Istanbul Technical University
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Throughput of Myopic Policy in Energy Harvesting Sensor Network for Environmental Monitoring
No full texthttps://doi.org/10.1109/metrolivenv64961.2025.1110701
Changes in community composition and functional diversity of European bats under climate change
No full textAbstract Climate change is predicted to drive geographical range shifts that will result in changes in species diversity and functional composition and have potential repercussions for ecosystem functioning. However, the effect of these changes on species composition and functional diversity (FD) remains unclear, especially for mammals, specifically bats. We used species distribution models and a comprehensive ecological and morphometrical trait database to estimate how projected future climate and land‐use changes could influence the distribution, composition, and FD of the European bat community. Future bat assemblages were predicted to undergo substantial shifts in geographic range and trait structure. Range suitability decreased substantially in southern Europe and increased in northern latitudes. Our findings highlight the potential for climate change to drive shifts in bat FD, which has implications for ecosystem function and resilience at a continental scale. It is important to incorporate FD in conservation strategies. These efforts should target species with key functional traits predicted to be lost and areas expected to experience losses in FD. Conservation strategies should include habitat and roost protection, enhancing landscape connectivity, and international monitoring to preserve bat populations and their ecosystem services.https://doi.org/10.1111/cobi.70025https://pubmed.ncbi.nlm.nih.gov/40165613http://dx.doi.org/10.1111/cobi.70025http://hdl.handle.net/10261/399387https://api.elsevier.com/content/abstract/scopus_id/105002076499https://hdl.handle.net/20.500.14243/542263http://dx.doi.org/10.13039/501100000270http://hdl.handle.net/10138/595219https://curis.ku.dk/ws/files/523029549/Changes_in_community_composition_and_functional_diversity_of_European_bats_under_climate_change.pdfhttps://hdl.handle.net/11573/1739868https://hal.inrae.fr/hal-05034834v1/documenthttps://hal.inrae.fr/hal-05034834v
Statistical analysis of Wind Characteristic and Wind Energy Potential Based on Weibull Distribution in Bingol Province, Turkey
No full textIn this study, the statistical analysis of wind energy density and wind speed distribution parameters in Bingol province was examined using hourly wind speed data measured by the General Directorate of Meteorology between 2014 and 2017. Weibull distribution was used for statistical modeling and k and c parameters were calculated for 10 m and 30 m height. According to statistical criteria, in the wind data analysis of Bingol province, it was determined that the months with the highest potential in terms of mean wind speed and wind power densities are March, April and May. In the months when mean wind speeds are the highest, the dominant wind direction is south. As a result, it is concluded that since the average monthly and annual power densities in Bingol province are about 100 W/m2. It is determined that the wind potential of the region can be used for small scale off-grid wind applications. The fact that the average speed is mostly higher than 4 m/s for 30 m hub height has shown that electrical energy generation from wind energy is promising.https://doi.org/10.31202/ecjse.1473486https://dergipark.org.tr/tr/pub/ecjse/issue/90309/147348
Hybrid TiO 2 ‐Coated Cement Materials for Pathogen and Pollutant Inactivation: Focus on SARS‐CoV‐2
No full textAbstract The current study induces nanocoated cementitious specimens with TiO 2 ‐based photocatalysts, doped with various metals including silver (Ag), zinc (Zn), and copper (Cu), supported on zeolite type X (ZXD) or nano‐graphite (NG). The objective was to attain cement‐based surfaces capable of effectively inactivating the SARS‐CoV‐2 virus. Antiviral efficacy is evaluated against the SARS‐CoV‐2 virus under laboratory room lighting conditions, revealing enhanced virus photodegradation after metal doping via the reactive oxygen species (ROS) generation mechanism. Further research focused on improving the synthesis process. Due to the same degradation mechanism and the high risks of working with the SARS‐CoV‐2 virus, Rhodamine B (RhB) dye was utilized to evaluate the photodegradation efficiency of the nano‐hybrids. Impressive outcomes were observed for Cu–TiO 2 /NG and Ag–TiO 2 /NG, demonstrating degradation rates of RhB at 45% and 98%, respectively, within a 60 min UV light irradiation period. Comprehensive sample characterization was performed utilizing XRD, XPS, SEM, and FE‐S/TEM. The characterization results indicate the presence of highly pure anatase titanium dioxide, along with well‐distributed metal and TiO 2 nanoparticles on nano‐graphite sheets. Based on the obtained results, this enhanced synthesis technique offers a promising approach for applying the prepared composites to the inactivation of the SARS‐CoV‐2 virus.https://doi.org/10.1002/slct.20250434
Görüntü sınıflandırması için verimli bir çoklu-sinir ağ topluluğu modeli
No full textThesis (M.Sc.) -- Istanbul Technical University, Graduate School, 2025Image classification is an artificial intelligence (especially deep learning) technique used to classify an image into specific categories or classes. Today, it is one of the cornerstones of computer vision and is of vital importance in many fields. For example, categorizing with high accuracy medical images into disease classes enables more efficient and accurate diagnosis. To achieve high accuracy in image classification tasks has encouraged the development of methods such as CNN. In addition, some methods such as ensemble technique and Transfer Learning etc. are commonly used for this objective. However, while trying to achieve high accuracy, other important parameters such as training time must also be considered. Therefore, especially Transfer Learning method is widely applied in image classification to reduce training time and enhance model efficiency. Even though transfer learning with pre-trained models such as AlexNet, VGG16, and DenseNet121 is widely used, when using these models for some image dataset, it demands a great amount of training time to reach high accuracy. The objective in this thesis is not only to increase accuracy but also to reduce training time for image classication tasks. Hence, it is proposed a model for image classification that incorporates five deep learning architectures with an ensemble technique. The proposed model consists of one MLP-based network and four CNN-based networks where one of them is a network that we call the auxiliary network. The auxiliary network is designed to recognize misclassified images in order to increase the accuracy of the model. The proposed model is tested on an image dataset called CIFAR-10. Then, it is compared the performance of the proposed model with pre-trained structures such as AlexNet, VGG16, and DenseNet121 on taking into account training time, the number of parameters, and accuracy. The results show that the proposed model outperforms pre-trained models in terms of achieving high accuracies and requiring less training time on CIFAR-10 dataset. The proposed model requires 15,38%, %10, and %87.78 of the training time of Alexnet, VGG16 and DenseNet121 to achieve %80 accuracy., respectively. While the proposed model achieves 85% and 90% accuracy, AlexNet and VGG16 cannot. In addition, it achieves 90% accuracy in 38.23 min, whereas DenseNet121 – more efficient than the other two pre-trained models - only reaches 87% accuracy in over three hours.M.Sc
Electrocatalytic Performances of PdZr/C, PdZrMo/C, and PtZrMo/C Cathode Catalyst for Proton Exchange Membrane Fuel Cells
No full textAbstract This study comprehensively investigates the synthesis and performance evaluation of nanoscale PdZr/C, PdZrMo/C, and PtZrMo/C catalysts as cathode electrocatalysts in proton exchange membrane (PEM) fuel cells. The structural and morphological characteristics of the catalysts were elucidated using XRD and SEM‐EDX. The electrochemical performance of catalysts was systematically analyzed via cyclic voltammetry (CV). The results indicate that the synthesized catalysts exhibit 2‐3 times higher catalytic activity than commercial Pt/C and Pd/C counterparts. Open circuit voltage (OCV) measurements conducted at 40 °C demonstrated values of 0.88 V for PdZr/C, 0.89 V for PdZrMo/C, and 0.92 V for PtZrMo/C. Furthermore, the maximum power densities achieved were 121.55 mW/cm 2 for PtZrMo/C, 89.96 mW/cm 2 for PdZr/C, and 82.92 mW/cm 2 for PdZrMo/C, signifying the superior performance of PtZrMo/C. At 70 °C, the PdZrMo/C and PdZr/C catalysts outperformed conventional Pd/C, underlining their enhanced electrocatalytic efficiency. Based on the maximum power density results, the electrocatalytic activity hierarchy was determined as PtZrMo/C > PdZrMo/C > PdZr/C > Pt/C > Pd/C. Additionally, efficiency analyses based on maximum power outputs further corroborated the potential of these catalysts in advancing PEM fuel cell technology. This study provides valuable insights into the development of high‐performance electrocatalysts for sustainable energy applications.https://doi.org/10.1002/slct.202500714https://hdl.handle.net/20.500.12604/879
Identification of New Candidate Inhibitors Able to Prevent Erythrocyte Invasion in Malaria by Drug Screening
No full textIn Nowadays malaria still remains the parasitic disease causing the highest number of deaths, accounting for 619,000 fatalities. The Plasmodium parasites that cause malaria have separate life cycles in both humans and female Anopheles mosquitoes, existing in various forms throughout this process. The main reason for observing the disease is that merozoites sustain their existence by invading erythrocytes. Existing drugs affect the parasite's ability to digest hemoglobin. Drug resistance is also involved in this process. In this study, have been focused to develop new drug candidate molecules for evade drug resistance. To evade drug resistance, the aim was to prevent merozoites from invading erythrocytes.The invasion of merozoites into erythrocytes consists of several stages: Attachment, deformation, apical junction formation, and tight junction formation. For this purpose, the docking calculations have been done between the invasion proteins such as MSP1, pvDBP, phRH5, AMA1 and candidates. The candidates obtained from the malaria box set were subjected to conformational scanning and geometry optimization in the Spartan'14 program to determine their physicochemical properties. According to the obtained results from the AutoDock Vina and multiple regression analyses were conducted for each protein to examine the relationship between binding affinities and the calculated physicochemical parameters of the candidates.In the regression study of 200 molecules examined for 4 different proteins, 108 molecules were included for DBP, 96 for MSP1, 90 for AMA1 and 96 for RH5, and 21 common molecules were observed for all proteins.Twenty-one molecules showed correlation with the proteins studied. Among these molecules, MMV019074, MMV019662 and MMV665881 were suggested as candidate drug leads in terms of their binding affinities, physicochemical properties and SwissADME values.https://doi.org/10.4274/tpd.galenos.2025.37928https://pubmed.ncbi.nlm.nih.gov/40340274https://doaj.org/article/65a194b8a9234e2ba22ed7b9b19e3ac
Poisson's Ratio as a Damage Index Sensed by Dual-Embedded Fiber Bragg Grating Sensor
No full textMonitoring the health of glass or carbon fiber reinforced polymer under dynamic loading conditions is still a challenge. When metals are tested under dynamic loading conditions, usually a single crack is a source of the failure in the material. On the other hand, composite materials include several damage modes such as transverse cracking, delamination and splitting under the dynamic loading and all of them contribute ultimate failure of the material. Due to the complexity of the damage in composite materials, it is very hard to estimate health or damage state of composite materials. In this study, we propose the usage of a novel embedded biaxial Fiber Bragg Grating sensor system to track the evolution of Poisson’s ratio which can be employed as a reliable damage index in composites. The fatigue experiments on specimen made of biaxial glass fiber infused with resin transfer molding system have shown that signal from novel embedded biaxial sensor system can be easily collected to evaluate Poisson’s ratio. The current study also indicates that the evolution behavior of Poisson’s ratio is consistent with other fatigue parameters such as temperature, force and strain energy that show very rapid change in the first region of fatigue with respect to number of cycles.https://doi.org/10.1007/978-3-319-22458-9_3https://dx.doi.org/10.1007/978-3-319-22458-9_
Short-Term Drainage Performance of Geotextile-Recycled Asphalt Pavement Filter Systems
No full texthttps://doi.org/10.1007/978-981-96-2710-3_1
Neighbor-Based Counterfactual Explanations: A Method for Balancing Similarity and Plausibility
No full texthttps://doi.org/10.1007/978-3-031-98304-7_2