International Journal of Advanced Natural Sciences and Engineering Researches

Balistik Zırh Uygulamaları İçin Al3Ti ve TiB₂ Takviyeli Fonksiyonel Derecelendirilmiş Katman İçeren Kompozitlerin Üretimi ve Karakterizayonu

Muhammed Soner BAŞER — 2025-06-04

Bu çalışmada, savurma ve sedimantasyon döküm teknikleri kullanılarak tabakalı balistik malzeme
üretimi amaçlanmıştır. Matris malzemesi olarak alüminyum, takviye elemanları olarak ise Al₃Ti ve TiB₂
partikülleri tercih edilmiştir. Bu partiküller, alüminyum alaşımlarının mekanik özelliklerini artırmaları
nedeniyle seçilmiştir.
İlk aşamada, savurma döküm yöntemiyle Al₃Ti partikülleri içeren katmanlı bir yapı elde edilmiştir.
Sonrasında, Al-Ti master alaşımına 1200 °C’de bor ilavesiyle TiB₂ partikülleri sentezlenmiş ve bu
partiküller hem savurma hem de sedimantasyon tekniğiyle TiB₂ katmanına sahip balistik malzemeler
üretilmiştir.
Üretilen numunelerin karakterizasyonu kapsamında, mikro yapı incelemeleri için optik mikroskop
analizleri gerçekleştirilmiş; partikül dağılımı ve katman yapısı detaylı şekilde gözlemlenmiştir. Ayrıca,
numunelerin yoğunlukları Arşimet prensibine dayalı yöntemle belirlenmiş; sertlik özellikleri ise Brinell
testi ile değerlendirilmiştir.
Çalışma sonuçları, hem savurma hem de sedimantasyon döküm yöntemleriyle Al₃Ti ve TiB₂ takviyeli
katman içeren kompozit balistik malzemelerin üretilebildiğini göstermiştir. Özellikle TiB₂ partiküllerinin
daha küçük taneli yapısı ve yüksek sertlik değeri sayesinde, Al₃Ti’ye kıyasla daha etkili bir takviye fazı
olduğu tespit edilmiştir. Döküm teknikleri karşılaştırıldığında, savurma yöntemi partiküllerin merkezkaç
kuvvetiyle daha homojen dağılmasını sağladığı ve daha yüksek takviye oranı sunduğu için avantajlı
görünsede, sedimantasyon yöntemi üretim sürecinin basitliği, düşük ekipman ihtiyacı ve büyük hacimli
dökümlere olan uygunluğu nedeniyle endüstriyel ölçekli üretim açısından daha uygun bulunmuştur.
Sonuç olarak, TiB₂ partiküllerinin sedimantasyon tekniği ile üretilmiş katmanlara entegre edilmesi,
özellikle zırh uygulamaları için yüksek sertlik ve performans sunması nedeniyle önemli bir avantaj
sağlamaktadır. Bu bağlamda, çalışmada elde edilen bulgular, katmanlı balistik malzeme tasarımlarında hem
takviye türü hem de üretim tekniğinin optimize edilmesinin kritik öneme sahip olduğunu ortaya koymuştur.

Intersection of Migration And Education Policies: Access to Education for Syrian Children in Turkey

Şengül BÜYÜKBOYACI — 2025-06-04

This study explores the access of Syrian children to education in Turkey within the context of
migration and education policies. Following the Syrian civil war that began in 2011, Turkey has become
host to over 3.6 million Syrian refugees, with children making up a significant portion. The research focuses
on the integration of these children into the Turkish education system, the obstacles they encounter, and the
national and international policy responses addressing these challenges.
Using qualitative document analysis, the study reviews legal regulations, policy documents, academic
literature, and international reports. Key barriers identified include language difficulties, curriculum
incompatibility, socioeconomic disadvantages, psychosocial issues, and experiences of discrimination.
The study emphasizes the need for sustainable and inclusive education policies. Recommendations include
enhancing Turkish language support, developing adaptable curricula, offering targeted teacher training,
expanding financial support, and increasing access to psychosocial services.
Ensuring education for Syrian children is vital not only for their personal development but also for Turkey’s
broader social cohesion and long-term policy goals. Therefore, the formulation of inclusive and responsive
education policies is imperative in addressing both humanitarian needs and integration efforts.

The Effect of Hot and Cold EGR Applications on Exhaust Emissions in Diesel Engines

Idris Cesur — 2025-06-05

In this study, the effects of hot and cold Exhaust Gas Recirculation (EGR) applications on
exhaust emissions in diesel engines were experimentally investigated. Tests were conducted by applying
both hot and cold EGR at 10% and 20% rates, and the resulting emission values were compared with
standard operating conditions. The results revealed that both types of EGR significantly reduced NOx
emissions. However, as the EGR rate increased, notable increases were observed in HC, CO, and smoke
emissions. These increases are attributed to the reduction in oxygen concentration and combustion
temperature in the combustion chamber caused by EGR, which negatively affects combustion efficiency.
In particular, the further decrease in combustion temperature with cold EGR applications exacerbated
these negative effects. In conclusion, although EGR applications are effective in controlling NOx
emissions, they must be carefully optimized due to their adverse impact on other harmful emissions. The
findings suggest that to balance engine emissions effectively, EGR rate, application type (hot/cold), and
engine operating parameters must be evaluated together.

Rapid and Accurate Estimation of Milk Fat by Near Infrared Spectroscopy: Comparison of Different Pre-processing and Regression Methods

Özcan Çataltaş — 2025-06-05

This study aims to rapidly and accurately estimate the fat content of milk using near-infrared
spectroscopy and various chemometric analysis methods. In the study, different pre-processing techniques
such as standard normal variate, multiplicative scatter correction, Savitzky-Golay smoothing, and spectral
differentiation were applied along with various modeling approaches such as partial least squares
regression, ridge regression, support vector regression, lasso regression, and random forest regression.
The findings show that pre-processing methods have a decisive impact on model success. In particular,
the use of standard normal variate and first derivative pre-processing methods in combination with partial
least squares regression and ridge regression resulted in the highest accuracy and lowest error values. The
results suggest that near-infrared spectroscopy can be an effective and reliable tool for automation and
real-time monitoring of quality control processes in the dairy industry.

Şerit Testere ile Kesilen Metalik Yüzeyin Pürüzlülük Değerinin Parametrik Optimizasyonu

Hakan SUMAK — 2025-06-05

Metal ve alaşım malzemelerin yarı mamul veya mamul hale getirilmesinde, şerit testere makineleri
yaygın olarak tercih edilen kesme sistemlerinden biridir. Şerit testereler, üretim süreçlerinin ilk aşamasında
yer almakta olup, yapısal özellikleri sayesinde diğer kesme yöntemlerine kıyasla daha ekonomik çözümler
sunmaktadır. Özellikle şerit bıçağının ince yapısı, malzeme israfını azaltarak ekonomik kesim imkânı
sağlar. Kesme işleminin hassasiyeti, sonraki işlem basamaklarını minimize ederek işçilik maliyetlerinin
düşürülmesine katkıda bulunmaktadır. Artan üretim maliyetleri göz önünde bulundurulduğunda, kesilen
parçaların nihai ürün olarak kullanılabilmesi, kesme kalitesi ve yüzey hassasiyetinin önemini artırmaktadır.
Bu çalışmada, imalat sektöründe yaygın olarak kullanılan şerit testere tezgâhı ile üç farklı metal
malzemenin, üç farklı kesme hızı ve üç farklı ilerleme değerinde kesilmesi sonucunda elde edilen yüzey
pürüzlülüğü değerlerinin optimizasyonu gerçekleştirilmiştir. Deneysel olarak yürütülen çalışmada, deney
sayısını azaltarak etkin sonuçlar elde etmek amacıyla Taguchi yöntemi uygulanmıştır. Kesilen numunelerin
yüzey pürüzlülüğü, yüzey ölçüm cihazları ile belirlenmiş ve elde edilen veriler varyans analizi (ANOVA)
ile değerlendirilerek optimum kesme parametreleri ortaya konulmuştur.

New Analytical Wave Solutions and Physical Interpretations of the Benjamin–Ono Equation

Damla Gezici — 2025-06-05

In this study, the G'/G2 method, one of the methods used to find analytical solutions of nonlinear
partial differential equations, is analysed. With the use of this method, travelling wave solutions of the
Benjamin Ono equation have been found and various solutions have been obtained depending on arbitrary
parameters. In this paper, the applicability and efficiency of this method on nonlinear evolution equations
(NLEEs) are investigated.

A Novel Framework for Accurate Multiclass Blood Cell Classification Using Deep Learning

Usama Irshad — 2025-06-05

This work introduces a new approach for classification of blood cells that handles issues
imbalance dataset between different categories and the presence of several classes. The main part of the
proposed approach is ResNet-18, a deep neural network designed for both strong and efficient feature
extraction and classification. We implemented class balancing techniques to eliminate the problem of
imbalanced classes in the training data. The model was able to classify Plasma Cell, Basophil, Eosinophils,
Erythroblast, Lymphocyte, Monocyte, nRBC and Platelet among the blood cell types. The study showed
that the model achieved an accuracy of 99.12%, precision of 99.12%, recall of 99.13% and F1-score of
99.12%. A deep learning framework makes blood cell analysis dependable and easily scalable, helping
solve major issues with classifying medical images.

A Framework for Multiclass Classification of Eye disease Using Deep Learning

Romail Khan — 2025-06-05

Eye diseases must be found and treated early to prevent problems with vision and ensure the
right treatment is given. A model based on deep learning and the VGG-16 structure is suggested in this
study for the automatic detection of six common ophthalmic conditions: AMD, Cataract, Diabetic
Retinopathy, Glaucoma, Retinal Detachment and Normal. Kaggle datasets that were made freely
available were selected for this study and used in a split of 80% for training and 20% for testing. In order
to match the VGG-16 model and improve image quality, we resized the images to 227×227 pixels, used
ImageNet statistics for normalization and added Gaussian blur to filter out noise. The model achieved a
total accuracy of 95%, macro and weighted average precision, recall and F1-scores were all recorded as
0.96 and 0.95. This confirms that the model can correctly detect a wide array of eye diseases in fundus
images, suggesting it will be useful for both early screening and automatic diagnosis in ophthalmology.
Although the model produced excellent results for all eye disease. This research shows that VGG-16 and
other deep learning techniques can greatly benefit medical image analysis and aid doctors in making
decisions about patients in ophthalmology.

Tribological behavior of choline chloride as additive to vegetable oil

Muhammad Deebaj — 2025-06-05

The tribological properties of sunflower oil were improved by using choline chloride-based IL
as sustainable bio-lubricant additive at various concentrations including 0.25 - 1 wt% IL. With a pin-on
disc tribometer, the lubricants were tested under realistic conditions of a 20 N load, 500 rpm rotational
speed and ambient temperature, to measure their ability to reduce friction and protect the surface from wear.
The oil blended with 1 wt% choline chloride IL showed a significant decrease of 60.43% in friction
coefficient and 53.65% in wear scar volume, compared to the neat oil. Scanning electron microscopy (SEM)
and stylus profilometry showed that a strong, protective tribofilm was formed which reduced surface
roughness and lowered the amount of abrasive wear. The reason for the excellent tribological performance
is that ILs are stable, have ionic structures and easily attach to metal surfaces which supports both extra
load capacity and more even shear stress distribution at the contact point. The results suggest that choline
chloride ILs are suitable eco-friendly additives for bio-lubricants and can replace the common use of
mineral oils in industrial applications. Following the right experimental approach and detailed surface
analysis, the study presents a good starting point for further improvements and wide use of IL-modified
bio-lubricants in industries. This work adds valuable information to the field of ionic liquid-based additives
and encourages the use of more environmentally friendly lubrication solutions.

Comparative Analysis of Square and Spiral Coils for Efficient Inductive Wireless Power Transfer

Mohammed Wadi — 2025-06-05

This paper presents the simulation and comparative analysis of different coil geometries used in
wireless power transfer systems, focusing on square and spiral coil configurations. Using the Ansys
Maxwell 3D simulation environment, key electromagnetic parameters such as self-inductance, mutual
inductance, and coupling coefficients were calculated to evaluate the performance of each design. For the
square coil, the simulation results demonstrated a consistent self-inductance and a varying coupling
coefficient depending on the distance between the coils. As the separation distance increased, the mutual
inductance and coupling coefficient values decreased, confirming the distance-sensitive nature of inductive
coupling. The spiral coil, formed by side-by-side winding of copper tubing, showed the highest efficiency
in magnetic field distribution and inductive characteristics. Visual representations revealed that the
magnetic field intensity was most concentrated near the inner diameter of the spiral coil due to the denser
turn count and reduced inter-turn spacing. These findings suggest that geometric parameters significantly
influence coil efficiency and coupling behavior in inductive wireless power systems. The study offers
practical insights into coil design optimization for improved power transfer efficiency and system reliability
in real-world applications.

Enhanced Performance in Inductive Wireless Energy Transfer Systems: A Study of Helical and Tubular Coils Configurations

Mohammed Wadi — 2025-06-05

This paper presents the simulation and comparative analysis of different coil geometries used in
wireless power transfer systems, focusing on square and spiral coil configurations. Using the Ansys
Maxwell 3D simulation environment, key electromagnetic parameters such as self-inductance, mutual
inductance, and coupling coefficients were calculated to evaluate the performance of each design. This
study examines two coil geometries' inductive and magnetic coupling characteristics—helical and
tubular—across different separation lengths. Inductance and coupling coefficient values were derived via
simulations using self- and mutual-inductance matrices. The helical coil demonstrated a coupling
coefficient of around 0.086 and an inductance of 83.4 µH at a spacing of 135 mm. The self-inductance
values of the tubular coil exhibited slight fluctuation with distance, affirming that self-inductance is
primarily independent of coil separation. Conversely, mutual inductance and coupling coefficients
markedly decreased as the inter-coil distance rose, with the tubular coil's coupling coefficient declining
from 0.276 at 50 mm to 0.038 at 200 mm. These findings validate the susceptibility of magnetic coupling
to coil configuration and separation, offering valuable insights for wireless power transmission and
magnetic sensing applications.

Arthrospira platensis in Family Medicine

Tayfun Bektaş — 2025-06-05

Arthrospira platensis (commonly known as Spirulina) has emerged as a prominent microalga
with significant therapeutic and nutritional potential. This review investigates the role and applications of
A. platensis within family medicine by exploring its nutrient composition, health benefits, clinical
evidence, and implications for preventive healthcare. A systematic review methodology was utilized,
drawing from databases including PubMed, ScienceDirect, and Scopus, with inclusion criteria
emphasizing peer-reviewed articles from the past ten years. The principal findings indicate substantial
antioxidant, anti-inflammatory, and immunomodulatory effects, highlighting the microalga's potential as
a supportive therapeutic agent in managing chronic diseases such as metabolic syndrome, cardiovascular
diseases, diabetes mellitus type 2, and inflammation-related disorders. Challenges related to
standardization, clinical validation, and regulatory considerations are identified as critical areas for future
research. Ultimately, integrating A. platensis into family medicine practice could enhance patient
centered care through preventive and complementary health strategies.

R-ESPCN ile Medikal Görüntülemede Süper Çözünürlük

Muhammed Fatih AĞALDAY — 2025-06-05

Hastalıkların erken teşhisinde Manyetik Rezonans (MR), Bilgisayarlı Tomografi (BT) ve Pozitron
Emisyon Tomografisi (PET), ultrason ve röntgen gibi tıbbi görüntüleme teknikleri uzun yıllardır
hastalıkların tanısı ve tedavisinde kullanılmaktadır. Tıbbi görüntüler üzerinde doğru tanı konulabilmesi ve
sonrasında tedavi süreçleri için kritik öneme sahip iken düşük çözünürlüklü tıbbı görüntüler tanının
doğruluğunu ve detaylı analiz yeteneğini sınırlayabilmektedir. Bu çalışmada, MR ve BT teknikleri ile elde
edilen beyin görüntülerinin çözünürlüğünü artırmak amacıyla R-ESPCN (Yenilenen ESPCN) modeli
kullanılmıştır. R-ESPCN modeli ile düşük çözünürlüklü MR görüntülerinin iyileştirilmesi amaçlanmış ve
elde edilen sonuçlar PSNR (Tepe Sinyal Gürültü Oranı) ve SSIM (Yapısal Benzerlik İndeksi Ölçümü)
metrikleri kullanılarak değerlendirilmiştir. Sonuçlara göre R-ESPCN'in beyin görüntülerinde önemli bir
çözünürlük iyileştirmesi sağladığını ve böylece tıbbi görüntülerin tanı değerinin artırılabileceğini
göstermektedir. Model karşılaştırıldığında ortalama olarak yüksek PSNR ve SSIM değerleri elde edilmiştir.
R-ESPCN 'ın MR ve BT görüntüleme teknikleri ile elde edilen verilerde güvenilir ve etkili bir çözünürlük
iyileştirme aracı olarak kullanılabileceği gösterilmiştir.

Deneysel Akışkanlar Mekaniğinde Eğitim Amaçlı Su Tüneli Tasarımı

Hilal AKBUNAR ÇAMURDIK — 2025-06-05

Su tünelleri, akışkanlar mekaniği alanında özellikle parçacık izleme, akış görselleştirme ve
türbülanslı akışların analizi gibi uygulamalarda yaygın olarak kullanılan, kontrollü bir ortamda akışın
oluşturulmasını sağlayan temel bir deney düzeneğidir. [3] Ancak, ticari su tünellerinin yüksek maliyetleri,
büyük boyutları ve kurulum zorlukları, eğitim ve araştırma kurumlarında bu sistemlere erişimi önemli
ölçüde kısıtlamaktadır. Bu çalışmada, eğitimde kullanılmak üzere tasarlanan, düşük maliyetli bir su
tünelinin tasarım ve imalat süreci ayrıntılı olarak sunulmaktadır.[3] Amaç deneysel su tünellerinin temel
yapısal özelliklerini, çalışma prensiplerini ve birçok farklı mühendislik alanlarındaki kullanım biçimlerini
farklı ölçüm teknikleri kullanarak kapsamlı bir biçimde incelemektir. Tasarım aşamasında kıyaslama
yapılırken ortam koşullarına en uygun kullanışlı tasarım tercih edilmiştir. Açık devre prensibiyle çalışan
sistem, 2 m × 0.7 m × 0.5 m boyutlarında cam bir tank, PVC merkez kanal, değişken debili bir su pompası
ve akış yönlendirici elemanlardan oluşmaktadır. Yapısal elemanlarda çelik, cam malzeme ve paslanmaz
çelik tercih edilerek uzun ömürlü ve stabil bir deney ortamı sağlanmıştır. Cam, akrilik veya şeffaf PVC'ye
göre üstün optik netlik, çizilme ve bulanıklık direnci sunduğundan görsel deney kalitesi açısından tercih
edilmiştir. İmalat sürecinde düşük maliyetli fakat etkili yapıştırıcı ve sızdırmazlık malzemeleri (silikon
macunu, PVC yapıştırıcısı vb.) kullanılarak toplam maliyet 3.250 – 3.750 USD aralığında tutulmuştur.
Sistem, 0.1–1 m/s arasında ayarlanabilir akış hızları ile farklı Reynolds sayılarında görsel ve nicel deneylere
olanak sağlamaktadır. Tünel tasarımı CAD yazılımları (SolidWorks, AutoCAD) ile optimize edilmiş;
prototip doğrulama çalışmalarıyla performansı test edilmiştir. Ayrıca, maliyet analizi sonuçları, benzer
özelliklere sahip ticari sistemlere kıyasla oldukça ekonomik ve erişilebilir bir çözüm sunduğunu
göstermektedir. Sistem, taşıma esnasında oluşabilecek yapısal deformasyonları önlemek amacıyla yerinde
montaj yöntemiyle kurulmuştur. Bu çalışma, düşük bütçeli araştırma ortamlarında deneysel akışkan
dinamiği çalışmalarının yaygınlaştırılmasına katkı sağlamayı ve eğitimde kullanılabilirliği artırmayı
hedeflemektedir.

Palynological study of spores of the species Asplenium septentrionale (L.) Hoffm. in Albania

A. GOLLOSHI — 2025-06-05

Asplenium septentrionale (L.) Hoffm.is a small herbaceous isospore plant. The study presents the
morphological description of the spores of Asplenium septentrionale (L.) Hoffm in Albania. At the same time,
a comparison of the palynological data of this species with those from the literature is made. The spores are
monolete and of the bilateral type. So, they have only one laesura. The spores have oval to ellipsoid, to bean
shaped contours. The surface of the exine is smooth (psilate) and of uniform thickness. The exine is about 1.5
um thick.
The perispora has spiny cristae, not uniform, but well developed. The perispore folds merge at the base. They
form wrinkles of different shapes, giving the impression that they are independent of each other. The size of
the perispore ornaments varies. They can reach up to 4-5 um. Through this study, more information is provided
on the morphological features of the spores of this species of our country compared to the literature data.
The material for the study was taken fresh in the Kolosian, Kukës area, Albania. The study was carried out
with a Motic BA310 light microscope with 400x and 1000x magnification.

Balık Pulu Atıklarının Asfalt Betonunun Performans Özeliklerine Etkisi

Zeynep ARSLAN — 2025-06-09

Tüm dünyada artan nüfus ve endüstriyel faaliyetler, araç kullanımını ve üretim miktarını sürekli olarak artırmaktadır. Bu durum, beraberinde ciddi miktarda atık oluşumunu getirmekte ve çevresel kirliliğe yol açarken, atık toplama alanları da giderek azalmaktadır. Bu çevresel zorluklara yanıt olarak, oluşan atıkların çeşitli sektörlerde değerlendirilmesi ve geri kazanımı yönünde yoğun çalışmalar yürütülmektedir. Yol inşaatı sektörü, bu atıkların değerlendirilmesi için önemli bir potansiyel sunmaktadır. Bu bağlamda, bu çalışma, balık pulu atıklarının asfalt betonu karışımlarının performans özellikleri üzerindeki etkilerini araştırmayı amaçlamıştır. Çalışma kapsamında, kontrol karışımı ile birlikte %3, %5 ve %7 oranlarında balık pulu atığı içeren asfalt betonu karışımları üretilmiş; bu karışımların mekanik özellikleri ve dayanıklılık parametreleri Marshall ve sünme deneyleri ile deneysel olarak incelenmiştir. Elde edilen deney sonuçlarına göre, %3 oranında balık pulu atıklarının asfalt betonunda kullanılması uygun bulunmuştur. Bu çalışma, balık pulu atıklarının yol inşaatında değerlendirilmesiyle hem çevresel kirliliğin azaltılmasına hem de doğal kaynakların daha etkin kullanılmasına katkıda bulunmayı hedeflemektedir.

Assessment of Technogenic Effects on the Water Quality of Major Iraqi Rivers

Ibraheem M. Alias — 2025-06-10

Iraq’s major rivers Tigris, Euphrates, Diyala, and Shatt Al-Arab are experiencing significant water quality degradation due to industrial discharge, agricultural runoff, and urban wastewater. This study assesses the water quality of these rivers using the Water Quality Index (WQI) and laboratory analyses of physical, chemical, and biological parameters. Water samples were collected from upstream, midstream, and downstream locations to evaluate spatial variations in pollution levels.

Findings reveal that while most river sections are classified as “fair,” water quality deteriorates to “poor” in the Shatt Al-Arab due to high levels of total dissolved solids (TDS) and heavy metals. Urban and industrial zones exhibit higher contamination, with heavy metals surpassing permissible limits, posing risks to public health and aquatic ecosystems. Nutrient-rich agricultural runoff has also contributed to eutrophication and reduced dissolved oxygen levels. Additionally, inefficient wastewater treatment systems and weak environmental regulations exacerbate pollution.

The study underscores the urgent need for integrated water resource management strategies, including stricter pollution controls, improved wastewater treatment infrastructure, and sustainable agricultural practices. Drawing from international case studies, it recommends policy interventions and technological advancements to enhance water quality. This comprehensive assessment of technogenic pollution’s impact on Iraqi rivers provides valuable insights for decision-makers, aiming to promote sustainable water management and long-term environmental conservation.

Binek Araç Salıncaklarının Destek Sacı ile Takviye Edilerek Ticari Araçlara Uygunluğunun Değerlendirilmesi

Furkan Göğer — 2025-06-10

Bu çalışmada, binek araçlarda kullanılan sac salıncak yapısına ilave destek sacı entegre edilerek, bu yeni yapı formunun ticari araçlarda kullanılabilirliği araştırılmıştır. Amaç, mevcut salıncak geometrisine yapısal takviye sağlayarak, ticari araçların maruz kaldığı daha yüksek yük ve yol koşullarına karşı dayanımı artırmaktır. Çalışma kapsamında, destek sacı eklenmiş ve eklenmemiş iki farklı tasarım sonlu elemanlar yöntemi ile modellenmiş ve her iki model üzerinde dinamik analiz gerçekleştirilmiştir. Uygulanan test senaryoları, ticari araç koşullarındaki çalışma yüklerini temsil edecek şekilde belirlenmiştir. Elde edilen sonuçlar, destek sacı ile güçlendirilmiş yapının gerilme ve deformasyon açısından daha güvenli sınırlar içerisinde kaldığını ve yapısal bütünlüğü koruduğunu göstermiştir. Bu doğrultuda, sac salıncak yapısının optimize edilerek ticari araçlarda kullanıma uygun hale getirilebileceği sonucuna ulaşılmıştır. Çalışma hem hafif yapı tasarımı hem de maliyet etkinliği açısından önemli bir mühendislik katkısı sunmaktadır.

Katı Atıkları Yönetimi için Depolama Alanı Seçiminde Geleneksel ve Hibrit Yaklaşımlar

Abdullah İzzeddin KARABULUT — 2025-06-10

Katı atıkların bertarafı için uygun alanların seçimi, çevresel sürdürülebilirlik, halk sağlığı ve arazi kullanım politikalarıyla ilgili bir planlama sürecidir. Bu süreç, yerleşim yerlerine yakınlık, hidrolojik ağlar, ulaşım altyapısı, topografya, jeolojik yapı ve ekolojik duyarlılık gerektiren biyofiziksel ve sosyoekonomik kriterlerin bütünsel bir değerlendirmesini gerektirir. Hızlı kentleşme, nüfus artışı ve sanayileşme ile sınırlı arazi kaynakları, karmaşık alan seçimini gerektirir. Katı atık yönetimi, çevresel bütünlük, ekonomik uygulanabilirlik ve planlama boyutları olan küresel bir öncelikli konudur. Teknoloji atık işleme kapasitelerini artırsa da genişleyen kentsel alanlar ve arazi kıtlığı nedeniyle optimum bertaraf alanlarının belirlenmesi hala zorluklar yaratmaktadır. Bu bağlamda, hijyenik sıhhi depolama alanlarının stratejik konumu, sürdürülebilir kentsel kalkınma ve çevresel etkinin azaltılması için esastır. Geleneksel yöntemler uzman görüşüne, öznel değerlendirmelere ve manuel harita kaplama tekniklerine dayalı zaman alıcı süreçler sunarken, Coğrafi Bilgi Sistemleri, Uzaktan Algılama ve Çok Kriterli Karar Analizi gibi coğrafi araçlar odaklanmış ve tekrarlanabilir değerlendirmelere olanak tanır. Bu teknolojiler, farklı veri katmanlarını bütünleştirerek mekânsal analizlere ve alan uygunluk değerlendirmelerine olanak tanır. Mekânsal çok kriterli değerlendirme çerçeveleri, arkeolojik miras alanları, kentsel yayılma ve tarım arazisi gibi sosyokültürel faktörleri göz önünde bulundurarak çatışan hedefleri dengelemek için umut verici karar destek sistemleri sunar. Bu çalışma, literatürdeki geleneksel ve hibrit katı atık depolama sahası yer seçimi yaklaşımlarını inceleyerek dijital coğrafi tekniklerin metodolojik evrimini değerlendirir.

Alterations in Plant Fatty Acid Composition Induced by Insect Herbivory and Elevated Carbon Dioxide: A Case Study on Halyomorpha halys (Stål, 1855) (Hemiptera: Pentatomidae)

İnanç Özgen — 2025-06-10

In this study, the impact of brown marmorated stink bug Halyomorpha halys (Stål, 1855) (Hemiptera: Pentatomidae) feeding activity on the saturated and unsaturated fatty acid composition of pepper, tomato, and bean leaves was investigated under two different atmospheric carbon dioxide (CO₂) concentrations (600 ppm and 670 ppm). Leaf samples were collected from plants subjected to varying levels of insect feeding damage under controlled laboratory conditions and analyzed using gas chromatography. The results revealed that both H. halys herbivory and elevated CO₂ levels induced significant alterations in plant fatty acid profiles. Specifically, the levels of saturated fatty acids such as palmitic acid (16:0) and stearic acid (18:0) increased proportionally with the intensity of feeding damage, while the concentrations of unsaturated fatty acids such as linoleic acid (18:2 n6) and linolenic acid (18:3 n3) varied depending on CO₂ concentration and damage severity. These findings indicate that fatty acid metabolism plays a crucial role in plant defense mechanisms and is modulated by both biotic stress (insect herbivory) and abiotic stress (CO₂ application). This study contributes to the biochemical understanding of plant–insect interactions and provides foundational data for the development of future biological control strategies and climate change-related impact assessments.

Noise Resilient Segmentation of Oil Spills in SAR Imagery

Ayesha Jabbar — 2025-06-18

Oil spills are one of the most detrimental environmental disasters, that often cause a serious threat to aquatic life, coastal habitats, and human livelihoods. Timely detection and accurate segmentation of oil spills are therefore critical for effective response and mitigation. This research particularly aims to detect and segment oil spills from low-quality SAR images using the SAR Oil Spill (SOS) dataset from ALOS and Sentinel-1A satellites covering spill-affected areas in the Gulf of Mexico and the Persian Gulf respectively. SAR images are inherently affected by various types of noise, such as speckle and thermal noise, as well as distortions like RF interference and motion artifacts, which complicate accurate analysis. To address these challenges, a multi-phase methodology was adopted. In the first phase, a baseline U-Net model was implemented on raw SAR imagery. The second phase integrated preprocessing techniques, specifically Fast & Flexible Denoising CNN (FFD-Net) for noise removal and unsharp masking for image sharpening, before U-Net-based segmentation. The third phase introduced an optimized U-Net model with a deeper and more lightweight architecture and improved training strategies, combined with the earlier preprocessing steps. Experimental evaluations demonstrated that the optimized model with preprocessing delivered the highest detection accuracy and lowest false positive rates, indicating its strong potential for integration into real-time, satellite-based oil spill monitoring systems.

The Evolution of Unmanned Aerial Systems in 2024: Turkey's Export Leadership and Global Military Applications

Kamil Aykutalp GÜNDÜZ — 2025-06-18

Unmanned aerial vehicles (UAVs) and armed unmanned aerial vehicles (UCAVs) technologies have developed at a remarkable pace across the world in 2024. These developments had an impact not only in the military domain, but also in economic and strategic areas. The aim of this research is to reveal the significant changes in the field of UAVs and UCAVs during 2024 and their impact on the world.

This study goes beyond the technical focus of the existing literature and sheds light on multidimensional developments such as increased production, examples of use in wars and international cooperation. In this respect, it aims to make an original contribution by addressing the issue from a broader perspective. In the following sections of the study, Turkey's export successes achieved by Baykar are presented first, followed by an examination of how Ukraine has emphasized UAVs in its war strategy. New generation projects developed by Turkey, such as the Anka-3, are introduced, and finally, the partnership agreement with Italy within the scope of international cooperation is evaluated.

Machine Learning-Based Prediction of LGS Scores from Middle School Exam Results in Kütahya

Zehra Bilici — 2025-06-18

Accurate prediction of student performance is crucial for improving educational outcomes and enabling early interventions. This study examines the predictability of national high school entrance exam (LGS) scores based on in-school exam results across six core subjects from 818 students in grades 6 to 8 at 15 middle schools in Kütahya, Turkey. Fourteen supervised machine learning regression models, including ensemble methods such as Extra Trees, Random Forest, and XGBoost, were applied independently for each subject to forecast LGS net scores. Performance was evaluated using Mean Squared Error (MSE) and Coefficient of Determination (R²). The results show that ensemble-based models significantly outperform traditional algorithms and achieve high accuracy in all subjects.The findings highlight the effectiveness of these models in capturing complex patterns in educational data and their potential for early identification of at-risk students. This research supports the integration of machine learning techniques into educational assessment systems to foster data-driven, personalized interventions.

Lane Detection in Foggy Images using Generative Adversarial Networks

Elif Filiz — 2025-06-18

autonomous vehicles (AVs) and advanced driver assistance systems (ADAS). These adverse conditions compromise the reliability of critical perception tasks such as lane detection and road environment understanding, thereby increasing the risk to driving safety and stability. In this study, a Generative Adversarial Network (GAN)-based approach was developed to address these issues using one of the leading techniques currently employed in the literature. Using GAN models, realistic foggy road images with varying density levels were generated, and lane detection performance was evaluated on this synthetic dataset. A comparative analysis was conducted between classical image processing techniques and deep learning-based methods. The effectiveness of each approach was evaluated using the Intersection over Union (IoU) metric, which balances both accuracy and spatial coverage in region-based tasks such as lane detection. While classical methods achieved an IoU performance of 89%, deep learning-based techniques reached up to 96%. The results demonstrate that deep learning approaches significantly outperform classical methods in identifying complex road structures, particularly under foggy conditions. These findings highlight the potential of GAN-based data generation and deep learning models to enhance the robustness of perception systems under adverse weather conditions, thereby contributing to safer and more reliable autonomous driving.

Measuring Innovation Capability on Individual Level: Challenges and Solutions

Laman Gasimzade — 2025-06-18

This paper explores the essential role of individual innovation capability in organizational growth and economic development. It highlights the importance of personal entrepreneurial traits, particularly in developed countries, and critiques commonly used assessment tools like 360-degree feedback and InnoQuotient for their limitations. The Berkeley Innovation Index is identified as a more effective alternative due to its focus on individual traits. The study also emphasizes the impact of psychological, organizational, and ethical factors, along with knowledge sharing, on individual innovation. It concludes that innovation at the individual level is complex and requires flexible, inclusive assessment frameworks.

Perceptions of Innovation in Remote vs. Onsite Work Environments: A Qualitative Study

Laman Gasimzade — 2025-06-18

This study explores how hybrid work environments affect employees’ perspectives and experiences of innovation. As existing research has largely focused on productivity and communication side, this qualitative research examines how different working environments establish innovation visibility, collaboration, and recognition. For getting the deep idea about the topic, twenty semi-structured interviews were conducted with hybrid workers from different international companies. Qualitive coding identified five key points; the significance of spontaneous interaction for idea generation, the autonomy and focus enabled by remote work, the risk of innovation being overlooked, and the unique recognition across work environments. This study suggests that hybrid work offers huge potential for innovation, but only when the organization can keep it up with the newly merged outcomes. The study concludes with recommendations for fostering innovation across all work modes through leadership, virtual collaboration, and equal recognition system.

The Contribution of Occupational Safety Management to Business Performance, the Link Between Employee Health and Productivity: The Role of Occupational Safety

Ayşenur ERDİL — 2025-06-18

Occupational safety has become an indispensable element of modern working life and has gained critical importance in terms of both protecting employee health and ensuring the sustainability of business operations. Although various practices and regulations have been developed in this field for many years, the increasing importance placed on human life in recent years has made it necessary to address occupational safety practices in a more systematic and comprehensive manner. The primary objective of this study is to reveal the effects of occupational safety on production efficiency in businesses and to evaluate the contributions of safe working environments to work performance. Within the scope of the study, the identification of potential hazard elements that may be encountered in workplaces, occupational safety management strategies aimed at eliminating these elements, and the role of trained human resources are examined. Both literature review and qualitative research were conducted within this scope. Research indicates that individuals working in safe environments demonstrate higher performance and experience fewer interruptions in production processes. In this regard, the development of effective occupational safety policies is both protective of employee health and conducive to increasing business productivity.

MAG Gazaltı Kaynak Yöntemi ile Birleştirilen SPH270-C Çeliğinin Mikroyapı ve Çekme Özelliklerine Kaynak Geriliminin Etkisinin İncelenmesi

Muhammed Elitaş — 2025-07-01

Bu çalışmada genellikle otomotiv endüstrisinde kullanılan SPH270-C çeliği ele alınmıştır. MAG
gazaltı kaynak yöntemi ile diğer kaynak parametreleri sabit tutularak 3 farklı gerilim değerlerinde (15 V,
17 V, 19 V) birleşimler elde edilmiştir. Kaynak işleminde meydana gelen yüksek sıcaklık sebebiyle
mikroyapıda değişikler meydana gelmektedir. Mikroyapı değişimlerini incelemek amacıyla metalografi
çalışması uygulanmıştır. Ayrıca mekanik özelliklerin değerlendirilmesi amacıyla çekme deneyleri
gerçekleştirilmiştir. Kaynak geriliminin birleşimlerin çekme dayanımına etkisi ele alınmıştır. Sonuç
olarak, kaynak işleminde meydana gelen yüksek sıcaklık sebebiyle esas metal (EM), ısının tesiri altındaki
bölge (ITAB) ve kaynak metali (KM) olmak üzere 3 farklı bölge meydana gelmiştir. EM yapısı ağırlıklı
olarak ferrit fazından oluşurken, EM’den ITAB’a doğru tanelerde irileşme ve KM’de ise kolonsal
tanelerin meydana geldiği görülmüştür. Çekme dayanımı kaynak gerilimindeki artışa bağlı olarak artış
göstermiştir.

Transforming the Construction Sector through Lean Management Practices: A Case of Turkey’s Construction Sector

Shurouq Mosad — 2025-07-01

This study explores the application of Lean Construction principles to improve efficiency and reduce
waste in Turkey’s civil and highway construction projects. Based on a survey of 269 industry professionals, it
examines the impact of five key Lean factors—Culture/People, Continuous Improvement, Customer Focus,
Waste Elimination, and Standardization—on project performance. Statistical analysis revealed that
standardization and waste elimination significantly enhance efficiency and cost control, while cultural and
continuous improvement practices showed weaker influence. The findings contribute a localized Lean
framework suited to the Turkish context and offer practical insights for improving infrastructure project
outcomes.

Microwave Antenna Systems for Accurate Weather Monitoring in Solar Power Plants

Faisal RAHIMI — 2025-07-01

Accurate and real -time weather monitoring is necessary for optimizing to the efficiency and
reliability of solar energy. This paper examines the integration of the microwave antenna system for
environmental sensing in the installation of solar photovoltaic (PV) systems, which focuses on their ability
to detect large atmospheric variables such as cloud cover, humidity and rainfall. By using ground -based
multichannel microwave radiometer (GMR), work with frequencies including 10 GHz, 18 GHz and 36
GHz, the system measures atmospheric brightness temperature (TB) to monitor the environmental
conditions affecting solar radiation. A sun-tracking calibration method is used to ensure all weather
operations, increase the accuracy and reliability of data acquisition. The architecture supports spontaneous
integration with the existing plant infrastructure using a 915 MHz wireless telemetry and is scalable for
both utility-scale and distributed photovoltaic systems. In addition, artificial neural networks (ANNs) are
used to model nonlinear between TB data and solar cell outputs, enabling accurately short-term energy
forecasting. The proposed approach supported by existing literature shows strong capacity to increase the
operational plan, improve forecast accuracy and support intelligent energy management in solar systems.
Limits and future directions, including phase-sensory antennas and hybrid sensor fusion, are also discussed.

Landslide Detection Using Transformers-Based Deep Learning Models

Fatih Uysal — 2025-07-01

Landslides are caused by earthquakes and rainstorms, especially in densely populated areas.
There are different types of landslides depending on the type of material such as soil, rock, rubble, and the
type of movement such as falling, sliding, and overturning. In order to automatically detect landslides
with artificial intelligence, a dataset was first created from open source images on the internet. There are
two different classes in the dataset itself, land and landslide. Data augmentation was done in order to have
an equal number of images containing classes in the datasets obtained in different amounts. Thus, a total
of 400 image datasets were created, 200 from each class. In addition, normalized and resized operations
were performed in data preprocessing. Then the dataset is randomly divided into 80% training and 20%
validation and testing. Transformers-based deep learning models were used to perform Landslide
detection. These models are Swin transformer, Vision Transformer (ViT) and Bidirectional Encoder
representation from Image Transformers (BEiT). For detection processes, the results obtained using a
total of three models, namely ViT, BEiT, and Swin Transformer, were compared. When the obtained
results were analyzed, it was observed that both f1 score and accuracy were over 90%. Thus, in the future,
this and similar classification studies can automatically detect landslides with artificial intelligence.

Development of a solar radiation prediction model using Fine Gaussian SVM in machine learning

F. Sidre OGLAKKAYA — 2025-07-01

The global transition toward renewable and clean energy sources is imperative for sustainable
development and for reducing the adverse environmental effects associated with fossil fuel use. Among
renewable options, solar energy is one of the most promising due to its abundance, scalability, and
minimal environmental impact. Effective utilization of solar energy in each region requires a
comprehensive understanding of solar radiation components, particularly diffuse radiation, which
significantly influences the performance of solar energy systems. Moreover, accurate estimation of solar
radiation is essential for climate change research, a critical area of global scientific inquiry. Currently,
satellite-based solar radiation forecasting systems offer high spatial and temporal resolution data,
including global, direct, and diffuse radiation components, making them valuable tools for the planning
and optimization of solar power systems. In this study, satellite-based forecasting models were utilized to
estimate diffuse solar radiation for the selected region. To enhance prediction accuracy, a Fine Gaussian
Support Vector Machine (SVM) algorithm was employed to model solar irradiance based on key
meteorological and geographical inputs. Additionally, the Ångström coefficients were determined using
MATLAB to support the development of the predictive model. The performance of the proposed model
was evaluated using various statistical error metrics, including root mean square error (RMSE), mean
absolute error (MAE), and coefficient of determination (R²). The results indicate that the Fine Gaussian
SVM model provides highly accurate predictions of diffuse solar radiation, demonstrating its
effectiveness for solar resource assessment and planning.

Fire Detection from Forest Images Using Multiple Deep Learning Models

Fatih Uysal — 2025-07-01

Forest fires can occur from natural or unnatural causes. While unnatural fires usually consist of
flammable materials thrown around unconsciously; Natural fires occur regularly depending on forest
cover, forest type, soil type and climate. Within the scope of this study, fire detection processes were
carried out with artificial intelligence using open source dataset in order to detect fires in forests. The
dataset used includes fire and non-fire images. There are 480 images in total, 240 for each class in the
binary classification study. For classification, special attention has been paid to the equal distribution of
classes so that the network training in deep learning models can be carried out in the best way. The
dataset is randomly split into 80% train, 20% validation. The dataset distribution consists of 384 images
for training and 96 images for validation. Artificial intelligence-based deep learning models used for
classification are Residual Networks (ResNet), Bidirectional Encoder representation from Image
Transformers (BEiT) and Swin Transformer. When the fire detection results were examined, it was
observed that the classification accuracies were above 87% and the f1-score was above 86%. In future
studies, hybrid and/or ensemble models can be developed by using more deep learning models to further
improve the detection process.

Forecasting Tourist Arrivals in Albania Using Time Series Models and Currency Fluctuations

Agresa Qosja — 2025-07-01

This study aims to forecast tourist arrivals in Albania by analysing quarterly data from 2016 to
the end of 2024, with a particular focus on the role of currency fluctuations, specifically the Euro, which is
commonly used in conjunction with the Albanian Lek. While a number of economic indicators were
considered, including GDP and other foreign currencies, the only one that showed a substantial correlation
with tourist traffic was the Euro. To predict and project tourist flows, time series models were employed,
including Exponential Smoothing (ETS), ARIMA, TBATS, and a regression model where the independent
variable is Euro and ARIMA-modeled residuals. The forecasting performance of each model was evaluated
using standard accuracy metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and
Mean Absolute Percentage Error (MAPE). Results indicate that the ETS model outperformed all others,
achieving the lowest error rates. ARIMA and TBATS indicated higher error margins, and the regression
residual hybrid approach performed moderately. These findings highlight the explanatory power of the
Euro in mimicking tourist demand in Albania and signal ETS to be the optimal model for short-term
forecasting in this regard. The study provides valuable guidelines for tourism planners and policymakers
seeking data-driven strategies for anticipating tourist flows.

Innovative Pedal Design Using a Double Rocker Mechanism: An Analysis of Link Length Suitability Across Rider Heights

Osman ACAR — 2025-07-01

This study explores the transmission performance of a novel tricycle pedal mechanism based on
an adjustable Double Rocker Mechanism (DRM), designed to enhance mechanical efficiency across a range
of rider anthropometries. Conventional crank-based tricycle systems often fail to accommodate user height
variation effectively, leading to suboptimal torque transfer and increased discomfort. To address this
challenge, the proposed DRM allows independent adjustment of both input and output link lengths, tailored
to individual rider dimensions. The relationship between link geometry, transmission angles, and
anthropometric parameters was evaluated using the Bee Algorithm, a bio-inspired optimization method
well-suited for multidimensional design problems. Constraints were introduced to ensure biomechanical
efficiency and mechanical feasibility. Results indicate that appropriate link length adjustments significantly
improve transmission angles and overall pedaling performance across various rider heights. The proposed
mechanism thus enables a single tricycle platform to serve a broader user population without compromising
mechanical advantage, comfort, or energy efficiency.

Determining Categories of Leaf Images Using Transfer Learning of VGGNet

Rıfat Aşlıyan — 2025-07-01

Recognizing plant species from their leaves holds great value across many domains, from
agriculture and forestry to pharmacology and the assessment of regional biodiversity. This research
focused on creating automated systems capable of identifying 12 distinct plant species solely from leaf
images. To accomplish this, we utilized the powerful VGGNet16 convolutional neural network. After
dividing our leaf images into a train set and a test set, we enriched the training data through an on-the-fly
augmentation process. By applying transformations like scaling, translation, and rotation to the images
during the network's training epochs, we improved its ability to generalize to future data, thereby
enhancing its overall performance. We applied with two approaches as training a standard VGGNet16
network and training a pre-trained VGGNet16 that had already learned features from a massive image
database. The effectiveness of these systems, configured with different parameters, was benchmarked on
the test dataset using classification metrics as F1-Score , Accuracy, and the ROC curve. Performance of
the pre-trained VGGNet16 network was observed to be markedly superior, achieving an F1-score of
98.8% compared to the 93.4% highest score from the standard VGGNet16.

FEM-Based Numerical Analysis of a Shielded Interdigitated Capacitive Humidity Sensor

Serhat Küçükdermenci — 2025-07-01

This study focuses on analyzing the humidity-dependent capacitive behavior of the sensor, driven
by a polymer dielectric layer whose permittivity is modulated by ambient relative humidity (RH). The
sensor's intricate architecture was meticulously modeled in 3D, incorporating a silicon substrate, copper
shielding layers, a silicon dioxide dielectric, and interdigitated copper electrodes embedded within the
humidity-sensitive polymer. The simulation methodology involved an electrostatic analysis with a 1 V
excitation applied to one set of interdigitated electrodes, while the other set and the shielding layers were
grounded to define the electric field. To systematically evaluate performance, a parametric sweep was
executed for RH, ranging from 0% to 100%. The principal results demonstrate a clear and highly linear
correlation between the sensor's capacitance magnitude and the RH, with an approximate change from 707
pF at 0% RH to 767 pF at 100% RH. This translates to a calculated sensitivity of approximately 0.6
pF/%RH, indicating effective detection of humidity variations. Furthermore, detailed field analyses of
electric field intensity, electric flux density, electrostatic potential, and stored energy density, visualized
across sensor surfaces and along a defined polyline, consistently confirmed the strategic concentration of
electric fields within the inter-electrode gaps. These findings validate the active sensing mechanism and
underscore the efficacy of the shielding layers in enhancing measurement stability. In conclusion, this
numerical investigation confirms the robust performance and design feasibility of the proposed shielded
interdigitated capacitive sensor for accurate and reliable humidity sensing applications.

Predicting Battery Health for Electric Vehicles using Machine Learning Approach

Abdelmounaim Bensabeur — 2025-07-01

Research utilized data-driven models to investigate SoH estimation methodologies for lithium
ion batteries, particularly focusing on their effectiveness in capturing degradation trends. The study
evaluated four different deep learning approaches-DNN, CNN, RNN, and LSTM-using various metrics,
including MAE, RMSE, R², and validation loss. Results reveal that the LSTM model outperforms the
others, achieving the lowest MAE (0.1293), RMSE (0.1680), and validation loss (0.0282), with an R² of
0.9790, making it the most reliable predictor of battery SoH. The study highlights a strong linear
correlation between SoH and parameters such as capacity and charge voltage, affirming their role as
critical indicators of battery health. Conversely, temperature exhibited negligible impact on SoH within
the narrow range studied, necessitating further research under diverse environmental conditions.
Anomalies in terminal current during charge-discharge cycles suggest potential operational irregularities
requiring deeper analysis. The study underscores the limitations of CNN in modeling temporal
dependencies, advocating for hybrid architectures like CNN-LSTM for enhanced predictive accuracy as
well as narrow temperature range of 25oc. Findings also demonstrate consistent SoC transitions across
cycles, emphasizing the stability of the battery's charge-discharge behavior and its implications for long
term durability.

Marketing social media of enterprises in Kosovo as a developing country. Challenges and benefits.

Dr. Bajram R Hasani — 2025-07-01

The primary aim of this study is to investigate the impact of social media marketing on the sales
performanceof businesses in Kosovo and to analyze the use of key social media indicators from the
perspective of business consumers in the country. The research methodology is based on both quantitative
and qualitative approaches. Quantitative data were collected through structured and semi-structured
questionnaires. These questionnaires were distributed to marketing managers or individuals responsible for
marketing in approximately 120 businesses in Kosovo, selected through purposive sampling, as well as to
400 consumers selected through probabilistic cluster sampling.
Three hypotheses were formulated and tested using ANOVA and SPSS software. In addition, cross
tabulation analysis was employed to interpret statistics regarding the most commonly used categories of
social media, the purposes of use, and the frequency of use by both enterprises and individuals in Kosovo.
The research findings reveal a noticeable increase in business sales as a result of the influence of social
media marketing elements when compared to the effects of traditional marketing components.

Effect of Thinning on Belowground Biomass in Chestnut Coppices

Ali Kemal ÖZBAYRAM — 2025-06-30

Thinning maintenance is frequently applied in forest management, and it regulates the stand structure by reducing stand density and encouraging faster growth of trees left in the stand. Tree roots have sensitivity to environmental factors that change due to interventions such as thinning. The aim of this study is to investigate the effects of thinning applied at different intensities on the belowground biomass in Anatolian chestnut (Castanea sativa Mill.) coppices in Gölcük region (Kocaeli). In thinning experiments set up in chestnut coppices in Gölcük region, thinning varying at rates of 0% (control), 20% (light), 35% (heavy) and 50% (very heavy) of the basal area were applied. Seven years after thinning, four different intensities of random trial plots were determined within the trial field and root sampling was performed from six different points of each plot according to the soil core technique. The roots cleaned from the soil were classified as fine root (0-2 mm), small root (2-5 mm) and dead root. The variance analysis revealed significant variability in the biomass belowground of chestnut coppices that were thinned at different intensities. Belowground biomass in chestnut coppices varied between 530.4 g m^-2and 994.2 g m^-2. Total root biomass, live root biomass, fine root biomass, and dead root biomass in very heavily thinned sites were 60%, 44%, 43%, and 300% higher than in other sites, respectively. Small root biomass did not vary among sites. The increase in rhododendron density under the forest caused by intense thinning led to the high belowground biomass in very heavy thinned sites. As a result, while the belowground biomass does not change significantly at thinning intensities of up to 35% in chestnut coppice, the underground biomass increases due to the increase in the density of the forest understory at very heavy thinning of up to 50%.

Analysis of Carbon Footprint Calculation Methods in Line with Carbon Neutral Targets; Industrial Facility Example

Uğur KILIÇ — 2025-06-30

Increasing environmental pollution concerns are one of the agenda items of the whole world. In this context, studies are being carried out to increase renewable energy, reduce fossil fuel consumption, and reach a carbon neutral target. One of the steps taken towards becoming carbon neutral is carbon footprint studies. Many studies are being carried out in the world and in EU countries to reduce carbon footprint. The most important of these are; European Green Agreement, Emission Trading System, Fit for 55 package, Renewable energy investments, carbon footprint monitoring and limitation methods, individual and institutional awareness studies. In this study conducted in line with the targets of carbon footprint monitoring and limitation, the carbon footprint of an example industrial facility was calculated and it was determined that the majority of this footprint was due to electrical consumption, and renewable energy applications that could reduce this electrical consumption were determined.