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GluN2B-mediated regulation of silent synapses for receptor specification and addiction memory
No full textPsychostimulants, including cocaine, elicit stereotyped, addictive behaviors. The reemergence of silent synapses containing only NMDA-type glutamate receptors is a critical mediator of addiction memory and seeking behaviors. Despite the predominant abundance of GluN2B-containing NMDA-type glutamate receptors in silent synapses, their operational mechanisms are not fully understood. Here, using conditional depletion/deletion of GluN2B in D1-expressing accumbal medium spiny neurons, we examined the synaptic and behavioral actions that silent synapses incur after repeated exposure to cocaine. GluN2B ablation reduces the proportion of silent synapses, but some of them can persist by substitution with GluN2C, which drives the aberrantly facilitated synaptic incorporation of calcium-impermeable AMPA-type glutamate receptors (AMPARs). The resulting precocious maturation of silent synapses impairs addiction memory but increases locomotor activity, both of which can be normalized by the blockade of calcium-impermeable AMPAR trafficking. Collectively, GluN2B supports the competence of cocaine-induced silent synapses to specify the subunit composition of AMPARs and thereby the expression of addiction memory and related behaviors. © The Author(s) 2025.TRUEsciescopuskc
Optimization of alginate/gelatin/dextran-aldehyde bioink for 3D bioprinting and cell engraftment
No full textThe development of bioinks with optimized printability, mechanical properties, and biocompatibility is critical for advancing three-dimensional (3D) bioprinting and tissue engineering. In this study, we introduce an alginate/gelatin/dextran-aldehyde (AGDA) bioink, designed to balance structural integrity and cellular functionality. Among the tested formulations, AGDA1 demonstrated superior performance, with optimized printability and high cell compatibility. AGDA bioinks involve dual crosslinking (ionic gelation of alginate and Schiff base formation between gelatin and dextran-aldehyde), permitting appropriate stiffness, viscosity, and thixotropic behavior. Fibroblasts encapsulated in AGDA, either as single cells, spheroids, or a combination of both, exhibited high viability and proliferative capacity. Notably, the combination method supported the highest cellular density and fibroblast-specific morphological transformations, surpassing the commercially available GelXA bioink. These findings highlight AGDA’s potential as a versatile bioink for fabricating complex and scalable tissue constructs. In summary, this study contributes to the development of bioinks tailored for enhanced cell engraftment and regenerative applications.TRUEsciescopu
RSCF: Relation-Semantics Consistent Filter for Entity Embedding of Knowledge Graph
No full textIn knowledge graph embedding, leveraging relation specific entity transformation has markedly enhanced performance. However, the consistency of embedding differences before and after transformation remains unaddressed, risking the loss of valuable inductive bias in- herent in the embeddings. This inconsistency stems from two problems. First, transforma- tion representations are specified for relations in a disconnected manner, allowing dissimilar transformations and corresponding entity em- beddings for similar relations. Second, a gener- alized plug-in approach as a SFBR (Semantic Filter Based on Relations) disrupts this consis- tency through excessive concentration of entity embeddings under entity-based regularization, generating indistinguishable score distributions among relations. In this paper, we introduce
a plug-in KGE method, Relation-Semantics Consistent Filter (RSCF). Its entity transfor- mation has three features for enhancing seman- tic consistency: 1) shared affine transformation
of relation embeddings across all relations, 2) rooted entity transformation that adds an en- tity embedding to its change represented by the transformed vector, and 3) normalization
of the change to prevent scale reduction. To amplify the advantages of consistency that pre- serve semantics on embeddings, RSCF adds relation transformation and prediction mod- ules for enhancing the semantics. In knowledge graph completion tasks with distance-based and tensor decomposition models, RSCF signifi- cantly outperforms state-of-the-art KGE meth- ods, showing robustness across all relations and their frequencies
Reinforcement learning-enabled strategic wargaming using LLMs for board game decision-making
No full textDual-stream hybrid architecture with adaptive multi-scale boundary-aware mechanisms for robust urban change detection in smart cities
Get PDFUrban environments undergo continuous changes due to natural processes and human activities, which necessitates robust methods for monitoring changes in land cover and infrastructure for sustainable developments. Change detection in remote sensing plays a pivotal role in analyzing these temporal variations and supports various applications, including environmental monitoring. Many deep learning-based methods have been widely investigated for change detection in the literature. Most of them are typically regarded as per-pixel labeling and show their dominance, but they still struggle in complex scenarios with multi-scale features, imprecise & blurring boundaries, and domain shifts between temporal shifts. To address these challenges, we propose a novel Dual-Stream Hybrid Architecture (DSHA) that combines the strengths of ResNet34 and Modified Pyramid Vision Transformer (PVT-v2) for robust change detection for smart cities. The decoder integrates a boundary-aware module, along with multiscale attention for accurate object boundary detection. For the experiments, we incorporated the LEVIR-MCI dataset, and the results demonstrate the superior performance of our approach by achieving an mIoU of 92.28% and an F1 score of 92.50%. Ablation studies highlight the contribution of each component by showing significant improvements in the evaluation metrics. In comparison with existing methods, DSHA outperformed the existing state-of-the-art methods on the benchmark dataset. These advancements demonstrate our proposed approach’s potential for accurate and reliable urban change detection, making it highly suitable for smart city monitoring applications focused on sustainable urban development. © 2025 Elsevier B.V., All rights reserved.TRUEsciescopu
Numerical Investigation of Electrical Performance and Scaling Properties in 6T1C IGZO-Based Synaptic Devices
No full textA 6T1C IGZO-based synaptic memory has excellent linearity, symmetry, and retention characteristics. In this work, we numerically investigate the performance of a 6T1C IGZO-based synaptic memory by examining leakage characteristics, synaptic behavior, and capacitance scaling. The impact of cell size reduction on retention characteristics is also evaluated. © 2025 Elsevier B.V., All rights reserved
High-Quality Unknown Object Instance Segmetation via Quadruple Boundary Error Refinement
No full textSynergistic Integration of Quantum Materials with Smart Electrolytes for Next‐Generation Multifunctional Supercapacitors: Advances, Challenges, and Future Prospects
No full textRapid advancements in artificial intelligence and growing global demand for sustainable energy solutions have accelerated the integration of intelligent functionalities into electrochemical energy storage devices, notably supercapacitors (SCs). Quantum materials (QMs), including quantum dots (QDs), MXenes, metal–organic frameworks (MOFs), covalent organic frameworks (COFs), and transition metal dichalcogenides (TMDs), combined with smart electrolytes, have emerged as critical components for achieving next-generation flexible, wearable, and intelligent SCs. Smart electrolytes, characterized by stimulus-responsiveness, self-healing, and multifunctionality, substantially enhance operational stability, electrochemical performance, and responsiveness. This review critically evaluates recent advancements in coupling QMs with smart electrolytes, emphasizing innovative design strategies, such as morphological engineering, interface tailoring, and surface functionalization. Synergistic interactions at QM-electrolyte interfaces are analyzed, highlighting enhancements in capacitance, energy density, and intelligent functionalities like electrochromism and shape memory, surpassing conventional SC capabilities. Computational modeling, particularly density functional theory, is discussed to elucidate quantum capacitance mechanisms and interfacial charge dynamics, optimizing device performance. This novel integration of QMs with smart electrolytes, previously unexplored comprehensively in existing literature, addresses current research challenges and identifies future research directions, emphasizing scalable synthesis, multifunctional materials development, and extensive mechanistic investigations to bridge laboratory innovations and practical technological applications.FALSEsciescopu
General Instantaneous Dynamic Phasor
No full textDynamic phasor has now been widely used in the fields of ac power and signal, such as power electronics, power systems engineering, and instrumentation. It is, however, broadly misunderstood by users that the dynamic phasor is valid for an averaged period of time. This misbelief stems from the definition of Fourier series coefficients for a variable where the time sliding integration is taken over a period to get its average. In this article, it is verified that the dynamic phasor is valid instantaneously for any time and that averaging over a period has nothing to do with the dynamics of phasor variables. An extended phasor transformation is newly proposed as a generalized instantaneous dynamic phasor. This instantly valid dynamic phasor is thoroughly verified by mathematics, simulation, and experiments. The instantaneous dynamic phasor is found to be valid for an arbitrary and even time-varying frequency, which is not necessarily the same as the source frequency. The transfer function of the phasor space system is found to be a frequency shift form of the original one of the real space system for the fixed frequency dynamic phasor. © 2025 Elsevier B.V., All rights reserved.FALSEsciescopu
Numerical homogenization for nonlinear multiscale analysis of electropermanent magnet composites
No full textThis study presents a numerical homogenization model to predict the effective nonlinear behavior of highly heterogeneous electropermanent magnet (EPM) composites. EPM composites consist of periodic microstructures composed of both soft and hard ferromagnetic materials (i.e., iron and permanent magnets). EPM composites possess unique ability to self-generate magnetic fields while adjusting them using external current, making them promising for use in electromechanical devices. However, direct numerical analysis of EPM composite structures requires huge computational costs, particularly in nonlinear ranges where electromechanical devices typically operate. This challenge can be alleviated through multiscale analysis using homogenization method. The developed homogenization model is constructed using the energy-based approach, assuming magnetic energy equivalence between heterogeneous and homogeneous media. Specifically, the effective B-H curve of EPM composite is computed by interpolating B-H pairs obtained by solving cell problems through finite element analysis. To validate the proposed homogenization model, three numerical examples including an actuator and a magnetic bearing, are investigated. In each example, the magnetic field distribution, magnetic energy, or magnetic force, along with computational time, of actual EPM heterogeneous structures are compared with those of equivalent structures having homogeneous effective B-H curve. These comparisons confirm the accuracy and computational efficiency of the developed numerical homogenization model. © 2016 IEEE.FALSEscopu