyes were determined as Nd amount in bead: 3%, pH: 7, contact time: 60 min, bead amount: 15 mg, initial dye concentration: 25 mg/L and temperature 298 K. Adsorption isotherm studies (Langmuir and Freundlich isotherms) and kinetic studies (pseudo-first-order and pseudo-second-order) were carried out under optimum conditions. The adsorption data were found to completely follow Langmuir model for CR and BBG dyes. The maximum adsorption capacities were found to be 46.03 mg/g for CR and 25.25 mg/g for BBG under optimum conditions. The adsorption kinetic data of both dyes were found to be compatible with pseudo-first-order and adsorption processes were explained by intraparticle diffusion. According to thermodynamic studies, nature of adsorption reaction was spontaneous, exothermic and increasing randomness for both dyes. As a result, it can be concluded that Nd/Alg beads obtained with a natural polymer have low cost and high efficiency and even small amounts of these beads have high potential for treatment of high concentration dye wastewater.
AtıfScopus10WoS8OpenAlex10İncele - MakaleAçık Erişim2023Scopus SJRQ1PatchResNet: Multiple Patch Division–Based Deep Feature Fusion Framework for Brain Tumor Classification Using MRI Images
Muezzinoglu, Taha; Baygin, Nursena; Tuncer, Ilknur; Barua, Prabal Datta; Baygin, Mehmet; Dogan, Sengul; Tuncer, Turker; Palmer, Elizabeth Emma; Cheong, Kang Hao; Acharya, U. Rajendra
Modern computer vision algorithms are based on convolutional neural networks (CNNs), and both end-to-end learning and transfer learning modes have been used with CNN for image classification. Thus, automated brain tumor classification models have been proposed by deploying CNNs to help medical professionals. Our primary objective is to increase the classification performance using CNN. Therefore, a patch-based deep feature engineering model has been proposed in this work. Nowadays, patch division techniques have been used to attain high classification performance, and variable-sized patches have achieved good results. In this work, we have used three types of patches of different sizes (32 × 32, 56 × 56, 112 × 112). Six feature vectors have been obtained using these patches and two layers of the pretrained ResNet50 (global average pooling and fully connected layers). In the feature selection phase, three selectors—neighborhood component analysis (NCA), Chi2, and ReliefF—have been used, and 18 final feature vectors have been obtained. By deploying k nearest neighbors (kNN), 18 results have been calculated. Iterative hard majority voting (IHMV) has been applied to compute the general classification accuracy of this framework. This model uses different patches, feature extractors (two layers of the ResNet50 have been utilized as feature extractors), and selectors, making this a framework that we have named PatchResNet. A public brain image dataset containing four classes (glioblastoma multiforme (GBM), meningioma, pituitary tumor, healthy) has been used to develop the proposed PatchResNet model. Our proposed PatchResNet attained 98.10% classification accuracy using the public brain tumor image dataset. The developed PatchResNet model obtained high classification accuracy and has the advantage of being a self-organized framework. Therefore, the proposed method can choose the best result validation prediction vectors and achieve high image classification performance.
AtıfScopus103OpenAlex101
İncele - MakaleYalnız künye2023Scopus SJRQ1Ultrasound assisted adsorptive removal of Cd+2 ions from the water system using Bi2S3 nano/micro flowers
Kurnaz Yetim, N.; Aslan, N.; Özcan, C.; Koç, M.M.
Heavy metal ions threaten public health and marine life; thus, removal of heavy metals is essential for the environment. Bi2S3 nanostructures are active materials which used in various applications. In this work, we produced Bi2S3 nanoflowers using hydrothermal synthesis and assessed their Cd2+ ions removal capability. Structural properties of the Bi2S3 nanoflowers were assessed by various like scanning electron microscopy, X-ray diffraction. Chemical characteristics of the materials were confirmed using energy dispersive X-ray spectra. Different parameters affecting the Cd2+ ion recovery was worked some of which are adsorbent amount, Cd2+ ion concentration, pH, adsorbent time, eluent type and amount, etc. For cadmium, the optimum pH was determined 8.0 for 1 M HNO3 eluent. Optimum adsorbent amount was determined as 150 mg while optimum recovery duration was found as 30 min. Bi2S3 nanoparticles managed to achieve 99.3% Cd2+ recovery rate in 5 mL eluent volume, for optimum condition. The adsorption capacity of the Bi2S3 nanoflowers was found to be promising and therefore, it was illustrated that Bi2S3 nanoflowers are good candidates as sorbent for water remediation applications.
AtıfScopus3WoS3OpenAlex3İncele - MakaleAçık Erişim2023Scopus SJRQ1The Effects of Changing the Effective Crest Length of Labyrinth Side Weir on Discharge Capacity
Dogan, Yusuf; Kaya, Nihat
Labyrinth side weirs have significant advantages according to conventional side weirs in terms of discharge capacity. Labyrinth side weirs are especially advantageous in places where more discharge is required from small weir openings lengths. The most significant feature of labyrinth side weirs is their effective crest length, which enables them to discharge more flow. In this study, the situation where the discharge capacity is decreased due to the narrowing of the crest length of the labyrinth side weir for any reason was experimentally investigated in detail. It was also considered that the potential narrowing could be in the upstream weir crest length. The overflow from the remaining length of the crest was enabled by the 1/3, 2/3, and 3/3 closure of the upstream crest length of the labyrinth side weir. Therefore, it was attempted to determine the decrease in discharge capacity due to the narrowing in the effective crest length. The values of the discharge coefficient were obtained by using the De Marchi approach. The study was conducted for subcritical flow regimes and steady-state flow conditions. A total of 980 experimental runs were conducted in this study. As the length of upstream effective crest length of the triangular labyrinth side weir at the straight channel decreases, the discharge coefficient also decreases. By narrowing the length of effective upstream crest length in 1/3, 2/3, and 3/3 ratios, the total discharge capacity decreased by approximately 10%, 23%, and 48%, respectively.
AtıfScopus7WoS6OpenAlex6İncele - DergiJournal of Digital Imaging