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- MakaleYalnız künye2026Scopus SJRQ1Thermal inactivation kinetics of Listeria monocytogenes in fat/water emulsions and fat tissue – effect of fat content
Ozturk, Samet; Huang, Lihan; Hwang, Cheng-An; Sheen, Shiowshuh
Significantly increased thermal resistance was found in Listeria monocytogenes inoculated to beef tallow. However, how the relative compositions of fat and water in emulsions affect bacterial survival during heating has not been evaluated. This study aimed to evaluate the effect of fat levels in solid fat/water (SF/W) emulsions on thermal resistance of L. monocytogenes. SF/W emulsions, containing 0, 25, 50, 60, and 75 % of tallow by weight, were prepared by mixing tallow with soybean lecithin and Tween 80 in 0.1 % peptone water using an ultrasonic processor. A 4-strain cocktail of L. monocytogenes was inoculated to the emulsions and subjected to isothermal heating between 55 and 65 °C. The survival curves were analyzed to estimate the kinetic parameters and evaluate thermal resistance. Linear survival was observed in emulsions with fat below 50 %. However, increased thermal resistance, evidenced by the increasing D and z values with the fat content, was observed. For example, the average D values in SF/W emulsions at 60 °C are 0.54, 1.04, and 2.53 min, respectively, with 0, 25, and 50 % fat. For SF/W emulsions with 60 and 75 % fat and in beef trim fat (BTF, 86.9 % fat), the survival curves became convexly nonlinear and could be described with the Weibull model. The exponents of the Weibull model range between 0.45 and 0.456, suggesting almost identical convexity of the survival curves. The convex survival curves indicate initial sensitivity to heat but the resistance increases with time at each temperature. The estimated time to a 5-log reduction becomes 11.9 and 26.4 min in 60 and 75 % SF/W emulsions, respectively, and 36.5 min in BTF at 60 °C. The study demonstrates the effect of fat levels on thermal resistance of L. monocytogenes in SF/W emulsions and BTF. The results of this study may help the food industry to properly kill L. monocytogenes in fat-containing meat products during thermal processing.
AtıfScopus2WoS2OpenAlex2
İncele - MakaleYalnız künye2026Scopus SJRQ2A Comprehensive Deep-Learning Framework Integrating Lesion Segmentation and Stage Classification for Enhanced Diabetic Retinopathy Diagnosis
İncir, Ramazan; Bozkurt, Ferhat
Diabetic retinopathy (DR), one of the most prevalent microvascular complications of diabetes, stands as a leading cause of vision loss globally. Due to its asymptomatic nature in early stages, delayed diagnosis and staging may result in irreversible visual impairment. Therefore, accurate and simultaneous lesion segmentation and stage classification of DR are of critical clinical importance. In this study, a two-stage, end-to-end, holistic framework is proposed for automated DR diagnosis. In the first stage, an Improved U-Net architecture enhanced with residual blocks and additional convolutional layers is employed to segment small and low-contrast lesions such as microaneurysms, hemorrhages, and hard/soft exudates with high precision. Model hyperparameters are optimized using the harmony search algorithm to enhance training efficiency. In the second stage, lesion-based weight maps obtained from the segmentation step are applied to fundus images from the APTOS dataset, generating enriched inputs for classification. A vision transformer (ViT)-based model, augmented with a Convolutional Block Attention Module (CBAM), is utilized to improve feature extraction. In addition, features derived from ViT are further refined using a graph convolutional network (GCN) and traditional machine-learning classifiers. The proposed approach achieves high performance in multi-class DR stage classification. Compared to existing studies, the framework demonstrates notable improvements in both segmentation and classification accuracy, offering a robust and generalizable solution for DR diagnosis.
AtıfScopus2WoS1OpenAlex3İncele - Derleme MakaleYalnız künye2026Scopus SJRQ1A review study of recent advancements in polymer heat exchanger technology in the aspect of heat transfer
Celen, Ali; Budak, Selahattin; Celen, Pınar; Dalkılıç, Ahmet Selim
Many applications use metallic heat exchangers, but some operating conditions, such as desalination, exhibit wear and corrosion problems. Thus, it is important to develop and use alternative materials to metals for such conditions. Although polymers have low thermal conductivity, corrosion resistance, cost-effectiveness, and lightweight, polymer heat exchangers can be constructed to overcome this problem. They were introduced in the last decades; advancements in 3D printing technology help manufacture complex, novel, and thin heat transfer surfaces. Although review studies are limited, recent research has not explored heat transfer-based reviews of heat exchangers produced by additive manufacturing and polymer hollow fibre methods. In this study, polymer heat exchanger works were reviewed and classified according to tube diameter as polymer and polymeric hollow fibre heat exchangers as the most updated ones. Generally, the studies focused on the thermal performance of heat exchangers constructed with metal and polymer materials, and the results were compared to determine the usability of polymer-based ones. As a result, polymeric hollow fibre heat exchangers may compete with metallic ones due to low wall resistance and enhanced heat transfer surface area because of the development of manufacturing technology. Also, the cost and weight of polymeric hollow fibre exchangers are lower, and they can be operated under corrosive conditions. Finally, it is obvious that increased research is needed to enhance the properties of polymer materials to increase the performance of this type of heat exchanger.
AtıfScopus3WoS4OpenAlex2İncele - MakaleAçık Erişim2026Scopus SJRQ1Predicting suicide probability in young adults: contributions of vulnerable narcissism, grudge, and demographic variables
Çelebi, Gülin Yazıcı
Suicide is a significant public health problem worldwide. The aim of this study is to examine the potential effects of levels of vulnerable narcissism and grudge on the likelihood of suicide. In addition to these variables, the potential effects of some demographic variables (gender, marital status, age, socioeconomic status, education level, perceived religiosity, number of siblings) were also investigated. A convenience sampling method was used to determine the study group. The study group consisted of a total of 500 individuals; 368 (73.6%) were women and 132 (26.4%) were men. The mean age of women was 26.31 (SD = 9.60, min-max = 18–64), the mean age of men was 29.01 (SD = 10.76, min-max = 18–55), and the mean age of the entire sample was 26.84 (SD = 9.88, min-max = 18–64). The Suicide Probability Scale, Vulnerable Narcissism Scale, Grudge Scale, and Personal Information Form were used as data collection tools in the study. Pearson correlation coefficient was used to examine the relationships between variables in the analysis of the data. Multiple linear regression analysis was conducted to examine the extent to which vulnerable narcissism, grudge, and sociodemographic characteristics are associated with suicide probability. The regression model showed that the independent variables significantly predicted the probability of suicide. According to the results obtained, the independent variables explained 38% of the variation in the probability of suicide {[R = 0.619, R2 = 0.383, F(8, 499)= 38.12, p < 0.0001]}, and vulnerable narcissism and grudge are positive predictors of the probability of suicide. Vulnerable narcissism (β = 0.34, p < 0.001) and grudge (β = 0.22, p < 0.001) are positive predictors of the probability of suicide. Age (β = −0.23, p < 0.001), education level (β = −0.08, p = 0.003), and SEL (β = −0.19, p < 0.001) are significant variables that negatively predict the probability of suicide. Based on the results, it can be said individuals with high levels of narcissism and grudge have a higher probability of suicide, and that some socio-demographic characteristics may influence these relationships. The findings are discussed in light of the literature.
- MakaleAçık Erişim2018Scopus SJRQ2Comparison of landscape metrics for three different level land cover/land use maps
Sertel, Elif; Topaloğlu, Raziye Hale; Şallı, Betül; Algan, Irmak Yay; Aksu, Gül Aslı
This research aims to investigate how different landscape metrics are affected by the enhancement of the thematic classes in land cover/land use (LC/LU) maps. For this aim, three different LC/LU maps based on three different levels of CORINE (Coordination of Information on The Environment) nomenclature were created for the selected study area using GEOBIA (Geographic Object Based Image Analysis) techniques. First, second and third level LC/LU maps of the study area have five, thirteen and twenty-seven hierarchical thematic classes, respectively. High-resolution Spot 7 images with 1.5 m spatial resolution were used as the main Earth Observation data to create LC/LU maps. Additional geospatial data from open sources (OpenStreetMap and Wikimapia) were also integrated to the classification in order to identify some of the 2nd and 3rd level LC/LU classes. Classification procedure was initially conducted for Level 3 classes in which we developed decision trees to be used in object-based classification. Afterwards, Level 3 classes were merged to create Level 2 LC/LU map and then Level 2 classes were merged to create the Level 1 LC/LU map according to CORINE nomenclature. The accuracy of Level 1, Level 2, Level 3 maps are calculated as; 93.50%, 89.00%, 85.50% respectively. At the last stage, several landscape metrics such as Number of Patch (NP), Edge Density (ED), Largest Patch Index (LPI), Euclidean Nearest Neighbor Distance (ENN), Splitting Index (SPLIT) and Aggregation Index (AI) metrics and others were calculated for different level LC/LU maps and landscape metrics values were compared to analyze the impact of changing thematic details on landscape metrics. Our results show that, increasing the thematic detail allows landscape characteristics to be defined more precisely and ensure comprehensive assessment of cause and effect relationships between classes.
AtıfScopus84WoS73OpenAlex88İncele - MakaleAçık Erişim2026Scopus SJRQ1Machine learning–enabled ECG arrhythmia classification: a systematic and educational study from signal processing to decision support
Melek, Negin
Electrocardiogram (ECG) signals play a critical role in the early detection of cardiac arrhythmias, which remain a major cause of morbidity and mortality worldwide. While deep learning approaches have achieved high classification accuracy, their increasing complexity often limits interpretability and practical applicability. This study presents a systematic and interpretable framework for multi-class ECG arrhythmia classification, examining the effects of signal processing, feature extraction, feature selection, and evaluation strategies on classification performance. Experiments were conducted on the MIT-BIH Arrhythmia Database using two feature representations: (i) morphological and temporal features, and (ii) a compact wavelet-based representation. Sequential Forward Feature Selection (SFFS) revealed that classification performance saturates at approximately 15 features, indicating that most discriminative information is captured within a compact subset. Using this feature space, the support vector machine (SVM) achieved the best overall performance, reaching 98.54% accuracy with stable results across different configurations. The wavelet-based representation further improved performance balance, yielding lower Golden Distance (GD) values (as low as 0.0249), indicating more consistent behavior across evaluation metrics. Overall, the results demonstrate that carefully designed feature extraction and selection enable classical machine learning methods, particularly SVM, to achieve high and reliable performance, providing an interpretable alternative to more complex black-box models in ECG arrhythmia classification.
AtıfScopus1İncele - MakaleYalnız künye2026Scopus SJRQ1Beyond reactive displacement: anticipatory mobility, climate governance, and the climate passport
Limon, Onur; Gürdal Limon, Elif
This article examines how existing mobility governance frameworks can be re-theorized to better align with the temporal dynamics of climate risk and the conditions shaping mobility decision-making. The analysis demonstrates that current mobility regimes systematically privilege discretionary, event-based protection while offering limited legal predictability or rights-based recognition for anticipatory and incremental mobility. Building on this analysis, the article develops the Climate Passport as a diagnostic framework rather than an operational policy instrument. The framework specifies three governance criteria that any adequate anticipatory mobility regime would need to satisfy: early recognition of climate risk before displacement becomes unavoidable, rights-based and legally predictable status for those facing slow-onset environmental harm, and institutional coherence across the currently fragmented domains of migration, refugee protection, and climate adaptation governance. Informed by the historical Nansen Passport and grounded in human rights and climate justice scholarship, the framework offers a systematic lens for examining how institutional architectures structure the timing, legitimacy, and accessibility of mobility under climate change. By reframing climate-related mobility as a structurally embedded component of adaptation rather than an exceptional failure of in situ resilience, the article contributes to interdisciplinary debates on migration, adaptation, and global justice.
- MakaleYayıncıda açık erişim2026Scopus SJRQ1Chitosan-Based Colloidal Delivery Systems as Natural Carriers for Propolis: Physicochemical Characterization and Application in Walnut Sausages
Mesci Dindar, Esra; Arserim-Uçar, Dılhun Keriman; Ayna, Adnan
In this study, a chitosan-based colloidal delivery systems (CDs) was developed as a natural delivery system for phenolic compounds from propolis extracts obtained via accelerated solvent extraction (ASE) and evaluated for improving the functional properties of walnut sausages. ASE propolis extract was incorporated into a chitosan (C)-based colloidal delivery system formulated with varying concentrations of diacetyl tartaric acid ester of mono- and diglycerides (DATEM) and lecithin (L). The formulation containing 2.5% chitosan, 12% DATEM and propolis, with a particle size of 265.9 nm and a ζ-potential of +40.8 mV, was selected for further characterization and food applications. The phenolic encapsulation of CDs depended on the structural characteristics of individual phenolic compounds, with chrysin (70.51%), vanillin (65.12%), and benzoic acid (51.26%) exhibiting the highest encapsulation; CD-treated walnut sausages decreased the lipid oxidation and improved the storage stability of the walnut sausages during storage. These findings indicate that the chitosan-based colloidal delivery system is a promising carrier for improving the encapsulation of propolis phenolics in walnut sausages.
- MakaleYalnız künye2026Scopus SJRQ2LC–MS/MS Phenolic Profiling and Antioxidant, Antimicrobial, and Antidiabetic Activities of Caucasian Propolis
Gültekin, Deniz Canbolat; Turan, İbrahim; Akmeşe, Osman; Gültekin, Ergün; Demir, Selim; Aliyazicioglu, Yüksel
Propolis is a natural bee product rich in phenolic and flavonoid compounds and is well known for its remarkable biological properties. In this study, the phenolic compositions of propolis samples collected from various localities in the Caucasus region of Türkiye were characterized using LC–MS/MS, and their antioxidant, antidiabetic, and antibacterial activities were also evaluated. LC–MS/MS analyses identified caffeic acid, ferulic acid, and p-coumaric acid as the major phenolic acid compounds, while pinocembrin, chrysin, and galangin were determined as the major flavonoid constituents. Overall, these findings indicate that the antioxidant capacity of propolis samples obtained from different regions may be particularly associated with these key phenolic and flavonoid compounds. Consistent with this composition, total phenolic and flavonoid contents ranged from 34.72 to 120.54 mg GAE/g and 6.88 to 47.19 mg QE/g, respectively. The extracts exhibited a relatively high antioxidant capacity, with FRAP values between 60.47 and 311.87 mg TE/g and DPPH IC50 values ranging from 215.80 to 37.96 µg/mL. Regarding antidiabetic activity, the IC50 values for α-amylase and α-glucosidase inhibition were determined as 5.86–3.47 and 12.22–5.02 mg/mL, respectively, indicating a pronounced antidiabetic potential. Antimicrobial analyses revealed activity against E. faecalis, B. cereus, E. coli, K. pneumoniae, and C. albicans. Overall, this study provides a comprehensive LC–MS/MS-based phenolic profile of Caucasian propolis and demonstrates its antioxidant properties together with its antidiabetic potential.
AtıfScopus2WoS2OpenAlex4İncele - MakaleYalnız künye2025Scopus SJRQ1The effect of testicular self-examination education on health beliefs in adult men: a systematic review and meta-analysis
Korkmaz, Mehmet; Yildirim, Tugba; Altun, Elif; Uzun, Sevda; Kayalar, Aycan
Purpose: Testicular cancer is primarily detected by the patient or their sexual partner and is most commonly diagnosed in men between the ages of 15 and 35. This study aims to assess the impact of self-examination education on health beliefs among adult men. Materials and methods: This study, which is a systematic review and meta-analysis, obtained studies by searching PubMed, Web of Scıence, EBSCOhost, Google Scholar, ProQuest and YOK Thesis Centre databases without a year limitation. After the reviews, 12 studies were included in the study. Data were synthesised by meta-analysis and narrative methods. Results: The findings of this systematic review and meta-analysis demonstrate that self-examination education significantly influences health beliefs in adult men (SMD: 1.790; 95% CI: 0.954 to 2.624; Z = 4.198, p = 0.00; I² = 99.109%). Conclusion: This study highlights that self-examination education may play an influential role in the shaping of adult men’s health beliefs, potentially contributing to the early detection and prevention of testicular cancer.
- MakaleYalnız künye2025Scopus SJRQ1A comparative study of Kolmogorov-Arnold networks and multilayer perceptrons for software defect prediction with SHAP analysis
Turk, Salih; Coskuncay, Ahmet
Software defect prediction is essential for enhancing software quality by detecting software errors early in the development lifecycle. This article presents the usage and a detailed evaluation of Kolmogorov-Arnold Networks (KAN) and Multilayer Perceptrons (MLP) over eleven prevalent software defect prediction datasets, namely CM1, JM1, KC1, KC3, MC1, MC2, MW1, PC1, PC3, PC4, and PC5. The defect prediction models were established and evaluated using five principal performance metrics: Accuracy, Precision, Recall, F1 Score, and Area Under the Curve (AUC). The experimental findings indicate that KAN consistently surpasses MLP in all datasets and metrics. KAN attains an average accuracy enhancement of almost 12% compared to MLP and demonstrates substantial improvements in precision and recall, rendering it more dependable in detecting defected modules. The paired t-test findings indicate that the performance disparities between KAN and MLP are statistically significant (p-value< 0.05) for the majority of metrics. Also SHAP analysis was performed to determine the important rankings of features for all datasets according to the FastKAN model. This study underscores the promise of KAN as a superior alternative to traditional neural networks for software defect prediction. The results highlight the significance of the usage of sophisticated neural network topologies in tackling intricate software engineering challenges.
AtıfScopus3WoS1OpenAlex2İncele - MakaleYalnız künye2026Scopus SJRQ3Purity of Density Operators for Non-Orthogonal Quantum States with Bounds and Applications to Quantum Fisher Information
Amirov, Orhan; Celik, Necati; Karabacak, Mesut
In this study, we investigate the purity of density operators constructed from non-orthogonal quantum states. Starting from a general statistical mixture of normalized states, we derive an explicit expression for the purity in terms of the associated probability distribution and the pairwise overlaps between the states, quantified by |⟨ψi|ψj⟩|2. This formulation clearly separates the contributions arising from classical probability weights and quantum interference effects induced by non-orthogonality. To further analyze the impact of state overlaps, we introduce an average overlap quantity that captures the global contribution of non-orthogonal components. Based on this representation, we establish upper bounds for the purity using both global and local overlap parameters. In particular, we show that the purity can be effectively controlled by the maximum pairwise overlap, eliminating the need to compute all individual inner products explicitly. A refined bound is also obtained by incorporating local overlap measures, leading to a tighter characterization of the system. In addition, we develop a general framework connecting overlap-based purity bounds with quantum Fisher information (QFI) in parameter-dependent quantum systems. We show that the overlap structure can be used to indirectly control and estimate the behavior of QFI, especially in cases where direct spectral analysis is not tractable. The applicability of the proposed approach is demonstrated through an explicit example involving non-orthogonal qubit states. These results provide a practical framework for estimating the mixedness of quantum states and offer useful insights into the interplay between non-orthogonality, purity, and quantum Fisher information in quantum information theory.
- Derleme MakaleYalnız künye2026Scopus SJRQ1Immunosuppressant adherence levels in kidney transplant recipients measured by electronic monitoring devices: A systematic review and meta-analysis
Tuncer, Metin; Yalcinkaya, Turgay; Erol, Ahmet; Özalp, Cemal
Background: Kidney transplantation improves survival and quality of life in patients with end-stage renal disease; however, long-term graft success depends on adherence to immunosuppressive therapy. Objective: To quantify adherence levels among kidney transplant recipients using electronic monitoring devices. Methods: PubMed, Web of Science, Scopus, and the Cochrane Library were systematically searched for studies published between 2000 and 2025. A random-effects meta-analysis was conducted including studies that assessed adherence using electronic monitoring devices. Results: Seven studies with a total of 1025 kidney transplant recipients were included. The pooled mean adherence measured using electronic monitoring devices was 94.39%. In studies with monitoring durations of three months or longer, adherence decreased to 90.50%. Substantial heterogeneity was observed across studies. Conclusion: Although electronically measured adherence appears generally high, its decline over longer monitoring periods indicates that adherence is dynamic and may decrease over time. However, these estimates should be interpreted cautiously because electronic monitoring devices cannot confirm actual medication ingestion. Furthermore, intentionally non-adherent patients may be less likely to participate in or remain engaged with electronic monitoring studies and may therefore be underrepresented, potentially leading to an overestimation of true adherence. Even modest reductions in adherence may carry important clinical consequences, underscoring the need for sustained monitoring.
- Derleme MakaleAçık Erişim2026Scopus SJRQ1Artificial-intelligence–based developmental screening for pre-school children (ages 3–6): a scoping review of deployed tools, clinical validity, equity, and implementation in guidance-center settings
Arslan, Adem; Gül, Fatih
Background – Early identification of neurodevelopmental conditions during the pre-school years (ages 3–6) is critical for timely intervention, yet traditional questionnaire-based screeners suffer from low positive predictive value, late detection, and inequitable access. Artificial intelligence (AI) and machine-learning (ML) methods promise more objective, scalable identification, and two devices (Cognoa Canvas Dx, 2021; EarliPoint Evaluation, 2023) are already US Food and Drug Administration (FDA)-authorized. Recent reviews summarize the technical literature, but few address how deployed tools perform on clinical validity, equity, and real-world implementation, particularly outside high-income, English-speaking settings. Methods – Following Joanna Briggs Institute methodology and the PRISMA extension for scoping reviews (PRISMA-ScR), we searched seven databases (1 January 2020–15 June 2026) and charted primary validation studies of AI/ML developmental-screening tools for children aged 72 months or younger. We mapped tools by modality, diagnostic performance, regulatory status, geographic origin, and, as a structured analytic axis, demographic subgroup (equity) reporting and deployment readiness. Results – Sixteen primary AI-tool studies met inclusion, spanning eight modality classes (eye-tracking, multimodal questionnaire-plus-video, computer-vision home/clinical video, tablet/smartphone digital phenotyping, voice/acoustic ML, electronic-health-record risk prediction, questionnaire-based ML, and wearable sensors) and eight countries (USA, South Korea, Australia, Switzerland, France, Israel, China, Vietnam). Reported sensitivities ranged from approximately 75 to 99.2% and specificities from 78.9 to 95.5%. Only one tool demonstrated stable performance across sex, race, and ethnicity; subgroup-stratified validation was otherwise near-absent. No CE-marked autism-specific screening device was identified. Conclusions – The evidence base has broadened in modality and geography but remains autism-concentrated and equity-blind, with a wide gap between analytic accuracy and primary-care deployment. We propose equity validation, deployment-readiness reporting, and an implementation pathway for low-resource guidance-center settings as priorities.
- MakaleYayıncıda açık erişim2026Scopus SJRQ1Maternal chrononutrition during pregnancy and the composition of intestinal and placental microbiota
Kurtbeyoglu, Emine; Caferoglu Akin, Zeynep; Ozdemir, Fatma
Background: Disrupted feeding timing may alter microbiota profiles and contribute to metabolic disturbances. This study aimed to investigate the impact of maternal chrononutrition during pregnancy on maternal gut and placental microbiota. Methods: This study was conducted between April 2022 and February 2023 at Erciyes University Faculty of Medicine Hospitals and Kayseri Private Dünyam Hospital in Kayseri, Türkiye, among healthy pregnant women with predominantly daytime feeding (pDT, n = 10) or predominantly nighttime feeding (pNT, n = 10). A questionnaire was administered, and three-day food consumption diaries were recorded at both 20–26 and 32–36 weeks of gestation. Fecal samples were collected at 32–36 weeks of gestation, and placental samples were collected at birth and stored at -80 °C until analysis. Results: Although total daily energy intake at both gestational periods (20–26 and 32–36 weeks) was similar between the groups, gestational weight gain was greater in the pNT group [16.30 (5.25) kg] than in the pDT group [11.90 (3.41) kg] (p = 0.039). Compared with the pDT group, the Firmicutes: Bacteroidetes ratio was higher in the maternal gut microbiota of the pNT group. Furthermore, Bacilli, Lactobacillales, Lactobacillaceae, and Lactobacillus were significantly more abundant in the pDT group than in the pNT group (p < 0.05). The intestinal and placental microbiota of the pDT and pNT groups had similar alpha and beta diversity. Conclusions: Our findings suggest that predominantly daytime or nighttime feeding during pregnancy may influence the composition of maternal gut and placental microbiota. These microbiome alterations may have potential implications for maternal and infant health; however, larger longitudinal studies are needed to clarify their long-term relevance, including possible links with fetal programming.
- Derleme MakaleYalnız künye2026Scopus SJRQ1A systematic and evolutionary analysis of software design pattern detection and recommendation approaches (2015–2025)
Turk, Salih; Coskuncay, Ahmet
Context: Automated detection and recommendation of software design patterns is crucial for software maintenance, reverse engineering, and architectural quality. Despite decades of research, the field has undergone a significant transformation with the emergence of deep learning and large language models (LLMs), leading to a fragmented landscape of methodologies and evaluation settings. Objective: This study aims to provide a systematic and evolutionary synthesis of design pattern detection and recommendation approaches published between 2015 and 2025, categorizing them into distinct technological paradigms and identifying open research challenges. Method: Following Kitchenham’s evidence-based software engineering guidelines and PRISMA principles, a systematic literature review was conducted. We executed a structured search on the Web of Science database, identifying 212 initial records. After rigorous multi-stage screening and quality assessment, 66 primary studies were selected for final synthesis and analysis. Results: The evolution of the field is characterized through six distinct paradigms: Rule-Based, Graph-Based, Feature-Driven Machine Learning, Representation Learning (Deep Learning), Hybrid, and Contextual Intelligence (LLM). Findings reveal a clear methodological shift from structural exactness to semantic and contextual understanding. However, the analysis also identifies a high concentration on specific datasets (JHotDraw and P-MARt) and a lack of standardized benchmarks for variant detection and multi-modal analysis. Conclusions: While LLM-based approaches show promise in handling pattern variants and semantic context, the field requires more diverse benchmarks and explainable models. This review provides a comprehensive research roadmap, emphasizing the need for multi-modal frameworks and industry-scale validation to bridge the gap between academic research and practical software engineering.
- MakaleAçık Erişim2026Scopus SJRQ1Physicochemical properties of fifteen flower honey samples from five districts in Türkiye
Ucuncu, Osman; Kul, Merve Kalafat; Baltaci, Cemalettin; Aykoc, Ayse Muslu
This study analyzed the physicochemical characteristics, mineral composition, and antioxidant potential of fifteen floral honey samples collected from five districts of Gümüşhane, Türkiye. The average values for moisture (17.44 g/100 g), insoluble solids (0.04 g/100 g), conductivity (0.35 mS/cm), diastase activity (54.15 DN), free acidity (38.18 meq/kg), and fructose + glucose content (68.95 g/100 g) were mostly within the acceptable limits defined by national honey standards. Proline content ranged from 562.66 to 1466.15 mg/kg, while hydroxymethylfurfural (0.37–18.89 mg/kg) confirmed the freshness of all samples. Potassium, calcium, sodium, and magnesium were identified as the major mineral elements in the analyzed honey samples, with considerable variation observed among samples collected from different districts. Antioxidant activity (Total phenolic content (mg GAE/100 g): 9.21–98.25; SC₅₀ (mg/mL): 5.23–41.29) revealed a strong antioxidant potential of Gümüşhane honeys. This work provides the first detailed characterization of Gümüşhane honeys, confirming their high physicochemical quality, rich mineral profile, and notable antioxidant potential, and underscores their value as a distinctive regional product with functional and commercial significance.
AtıfScopus4WoS5İncele - MakaleYalnız künye2019Scopus SJRQ4Object based classification of unmanned aerial vehicle (UAV) imagery for forest fires monitoring
Baha Bilgilioglu, B.; Ozturk, Ozan; Sariturk, Batuhan; Seker, Dursun Zafer
In case of fire, determination of burned trees and fire direction is very important. Until now, satellite images and aerial photographs have been widely used in forest fire studies. However, these data can be ineffective in terms of temporal and spatial resolution. In recent years, due to high resolution of provided images, use of Unmanned Aerial Vehicle (UAV) rapidly increased in forest related monitoring studies. Images obtained soon after the forest fires by means of UAVbecome the most important data to evaluate the damage level in the forestry area using different classification techniques. Conventional image classification methods are inefficient for evaluation of high resolution images. However, object-based classification is more accurate than conventional methods. Because, this method uses spectral, neighborhood, texture, hierarchy and size based relationships. In this study, forest fires occurred in Camburnu Natural Park in Surmene District of Trabzon Province located in the Black Sea Region of Turkey was selected as the study area. To determine the destroyed area, high resolution UAV images of the study area were obtained and image pre-processing steps were employed. Object-based classification and pixel-based classification have applied to these images. The boundaries of destroyed forest have been extracted by means of two classification methods. Additionally, combining of these two classification results was investigated to improve the results of the burned area.
AtıfScopus5İncele - MakaleAçık Erişim2017Scopus SJRQ2Sexual health literacy as a predictor of reproductive health and family planning attitudes among university students: a cross-sectional study
Karaahmet, Aysu Yıldız; Bilgiç, Fatma Şule; Karakaş, Sevda
Introduction: This study was conducted to investigate the relationship between sexual health literacy (SHL) and reproductive health and family planning (RHFP) attitudes among university students and to identify the sociodemographic and informational predictors influencing these variables. Methods: A descriptive, cross-sectional design was employed with a sample of 867 university students aged 18-35 in Türkiye between May and September 2024. Data were collected using a Sociodemographic Questionnaire, the Sexual Health Literacy Scale (SHLS), and the Reproductive Health and Family Planning Attitude Scale (RHFPAS). Data analysis included Pearson correlation and multiple linear regression models to determine predictors and relationships. Results: The findings revealed a strong positive correlation between SHL and RHFP attitudes (r = 0.880, P < .05). Multiple linear regression analysis showed that total SHLS scores and students’ conceptual definitions of sexual health were significant predictors of RHFP attitudes. In particular, the knowledge and attitude subscales of the SHLS were identified as key determinants. Furthermore, residence type, sexual activity status, and information sources were found to significantly influence both SHL levels and RHFP attitudes. Discussion: SHL is a critical determinant in shaping the RHFP attitudes of young adults. Higher literacy levels directly correlate with more positive attitudes toward reproductive health services, suggesting that targeted educational interventions are necessary to improve health outcomes in this population.
AtıfScopus1WoS1OpenAlex1İncele - MakaleYalnız künye2018Scopus SJRQ2Bearing capacity and slope stability assessment of rock masses at the Subasi viaduct site, NE, Turkey
Kaya, Ayberk; Bulut, Fikri; Dağ, Serhat
This study investigates the bearing capacity of rock masses and stability of slopes at the Subasi viaduct site which is a part of the improvement project of the Artvin–Hopa government highway between KM 6 + 500 and 13 + 787 in NE Turkey. The geotechnical studies were performed in three stages. Firstly, the bearing capacity of moderately weathered andesitic tuff was evaluated using the empirical equations. Secondly, the major principal stress and vertical displacement due to the viaduct and traffic loadings at the level of foundations were determined by the finite element method (FEM). The vertical displacement value and comparison of bearing capacity and major principal stress show that any problem is not expected at the viaduct site in terms of the bearing capacity. Finally, the stability of slopes at the viaduct site was investigated using kinematic, limit equilibrium, and finite element method-based shear strength reduction analyses methods. It was concluded that no discontinuity controlled failures at the slopes are expected. However, a circular failure is possible to occur at the face slope excavated in highly weathered andesitic tuff. After support application, the long-term stabilization of face slope has been achieved. Consequently, it is suggested that the empirical, analytical, and numerical methods should be combined for a more reliable construction design.
AtıfScopus6WoS5OpenAlex10İncele
