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Active Distribution Networks State Estimation Under Variable Generation and Load Conditions
No full textTraditional distribution networks consist of only centralized generation sources and carry electric energy to consumers. However, today, with the widespread use of Distributed Energy Resources (DER), Energy Storage Systems (ESS), and Electric Vehicle Charging Stations (EVCS), these systems have transformed into active structures having bi-directional power flow, that is, Active Distribution Networks (ADNs). This transformation has made distribution networks more complex and increased the need for network monitoring, control, and analysis methods. In this study, an approach is presented that power flow analysis and state estimation algorithms work together to rapidly monitor and analyze ADNs under variable generation and load conditions. This approach is implemented by combining Direct Load Flow (DLF) analysis with Weighted Least Squares (WLS) based state estimation algorithm and using measurement data obtained from Micro-Phasor Measurement Units (micro-PMU). In order to simulate real-time conditions, modified IEEE 33 bus distribution system is used, and time-varying load profiles are created with Monte Carlo Simulation. Renewable Energy Sources (RES), Battery Energy Storage Systems (BESS), and EVCS are integrated into the distribution system to create a realistic ADN structure. The obtained results show that the proposed method performs state estimation with high accuracy, and the average voltage magnitude and angle errors remain at the level of 0.15% and 1.1%, respectively. Also, the average execution time of power flow analysis and state estimation is 0.0012 seconds and 0.5265 seconds, respectively. As a result, the proposed approach is a suitable solution for real-time monitoring and analysis of ADNs, thanks to its high estimation accuracy and fast execution time
Foliar application of chitosan nanoparticles loaded with Shilajit modulates biochemical response in wheat under salinity stress
No full textSalinity is a major abiotic stress that limits plant growth and productivity, particularly in arid and semi-arid regions. This study presents a novel nano-biotechnological approach to improving salt stress tolerance in wheat (Triticum aestivum L.) through the foliar application of chitosan nanoparticles loaded with Shilajit (Shilajit@CNPs). Shilajit@CNPs were synthesized and characterized using dynamic light scattering, FTIR spectroscopy, and scanning electron microscopy; their release behavior was also evaluated. Wheat plants exposed to 100 mM NaCl were treated foliarly with 100 ppm Shilajit@CNPs every five days for one month. The treatment improved growth, physiological performance, and photosynthetic pigment content while reducing lipid peroxidation, as indicated by a decrease in malondialdehyde levels (from 0.07 to 0.05 μmol g−1 fresh weight). Total phenolic content increased, and antioxidant capacity was enhanced by up to 15 %, based on free radical scavenging activity. Furthermore, key antioxidant enzymes were significantly activated, with catalase, ascorbate peroxidase, and superoxide dismutase activities increasing by 29 %, 25 %, and 90 %, respectively, compared to salt-stressed plants. Overall, this formulation, merging traditional bioactives with nanoscale delivery, represents an untested strategy in the context of salinity stress management in wheat. These findings underscore its potential as a sustainable solution to enhance crop resilience in salt-affected environments
İç Mekanda İşleve Göre Renk Tercihlerinin Mekansal Deneyim ve Sanal Gerçeklik Yöntemleriyle Değerlendirilmesi: Yaşam Alanı ve Hasta Odası Örneği
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Multiplicity-dependent inclusive J/ψ production at forward rapidity in pp collisions at s = 13 TeV
No full textThis paper presents a study of the inclusive forward J/ψ yield as a function of forward charged-particle multiplicity in pp collisions at s = 13 TeV using data collected by the ALICE experiment at the CERN LHC. The results are presented in terms of relative J/ψ yields and relative charged-particle multiplicities with respect to these quantities obtained in inelastic collisions having at least one charged particle in the pseudorapidity range |η| < 1. The J/ψ mesons are reconstructed via their decay into μ+μ− pairs in the forward rapidity region (2.5 < y < 4). The relative multiplicity is estimated in the forward pseudorapidity range which overlaps with the J/ψ rapidity region. The results show a steeper-than-linear increase of the J/ψ yields versus the multiplicity. They are compared with previous measurements and theoretical model calculations
Enhancing Lung Disease Diagnosis: A High Performance Hybrid Deep Learning Framework for Multi-Class Chest X-Ray Analysis
No full textThis study presents a high performance hybrid deep learning model for the classification of 14 lung diseases using chest X-ray (CXR) images. Manual evaluation of CXR images is labor-intensive and prone to human error. Therefore, automated systems are required to improve diagnostic accuracy and efficiency. Our model integrates ResNet18 and EfficientNet-V2-S architectures, combining residual connections with efficient scaling to achieve high accuracy while maintaining computational efficiency. Trained on the NIH ChestX-ray14 dataset, comprising 112 120 images across 14 disease classes, the model mitigates class imbalances with extensive data augmentation techniques. Achieving an impressive average AUC of 0.872, the model outperforms previous approaches. This performance was enhanced by a refined, anatomically-aware data augmentation strategy that improved the model's robustness and clinical relevance, particularly in challenging disease categories such as Pneumothorax, Emphysema, and Hernia. To further validate its generalizability, the proposed model was tested on three additional datasets for pneumonia, COVID-19, and tuberculosis. The results demonstrate superior performance, achieving an accuracy of 0.958, F1 score of 0.944, and ROC AUC of 0.989 for pneumonia; an accuracy of 0.974, F1 score of 0.969, and ROC AUC of 0.995 for COVID-19; and an accuracy of 0.999, F1 score of 0.999, and ROC AUC of 0.999 for tuberculosis. These outstanding results confirm the robustness and clinical applicability of the model across diverse datasets. This research introduces a reliable and efficient diagnostic tool that enhances the potential of automated lung disease classification. By alleviating radiologists' workload and promoting timely, accurate diagnostic outcomes, the model contributes significantly to medical imaging applications and demonstrates its capacity for practical use in real-world clinical settings
Uncertainty in Generalization: A Comparative Analysis of Selection/Elimination by Algorithms and Cartographers
No full textIn conventional cartography, while base maps are directly produced, smaller scale maps are derived through cartographic generalization. Efforts to automate the process of generalization began in the 1970s; however, it faced challenges due to its uncertainty nature. The generalization commences with the object selection/elimination process. The goal of this study is to once again bring to light the inherent uncertainty present in the selection/elimination process. To this end, the streams selected by two algorithms and seven cartographers in addition to the streams in the original 50K were compared according to geometric properties, performance metrics, and quantitative geomorphic measurements. Our findings emphasize the varying nature of outcomes: no two results were completely identical, underscoring the variable nature of manual and digital generalization. This variation reveals the need for balancing human intuition with algorithmic consistency in the selection of geographical objects, a challenge that has broad implications for the future of cartographic generalization
