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ABSTRACTβ-Si3N4 whiskers were synthesized by using a moderately low-temperature heat-treatment process, with the aid of a low weight fraction of multiple aids (Y2O3-MgO). The synthesis conditions and aspect ratio of the whiskers were adjusted by changing the MgO content in the composite additive system, heat treatment temperature and holding time. The phase composition and morphology of the whiskers were analyzed by XRD and SEM, respectively. The phase transformation could be completed at 1700°C by introducing only 0.5 wt% MgO. The heat-treated temperature was successfully further reduced to 1650°C by extending the holding time to 5 h. The introduction of MgO can make the whiskers form rapidly at a lower temperature. By introducing a small amount of MgO and appropriately extending the holding time at a lower treatment temperature has been proved to be an effective method to obtain β-Si3N4 whiskers with higher aspect ratio under mild conditions.

The problem of efficient quantum state learning, also called shadow tomography, aims to comprehend an unknown $d$-dimensional quantum state through POVMs. Yet, these states are rarely static; they evolve due to factors such as measurements, environmental noise, or inherent Hamiltonian state transitions. This paper leverages techniques from adaptive online learning to keep pace with such state changes. The key metrics considered for learning in these mutable environments are enhanced notions of regret, specifically adaptive and dynamic regret. We present adaptive and dynamic regret bounds for online shadow tomography, which are polynomial in the number of qubits and sublinear in the number of measurements. To support our theoretical findings, we include numerical experiments that validate our proposed models.

Digital quantum simulation has broad applications in approximating unitary evolution of Hamiltonians. In practice, many simulation tasks for quantum systems focus on quantum states in the low-energy subspace instead of the entire Hilbert space. In this paper, we systematically investigate the complexity of digital quantum simulation based on product formulas in the low-energy subspace. We show that the simulation error depends on the effective low-energy norm of the Hamiltonian for a variety of digital quantum simulation algorithms and quantum systems, allowing improvements over the previous complexities for full unitary simulations even for imperfect state preparations due to thermalization. In particular, for simulating spin models in the low-energy subspace, we prove that randomized product formulas such as qDRIFT and random permutation require smaller Trotter numbers. Such improvement also persists in symmetry-protected digital quantum simulations. We prove a similar improvement in simulating the dynamics of power-law quantum interactions. We also provide a query lower bound for general digital quantum simulations in the low-energy subspace.

An NMR-centered approach demonstrates that the liquid-liquid phase separation behavior of the SARS-CoV-2 nucleocapsid (N) protein is modulated by competitive interactions with ATP and nucleic acids.

Probing ssDNA binding to TAR-DNA-binding protein-43 (TDP-43) prion-like domain (PLD) through NMR shows key interactions for nucleic acid to drive liquid-liquid phase separation (LLPS) of intrinsically-disordered proteins.

Autonomous driving is a promising way to future safe, efficient, and low-carbon transportation. Real-time accurate target detection is an essential precondition for the generation of proper following decision and control signals. However, considering the complex practical scenarios, accurate recognition of occluded targets is a major challenge of target detection for autonomous driving with limited computational capability. To reveal the overlap and difference between various occluded object detection by sharing the same available sensors, this paper presents a review of detection methods for occluded objects in complex real-driving scenarios. Considering the rapid development of autonomous driving technologies, the research analyzed in this study is limited to the recent five years. The study of occluded object detection is divided into three parts, namely occluded vehicles, pedestrians and traffic signs. This paper provided a detailed summary of the target detection methods used in these three parts according to the differences in detection methods and ideas, which is followed by the comparison of advantages and disadvantages of different detection methods for the same object. Finally, the shortcomings and limitations of the existing detection methods are summarized, and the challenges and future development prospects in this field are discussed.

BackgroundWhile the effectiveness of several occupational healthcare interventions has been demonstrated, successful implementation of such programs among internal migrant workers (IMWs) in small and medium-sized enterprises (SMEs) has been limited. This study aimed to evaluate the implementation of a three-arm cluster randomized controlled trial promoting respiratory protective equipment (RPE) use among IMWs exposed to organic solvents in SMEs and to assess the association between participants' compliance and effectiveness of intervention.MethodsA total of 60 SMEs were randomly allocated to a low- or high-intensive intervention group, or a control group that did not receive any intervention. The low-intensive intervention group was subjected to both traditional and mHealth occupational health education. The high-intensive intervention group was subjected to the low-intensive group activities and peer education. The Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework was used to guide implementation evaluation of this 6-months intervention. Generalized linear mixed models (GLMMs) were used to evaluate the effects of participants' compliance with the intervention on the primary outcomes, regarding the appropriate use of RPE.ResultsOf 4,527 potentially eligible participants, 1,211 individuals were enrolled, with a reach rate of 26.8%. Sixty of the 66 SMEs approached (90.9%) SMEs adopted the intervention. Fidelity to traditional education (100.0%) and mHealth intervention (97.5%) was higher than fidelity to peer education (20.0%). Peer leaders cited inconvenient time and unfamiliarity with peers as two major barriers to delivering peer education. Compared with the control group, IMWs who complied with the interventions in both groups were more likely to wear RPE appropriately [low-intensive group: adjusted odds ratio (aOR) = 2.58, 95% confidence interval (CI): 1.56–4.28; high-intensive group: aOR = 7.52, 95% CI: 3.72–15.23]. Most participants (95.8%) were satisfied with the program and 93.1% stated that they would maintain the use of RPE in the future.ConclusionsA multi-component occupational health intervention to promote the use of RPE among IMWs in SMEs was feasible and acceptable. Peer education had great potential to enhance the occupational health behavior of IMWs, and thus strategies to improve participants' adherence to this component warrant further investigation.Clinical Trial Registrationhttp://www.chictr.org.cn, identifier: ChiCTR-IOR-15006929.

Classical algorithms are often not effective for solving nonconvex optimization problems where local minima are separated by high barriers. In this paper, we explore possible quantum speedups for nonconvex optimization by leveraging the $global$ effect of quantum tunneling. Specifically, we introduce a quantum algorithm termed the quantum tunneling walk (QTW) and apply it to nonconvex problems where local minima are approximately global minima. We show that QTW achieves quantum speedup over classical stochastic gradient descents (SGD) when the barriers between different local minima are high but thin and the minima are flat. Based on this observation, we construct a specific double-well landscape, where classical algorithms cannot efficiently hit one target well knowing the other well but QTW can when given proper initial states near the known well. Finally, we corroborate our findings with numerical experiments.

Quantum simulation is a prominent application of quantum computers. While there is extensive previous work on simulating finite-dimensional systems, less is known about quantum algorithms for real-space dynamics. We conduct a systematic study of such algorithms. In particular, we show that the dynamics of a $d$-dimensional Schrödinger equation with $\eta$ particles can be simulated with gate complexity $\tilde{O}\bigl(\eta d F \text{poly}(\log(g'/\epsilon))\bigr)$, where $\epsilon$ is the discretization error, $g'$ controls the higher-order derivatives of the wave function, and $F$ measures the time-integrated strength of the potential. Compared to the best previous results, this exponentially improves the dependence on $\epsilon$ and $g'$ from $\text{poly}(g'/\epsilon)$ to $\text{poly}(\log(g'/\epsilon))$ and polynomially improves the dependence on $T$ and $d$, while maintaining best known performance with respect to $\eta$. For the case of Coulomb interactions, we give an algorithm using $\eta^{3}(d+\eta)T\text{poly}(\log(\eta dTg'/(\Delta\epsilon)))/\Delta$ one- and two-qubit gates, and another using $\eta^{3}(4d)^{d/2}T\text{poly}(\log(\eta dTg'/(\Delta\epsilon)))/\Delta$ one- and two-qubit gates and QRAM operations, where $T$ is the evolution time and the parameter $\Delta$ regulates the unbounded Coulomb interaction. We give applications to several computational problems, including faster real-space simulation of quantum chemistry, rigorous analysis of discretization error for simulation of a uniform electron gas, and a quadratic improvement to a quantum algorithm for escaping saddle points in nonconvex optimization.

In recent years, strontium-substituted calcium phosphate bone cement (Sr-CPC) has attracted more and more attentions in the field of bone tissue repair due to its comprehensive advantages of both traditional CPC and Sr ions. In this study, a crucial Sr-containing α-Ca3–xSrx(PO4)2 salt has been synthesized using a simplified one-step method at lower synthesis temperature. A novel Sr-CPC has been developed based on the simple binary Sr-containing α-Ca3–xSrx(PO4)2/Ca4(PO4)2O cement powder. The physicochemical properties and hydration mechanism of this Sr-CPC at various Sr contents were intensively investigated. The setting product of this Sr-CPC after a set for 72 h is a single-phase Sr-containing hydroxyapatite, and its compressive strength slightly decreased and its setting time extended with the increase of Sr content. The hydration process included the initial formation of the medium product CaHPO4⋅2H2O (30 min∼1 h), the following complete hydration of Ca4(PO4)2O and the initially formed CaHPO4⋅2H2O (2∼6 h), and the final self-setting of α-Ca3–xSrx(PO4)2 (6 h∼). The compressive strength of Sr-CPC, which was closely related to the transformation rate of Sr-containing hydroxyapatite, tended to increase with the extension of hydration time. In addition, Sr-CPC possessed favorable cytocompatibility and the effect of Sr ions on cytocompatibility of Sr-CPC was not obvious at low Sr contents. The present study suggests α-Ca3–xSrx(PO4)2 is a kind of vital Sr-containing salt source which is useful to develop some novel Sr-containing biomaterials. In addition, the new Sr-containing cement system based on this simple binary α-Ca3–xSrx(PO4)2/Ca4(PO4)2O cement powder displayed an attractive clinical application potential in orthopedics.

We initiate the study of quantum algorithms for escaping from saddle points with provable guarantee. Given a function $f\colon\mathbb{R}^{n}\to\mathbb{R}$, our quantum algorithm outputs an $\epsilon$-approximate second-order stationary point using $\tilde{O}(\log^{2} (n)/\epsilon^{1.75})$ queries to the quantum evaluation oracle (i.e., the zeroth-order oracle). Compared to the classical state-of-the-art algorithm by Jin et al. with $\tilde{O}(\log^{6} (n)/\epsilon^{1.75})$ queries to the gradient oracle (i.e., the first-order oracle), our quantum algorithm is polynomially better in terms of $\log n$ and matches its complexity in terms of $1/\epsilon$. Technically, our main contribution is the idea of replacing the classical perturbations in gradient descent methods by simulating quantum wave equations, which constitutes the improvement in the quantum query complexity with $\log n$ factors for escaping from saddle points. We also show how to use a quantum gradient computation algorithm due to Jordan to replace the classical gradient queries by quantum evaluation queries with the same complexity. Finally, we also perform numerical experiments that support our theoretical findings.

Abstract Background In China, most of migrant workers work in the small and medium-sized enterprises (SMEs) and are a vulnerable group for occupational health. Migrant workers are at increased risk of occupational health risks due to poor occupational health behaviours such as the low use of personal protective equipment (PPE). However, there is a lack of solid evidence regarding how to improve the use of PPE among migrant workers in SMEs. The current study will assess the effectiveness of a multi-pronged behavioural intervention designed to promote PPE utilization among migrant workers exposed to organic solvents in SMEs. Methods/Design This is a single blind, three-arm cluster randomized trial with 60 SMEs equally randomized to receive a top-down intervention (i.e. general health education and mHealth intervention provided by researchers) or a comprehensive intervention (which includes both top-down intervention and peer education) or a control condition (participants will not receive the intervention, but study measures will be obtained). Interventions will be conducted at the SMEs level for 6 months and all eligible migrant workers in these SMEs will be enrolled into the trial. The primary outcome is effective use of PPE during the last week. The secondary outcomes are occupational health knowledge and attitude and participation in occupational health check-up. Data will be collected and assessed at baseline; 3 months post baseline and the end of the intervention. Discussion This theory- and evidence based intervention will contribute to the limited evidence of behaviour change intervention in improving PPE utilization of migrant workers in SMEs, and provide timely evidence for the development of basic occupational health services in China and elsewhere with similar industrialization contexts. Trial registration ChiCTR-IOR-15006929 . Registered on 16 August 2015.

While recent work suggests that quantum computers can speed up the solution of semidefinite programs, little is known about the quantum complexity of more general convex optimization. We present a quantum algorithm that can optimize a convex function over an $n$-dimensional convex body using $\tilde{O}(n)$ queries to oracles that evaluate the objective function and determine membership in the convex body. This represents a quadratic improvement over the best-known classical algorithm. We also study limitations on the power of quantum computers for general convex optimization, showing that it requires $\tilde{\Omega}(\sqrt n)$ evaluation queries and $\Omega(\sqrt{n})$ membership queries.

This paper addresses a new reliability model, based on energy dissipation, considering performance degradation behaviors. Different from the two-state reliability model and traditional reliability model based on failure rate statistics, this paper focuses on the component energy loss due to its fault evolution, such as fatigue, aging and wear, and presents a reliability model based on the component’s energy dissipation, as well as establishing a power dissipation constrained reliability model for degradation-based reliability assessment. As a demonstration, the proposed method is applied to model and evaluate the failure behavior of the electro-hydrostatic actuation system. The results indicate that the proposed method is effective in describing its life-cycle degradation in the energy field, and provides a reliability assessment based on energy dissipation.

Leakage is the most important failure mode in aircraft hydraulic systems caused by wear and tear between friction pairs of components. The accurate detection of abrasive debris can reveal the wear condition and predict a system’s lifespan. The radial magnetic field (RMF)-based debris detection method provides an online solution for monitoring the wear condition intuitively, which potentially enables a more accurate diagnosis and prognosis on the aviation hydraulic system’s ongoing failures. To address the serious mixing of pipe abrasive debris, this paper focuses on the superimposed abrasive debris separation of an RMF abrasive sensor based on the degenerate unmixing estimation technique. Through accurately separating and calculating the morphology and amount of the abrasive debris, the RMF-based abrasive sensor can provide the system with wear trend and sizes estimation of the wear particles. A well-designed experiment was conducted and the result shows that the proposed method can effectively separate the mixed debris and give an accurate count of the debris based on RMF abrasive sensor detection.