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Based on grain refinement and secondary phase strengthening, minor boron (B) was added to near β-Ti alloy to strengthen the alloys. Ti85Fe6Cu5Sn2Nb2 alloys with various B contents were designed, prepared by using a non-consumable vacuum arc melting furnace, and hot rolled at 800℃followed by quenching. The effects of minor B addition on the microstructure and mechanical properties of Ti85Fe6Cu5Sn2Nb2 alloy were investigated through microstructure observation, tensile mechanical test, fracture observation and transmit electron microscopy. The results reveal that minor B addition can refine the grains, improve the strength whereas the plasticity of the alloy is decreased. The alloy containing 0.15% (mass fraction)B possesses the better comprehensive mechanical properties(σ0.2=1105 MPa, δb=4.5%).With the increase of B content, the strength of the alloy is increased and reaches up to 1156 MPa. Orthorhombic TiB compounds are formed in the alloy, distributed in the β-Tialloy matrix. Upon deformation, the fracture of TiB phases, cutting and debonding of TiB phases to the alloy matrix, formed the fracture source, resulted in the decrease of the alloy plasticity.

With the increasing adoption of industrial Ethernet in train communication network (TCN), the limitations of insufficient reliability and incompatibility between different Ethernet technologies have become increasingly apparent. To address these challenges, time‐sensitive networking (TSN) has emerged as a solution, enabling multi‐network convergence and enhancing data transmission determinism and reliability through techniques such as frame replication and elimination for reliability (FRER). However, the utilization of these policies will adversely affect schedulability due to increased network load. To address the issue of link failures in TCN topologies, a routing policy based on faulty link diagnosis is proposed. The proposed policy utilizes an improved max‐min ant system (IMMAS) for diagnosing and resolving faulty links. Furthermore, a routing optimization algorithm is introduced to enhance transmission reliability by planning alternative paths for traffic. To evaluate the performance of the proposed routing algorithm, multiple sets of test cases are conducted separately under different failure scenarios to compare them with other routing policies. The results demonstrate the superior reliability of the proposed routing policy over alternative approaches. Additionally, the traffic utilizing the proposed routing policy show improved schedulability following integer linear programming (ILP) scheduling. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction for bearing is a significant part of the maintenance of urban rail transit trains. Bearing RUL is closely linked to the reliability and safety of train running, but the current prediction accuracy makes it difficult to meet the requirements of high reliability operation. Aiming at the problem, a prediction model based on an improved long short-term memory (ILSTM) network is proposed. Firstly, the variational mode decomposition is used to process the signal, the intrinsic mode function with stronger representation ability is determined according to energy entropy and the degradation feature data is constructed combined with the time domain characteristics. Then, to improve learning ability, a rectified linear unit (ReLU) is applied to activate a fully connected layer lying after the long short-term memory (LSTM) network, and the hidden state outputs of the layer are weighted by attention mechanism. The Harris Hawks optimization algorithm is introduced to adaptively set the hyperparameters to improve the performance of the LSTM. Finally, the ILSTM is applied to predict bearing RUL. Through experimental cases, the better performance in bearing RUL prediction and the effectiveness of each improving measures of the model are validated, and its superiority of hyperparameters setting is demonstrated. [ABSTRACT FROM AUTHOR]

As one of the important components of train traction motor, the bearing plays a key role in ensuring the safety of train operation. At present, the existing fault diagnosis methods for train traction motor bearings have the disadvantages of poor feature extraction ability, slow diagnosis speed, and low diagnosis accuracy. To solve the above problems, an improved deep residual network (IDRN) is proposed in this paper. Firstly, a new residual convolution block (RCB) is proposed to improve the extraction ability of hidden features. Secondly, the squeeze and excitation block (SE) is embedded in the residual block to increase the sensitivity of the model to features. Lastly, Adan is selected as the optimizer to increase the convergence speed of IDRN. The train traction motor bearing bench built by our research team is selected for the experiment. The results show that the test accuracy of IDRN on the bearing dataset of train traction motor is 98.72%, which is at least 2.27% higher than other comparative models. The IDRN model converges with fewer iterations in the training process, shortening the entire experiment time. At the same time, the CWRU bearing dataset and the bearing dataset with natural defects are selected for comparative verification. The experimental results show that the IDRN model exhibits strong robustness and practicability on different datasets. [ABSTRACT FROM AUTHOR]

(Ti40Zr25Cu9Ni8Be18)100-xYx (x=0,1.0,1.5,2.0,3.0) alloy rods with 3mm in diameter were fabricated by copper mold casting method. The microstructures, nano-crystallization and mechanical properties of the alloys were investigated by X-ray diffractometry(XRD), scanning electron microscopy(SEM), transmission electron microscopy(TEM), differential scanning colorimetry(DSC) and uniaxial compressive test.The results show that with 1.0%(atom fraction, the same below) addition of Y, the alloy presents amorphous nature by X-ray examination, with 1.5% of Y addition, nano-crystallization of the amorphous alloy is induced. Crystalline phases with the size of 5-20nm can be observed distributed in the amorphous matrix by high resolution transmission electron microscopy. The alloy with 1.5% of Y addition presents the fracture strength of 1990MPa, at the plastic strain of 3% upon uniaxial compressive test. The number of shear bands is increased in the fracture surfaces of the alloys with Y addition. The interference of shear bands hinders inhomogeneous deformation, increases the fracture strength and plasticity of the alloy.

To observe the clinical effect of combined acupuncture and kinesiotherapy on upper cross syndrome (UCS) by a parallel randomized clinical trial. A total of 45 patients with UCS were recruited from the outpatients of Acupuncture-Moxibustion, Tuina and Rehabilitation Department of the First Affiliated Hospital of Hunan University of Chinese Medicine, the students of Hunan University of Chinese Medicine and the patients from the nearby communities in accordance with the inclusion criteria. Using the random number table method, they were divided into a combined treatment group (acupuncture plus kinesiotherapy, 23 cases) and a simple kinesiotherapy group (22 patients). Treatment for 4 weeks was one course, and two consecutive courses were required. The visual analog scale (VAS) score, the score of the assessment scale for cervical spondylosis, the value of surface electromyography (root mean square, RMS), and the cervical curvature value were used in the evaluation. The allocation scheme was concealed from the outcome assessors. The data from 23 cases of the combined treatment group and 22 cases of the simple kinesiotherapy group were analyzed. Before treatment, the differences were not statistically significant in the general conditions, VAS score, assessment score of cervical spondylosis, cervical curvature value, and RMS in UCS patients between the two groups (all P > 0.05). After treatment, the VAS score was reduced compared with that before treatment in both groups (all P < 0.05). In two courses of treatment, the VAS score decreased as compared with that in one course of treatment in both groups (both P < 0.05), and the VAS score in the combined treatment group decreased more obviously after each course of treatment (both P < 0.05). The RMS decreased compared with that before treatment in each group (both P < 0.05), and the decrease in the combined treatment group was more obvious (P < 0.05). After treatment of each course, the assessment score was all increased as compared with that before treatment in two groups (all P < 0.05). In two courses of treatment, the assessment score was increased as compared with that in one course of treatment in both groups (both P < 0.05), and the score in the combined treatment group was increased more obviously in the two courses of treatment (P < 0.05). Regarding either the intra-group comparison or the inter-group comparison before and after treatment, the differences were not statistically significant (all P > 0.05), suggesting no obvious improvement of cervical curvature in the two courses of treatment in patients with UCS. However, cervical curvature tended to improve in the combined treatment group. The total effective rate was significantly different between the two groups (P < 0.05), indicating that the total effective rate in the combined treatment group was better than that in the simple kinesiotherapy group.No any adverse reactions occurred. Combined treatment with acupuncture, kinesiotherapy, and kinesiotherapy alleviated pain, relieved the symptoms and physical signs, and improved the daily movement of the patients. However, the combined treatment of acupuncture and kinesiotherapy had a much better effect on UCS. [ABSTRACT FROM AUTHOR]

The obstacle detection in the dangerous area of railway track is an important research direction in the field of the driverless train. Traditional obstacle detection methods have many issues, such as complicated steps, low detection accuracy, and slow inspection speed. To overcome these defects, a detection network based on deep learning, named Mask R-CNN, was adopted, as described in this paper. The detection network uses the Mask-RCNN model with ResNet101 as its backbone feature extraction network, which has deeper network layers. Therefore, this network has high detection accuracy for small targets. Data from a subway obstacle test was used to train this network. In addition, data augmentation and transfer learning were adopted to improve the efficacy of the training. To improve the detection speed, the technical framework of the detection was also improved. The test results showed that the precision of the Mask-RCNN model with ResNet101 as its backbone feature extraction network reached 95.7% and that it required an average time of 0.18 s. The proposed model shows satisfactory performance when used for obstacle detection in the dangerous area of the railway track, compared with other networks. [ABSTRACT FROM AUTHOR]

Introduction. It has well established that metabolic syndrome (MetS) can predict the risk of type 2 diabetes mellitus (T2DM) in some population groups. However, limited evidence is available regarding the predictive effect of MetS for incident T2DM in mainland Chinese population. Methods. A 3-year cohort study was performed for 9735 Chinese without diabetes at baseline. MetS and its components were assessed by multivariable analysis using Cox regression. Prediction models were developed. Discrimination was assessed with area under the receiver operating characteristic curves (AUCs), and performance was assessed by a calibration curve. Results. The 3-year cumulative incidence of T2DM was 11.29%. Baseline MetS was associated with an increased risk of T2DM after adjusting for age (HR = 2.68, 95% CI, 2.27–3.17 in males; HR = 2.59, 95% CI, 1.83–3.65 in females). Baseline MetS exhibited relatively high specificity (88% in males, 94% in females) and high negative predictive value (90% in males, 94% in females) but low sensitivity (36% in males, 23% in females) and low positive predictive value (31% in males and females) for predicting the 3-year risk of T2DM. AUCs, including age and components of MetS, for the prediction model were 0.779 (95% CI: 0.759–0.799) in males and 0.860 (95% CI: 0.836–0.883) in females. Calibration curves revealed good agreement between prediction and observation results in males; however, the model could overestimate the risk when the predicted probability is >40% in females. Conclusions. MetS predicts the risk of T2DM. The quantitative MetS-based prediction model for T2DM risk may improve preventive strategies for T2DM and present considerable public health benefits for the people in mainland China. [ABSTRACT FROM AUTHOR]

A highly efficient denitrifying bacterium aHD7 was screened from activated sludge. After static culture at 30 °C for 3 days, the denitrification rate of the aHD7 reached 91.7%, and during denitrification, nitrite had lower accumulation, with its concentration basically maintained at 1.8 μmg · L-1. The microscopy observation demonstrated that the aHD7 was a gram-negative bacillus, with an average size of 0.5 μm×(1.5-2.5) μm. Based on its biochemical/morphological characteristics and homologic analysis of 16S rDNA sequence, the aHD7 was identified as Pseudomonas mendocina. The investigation on the factors affecting the denitrification capacity of aHD7 showed that at the initial concentration of nitrate nitrogen being less than 276.95 mg · L-1, the denitrification rate was almost 100%, and when the initial concentration of nitrate nitrogen was as high as 553.59 μmg · L-1, the denitrification rate could reach 66.8%, with little nitrite accumulated. Ethanol was the most suitable carbon source. C/N ratio 6-8 and pH value 6-9 benefited the denitrification. The aHD7 had a good ability of anaerobic ammonium oxidation, and its average ammonium ?utilization? rate reached 4.56 mg · L-1· d-1. [ABSTRACT FROM AUTHOR]

Abstract: Reactions of the title complex (1), which features the rigid doubly-bridged biscyclopentadienyl ligand and the unusually long Mo–Mo bond in the structure, with a variety of classic substrates are explored. Complex 1 reacts with I2 in CH2Cl2 to give the expected Mo–Mo cleaved product [(η 5-C5H3)2(CMe2)(SiMe2)]Mo2(CO)6I2 (2A) with a Mo–Mo distance of 5.329Å. While the same reaction in benzene forms the product 2B, which has the same structural formula as 2A but a different conformation, in which the Mo–Mo distance is 6.442Å. Reaction of 1 with Ph2E2 (E=S, Se) in refluxing toluene or under UV irradiation yields products with the carbonyls completely removed, [(η 5-C5H3)2(CMe2)(SiMe2)]Mo2(EPh)2(μ-EPh)2 (3a–b). Reaction of 1 with [Et2NC(S)S]2 results in the di-substituted product [(η 5-C5H3)2(CMe2)(SiMe2)]Mo2(CO)4(Et2NC(S)S)2 (4). Reaction of 1 with C2Ph2 affords the acetylene-bridged derivative [(η 5-C5H3)2(CMe2)(SiMe2)]Mo2(CO)4(μ-C2Ph2) (5). Reaction of 1 with an excess of PPh3 in refluxing toluene only provides the mono-substituted derivative [(η 5-C5H3)2(CMe2)(SiMe2)]Mo2(CO)5(PPh3) (6), while the reaction of 1 with excess P(OPh)3 under the same conditions produces not only the mono-substituted derivative [(η 5-C5H3)2(CMe2)(SiMe2)]Mo2(CO)5(P(OPh)3) (7), but also the di-substituted derivative [(η 5-C5H3)2(CMe2)(SiMe2)]Mo2(CO)4(P(OPh)3)2 (8). The reactivity and properties of the doubly-bridged molybdenum complex 1 and the corresponding non-bridged and singly-bridged analogs are compared. The molecular structures of 2A, 2B, 3a, 5 and 8 have been determined by single crystal X-ray diffraction. [Copyright &y& Elsevier]

Electronic structures of graphene nanoribbons with armchair edges (AGNRs) containing N-substitutional impurity have been investigated, using ab initio density functional theory. It is shown that the electronic structures of the doped AGNRs are different from those of doped carbon nanotubes (CNTs). N introduces an impurity level above the conduction band minimum (CBM) in the AGNRs while an impurity level introduced by N is below the CBM in the CNTs. This character can be explained as a consequence of the edge polarization effects, which ionize the impurity level so that the relevant charge carriers occupy the conduction bands, which is independent of curvature and doping site. Although the N-doped AGNR and CNT are all n-type semiconductors, an implication of the result is that edge polarization effects could make some properties of the N-doped AGNRs different from those of N-doped CNTs. It suggests the possibility to make 1-D doped structures with novel physical and device characteristics. [ABSTRACT FROM PUBLISHER]

Reactions of phenylethynyl lithium with substituted cyclopentenones gave the corresponding pendant phenylethynyl substituted cyclopentadienes. Subsequent deprotonation and transmetallation with TiCl4·2THF, ZrCl4, and Cp∗ZrCl3 yielded the alkyne-functionalized metallocene complexes [C5Me4(C≡CPh)]2MCl2 [M=Ti (1), Zr (2)], Cp∗[C5Me4(C≡CPh)]ZrCl2 (3), and Cp∗[C5H2R′2(C≡CPh)]ZrCl2 [R′=Me (4), Ph (5)]. These complexes were fully characterized by 1H NMR, 13C NMR, MS spectra, and elemental analysis. The molecular structure of 2 was determined by single crystal X-ray diffraction analysis. Ethylene polymerization was studied with these complexes in the presence of methylaluminoxane (MAO). [ABSTRACT FROM AUTHOR]

Biogas slurry is a cheap source of plant nutrients and can offer extra benefits to soil fertility and fruit quality. However, its current utilization mode and low content of active ingredients limit its further development. In this paper, a one-growing-season field study was conducted to assess the effects of concentrated biogas slurry on soil property, tomato fruit quality, and composition of microflora in both nonrhizosphere and rhizosphere soils. The results showed that application of concentrated slurry could bring significant changes to tomato cultivation, including increases in organic matter, available N, P, and K, total N and P, electrical conductivity, and fruit contents of amino acids, protein, soluble sugar, β-carotene, tannins, and vitamin C, together with the R/S ratios and the culturable counts of bacteria, actinomycetes, and fungi in soils. It was concluded that the application is a practicable means in tomato production and will better service the-is area of sustainable agriculture. [ABSTRACT FROM AUTHOR]

Density-functional theory based on first principles is used to investigate oxidation of a semiconducting graphene nanoribbon with armchair edges (9-AGNR) by oxygen. The calculated results demonstrate that the oxygen is favorably physisorbed on the inner of 9-AGNR, while the oxygen is chemisorbed at the edge of 9-AGNR. Compared to the oxygen chemisorbed (cyclo- addition) 9-AGNR, two epoxy groups formed from cyclo-additions at the edge of 9-AGNR is energetically preferred. It is also found that the uniaxial strain generated by the cooperative alignment of two epoxy groups can significantly change the band gaps of 9-AGNR, leading to a change in the band gap for 9-AGNR as the concentration of epoxy groups varies. The pronounced change in the electronic properties, in particular, the band gaps, of 9-AGNR, as oxygen is adsorbed on 9-AGNR, may indicate that a semiconducting AGNR could have a potential application as a chemical (oxygen) sensor. [ABSTRACT FROM PUBLISHER]

Aim: To discriminate 32 phenethyl-amines between antagonists and agonists, and predict the activities of these compounds. Methods: The support vector machine (SVM) is employed to investigate the structure-activity relationship (SAR)/quantitative structure-activity relationship (QSAR) of phenethyl-amines based on molecular descriptors. Results: By using the leave-one-out cross-validation (LOOCV) test, 1 optimal SAR and 2 optimal QSAR models for agonists and antagonists were attained. The accuracy of prediction for the classification of phenethyl-amines by using the LOOCV test is 91.67%, and the accuracy of prediction for teh classification of phenethyl-amines by using the independent test is 100%; the results are better than those of the Fisher, the artificial neural network (ANN), and the K-nearest neighbor models for this real world data. The RMSE (root mean square error) of antagonists' QSAR model is 0.5881, and the RMSE of agonists' QSAR model is 0.4779, which are better than those of the multiple linear regression, partial least squares, and ANN models for this real world data. Conclusion: The SVM can be used to investigate the SAR and QSAR of phenethyl-amines and could be a promising tool in the field of SAR/QSAR research. [ABSTRACT FROM AUTHOR]

In this work, support vector classification (SVC) algorithm was used to build structure-activity relationship (SAR) model of the 5-hydroxytryptamine type 3 (5-HT3) receptor antagonists with 26 compounds. In a benchmark test, SVC was compared with several techniques of machine learning currently used in the field. The prediction performance of the model was discussed on the basis of the leave-one-out cross-validation. The results show that the accuracy of prediction of SVC model was higher than those of back propagation artificial neural network (BP ANN), K-nearest neighbor (KNN) and Fisher methods. [ABSTRACT FROM AUTHOR]