Orthogonal Research Articles

Effect of Dynamic Corrosion and Degradation on Fatigue Life of an AA2024-T3 Aircraft Profile: Experimental, Statistical, and Numerical Analyses

The effect of parameters such as angle of attack, flow velocity, and electrolyte concentration (saline solution) on the extent of material degradation, as well as the morphology and depth of corrosion pits in an airfoil made of 2024-T3 aluminum alloy, was studied in detail. An orthogonal L9 design of experiments (Taguchi method) was applied to promote high pitting corrosion through the quality characteristic “the higher the better”. Potentiodynamic curves of the three experiments with low, medium, and high saline concentration were obtained through the CorrTest CS350 equipment. The above allowed the determination of the electrochemical corrosion parameters under static test conditions. The corroded airfoils were analyzed using light optical microscopy (LOM). In addition, the roughness measurements correlated with the extent of the degraded surface. A complete pit shape and depth characterization was obtained by applying mechanical ground, surface wear level monitoring (every 0.01 mm), and LOM observations. Pitting defects (depth and morphology) and mechanical strength reduction were considered by a finite element (FE) model to simulate the airfoil fatigue behavior. Numerical results helped to determine the contribution of pitting and the extent of degradation on sudden airfoil failure.

Cosine Distance Loss for Open-Set Image Recognition

Traditional image classification often misclassifies unknown samples as known classes during testing, degrading recognition accuracy. Open-set image recognition can simultaneously detect known classes (KCs) and unknown classes (UCs) but still struggles to improve recognition performance caused by open space risk. Therefore, we introduce a cosine distance loss function (CDLoss), which exploits the orthogonality of one-hot encoding vectors to align known samples with their corresponding one-hot encoder directions. This reduces the overlap between the feature spaces of KCs and UCs, mitigating open space risk. CDLoss was incorporated into both Softmax-based and prototype-learning-based frameworks to evaluate its effectiveness. Experimental results show that CDLoss improves AUROC, OSCR, and accuracy across both frameworks and different datasets. Furthermore, various weight combinations of the ARPL and CDLoss were explored, revealing optimal performance with a 1:2 ratio. T-SNE analysis confirms that CDLoss reduces the overlap between the feature spaces of KCs and UCs. These results demonstrate that CDLoss helps mitigate open space risk, enhancing recognition performance in open-set image classification tasks.

Space-charge-limited current for nonplanar relativistic diodes with nonzero monoenergetic initial velocity using point transformations

Bijective point transformations were recently used to derive the classical space-charge-limited current (SCLC) in one-dimensional (1D) nonplanar devices for electrons emitted into vacuum with nonzero monoenergetic initial velocity. Using these transformations, we first derive a canonical form of SCLC for a relativistic diode with zero initial velocity that holds for any orthogonal 1D geometry and recovers the previously derived planar result. We extend this result to derive a canonical form of SCLC that accounts for nonzero monoenergetic initial velocity and relativistic effects, while recovering SCLC for nonrelativistic diodes with zero and nonzero initial velocity and the relativistic diode with zero initial velocity in appropriate limits. We then use appropriate bijective point transformations to convert from the canonical solution to concentric cylindrical and spherical coordinates. This equation has no closed form solution and must be numerically integrated. The relativistic effects of initial velocity do not become significant until the Lorentz factor γ0≳1.1; for lower γ0, nonrelativistic SCLC gives a reasonable approximation. In the ultra-relativistic limit, Jr,SCLC/JSCLC∝V1/2, where Jr,SCLC and JSCLC are the SCLC for the relativistic diode with general initial velocity and nonrelativistic diode with zero initial velocity, respectively. These asymptotic equations match the exact solutions for sufficiently large γ0 and V. This analysis provides an exact, numerical solution for SCLC for nonzero monoenergetic initial velocity that incorporates relativistic effects for any 1D orthogonal geometry.

Direct mathematical method for real-time ischemic episodes detection from electrocardiograms using the discrete Hermite transform

A real-time automated identification technique is developed for the detection of ischemic episodes in long-term electrocardiographic (ECG) signals using mathematical expansions involving the Discrete Dilated Hermite Transform. The Discrete Hermite functions could be viewed as a set of orthogonal vectors that resemble a finite Fourier series. They are generated easily as eigenvectors of a symmetric tridiagonal matrix that commutes with the centered Fourier matrix. The Discrete Hermite Transform (DHmT) values are computed from a simple dot product between an individual ECG complex extracted from the European Society of Cardiology (ESC) ST-T database and the corresponding discrete Hermite function. These values are found to contain information about the ECG shape, highlighting changes between ST segment and T wave alterations which are the features of ischemic episodes. This information from the discrete Hermite transform, based on an orthonormal set of n-dimensional digital Hermite functions that serve as shape-identification functions, can be used to identify ischemic episodes from the ECG. The performance measures resulting from applying this method to detect ischemic episodes were Sensitivity 87 %, Specificity 86 %, and positive predictive accuracy 81 %. The computer time to analyze one heartbeat for ischemia with this method is 0.031 s on a standard PC.

A recursive filtering approach to power harmonic detection with stochastic communication delays: Tackling amplify-and-forward relays

This paper focuses on the power harmonic detection problem in smart grids with relay transmissions and stochastic communication delays. A model is established using an orthogonal vector to capture the dynamics of harmonic signals with direct current attenuation components. For the assurance of reliable information delivery, a strategy employing an amplify-and-forward relay with stochastic transmission power is used to schedule data from sensors to the remote filter. Furthermore, a set of random variables that obey Bernoulli distributions is used to characterize the stochastic nature of the communication delays. With the goal of achieving accurate power harmonic detection, a recursive filtering algorithm is aimed to be designed in the presence of the amplify-and-forward relay strategy and stochastic communication delays, which ensures that the desired filter gain parameter is derived recursively by minimizing the upper bound on the filtering error covariance. Ultimately, the effectiveness of the proposed filtering algorithm is demonstrated through simulation experiments on power harmonic detection, thereby verifying its capability in tracking harmonic dynamics.

Azophotoswitches containing thiazole, isothiazole, thiadiazole, and isothiadiazole.

We report a novel class of azophotoswitches incorporating various five-membered heteroaryl units such as thiazole, isothiazole, thiadiazole, and isothiadiazole. These azophotoswitches were developed through an initial screening of 24 compounds using DFT calculations to identify those with the wavelength of maximum absorption (λmax) at a long wavelength. Subsequently, eight selected azophotoswitches were synthesized. Compounds containing both thiazole and isothiazole moieties showed relatively long λmax compared to the other synthesized compounds. These azophotoswitches exhibited reversible isomerization under visible light irradiation at 430 nm, 450 nm, 470 nm (trans to cis) and 525 nm (cis to trans). Analysis of the X-ray crystal structures of the cis isomer of phenylazo[1,3,4-thiadiazole] exhibited a unique orthogonal geometry.

Isotropic sampling of tensor-encoded diffusion MRI.

The purpose of this study is to develop a method for selecting uniform wave vectors for double diffusion encoding (DDE) to improve the accuracy and reliability of diffusion measurements. The method relies on identifying orthogonal wave vectors with rotations, and representing these rotations as points on a three-dimensional sphere in four dimensions using quaternions. This enables an electrostatic repulsion algorithm to achieve a uniform distribution of these points. The optimal points are then converted back into orthogonal wave vectors (or rotations). The method was validated by comparing the distribution of directions to those generated by uniform sampling and by evaluating the error in the powder-averaged signal for various models. Our results demonstrate that the electrostatic repulsion approach effectively achieves a uniform distribution of wave vectors. The proposed method provides a systematic way to generate uniform diffusion directions suitable, for example, for DDE, enhancing the precision of diffusion measurements and reducing potential bias in experimental results. The method is also capable of generating uniform sets of B-tensors, and is thus applicable for general free waveform encoding.

Working memory updating in the macaque lateral prefrontal cortex.

Working memory updating is an important executive process. Here, we study the single-neuron mechanisms involved in updating versus protecting memory from distractors in the macaque prefrontal cortex. We recorded single-neuron activity from the lateral prefrontal cortex (LPFC) and prearcuate cortex (PAC) while male monkeys performed a task that required them to update their memory of target locations while ignoring distractors. Our findings revealed that neurons in the PAC signaled updated memory locations approximately ∼100 ms after stimulus onset, significantly faster than the ∼400 ms observed in the LPFC. Additionally, PAC neurons exhibited longer encoding of distractor information. Population decoding analyses further indicated that distractor information was maintained in orthogonal subspaces from target information in both regions, minimizing interference. These results demonstrate the distinct temporal dynamics in memory updating processes between the PAC and LPFC and highlight the interplay between robust memory maintenance and updating, suggesting that local neural mechanisms may contribute to these processes.Significance Statement Working memory is a fundamental cognitive function. It stored information in the short term, and this information can be manipulated to allow intelligent behaviors. The lateral prefrontal cortex is involved in this process, but the mechanisms of working memory manipulation remain unclear. Here, we studied one type of manipulation; working memory updating, which refers to the exchange of one memory for another. We found that two adjacent regions in the lateral prefrontal cortex show different updating times: while a posterior region updates memory content very fast, the more anterior region takes significantly longer. These results show that working memory updating may involve multiple operations, such as updating of memory or attention versus updating of motor plans.

Biomimetic Approach for Optimal Designing of the Shape and Controlled Release of Therapeutics from Tricompartmental Microcarriers for Managing Parkinson's Disease.

Inspired by the intricate cellular morphology and the discoid shape of red blood cells (RBCs), biomimetic tricompartmental microcarriers (TCM) with controlled release profiles were engineered using an electrohydrodynamic-co-jetting technique for efficient management of Parkinson's disease (PD). While jetting, Levodopa (LD), CD (Carbidopa), and ENT (Entacapone) (3 PD drugs) were directly encapsulated in the three individual compartments of the TCM used for oral administration. The optimal shape and controlled release profiles were obtained by employing the Taguchi orthogonal L9 design-of-experiment approach by systematically varying the processing parameters, i.e., solvent ratio, polymer concentration, and flow rate. The "smaller-the-better" norm for the S/N ratio demonstrated the solvent ratio (DMF content) and polymer concentration as the most influential parameters in ensuring the RBC shape and controlling the release of drugs. Analysis of variance and response surface methodology approach provided insights into the optimal influence of control factors on the response variables. Confirmation experiments further validated the optimized microparticles (Poptimized), demonstrating an error of only ∼0.13% in aspect ratio deviation (ARDEV) and ∼19% (within the tolerance limit) in release factor (RF) from the predicted experiment. Moreover, Poptimized exhibits ∼100% encapsulation efficiency of all three PD drugs, with the cumulative release of ∼100% LD, ∼97% CD, and ∼65% ENT within 5 h of the in vitro study. In addition, in vivo studies such as pharmacokinetics (using healthy rats) and pharmacodynamics [using the MPTP (methyl-4-phenyl-1,2,3,6-tetrahydropyridine)-injected PD-induced mice model] showed that the TCM can effectively control the release of LD (primary drug) for a prolonged period, thereby promising sustained drug delivery and improved therapeutics outcomes.

Casimir effect of a doubly Lorentz-violating scalar in magnetic field

In this work, I study the Casimir effect caused by a charged and massive scalar field that violates Lorentz symmetry in an aether-like and CPT even manner, by direct coupling between the field derivatives and two fixed orthogonal unit vectors. I call this a “double” Lorentz violation. The field will satisfy either Dirichlet or mixed boundary conditions on a pair of plane parallel plates. I use the generalized zeta function technique to examine the effect of a uniform magnetic field, perpendicular to the plates, on the Casimir energy and pressure. Three different pairs of mutual directions of the unit vectors are possible: timelike and spacelike perpendicular to the magnetic field, timelike and spacelike parallel to the magnetic field, spacelike perpendicular and spacelike parallel to the magnetic field. I fully examine all of them for both types of boundary conditions listed above and, in all cases and for both types of boundary conditions, I obtain simple expressions of the zeta function, Casimir energy and pressure in the three asymptotic limits of strong magnetic field, large mass, and small plate distance.

Progress using collective Thomson scattering of microwave radiation to detect fast ions and plasma instabilities at the GDT open magnetic trap

For the big mirror magnetic trap (Gas-Dynamic Trap, Budker Institute, Russia), a system for recording of collective Thomson scattering spectra of microwave radiation has been developed to focus on the study of velocity distribution function of fast ions. A diagnostic complex includes a high-power 450 kW/54.5 GHz gyrotron as a source of probing radiation, two independent highly sensitive radiometers operating in the range of 54.47 ± 0.55 GHz for simultaneous registration of scattered radiation in two orthogonal geometries, and quasi-optical systems for focusing the probing and diagnostic microwave beams. We report the results of the last experimental campaign of 2023 with plasma heating by neutral beams, in which the scattering signals from the fast ions have been detected accompanied by high-frequency instability of plasma.

Reduced-Order Modeling for Subsurface Flow Simulation in Fractured Reservoirs

Summary Reservoir simulation for fractured reservoirs is often challenging and time-consuming due to the strong heterogeneity and complex flow dynamics introduced by fracture-matrix interactions. In this study, we introduce a novel reduced-order modeling procedure to speed up the flow simulation of fractured reservoirs. The reduced-order model (ROM) is developed based on proper orthogonal decomposition (POD) in conjunction with the embedded discrete fracture model (EDFM) that provides full-order simulation results. With the full-order training simulation, snapshots of reservoir pressure and saturation state at different timesteps are captured and assembled into separate data matrices. Singular value decomposition (SVD) is then applied to these data matrices to obtain a reduced set of orthogonal base vectors for pressure and saturation solutions, respectively. These base vectors enable the projection of high-dimensional linear equations into much lower-dimensional spaces, which significantly accelerates the process of solving nonlinear governing equations under the EDFM approach. The developed reduced-order modeling procedure is implemented in the MATLAB reservoir simulation toolbox (MRST) and tested via multiple cases for both 2D and 3D fractured reservoirs under different boundary and well control scenarios. In certain challenging cases, the use of multiple training simulations is explored and is shown to provide improved predictions. Overall, the proposed ROM approach is able to provide simulation results that are very consistent with those obtained from the full-order simulations while achieving computational speedups of about an order of magnitude for large-scale cases. These observations indicate that the proposed ROM exhibits satisfactory generalization performance, making it suitable for problems that require many flow simulations under different settings, such as production optimization.

The index of equidimensional flag manifolds

AbstractIn this paper, we consider the flag manifold of orthogonal subspaces of equal dimension that carries an action of the cyclic group of order . We provide a complete calculation of the associated Fadell–Husseini index. This may be thought of as an odd primary version of the computations of Baralić, Blagojevic, Karasev, and Vucic, for the Grassmann manifold . These results have geometric consequences for ‐fold orthogonal shadows of a convex body.

Spin-Orbit Torque Booster in an Antiferromagnet via Facilitating a Global Antiferromagnetic Order: A Route toward an Energy-Efficient Memory.

Spin transport and the associated spin torque effects in antiferromagnets (AFMs) are scientifically interesting but have remained elusive due to the varied observations of spin transport in AFMs. This study revisits the role of a global Néel order in nickel oxide (NiO) facilitated through a spin-orbit torque (SOT) and examines the enhanced SOT efficiency in a heavy metal (W)/AFM (NiO)/ferromagnet (FM, CoFeB) trilayer with varying NiO thicknesses ranging from 1 to 5 nm. At the as-grown state, the Néel order of NiO is randomly oriented due to the polycrystalline nature of the film structure, leading to increased spin absorption and blocking spin transport from the adjacent W layer. When the spin current amplitude exceeds a threshold value, SOT enables reorientation of the Néel order in NiO to an equilibrium state, forming a global Néel order aligned with the applied current. This long-range Néel order reduces spin absorption and enhances spin transport through NiO, hence boosting the SOT efficiency in the adjacent CoFeB layer. X-ray magnetic linear dichroism spectroscopy and rewritable Néel order reorientation experiments in a device with orthogonal geometry confirmed the strong correlation between the global Néel order facilitation and the boosted SOT efficiency, which is enhanced larger than 4-fold for both damping- and field-like torques in the trilayer with 5 nm NiO. This study not only reveals the strong correlation between globally facilitated Néel order and spin transport in NiO but also offers a promising manner to promote AFM-based SOT devices toward energy-efficient computing technology.

Mangrove Extraction Algorithm Based on Orthogonal Matching Filter-Weighted Least Squares.

High-precision extraction of mangrove areas is a crucial prerequisite for estimating mangrove area as well as for regional planning and ecological protection. However, mangroves typically grow in coastal and near-shore areas with complex water colors, where traditional mangrove extraction algorithms face challenges such as unclear region segmentation and insufficient accuracy. To address this issue, in this paper we propose a new algorithm for mangrove identification and extraction based on Orthogonal Matching Filter-Weighted Least Squares (OMF-WLS) target spectral information. This method first selects GF-6 remote sensing images with less cloud cover, then enhances mangrove feature information through preprocessing and band extension, combining whitened orthogonal subspace projection with the whitened matching filter algorithm. Notably, this paper innovatively introduces Weighted Least Squares (WLS) filtering technology. WLS filtering precisely processes high-frequency noise and edge details in images using an adaptive weighting matrix, significantly improving the edge clarity and overall quality of mangrove images. This innovative approach overcomes the bottleneck of traditional methods in effectively extracting edge information against complex water color backgrounds. Finally, Otsu's method is used for adaptive threshold segmentation of GF-6 remote sensing images to achieve target extraction of mangrove areas. Our experimental results show that OMF-WLS improves extraction accuracy compared to traditional methods, with overall precision increasing from 0.95702 to 0.99366 and the Kappa coefficient rising from 0.88436 to 0.98233. In addition, our proposed method provides significant improvements in other metrics, demonstrating better overall performance. These findings can provide more reliable technical support for the monitoring and protection of mangrove resources.

Path Planning for Mobile Robots Based on the Improved DAPF-QRRT* Strategy

The rapidly exploring random tree star (RRT*) algorithm is widely used to solve path planning problems. However, the RRT* algorithm and its variants fall short of achieving a balanced consideration of path quality and safety. To address this issue, an improved discretized artificial potential field-QRRT* (IDAPF-QRRT*) path planning strategy is introduced. Initially, the APF method is integrated into the Quick-RRT* (Q-RRT*) algorithm, utilizing the attraction of goal points and the repulsion of obstacles to optimize the tree expansion process, swiftly achieving superior initial solutions. Subsequently, a triangle inequality-based path reconnection mechanism is introduced to create and reconnect path points, optimize the path length, and accelerate the generation of sub-optimal paths. Finally, by refining the traditional APF method, a repulsive orthogonal vector field is obtained, achieving the orthogonalization between repulsive and attractive vector fields. This places key path points within the optimized vector field and adjusts their positions, thereby enhancing path safety. Compared to the Q-RRT* algorithm, the DPF-QRRT* algorithm achieves a 37.66% reduction in the time taken to achieve 1.05 times the optimal solution, and the IDAPF-QRRT* strategy nearly doubles generated path safety.

Cooperative Nernst effect of multilayer systems: Parallel circuit model study

Transverse thermoelectric power generation has emerged as a topic of immense interest in recent years owing to the orthogonal geometry which enables better scalability and fabrication of devices. Here, we investigate the thickness dependence of longitudinal and transverse responses in film-substrate systems, i.e., the Seebeck coefficient, the Hall coefficient, the Nernst coefficient, and the anomalous Nernst coefficient in a unified and general manner based on the circuit model, which describes the system as the parallel setup. By solving the parallel circuit model, we show that the transverse responses exhibit a significant peak, indicating the importance of a cooperative effect between the film and the substrate, arising from circulating currents that occur in these multilayer systems in the presence of a temperature gradient. Finally, on the basis of realistic material parameters, we predict that the Nernst effect in bismuth thin films on doped silicon substrates is boosted to unprecedented values if the thickness ratio is tuned accordingly, motivating experimental validation. Published by the American Physical Society 2024

Necessity of orthogonal basis vectors for the two-anyon problem in a one-dimensional lattice* *Supported by National Science Foundation of China (Grant Nos. 12074340 and 12474492) and the Science Foundation of Zhejiang Sci-Tech University (Grants No. 20062098-Y, 19062463-Y and 22062344-Y).

Few-body physics for anyons has been intensively studied within the anyon-Hubbard model, including the quantum walk and Bloch oscillations of two-anyon states. Recently, theoretical and experimental simulations of two-anyon states in a one-dimensional lattice have been carried out by expanding the wavefunction in terms of non-orthogonal basis vectors, resulting in non-physical degrees of freedom. In the present work, we deduce finite difference equations for the two-anyon state in a one-dimensional lattice by solving the Schrödinger equation with orthogonal and complete basis vectors. Such an orthogonal scheme gives all the orthogonal physical eigenstates, while the conventional (non-orthogonal) method produces many non-physical redundant eigensolutions whose components violate the anyonic commutation relations. The dynamical property of the two-anyon states in a sufficiently large lattice is investigated and compared in both the orthogonal and conventional schemes. For initial states with two anyons at the same site or two (next-)neighboring sites, we observe the same dynamical behavior in both schemes, including the revival probability, probability density function and two-body correlation. For other initial states, the conventional scheme produces erroneous states that no longer obey the anyonic relations. The period of Bloch oscillations in the pseudo-fermionic limit has been found to be twice that in the bosonic limit, while these oscillations disappear at other statistical parameters. Our findings are vital for quantum simulations of few-body anyonic physics in lattice models.

Range-Domain Subspace Detector in the Presence of Direct Blast for Forward Scattering Detection in Shallow-Water Environments

This paper aims to detect a target that crosses the baseline connecting the source and the receiver in shallow-water environments, which is a special scenario for a bistatic sonar system. In such a detection scenario, an intense sound wave, known as the direct blast, propagates directly from the source to the receiver without target scattering. This direct blast usually arrives at the receiver simultaneously with the forward scattering signal and exhibits a larger intensity than the signal, posing a significant challenge for target detection. In this paper, a range-domain subspace is constructed by the horizontal distance between the source/target and each element of a horizontal linear array (HLA) when the ranges of environmental parameters are known a priori. Meanwhile, a range-domain subspace detector based on direct blast suppression (RSD-DS) is proposed for forward scattering detection. The source and the target are located at different positions, so the direct blast and the scattered signal are in different range-domain subspaces. By projecting the received data onto the orthogonal complement subspace of the direct blast subspace, the direct blast can be suppressed and the signal that lies outside the direct blast subspace is used for target detection. The simulation results indicate that the proposed RSD-DS exhibits a performance close to the generalized likelihood ratio detector (GLRD) while requiring less prior knowledge of environments (only known are the ranges of the sediment sound speed and the bottom sound speed), and its robustness to environmental uncertainties is better than that of the latter. Moreover, the proposed RSD-DS exhibits better immunity against the direct blast than the GLRD, since it can still work effectively at a signal-to-direct blast ratio (SDR) of −30 dB, while the GLRD stops working in this case.

Modular representations emerge in neural networks trained to perform context-dependent tasks.

The brain has large-scale modular structure in the form of brain regions, which are thought to arise from constraints on connectivity and the physical geometry of the cortical sheet. In contrast, experimental and theoretical work has argued both for and against the existence of specialized sub-populations of neurons (modules) within single brain regions. By studying artificial neural networks, we show that this local modularity emerges to support context-dependent behavior, but only when the input is low-dimensional. No anatomical constraints are required. We also show when modular specialization emerges at the population level (different modules correspond to orthogonal subspaces). Modularity yields abstract representations, allows for rapid learning and generalization on novel tasks, and facilitates the rapid learning of related contexts. Non-modular representations facilitate the rapid learning of unrelated contexts. Our findings reconcile conflicting experimental results and make predictions for future experiments.