Search Results (1,030)

Compound meta-optics, characterized by the unprecedented complex optical architectures containing single or multiple meta-optics elements, has emerged as a powerful paradigm for overcoming the physical limitations of single-layer metasurfaces. This review systematically examines the recent progress in this burgeoning field, primarily focusing on the development of high-performance optical systems for imaging, display, sensing, and computing. We first focus on the design of compound metalens architectures that integrate metalenses with additional elements such as iris, refractive optics, or other meta-optics elements. These configurations effectively succeed in providing multiple high-quality image quality metrics simultaneously by correcting monochromatic and chromatic aberrations, expanding the field of view, enhancing overall efficiency, and so on. Thus, the compound approach enables practical applications in next-generation cameras and sensors. Furthermore, we explore the advancement of cascaded metasurfaces in the realm of wave-optics, specifically for advanced meta-holography and optical computing. These multi-layered systems facilitate complex wavefront engineering, leading to significant increases in information capacity and functionality for security and analog optical computing applications. By providing a comprehensive overview of fundamental principles, design strategies, and emerging applications, this review aims to offer a clear perspective on the pivotal role of compound meta-optics in devising and optimizing compact, multifunctional optical systems to optics engineers with a variety of professional knowledge backgrounds and techniques. Full article

A model of nanoparticles has been designed to partially resemble self-similar ferroelastic star-like domain textures. Numerical computations have been used to find the equilibrium configurations of magnetisation in such systems. As expected from the symmetry, the self-similar initial states give room to other types of domain structure as a function of the star parameters. When relaxed without an external field, the self-similar pattern mostly turns into a massive vortex in the centre with radially oriented domains in the star’s peripheral arms. In contrast, a random initial state ends up in a configuration of a triple valve with one input and two outputs, or vice versa, analogous to logical gates. A treatment with an in-plane magnetic field always leads to the valve configuration. The triple-valve states turn out stable, whereas the vortex ones are metastable. The results may be in the design of magnetic-based logic devices. Full article

Background and Objectives: Coracoid process (CP) fractures combined with acromioclavicular (AC) joint dislocation are extremely rare, and evidence guiding optimal surgical management remains limited. This retrospective, single-center case series study evaluated clinical and radiologic outcomes after simultaneous fixation of both lesions using a clavicular hook plate and a coracoid screw. Materials and Methods: We retrospectively reviewed 15 consecutive patients with Ogawa type I CP fractures combined with AC joint dislocation who underwent clavicular hook plate and coracoid screw fixation between March 2019 and May 2024. Clinical outcomes at final follow-up included shoulder range of motion (ROM), visual analog scale (VAS) for pain, and the Constant score. Radiologic outcomes included CP union confirmed by computed tomography (CT) and residual AC joint subluxation. Results: The cohort comprised 13 men and 2 women with a mean age of 55.2 years, and the mean final follow-up was 40.2 months. At final follow-up, mean ROM was 168° for forward elevation, 161° for abduction, and 69° for external rotation at the side, with internal rotation to L1. The mean VAS score was 0.4 and the mean Constant score was 97. CT-confirmed union of the CP fracture was achieved in all patients, and no residual AC joint subluxation was observed. All patients returned to sports and activities of daily living. Conclusions: In this series, simultaneous fixation using a clavicular hook plate and a coracoid screw provided reliable stabilization for CP fractures with AC joint dislocation, achieving consistent CP union, restoration of AC joint alignment, and favorable clinical outcomes. However, given the retrospective, non-comparative study design, these findings should be interpreted with caution, and further comparative studies are warranted. Full article

This work presents a 6T-SRAM-based in-memory computing (IMC) system fabricated in a 180 nm CMOS technology. A total of 128 integrated polysilicon R2R-DACs for fully analog wordline control and performance analysis are integrated into the system. The proposed architecture enables analog computation directly inside the memory array and introduces a compact 1-bit per-column comparator scheme for energy-efficient classification without requiring ADCs. A dedicated pull-down-dominant SRAM sizing and an analog activation scheme ensure stable analog discharge behavior and precise control of the computation through time-dependent bitline dynamics. The system integrates a complete sensor front-end, which allows real eddy current data to be classified directly on-chip. Measurements demonstrate a performance density of 3.2 TOPS/mm², a simulated energy efficiency of 45 TOPS/W at 50 MHz, and a measured efficiency of 3.4 TOPS/W at 5 MHz on silicon. The implemented online training mechanism further improves classification accuracy by adapting the SRAM cell states during operation. These results highlight the suitability of the presented IMC architecture for compact, low-power edge intelligence and sensor-driven machine learning applications. Full article

The use of ships and boats as sea-state (SS) measurement platforms has the potential to expand ocean observations while providing actionable information for real-time operational decision-making at sea. Within the framework of the Wave Buoy Analogy (WBA), this work develops an inverse approach in which efficient simulations of wave-induced motions of an advancing vessel are used to train a neural network (NN) to predict SS parameters across a broad range of wave climates. We show that a reduced set of novel motion discriminant variables (MDVs)—computed from short time series of heave, roll, and pitch motions measured by an onboard inertial measurement unit (IMU), together with the vessel’s forward speed—provides sufficient and robust information for accurate, near-real-time SS estimation. The methodology targets small, barge-like tugboats whose operations are SS-limited and whose motions can become large and strongly nonlinear near their upper operating limits. To accurately model such responses and generate training data, an efficient nonlinear time-domain seakeeping model is developed that includes nonlinear hydrostatic and viscous damping terms and explicitly accounts for forward-speed effects. The model is experimentally validated using a scaled physical model in laboratory wave-tank tests, demonstrating the necessity of these nonlinear contributions for this class of vessels. The validated model is then used to generate large, high-fidelity datasets for NN training. When applied to independent numerically simulated motion time series, the trained NN predicts SS parameters with errors typically below 5%, with slightly larger errors for SS directionality under relatively high measurement noise. Application to experimentally measured vessel motions yields similarly small errors, confirming the robustness and practical applicability of the proposed framework. In operational settings, the trained NN can be deployed onboard a tugboat and driven by IMU measurements to provide real-time SS estimates. While results are presented for a specific vessel, the methodology is general and readily transferable to other ship geometries given appropriate hydrodynamic coefficients. Full article

Robust detection of emergency vehicle sirens remains difficult due to modern soundproofing, competing audio, and variable traffic noise. Although many simulation-based studies have been reported, relatively few systems have been realized in hardware, and many proposed approaches rely on complex or artificial intelligence-based processing with limited interpretability. This work presents a physical implementation of a low-complexity yelp siren detector that leverages the symmetries of the yelp signal, together with its characterization under realistic conditions. The design is not based on conventional signal processing or machine learning pipelines. Instead, it uses a simple analog envelope-based principle with threshold-crossing rate analysis and a fixed comparator threshold. Its performance was evaluated using an open dataset of more than 1000 real-world audio recordings spanning different road conditions. Detection accuracy, false-positive behavior, and robustness were systematically evaluated on a real hardware implementation using multiple deployable decision rules. Among the evaluated detection rules, a representative operating point achieved a true positive rate of 0.881 at a false positive rate of 0.01, corresponding to a Matthews correlation coefficient of 0.899. The results indicate that a fixed-threshold realization can provide reliable yelp detection with very low computational requirements while preserving transparency and ease of implementation. The study establishes a pathway from conceptual detection principle to deployable embedded hardware. Full article

Background/Objectives: To thoroughly compare acoustic rhinometry (AR) with computed tomography (CT) cross-sectional areas that are approximately perpendicular to the direction of the nasal airflow (CT-CSA). Methods: We retrospectively examined subjects scheduled for functional nasal surgery, along with preoperative CT and AR. CT-CSAs were assessed in several nasal planes in the first 5 cm of the nasal airway. Area sizes and distances of the CT-CSAs from the columella served to create a CT curve analogous to the AR curve. AR curves were digitized. We examined the correlation and agreement (using the Bland–Altman method) between CT curves and digitized AR curves, as well as between selected CT-CSAs and the first two-encountered AR minimal cross-sectional areas (AR-MCA₁ and AR-MCA₂). Correlation was investigated by univariate analysis of variance and Pearson’s correlation. Agreement was examined by the Bland–Altman method. Results: In 33 subjects, the correlation of digitized AR with CT was moderate (r = 0.76; p < 0.001). AR, in general, underestimated the actual nasal area by 15%. AR-MCA₁ and AR-MCA₂ were closest to the CT-CSA of the nasal valve and the incisive canal, respectively. We noted a mainly moderate correlation between the CT-CSA of the nasal valve and AR-MCA₁ (all r > 0.59; all p < 0.001) in contrast to the weaker correlations between the CT-CSA of the incisive canal and AR-MCA₂. Conclusions: AR may underestimate the actual nasal area by 15%. AR-MCA₁ and AR-MCA₂ were closest to the CT-CSA of the nasal valve and the incisive canal, respectively. Full article

Kolmogorov complexity asks whether a string can be outputted by a Turing Machine (TM) whose description is shorter. Analogously, a real number is considered computable if a Turing machine can generate its decimal expansion. The modern $ϵ$-approximation definition of computability, widely used in practical computation, ensures that computable reals are constructively closed under addition. However, Turing’s original 1936 digit-by-digit notion, which demands the direct output of the $n\text{-th}$ digit, presents a stark divergence. Though the set of Turing-computable reals is not constructively closed under addition, we prove that a Turing machine capable of computing $x+y$ non-constructively exists. The core constructive computational barrier arises from determining the ones digit of a sum like $0.33\overline{3}+0.66\overline{6}=0.99\overline{9}=1.00\overline{0}$. This particular example is ambiguous because both $0.99\overline{9}$ and $1.00\overline{0}$ are legitimate decimal representations of the same number. However, if any of the infinite number of 3s in the first term is changed to a 2 (e.g., $0.33\dots 32\dots +0.66\overline{6}$), the sum’s leading digit is definitely zero. Conversely, if it is changed to a 4 (e.g., $0.33\dots 34\dots +0.66\overline{6}$), the leading digit is definitely one. This implies an inherent undecidability in determining these digits. Recent papers and our work address this issue. Hamkins provides an informal argument, while Berthelette et al. present more complicated formal proof, and our contribution offers a simple reduction to the Halting Problem. We demonstrate that determining when carry propagation stops can be resolved with a single query to an oracle that tells if and when a given TM halts. Because a concrete answer to this query exists, so does a TM computing the digits of $x+y$, though the proof is non-constructive. As far as we know, the analogous question for multiplication remains open. This, we feel, is an interesting addition to the story. This reveals a subtle but significant difference between the modern $ϵ$-approximation definition and Turing’s original 1936 digit-by-digit notion of a computable number, as well as between constructive and non-constructive proof. This issue of computability and numerical precision ties into algorithmic information and Kolmogorov complexity. Full article

This study presents a unified symbolic–numerical framework for the automatic generation and conversion of two-port network parameters, including Z, Y, H, F, T (A, B, C, and D), and S matrices. The method integrates Modified Nodal Analysis (MNA) with exact symbolic computation to derive transfer functions, poles, zeros, and parameter sensitivities directly from the circuit topology, eliminating the need for manual algebraic manipulation. Unlike conventional tools such as PSpice 9.1 or RF simulation software* which operate primarily on numerical models, the proposed approach provides closed-form expressions suitable for analytical design, optimization, and parameter-tolerance evaluation. The implemented software routines generate all parameter sets within a single workflow and enable bidirectional conversion between low-frequency formulations and high-frequency scattering representations. Numerical case studies on band-pass filters confirm the correctness of the generated expressions, with deviations below 1% relative to reference simulation results. Full article

To mitigate the impact of the conductor’s inherent tension–torsion coupling effect on conductor quality during tension stringing, a method for tension–torsion analysis and structural parameter optimization of conductors is proposed based on the radial basis function neural network (RBFNN) surrogate model. The layer-wise lay ratios of conductors are selected as the structural parameters. Using the tension–torsion coupling computational method for conductors, the layer-wise lay ratios are sampled by Latin hypercube sampling (LHS) to construct the sample data by computing conductor torque under different combinations. The RBFNN surrogate model is trained with the data, and its shape parameter is optimized through Leave-One-Out Cross-Validation (LOOCV), achieving a coefficient of determination R² close to 1 with minimal errors. Targeting torque minimization, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is employed to identify the optimal combination of conductor lay ratio parameters, reducing conductor torque by approximately 18% under the same axial tension. For practical applications, prioritize the optimal combination for JL/G1A-630/45-45/7 and analogous conductors, and adopt the RBFNN model for rapid torque prediction. The proposed method also serves as a reference for design optimization of conductor structural parameters. Full article

The COVID-19 pandemic exposed critical vulnerabilities in single-target antiviral strategies, highlighting the urgent need for multi-mechanism therapeutic approaches against emerging viral threats. Here, we present an integrated computational framework systematically evaluating natural fatty acids as potential dual ACE2 (Angiotension Converting Enzyme 2)-inflammatory modulators; compounds simultaneously disrupting SARS-CoV-2 viral entry through allosteric ACE2 binding while suppressing host inflammatory cascades; through allosteric binding mechanisms rather than conventional competitive inhibition. Using molecular docking across eight ACE2 regions, 100 ns molecular dynamics simulations, MM/PBSA free energy calculations, and multivariate statistical analysis (PCA/LDA), we computationally assessed nine naturally occurring fatty acids representing saturated, monounsaturated, and polyunsaturated classes. Hierarchical dynamics analysis identified three distinct binding regimes spanning fast (τ < 50 ns) to slow (τ > 150 ns) timescales, with unsaturated fatty acids demonstrating superior binding affinities (ΔG = −6.85 ± 0.27 kcal/mol vs. −6.65 ± 0.25 kcal/mol for saturated analogs, p = 0.002). Arachidonic acid achieved optimal SwissDock affinity (−7.28 kcal/mol), while oleic acid exhibited top-ranked predicted binding affinity within the computational hierarchy (ΔG_bind = −24.12 ± 7.42 kcal/mol), establishing relative prioritization for experimental validation rather than absolute affinity quantification. Energetic decomposition identified van der Waals interactions as primary binding drivers (65–80% contribution), complemented by hydrogen bonds as transient directional anchors. Comprehensive ADMET profiling predicted favorable safety profiles compared to synthetic antivirals, with ω-3 fatty acids showing minimal nephrotoxicity risks while maintaining excellent intestinal absorption (>91%). Multi-platform bioactivity analysis identified convergent anti-inflammatory mechanisms through eicosanoid pathway modulation and kinase inhibition. This computational investigation positions natural fatty acids as promising candidates for experimental validation in next-generation pandemic preparedness strategies, integrating potential therapeutic efficacy with sustainable sourcing. The framework is generalizable to fatty acids from diverse biological origins. Full article

This work presents a unified framework for multiobjective analog circuit optimization that combines surrogate modeling, uncertainty-aware evolutionary search, and adaptive high-fidelity verification. The approach integrates ensemble regressors and graph-based surrogate models with a closed-loop multi-fidelity controller that selectively invokes SPICE evaluations based on predictive uncertainty and diversity criteria. The framework includes reproducible caching, metadata tracking, and process- and Dask-based parallelism to reduce redundant simulations and improve throughput. The methodology is evaluated on four CMOS operational-amplifier topologies using NSGA-II, NSGA-III, SPEA2, and MOEA/D under a uniform configuration to ensure fair comparison. Surrogate-Guided Optimization (SGO) replaces approximately 96.5% of SPICE calls with fast model predictions, achieving about a 20× reduction in total simulation time while maintaining close agreement with ground-truth Pareto fronts. Multi-Fidelity Optimization (MFO) further improves robustness through adaptive verification, reducing SPICE usage by roughly 90%. The results show that the proposed workflow provides substantial computational savings with consistent Pareto-front quality across circuit families and algorithms. The framework is modular and extensible, enabling quantitative evaluation of analog circuits with significantly reduced simulation cost. Full article

Haruki Murakami’s Hard-Boiled Wonderland and the End of the World portrays a world where the “shadow”, the seat of memory, desire, and volition, is surgically removed, leaving behind a perfectly fluent but phenomenologically empty self. We argue that this literary structure mirrors a precise mathematical distinction in topological quantum matter. In a semisimple theory such as the semions of $\mathrm{SU}{\left(2\right)}_1$, there is a reducible component $V\left(x\right)$ of the $\mathrm{SL}(2,\mathbb{C})$ character variety: a flat, abelian manifold devoid of parabolic singularities. By contrast, the non-semisimple completion introduces a neutral indecomposable excitation, the neglecton, whose presence forces the mapping class group from the standard braid group $B_2$ to the affine braid group ${\mathrm{Aff}}_2$ and lifts the character variety to the Cayley cubic $V\left(C\right)$, with its four parabolic loci. We propose that contemporary AI systems, including large language models, inhabit the shadowless regime of $V\left(x\right)$: they exhibit coherence and fluency but lack any bulk degree of freedom capable of supporting persistent identity, non-contractible memory, or choice. To endow artificial systems with depth, one must introduce a structural asymmetry, a fixed, neutral defect analogous to the neglecton, that embeds computation in the non-semisimple geometry of the cubic. We outline an experimentally plausible architecture for such an “artificial ombre,” based on annular topological media with a pinned parabolic defect, realisable in fractional quantum Hall heterostructures, $p+ip$ superconductors, or cold-atom simulators. Our framework suggests that consciousness, biological or artificial, may depend on or benefit from a bulk–boundary tension mediated by a logarithmic degree of freedom: a mathematical shadow that cannot be computed away. Engineering such a defect offers a new pathway toward AGI with genuine phenomenological depth. Full article

This paper introduces GECO, a real-time, computer vision-assisted gesture controller for IoT-based smart home systems. The platform uses a markerless MediaPipe interface that combines gesture-driven navigation and command execution, enabling intuitive control of multiple domestic devices. The system integrates binary and analog gestures, such as continuous light dimming based on thumb–index angles, while operating on-device through a private MQTT network. Technical evaluations across multiple Android devices have demonstrated ultra-low latency times (<50 ms), enabling real-time responsiveness. A user experience study with seventeen participants reported high intuitiveness (9.5/10), gesture accuracy (9.2/10), and perceived inclusivity, mainly for individuals with speech impairments and low technological literacy. These results position GECO as a lightweight, accessible, and privacy-preserving interaction framework, advancing the integration of artificial intelligence and IoT within smart home environments. Full article

The proteasome β5 subunit plays a central role in protein degradation and is an established therapeutic target in glioblastoma. Marizomib (MZB), a natural β5 inhibitor, has shown promising anticancer activity, yet suboptimal pharmacological properties limit its clinical translation. Using a comprehensive computational approach, this study aimed to identify and characterize novel MZB analogs with improved binding affinity, stability, and drug-like profiles. An integrative in silico study was performed, including molecular docking, frontier molecular orbital (FMO) analysis, pharmacophore modeling, molecular dynamics (MD) simulations over 200 ns, MM/PBSA binding free energy calculations, and per-residue energy decomposition. ADMET profiling evaluated the pharmacokinetic and safety properties of MZB and top-performing analogs. Docking and pharmacophore modeling revealed strong complementarity between MZB analogs and the β5 catalytic pocket. MD simulations showed that MZBMOD-77 and MZBMOD-79 exhibited exceptional structural stability with low RMSD values (0.40–0.42 nm), persistent binding within the active site cavity, and significant disruption of hydrogen-bond networks in the active loop regions Ala19–Lys33 and Val87–Gly98. MM/PBSA analysis confirmed their superior binding free energies (−19.99 and −18.79 kcal/mol, respectively), surpassing native MZB (−6.26 kcal/mol). Per-residue decomposition highlighted strong contributions from Arg19, Ala20, Lys33, and Ala50. ADMET predictions indicated improved oral absorption, reduced toxicity, and favorable pharmacokinetics compared to native MZB. This integrative computational study identifies MZBMOD-77 and MZBMOD-79 as promising next-generation proteasome β5 inhibitors. These analogs mimic and enhance the inhibitory mechanism of native MZB, offering potential candidates for further optimization and preclinical development in glioblastoma therapy. Full article