SDG 9 · Sanayi, Yenilikçilik ve Altyapı · SDG · Erzincan Binali Yıldırım Üniversitesi

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ArticleYalnız künye2026/r/ebyu/10499
Out-of-plane performance of masonry infill walls strengthened with CFRP, polyurea spray, and aluminum stiffeners
Avcı, Hasan; Kucuk, Mesut; Sorkhab, Atabek; Sari, Ali
In this study, the out-of-plane behavior of lateral hollow clay brick (LHCB) infill walls embedded within reinforced concrete beams was experimentally investigated. Within the scope of the study, specimens strengthened with CFRP strips, a polyurea-based sprayed coating, a hybrid CFRP + polyurea system, and vertical aluminum profiles were tested, in addition to reference walls with thin and thick plaster. The out-of-plane behavior of the specimens was compared in terms of maximum load-bearing capacity, initial stiffness, displacement capacity, energy dissipation performance, and failure modes. The experimental results showed that increasing the plaster thickness significantly improved the load-bearing capacity and energy dissipation performance. Although CFRP and CFRP + polyurea hybrid reinforcements increased the maximum load capacity by 110–140%, their energy dissipation capacity remained low due to brittle failure behavior. It was determined that the polyurea coating preserved the surface integrity of the brickwork but limited its ductile behavior. The highest performance was achieved in specimens reinforced with vertical aluminum profiles; this method increased out-of-plane strength by over 220% while increasing energy dissipation capacity by approximately 12 times. Additionally, it was found that the aluminum profiles prevented brittle spalling, enabling the walls to exhibit consistent behavior at high displacement levels.
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ArticleYalnız künye2026/r/ebyu/10493
The role of air transportation on CO2 emissions in BRICS countries: Novel findings from MMQR methodology
Gümüş Akar, Pınar; Balli, Esra; Manga, Müge; Çatik, Abdurrahman Nazif
This article examines the impact of air passenger and freight transportation, economic growth, energy consumption, trade openness, and FDI on the CO2 emissions in BRICS countries for the 1993–2021 period, utilising panel Fully Modified Ordinary Least Square (FMOLS) and Dynamic Ordinary Least Square (DOLS) estimators and Method of Moments Quantile Regression (MMQR) methodologies. According to the findings, air passenger transportation, economic growth, energy consumption and FDI have a positive impact on CO2 emissions, while squared economic growth and trade openness have a negative impact. Findings indicate that air freight transportation has no statistically significant effect on CO2 emissions. According to the causality analysis, there is a bidirectional causality between air passenger-CO2 emissions and GDP-CO2 emissions. There is a unidirectional causality from air freight to CO2 emissions, from energy consumption to CO2 emissions and from CO2 emissions to trade openness.
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ArticleYalnız künye2026/r/ebyu/10470
Automated identification of structural elements in RC buildings based on structural floor plan data
İnce, Ozan; Karaköse, Perihan; İnce, Elif Gökçe; Eren, Erkin; Çakil, Burak; Atar, Muhammed
Proper placement of load-bearing elements plays a critical role in earthquake-resistant building design. Design mistakes and deficiencies in the arrangement of columns, shear walls, and beams may reduce seismic performance, increase torsional effects, cause frame discontinuities, and lead to inefficient or uneconomical structural solutions. Therefore, early identification of such deficiencies directly from structural floor plans can provide important support during both preliminary design and rapid assessment stages. This study presents an automated methodology for detecting, classifying, and evaluating load-bearing elements in RC building floor plans by integrating image processing and deep learning techniques. The YOLO object detection algorithm was employed to identify structural components within floor plan images. A dataset comprising 500 RC building floor plans was developed, and structural elements were manually annotated and labeled for model training and validation. Unlike previous approaches that mainly focus on limited structural components, the proposed model detects columns, shear walls, and four different beam types according to their support conditions. In addition, detected bounding box coordinates are converted into real-world dimensions using grid distances obtained from drawings, allowing the cross-sectional dimensions of each element to be determined. The model achieved high detection performance, particularly for columns, shear walls, and main beams, with accuracy values exceeding 93%. The results show that the proposed approach can accurately obtain information from structural floor plans and provide a promising decision-support system.
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ArticleAçık Erişim2026/r/ebyu/10465
Investigation of production and performance evaluation of high-strength lightweight concrete (HSLWC) produced with regional volcanic aggregates (RVA)
Ulusu, İsmet; Ulusu, Hayati; Gül, Rüstem
This study investigates the feasibility of producing high-strength lightweight concrete using regional volcanic aggregate (RVA) and examines the performance of the produced concrete. Various mix designs conforming to ASTM and TS EN standards were applied to achieve the targeted strength and density. The study also investigated the effect of water/cement ratio (W/C) and cement content on the mechanical and physical properties of high-strength lightweight concrete. Experimental tests such as compressive strength, tensile strength, density, water absorption, and elastic modulus were conducted to determine the effect of cement content and lightweight aggregate on the overall behavior of the concrete. To ensure adequate workability, superplasticizer was added to all mixes at a rate of 1.5% of the cement weight. The slump values of fresh concrete met modern requirements for good workability, pumpability, and high strength. The results showed that, provided a superplasticizer is used, high-strength concrete with a density range of 1830 to 1915 kg/m³ can be produced using 450 kg/m³ of cement. The results show that the porous structure and pozzolanic properties of RVA significantly increase the mechanical strength of concrete and, thanks to its porous structure, reduce its unit weight by 20–25%. The highest compressive strength recorded within 90 days for a mixture containing 650 kg/m³ of cement was 87 MPa. It was concluded that the W/C ratio and cement content have a significant effect on the mechanical and physical properties of these concretes.
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ArticleAçık Erişim2026/r/ebyu/10414
Model-Based Exergy and Partial Techno-Economic Screening of Hydrogen, Ammonia, E-Diesel, and Waste Cooking Oil Biodiesel Pathways for Heavy-Duty Internal Combustion Engines
Demirci, Oğuz Kürşat
Five pathways—diesel, renewable hydrogen, green ammonia, Fischer–Tropsch e-diesel, and B100 waste cooking oil fatty acid methyl ester (WCO-FAME)—were compared on a common service basis using a reduced-order engine exergy model, a partial techno-economic cost model, and 10,000 Latin hypercube simulation samples; each alternative was paired with diesel by sample index. The results were normalised to 1 kWh of net brake work (kWhb) and 1000 tonne-kilometres of freight work (1000 tkm). A carbon balance back-calculation from European Commission Vehicle Energy Consumption Calculation Tool (VECTO) monitoring data for subgroup 5-Long Haul (5-LH; a 4 × 2 tractor–semitrailer) yielded 89.31 kWhb/1000 tkm; first-order payload penalties increased this requirement to 94.79 kWhb/1000 tkm for hydrogen and 91.97 kWhb/1000 tkm for ammonia. Deterministic central-case exergy efficiencies were 43.02% for hydrogen, 39.67% for e-diesel, 39.35% for diesel, 37.56% for B100, and 33.81% for ammonia. The study-specific partial cost was expressed in 2025 EUR per 1000 tkm and included delivered fuel, maintenance, and annualised incremental vehicle capital; common fleet costs, including driver, tyre, toll, insurance, and tax costs, and externalities were excluded. Therefore, this metric was not a full freight cost or tariff. Deterministic central-case costs were EUR 25.85/1000 tkm for diesel, EUR 26.72/1000 tkm for B100, EUR 58.64/1000 tkm for hydrogen, EUR 59.84/1000 tkm for ammonia, and EUR 67.44/1000 tkm for e-diesel. Hydrogen exceeded diesel in exergy efficiency in 96.50% of the paired simulation samples; all three electrofuel pathways were costlier than diesel in every paired simulation sample, whereas B100 was cheaper than diesel in 35.62% of the paired simulation samples. Thus, hydrogen led in exergy efficiency, diesel in cost under the defined partial economic boundary, and B100 was the closest cost competitor. These are conditional model-screening results rather than a completed comparative life-cycle assessment: no quantitative life-cycle impact assessment (LCIA), environmental ranking or marginal abatement cost result is reported.
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ArticleAçık Erişim2026/r/ebyu/10404
Nonlinear effects of financial development on energy infrastructure: Insights from nonlinear panel models
Helmi, Mohamad Husam; Balli, Esra; Çatik, Abdurrahman Nazif; Kuziboev, Bekhzod; Ruzmetova, Maxbuba; Makhmudov, Samariddin
This study examines the impact of financial development on energy infrastructure needs in a sample of 85 countries over the period 2000–2023. The analysis combines linear panel estimators with advanced nonlinear methods, including Method of Moments Quantile Regression (MMQR), the Instrumental-Variable Partially Linear Functional-Coefficient model (IVPLFC), and the dose–response function. These methods allow us to examine heterogeneity, dynamic persistence, and nonlinear marginal effects. A key contribution of the study is the use of the Energy Infrastructure Needs index, which captures deficiencies across electricity generation, transmission, distribution, and storage, rather than relying on narrower traditional proxies. The results show that stronger financial development significantly reduces energy infrastructure needs. This effect is strongest at earlier stages of financial development, while diminishing marginal effects emerge as financial systems mature. The MMQR results reveal distributional differences across levels of energy infrastructure needs, while the IVPLFC and dose–response results confirm clear nonlinear effects. Overall, the findings show that financial development plays an important but uneven role in reducing energy infrastructure needs. Financially underdeveloped economies benefit most from expanding access to credit, strengthening banking intermediation, and improving liquidity, whereas more financially developed economies require complementary institutional and regulatory reforms to achieve further progress.
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ArticleYalnız künye2026/r/ebyu/10385
Data-driven forecasting of photovoltaic performance with explainable artificial intelligence: Experimental validation using environmental and thermal predictors
Karakoyun, Yakup; Katipoğlu, Okan Mert; Dogan, Ahmet; Canbaz, Ali
PV systems meet energy demand, but environmental and operational factors affect their performance. This study used a three-scenario ablation framework to predict PV power from baseline meteorological and operational variables to under-panel temperature and finally to the full thermal configuration, including water temperature (a newly studied variable). Scenario 1 included time, air temperature, panel angle, relative humidity, and average wind speed; Scenario 2 added under-panel temperature; and Scenario 3 added water temperature. Direct thermal predictors were evaluated separately from meteorological inputs using this design. Four sophisticated ML algorithms, Random Forest (RF), CatBoost, Support Vector Machine (SVM), and one-dimensional Convolutional Neural Network (1D CNN), were refined by Randomized Search (RS) and evaluated using metrics. According to the first group of power prediction results under the full Scenario 3 configuration, the CatBoost-RS model demonstrated superior performance with RMSE = 10.61, MAE = 8.41, AIC = 222.22, KGE = 0.957, and R2 = 0.99. For the second group, the SVM-RS model outperformed others, achieving RMSE = 8.02, MAE = 5.50, AIC = 200.36, KGE = 0.994, and R2 = 0.992. Comparative analysis showed that CatBoost-RS was the most robust predictor, while RF exhibited proficiency in managing nonlinearity in this experimental setting. One-way ANOVA, paired t -test, Wilcoxon signed-rank test, bootstrap confidence intervals, and effect size (Cohen’s d) analyses across both groups show that optimised ensemble and kernel-based models outperform the convolutional approach in predictive performance. Experimental validation and high predictive accuracy improve PV performance prediction in the proposed method.
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ArticleYalnız künye2026/r/ebyu/10371
Data-driven resilience modeling: Shock response of Turkish logistics corridorss
Sünbül, Şeyma; Tortum, Ahmet
Traditional transport planning often fails to manage changes in people's mobility during crises. This study uses data from Meta's "Data for Good" platform to analyse how well Türkiye's transportation network can deal with external shocks. The mobility of 81 provinces was modelled using K-Means clustering and validated through spatial analyses and a Logistics Dominance Index (LDI). Results identify four distinct functional zones. Large metropolises (Cluster 4) showed high vulnerability, while logistics corridors (Cluster 2) demonstrated operational robustness, showing +3.69% continuity during crisis periods. LDI results indicate that road capacity in these regions is predominantly influenced by commercial freight. Despite the presence of high functional resilience, significant spatial risks are identified due to geographical overlap with the North Anatolian Fault Line. The findings of the present study suggest that future planning should prioritise network redundancy and data-driven dynamic management over static capacity expansion.
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ArticleYalnız künye2026/r/ebyu/10317
UAV-supported visual inspection of aircraft for corrosion and crack detection
Kurt, B.; Soylak, M.; Köse, O.
Maintenance procedures are critically important for preserving the structural integrity, maintaining the functionality and ensuring the operational safety of aircraft. Traditional inspection techniques used in aircraft are often costly, time-consuming and prone to human mistake. Today, the opportunities provided by digitalisation and automation in aircraft maintenance and inspection processes are paving the way for innovative approaches. In this context, the use of inspection systems supported by image processing technologies has the potential to bring about a significant transformation in aircraft maintenance. Visual inspection methods integrated with unmanned aerial vehicles (UAVs) enable the rapid, accurate and repeatable detection of defects such as corrosion and cracks on the external surfaces of aircraft. This study focuses on the automatic detection and classification of defects on the external surfaces of aircraft, based on tests and analyses carried out by artificial intelligence algorithms using high-resolution data. The model developed in this study was implemented in Python in the Google Colab environment and supported by AI algorithms trained on visual data. The main objective is to investigate the feasibility of UAV-based systems for aircraft visual inspection and to provide concrete evidence of their practical applicability. In this regard, the UAV platform selected for image acquisition is intended to comprehensively scan the target areas and capture images with sufficient resolution for processing by artificial intelligence algorithms. A review of the literature reveals that UAV- and AI-based integrated approaches have been explored in only a limited number of studies related to aircraft maintenance. In this context, the present study proposes a system that enables the rapid and accurate detection of structural defects such as corrosion and cracks on the external surfaces of aircraft.
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ArticleAçık Erişim2026/r/ebyu/10267
A new UAV-based approach for detection of paint damage on aircraft surfaces
Kurt, Berces; Soylak, Mustafa; Köse, Oğuz
In aircraft maintenance operations, the early detection of defects such as cracks, corrosion, impact damage and paint damage on aircraft external surfaces is of critical importance. Traditional visual inspection methods have significant limitations, including lengthy inspection times, high operational costs and results that vary depending on the operator. In this study, an approach combining Unmanned Aerial Vehicle (UAV)-based imaging with deep learning-based image processing techniques is proposed to address these limitations. As part of the study, a dataset comprising high-resolution images of aircraft external surfaces was created, with the aim of automatically detecting paint damage. YOLO-based object detection models (YOLOv11 and YOLO26) were used for defect detection, and model performance was evaluated comparatively through training conducted on the same dataset using the Roboflow and Google Colab platforms. The results obtained demonstrate that the proposed approach provides high accuracy and reliability in the detection of paint damage on aircraft surfaces. Furthermore, it was observed that the choice of training platform influences the model’s temporal consistency in video-based analyses. Tests conducted on different hardware platforms demonstrate that the method is applicable across various systems; however, hardware capacity is a critical factor affecting real-time performance. In conclusion, the proposed UAV-supported deep learning approach offers a faster, non-contact and repeatable inspection method for aircraft maintenance processes, demonstrating significant potential for digitalisation and automation in Maintenance, Repair and Overhaul (MRO) operations.
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ArticleAçık Erişim2026/r/ebyu/10263
Effects of maximum aggregate grain size and gradation on roller compacted concrete pavements
Keleş, Ömer Faruk
Roller compacted concrete (RCC) pavements, which stand out as an alternative to conventional concrete and asphalt pavements, are increasingly preferred because of their high strength, fast construction, and low cost advantages. The properties of aggregates directly affect the performance in roller compacted concrete mixtures. In particular, the maximum aggregate particle size (Dmax) and gradation play a determining influence on the mixture's density, compressibility, and mechanical strength. This study aimed to investigate the effects of maximum aggregate particle size and gradation on the mechanical performance of roller compacted concrete pavement mixtures. Within this scope, roller compacted concrete pavement mixtures were produced using three Dmax (12.5, 19, and 25 mm) and three different cement dosages (300, 350, and 400 kg). Tests for physical properties on mixtures were conducted using unit weight and ultrasonic pulse velocity (UPV) tests, while tests for mechanical properties were conducted using compressive strength, flexural strength, and splitting tensile strength tests. Increasing the Dmax to 25 mm significantly improved the compressive and flexural strength of the mixtures. However, the splitting tensile strength increased up to 19 mm and showed a slight decrease when reaching 25 mm. The increase in cement content consistently improved all mechanical properties. Overall, the use of well-graded coarse aggregates with Dmax in the range of 19–25 mm was found to improve the mechanical properties and compressibility of roller compacted concrete mixtures.
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ArticleAçık Erişim2026/r/ebyu/10258
Revealing the effectiveness of hydropower innovations on emission level in Japan: evidence from machine-learning algorithm
Özkan, O.; Destek, M.A.; Khan, Z.; Manga, M.
This study explores how oil efficiency and hydropower innovation affect carbon emissions in Japan. In this direction, the novel multivariate quantile on quantile regression technique is applied to explore the period from 2000 to 2020. In addition, to prevent omitted variable bias, the environmental implications of industrialization, urbanization, and economic growth are also observed. The empirical findings indicate that oil efficiency, industrialization, and hydropower innovations are all lower carbon emissions. However, urbanization and economic growth harm the environmental quality. Lastly, a robustness check is performed using a kernel-based regularized least squares technique based on machine learning. The results of this investigation support the claim that oil efficiency and hydropower innovation can reduce emissions. Furthermore, it is found that the emission-reducing impact of both gets stronger as the emission level rises.
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ArticleYalnız künye2026/r/ebyu/10172
Diode-based programmable network array (DiProNA) for high-impact reconfigurable instrumentation
Can, Erol
The increasing complexity and integration density of modern mixed-signal and high-frequency systems demand reconfigurable, precise, and fault-tolerant signal-routing architectures. Conventional switching matrices based on multiplexers or crossbar networks suffer from crosstalk, leakage, limited scalability, and weak fault tolerance, making them unsuitable for emerging precision analog platforms. Addressing these limitations, this work introduces the Diode-Based Programmable Network Array (DiProNA), a novel hybrid switching architecture that differs fundamentally from existing structures by combining MOSFET-based programmability with unidirectional diode-assisted conduction paths to achieve intrinsic fault isolation, current sharing, and enhanced thermal robustness. A comprehensive analytical model—developed using Thevenin equivalents, nodal analysis, and explicit inclusion of diode and MOSFET parasitics—is presented and validated through extensive circuit-level simulations. Clear modeling assumptions (such as quasi-static device behavior and linearized small-signal parasitics) are stated to ensure transparency and reproducibility. Quantitative comparisons with traditional multiplexer and crossbar matrices demonstrate that DiProNA achieves higher voltage accuracy, lower effective resistance, and significantly reduced short-circuit current. Series ballast resistors effectively constrain fault currents and mitigate thermal stress, enabling reliable operation under open-circuit, short-circuit, and partial-failure scenarios. Multi-source configurations further enhance efficiency and distribute power-thermal load, while dynamic evaluations confirm microsecond-level response times with minimal overshoot, supporting high-speed instrumentation front-ends. Collectively, the results position DiProNA as a scalable, efficient, and fault-tolerant routing solution suitable for adaptive data converters, SoC subsystems, wireless front-ends, and biomedical instrumentation. This work establishes DiProNA as a robust and versatile platform advancing next-generation programmable mixed-signal systems.
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ArticleAçık Erişim2026/r/ebyu/10167
The Impact of Agricultural Value Added and Energy Consumption on Economic Growth in Türkiye: An Empirical Analysis; [Türkiye’de Tarımsal Katma Değer ile Enerji Tüketiminin Ekonomik Büyümeye Etkileri: Ampirik Bir Analiz]
Akın, Fatih; Dinçer, Selin
Climate change, global warming, natural disasters, wars, and global crises, which threaten the world's food supply, have made the agricultural sector fragile and strategically important, especially over the last century. Accordingly, it has become even more crucial for countries to have a strong agricultural infrastructure to ensure food security, maintain social stability, and reduce external dependence. In developing countries, in particular, the agricultural sector plays a critical role through functions such as job creation, reducing income inequality, and contributing to economic growth. For the Türkiye economy, the agricultural sector has historically been one of the cornerstones of the economic structure, providing raw materials to the industrial sector, supporting rural development, creating employment, and generating exports and foreign exchange earnings. On the other hand, energy consumption has become an indispensable element of production processes today. This study examines the impact of agricultural value added, gross fixed capital investment, and energy consumption on economic growth in Türkiye during the period 1968–2023 using the Fourier Engle-Granger cointegration test, Fourier Fully Modified Ordinary Least Squares (FMOLS), Fourier Dynamic Ordinary Least Squares (DOLS), and Fourier Canonical Cointegration Regression (CCR) methods. The results of the Fourier Engle-Granger cointegration test reveal a long-term relationship among the variables. The Fourier FMOLS, Fourier DOLS, and Fourier CCR estimation results indicate that agricultural value added, gross fixed capital investment, and energy consumption have positive and statistically significant effects on economic growth in both the short and long term.
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Conference ObjectYalnız künye2026/r/ebyu/10153
Traffic Flow Optimization in TMAs: Modeling and User Interface Implementation
Demirel, Soner; Socha, V.; Hanakova, L.
This study aims to manage air traffic flow in the terminal maneuvering area (TMA) of Istanbul Sabiha Gokcen Airport by generating delay-based solutions. For this purpose, a mixed-integer mathematical modeling technique is used. The model is integrated into a graphical user interface that includes two modules: Generic Data and Real Data. In this way, planning air traffic controllers can gain foresight into the traffic flow within the TMA by using parameters derived from the traffic outside the TMA. Through the Generic Data module, preliminary runs with different traffic mixes allow researchers to observe the outcomes of various scenarios, while the Real Data module provides both planning support and delay-based recommendations for controllers. The interface includes a Gantt chart, a heatmap visualizing delays between waypoints, the frequency of radio usage by air traffic controllers for solutions, emission-related graphs and tables presenting statistics such as flight durations of aircraft within the TMA. By using the model and the interface, traffic flow can be managed optimally with the objective of minimizing delays. At the same time, the user-friendly interface offers visual references and can serve as an easy-To-use assistant for planning controllers.
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ArticleAçık Erişim2026/r/ebyu/10084
Seismic Performance and Nonlinear Flexural Behavior of Corroded Reinforced Concrete Columns: An Analytical and Parametric Study
Çelik, Alper; Turan, Ahmet İhsan; Yalciner, Hakan; Kumbasaroglu, Atila
This study presents an analytical investigation and a parametric evaluation of the structural behavior and seismic performance of highly corroded reinforced concrete (RC) columns, based on previously conducted experimental studies by the authors. In the analytical phase, moment–curvature relationships were obtained by considering the deterioration of the mechanical properties of both concrete and reinforcing steel due to corrosion in RC column specimens. By linking the sectional moment–curvature response with the element-level behavior observed in the experimental program, the plastic hinge lengths and rotational capacities of the corroded RC columns were determined. Subsequently, a parametric study was carried out using the analytical framework developed in the first phase on a set of 48 RC column models. In this investigation, axial load ratio, concrete compressive strength, corrosion level, section type, and concrete cover depth were considered as key parameters. The results of the combined experimental and analytical investigations demonstrate that the adopted section analysis approach successfully captures the nonlinear flexural behavior observed in the corroded specimens and provides a reliable basis for evaluating the structural performance and for supporting the assessment of seismic performance of deteriorated RC columns.
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Conference ObjectYalnız künye2026/r/ebyu/10021
Hotspot Capacity-Driven Scheduling Strategies for Reducing Airborne Delays in TMAs
Demirel, Soner; Socha, V.; Hanakova, L.
This paper investigates the reduction of airborne delays in Terminal Maneuvering Areas (TMAs) through departure time adjustments based on hotspot levels at origin airports. While existing studies primarily focus on reducing airborne delays to support environmentally sustainable airport operations, this research proposes shifting such delays to the ground within the predefined capacity limits of critical areas (hotspots) at destination airports. Istanbul Sabiha Gokcen International Airport (LTFJ), its associated TMA routes and Point Merge System (PMS) were selected as the case study. Four virtual origin airports were defined for analysis. The proposed model represents hotspot locations (gates, taxiways, and runway holding positions) as virtual nodes with predefined congestion levels (low, medium, and high). The problem is formulated as a mixed integer linear programming model and solved using the CPLEX solver. The model achieves computational efficiency within reasonable solution times. Under the implementability bound in Eq. (31), the model reduces average airborne delay from 1552 s to 931.2 s and it corresponds to a 40 percent reduction within the prescribed acceptability band. Overall, the findings demonstrate that the proposed methods align with existing literature by enhancing delay management through realistic modeling at hotspot points.
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ArticleAçık Erişim2025/r/ebyu/19572
Mıst ülkelerinde sürdürülebilir kalkınma ve yeşil enerjinin yeşil üretime etkisi
Akçayır, Ömer; Akın, Fatih
Çalışmada, MIST (Meksika, Endonezya, Güney Kore ve Türkiye) ülkelerinde sürdürülebilir kalkınma (HDI) ve yenilenebilir (REN) enerji tüketiminin yeşil üretim (CO2) üzerindeki etkilerini araştırmak amacıyla 1990-2021 yılları verileri kullanılarak panel veri analizi yöntemleri kullanılmıştır. CIPS birim kök testinin kullanıldığı analizde tüm serilerin birinci farkta durağan oldukları belirlenmiş, LM Bootstrap eşbütünleşme yöntemiyle eşbütünleşme ilişkisi tespit edilmiştir. Modelin katsayıları Artırılmış Ortalama Grup (AMG) yöntemi ile tahmin edilmiş ve nihayetinde Dumitrescu & Hurlin (2012) panel nedensellik testi ile analiz tamamlanmıştır. Panelin genelinde ve ülke bazında istatistiksel olarak anlamlı sonuçlar ortaya çıkmış, HDI ve yenilenebilir enerji tüketimi CO2’yi azaltırken, kontrol değişken olan GDP ve yenilenemeyen enerji tüketimi CO2’yi artırmaktadır. Sürdürülebilir kalkınmanın yeşil üretim üzerindeki etkisinin yenilenebilir enerjinin etkisinden daha fazla olduğu tespit edilmiştir. Ayrıca bağımsız değişkenlerin her birinden bağımlı değişkene (CO2) doğru, doğrudan ya da diğer değişkenler aracılığıyla dolaylı olarak nedensellik ilişkisi tespit edilmiştir.
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ArticleAçık Erişim2025/r/ebyu/19521
Strength and Microstructural Analysis of Nano Powder and Basalt Fibre Reinforced RCC Pavement Concrete
Tortum, Ahmet; Sünbül, Şeyma
Nowadays, durability and longevity represent the main issues in the field of road concrete. Roller Compacted Concrete (RCC) is a sustainable option for road pavements due to its low binder content, fast application and high strength. Nevertheless, there is a necessity for the improvement of RCC road concretes through the incorporation of various additives to enhance their performance. The present study investigates the mechanical and microstructural properties of RCC road concretes containing Fe₂O₃ and TiO₂ nano powders and basalt fiber additives. Three distinct mixtures were formulated, and a series of compressive strength, flexural strength and abrasion resistance tests were conducted. The results were verified by BET (Brunauer-Emmett-Teller) and SEM (Scanning Electron Microscope) analyses. The experimental findings demonstrated that the incorporation of nano powder and basalt fiber additives was effective in increasing the durability of concrete. The third mixture demonstrated optimal performance, exhibiting a compressive strength of 61.20 MPa, a flexural strength of 8.80 MPa, and a mass loss of 2.5 g. BET analysis revealed that the incorporation of nanopowders enhanced the matrix density by increasing the surface area. Furthermore, the SEM images demonstrated that the nanopowders were uniformly dispersed within the matrix, and the basalt fibres exhibited robust interfacial interactions. In conclusion, the combined use of Fe2O3 and TiO2 nanopowders with basalt fibre additive is considered as an effective approach to improve the performance of RCC road concretes.
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ArticleAçık Erişim2025/r/ebyu/19513
Yeni sanayileşmiş ülkelerde yenilenebilir enerji ve insani gelişmenin ekonomik büyümeye katkısı
Akın, Fatih; Dinçer, Selin
Bu çalışmada 1990-2022 döneminde yeni sanayileşmiş ülkelerde (NIC) (Brezilya, Çin, Hindistan, Endonezya, Malezya, Meksika, Filipinler, Güney Afrika, Tayland, Türkiye) yenilenebilir enerji ve HDI düzeyinin ekonomik büyüme üzerindeki etkisi Artırılmış Ortalama Grup (AMG) tahmincisi ve Juodis, Karavias & Sarafidis (JKS, 2021) Granger nedensellik analizi ile incelenmiştir. LM Bootstrap Panel Eşbütünleşme testi sonuçları yenilenebilir enerji, HDI ve ekonomik büyüme değişkenleri arasında uzun dönemli bir ilişki olduğunu ortaya koymaktadır. Panel AMG tahmin sonuçları panel genelinde yenilenebilir enerji ve HDI düzeyinin ekonomik büyüme üzerinde pozitif ve anlamlı etkisi olduğunu göstermektedir. Ülkelere göre sonuçlar Çin, Hindistan, Endonezya ve Türkiye’de yenilenebilir enerjinin büyüme üzerinde anlamlı ve pozitif bir etkisi olduğunu göstermektedir. Ayrıca, Güney Afrika hariç tüm ülkelerde HDI düzeyinin büyüme üzerinde pozitif ve istatistiksel olarak anlamlı bir etkisi olduğunu göstermektedir. JKS Granger nedensellik testi sonuçları ise HDI ile ekonomik büyüme arasında ve HDI ile yenilenebilir enerji arasında çift yönlü nedensellik bulunduğunu ortaya koymaktadır. Ayrıca ekonomik büyümeden yenilenebilir enerjiye doğru tek yönlü nedensellik tespit edilmiştir. Bulgular, NIC ülkelerinde yenilenebilir enerji ve HDI değerinin ekonomik büyüme üzerinde önemli rol oynadığını vurgulamaktadır.
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