Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Uzun, Sevda(62)
- Merdan, Mehmet(58)
- Duran, Celal(50)
- Ozdes, Duygu(47)
- Kaygusuz, Abdullah(43)
- Ashyralyyev, Charyyar(38)
- Karsli, Orhan(33)
- Öztürk, Serkan(32)
- Araz, Onur(31)
- Demir, Selim(29)
- Kulakaç, Nurşen(29)
- Dokuz, Abdurrahman(28)
- Arslan, Mehmet(27)
- Baltacı, Cemalettin(27)
- Maden, Nafiz(27)
- Sipahi, Ferkan(27)
- Topal, Fevzi(27)
- Turan, Ibrahim(27)
- Kandemir, Raif(25)
- Kurt, Kemal(24)
- Ozturk, Samet(24)
- Peker, Iskender(24)
- Vural, Alaaddin(24)
- Reis, Hatice Catal(23)
- Yücel, Cem(23)
- Aydin, Faruk(22)
- Baltaci, Cemalettin(22)
- Gücer, Mehmet Ali(22)
- Günen, Mehmet Akif(22)
- Kankal, Murat(22)
- Topal, Meryem(22)
- Karimullah, Suud Sarim(21)
- Uysal, Ibrahim(20)
- Kurnaz, Muammer(19)
- Tuncer, Metin(19)
- Yolcu, Halil(19)
- Çelik, Necati(19)
- Ürkek, Bayram(19)
- Şan, Murat(19)
- Aliyazicioglu, Yuksel(18)
Yayınlar
- MakaleYayıncıda açık erişim2025Scopus SJRQ2Convergence on Kirk Iteration of Cesàro Means for Asymptotically Nonexpansive Mappings
Cona, Lale; Şimşek, Deniz
This article addresses the convergence of iteration sequences in Cesàro means for asymptotically nonexpansive mappings. Specifically, this study explores the behavior of Kirk iteration in the Cesàro means in the context of uniformly convex and reflexive Banach spaces equipped with uniformly Gâteaux differentiable norms. The focus is to determine the conditions under which the Kirk iteration sequence converges strongly or weakly to a fixed point. Finally, some examples are given in this article to demonstrate the advantages of the preferred iteration method and to verify the results obtained.
AtıfScopus1WoS1OpenAlex1
İncele - MakaleYalnız künye2025Scopus SJRQ1A deep neural network combined with a two-stage ensemble model for detecting cracks in concrete structures
Reis, Hatice Catal; Turk, Veysel; Kaya Yildiz, Cagla Melisa; Bozkurt, Muhammet Furkan; Karagoz, Seray Nur; Ustuner, Mustafa
Detection of cracks in concrete structures is critical for their safety and the sustainability of maintenance processes. Traditional inspection techniques are costly, time-consuming, and inefficient regarding human resources. Deep learning architectures have become more widespread in recent years by accelerating these processes and increasing their efficiency. Deep learning models (DLMs) stand out as an effective solution in crack detection due to their features such as end-to-end learning capability, model adaptation, and automatic learning processes. However, providing an optimal balance between model performance and computational efficiency of DLMs is a vital research topic. In this article, three different methods are proposed for detecting cracks in concrete structures. In the first method, a Separable Convolutional with Attention and Multi-layer Enhanced Fusion Network (SCAMEFNet) deep neural network, which has a deep architecture and can provide a balance between the depth of DLMs and model parameters, has been developed. This model was designed using a convolutional neural network, multi-head attention, and various fusion techniques. The second method proposes a modified vision transformer (ViT) model. A two-stage ensemble learning model, deep feature-based two-stage ensemble model (DFTSEM), is proposed in the third method. In this method, deep features and machine learning methods are used. The proposed approaches are evaluated using the Concrete Cracks Image Data set, which the authors collected and contains concrete cracks on building surfaces. The results show that the SCAMEFNet model achieved an accuracy rate of 98.83%, the ViT model 97.33%, and the DFTSEM model 99.00%. These findings show that the proposed techniques successfully detect surface cracks and deformations and can provide practical solutions to real-world problems. In addition, the developed methods can contribute as a tool for BIM platforms in smart cities for building health.
AtıfScopus2WoS2OpenAlex1İncele - MakaleYalnız künye2025Scopus SJRQ3Interpretable machine learning for rapid prediction and calibration of HPGe detector efficiency: a physics-informed approach and online platform
Yesilkanat, Cafer Mert; Celik, Necati; Celik, Ahmet; Cevik, Ugur
This study introduces a novel, physics-informed, and calibration-friendly hybrid machine learning framework for the rapid and accurate prediction of the Full Energy Peak (FEP) efficiency in High-Purity Germanium (HPGe) detectors. To overcome the limitations of conventional “black-box” models, our two-stage approach first represents the FEP efficiency curve using a physically interpretable logarithmic polynomial. Subsequently, machine learning models were trained to predict the polynomial coefficients directly from the detector geometric parameters using a comprehensive dataset generated via Monte Carlo simulations. Among the various algorithms tested, the Generalized Linear Model yielded superior performance, achieving R2 values of 0.975–0.992 for the coefficients. While raw model predictions showed expected variability, a key feature of our framework (a single-point calibration protocol using one known efficiency value) dramatically improved accuracy, reducing the mean absolute percentage error by an average of 80% on the independent test data. The calibrated model was further validated using an experimental detector. The entire framework was deployed as a user-friendly online platform, enabling researchers to instantly generate and calibrate efficiency curves. Our work successfully harmonizes interpretability, speed, and accuracy, offering a powerful tool for the design, optimization, and routine calibration of HPGe detectors.
AtıfScopus4WoS3OpenAlex3İncele - MakaleAçık Erişim2025Scopus SJRQ1A strategy for the one-pot direct production of 5-hydroxymethylfurfural (HMF) from sucrose using organic weak acid in situ as a catalyst
Gül, Abdulkadir; Tanyıldızı, Muhammet Şaban
5-Hydroxymethylfurfural (HMF), which can be produced in high yields by acid-catalyzed dehydration of carbohydrates, is used as a feedstock in biofuel and biopolymer production. However, these acidic catalysts are usually added externally, which increases HMF production costs. Furthermore, other important parameters for high yields of HMF production are raw material selection, reaction medium, and separation-purification processes. The aim of this study is to produce HMF in one-pot without the addition of any exogenous catalyst by evaluating the fructose remaining in the production medium in which the glucose from sucrose hydrolysis is microbially fermented into gluconic acid (GA). HMF production from fructose in a biphasic system consisting of a fermentation liquid containing GA and fructose and 2-Methyltetrahydrofuran (2-MeTHF) was optimized using Central Composite Design (CCD). The variables examined included reaction time, reaction temperature, solvent type, salt type, and organic/aqueous phase ratio. In the study, by chelating GA and calcium ions, the low degree of ionization of GA was increased, and HMF was produced in high yields without the addition of a catalyst. Under optimum conditions, using 2-MeTHF/ fermentation liquid (6:1) and 7.56 wt% CaCl2, the yield of HMF produced in pressurized (hydrothermal) vessels at about 147 °C for 158 min reached 77.6 % of the theoretical yield. The amount of GA in the production medium was 92.64 % preserved from degradation. GA and HMF in different phases of the biphasic system were purified from the production medium in high yields. Furthermore, 2-MeTHF, which constitutes the organic phase of the biphasic system, was recovered at approximately 95 % by vacuum evaporation. As a result, HMF production from sucrose, which is a more economical and abundant raw material compared to glucose and fructose, was realized in one-pot without the addition of any catalyst.
AtıfScopus6WoS5OpenAlex7İncele - MakaleAçık Erişim2025Scopus SJRQ1The Protective Effect of Boric Acid Against High Fructose-Induced Liver and Kidney Damage in Rats
Yüksel, Duygu; Başeğmez, Mehmet; Kan, Fahriye
This study aimed to determine the protective role of boric acid (BA) in high fructose (HF)-induced liver and kidney toxicity in a young rat model. High-fructose consumption causes serious damage to liver and kidney tissue in healthy individuals and contributes to the emergence of various metabolic diseases. Thirty-two healthy female Wistar albino rats (250–300 g weight and 3–4 months) were randomly distributed into four equal groups (n = 8): control, high fructose % 20 (HF), boric acid 20 mg/kg (BA), and HF + BA. High fructose was freshly prepared and administered to the rats as 20 g of d-fructose dissolved in 100 mL of tap water daily for a duration of 30 days. Boric acid (20 mg/kg) was administered through gastric gavage throughout the 30-day study period. At the end of study, blood, liver, and kidney were collected from rats. The results indicated that high fructose induced increased glucose, total cholesterol, triglyceride, and urea levels in rat serum. Boric acid administration significantly decreased glucose, total cholesterol, triglyceride, and urea levels in HF + BA groups. The results indicated that high fructose-induced oxidative stress by increasing the level of MDA and by decreasing GSH levels, and CAT activity in the liver and kidney of rats. However, oral BA administration significantly decreased MDA levels and increased GSH levels, and CAT activity (p < 0.05). Furthermore, BA significantly reduced high fructose-induced histopathological and Immunohistochemistry alteration in the liver and kidney tissues. In conclusion, BA may prevent the oxidative imbalance and histopathological and immunohistochemical damage caused by high fructose in liver and kidney tissues in rats.
AtıfScopus4WoS3OpenAlex3
İncele - MakaleYayıncıda açık erişim2025Scopus SJRQ3An Analysis on The Relationship of Organic Honey Production and The Number of Producers with Growth, Employment, and Agricultural Employment in Türkiye; [Türkiye’de Organik Bal Üretimi ve Üretici Sayısı ile Büyüme, İstihdam ve Tarımsal İstihdam Arasındaki İlişkinin Tespitine Yönelik Bir Analiz]
Merdan, Kurtuluş
The growing world population has driven humanity to produce more food, potentially leading to the neglect of quality and safety in food production. Evolving life conditions and technological progress have altered the consciousness level of humanity, bringing about new demands in some communities due to high income. Organic beekeeping has developed within this structure and tends to flourish as a model in Türkiye. Organic beekeeping has increased the income from beekeeping. It is practiced in natural unspoiled areas with no conventional feeding and chemical pesticides and requires supervising all processes through inspection and certification. Besides organic honey, the most common organic beekeeping product, organic bee products include bee pollen, propolis, royal jelly, beeswax, and bee venom. This study presents the economic analysis of organic honey production volume in Türkiye. For this purpose, the short and long-term relationships of organic honey production and the number of producers with economic growth, employment, and agricultural employment were analyzed. In addition, organic honey production and the number of organic honey producers were examined as driving forces for growth, employment, and agricultural employment. In this context, annual time series data between 2004 and 2022 were used as the sample. According to the analysis, organic honey production, an independent variable, had no long-term relationship with employment and agricultural employment variables, whereas it had a long-term relationship with the growth variable. The number of organic honey producers, another independent variable, was found to have no long-run relationship with the employment variable; however, it had a long-term relationship with the growth and agricultural employment variables. The findings of the study indicated that organic honey production did not drive growth, employment, or agricultural employment. On the other hand, the number of organic honey producers led to growth, yet not to employment or agricultural employment.
- MakaleAçık Erişim2025Scopus SJRQ2Some physical and mechanical properties of particle boards produced from industrial wood chips and Scots pine (Pinus sylvestris) cones
Karahan, Selim; Guler, Cengiz
This study investigated the potential use of Scots pine cones as an alternative raw material to larch wood chips for particle board production. Due to the increasing scarcity of forest resources, exploring alternative raw material for the forest industry has gained importance in in recent years. Particle boards were produced in laboratory conditions by blending industrial wood chips with Scots pine cones in varying proportions (25 %, 50 %, 75 % and 100 %) and usingr urea formaldehyde glue. The adhesice mixture contained 55 % urea formaldehyde glue and 33 % ammonium chloride as a hardener. Te production parameters included a press temperature of 150 °C, a press time was 7 minutes, and a press pressure of 2,4 MPa to 2,6 MPa. The resulting boards had a thickness of 16 mm and a density ranging from 730 kg/m3 to 740 kg/m3. Tests for thickness iswelling, water absorption, bending strength, modulus of elasticity and tensile strength perpendicular to the surface were conducted. Results showed that the physical and mechanical properties of the boards containing up to 25 % Scots pine cone met the required standards.
AtıfScopus2WoS2OpenAlex3İncele - BildiriYalnız künye2025Multi Objective Reinforcement Tuning for Responsible Generative AI in Automated Financial Advisory Systems
Bhandarkar, Vishakha D; Genemo, Musa Dima; Muda, Iskandar; Murugan, Rekha; Sreeram, G.; Dharani, N.
Generative AI in a financial context poses risks to the areas of regulatory compliance, accuracy in the content being delivered, and appropriateness in terms of emotion. This research proposes ReFiGen, a framework that can be used to create compliant financial content that is compliant with the regulatory framework and the sentiment of the users. Following domain-specific fine-tuning, ReFiGen blends sentiment alignment with reinforcement learning to optimize on compliance. It demonstrates better performance than baseline models with a high data accuracy rate of 98.35 percent and can generate a fluent output that is emotionally congruent, and regulation-minded. The results of experiments indicate that ReFiGen is both clear and coherent and exactly follows financial guidelines. This practice affirms that the combination of unstructured compliance modeling and affective control is a successful aspect of LLMs. ReFiGen is one of the milestones on the journey of trustworthy and ethically aligned AI in the financial advisory market.
- Kitap BölümüYalnız künye2025Scopus SJRQ4The Robust Method for Solving the Conformable Time-Fractional Klein–Gordon Equation
Anaç, Halil
The present study used the conformable fractional q-Shehu homotopy analysis transform method to investigate the conformable time-fractional Klein–Gordon equations with proportional delay. The numerical answers to this problem are graphed using Maple software. The efficacy and consistency of the proposed method have been demonstrated through numerical simulations.
- Kitap BölümüYalnız künye2025Scopus SJRQ4Investigation of Fractional Order Genesio-Tesi System Using Laplace Transformation
Bekiryazici, Zafer; Sengul, Suleyman; Merdan, Mehmet
In this paper the fractional-order Genesio-Tesi chaotic system is investigated with Laplace transformation method. Genesio-Tesi system is a three-dimensional equation system with quadratic nonlinearity and is frequently used for exemplifying chaotic systems. In this study, Caputo fractional derivative is used to obtain the fractional system and Laplace transformation is applied in order to analyze the explicit solutions of the system.
- MakaleYalnız künye2025Scopus SJRQ2Development of gluten-free cookie using composite flour from corn and lentil flours: Study by response surface methodology
Bursa, Batuhan; İlyasoğlu, Huri
The demand for gluten-free foods has increased due to health-based issues and lifestyle choices. This study aimed to develop a gluten-free cookie with enhanced nutritional value. For this reason, the composite flour from corn flour (CF) and lentil flour (LF) was prepared for cookie formulation. To eliminate the possible negative impact of LF on the sensorial properties of the cookie, lemon peel powder (LPP) was incorporated into the cookie formulation. The effects of the LF level of composite flour and the incorporation level of LPP on the physical, textural, and sensorial properties of the cookie were investigated via response methodology. The optimal levels were found as 16 g LF and 1 g LPP for 100 g composite flour. The physical, textural, and sensorial properties, proximate composition, vitamin C and total phenolic contents, and antioxidant capacity of the developed cookie and control cookies were determined. The developed cookie had a higher nutritional value than the control cookie made from CF in terms of protein, dietary fibre, ash, and vitamin C. Moreover, it presented higher total phenolic content and radical scavenging activity. According to these results, the developed cookie can be proposed for a gluten-free diet.
AtıfScopus3WoS2İncele - MakaleAçık Erişim2025Scopus SJRQ3Superconducting Properties of Welded Three Top-Seeded YBa2Cu3O7−x Produced with NdBCO/YBCO/MgO Thin-Film Seed
Batur, Kübra Nur; Duman, Şeyda; Çakir, Bakiye; Başoğlu, Mehmet; Aydiner, Alev
This study investigates hollow single-crystal top-seeded melt growth (TSMG) YBa2Cu3O7−x (YBCO or Y123) samples welded with Ag-added TSMG YBCO solder materials produced using NdBCO/YBCO/MgO thin-film seeds. Three main samples and one solder material are produced by the TSMG method. In order to increase the dimensions of the bulk YBCO superconductor, two slices of the solder material and the main samples are stacked on top of each other with the c-axis parallel and welded at 1030 °C. X-ray diffraction patterns, polarized optical photographs, and SEM micrographs are taken to determine the structural properties of the samples. A preferential orientation in the c-axis is observed with a good connection. Low-temperature resistance and magnetization measurements are made for the specimens taken from the welding region. A sharp superconducting transition occurs at 0 T around 93 K for welding regions of W-ab and W-bc specimens. The critical current densities (Jc) are high as in the main samples. At the same time, the Jc values at 77 K and 0 T are found as 1.54 × 104, 1.98 × 104, 2.21 × 104, 2.31 × 104, and 2.36 × 104A cm−2 for the W-a, W-ab, W-b, W-bc, and W-c specimens, respectively. The single-grain feature is not impaired; thus, the absence of additional grain boundaries facilitates the current passage.
- MakaleAçık Erişim2025Scopus SJRQ4First Molecular Data for the Genus Kovalius (Opiliones: Sclerosomatidae: Leiobuninae) and their Phylogenetic Relationships; [Kovalius (Opiliones: Sclerosomatidae: Leiobuninae) Cinsinin İlk Moleküler Verileri ve Filogenetik İlişkileri]
Kurt, Pınar; Yildirim Doğan, Nalan
Kovalius (Opiliones: Sclerosomatidae: Leiobuninae) is a small genus of harvestmen described by Tchemeris, 2023. The first description of Kovalius logunovi was made from Russia and it was subsequently recorded from Türkiye on the basis of the morphological data. The use of morphological data alone for the identification of taxa and determination of their relationships may lead to limitations and difficulties in some cases. As a result, the use of molecular data in addition to the morphological data in the identification of new taxa and determination of phylogenetic relationships increases the reliability of studies. In this study, the mitochondrial 16S rRNA gene region of the Kovalius logunovi species was examined for the first time and a sequence of approximately 408 bp was obtained. Based on these data, the phylogenetic relationships of the species with similar species were revealed.
AtıfScopus1OpenAlex3
İncele - MakaleAçık Erişim2025Scopus SJRQ3Mathematical analysis of fractional order Covid 19 epidemic model
Açikgöz, Pınar; Merdan, Mehmet
This article aims to examine the dynamics of the fractional epidemic model of covid 19. The model in question incorporates the notion of a compatible derivative. Given their consistent and worldwide characteristics, fractional derivatives are presently employed to address nu-merous practical issues. In addition, we employ two numerical techniques, namely the con-formal differential transform and the variational iteration approach, to provide an approxi-mate solution for the given model. The research closes by providing an in-depth analysis and visually representing the numerical findings. Furthermore, it has been demonstrated that the solution obtained is convergent.
AtıfOpenAlex1İncele - MakaleAçık Erişim2025Scopus SJRQ1The psychological consequences of 6 February 2023 Kahramanmaraş earthquakes
Kiymis, Ibrahim; Fakioglu, Merve
The Kahramanmaraş earthquakes (2023) are the largest and most destructive earthquakes witnessed in the era of the Republic of Turkey. Earthquakes lead to serious mental health disorders (MHD) such as Post-Traumatic Stress Disorder (PTSD) in individuals. Earthquakes that cause significant death and injury on a large scale, such as the Kahramanmaraş earthquakes, can result in various mental health disorders. Therefore, the aim of the study was to examine the effects of variables such as PTSD, event impact, psychological resilience, depression, and anxiety levels on adult earthquake survivors affected by the Kahramanmaraş earthquakes, using a quantitative research approach with survey techniques. A total of 662 earthquake survivors were reached. The effect of variables on each other was analyzed using Structural Equation Modeling (SEM). According to the findings of the study, event impact had a significant and positive effect on PTSD (β = 0.780, p = 0.000). Unexpectedly, resilience did not have a significant effect on PTSD (β=-0.04, p > 0.05). PTSD had a significant and positive effect on anxiety (β = 0.936, p = 0.000) and depression (β = 0.643, p = 0.000). Consequently, event impact’s severity increased PTSD, which in turn led to anxiety and depression. Surprisingly, psychological resilience did not serve as a protective factor against PTSD. For future research, it is recommended to examine psychological resilience with different event types, on different sample groups, and using more comprehensive and varied methods.
AtıfScopus9WoS10OpenAlex13İncele - MakaleAçık Erişim2025Scopus SJRQ3The effect of using a local skin cooling device on arteriovenous fistula cannulation pain and comfort level of patients on hemodialysis: A single-blind randomized controlled study
Demırağ, Hatice; Kulakaç, Nurşen
Introduction: The study aimed to investigate the impact of the using local skin cooling on arteriovenous fistula cannulation pain and the comfort levels of hemodialysis patients. Methods: The single-blind randomized controlled trial was formed with 50 patients between June 20, 2023, and July 31, 2023, in the hemodialysis units of two state hospitals in Turkey. Patients were randomly assigned to either the experimental group (n = 25) or the control group (n = 25) using the block randomization method. This data were collected through the use of the CoolSense device, the “Patient Information Form,” the “Verbal Category Scale,” and the “General Comfort Scale.” Patients in the experimental group received the local skin cooling for five seconds during arteriovenous fistula cannulation, while the control group followed the standard hospital protocol. The study was registered on Clinical Trials (NCT06144801). Findings: The study revealed that the application of the local skin cooling during arteriovenous fistula cannulation significantly reduced pain and increased patients’ comfort levels, demonstrating an anesthetic effect (p < 0.001). Furthermore, it was observed that as pain levels decreased, patient comfort levels increased (p < 0.001). Discussion: It is concluded that the using lokal skin cooling is an effective tool for reducing pain and enhancing comfort during arteriovenous fistula cannulation in hemodialysis patients.
AtıfScopus3WoS3OpenAlex3İncele - MakaleYalnız künye2025Scopus SJRQ3Golden Distance: A New and Comprehensive Metric Definition Study Facilitating Classification Performance Evaluations
Melek, Mesut; Melek, Negin
Machine/deep learning has great potential in many classification studies that make our lives easier. Performance evaluation of the classification algorithm is of great importance for making accurate predictions in machine learning, which, itself, learns from data without any human intervention. In these learning models, high-dimensional data in classes with complex features belonging to balanced or unbalanced distributions are analysed with different classification algorithms and take their place in the decision stage. While the difficulties in implementing classification algorithms continue today, evaluating the superiority of developed systems over each other continues to be both a time-consuming and very difficult process. In models proposed in the literature, only a single parameter, such as accuracy, is not sufficient to determine model adequacy. In this context, among different classification algorithm performance parameters, some metrics such as sensitivity, specificity, F-measure (F1), Jaccard index, area under the curve, and kappa coefficient are used for system evaluation. As these parameters are placed in the analysis tables, the study should be evaluated at length by including large tables in the article. Instead of using various parameters that make performance analysis difficult in system evaluation, this study defines a new Golden Distance-based parameter that summarizes the model comprehensively from all dimensions. To understand this new metric’s working principle, a detailed comparative analysis of research conducted on three datasets was performed. Based on the final evaluation, the proposed inclusive metric demonstrated successful and fair behaviour in all conditions. Thus, a fair and simple comparison will be achieved with a single metric, and long analyses can be avoided by presenting many performance parameters in systems’ superiority indicator tables.
AtıfScopus9WoS10OpenAlex12İncele - MakaleYayıncıda açık erişim2025Scopus SJRQ2Optimizing Visibility of Historical Structures Using Modified Weighted Differential Evolution: Insights from the Kromni Valley, Gümüşhane, Türkiye
Günen, Mehmet Akif; Öztürk, Kaşif Furkan; Aliyazıcıoğlu, Şener
It is very important for historical structures to see each other in order to reveal the historical and cultural identity of a region. Historical structures in the Kromni Valley of Gümüşhane, located near the Sümela Monastery, served as places of worship, communication, trade, and social activity centers during their period of active use. This study analyses the spatial relationships of 38 historic buildings, including churches, chapels and castles, whose 3D models are created by in-situ measurements and point clouds obtained by unmanned aerial vehicles, using a 3D viewshed analysis using geographic information systems and remote sensing data. The research introduces a modified weighted differential evolution-based viewshed analysis (mWDE-WS) to enhance the visibility of these structures. In order to assess the applicability of the proposed method, a statistical comparison was conducted between four different Differential Evolution (DE) algorithms (standard DE, LSHADE, CobiDE, JADE and WDE) and the mWDE. The Wilcoxon signed-rank test indicates that mWDE is a more effective solution than alternative methods for addressing the relevant real-world issues. The study also integrates drainage network analysis to assess flood risks and the relationship between cultural structures and water flow. Findings show that historical structures in the region were built not randomly but within a rational approach and 64% of the study area is visible from structures and 2% of the area is visible from ten or more structures. mWDE-WS analysis revealed that the visible area could increase by 20% to 84.37% if the historic structures were placed in optimal locations. In addition, the historical structures were built away from 3rd order streams to minimize flood risk and humidity, demonstrating the community's awareness of the local topography and hydrology.
AtıfScopus1OpenAlex3İncele - MakaleAçık Erişim2025Scopus SJRQ1DAPNet++: density adaptive PointNet + + for airborne laser scanning data
Akbulut, Zeynep; Karsli, Fevzi
Addressing the challenges arising from the irregularity and varying density of Airborne Laser Scanning (ALS) point clouds, which particularly affect the performance and generalization ability of 3D Deep Neural Networks (DNNs), is essential for their effective application in direct semantic segmentation tasks. In this study, we investigated the underexplored aspect of adapting PointNet + + for semantic segmentation of ALS point clouds. We introduced Density-Adaptive PointNet++ (DAPNet++) to enhance robustness against variable point densities through modifications made to the original PointNet + + architecture. Our methodology includes controlled block partitioning based on point density replacing the original batching strategy. Another key advancement in the study is the automation of the initial value of the neighborhood search radius by taking into account the characteristics of the dataset. This approach optimizes receptive field determination, crucial for effective semantic segmentation. The effectiveness of DAPNet + + is validated through extensive experiments on various datasets, including ISPRS Vaihingen, DALES, subsampled DALES, and OpenGF. Notable improvements include up to 11% increase in weighted mean Intersection-over-Union (mIoU) on the highly variable OpenGF test dataset and 3% increase in mIoU on the subsampled DALES dataset. Furthermore, the generalization capability of the DAPNet + + was tested, revealing an approximately 5% improvement in evaluation metrics compared to PointNet++. In summary, DAPNet + + minimizes trial-and-error in the selection of parameters for block partitioning and radius, and it enhances robustness against variable point density. With the proposed approach, it has been observed that there is a significant improvement in accuracy for underrepresented classes, mitigating class imbalance in ALS point clouds.
AtıfScopus9WoS5OpenAlex6İncele - MakaleAçık Erişim2025Scopus SJRQ1The Effect of Audio-Visual Methods on Preoperative Anxiety in Children: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Kulakaç, Nurşen; Ustuner Top, Fadime
Background: Children presenting to hospitals during the preoperative period are often exposed to procedures that cause significant stress. Through preoperative interventions, nurses play an important role in relieving the anxiety of children and their parents. Objective: This study was conducted to determine the effect of audio-visual methods on preoperative anxiety levels in children. Methods: PubMed (including MEDLINE), Cochrane, EBSCOHost, Web of Science, YÖK National Thesis Center, and Google Scholar databases of randomized controlled studies published in the last 10 years were searched for this meta-analysis. 32 studies were included in the meta-analysis, and the total sample size was 2795. Results: Q According to the results of the meta-analysis using the random-effects model, audio-visual methods had a positive effect on reducing anxiety (SMD: -1.312, %95 CI -1,666-(0,958), Z=-7,260, p < 0,001, Q-value = 556,572 I2 = 94.519). The moderator analysis demonstrated that cartoons (SMD:-1.645, p < 0.001), games (SMD:-1.931, p = 0.001), music (SMD:-0.534, p < 0.001), video showings (SMD:-1.363, p = 0.001), and virtual reality (SMD:-1.782, p < 0.001) reduced preoperative anxiety in children. Conclusions: Audio-visual methods are effective in reducing preoperative anxiety in children. The results of this study may provide pediatric perioperative nurses with additional information to help them decrease anxiety for patients and their families.
AtıfScopus4WoS3OpenAlex4İncele