Search Results (82)

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

In this paper, a coupled negative bias temperature instability (NBTI)/hot carrier degradation (HCD) failure model is proposed on the 2-D voltage plane for aging simulation of SRAM circuits. According to the physical mechanism of failure, based on the reaction–diffusion and hot carrier energy-driven theory, revised degradation models of threshold voltage shift (∆Vth) for the NBTI and HCD are established, respectively, with explicit expressions for gate voltage (V_G)/drain voltage (V_D). An NBTI/HCD coupling model is built on the 2-D {V_G, V_D} voltage plane with a weighting factor in the form of V_G and V_D power law. The model also takes into account the AC effect and long-term saturation behavior. The predicted ∆Vth under various stress conditions shows an average relative error of 11.6% with experimental data across the entire bias space. SRAM circuit simulation shows that the read static noise margin (RSNM) and write static noise margin (WSNM) have a maximum absolute error of 4.2% and 3.1%, respectively. This research provides a valuable reference for the reliability simulation of nanoscale integrated circuits. Full article

A special class of complex adaptive systems—biological and social—thrive not by passively accumulating patterns, but by engineering coherence, i.e., the deliberate alignment of prior knowledge, real-time updates, and teleonomic purposes. By contrast, today’s AI stacks—Large Language Models (LLMs) wrapped in agentic toolchains—remain rooted in a Turing-paradigm architecture: statistical world models (opaque weights) bolted onto brittle, imperative workflows. They excel at pattern completion, but they externalize governance, memory, and purpose, thereby accumulating coherence debt—a structural fragility manifested as hallucinations, shallow and siloed memory, ad hoc guardrails, and costly human oversight. The shortcoming of current AI relative to human-like intelligence is therefore less about raw performance or scaling, and more about an architectural limitation: knowledge is treated as an after-the-fact annotation on computation, rather than as an organizing substrate that shapes computation. This paper introduces Mindful Machines, a computational paradigm that operationalizes coherence as an architectural property rather than an emergent afterthought. A Mindful Machine is specified by a Digital Genome (encoding purposes, constraints, and knowledge structures) and orchestrated by an Autopoietic and Meta-Cognitive Operating System (AMOS) that runs a continuous Discover–Reflect–Apply–Share (D-R-A-S) loop. Instead of a static model embedded in a one-shot ML pipeline or deep learning neural network, the architecture separates (1) a structural knowledge layer (Digital Genome and knowledge graphs), (2) an autopoietic control plane (health checks, rollback, and self-repair), and (3) meta-cognitive governance (critique-then-commit gates, audit trails, and policy enforcement). We validate this approach on the classic Credit Default Prediction problem by comparing a traditional, static Logistic Regression pipeline (monolithic training, fixed features, external scripting for deployment) with a distributed Mindful Machine implementation whose components can reconfigure logic, update rules, and migrate workloads at runtime. The Mindful Machine not only matches the predictive task, but also achieves autopoiesis (self-healing services and live schema evolution), explainability (causal, event-driven audit trails), and dynamic adaptation (real-time logic and threshold switching driven by knowledge constraints), thereby reducing the coherence debt that characterizes contemporary ML- and LLM-centric AI architectures. The case study demonstrates “a hybrid, runtime-switchable combination of machine learning and rule-based simulation, orchestrated by AMOS under knowledge and policy constraints”. Full article

This study proposes an automated, visual–geometric fusion method for measuring pupillary height (PH) and interpupillary distance (PD), aiming to replace manual measurements while balancing accuracy, efficiency, and cost accessibility. To this end, a two-layer Ensemble of Regression Tree (ERT) is used to coarsely localize facial landmarks and the pupil center, which is then refined via direction-aware ray casting and edge-side-stratified RANSAC followed by least-squares fitting; in parallel, an RC-BlendMask instance-segmentation module extracts the lowest rim point of the spectacle lens. Head pose and lens-plane depth are estimated with the Perspective-n-Point (PnP) algorithm to enable pixel-to-millimeter calibration and pose gating, thereby achieving 3D quantification of PH/PD under a single-camera setup. In a comparative study with 30 participants against the Zeiss i.Terminal2, the proposed method achieved mean absolute errors of 1.13 mm (PD), 0.73 mm (PH-L), and 0.89 mm (PH-R); Pearson correlation coefficients were r = 0.944 (PD), 0.964 (PH-L), and 0.916 (PH-R), and Bland–Altman 95% limits of agreement were −2.00 to 2.70 mm (PD), −0.84 to 1.76 mm (PH-L), and −1.85 to 1.79 mm (PH-R). Lens segmentation performance reached a Precision of 97.5% and a Recall of 93.8%, supporting robust PH extraction. Overall, the proposed approach delivers measurement agreement comparable to high-end commercial devices on low-cost hardware, satisfies ANSI Z80.1/ISO 21987 clinical tolerances for decentration and prism error, and is suitable for both in-store dispensing and tele-dispensing scenarios. Full article

Background and Objective: Accurate classification of standard fetal ultrasound planes is a critical step in prenatal diagnostics, enabling reliable biometric measurements and anomaly detection. Conventional deep learning approaches, particularly convolutional neural networks (CNNs) and transformers, often face challenges such as domain variability, noise artifacts, class imbalance, and poor calibration, which limit their clinical utility. This study proposes a hybrid state–space and vision transformer framework designed to address these limitations by integrating sequential dynamics and global contextual reasoning. Methods: The proposed framework comprises five stages: (i) preprocessing for ultrasound harmonization using intensity normalization, anisotropic diffusion filtering, and affine alignment; (ii) hybrid feature encoding with a state–space model (SSM) for sequential dependency modeling and a vision transformer (ViT) for global self-attention; (iii) multi-task learning (MTL) with anatomical regularization leveraging classification, segmentation, and biometric regression objectives; (iv) gated decision fusion for balancing local sequential and global contextual features; and (v) calibration strategies using temperature scaling and entropy regularization to ensure reliable confidence estimation. The framework was comprehensively evaluated on three publicly available datasets: FETAL_PLANES_DB, HC18, and a large-scale fetal head dataset. Results: The hybrid framework consistently outperformed baseline CNN, SSM-only, and ViT-only models across all tasks. On FETAL_PLANES_DB, it achieved an accuracy of 95.8%, a macro-F1 of 94.9%, and an ECE of 1.5%. On the Fetal Head dataset, the model achieved 94.1% accuracy and a macro-F1 score of 92.8%, along with superior calibration metrics. For HC18, it achieved a Dice score of 95.7%, an IoU of 91.7%, and a mean absolute error of 2.30 mm for head circumference estimation. Cross-dataset evaluations confirmed the model’s robustness and generalization capability. Ablation studies further demonstrated the critical role of SSM, ViT, fusion gating, and anatomical regularization in achieving optimal performance. Conclusions: By combining state–space dynamics and transformer-based global reasoning, the proposed framework delivers accurate, calibrated, and clinically meaningful predictions for fetal ultrasound plane classification and biometric estimation. The results highlight its potential for deployment in real-time prenatal screening and diagnostic systems. Full article

Day-ahead wind power forecasting is often limited to on-site Supervisory Control and Data Acquisition (SCADA) datasets without Numerical Weather Prediction (NWP) information. In this regime, practitioners extend autoregressive windows over many variables, so the input size grows with both features and lags. Many lag–feature pairs are redundant, increasing the training time and overfitting risk. A lightweight, differentiable joint gate over the lag–feature plane trained with a temperature-annealed sigmoid is proposed. Sparsity is induced by capped penalties that (i) bound the total open mass to the top-M features and (ii), within each selected feature, bound the mass to the top-k lags. An additional budget-aware off-state term pushes unused logits negative in proportion to the excess density over the $(M\times k)$ budget. A lightweight, per-feature softmax pooling head supplies the forecasting loss during selection. After training, the learned probabilities are converted into compact, non-contiguous lag–feature subsets (top-M features; per-feature top-k lags) and reused by downstream predictors. Tests on the Offshore Renewable Energy (ORE) Catapult Platform for Operational Data (POD) from the Levenmouth Demonstration Turbine (LDT) dataset show that the joint gate reduces the input dimensionality and training time while improving accuracy and stability relative to Pearson’s correlation, mutual information, and cross-correlation function selectors. Full article

After rough planing, defects such as wormholes and small patches of green bark residue and decay are often overlooked and misclassified. Strip-like defects, including splinters and chipped edges, are easily confused with the natural bamboo grain, and a single elongated defect is frequently fragmented into multiple detection boxes. This study proposes a modified TASNet-YOLO model, an improved detector built on YOLO11n. Unlike prior YOLO-based bamboo defect detectors, TASNet-YOLO is a mechanism-guided redesign that jointly targets two persistent failure modes—limited visibility of small, low-contrast defects and fragmentation of elongated defects—while remaining feasible for real-time production settings. In the backbone, a newly designed TriMAD_Conv module is introduced as the core unit, enhancing the detection of wormholes as well as small-area defects such as green bark residue and decay. The additive-gated C3k2_AddCGLU is further integrated at selected C3k2 stages. The combination of additive interaction and CGLU improves channel selection and detail retention, highlighting differences between splinters and chipped edges and bamboo grain strips, thereby reducing false positives and improving precision. In the neck, the neck replaces nearest-neighbor upsampling and CBS with SNI-GSNeck to improve cross-scale alignment and fusion. Under an acceptable real-time budget, predictions for splinters and chipped edges become more contiguous and better aligned to edges, while wormholes predictions are more circular and less noisy. Experiments on our in-house dataset (8445 bamboo-strip defect images) show that, compared with YOLO11n, the proposed model improves detection accuracy by 5.1%, achieves 106.4 FPS, and reduces computational costs by 0.4 GFLOPs per forward pass. These properties meet the throughput demand of 2 m/s conveyor lines, and the compact model size and compute footprint make edge deployment straightforward for fast online screening and preliminary quality grading in industrial production. Full article

This article presents a new approach to the problem of transforming one quantum state into another. It is shown that an $r$-qubit superposition $|\mathit{x}⟩$ can be obtained from another $r$-qubit superposition $|\mathit{y}⟩$, by using only ${(2}^r-1)$ rotations, each presented by one controlled rotation gate. The quantum superpositions with real amplitudes are considered. The traditional two-stage approach $U_y^{-1}{U}_x:|\mathit{x}⟩\to |{0⟩}^{\oplus r}\to |\mathit{y}⟩$ requires twice as many rotations. Here, both transformations to the conventual basis state, $U_x: |\mathit{x}⟩\to |{0⟩}^{\oplus r}$ and $U_y: |\mathit{y}⟩\to |{0⟩}^{\oplus r}$, use ${(2}^r-1)$ rotations each on two binary planes, and many of these rotations require additional sets of CNOTs to be represented as 1- or 2-qubit-controlled gates. The proposed method is based on the concept of the discrete signal-induced heap transform (DsiHT) which is unitary and generated by a vector and a set of angular equations with given parameters. The quantum analog of this transform is described. The main characteristic of the DsiHT is the path of processing the data. It is shown that there exist such fast paths that allow for effective computing of the DsiHT, which leads to the simple quantum circuits for state preparation and transformation. Examples of such paths are given and quantum circuits for preparation and transformation of 2-, 3-, and 4-qubits are described in detail. CNOT gates are not used, but only controlled gates of elementary rotations around the $y$-axis. It is shown that the transformation and, in particular, only rotation gates with control qubits are required for initialization of 2-, 3-, and 4-qubits. The quantum circuits are simple and have a recursive form, which makes them easy to implement for arbitrary $r$-qubit superposition, with $r\ge 2.$ This approach significantly reduces the complexity of quantum state transformations, paving the way for more efficient quantum algorithms and practical implementations on near-term quantum devices. Full article

Magnetic 2D materials offer a compelling platform for next-generation electrocatalysis by enabling spin-dependent reaction pathways. Among them, layered ferromagnets such as Fe₃GeTe₂ (FGT) have garnered attention for combining intrinsic ferromagnetism with high predicted oxygen evolution activity. However, the stability of non-oxide ferromagnets in electrochemical environments remains an unresolved challenge, limiting their envisioned applications. In this study, we introduce a structural homolog approach to investigate the origin of FGT’s catalytic behavior and the mechanisms underlying its degradation. By comparing FGT with its isostructural analog Fe₃GaTe₂ (FGaT), we demonstrate that the electrochemical activity of FGT arises primarily from Fe orbitals and is largely insensitive to changes in sublayer composition. Although both materials exhibit similar basal-plane hydrogen evolution performance, FGaT demonstrates significantly lower long-term stability. Density functional theory calculations reveal that this instability arises from weaker Te bonding introduced by Ga substitution. These findings establish structural homologs as a powerful strategy for decoupling catalytic activity from electrochemical deterioration and for guiding the rational design of stable magnetic electrocatalysts. Full article

The article presents the quantum signal-induced heap transform (QsiHT) method of the QR-decomposition of multi-qubit operations. This transform can be generated by a given signal, by using different paths, or orders, of processing the data. We propose using the concept of the fast path of calculation of the QsiHT and applying such transforms on each stage of the matrix decomposition. This allows us to build quantum circuits for multi-qubit unitary operation without permutations. Unitary operations with real and complex matrices are considered. The cases of 3- and 4-qubit operations are described in detail with quantum circuits. These circuits use a maximum of 28 and 120 Givens rotation gates for 3- and 4-qubit real operations, respectively. All rotations are performing only on adjacent bit planes. For complex unitary operation, each of the Givens gates is used in pairs with two Z-rotation gates. These two types of rotations and the global phase gate are the universal gate set for multi-qubit operations. The presented approach can be used for implementing quantum circuits for $n$-qubits when $n\ge 2$, with a maximum of ($4^n/2-2^{n-1})$ Givens rotations and no permutations. Full article

As populations age, particularly in countries like Japan, mobility impairments related to ankle joint dysfunction, such as foot drop, instability, and reduced gait adaptability, have become a significant concern. Active ankle–foot orthoses (AAFO) offer targeted support during walking; however, most existing systems rely on rule-based or threshold-based control, which are often limited to sagittal plane movements and lacking adaptability to subject-specific gait variations. This study proposes an approach driven by neuromuscular activation using surface electromyography (EMG) and a Gated Recurrent Unit (GRU)-based deep learning model to predict plantar pressure distributions at the heel, midfoot, and toe regions during gait. EMG signals were collected from four key ankle muscles, and plantar pressures were recorded using a customized sandal-integrated force-sensitive resistor (FSR) system. The data underwent comprehensive preprocessing and segmentation using a sliding window method. Root mean square (RMS) values were extracted as the primary input feature due to their consistent performance in capturing muscle activation intensity. The GRU model successfully generalized across subjects, enabling the accurate real-time inference of critical gait events such as heel strike, mid-stance, and toe off. This biomechanical evaluation demonstrated strong signal compatibility, while also identifying individual variations in electromechanical delay (EMD). The proposed predictive framework offers a scalable and interpretable approach to improving real-time AAFO control by synchronizing assistance with user-specific gait dynamics. Full article

To increase the power of a trapped-ion quantum information processor, the qubit number, gate speed, and gate fidelity must all increase. All three of these parameters are influenced by the trapping field, which, in turn, depends on the electrode geometry. Here, we consider how the electrode geometry affects the following radial trapping parameters: trap height, harmonicity, depth, and trap frequency. We introduce a simple multi-wafer geometry comprising a ground plane above a surface trap and compare the performance of this trap to a surface trap and a multi-wafer trap that is a miniaturized version of a linear Paul trap. We compare the voltage and frequency requirements needed to reach a desired radial trap frequency and find that the two multi-wafer trap designs provide significant improvements in expected power dissipation over the surface trap design in large part due to increased harmonicity. Finally, we consider the fabrication requirements and the path towards the integration of the necessary optical control. This work provides a basis to optimize future trap designs with scalability in mind. Full article

As a novel energy-saving and maneuvering device for ships, the gate rudder system (GRS) functions similarly to an accelerating duct. While providing additional thrust, its independently controllable rudder blades on either side of the propeller also enhance ship maneuverability. The GRS was first fully implemented on a container ship in Japan, demonstrating improved propulsion efficiency, fuel savings, and excellent performance in maneuvering, noise, and vibration reduction. In recent years, extensive research has been conducted on the hydrodynamic performance, acoustic characteristics, and energy-saving effects of the GRS. However, certain gaps remain in the research, such as a lack of systematic studies on optimal GRS design in the publicly available literature. Only Ahmet Yusuf Gurkan has investigated the sensitivity of propulsion performance to parameters such as rudder angle, rudder X-shift, rudder tip skewness, and blade tip chord ratio. Therefore, this study employs the JBC benchmark vessel and adopts a coupled CFD-CAESES approach to develop a matching optimization design for the GRS. The influence of geometric parameters—including GRS airfoil camber, maximum camber position, chord length, thickness, distance from the leading edge to the propeller plane, and the gap between the GRS and propeller blades—on ship propulsion performance is investigated. The sensitivity of these design variables to propulsion performance is analyzed, and the optimal GRS design is selected to predict and evaluate its energy-saving effects. This research establishes a rapid and comprehensive CFD-based optimization methodology for GRS matching design. The findings indicate that the gap between the GRS and propeller, the distance from the GRS to the stern, and the airfoil camber of the GRS significantly contribute to various performance responses. After GRS installation, the viscous pressure resistance of the JBC ship decreases, resulting in an 8.05% energy-saving effect at the designated speed. Full article

The optimization of photoinjector brightness is crucial for achieving the highest performance at X-ray free-electron lasers. To this end, photocathode laser pulse shaping has been identified as a key technology for enhancing photon flux and lasing efficiency at short wavelengths. Supported by beam dynamics simulations, we identify transversely and longitudinally truncated Gaussian electron bunches as a beneficial bunch shape in terms of the projected emittance and 5D brightness. The realization of such pulses from chirped Gaussian pulses is studied for 514 nm and 257 nm wavelengths by inserting an amplitude mask in the symmetry plane of the pulse stretcher to achieve longitudinal shaping and an aperture for transverse beam shaping. Using this scheme, transversely and longitudinally truncated Gaussian pulses can be generated and later used for the production of up to 3 nC electron bunches in the photoinjector. The 3D pulse shape at a wavelength of 514 nm is characterized via imaging spectroscopy, and second-harmonic generation frequency-resolved optical gating (SHG FROG) measurements are also performed to analyze the shaping scheme’s efficacy. Furthermore, this pulse-shaping scheme is transferred to a UV stretcher, allowing for direct application of the shaped pulses to cesium telluride photocathodes. Full article

Content Addressable Memories (CAMs) are pivotal in high-speed packet processing systems, enabling rapid data lookup operations essential for applications such as routing, switching, and network security. While traditional Register-Transfer Level (RTL) methodologies have been extensively used to implement CAM architectures on Field-Programmable Gate Arrays (FPGAs), they often involve complex, time-consuming design processes with limited flexibility. In this paper, we propose a novel templated High-Level Synthesis (HLS)-based approach for the design and implementation of CAM architectures such as Binary CAMs (BCAMs) and Ternary CAMs (TCAMs) optimized for data plane packet processing. Our HLS-based methodology leverages the parallel processing capabilities of FPGAs through employing various design parameters and optimization directives while significantly reducing development time and enhancing design portability. This paper also presents architectural design and optimization strategies to offer a fine-tuned CAM solution for networking-related arbitrary use cases. Experimental results demonstrate that HLSCAM achieves a high throughput, reaching up to 31.18 Gbps, 9.04 Gbps, and 33.04 Gbps in the $256\times 128$, $512\times 36$, and $1024\times 150$ CAM sizes, making it a competitive solution for high-speed packet processing on FPGAs. Full article