Chinese characters hold great potential to help inform and enrich psycholinguistic research on lexical ambiguity as a large portion of them are ambiguous in nature with meaning varying from context to context. This report presents a psycholinguistic database that contains over 2000 characters with normative measures for meaning dominance and meaning balance, that is, the relative frequency of each meaning associated with a target character and the degree of balance across the meanings of the character. The measurement process takes advantage of the fact that, in Chinese, generating words containing a target character is the most convenient way to specify and disambiguate character meanings. Character meanings stored in ordinary people’s mental lexicon are identified based on the words, along with a small portion of meaning descriptions, listed by over 900 native speakers. The measures of meaning dominance and meaning balance for the characters are derived from computing the relative frequencies of the meanings. Potential research and practical applications of the database, as a valuable tool, to enhance our understanding of the acquisition, representation, and processing of ambiguous lexical items are discussed.

This study compared the concentrations, types and distributions of sialic acid (SA) in human milk at different stages of the postnatal period with those in a range of infant formulas. Breast milk from mothers of healthy, full-term and exclusively breastfed infants was collected on the 2nd (n 246), 7th (n 135), 30th (n 85) and 90th (n 48) day after birth. The SA profiles of human milk, including their distribution, were analysed and compared with twenty-four different infant formulas. Outcome of this observational study was the result of natural exposure. Only SA of type Neu5Ac was detected in human milk. Total SA concentrations were highest in colostrum and reduced significantly over the next 3 months. Approximately 68·7–76·1 % of all SA in human milk were bound to oligosaccharides. Two types of SA, Neu5Ac and Neu5Gc, have been detected in infant formulas. Most SA was present in infant formulas combined with protein. Breastfed infants could receive more SA than formula-fed infants with the same energy intake. Overall, human milk is a preferable source of SA than infant formulas in terms of total SA content, dynamics, distribution and type. These SA profiles in the natural state are worth to be considered by the production of formulas because they may have a great effect on infant nutrition and development.

Chinese herbal medicine (CHM), which includes herbal slices and proprietary products, is widely used in China. Shenqi Dihuang (SQDH) is a traditional Chinese medicine (TCM) formula with ingredients that affect tumor growth. Despite recent advances in prognosis, patients with renal cell carcinoma (RCC) cannot currently receive curative treatment. The present study aimed to explore the potential target genes closely associated with SQDH. The gene expression data for SQDH and RCC were obtained from the TCMSP and TCGA databases. The SQDH-based prognostic prediction model reveals a strong correlation between RCC and SQDH. In addition, the immune cell infiltration analysis indicated that SQDH might be associated with the immune response of RCC patients. Based on this, we successfully built the prognostic prediction model using SQDH-related genes. The results demonstrated that CCND1 and NR3C2 are closely associated with the prognosis of RCC patients. Finally, the pathways enrichment analysis revealed that response to oxidative stress, cyclin binding, programmed cell death, and immune response are the most enriched pathways in CCND1. Furthermore, transcription regulator activity, regulation of cell population proliferation, and cyclin binding are closely associated with the NR3C2.

Aerospace represents the development of national science and technology. It is an important foundation for exploring space and an important guarantee for the construction of aerospace power. There are many large workpieces in the aerospace field. The box insulation layer of large workpieces is an important processing problem. A new thick processing equipment is proposed to process the box insulation layer of large workpieces. The thick processing equipment consists of the XYZ shaft long guide rail and five degrees of freedom (5-DOF) RAPA. The mechanical structure of the 5-DOF RAPA is a redundantly actuated parallel mechanism (RAPM). Meanwhile, this paper proposes a new method to design 5-DOF redundantly actuated parallel mechanisms (RAPMs) with large output rotational angles. Based on configuration evolution and Li group, two articulated moving platforms (AMPs) and four kinds of limbs are designed, and a series of 3T2R (T represents translation, R represents rotation) RAPMs and 2T3R RAPMs are synthesized. To verify the designed RAPMs with large angle, an example of RAPMs, 4UPS-{2UPR}-R is analyzed. To ensure that the RAPM has no mechanism vibration impact in movement, this paper represents the RAPM adopts a newly proposed trajectory planning method. The results show that the 4SPU-(2UPR)R mechanism possesses large angles and verifies the efficiency of the new proposed trajectory planning method in simplified trajectories. This work lays the foundation for processing the box insulation layer of large workpieces with straight lines and arcs paths.

The type species of Squamarina has two varieties, S. gypsacea var. gypsacea and S. gypsacea var. subcetrarioides. In this study, a phylogenetic and taxonomic analysis of these two varieties shows that S. var. subcetrarioides merits treatment as a species separate from S. gypsacea. Therefore, we raise this variety to species level as S. subcetrarioides (Zahlbr.) Y. Y. Zhang. Squamarina subcetrarioides is phylogenetically not closely related to S. gypsacea and differs from that species in the thallus forming rosettes when young, later becoming cracked and irregular in outline, and consisting of numerous small squamules.

The strongly nonlinear Miyata–Choi–Camassa model under the rigid lid approximation (MCC-RL model) can describe accurately the dynamics of large-amplitude internal waves in a two-layer fluid system for shallow configurations. In this paper, we apply the MCC-RL model to study the internal waves generated by a moving body on the bottom. For the case of the moving body speed $U=1.1c_{0}$, where ${c_0}$ is the linear long-wave speed, the accuracy of the MCC-RL results is assessed by comparing with Euler's solutions, and very good agreement is observed. It is found that when the moving body speed increases from $U=0.8c_{0}$ to $U=1.241c_{0}$, the amplitudes of the generated internal solitary waves in front of the moving body become larger. However, a critical moving body speed is found between $U=1.241c_{0}$ and $U=1.242c_{0}$. After exceeding this critical speed, only one internal wave right above the body is generated. When the moving body speed increases from $U=1.242c_{0}$ to $U=1.5c_{0}$, the amplitudes of the internal waves become smaller.

Electromagnetic simulation software has become an important tool for antenna design. However, high-fidelity simulation of wideband or ultra-wideband antennas is very expensive. Therefore, antenna optimization design by using an electromagnetic solver may be limited due to its high computational cost. This problem can be alleviated by the utilization of fast and accurate surrogate models. Unfortunately, conventional surrogate models for antenna design are usually prohibitive because training data acquisition is time-consuming. In order to solve the problem, a modeling method named progressive Gaussian process (PGP) is proposed in this study. Specially, when a Gaussian process (GP) is trained, test sample with the largest predictive variance is inputted into an electromagnetic solver to simulate its results. After that, the test sample is added to the training set to train the GP progressively. The process can incrementally increase some important trusted training data and improve the model generalization performance. Based on the proposed PGP, two monopole antennas are optimized. The optimization results show effectiveness and efficiency of the method.

The microstructure and texture evolution of Ni-5 at%W (Ni5W) alloy substrates were investigated by in situ tensile testing along the rolling direction (RD), transverse direction (TD), and at 45° to the RD (45°-RD), as well as by electron backscatter diffraction characterization. The tensile stress direction had a significant influence on the texture evolution. The cubic texture in the Ni-5 at%W alloy exhibited severe degradation when the tensile angle was 45°-RD. In contrast, the cubic texture was relatively stable under high deformation along the RD or TD. It was found that the slip line system in the 45°-RD specimen was the key to the contrasting behavior. The effect of the tensile testing angle on the cubic texture evolution for Ni–W substrates was investigated, and the corresponding effect on the superconducting properties of coated materials was studied.

Advanced Ni8W/Ni12W/Ni8W alloy composite substrates used in YBCO-coated conductors with a strong cube texture and high yield strength have been fabricated, and a CeO2 buffer layer film was successfully deposited on the composite substrates. Through in situ tensile testing coupled with electron backscattered diffraction (EBSD) analysis, the stability of the cube texture of Ni8W/Ni12W/Ni8W alloy composite substrates has been investigated. The stress–strain curve shows that the yield strength (at 0.2% strain) of the composite substrates exceeds 250 Mpa. The orientation of grains and boundaries on the surface of the substrates was almost unchanged, while the strain exceeds 0.2%, which indicated that the composite substrates are adequate for depositing buffer layers and YBCO layers by the reel-to-reel process.

In this article, we present a comprehensive study of asymptotic optimality of least squares model averaging methods. The concept of asymptotic optimality is that in a large-sample sense, the method results in the model averaging estimator with the smallest possible prediction loss among all such estimators. In the literature, asymptotic optimality is usually proved under specific weights restriction or using hardly interpretable assumptions. This article provides a new approach to proving asymptotic optimality, in which a general weight set is adopted, and some easily interpretable assumptions are imposed. In particular, we do not impose any assumptions on the maximum selection risk and allow a larger number of regressors than that of existing studies.

Light-absorbing impurities (LAIs, e.g. black carbon (BC), organic carbon (OC), mineral dust (MD)) deposited on snow cover reduce albedo and accelerate its melting. Northern Xinjiang (NX) is an arid and semi-arid inland region, where snowmelt leads to frequent floods that have been a serious threat to local ecological security. There is still a lack of quantitative assessments of the effects of LAIs on snowmelt in the region. This study investigates spatial variations of LAIs in snow and its effect on snow albedo, radiative forcing (RF) and snowmelt across NX. Results showed that concentrations of BC, OC (only water-insoluble OC), MD ranged from 32 to 8841 ng g−1, 77 to 8568 ng g−1 and 0.46 to 236 µg g−1, respectively. Weather Research and Forecasting Chemistry model suggested that residential emission was the largest source of BC. Snow, Ice, and Aerosol Radiative modelling showed that the average contribution of BC and MD to snow albedo reduction was 17 and 3%, respectively. RF caused by BC significantly exceeded RF caused by MD. In different scenarios, changes in snow cover duration (SCD) caused by BC and MD decreased by 1.36 ± 0.61 to 6.12 ± 3.38 d. Compared with MD, BC was the main dominant factor in reducing snow albedo and SCD across NX.

Altered resting-state functional connectivity (rsFC) has been noted in large-scale functional networks in attention-deficit/hyperactivity disorder (ADHD). However, identifying consistent abnormalities of functional networks is difficult due to varied methods and results across studies. To integrate rsFC alterations and search for coherent patterns of intrinsic functional network impairments in ADHD, this research conducts a coordinate-based meta-analysis of voxel-wise seed-based rsFC studies comparing rsFC between ADHD patients and healthy controls. A total of 25 datasets from 21 studies including 700 ADHD patients and 580 controls were analyzed. We extracted the coordinates of seeds and between-group effects. Each seed was then categorized into a seed-network by its location within priori 7-network parcellations. Then, pooled meta-analyses were conducted for the default mode network (DMN), frontoparietal network (FPN) and affective network (AN) separately, but not for the ventral attention network (VAN), dorsal attention network (DAN), somatosensory network (SSN) and visual network due to a lack of primary studies. The results showed that ADHD was characterized by hyperconnectivity between the FPN and regions of the DMN and AN as well as hypoconnectivity between the FPN and regions of the VAN and SSN. These findings not only support the triple-network model of pathophysiology associated with ADHD but also extend this model by highlighting the involvement of the SSN and AN in the mechanisms of network interactions that may account for motor hyperactivity and impulsive symptoms.

Using ethanol adsorption calorimetry, the surface energetics of two carbon substrates and two products in microwave-assisted carbon nanotube (CNT) growth was studied. In this study, the ethanol adsorption enthalpies of the two graphene-based samples at 25 °C were measured successfully. Specifically, the near-zero differential enthalpies of ethanol adsorption are −75.7 kJ/mol for graphene and −63.4 kJ/mol for CNT-grafted graphene. Subsequently, the differential enthalpy curve of each sample becomes less exothermic until reaching a plateau, −55.8 kJ/mol for graphene and −49.7 kJ/mol for CNT-grafted graphene, suggesting favorable adsorbate–adsorbent binding. Moreover, the authors interpreted and discussed the partial molar entropy and chemical potential of adsorption as the ethanol surface coverage (loading) increases. Due to the low surface areas of carbon black–based samples, adsorption calorimetry could not be performed. This model study demonstrates that using adsorption calorimetry as a fundamental tool and ethanol as the molecular probe, the overall surface energetics of high–surface area carbon materials can be estimated.

The absence of a regional, open water vessel collision risk assessment system endangers maritime traffic and hampers safety management. Most recent studies have analysed the risk of collision for a pair of vessels and propose micro-level risk models. This study proposes a new method that combines density complexity and a multi-vessel collision risk operator for assessing regional vessel collision risk. This regional model considers spatial and temporal features of vessel trajectory in an open water area and assesses multi-vessel near-miss collision risk through danger probabilities and possible consequences of collision risks via four types of possible relative striking positions. Finally, the clustering method of multi-vessel encountering risk, based on the proposed model, is used to identify high-risk collision areas, which allow reliable and accurate analysis to aid implementation of safety measures.

In order to investigate the benefits of compound waterways more fully, this study reveals vessel navigational mode and traffic conflicts in a compound waterway through a case analysis, following which a type of simplified prototype of a compound waterway is proposed and three key conflict areas are specified. Based on the three key sub-models of slot allocation for vessels in a waterway entrance, traffic flow conversion of a main and auxiliary waterway in a precautionary area, and traffic flow coordination of division and confluence in a Y crossing area, a vessel traffic scheduling optimisation model is presented, with the minimum waterway occupancy time and minimum total waiting time of vessels as the objective. Furthermore, a multi-objective genetic algorithm is proposed to solve the model and a simulation experiment is carried out. By analysing the optimised solution and comparing it with other scheduling schemes in common use, the results indicate that this method can effectively improve navigation safety and efficiency in a compound waterway.

A mixture copula is a linear combination of several individual copulas that can be used to generate dependence structures not belonging to existing copula families. Because different pairs of markets may exhibit quite different dependence structures in empirical studies, mixture copulas are useful in modeling the dependence in financial data. Therefore, rather than selecting a single copula based on certain criteria, we propose using a model averaging approach to estimate financial data dependence structures in a mixture copula framework. We select weights (for averaging) by a J-fold Cross-Validation procedure. We prove that the model averaging estimator is asymptotically optimal in the sense that it minimizes the squared estimation loss. Our simulation results show that the model averaging approach outperforms some competing methods when the working mixture model is misspecified. Using 12 years of data on daily returns from four developed economies’ stock indexes, we show that the model averaging approach more accurately estimates their dependence structures than some competing methods.