Tokyo City University

Abstract Ferroelectric AlScN is a premier candidate for next-generation non-volatile memories and in-memory computing, yet its reliability degradation mechanisms remain unclear. Here, we demonstrate a pronounced polarity dependence in the time-dependent dielectric breakdown (TDDB) statistics of AlScN capacitors and its suppression via plasma oxidation (PO). For as-deposited Al0.7Sc0.3N and Al0.8Sc0.2N, Weibull analysis reveals that the shape parameter (β) is significantly smaller under positive top-electrode bias than under negative bias, suggesting a universal, polarity-sensitive breakdown process. We propose that field-driven migration of near-surface oxygen species modulates defect-network evolution in a polarity-dependent manner. To validate this, the Al0.8Sc0.2N surface was modified by PO. Angle-resolved HAXPES confirmed enhanced near-surface oxidation, and J–V measurements showed reduced leakage current. Crucially, TDDB analysis demonstrated that PO strongly suppresses the polarity dependence of β, consistent with our proposed model. Finally, the voltage acceleration coefficient and 10-year lifetime voltage were evaluated.

This paper proposes an analysis of power distribution between onshore terminals during output fluctuations of offshore wind farms, considering power interchange from wind farms and transmission stability.

Integrating electric vehicles (EVs) into microgrids offers a promising solution to enhance renewable energy utilization, especially in cold regions where EV adoption remains challenging. This paper determines operational data using telematics devices over one year from six municipal vehicles, which include EV and conventional vehicles. The data includes key parameters, such as initial state of charge (SOC) and parking ratio, for evaluating power supply and demand balancing. This paper analyzes the temperature dependence of key parameters and the daily operation patterns of vehicles using the GPS data. This paper also evaluates the correlation between facility demand and weather conditions to realize weather-linked simulations.

The increasing complexity of modern neural networks impedes systematic understanding and formal analysis,often resulting in “black box” behavior. Our paper introduces Typed Petri Nets (TPNs) as a formal framework formodeling the structure and dynamics of neural networks, aiming to address these challenges. We define the corecomponents of TPNs―places, transitions, arcs, a type system (e.g., for tensors), and computation assignments―anddemonstrate how they can compositionally represent basic network layers. Furthermore, we illustrate the capability ofTPNs to model advanced architectures, including convolutional layers, skip connections, and multi-branch structures.TPN dynamics naturally simulate forward propagation through markings and firing rules. The framework’sformal semantics can be rigorously explored using category theory, as illustrated by applications in probabilistic contextsusing Markov Categories. Key advantages of the TPN approach include modularity through compositionality,explicit information flow representation, type safety, and a foundation for formal analysis. This work establishesTPN as a structured, mathematically grounded perspective for understanding complex neural networks, offering apromising basis for future research into learning dynamics, verification, and tool development.

In this paper, several optimized design results of the HTGR-based 10 MWth Reaktor Daya Eksperimental (RDE) (Experimental Power Reactor), so far conducted, are reviewed and compared from the neutronics, reactor types, refueling schemes, and fuel cycle points of view. The review covers the multipass and once-through-then-out (OTTO) pebble-bed cores, as well as block/prismatic type cores with several fuel shuffling options. As for the fuel cycle, uranium and thorium fuels are considered. The fuel burnup performance and power distribution are evaluated and compared among other important design parameters. Reactor physics codes, nuclear data libraries, and calculation models and procedures used for the design and analysis are reviewed, and challenges for future improvements are discussed.

To reduce network operations workload, there will be strong demand for more efficiency in the inspection and repair of network facilities. Dispatch-less operations supported by remotely controlled drones can significantly improve work efficiency, but current network systems cannot meet the strict latency required for such operations. To achieve end-to-end links that have excellent stability and low-latency, this work proposes a real-time optical path control scheme that supports the a combination of optical and wireless network domains. Evaluations confirm that the proposed scheme can perform optical path control based on information from multiple domains fast enough for remote drone control. Further study will be needed to investigate the effects of coordination between a wireless controller, and to perform system-level evaluations including telemetry and optical switch response time.

Juggling is a complex motor skill that requires multiple sub-skills and cannot be mastered without extensive practice. Although prior studies have quantified performance differences between novice and expert jugglers, none have attempted to quantitatively decompose these components or model their contribution to juggling performance. This longitudinal study presents a multimodal evaluation system that integrates computer vision, motion capture, and biosensing to quantify three key elements of juggling ability: Sequencing, Prediction, and Accuracy. Twenty beginners completed a 10-day, three-ball juggling experiment combining visuo-haptic virtual reality (VR) and real-world practice, with half training in reduced gravity, previously shown to enhance early-stage motor learning. The fitted Gamma-Log generalized linear model (GLM) indicated that Sequencing is the dominant factor of early skill acquisition, followed by Prediction and Accuracy. This study provides the first computational decomposition of juggling, demonstrates how multiple elements jointly contribute to performance, and results in a principled approach to characterizing motor learning in complex real-world tasks.

This paper proposes a multiplexing scheme of control signals for initial access using a dedicated frequency bandwidth for the discrete Fourier transform-spread orthogonal frequency division multiplexing (DFT-S-OFDM) waveform in the sub-Terahertz (THz) bands. We employ a subcarrier spacing (SCS) in the dedicated frequency bandwidth that is narrower than that for other frequency bandwidths in which the shared channels used to convey end-user application data and layer 1/layer 2 control channels in the physical layer are multiplexed in the downlink (DL) and uplink (UL). The estimation accuracy of fractional frequency offset (FFO) based on the autocorrelation using a cyclic prefix (CP) that is attached to each DFT-S-OFDM symbol is improved due to the increase in the CP power as the SCS becomes narrower. Accordingly, the narrower SCS for the control signals in the initial access enable us to achieve a high physical layer cell ID detection probability by making effective use of the FFO estimation and joint estimation of the integer frequency offset (IFO) and primary synchronization signal (PSS) in the DL. Moreover, the narrow SCS achieves a relaxed round-trip time delay measurement based on the physical random access channel (PRACH). This enables a cyclic-shift based multiplexing scheme for the multiple PRACH preambles from the simultaneously accessing sets of user equipment (UE), which leads to a low miss-detection probability for the PRACH. We also present a synchronization signal block (SSB) structure in the DL and PRACH structure in the UL that are suited to the proposed multiplexing scheme for initial-access control signals in the sub-THz bands.

Binders affect both the mechanical properties and electrochemical performance of electrode materials for lithium-ion batteries. This study has developed and applied a mechanical model of electrode materials on negative electrodes made of polyvinylidene fluoride (PVDF) and styrene-butadiene rubber (SBR) based binders. A series of tensile tests was performed on carbon-based negative electrodes with several concentration ratios of the binders, and the stress, strain and dissipated strain energy of the binders inside the electrodes were estimated using the mechanical model. The results were summarized as follows. (1) Both the PVDF-based and SBR-based specimens fractured microscopically due to the rupture of the binder inside the specimen. This indicates that the structure of the negative electrode is supported by the binder. (2) The mechanical model was valid for the PVDF-based specimen in that the constituent of the PVDF-based binder formed inside the specimen did not change with respect to the binder concentration. (3) The tensile strength and permanent strain of the SBR-based specimen showed the same tendency as those of the PVDF-based specimen, and the mechanical model was valid for the SBR-based specimen as well. The dissipated strain energy showed a bipolarized tendency between the low and middle binder concentrations. This tendency would be caused by the adhesion mechanism between carbon particles, SBR and CMC, and the dispersibility of carbon particles.