Chinese spelling correction has achieved significant progress, but critical challenges remain, especially in handling visually and phonetically similar errors within complex syntactic structures. This paper introduces a novel approach combining a Long Short-Term Memory Network (LSTM)-enhanced Transformer for error detection and Bidirectional Encoder Representations from Transformers (BERT)-based correction with a dynamic adaptive weighting scheme. Transformer uses global attention mechanism to capture dependencies between any two positions in the input sequence. By processing each token in the sequence recursively, LSTM is able to more finely capture local context and sequential information within the sequence. Based on adaptive weighting coefficient, weights of multi-task learning are automatically adjusted to help the model better balance the learning process between the detection and correction network, enabling it to converge faster and achieve higher precision. Comprehensive evaluations demonstrate improved performance over existing baselines, particularly in addressing complex error patterns.

This study explores the dynamics of flexible ribbons with an added weight $G$ at the tail in uniform flow, considering key parameters like inflow Reynolds number ($Re_u$), mass ratio ($M_t$) and aspect ratio (${A{\kern-4pt}R}$). For two-dimensional ribbons, a simplified theoretical model accurately predicts equilibrium configurations and forces. Inspired by Barois & De Langre (J. Fluid Mech., vol. 735, 2013, R2), we introduce an important control parameter ($C_G$) that effectively collapses normalized forces and angle data. Vortex-induced vibration is observed, and Strouhal number ($St$) scaling laws with $C_G$ are identified. In three-dimensional scenarios, the model effectively predicts lift, but its accuracy in predicting drag is limited to situations with small $Re_u$ values. The flow along the side edges mitigates pressure differences, thereby suppressing vibration and uplift, particularly noticeable in the case of narrow ribbons. This study offers new insights into the dynamics of flexible bodies in uniform flow.

Early detection and correction of defects are critical in additive manufacturing (AM) to avoid build failures. In this paper, we present a multisensor fusion-based digital twin for in-situ quality monitoring and defect correction in a robotic laser-directed energy deposition process. Multisensor fusion sources consist of an acoustic sensor, an infrared thermal camera, a coaxial vision camera, and a laser line scanner. The key novelty and contribution of this work are to develop a spatiotemporal data fusion method that synchronizes and registers the multisensor features within the part's 3D volume. The fused dataset can be used to predict location-specific quality using machine learning. On-the-fly identification of regions requiring material addition or removal is feasible. Robot toolpath and auto-tuned process parameters are generated for defect correction. In contrast to traditional single-sensor-based monitoring, multisensor fusion allows for a more in-depth understanding of underlying process physics, such as pore formation and laser-material interactions. The proposed methods pave the way for self-adaptation AM with higher efficiency, less waste, and cleaner production.

The laser-induced damage threshold (LIDT) of plate laser beam splitter (PLBS) coatings is closely related to the subsurface absorption defects of the substrate. Herein, a two-step deposition temperature method is proposed to understand the effect of substrate subsurface impurity defects on the LIDT of PLBS coatings. Firstly, BK7 substrates are heat-treated at three different temperatures. The surface morphology and subsurface impurity defect distribution of the substrate before and after the heat treatment are compared. Then, a PLBS coating consisting of alternating HfO2–Al2O3 mixture and SiO2 layers is designed to achieve a beam-splitting ratio (transmittance to reflectance, s-polarized light) of approximately 50:50 at 1053 nm and an angle of incidence of 45°, and it is prepared under four different deposition processes. The experimental and simulation results show that the subsurface impurity defects of the substrate migrate to the surface and accumulate on the surface during the heat treatment, and become absorption defect sources or nodule defect seeds in the coating, reducing the LIDT of the coating. The higher the heat treatment temperature, the more evident the migration and accumulation of impurity defects. A lower deposition temperature (at which the coating can be fully oxidized) helps to improve the LIDT of the PLBS coating. When the deposition temperature is 140°C, the LIDT (s-polarized light, wavelength: 1064 nm, pulse width: 9 ns, incident angle: 45°) of the PLBS coating is 26.2 J/cm2, which is approximately 6.7 times that of the PLBS coating deposited at 200°C. We believe that the investigation into the laser damage mechanism of PLBS coatings will help to improve the LIDT of coatings with partial or high transmittance at laser wavelengths.

Cognitive decline is a public health problem for the world’s ageing population. This study was to evaluate the relationships between serum Fe, blood Pb, Cd, Hg, Se and Mn and cognitive decline in elderly Americans. Data of this cross-sectional study were extracted from the National Health and Nutritional Examination Survey (NHANES 2011–2014). Cognitive performance was measured by the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), Animal Fluency and Digit Symbol Substitution Test (DSST) tests. Weighted univariable and multivariate logistic regression analyses were used to assess the associations between six trace elements and low cognitive performance. Subgroup analyses based on diabetes and hypertension history were further assessed the associations. A total of 2002 adults over 60 years old were included. After adjusting covariates, elevated serum Fe levels were associated with the decreased risk of low cognitive performance, especially in the elderly without diabetes history and with hypertension history. High blood Cd levels were associated with the high odds of low cognitive performance in old adults with diabetes and hypertension history. Elevated blood Mn levels were connected with low cognitive performance in old hypertensive people. High blood Pb levels were related to the high odds of low cognitive performance, especially in the elderly without diabetes and hypertension history. High blood Se levels were linked to the decreased risk of low cognitive performance in all the elderly. Appropriate Fe, Se supplementation and Fe-, Se-rich foods intake, while reducing exposure to Pb, Cd and Mn may be beneficial for cognitive function in the elderly.

Fine fibre immersed in different flows is ubiquitous. For a fibre in shear flows, most motion modes appear in the flow-gradient plane. Here the two-dimensional behaviours of an individual flexible flap in channel flows are studied. The nonlinear coupling of the fluid inertia ($\textit {Re}$), flexibility of the flap ($K$) and channel width ($W$) is discovered. Inside a wide channel (e.g. $W=4$), as $K$ decreases, the flap adopts rigid motion, springy motion, snake turn and complex mode in sequence. It is found that the fluid inertia tends to straighten the flap. Moreover, $\textit {Re}$ significantly affects the lateral equilibrium location $y_{eq}$, therefore affecting the local shear rate and the tumbling period $T$. For a rigid flap in a wide channel, when $\textit {Re}$ exceeds a threshold, the flap stays inclined instead of tumbling. As $\textit {Re}$ further increases, the flap adopts swinging mode. In addition, there is a scaling law between $T$ and $\textit {Re}$. For the effect of $K$, through the analysis of the torque generated by surrounding fluid, we found that a smaller $K$ slows down the tumbling of the flap even if $y_{eq}$ is comparable. As $W$ decreases, the wall confinement effect makes the flap easier to deform and closer to the centreline. The tumbling period would increase and the swinging mode would be more common. When $W$ further decreases, the flaps are constrained to stay inclined, parabolic-like or one-end bending configurations moving along with the flow. Our study may shed some light on the behaviours of a free fibre in flows.

This paper proposes an aerodynamic analysis of the shuttlecock and a novel method for predicting shuttlecock trajectory. First, we have established a shuttlecock track data set by an infrared-based binocular vision system. Then the unscented Kalman filter algorithm is designed to further filter the noise and visual recognition algorithm errors. Third, the radial basis function (RBF)-based track prediction model is designed. This method offers a concept to obtain the neural network model of different kinds of flying or moving objects. The experimental results show that the proposed method can predict the shuttlecock trajectory in real time at high accuracy and can be used for implementing the algorithm of return strategies in the near future.

Self-propulsive performances of the flexible plates undergoing pitching and heaving motions are investigated numerically. The effects of multiple key dimensionless parameters are considered, such as bending stiffness, heaving amplitude, pitching amplitude and flapping frequency. Despite so many influence factors, results indicate that the cruising speed $U$ (or the cruising Reynolds number $Re_c$), the thrust $T$ and the input power $P$ can be summarized as some simple scaling laws vs the flapping Reynolds number $Re_f$. In the heaving motion, the scaling laws may be not fully independent of bending stiffness because in the motion the role of bending stiffness is more complicated for the thrust generation. Our scaling laws are well supported by biological data on swimming aquatic animals.

The purpose of our study was to elucidate the functions of miR-30c-5p on adenomyosis for exploring novel treatment strategies. We first detected the expression of miR-30c-5p in clinical adenomyotic tissues and isolated endometrial cells from adenomyotic tissues. Next, gain and loss-of-function assays were performed to detect the effect of miR-30c-5p on adenomyotic endometrial cells. Further, luciferase assay and real-time polymerase chain reaction as well as western blot were conducted to investigate the potential target of miR-30c-5p; and transwell assay, wound-healing assay and CCK-8 assay were used to evaluate the effects of miR-30c-5p and its target on regulating biological functions of adenomyotic endometrial cells. Our results found that miR-30c-5p was down-regulated in both adenomyosis tissues and adenomyotic epithelial cells, which correlated with dysmenorrhea, longer duration of symptoms and more menstrual bleeding. Moreover, the overexpression of miR-30c-5p inhibited the proliferation, migration and invasion of adenomyotic epithelial cells, where miR-30c-5p knockdown had an opposite effect. Furthermore, we confirmed mitogen-activated protein kinase 1 (MAPK1) was one of the direct targets of miR-30c-5p, indicating its important role in miR-30c-5p-mediated suppression of proliferation, invasion and migration in adenomyotic epithelial cells. This study showed that the interaction of miR-30c-5p with MAPK1 can regulate the proliferation, invasion and migration in adenomyotic epithelial cells.

The development of a consistent framework for Calphad model sensitivity is necessary for the rational reduction of uncertainty via new models and experiments. In the present work, a sensitivity theory for Calphad was developed, and a closed-form expression for the log-likelihood gradient and Hessian of a multi-phase equilibrium measurement was presented. The inherent locality of the defined sensitivity metric was mitigated through the use of Monte Carlo averaging. A case study of the Cr–Ni system was used to demonstrate visualizations and analyses enabled by the developed theory. Criteria based on the classical Cramér–Rao bound were shown to be a useful diagnostic in assessing the accuracy of parameter covariance estimates from Markov Chain Monte Carlo. The developed sensitivity framework was applied to estimate the statistical value of phase equilibria measurements in comparison with thermochemical measurements, with implications for Calphad model uncertainty reduction.

Corona Virus Disease 2019 (COVID-19) has presented an unprecedented challenge to the health-care system across the world. The current study aims to identify the determinants of illness severity of COVID-19 based on ordinal responses. A retrospective cohort of COVID-19 patients from four hospitals in three provinces in China was established, and 598 patients were included from 1 January to 8 March 2020, and divided into moderate, severe and critical illness group. Relative variables were retrieved from electronic medical records. The univariate and multivariate ordinal logistic regression models were fitted to identify the independent predictors of illness severity. The cohort included 400 (66.89%) moderate cases, 85 (14.21%) severe and 113 (18.90%) critical cases, of whom 79 died during hospitalisation as of 28 April. Patients in the age group of 70+ years (OR = 3.419, 95% CI: 1.596–7.323), age of 40–69 years (OR = 1.586, 95% CI: 0.824–3.053), hypertension (OR = 3.372, 95% CI: 2.185–5.202), ALT >50 μ/l (OR = 3.304, 95% CI: 2.107–5.180), cTnI >0.04 ng/ml (OR = 7.464, 95% CI: 4.292–12.980), myohaemoglobin>48.8 ng/ml (OR = 2.214, 95% CI: 1.42–3.453) had greater risk of developing worse severity of illness. The interval between illness onset and diagnosis (OR = 1.056, 95% CI: 1.012–1.101) and interval between illness onset and admission (OR = 1.048, 95% CI: 1.009–1.087) were independent significant predictors of illness severity. Patients of critical illness suffered from inferior survival, as compared with patients in the severe group (HR = 14.309, 95% CI: 5.585–36.659) and in the moderate group (HR = 41.021, 95% CI: 17.588–95.678). Our findings highlight that the identified determinants may help to predict the risk of developing more severe illness among COVID-19 patients and contribute to optimising arrangement of health resources.

This article presents a brief review of our case studies of data-driven Integrated Computational Materials Engineering (ICME) for intelligently discovering advanced structural metal materials, including light-weight materials (Ti, Mg, and Al alloys), refractory high-entropy alloys, and superalloys. The basic bonding in terms of topology and electronic structures is recommended to be considered as the building blocks/units constructing the microstructures of advanced materials. It is highlighted that the bonding charge density could not only provide an atomic and electronic insight into the physical nature of chemical bond of materials but also reveal the fundamental strengthening/embrittlement mechanisms and the local phase transformations of planar defects, paving a path in accelerating the development of advanced metal materials via interfacial engineering. Perspectives on the knowledge-based modeling/simulations, machine-learning knowledge base, platform, and next-generation workforce for sustainable ecosystem of ICME are highlighted, thus to call for more duty on the developments of advanced structural metal materials and enhancement of research productivity and collaboration.

Archaeological research on food-production systems has focused heavily on the origins of agriculture and animal domestication; the agricultural practices of early states are comparatively less well understood. This article explores archaeological evidence for crop cultivation, field-management practices and the use of farming implements at the Western Han (202 BC–AD 8) village of Sanyangzhuang in Henan Province, China. The authors analyse the implications of these practices for the newly developed smallholder mode of production. By combining diverse strands of evidence, this investigation provides new insights into the status of agricultural production in the Central Plains during the Western Han Dynasty.

The software package ESPEI has been developed for efficient evaluation of thermodynamic model parameters within the CALPHAD method. ESPEI uses a linear fitting strategy to parameterize Gibbs energy functions of single phases based on their thermochemical data and refines the model parameters using phase equilibrium data through Bayesian parameter estimation within a Markov Chain Monte Carlo machine learning approach. In this paper, the methodologies employed in ESPEI are discussed in detail and demonstrated for the Cu–Mg system down to 0 K using unary descriptions based on segmented regression. The model parameter uncertainties are quantified and propagated to the Gibbs energy functions.

We obtain a non-trivial bound for cancellations between the Kloosterman sums modulo a large prime power with a prime argument running over very short intervals, which in turn is based on a new estimate on bilinear sums of Kloosterman sums. These results are analogues of those obtained by various authors for Kloosterman sums modulo a prime. However, the underlying technique is different and allows us to obtain non-trivial results starting from much shorter ranges.

Functionally graded materials (FGMs) in which the elemental composition intentionally varies with position can be fabricated using directed energy deposition additive manufacturing (AM). This work examines an FGM that is linearly graded from V to Invar 36 (64 wt% Fe, 36 wt% Ni). This FGM cracked during fabrication, indicating the formation of detrimental phases. The microstructure, composition, phases, and microhardness of the gradient zone were analyzed experimentally. The phase composition as a function of chemistry was predicted through thermodynamic calculations. It was determined that a significant amount of the intermetallic σ-FeV phase formed within the gradient zone. When the σ phase constituted the majority phase, catastrophic cracking occurred. The approach presented illustrates the suitability of using equilibrium thermodynamic calculations for the prediction of phase formation in FGMs made by AM despite the nonequilibrium conditions in AM, providing a route for the computationally informed design of FGMs.

Phase stability, elastic, and thermodynamic properties of (Co,Ni)3(Al,Mo,Nb) with the L12 structure have been investigated by first-principles calculations. Calculated phonon density of states show that (Co,Ni)3(Al,Mo,Nb) is dynamically stable, and calculated elastic constants indicate that (Co,Ni)3(Al,Mo,Nb) possesses intrinsic ductility. Young’s and shear moduli of the simulated polycrystalline (Co,Ni)3(Al,Mo,Nb) phase are calculated using the Voigt–Reuss–Hill approach and are found to be smaller than those of Co3(Al,W). Calculated electronic density of states depicts covalent-like bonding existing in (Co,Ni)3(Al,Mo,Nb). Temperature-dependent thermodynamic properties of (Co,Ni)3(Al,Mo,Nb) can be described satisfactorily using the Debye–Grüneisen approach, including heat capacity, entropy, enthalpy, and linear thermal expansion coefficient. Predicted heat capacity, entropy, and linear thermal expansion coefficient of (Co,Ni)3(Al,Mo,Nb) show significant change as a function of temperature. Furthermore the obtained data can be used in the modeling of thermodynamic and mechanical properties of Co-based alloys to enable the design of high temperature alloys.