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FeS2 Nanopartiküllerinin Antibakteriyel Fototerapi Özelliklerinin Geliştirilmesine Yönelik Yeni Yaklaşımlar
No full textIn recent years, the increasing prevalence of antibiotic resistance has rendered conventional treatment methods inadequate, thereby intensifying the need for non–antibiotic therapeutic approaches. In this context, phototherapy has emerged as a promising, non–invasive, and targeted treatment. However, the therapeutic efficiency of phototherapeutic agents is considerably dependent on the optical, physical, and surface properties of the materials. Accordingly, semiconductor materials such as iron disulfide FeS2, which are non–toxic, cost–effective, abundant in nature, and capable of broad–spectrum light absorption, offer significant advantages. In this thesis study, two innovative strategies were developed and systematically investigated to enhance the antibacterial phototherapy efficiency of FeS2 nanoparticles. The first strategy involves the construction of a p–n type FeS2/WS2 heterostructure by combining FeS2 nanoparticles with n–type semiconductor tungsten disulfide (WS2) to enhance their photodynamic effect. This heterostructure suppresses the recombination of photo–excited electron–hole pairs and increases the generation of reactive oxygen species (ROS), leading a significant improvement in both photodynamic and photothermal efficiency. The heterostructure containing 30 wt.% WS2 exhibited a photothermal conversion efficiency of 52.6%, and its antibacterial activity with the concentration of 100 μg mL-1 was determined to be 99.4% and 100% against E. coli and S. aureus, respectively The second strategy involves the surface modification of the FeS2 nanoparticles using ε–poly–L–lysine, a cationic and water–soluble biopolymer, to enhance interactions between bacteria and material with reducing agglomeration tendency. The surface modification increased the surface charge of the nanoparticles resulting in improved colloidal stability and strengthened electrostatic interactions with bacteria cells. The modified structures achieved photothermal efficiency of up to 65.5%, exhibited enhanced ROS production, and provided over 99% inactivation of both bacterial species at a material concentration of 125 μg mL-1. The findings demonstrate that both strategies significantly improved the antibacterial phototherapy performance of FeS2 nanoparticles.Son yıllarda, artan antibiyotik direnci, geleneksel tedavi yöntemlerini yetersiz kılmakta ve antibiyotik kullanılmayan tedavi yöntemlerine olan ihtiyacı giderek artırmaktadır. Fototerapi bu noktada canlı hücrelere zarar vermeyen ve hedefe yönelik bir yöntem olarak ön plana çıkmaktadır. Ancak, fototerapötik malzemelerin etkinliği büyük oranda malzemenin optik, fiziksel ve yüzeysel özelliklerine bağlı kalmaktadır. Bu doğrultuda, FeS2 gibi düşük toksisite ve geniş spektrumlu ışık absorpsiyonuna sahip, bol bulunan ve düşük maliyetli yarı iletken malzemeler önemli avantajlar sunmaktadır. Bu tez çalışmasında, demir disülfür (FeS2) nanopartiküllerinin antibakteriyel fototerapi uygulamalarındaki etkinliğini artırmaya yönelik iki yenilikçi strateji geliştirilmiştir. İlk strateji, FeS2 nanoparçacıklarının fotodinamik etkinliğini artırmak amacıyla n–tipi bir yarı iletken olan tungsten disülfür (WS2) ile birlikte sentezlenerek p–n tipi FeS2/WS2 heteroyapısının oluşturulmasıdır. Bu heteroyapı, foto–uyarılmış elektron–boşluk çiftlerinin yeniden birleşmesini azaltarak reaktif oksijen türlerinin (ROS) oluşumunu artırmış ve böylece hem fotodinamik hem de fototermal etkide artış sağlamıştır. Heteroyapının ağırlıkça %30 WS2 içeren formunun fototermal dönüşüm verimliliği %52,6 olarak hesaplanmıştır. 100 μg mL–1 malzeme derişiminde antibakteriyel fototerapi aktivitesi ise E. coli ve S. aureus için sırasıyla %99,4 ve %100 olarak tespit edilmiştir. İkinci strateji, FeS2 nanopartiküllerinin aglomerasyonunu engelleyerek bakteri ile etkileşimini arttırmak amacıyla ε–poli–L–lizin ile yüzey modifikasyonunun gerçekleştirilmesidir. Katyonik ve suda çözünebilen bu biyopolimer, nanopartiküllerin yüzey yükünü artırarak daha iyi süspansiyon kararlılığı sağlamış ve malzemenin bakteri hücresi ile elektrostatik etkileşimi güçlendirmiştir. Bu yapıların fototermal verimliliği %65,5'e ulaşmış ve ROS üretimi artmıştır. Antibakteriyel aktivite açısından 125 μg mL–1 malzeme derişiminde her iki bakteri türü için %99'un üzerinde inaktivasyon sağlanmıştır. Elde edilen bulgular, her iki yaklaşımın FeS2 nanopartiküllerinin antibakteriyel fototerapi uygulamalarındaki etkinliğini artırdığını göstermektedir
Shake Table Experiments and Numerical Simulation on the Effects of Damage and Strengthening on Dynamic Behavior of RC Frames
No full textThis study presents an investigation on the effects of damage and strengthening on the dynamic behavior of buildings. Forced vibration tests were carried out on the shake table of two 1/3 scale, 3D, 2-story, single-span reinforced concrete frame specimens produced in laboratory. The damage was created by weakening the joint areas. Then the damaged zones were repaired and strengthening methods using in-plane reinforced concrete shear walls and X-shaped steel diagonal bracings were applied. The aim here is to perform a dynamic-based performance evaluation of these two commonly used global systemic strengthening techniques in practice. A total of more than 105 forced vibration experiments were carried out under 4 different intensities of dynamic load in different conditions of the specimens. Dynamic parameters were determined with the experimental modal analysis method. Moreover, story displacements time history, base shears time history, base shear-top displacement hysteresis curves, and lateral translational stiffnesses were obtained. In addition, numerical analyses using ETABS finite element software were also conducted. As a result, it was observed that the damage reduced the lateral translational stiffnesses by about 50%, steel bracings increased the stiffness in the damaged condition by 147% and RC shear walls increased it by 381%. On average, the 1st natural frequency decreased by 36.5% in the damaged conditions, increased by 83% in the strengthened conditions compared to the damaged conditions. Strengthening of the members tends to limit the soft story behavior. In general, although its application is difficult, the best performance in all studied parameters was obtained from the specimen strengthened with in-plane reinforced concrete shear walls.This study was part of the doctoral dissertation of the first author accepted by KTUN Graduate Education Institute in 2024. The authors would like to thank the KTUN Coordinatorship of Instructor Training Programme for the collaboration.Konya Technical Universit
Predictive Modeling of MB Adsorption on Activated Olive Stone Through Artificial Neural Networks
No full textThe primary objective of this study was to evaluate the potential of activated olive stone (AOS), an organic waste material, for adsorbing Methylene Blue (MB) dye from aqueous solutions and to develop a predictive model using Artificial Neural Networks (ANNs). This research aimed to explore AOS as an eco-friendly and cost-effective adsorbent for wastewater treatment, emphasizing its potential for large-scale applications. Additionally, the study sought to enhance the understanding of how various factors-such as pH, contact time, and adsorbent dosage-affect the adsorption process and to optimize the conditions for maximum dye removal efficiency. The material's structure and functional groups were analyzed using Fourier Transform Infrared (FTIR) spectroscopy. Adsorption experiments conducted in a batch system demonstrated a removal efficiency of 93% under optimal conditions, with a maximum adsorption capacity of 446 mg/g for MB. The optimal conditions were identified as pH 7, a contact time of 30 min, 10 g/L of AOS, and an MB concentration of 250 mg/L. To better understand the influence of various parameters on MB adsorption, an ANN model was developed. The model analysis revealed a strong correlation coefficient (R2) of 91%, indicating that the model could reliably predict MB removal. Overall, the study highlights the promising potential of AOS as an adsorbent for wastewater treatment and demonstrates the effectiveness of ANN models for optimizing adsorption processes
Self-Powered Narrowband Near-Infrared Photodetector Based on Polyoxometalate Compound
No full textNarrowband photodetectors are employed in optical communication, where designated wavelengths are used to transmit data, and in environmental sensing to identify specific gases that absorb distinct wavelengths of light. In this study, we developed a novel polyoxometalate compound for application as a near-infrared (NIR) photodetector. Li6 [alpha-P2W18O62]-.28H2O compound was synthesized successfully and was characterized using 31P NMR, FT-IR, UV-Vis, C-V, SEM, TEM, and AFM. We used Li6 [alpha-P2W18O62]-.28H2O compound as interlayer in Schottky type photodetector structure. Photodiode and photodetector measurements were performed under various solar intensities (20, 40, 60, 80, and 100 mW), ultraviolet, visible, and near-infrared wavelengths ranging from 351 to 1600 nm. Notably, the device exhibited an excellent responsivity, external quantum efficiency, and detectivity under near-infrared wavelengths. It showed 64.17 mA/W responsivity, 4.34 x 1010 Jones detectivity and 7.96 % external quantum efficiency at 1000 nm and 0 bias voltage. Moreover, the device demonstrated 4.736 A/W responsivity and 8.57 x 1011 Jones detectivity under solar light. Furthermore, this research introduces a novel compound for developing narrowband photodetectors utilizing polyoxometalate.Seluk University BAP office [23401011]TUBITAK 1001 Scientific and Technological Research Projects Funding Program [122Z293]This publication has been produced, benefiting from the TUBITAK 1001 Scientific and Technological Research Projects Funding Program (Project No. 122Z293) . This work was supported by Selcuk University BAP office with the research Project Number of 23401011
Zero Waste Applications for the Apple Processing Wastes: Recovery of Valuable Compounds by Supercritical Co2 and Wastewater Treatment by Advanced Oxidation
No full textThis study has focused on the characterization, recovery and treatment of apple processing wastes, which are important in terms of sustainability and circular economy. Apple processing wastewater (AW) was noted for its acidity (pH 4.2), high chemical oxygen demand (COD) (114 g/L), total solids (TS; 13.5 %) and oil/grease content (16.5 g/L), as well as the presence of valuable fatty acids and phenolics. Total phenolic content (TPC), total flavonoids content (TFC) and antioxidant activity were determined in the wastewater as 1660 mg GAE/L, 421 mg QE/L and 2.1 mM TE, respectively. The extraction of phenolic compounds from AW and apple pomace (AP) using supercritical carbon dioxide (SC-CO2) extraction was investigated as a first step. The results indicate that SC-CO2 extraction is effective in recovering phenolic compounds from apple processing wastes. The extraction yields for AW and AP using SC-CO2 reached up to 76 % and 43 % of the extractable oils, respectively. Recovery efficiencies of the TPC were observed to be up to 3.8 % for AW and 11.4 % for AP. Furthermore, the phenolic and fatty acid profiles of extracts were also evaluated, indicating the recovery of valuable compounds such as quercetin, catechin, procyanidin B2, a)-6 and a)-9 fatty acids. In terms of wastewater treatment, the use of ozone oxidation (OO) and supercritical water oxidation (SCWO) for the removal of COD, suspended solids (SS), TPC and toxicity from AW were also investigated. The OO exhibited relatively low COD removal efficiency, while SCWO demonstrated high efficiency ranging from 84 % to 99.8 %. This study has revealed that while SCWO effectively removes COD, TPC, SS and color from fruit processing wastewater, OO demonstrates greater effectiveness in reducing toxicity in the Vibrio fischeri test.The authors would like to acknowledge for the financial support provided by TUBITAK (project number: 120Y351) and KTUN-BAP (project number: 222301003) . Additionally, the authors express their gratitude to Muberra Nur Kilicarslan, Esra Bircan, and Aslihan Ozturk for their contributions to the laboratory experiments.TUBITAK [120Y351]; KTUN-BAP [222301003
Measurement of Multidifferential Cross Sections for Dijet Production in Proton-Proton Collisions at √s=13tev
No full textNavarrete Ramos, Efren/0000-0002-5180-4020A measurement of the dijet production cross section is reported based on proton-proton collision data collected in 2016 at root s = 13 TeV by the CMS experiment at the CERN LHC, corresponding to an integrated luminosity of up to 36.3 fb(-1). Jets are reconstructed with the anti-k(T) algorithm for distance parameters of R = 0.4 and 0.8. Cross sections are measured double-differentially (2D) as a function of the largest absolute rapidity vertical bar y vertical bar(max) of the two jets with the highest transverse momenta p(T) and their invariant mass m(1,2), and triple-differentially (3D) as a function of the rapidity separation y*, the total boost y(b), and either m(1,2) or the average p(T) of the two jets. The cross sections are unfolded to correct for detector effects and are compared with fixed-order calculations derived at next-to-next-to-leading order in perturbative quantum chromodynamics. The impact of the measurements on the parton distribution functions and the strong coupling constant at the mass of the Z boson is investigated, yielding a value of alpha(S)(m(Z)) = 0.1179 +/- 0.0019.We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: SC (Armenia), BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); SRNSF (Georgia); BMBF, DFG, and HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Science Committee, project no. 22rl-037 (Armenia); the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the "Excellence of Science - EOS" - be.h project n. 30820817; the Beijing Municipal Science ; Technology Commission, No. Z191100007219010 and Fundamental Research Funds for the Central Universities (China); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Shota Rustaveli National Science Foundation, grant FR-22-985 (Georgia); the Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy - EXC 2121 "Quantum Universe" - 390833306, and under project number 400140256 - GRK2497; the Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288 (Greece); the Hungarian Academy of Sciences, the New National Excellence Program - uNKP, the NKFIH research grants K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council of Science and Industrial Research, India; ICSC - National Research Center for High Performance Computing, Big Data and Quantum Computing, funded by the EU NexGeneration program (Italy); the Latvian Council of Science; the Ministry of Education and Science, project no. 2022/WK/14, and the National Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the FundacAo para a Ciencia e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF "a way of making Europe", and the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources ; Institutional Development, Research and Innovation, grant B37G660013 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).FWF (Austria); FNRS (Belgium); FWO (Belgium); CNPq (Brazil); CAPES (Brazil); FAPERJ (Brazil); FAPERGS (Brazil); FAPESP (Brazil); BNSF (Bulgaria); MoST (China); NSFC (China); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER (Estonia); ERDF (Estonia); Academy of Finland (Finland); MEC (Finland); CEA (France); CNRS/IN2P3 (France); BMBF (Germany); DFG (Germany); HGF (Germany); NKFIH (Hungary); DAE (India); DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); NRF (Republic of Korea); MES (Latvia); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CONACYT (Mexico); UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); FCT (Portugal); MESTD (Serbia); PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); NSTDA (Thailand); TUBITAK (Turkey); NASU (Ukraine); NSF (USA); Marie-Curie program (European Union); European Research Council (European Union); Horizon 2020 Grant (European Union) [675440, 724704, 752730, 758316, 765710, 824093, 884104]; COST Action (European Union) [CA16108]; Leventis Foundation; Alfred P. Sloan Foundation; Alexander von Humboldt Foundation; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); FWO (Belgium) under the "Excellence of Science - EOS - be.h project [30820817]; Beijing Municipal Science ; Technology Commission [Z191100007219010]; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Hellenic Foundation for Research and Innovation (HFRI) (Greece) [2288]; Deutsche Forschungsgemeinschaft (DFG) [EXC 2121, 390833306, 400140256 - GRK2497]; Hungarian Academy of Sciences (Hungary); Council of Science and Industrial Research, India; Latvian Council of Science; National Science Center (Poland) [Opus 2021/41/B/ST2/01369, 2021/43/B/ST2/01552]; National Priorities Research Program by Qatar National Research Fund; MCIN/AEI, ERDF "a way of making Europe"; Programa Severo Ochoa del Principado de Asturias (Spain); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); National Science, Research and Innovation Fund via the Program Management Unit for Human Resources ; Institutional Development, Research and Innovation (Thailand) [B05F650021]; Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (USA); BMBWF (Austria); MES (Bulgaria); CERN; CAS (China); MINCIENCIAS (Colombia); MSES (Croatia); ERC PUT (Estonia); HIP (Finland); GSRI (Greece); MSIP (Republic of Korea); LAS (Lithuania); CINVESTAV (Mexico); LNS (Mexico); SEP (Mexico); MOS (Montenegro); MES (Poland); NSC (Poland); MCIN/AEI (Spain); MST (Taipei); MHESI (Thailand); TENMAK (Turkey); STFC (United Kingdom); DOE (USA); F.R.S.-FNRS (Belgium); New National Excellence Program - UNKP (Hungary); NKFIH (Hungary) [K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, TKP2021-NKTA-64]; Ministry of Education and Science [2022/WK/14]; Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu (Spain) [MDM-2017-0765]; SC (Armenia
Devastating Natural Hazard Observation With the Combination of Optical and Microwave Remote Sensing Datasets, Valencia 2025 Flood
No full textFloods represent some of the most catastrophic natural hazards, impacting infrastructure, ecosystems, and human lives significantly. The flood event in Valencia in 2025 serves as a critical case for investigating flood dynamics and developing disaster preparedness strategies. In response, remote sensing datasets, including Sentinel-1 SAR and PlanetScope MSI, provide invaluable insights by capturing changes in land cover and fluctuations in water extent both before and after flood occurrences. This study employs a multi-sensor approach to analyze the 2025 Valencia flood, integrating optical and microwave datasets to produce comprehensive and precise observations of the flood's impact, spatial patterns, and potential causal factors. Furthermore, assessments based on the Normalized Difference Water Index (NDWI) further illustrate the limitations associated with optical methods in flood mapping, thereby reinforcing the indispensable role of SAR in crisis management. The findings highlight the critical importance of flexible urban planning, including creating flood protection zones to prevent loss of life and reduce structural damage in cities vulnerable to flooding. A comparison with previous flood incidents indicates rising extreme weather, reinforcing the need for proactive government measures. This study confirms the essential role of remote sensing in contemporary disaster management, providing essential, large-scale, real-time data for informed policymaking, effective emergency response, and building long-term resilience. By incorporating advanced satellite technologies, this research establishes a new standard for flood evaluation and early warning systems, with far-reaching effects on climate adaptation and strategies for risk reduction. © 2025 Hasan Bilgehan Makineci.European Space Agency, ES
Futbolcularda Görülen Hamstring Yaralanmalarının Termal Görüntüleme ve Yapay Zeka ile Tespiti
No full textAmong the key factors influencing the success of football teams are player quality, tactical arrangements, and training programs. However, one of the primary factors negatively impacting this success is player injuries. An active injury prevents a player from contributing to the team, thereby reducing team performance and incurring additional costs for treatment. Consequently, football teams utilize various medical imaging methods, including ultrasonography, magnetic resonance imaging, computed tomography, blood tests, and isokinetic devices, to examine muscle injuries, enable early diagnosis, monitor rehabilitation processes, and determine return-to-play decisions. Recent advancements in thermography have introduced thermographic imaging as a cost-effective, portable, non-invasive, and user-friendly method for injury analysis in football teams. By utilizing temperature asymmetry and increased heat in the injured muscle region, thermography enables detailed examination of injuries. This has led to a rise in studies focusing on the use of thermography in sports injuries in recent years. This thesis investigates the use of thermographic imaging in analysing football player injuries and its applicability in injury detection and rehabilitation planning. The study utilizes deep learning methods to segment muscle regions from lower extremity thermographic images and classify injuries in these regions. Additionally, decision support systems for the detection of active injuries and planning of rehabilitation processes in football players were developed. For the research, lower extremity thermographic images were collected from football players in a Turkish Super League team over two seasons. Although large datasets are generally required to achieve successful results in deep learning methods, the limited number of injured players per season presents challenges in achieving balanced datasets. To overcome this issue, in addition to traditional deep learning methods, few-shot learning techniques and novel deep learning methods were employed. In this thesis, the thermographic images of football players with active injuries were analyzed, and their injuries were detected, followed by planning their rehabilitation processes. Subsequently, deep learning algorithms were utilized to segment muscle regions for more detailed analysis of these injuries, and the segmented regions were classified to identify injuries. To address the issue of insufficient and imbalanced datasets, few-shot learning and modern deep learning methods were employed to enhance classification accuracy. The implementations conducted within the scope of the thesis were carried out under three main stages. First, injuries in football players were detected from thermographic images, and the thermal response of injuries under exercise was analyzed. Additionally, thermography was used to assist in planning rehabilitation processes and determining return-to-play decisions. Subsequently, muscle regions identified based on the anatomic atlas were segmented using deep learning methods such as U-Net, Pyramid Scene Parsing Network, LinkNet, and Feature Pyramid Network. The segmentation results demonstrated that U-Net achieved the highest performance with a success rate of 99%. Following the segmentation, injuries in the segmented muscle regions were detected using deep learning methods such as DenseNet, Visual Geometry Group, ResNet, and EfficientNet. An end-to-end algorithm structure encompassing both segmentation and classification was designed for injury detection. The classification results indicated that the highest accuracy rates were achieved with EfficientNetB0 (83.9%) and EfficientNetB1 + Feature Pyramid Network (81.0%). To further enhance accuracy and address the issue of imbalanced datasets, classification was also performed using Siamese Networks, Prototypical Networks, and Kolmogorov-Arnold Network. The classification results showed that the Siamese and Prototypical Networks achieved success rates of 94% and 97.78%, respectively, while the Kolmogorov-Arnold Network achieved a success rate of 93.1%. This demonstrates the effectiveness of few-shot learning methods and the Kolmogorov-Arnold Network, a novel deep learning method, in detecting sports injuries using thermography. Furthermore, this study is among the first in the literature to use Kolmogorov-Arnold Networks for injury detection from thermographic images. In conclusion, the studies conducted within the scope of this thesis demonstrate the significant contribution of thermography and deep learning methods to sports medicine by enabling effective injury detection and management.Futbol takımlarının başarısını etkileyen en önemli faktörler arasında oyuncu kalitesi, taktiksel düzenlemeler ve antrenman programları yer almaktadır. Ancak bu başarıyı olumsuz etkileyen başlıca faktörlerin başında futbolcu yaralanmaları gelmektedir. Aktif yaralanma yaşayan bir oyuncunun takıma katkı sağlayabilmesi mümkün olmamaktadır. Bundan dolayı yaralanmalar hem futbol takımlarının başarısını düşürmekte ve hem de tedavi masraflarından dolayı takımlara maliyetli olmaktadır. Bu olumsuzluklardan dolayı futbol takımları kas yaralanmalarının incelenmesi, erken evre tespiti, rehabilitasyon sürecinin takibi ve sahaya dönüşün karar verilmesinde ultrasonografi (USG), manyetik rezonans görüntüleme (MRG), bilgisayarlı tomografi (BT), kan testleri ve izokinetik cihazlar gibi çeşitli tıbbi görüntüleme metotları kullanmaktadır. Günümüzde termografi alanındaki gelişmelerle birlikte termografik görüntüleme futbol takımlarına yaralanmaların incelenmesinde kullanımı kolay, maliyetsiz, kolay taşınabilir ve non-invaziv bir metot sunmaktadır. Yaralanmanın oluştuğu kas bölgesinde oluşan sıcaklık artışı ve kas bölgelerindeki sıcaklık asimetrisinden faydalanarak yaralanmaları incelemek mümkün olmaktadır. Bundan dolayı son dönemlerde sporcu yaralanmalarında termografinin kullanımı üzerine yapılan çalışmalar artış göstermektedir. Bu tez çalışmasında, futbolcu yaralanmalarının termal görüntüleme ile incelenmesi ve bu görüntülerin yaralanma tespiti ile rehabilitasyon süreçlerinin planlanmasında kullanılabilirliği araştırılmıştır. Çalışmada, derin öğrenme yöntemleri kullanılarak alt ekstremite termal görüntülerinden kas bölgelerinin segmentasyonu ve bu bölgelerdeki yaralanmaların sınıflandırılması yapılmıştır. Ayrıca, futbolcuların aktif yaralanmalarının tespiti ve rehabilitasyon süreçlerinin planlanmasına yönelik karar destek sistemleri geliştirilmiştir. Araştırma için Türkiye Süper Lig takımı futbolcularından 2 sezon boyunca termografi ile alt ekstremite termal görüntüleri elde edilmiştir. Derin öğrenme metotlarında başarılı sonuçlar elde edebilmek için büyük miktarda veri gerekliliği olduğu bilinse de bir sezonda yaralanma yaşayan futbolcu sayısının sınırlı olması yetersiz ve dengesiz veri seti problemini ortaya çıkarmaktadır. Bu problemin üstesinden gelmek için geleneksel derin öğrenme metotlarına ek olarak, az örnekli öğrenme metotları ve yeni derin öğrenme metotları kullanılmıştır. Bu tez çalışmasında, aktif yaralanması bulunan futbolcuların termal görüntüleri incelenerek yaralanmaların tespiti yapılmış ve rehabilitasyon süreçleri planlanmıştır. Daha sonra bu yaralanmaların daha detaylı analiz edilebilmesi için derin öğrenme algoritmaları ile kas bölgelerinin segmentasyonu yapılmış ve segmente edilen bölgeler üzerinden yaralanmaların sınıflandırılması gerçekleştirilmiştir. Yetersiz ve dengesiz veri seti problemine yönelik olarak, az örnekli öğrenme ve modern derin öğrenme metotları ile sınıflandırma süreçlerinin doğruluğu artırılmıştır. Tez kapsamında yürütülen uygulamalar üç başlık altında gerçekleştirilmiştir. İlk olarak termal görüntülerden futbolcuların yaralanmaları tespit edilmiş ve yaralanmaların egzersiz etkisi altında verdiği termal tepki incelenmiştir. Aynı zamanda futbolcuların rehabilitasyon süreçlerinin planlanması ve sahaya dönüş kararının verilmesinde termografi desteği sağlanmıştır. Daha sonra futbolcularda anatomik atlasa göre belirlenen kas bölgeleri derin öğrenme metotlarından U-Net, Piramit Sahne Ayrıştırma Ağı, LinkNet ve Özellik Piramit Ağı kullanılarak segmente edilmiştir. Bu segmentasyon işlemi sonucunda; en başarılı metodun %99 başarı oranı ile U-Net olduğu görülmüştür. Daha sonra segmente edilen kas bölgelerindeki yaralanmaların tespit edilmesi için Densenet, Görsel Geometri Grubu, Artık Ağlar, EfficientNet gibi derin öğrenme metotları kullanılmıştır. Yaralanmaların tespiti için hem segmentasyon hem de sınıflandırma içeren bir uçtan uca bir algoritma yapısı tasarlanmıştır. Sınıflandırma sonucunda en başarılı sonuçları EfficientNetB0 %83.9 ve EfficientNetB1 + Özellik Piramit Ağı %81.0 doğruluk oranı ile vermiştir. Yetersiz ve dengesiz veri seti problemine çözüm bulmak ve elde edilen sonuçların başarı oranını daha da arttırmak için Siyam Ağları, Prototip Ağları ve Kolmogorov-Arnold Ağı ile sınıflandırma işlemi gerçekleştirilmiştir. Sınıflandırma sonuçları incelendiğinde Siyam ve Prototip Ağları sırasıyla %94 ve %97.78 başarı oranı elde ederken, Kolmogorov-Arnold Ağı ile %93.1 başarı oranı elde edilmiştir. Bu durum az örnekli öğrenme metotları ve derin öğrenmede yeni bir metot olan Kolmogorov-Arnold Ağının sporcu yaralanmalarının termografi ile tespit edilmesinde oldukça başarılı olduğunu göstermektedir. Bunun yanında bu çalışma, Kolmogorov-Arnold Ağı ile termal görüntülerden yaralanma tespiti üzerine yapılan literatürdeki ilk çalışmalardandır. Sonuç olarak bu tez kapsamında yürütülen çalışmalar termografi ve derin öğrenme yöntemlerinin spor hekimliğinde etkin bir şekilde kullanılarak yaralanmaların tespitinde ve yönetiminde önemli bir katkı sunduğunu göstermektedir
Leeb Hardness Approach in the Determination of Strength After Accelerated Weathering Tests
No full textThis study aimed to investigate the usability of the Leeb hardness test in determining changes in strength as a result of atmospheric weathering in works of cultural heritage built with low-strength pyroclastic rocks. To this end, the effects of weathering processes on strength properties were investigated in two building stones commonly used in Niğde province located in the Cappadocia (Turkey) region, which contains the most important works of cultural heritage created using low-strength pyroclastic rocks. The index, strength, mineralogical, and petrographic properties of rocks were first investigated. Then, freeze–thaw (F-T) and salt crystallization (SC) tests, the weathering processes of which consisted of six periods, were performed on samples prepared in cubic form. After the F-T and SC processes, the macro change in the samples and changes in weight loss, uniaxial compressive strength (UCS), and Leeb hardness (HL) values were determined. Highly correlated linear relationships were obtained between the SC and F-T cycles of the samples and the UCS and HL values. The HL test was applied to samples for which the UCS test could not be applied due to the loss of sample integrity after the advancing cycles of the accelerated weathering tests. Linear relationships with high correlation were determined between the UCS and HL values obtained from the samples after the accelerated weathering test. This study revealed that the HL approach could be used as an alternative in modeling the strength parameters of the weathering processes of the structures of cultural heritage built using low-strength rocks. © The International Institute for Conservation of Historic and Artistic Works 2024
Lightweight and Sustainable Recycled Cellulose Based Hybrid Aerogels With Enhanced Electromagnetic Interference Shielding
No full textDeveloping lightweight, sustainable, high porosity, and high-performance electromagnetic interference (EMI) shielding apparatus is essential to diminish electromagnetic contamination for protecting human health and electronic devices. Herein, 1D carbon nanotubes (CNTs) and 2D graphene nanoplatelets (GNPs) functionalized recycled cellulose aerogel (RCA) were fabricated via a facile method by freeze, solvent exchange, and ambient drying. The effect of nanofiller type and quantity on the structural, morphological, electrical, thermal and EMI shielding performance of the RC-based aerogel were investigated. The as-prepared hybrid aerogel displays the maximum 40.2 dB electromagnetic interference shielding efficiency (SE) at 8.92 dB GHz with absorption dominant characteristic. CNTs:GNPs nanofillers in recycled cellulose matrix provoked conductivity mismatching and increased interfacial polarization loss. At a density of 0.087 gcm-3, CNTs:GNPs; 7:7%wt. doped RCA exhibits a highly specific SE (SSE) value of 461.95 dBcm3g-1 and an absolute SE (SSE/t) value of 2309.29 dBcm2g-1. These results show that the CNTs:GNPs; 7:7%wt. doped RCA can meet practical applications' lightweight and high-efficiency EMI shielding requirements.The authors acknowledge funding support from The Scientific and Technical Research Council of Turkiye under grant number 121C507.Scientific and Technical Research Council of Turkiye [121C507