Search Results (3,251)

Broadband, low-frequency sound absorption within a compact device remains a central unsolved problem in noise control engineering, arising from fundamental trade-offs among resonator volume, absorption bandwidth, panel thickness, and frequency tunability that no passive, linear, time-invariant system can simultaneously circumvent. This review establishes a unified mass–spring–damping (MSD) framework applied systematically across the full spectrum of resonance-based absorber technologies. From first principles, we derive the mass–stiffness coupling result (the mass-disappearing result of Shen and Liu): fixing the resonance frequency imposes $K=M{\omega }_{\mathrm{res}}^2$, so acoustic mass and stiffness cannot be adjusted independently; the half-absorption bandwidth ${\Pi }_1=\eta /\left(M{\omega }_{\mathrm{res}}\right)+V{\omega }_{\mathrm{res}}/\left(c_0{S}_{\mathrm{tar}}\right)$ then depends explicitly on the cavity volume V (system stiffness) and on the damping coefficient $\eta$, rather than on mass as an independent lever. This explains why neck extension, space-coiling, and membrane loading—which merely add mass while leaving the cavity stiffness unchanged—fail to broaden the absorption band at fixed volume, and refocuses the design effort on stiffness reduction and damping control. Five non-dimensional performance metrics are introduced that collapse the scattered literature into a single, scale-independent language for rigorous comparison across all absorber families: normalised half-absorption bandwidth ${\Pi }_1$, volume efficiency ${\Pi }_2$, integral absorption criterion ${\Pi }_3$ tied to the Rozanov causality bound, quality factor $Q=1/{\Pi }_1$, and frequency-thickness ratio ${\Pi }_4$. A two-degree-of-freedom acoustic–structural coupling model yields closed-form effective stiffness and damping, revealing how structural loss augments acoustic damping, how modal veering produces split absorption peaks, and how the anti-resonance frequency becomes a designable parameter. A critical distinction is drawn between mathematical negative stiffness (a fitting artefact) and physical negative stiffness via repulsive magnets, bistable elements, or negative-capacitance piezoelectric shunts, which genuinely reduces cavity stiffness, lowers resonance frequency, and widens bandwidth beyond the passive causality bound. The shunt electromechanical diaphragm further demonstrates $\alpha >0.9$ at nine tonal frequencies spanning three octaves without mechanical modification. Finally, embedding MSD equations and ${\Pi }_1$–${\Pi }_4$ bounds as hard physical priors in AI/LLM-assisted design frameworks is identified as the key step toward provably physically consistent absorber synthesis. Full article

In this paper, we introduce and study proximal $\mathfrak{Z}$-condensing operators in strictly convex Banach spaces by combining simulation functions with measures of noncompactness. A Darbo-type best proximity point theorem is established, and several consequences corresponding to nonlinear condensing conditions are obtained. As an application, a system of nonlinear ordinary differential equations is embedded into a non-self operator problem on an enlarged product space; in this formulation, best proximity points are shown to be equivalent to classical solutions of the system. We also prove a Krasnoselskii-type best proximity point theorem for the sum of a simulation-function contraction and a compact operator and apply it to a nonlinear matrix-valued integral equation. Finally, a multiplicative best proximity point theorem is obtained in strictly convex Banach algebras and is used to study a nonlinear integral equation. The results provide a unified operator-theoretic framework for additive and multiplicative equations involving non-self mappings. Full article

In drug-induced liver injury (DILI) prediction field, animal models and in vitro cell models are most commonly used. However, animal models require long experimental timelines and may exhibit species-specific differences compared with humans, whereas conventional two-dimensional (2D) cell culture models lack cell-to-cell and cell-to-extracellular matrix (ECM) interaction. Liver organoid models and liver organ-on-a-chip can better simulate the human liver microenvironment; however, the construction of liver organoids requires a long cycle and high costs, while liver organ-on-a-chip systems demand specialized equipment and professional technicians. Herein, we selected the human C3A cell line, characterized by its low cost and facile culture conditions to establish an in vitro three-dimensional (3D) liver model. Briefly, C3A cells were embedded in Matrigel and cultured for 7 days to allow model maturation. Compared with their 2D-cultured cell model, the established 3D model exhibited elevated mRNA expression levels of drug-metabolizing cytochrome P450 enzymes (CYPs). Moreover, the model displayed robust expression of key hepatic biomarkers, as well as bile duct biomarkers. To evaluate the model’s applicability for DILI prediction, we performed toxicity assessments using a panel of six well-characterized hepatotoxicants and three non-hepatotoxic compounds. Notably, the 3D C3A model achieved a sensitivity of 83.3%, specificity of 100%, and overall accuracy of 88.9%. Furthermore, treatment of this model with chlorpromazine, a well-characterized cholangiotoxic agent, resulted in suppressed expression of the bile duct biomarker cytokeratin 19 (CK19) and bile salt export pump (BSEP), accompanied by impaired bile acid transport capacity. Taken together, this study provided a simple, low-cost, easy to culture, and more readily scalable 3D hepatic model in comparison with conventional 2D primary human hepatocyte (PHHs) models and other advanced 3D liver models. Notably, the model displayed dual hepatic and biliary characteristics, supporting predictions of both DILI and drug-induced bile duct injury. It provided a promising in vitro platform for assessing drug-induced hepatobiliary toxicity, with potential to reduce reliance on animal experiments and accelerate early-stage screening of novel pharmaceutical candidates. Full article

Occluded person re-identification (Re-ID) is a challenging task, as non-target pedestrians or surrounding obstacles often interfere with the visual cues of the target person, making it difficult for models to effectively learn discriminative feature representations. Most existing methods focus on salient body parts via spatial partitioning or external cues; however, they are either limited in capturing diverse semantic information or tend to introduce additional network complexity. To address these issues, we propose a Diversity Feature Learning Network (DFLNet). Specifically, a Scene-Level Occlusion (SLO) strategy is designed to automatically simulate two common occlusion scenarios by modeling the relative spatial relationships between the target person and surrounding occluders in real-world scenes. Subsequently, multiple class tokens are introduced to capture diverse representations of the target identity. A Token Diversity Constraint (TDC) loss is further imposed on these class tokens to encourage the learning of discriminative and diverse feature embeddings. Finally, we design a Diversity Feature Fusion (DFF) module, which facilitates the interaction and integration of dual-branch features by modeling global feature correlations and optimizing inter-feature distribution distances. Extensive experiments on occluded, partial, and holistic Re-ID datasets demonstrate the effectiveness of the proposed DFLNet. Full article

Stock index forecasting is essential for financial market analysis and risk monitoring, yet it remains challenging because index price series are nonlinear, non-stationary, and driven by heterogeneous market factors. Existing methods remain limited in preserving local price patterns, capturing multi-scale local dependencies, and integrating attention-derived structures with long-range state-space representations. To address these limitations, we propose AG-SSM, an attention-guided state-space model for multi-step stock index forecasting. The model first uses variable-wise patch embedding to construct local semantic units, which are then processed by the AG-SSM architecture for temporal representation learning. Its core block integrates dual-path local attention (DPLA), S4D-based state-space feature generation, attention-guided aggregation (AGA), and gated update (GU). Specifically, DPLA combines sliding and dilated local attention to capture contiguous and sparsely distributed dependencies, while AGA reuses local attention maps to refine state-space features, thereby coupling local market structures with long-range sequential dynamics. Experiments on six stock index datasets (SSE, SZSE, SMESE, SP500, DJIA, and NIKKEI225) under one-, five-, ten-, and fifteen-step forecasting horizons show that AG-SSM achieves the lowest horizon-averaged MAPE on all six datasets while maintaining competitive performance across other metrics and individual horizons. Averaged over five independent runs, the horizon-averaged MAPE values are 1.5466%, 2.2173%, 2.2293%, 1.4373%, 1.2995%, and 1.8738% on the six datasets, respectively. Ablation studies, state-space variant comparisons, sensitivity analyses, and statistical tests further support the effectiveness and robustness of the proposed framework. Full article

Aiming at the difficulty of extracting fault features from nonlinear and non-stationary vibration signals of planetary gearboxes, a planetary gearbox fault diagnosis method based on Refined Composite Multiscale Fuzzy Entropy (RCMFE), Parametric-t-distributed Stochastic Neighbor Embedding (P-t-SNE), and Artificial Jellyfish Search Algorithm Optimized Support Vector Machine (JS-SVM) is proposed. Firstly, RCMFE is used to calculate and combine the feature vectors of the original fault signals of the planetary gearbox to construct the original high-dimensional fault feature set. Secondly, Parametric-t-SNE (P-t-SNE) based on a deep feedforward neural network is employed to reduce the dimensionality of the high-dimensional features, thereby extracting sensitive low-dimensional features and achieving out-of-sample mapping. Finally, the low-dimensional features are inputted into the JS-SVM for the identification of fault types. The experimental results of planetary gearbox fault diagnosis show that the proposed method can accurately identify common faults in planetary gearboxes, demonstrating promising application prospects. Full article

Liquid-lubricated spherical spiral-groove hydrodynamic bearings (SSGB) sustain composite loads under high-speed conditions. This study establishes a numerical framework for SSGB lubrication and proposes a physics-guided hybrid surrogate model to accelerate rotor dynamic analysis. The spherical Reynolds equation is solved via the finite-difference method to determine pressure distributions and dynamic coefficients. Results indicate SSGB exhibits quasi-isotropic behavior, with load capacity and stiffness increasing linearly with rotational speed. Direct stiffness rises with groove depth but varies non-monotonically with groove width ratio, whereas damping generally declines as these geometric parameters increase. The developed surrogate model achieves high prediction accuracy ($R^2=0.9969$). Embedding this model into rotor equations reveals typical soft-spring nonlinearities: deeper grooves increase critical speed and resonance amplitude, while excessive groove width ratios trigger amplitude jumps and system instability. This work provides an efficient approach for predicting the dynamic response of complex rotor-bearing systems. Full article

This paper introduces the concept of rural mining, defined as the systematic recovery and reuse of construction materials from abandoned rural building stock within a circular economy framework. Although rural depopulation and its consequences have been extensively studied, the material dimension of abandonment, the substantial quantities of construction materials embedded in deserted rural settlements, has received comparatively little systematic analytical attention. Three interconnected contributions respond to this gap. First, the paper operationalizes the concept of rural mining, consolidating a term that remains terminologically rare and theoretically underdeveloped. Second, it develops the Rural Mining Potential Assessment Framework (RMPAF), an original ten-criterion analytical tool spanning the material, infrastructural, legal, economic, institutional, cultural, and technological dimensions of rural mining potential. Third, it articulates an ethical foundation establishing the non-negotiable boundaries of rural mining practice. The RMPAF is applied through a comparative assessment of three European national contexts, Serbia, Slovenia, and Greece, revealing that rural mining potential is neither uniform nor straightforwardly derivable from the scale of depopulation alone. Across all three contexts, ownership fragmentation, the absence of legal recognition, and the lack of dedicated policy instruments emerge as shared structural barriers. Development of operational protocols for culturally mediated rural mining and legal recognition of the concept are identified as priority directions for future action. Full article

Accurate detection of internal defects in concrete structures is critical to structural safety. This study proposes a three-stage method for segmentation and geometric quantification of defects from 3D ultrasonic point clouds to support non-destructive testing (NDT). Sparse point clouds are densified by grid-based, acoustically constrained trilinear interpolation in the linear amplitude domain to preserve defect-boundary fidelity. An Amplitude-aware PointNet (AAPNet) integrates spatial coordinates with normalized and thresholded echo amplitude for defect segmentation. Connected-component analysis on voxelized defect regions enables estimation of length, width, height, and volume. Laboratory specimens containing voids, cracks, and delaminations were scanned with an Elop Insight 3D ultrasonic system. Segmentation was evaluated by nine-fold leave-one-specimen-out cross-validation (LOSO-CV) on 250 scans from nine independent single-defect specimens, yielding a mean IoU of 82.75 ± 2.66% (precision 92.03 ± 1.58%, recall 89.11 ± 1.73%, F1 90.54 ± 1.59%). Dimensional quantification on embedded defects gave relative volume errors of 2.6–14.9% and axis-aligned deviations within 2.0 cm. Full article

Acoustic emission (AE) monitoring is widely used to characterize coal failure, but specimens with different burst tendencies cannot be distinguished reliably using a single count, energy, b-value, or fractal indicator. This study reanalyzed archived Vallen AE data from uniaxial-compression tests on five strong-burst, five weak-burst, and three specimen-matched non-burst coal specimens. Thirteen VisualAE event tables were verified against independently decoded primary-data files; hit counts were identical and cumulative-energy differences were below 1%. Vallen C and c records were identified as transmitted and received calibration pulses and were excluded consistently from the physical-AE analysis. Calibration records contributed mean energy shares of 11.1%, 4.3%, and 92.5% in the strong-, weak-, and non-burst groups, respectively. After exclusion, the top 1% of retained events contributed 96.6%, 97.9%, and 66.6% of the AE energy; mean b-values at Ht + 5 dB were 0.788, 0.793, and 1.650; and raw-energy multifractal widths were 1.933, 1.729, and 1.219. The correlation dimension depended strongly on embedding, delay, and scaling-range choices and did not show a universal late-sequence decrease. An exploratory six-component AE multi-parameter index yielded group means of 0.659, 0.798, and 0.150. The results support complementary, explicitly parameterized AE sequence descriptors, while the non-burst sample size (n = 3), absence of strict machine-AE time synchronization, and field-scale transfer requirements limit generalization. Full article

Distribution system restoration (DSR) has become increasingly challenging due to frequent topology changes and complex nodal interactions, especially in systems with a high penetration of distributed energy resources (DERs). Existing deep reinforcement learning (DRL) methods remain limited in representing inter-nodal relationships and incorporating domain prior knowledge. Therefore, this paper proposes a graph attention-based coupling-aware reinforcement learning method. From a non-Euclidean spatial perspective, the proposed method uses the distribution power transfer factor (DPTF) to quantify the strength of electrical coupling between nodes. The resulting coupling strengths are embedded as entries of the graph adjacency matrix, allowing the model to capture complex nodal interactions driven by power transfer. An aware graph attention network (AGAT) is further developed, where adjacency matrix with prior knowledge is introduced as a bias term in the attention coefficient calculation. This design guides GAT to generate differentiated node representations enriched with physical information. Based on the extracted graph features, proximal policy optimization (PPO) is employed to determine restoration decisions. Case studies on the IEEE 34-bus system demonstrate that the proposed method outperforms benchmark algorithms in training convergence, restored power, and online decision efficiency, enabling fast and effective distribution system restoration. Full article

Motion image deblurring remains challenging because many existing models rely on complex architectures, leading to high computational cost and parameter redundancy, particularly under non-uniform blur in real-world scenes. To mitigate these limitations, we propose a Lightweight Asymmetric Convolutional Residual Network (LACR) for efficient motion image deblurring. LACR introduces an asymmetric convolutional residual module that combines local spatial embedding with horizontal and vertical asymmetric refinement, enabling direction-sensitive blur modeling with reduced spatial redundancy. A shallow deep feature fusion mechanism is further designed to integrate low-level convolutional cues with deep restoration representations, thereby complementing low-frequency structural information with high-frequency texture details. Experiments on four benchmark datasets show that LACR improves the reconstruction of edges, textures, and structural details while maintaining a lightweight design. Compared with representative lightweight deblurring methods under consistent evaluation settings, LACR achieves an average PSNR gain of 0.38 dB and reduces computational cost by more than 20%. Quantitative and qualitative results demonstrate that LACR achieves a favorable balance between restoration quality and computational efficiency. Full article

Background and objectives: Artificial intelligence (AI) is increasingly embedded in public health workflows, yet adoption among practitioners remains uneven and is shaped by knowledge, legal awareness, and operational barriers. This cross-sectional study characterised determinants of AI adoption among healthcare professionals and examined how legal concern moderates the translation of technical knowledge into practical use, at a single Romanian tertiary academic centre. Methods: We surveyed 93 healthcare professionals (physicians, nurses, public health specialists, residents) at the “Pius Brînzeu” Clinical Emergency County Hospital and “Victor Babeș” University of Medicine and Pharmacy Timișoara. Participants were classified as AI adopters or non-adopters. Likert-derived composite scores (0–100; Cronbach’s α 0.79–0.88) quantified knowledge, trust, legal concern, privacy concern, and workflow confidence. Group comparisons used independent-samples t-tests and χ² tests; associations used Pearson correlation; predictors of adoption and usage intensity were modelled with logistic and multiple linear regression; a two-way ANOVA tested profession-by-training effects. Significance was set at p < 0.05. Benjamini–Hochberg false-discovery-rate correction was applied across the 18 bivariate tests reported in this study, and adjusted q-values are reported alongside unadjusted p-values. Results: Adopters (n = 51) were younger (34.7 ± 7.5 vs. 43.2 ± 8.5 years; p < 0.001) and reported higher knowledge (67.3 vs. 48.6; p < 0.001) and workflow confidence (64.2 vs. 41.9; p < 0.001) but lower legal concern (58.4 vs. 71.2; p < 0.001). Knowledge correlated positively with usage intensity (r = 0.536; p < 0.001), whereas legal concern correlated negatively (r = −0.426; p < 0.001). In multivariable models, younger age (OR = 0.91; p = 0.004), knowledge (OR = 1.06; p = 0.005), and trust (OR = 1.07; p = 0.005) independently predicted adoption. The linear model explained 46.1% of usage variance. Stratified analysis suggested legal concern attenuated the knowledge–usage slope (β: 0.51→0.18); however, the formal knowledge-by-concern interaction term was not statistically significant (p = 0.191), and this pattern is therefore exploratory. Conclusions: In this modest, single-centre sample, AI adoption was independently associated with knowledge and trust, and legal concern was independently and negatively associated with usage intensity; the apparent dampening of the knowledge–usage relationship by legal concern was suggestive but not statistically confirmed. Targeted legal-regulatory literacy and structured training may support practical AI uptake in public health settings, pending confirmation in larger, multicentre studies. Full article

High-chromium white cast irons represent an important group of wear-resistant engineering materials widely used in mining, mineral processing and cement industries due to their excellent abrasion resistance and high hardness. The present study investigates the influence of chemical composition and magnesium modification on the microstructure and hardness of two high-chromium cast irons. Two alloys were examined: a 28 mass% Cr cast iron without magnesium addition and a modified alloy containing 14 mass% Cr and 0.88 mass% Mg. Optical metallographic analysis revealed significant differences in carbide morphology between the investigated alloys. The alloy without magnesium exhibited coarse primary M7C3 chromium carbides embedded in the metallic matrix, whereas the Mg-modified alloy showed a significantly refined eutectic structure with fine carbide distribution. Hardness measurements revealed values of approximately 475 HV for the non-modified alloy and 750 HV for the Mg-modified alloy. The obtained results demonstrate the strong relationship between chemical composition, microstructure and hardness of high-chromium cast irons. Full article

With the increasing diversity of multimedia data types, single-modal steganography is insufficient to meet the demand for the simultaneous covert communication of multiple types of data, and single optical transformation encryption schemes are insufficiently secure against cryptanalytic attacks. To address these challenges, this paper designs and implements a multimodal secret information steganography system based on the optical Gyrator transform. We propose a multimodal steganographic system for the covert transmission of three types of heterogeneous secret data—text, color images, and audio—using a three-level cascaded optical encryption architecture. The system first uniformly encapsulates the multimodal data into a unified bitstream via a Type–Length–Value (TLV) format; it then uses the Ushiki chaotic map to generate a pure phase mask for random phase modulation of the carrier image, followed by spatial-frequency scrambling via a fractional Fourier transform (FrFT, order γ = 1.6). The secret bitstream is embedded into all eight bit planes of the amplitude components in the FrFT domain using binary square representation, achieving an embedding capacity of 2.23 × 10⁶ bits—approximately 8.5 times that of traditional LSB methods; finally, a speckle-noise-like ciphertext is output via a Gyrator transform (angle α = 0.5). Experiments demonstrate that under non-attack conditions, the system achieves lossless recovery with a zero bit-error rate. Under known-plaintext and chosen-plaintext attacks on the Gyrator layer, the PSNR of the recovered images was only 4.89 dB and 4.80 dB, respectively, and the secret information could not be effectively extracted, as the chaotic-FrFT pre-encryption statistically isolates the intermediate image from natural image statistics. This system provides a functionally complete and practically secure solution for multimodal covert communication. Full article