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Objective: Artificial hip arthroplasty (AHA) is widely accepted in elderly patients with femoral neck fractures, but it is associated with high risk of death and various postoperative complications due to old age and accompanying chronic diseases. Therefore, this study aimed to explore the risk factors for death in elderly patients with femoral neck fractures after AHA and to establish a nomogram risk prediction model, which is expected to reveal high‐risk patients and improve the postoperative quality of life and survival rate of patients. Methods: Elderly patients who underwent AHA for femoral neck fractures in our hospital from September 2014 to May 2021were retrospectively analyzed. These patients were divided into a survival group and a death group according to their clinical outcomes. The following clinical data were recorded for the patients in the two groups: sex, age, underlying diseases, smoking and drinking history, preoperative nutritional risk score (NRS) and American Society of Anesthesiologists (ASA) score, as well as relevant indicators about the operation. These data were subject to univariate analysis and then logistic analysis to determine the risk factors of death. Subsequently, a nomogram risk prediction model was established and further validated with the receiver operating characteristic curve (ROC) and the Hosmer–Lemeshow test. Finally, the effects of predictive risk factors were analyzed using the Kaplan–Meier survival curve. Results: Follow‐up was completed by 260 patients, including 206 patients in the survival group and 54 patients in the death group; the overall death rate was 20.77%, and the follow‐up time, age, postoperative 1, 3 and 5‐year death rates were 3.47 ± 1.93 years, 75.32 ± 9.12 years, 5.77%, 12.51%, and 25.61%, respectively. The top three causes of death in 54 patients were respiratory disease, cerebrocardiovascular disease, and digestive disease, respectively. The logistic analysis indicated that elderly patients with femoral neck fractures, the risk factors for death after AHA were age ≥ 80 years, preoperative NRS ≥ 4, HB ≤ 90 g/L, CR ≥ 110 umol/L, and ASA score ≥ 3, as well as postoperative albumin ≤ 35 g/L, the nomogram was established, and then its predictive performance was successfully validated using the ROC curve (AUC = 0.814, 95% confidence interval = 0.749–0.879) and the Hosmer–Lemeshow test (p = 0.840). Furthermore, Kaplan–Meier survival curve analysis revealed that the abovementioned six indicators were correlated with the post‐AHA survival time of elderly patients with femoral neck fractures (pLog Rank < 0.05). Conclusion: Old age, preoperatively high NRS and ASA score, anemia, poor renal function, and postoperative hypoproteinemia are the major risk factors for death in elderly patients with femoral neck fractures after AHA; they are also associated with postoperative survival. Early identification and effective interventions for optimization of modifiable risk factors are recommended to improve the postoperative quality of life and survival rates. [ABSTRACT FROM AUTHOR]

Surrogate models have been used to emulate mathematical simulators of physical or biological processes for computational efficiency. High‐speed simulation is crucial for conducting uncertainty quantification (UQ) when the simulation must repeat over many randomly sampled input points (aka the Monte Carlo method). A simulator can be so computationally intensive that UQ is only feasible with a surrogate model. Recently, deep neural network (DNN) surrogate models have gained popularity for their state‐of‐the‐art emulation accuracy. However, it is well‐known that DNN is prone to severe errors when input data are perturbed in particular ways, the very phenomenon which has inspired great interest in adversarial training. In the case of surrogate models, the concern is less about a deliberate attack exploiting the vulnerability of a DNN but more of the high sensitivity of its accuracy to input directions, an issue largely ignored by researchers using emulation models. In this paper, we show the severity of this issue through empirical studies and hypothesis testing. Furthermore, we adopt methods in adversarial training to enhance the robustness of DNN surrogate models. Experiments demonstrate that our approaches significantly improve the robustness of the surrogate models without compromising emulation accuracy. [ABSTRACT FROM AUTHOR]

The architectures of many neural networks rely heavily on the underlying grid associated with the variables, for instance, the lattice of pixels in an image. For general biomedical data without a grid structure, the multi‐layer perceptron (MLP) and deep belief network (DBN) are often used. However, in these networks, variables are treated homogeneously in the sense of network structure; and it is difficult to assess their individual importance. In this paper, we propose a novel neural network called Variable‐block tree Net (VtNet) whose architecture is determined by an underlying tree with each node corresponding to a subset of variables. The tree is learned from the data to best capture the causal relationships among the variables. VtNet contains a long short‐term memory (LSTM)‐like cell for every tree node. The input and forget gates of each cell control the information flow through the node, and they are used to define a significance score for the variables. To validate the defined significance score, VtNet is trained using smaller trees with variables of low scores removed. Hypothesis tests are conducted to show that variables of higher scores influence classification more strongly. Comparison is made with the variable importance score defined in Random Forest from the aspect of variable selection. Our experiments demonstrate that VtNet is highly competitive in classification accuracy and can often improve accuracy by removing variables with low significance scores. [ABSTRACT FROM AUTHOR]

2101 duplex stainless steel with low Ni content is developed to replace austenitic stainless steel in many corrosive environments. Herein, the effects of Ce addition on the microstructure and corrosion resistance of 2101 duplex stainless steel are studied. The electrochemical corrosion behavior of 2101 duplex stainless steel with Ce contents of 0, 0.005, and 0.020 wt% is studied in 3.5 wt% NaCl solution and 0.05 mol L−1 H2SO4 + 0.05 mol L−1 KSCN solution at 25 °C. The situation is evaluated in 6 wt% FeCl3 solution at 35 °C. The results show that with increasing Ce content, the proportion of α phase in 2101 duplex stainless steel decreases, whereas the proportion of γ phase increases. At 0.005 wt% Ce, the two‐phase ratio is close to 1:1. Moreover, the partition coefficient (K) of elements gradually approaches 1 with increasing Ce, indicating a more uniform element distribution between the two phases. The electrochemical experiments in simulated seawater and acidic environments show that Ce addition gradually increases the corrosion potential and pitting resistance, leading to the formation of more stable passivation film. [ABSTRACT FROM AUTHOR]

In this paper, based on the Hirota and Maxwell–Bloch (H‐MB) system and its application in the theory of the femtosecond pulse propagation through an erbium doped fiber, we define two kinds of nonlocal Hirota and Maxwell–Bloch (NH‐MB) systems, namely, PT‐symmetric NH‐MB system and reverse space‐time NH‐MB system. Then, we construct the Darboux transformations of these NH‐MB systems. Meanwhile, we derive the explicit solutions by the Darboux transformations. [ABSTRACT FROM AUTHOR]

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Carbon nitride quantum dots (CNQDs) were doped into melamine (MA) and silver (Ag) matrix yielding CNQDs@MA−Ag nanocomposites by the supramolecular self‐assembly route. To our surprise, the resulting mesoporous nanocomposites could present different morphologies (i. e., nanowires, nanoflowers, and nanosheets) and especially tunable visible‐light photocatalysis performances depending on the percentages of CNQDs used. It was discovered that the CNQDs@MA−Ag ones with the pinecone‐like structure (containing 30 % CNQDs) could exhibit the strongest visible‐light photocatalysis for splitting water to yield H2O2 with a production efficiency of 39.82 μmol ⋅ g−1 h−1, which is more than ten‐fold higher than that of CNQDs alone. Herein, the nano‐scaled Ag could facilitate the increased light harvesting and the separation as well as transmission of electron‐hole pairs of CNQDs resulting in the enhanced visible‐light photocatalysis of nanocomposites. More importantly, the pinecone‐like ones might additionally enable more exposed active sites of photocatalysis and larger mesoporous specific area of contacting dissolved oxygen in water, leading to the higher photocatalytic efficiency of H2O2 generation. This CNQDs‐based fabrication strategy may open a new door towards the design of a variety of photocatalysts with different morphologies and tunable photocatalysis performances for the efficient splitting water toward H2O2 evolution under sunlight. [ABSTRACT FROM AUTHOR]