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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]

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]

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]

Ti-based bulk metallic glasses (Ti4oZr25Cu9Ni8Be18)100-xNbx with x = 0 to 5at.% are prepared by copper-mold casting. The glass formation ability is almost unchanged by addition of Nb. The compression plasticity is, however, apparently changed, from 3% at x = 0 to 13% at x = 3, about 330% increases at the strain rate of 1 × 10[?]4s[?]1. The increment of the plasticity can be attributed to the segregation of Nb in the area of shear bands during the compression processing. An effective way to increase the plasticity of Ti-based bulk metallic glasses is thus proposed. [ABSTRACT FROM AUTHOR]

In the title compound, [Fe(C5H5)(C20H23)], the two five-membered cyclo­penta­dienyl rings are nearly parallel, with a dihedral angle of only 3.8 (1)°. The dihedral angle between the indenyl plane and the substituted cyclo­penta­dienyl ring is 58.1 (2)°. [ABSTRACT FROM AUTHOR]

In the title compound, [Fe(C5H5)(C21H23)], the cyclo­penta­dienyl (Cp) rings deviate slightly from an eclipsed geometry. The cyclo­heptane ring adopts a chair conformation. The unsubstituted and substituted Cp rings form dihedral angles of 55.62 (12) and 54.13 (10)°, respectively, with the indene ring system. [ABSTRACT FROM AUTHOR]

The turnout switch machine (TSM) is the critical signal equipment of the interlocking system, which directly affects the efficiency and safety of rail transit. However, the incomplete feature information of single-source data and the scarcity of fault data in real scenes make the existing methods unable to obtain satisfactory diagnostic results. Given the above issues, a few-shot fault diagnosis method based on data fusion and balanced regularized prototypical network (BRPN) is proposed. Firstly, a multi-signal adaptive fusion strategy is proposed to adaptively fuse three currents of the TSM to achieve a comprehensive and accurate expression of fault information. Secondly, a multi-level feature fusion network is designed to enhance the feature extraction ability of the fusion current signal. Furthermore, a prototype distribution balanced regularized strategy is proposed to balance the distribution differences between prototype interclass, so as to improve the diagnostic performance of the BRPN. Finally, the proposed BRPN model is verified by data fusion experiments and different small sample fusion datasets. Compared with other methods, the proposed method shows satisfactory diagnostic results in 1-shot and 5-shot experiments with two conversion processes, and the highest accuracy of fault diagnosis reaches 99.60%, which can provide a solution for solving the scarce fault samples of switch machines in a real-world situation. • A MSAFS method is proposed to realize the accurate expression of fault information. • A MLFFN method is presented to improve the extraction ability for fusion signals. • A PDBRS approach is proposed to balance the distribution difference of prototypes. • The BRPN model is presented for switch machines few-shot fault diagnosis. • Data fusion and few-shot experiments verify the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

As a crucial load-bearing component of the cable-stayed bridge, the cable requires surface defect detection to maintain its safety. The current deep-learning-based stay-cable surface defect detection still faces challenges, including insufficient actual defect samples, cumbersome labeling processes, and an effective method for further evaluation and classification of defects. To address these problems, an unsupervised vision transformer-based lightweight reconstruction network called ViTR-Net is proposed, which only uses normal stay-cable surface images to achieve end-to-end localization of defect areas in the image. ViTR-Net is a generative-based method that uses a hybrid network of convolutional neural networks and visual transformers as a feature extraction network. A feature correction fusion module is designed by integrating differential convolution and attention mechanisms to compensate for potential detail loss caused by feature extraction. Subsequently, a severity index for defects is established to assess the severity of various defects, and the detected defects are adaptively classified using the affinity propagation clustering algorithm. ViTR-Net achieves the highest defect detection accuracy and exquisite defect segmentation results on a realistic cable defect dataset through experimental validation. The proposed model can realize the surface defect assessment and adaptive classification of the stay cable, which has a good application prospect in the actual maintenance process of the stay cable. • Unsupervised vision transformer-based lightweight reconstruction network. • Feature correction fusion method combining differential convolution and attention. • Address the issue of limited defect samples and labor-intensive manual labeling. • Introduce a metric for evaluating different defect severity levels. • Realize surface defect assessment and adaptive classification of the stay cable. [ABSTRACT FROM AUTHOR]

• A novel bearing RUL prediction approach with hypergraph structure is proposed. • The MHG is constructed to represent the rolling bearings' underlying structure. • The HGAT is proposed to learn the high-order relationships between signal samples. Accurate remaining useful life (RUL) prediction of rolling bearings plays a vital role in ensuring the safe operation of mechanical equipment. Graph-based models have become an emerging trend in RUL prediction by converting monitoring samples into graph structures to capture samples' relationships effectively. However, graph-based models only use pairwise samples to model the relationships between samples and cannot capture the non-pairwise high-order relationships between multiple samples. Besides, graph-based models rely heavily on predefined graphs to aggregate relevant features. The bearing monitoring datasets have no explicit structure, and the predefined graph structures cannot characterize datasets. Aiming at these issues, a temporal multi-resolution hypergraph attention network (T-MHGAT) is proposed. Firstly, the bearings' monitoring samples are established and fused into a multi-resolution hypergraph (MHG) to characterize the potential structure of bearings monitoring datasets. Then, a hypergraph attention network (HGAT) is designed to mine the high-order relationships between signal samples on hypergraph data. Meanwhile, multiple gated recurrent units (GRUs) are constructed to capture the signal samples' temporal information. Finally, the linear layer is built after GRUs to output RUL prediction values. Many experiments on two rolling bearing datasets showed the effectiveness of T-MHGAT, which can lay the foundation for predictive equipment maintenance. [ABSTRACT FROM AUTHOR]

The positive- and negative-ion electrospray ionization (ESI) coupled with Fourier transform-ion cyclotron resonance mass spectrometry (FT-ICR MS) was employed to identify the chemical composition of heteroatomic compounds in four distillates of Fushun shale oil, and their catalytic cracking performance was investigated. There are nine classes of basic nitrogen compounds (BNCs) and eleven classes of non-basic heteroatomic compounds (NBHCs) in the different distillates. The dominant BNCs are mainly basic N1 class species. The dominant NBHCs are mainly acidic O2 and O1 class species in the 300–350 °C, 350–400 °C, and 400–450 °C distillates, while the neutral N1, N1O1 and N2 compounds become relatively abundant in the > 450 °C fraction. The basic N1 compounds and acidic O1 and O2 compounds are separated into different distillates by the degree of alkylation (different carbon number) but not by aromaticity (different double-bond equivalent values). The basic N1O1 and N2 class species and neutral N1 and N2 class species are separated into different distillates by the degrees of both alkylation and aromaticity. After the catalytic cracking of Fushun shale oil, the classes of BNCs in the liquid products remain unchanged, while the classes and relative abundances of NBHCs vary significantly. [ABSTRACT FROM AUTHOR]

Continuous growth of energy consumption for metro systems has raised the public's concern. Meanwhile, both operators and passengers pursue for efficient metro services, especially during peak hours. This paper aims at designing a bi-objective optimization model and a new solution approach so that the energy conservation of metro trains and less passenger waiting time can be achieved. Firstly, considering complex routes, a multi-particle train operation model with the objectives of punctuality, parking accuracy, energy efficiency and safety provided is established for the minimization of traction energy consumption through investigating the optimal force coefficients and coast positions, while suitable dwell times and headway times of metro trains are found trough building a timetable model, which balances the regenerative energy, transfer and non-transfer passenger waiting time. Secondly, a deep reinforcement learning (DRL) and non-dominated sorting genetic algorithm II (NSGA-II) based two-layer solution approach is introduced for model calculation. Finally, comparison shows that the proposed model can achieve the energy conservation of metro trains by 9.16% with passenger waiting time decreased by 15.88%. More experiments further demonstrate the superiority of the developed model and solution approach. [Display omitted] [ABSTRACT FROM AUTHOR]

The rapid development of intelligent rail transportation equipment is raising higher requirements for the real-time performance of Train Communication Network (TCN) data transmission. Time-Sensitive Network (TSN) has gained widespread attention in train communication due to its advantages of high transmission rates and deterministic latency. Firstly, for addressing the flow scheduling problem in TSN, a TCN topology model supports TSN is designed. It establishes a multi-objective flow scheduling model based on a priority strategy, aiming to minimize average response time and makespan while ensuring a scheduling success rate. Then, to solve the routing selection problem, a joint scheduling algorithm based on multi-level routing selection is adopted, providing feasible scheduling solutions. Finally, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm is introduced to iteratively and selectively optimize the scheduling solutions. This approach resolves the flow ordering problem while achieving optimization for the scheduling model. The feasibility of this scheduling approach is validated through simulations, and the results demonstrate significant advantages of the proposed model and algorithm in terms of scheduling success rate and real-time performance, providing effective solutions for flow scheduling in TCN. [ABSTRACT FROM AUTHOR]

• In order to overcome the problems of low parameter estimation accuracy and poor versatility in existing reliability models of train system components, an improved beluga whale optimization algorithm (IBWO) is proposed to calculate the reliability of bogie key components. • A new total maintenance cost model of bogie components is established based on the failure risk cost and preventive maintenance (PM) cost. • A PM model for key components of subway train bogie with consideration of failure risk is established. With the acceleration of urbanization, the number of urban rail transit trains has increased dramatically, and the economy and safety of subway trains have become important standards. As an essential subsystem of the subway train, the bogie has a long maintenance process and serious failure consequences. So, it is essential to formulate a reasonable maintenance strategy to ensure its operation. To solve the above problems, a preventive maintenance (PM) strategy for key components of train bogie is established with consideration of failure risk in this paper. Firstly, the failure risk factors of the bogie components are scored and weighted, and the failure risk cost model and PM cost model are established. Next, a Weibull distribution parameter estimation method for bogie components based on the improved beluga whale optimization algorithm (IBWO) is proposed, providing a theoretical basis for PM decision-making optimization. Then, the Lévy flight is introduced into particle swarm optimization to enhance the optimization performance of the model and obtain the optimal solution. Finally, the bogie key components of Nanning Metro line 1 are selected as a case study. The results show that the IBWO algorithm has strong applicability and feasibility, and can accurately calculate the Weibull parameter values of the bogie key components. Compared with the PM plan without considering the failure risk, the proposed method is more economical and safer, which can provide necessary theoretical support for the maintenance decision optimization of urban rail transit train components. [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]

In this study, two synthesized cutinase genes from Fusarium solani and Aspergillus fumigatus were expressed in Pichia pastoris X33. The characteristics of these two cutinases were investigated and compared. The results indicated that F. solani and A. fumigatus cutinases hydrolyzed p -nitrophenyl substrates with different carbon chain lengths. A. fumigatus cutinase predominately hydrolyzed p -nitrophenyl butyrate, but F. solani cutinase preferred p -nitrophenyl decanoate. The abilities of polymer synthesis and bioplastic degradation were tested and compared between F. solani and A. fumigatus cutinases. The results showed that F. solani cutinase had degradation ability on poly(ε-caprolactone) (PCL) and synthesized polymer with a molecular weight (MW) of 2300 in organic solvent. However, A. fumigatus cutinase completely degraded PCL and synthesized molecules with a MW of 25,000, suggesting that A. fumigatus cutinase has more promising applications. [ABSTRACT FROM AUTHOR]

Background Evolocumab (AMG 145), a PCSK9 inhibitor, has been shown to decrease low-density lipoprotein cholesterol (LDL-C) levels. Doses of 140 mg administered every 2 weeks (Q2W) and 420 mg administered every 4 weeks (Q4W) are widely used, and both dosing schedules were effective in clinical trials. However, some researchers have speculated that 140 mg Q2W administration has equal or even greater efficacy. This meta-analysis was performed to assess the differences in efficacy and safety between the two doses. Methods We searched the PubMed, EMBASE, and Web of Science databases to identify relevant clinical trials published before January 2016. A total of 2403 patients from 8 randomized controlled trials were identified and included in the analysis. Results Evolocumab administered at 140 mg Q2W resulted in a greater percent change from baseline in LDL-C concentration (− 7.27; 95% confidence interval (CI), − 10.36 to − 4.18) and had greater efficacy in achieving the treatment goal of LDL-C ≤ 1.8 mmol/L with an relative risk (RR) of 1.09 (95% CI, 1.00 to 1.18) compared with 420 mg Q4W in patients who were concomitantly treated with statins. These findings were not significantly different between the 140 mg Q2W and 420 mg Q4W groups when evolocumab was administered as monotherapy. There was no difference in the rate of occurrence of the main treatment-related adverse events between the two doses. Conclusions Evolocumab administered at 140 mg Q2W was more effective than the 420 mg Q4W dosage at lowering lipid concentrations, especially in patients who concomitantly received stable statin therapy. [ABSTRACT FROM AUTHOR]

The global energy crunch and rising demand for electricity make it all the more important to develop energy-efficient timetable for urban rail transit trains. Under normal circumstances, the train can operate normally according to the energy-saving timetable that has been formulated. However, trains may deviate from the original schedule and increase energy consumption due to station delays. For this, how to quickly restore the train to an efficient state and effectively transport the delayed passengers to the destination as soon as possible is a challenging problem. Therefore, this paper proposed a two-stage optimization method to solve the above problem. In the first stage, an improved differential evolution algorithm was used to optimize the energy-saving operation strategy of the train without delay, and the optimal running time-energy consumption solution set of the train in the redundant running time of each section on the entire line was obtained. Then, based on this, the second stage proposed a rapid iterative method to optimize and reschedule the timetable of the delayed train in the remaining sections, and an efficient solution was obtained. Finally, Nanning Rail Transit (NNRT) Line 5 is selected for a case study, and the proposed model and method are verified. Compared with the existing solution that temporarily shortens the running time of the next section in the delayed station, the proposed method reduces the energy consumption by 3.32% on average, and the average calculation time is about 0.36 s, which showed that the method is effective and meets the real-time requirements. [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]

Flywheel energy storage system is widely used in train braking energy recovery, and has achieved excellent energy-saving effect. As a key component of the flywheel energy storage system, the health of the bearing is greatly significant to realize the effective recovery of train braking energy. The vibration signal of the bearing presents complex nonlinear and non-stationary characteristics, which makes it difficult to diagnose the fault of the bearing. To solve this problem, a fault diagnosis method for bearing of flywheel energy storage system based on parameter optimization Variational Mode Decomposition (VMD) energy entropy is proposed. Firstly, the improved Sparrow Search Algorithm is used to optimize VMD parameters with the dispersion entropy as the fitness value. Then, the original signal is decomposed into a series of intrinsic mode components by using the optimized VMD algorithm, and the energy entropy of each component is calculated to construct the feature vector. Finally, an Inverted Residual Convolutional Neural Network (IRCNN) is used as feature vector input model for fault diagnosis. The experimental results show that the proposed method can effectively extract the bearing fault characteristics and realize accurate fault diagnosis, and the recognition rate reaches 97.5%, which is better than the comparison method. • The CSSA-VMD method can effectively carry out bearing vibration signal analysis. • The energy entropy is introduced into the feature extraction. • The input model of IRCNN as feature vector is superior to the comparison models. • Experiments verify the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: Structure propensities of amino acids are important determinants in guiding proteins'' local and global structure formation. We constructed a phage display library—a hexa-HIS tag upstream of a CXXC (X stands for any of the 20 natural amino acids) motif appending N-terminal to the minor capsid protein pIII of M13KE filamentous phage—and developed a novel directed-evolution procedure to select for amino acid sequences forming increasingly stable β-turns in the disulfide-bridged CXXC motif. The sequences that emerged from the directed-evolution cycles were in good agreement with type II β-turn propensities derived from surveys of known protein structures, in particular, Pro-Gly forming a type II β-turn. The agreement strongly supported the notion that β-turn formation plays an active role in initiating local structure folding in proteins. [Copyright &y& Elsevier]

The title centrosymmetric complex, [Mo2(C19H19)(CO)6] or [{(η5-C5H4)C(CH2)4(C9H7)}Mo(CO)3]2, is a binuclear metal carbonyl complex containing an Mo—Mo single bond [3.2405 (9) Å]. The cyclo­pentane ring adopts an envelope conformation. The dihedral angle between the indene and cyclo­penta­diene rings is 57.0 (2)°. [ABSTRACT FROM AUTHOR]

Urban rail transit is an efficient public transport, and reducing their energy consumption is beneficial to climate change and sustainable development. Current researches in metro train trajectory optimization are mainly based on the single-particle train operation model, and the selection of energy-saving operation strategy under time-varying passenger flow is not taken into consideration. Therefore, considering the practical line environment and uncertain trainload simultaneously, this paper establishes a multi-particle operation model and develops a new optimization method based on mutated dichotomy and differential evolutionary algorithm to solve the model. Then, the influence of various passenger flow and line information on the train trajectory optimization is analyzed from the perspective of trainload capacity based on the proposed method, and the selection basis of the optimal energy-saving train operation strategy was determined. Finally, a case study was conducted in the Nanning Rail Transit Line 1 and Line 5, respectively. The results show that the proposed method has strong efficiency for energy conservation and better optimization performance than the conventional differential evolution algorithm in solving train trajectory optimization problem. And the simulation results of the Line 5, which is a fully automatic operation line, also verified the reliability of the selection basis. • Passenger flow was considered in a multi-particle metro energy conservation model. • An improved evolution algorithm was proposed to optimize train trajectory. • The optimal operation strategy selection basis was determined for the metro line. [ABSTRACT FROM AUTHOR]

• A deep learning method for obstacle detection of rail transit was proposed. • A color mask method was proposed to divide the region of interest. • Data augmentation, transfer learning and staged training strategy was used in model. Obstacle detection plays an important role in train automatic operation. To overcome the low accuracy and poor real-time performance of traditional detection methods, and better detect medium and long distances obstacles, the Improved-YOLOv4 network based on deep learning was proposed. The D-CSPDarknet was designed as feature extraction network. A combination of path aggregation and feature pyramid networks were used in feature fusion network, and a spatial pyramid pooling network was set up at each fusion layer. A method of dividing the ROI using a mask was proposed to improve the accuracy of the model while the processing speed can reach 0.004 s. Data augmentation, transfer learning and phased training strategies were used to improve model performance. Based on the data collected in the real operating environment of the train, Improved-YOLOv4 obtained the mAP of 93% on NVIDIA Jetson AGX, which is more suitable for the obstacle detection of rail transit. [ABSTRACT FROM AUTHOR]

• A deep learning method for inspection of exterior substance is proposed. • Dense connection and spatial pyramid pooling are used to extract features. • Data augmentation and transfer learning was used to build inspection model. • The proposed model significant outperformed other deep learning method. When a high-speed train is running, exterior substances, such as rail-side plastic bags, are easily rolled into the bogies, cables and equipment gaps, which can easily cause smoke, odor and even equipment short-circuits and fires. Current inspection methods have many disadvantages. To overcome these defects, this paper presents a fast intelligent inspection model based on the YOLO V3 deep learning method for exterior substance inspection. We utilized the densely convolutional network as the feature extractor to enhances the feature propagation and ensure maximum information flow in the YOLO V3 method. Spatial pyramid pooling networks was used in the YOLO V3 method to reduce context information loss. The transfer learning and data augmentation technologies were applied to make the present method easier to train. Compared with other method, the proposed method shows satisfactory performance in terms of precision and recall, which is the stare-of-art exterior substance inspection method. [ABSTRACT FROM AUTHOR]

• Time cost of transfer passenger was considered in a metro energy conservation model. • A tab encoding method was applied for calculating the regenerative energy. • The transfer waiting time was optimized based on the arrival time matrix. Energy consumption by metro trains has attracted considerable attention due to economic and environmental concerns. Passengers want convenient travel that takes less time. Based on the requirements of reduced train costs and more passenger comfort, the train control strategy and time parameters need to be adjusted properly. This paper proposes an integrated method for the minimum energy consumption of metro trains and minimum transfer waiting time cost for transfer passengers. First, we used a four-stage control strategy to optimize the train trajectory and generated a power matrix. Then, a tab encoding method was established for the regenerative energy calculation on the basis of the power matrix. To obtain the transfer waiting time of passengers, an arrival time matrix was built that was inspired by the matching operation of the regenerative energy. The genetic algorithm was employed for optimization because of its good searching ability, and the matrix form in this model was suitable for computer programming. Finally, two metro lines from the Nanning rail transit system were selected for the case study. The results show that the proposed method has good efficiency for energy conservation and decreased the transfer waiting time between two metro lines. Further research can be conducted by considering more kinds of passengers from the travel flow in the future. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) organic nanomaterials are fascinating because of their unique properties and pentential applications in future optoelectronic devices. Polyaniline (PANI) has attracted much attention for its high conductivity, good environmental stability and unusual doping chemistry. We report on liquid-phase exfoliation of layered PANI films grown by electrochemical polymerization. Atomic force microscopy images demonstrate that few- or even mono-layer PANI nanosheets can be fabricated. The PANI nanosheets can be transferred onto a variety of surfaces, providing a promising route to their incorporation into a variety of devices for further studies and various applications. [ABSTRACT FROM AUTHOR]

In the title mol­ecule, C23H25NO, the hydroxy H atom is involved in the formation of an intramolecular O—H⋯N hydrogen bond. The dihedral angle between the benzene ring and the 2-naphthol system is 78.1 (1)°. The crystal packing is stabilized by van der Waals forces. [ABSTRACT FROM AUTHOR]