Mardin Meslek Yüksekokulu

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Whisker Behavior and Tool Wear in Cutting of Unidirectional SiC Whisker Reinforced Plastics
(Elsevier Science Pub., 1996) Jamal ElDeen AFAGHANI; K. Yamaguchi
This study concerns the cutting process of unidirectional Sic whisker-reinforced plastic composite. The effects of the grain size of the polycrystalline diamond tool and the Sic whisker orientation on the tool wear were investigated. The tool with fine grain size exhibited higher wear rates. The greatest tool wear was with the composite having longitudinal alignment of whiskers. The cutting processes of various orientations of whiskers were observed by scanning electron microscopy at low speed using a specially designed device. Moreover, models were proposed for cutting the Sic whisker-plastic composite and for wear of sintered diamond tools.
Citation - WoS: 5
Near ideals in near semigroups
(EUROPEAN JOURNAL PURE & APPLIED MATHEMATICS, 2018) Bağırmaz, Nurettin
In this paper, we introduced the notion of near subsemigroups, near ideals, near bi-ideals and homomorphisms of near semigroups on near approximation spaces. Then we give some properties of these near structures.
Sleep arousal detection using one dimensional local binary pattern-based convolutional neural network
(IEEE, 2021) Zan, Hasan; Yıldız, Abdulnasır
Sleep arousal is defined as a shift from deep sleep to light sleep or complete awakening. Arousals cause sleep deprivation by fragmenting sleep, and ultimately, many health problems. Arousals can be induced by well-studied apneas and hypopneas or other sleep orders such as hypoventilation, bruxism, respiratory effort-related arousals. Thus, detection of less-studied non-apnea/hypopnea arousals is important for diagnosis and treatment of sleep disorders. Traditionally, polysomnography (PSG) test that is recording and inspecting overnight physiological signals is used for sleep studies. In this work, a novel method based on one dimensional local binary pattern (1D-LBP) and convolutional neural network (CNN) for automatic arousal detection from polysomnography recordings is proposed. 25 recordings from PhysioNet Challenge 2018 PSG dataset are used for experiments. Each signal in PSG recordings is transformed to a new signal using 1D-LBP, and then segmented using 10-s-long sliding window. The segments are fed to a CNN model formed by stacking 25 layers for classification of non-apnea/hypopnea arousal regions from non-arousal regions. Area under precision-recall curve (AUPRC) and area under receiver operating characteristic curve (AUROC) metrics are used for performance measurement. Experimental results reflect that the proposed method shows a great promise and obtains an AUPRC of 0.934 and an AUROC of 0.866.
Citation - WoS: 32
Citation - Scopus: 32
Development software program for extraction of photovoltaic cell equivalent circuit model parameters based on the Newton–Raphson method
(SpringerLink, 2022) Adak, Süleyman; Cangi, Hasan; Yılmaz, Ahmet Serdar; Arifoğlu, Uğur
Finding the equivalent circuit parameters for photovoltaic (PV) cells is crucial as they are used in the modeling and analysis of PV arrays. PV cells are made of silicon. These materials have a nonlinear characteristic. This distorts the sinusoidal waveform of the current and voltage. As a result, harmonic components are formed in the system. The PV cell is the smallest building block of the PV system and produces voltages between 0.5 V and 0.7 V. It serves as a source of current. The amount of radiation hitting the cell determines how much current it produces. In an ideal case, a diode and a parallel current source make up the equivalent circuit of the PV cell. In practice, the addition of a series and parallel resistor is made to the ideal equivalent circuit. There are many equivalent circuits in the literature on modeling the equivalent circuit of a PV cell. The PV cell single–diode model is the most used model due to its ease of analysis. In this study, the iterative method by Newton–Raphson was used to find the equivalent circuit parameters of a PV cell. This method is one of the most widely used methods for determining the roots of nonlinear equations in numerical analysis. In this study, five unknown parameters (Iph, Io, Rs, Rsh and m) of the PV cell equivalent circuit were quickly discovered with the software program prepared based on the Newton–Raphson method in MATLAB
Citation - WoS: 46
Citation - Scopus: 57
Harmonics Mitigation of Stand-Alone Photovoltaic System Using LC Passive Filter
(Journal of Electrical Engineering and Technology, 2021) Adak, Süleyman
This article investigates modeling and simulation of the off-grid photovoltaic (PV) system, and elimination of harmonic components using an LC passive filter. Pulse width modulation (PWM) inverter is used to convert the direct current to alternating current. It is very important in terms of energy quality that the inverter output current total harmonic distortion (THDI) is below the value given by standards. Harmonic components have negatively effect on off-grid PV power system. THDI should be kept below a certain level in order to prevent damage to the equipment in the off-grid system and to ensure a higher quality energy flow to reduce the total harmonic distortion (THD) of the solar inverter output current; LC passive filter must be connected to the output of the PWM inverter. There are many types of passive filters for solar inverters. One of the most widely used filter types is the LC filter. LC filters are used in off-grid systems. LC filter is smaller in size and lower cost than other filters. But it is more complicated to determine the parameters of the LC filter. Therefore, in order for the system to remain in a steady state, the parameters must be accurately calculated and analyzed. In this study, the output power of the solar inverter, switching frequency, bus voltage etc. values were determined and LC filter parameters were calculated. Since high inductance values are used in LC filters, the voltage drop increases in these filters. To reduce the voltage drop, the DC bus voltage must be increased, which increases the switching losses. LC filter is connected between the inverter and the nonlinear load to filter the harmonic components produced by the DC/DC boost converter, DC/AC inverter and non-linear load. Matlab/Simulink program was used in Simulation and analysis of off-grid solar system. Solar inverter output current THD was measured as 91.55%. After the LC filter is connected to the system, this value has dropped to 2.62%.
Citation - WoS: 2
Citation - Scopus: 4
The convolutional neural network approach from electroencephalogram signals in emotional detection
(Concurrency Computation, 2021) Türk, Ömer; Özerdem, Mehmet Siraç
Although brain-computer interfaces (BCI) progress rapidly, the desired success has not been achieved yet. One of these BCI is to detect emotional states in humans. An emotional state is a brain activity consisting of hormonal and mental reasons in the face of events. Emotions can be detected by electroencephalogram (EEG) signals due to these activities. Being able to detect the emotional state from EEG signals is important in terms of both time and cost. In this study, a method is proposed for the detection of the emotional state by using EEG signals. In the proposed method, we aim to classify EEG signals without any transform (Fourier transform, wavelet transform, etc.) or feature extraction method as a pre-processing. For this purpose, convolutional neural networks (CNNs) are used as classifiers, together with SEED EEG dataset containing three different emotional (positive, negative, and neutral) states. The records used in the study were taken from 15 participants in three sessions. In the proposed method, raw channel-time EEG recordings are converted into 28 × 28 size pattern segments without pre-processing. The obtained patterns are then classified in the CNN. As a result of the classification, three emotion performance averages of all participants are found to be 88.84%. Based on the participants, the highest classification performance is 93.91%, while the lowest classification performance is 77.70%. Also, the average f-score is found to be 0.88 for positive emotion, 0.87 for negative emotion, and 0.89 for neutral emotion. Likewise, the average kappa value is 0.82 for positive emotion, 0.81 for negative emotion, and 0.83 for neutral emotion. The results of the method proposed in the study are compared with the results of similar studies in the literature. We conclude that the proposed method has an acceptable level of performance.
Citation - WoS: 141
Citation - Scopus: 184
Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals
(MDPI, 2019) Türk, Ömer; Özerdem, Mehmet Siraç
The studies implemented with Electroencephalogram (EEG) signals are progressing very rapidly and brain computer interfaces (BCI) and disease determinations are carried out at certain success rates thanks to new methods developed in this field. The effective use of these signals, especially in disease detection, is very important in terms of both time and cost. Currently, in general, EEG studies are used in addition to conventional methods as well as deep learning networks that have recently achieved great success. The most important reason for this is that in conventional methods, increasing classification accuracy is based on too many human efforts as EEG is being processed, obtaining the features is the most important step. This stage is based on both the time-consuming and the investigation of many feature methods. Therefore, there is a need for methods that do not require human effort in this area and can learn the features themselves. Based on that, two-dimensional (2D) frequency-time scalograms were obtained in this study by applying Continuous Wavelet Transform to EEG records containing five different classes. Convolutional Neural Network structure was used to learn the properties of these scalogram images and the classification performance of the structure was compared with the studies in the literature. In order to compare the performance of the proposed method, the data set of the University of Bonn was used. The data set consists of five EEG records containing healthy and epilepsy disease which are labeled as A, B, C, D, and E. In the study, A-E and B-E data sets were classified as 99.50%, A-D and B-D data sets were classified as 100% in binary classifications, A-D-E data sets were 99.00% in triple classification, A-C-D-E data sets were 90.50%, B-C-D-E data sets were 91.50% in quaternary classification, and A-B-C-D-E data sets were in the fifth class classification with an accuracy of 93.60%.
Citation - Scopus: 2
Classification of Epilepsy Types from Electroencephalogram Time Series Using Continuous Wavelet Transform Scalogram-Based Convolutional Neural Network
(ASTM International, 2020) Türk, Ömer; Akpolat, Veysi; Varol, Sefer; Aluçlu, Mehmet Ufuk; Özerdem, Mehmet Siraç
During the supervisory activities of the brain, the electrical activities of nerve cell clusters produce oscillations. These complex biopotential oscillations are called electroencephalogram (EEG) signals. Certain diseases, such as epilepsy, can be detected by measuring these signals. Epilepsy is a disease that manifests itself as seizures. These seizures manifest themselves in different characteristics. These different characteristics divide epilepsy seizure types into two main groups: generalized and partial epilepsy. This study aimed to classify different types of epilepsy from EEG signals. For this purpose, a scalogram-based, deep learning approach has been developed. The utilized classification process had the following main steps: the scalogram images were obtained by using the continuous wavelet transform (CWT) method. So, a one-dimension EEG time series was converted to a two-dimensional time-frequency data set in order to extract more features. Then, the increased dimension data set (CWT scalogram images) was applied to the convolutional neural network (CNN) as input patterns for classifying the images. The EEG signals were taken from Dicle University, Neurology Clinic of Medical School. This data consisted of four classes: healthy brain waves, generalized preseizure, generalized seizure, and partial epilepsy brain waves. With the proposed method, the average accuracy performance of three of the EEG records' classes (healthy, generalized preseizure, and generalized seizure), and that of all four classes of EEG records were 90.16 % (± 0.20) and 84.66 % (± 0.48). According to these results, regarding the specific accuracy ratings of the recordings, the healthy EEG records scored 91.29 %, generalized epileptic seizure records were at 96.50 %, partial seizure EEG records scored 89.63 %, and the preseizure EEG records had a 90.44 % rating. The results of the proposed method were compared to the results of both similar studies and conventional methods. As a result, the performance of the proposed method was found to be acceptable.
Citation - WoS: 5
Citation - Scopus: 6
THERMAL ANALYSIS OF DIFFERENT REFUSE DERIVED FUELS SAMPLES
(DoiSerbia, 2021) Ayas, Gizem; Öztop, Hakan F.
As a result of the activities carried out by people to maintain their daily lives in different places such as homes, hospitals, hotels or workplaces, waste consisting of furniture, paint, batteries, food waste, sachets, bottles, fabrics, and fibers with the heterogeneous structure is called municipal solid waste. Secondary fuels with higher heating value, which are generated by recycling of non-recyclable and reusable wastes in municipal solid wastes, are called as refuse derived fuel (RDF). In this study, RDF1 (taken in December, winter season) and RDF2 (taken in June, summer season) samples obtained from different dates were used. The ultimate, proximate, calorific value, X-ray fluorescence, thermogravimetric analysis, and differential scanning calorimetry analysis were performed for these samples. Combustion characterization from RDF samples was investigated in the applied analyzes. The results of the content analysis made were examined separately and compared with the thermogravimetric analysis and differential thermal analysis combustion graph curves. It was revealed that the RDF1 sample had a better combustion compared to the RDF2 sample, as the ash amount and content obtained as a result of the combustion also supported other data. In addition, the results of the analysis show how different the RDF samples taken from the same region in two different months are different from each other.
Performance Improvement of Genetic Algorithm Based Exam Seating Solution by Parameter Optimization
(Journal of Innovative Science and Engineering (JISE), 2022) Ağalday, Fatih; Nizam, Ali
Exam seat allocation has become a complex problem, with an increasing number of students, subjects, exams, departments, and rooms in higher education institutions. The requirements and constraints of this problem demonstrate characteristics similar to extensively researched exam timetabling problems. They plan for a limited capacity effectively and efficiently. Additionally, exam seating requires a seating arrangement to reduce the number of cheating incidents. In the literature, several genetic algorithm-based methods have been recommended to prevent students, who are close friends, from sitting close during the exams while providing the best exam session arrangement. We improved the performance of the genetic algorithm using parameter optimization and a new elitism method to increase the saturation rate and accuracy. The algorithm was tested on a real-world dataset and demonstrated high potential for the realization of a high-quality seating arrangement compatible with the requirements of educational institutions.
Characterization of yeast flora of “hurma” olives using molecular methods and mid-IR spectroscopy
(2015) Canal, Canan; Baysal, A. Handan; Özen, Fatma Banu
Among the olive varieties in Turkey, Erkence olives, grown in nearby area around Karaburun Peninsula of Izmir, go through a natural debittering phase on the tree during its ripening. As a result of this phase, the olives lose their bitter taste while still on the tree and have a dark brownish color in the inside and a wrinkled outer layer which are their differentiating appearance characteristics from olives that do not undergo this process. This naturally debittered olive type is known by the name of Hurma (Aktas et al., 2014). According to an old study performed in Greece with a similar type of olive, the debittering process was attributed to the action of a fungus, Phoma olea,which hydrolyses oleuropein, a bitter phenolic compound of olives (Kalogeras, 1932). There is no study in the literature related to the characterization of yeasts on this unique type of olive, Hurma. Until present, the characterization of yeasts associated with table olives has been made through biochemical and morphological methods, using the taxonomic keys (Kurztman and Fell, 1998). More recently, molecular methods and FTIR spectroscopy using chemometric techniques have been used for the identification of yeasts due to being rapid, easy and more precise methods for yeast identification. In order to understand the role of yeasts in maturation and debittering process of natural Hurma olives, characterization of olive yeasts from two olive types, Hurma and Gemlik, an olive variety which is commonly consumed as table olive, was aimed using molecular methods and mid-IR spectroscopy in comparison with cultural methods.
Citation - WoS: 10
Citation - Scopus: 9
Identification of cotton and corn plant areas by employing deep transformer encoder approach and different time series satellite images: A case study in Diyarbakir, Turkey
(ScienceDirect, 2023) Türk, Ömer; Şimşek Bağcı, Reyhan; Acar, Emrullah
It is very important to determine the crops in the agricultural field in a short time and accurately. Thanks to the satellite images obtained from remote sensing sensors, information can be obtained on many subjects such as the detection and development of agricultural products and annual product forecasting. In this study, it is aimed to automatically detect agricultural crops (corn and cotton) by using Sentinel-1 and Landsat-8 satellite image indexes via a new deep learning approach (Deep Transformer Encoder). This work was carried out in several stages, respectively. In the first stage, a pilot area was determined to obtain Sentinel-1 and Landsat-8 satellite images of agricultural crops used in this study. In the second stage, the coordinates of 100 sample points from this pilot area were taken with the help of GPS and these coordinates were then transferred to Sentinel-1 and Landsat-8 satellite images. In the next step, reflection and backscattering values were obtained from the pixels of the satellite images corresponding to the sample points of these agricultural crops. While creating the data sets of satellite images, the months of June, July, August and September for the years 2016–2021, when the development and harvesting times of agricultural products are close to each other, were preferred. The image data set used in the study consists of a total of 434 images for Sentinel-1 satellite and a total of 693 images for Landsat-8. At the last stage, the datasets obtained from different satellite images were evaluated in three different categories for crop identification with the aid of Deep Transformer Encoder approach. These are: (1-) Crop identification with only Sentinel-1 dataset, (2-) Crop identification only with Landsat-8 dataset, (3-) Crop identification with both Sentinel-1 and Landsat-8 datasets. The results showed that 85%, 95% and 87.5% accuracy values were obtained from the band parameters of Sentinel-1 dataset, Landsat-8 dataset and Sentinel-1&Landsat-8 datasets, respectively
Effects of Electric Vehicles and Charging Stations on Microgrid Power Quality
(Gazi University Journal of Science Part A: Engineering and Innovation, 2022) Adak, Süleyman; Cangi, Hasan; Kaya, Rıdvan; Yılmaz, Ahmet Serdar
In this study, integration of renewable energy sources and Electric Vehicles (EVs) into a micro-grid was modeled and analyzed. The microgrid is divided into four important parts; a diesel generator, acting as the base power generator; a photovoltaic (PV) farm combined with a wind farm, to produce electrical energy; a vehicle to grid (V2G) system installed next to the last part of the microgrid which is the load of the microgrid. The size of the microgrid represents approximately a community of a thousand households during a low consumption day in spring or fall. There are 100 electric vehicles in the base model which means that there is a 1:10 ratio between the cars and the households. This is a possible scenario in a foreseeable future. The continuous increase in their rate in energy production makes micro-grids important. Microgrids can be designed to meet the energy needs of hospitals, universities or charging stations of electric cars, as well as to meet the energy needs of a district, village or industrial site. Charging stations are needed to charge the electric vehicle battery. In this study, the effects of electric vehicles on the microgrid network are analyzed. Electric vehicles have non-linear circuit elements in their structures. Therefore, they are a source of harmonic current in the microgrid. They negatively affect the power quality of the microgrid. The battery in electric vehicles is charged with direct current. The alternating current from the microgrid needs to be converted to direct current.
An Assessment of Post-Earthquake Issues in UNESCO (United Nations Educational, Scientific and Cultural Organization) Gastronomic Cities Gaziantep, Hatay and Şanlıurfa in Turkey
(2023) Kızılgeçi, çiğdem
estinations catering to tourists with specific gastronomic preferences or diverse motivations may experience occasional disruptions in the range of offerings available. This scenario may arise due to anthropogenic factors or natural phenomena that result in varying degrees of environmental degradation. The literature commonly reports that regions experiencing disasters such as wars, floods, epidemics, earthquakes, and hurricanes are susceptible to significant life, property, and economic losses. The seismic events thatcommenced on February 6th, 2023 and persist to the present have engendered a consequential phenomenon within the nation of Turkey. The present research has been conducted to examine the prospective impacts of the Gaziantep/Kahramanmaraş earthquakes of 2023 on Gastronomy tourism and to propose viable remedies for any associated issues. The study employed qualitative research methods, specifically observation and literature review, to gather data. The data that was acquired was subjected to analysis using the descriptive analysis methodology. In summary, based on the scientific literature review and contemporary scientific assessments of gastronomic tourism, it has been observed that this phenomenon can be leveraged as a tourism asset in the future, despite certain criticisms. Upon evaluating both domestic and foreign visual and printed media, it is apparent that there is a prevalence of favorable news regarding gastronomy tourism. Based on the literature and observational data gathered in the study, it is believed that the impact of the earthquake on the gastronomic tourism of Gaziantep, Hatay, and Şanlıurfa, which are recognized as UNESCO (United Nations Educational, Scientific and Cultural Organization) gastronomic cities, can be mitigated through appropriate measures. With multidimensional planning, the gastronomy of these cities is expected to emerge even stronger from the aftermath of the earthquake
Citation - WoS: 1
Citation - Scopus: 1
The Karapapaks and their shifting loyalties on the imperial borderlands during the nineteenth century
(Taylor & Francis Online, 2022) Çiftçi, Erdal
The Karapapaks were one of the less known native Turkish ethnic groups of the Transcaucasia, who overwhelmingly took refuge in the Ottoman and Qajar Empires in the late 1820s, after the expansion of Tsarist Russia into their homelands. This paper analyses how the literature regarding Karapapak movements and society was overwhelmingly shaped by selective, essentialist, and anachronistic approaches by some historians in Turkey and Iran. While the former determined that they were a loyal pro-Ottoman and pro-Sunni Karapapak society, the latter constructed an opposing pro-Iranian and pro-Shiite narrative. This paper deconstructs both approaches, and asserts that the collective ethnic and sectarian identities of this society played a secondary role in regards to influencing their cross-border movements. This paper argues that the approach of the current literature cannot explain this borderland society’s perpetual, multiple and multi-directional cross-border movement. Instead, the Karapapaks often manoeuvred the frontiers of the empires, and defected to another empire when it was necessary to, first and foremost, satisfy the needs of their own society, over those of any imperial allies.
Citation - WoS: 3
Citation - Scopus: 3
Investigating the limestone quarries as geoheritage sites: Case of Mardin ancient quarry
(De Gruyter, 2023) Karataş, Lale; Alptekin, Aydın; Yakar, Murat
Abstract: Abandoned quarries are valuable as a tourism element, as they exhibit the building material of the buildings built in the geographical area they are located in as historical objects. However, in order to determine how the quarries can be used for tourism purposes, it is necessary to determine the constraints on the choice of solution in spatial arrangements. The aim of this study is to investigate how the ancient limestone quarry of Mardin, which is a natural and cultural geological heritage, can be used for tourism and to develop suggestions. Within the scope of the study, in order to examine the possibilities of how an idle quarry located in Mardin province in Turkey can be used for tourism, the constraints in the selection of the post-use solution will be determined. In order to determine whether the Mardin quarry is accessible and safe to visit, various field studies were carried out in the study area, laboratory experiments and analyses. The caves were scanned with a 3D laser scanner, and its plans and sections were obtained. The findings were evaluated and suggestions were developed for the use of the ancient limestone quarry for tourism.
Citation - WoS: 3
Citation - Scopus: 6
Thin-Layer Drying Modeling in the Hot Oil-Heated Stenter
(International Journal of Thermophysics, 2020) Ünal, Fatih; Akan, Ahmet Erhan
Although the drying processes have an important place in the textile industry in terms of drying or various textile finishing applications, they are considered as an expensive process in terms of energy and time consumed. Therefore, it is of great importance to simulate with mathematical models the drying behavior of a stenter (ram machine), one of the most preferred convection dryers in the textile industry. For this purpose, in this study, modeling was attempted of the drying behavior of 67 % Cotton + 33 % Polyester containing Thessaloniki knit fabrics, using experimental data obtained from drying processes performed in 9 different drying operations in a 10-chamber hot oil-heated stenter and 12 different empirical and semi-empirical thin-layer models that are frequently used in the literature. R2 values from regression analysis were evaluated as the primary factor in the model fit selection. According to the results obtained, it was understood that the Diffusion Approach model with R2 values ranging from 0.9991 to 0.9999, Two Term Model with R2 values ranging from 0.9995 to 0.9999, and the Modified Henderson and Pabis model with R2 values ranging from 0.9995 to 0.9999 gave the most appropriate results upon simulating drying behavior. In this regard, this study, which contains explanatory information on the drying behavior in a stenter, is thought to be useful to researchers.
Citation - WoS: 7
Citation - Scopus: 13
Palmprint recognition system based on deep region of interest features with the aid of hybrid approach
(SpringerLink, 2023) Türk, Ömer; Çalışkan, Abidin; Acar, Emrullah; Ergen, Burhan
Palmprint recognition system is a biometric technology, which is promising to have a high precision. This system has started to attract the attention of researchers, especially with the emergence of deep learning techniques in recent years. In this study, a deep learning and machine learning-based hybrid approach has been recommended to recognize palmprint images automatically via region of interest (ROI) features. The proposed work consists of several stages, respectively. In the first stage, the raw images have been collected from the PolyU database and preprocessing operations have been implemented in order to determine ROI areas. In the second stage, deep ROI features have been extracted from the preprocessed images with the aid of deep learning technique. In the last stage, the obtained deep features have been classified by employing a hybrid deep convolutional neural network and support vector machine models. Finally, it has been observed that the overall accuracy of the proposed system has achieved very successful results as 99.72% via hybrid approach. Moreover, very low execution time has been observed for whole process of the proposed system with 0.10 s.
CONDENSATION ANALYSIS OF THE INSULATION OF WALLS IN MARDIN PROVINCE ACCORDING TO DIFFERENT LOCATIONS
(2019) Ünal, Fatih
In this study, condensation and vapor diffusion caused by different positioned insulation in the wall were analyzed for Mardin province. In the analysis, according to the 2008 standard of TS 825, the MATLAB calculation program was used with the Glaser graphing method and graphical user interface (GUI). Extruded polyurethane foam was used as the insulation material and normal unreinforced concrete was chosen as the wall. Evaporation and condensation values were determined by creating 6 different wall models with the same insulation thickness of 20 cm and an unreinforced concrete wall was covered with 2 cm plaster on the inside with a 3 cm thickness on the outside. The data obtained for 2 cm and 4 cm insulation thicknesses are presented in tables and the results are interpreted for Mardin province. Consequently, it was seen that the worst wall structure in terms of condensation and evaporation was obtained in the middle insulated wall and later in the interior insulated wall structure. The externally insulated wall did not show any condensation.
Qualitative properties of certain non-linear differential systems ofsecond order
(Elsevier, 2017) TUNÇ, CEMİL; DİNÇ, Yavuz
In this paper, we study the boundedness and square integrability of solutions in certain non-linear systems of differential equations of second order. We establish two new theorems, which include suitable sufficient conditions guaranteeing the boundedness and square integrability of solutions to the considered systems. The presented proofs simplify previous works since the Gronwall inequality is avoided which is the usual case. The technique of proof involves the integral test, and two examples are included to illustrate the results.

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