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Design of an Integrated Logistics Management System for Natural Disaster
No full textDoğal afetler, insan hayatı ve mülkler üzerinde ciddi tehditler oluşturarak etkili ve entegre lojistik çözümleri zorunlu hale getirmektedir. Bu tür olaylar, hem anlık müdahaleler hem de uzun vadeli iyileştirme süreçleri için doğru planlama ve koordinasyonu gerektirir. Bu tez, afet müdahale operasyonlarının verimliliğini ve etkinliğini artırmak amacıyla Entegre Lojistik Yönetim Sistemi'nin (ILMS) tasarımını hedeflemektedir. Çok yönlü bir araştırma yaklaşımıyla, mevcut lojistik uygulamaları incelenmekte, temel zorluklar belirlenmekte ve Coğrafi Bilgi Sistemleri (GIS), Nesnelerin İnterneti (IoT) ve Yapay Zeka (AI) gibi ileri teknolojileri içeren bir çerçeve geliştirilmektedir. Araştırma, literatür incelemeleri ve geçmiş afet olaylarına ilişkin vaka çalışmalarından elde edilen verilere dayanmaktadır. Önerilen ILMS, paydaşlar arasında gerçek zamanlı koordinasyonun, dinamik kaynak tahsisinin ve verilerin sorunsuz entegrasyonunun iyileştirilmesine odaklanmaktadır. Bunun yanı sıra, lojistik süreçlerin hızlandırılması ve kaynakların etkin kullanımı hedeflenmektedir. Geçerlilik yöntemleri arasında simülasyon modellemesi, uzman değerlendirmeleri ve lojistik performansının karşılaştırmalı analizi bulunmaktadır. Beklenen sonuçlar arasında mevcut uygulamalardaki kritik boşlukların belirlenmesi, afet lojistik yönetimini iyileştirmek için pratik öneriler sunulması ve afet hazırlık ve müdahalelerini geliştirmek için kapsamlı bir çözüm geliştirilmesi yer almaktadır. Çalışma, gelişmiş teknolojilerden ve yenilikçi metodolojilerden yararlanarak afet lojistiğini yönetmek için bütüncül bir yaklaşım sunmaktadır. Bu bağlamda, önerilen sistemin, afet sonrası müdahale süreçlerinde daha etkin bir yönetim sağlayarak can kaybı ve ekonomik zararı en aza indirmesi beklenmektedir.Natural disasters continue to pose significant challenges to human lives and properties, necessitating effective and integrated logistical responses. These events, including earthquakes, floods, and hurricanes, often result in devastating loss of life, widespread displacement, and severe damage to infrastructure and economies. This thesis aims to design an Integrated Logistics Management System (ILMS) to enhance the efficiency and effectiveness of disaster response operations. Through a multi-faceted research approach, the study examines current logistics practices, identifies major challenges, and develops a framework that incorporates advanced technologies, including Geographic Information Systems (GIS), the Internet of Things (IoT), and Artificial Intelligence (AI). The research relies on data collected through comprehensive literature reviews, in-depth analyses of past disaster events, and case studies. The proposed ILMS focuses on improving real-time coordination among stakeholders, dynamic resource allocation, and seamless data integration. By leveraging advanced technology, the system is expected to address logistical bottlenecks, improve resource utilization, and ensure faster response times during crises. Validation methods include simulation modeling to test system robustness, expert reviews to refine the framework, and comparative analysis of logistics performance against existing systems. The study not only highlights critical gaps in current practices but also offers practical recommendations for policymakers and disaster response teams. Ultimately, this research contributes to the development of a comprehensive, technology-driven approach to disaster logistics management, aiming to minimize human and economic losses
A New Method Based on Local Binary Gaussian Pattern for Classification of Rat Estrous Cycle Stages Using Smear Images
No full textKilic, Irfan/0000-0001-5079-2825In this study, a unique dataset was created by classifying the images of vaginal smears taken from rats under a microscope for 4 different cycles. Classifying a new case image with the help of this dataset is a computer vision problem. In this study, to improve the weaknesses of the LBP algorithm, a new feature extraction method called Local Binary Gaussian Pattern (LBGP) is developed based on the Gaussian matrix, which helps to remove noise in images. Local Binary Gaussian Pattern proposes a Gaussian-like filter inspired by the Gaussian matrix. After converting the smearing image to the gray histogram, the image features obtained with the help of the Local Binary Pattern (LBP) and our proposed Local Binary Gaussian Pattern (LBGP) feature extractor are combined to obtain features that we call hybrid features. From these features, the ones above a certain threshold value are selected with the help of Neighborhood Component Analysis (NCA), and a Hybrid + Neighborhood Component Analysis (NCA) approach is presented. All hybrid features and hybrid features reduced by Neighborhood Component Analysis were trained with Support Vector Machine (SVM), Decision Trees (DT), Naive Bayes (NB), and k-nearest Neighbors (k-NN) classifiers. According to the classification results, it is seen that the Support Vector Machine (SVM) is effectively classified with the trained classifier. With the Support Vector Machine (SVM) classifier, a success rate of over 90 % (90.25 %) was achieved. Considering the difficulty of classifying smearing images, this result is promising for the future stages of this study.Research Council of Turkey (TUBITAK) [220S744]This work was supported by the Scientific and TechnologicalScience Citation Index Expande
Develop a Cathodic Protection System (CP) Using a Mix of Green Energy Sources
No full textIrak'ın Kerbela Petrol Rafinerisi Tüm ülkedeki ağır akaryakıt, gaz ve ham petrol için en büyük yeraltı boru hattı ağlarından biri olarak anlaşıldı. Hassas boru hatları, uygun şekilde korunmadığı takdirde paslanmanın yanı sıra yoğun korozyon nedeniyle tehlikeli hasarlara maruz kalabilir. Makale, pas için bilimsel araştırmaları özetlemektedir. Korozyonun mekanizmaları, etkileri ve dünya çapındaki etkileri, iklimsel değişkenlere, katodik koruma değişkenlerine ve bu faktörleri simüle ederek toprağın direncine dayalı olarak tasarımın iyileştirilmesi yoluyla araştırılmaktadır. COMSOL Multiphysics kullanarak bu tür bir simülasyon gerçekleştirin. Burada, herhangi bir geleneksel AC gücüne ihtiyaç duymadan yeraltı boru hatlarını akıllıca koruyan bir teknoloji tartışıldı. MATLAB'da oluşturuldu ve katodik koruma sistemi, kullanımı korozyon olasılığını ve ardından büyük mali kayıplara yol açabilecek hasarı azaltmak için kullanılan güneş panelleri ve rüzgar türbinlerinin bir karışımı tarafından destekleniyor. Sistemin uzun ömürlü olması ve her türlü hava koşulunda iyi çalışması için en kötü durumları göz önünde bulundurmalıyız. HOMER kullanılarak yapılan testlerden elde edilen sonuçlara da bakılır ve projenin ömür boyu maliyeti kontrol edilir.The Karbala Oil Refinery of Iraq Understood as one of the largest networks of subterranean pipelines for heavy fuel oil, gas, and crude oil in the whole country. The vulnerable pipelines can undergo hazardous damages from extensive corrosion as well as rusting if not properly protected. The paper outlines scientific investigations for rust. Mechanisms, impacts, and worldwide impacts of corrosion are researched through the improvement of the design based on climatic variables, cathodic protection variables, and resistivity of the soil by simulating those factors. Perform such simulation using COMSOL Multiphysics. A technology was discussed here that smartly protects underground pipelines without needing any conventional AC power. It was created in MATLAB, and the cathodic protection system is powered by a mix of solar panels and wind turbines, the usage of which is meant to reduce the possibility of corrosion and, subsequently, damage that might lead to huge financial losses. For the system to have a long life and work well in all kinds of weather, we should consider the worst situations. Results from tests using the HOMER are also looked at, and the project's lifetime cost is checked
Thermal and Mechanical Attributes and Swelling Percentage of Hydrogels by Changing in Magnetic Field Frequency Using Computer Simulation
No full textThe thermodynamic, mechanical, and expansion properties of synthetic hydrogels derived from polyacrylamide (PAM) are investigated in this study to the impact of magnetic field frequency (MFF) as an external stimulus. The impact of various MFFs on essential parameters, such as swelling percentage (SP), ultimate strength (US), Young's modulus (YM), heat flux (HF), and thermal conductivity (TC), is assessed using Molecular Dynamics (MD) simulation with LAMMPS software, ranging from 0.01 to 0.05 1/fs. It is important to note that our results indicate that the structural volume decreased from 356,985 to 349,982 & Aring; at 0.05 1/fs as the MFF increased. The alignment of polymer chains in the hydrogel was improved by increasing the MFF, resulting in a more compact structure. Through this compaction, the total structural volume diminished as the chains were drawn closer together, thereby reducing the spaces among them. US experienced a decrease from 0.0325 to 0.0331 MPa, while YM converged to 0.0008 MPa. The alignment and packaging of polymer chains improved, resulting in an increase in the US of hydrogels as the MFF increased. This enhanced alignment resulted in a material that can withstand a larger amount of stress before failing, as a result of the stronger intermolecular interactions. Additionally, the temperature coefficient (TC) increased to 0.56 W/m & sdot;K as the MFFs increased. An increase in molecular alignment and a decrease in free volume within the hydrogel can be attributed to the higher MFF. This enhanced alignment enabled the molecules to transfer heat more efficiently, resulting in improved TC and increased HF. These findings illustrate the substantial influence of MFF on hydrogel properties, offering valuable insights for the development of drug delivery systems and responsive materials.Science Citation Index Expande
C-flip/Ku70 Complex; a Potential Molecular Target for Apoptosis Induction in Hepatocellular Carcinoma
No full textVosough, Massoud/0000-0001-5924-4366Hepatocellular carcinoma (HCC) is one of the most lethal malignancies worldwide and the most common form of liver cancer. Despite global efforts toward early diagnosis and effective treatments, HCC is often diagnosed at advanced stages, where conventional therapies frequently lead to resistance and/or high recurrence rates. Therefore, novel biomarkers and promising medications are urgently required. Epi-drugs, or epigenetic-based medicines, have recently emerged as a promising therapeutic modality. Since the epigenome of the cancer cells is always dysregulated and this is followed by apoptosis-resistance, reprogramming the epigenome of cancer cells by epi-drugs (such as HDAC inhibitors (HDACis), and DNMT inhibitors (DNMTis)) could be an alternative approach to use in concert with established treatment protocols. C-FLIP, an anti-apoptotic protein, and Ku70, a member of the DNA repair system, bind together and make a cytoplasmic complex in certain cancers and induce resistance to apoptosis. Many epi-drugs, such as HDACis, can dissociate this complex through Ku70 acetylation and activate cellular apoptosis. The novel compounds for dissociating this complex could provide an innovative insight into molecular targeted HCC treatments. In this review, we address the innovative therapeutic potential of targeting c-FLIP/Ku70 complex by epi-drugs, particularly HDACis, to overcome apoptosis resistance of HCC cells. This review will cover the mechanisms by which the c-FLIP/Ku70 complex facilitates cancer cell survival, the impact of epigenetic alterations on the complex dissociation, and highlight HDACis potential in combination therapies, biomarker developments and mechanistic overviews. This review highlights c-FLIP ubiquitination and Ku70 acetylation levels as diagnostic and prognostic tools in HCC management.Science Citation Index Expande
Synthesis of Copper Oxide Nanoparticles and Their Efficiency in Automotive Radiator Heat Transfer Systems
No full textEnhancing heat transfer in automotive radiators is a matter of concern in the automotive industry. Accordingly, the role of using oxide nanoparticles in various heat exchangers has been extensively studied. However, fewer studies addressed the role of these nanoparticles in radiators. In the present study, copper oxide nanoparticles were synthesized by recycling the spent batteries as copper-rich sources, which is a rather inexpensive and environmentally friendly method of preventing electronic waste production. Subsequently, a homemade singletube heat exchanger apparatus was designed to perform a series of nanofluid heat transfer experiments using the response surface methodology. The performance of copper oxide nanofluid heat transfer effects was investigated using varying Reynolds numbers in the range of 2000 to 12,000, volume fractions in the range of 0.1 to 0.3 %, and inlet temperature of the nanofluid between 30 and 40 degrees C. The results indicated that the Nusselt number increases with the enhancement of nanoparticle concentration, Reynolds number, and temperature. The optimal Nusselt number of 123.4 was observed at a temperature of 40 degrees C, volume concentration of 0.3 %, and Reynolds number of 12,000. The quadratic model demonstrated the best correlation for the Nusselt number, with mean squared error, root mean squared error, and correlation coefficient values of 3.589, 1.894, and 0.9901, respectively. Under such conditions, a satisfactory fit between the experimental data and the proposed rela- tionship was achieved with deviation in the range of +2.1051 and- 2.8369. The corresponding maximum positive and negative errors were 8.0895 and- 10.6169, respectively. The obtained results confirm that the proposed method is not only cost-effective but is also advantageous from environmental considerations.Science Citation Index Expande
Efficient Mri Segmentation of Spine Hemangiomas: a Novel Modified U-Net Approach To Enhance Tumor Boundary Detection
No full textMagnetic Resonance Imaging (MRI) is a widely used, non-invasive method for medical imaging, particularly effective in visualizing soft tissues and identifying abnormalities like spine hemangiomas. One of the main challenges remains the low segmentation accuracy of skeletal MRI images. Spine hemangioma segmentation involves algorithmically identifying and localizing these tumors within MRI scans, a process crucial for accurate diagnosis and treatment planning. Although several segmentation methods exist, this paper introduces a U-Net-based approach, implemented in PyTorch and optimized with the Adam optimizer. This setup refines model weight adjustments and harnesses the full capabilities of a fully connected convolutional neural network (CNN) for precise semantic segmentation, including pixel-wise classification through an encoder-decoder structure. This U-Net architecture is versatile and adaptable to various analytical tasks across diverse applications. The model was trained on a substantial dataset spanning the three primary anatomical planes used in medical imaging—Axial, Coronal, and Sagittal without additional data augmentation. It achieved real-time segmentation with a remarkable accuracy of 94.13% and demonstrated strong performance metrics, including a Dice coefficient of 0.634 and Precision of 0.711, underscoring its robustness and potential clinical utility. This work highlights U-Net’s effectiveness in spine hemangioma segmentation and explores its matching capabilities, indicating promising potential for advancements in automated MRI analysis. © Little Lion Scientific
Impact of Investor Confidence on Government Debt-Induced Financial System With Investment Delay
No full textIn this paper, we have developed and analyzed a financial model that investigates the influence of government debt and investor confidence on the system's stability with investment delay feedback. The model's solution's basic properties, existence and uniqueness are discussed. The equilibria of the system are obtained, and local stability at each equilibrium is examined. Hopf bifurcation analysis has been performed at each existing equilibria of the model. It is reviewed that when investor confidence was negligible, the system dynamics changed from stable to unstable as the parameter value of government debt increased. Also, it shows that a higher level of investor confidence can significantly affect the system's dynamics. Furthermore, we investigate the impact of time delay on system behaviors, and numerical results are given to show the effectiveness of the theoretical results.Science Citation Index Expande
Thermal Management in a Phase Change Material-Based Microchannel Heat Sink Manifold System for Cooling Electronic Boards
No full textThe growth of technology and the development of electronic devices made the need for efficient thermal management. By the ability of latent thermal energy storage, the application of phase change materials (PCMs) for cooling electronic boards has been investigated. The heat sink is an air-cooled micro heat sink cooling system (MHCS) with the possibility of embedding the PCM board. A type of metal-based paraffin is the PCM and free and forced modes of convective cooling studied numerically. Five input heat fluxes and three Reynolds (Re) numbers were investigated and the system performance was analyzed. The results show the significant influence of PCM on the control of the EC temperature; the reduction of heat sink temperature by 72 % and 78 % in natural and forced convection modes respectively, are the results of employing PCM in MHCS. It was shown that the best result of PCM employment in forced convection mode is at the lowest Re number. Furthermore, the Re number increase has the best effect on cooling efficiency at the highest heat flux. The results of this study could help in justifying the application of PCM from technical and economic viewpoints.Emerging Sources Citation Inde
A Novel Deep Learning Approach To Enhance Creditworthiness Evaluation and Ethical Lending Practices in the Economy
No full textEvaluating a borrower's creditworthiness and enabling ethical lending practices are two of the most essential functions of credit scoring, making it an integral part of the economy. Credit risk management is an essential aspect of the financial industry, with the primary goal of minimising potential losses caused by customers failing to meet their credit responsibilities, such as fails to pay and bankruptcies. This risk is inherent in lending activities, where lenders extend credit to individuals or businesses. The traditional credit scoring approaches, which rely on statistical and machine learning techniques to analyse complex data and non-linear correlations in credit data has to be improved. Because the current financial sector lacks credit scoring, a deep learning network-based credit ranking model is presented in this research. This paper applies the complicated field of deep learning known as the stacked unidirectional and bidirectional long short-term memory model in the network to resolve credit scoring issues. Since scoring is not a time sequence issue, the suggested model uses the three-layer stacked LSTM and bidirectional LSTM architecture by modelling public datasets in a new way. Our suggested models beat state-of-the-art, considerably more difficult deep learning methods, proving that we could keep complexity to a minimum. The research findings indicate that the model demonstrates high levels of accuracy across various datasets. The model obtains an accuracy of 99.5% on the Australian dataset, 99.4% on the German dataset (categorical), 99.7% on the German dataset (numerical), 99.2% on the Japanese dataset, and 99.8% on the Taiwanese dataset. These results highlight the robustness and effectiveness of the model in accurately predicting outcomes for different geographical regions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.AlMaarefa University, UM; University Philosophy and Social Science Major Fund Project in Jiangsu Province; National Natural Science Foundation of China, NSFC, (72372073); National Natural Science Foundation of China, NSFC; University Philosophy and Social Science Major Fund Project in Jiangsu, (2023SJZD061); Nanjing Vocational University of Industry Technology, (2022SKYJ03)Science Citation Index Expande