Elektrik-Elektronik Mühendisliği Bölümü Koleksiyonu

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https://hdl.handle.net/20.500.12428/2641

Listeleniyor 1 - 19 / 19

Structural inequalities exacerbate infection disparities
(Nature Portfolio, 2025) Sajjadi, Sina; Toranj Simin, Pourya; Shadmangohar, Mehrzad; Taraktas, Basak; Bayram, Ulya; Ruiz-Blondet, Maria V.; Karimi, Fariba
During the COVID-19 pandemic, the world witnessed a disproportionate infection rate among marginalized and low-income groups. Despite empirical evidence suggesting that structural inequalities in society contribute to health disparities, there has been little attempt to offer a computational and theoretical explanation to establish its plausibility and quantitative impact. Here, we focus on two aspects of structural inequalities: wealth inequality and social segregation. Our computational model demonstrates that (a) due to the inequality in self-quarantine ability, the infection gap widens between the low-income and high-income groups, and the overall infected cases increase, (b) social segregation between different socioeconomic status (SES) groups intensifies the disease spreading rates, and (c) the second wave of infection can emerge due to a false sense of safety among the medium and high SES groups. By performing two data-driven analyses, one on the empirical network and economic data of 404 metropolitan areas of the United States and one on the daily Covid-19 data of the City of Chicago, we verify that higher segregation leads to an increase in the overall infection cases and higher infection inequality across different ethnic/socioeconomic groups. These findings together demonstrate that reducing structural inequalities not only helps decrease health disparities but also reduces the spread of infectious diseases overall.
Discrimination accuracy of haploid and diploid maize seeds using NIR spectroscopy coupled with different machine learning algorithms and data pretreatment methods
(Taylor & Francis Inc, 2025) Kahrıman, Fatih; Polat, Adem; Tiryaki, Ali Murat; Eskizeybek, Volkan; Fidan, Sertuğ; Songur, Umut
Spectral data collected at the single seed level allows determination of the biochemical content of the seed sample, as well as to identify the seed class. NIR (Near Infrared) spectroscopy provides a more precise method for differentiating haploid and diploid seeds in maize than traditional visual examination. In this study, classification models that can be used in the separation of haploid and diploid maize seeds were developed using spectra collected between 900-1700 nm from a single maize seed. In the study, 427 diploid and 311 haploid samples obtained by crossing 10 donor materials and 3 inducer lines and separated by eye according to the Navajo marker were used. Spectral measurements were conducted over the wavelength range of 900 to 1700 nm for each sample. The robust PCA (Principal Component Analysis) method was used to detect spectral outliers. Spectral data were treated with none, FD (First Derivative), SD (Second Derivative), SNV (Standard Normal Variate), and their binary combinations. Logistic Regression, Support Vector Machine with a linear kernel (SVM-C), Random Forest, and XGBoost methods were employed as machine learning techniques. The performance of the developed machine learning models was assessed using metrics such as Sensitivity, Specificity, Recall, F1-Score, and Accuracy. The Boosting method demonstrated the best performance with 94.9% accuracy, 95.1% sensitivity, 94% specificity, and an F1 Score of 96%, particularly when using raw reflectance data. These results obtained from raw data show that high accuracy can be achieved in classification models without requiring additional preprocessing steps. D2 preprocessing was found to be unsuitable for intact seed spectra, whereas SNV and D1 applications improved the classification success of other modeling techniques. The study revealed that the Boosting-Raw combination is a powerful and feasible method for classifying haploid and diploid samples.
Memristör tabanlı bir Liénard Osilatörü tasarımı
(Gazi Univ, Fac Engineering Architecture, 2025) Çakır, Kübra; Mutlu, Reşat; Karakulak, Ertuğrul
Yeni devre elemanı memristör sensör yapımında, hücresel sinir ağları, kaotik sistemler, programlanabilir analog devreler, kalıcı bellek cihazları gibi devre uygulamalarında kullanılabilmektedir. 1928’de salınımlı devrelerin modellenmesi için Alfred-Marie Liénard tarafından Liénard denklemleri önerilmiştir. İlk Liénard Osilatörü yapıldığında yarı iletken teknolojisi mevcut değildi ancak günümüzde çeşitli yarı iletken devre elemanları yeni tür Liénard Osilatörlerinin yapımında kullanılmaktadır. Günümüzde Knowm firmasının ürettiği ve piyasada satılan Karbon tabanlı memristörler mevcuttur. Yapılan literatür çalışmasına göre, henüz yapılmış Karbon Knowm memristör tabanlı bir Liénard Osilatörü bulunmamaktadır. Bu çalışmanın amacı iki adet Karbon tabanlı Knowm memristör kullanarak literatüre yeni bir Liénard Osilatör çeşidi kazandırmaktır. Bu çalışmada önce memristör tabanlı bir Liénard Osilatörü devre topolojisi önerilmiş, devreyi tanımlayan denklemler verilmiş, ardından Karbon tabanlı bir memristör entegresi kullanılarak devre kurulmuş ve kurulan osilatör üzerinde gerçekleştirilen deneyler sayesinde literatürde bir ilk olarak iki tane ters-paralel bağlı Karbon Knowm memristör kullanarak bir Liénard Osilatörü yapılabileceği gösterilmiştir.
Design of EM-artifact-free earphone based on the photoacoustic effect
(Elsevier GmbH, 2021) Muşdal, Bengi Derya; Kurt, Mustafa
Electromagnetic interactions between conventional earphones and the electroencephalography (EEG) electrodes used for analyzing brain waves give rise to efficiency problems in neurophysiological studies of auditory perception. Currently used speakers and headphones are electromagnetic devices based on strong magnets. In spite of intensive use of such systems, there has been no effective way to eliminate the electromagnetic artifacts produced by such audio transmitting devices to date. The ability for transferring audible sounds without the use of electromagnetic devices that can affect the EEG signal would open up many innovative possibilities in Audio Technologies. Audible sound transfer over long distances is possible by the photoacoustic effect. In such studies, the modulated optical signal can be converted into an audible signal arising from the absorption of the light energy of relevant molecules. In this study, we propose an earphone based on the photoacoustic effect, and calculated the dB SPL (Sound Pressure Level) values for a spherical cell filled with olive pomace. By the use of the method of Diebold and Westervelt, we theoretically calculated the sound pressure levels for our cell and determined a 60 dB SPL at a sound frequency of 1000 Hz for our preliminary earphone design.
Linear and non-linear dynamics of the epidemics: System identification based parametric prediction models for the pandemic outbreaks
(ISA - Instrumentation, Systems, and Automation Society, 2022) Tutsoy, Önder; Polat, Adem
Coronavirus disease 2019 (COVID-19) has endured constituting formidable economic, social, educational, and phycological challenges for the societies. Moreover, during pandemic outbreaks, the hospitals are overwhelmed with patients requiring more intensive care units and intubation equipment. Therein, to cope with these urgent healthcare demands, the state authorities seek ways to develop policies based on the estimated future casualties. These policies are mainly non-pharmacological policies including the restrictions, curfews, closures, and lockdowns. In this paper, we construct three model structures of the SpInItIbD-N (suspicious Sp, infected In, intensive care It, intubated Ib, and dead D together with the non-pharmacological policies N) holding two key targets. The first one is to predict the future COVID-19 casualties including the intensive care and intubated ones, which directly determine the need for urgent healthcare facilities, and the second one is to analyse the linear and non-linear dynamics of the COVID-19 pandemic under the non-pharmacological policies. In this respect, we have modified the non-pharmacological policies and incorporated them within the models whose parameters are learned from the available data. The trained models with the data released by the Turkish Health Ministry confirmed that the linear SpInItIbD-N model yields more accurate results under the imposed non-pharmacological policies. It is important to note that the non-pharmacological policies have a damping effect on the pandemic casualties and this can dominate the non-linear dynamics. Herein, a model without pharmacological or non-pharmacological policies might have more dominant non-linear dynamics. In addition, the paper considers two machine learning approaches to optimize the unknown parameters of the constructed models. The results show that the recursive neural network has superior performance for learning nonlinear dynamics. However, the batch least squares outperforms in the presence of linear dynamics and stochastic data. The estimated future pandemic casualties with the linear SpInItIbD-N model confirm that the suspicious, infected, and dead casualties converge to zero from 200000, 1400, 200 casualties, respectively. The convergences occur in 120 days under the current conditions.
Asma Köprüler için Bir Yıldırımdan Korunma Sistemi Tasarımı
(Gazi Üniversitesi, 2024) Koç, Cüneyt; Kaymakçı, Özgür Turay
Suspension bridges are located on the connection point of big cities. Therefore lightning protection is crucial for that kind of critical structures. Lightning strike not only can cause casualties, but also can destroy the bridge and other electro-mechanical systems. Due to this, lightning protection system design is highly critical. In this article, world's 7th biggest suspension bridge Osmangazi Bridge located in key control projection of Gebze-Izmir Highway between Izmit and Yalova cities are selected for case study calculations. The Lightning Protection System (LPS) has been examined as a whole system. Unlike similar studies, it has been shown that when the suspension bridge structure is used as the down conductor, the tower body with a smaller impedance than the external down conductor in terms of the same peak value and so lightning current will always prefer tower body as down conductor. In this study, unlike the examples in the literature, the lightning protection system in suspension bridges was examined as a whole system and the most reliable system for suspension bridges was proposed with the necessary calculations.
Robotik Kaynak Hatlarının Bulanık Hata Ağacı Analizi
(Gazi Üniversitesi, 2024) Kaymakçı, Özgür Turay
Endüstri 4.0 ve dijitalleşmenin bir sonucu olarak robotlu üretim sistemleri yaygınlaşmaktadır. Özellikle otomotiv endüstrisinde kaynak, paletleme, montaj ve taşıma gibi ağır işlerde kullanılan robotların sahip oldukları yüksek potansiyel iş anlamında ciddi avantajlar vadederken beraberinde birçok iş güvenli riskini de açığa çıkartmaktadır. Ayrıca günümüzde işbirlikçi robotların kullanımının artması ile birlikte güvenlik konusu en önemli konulardan biri haline gelmektedir. İş güvenliği çerçevesinde robot hatları için birçok standart oluşturulmuş olup günümüzde yaygın olarak kullanılmaktadır. Geliştirilen bu standartlar ile üreticilerin tutarlı kalite ve performansa sahip ürünleri üretmesine olanak tanırken risklerin kabul edilebilir seviyede kalmasını garanti altına alınmaya çalışılmaktadır. Ne yazık ki bu çalışmalar yapılır iken genellikle insan faktörü göz ardı edilmiş ve risk hesaplamalarında kapsam dışında bırakılmıştır. Bu çalışmada bu eksiği gidermek için otomotiv endüstrisinde kaynak işlemi için kullanılan bir robot hattı üzerinde risk analiz ve risk azaltma çalışmaları hayata geçirilmiştir. Robotik güvenlik standartları da kapsam dâhilinde tutularak ilgili fonksiyonel güvenlik hesaplamaları yapılmıştır. Bu aşamada insan faktörü de analize dâhil edilmiştir. Bu kapsamda ilgili belirsizlikleri modelleyebilmek için bulanık setleri kullanan bir hata ağacı analiz yöntemi geliştirilmiştir. Böylelikle sadece ilgili ekipmanların kategorileri ve güvenilirlik parametreleri değerlendirilmemiş aynı zamanda insan-robot etkileşiminin sistem güvenilirliğine olan etkisi incelenmiştir. Son olarak elde edilen sonuçların sektör bazlı standartlar kapsamında yeterliliği irdelenmiştir.
Cybersecure and scalable, token-based renewable energy certificate framework using blockchain-enabled trading platform
(Springer, 2024) Çalı, Ümit; Kuzlu, Murat; Sebastian-Cardenas, D. Jonathan; Elma, Onur; Pipattanasomporn, Manisa; Reddi, Ramesh
Decarbonization of energy systems has been a recent trend during the last two decades where large-scale renewable energy sources (RES) are integrated into the modern power systems. Various countries have developed new energy policy instruments, such as Renewable Energy Certificates (RECs), to promote the growth of RES. RECs are tradable, non-tangible assets, which have a monetary value. Tracking and certification of the origin of an energy resource regardless of its type (e.g., a conventional power plant or RES) are a critical operation. In addition to the certification of origin, trading transactions must be performed using a secure method. Energy industry participants need to secure the data and applications related to RECs. Digitalization technologies such as artificial intelligence (AI) and distributed ledger technology (DLT) are among the most popular and promising options. DLT is a perfect framework that can support such REC functionalities. This paper addresses the cybersecurity aspects in REC trading using blockchain and distributed ledger technology, considering detailed cybersecurity perspectives and aspects of adopting technology from an organizational perspective.
Analysis of deceptive data attacks with adversarial machine learning for solar photovoltaic power generation forecasting
(Springer, 2024) Kuzlu, Murat; Sarp, Salih; Catak, Ferhat Özgür; Çalı, Ümit; Zhao, Yanxiao; Elma, Onur; Güler, Özgür
The solar photovoltaics (PV) energy resources have become more important with their significant contribution to the current power grid among renewable energy resources. However, the integration of the solar PV causes reliability issues in the power grid due to its high dependence on the weather condition. The predictability and stability of forecasting are critical for fully utilizing solar power. This study presents an Artificial Neural Network (ANN)-based solar PV power generation forecasting using a public dataset to form a basis experimental testbed to demonstrate analysis and impact of deceptive data attacks with adversarial machine learning. In addition, it evaluates the algorithms' performance using the Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Average Error (MAE) metrics for two main cases, i.e., with and without adversarial machine learning attacks. The results show that the ANN-based models are vulnerable to adversarial attacks.
Automated fabric inspection system development aided with convolutional autoencoder-based defect detection
(Niğde Ömer Halisdemir Üniversitesi, 2024) Mercimek, Muharrem; Öz, Muhammed Ali Nur; Kaymakçı, Özgür Turay
Industrial automatic fabric inspection system, a critical technology in the industry, enhances both total production quantity and quality compared to conventional inspection techniques. This study aims to create a reliable and effective real-time automated visual inspection system for fabrics, focusing on defect detection. The goals of the study can be stated as; installing a system with advanced technology for capturing and processing images swiftly, the development and deployment of a system capable of autonomously learning and scanning fabrics in use, and the creation of a smart framework for accurate fabric defect detection and classification. We focus on the development of unsupervised fabric defect detection using a convolutional autoencoder model, and defect classification using a convolutional neural network model, which takes input as the feature vector generated by the convolutional autoencoder. The experimental outcomes have displayed significant success rates in both detecting defects and classifying them, confirming the effectiveness of the framework in real-time visual inspection systems.
Human factors in hydrogen storage: An analysis of safety implications
(Elsevier Ltd, 2024) Bulat, Hasan Hüseyin; Kaymakçı, Özgür Turay; İlhan, Hacı
In recent years, due to the growing interest in sustainable energy, technologies utilizing hydrogen have become increasingly significant. However, the potential risks accompanying this progress should not be overlooked. This study makes a significant contribution to the literature by detailing the effects of human error in hydrogen storage systems through both qualitative and quantitative analyses. Our work aims to enhance awareness and provide new perspectives for future research by quantitatively demonstrating the impact of human error on system safety and performance. This approach lays the groundwork for developing new strategies to make hydrogen storage systems safer and more efficient. In our proposed model, questions related to the system were directed to 10 experts to better understand the uncertain effects of human errors on safety. Based on the responses received, a method has been suggested to form fuzzy numbers representing risks that may arise due to human errors. Referring to a hydrogen storage system actively utilized in industry, a fuzzy fault tree was prepared for the highest risk, namely “hydrogen explosion,” and these fuzzy numbers were evaluated within the fault tree to analyze their impact on safety. As a result of this analysis, no change was observed in the SIL level, and only a minimal difference was seen in the PFDAvg value. In this context, the fault tree results indicate that the effect of human-induced errors on the system's safety is limited. This study clearly emphasizes the critical importance of safety management for hydrogen storage systems and the standards and regulations that need to be considered in this field.
The comprehensive analysis of the determination of wavelet function-level pair for the decomposition and reconstruction of artificial S1 heart signals by using multi-resolution analysis
(Çanakkale Onsekiz Mart Üniversitesi, 2021) Polat, Adem
Two major sounds of the normal heart sound like “lub dub”. The “lub” is the first heart sound, commonly termed S1 results from mitral (M1) and tricuspid (T1) valve closure at the start of systole. In this work, the noisy-S1 heart signal was investigated to separate M1 and T1 components of it by making the comprehensive analysis of the determination of wavelet function-level pair for the decomposition and reconstruction of artificial S1 by using multiresolution analysis (MRA) and discrete wavelet transform (DWT). For this purpose, a synthetic S1 and its three different noisy-S1 signals were created by using the linear chirp transient model and then decomposed to their approximations and details at three different decomposition levels (3,4,5). 86 daughter wavelets of Biorthogonal, Coiflet, Daubechies, and Symlet were used to reconstruct noisy-S1 signals using comprehensive MRA&DWT. S1 and reconstructed noisy-S1 were compared qualitatively and quantitatively. For quantitative assessment, signal-to-noise-ratio (SNR), peak-SNR (PSNR), root-mean-square-error (RMSE), and structural-similarity (SSIM(%)) metrics were used for noisy-S1 signals at 3–4-5 decomposition levels. In the final evaluation of 86 daughter wavelets, db5, bior3.3, and bior3.9 performed superior results both qualitatively and quantitatively. The db5 was the superior one qualitatively at level 5, and quantitatively, the SNR values of the reconstructed signal by db5 were 8.620, 8.009, and 6.333 for %5-,%10-, and %20-noisy heart signals, respectively. The study proved that MRA&DWT provides a comprehensive analysis opportunity consisting of 86 daughter wavelets for perfect reconstruction of the S1 heart signals and detecting transients between M1 and T1 components.
Introducing a novel fast algebraic reconstruction technique and advancing 3D image reconstruction in a specialized bioimaging system
(Elsevier Ltd, 2024) Adem, Polat
In this study, our primary goal was to remarkably reduce computation time and enhance the efficiency of 3D image reconstruction in bioimaging applications by focusing on iterative image reconstruction methods, particularly algebraic reconstruction technique (ART). To achieve this, we introduced a novel fast algebraic reconstruction technique called the mining-ART, consisting of two versions. We validated our proposed method using the mini-Opto tomography device, a specialized bioimaging system, and a synthetic biological phantom. This phantom, developed in our laboratory for bioimaging experiments, was composed of polydimethylsiloxane (PDMS) and had dimensions of 8 mm × 8 mm × 500 µm. We acquired two-dimensional (2D) projections of the phantom from 11 different angles using the bioimaging device, and then reconstructed these projections in 3D using both ART and the mining-ART. The dimensions of the 3D reconstructed images ranged from 100 × 100 × 50 to 800 × 800 × 50, and voxel resolutions varied correspondingly from 80 × 80 × 10 µm to 10 × 10 × 10 µm. Our experimental results demonstrated that the proposed mining-ART outperformed ART in terms of superior 3D image reconstruction speed across various sizes, while maintaining similar image quality. The mining-ART achieved a significant acceleration in computation time, ranging from 5.89 to 92.77 times faster than ART, depending on the dimensions. Furthermore, we extensively explored the feasibility of integrating compressed sensing-based three-dimensional total variation (3DTV) into the mining-ART. In conclusion, our proposed mining-ART demonstrated its potential in dramatically enhancing the computational performance of image reconstruction methods in bioimaging and made a significant contribution to advancing 3D image reconstruction in various research fields.
Tracing 2D Growth of Pancreatic Tumoroids Using the Combination of Image Processing Techniques and Mini-Opto Tomography Imaging System
(SAGE Publications Inc., 2023) Akbaba, Cihat Ediz; Polat, Adem; Göktürk, Dilek
Objectives: In this study, we aimed to trace the 2D growth development of tumoroids produced with MIA PaCa-2 pancreatic cancer cells at different time points. Methods We cultured 3 different tumoroids with 0.5%, 0.8%, and 1.5% agarose concentrations and calculated the growth rate of the tumoroids with their images acquired at 9 imaging time points by mini-Opto tomography imaging system applying image processing techniques. We used the metrics contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR), and mean squared error (MSE) to analyze the distinguishability of the tumoroid structure from its surroundings, quantitatively. Additionally, we calculated the increase of the radius, the perimeter, and the area of 3 tumoroids over a time period. Results In the quantitative assessment, the bilateral and Gaussian filters gave the highest CNR values (ie, Gaussian filter: at each of 9 imaging time points in range of 1.715 to 15.142 for image set-1). The median filter gave the highest values in PSNR in the range of 43.108 to 47.904 for image set-2 and gave the lowest values in MSE in the range of 0.604 to 2.599 for image set-3. The areas of tumoroids with 0.5%, 0.8%, and 1.5% agarose concentrations were 1.014 mm2, 1.047 mm2, and 0.530 mm2 in the imaging time point-1 and 33.535 mm2, 4.538 mm2, and 2.017 mm2 in the imaging time point-9. The tumoroids with 0.5%, 0.8%, and 1.5% agarose concentrations grew up to times of 33.07, 4.33, and 3.80 in area size over this period, respectively. Conclusions The growth rate and the widest borders of the different tumoroids in a time interval could be detected automatically and successfully. This study that combines the image processing techniques with mini-Opto tomography imaging system ensured significant results in observing the tumoroid's growth rate and enlarging border over time, which is very critical to provide an emerging methodology in vitro cancer studies.
Editorial: Internet of energy for renewable energy-based decarbonized electrical energy systems
(Frontiers Media S.A., 2023) Elma, Onur; Kuzlu, Murat; Zohrabi, Nasibeh
This research topic aims to highlight the current state-of-the-art technologies in digitalized smart grids, including renewable energy and others with the Internet of Energy (IoE) under the decarbonized electrical energy systems. These approaches will increase the overall efficiency of electrical power systems, along with emerging technologies and applications in the current system.
A Nested Autoencoder Approach to Automated Defect Inspection on Textured Surfaces
(Sciendo, 2021) Öz, Muhammed Ali Nur; Kaymakçı, Özgur Turay; Mercimek, Muharrem
In recent years, there has been a highly competitive pressure on industrial production. To keep ahead of the competition, emerging technologies must be developed and incorporated. Automated visual inspection systems, which improve the overall mass production quantity and quality in lines, are crucial. The modifications of the inspection system involve excessive time and money costs. Therefore, these systems should be flexible in terms of fulfilling the changing requirements of high capacity production support. A coherent defect detection model as a primary application to be used in a real-time intelligent visual surface inspection system is proposed in this paper. The method utilizes a new approach consisting of nested autoencoders trained with defect-free and defect injected samples to detect defects. Making use of two nested autoencoders, the proposed approach shows great performance in eliminating defects. The first autoencoder is used essentially for feature extraction and reconstructing the image from these features. The second one is employed to identify and fix defects in the feature code. Defects are detected by thresholding the difference between decoded feature code outputs of the first and the second autoencoder. The proposed model has a 96% detection rate and a relatively good segmentation performance while being able to inspect fabrics driven at high speeds.
Anomaly localization in regular textures based on deep convolutional generative adversarial networks
(Springer, 2022) Öz, Muhammed Ali Nur; Mercimek, Muharrem; Kaymakçı, Özgur Turay
Pixel-level anomaly localization is a challenging problem due to the lack of abnormal training samples. The existing adversarial network methods attempt to segment anomalies by reconstructing the image then comparing the reconstructed image with the original. However, reconstructing an image with adversarial networks involve complex training procedures and result in long run-times. This paper proposes a simpler and intuitive anomaly localization approach based on generative adversarial networks (GAN) for regular textured images. In the proposed method, a discriminator network generates an anomaly map and is trained by a generator network that generates imitations of anomalous samples. To lower computational costs, strided convolutions are used in the discriminator network to produce anomaly map for pixel blocks instead of individual pixels. Discriminator that is trained in the proposed scheme gains ability to segment the anomalies in images. The experimental results show that the performance of the proposed method is almost equivalent to that of the state-of-the-art methods. Besides, with an accompanying low-cost training phase it is faster and simpler to implement.
An alternative approach to tracing the volumic proliferation development of an entire tumor spheroid in 3D through a mini-Opto tomography platform
(Elsevier Ltd, 2022) Polat, Adem; Gökturk, Dilek
Microscopy, which is listed among the major in-situ imaging applications, allows to derive information from a biological sample on the existing architectural structures of cells and tissues and their changes over time. Large biological samples such as tumor spheroids cannot be imaged within one field of view, regional imaging in different areas and subsequent stitching are required to attain the full picture. Microscopy is not typically used to produce full-size visualization of tumor spheroids measuring a few millimeters in size. In this study, we propose a 3D volume imaging technique for tracing the growth of an entire tumor spheroid measuring up to 10 mm using a miniaturized optical (mini-Opto) tomography platform. We performed a primary analysis of the 3D imaging for the MIA PaCa-2 pancreatic tumoroid employing its 2D images produced with the mini-Opto tomography from different angles ranging from -25 ° to +25 ° at six different three-day-apart time points of consecutive image acquisition. These 2D images were reconstructed by using a 3D image reconstruction algorithm that we developed based on the algebraic reconstruction technique (ART). We were able to reconstruct the 3D images of the tumoroid to achieve 800 × 800-pixel 50-layer images at resolutions of 5–25 μm. We also created its 3D visuals to understand more clearly how its volume changed and how it looked over weeks. The volume of the tumor was calculated to be 6.761 mm3 at the first imaging time point and 46.899 mm3 15 days after the first (at the sixth time point), which is 6.94 times larger in volume. The mini-Opto tomography can be considered more advantageous than commercial microscopy because it is portable, more cost-effective, and easier to use, and enables full-size visualization of biological samples measuring a few millimeters in size.
An expansion planning method for extending distributed energy system lifespan with energy storage systems
(SAGE Publications, 2022) Tercan, Said Mirza; Elma, Onur; Gökalp, Erdin; Çalı, Ümit
The recent advances in the modern power grids, such as growing energy demand and penetration of higher amounts of distributed energy generators like renewable energy resources, caused additional grid integration challenges for the distributed energy system operators. Besides, deep electrification impacts triggered by a growing share of electric vehicles as additional electric loads made it essential for the distributed energy system operators to re-investigate their upgrade plans in terms of distributed energy system lines and corresponding infrastructure investments. An energy storage system offers the opportunity to use energy flexibly, resulting in deferring the inevitable upgrade costs of the distribution grid elements and increasing the power quality. In this study, a new method is proposed to extend the lifespan of distributed energy systems with an energy storage system and reduce line upgrade costs. The proposed method is tested on the IEEE-33 with different case studies. The findings of this study indicated that the investigated energy storage option has a positive impact on the distributed energy system components and assets in terms of extending their lifespan and helping to mitigate growing demand peaks because of the load increase. According to the results, the proposed method reduces the total cost by up to 66%. Furthermore, the power losses are reduced by an average of 34.8%, and the voltage profiles are improved with the energy storage system.

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