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In distributed storage systems, an r-Locally Repairable Code (r-LRC) ensures that a failed symbol can be recovered by accessing at most r other symbols. Prakash et al. in (Proceedings of IEEE International Symposium on Information Theory, pp. 2776–2780, 2012) further introduced the concept of (r , δ) -LRC, where δ ≥ 2 , which can deal with the symbol failure in the presence of extra δ - 2 symbol failures still by accessing at most r other symbols. In particular, an r-LRC is just an (r, 2)-LRC. Luo and Ling in (Des Codes Cryptogr 90:1271–1287, 2022) obtained some alphabet-optimal r-LRCs concerning the Cadambe–Mazumdar bound from optimal linear codes constructed by special projective spaces. In this paper, we generalize the results of Luo and Ling in (Des Codes Cryptogr 90:1271–1287, 2022). Firstly, we generalize the result of constructing optimal linear codes to larger code length. In particular, we present the conditions for the constructed linear codes to qualify as Griesmer codes or distance-optimal codes. Secondly, we explore the locality of the constructed codes. The novelty of our work lies in establishing the locality as (r , δ) -locality and (r , δ) -locality with availability, in contrast to the previous literature that only considered r-locality. In addition, through the analysis combining the code parameters and the Cadambe–Mazumdar-like bound for (r , δ) -LRCs, we obtained some alphabet-optimal (r , δ) -LRCs and alphabet-optimal (r , δ) -LRCs with availability. [ABSTRACT FROM AUTHOR]

We present fourth-order conservative non-splitting semi-Lagrangian (SL) Hermite essentially non-oscillatory (HWENO) schemes for linear transport equations with applications for nonlinear problems including the Vlasov–Poisson system, the guiding center Vlasov model, and the incompressible Euler equations in the vorticity-stream function formulation. The proposed SL HWENO schemes combine a weak formulation of the characteristic Galerkin method with two newly constructed HWENO reconstruction methods. The new HWENO reconstructions are meticulously designed to strike a delicate balance between curbing numerical oscillation and introducing excessive dissipation. Mass conservation naturally holds due to the weak formulation of the semi-Lagrangian discontinuous Galerkin method and the design of the HWENO reconstructions. We apply a positivity-preserving limiter to maintain the positivity of numerical solutions when needed. Abundant benchmark tests are performed to verify the effectiveness of the proposed SL HWENO schemes. [ABSTRACT FROM AUTHOR]

Lower limb augmentation exoskeletons (LLAE) have been applied in several domains to enforce human walking capability. As humans can adjust their joint moments and generate different amounts of mechanical energy while walking on different terrains, the LLAEs should provide adaptive augmented torques to the wearer in multi-terrain environments, which requires LLAEs to implement accurate terrain parameter recognition. However, the outputs of previous terrain parameter recognition algorithms are more redundant, and the algorithms have higher computational complexity and are susceptible to external interference. Therefore, to resolve the above issues, this paper proposed a neural network regression (NNR)-based algorithm for terrain slope parameter recognition. In particular, this paper defined for the first time a unified representation of terrain parameters: terrain slope (TS), a single parameter that can provide enough information for exoskeleton control. In addition, our proposed NNR model uses only basic human parameters and LLAE joint motion posture measured by an Inertial Measurement Unit (IMU) as inputs to predict the TS, which is computationally simpler and less susceptible to interference. The model was evaluated using K-fold cross-validation and the results showed that the model had an average error of only 2.09 ∘ . To further validate the effectiveness of the proposed algorithm, it was verified on a homemade LLAE and the experimental results showed that the proposed TS parameter recognition algorithm only produces an average error of 3.73 ∘ in multi-terrain environments. The defined terrain parameters can meet the control requirements of LLAE in urban multi-terrain environments. The proposed TS parameter recognition algorithm could facilitate the optimization of the adaptive gait control of the exoskeleton system and improve user experience, energy efficiency, and overall comfort. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things, numerous devices have been deployed in complex environments for environmental monitoring and information transmission, which brings new power supply challenges. Wireless power transfer is a promising solution since it enables power delivery without cables, providing well-behaved flexibility for power supplies. Here we propose a compact wireless power transfer framework. The core components of the proposed framework include a plane-wave feeder and a transmissive 2-bit reconfigurable metasurface-based beam generator, which constitute a reconfigurable power router. The combined profile of the feeder and the beam generator is 0.8 wavelengths. In collaboration with a deep-learning-driven environment sensor, the router enables object detection and localization, and intelligent wireless power transfer to power-consuming targets, especially in dynamic multitarget environments. Experiments also show that the router is capable of simultaneous wireless power and information transfer. Due to the merits of low cost and compact size, the proposed framework may boost the commercialization of metasurface-based wireless power transfer routers. The rapid emergence of the Internet of Things has led to new power supply challenges. Here, authors propose a compact wireless power transfer framework that can intelligently deliver wireless power to devices in dynamic environments and simultaneously transfer information. [ABSTRACT FROM AUTHOR]

We propose an Eulerian–Lagrangian (EL) Runge–Kutta (RK) discontinuous Galerkin (DG) method for a linear hyperbolic system. The method is designed based on the ELDG method for transport problems (J Comput Phys 446:110,632, 2021), which tracks solutions along approximations to characteristics in the DG framework, allowing extra large time stepping sizes with stability with respect to the classical RKDG method. Considering each characteristic family, a straightforward application of ELDG for the hyperbolic system will be to transform to the characteristic variables, evolve them on associated characteristic-related space–time regions, and transform them back to the original variables. However, the conservation could not be guaranteed in a general setting. In this paper, we formulate a conservative semi-discrete ELDG method by decomposing each variable into two parts, each of them associated with a different characteristic family. As a result, four different quantities are evolved in EL fashion and recombined to update the solution. The fully discrete scheme is formulated by using method-of-lines RK methods, with intermediate RK solutions updated on the background mesh. Numerical results for 1D and 2D wave equations are presented to demonstrate the performance of the proposed ELDG method. These include the high order spatial and temporal accuracy, stability with extra large time stepping size, and conservative property. [ABSTRACT FROM AUTHOR]

With the development of blockchain technology, smart contracts have attracted a lot of attention in recent years. They are widely used because they can reduce the cost of trust compared with traditional contracts. At the same time, they are at risk of being hacked. Therefore, the current research on smart contract vulnerability detection is particularly important. We proposed a novel smart contract reentrancy vulnerability detection model based on BiGAS. We had conducted numerous experiments, and the experimental results showed that our model (BiGAS Detection Model) has a strong vulnerability detection ability. It achieves an accuracy and F1-score of over 93% for the detection of reentrancy vulnerabilities in smart contracts. To verify that the choice of SVM is one of the reasons for improving the performance of our method, Softmax was replaced by the SVM classifier in the model. The accuracy of the model with the classifier replaced with Softmax was 89.78% and the F1-score was 89.83%. In addition, we compared our approach with advanced automated audit tools and other deep learning-based vulnerability detection methods. Compared with the existing advanced methods, the accuracy and F1-score improvement of our model ranges from 4 to 23%. The conclusion was that our method is significantly better than the existing advanced methods in detecting smart contract reentrancy vulnerabilities. [ABSTRACT FROM AUTHOR]

In order to study the damage behaviors of optical mirrors induced by film defects (as film bumps, film pits and film contaminants) in high-power CW laser, a simulation study was carried out, which considered temperature, thermal stress and thermal deformation in results. The results show that the bumps of the film will primarily absorb laser energy, and its temperature, thermal stress and thermal deformation will become extremely high, which is easy to damage the film; similarly, the film pits will also absorb laser energy for its temperature rise, and the pits will expand to the inwards. The larger the size of the pits, the larger the expansion of melting range will be, and eventually damage the film layer; after absorbing the laser energy, the temperature of the film contaminants is very high (for film contaminants size of 150 μm, its maximum temperature reaching 900 K), even exceeding the damage temperature (523 K) that the film can withstand, and finally causing the film to be ablated. [ABSTRACT FROM AUTHOR]

The purpose of this study is to explore a storage solution for titanium implants and investigate its osteogenic properties. The commercial pure titanium (cp-Ti) surface and double-etched (SLA) titanium surface specimens were preserved in air, saline, 10 mM Vitamin C (VitC)-containing saline and 100 mM VitC-containing saline storage solutions for 2 weeks. The surface microtopography of titanium was observed by scanning electron microscopy (SEM), the surface elemental compositions of the specimens were analyzed by Raman and X-ray photoelectron spectroscopy (XPS), and water contact angle and surface roughness of the specimens were tested. The protein adsorption capacity of two titanium surfaces after storage in different media was examined by BCA kit. The MC3T3-E1 osteoblasts were cultured on two titanium surfaces after storage in different media, and the proliferation, adhesion and osteogenic differentiation activity of osteoblasts were detected by CCK-8, laser confocal microscope (CLSM) and Western blot. The SEM results indicated that the titanium surfaces of the air group were relatively clean while scattered sodium chloride or VitC crystals were seen on the titanium surfaces of the other three groups. There were no significant differences in the micromorphology of the titanium surfaces among the four groups. Raman spectroscopy detected VitC crystals on the titanium surfaces of two experimental groups. The XPS, water contact angle and surface roughness results suggested that cp-Ti and SLA-Ti stored in 0.9% NaCl and two VitC-containing saline storage solutions possessed less carbon contamination and higher surface hydrophilicity. Moreover, the protein adsorption potentials of cp-Ti and SLA-Ti surfaces were significantly improved under preservation in two VitC-containing saline storage solutions. The results of in vitro study showed that the preservation of two titanium surfaces in 100 mM VitC-containing saline storage solution upregulated the cell adhesion, proliferation, osteogenic related protein expressions of MC3T3-E1 osteoblasts. In conclusion, preservation of cp-Ti and SLA-Ti in 100 mM VitC-containing saline storage solution could effectively reduce carbon contamination and enhance surface hydrophilicity, which was conducive to osteogenic differentiation of osteoblasts. [ABSTRACT FROM AUTHOR]