Japan Advanced Institute of Science and Technology

Methylaluminoxane (MAO) is commonly employed to activate molecular pre-catalysts in polyolefin synthesis, both indus-trially and in the laboratory. Despite the extensive use of this compound, the ambiguity related to its structure hampers the understanding of its structure–function relationship. The current study therefore employed synchrotron X-ray total scattering to elucidate the nano-sized molecular structure of MAO. The MAO samples, which were prepared using various synthetic protocols, exhibited consistent X-ray scattering patterns and atomic pair distribution function curves, indicating similar molecular structures. However, the scattering intensity in the small-angle region revealed differences in the high-er-order structures. A fitting study performed using 172 molecular models showed that small molecule and tube models were inadequate to reproduce the experimental results, whereas cage and sheet models provided comparably better fits. The sheet model was found to be consistent with the observed molecular weight and the molecular weight distribution, in addition to accounting for the intensity in the small-angle scattering region. These results align with recent crystallo-graphic findings reported in Science, where a stacked sheet model successfully reproduced an experimental X-ray dif-fraction pattern. Ultimately, determination of the structural motif of MAO is expected to be beneficial to systematic re-search and development using this compound.

In the Industrial Internet of Things (IIoT), Multi-access Edge Computing (MEC) emerges as a transformative paradigm for managing computation-intensive tasks, where task offloading plays an important role. However, due to the complex environment of IIoT, existing deep reinforcement learning-based schemes suffer from significant shortcomings in accuracy and convergence speed during model training when addressing the issue of task offloading. In this paper, to solve this problem, we propose an online task offloading scheme based on reinforcement learning, leveraging the double deep Q network (DQN) and dueling DQN with a prioritized experience replay mechanism, called the Prioritized experience-based Double Dueling DQN task offloading scheme (P-D3QN). P-D3QN enhances action selection accuracy using double DQN and mitigates Q-value overestimation by decomposing state and advantage using dueling DQN. Additionally, we adopt the prioritized experience replay mechanism to enhance the convergence speed of model training by selecting transitions that induce a higher training error between the evaluation network and the target network. Experimental results demonstrate that P-D3QN outperforms several state-of-the-art schemes, achieving a reduction of 21.0% in the average cost of the task and improving the completion rate of the task by 19.5%.

In this paper, we will report results of two sets of cross-linguistic studies about truth judgments and correctness judgments by speakers of English and Japanese, which will show a significant influence of a moral-political factor in an utterance on Japanese truth/correctness judgments. Following up Mizumoto (2022), which demonstrated such an effect on Japanese truth judgments and correctness judgments about utterances containing a contrastive conjunction (such as “but”), Study 1 shows the same effect on Japanese correctness judgments about utterances containing a pejorative. Study 2 then shows that a moral-political factor in utterances can affect Japanese truth/correctness judgments about them even if they are simple utterances containing neither a contrastive conjunction nor a pejorative. In conclusion, we will briefly discuss whether this effect is linguistic or psychological, and present three hypotheses: the semantic hypothesis, pragmatic hypothesis, and error theory hypothesis, to account for the data, which we leave open for future studies.

Methylaluminoxane (MAO) is commonly employed to activate molecular pre-catalysts in polyolefin synthesis, both indus-trially and in the laboratory. Despite the extensive use of this compound, the ambiguity related to its structure hampers the understanding of its structure–function relationship. The current study therefore employed synchrotron X-ray total scattering to elucidate the nano-sized molecular structure of MAO. The MAO samples, which were prepared using various synthetic protocols, exhibited consistent X-ray scattering patterns and atomic pair distribution function curves, indicating similar molecular structures. However, the scattering intensity in the small-angle region revealed differences in the high-er-order structures. A fitting study performed using 172 molecular models showed that small molecule and tube models were inadequate to reproduce the experimental results, whereas cage and sheet models provided comparably better fits. The sheet model was found to be consistent with the observed molecular weight and the molecular weight distribution, in addition to accounting for the intensity in the small-angle scattering region. These results align with recent crystallo-graphic findings reported in Science, where a stacked sheet model successfully reproduced an experimental X-ray dif-fraction pattern. Ultimately, determination of the structural motif of MAO is expected to be beneficial to systematic re-search and development using this compound.

When offering services such as e-commerce on the cloud, there is a necessity to adjust the amount of server resources provided in response to the irregularly increasing and decreasing traffic. This need arises when there is a desire to maintain a constant level of service while, at the same time, minimizing costs as much as possible. There is an abundance of prior research regarding the prediction of future loads from past time-series data. Many of these approaches rely on traditional time-series forecasting, which necessitates that the data used for learning adhere to stationary or unit root processes, or they use deep learning approaches that include using a vast amount of data and parameters. In this study, we propose a traffic forecasting method that includes dynamic window size changes that can follow even slight variation in trends. This method incorporates a fuzzy-entropy-based burst traffic detection in the regression estimation with sliding-window learning. In the evaluation, we conduct four comparative experiments using actual traffic rather than simulations. As a result, compared to the baseline, the proposed reduced the number of request failures and improved the Mean Squared Error between the ideal and the actual container count by 26.4 points on average.

The abundance of raw materials is a significant advantage that positions sodium-ion batteries (SIBs) as a promising energy storage solution for the future. However, the low cycle efficiency and poor rate capacity of cathode materials have hindered the commercialization of SIBs, prompting extensive research efforts to address these challenges. Ion doping into the material structure is considered to be an effective, simple, and scalable approach to enhancing the electrical performance of cathode materials. In this work, B and F elemental ions were selectively doped into the structure of sodium-lithium-manganese-cobalt oxide material via the sol–gel method combined with calcination. The effects of B and F ion doping on the properties of the obtained materials exhibited distinct variations, particularly in the electrochemical performance. While the B-doped material (B-NLMC) exhibited a specific capacity of up to 166.5 mAh g–1 at a current density of 10 mA g–1 and maintained 72.1% capacity after 100 cycles, the material simultaneously doped with B and F ions (BF-NLMC) demonstrated lower specific capacity and cycling efficiency compared to B-NLMC. However, the BF-NLMC material exhibited significantly improved rate capability, delivering a specific capacity of up to approximately 80 mAh g–1 at a high current density of 200 mA g–1. Obviously, this research offers valuable insights into diversifying doping strategies to enhance the electrochemical performance of cathode materials, contributing to the advancement of SIB technology.

Designing cathode materials that exhibit excellent rate performance and extended cycle life is crucial for the commercial viability of aqueous zinc (Zn)‐ion batteries (ZIBs). This report presents a hydrothermal synthesis of stable Ni0.22V2O5·1.22H2O (NVOH) cathode material, demonstrating high‐rate performance and extended cycle life. A successful in situ phase transformation yields Zn3(OH)2V2O7·nH2O (ZVO), which undergoes an irreversible phase transition and exhibits exceptional energy storage properties. The procedure maintains the lattice structure of ZVO and ensures high structural stability throughout the phase transformation. The NVOH cathode material exhibits the discharge capacities of 399 mA h g⁻¹ at a rate of 1 A g⁻¹ after 400 cycles and 303 mA h g⁻¹ at 10 A g⁻¹ after 2000 cycles. Density functional theory calculations indicate that the material is protected by electrostatic forces and exhibits structural stability, with a Zn‐ion migration barrier of 0.32 eV across the host lattice and the electrode–electrolyte interface. Due to these properties, NVOH also exhibits high energy/power densities of 395 Wh kg⁻¹/406 W kg⁻¹ at 0.5 A g⁻¹ and 288 Wh kg⁻¹/8830 W kg⁻¹ at 10 A g⁻¹. Ex situ characterizations indicate structural modifications and irreversible phase changes of NVOH, highlighting the potential of H⁺ intercalation and in situ phase transitions for high‐performance aqueous ZIBs.

Projection dynamic is an evolutionary game with a nonsmooth transition rate. Projection dynamic has been less studied compared to major evolutionary game models such as the replicator and logit dynamics due to its lower regularity, which is more challenging to theoretically address. We propose a regularized version of the dynamic, called regularized projection dynamic (RPD), where the transition rate is Lipschitz continuous, making its solution a time-dependent probability measure in a suitable Banach space. This regularization not only enables us to derive a more tractable model, but also leads to a mean field game (MFG) whose formal large-discount limit is the RPD, resulting in a forward–backward generalization of the RPD. The vanishing regularization limit of the MFG leads to an essentially unbounded control, making the incorporation of regularization essential for its analysis. We present finite difference methods that can handle the RPD and MFG, where the regularization guarantees nonnegativity of their probability densities.

Parks are important urban design settings to promote physical activity within urban areas. However, existing park audit tools often do not address the unique challenges of high-density areas, especially in Asian contexts. This study presents the development and testing of the audiT tool for Activity-friendly Parks in denSe urban areas (TAPS) that support park-related physical activity in highly dense urban settings. Created through a Delphi consultation process that incorporated expert consensus, TAPS focuses on five key domains: park surroundings and accessibility, activity areas, facilities and amenities, aesthetics, and safety. The tool was tested in 25 parks across Tokyo. Of the 24 important park attributes identified by interdisciplinary experts (n=27), open/green space and pathways had the highest expert consensus. Inter-rater reliability was measured using Cohen’s kappa and percent agreement; validity was confirmed through comparison to a gold standard. Across the items, 91.1% achieved a kappa of over 0.4 indicating at least moderate agreement. 95.9% showed more than 70% agreement, and overall dimension validity displayed 87.5% agreement. TAPS is a user-friendly tool that provides a reliable and valid evaluation framework for improving parks to support physical activity in dense urban areas in Asia.

This Feature Article outlines the design of polymer networks for water splitting induced by visible light to develop artificial chloroplasts. The network serves as a mediator for photoinduced electron transfer among precisely organised molecules.

A reliable speech watermarking technique must balance satisfying four requirements: inaudibility, robustness, blind detectability, and confidentiality. A previous study proposed a speech watermarking technique based on direct spread spectrum (DSS) using a linear prediction (LP) scheme, i.e., LP-DSS, that could simultaneously satisfy these four requirements. However, an inaudibility issue was found due to the incorporation of a blind detection scheme with frame synchronization. In this paper, we investigate the feasibility of utilizing a psychoacoustical model, which simulates auditory masking, to control the suitable embedding level of the watermark signal to resolve the inaudibility issue in the LP-DSS scheme. Evaluation results confirmed that controlling the embedding level with the psychoacoustical model, with a constant scaling factor setting, could balance the trade-off between inaudibility and detection ability with a payload up to 64 bps.