Kanagawa Institute of Technology

Abstract Horizontal slot waveguides allow light to be partially confined in the low index region. Furthermore, it is possible to realize a sensor that utilizes the change in effective refractive index depending on the state of the material in the hollow slot structure. We present a method for fabricating microring resonators using Nb2O5 horizontal hollow slot waveguides for sensor devices. We used a process using an electron beam lithography technique for forming hollow slot region. Hollow slot waveguides were formed in the ring resonator by selective etching using the patterned PMMA as a protective film. The resonant characteristics were obtained by using the fabricated microring resonator using Nb2O5 horizontal hollow slot waveguides. As a preliminary study, resonant characteristics dependent on the material filled in the horizontal slot region were also obtained.

This paper introduces a novel neural architecture that significantly transforms how neural networks process information. While traditional neural networks rely on training to map inputs to predefined output values, our approach, derived from space mapping theory, establishes a framework that generates mathematically distinguishable scalar values through random space mapping. The architecture consists of neural units that function as random space mapping operators, progressively transforming high-dimensional inputs into scalar values while maintaining their discriminative properties. This approach is particularly advantageous in closed dataset environments—where all data items are known and finite—as it eliminates the computational overhead of traditional training while ensuring reliable pattern distinguishability. Instead of training networks to achieve specific output values, we establish a database-driven recognition mechanism that links these scalar values to desired outputs. Through implementations in both flower recognition and robot navigation tasks, we demonstrate how our architecture achieves effective high-dimensional data processing while offering significant interpretability and efficiency. The results suggest a new direction for neural architecture design, offering a training-free alternative that could significantly advance high-dimensional data processing tasks across various domains.

The present study reports on a preliminary experiment designed to experimentally verify the effectiveness of a newly developed rehabilitation support system using a torqueload wrist manipulandum. Evidence-based rehabilitation requires devices that can objectively and quantitatively evaluate motor function, yet conventional methods often rely on subjective assessments or limited clinical indicators. To overcome these limitations, we constructed a system capable of assessing two representative neural motor control strategies: feedback (FB) and feedforward (FF) control. In order to evaluate the feasibility and effectiveness of the proposed system, experimental trials were conducted with nine healthy participants. The device allows torque loads to be applied in arbitrary directions to wrist movements. Pursuit tasks were used to assess FB control, and step tasks to assess FF control. Participants performed both tasks under three leftward torque-load conditions ($0,0.9$, and 1.5 kgf.cm) simulating a resistive (pseudo-spastic) environment. Response time and positional error were analyzed as outcome measures. The results confirmed that the system could successfully differentiate neural control strategies. FB control showed delayed responses and increased positional errors, while FF control exhibited enhanced internal model formation under mild spasticity. This preliminary verification demonstrates that the proposed system can experimentally evaluate adaptive processes of FB and $F F$ control under different task environments. These findings support its potential as a platform for quantitative assessment and rehabilitation support. Future clinical studies are required to further validate the system and explore its contribution to functional recovery in stroke patients.

Abstract Negative Thermal Expansion (NTE) is a key property for achieving dimensional stability in advanced technologies, yet understanding its underlying mechanisms, particularly the dynamic processes of phase transitions, remains a formidable challenge. In this study, we applied large-scale molecular dynamics simulations using the Crystal Hamilton Graph Network (CHGNet), a pretrained universal machine learning force field (MLFF), to elucidate the atomic-level mechanisms of the NTE phenomenon in the perovskite oxide system Bi 1 − x La x CoO 3 . CHGNet achieved a computational speed approximately 20,000 times faster than first-principles calculations and qualitatively reproduced the chemical trend of the NTE transition temperature systematically decreasing as La substitution increased. Leveraging this computational efficiency, we successfully visualized the dynamic process of the NTE phase transition at the atomic level, which had previously been unobservable. The results revealed a nucleation and propagation mechanism wherein the phase transition initiates heterogeneously in La-rich regions and propagates to Bi-rich regions. These findings demonstrate that universal MLFFs are powerful tools for more quickly elucidating complex phase transition phenomena and open up new possibilities for computational science-driven exploration of new materials.

Formamidinium lead iodide FAPbI3 (FA+ = CH(NH2)2+) with a cubic perovskite structure is among the most-studied or-ganic-inorganic hybrid perovskites. Despite several reports only on thin films of FA-rich lead iodide perovskites, such as FA2PbI4, their structures remain unknown due to challenges in characterizing polycrystalline films. Here, we report two new phases of FA-rich lead iodide perovskite-derived structures, FA2PbI4 and FA3PbI5, in the bulk form. These materials feature two- and one-dimensional octahedral networks of perovskite, respectively, allowing for the bandgap tuning among FA-Pb-I compositions. This study presents a novel approach to controlling the structural dimensionality of perov-skites and their optical properties.

Rhizospheric microbiomes differ between active and dormant plants due to changes in root activity and exudate production, especially under environmental stress. In arid regions, native plants such as Potaninia mongolica Maxim enter dormancy to survive harsh conditions. However, rhizospheric microbial and chemical differences between active and dormant states of plants remain poorly described. This study investigated rhizospheric microbial communities and soil chemical changes in the case of active and dormant P.mongolica plants. Rhizospheric soil samples were collected, and soil texture and chemical variables were analyzed. High-throughput sequencing targeting the 16S rRNA and ITS regions was conducted to profile bacterial and fungal communities, respectively. Results showed that the dominant fungal phyla were Ascomycota and Basidiomycota, while Proteobacteria and Actinobacteria were the dominant bacterial phyla in both plant states. Although bacterial diversity did not differ significantly between active and dormant plants (p > 0.05, Welch’s t-test), fungal diversity was significantly different. Among soil chemical variables, total nitrogen was notably elevated in the rhizosphere of dormant plants (mean = 7.93; SD = 5.91). These findings reveal differences in fungal community structure and nitrogen levels in the rhizosphere between active and dormant plant states. Understanding these interactions contributes to our knowledge of desert plant microbiome dynamics and may inform the use of microbial indicators or amendments to support vegetation restoration in arid environments.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-22696-7.

The global rise in electronic waste highlights the urgent need for green electronics that minimize environmental impact through sustainable material selection and fabrication methods. In this work, multifunctional, biodegradable paper electrodes, designated as MXNx/B‐CP, are prepared via a simple vacuum‐assisted assembly of homogenized MXene (Ti3C2Tx) nanosheets within bamboo‐derived cellulose nanofiber (CNF). These freestanding paper electrodes offer tunable electrical conductivity, mechanical flexibility, and low‐cost, scalable production. To enhance their stability, the electrodes are encapsulated in a breathable, porous Ecoflex layer, which imparts waterproofing while maintaining gas permeability. Strong hydrogen bonding at the MXene‐CNF interface facilitates continuous electron transport and structural integrity, yielding a nonlinear piezoresistive response with a gauge factor increasing from 3.7 to 11.42 at small strain range, alongside a strain‐adaptive Young's modulus ranging from 0.064 to 1.768 MPa. Benefiting from this synergistic design, the electrodes support a wide range of sensing applications, including bending strain detection, surface electromyography, and human‐machine interfaces for exoskeleton control while exhibiting excellent stability, low noise, and long‐term durability under repeated deformation. This innovation not only expands the potential of paper‐based electronics but also offers a scalable pathway toward sustainable, high‐performance solutions for next‐generation wearable and assistive technologies.

This paper gives a comparative study of Sliding Mode Control (SMC) with smoothing factors and Linear State-Feedback Control (LSFC) with internal saturation for satellite attitude control. It is well-known that SMC has a strong robustness against so-called “matched uncertainty”, which is characterized as the uncertainties belonging to the image of the control input matrix of plant system. Thanks to this property, visual-feedback based satellite attitude control using SMC is discussed for Hayabusa2 in a flyby imaging mission near an asteroid “2001 CC21” on the way for its “Extended mission” to the asteroid “1998 KY26.” The feasibility study of capturing the asteroid image of “2001 CC21” using SMC with smoothing factors during the flyby has been conducted, and the usefulness of SMC with smoothing factors compared to PD (Proportional and Derivative) controller has been demonstrated. We show the result of applying LSFC combined with internal saturation to the same mission, i.e. capturing the asteroid image of “2001 CC21” during the flyby, and also point out issues for further control performance improvement.

This paper proposes a hyperspectral (HS) anomaly detection method using a novel background model based on convolutional sparse coding (CSC). One promising HS anomaly detection approach is to simultaneously estimate background and anomaly components (and noise) from an HS image by solving an optimization problem that appropriately models each component. Existing methods are designed to capture the local pixel-wise relationships of a background component, but do not capture non-local similarity structures. As a result, these methods misclassify widely distributed and relatively large objects in a background component as a part of an anomaly component. Therefore, we propose improving the modeling of a background component by using CSC. Specifically, we formulate an optimization problem that incorporates HS total variation and CSC to capture local and non-local similarity structures, respectively. We then develop an algorithm to solve this problem based on alternating minimization. Experiments on HS datasets show that our method achieves better detection performance than existing state-of-the-art methods.