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Abstract Anomaly detection is a highly important task in the field of data analysis. Traditional anomaly detection approaches often strongly depend on data size, structure and features, while introducing the idea of ensemble into anomaly detection can greatly improve the generalization ability. Ensemble-based anomaly detection methods still face some challenges, however, such as data imbalance, time and space demand and the selection of base detectors. To this end, we propose a selective ensemble method for anomaly detection based on parallel learning (SEAD-PL). First, a differentiated stratified sampling method is designed to alleviate the problem of data imbalance. Then, a distributed parallel training frame is built to address the problem of excessive time and space consumption for base detector training. Finally, a clustering-based ensemble selection strategy is introduced to balance the accuracy and diversity of base detectors. Experiments are performed on six datasets, which demonstrate that the proposed method has obvious advantages over four selected methods.

The avionics network supports high-safety-level flight operations, with the analysis of transmission failures serving as a crucial means for its safety evaluation. Due to the time-dependent nature of the failure probability in avionics networks, traditional constant and unchangeable probability values can deviate from the actual situation under specific conditions. This deviation may lead to inadequate responses to occasional events and potentially cause flight accidents. A Dynamic Fault Tree (DFT) model for civil aircraft avionics network transmission failures, based on an optimized extended fuzzy algorithm, is introduced in this paper. Initially focusing on event correlations, a DFT is established for the transmission failure of the Avionics Full Duplex Switched Ethernet (AFDX). Subsequently, considering the variations between events, triangular fuzzy processing is applied to the event failure rates based on relative confidence levels. Finally, by optimizing the weakest t-norm operator, the failure probability intervals are aggregated and the fuzzy scale is regulated. Experimental results demonstrate that, compared to the static-minimum t-norm and traditional weakest t-norm methods, the proposed approach enhances the accuracy of the fuzzy failure probability intervals by 66.15% and 40.59%, respectively. Concurrently, it maintains consistency in the ranking of event importance, highlighting the superior effectiveness of the proposed method in analyzing transmission failures in avionics networks.

Background and aimsDyslipidemia is known to contribute to arterial stiffness, while the inverse association remains unknown. This study aimed to explore the association of baseline arterial stiffness and its changes, as determined by brachial-ankle pulse wave velocity (baPWV), with dyslipidemia onset in the general population.MethodsThis study enrolled participants from Beijing Health Management Cohort using measurements of the first visit from 2012 to 2013 as baseline, and followed until the dyslipidemia onset or the end of 2019. Unadjusted and adjusted Cox proportional regression models were used to evaluate the associations of baseline baPWV and baPWV transition (persistent low, onset, remitted and persistent high) with incident dyslipidemia.ResultsOf 4362 individuals (mean age: 55.5 years), 1490 (34.2%) developed dyslipidemia during a median follow-up of 5.9 years. After adjusting for potential confounders, participants with elevated arterial stiffness at baseline had an increased risk of dyslipidemia (HR, 1.194; 95% CI, 1.050-1.358). Compared with persistent low baPWV, new-onset and persistent high baPWV were associated with a 51.2% and 37.1% excess risk of dyslipidemia.ConclusionThe findings indicated that arterial stiffness is an early risk factor of dyslipidemia, suggesting a bidirectional association between arterial stiffness and lipid metabolism.

PurposeThyroid hormones sensitivity is a newly proposed clinical entity closely related with metabolic health. Prior studies have reported the cross-sectional relationship between thyroid hormones sensitivity and diabetes; however, the longitudinal association is unclear to date. We aimed to explore the relationship between impaired thyroid hormone sensitivity at baseline and diabetes onset using a cohort design.MethodsThis study enrolled 7283 euthyroid participants at the first visit between 2008 and 2009, and then annually followed until diabetes onset or 2019. Thyrotropin (TSH), free triiodothyronine (FT3) and free thyroxine (FT4) were measured to calculate thyroid hormone sensitivity by thyroid feedback quantile-based index (TFQI), Chinese-referenced parametric thyroid feedback quantile-based index (PTFQI), thyrotropin index (TSHI), thyrotroph thyroxine resistance index (TT4RI) and FT3/FT4 ratio. Cox proportional hazard model and cross-lagged panel analysis were used.ResultsThe mean baseline age was 44.2 ± 11.9 years, including 4170 (57.3%) male. During a median follow-up of 5.2 years, 359 cases developed diabetes. There was no significant association between thyroid hormones sensitivity indices and diabetes onset, and adjusted hazard ratios per unit (95% CIs) were 0.89 (0.65-1.23) for TFQI, 0.91 (0.57-1.45) for PTFQI, 0.95 (0.70-1.29) for TSHI, 0.98 (0.70-1.01) for TT4RI and 2.12 (0.17-5.78) for FT3/FT4 ratio. Cross-lagged analysis supported the temporal association from fasting glucose to impaired thyroid hormones sensitivity indices.ConclusionsOur findings could not demonstrate that thyroid hormones sensitivity status is a predictor of diabetes onset in the euthyroid population. Elevated fasting glucose (above 7.0 mmol/L) appeared to precede impaired sensitivity indices of thyroid hormones.

BackgroundStroke is a major disease with high morbidity and mortality worldwide. Currently, there is no quantitative method to evaluate the short-term prognosis and length of hospitalization of patients.PurposeWe aimed to develop nomograms as prognosis predictors based on imaging characteristics from non-contrast computed tomography (NCCT) and CT perfusion (CTP) and clinical characteristics for predicting activity of daily living (ADL) and hospitalization time of patients with ischemic stroke.Materials and methodsA total of 476 patients were enrolled in the study and divided into the training set (n = 381) and testing set (n = 95). Each of them owned NCCT and CTP images. We propose to extract imaging features representing as the Alberta stroke program early CT score (ASPECTS) values from NCCT, ischemic lesion volumes from CBF, and TMAX maps from CTP. Based on imaging features and clinical characteristics, we addressed two main issues: (1) predicting prognosis according to the Barthel index (BI)–binary logistic regression analysis was employed for feature selection, and the resulting nomogram was assessed in terms of discrimination capability, calibration, and clinical utility and (2) predicting the hospitalization time of patients–the Cox proportional hazard model was used for this purpose. After feature selection, another specific nomogram was established with calibration curves and time-dependent ROC curves for evaluation.ResultsIn the task of predicting binary prognosis outcome, a nomogram was constructed with the area under the curve (AUC) value of 0.883 (95% CI: 0.781–0.985), the accuracy of 0.853, and F1-scores of 0.909 in the testing set. We further tried to predict discharge BI into four classes. Similar performance was achieved as an AUC of 0.890 in the testing set. In the task of predicting hospitalization time, the Cox proportional hazard model was used. The concordance index of the model was 0.700 (SE = 0.019), and AUCs for predicting discharge at a specific week were higher than 0.80, which demonstrated the superior performance of the model.ConclusionThe novel non-invasive NCCT- and CTP-based nomograms could predict short-term ADL and hospitalization time of patients with ischemic stroke, thus allowing a personalized clinical outcome prediction and showing great potential in improving clinical efficiency.SummaryCombining NCCT- and CTP-based nomograms could accurately predict short-term outcomes of patients with ischemic stroke, including whose discharge BI and the length of hospital stay.Key ResultsUsing a large dataset of 1,310 patients, we show a novel nomogram with a good performance in predicting discharge BI class of patients (AUCs > 0.850). The second nomogram owns an excellent ability to predict the length of hospital stay (AUCs > 0.800).

ObjectiveTo develop a deep learning-based model using esophageal thickness to detect esophageal cancer from unenhanced chest CT images.MethodsWe retrospectively identified 141 patients with esophageal cancer and 273 patients negative for esophageal cancer (at the time of imaging) for model training. Unenhanced chest CT images were collected and used to build a convolutional neural network (CNN) model for diagnosing esophageal cancer. The CNN is a VB-Net segmentation network that segments the esophagus and automatically quantifies the thickness of the esophageal wall and detect positions of esophageal lesions. To validate this model, 52 false negatives and 48 normal cases were collected further as the second dataset. The average performance of three radiologists and that of the same radiologists aided by the model were compared.ResultsThe sensitivity and specificity of the esophageal cancer detection model were 88.8% and 90.9%, respectively, for the validation dataset set. Of the 52 missed esophageal cancer cases and the 48 normal cases, the sensitivity, specificity, and accuracy of the deep learning esophageal cancer detection model were 69%, 61%, and 65%, respectively. The independent results of the radiologists had a sensitivity of 25%, 31%, and 27%; specificity of 78%, 75%, and 75%; and accuracy of 53%, 54%, and 53%. With the aid of the model, the results of the radiologists were improved to a sensitivity of 77%, 81%, and 75%; specificity of 75%, 74%, and 74%; and accuracy of 76%, 77%, and 75%, respectively.ConclusionsDeep learning-based model can effectively detect esophageal cancer in unenhanced chest CT scans to improve the incidental detection of esophageal cancer.

In order to have more accurate control over the compression molding of automobile coat rack, improve the quality of molding products, and achieve the goal of lightweight design, a novel mechanical model for the main two-layer composite structure of the coat rack is proposed. In this regard, the main factors affecting the mechanical properties of the composite structure are obtained. The hot air convection is selected for the sheet preheating. During the experiment, the hot air temperature, preheating time, molding pressure, and pressing holding time are set to 250°C, 110 s, 13 MPa, and 80 s, respectively. Moreover, the error compensation method is applied to compensate for the shrinkage of the product during solidification and cooling. The LS-DYNA finite element software is used to simulate the molding process of the main body of the coat rack, and the node force information with large deformation is obtained accordingly. The load mapping is used as the boundary condition of mold topology optimization, and the compression molding of the main body of the coat rack is optimized. A lightweight design process and method for the compression molding of automotive interior parts and a mathematical model for the optimization of the solid isotropic material penalty (SIMP) (power law) material interpolation of the concave and convex molds are established. Based on the variable density method, OptiStruct is used for the lightweight design of the convex and concave molds of the main body of the coat rack, which reduces the mold weight by 15.6% and meets the requirements of production quality and lightweight.

In this work, we propose a novel cascaded V-Nets method to segment brain tumor substructures in multimodal brain magnetic resonance imaging. Although V-Net has been successfully used in many segmentation tasks, we demonstrate that its performance could be further enhanced by using a cascaded structure and ensemble strategy. Briefly, our baseline V-Net consists of four levels with encoding and decoding paths and intra- and inter-path skip connections. Focal loss is chosen to improve performance on hard samples as well as balance the positive and negative samples. We further propose three preprocessing pipelines for multimodal magnetic resonance images to train different models. By ensembling the segmentation probability maps obtained from these models, segmentation result is further improved. In other hand, we propose to segment the whole tumor first, and then divide it into tumor necrosis, edema, and enhancing tumor. Experimental results on BraTS 2018 online validation set achieve average Dice scores of 0.9048, 0.8364, and 0.7748 for whole tumor, tumor core and enhancing tumor, respectively. The corresponding values for BraTS 2018 online testing set are 0.8761, 0.7953, and 0.7364, respectively. We also evaluate the proposed method in two additional data sets from local hospitals comprising of 28 and 28 subjects, and the best results are 0.8635, 0.8036, and 0.7217, respectively. We further make a prediction of patient overall survival by ensembling multiple classifiers for long, mid and short groups, and achieve accuracy of 0.519, mean square error of 367240 and Spearman correlation coefficient of 0.168 for BraTS 2018 online testing set.

Background: Depression manifests as a mental disorder characterized by a low mood, suicidal tendencies, disturbances in sleep-wake cycles, psychomotor agitation, and pronounced feelings of hopelessness and anhedonia. Baicalin, a natural flavonoid compound, shows significant promise in alleviating depressive symptoms in animals. This study aims to assess the impact of baicalin on experimental models of depression. Methods: A systematic search of electronic databases was conducted using the search terms “baicalin” AND “depression” OR “depressed” OR “anti-depression”. Preclinical animal models representing experimental depression were included in the analysis. The risk of bias in the included studies was evaluated using the CAMARADES tools. Results: Baicalin significantly increased sucrose preference test (SPT) [SMD= 21.31, 95%CI (16.32, 26.31), P < 0.00001]. mThe tail suspension test (TST) duration significantly decreased in the baicalin group compared to the model group [SMD = −39.3, 95%CI (−49.71, −28.89), P < 0.0001]. Furthermore, baicalin reduced immobility time in rats subjected to the forced swim test (FST) [SMD = −39.73, 95%CI (−48.77, −30.69) P < 0.0001]. Compared to the model group, baicalin treatment also significantly increased the frequency of crossings in the open field test (OFT) [SMD = 32.44, 95%CI (17.74, 47.13), P < 0.00001]. Conclusion: Baicalin significantly improves the manifestations of depressive symptoms. The effect of baicalin against depression is exerted through its anti-inflammatory actions, inhibition of oxidative stress, regulation of the HPA axis, and restoration of neuroplasticity. Future studies will be needed to further explore how these promising preclinical findings can be translated into clinical treatment for depression. [ABSTRACT FROM AUTHOR]

The electronic structure near the Fermi surface determines the electrical properties of the materials, which can be effectively tuned by external pressure. Bi0.5Sb1.5Te3 is a p-type thermoelectric material which holds the record high figure of merit at room temperature. Here it is examined whether the figure of merit of this model system can be further enhanced through some external parameter. With the application of pressure, it is surprisingly found that the power factor of this material exhibits λ behavior with a high value of 4.8 mW m-1 K-2 at pressure of 1.8 GPa. Such an enhancement is found to be driven by pressure-induced electronic topological transition, which is revealed by multiple techniques. Together with a low thermal conductivity of about 0.89 W m-1 K-1 at the same pressure, a figure of merit of 1.6 is achieved at room temperature. The results and findings highlight the electronic topological transition as a new route for improving the thermoelectric properties. [ABSTRACT FROM AUTHOR]

Cognitive metaphor theory provides a systematic framework to better understand the working mechanism of metaphor. Its recent development further allows translation researchers to have a clearer insight into the movement of metaphor across languages and culture. Building on an empirical study, this paper examines the complementary relationship between two prominent cognitive metaphor theories – Conceptual Metaphor Theory (CMT) and Conceptual Blending Theory (CBT), and discusses the practical contribution that this relationship could make to the existing research on metaphor translation. To construct a comparable basis for CMT and CBT, two parameters are adopted for data analysis, which is proven useful to serve the purpose. The two chosen parameters are: projection and provenance, denoting the content and the type of metaphor respectively. Metaphorical expressions analyzed in this paper are sourced from cosmology-themed articles published in Scientific American in 2017 and their Simplified Chinese translations published in Huanqiukexue. Findings show that delineated by the two parameters, CMT and CBT indeed share a complementary relationship owing to their different focuses and organizing mechanisms. Furthermore, the collaboration between CMT and CBT offers a well-rounded analytical framework for translation studies. In turn, the correlation between metaphor parameters and translation solutions provides detailed clues for studying metaphor across culture. Finally, the reflection of this dual-model parametric approach regarding its pros and cons is also shown to shed light on future research. [ABSTRACT FROM AUTHOR]

The VAD (vincristine-doxorubicin-dexamethasone) regimen has been used for decades to treat multiple myeloma (MM). Based on reports that vascular endothelial growth factor- (VEGF-) mediated angiogenesis is critical for MM pathogenesis, the antiangiogenic compound thalidomide has been added to VAD (T-VAD). However, it remains unclear whether T-VAD is more efficacious than VAD for serum VEGF reduction or if the difference influences clinical outcome. Pubmed, Cochrane library, China Biomedical Literature (CBM) database, China National Knowledge Infrastructure (CNKI) database, Vip database, and Wanfang database were searched for relevant studies published up to June 2017. RevMan5.2 was used for methodological quality evaluation and data extraction. Thirteen trials (five randomized, seven nonrandomized, and one historically controlled) involving 815 cases were included. Serum VEGF was significantly higher in MM cases than non-MM controls (MD=353.01, [95%CI 187.52–518.51], P<0.01), and the overall efficacy of T-VAD was higher than that of VAD (RR=1.36, [1.21–1.53], P <0.01). Further, T-VAD reduced VEGF to a greater extent than VAD does ([MD=-49.85, [-66.28− -33.42], P<0.01). The T-VAD regimen also reduced VEGF to a greater extent in newly diagnosed MM patients than it did in recurrent patients ([MD=-120.20, [-164.60–-39.80], P<0.01). There was no significant difference in VEGF between T-VAD patients (2 courses) and nontumor controls (MD=175.94, [-26.08–377.95], P=0.09). Greater serum VEGF reduction may be responsible for the superior efficacy of T-VAD compared to VAD. [ABSTRACT FROM AUTHOR]

Longitudinal data often arise in longitudinal follow-up studies, and there may exist a dependent terminal event such as death that stops the follow-up. In this article, we propose a new joint modeling for the analysis of longitudinal data with informative observation times via a dependent terminal event and two latent variables. Estimating equations are developed for parameter estimation, and asymptotic properties of the resulting estimators are established. In addition, a generalization of the joint model with time-varying coefficients for the longitudinal response variable is considered, and goodness-of-fit methods for assessing the adequacy of the model are also provided. The proposed method works well in our simulation studies, and is applied to a data set from a bladder cancer study. [ABSTRACT FROM AUTHOR]

For shortcomings of current smart home system and power line carrier insufficient data transferring, an intelligent sensor networks system based on intelligent gateway is studied in this paper. Smart home system includes internal network, intelligent gateway, and external network. Multiple servers are designed in gateway, which start at the same time and receive various forms of data. Then data is processed for internal network format. External network includes several modes of communication such as TCP/IP, UDP, and pipe. Internal network includes ZigBee wireless and power line carrier (PLC) communication. Power line carrier protocol is improved, and it expands data transmission capacity to accommodate communication needs of modern family. Smart home system is designed as modules, such as intelligent gateway module, power line carrier communication module, ZigBee wireless communication modules, and appliance within electric equipment. Finally this system is physically designed and verified. The smart home system introduced in this paper has easily used and has strong compatibility. [ABSTRACT FROM AUTHOR]

In this paper, we present a natural extension of accelerated failure time model for survival data to formulate the effects of covariates on the mean function of the counting process for Clustered Recurrent event data. Making use of the theory of estimating equation, the unknown parameter in the model is estimated. Based on modern empirical process theory, the consistency and asymptotic normality of the proposed estimators are proved. [ABSTRACT FROM AUTHOR]

The microstructure, martensite transformation behavior, thermal stability and shape memory behavior of Ti—20Zr—10Ta high temperature shape memory alloy were investigated. The Ti—20Zr—10Ta alloy exhibited a reversible transformation with the high martensite transformation temperature of 500 °C and good thermal stability. The alloy displayed the elongation of 15% and a maximum recovery stain of 5.5% with 8% pre-strain. [ABSTRACT FROM AUTHOR]

In order to avoid the "metallic smile" appearance of metal wires when undergoing orthodontic treatment, epoxy resin/polytetrafluorethylene coating TiNi arch wires were made by dipping method. TiO2 and FeFe2O4 were chosen as dyes in order to match the color of teeth and the color schemes were fixed by spectrophotometer method. The biocompatibility of coating was also examined. The results showed that the cytotoxicity of the coating was grade I, and without mutagenesis and carcinogenesis. Skin sensitization assay showed no erythema or oedema response and epithelial was integrated according to mucous membrane irritation. Thus, good behavior in clinic can be anticipated. [ABSTRACT FROM AUTHOR]

The effect of thermo-mechanical treatment on microstructure evolution, martensite transformation, and shape memory behavior of Ti–15Ta–15Zr high temperature shape memory alloy were investigated. Different martensite morphologies were found with different annealing temperatures. The Ti–15Ta–15Zr alloy exhibits almost perfect shape memory recovery strain of 6% after annealing at 973 K for 0.5 h. [ABSTRACT FROM AUTHOR]

The electronic structure near the Fermi surface determines the electrical properties of the materials, which can be effectively tuned by external pressure. Bi0.5Sb1.5Te3 is a p-type thermoelectric material which holds the record high figure of merit at room temperature. Here it is examined whether the figure of merit of this model system can be further enhanced through some external parameter. With the application of pressure, it is surprisingly found that the power factor of this material exhibits λ behavior with a high value of 4.8 mW m-1 K-2 at pressure of 1.8 GPa. Such an enhancement is found to be driven by pressure-induced electronic topological transition, which is revealed by multiple techniques. Together with a low thermal conductivity of about 0.89 W m-1 K-1 at the same pressure, a figure of merit of 1.6 is achieved at room temperature. The results and findings highlight the electronic topological transition as a new route for improving the thermoelectric properties. [ABSTRACT FROM AUTHOR]

Abstract The electronic structure near the Fermi surface determines the electrical properties of the materials, which can be effectively tuned by external pressure. Bi0.5Sb1.5Te3 is a p‐type thermoelectric material which holds the record high figure of merit at room temperature. Here it is examined whether the figure of merit of this model system can be further enhanced through some external parameter. With the application of pressure, it is surprisingly found that the power factor of this material exhibits λ behavior with a high value of 4.8 mW m−1 K−2 at pressure of 1.8 GPa. Such an enhancement is found to be driven by pressure‐induced electronic topological transition, which is revealed by multiple techniques. Together with a low thermal conductivity of about 0.89 W m−1 K−1 at the same pressure, a figure of merit of 1.6 is achieved at room temperature. The results and findings highlight the electronic topological transition as a new route for improving the thermoelectric properties.

• Multitude advances were found for V-rich Bi 2 S 3 nanowire than pristine Bi 2 S 3 photoelectrode. • V-rich Bi 2 S 3 nanowires achieve excellent photocurrent density for water splitting under visible light. • Deep insights on how the V-rich feature affects the PEC performance of Bi 2 S 3. Bi 2 S 3 is a n-type (negative type) semiconductor that has a narrow band gap and an ideal band edge position for hydrogen generation. However, high charge recombination rate, low photoelectrochemical (PEC) efficiency and poor stability are of major concerns of Bi 2 S 3 as a photoelectrode. Herein, we report an efficient Vanadium-rich Bi 2 S 3 nanowires (atomic ratio of V to Bi is 1/22) that have been prepared on fluorine-doped tin oxide (FTO) substrate by an in-situ solution-processed reaction with BiVO 4 film as precursor. The resulting V-rich photoelectrode demonstrates notably high charge separation efficiency and fast charge transport with respect to pristine Bi 2 S 3 nanowires. As a result, the photocurrent density of ca. 10 mA·cm−2 at -0.2 V vs Ag/AgCl is obtained under visible light illumination. In addition, V-rich Bi 2 S 3 generates IPCE (incident photocurrent-to-current conversion efficiency) of 50% at 460 nm and 45% at 760 nm, demonstrating a 2.0 and 2.8-fold increase, respectively, when compared with pristine Bi 2 S 3. This enhancement is probably due to increased light absorption, less charge recombination, and faster charge transfer. More importantly, in contrast to pristine Bi 2 S 3 sample that suffers detrimental photocorrosion in sulfide containing electrolyte, V-rich Bi 2 S 3 nanowires retain 84.6% of its initial photocurrent over the course of 1 h. These findings are expected to shed light on engineering high-performance and robust metal sulfide materials for photoelectrochemical application. [ABSTRACT FROM AUTHOR]

Highlights: [•] N–Si codoped TiO2 fibers modified with aminosilane coupling agents were prepared. [•] N–Si codoping is responsible for a mesostructure and wide spectral response. [•] γ-Aminopropyltriethoxysilane modified TiO2 fibers exhibit a higher photoactivity. [•] The regenerated TiO2 fibers can be reused maintaining high photoactivity. [Copyright &y& Elsevier]