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A novel integration of Hodrick–Prescott filter (Hp-filter) and wavelet transform (WT) with optimize support vector machine (PSO-SVM) in predicting solar radiation
Abstract Previous research has shown that predicting solar radiation is a challenging issue due to highly nonlinear and noisy climate data. Various hybrid approaches have been applied earlier for solar radiation prediction, which integrates the Wavelet Transform with various Machine Learning models. This research, therefore, intends to further improve the performance of these existing hybrid models. To address the limitations in handling nonlinear and noisy climate patterns, this study proposes a multi-hybrid model for accurately predicting solar radiation that incorporates the Hodrick–Prescott Filter (HP-Filter), Discrete Wavelet Transform (DWT), and Support Vector Machine (SVM). The collected data from the Bangladesh Meteorological Department for two different geological locations in Bangladesh, namely Dhaka and Chittagong, is divided into three categories for modeling: 70% for training, 15% for validation, and 15% for testing, whereas the model hyper-parameters of the SVM were optimized using the Particle Swarm Optimization algorithm. The proposed approach applies the Hodrick–Prescott Filter before analyzing DWT to strengthen the SVM model’s ability to capture complicated climate patterns in great detail and also make the model more precise and reliable. Several performance metrics, such as Mean Squared Error (MSE), Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error, and Coefficient of Determination (R2), were considered for model evaluation. The results showed that it improves upon traditional SVM by 99.76% and 99.77% and hybrid DWT-SVM by 39% and 57% in terms of MSE reduction at Dhaka and Chittagong, respectively. R2 also improved by 49% and 54% over traditional SVM and by 4.40% and 3.16% over hybrid DWT-SVM model. The model well captures the complex nonlinear trend existing in solar radiation; thus, it shows its potential to be applied to other regions for efficient prediction of solar radiation.
Clinical performance validation and four diagnostic strategy assessments of high-sensitivity troponin I assays
Optimal application research of superconducting fault current limiters on medium voltage direct current shipboard power system
Spatial ecology of two emblematic deep-sea crustaceans in the Salas y Gómez, Nazca and Juan Fernández ridges Southeast Pacific
Abstract The Salas & Gómez, Nazca, and Juan Fernández ridges are among the least studied regions on the planet. Ecological knowledge of deep-sea organisms inhabiting islands and seamounts along these ridges is limited. Using various sources, including published data and in situ information from five scientific expeditions in the region, we analyzed the spatial ecology of Paromola rathbuni and Projasus bahamondei, two mobile species of benthic megafauna. Random Forest models detected different combinations of abiotic variables affecting species distribution. The distribution of P. bahamondei was primarily influenced by dissolved oxygen, longitude (W), and temperature. The distribution of P. rathbuni was mainly explained by longitude (W), followed by depth, temperature, and oxygen. Generalized Linear Models quantified significant effects of low dissolved oxygen and temperature on species presence, influencing their preference for bathymetric ranges (P. bahamondei at 400–500 m, P. rathbuni at 300–400 m). A distribution break to the west of ~ 85°W was confirmed, potentially due to the weakening of the Oxygen Minimum Zone altering oxygen and thermal conditions. This feature could act as a barrier for both species despite their high dispersal potential. Under projected climate change, shifts in latitudinal, longitudinal, and bathymetric distribution patterns are expected for both species.
Investigation of deformation of existing tunnel due to above excavation unloading considering tunnel lateral response
Partial organic substitution for chemical fertilizer reduces N2O emissions but increases the risk of N loss through nitrification in Tibetan farmland
Graphical model analysis of subjective well-being and various factors in Japanese adults from the Iwaki cross-sectional study
Direct cell interactions potentially regulate transcriptional programmes that control the responses of high grade serous ovarian cancer patients to therapy
Abstract The tumour microenvironment is composed of a complex cellular network involving cancer, stromal and immune cells in dynamic interactions. A large proportion of this network relies on direct physical interactions between cells, which may impact patient responses to clinical therapy. Doublets in scRNA-seq are usually excluded from analysis. However, they may represent directly interacting cells. To decipher the physical interaction landscape in relation to clinical prognosis, we inferred a physical cell–cell interaction (PCI) network from ‘biological’ doublets in a scRNA-seq dataset of approximately 18,000 cells, obtained from 7 treatment-naive ovarian cancer patients. Focusing on cancer-stromal PCIs, we uncovered molecular interaction networks and transcriptional landscapes that stratified patients in respect to their clinical responses to standard therapy. Good responders featured PCIs involving immune cells interacting with other cell types including cancer cells. Poor responders lacked immune cell interactions, but showed a high enrichment of cancer-stromal PCIs. To explore the molecular differences between cancer-stromal PCIs between responders and non-responders, we identified correlating gene signatures. We constructed ligand-receptor interaction networks and identified associated downstream pathways. The reconstruction of gene regulatory networks and trajectory analysis revealed distinct transcription factor (TF) clusters and gene modules that separated doublet cells by clinical outcomes. Our results indicate (i) that transcriptional changes resulting from PCIs predict the response of ovarian cancer patients to standard therapy, (ii) that immune reactivity of the host against the tumour enhances the efficacy of therapy, and (iii) that cancer-stromal cell interaction can have a dual effect either supporting or inhibiting therapy responses.
Preclinical and clinical evaluation of [64Cu]Cu-PSMA-Q PET/CT for prostate cancer detection and its comparison with [18F]FDG imaging
Development of anti-fouling endoscope tip hood for gastrointestinal endoscopy
Using pet insurance claims to predict occurrence of vector-borne and zoonotic disease in humans in the United States
Synchronizing controlled logistics terminals between simulated and visualized production lines using an ASTAK method
Cannabidiol exerts teratogenic effects on developing zebrafish through the sonic hedgehog signaling pathway
Clinical experience of the expanded carrier screening for recessive genetic diseases in a large cohort study in Southern central China
Accessing the role of tourism, renewable energy, and green finance in shaping the sustainable future
Machine-learning-aided analysis of relationship between crystal defects and macroscopic mechanical properties of TWIP steel
Abstract Establishing efficient methods to obtain quantitative data on crystal defect evolution is vital for understanding material properties. Dynamic Transmission Electron Microscopy (TEM) captures crystal defects in materials undergoing plastic deformation, generating vast datasets with high temporal and spatial resolution. However, manual analysis of these images is labor-intensive, and automated, unbiased analysis remains a challenge. In this study, we developed a U-net-based machine learning approach to analyze TEM videos of crystal defect evolution in Twinning-Induced Plasticity (TWIP) steels with different grain sizes. The method overcame challenges like field-of-view translation and nonuniform defect motion. This approach quantitatively measured defect evolution as a function of time and strain with the same temporal resolution as the original videos, detecting even minor changes with high accuracy. We use this technique to quantitatively reveal the switch of the dominant plastic deformation mechanism with grain size and the relaxation of elastic strain due to the rapid increase in stacking faults. Our results validate the use of U-net models for efficient semantic segmentation of TEM videos, enabling accurate quantitative analysis. This work advances TEM video analysis and provides new insights into the deformation mechanisms of materials.
Frequent failure of nutrients to increase plant biomass supports the need for precision fertilization in agriculture
Genome-wide association study of plasma amino acids and Mendelian randomization for cardiometabolic traits
Deep learning based dual stage model for accurate nasogastric tube positioning in chest radiographs
Abstract Accurate placement of nasogastric tubes (NGTs) is crucial for ensuring patient safety and effective treatment. Traditional methods relying on manual inspection are susceptible to human error, highlighting the need for innovative solutions. This study introduces a deep-learning model that enhances the detection and analysis of NGT positioning in chest radiographs. By integrating advanced segmentation and classification techniques, the model leverages the nnU-Net framework for segmenting critical regions and the ResNet50 architecture, pre-trained with MedCLIP, for classifying NGT placement. Trained on 1799 chest radiographs, the model demonstrates remarkable performance, achieving a Dice Similarity Coefficient of 65.35% for segmentation and an Area Under the Curve of 99.72% for classification. These results underscore its ability to accurately distinguish between correct and incorrect placements, outperforming traditional approaches. This method not only enhances diagnostic precision but also has the potential to streamline clinical workflows and improve patient care. A functional prototype of the model is accessible at https://ngtube.ziovision.ai.