Search Results (78)

Since its publication in the 1960s, the Kalman Filter (KF) has been a powerful tool in optimal state estimation. However, the KF and most of its variants have mainly focused on the state estimation of smooth systems. In this work, we propose a new algorithm called the Discrete Unilateral Constrained Extended Kalman Filter (DUCEKF) that expands the capabilities of the Extended Kalman Filter (EKF) to a class of hybrid mechanical systems known as systems with unilateral constraints. Such systems are non-smooth in position and discontinuous in velocity. Lyapunov stability theory is invoked to establish sufficient conditions for the estimation error stability of the proposed algorithm. A comparison of the proposed algorithm with the EKF is conducted in simulation through a case study to demonstrate the superiority of the DUCEKF for the state estimation tasks in this class of systems. Simulations and an experiment were developed in this case study to validate the performance of the proposed algorithm. The experiment was conducted using electronic hardware that consists of an Embedded System (ES) called “Mikromedia for dsPIC33EP” and an external DAC-12 Click board, which includes a Digital-to-Analog Converter (DAC) from Texas Instruments. Full article

In this work, a sub-modeling technique is proposed for the analysis of large-scale masonry structures. The approach couples an anisotropic macroscale formulation, derived by incorporating the notion of a fabric tensor for an orthotropic material, with mesoscale analysis. The latter employs distinct inelastic constitutive relations assigned to the brick material and brick-mortar interfaces, which enable the tracing of localized damage propagation. The mechanical properties at the macro-level are identified from the ‘virtual’ set of data generated through mesoscale analysis, ensuring consistency between the two approaches in representing the masonry material across different scales. In the numerical analysis, the macroscale approach is first applied over the entire domain to interpolate the kinematic boundary conditions in a local region of interest, which is then re-analyzed based on the mesoscale framework. The developed strategy is illustrated by simulating the shear response of a large-scale unreinforced masonry wall with multiple window openings. Full article

Facing severe depopulation and aging, rural Japanese communities—particularly marginal settlements (genkai shūraku)—increasingly require revitalization strategies that integrate local culture and elder well-being. This study examines the Ōsawa Engawa Café, a community-led initiative in a mountainous tea-growing village, as a site of ikigai-zukuri—the active creation of life purpose among elderly residents. With the use of a mixed-methods approach, including spatial analysis, household surveys, and interviews, Chi-square Automatic Interaction Detection (CHAID) decision tree analysis was applied to identify factors shaping distinct household café operational states: Operating, Discontinued, and Never Operated. Qualitative findings reveal that support from local leaders, experts, and the government enabled the Ōsawa Engawa café’s launch. Broad household participation, often guided by elderly women, sustained the initiative by sharing local culture—such as engawa (verandas), Zairai tea (native variety), and omotenashi (hospitality)—thereby nurturing residents’ ikigai through daily engagement. Complementing these insights, the CHAID analysis revealed a hierarchy of influential factors: high-frequency support from out-migrated family members was the strongest predictor of continued operation; in the absence of such support, co-resident family cooperation proved essential; where both were lacking, agricultural engagement distinguished households that discontinued from those that never operated. Practically, the Ōsawa model offers a replicable, bottom-up strategy that activates the Rural Cultural Landscape (landscapes shaped by traditional rural life and culture, RCL) through community engagement grounded in cultural practices and elderly ikigai-zukuri, contributing to sustainable rural livelihoods. Theoretically, this study reframes ikigai-zukuri as a key socio-cultural pillar of community resilience in aging rural areas. Fostering such culturally embedded, purpose-driven initiatives is essential for building vibrant, adaptive rural communities in the face of demographic decline. However, the study acknowledges that the Ōsawa model’s success is rooted in its specific socio-cultural context, and its replication in other cultural settings may be limited without contextual adaptation. Full article

This work investigates the applicability of Bayesian inverse analysis for identifying parameters from non-standard aluminum specimens with notches that induce stress concentrations. Unlike conventional standardized specimens, the notched samples used in this work are typically unsuitable for direct parameter extraction due to geometric irregularities and size effects. The experimental procedure involved tensile tests conducted using a universal testing machine, with deformation data collected via LVDT sensors and optical measurements with digital image correlation. The numerical simulations were performed using a quadrilateral finite element model with embedded strong discontinuities to capture the complete material response, including elastic, plastic, and fracture behavior. The proposed identification procedure successfully provided reliable posterior parameter estimates on aluminum rectangular and single-notch specimens. Furthermore, the identified parameters were validated on a double-notch specimen made of the same material. The results highlight the importance of parameter interpretation and show that the Bayesian framework can reliably identify key material and model-dependent parameters from non-standard specimens while accounting for uncertainty in both measurements and model formulation. Full article

Structural microheterogeneity arising from the cooperative nature of hydrogen bonding is a critical yet often overlooked factor in the mechanistic understanding of physicochemical and biological processes occurring in aqueous environments. MD simulations using a potential that accounts for molecular flexibility and directional interactions revealed inhomogeneity arising from patches of continuously connected, four-bonded molecules embedded within a less ordered, space-filling hydrogen-bond network. The size of these patches follows a statistical distribution that is strongly temperature-dependent. With increasing temperature, the average size of the patches decreases, whereas the contribution of molecules forming the inter-patch zones becomes more pronounced. The nature of microheterogeneity is evidenced by temperature-dependent changes in the asymmetry of calculated power spectra as well as in the measured IR absorption within the stretching, bending, and combination band regions. A novel method for band analysis incorporates the calculation of skewness and a mirroring procedure for more accurate determination of FWHM of asymmetric bands. Discontinuities in the temperature dependence of spectral parameters observed within the 5–80 °C range correspond to the thermodynamic anomalies of liquid water. We show that structural microheterogeneity persists near 100 °C, suggesting that aqueous processes are better described by statistical distributions than by uniform models. Molecular simulations and IR spectroscopy offer key insights into these distributions. Full article

Spontaneous counter-current imbibition is a crucial recovery mechanism in water-wet fractured reservoirs, especially in unconventional formations like tight and shale reservoirs. The geometric characteristics of microscale fractures require further clarification regarding their impact on imbibition. In this paper, the numerical simulation method is used to study the influence of fracture aperture, length, density, and relative position between fracture and imbibition open face on the counter-current imbibition process of a matrix block. For fractures perpendicular to the imbibition surface and in contact with water, the embedded discrete fracture model is utilized to simulate the impact of varying fracture apertures on counter-current imbibition. For fractures parallel to the imbibition surface, considering the impact of fracture on the capillary discontinuity of the matrix, the effects of varying fracture lengths and densities on counter-current imbibition are simulated. The results show that when fractures are perpendicular to the imbibition surface and in contact with water, the imbibition rate can be increased, and as the fracture aperture decreases, the imbibition rate first increases and then decreases. On the other hand, fractures parallel to the imbibition surface inhibit the imbibition process, with the imbibition rate decreasing as fracture length or density increases. This paper proposes an empirical shape factor considering the geometric characteristics of fractures, which can effectively characterize the influence of microfractures on matrix block imbibition, thus improving the dual-medium numerical simulation model. Full article

Seismic data are easily contaminated by random noise, impairing subsequent geological interpretation tasks. Existing denoising methods like low-rank approximation (LRA) and deep learning (DL) show promising denoising capabilities but still have limitations; for instance, LRA performance is parameter-sensitive, and DL networks lack interpretation. As an alternative, this paper introduces the low-rank tensor network (LRTNet), an innovative approach that integrates low-rank tensor approximation (LRTA) with DL. Our method involves constructing a noise attenuation model that leverages LRTA, total variation (TV) regularization, and weighted tensor nuclear norm minimization (WTNNM). By applying the alternating direction method of multipliers (ADMM), we solve the model and transform the iterative schemes into a DL framework, where each iteration corresponds to a network layer. The key learnable parameters, including weights and thresholds, are optimized using labeled data to enhance performance. Quantitative evaluations on synthetic data reveal that LRTNet achieves an average signal-to-noise ratio (SNR) of 9.37 dB on the validation set, outperforming Pyseistr (6.46 dB) and TNN-SSTV (6.10 dB) by 45.0% and 53.6%, respectively. Furthermore, tests on real field datasets demonstrate consistent enhancements in noise suppression while preserving critical stratigraphic structures and fault discontinuities. The embedded LRTA mechanism not only improves network interpretability, but also reduces parameter sensitivity compared to conventional LRA methods. These findings position LRTNet as a robust, physics-aware solution for seismic data restoration. Full article

So far, discontinuous Named Entity Recognition (NER) has become a focus of attention for many researchers and has spawned numerous methodologies. Among them, the grid tagging model has particularly stood out due to its flexibility and adaptability in discontinuous NER tasks. The core idea of the model is to convert text into a two-dimensional grid format and capture entity segments as well as their relationships within the text by tagging elements in the grid. However, while grid tagging models typically emphasize learning tag embeddings to represent entity segment information and relationships between word pairs within the same contextual space, they often neglect the extraction of global features. We believe that designing two distinct feature generators to capture two different types of information, namely entity segment tag features and relationship tag features, during the learning process, is beneficial for improving the model’s performance. In this work, we propose novel tag feature generators, specifically designing two different generators–a relationship tag feature generator and an entity segment tag feature generator. These generators are designed to assist each other in more effectively generating feature information during the representation learning process. Experimental results demonstrate that our model achieves significant performance improvements on multiple discontinuous NER datasets, exhibiting higher accuracy and efficiency compared to other state-of-the-art (SOTA) methods. Furthermore, we conducted detailed analyses and discussions on the different components of the model, verifying the effectiveness of each component and its contribution to the overall model performance. Full article

Background/Objectives: Medical therapies that apply biodegradable materials, such as polydioxanone, are widely used to treat various disorders. Thread-embedding acupuncture (TEA) is a unique form of acupuncture that exerts long-lasting therapeutic effects by inserting absorbable threads at specific acupuncture points, and is widely used to treat various diseases. However, there is currently a lack of research regarding the safety of TEA. This prospective observational trial aims to evaluate the safety of TEA by collecting and analyzing data related to adverse events in patients receiving TEA in actual practice. Methods: A total of 350 eligible participants who undergo TEA at one of three university-affiliated hospitals and two traditional Korean medicine clinics will be systemically observed for post-treatment adverse reactions. The patients will be monitored at three time points: 1 week, 1 month, and 3 months post-treatment. Safety evaluations will assess the incidence of adverse events and treatment discontinuation rates during the 3-month post-treatment period. Conclusions: This study will evaluate the safety of TEA and provide information for decision-making in clinical practice as well as basic data for future large-scale research. Full article

To guarantee the reliability and integrity of audio, data have been focused on as an essential topic as the fast development of generative AI. Significant progress in machine learning and speech synthesis has increased the potential for audio tampering. In this paper, we focus on the digital watermarking method as a promising method to safeguard the authenticity of audio evidence. Due to the integrity of the original data with probative importance, the algorithm requires reversibility, imperceptibility, and reliability. To meet the requirements, we propose a reversible digital watermarking approach that embeds feature data concentrating in high-frequency intDCT coefficients after transforming data from the time domain into the frequency domain. We explored the appropriate hiding locations against spectrum-based attacks with novel proposed methodologies for spectral expansion for embedding. However, the drawback of fixed expansion is that the stego signal is prone to being detected by a spectral analysis. Therefore, this paper proposes two other new expansion methodologies that embed the data into variable locations—random expansion and adaptive expansion with distortion estimation for embedding—which effectively conceal the watermark’s presence while maintaining high perceptual quality with an average segSNR better than 21.363 dB and average MOS value better than 4.085. Our experimental results demonstrate the efficacy of our proposed method in both sound quality preservation and log-likelihood value, indicating the absolute discontinuity of the spectrogram after embedding is proposed to evaluate the effectiveness of the proposed reversible spectral expansion watermarking algorithm. The result of EER indicated that the adaptive hiding performed best against attacks by spectral analysis. Full article

In this work, the fractal continuum Maxwell law for the creep phenomenon is introduced. By mapping standard integer space-time into fractal continuum space-time using the well-known Balankin’s approach to variable-order fractal calculus, the fractal version of Maxwell model is developed. This methodology employs local fractional differential operators on discontinuous properties of fractal sets embedded in the integer space-time so that they behave as analytic envelopes of non-analytic functions in the fractal continuum space-time. Then, creep strain $\epsilon \left(t\right)$, creep modulus $J\left(t\right)$, and relaxation compliance $G\left(t\right)$ in materials with fractal linear viscoelasticity can be described by their generalized forms, ${\epsilon }^{\beta }\left(t\right),J^{\beta }\left(t\right) \mathrm{and} G^{\beta }\left(t\right)$, where $\beta =dimS/dimH$ represents the time fractal dimension, and it implies the variable-order of fractality of the self-similar domain under study, which are $dimS$ and $dimH$ for their spectral and Hausdorff dimensions, respectively. The creep behavior depends on beta, which is characterized by its geometry and fractal topology: as beta approaches one, the fractal creep behavior approaches its standard behavior. To illustrate some physical implications of the suggested fractal Maxwell creep model, graphs that showcase the specific details and outcomes of our results are included in this study. Full article

Unlike traditional base materials such as concrete or masonry, there are no guidelines for rock as a base material for post-installed anchors. The varying rock properties (e.g., rock type, discontinuities) and numerous installation parameters (e.g., embedment depth, anchor diameter) leave engineers with limited information on design resistances, leading to an uncertain basis for anchor applications in rock. To identify the key parameters that determine rock as a base material, an evaluation of rock characteristics was conducted, combined with in situ pull-out tests in different key geologies (granite, limestone, mica schist, dolomite, granulite) and discrete element modeling, which has been found to be suitable for investigating the load-bearing behavior of post-installed anchors in rock. Discontinuities were identified as the main factor influencing the load-bearing capacity of post-installed anchors in rock mass. Based on the in situ investigations, assessment methods for rock as a base material were proposed, along with corresponding resistance partial safety factors for design of 2.5, 2.0, and 1.7 for high, medium, and low levels of uncertainty regarding possible inhomogeneities. A limit value R ≥ 36, associated with rebound hammer assessments, was defined for the low degree of uncertainty, showing limitations for schistose rock. This is concluded by a design approach for determining design resistances of shallow fasteners in rock mass. Full article

It is known that swift heavy ion (SHI) irradiation induces the shape elongation of metal nanoparticles (NPs) embedded in transparent insulators, which results in anisotropic optical absorption. Here, we report another type of the optical anisotropy induced in CaF₂ crystals without including intentionally embedded metal NPs. The CaF₂ samples were irradiated with 200 MeV Xe¹⁴⁺ ions with an incident angle of 45° from the surface normal. With the increasing fluence, an absorption band at ~550 nm, which is ascribed to Ca aggregates, increases both the intensity and the anisotropy. XTEM observation clarified the formation of the continuous line structures and the discontinuous NP chains parallel to the SHI beam. Numerical simulations of the optical absorption spectra suggested the NP chains but not the continuous line structures as the origin of the anisotropy. The optical anisotropy in CaF₂ irradiated with SHIs is different from the shape elongation of NPs. Full article

Grit particles remaining on the substrate surface after grit blasting are generally considered to impair the thermal performance of thermal barrier coatings (TBCs). However, the specific mechanisms by which these particles degrade the multilayer structure of TBCs during thermal cycling have not yet been fully elucidated. In this study, the superalloy substrate was grit-blasted using various processing parameters, followed by the deposition of thermal barrier coatings (TBCs) consisting of a metallic bond coat (BC) and a ceramic top coat (TC). After thermal shock tests, local thinning or discontinuities in the thermally grown oxide (TGO) layer were observed in TBCs where large grit particles were embedded at the BC/substrate interface. Moreover, cracks originated at the concave positions of the TGO layer and propagated vertically towards BC; these cracks may be associated with additional stress imposed by the foreign grit particles during thermal cycling. At the BC/substrate interface, crack origins were observed in the vicinity of large grit particles (~50 μm). Full article

In modern urban environments, visual sensors are crucial for enhancing the functionality of navigation systems, particularly for devices designed for visually impaired individuals. The high-resolution images captured by these sensors form the basis for understanding the surrounding environment and identifying key landmarks. However, the core challenge in the semantic segmentation of blind roads lies in the effective extraction of global context and edge features. Most existing methods rely on Convolutional Neural Networks (CNNs), whose inherent inductive biases limit their ability to capture global context and accurately detect discontinuous features such as gaps and obstructions in blind roads. To overcome these limitations, we introduce Dual-Branch Swin-CNN Net(DSC-Net), a new method that integrates the global modeling capabilities of the Swin-Transformer with the CNN-based U-Net architecture. This combination allows for the hierarchical extraction of both fine and coarse features. First, the Spatial Blending Module (SBM) mitigates blurring of target information caused by object occlusion to enhance accuracy. The hybrid attention module (HAM), embedded within the Inverted Residual Module (IRM), sharpens the detection of blind road boundaries, while the IRM improves the speed of network processing. In tests on a specialized dataset designed for blind road semantic segmentation in real-world scenarios, our method achieved an impressive mIoU of 97.72%. Additionally, it demonstrated exceptional performance on other public datasets. Full article