Intramuscular EMG classifier for detecting myopathy and neuropathy

This article presents an automatic diagnostic system to classify intramuscular electromyography (iEMG) signals, thereby detecting neuromuscular disorders. To this end, we tailored the center symmetric local binary pattern (CSLBP) to analyze one-dimensional ($1$-D) signals. In this approach, the $1$-D CSLBP feature extracted from a decimated iEMG signal is fed to a combination of classifiers, which in turn assigns a set of labels to the signal, and ultimately the signal category is determined by the Boyer-Moore majority voting (BMMV) algorithm. The proposed framework was investigated with a benchmark iEMG dataset that contains signals recorded from three different muscles: biceps brachii (BB), deltoideus (DE), and vastus medialis (VM). In a repeated $10$-fold cross-validation, CSLBP-Combined-Classifiers-BMMV (CSLBP-CC-BMMV) achieved an average classification accuracy of $92.80$%, $94.25$%, and $93.71$% for the iEMG signals recorded from BB, DE, and VM muscle, respectively. Interestingly, the performance of CSLBP-CC-BMMV surpassed the other published approaches and ensemble learning methods that are akin to our scheme in terms of classification accuracy and computational time.     

A High-Performance Li–Mn–O Li-rich Cathode Material with Rhombohedral Symmetry via Intralayer Li/Mn Disordering

The search for new high-performance and low-cost cathode materials for Li-ion batteries is a challenging issue in materials research. Commonly used cobalt- or nickel-based cathodes suffer from limited resources and safety problems that greatly restrict their large-scale application, especially for electric vehicles and large-scale energy storage. Here, a novel Li–Mn–O Li-rich cathode material with $R\overline{3}m$ symmetry is developed via intralayer Li/Mn disordering in the Mn-layer. Due to the special atomic arrangement and higher $R\overline{3}m$ symmetry with respect to the C2/m symmetry, the oxygen redox activity is modulated and the Li in the Li-layer is preferentially thermodynamically extracted from the crystal structure instead of Li in the Mn-layer. The as-obtained material delivers a reversible capacity of over 300 mAh g⁻¹ at 25 mA g⁻¹ and rate capability of up to 260 mAh g⁻¹ at 250 mA g⁻¹ within 2.0–4.8 V. The excellent performance is attributed to its highly structural reversibility, mitigation of Jahn–Teller distortion, lower bandgap, and faster Li-ion 2D channels during the lithium-ion de/intercalation process. This material is not only a promising cathode material candidate but also raises new possibilities for the design of low-cost and high-performance cathode materials.     

Ultrafast Orbital‐Oriented Control of Magnetization in Half‐Metallic La0.7Sr0.3MnO3 Films

Manipulating spins by ultrafast pulse laser provides a new avenue to switch the magnetization for spintronic applications. While the spin–orbit coupling is known to play a pivotal role in the ultrafast laser‐induced demagnetization, the effect of the anisotropic spin–orbit coupling on the transient magnetization remains an open issue. This study uncovers the role of anisotropic spin–orbit coupling in the spin dynamics in a half‐metallic La_0.7Sr_0.3MnO₃ film by ultrafast pump–probe technique. The magnetic order is found to be transiently enhanced or attenuated within the initial sub‐picosecond when the probe light is tuned to be s‐ or p‐polarized, respectively. The subsequent slow demagnetization amplitude follows the fourfold symmetry of the ${\mathrm{d}}_{x^2?y^2}$ orbitals as a function of the polarization angles of the probe light. A model based on the Elliott–Yafet spin‐flip scatterings is proposed to reveal that the transient magnetization enhancement is related to the spin‐mixed states arising from the anisotropic spin–orbit coupling. The findings provide new insights into the spin dynamics in magnetic systems with anisotropic spin–orbit coupling as well as perspectives for the ultrafast control of information process in spintronic devices.     

Comb and Bottlebrush Polymers with Superior Rheological and Mechanical Properties

Comb and bottlebrush polymers present a wide range of rheological and mechanical properties that can be controlled through their molecular characteristics, such as the backbone and side chain lengths as well as the number of branches per molecule or the grafting density. This review investigates the impact of these characteristics specifically on the zero shear viscosity, strain hardening behavior, and plateau shear modulus. It is shown that for a comb polymer with an entangled backbone and entangled side chains, a maximum in the strain hardening factor and minimum in the zero shear viscosity η₀ can be achieved through selection of an optimum number of branches q. Bottlebrush polymers with flexible filaments and extremely low plateau shear moduli relative to linear polymers open the door for a new class of solvent‐free supersoft elastomers, where their network modulus can be controlled through both the degree of polymerization between crosslinks, n_x, and the length of the side chains, n_sc, with $G_{BB}^0\approx \rho kT{n}_x^{?1}{(n_{sc}+1)}^{?1}$.     

Exploiting a Single-Crystal Environment to Minimize the Charge Noise on Qubits in Silicon

Electron spins in silicon offer a competitive, scalable quantum-computing platform with excellent single-qubit properties. However, the two-qubit gate fidelities achieved so far have fallen short of the 99% threshold required for large-scale error-corrected quantum computing architectures. In the past few years, there has been a growing realization that the critical obstacle in meeting this threshold in semiconductor qubits is charge noise arising from the qubit environment. In this work, a notably low level of charge noise of S₀ = 0.0088 ± 0.0004 μeV² Hz⁻¹ is demonstrated using atom qubits in crystalline silicon, achieved by separating the qubits from surfaces and interface states. The charge noise is measured using both a single electron transistor and an exchange-coupled qubit pair that collectively provide a consistent charge noise spectrum over four frequency decades, with the noise level S₀ being an order of magnitude lower than previously reported. Low-frequency detuning noise, set by the total measurement time, is shown to be the dominant dephasing source of two-qubit exchange oscillations. With recent advances in fast (≈μs) single-shot readout, it is shown that by reducing the total measurement time to ≈1 s, 99.99% two-qubit $\text{◂√▸}\sqrt{SWAP}$ gate fidelities can be achieved in single-crystal atom qubits in silicon.     

A new double EWMA-t chart with auxiliary information for the process mean

In this paper, we propose an auxiliary-information-based (AIB) double EWMA-$t$ (AIB-DEWMA-$t$) chart for monitoring the process mean. The DEWMA-$t$ chart encompasses the EWMA-$t$ and AIB-EWMA-$t$ charts. The Monte Carlo simulations are used to compute the run length characteristics of the AIB-DEWMA-$t$ chart. Based on detailed run length comparisons, it is found that the AIB-DEWMA-$t$ chart may uniformly and substantially outperform the AIB-EWMA-$t$ chart when detecting different shifts in the process mean. In addition, the AIB-DEWMA-$t$ chart is uniformly more sensitive than the DEWMA-$t$ chart. Similar trends are observed when comparing these control charts with the variable sampling interval feature. A real dataset is also considered to demonstrate the implementation of the proposed chart.     

A one-class peeling method for multivariate outlier detection with applications in phase I SPC

In phase I of statistical process control (SPC), control charts are often used as outlier detection methods to assess process stability. Many of these methods require estimation of the covariance matrix, are computationally infeasible, or have not been studied when the dimension of the data, $p$, is large. We propose the one-class peeling (OCP) method, a flexible framework that combines statistical and machine learning methods to detect multiple outliers in multivariate data. The OCP method can be applied to phase I of SPC, does not require covariance estimation, and is well suited to high-dimensional data sets with a high percentage of outliers. Our empirical evaluation suggests that the OCP method performs well in high dimensions and is computationally more efficient and robust than existing methodologies. We motivate and illustrate the use of the OCP method in a phase I SPC application on a $N=354$, $p=1917$ dimensional data set containing Wikipedia search results for National Football League (NFL) players, teams, coaches, and managers. The example data set and R functions, OCP.R and OCPLimit.R, to compute the respective OCP distances and thresholds are available in the supplementary materials.     

Memory-type t charts with multiple auxiliary information for the process mean

Multiple auxiliary information-based (MAIB) memory-type $t$ charts are proposed with fixed and variable sampling intervals for an improved monitoring of the process mean, which include adaptive/nonadaptive cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) $t$ charts. These control charts are constructed based on a unique uniformly minimum variance unbiased estimator of the process mean that requires information on a study variable as well as on several correlated auxiliary variables. The Monte Carlo simulation technique is used to compute the run length characteristics of the proposed charts when sampling from a multivariate normal distribution. The run length comparisons show that the proposed MAIB-$t$ charts outperform their existing auxiliary information based (AIB) and non-AIB $t$ charts, where the normalizing transformation is used for all considered $t$ charts in order to have uniformity in the comparisons. A real data application is also given to support the proposed theory.     

Automated segmentation of diabetic macular edema in OCT B-scan images based on RCU-Net

Diabetic macular edema (DME) is a typical fundus disease that can cause blindness in severe cases. The morphology of the inner limiting membrane (ILM) to the retinal pigment epithelium (RPE) layer in the retina and the macular edema (ME) area are important features for the diagnosis of DME. Doctors usually use non-invasive and high-resolution optical coherence tomography (OCT) to examine the fundus of the patient. However, manual diagnosis has low efficiency and strong subjectivity. Realizing the automatic segmentation of the ILM-RPE layer and ME is extremely important for the early diagnosis of DME. In this paper, the attention mechanism based on residual convolution module U-Net (RCU-Net) is proposed for the automatic segmentation of the retinal layer and cystoid edema lesions. Through the fusion of the residual structure and CBAM for feature extraction, the useful features in the channel and space are effectively strengthened, and the network can better learn different levels of information. The proposed network is combined with the Lovász-softmax loss, which can better target the correlation between targets to obtain the optimal segmentation model during training. Finally, this paper compares the proposed method with several other segmentation methods. The experimental results show that the $\mathit{\text{MIoU}}$ of the method in this model reaches 88.595%, and the $\mathit{\text{Accuracy}}$ reaches 99.171%. The RCU-Net proposed in this paper is used to segment the ILM-RPE layer and ME region in the retina OCT B-scan images, and its overall performance is better than other networks.     

VI3—a New Layered Ferromagnetic Semiconductor

2D materials are promising candidates for next‐generation electronic devices. In this regime, insulating 2D ferromagnets, which remain rare, are of special importance due to their potential for enabling new device architectures. Here the discovery of ferromagnetism is reported in a layered van der Waals semiconductor, VI₃, which is based on honeycomb vanadium layers separated by an iodine–iodine van der Waals gap. It has a BiI₃‐type structure ( $R\overline{3}$, No.148) at room temperature, and the experimental evidence suggests that it may undergo a subtle structural phase transition at 78 K. VI₃ becomes ferromagnetic at 49 K, below which magneto‐optical Kerr effect imaging clearly shows ferromagnetic domains, which can be manipulated by the applied external magnetic field. The optical bandgap determined by reflectance measurements is 0.6 eV, and the material is highly resistive.