Yildiz Technical University
Recent publications
Wastewater treatment plants (WWTPs) are the main source of emerging pollutants encountered in surface water, albeit existing standards apply to a very limited number of emerging compounds. This paper presents a case study on non-target analysis, performed on influent and effluent samples from the WWTPs in Kocaeli, Türkiye. The samples were concentrated through solid-phase extraction, followed by LC-MS/MS analysis to identify emerging compounds, typically found in wastewater and treated wastewater. The data obtained was evaluated based on wastewater characteristics and the flow rate of the selected WWTPs. The removal of detected emerging compounds was calculated and classified as — “not removed”, and “partially removed”. Our analysis showed pharmaceutics as the most prevalent detected compounds, with the highest level of removal efficiency. The study demonstrated the necessity for redesigning conventional WWTPs to reduce the potential escape of emerging pollutants, with potential accumulation and transformation into harmful by-products in the environment.
This study focuses on predicting the output power of wind turbines (WTs) using the wind speed and WT operational characteristics. The main contribution of this work is a model identification method based on an adaptive neuro-fuzzy inference system (ANFIS) through multi-source data fusion on a moving window (MoW). The proposed ANFIS-MoW-based approach was applied to data in different time series windows, namely the very short-term, short-term, medium-term and long-term time horizons. Data collected from a 30-MW wind farm on the west coast of Nouakchott (Mauritania) were used in the computational analysis. In comparison to nonparametric models from the literature and models employing artificial intelligence machine learning techniques, the proposed ANFIS-MoW model demonstrated superior predictions for the output power of the WT with the fusion of very few data collected from different WTs. Moreover, for various time series windows (TSW) and meteorological conditions, additional benchmarking demonstrated that the ANFIS-MoW-based method outperformed five existing ANFIS-based models, including grid partition (ANFIS-GP), subtractive clustering (ANFIS-SC), fuzzy c-means clustering (ANFIS-FCM), genetic algorithm (ANFIS-GA), and particle swarm optimization (ANFIS-PSO). The results indicated that the suggested methodology is a promising soft-computing tool for accurately estimating the WT output power for WTs' sustainability through better control of their operation.
The difficulty of the production stages of panel radiators used for heating purposes reveals the importance of determining the heat transfer performance and panel radiator weight values, which are determined depending on the design parameters. In the present work, an artificial neural network model is proposed for predicting the heat transfer and weight values of a panel radiator as outputs depending on the design parameters of convectors. In the multilayer network model developed with 78 numerically obtained data sets, 8 different design parameters were defined as input parameters and heat transfer and in the output layer panel weight values were obtained. The design parameters of the convectors, in other words, input parameters of network model were chosen as the height of convector, thickness of convector sheet, the trapezoidal height of convector, convector base length, opposing convector distance, tip width of convector, convector vertical location and distance between convectors. For the proposed neural network model, the mean squared errors obtained for the heat transfer and panel radiator weight are −1.25E-04 and -7.54E-05 respectively. In addition, an R-value of 0.99999 has been obtained, and the average deviation value has been calculated as 0.001%. The obtained results show that, depending on the design parameters, the proposed artificial neural network model can predict the rate of heat transfer and weight of the panel radiator with high accuracy. This investigation is supposed to fill a significant gap since it is the pioneer one in open sources on machine learning modeling of panel radiators. Thus, it can possibly make a crucial contribution to the related manufacturing industry.
The boundary of real property is the fundamental element for securing rights attached to land. Countries, even with a long-standing cadastral tradition, often face the challenge of interpreting the course of a parcel boundary on the ground based on the available evidence, as data quality is very heterogeneous. Various cadastral principles and procedures have been developed for the determination of parcel boundaries in the field, which may also be associated with resolving boundary disputes. This article documents and compares the principles and procedures applied in the determination of property boundaries in selected civil law countries based on a novel conceptual model developed for that purpose. The notion of ‘boundary determination’ used in this article refers to demarcating and surveying land parcel boundaries during the initial cadastral survey and cadastral update procedures. The selected countries include Denmark and Sweden, which apply Nordic civil law; Slovenia and Turkey, which apply German civil law; and Spain, which applies Napoleonic civil law. The demarcation principles and processes applied in the different cadastral systems, the parties involved, and the evidence taken into consideration in these processes are described and compared. The main aim is to contribute to the documentation of the reasoning applied to the property boundary determination in the selected civil law countries.
With the expanding discussion over climate change mitigation, the shift to clean energy is a crucial challenge. The intricate structure of the nuclear energy-emissions nexus renders predicting whether nuclear acts as a green source of energy problematic. Hence, this study evaluates the effect of nuclear and renewable energy consumption on ecological quality proxied by carbon dioxide (CO2) emissions, ecological footprint (EF), and load capacity factor (LCF) by considering the role of financial development and economic growth. In this context, the United States (the US) is selected as the country example because of the leading position in nuclear energy production and consumption in the world; quarterly data from 1965/Q1 to 2018/Q4 is used; and novel Bootstrap Fourier Granger Causality in Quantile approach (Cheng et al., 2021) is applied. The main novelty of this approach is to examine the causality effect in a more detailed and flexible way. Whereas the traditional Granger test provides information about only conditional mean causality, this approach presents information about a tail causal relation or non-linear causalities. The empirical outcomes show that nuclear energy, renewable energy, and financial development lessen ecological deterioration in middle and higher tails while economic growth affects ecological quality negatively in higher quantiles. Thus, findings highlight governmental measures that favor nuclear energy transition and ecological sustainability to achieve and sustain a better environmental quality.
In this paper, an experimental research was conducted to investigate some mechanical and microstructural properties of geopolymers composites based on red mud-metakaolin reinforced with four different fiber types and filled with brick dust-recycle aggregate filling material. Polypropylene, glass, polyvinyl alcohol and basalt fibers were used. The effect of fiber reinforcements on the strength properties and abrasion resistance of geo-polymer composites was investigated, and microstructural analysis was performed to reveal the geopolymeric matrix composition, structure and bonding of the fibers. The results revealed that fiber reinforcement to meta-kaolin red mud-based geopolymers with brick waste powder and recycling aggregate improved the strength properties. For example, the presence of glass and polyvinyl alcohol fibers increased the flexural strength of geopolymer composites by 39% and 61%, respectively, compared to the control sample. Microstructural analysis revealed that the fibers gave an acceptable interfacial bond with the geopolymeric binder. The findings of the current article show a potential solution to becoming a sustainable geopolymer by presenting the successful use of red mud, recycling aggregate, and brick dust waste with different fibers as important asset materials.
The present study deals with the production of multifunctional polymer-based nanocomposites having the ability of ionizing radiation shielding and enhanced electric conductivity. In this context, it is aimed to obtain a new environmentally-friendly lead-free material with both high electric conductivity and ionizing radiation shielding features by reinforcing polycarbazole (PCz), a conductive polymer with BaO nanoparticles having a high effective atomic number. PCz matrix and BaO nanoparticles have been prepared by polymerization and co-precipitation methods, respectively. The surface morphology of the PCz matrix and nano BaO particles have been characterized by SEM and TEM images. Additionally, the optical band gaps of the samples have been calculated by using UV–visible spectroscopy. The lowest optical bandgap i.e. the highest electric conductivity has been observed for the 10 wt% BaO nanoparticle doped composite. On the other hand, the ionizing radiation shielding abilities of the samples have been evaluated in the context of mass attenuation coefficient, half-value layer, mean free path, effective atomic number, and effective electron density determined both experimentally and theoretically. Experimental measurements have been carried out by using NaI(Tl) detector and point radioactive sources (Ba-133, Cs-137, and Co-60) in the narrow beam geometry. The theoretical calculations have been performed by WinXCom software. A reasonable agreement between the experimental and WinXCom results has been found. Consequently, it has been determined that the maximum radiation shielding ability has been detected against 81 keV radiation for the PCz/30 wt% BaO nanocomposite. The composite has also a lower bandgap than pristine PCz. In this respect, the PCz/30 wt% BaO nanocomposite can have a promising potential in both medical and electronic applications that require high electric conductivity and ionizing radiation shielding ability.
Geopolymers are an important composite material alternative to cement with high CO2 emission, which allows the use of waste materials in its structure. However, which waste material contributes to the geopolymer composite structure to what extent is still an ongoing research area. This study has been prepared to contribute to closing the gap in this field. In this study, industrial waste materials (waste marble powder (MP), waste brick powder (BP), ceramic waste powder (CW), waste glass powder (GP), and rice husk ash (RHA)) were used 25%, 50%, and %75 percentage as aggregate instead of recycled concrete (RC) on red mud-metakaolin based geopolymer mortars. Mechanical properties (ultrasonic pulse velocity (UPV), splitting tensile, compressive, and flexural strengths), physical properties (void ratio, water absorption, unit weight, and workability properties), abrasion test, and microstructure analyzes (SEM-EDS and XRD) were performed. The highest compressive strength results were in seen the 25% brick powder substitution, with an increase of 74.01% compared to the control sample. Also, the compressive strength results increased in the case of increasing the use of ceramic waste. High silica and alumina contents in both materials were effective in these cases. In terms of the abrasion test, the highest performance was seen as a 49.2% decrease in the mass loss in 75% marble powder replacement. As a result, substitute materials have shown a successful performance, creating significant potential in the production of a sustainable geopolymer.
Dy³⁺-doped luminescent glasses have received great attention due to their ability to emit white light at a suitable yellow-to-blue intensity ratio. However, achieving bright white light using single Dy³⁺-doped glasses remains a challenge due to the hypersensitivity of the emission band at 575 nm — usually resulting in intense yellow emission. In this work, we present a novel approach for compensating for the blue emission deficiency of Dy³⁺ to produce resin-free white light-emitting diodes (WLEDs) by synthesizing two series of Dy³⁺-doped glasses from tellurite and silicate systems on which blue-emitting carbon dots (BCDs) are spin-coated. The structural, chemical, optical, and luminescence properties of tellurite and silicate glasses are compared and discussed in detail. White light emissions are obtained upon 365-nm excitation for BCD-coated Dy³⁺-doped tellurite and silicate glasses with color coordinates of (x=0.31, y=0.33) and (x=0.31, y=0.34) and correlated color temperatures (CCT) of 5518 K and 5316 K, respectively. BCDs coating increases photoluminescence quantum yield (PLQY) values from 3.10% to 5.62% and from 20.81% to 31.49% for tellurite and silicate glasses, respectively. Ultimately, the findings in this work show the potential of BCD-coated luminescent glasses with excellent luminescent properties to be considered in solid-state lighting applications.
Within a p-i-n LED structure, energy band gaps of MOCVD grown %10 In content InGaN and GaN layers were obtained through traditional UV–Visible transmission-absorption measurements and utilizing surface photovoltage (SPV) and spectral photoconductivity (SPC) measurements. Moreover, all applied techniques determined that the total band offset between InGaN and GaN layers were around 0.3–0.4 eV. Furthermore, the temperature dependence of band gaps of each InGaN and GaN layer was analyzed through a modified Varshni relation where both Varshni coefficients and band tail energy width were explored. Band tails in a modified Varshni relation, SPV and SPC measurements were in agreement with each other and originated Franz-Keldysh Effect attributed to the presence of photon assisted tunneling.
The demand for video analysis has been rapidly increasing in the last decade. Video analysis plays a critical role in various technologies, including medical diagnosis, security surveillance, robotics, and sport. Soccer is the most popular sport in our culture, with millions of fans. Many video analysis approaches have been developed in recent years to assist and provide important information to spectators, referees, coaches, and players. Most of these approaches are aimed towards detecting and tracking players or the ball, event detection, and analysis of the game. For this purpose, various classical or deep learning-based strategies have been used. This study investigates deep learning-based techniques that have been proposed over the last few years to analyze football videos. The purpose of this study is not to compare current methodologies, but to show the most recent research in the field. This paper investigates the challenges of soccer video analysis and its application groups, e.g., player/ball detection and tracking, event detection, and game analysis. This paper also reviews the used deep learning-based methods, their performance, advantages, and disadvantages in soccer videos, and finally, concludes with future potential in the analysis of soccer videos.
Monitoring and management of agricultural lands are essential due to reasons affecting agriculture, such as increasing population and global climate. With the increase in the temporal resolution of satellite systems, time-series classifications have become popular in cropland mapping. Because annual plants can give similar spectral reflectance values on the same date. In this context, agricultural land (∼500 km2) was selected in the south of South Dakota in the United States. The area includes alfalfa, corn, soybeans, winter wheat plants, developed, grassland/pasture, herbaceous wetlands, and open water areas. The study aims to map croplands with vegetation indices produced by annual Sentinel-1 and Sentinel-2 satellites. In this context, Radar Vegetation Index (RVI) produced from 25 Sentinel-1, and the Normalized Difference Vegetation Index (NDVI) produced from 26 Sentinel-2 satellites were used for 2020. We used the Time-Weighted Dynamic Time Warping (TWDTW) algorithm, which separates and classifies the similarities between two time series with variable speeds with time constraints. For mapping, the indices were classified both individually and combined. The highest overall accuracy (77.2%) was obtained with the combined use of NDVI and RVI. Among the plant classes, the lowest accuracy (83.71%) was found, and it was determined that the plant classes did not mix much. Sentinel-2 satellite is not available before April due to weather conditions in the region. For this reason, since the Sentinel-1 satellite is not affected by weather conditions, it is thought that the use of two satellites together will be beneficial in time series analysis.
This study presented a novel liquid-cooled heat sink based on constructal theory. An experiment was conducted to investigate the influence of boundary conditions, such as the mass flow rate (ṁ), on heat transfer rate (Qin) and pressure drop. Five cylinder heater cartridges were used in the experiment for 11 different mass flow rates (0.008292 < ṁ (kg/s) < 0.03307). Through numerical simulation, the effects of changing the number of clusters on heat transfer and pressure drop were studied. The results showed that the optimal combination of pressure drop and Nusselt number occurs in four clusters. According to the results, increasing the number of clusters can increase the Nusselt number by up to 11.98% and 13.62% for the highest (ṁ = 0.03307 kg/s) and lowest (ṁ = 0.008292 kg/s) mass flow rates, respectively. This work may lay the foundation for creating the next generation of thermal management systems for compact heat sources, such as the CPU in a self-driving car, robots and high-performance computers (HPC).
The adsorption efficiency of Pb(II) and Cd(II) from aqueous solutions on m-phenylenediamine-modified Amberlite XAD-4 resin was investigated. The effects of pH, adsorbent amount, initial metal concentration, eluent type and volume and flow rate on the retention of the metal ions have been studied on column studies. The optimum parameters were determined as pH 5, concentration 10 mg/L, stirring time 30 min and 0.2 g adsorbent amount and flow rate 2.5 mL/min for a quantitative adsorption. Sorption data were interpreted in terms of Langmuir and Freundlich equations, and both models were found to be fully appropriate. Each column can be used up to 10 sequential analyses without considerable change. The results indicate high metal adsorption capacity and satisfactory recovery of Pb(II) and Cd(II).
Background Qigong embraces a range of self-care exercises originating from China. Lung-Strengthening Qigong (LSQ) is a specific technique for maintaining and improving physical and mental wellbeing. Methods We recruited 170 practitioners and 42 non-practitioner/control samples to investigate the impacts of LSQ practice on body, mind, thoughts, and feelings. This is a pilot study pursued to plan for an adequately powered, non-clinical randomized controlled trials (RCT) on overall wellbeing and health and to evaluate the adequacy of delivering the physical activity intervention with fidelity. Self-evaluation-based data collection schemes were developed by regularly requesting completion of a questionnaire from both practitioner and control group, and an online diary and end of study survey (EOS) completion only from the practitioners. Diverse types of analyses were conducted, including statistical tests, machine learning, and qualitative thematic models. Results We evaluated all different data resources together and observed that (a)the impacts are diverse, including improvements in physical (e.g., elevated sleep quality, physical energy, reduced fatigue), mental (e.g., increased positivity, reduced stress), and relational (e.g., enhanced connections to self and nature) wellbeing, which were not observed in control group; (b)measured by the level-of-effectiveness, four distinct clusters were identified, from no-effect to a high-level of effect; (c)a majority (84 %) of the LSQ practitioners experienced an improvement in wellbeing; (d)qualitative and quantitative analyses of the diary entries, questionnaires, and EOS were all found to be consistent, (e)majority of the positively impacted practitioners had no or some little prior experience with LSQ. Conclusions Novel features of this study include (i)an increased sample size vis-à-vis other related studies; (ii)provision of weekly live-streamed LSQ sessions; (iii)integration of quantitative and qualitative type of analyses. The pilot study indicated that the proportion of practitioners who continued to engage in completing the regular-interval questionnaires over time was higher for practitioners compared to the control group. The engagement of practitioners may have been sustained by participation in the regular live LSQ sessions. To fully understand the impacts of LSQ on clinical/physiological outcomes, especially for specific patient groups, more objective biomarkers (e.g. respiratory rate, heart rate variation) could be tracked in future studies.
There is a rising necessity concerning low energy consumption and thermal comfort, thus, new systems combining displacement ventilation and radiant heating are becoming common as a research topic. The current paper covers free, mixed, and forced convection in an experimental chamber consisting of a radiant wall heating system and a baseboard level diffuser. Air temperature in the test room is adjusted using an air blower via a novel designed baseboard level slot diffuser along with a hydronic wall heating circuit. Simultaneously; an upward direction air jet is blown through the heated or the opposite wall under two scenarios. The ranges of air inlet velocity and temperature are selected to be between 0.25 and 12.5 m/s and 14-26 • C, correspondingly. The heat transfer characteristics pertaining to the heated wall are studied with respect to the influences of wall and air temperatures, air inlet velocities, and the location of the diffuser. To derive convective heat transfer coefficient correlations valid for mixed and forced types for a radiant heated wall, acquired data have been processed. The convective, radiative, and total heat transfer coefficient intervals of 3-14.1, 5.2-5.5, and 9.3-21.7 W/m 2 K have been obtained for the radiant heated wall coupled with airflows, correspondingly.
In this study, the effects of using various fruit juice concentrates (pomegranate, grape and sour cherry) instead of corn syrup in marshmallows on the physico-chemical, textural and microstructural properties of the products were investigated. Experimental points of the study were determined by D-optimal mixture design, and accordingly, special cubic, linear and quadratic models were used for the effects on physico-chemical, color and texture parameters according to independent variables. Considering the results, pH, water activity and density values of the marshmallow samples were found to be in the range of 2.54–3.21, 0.48–0.55 and 0.44–0.66 g/mL, respectively. As expected, the use of fruit juice concentrates affected the visual properties of marshmallows, and in particular sour cherry and pomegranate juice concentrates caused a positive increase in the +a* values of the samples. According to the texture profile analysis, the hardness, springiness, cohesiveness, gumminess and resilience values of the samples were determined as 6.43–10.5 N, 0.84–97, 0.74–0.90, 5.18–8.22 g, 0.34–0.81, respectively. R² values of investigated parameter models were determined between 0.4971 and 0.8901. It was concluded that fruit juice concentrates can be used instead of corn syrup in marshmallows to meet consumer demands and expectations. Finally, it is thought that it will be useful to investigate the effects of various hydrocolloids on the color, texture stability and sensory properties of the products with further studies using fruit juice concentrates in marshmallow production.
Reducing greenhouse gas emissions is an important task to reduce the adverse effects of climate change. A large portion of greenhouse gas emissions apparently originates from the transportation sector. Therefore, adopting cleaner technologies with lower emission footprints has become vital. For this reason, in this study, a life cycle impact analysis of hydrogen production technologies as an alternative to fossil fuels and the utilization of hydrogen in fuel cell electric buses is carried out. According to the results of this study, the operational contributions of internal combustion engines have a significant impact on life cycle impact analysis indicators. The global warming potentials of clean hydrogen production technologies result in much lower results compared to conventional hydrogen production technologies. Also, almost all indicators for biohydrogen production technologiess yield lower results because of the wastewater removal. The global warming potential results of hydrogen production methods are found to be 6.8, 1.9, 2.1, 0.5, 0.2, and 7.9 kg CO2 eq./kg H2 for PV electrolysis, wind electrolysis, high temperature electrolysis, dark fermentation, photo fermentation and conventional hydrogen production, respectively. However, the chemicals used in PV and wind turbine production increased the ecotoxicological indicators. On the other hand, hydrogen utilization in buses is a better option environmentally. The global warming potentials for PV electrolysis, wind electrolysis, high temperature electrolysis, dark fermentation, photo fermentation, conventional hydrogen, compressed natural gas bus, and diesel bus are found to be 0.060, 0.016, 0.018, 0.007, 0.006, 0.053, 0.082, and 0.125 kg CO2 eq./p.km, respectively. The results are especially important in terms of reducing the effects at the source and optimizing the systems.
We evaluated spatiotemporal characteristics of the wind power density (WPD) in the Mediterranean and the Black Sea. An hourly wind speed data from ERA5 of ECMWF is used covering 62 years between 1959 and 2020. Spatial distributions of WPD at 100 m altitude at monthly, yearly, and seasonal timescales are revealed. The average WPD in the study area is 373 W/m², where the spatial maximums in the 62-year average map reach up to 1116 W/m². The spatial distribution of yearly mean WPD considering 13 sub-basins revealed that the two most prominent regions are the Gulf of Lion and the Aegean Sea. The diurnal variation of WPD showed over ± 10 % variability within the considered sub-basins. Five commercial turbines are selected to investigate their performance in the study area. The capacity factors, and operational and rated power time percentages are spatially mapped. The variability in the WPD is investigated using several indicators of yearly variability index, intra-annual variability index, coefficient of variation, robust coefficient of variation, inter-quartile range, and inter-quartile ratio. The long-term trend in the monthly-mean values is investigated and it is revealed that the statistically significant downward trends manifest themselves in the Levantine Basin, Ligurian-Provençal Basin, Balearic Sea, and the Alboran Sea. The capacity factors of five widely used wind turbines are computed in the study area using both yearly and seasonal timescales.
The current study investigates the green hydrogen production from renewable energy for a sustainable yacht marina. The main idea is to create a sustainable harbor for utilizing renewable energy sources using electrolysers to produce hydrogen and providing power for both marina energy demand and transportation fuel needs. Generating electricity from renewable energy resources and converting into green hydrogen in marinas are the main objectives of the current study. Solar and wave energy resources are used for calculating total hydrogen production potential. Four different sizes of luxury yachts are considered in the calculations. The considered three yacht marinas are located in Malaga, Mugla, and Istanbul. The average annual green hydrogen production potential of a marina from offshore solar energy is estimated to be 1.34, 1.38, 1.47 kt, and annual green hydrogen production from wave energy is 2.66, 3.06, 3.99 kt for the considered marinas locates in Marbella, Bodrum, and Atakoy, respectively. During the summer season, 400 small-sized, 250 medium-sized, or 100 large-sized yachts are potentially considered for fueling with green hydrogen. At maximum capacity, in Atakoy marina, which is chosen for the case study, a total of 243 hydrogen-fueled mega-yachts can potentially be refueled with green hydrogen. The study results show that the marinas selected in the specified locations appear to feasible for sustainable hydrogen applications.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.
11,632 members
Bulent Bayram
  • Department of Geomatics Enginering
Osman Sagdic
  • Department of Food Engineering
Banu Mansuroglu (Mansour Karaman)
  • Department of Molecular Biology and Genetics Department
Tufan Demirel
  • Department of Industrial Engineering
Fusun Balik Sanli
  • Department of Geomatics Enginering
Information
Address
Yildiz Campus, Barbaros Boulevard, Yildiz - Besiktas, 34349, Istanbul, Turkey
Head of institution
Prof. Dr. Tamer Yılmaz
Website
http://www.yildiz.edu.tr
Phone
00902123837070
Fax
00902123834011