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Discovery of Ni^(I) Complexes for CO₂ Insertion Enabled by a Machine Learning-Computational-Selection Sequence

Assessment of dispensing practices and patient’s knowledge of dispensed medicines in a hospital pharmacy, Ethiopia

Unique Conductivity Behavior in Water-In-Salt Electrolytes Driven by Ion Clusters

Correction: Robotic flexible ureteroscopy system, Zamenix R, demonstrates efficacy and safety in initial clinical evaluation for retrograde intrarenal surgery

Pressure-Activated Efficient Near-Infrared Luminescence in Atomically Precise Gold Nanoclusters

Publisher Correction: The effect of long-term exposure to diatrizoate on microbial communities in activated sludge

A Redox-Active Frustrated Lewis Pair for the Activation and N–O Scission of Nitrosonium Cations

Improving energy autonomy of positive energy districts using multi-agent deep reinforcement learning

Abstract In recent years, Positive Energy Districts (PEDs) have emerged at the forefront of urban innovation, rapidly transforming communities by integrating shared Energy Storage Systems (ESS) and Electric Vehicles (EVs) to redefine the future of sustainable communities. However, energy management in such communities remains extremely challenging due to the dynamic nature of EV availability, unpredictable renewable energy generation, and the necessity to maintain user comfort while optimizing energy use. Overcoming these challenges is critical for enabling PEDs to achieve carbon neutrality, reduce costs, and improve energy sharing. In addition, Vehicle-to-Grid (V2G) technology and shared ESS offer unique opportunities to optimize energy consumption and facilitate access to the open energy market, but fully exploiting their potential requires advanced strategies such as Deep Reinforcement Learning (DRL). To address these needs, this work proposes a novel Community Multi-Agent Deep Reinforcement Learning Vehicle-to-Grid (CoMAD V2G) solution based on Multi-Agent Reinforcement Learning (MARL), which enhances the utilization of community-generated energy and increases community autonomy by controlling the charging and discharging cycles of V2G-enabled EVs. Real data on household consumption, solar energy production, EV dynamics, and electricity prices are used to evaluate and verify the effectiveness of the proposed solution in a realistic environment. Under these conditions, the proposed solution achieves improved energy exchange with the external grid on an annual basis, a result not attained with comparable conventional heuristic or alternative learning-based approaches for the community under consideration. Furthermore, the solution reduces household electricity costs by up to 25%, highlighting its potential to deliver significant economic and sustainability benefits for PEDs.

Structural Basis for Oxidized Glutathione Recognition by Yeast Cadmium Factor 1

Ensemble of deep learning and IoT technologies for improved safety in smart indoor activity monitoring for visually impaired individuals

Alkene Borylation–Hydrogenation Enables Highly Active, Site-Selective Cobalt-Catalyzed Borylation

Structural equation modeling of factors influencing women’s attitudes, comfort and willingness toward risk-stratified breast cancer screening

Stereocomplexation-Promoted Alternating Supramolecular Copolymerization of Peptide-<i>Oligo</i>(Lactic Acid) Conjugates

Contrastive learning-driven framework for neuron morphology classification

Macrocyclic Phage Display for Identification of Selective Protease Substrates

Cross-sectional relationships between spinal cord gray matter volume and pain in individuals with fibromyalgia and opioid use

Photoactivated Ion Transport: Role of Intrinsic Defects and Plasmonics for Efficient Ionic Power Harvesting

Self-oriented affective empathy is associated with increased negative affect

Abstract An increasing body of research suggests that empathic traits at high levels may predict negative affectivity. Here, we investigate the combinatory and differential role of affective (personal distress, empathic concern) and cognitive (perspective taking) facets of empathy for their contribution to negative affectivity in two general population samples (N1 = 259, N2 = 938). A latent profile analysis revealed four combinatory groups of affective and cognitive empathic facets (i.e., high affective high cognitive [A+/C+], high affective low cognitive [A+/C−], low affective high cognitive [A−/C+], low affective low cognitive [A−/C−]). These groups were differentially associated with negative affectivity, showing that greater affective empathy was associated with increased negative affect. Moreover, moderation and subsidiary simple slopes analyses demonstrated that self-oriented affective empathy (personal distress) was generally positively associated with depression and anxiety. In case of depressive symptomatology, this correlation was lower under circumstances of high cognitive empathy, but only in the larger, second sample. Other-oriented affective empathy (empathic concern) was not related to negative affect. Our findings suggest that enhanced self-focused affective empathy may be associated with exaggerated involvement in the emotional experience of others, with the potential to reduce the negative correlation of accurate emotion recognition with negative affect.

Convergent Total Synthesis of Erchinines A and B and Their C20 Epimers

A hybrid phase-shifted PWM technique for a 13-level inverter with low switch count and suppressed charging spike