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Accumulating evidence indicates that gut microbiota closely correlates with the tumorigenesis of digestive system cancers (DSCs). However, whether the causality between gut microbiota and DSCs exists is unknown. Genome-wide association study (GWAS) summary statistics for gut microbiota and DSCs and the bidirectional two-sample Mendelian randomization (MR) analysis were utilized to assess the causality between gut microbiota and DSCs. Sensitivity analyses were performed to evaluate the robustness of our results. We found that the genus Eggerthella (OR = 0.464, 95%CI: 0.27 to 0.796, p = 0.005) was negatively associated with the risk of gastric cancer. The genetically predicted genus Lachnospiraceae FCS020 group (OR = 0.607, 95%CI: 0.439 to 0.84, p = 0.003) correlated with a lower risk of colorectal cancer, and genus Turicibacter (OR = 0.271, 95%CI: 0.109 to 0.676, p = 0.005) was a protective factor for liver cancer. In the reverse MR, DSCs regulated the relative abundance of specific strains of gut microbiota. We comprehensively screened the association between gut microbiota and DSCs using a bidirectional two-sample MR analysis and identified the causality between several microbial taxa and DSCs. Our discoveries are beneficial for the development of novel microbial markers and microbiota-modifying therapeutics for DSC patients.

The rapid development of cities in recent years has increased the operational pressure of rail vehicles, and due to the characteristics of rail vehicles, including harsh operating environment, frequent starting and braking, resulting in rails and wheels being prone to rail corrugation, polygons, flat scars and other faults. These faults are coupled in actual operation, leading to the deterioration of the wheel–rail contact relationship and causing harm to driving safety. Hence, the accurate detection of wheel–rail coupled faults will improve the safety of rail vehicles’ operation. The dynamic modeling of rail vehicles is carried out to establish the character models of wheel–rail faults including rail corrugation, polygonization and flat scars to explore the coupling relationship and characteristics under variable speed conditions and to obtain the vertical acceleration of the axle box. An APDM time–frequency analysis method is proposed in this paper based on the PDMF adopting Rényi entropy as the evaluation index and employing a WOA to optimize the parameter set. The number of iterations of the WOA adopted in this paper is decreased by 26% and 23%, respectively, compared with PSO and SSA, which means that the WOA performs at faster convergence speed and with a more accurate Rényi entropy value. Additionally, TFR obtained using APDM realizes the localization and extraction of the coupled fault characteristics under rail vehicles’ variable speed working conditions with higher energy concentration and stronger noise resistance corresponding to prominent ability of fault diagnosis. Finally, the effectiveness of the proposed method is verified using simulation and experimental results that prove the engineering application value of the proposed method.

Gear tooth spalling is one of the inevitable fault modes in the long-term service of the traction transmission system of railway vehicles, which can worsen the dynamic load of the rotating mechanical system and reduce the operating quality. Therefore, it is necessary to study its fault mechanism to guide fault diagnosis scientifically. This paper established a planar railway vehicle model with a traction transmission system and an analytical time-varying meshing stiffness (TVMS) model of the spalling spur gear. Then, it analyzed the dynamic characteristics under traction conditions. The research found that the spalling length and depth affect the amplitude of the TVMS at the defect, while the width affects the range of the TVMS loss. The crest factor is the best evaluation indicator in ideal low-noise environments due to its sensitivity and linearity, but it is not good in strong-noise environments. Similarly, a time–frequency analysis tool cannot significantly detect the sideband characteristics that are excited by spalling. After high-pass filtering, the root mean square and variance exhibit excellent classification and vehicle speed independence in strong-noise environments. This research achievement can provide adequate theoretical support for feature selection and making strategies for fault diagnosis of railway vehicle gear systems.

A two-switch nonisolated DC–DC three-port converter (TPC) based on a modified SEPIC circuit with a high voltage gain is introduced and a unified power control strategy is proposed in this article. The proposed TPC is integrated with one low-voltage input and one high-voltage port and is suitable for photovoltaic (PV) applications. The operating principle of this TPC is analyzed in detail and then its static voltage gain is determined. In addition, the advantage of using a modified SEPIC circuit is that fewer components are employed compared to a similar topology. Furthermore, the state space method is adopted to analyze this multiple-input multiple-output (MIMO) system and then a small signal model is built to design an appropriate control strategy. Finally, the proposed 100 W TPC connects a solar PV energy storage system to a load demonstrating efficiency ranging from 85% to 94%. Moreover, the steady- and dynamic-state performances of this TPC are verified in the SimStar MT3200 HIL platform, which illustrates the feasibility of the proposed TPC and its power control strategy for solar PV applications.

The energy management system (EMS) of hybridization and electrification plays a pivotal role in improving the stability and cost-effectiveness of future vehicles. Existing efforts mainly concentrate on specific optimization targets, like fuel consumption, without sufficiently taking into account the degradation of on-board power sources. In this context, a novel multi-objective energy management strategy based on deep reinforcement learning is proposed for a hybrid electric vehicle (HEV), explicitly conscious of lithium-ion battery (LIB) wear. To be specific, this paper mainly contributes to three points. Firstly, a non-parametric reward function is introduced, for the first time, into the twin-delayed deep deterministic policy gradient (TD3) strategy, to facilitate the optimality and adaptability of the proposed energy management strategy and to mitigate the effort of parameter tuning. Then, to cope with the problem of state redundancy, state space refinement techniques are included in the proposed strategy. Finally, battery health is incorporated into this multi-objective energy management strategy. The efficacy of this framework is validated, in terms of training efficiency, optimality and adaptability, under various standard driving tests.

To take full advantage of the multicomponent transient electromagnetic method, we summarize the advantages of multicomponent exploration based on simulation calculation. We have carried out experiments on the effectiveness of the method in the known copper-nickel mining area. The results show that the characteristic curve of the horizontal component can effectively point to the central direction of the low-resistivity ore body and reflect the occurrence of the ore body. The degree of coupling between the vertical component and the ore body is high, which is beneficial for quantitative inversion. The results for the horizontal component and vertical component interpretations are consistent, which reduces the limitation of conventional single vertical component interpretation and effectively improves the work efficiency in field work.

Noise tends to limit the quality of wide field electromagnetic method (WFEM) data and exploration results. The existing WFEM denoising methods lack the signal identification process and are only able to filter or eliminate abnormalities in the time or frequency domain, which easily leads to the loss of more abundant real data and to low data quality. Thus, we built the WFEM data sample library to extract the multi-domain features. Then, neighborhood search and location sharing were used to improve the grey wolf optimizer (IGWO) algorithm. The support vector machine (SVM) parameters were optimized by IGWO to train multi-domain features, and an IGWO-SVM data model was generated. We used the data model to quantitatively test the WFEM signal and noise in the simulation and measured data. This method can effectively identify the WFEM signal and noise, eliminate the identified noise, and use the identified signal to reconstruct the effective data. Finally, the digital coherence technique was used to extract the spectrum amplitude of the effective frequency points. The experiments demonstrated the advantage of the convergence of IGWO algorithms and the comparison of the SVM parameters optimization techniques. The proposed method can quickly and effectively search the optimal SVM parameters, significantly improve the identification effect of WFEM signal noise, and completely remove the abnormal noise waveform in the reconstructed data. The more stable electric field curves in the results verify the effectiveness of the algorithm design and optimized identification method.

In the signal processing of real subway vehicles, impacts between wheelsets and rail joint gaps have significant negative effects on the spectrum. This introduces great difficulties for the fault diagnosis of gearboxes. To solve this problem, this paper proposes an adaptive time-domain signal segmentation method that envelopes the original signal using a cubic spline interpolation. The peak values of the rail joint gap impacts are extracted to realize the adaptive segmentation of gearbox fault signals when the vehicle was moving at a uniform speed. A long-time and unsteady signal affected by wheel–rail impacts is segmented into multiple short-term, steady-state signals, which can suppress the high amplitude of the shock response signal. Finally, on this basis, multiple short-term sample signals are analyzed by time- and frequency-domain analyses and compared with the nonfaulty results. The results showed that the method can efficiently suppress the high-amplitude components of subway gearbox vibration signals and effectively extract the characteristics of weak faults due to uniform wear of the gearbox in the time and frequency domains. This provides reference value for the gearbox fault diagnosis in engineering practice.

Stimulus frequency otoacoustic emission (SFOAE) is an active acoustic signal emitted by the inner ear providing salient information about cochlear function and dysfunction. To provide a basis for laboratory investigation and clinical use, we investigated the characteristics of SFOAEs, including detection rate, musical training influence, and gain function. Sixty-five normal hearing subjects (15 musicians and 50 non-musicians, aged 16&ndash, 45 years) were tested and analyzed at the probe level of 30 and 50 dB sound pressure levels (SPL) in the center frequency of 1 and 4 kHz in the study. The results indicate that (1) the detection rates of SFOAE are sensitive to the gender, (2) musicians reveal enhanced hearing capacity and SFOAE amplitudes compared with non-musicians, and (3) probe frequency has a significant effect on the compression threshold of SFOAE. Our findings highlight the importance of SFOAE in the clinical hearing screening and diagnosis and emphasize the use of musical training for the rehabilitation enhancement of the auditory periphery and hearing threshold.