Pamukkale University

Aim: Despite the survival benefit achieved with immune checkpoint inhibitors in advanced non–small-cell lung cancer (NSCLC), outcomes remain heterogeneous. While PD-L1 expression is an established biomarker, less is known about the prognostic relevance of on-treatment systemic inflammatory dynamics and the utility of endothelial stress–based indices such as the Endothelial Activation and Stress Index (EASIX) in patients receiving immunotherapy.Material and Methods: We retrospectively evaluated patients with advanced NSCLC treated with nivolumab. PD-L1 expression, metastatic burden, baseline inflammatory parameters, and baseline EASIX were recorded. On-treatment inflammatory markers, including derived neutrophil-to-lymphocyte ratio (dNLR) at 3 months and longitudinal changes in NLR (ΔNLR), were analyzed. Overall survival (OS) and progression-free survival (PFS) were assessed using Kaplan–Meier and Cox regression models. To account for time-dependent bias, 3-month landmark analyses were performed.Results: Eighty-one patients were included. High PD-L1 expression (≥50%) was associated with significantly improved OS but did not retain independent prognostic significance for PFS. A high metastatic burden, particularly involvement of four or more metastatic organs, was associated with inferior OS. In landmark-adjusted analyses, dNLR at 3 months was independently associated with both OS and PFS, while ΔNLR was associated with OS, indicating that favorable on-treatment immune–inflammatory changes correlated with improved survival. Baseline EASIX was not associated with either OS or PFS.Conclusion: In nivolumab-treated advanced NSCLC, survival outcomes are primarily driven by tumor biology, disease burden, and immune–inflammatory dynamics evolving during therapy. On-treatment inflammatory markers, particularly dNLR and ΔNLR, provide more clinically relevant prognostic information than baseline indices, whereas baseline EASIX appears to have limited utility in this setting. These findings support a treatment-specific, time-dependent approach to biomarker-based risk stratification in immunotherapy.

The cotton bollworm [Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae)] is one of the most important pests of cotton and causes significant crop losses in cotton, especially to generative organs, by doing damage. This study was carried out in three neighborhoods of the Söke district in second-crop cotton fields where delta-type sexual attractant traps were set in order to determine the adult population fluctuation and infestation rate of the cotton bollworm in 2023 and 2024. For this purpose, three second-crop cotton fields were determined in the Akçakonak, Doğanbey, and Güllübahçe neighborhoods, and the adult individuals in the traps were counted regularly. The infestation rate was calculated as a percentage using the numerical data obtained. The number of adult individuals in the traps set to determine the population fluctuation of cotton bollworms reached the highest level in September 2023 in the Akçakonak neighborhood and in July in the Doğanbey and Güllübahçe neighborhoods. In 2024, the number of adult individuals reached the highest level in September in the Akçakonak, Doğanbey, and Güllübahçe neighborhoods. The infestation rates were recorded as 57, 61, and 45% in the Akçakonak, Doğanbey, and Güllübahçe neighborhoods in 2023, respectively, and 12, 16, and 16% in 2024, respectively. In conclusion, in this study conducted to determine the population fluctuation and infestation rate of the cotton bollworm, it was concluded that monitoring the adult individuals with delta-type sexual attractant traps is important in determining the time of control and in deciding on the control.

This study examines the relationships between friendship quality, loneliness, and subjective well-being among Roma primary school children in Türkiye, who face socio-cultural disadvantages and structural barriers in educational processes. Additionally, the study examines whether loneliness serves as a mediator in the relationship between friendship quality and subjective well-being. A total of 279 Roma children participated, and data were collected using the Friendship Quality Questionnaire (FQQ), the Children’s Loneliness Scale (CLS), and the Subjective Well-Being Scale (ESSWBS). The data were analyzed using a general linear mediation (GLM) analysis. The study’s findings revealed a positive correlation between subjective well-being and friendship, and a negative correlation between friendship and loneliness. Additionally, a negative relationship between subjective well-being and loneliness was found, and loneliness mediated the relationship between subjective well-being and the quality of friendships. These results highlight the role of friendship as a protective factor for children with novels.

Fen bilimleri, mühendislik bilimleri ve sosyal bilimler gibi birçok farklı bilim dalında çizge teori çalışılmaktadır. Özellikle matematik, kimya, bilgi ve ağ analizi alanlarda çizge teorinin uygulamaları görülmektedir. Son zamanlarda matematiksel kimya alanında bir molekülün kimyasal analizi çizge teorik parametreleri ile hesaplanabilmektedir. Bu parametreler topolojiksel çizge indeksleri olarak tanımlanır. Bu indekslerin farklı özelliklere göre tanımları vardır, örneğin dereceye bağlı topolojiksel indeksler ve dışmerkezliğe dayalı topolojiksel indeksler literatürde çalışılmaktadır. Son zamanlarda ters topolojiksel indeksler tanımlanmıştır. Bu çalışmada, elmas çizgelerin bazı önemli ters derece tabanlı topolojiksel indeks değerleri incelenmiş ve bunlar için tam sonuçlar elde edilmiştir.

Yapay zekâ küresel boyutta ülkelerin yenilikçilik kapasitelerini, sürdürülebilir kalkınma hedeflerine ulaşma yetkinliklerini destekleyen stratejik bir unsurdur. Yapay zekânın gelişmesiyle birlikte küresel politikaların izlenmesi daha önemli hale gelmektedir. Bu doğrultuda, ülkeler yapay zekâ ile ilişkili fırsatları ve tehditleri farkında olarak mevcut politikalarını değiştirmeli, yapay zekâ kapasitelerini izlemeli ve performanslarını iyileştirici önlemler almalıdır. Yapay zekâ kapasite performanslarının takip edilebilmesi için subjektif değerlendirmelere dayanan endeksler bulunmaktadır. Ancak sıralamaların güvenilirliği ve nesnelliğini sağlayabilmek adına objektif ağırlıklandırma yöntemleri kullanılarak değerlendirmenin yapılması gerekmektedir. Bu motivasyonla çalışmada üst-orta gelir grubunda yer alan ve ulusal yapay zekâ stratejisine sahip ülkelerin yapay zekâ kapasitelerini nesnel ve karşılaştırmalı bir biçimde değerlendirilmesi amaçlanmıştır. Bu doğrultuda, Global AI Index verileri kullanılarak yapay zekâ kapasitesini belirleyen faktörlerin önem dereceleri D-CRITIC yöntemiyle ortaya konmuş, ülkelerin performansları da SPOTIS yaklaşımıyla sıralanmıştır. Ayrıca farklı ağırlıklandırma senaryoları altında yapılan duyarlılık analizleriyle sıralamaların duyarlılığı test edilmiş, elde edilen sonuçların sağlamlığı incelenmiştir. D-CRITIC ve SPOTIS ile elde edilen sıralamaların diğer yöntemlerle tutarlılığı da karşılaştırmalı analiz ile ölçülmüştür. Sonuçlar, yapay zekâ kapasitesini belirlemede en kritik boyutun işletim ortamı olduğunu göstermiştir. Ülkeler arası performanslarda ise Çin, Türkiye ve Arjantin’in üst-orta gelir grubunda en yüksek yapay zekâ kapasitesine sahip ülkeler olduğu ortaya koyulmuştur. Duyarlılık analizi sonucunda ise özellikle üst sıralarda yer alan ülkelerin yapay zekâ kapasite sıralamalarının genel olarak istikrarlı olduğu görülmüştür. Buna karşılık orta ve alt sıralarda yer alan bazı ülkelerin performanslarının kriter ağırlıklarına duyarlı olduğu tespit edilmiştir. Karşılaştırmalı analiz sonucunda çalışmanın sonuçlarının tutarlı ve diğer yöntemlerle uyumlu olduğu, sıralamaların yöntem seçimine duyarlı olmadığı gösterilmiştir. Elde edilen bulgular, ülkelerin performanslarının sağlamlığını teyit etmekle birlikte, belirli ülkelerin stratejik olarak daha kırılgan bir yapıya sahip olduklarını da ortaya koymuştur.

In this study, the effects of fuel blend ratio and engine speed on the performance and emissions of a spark-ignition (SI) engine fueled with gasoline-isopropanol blends were experimentally investigated, modeled, and optimized. Despite the potential benefits of gasoline-alcohol blends for SI engines, many response-surface-based studies adopt simplified surrogate models and fixed second-order formulations, which may not adequately capture coupled and non-linear effects, particularly when experimental data are limited. To address this gap, a data-driven multi-model strategy is adopted to systematically evaluate seven multivariate polynomial regression structures for each response, instead of imposing a single fixed-form model. Model performance is assessed using a hold-out validation scheme, and the model with the highest predictive accuracy is selected for each response as the final predictive model, enabling accurate prediction of torque, fuel consumption (FC), and carbon monoxide (CO), hydrocarbons (HC), and carbon dioxide ([Formula: see text]) emissions. As an additional contribution, a multi-objective scalarized function constructed from normalized response values is minimized using a PID-based search algorithm (PSA) to simultaneously maximize torque, power, and brake thermal efficiency (BTE) while minimizing FC, CO, HC, [Formula: see text], and brake-specific fuel consumption (BSFC). The results indicate that increasing the isopropanol ratio has pronounced and non-linear effects on engine behavior: torque and power increase up to an intermediate isopropanol fraction and then decrease at higher ratios, BSFC rises with increasing isopropanol content, and HC, CO, and [Formula: see text] emissions decrease as the isopropanol share increases. The optimization identifies an optimal operating condition at a 50% isopropanol-50% gasoline blend and an engine speed of 2783 rpm. Overall, the study delivers a compact modeling-optimization framework for gasoline-isopropanol operation in SI engines and supports the design of more efficient and environmentally friendly fuel strategies based on alternative fuel usage.