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Multi-stage iterative compressed sensing framework based on DIFF transformer and ISTA for remote sensing images
Radiomics models with baseline MRI and clinical data to predict target therapy response and high-risk mortality in metastatic GIST
Enhancing cardiotocography classification via ensemble learning and threshold optimization
Abstract Machine learning classifiers trained on imbalanced healthcare datasets often exhibit bias, leading to poor performance on critical cases. The cardiotocography (CTG) dataset exemplifies this issue, where misclassification of pathological cases arises due to both class imbalance and non-optimal probability thresholds. Statistical analysis suggests refining classification thresholds, but this approach has been largely overlooked in CTG data research. To address these challenges, we propose a multifusion method integrating undersampling, threshold-moving optimization, and ensemble classifiers to enhance classification precision while maintaining computational efficiency. Applied to a CTG dataset of 502 cases from Czech Technical University and University Hospital Brno, our method showed significant improvements in identifying pathological cases. While baseline models correctly classified only about 2 out of 11 cases per test, our approach achieved 76.92, 75, and 41.67% precision, accurately identifying 9, 9, and 3 cases out of 12, respectively.
Ribosomal Elongation of Trans-4-Aminocrotonic Acids into Nascent Peptide Chain
Study of the effects and mechanism of allogeneic platelet-rich plasma gel on layered skin flaps
Correction to “Photocatalyzed Decarboxylative B–C Couplings for the Synthesis of Carboranyl Amino Acids and Peptides”
Assessment of U = U knowledge and influencing factors among the population in Henan Province China based on an internet-based survey
<i>De Novo</i> Discovery of α,α-Disubstituted α-amino Acid-Containing α-helical Peptides as Competitive PPARγ PPI Inhibitors
The utility of novel echocardiographic techniques in evaluating cardiac function in patients undergoing hematopoietic stem cell transplantation: a pilot study
DNA Nanostructure Self-Assembly in an Aqueous Ionic Liquid Solution with Enhanced Stability and Target Binding Affinity
Impact of behavioral economics to improve antihypertensive therapy adherence, a pilot randomized controlled trial in Los Angeles
Unravelling the Origin of Water’s Thermal Conductivity Maximum: Compressibility, Tetrahedrality and Nuclear Quantum Effects
A sensitive green HPLC-fluorescence method for simultaneous analysis of sacubitril and valsartan in pure forms, pharmaceutical dosage form and human plasma
Abstract The development of green methods of analysis has recently become a global trend in analytical chemistry, especially in routine HPLC analysis. This study presents a sensitive and economic green HPLC method with fluorescence detection for the simultaneous determination of sacubitril and valsartan in bulk, and pharmaceutical dosage forms in spiked human plasma using ibuprofen as an internal standard. Analysis was performed on a C18 column (150 mm × 4.6 mm, 5 mm) at ambient temperature with isocratic elution using 30 mM phosphate (pH 2.5) and ethanol in a ratio of (40:60 v/v) at a flow rate of 1.0 mL/min. The fluorescence detector was programmed for excitation and emission wavelengths from 0 to 3.2 min, where λ Excitation = 250.0 nm and λ Emission = 380.0 nm. Then the λ Emission was changed to 320.0 nm from 3.2 to 5.2 min while the λ Excitation was not changed. After 5.2 min, the λ Excitation and λ Emission were changed to 220.0 and 289.0 nm, respectively. Validation of the proposed method was performed in accordance with the ICH guidelines. The method showed good linearity for sacubitril and valsartan in the concentration ranges of 0.035 to 2.205 µg/mL and 0.035 to 4.430 µg/mL, respectively. The proposed method was proven eco-friendly and applicable for routine analysis of the studied drugs in human plasma through assessment by the Analytical Eco-Scale, AGREE, the complex GAPI, the AGSA, the CaFRI, the RGBfast and the Click Analytical Chemistry Index methods.
From the archive: Do sunspots affect the price of corn?
Bicarbonate-Dependence for Pd-Catalyzed CO <sub>2</sub> Hydrogenation to Formate over an Electronegativity-Induced Bimetallic Center
Efficient energy management of a low-voltage AC microgrid with renewable and energy storage integration using nonlinear control
Abstract This paper proposes an enhanced nonlinear control strategy combined with efficient energy flow management for a low-voltage AC microgrid integrating a wind turbine, a photovoltaic system, and a battery energy storage unit. The microgrid operates in a grid-connected configuration, aiming to optimize energy generation, storage, and consumption. To achieve this, a comprehensive mathematical model of the system is developed, and backstepping controllers are designed to fulfill the control objectives. The stability of the closed-loop system is rigorously verified using Lyapunov theory. Furthermore, a novel algorithm is introduced to maximize renewable energy extraction while effectively managing battery storage to enhance system performance and reliability. The proposed approach also ensures grid stability through power factor correction and ensures the load demand is met. Simulation results validate the effectiveness of the control strategy, demonstrating significant improvements in energy efficiency, system stability, and overall dynamic performance under varying load and environmental conditions.