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Evaluation of the impact of Comprehensive Medication Management on health outcomes in people living with hypertension: a pragmatic clinical trial
Glucagon-Like Peptide-1 Receptor Agonists Inhibit the Initiation of Toxic Amyloid-β42 Aggregation
AAV-only targeting of ventral tegmental area dopamine neurons for optical self-stimulation studies in mice
Cobalt-Catalyzed Asymmetric Cyclopropanation of Heteroaryl Alkenes with Homogeneous Zinc Carbenoids
Double deep reinforcement learning twin-delayed agents for performance improvement of a grid-connected wave energy conversion system
Abstract This study introduces a novel approach using two deep learning agents, trained with the twin-delayed deep deterministic policy gradient (TD3) algorithm, to replace the PI controllers used for the control of grid-connected Archimedes Wave Swing (AWS) wave energy conversion systems. The generator converter’s controller has two mandatory objectives: minimizing losses in the stator and maximizing energy extraction from incident sea waves. These goals are achieved by controlling the generator’s dq currents using a TD3 agent on the rectifier side. In addition, the grid-side inverter’s controller is responsible for regulating both the DC link and the point-of-common-coupling voltages. In the new configuration, two approaches are proposed in this work: either a single deep learning agent replaces the four proportional-integral (PI) controllers on the inverter side, or a hybrid approach combining two PI controllers with a TD3 agent. To verify the reliability of the TD3 agents, the system is analyzed in both steady and transient states under fault conditions. Furthermore, the TD3 agents’ performance is benchmarked against the classical PI controller configuration in MATLAB Simulink. The results demonstrate better dynamic and steady-state responses from the hybrid-TD3 agent on the grid side than from the full PI classical configuration.
Advancing behavioural guidance systems to help conserve the European eel (Anguilla anguilla)
Abstract This study investigated yellow (experiment 1) and silver-phase (experiment 2) European eel ( Anguilla anguilla ) behavioural response to pulsed direct current electric fields. Eel were offered a choice of two channels through which to pass under either: (1) a treatment condition, in which eel could select either an Electrified Channel (EC) or one in which the electric field was negligible (Non-Electrified Channel – NEC), or (2) a control in which the electric field was absent in both routes. In experiment 1, the influence of the EC field strength and direction of approach (upstream or downstream) on both initial and total channel passage and avoidance ( reaction , route change and rejection ) was assessed. In experiment 2, the influence of EC field strength and pulse frequency on initial channel passage and avoidance was investigated. The percentage of eel that passed the NEC under the treatment did not differ from the control in either experiment. In experiment 1, yellow-phase eel exhibited greater total avoidance in the EC when travelling upstream; but field strength had no effect. In experiment 2, silver-phase eel exhibited greater initial avoidance in the EC, but neither field strength nor frequency were influential. These findings will help optimise the design of devices that employ electric fields to guide eel.
Challenges and Opportunities for Cleavable Linkers Used in Polymer–Drug Conjugates
Temporal evolution of spatial water quality heterogeneity in Oman’s largest water supply reservoir
Inorganic Antibody-Mimetic H-Zeolite Blocks Airborne Viruses via E340-Targeted Biorecognition
A High-resolution metal–insulator–metal plasmonic biosensor with concentric ring resonators for carcinoembryonic antigen detection
Microenvironment Magnesium Overload Disrupts Bacterial Membrane Functions for the Central Nervous System Infection Treatment
An enhanced hypergraph CNN with adaptive focal loss for automated ECG heartbeat classification
Abstract Deep learning techniques have shown significant promise for the automated diagnosis of CVD using ECG analysis. Nevertheless, several critical challenges persist with current approaches: severe class imbalance, intricate temporal dependencies, and poor modeling of inter-beat relationships ultimately limit clinical applicability. This work presents a novel hybrid deep learning framework that combines a CNN for time-domain feature extraction, k-nearest neighbor-based hypergraph construction with cosine similarity to model inter-beat dependencies, and a residual-connection-enhanced hypergraph neural network (EHGNN) for robust classification. To address class imbalance, focal loss is implemented with adaptive class weighting. For evaluation, our model was tested on two benchmark datasets: the MIT-BIH Arrhythmia Database and the St. Petersburg Institute of Cardiological Technics (INCART) 12-lead Arrhythmia Database. In the case of the MITBIH dataset, a classification accuracy of 98.66% was achieved using the AAMI five-class classification system, whereas the results indicated a three-class arrhythmia detection accuracy of 95.46% for the INCART database. The model demonstrated consistent performance on two separate data sets, thus emphasizing its ability to generalize well. These results confirm that the proposed framework effectively addresses class imbalance through focal loss and SMOTE, temporal dependencies through CNN-based feature extraction, and inter-beat relationships through EHGNN, demonstrating great potential for clinical diagnostic systems by capturing complex heartbeat patterns that are otherwise overlooked by standard models