Erciyes University - AVESIS
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PERLİT VE POMZA AGREGALI UÇUCU KÜL BAZLI GEOPOLİMER HARCIN MEKANİK ÖZELLİKLERİ ÜZERİNE KENEVİR LİFİ İLAVESİNİN ETKİSİ VE YAŞAM DÖNGÜSÜ DEĞERLENDİRMESİ
No full textİnşaat sektörü ve yapı malzemeleri, küresel ölçekte ciddi çevresel etkilere sahiptir.Özellikle çimento, dünya genelindeki CO₂ salınımının yaklaşık %8’ini oluşturmaktadır.Avrupa Yeşil Mutabakatı gibi uluslararası politikalar doğrultusunda, yapı sektöründekarbon salımının azaltılması hedeflenmektedir. Bu kapsamda, alternatif bağlayıcısistemler olarak geopolimerler giderek daha fazla önem kazanmaktadır. Bu tezçalışmasında, genleştirilmiş perlit, pomza ve kenevir kıtığı gibi hafif agregalarkullanılarak uçucu kül bazlı geopolimer harçlar üretilmiş; sodyum hidroksit aktivatörolarak tercih edilmiştir. Ayrıca, bu harçlara farklı oran ve uzunluklarda kenevir lifi ilaveedilerek lif katkısının performans üzerindeki etkileri araştırılmıştır. Harçlar 90 °C’de 24,48 ve 72 saat kürlenmiştir. Kür sonrası işlenebilirlik, birim ağırlık, UPV, eğilme ve basınçdayanımı, yüksek sıcaklık dayanımı (300 °C, 600 °C, 900 °C), iç yapı (FESEM, EDX,Mapping, XRD) ve yaşam döngüsü analizleri yapılmıştır. Yaşam döngüsüdeğerlendirmesi kapsamında, en yüksek küresel ısınma potansiyeli (GWP) değeritamamen kırtık agregalı PO-W100 ve MO-W100 harçlarında tespit edilirken, en düşükGWP değeri ise pomza ve kenevir lifi içeren M-3-0.75% karışımında elde edilmiştir.Sonuçlar, lif katkısının mekanik özellikleri iyileştirdiğini, çevresel etkileri ise azalttığınıgöstermiştir. Bu yönüyle çalışma, yapı malzemeleri alanına çevreci ve yenilikçi katkılarsunmaktadır.</p
Performance comparison of deep and transfer learning models for smart soil texture classification
No full textClassification of soil is crucial for implementing precision agriculture practices to achieve better crop planning and resource utilization. The method of this study involved utilizing a deep learning-based transfer learning model for soil texture classification using image data. For background removal to precisely extract soil features, we processed a dataset of 720 soil images from six different soil types using YOLOv5. Nine state-of-the-art transfer learning models, namely, DenseNet121, Xception, MobileNetV2, and VGG19, were evaluated in terms of classification accuracy, computational efficiency, and memory usage. Experimental results showed that the test accuracy for DenseNet121 was the best with 97.22 %, Xception was 93.52 %, and MobileNetV2 was 72.22 %. The computational efficiency analysis showed that MobileNetV2 converged the fastest (769.53 sec) and used the smallest memory (0.74 GB). On the contrary, DenseNet121 and Xception, though consuming more memory, showed a better reliability of classification. Future research may focus on improving lightweight architecture or optimizing facility extraction techniques to enhance classification accuracy and reduce computational costs. These results indicate the promise of deep learning models for soil texture classification, which can be applied in accurate agriculture and sustainable land management
Epidemiology and risk factors for hypopituitarism due to traumatic brain injury
No full textDuring the last two decades traumatic brain injury (TBI) was also found as an important cause of hypopituitarism. Although the most common causes of TBI are traffic accidents and falls, others such as blast-related injuries, acts of violence and combative sports are also considered in the etiology. TBI may lead to transient or permanent pituitary dysfunction. The definition of TBIinduced hypopituitarism cover alterations in pituitary hormone levels which may occur even after five years of injury and characterised by hormonal deficiencies but rarely recovery of some hormones during the course of the disease. It has been shown that between 5 % and 70 % of the TBI patients suffer from hypopituitarism. This large variation in the prevalence may be explained by diverse diagnostic criteria used in different studies, different time points of interventions after TBI, severity of trauma etc. Patients with advanced age, low Glasgow Coma Scale, needing intensive care unit stay, presence of skull fractures, brain edema are particularly make patients vulnerable to TBI-induced hypopituitarism
Molecular Identification of Extended-Spectrum Beta-Lactamase and Aminoglycoside-Modifying Enzyme Genes Among Lactose-Fermenting Enterobacteriaceae Clinical Isolates
No full textThe routine phenotypic methods originally identified the NA22isolates as Klebsiella pneumoniae. Nevertheless, WGS and NCBI annotationhave subsequently confirmed that the microorganism is an Escherichia coli.The isolate had been recovered from the bloodstream of a patient and wasresistant to many of the most important classes of antibiotics. A total ofeight contigs >1,000 bp were generated through sequencing on the OxfordNanopore platform. Three plasmids, NA22_1, NA22_2, and NA22_3, were identifiedusing PlasmidFinder. Among them, NA22_3 harbored significant resistance genes,including blaNDM-4, blaCTX-M-15, and aac(6’)-Ib-cr, along withthe tra operon genes that may facilitate plasmid transfer. Functionalannotation was performed against the PATRIC, CARD, and VFDB databases, and 76resistance and over 200 virulence, as well as over 100 metabolism and transportinvolved subsystems, were found. The antimicrobial susceptibility testsrevealed resistance to β-lactams (including carbapenems), fluoroquinolones,aminoglycosides, and sulfonamides, which were consistent with the genomeprofile. The complete genome sequence is included in BioProject ID:PRJNA1270365. These results highlight the importance of routine genomicsurveillance of multidrug-resistant strains within hospitals.</p
Microfluidics-Based Nanoparticle Formulations: Preparation and Evaluation of Protein Delivery Systems
No full textPolymeric nanoparticles have attracted significant attention due to their potential in drug delivery, material science, and chemistry. In general, the targeted activities of nanoparticles (NPs) are affected by their size and morphology. Microfluidic methods offer precise control over nanoparticle properties, providing better reproducibility and uniformity. This study investigates the effects of microfluidic method parameters on the physicochemical properties and protein delivery potential of synthesized poly(lactic-co-glycolic acid) (PLGA) nanoparticles. The size of the nanoparticles was precisely tuned by varying the flow rate ratios (FRR), total flow rate (TFR), polymer, protein, and surfactant concentrations. Proteins with various molecular weights, including bovine serum albumin (BSA), lysozyme, and aprotinin, were effectively encapsulated, and their drug release kinetics and structural integrity were investigated. By simultaneously evaluating three structurally distinct model proteins within a single microfluidic system, this study provides a comprehensive insight into the role of protein size and charge on nanoparticle formation and release behavior for the first time. This research contributes to the advancement of nanoparticle formulation strategies using microfluidic technology. Microfluidic systems hold great potential for rapid, easy, effective, low-cost, and high-yield NP production
Exploring the Roles of Pro-Inflammatory TNF-α and Anti-Inflammatory IL-35 Cytokines in the Pathogenesis of Rheumatoid Arthritis among Patients in Baghdad, Iraq
No full textBackground: Rheumatoid arthritis (RA) is a chronic, immune-mediated inflammatory disease characterized by synovitis, particularly affecting the small joints of the hands and feet. Tumor necrosis factor-alpha (TNF-α), a pro-inflammatory cytokine, plays a critical role in the etiology and pathogenesis of RA. Objectives: To evaluate the levels of TNF-α and IL-35, along with various clinical indicators, to assess their potential in predicting disease outcomes in Iraqi patients with RA. Methodology: A total of 125 participants were enrolled in this study, including 100 patients diagnosed with RA and 25 healthy controls. The study was conducted at the Rheumatology Consultation Clinic, Baghdad Teaching Hospital, Medical City, between September 2024 and March 2025. Participants ranged in age from 15 to 70 years. Serum levels of the pro-inflammatory cytokine TNF-α and the anti-inflammatory cytokine IL-35 were measured using the ELISA technique. Results: The study found no statistically significant differences in TNF-α concentration among RA patients based on disease duration or family history of RA. However, TNF-α levels were significantly higher in RA patients compared to healthy controls (p=0.0055 and p<0.0001, respectively). Furthermore, female patients exhibited significantly higher TNF-α levels than males (p = 0.0256), whereas the difference in males was not significant (p=0.6200). TNF-α concentration were also significantly higher in older patients compared to younger individuals. Regarding IL-35, its concentration was significantly decreased in both male and female RA patients compared to healthy individuals (p < 0.0001). Conclusion: The findings suggest a clear distinction between the roles of pro-and anti-inflammatory cytokines in RA. The decreased levels of IL-35 in RA patients indicate its potential involvement in joint inflammation and damage. IL-35 may contribute to disease progression by modulating immune responses affecting bone and joint health
MÜHENDİSLİK TASARIM SÜRECİ İÇEREN SÜRDÜRÜLEBİLİR KALKINMA MODÜLÜNÜN GELİŞTİRİLMESİ VE ETKİLERİNİN İNCELENMESİ
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Analyzing Public Environmental Awareness Using Advanced Machine Learning for Sustainable Urban Transportation
No full textEnvironmental awareness and sustainable transportation are critical in addressing climate change and urbanization. This research enhances understanding of environmental awareness through advanced machine learning (ML), focusing on cycling as a key component of sustainable urban mobility. Based on survey data from 550 participants in Kayseri, Türkiye, the study examines demographic, behavioral, and attitudinal factors influencing environmental awareness. Bioinspired feature selection algorithms, including genetic algorithm and particle swarm optimization, identified key predictors. Generative Adversarial Networks (GANs) generated synthetic data for underrepresented groups, improving dataset balance and reliability. Seven classification models were evaluated using 10-fold cross-validation. Ensemble methods, particularly CatBoost and LightGBM, achieved over 0.82 accuracy with balanced precision, recall, and F1-score. Behavioral factors, such as reasons for choosing a bicycle and environmental expectations, were the most significant determinants. These findings can inform targeted cycling infrastructure planning, inclusive environmental campaigns, and the development of predictive tools to identify vulnerable or responsive user groups
A Decade-Long Study Focused on the Clinical and Microbiological Assessment of Patients Infected by Achromobacter Species
No full textBackground: This study aimed to assess the characteristics of patients with Achromobacter spp. infections over a ten-yearperiod at a tertiary care hospital.Methods: A retrospective evaluation was conducted on the patients' demographic information, co-morbid conditions,laboratory results, antimicrobial susceptibilities, and treatments.Results: The study included 154 patients along with their clinical isolates. The majority of Achromobacter species isolates werefrom surgical clinics (43%). A history of immunosuppressive disease or treatment was present in 40% of the individuals.Polymicrobial infections were identified in 50 patients, and a total of 18 patients (12%) died within 28 days. Trauma was lessfrequent (8% vs. 16%; P = 0.035), and the rate of chronic disease was lower (58% vs. 75%; P = 0.032) among patients withpolymicrobial infections. These patients had a higher occurrence of abscess samples (68% vs. 36%; P < 0.001) and a lower 28-daymortality rate (4% vs. 18%; P = 0.037). Achromobacter spp. was isolated from blood cultures in 45 patients, with higher rates ofhypertension (22% vs. 8.3%; P = 0.017) and coronary artery disease (45% vs. 7.3%; P = 0.003). Meropenem usage was more commonin patients with bacteremia (29% vs. 11%; P = 0.006). The mortality rate was higher in the bacteremic patient group than in thenon-bacteremic group (20% vs. 10.5%; P = 0.139).Conclusions:Achromobacter spp. are frequently isolated from immunocompromised patients, but they can also be part ofpolymicrobial infections, especially in wound or abscess samples from surgical clinics. This is the first study linkingpolymicrobial Achromobacter spp. abscesses to reduced mortality. It has been observed that the mortality rate is higher inbacteremic patients even when broad-spectrum antibiotics are used.Keywords: Achromobacter spp., Polymicrobial, Bacteremia, Resistance</p