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Study of PV solar thermal absorber and collector coupling as a combined PVT model
Get PDFA PVT (photovoltaic thermal) system is a type of solar energy system that combines the
functionality of both photovoltaic (PV) and solar thermal systems. In a PVT system, the
solar cells are used to generate electricity, while the thermal absorber is used to collect and
store heat energy. The heat energy can then be used for various applications, such as space
heating, water heating, or even power generation through a thermoelectric generator. The
combination of the two technologies in a PVT system can lead to increased energy efficiency
and overall system performance. The collector model is used to predict the heat transfer
between the thermal absorber and the heat transfer fluid, taking into account factors such as
the collector design and the fluid flow rate. The coupling of these models is important to
predict the overall performance of the PVT system, taking into account the interactions
between the electrical and thermal components. The models can be used to optimize the
design of the PVT system and predict its performance under different operating conditions.
It is important to note that the accuracy of the PVT model depends on the accuracy of the
individual component models and the validity of the assumptions made in the model. The
effect of packing factor of thermoelectric cooler (TEC) module on the performance of
photovoltaic thermal (PVT) integrated thermoelectric cooler (TEC) fluid collectors has been
analyzed in this thesis, by considering three different types of PV modules, namely opaque
(glass-to-tedlar), semitransparent (glass-to-glass) and aluminium base (glass-to-aluminium).
The performance of the opaque PV-TEC collector without duct/tube was studied when
partially and fully covered with TEC
The impact of consumer knowledge on sustainability and sustainable clothing: A case study from Iraq
Get PDFThe research study examines the character of consumer knowledge in manipulating
sustainability and the sustainable clothing industry in Iraq. Consumer awareness plays an
important part in developing conscious sequences of utilization; ultimately stimulating and
eco-ecological products, and influencing industry practices. Informed consumers make more
informed choices based on understanding the environmental and social implications of their
clothing purchases. As consumer knowledge spreads, it leads to increased awareness,
education, and a shift in consumer behavior towards more sustainable practices. Moreover,
consumer knowledge fosters collaboration and engagement, enabling stakeholders to work
together towards creating a more environmentally conscious fashion industry. The synopsis
underlines how crucial shopper awareness has become as a change agent for longevity and
the adoption of environmentally conscious apparel procedures.
The study examines customer knowledge, attitudes, behaviours, and buying habits for
sustainable fashion. The research study uses SEM (structural equation modelling), CFA
(confirmatory factor analysis), and exploratory analysis of factors (EFA) Modelling (SEM)
to examine how observed features relate to the direction of underlying structures and how these structures affect sustainable clothing solutions. The study suggests that educating
customers about sustainable fashion improves their awareness and opinions. This highlights
how well-informed clients drive beneficial industry change. Despite better comprehension,
recycling has dropped dramatically, suggesting a complex link that needs further
investigation.
The study's rigorous analytical methodology ensures construct internal coherence and
validity, providing a solid foundation for understanding sustainable fashion consumer
behaviour dynamics. Consumers, corporations, and politicians can profit from the research'
insightful and important insights. Businesses can utilise the insights to create focused
marketing strategies and creative goods that meet consumer needs for sustainability.
Policymaker insights can be used to design fashion industry rules and policies that promote
environmentally responsible business practices. According to the survey, people should
educate themselves on sustainable apparel and make informed shopping decisions to create
a more enduring future
Fortifying IoT infrastructure using machine learning for DDoS attack within distributed computing-based routing in networks
No full textThe DDoS, Also known as the Denial-of-Service cyber-attack, is now widely used, especially after such technologies as the IoT (Internet of Things) became mainstream and data traffic prefers link routes. Their effectiveness is limited by the attacks being controlled by the center, the limited data transmission capacity and also the viruses' ability to operate under-the-roof while using the mobile nodes which helps them move covertly. On the other hand, if we consider the conventional security approaches, these security devices mostly use traditional security protocols such as password encryption and user authentication respectively. The aim of this paper is to analyze the architecture of attack detection that are deployed in the IoT network and correspondingly demonstrate whether their function is to track and follow or even attack subjects. Moreover, the paper demonstrates the proper job of the detectors in keeping the networks to be safe. The algorithms that are tended to do the machine learning which is about past occurrences of these attacks and then soon to come up with new solutions which somehow or very likely could control or minimize attacks that might be prejudicial are the typical way that attacks are prevented. This research aims to compare the key machine learning approaches, Namely Support Vector Machines (SVM), Random Forest (RF) and Decision Trees (DT), in their ability to classify Intrusion Detection Systems (IDS) via routing networks over distributed computing systems. In addition, Algorithms perform quality control to determine the optimal hyperplane for the given data, Find neighboring data points and preserve the structure of the tree. We evaluate these algorithms using metrics such as the confusion matrix, F1 score, and AUC-ROC to determine their performance in managing imbalanced datasets and generating meaningful insights. Our results indicate that Random Forest outperforms the other models, achieving an accuracy of 99.2%, a false positive rate of 0.8%, and an AUC-ROC of 0.997
Ureteropelvic junction obstruction in the first three months of life: is sex a prognostic factor?
No full textOBJECTIVE: Ureteropelvic junction obstruction (UPJO) is a blockage that occurs at the point where the renal pelvis (the part of the kidney where urine collects) meets the ureter (the tube that carries urine from the kidney to the bladder). This study compared outcomes between male and female patients with UPJO. PATIENTS AND METHODS: 402 UPJO patients diagnosed and treated before the age of three months were divided into two groups: males and females. The following information was extracted: age at diagnosis, age at surgery, the parenchymal thickness of the UPJ and contralateral sides (preoperatively and at 1 and 3 years postoperatively), pelvic diameter, and kidney function. RESULTS: There were 287 male and 115 female patients (a ratio of 2.5:1). The parenchymal thickness (PTs) at diagnosis and surgery were 5(4) mm and 5(3) mm in males, respectively. In females, these values were 5(3) mm and 6(5) mm, respectively. There was a significant decrease in male PT at the time of surgery compared to diagnosis (p<0.05). After the first postoperative year, PTs were 8(4) mm and 9(4) mm in males and females, respectively, and after the third postoperative year, PTs were 9(4) mm and 10(4.75) mm in males and females, respectively. CONCLUSIONS: Among patients diagnosed with UPJO during the first three months of life, males had a more severe disease course than females. Additionally, females experienced better clinical improvement during the long-term postoperative period
Empowering healthcare innovation: IoT-enabled smart systems and deep learning for enhanced diabetic retinopathy in the telehealth landscape
No full textBackground: Among prevalent medical complications, Diabetic Eye Disease (DED) stands as a significant contributor to vision loss. To forecast its progression and accurately assess the various stages, diverse methodologies have emerged. Machine Learning (ML) and Deep Learning (DL) algorithms have become essential tools in this endeavor, primarily through their adept analysis of Diabetic Retinopathy (DR) images. However, there is still a need for a more efficient and accurate method to predict DR performance. Method: We have developed an innovative method for classifying and predicting diabetic retinopathy. The novel idea in this research is to combine several techniques, including ensemble learning and a 2D convolutional neural network; we utilized transfer learning and a correlation method in our approach. Initially, the Stochastic Gradient Boosting process was employed for predicting diabetic retinopathy. We then used a boosting-based Ensemble Learning method for predicting images of diabetic retinopathy. Next, we applied a 2D Convolutional Neural Network. We successfully employed Transfer Learning to classify different stages of diabetic retinopathy images accurately. This research explores the role of artificial intelligence in identifying and categorizing diabetic retinopathy at an early stage, using techniques such as machine learning and deep learning. It also use techniques like transfer learning, domain adaptation, multitask learning, and explainable AI to accurately classify different stages of diabetic retinopathy images. Our proposed technique achieves impressive results through experiments, with a 97.9% accuracy in forecasting DR images and a 98.1% accuracy in image grading. Additionally, sensitivity and specificity metrics measure 99.3% and 97.6%, respectively. Comparative analysis with existing methods underscores the high predictive accuracy achieved by our proposed approach
Identification of exosomal microRNAs and related hub genes associated with imatinib resistance in chronic myeloid leukemia
Get PDFTUBITAK 1001 Project no. 115Z400Chemotherapy resistance is a major obstacle in cancer therapy, and identifying novel druggable targets to reverse this phenomenon is essential. The exosome-mediated transmittance of drug resistance has been shown in various cancer models including ovarian and prostate cancer models. In this study, we aimed to investigate the role of exosomal miRNA transfer in chronic myeloid leukemia drug resistance. For this purpose, firstly exosomes were isolated from imatinib sensitive (K562S) and resistant (K562R) chronic myeloid leukemia (CML) cells and named as Sexo and Rexo, respectively. Then, miRNA microarray was used to compare miRNA profiles of K562S, K562R, Sexo, Rexo, and Rexo-treated K562S cells. According to our results, miR-125b-5p and miR-99a-5p exhibited increased expression in resistant cells, their exosomes, and Rexo-treated sensitive cells compared to their sensitive counterparts. On the other hand, miR-210-3p and miR-193b-3p were determined to be the two miRNAs which exhibited decreased expression profile in resistant cells and their exosomes compared to their sensitive counterparts. Gene targets, signaling pathways, and enrichment analysis were performed for these miRNAs by TargetScan, KEGG, and DAVID. Potential interactions between gene candidates at the protein level were analyzed via STRING and Cytoscape software. Our findings revealed CCR5, GRK2, EDN1, ARRB1, P2RY2, LAMC2, PAK3, PAK4, and GIT2 as novel gene targets that may play roles in exosomal imatinib resistance transfer as well as mTOR, STAT3, MCL1, LAMC1, and KRAS which are already linked to imatinib resistance. MDR1 mRNA exhibited higher expression in Rexo compared to Sexo as well as in K562S cells treated with Rexo compared to K562S cells which may suggest exosomal transfer of MDR1 mRNA
Antioxidant, anti-tyrosinase activities and characterization of phenolic compounds for some plants from the Marmara Region, Türkiye
Get PDFThis research was financially supported by the Marmara University Scientific Research CommitteeIn this study, antioxidant, anti-tyrosinase, and sun protection factor (SPF) values of 26 extracts obtained from 24 plants naturally grown in the Marmara Region were investigated, and phenolic compound characterization of 8 active plants was performed. All of the plants mentioned in this study have been evaluated for their Sun Protection Factor (SPF) values for the first time, as well as 3 of them evaluated for antioxidant activity and 15 of them evaluated for tyrosinase inhibition for the first time. The results showed that the plant extracts generally exhibited high antioxidant activities. In terms of DPPH radical scavenging activity, Cota tinctoria (L.) J. Gay exhibited a very close IC50 value (0.038 mg/mL) to the standard compounds, ascorbic acid and quercetin. Plantago major L. subsp. intermedia (Gilib.) Lange demonstrated the highest CUPRAC radical scavenging activity (0.187 mM ascorbic acid equivalent). Hypericum perforatum L. was determined to have the highest total phenolic content (0.268 mg GAE g/extract). Among the plant extracts, Sambucus ebulus L. fruit extract exhibited the highest tyrosinase inhibition (IC50 0.08 mg/mL), showing a similar effect to the standard compound kojic acid. The extract with the highest SPF value was calculated Inula oculuschristi L. extract, with a value of 28.55. The phenolic compound analysis of eight plants, which have been determined to exhibit high efficacy in both antioxidant activities and tyrosinase inhibition, was conducted. Some of phenolic compounds obtained from these eight plants were novel for these species. According to the experiments conducted in this study, Euphorbia helioscopia has high potential as natural sources of antioxidants and skin whiteners
Numerical analysis and design for thermal efficiency optimization using Al2 O3 nanofluids in shell and tube heat exchangers
No full textIn this study, the efficacy of aluminum oxide (Al2O3) nanofluids at a 2% concentration for enhancing heat transfer in shell and tube heat exchangers is evaluated. By employing SOLIDWORKS for the innovative design and Computational Fluid Dynamics (CFD) for simulation, an improvement in heat transfer efficiency over traditional hot water systems is identified. Emphasizing the unique properties of Al2O3 nanofluids in augmenting heat transfer rates, the role of advanced CAD and simulation tools in engineering practices is highlighted. Results confirm nanofluids' benefits in improving thermal management systems, indicating their potential to decrease energy consumption and operational costs. Furthermore, the exploration of a novel design for heat exchangers, inspired by but distinct from existing market standards, suggests new avenues for the application of nanofluids in various industrial settings, marking a step towards more energy-efficient technologies
Afet yönetiminde jeomanyetik fırtınaların geliştirilmiş tahmini için derin öğrenme
No full textBu araştırma çalışması, afet yönetimi bağlamında Jeomanyetik fırtınaların gelişmiş tahminlerine odaklanmaktadır. Dünyanın manyetosferindeki bozuklukların neden olduğu jeomanyetik fırtınaların kritik altyapılar ve iletişim sistemleri üzerinde önemli etkileri olabilir. Bu fırtınaların doğru tahmin edilmesi, etkili afet hazırlığı ve müdahale stratejileri için çok önemlidir. Bu çalışmada, jeomanyetik aktiviteyi tahmin etmek için Uzun Kısa Süreli Bellek (LSTM) modellerine dayalı bir yaklaşım öneriyoruz. İlgili zaman serisi verilerini topluyor ve ön işleriz, veri araştırma ve analiz tekniklerini kullanır ve özel bir LSTM modeli geliştiririz. Model, kayıp fonksiyonları ve Ortalama Karekök Hata (RMSE) dahil olmak üzere çeşitli performans ölçümleri kullanılarak eğitilir ve değerlendirilir. Sonuçlarımız, LSTM modelinin zamansal bağımlılıkları ve kalıpları yakalamadaki etkinliğini ve doğru tahminlere yol açtığını göstermektedir. Ayrıca, yaklaşımımızın afet yönetimi senaryolarına pratik uygulanabilirliğini tartışıyor ve gelecekteki araştırmalar için yolları vurguluyoruz. Bu araştırmanın sonuçları değerli bilgiler sağlıyor ve Jeomanyetik fırtınaların tahmininin geliştirilmesine, sonuçta afet yönetimi stratejilerinin ve bu doğal olaylar karşısında dayanıklılığın geliştirilmesine katkıda bulunuyor.This research work focuses on the improved forecasting of Geomagnetic storms in the context of disaster management. Geomagnetic storms, caused by disturbances in Earth's magnetosphere, can have significant impacts on critical infrastructures and communication systems. Accurate forecasting of these storms is essential for effective disaster preparedness and response strategies. In this study, we propose an approach based on Long Short-Term Memory (LSTM) models to forecast geomagnetic activity. We gather and preprocess relevant time series data, employ data exploration and analysis techniques, and develop a tailored LSTM model. The model is trained and evaluated using various performance metrics, including loss functions and the Root Mean Squared Error (RMSE). Our results demonstrate the effectiveness of the LSTM model in capturing temporal dependencies and patterns, leading to accurate predictions. Furthermore, we discuss the practical applicability of our approach in disaster management scenarios and highlight avenues for future research. The outcomes of this research provide valuable insights and contribute to enhancing the forecasting of Geomagnetic storms, ultimately improving disaster management strategies and resilience in the face of these natural phenomena
An in vitro assessment of ionizing radiation impact on the efficacy of radiotherapy for breast cancer
Get PDFFunding agency : Yildiz Technical University.Objectives Ionizing radiation is still one of the most effective treatment options for various cancers. It is possible to reduce the side effects of this effective treatment method and increase the chance of success by elucidating the responses it creates at the molecular level in the cell. This study aims to investigate of the molecular effects of therapeutic ionizing radiation on breast cancer, which is the most prevalent cancer type. Methods MDA-MB-231 and MCF7 cell lines were irradiated with 4 and 8 Gy ionizing radiation and monitored for up to 7 days. RNA was collected at 48 and 96 h, when cellular molecular mechanisms became most evident, and quantitative expression levels of microRNAs (miR-208a, miR-124, miR-145), for which cancer-radiation associations have been determined from existing literature and databases, were evaluated. Results Exposure to ionizing radiation resulted in a dose-dependent reduction in cell viability in both MCF7 and MDA-MB-231 breast cancer cell lines. Furthermore, microRNA expression analysis revealed notable changes at all levels. The research demonstrates that miR-208a, miR-145, and miR-124 are crucial in the biological response to ionizing radiation. Conclusions Therapeutic ionizing radiation profoundly affects cell viability and microRNA expression in breast cancer cell lines, showing dose and time-dependent effects. The observed microRNA expression patterns suggest potential biomarkers for radiation response and therapeutic targets to improve radiotherapy efficacy. Further in vivo validation and exploration of these microRNAs' roles in modulating cellular response to ionizing radiation are needed