International Journal of Advanced Natural Sciences and Engineering Researches

Enhancing Customer Churn Prediction in the Finance Sector through Explainable AI and Machine Learning

Fidan Khalilbayli — 2025-10-01

Customer churn prediction has emerged as a critical research domain in various industries,
including telecommunications, retail, and finance, due to its significant impact on business profitability,
customer satisfaction, and long-term sustainability. Churn refers to the rate at which customers terminate
their relationship with a company, necessitating the development of accurate predictive models to
facilitate effective retention strategies. In this context, machine learning models have demonstrated
substantial potential by processing large datasets to identify patterns indicative of customer attrition.
However, despite their predictive accuracy, the widespread adoption of these models is often limited by
their lack of transparency, commonly referred to as the “black box” problem. This challenge is
particularly observed in sectors such as finance and risk management, where customer trust is of
paramount importance. Explainable AI (XAI) addresses this limitation by enhancing the interpretability
of machine learning models, enabling stakeholders to comprehend the rationale behind predictions while
maintaining model performance. This study investigates the integration of XAI methodologies into
customer churn prediction models within the financial sector, with a focus on Eminevim, addressing the
challenges posed by complex data and the necessity for actionable insights. The performance and
interpretability of various machine learning algorithms, such as Random Forest, XGBoost, Light-GBM,
and CatBoost are assessed utilizing explainability techniques such as SHAP (Shapley Additive
Explanations). The findings demonstrate that XAI-augmented churn prediction models not only preserve
high predictive accuracy but also enhance transparency, empowering financial institutions to make
informed, data-driven decisions that mitigate customer attrition and promote long-term business
sustainability.

The Impact of Relationship Marketing in Financial Institutions in Emerging Markets: Evidence from Albania

Dr. Ledia Sula — 2025-10-01

In recent years, managers of financial institutions have increasingly acknowledged the strategic
importance of retaining existing customers as a critical determinant of competitiveness and long-term
sustainability. Marketing literature consistently highlights the advantages of cultivating a loyal customer base,
including reduced churn, enhanced profitability, and stronger advocacy. At the core of customer loyalty lies
the quality and continuity of the relationship between the client and the institution. The deeper and more
sustained this relationship, the greater the mutual value generated for both parties.
This study examines the role of relationship marketing in the context of financial institutions in Albania, an
emerging market where competition and customer expectations are intensifying. Specifically, it investigates
how Customer Relationship Management (CRM) practices influence customer satisfaction and behavioral
loyalty. A quantitative research design was employed, with data collected through structured questionnaires
administered to clients and employees of selected financial institutions across Albania. A purposive sampling
method ensured the inclusion of respondents with direct experience in banking and other financial services. A
total of over 400 valid responses (300 customers and 100 employees) were analyzed using SPSS version 25.
The Friedman test was applied to assess differences in customer awareness across CRM practices, while a
Neural Network Model was used to predict satisfaction based on demographic and behavioral variables.
Findings revealed that CRM attributes are not perceived equally, with “CRM enhances customer loyalty”
emerging as the most influential factor. The results underscore that effective CRM implementation in Albanian
financial institutions relies not only on technological infrastructure but also on human interaction, service
accessibility, and personalized engagement strategies. Hence, financial institutions in Albania are advised to
prioritize customer-centric CRM strategies, continuous staff training, and targeted communication to
strengthen trust, satisfaction, and long-term loyalty.

Ön Şarjlı Gerilim Kaynaklı Dönüştürücü

Yasin BEKTAŞ — 2025-10-01

Güç elektroniği sistemleri, enerji dönüşümü ve kontrolü açısından büyük bir öneme sahiptir. Bu
sistemlerin verimliliği ve güvenliği, kullanılan dönüştürücüler ve kontrol tekniklerine doğrudan bağlıdır.
Gerilim kaynaklı dönüştürücüler, doğru akım gücünü alternatif akım gücüne dönüştürmek için yaygın
olarak kullanılan bir güç dönüştürücüsüdür. Özellikle, ön şarjlı gerilim kaynaklı dönüştürücüler, şebeke
bağlantılı sistemlerde veya batarya tabanlı güç sistemlerinde tercih edilir. Bu sistem, devreye giriş sırasında
oluşabilecek yüksek akım darbelerini engelleyerek ekipmanları korumak amacıyla tasarlanmıştır. Bu
çalışmada, ön şarjlı ve kapalı devre kontrollü 3 fazlı gerilim kaynaklı dönüştürücü devre yapısı Plexim
ortamında modellenmiş ve incelenmiştir.

Exploring the Educational Usability of Popular 3D Scanning Applications

Mgr. Gergely Kocsis — 2025-10-01

This study explores the educational usability of four popular 3D scanning applications - LumaAI,
Polycam, KIRI Engine, and 3D Scanner App, by comparing their performance, user experience, and
potential integration into teaching and learning contexts. Three of the applications (LumaAI, Polycam, and
KIRI Engine) were tested on an Android device, while the 3D Scanner App was examined on an Apple
iPad Pro to leverage its built-in LiDAR sensor. To assess their ability to handle challenging conditions, the
target object was placed under a transparent glass dome, allowing us to evaluate how each application
processes light refraction and reflections during the scanning process.
The analysis focused on two main aspects: (1) the quality and accuracy of the generated 3D models, and
(2) the overall user experience, including accessibility, workflow simplicity, and platform compatibility.
These criteria were then evaluated in relation to their potential application in educational settings,
particularly for subjects where interactive visualization, digital modeling, or augmented learning
experiences can enhance engagement and understanding.
Findings indicate differences among the applications in terms of usability, rendering fidelity, and suitability
for classroom use. While some applications demonstrated robustness and ease of use, others struggled with
reflective surfaces or required more advanced features. The results highlight both opportunities and
limitations of current mobile 3D scanning tools, providing insights for educators seeking to integrate such
technologies into pedagogical practice.

Hastane Yangınlarının Etkileri: Sağlık, Çevre ve Toplum Üzerine Bir İnceleme

Mehmet Erensoy — 2025-10-01

Yangınlar, çevre ve sağlık sistemleri üzerinde önemli etkiler yaratabilen doğal ve insan kaynaklı
olaylardır. Özellikle hastane yangınları, sağlık hizmetlerinin sürekliliğini tehdit eden, altyapı ve hasta
güvenliğini doğrudan etkileyen olaylardır. Yangın, her yerde görülebilen, ısı kaynağı, yanıcı madde ve
yeterli seviyedeki oksijenin bir araya gelmesiyle oluşan bir felaket türüdür. Yangınlar orman yangınları gibi
doğada görülebildiği gibi binalarda da görülebilmektedir. Hastaneler insanlara sağlık hizmeti veren
binalardır. Ancak bu binalarda da yangınlara rastlanabilmektedir. Özellikle yoğun bakım ünitelerinin ve
yatan hastaların olduğu hastanelerdeki yangınlar yardıma muhtaç insanların hayatı söz konusu olduğundan
daha kritik bir hal almaktadır. Türkiye’de hastane yangınları konusunda geniş kapsamlı ve güvenilir bir
kaynak bulunmamaktadır. Ülke genelinde tutulan kayıtların toplandığı ulusal bir veri merkezi de maalesef
yoktur. Yangınlar ile ilgili veriler lokal ve dağınık olarak çeşitli resmi kurumlarda tutulmaktadır. Bu
çalışmayla ülkemizde 2013-2022 tarihleri arasında sosyal medyaya yansımış hastane yangınları kayıt altına
alınmıştır. Kayıt altına alınan hastane yangınları MS Excel ve Spss 22.0 ile frekans dağılımı
değerlendirilmiştir. Toplam 271 hastane yangını tespit edilmiştir. En önemli yangın nedeni olarak elektrik
tesisatı ve en çok yangın çıkan bölüm ise hasta servisi/poliklinikler olarak tespit edilmiştir. Bu çalışma,
çevre ve hastane yangınlarının sağlık, çevre ve toplum üzerindeki etkilerini incelemeyi amaçlamaktadır.

Sustainability as a Driver of Digital Transformation: The Role of Emerging Technologies

Ayşenur ERDİL — 2025-10-01

Digital transformation has emerged as a defining force in reshaping economic, social, and
environmental domains, with sustainability increasingly influencing the trajectory of this transformation.
The relationship between digitalization and sustainability is mutually reinforcing: sustainability sets the
direction for the development, adoption, and diffusion of digital technologies, while digital tools and
processes provide essential mechanisms for realizing sustainable outcomes. Emerging technologies such as
artificial intelligence (AI), big data analytics, the Internet of Things (IoT), and blockchain are playing a
pivotal role in enabling greener business models, enhancing transparency, optimizing resource efficiency,
and reducing environmental footprints. At the same time, organizations face challenges related to high
implementation costs, data security concerns, regulatory complexity, and digital skill gaps. This research
examines the multidimensional impact of sustainability on digital transformation through both SWOT and
PESTLE analyses. The findings reveal that sustainability not only drives innovation and efficiency but also
creates new opportunities for circular economy models, global collaboration, and investment in green
technologies. However, threats such as digital divides, technological lock-ins, regulatory challenges and
the rising energy demand of digital infrastructures pose significant risks. By combining theoretical insights
with strategic analysis, this study underscores the importance of organizational capabilities, governance,
and innovation in leveraging digital transformation to achieve long-term sustainability goals. The
discussion contributes to the growing body of literature by offering a comprehensive framework to
understand how sustainability can shape digital transformation in practice and by highlighting the need for
holistic approaches to address both opportunities and challenges, including political, economic, social,
technological, legal, and environmental factors.

Derin Öğrenme Tabanlı Melanoma Tespiti: Karşılaştırmalı Model Analizi

Halil İbrahim KOCAGÖZ — 2025-10-01

Cilt kanseri, cilt hücrelerinin kontrolsüz bir biçimde büyümesi ile meydana gelen bir kanser
biçimidir. Melanom, bu kanser türünün en nadir görülen fakat en çok ölüme sebep olan türüdür. Bu
çalışmada, melanoma tespiti amacıyla dermoskopi görüntülerini kullanarak bir sınıflandırma modeli
geliştirilmiştir. Benign (iyi huylu) ve malign (kötü huylu) olarak etiketlenmiş 10.000 deri üzeri iz
fotoğrafından oluşan bir veri seti kullanılarak, deri üzerinde bulunan izlerin kötü huylu melanoma cilt
kanseri mi yoksa iyi huylu mu olduğunu bulma amacıyla bir sınıflandırma modeli geliştirilmiştir.
Bu çalışmada, üç farklı derin öğrenme mimarisi (AlexNet, EfficientNet ve ResNet50) kullanılarak
melanoma tespiti için karşılaştırmalı bir analiz gerçekleştirilmiştir. Modellerin genelleme yeteneğinin
artması, doğruluk oranının yükselmesi ve aşırı öğrenmeyi engellemek için çeşitli veri artırma yöntemleri
ve görüntü zenginleştirme yöntemleri kullanılmıştır. Bu yöntemler modellerin farklı görüntülerle bile daha
etkili performans göstermesini sağlamıştır. Bu çalışmada elde edilen Out-of-Fold (OOF) ROC-AUC
skorları 0.952(AlexNet), 0.973 (EfficientNet) ve 0.963 (ResNet50) olarak hesaplanmıştır. Bu değerler,
modellerin genel performansını ve genelleme yeteneğini doğrulamaktadır. Modellerin doğrulama veri seti
üzerindeki doğruluk oranı maksimum %97.3 olarak ölçülmüş ve bu da modellerin yüksek bir doğruluk
oranına ulaştığını göstermektedir.
Sonuç olarak, bu araştırma, yapay zekâ ile kanser teşhisi gibi kritik alanlardaki başarısını göstermektedir.
Çalışmanın temel başarı unsurlarından biri olarak; transfer öğrenme yöntemi ile büyük veri setlerinde
eğitilmiş modellerin daha küçük ölçekli medikal veri kümelerine başarıyla uyum sağlayabilmesi olarak
belirtilebilir. Bu güncel literatür karşılaştırması, çalışmamızın mevcut en iyi uygulamalarla rekabet edebilir
ve bazı durumlarda daha iyi sonuçlar elde edebildiğini göstermektedir. Özellikle EfficientNet modelimizin
performansı, literatürdeki benzer çalışmaların literatürdeki bazı çalışmaların önüne geçmiştir.

Machine Learning in Biosciences: A Review of Applications

Ahmet TOPRAK — 2025-10-01

Studies have shown that only about 2% of the genome encodes proteins, while the remaining 98% consists of non-coding RNAs (ncRNAs). Based on length, ncRNAs are classified as small (<200 nt) or long (>200 nt) and play key roles in biological processes. Experimentally verified associations between ncRNAs (miRNAs, lncRNAs, circRNAs) and diseases remain limited, since laboratory studies are costly and time-consuming. Thus, computational approaches have become essential for predicting disease related ncRNAs. Similarly, drug-target interactions are vital for drug discovery, as drugs act by binding to and inhibiting target molecules. Yet, experimental identification of these interactions is expensive, driving the development of computational prediction methods. Microbes also influence human health, with microbiomes playing essential physiological roles. Identifying disease-related microbes is crucial, but experimental approaches are limited by cost and time. Hence, computational methods are widely employed. Overall, computational strategies can be grouped into score functions, network-based algorithms, multi source biological integration, and machine learning. This review highlights machine learning approaches for predicting ncRNA-disease associations, drug-target interactions, and disease-related microbes. It also summarizes key databases and successful methodologies, serving as a guide for future research in this field.

Statistical and experimental analysis of machining parameters and tool radius in turning of low carbon steels

Necmettin Aydın — 2025-10-01

In this study, the effects of basic machining parameters and insert radius on machinability
during turning of low-carbon steels were investigated using experimental and statistical methods. The
experiments were conducted on AISI 1015 case-hardened steel, and speed, depth, feed, and insert radius
were considered as input parameters. Output parameters were determined as roughness and cutting force.
The full factorial method was used for the experimental design, and the obtained data were evaluated
using ANOVA. The results show that feed and insert radius are particularly decisive on roughness. At
low feed and high cutting speed, a 0.8 mm insert radius provides optimum surface quality. Cutting force
increases in direct proportion to depth of cut and feed, while decreasing with increasing speed. It was also
observed that tools with a 0.4 mm insert radius exhibit lower force values. According to the ANOVA
results, feed was the parameter that most affected surface roughness at a 0.4 mm tip radius, while depth
was the most critical parameter determining force at a 0.8 mm tip radius. Consequently, this study
provides guidance in selecting optimal cutting parameters to achieve high efficiency and desired
tolerances in manufacturing processes.

Türkiye’nin Hava Yolu İhracatında Sosyal ve Çevresel Sürdürülebilirlik için Yapay Zekâ Destekli Çok Amaçlı Optimizasyon Yaklaşımı

Nesrin Kolukırık — 2025-10-01

Bu çalışma, Türkiye’nin hava yolu ihracatında sürdürülebilirliği hem sosyal hem de çevresel
boyutlarıyla değerlendiren çok amaçlı bir optimizasyon yaklaşımı sunmaktadır. Doğrusal programlama
modeli, istihdamı artırmayı ve CO₂ emisyonlarını sınırlandırmayı amaçlamaktadır. 2000–2023 yıllarına ait
TÜİK ihracat verileri, ülkeler arası hava mesafeleri ve dört farklı uçak tipine /taşımacılık türüne (Belly
hold taşımacılığı, Boeing 747F, Boeing 777F ve Airbus A330F) ilişkin katsayılar değerlendirilmiştir.
Modelin çözümünde PuLP kütüphanesi kullanılmıştır. TÜİK'ten elde edilen 2000-2023 yıllarına ait veriler
yapay sinir ağına aktarılmış ve gelecek yılların (2024-2025) tahmini için modelden yararlanılmıştır.
Bulgular, istihdamın artış eğiliminde olduğunu göstermektedir. Emisyonların ise dönemsel dalgalanmalar
sergilediğini göstermektedir. Tahmin sonuçlarına göre, 2024 ve 2025 yılları için istihdamda yaklaşık %4,1
oranında bir artış olduğu ve CO₂ emisyonlarında ise %5,0 düzeyinde bir yükseliş yaşanacağı
öngörülmektedir. Bu çalışma, havayolu taşımacılığında toplumsal faydalar ile çevresel etkilerin ele
alınmasına imkân tanımaktadır ve karar vericiler için de oldukça önemli uygulanabilir stratejik öneriler
sunmaktadır. Ek olarak, bu araştırma hava yolu taşımacılığında sürdürülebilir kalkınma hedeflerine
ulaşmak için yol gösterici bir çerçeve ortaya koymaktadır. Çalışma, çevresel maliyetlerin azaltılmasıyla
birlikte, toplumsal faydanın artmasına katkı sağlayacak politikaların tasarlanmasına destek olması
beklenmektedir. Özellikle çevresel verimlilik ile sosyal kazanımların dengelenmesi gerektiğine vurgu
yapılmakta ve mevcut uygulamaların yanı sıra gelecek kuşakların ihtiyaçlarını gözeten stratejik adımlar
için de sağlam bir temel sunulmaktadır.

Personal data ethics in forensic informatics: Developing innovative solution methods with TRIZ method

Semih Balcı — 2025-10-01

This study addresses the ethical tensions between the protection of personal data and the need
for evidence integrity and rapid work in forensics through the TRIZ approach. First, four basic
contradictions are defined: confidentiality-access, speed-procedurality, scope-proportionality, and
transparency-operational security. Then, feasible solutions are proposed using TRIZ's practical principles.
Selective acquisition and automated masking, focusing only on event-relevant data, an ethical compliance
matrix based on objective–data mapping. Evidence quarantine and recorded access for sensitive content.
Role-based authorizations that are time-bound and can be easily revoked when needed. Records that track
all transactions, maintaining integrity. This approach reduces unnecessary personal data exposure,
maintains the integrity of evidence, makes decisions visible, and facilitates auditing. Qualitative evaluations
show that selective acquisition and masking enable data minimization while accelerating review. It shows
that approval and registration flows strengthen defensibility. Proper tool support, team training, and clear
corporate policies are important for successful implementation. In the future, it is recommended to develop
evaluations with measurable metrics (completion time, mask opening rate, number of access requests), tool
integrations, and training programs across different types of cases. This framework offers a streamlined and
scalable roadmap that makes it easier to achieve operational goals while adhering to ethical principles.

Tekstil ve Hazır Giyim Ürünlerinin Dijital Kimliği: Dijital Ürün Pasaportu Tekstil ve Hazır Giyim Ürünlerinin Dijital Kimliği: Dijital Ürün Pasaportu

Esra Dirgar — 2025-10-05

Avrupa Birliği pazarındaki mevcut tekstil ürünlerinin, 2030 yılına kadar, geri dönüştürülebilir ve
sürdürülebilir olmasını zorunlu kılan yeni Avrupa Komisyonu düzenlemeleri, Tekstil ve hazır giyim
sektöründe faaliyet gösteren firmaları, döngüsel bir iş modeline doğru yönlendirmektedir. Döngüselleşme
için, ürün yaşam döngüsü boyunca veri toplama, depolama ve paylaşımını kolaylaştırmak ve ürün
izlenebilirliğini ve sürdürülebilirliğini sağlamak için Dijital Ürün Pasaportlarının kullanımı gerekmektedir.
Bu çalışmada, dijital ürün pasaportlarının sağlayacağı avantajlar, hazır giyim sektöründe uygulanmasındaki
zorluklar incelenmiştir.

Heat Transfer Analysis of Radiative MHD Casson Fluid over Nonlinear Extended Sheet under Influence of Suction/Injection: non Similarity Numerical Investigation

Saqib Hussain — 2025-10-05

The present study investigates the heat transfer characteristics of radiative
magnetohydrodynamic (MHD) Casson fluid flow over a nonlinear extended sheet subject to suction and
injection effects. A non-similarity approach is employed to capture the more general behavior of the
governing boundary layer equations, which are derived under appropriate physical assumptions. The
mathematical model incorporates the influence of magnetic field, thermal radiation, nonlinear stretching,
and suction/injection on the velocity and thermal boundary layers. By applying suitable similarity and non
similarity transformations, the governing partial differential equations are reduced to a coupled system of
nonlinear equations, which are then solved numerically. The effects of key controlling parameters including
the Casson parameter, magnetic field strength, nonlinear stretching index, radiation parameter, and
suction/injection parameter on the flow and thermal distributions are analyzed in detail. The results
highlight that suction enhances the cooling rate by thinning both velocity and thermal boundary layers,
while injection produces the opposite effect. Moreover, the interaction between radiation and MHD
significantly alters the heat transfer rate near the sheet. The findings provide useful insights into the control
of transport phenomena in industrial processes involving polymer extrusion, coating, and cooling
technologies.

Duvar yönlendirme özelliğine sahip bir yanma odasında yakıt enjeksiyon konum açılarının araştırılması

İlker Temizer — 2025-10-13

Yanma odası içerisinde yakıt ve havanın karışım şekli yanma olayında kimyasal reaksiyonlar için
önemli bir etkendir. Bu etkenlerin iyileştirilmesinde asıl amaç; yüksek yanma performansına sahip yeni
nesil motorları geliştirerek, kirletici emisyonları kontrol etmektir. Bu çalışmada, yakıt yönlendirme
özelliğine sahip bir yanma odasında enjektörün farklı konum açılarına göre dağılım gösteren yakıt
spreylerinin motor parametreleri üzerindeki etkileri sayısal olarak incelenmiştir. Ansys Forte yazılımı
kullanılarak simule edilen tek silindirli bir motorun, farklı devir sayılarında elde edilen sıcaklık/yakıt
püskürtme dağılımları görsel çıktılarla desteklenmiştir. Sonuçlar incelendiğinde, yakıt yönlendirme
özelliğine sahip bir yanma odasında duvar yapısı/püskürtme ekseni arasında 10°’lik bir açı farkının CO,
NOx ve is emisyonları üzerinde önemli avantajları beraberinde getirdiği tespit edilmiştir. Enjektör
nozulundan belirli bir kinetik enerjide oda içerisine püskürtülen yakıt zerrelerinin, duvar bölgesinde
birikmeden farklı lokasyonlara yönelimi, beraberinde daha iyi bir yanmanın oluşmasına neden olmuştur.
Yakıt yönlendirme çıkıntılarına sahip bir oda içerisinde, iki farklı oyuğa gerçekleştirilebilen eşit dağılımla
daha dengeli bir yanma söz konusu olabilir. Yakıtın oda içerisindeki dağılım etkinliğinin artışına bağlı
olarak, is emisyonlarında önemli azalmaların sağlanması, aynı zamanda yerel sıcaklıkların azaltılarak
daha homojen bir sıcaklık artışı ile NOx emisyonlarının kontrol altına alınabileceği belirtilmiştir.

On the Biomorphological, Ethological Characteristics and Biocontrol Potential of Forficula auricularia L., 1758 (Dermaptera: Forficulidae)

A. Kekillioğlu — 2025-10-13

Forficula auricularia L., 1758 (Dermaptera: Forficulidae) is an insect species native to Europe
and Western Asia. The common name "earwig" for this species has been suggested to be derived from the
Anglo-Saxon term "earwicga." Earwigs have distinctive physical characteristics, including a shield
shaped pronotum, chewing mandibular mouthparts, and heavily sclerotized forceps-like cerci. Earwigs
are primarily nocturnal and spend the day hiding in plant debris, cracks, crevices, and dark, moist places,
where they can sometimes congregate in large numbers. Social and maternal behaviors in earwigs are
surprisingly complex and sophisticated. The timing of seasonal mating behavior appears to be strongly
dependent on the geographic location of populations. Earwigs are known to be polyphagous insects that
feed on a wide variety of plant and animal matter, but they are classified as generalist predators. This
raises the question of whether the earwig is a beneficial or a plant pest due to its capacity to prey on
garden and agricultural pests. Earwigs are primarily recorded as plant pests on fruits such as apples,
apricots, and grapes, and vegetables such as beans, cabbage, cucumbers, lettuce, peas, and potatoes. The
overall objective of this study was to investigate the biomorphological and ethological characteristics of
Forficula auricularia L., 1758 (Dermaptera: Forficulidae) particularly its biocontrol potential for pest
management.

A High-Gain Wideband Circular Fractal Slotted Ring Antenna Integrated with PRS for M2M Communication

Sunbal Bibi — 2025-10-13

This paper presents the design and analysis of a planar circular fractal slotted-ring antenna
integrated with a Partially Reflective Surface (PRS), aimed at achieving high gain and wideband
performance for Machine-to-Machine (M2M), WLAN, Wi-Fi, and WiMAX applications. The proposed
antenna features a compact geometry utilizing fractal slotting to enhance current distribution and
bandwidth without increasing the physical size. To further improve gain and impedance matching, a 5×5
array of circular slotted PRS unit cells is strategically positioned above the radiating structure. The entire
antenna system is designed and simulated using CST Studio Suite 2024, a powerful electromagnetic
simulation tool. Simulated results demonstrate a significant enhancement in both S-parameters and
realized gain, with deep S₁₁ notches at 3.5 GHz and 5.5 GHz, and a peak gain reaching 8 dB, compared
to 4.5 dB without PRS. Radiation pattern analysis confirms stable and directional behavior across the
band. The proposed antenna is highly suitable for modern wireless communication systems requiring
compactness, efficiency, and robust performance.

Fırfırlı Kertenkele Optimizasyon Algoritması

Yasin BEKTAŞ — 2025-10-13

Optimizasyon algoritmaları, mühendislik, yapay zekâ, finans ve birçok endüstriyel alanda en iyi
çözümü bulmak için kullanılır. Geleneksel yöntemlerle çözülmesi zor olan karmaşık ve büyük ölçekli
problemler için bu algoritmalar daha verimli çözümler sunar. Yeni optimizasyon algoritmalarına ihtiyaç,
sürekli değişen ve karmaşıklaşan problemlere daha esnek ve etkili çözümler geliştirme gerekliliğinden
doğmaktadır. Bu çalışmada, Fırfırlı Kertenkele Optimizasyon (FKO) adlı doğa esinli bir meta-sezgisel
algoritma sunulmaktadır. FKO, kertenkelelerin otur ve bekle stratejisinden ilham alarak, optimizasyon
problemlerine etkili çözümler sunmayı amaçlar. FKO algoritmasının performansı, F1–F23 benchmark
fonksiyonları üzerinde test edilmiş ve GA (Genetik Algoritma), PSO (Parçacık Sürü Optimizasyonu), WOA
(Balina Sürüsü Optimizasyon Algoritması) ve Gri Kurt Optimizasyonu (GWO) gibi algoritmalarla
karşılaştırılmıştır. Sonuçlar, FKO algoritmasının keşif ve istismar aşamalarında dengeyi başarılı bir şekilde
sağladığını ve çeşitli optimizasyon problemleri için güçlü bir çözüm sunduğunu göstermektedir.

Modeling and Simulation of DC Motor Speed PID Control Using Outseal with LabVIEW Integration

Serhat Küçükdermenci — 2025-10-13

This study presents a simulation-based approach to implementing speed control of a DC motor
using a Proportional-Integral-Derivative (PID) algorithm, developed through Outseal ladder logic and
integrated with LabVIEW for real-time monitoring. The primary objective is to model and simulate a
closed-loop control system that enables precise speed regulation under varying load conditions. The system
architecture combines Outseal Nano hardware logic with Proteus simulation to emulate encoder feedback
and motor driver behavior, while LabVIEW serves as the visualization and data acquisition platform via
Modbus communication. The ladder diagram is programmed to compute PID parameters dynamically, and
the simulation environment allows for real-time adjustment of setpoints and observation of process
variables, control outputs, and error signals. Fourteen holding registers are utilized to exchange key control
data, including Kp, Ki, Kd, SP, PV, and OP values. The results demonstrate stable speed tracking
performance, minimal overshoot, and responsive adaptation to input changes. The integration of Outseal
and LabVIEW within Proteus provides a flexible and scalable framework for educational and industrial
applications. This work contributes to the field of embedded control systems by offering a modular and
replicable simulation model that bridges hardware logic programming with graphical monitoring tools.

Comparing Additive and Multiplicative Attention in CNN–LSTM for Ictal Detection

Mustafa Erginli — 2025-10-13

This study comparatively analyzes the performance of six deep learning-based models in
epileptic seizure detection. In particular, the effects of additive (Bahdanau) and multiplicative (Luong)
attention mechanisms, integrated into the CNN-LSTM backbone, are evaluated in detail. EEG signals
were divided into 256 sample-length windows with a stride value of 256, and the training, validation, and
test sets were separated by 70, 15, and 15 percent, respectively. Z-score standardization was calculated
only with the training statistics and applied to the other subsets. The Bahdanau attention mechanism
added to the CNN-LSTM structure achieved the highest performance, with an area under the curve
(AUPRC) value of 0.9884, receiver operating characteristic (AUROC) value of 0.9876, and F1 score of
0.9436. The CNN-LSTM model, utilizing the Luong attention mechanism, achieved a similarly high level
of success with an AUPRC value of 0.9857 and an AUROC value of 0.9849. The results reveal that
attentional mechanisms enhance the model's ability to focus on critical temporal patterns in EEG signals,
thereby improving both sensitivity and overall accuracy in seizure detection. These findings suggest that
the addition of an attention mechanism to the LSTM and CNN-LSTM architecture offers a practical and
reliable improvement in terms of sensitivity-specificity trade-off, training time, and clinical applicability.

AI ile Reklam Afişi Otomasyonuna Yönelik Güncel Sistemler

Mustafa Merdin — 2025-10-21

Bu çalışma, reklam afişi tasarımında yapay zekânın [YZ] giderek artan rolünü ve bu teknolojinin
getirdiği inovatif otomasyon potansiyelini kapsamlı biçimde incelemektedir. Özellikle son dönemde
geliştirilen AutoPoster ve POSTA gibi YZ tabanlı sistemler, kullanıcı girdilerine dayalı olarak afiş
tasarım sürecinde görsel düzen, tipografi, renk uyumu, metin ve kompozisyon gibi öznitelikleri otomatik
biçimde üretebilme yeteneğine sahiptir. Bu sistemler, tasarım süresini önemli ölçüde kısaltmakta, üretim
maliyetlerini azaltmakta ve aynı zamanda profesyonel düzeyde estetik sonuçlar elde edilmesini mümkün
kılmaktadır. Literatürde “AI-assisted creative automation” olarak adlandırılan bu yaklaşım, insan
yaratıcılığı ile makine öğrenmesinin etkileşimini yeniden tanımlamakta; tasarımcıların yönlendirici, seçici
ve denetleyici roller üstlenmesini teşvik etmektedir. Bununla birlikte, çalışmada şeffaf algoritmaların
kullanımı, kullanıcı kontrolünün korunması, özgünlük ve etik tasarım ilkelerinin önemi de
vurgulanmaktadır. Araştırma, yapay zekâ destekli afiş otomasyonunun yalnızca teknik bir kolaylık değil,
aynı zamanda yeni bir yaratıcı paradigma sunduğunu öne sürmektedir. Sonuç olarak, bu çalışma,
gelecekte YZ’nin reklam ve grafik tasarımı alanlarında yaratıcı süreçleri nasıl dönüştürebileceğine, insanmerkezli tasarım değerlerini koruyarak nasıl sürdürülebilir bir inovasyon zemini oluşturabileceğine dair
öngörüler sunmaktadır.

Mikro-Robotik Sistemde PID Kontrol Parametrelerinin Deniz Ayısı Optimizasyon Algoritması ile Gerçek Zamanlı Ayarlanması

Yasin BEKTAŞ — 2025-10-21

Bu çalışma, bir mikro-robotik sistemin konum kontrolünü sağlamak amacıyla Oransal-İntegralTürev (PID) kontrolcüsü parametrelerinin ayarlanması için yenilikçi bir yaklaşım sunmaktadır. Çalışmanın
merkezinde, en uygun kontrolcü kazançlarını bulmak üzere yeni bir meta-sezgisel algoritma olan Deniz
Ayısı Optimizasyonu (WO) kullanılmaktadır. Optimum PID göstergeleri, sistemin hızlı ve hatasız
yerleşmesini hedefleyen, İntegral Zaman Ağırlıklı Karesel Hata (ISTES) amaç fonksiyonunun minimize
edilmesiyle elde edilmiştir. Önerilen WO tabanlı kontrolcünün etkinliği, aynı ISTES kriteri üzerinden Gri
Kurt Optimizasyonu (GWO) ve Sinüs-Kosinüs Algoritması (SCA) ile yapılan karşılaştırmalarla
doğrulanmıştır. Tüm kontrol teknikleri, sistemin tanımlanan modeline MATLAB/Simulink ortamında
uygulanmıştır. Yapılan simülasyon sonucunda WO, hem yükselme süresi (tr) hem de yerleşme süresi (ts)
açısından en düşük değerleri sağlayarak diğer iki optimizasyon algoritmasına kıyasla üstün performansı
göstermiştir. Bu bulgular, WO algoritması mikro-robotik sistemlerin hassas konum kontrolü gereksinimleri
için en uygun optimizasyon tekniği olduğunu açıkça ortaya koymaktadır.

The Influence of Age and Breed on the Prevalence of Canine Leishmaniasis in Albania

Petrika Qafëzezi — 2025-10-21

Canine Leishmaniasis (CanL), caused by Leishmania infantum and transmitted by
phlebotomine sand flies, remains a significant zoonotic and veterinary health concern in Albania, a
country considered highly endemic within the Mediterranean Basin. This study investigates the influence
of intrinsic host factors such as age and breed on the prevalence of CanL in Albanian dog population.
Overall seroprevalence estimates in Albania have historically varied, with more recent figures indicating
a substantial public health threat, particularly in specific geographical districts. Age was consistently
identified as a risk factor, with infection rates often showing an elevated risk in young dogs aged (0-3
years), with a prevalence value of 11.4 % and dogs aged (6-9 years) with a value of 12.9 %. From the
survey resulted that certain breeds, such as the Mix Breed (17%) followed by Belgian Shepherd (12.5%)
and German Shepherd (9.8%), were frequently represented among most infected populations, suggesting
potential genetic susceptibility, though further controlled studies are warranted. A clear understanding of
these host-specific risk factors is crucial for targeted surveillance, vector control, and mitigating the
associated human health risk.

Classification of Student Stress Levels Using Machine Learning Methods

Kadircan Yeler — 2025-10-21

Nowadays, students feel under pressure and experience stress for various reasons. These reasons
generally manifest as academic pressures, the social environment, and personal anxieties. Intense stress is
a significant factor that negatively impacts students' academic success, psychological health, and overall
quality of life. The inadequacy of traditional subjective assessment methods in determining stress levels
accurately and reliably has increased the need for objective, data-driven solutions. The main purpose of this
study is to automatically classify student stress levels (Low, Medium, High) using machine learning
algorithms and the Student Stress Monitoring Dataset from Kaggle. This dataset contains 1,100
observations, 21 features, and no missing values. In this study, Logistic Regression, Random Forest, and
XGBoost models were applied for classification. The accuracy of these models was measured as 88.1%,
86.2%, and 86.8%, respectively. The results show that machine learning methods can be used effectively
in predicting student stress levels.

Integrating Complex Network Analysis and Machine Learning for Biomarker Discovery in the Human Gut Microbiome

Oltiana Bame — 2025-10-21

The human gut microbiome is a complex ecosystem whose structural and functional
equilibrium is essential for host health. Imbalances in this equilibrium have been linked to numerous
chronic diseases, underscoring the need for sophisticated analytical techniques to elucidate microbiome
composition and predict disease-associated phenotypes. This study proposes an integrated methodology
that combines complicated network spectral analysis with machine learning to discover physiologically
significant patterns from multi-omics microbiome data. Utilizing metagenomic datasets, we calculated
essential measures, including LATENT, EXPLAINED, and MU, which encapsulate the variance structure
and network impact of microbial taxa. Our findings demonstrated a long-tail distribution of LATENT
values, aligning with scale-free network characteristics, suggesting the existence of highly linked taxa that
may function as keystone species or biomarkers. Positive correlations between EXPLAINED and MU
indicate that taxa contributing more to variation also exert a bigger impact within the functional
microbiome network. Statistical distribution analysis, ECDF plots, and comparison boxplots validated a
significant level of variability within the dataset, a characteristic commonly observed in microbial
communities. This integrated framework optimises predictive performance and biological interpretability,
offering a scalable approach for biomarker discovery and the construction of personalised diagnostic
models.

Rubber Waste Management and Waste Management Practices in Rubber Production Facilities

Aziz Tepebaş — 2025-10-21

It is noted that in countries that have completed the industrial revolution, the cost of repairing
environmental damage far outweighs the cost of protecting the environment. Within the framework of
sustainable use of natural resources and effective waste management, recycling and utilising rubber waste
for energy generation is of paramount importance. The significant increase in the number of vehicles
worldwide and the lack of technical and economic mechanisms for processing end-of-life automobile tires
have led to the accumulation of waste tires in landfills, creating a serious environmental problem. It is
estimated that approximately 80% of waste tires globally remain unused, and the amount of waste tires in
Turkey is doubling annually. This situation highlights the need for urgent action plans for waste tire
management. Rubber waste can be used both in businesses by converting it into more durable materials
(vulcanisation) and in energy production in power plants.
In this study, the use of rubber in the automotive sector and recycling alternatives of waste tires were
evaluated and the methods of reusing waste rubber were examined through a sample enterprise.

Attention-Enhanced Nested U-Net with Fuzzy Pooling for Medical Image Analysis: A Review

Noor M. Basheer — 2025-10-21

Magnetic resonance imaging (MRI) is the cornerstone of medical diagnosis; however, accurate
segmentation of MRI images remains a challenging task due to noise, low contrast, and complex anatomical
structures. Traditional machine learning and early deep learning methods have achieved moderate success,
but they often struggle to maintain precise boundaries and handle ambiguous areas. Recent innovations,
such as attention mechanisms and multi-scale architectures like the Nested U-Net, have significantly
improved the accuracy of locating and segmenting features. Despite all this, traditional clustering processes
can still cause information loss, especially at object boundaries. In this review, the evolution of MRI
segmentation will be systematically explored through four developmental stages: (1) classical machine
learning, (2) convolutional neural networks (CNNs), (3) deep learning architectures, and (4) optimized
networks. Using attention and fuzzy logic. We will highlight the strengths and limitations of each stage,
and propose an advanced segmentation framework that combines an attention-enhanced nested U-Net with
fuzzy pooling, a technique that integrates soft decision-making to retain uncertain and boundary
information. Preliminary results show improved dice similarity coefficient (DSC) and sensitivity, as well
as decreased Hausdorff distance (HD), especially in complex MRI data sets of the brain and liver. Our
approach shows superior generalization and accuracy to traditional clustering strategies. Future work will
also focus on cross-media adaptation, real-time deployment in clinical settings, and integration of
automated diagnostics.

Deploying Deep Medical Segmentation Models on Edge Devices: A Case Study of Nested U-Net with Attention and Fuzzy Pooling on Raspberry Pi

Noor M. Basheer — 2025-10-21

The process of medical image segmentation plays a critical role in modern diagnosis, but most
high-performance deep learning models require significant computational resources that limit their use in
real-time or remote clinical settings. This study addresses this gap by proposing a lightweight hybrid
segmentation framework optimized for Raspberry Pi deployment, which can be defined as a cost-effective,
portable, energy-efficient platform suitable for point-of-care medical applications. The model integrates the
improved U-Net backbones with attention and fuzzy pooling mechanisms to improve segmentation
accuracy while maintaining a compact computational footprint. The proposed model was evaluated on the
cardiac magnetic resonance imaging ACDC dataset, achieving a mean Dice score of 98.21% and a
classification accuracy of 98%. Most importantly of all, real-time inference on Raspberry Pi was achieved
with minimal latency and resource consumption, and this demonstrates the model's suitability for edge
deployment in low-resource or remote environments. These results underscore the possibility of embedding
advanced segmentation models into lightweight devices to enable scalable medical image analysis that can
be accessed across diverse clinical scenarios.

Traveling Wave Solutions for Non-linear Biological Model using the Improved (G’/G)-Expansion Method

Attia Rani — 2025-10-21

This article presents a broader variety of accurate and more generic travelling wave solutions
for a nonlinear biological model of fractional order using the improved (G’/G)-expansion method.The
Caputo sense is used to characterize the time fractional derivatives. A variety of latest exact solutions
likes exponential, hyperbolic, trigonometric functions, have been successfully determined with
considerable physical significance. The obtained solutions are practical, trustworthy and appropriate for
understanding chaotic vibrations, scalar electrodynamics, electromagnetic interactions, and optimal
control theory, as well as global and local bifurcations and echoes phenomena. We investigate the
solutions by drawing graphs for the various numeric values of the free parameters in order to observe the
inner substructure of the model. The acquired findings further show that the proposed technique is
concise and simple, and that it could be useful in illustrating the infrastructure of physical phenomena
related with the considered model.

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Computational Analysis of MHD Mixed Convection Jeffrey Nanofluid Flow Past a Stretching Sheet With Thermal Radiation

Saira Kanwal — 2025-10-21

This work considers the effects of thermal radiation, Brownian and thermophoretic diffusion,
and viscous dissipation in the mixed convection magnetohydrodynamic (MHD) flow of a Jeffrey nanofluid
over a nonlinear stretching surface. A set of ordinary differential equations was created from the governing
partial differential equations for momentum, energy, and concentration using the proper similarity
transformations. These nonlinear equations are splved numerically under predetermined boundary
conditions. The results show that increasing the magnetic parameter suppresses the velocity field because
of the increased Lorentz force. Thermal radiation and viscous dissipation enhance the temperature
distribution. The results shed light on a number of industrial procedures, such as viscoelastic nanofluid heat
transfer systems, wire coating, and polymer extrusion.

Enhancing Educational Visualization Through 3D Models

Gergely Kocsis — 2025-10-21

This study explores the pedagogical potential of 3D models in contemporary education and
evaluates their effectiveness in enhancing the learning process. The primary objective was to examine
how interactive three-dimensional representations can support students more efficiently than conventional
two-dimensional illustrations typically found in textbooks. To achieve this, we selected exotic fruits -
papaya, avocado, and mango - as case examples. These items frequently appear as static images in
educational materials, making them ideal for comparison with dynamically rendered 3D models.
The research employed accessible, freely available mobile applications capable of generating 3D models
directly from smartphone cameras. This approach eliminated the need for specialized equipment, thereby
demonstrating the feasibility of integrating 3D modeling activities into regular classroom settings. The
usability, accessibility, and output quality of the selected applications were systematically examined,
focusing on their suitability for educational purposes and their potential to promote student engagement
and experiential learning.
Furthermore, the study investigated the role of artificial intelligence (AI) in refining the quality and
realism of the 3D models. The inclusion of AI-based post-processing highlights emerging opportunities
for automated enhancement, texture reconstruction, and surface detail optimization. The findings
emphasize that low-cost mobile solutions combined with AI-driven improvements can significantly
expand the availability and educational value of 3D content, offering a scalable and motivating tool for
teaching and learning in various disciplines.

Agricultural Waste Management: A Review of Sources, Classification, and Sustainable Strategies

Esra Nermin Ertekin — 2025-10-21

Agricultural waste (AW) represents one of the largest global waste streams, posing major
challenges for environmental sustainability, economic efficiency, and public health. Generated from both
crop and livestock production, AW includes diverse organic and inorganic fractions requiring different
handling approaches. Improper disposal of AW leads to greenhouse gas (GHG) emissions, nutrient runoff,
and resource depletion. However, the transition toward circular economy frameworks has redefined AW as
a resource for bioenergy, biofertilizer, and other value-added products. This review comprehensively
explores the sources, classification, and composition of agricultural waste, as well as traditional disposal
and innovative valorization strategies. Organic wastes from livestock (manure, bedding, feed residues) and
crops (straw, husks, leaves) are analyzed with respect to their nutrient potential, biogas generation, and
composting efficiency. Inorganic wastes such as plastics, packaging materials, and agrochemical containers
are discussed in terms of recycling feasibility and regulatory compliance. Emphasis is placed on advanced
technologies such as anaerobic digestion, thermochemical conversion, and biorefinery systems that enable
nutrient recovery, renewable energy production, and pollution reduction. The paper further outlines policy
recommendations and sustainable management frameworks to promote integrated AW management in line
with circular economy principles.

The Role of Digital Transformation on the Way to Environmental Sustainability in the Turkish Context

Seyfettin Uğuz — 2025-10-21

Digital transformation refers to increased productivity in the production and service sectors
through the automation of industrial processes, the use of data analytics, and various technological
innovations. This process, which forms the basis of industrial transformation, is a critical issue that must
be addressed in terms of both its positive and negative aspects, particularly in relation to the point of its
environmental impacts. Although digitalisation in Turkey has rapidly increased in recent years, studies
assessing its potential impacts on sustainability and the environment are limited. However, a
comprehensive assessment is needed on this issue from many perspectives. This study examines the
current status of digitalisation in Turkey and its environmental impacts on industrial processes. In light of
the obtained data, environmental considerations and proposed solutions are presented when expanding
digital transformation processes. The aim of this study is to analyse the current status of the integration of
digital transformation and environmental sustainability from an environmental engineering perspective in
the Turkish context.

Kurumlarda Dijital Dönüşüm Uygulamaları

Ezgi GÜL — 2025-10-21

Bu çalışma, kurumlarda dijital dönüşüm uygulamalarını teknoloji, süreç ve insan boyutlarıyla
bütüncül bir bakış açısıyla incelemeyi amaçlamaktadır. Kurumlarda dijital dönüşüm, iş süreçlerinin dijital
teknolojilerle bütünleştirilmesi ve organizasyonun tüm kademelerinde veriye dayalı karar alma kültürünün
yerleşmesiyle gerçekleşir. Dolayısıyla dijital dönüşüm, yalnızca teknoloji entegrasyonu değil; aynı
zamanda kurumların iş modellerini, süreç yönetimini ve değer yaratma biçimlerini yeniden tanımlayan
stratejik bir dönüşüm sürecidir. Araştırmada, dijital dönüşüm literatüründe öne çıkan kavramsal modeller,
teorik yaklaşımlar ve stratejik planlama örnekleri analiz edilmiş, farklı sektörlerdeki uygulamalar
karşılaştırmalı biçimde değerlendirilmiştir. Sonuç olarak çalışma, dijital dönüşümün sürdürülebilir biçimde
yürütülebilmesi için insan, süreç ve teknoloji boyutlarının stratejik bütünlük içinde yönetilmesi gerektiğini
ortaya koymakta; kurumların dijitalleşme yolculuklarında yönetişim modelleri, kültürel dönüşüm, insan
kaynağı politikaları ve yenilik stratejilerine ilişkin kapsamlı bir çerçeve sunmaktadır.

Application of Artificial Neural Networks (ANN) in Climate Modeling: Predicting Rainfall and River Discharge

Abbas Gulistani — 2025-10-21

Accurate prediction of rainfall and river discharge is essential for effective water resource
management, flood forecasting, and climate adaptation. This study investigates long-term variations in
temperature, precipitation, river discharge, and land cover from 1970 to 2023 in the Kokcha River Basin,
Afghanistan, using hydrometeorological records and GIS-based analysis. A feedforward Multilayer
Perceptron (MLP) artificial neural network, trained with the backpropagation algorithm, was developed to
forecast rainfall and river discharge based on long-term hydroclimatic data. Model performance was
evaluated using the coefficient of determination (R²), Root Mean Square Error (RMSE), and Mean
Absolute Error (MAE). The ANN model achieved high predictive accuracy (R² = 0.87 for rainfall and
0.91 for discharge), outperforming conventional regression and time-series models. These results
demonstrate the capability of ANN-based models to capture complex nonlinear hydrological relationships
and support robust short-term forecasting and adaptive water management in data-scarce regions.

New Simpson Type Quadrature Formulas for Any Order Differentiable Functions Simpson Type Quadrature Rules

Samet ERDEN — 2025-10-21

This paper is devoted to establishing error estimates for the Simpson-type quadrature formula in
the case of functions whose higher-order derivatives are either bounded or belong to the Lp space. Initially,
the fundamental inequalities introduced by Erden et al. in [7] are stated. Subsequently, the Simpson-type
quadrature rules obtained by means of these inequalities are examined for the mentioned classes of
functions. In addition, the relationships between the particular cases of the derived quadrature formulas and
the existing formulas reported in the literature were analyzed.

Synthesis and Characterization of TiO₂-Coated n-Dodecanol@MFB Microparticles

Cemil Alkan — 2025-10-21

Current research on phase change materials for thermal energy storage focuses on developing
hybrid functionalities to enhance their applications. These hybrid properties typically include solar heat
harvesting, flame retardancy, and antibacterial properties. Furthermore, cost-effective particle production
remains a key goal for wider availability.
In this study, n-dodecanol, an organic phase change material widely used in cooling and anti-icing systems,
was incorporated into a melamine-formaldehyde resin with boric acid to obtain low-cost microcapsules
with hybrid properties, particularly flame retardancy. Subsequent deposition of TiO2 onto the particle
surface conferred photothermal heat generation and antibacterial properties.
The microencapsulated phase change materials were characterized using DSC to determine their
physicochemical and thermal energy storage properties. The results show that the microcapsules produced
retained more than 50% of the core's latent heat capacity and exhibited high thermal stability over repeated
heating-cooling cycles. The particles were also found to be homogeneous, spherical, and 50-115 um in
diameter.

İletişim Tarzlarının Müzakere Stratejileri Üzerindeki Etkisi: Norton Modeli Üzerine Bir Akademik Literatür Derlemesi

Sinem Ataseven — 2025-10-21

Bu literatür taraması, Norton’un iletişim tarzları modeli ile Tero International müzakere öz
değerlendirme ölçeği temelinde iletişim tarzları ile müzakere stratejileri arasındaki ilişkiyi incelemektedir.
Norton, dokuz iletişim tarzı tanımlarken; Tero, beş müzakere tarzı belirlemiştir: rekabetçi, işbirlikçi,
uzlaştırıcı, kaçınmacı ve uyum sağlayıcı.
Araştırmalar, baskın ve tartışmacı iletişim tarzlarının genellikle rekabetçi müzakere ile örtüştüğünü, açık
ve arkadaşça tarzların ise işbirliğini desteklediğini göstermektedir. Rahat ve izlenim bırakan tarzlar
uzlaşma ile ilişkilendirilirken, dikkatli iletişimciler uyum ve işbirliğine eğilimlidir. Dramatik ve
canlandırıcı tarzların etkisi ise bağlama göre değişmektedir.
Kültürel faktörler de bu ilişkileri şekillendirmektedir. Yüksek bağlamlı kültürler dolaylı ve uyum
sağlayıcı stratejileri, düşük bağlamlı kültürler ise doğrudan ve rekabetçi yaklaşımları tercih eder.
Bu çalışma, iletişim tarzı farkındalığının müzakere eğitimine entegre edilmesinin önemini vurgular.
Tarzların etkileşimini anlamak, daha etkili stratejiler geliştirmeye katkı sağlar.

Systematic Evaluation of Deep Learning Architectures for Epilepsy Detection with Patient-Based Validation

Mustafa Erginli — 2025-10-21

EEG signal processing is crucial for diagnosing neurological diseases, such as epilepsy. This
study systematically compares the performance of time-domain and time-frequency domain
representations in detecting epilepsy seizures. The data were divided into four-second windows at a
sampling frequency of 256 Hz, and data leakage was prevented by applying a patient-based
discrimination protocol. The training, validation, and test sets were separated by patient IDs, and
windows from the same patient were not included in more than one partition. Six deep learning models,
including BiLSTM, 1D-CNN, STFT-CNN, STFT-CNN with attention mechanism, ResSTFT-CNN, and
ResSTFT-CNN with attention mechanism, were evaluated under the same training conditions. AdamW
optimization, label smoothing, and early stopping algorithms were applied to all models. The models
were compared in terms of accuracy, F1-Macro, AUROC, AUPRC, calibration metrics, and inference
rate. Results showed that the 1D-CNN model exhibited significant superiority. The model offered the
most balanced performance with an accuracy of 0.957, an AUROC of 0.993, and an inference time per
sample of 0.09 milliseconds. BiLSTM came in second with 0.936 accuracy. For STFT-based models, the
addition of an attention mechanism significantly improved performance, with STFT-CNN accuracy
increasing from 0.78 to 0.91. Model complexity analyses revealed that 1D-CNN strikes a balance
between high performance and a low parameter count. Working in the time-domain representation, 1D
CNN offers an optimal solution for epilepsy detection, striking a balance between accuracy, speed, and
clinical applicability. The patient-based discrimination strategy allows for the realistic generalization
capacity of the models to be evaluated.

AI-Based Energy Management of Net-Zero Energy Buildings Through Renewable Integration and Storage

Özlem Boydak — 2025-10-21

This paper explores the role of AI in energy management of NZEBs, emphasizing renewable
integration and storage strategies, and presents future research directions to enhance efficiency, resilience,
and cost-effectiveness. Net-zero energy buildings (NZEBs) represent a transformative approach to
sustainable architecture, integrating advanced renewable energy technologies, energy-efficient designs, and
intelligent control strategies to achieve an annual energy balance between consumption and on-site
renewable generation. The increasing complexity of NZEB energy systems, driven by fluctuating
renewable generation, variable demand patterns, and multi-scale energy storage, has created the need for
advanced energy management strategies. Artificial intelligence (AI) techniques, particularly those
leveraging machine learning, reinforcement learning, and optimization algorithms, offer significant
potential for improving energy efficiency, operational flexibility, and occupant comfort.

SAĞLIK KURUMLARINDA AKILLI LABORATUVAR SİSTEMLERİNİN YALIN HASTANE UYGULAMALARIYLA ENTEGRASYONU

İrem Düzdar — 2025-10-27

Günümüzde sağlık kurumlarında laboratuvar hizmetleri, tanı ve tedavi süreçlerinin temel
bileşenlerinden biri haline gelmiştir. Ancak laboratuvar süreçlerinde yaşanan hata, gecikme ve kaynak israfı
gibi sorunlar hem hizmet kalitesini düşürmekte hem de maliyetleri artırmaktadır. Bu durum, sağlık
kurumlarında verimlilik ve güvenilirliği artıracak yenilikçi yaklaşımların gerekliliğini ortaya koymaktadır.
Bu çalışmada, laboratuvar süreçlerinde ortaya çıkan hata ve israf problemlerine karşı akıllı laboratuvar
sistemleri ile yalın hastane yaklaşımının entegrasyonu incelenmiştir. Ulusal ve uluslararası örnekler
üzerinden yapılan analizlerde, süreç iyileştirmesi, hata oranlarının azaltılması ve maliyetlerin düşürülmesi
hedeflenmiştir. Ayrıca, 5S yaklaşımı temel alınarak akıllı sistemlerle desteklenen yalın çözüm önerileri
sunulmuştur. Çalışmanın bulguları, akıllı teknolojilerin yalın yönetim ilkeleriyle birlikte uygulanmasının
sağlık
laboratuvarlarında etkinlik, doğruluk ve sürdürülebilirlik açısından önemli faydalar
sağlayabileceğini göstermektedir.

Dijital Dönüşüm Teknolojilerinin Çok Boyutlu Yapısı Üzerine Bir Derleme: Teknolojik Yenilikler ve Uygulama Alanları

Berk DEMİRSOY — 2025-10-27

Bu çalışma, dijital dönüşüm kavramını çok boyutlu bir perspektiften ele alarak gelişen teknolojiler,
sektörel uygulamalar ve yönetişim dinamikleri arasındaki etkileşimi incelemektedir. Literatürde yer alan
güncel araştırmalar, dijital dönüşümün yalnızca teknik bir yenilenme süreci değil, aynı zamanda
kurumsal, toplumsal ve kültürel dönüşümleri de içeren bütüncül bir paradigma olduğunu göstermektedir.
Bu bağlamda çalışma, altı temel eksen üzerinden derlenmiştir: ileri düzey dijital teknolojilerin
entegrasyonu, akıllı üretim ve dijital ikiz uygulamaları, sağlıkta dijital dönüşüm, elektronik ölçüm ve
enstrümantasyon teknikleri, siber güvenlik ve dijital yönetişim ile Nesnelerin İnterneti (IoT) tabanlı
dönüşüm yaklaşımlarıdır. Elde edilen bulgular, bu alanlarda önemli ilerlemeler kaydedilmiş olmasına
karşın, araştırmaların çoğunlukla teknolojik yeniliklere odaklandığını ve insan, organizasyon, etik ve
sürdürülebilirlik boyutlarının sınırlı biçimde ele alındığını ortaya koymaktadır. Literatürdeki temel
eksiklikler arasında teknolojiler arası entegrasyon modellerinin yetersizliği, birlikte çalışabilirlik
standartlarının eksikliği, veri güvenliği ile enerji verimliliği arasındaki dengenin kurulamayışı ve siber
yönetişimde davranışsal faktörlerin ihmal edilmesi öne çıkmaktadır. Bu derleme, mevcut literatürdeki bu
boşlukları bütüncül bir bakış açısıyla değerlendirerek, gelecekteki araştırmalar için çok katmanlı hibrit
mimariler, etik ve açıklanabilir yapay zekâ modelleri, kenar-bulut bilişim tabanlı dijital ikiz sistemleri ve
insan merkezli yönetişim çerçevelerinin geliştirilmesini önermektedir. Sonuç olarak, dijital dönüşümün
sürdürülebilir, güvenli ve insan merkezli biçimde ilerlemesi; yalnızca teknolojik gelişmelere değil, bu
gelişmeleri yönlendirecek stratejik planlama, kurumsal uyum ve disiplinler arası bütünleşmeye dayalı bir
ekosistemin oluşturulmasına bağlı olacaktır.

Formation of diazocyclic compounds from o-amino-biphenyl aromatics with nitric oxide: Investigation of the response of a fluorescent sensor containing coumarin and indoline salt to NO using DFT

Tahir Savran — 2025-10-27

Reactive Oxygen Species (ROS), produced in biological reactions in living organisms, are
entities that can have significant chemical effects in cellular environments. While normal levels of ROS
production are positive, excessive production is negative. ROS are gasotransmitters and play important
roles in physiopathological processes. Furthermore, ROS are categorized as radicals (RO2.,-OH, NO.,
and O2.-) and non-radicals (HOCl, ONOO-, 1O2, H2O2, and O3). ROS are associated with oxidative stress
and are linked to various diseases triggered by it, such as cardiovascular disease, diabetes, and cancer.
Nitric oxide (NO.) is a gaseous molecule with a very short half-life. NO. plays a role in the regulation of
nerve function, making its identification crucial. Because NO. is a communication molecule in living
organisms, the development of simple and selective fluorescent sensors for its monitoring is invaluable.
Techniques for the determination of ROS include chromatography, Electron Spin Resonance, Nuclear
Magnetic Resonance, Mass Spectrometric Analysis, Chemiluminescence, and Fluorescence. While these
techniques are useful, the fluorescence method is superior. It is portable, requires no expertise, and is
inexpensive. Consequently, the results of applying Density Functional Theory (DFT) to the sensor
designed for NO. detection are analyzed here.

Assessment of radiation protection characteristics in Bi-2212 superconductor poly-crystalline specimens

Selim KAYA — 2025-10-27

Investigating the effects of Erbium (Er) doping on the radiation shielding properties of
Bi2Sr2(Ca1-xErx)Cu2O8 (x = 0.0, 0.1, and 0.3) polycrystalline superconducting ceramics is the goal of the
current work. Both the WinXcom database and the EGS4 Monte Carlo simulation algorithm were used to
theoretically estimate the mass attenuation coefficients (u/p) of the produced samples across a range of
gamma-ray energies. The mean free path (MFP), half value layer (HVL), radiation protection efficiency
(RPE), effective atomic number (Zeff), and gamma-ray air kerma coefficients (k
) were among the
important shielding characteristics that were methodically determined from these coefficients. The
shielding performance was thoroughly evaluated using the EGS4 simulations, and any discrepancies were
identified by rigorously comparing the outcomes with the theoretical predictions derived from XCOM.
First, the Er-doped Bi-Sr-Ca-Cu-O superconducting system's physical and structural characteristics were
carefully investigated. Er incorporation dramatically alters the radiation attenuation behavior, according
to
subsequent comparisons between the Er-doped and undoped samples. This improves our
comprehension of the compositional dependency of shielding efficiency in BSCCO-based
superconductors.

Thermoanalytical Evaluation of YBCO Superconductors via Energy Reaction Index (ERI)

İbrahim DÜZGÜN — 2025-10-27

The thermochemical efficiency and phase evolution of YBa2Cu3O7-S (Y123) and Y3Ba5Cu8O18
(Y358) superconductors were investigated using differential thermal analysis (DTA). To provide a
complementary metric to conventional conversion-factor (CF) methods, an Energy–Reaction Index (ERI)
was introduced and applied to quantify the balance between exothermic and endothermic contributions
during synthesis. The DTA profiles exhibited a single sharp exothermic peak for Y123 near 940 C and
multiple broader peaks for Y358 between 850 C and 970 C, indicating distinct formation kinetics. The
calculated ERI values (1.84 for Y123 and 1.29 for Y358) revealed that Y123 undergoes a more complete
and thermochemically efficient reaction process, while Y358 experiences distributed energy release
associated with multi-step crystallization. Segmental ERI analysis across temperature intervals confirmed
the kinetic limitation of Y358 and the progressive energy coherence of Y123. The findings demonstrate
that ERI is a reliable and straightforward tool for evaluating reaction completeness and energy utilization
in complex oxide superconductors. This approach can assist in optimizing heat-treatment schedules,
oxygen partial pressure control, and precursor ratios for future YBCO-based materials.

Finding the PID Controller Parameters in Synchronization Between Hyperchaotic Oscillators in QAM (Quadrature Amplitude Modulation) Communication System Using Particle Swarm Optimization

Ali Can Çabuker — 2025-10-27

Oscillators, which form the basis of chaotic communication, can be designed using different
mathematical models. Hyperchaotic oscillators, unlike classical chaotic oscillators such as Chua and Sprott,
have been developed to generate more chaotic signals. To ensure synchronization of such oscillators, the
controller coefficients were determined using particle swarm optimization. This study attempted to achieve
synchronization between hyperchaotic oscillator systems using various optimization algorithms, and the
results demonstrated that the PID controller was inadequate for these systems.

Termodinamik Model Kullanılarak Farklı Çalışma Şartlarında Motor İç Parametrelerinin Hesaplanması

İlker TEMİZER — 2025-10-27

Motorlarda yanma modeli olarak, çok boyutlu modellerden daha sade yapıda ve daha kısa
zamanda, fakat yanma olayının fiziksel süreci ve çeşitli faktörlerin bu sürece etkilerini çok boyutlu
modellerden geri kalmayacak ölçüde deneysel şartlara yakın bir şekilde sonuç veren tek boyutlu modelin
uygulanması büyük önem taşımaktadır. Bu doğrultuda yapılan bu çalışmada, termodinamik bir model
kullanılarak motor 1800 d/d, 2000 d/d ve 2200 d/d çalışma şartlarında oluşan indike ve efektif değerleri
hesaplanmıştır. Standart yanma odasına sahip dizel motor için tek boyutlu bir çevrimin termodinamik
model hesabı yapılmıştır. Aynı zamanda deney motoru farklı hızlarda tam yük şartları altında
çalıştırılarak silindir içerisinde oluşan ortalama indike basınç değerleri çevrimsel olarak elde edilmiştir.
Sonuçlar incelendiğinde, termodinamik yaklaşımla yapılan hesaplamaların, deneysel sonuçlarla uyumlu
olduğu görülmüştür. Bu model farklı yanma şartları için uygulanabilir olduğu da aynı zamanda test
edilmiştir. En yüksek ortalama indike basınç değeri, motorun 1800 d/d çalışma şartlarında teorik olarak
9.3 bar, deneysel olarak ise 8.73 bar tespit edilmiştir. Bu sonuçların teorik yapılan kabuller, deneysel
çevrimsel farklılıklar ve kayıplar düşünüldüğünde, kabul edilebilir sınırlar içerisinde olduğu
değerlendirilmektedir.

Leaf Sampling (Quince Tree Leaf: Cydonia Oblonga Miller)

Melek Gökmen Karakaya — 2025-10-27

This study was conducted with first-year students of the Laboratory Technology Program at
Uşak University, Banaz Vocational School of Higher Education, within the scope of the “Sampling
Methods” course. The main aim of the activity was to demonstrate the importance of correct and reliable
sampling practices in agricultural and laboratory applications through hands-on leaf sampling from quince
(Cydonia oblonga) trees. During the practical session, students learned how to select, collect, label, and
transport leaf samples properly, in accordance with the principles outlined in ISO/IEC 17025 related to the
sampling process. The activity not only enhanced students’ technical skills but also provided a strong
connection between theoretical knowledge and real-world laboratory applications. Feedback from
participants indicated that the exercise increased their motivation, deepened their understanding of
sampling techniques, and improved the overall efficiency of the course. Furthermore, the experience
fostered awareness of quality assurance, traceability, and sustainable agricultural practices, aligning with
the United Nations Sustainable Development Goals (SDGs)—particularly Goal 4 (Quality Education), Goal
12 (Responsible Consumption and Production), and Goal 15 (Life on Land). In conclusion, this study
demonstrated that integrating applied sampling activities into laboratory education significantly improves
student engagement and understanding while promoting environmentally responsible practices. Such
training contributes to the development of competent, quality-oriented, and sustainability-conscious
laboratory professionals prepared to meet the needs of modern scientific and industrial fields.

Weight Reduction of Fused Deposition Modeling Manufactured PLA Structures through Topology Optimization

Murat Işık — 2025-10-27

The interaction between additive manufacturing (AM) and Topology Optimization (TO) offers
a powerful example to create lightweight, high performance structural components. This study
investigates this combined approach by designing and analyzing a bracket made of Polylactic Acid (PLA)
using the fused deposition modeling (FDM) manner. Finite Element Analysis (FEA) was conducted using
ANSYS software to perform a static structural analysis on an initial bracket model. Subsequently,
topology optimization was employed to minimize compliance (maximize stiffness) under two distinct
volume constraints, targeting final volumes of 70% and 50% of the original design space. The initial
analysis revealed a notable dissociation between the locations of maximum total deformation (2.130 mm
at the component’s tip) and maximum equivalent stress (153.48 MPa at the bracket’s base and fixture
points). The optimization process successfully yielded two distinct lightweight geometries that effectively
follow the identified primary load paths, validating the TO strategy. The findings demonstrate that
combining FDM and TO is a highly effective method for achieving significant weight reduction. The
comparison between the 70% and 50% volume models illustrates a critical balance between mass savings
and increased stress concentration. This study validates the simulation-driven design process as a robust
tool for developing structurally efficient components tailored for applications where weight is a critical
parameter.

Kablosuz Enerji Transferi Uygulaması için PCB Çift D Bobin Tasarımı

Mahmut Ersin ERÇİN — 2025-10-27

Bataryalı cihazların kullanımı gün geçtikçe artmaktadır. Bu nedenle bu cihazların şarj edilmesi bazı
durum ve ortamlarda yönetilmesi gereken bir problem haline gelmiştir. Özellikle mobil sistemler amaçları
gereği hafif olmalıdırlar. Bu tarz cihazların çoğunda ağırlık bütçesinin büyük kısmını bataryalar
oluşturmaktadır. Batarya miktarının/ağırlığının azaltılması önem arz etmektedir. Azaltılan batarya miktarı,
daha sınırlı enerji depolama kabiliyeti anlamına gelmekte ve daha sık şarj olma ihtiyacını ortaya
çıkarmaktadır. Bu ihtiyacı gidermek için en çok kullanılan ve düşük maliyetli yöntem kablolu şarj işlemidir.
Kablolu şarj ise beraberinde, kablo karmaşası, soketlerin yıpranması, ısınma, kıvılcım, güvenlik vb.
konularda problemler ortaya çıkarmaktadır. Kablosuz enerji transferi (KET) sistemleri, herhangi bir fiziksel
bağlantı olmadan enerji transferi yapabildiğinden, bahsedilen problemleri ortadan kaldırmaktadır. Bu
çalışmada KET sistemi bileşenlerinden olan örnek bir alıcı verici PCB çift D bobin tasarımı incelenmiştir.

Comparison of Large Scale Multiobjective Handling Mechanisms: Cooperative Coevolution, Dimension Handling, Enhanced and Problem Reformulation Mechanisms

Okkes Tolga Altinoz — 2025-10-27

Large scale optimization problems map situations where the decision space is very large. Such
problems are frequently encountered in engineering. Large scale problems are called large scale because
of the size of the decision space, but this does not mean that these problems are expe4nsive. Nevertheless,
due to the large size, traditional selection procedures do not work efficiently. Although these problems
can be solved with traditional optimization algorithms, some mechanisms have been proposed to obtain
better solutions for the same computational resource. The aim of these mechanisms is to simplify the
problem and solve it faster and more accurately. In this research, these mechanisms will be compared
with each other. For this comparison, two algorithms from each category will be selected and applied to
nine benchmark problems. The empirical results obtained and the algorithms and the categories to which
they belong will be compared.

Investigation of the Adsorption of Eosin B Dye with Graphene Oxide: Effect of Synthesis Methods and Adsorption Conditions

Mahad Quesleyeh ALI — 2025-10-27

The contamination of industrial wastewater with dyes causes toxic effects on aquatic
organisms and poses risks to human health. Among existing wastewater treatment methods, adsorption
stands out due to its simplicity, efficiency, and low cost. Graphene oxide (GO) is one of the most studied
materials due to its high specific surface area, good stability in aqueous environments, and oxygenated
functional groups (hydroxyl, carboxyl, and epoxide) that facilitate interactions with organic molecules. In
this study, GO was synthesized using three different methods (Staudenmaiermaier, Staudenmaiermaier
Hummers, and Hummers) and subsequently used for the adsorption of eosin B in aqueous solutions.
Furthermore, the effects of the type of base (NaOH, KOH, NH₄OH) and different pH values (pH: 2, 5,
and 9) on the adsorption process were investigated. Samples were analyzed in terms of % eosin B
adsorbed and the amounts of eosin B (mg)/GO (g). Experiments were conducted at 25 °C, at an initial
concentration of 300 ppm, using 0.1 g of GO and stirring for 24 h. The results show that the Hummers
method has the highest adsorption capacity. Adsorption efficiency reaches its maximum value,
particularly in the basic environment created by using NaOH, and the highest efficiency under pH
influence was obtained at pH 2. These findings confirm the critical role of surface functionalization and
electrostatic charges in the GO-dye interaction and demonstrate the promising potential of GO as an
effective absorbent in the treatment of colored wastewater.

Structural Integrity Evaluation of SLM Ti-6Al-4V Components with Optimized Geometrical Features

Murat IŞIK — 2025-10-27

This study investigates the integration of topology optimization (TO) and additive
manufacturing (AM) for lightweighting aerospace components, focusing on a structural bracket fabricated
from Ti-6Al-4V (Ti64) via selective laser melting (SLM). The primary objective was to analyze the
component's structural response under static loading and to evaluate the influence of load magnitude on
topology optimization outcomes for 30% and 50% mass reduction targets. Finite element analysis (FEA)
was conducted using assigned SLM Ti6Al4V material properties, applying static loads of 20 kN and 40
kN. The simulation results demonstrate that the bracket operates well within its linear elastic range,
exhibiting a high factor of safety; the maximum observed elastic stress (approximately 81 MPa at 20 kN)
remained significantly below the material's typical tensile strength (800-1200 MPa). Critically, the
topology optimization algorithm produced nearly identical optimized geometries for both 30% and 50%
mass reduction targets, irrespective of whether the 20 kN or 40 kN load was applied. This main finding
indicates that for this linear system, the optimal material layout is conducted by the mass reduction target
and initial geometry, not the load's magnitude. This conclusion supports the use of SLM Ti6Al4V and TO
for creating strong, lightweight structures and suggests a potential to update the design process by
reducing redundant optimization analyses within a linear loading range.

Investigation of the Effects of Floating Solar Energy Systems on Water Quality and Aquatic Ecosystems: A Literature-Based Assessment

Orhan Türkoğlu — 2025-10-27

Floating photovoltaic (FPV) systems have recently emerged as a promising renewable energy
solution that minimizes land use conflicts while enhancing water resource efficiency. However, their
ecological and environmental implications remain poorly understood. This study provides a comprehensive
literature-based assessment of the effects of FPV systems on water quality and aquatic ecosystems. Through
a systematic review of published research, the study synthesizes current findings regarding physical
(temperature, light, evaporation), chemical (oxygen, pH, nutrients), and biological (algae, fish,
microorganisms) impacts. The results indicate that FPV systems can significantly reduce evaporation and
surface temperature but may also modify light penetration and dissolved oxygen dynamics, influencing
aquatic biodiversity. The paper concludes by identifying critical research gaps and suggesting future
directions for sustainable FPV design and deployment. Additionally, this study highlights the importance
of integrating ecological monitoring and adaptive management strategies into FPV project planning to
mitigate potential adverse impacts. The findings underscore the need for interdisciplinary research
combining hydrology, ecology, and renewable energy engineering to ensure environmentally responsible
FPV system deployment. This review also identifies policy and management considerations essential for
balancing renewable energy development with aquatic ecosystem conservation.

Influence of Geometrical Design on the Load Bearing Performance of Structural Steel Components

Murat IŞIK — 2025-10-27

The increasing demand for lightweight and resource efficient components in critical sectors
like aerospace and structural engineering has driven the integration of advanced methods such as additive
manufacturing (AM) and topology optimization (TO). Structural steel, while valued for its strength, often
results in components that are significantly over engineered for their specific applications. This study
investigates the lightweighting potential of an S275N structural steel bracket using the finite element
method (FEM). A 3D model with an initial mass of 1.8636 kg was analyzed under a 20 kN static load
with fixed supports to establish baseline performance. The initial FEM analysis revealed a minimal
maximum deformation of 0.02 mm and a maximum von Mises stress of 84 MPa, which is well below the
material's yield strength. Crucially, the analysis showed that stress was highly concentrated around
support features, leaving large regions of the bracket under utilized. Following this analysis, Topology
Optimization (TO) was applied to the design, successfully generating a new, efficient geometry with a
50% mass reduction. The optimized topology strategically maintains a continuous load path and retains
material in high stress concentration zones while eliminating non structural mass. This research serves as
a practical validation of the design for additive manufacturing (DfAM) workflow, demonstrating how
computational analysis can produce structurally strong, lightweight designs perfectly suited for
fabrication using modern AM processes.

Endüstriyel Elektrikli Araçlar Motor Sürücülerinde Kullanılmak Üzere Yüksek Frekanslı İzoleli DC-DC Düşürücü Tasarımı

Seyfullah İsmail ÇELİK — 2025-10-27

Günümüzde elektrikli araç teknolojileri, enerji verimliliği ve sürdürülebilirlik hedefleri
doğrultusunda hızla gelişmektedir. Bu gelişimle birlikte, motor sürücü sistemlerinin güç gereksinimlerini
karşılayan yardımcı besleme devrelerinin de yüksek verim, güvenilirlik ve kompakt yapı özelliklerini bir
arada sunması büyük önem taşımaktadır. Özellikle geniş giriş gerilim aralığında kararlı çalışabilen,
izolasyonlu DC-DC dönüştürücüler, endüstriyel motor sürücülerde kontrol ve haberleşme devrelerinin
güvenli beslenmesi açısından kritik bir role sahiptir. Bu nedenle, yüksek performanslı ve maliyet etkin
izole güç çözümleri geliştirmek, modern endüstriyel uygulamaların temel gereksinimlerinden biri haline
gelmiştir.
Bu çalışmada, 18 V – 100 V geniş giriş gerilimi aralığında çalışabilen, yüksek frekanslı ve izoleli bir DC
DC düşürücü dönüştürücü tasarımı sunulmaktadır. Fly-Buck topolojisine dayanan tasarım, geleneksel
flyback yapısına kıyasla daha az bileşenle izolasyon ve çoklu çıkış elde etme avantajı sağlamaktadır.
Dönüştürücü, LM5161 entegresiyle Sabit Açık Kalma Süresi (COT – Constant On-Time) kontrol yöntemi
kullanılarak 300 kHz anahtarlama frekansında çalıştırılmıştır. Bu yöntem sayesinde, yüksek giriş
gerilimlerinde dahi hızlı geçici tepki ve kararlı çıkış performansı elde edilmiştir.
Tasarımın birincil çıkışı 15 V / 1 A, izole ikincil çıkışı ise 15 V / 100 mA olup toplam çıkış gücü 17.5
W’tır. Küçük boyutlu manyetik elemanlar sayesinde dönüştürücü yüksek güç yoğunluğu ve alan
verimliliği sağlamaktadır. Yapılan deneysel doğrulamalar sonucunda dönüştürücünün verimi %80’in
üzerinde ölçülmüş ve sistemin endüstriyel motor sürücülerde kullanılabilirliği başarıyla ortaya konmuştur.

Investigation of the Mechanical Behavior and Structural Characteristics of an Accessory Mount Using Finite Element Method

Murat IŞIK — 2025-10-27

The continuous push for lightweighting in the many industries to enhance fuel efficiency
requires a detailed understanding of existing structural components. This research presents a
comprehensive structural assessment of an accessory mount to establish a detailed baseline of its
mechanical performance. The primary objective was to characterize the component's response to
simulated operational loads. Using the finite element method (FEM) within the ANSYS software
platform, a static structural analysis was conducted. The model utilized a magnesium alloy, defined by its
main elastic properties and density of 1800 kg/m3. The analysis successfully mapped the distribution of
internal stresses, strains, and physical displacements. The results identified a maximum von Mises stress
of 237.2 MPa, which was found to be highly localized at the fillets of the mounting holes. Concurrently, a
separate analysis of displacement showed a maximum total deformation of 2.0683 mm, with this peak
deflection occurring in the component's unsupported central span. The equivalent elastic strain
distribution was found to be in direct correlation with the stress results. The study also highlighted that
significant regions of the component's main body are subjected to minimal stress levels. This work
concludes by providing a complete quantitative characterization of the accessory mount's structural
behavior, establishing a critical performance baseline for future design iterations.

Temporal Convolutional Networks Enhanced RNN Models for EEG Signal Classification

Mustafa Erginli — 2025-10-31

This study systematically compares four deep learning architectures for classifying EEG signals. LSTM and GRU models, as well as TCN + LSTM and TCN + GRU models, are evaluated under the same conditions of data augmentation, hyperparameterization and early stopping. The data consisted of recordings with a sampling frequency of 173.61 Hz, categorized into normal, interictal and ictal classes. Normalization and time domain data augmentation strategies were used in the preprocessing steps. The results show that hybrid structures provide significant superiority. LSTM produced 86.67 percent accuracy, 0.8646 macro F1, 0.7908 Matthews correlation coefficient, and 0.9699 AUROC. GRU achieved 81.33 percent accuracy, 0.8220 macro F1, 0.7083 Matthews correlation coefficient and 0.9664 AUROC. TCN + LSTM performed more strongly with 92.00 percent accuracy, 0.9221 macro F1, and 0.9953 AUROC. The highest success was achieved with TCN + GRU. This model achieved 96.00% accuracy, 0.9610 macro F1 score, 0.9375 Matthews correlation coefficient and 0.9993 AUROC. In probabilistic calibration, the best value was obtained for TCN + GRU with a log loss of 0.0982 and a Brier score of 0.0426. In terms of inference efficiency, GRU is the fastest model with an average of 0.39 milliseconds. The hybrid models meet the real-time usage threshold with a delay of about 7.6 milliseconds. The results show that the TCN + GRU architecture is successful in achieving a balance between accuracy, calibration and latency.