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A comprehensive understanding of carrier transport in photoisomeric molecular junctions is crucial for the rational design and delicate fabrication of single-molecule functional devices. It has been widely recognized that the conductance of azobenzene (a class of photoisomeric molecules) based molecular junctions is mainly determined by photoinduced conformational changes. In this study, it is demonstrated that the most probable conductance of amine-anchored azobenzene-based molecular junctions increases continuously upon UV irradiation. In contrast, the conductance of pyridyl-anchored molecular junctions with an identical azobenzene core exhibits a contrasting trend, highlighting the pivotal role that anchoring groups play, potentially overriding (even reversing) the effects of photoinduced conformational changes. It is further demonstrated that the molecule with cis-conformation cannot be fully mechanically stretched into the trans-conformation, clarifying that it is a great challenge to realize a reversible molecular switch by purely mechanical operation. Additionally, it is revealed that the coupling strength of pyridyl-anchored molecules is dramatically weakened when the UV irradiation time is prolonged, whereas it is not observed for amine-anchored molecules. The mechanisms for these observations are elucidated with the assistance of density functional theory calculations and UV–Vis spectra combined with flicker noise measurements which confirm the photoinduced conformational changes, providing insight into understanding the charge transport in photoisomeric molecular junctions and offering a routine for logical designing synchro opto-mechanical molecular switches.

Purpose The albumin to alkaline phosphatase ratio (AAPR) is a newly developed blood biomarker that has been reported to have prognostic value in several types of cancers. The aim of this study was to investigate the predictive value of AAPR in overall survival after radical colon cancer surgery in patients with stage I-III colorectal cancer (CRC).Methods The clinical data of 221 eligible patients with stage I ∼ III CRC were retrospectively analyzed. A series of survival analyses were performed to assess the prognostic value of AAPR. Univariate and multifactorial Cox analyses were performed to identify independent risk factors. Columnar graph prediction models were further constructed based on independent risk factors such as AAPR, and their predictive properties were validated.Results The optimal cutoff value of preoperative AAPR for postoperative overall survival (OS) in patients undergoing laparoscopic radical CRC was .495 as shown by univariate and multifactorial Cox regression analysis. The factors of age ≤65 years, Tumor-Node-Metastasis (TNM) stage I-II, tumor grading (high/medium differentiation), CEA ≤5, and AAPR ≥.495 were associated with better OS (P< .05).Conclusions Preoperative AAPR level was a good predictor of postoperative survival in patients undergoing laparoscopic radical CRC surgery, and AAPR <.495 was an independent risk factor for decreased postoperative OS.

The condition of the rotor system remains difficult to assess due to system nonlinearity and nosiy measurements. To deal with the problem, we proposed a hierarchical physics-informed neural network (HPINN) to discover the ordinary differential equation (ODE) of a healthy/faulty rotor system from noise measurements and then assess the machine condition based on the discovered ODE. Specifically, the ODE of a healthy rotor system is first stably identified from noise measurement through HPINN guided by rotor dynamics. Based on the identified healthy ODE, the extra fault terms in the ODE of the faulty rotor system are then sparsely regressed from the predefined library embedded in HPINN. Moreover, with the mathematical terms of discovered fault, the potential fault and the health indicator (HI) are diagnosed and constructed to assess the condition of the rotor system, respectively. Finally, the effectiveness of the proposed method is verified by the data set collected on the circulating water test bench, showing the potential for practical applications.

In mechanical anomaly detection, algorithms with higher accuracy, such as those based on artificial neural networks, are frequently constructed as black boxes, resulting in opaque interpretability in architecture and low credibility in results. This article proposes an adversarial algorithm unrolling network (AAU-Net) for interpretable mechanical anomaly detection. AAU-Net is a generative adversarial network (GAN). Its generator, composed of an encoder and a decoder, is mainly produced by algorithm unrolling of a sparse coding model, which is specially designed for feature encoding and decoding of vibration signals. Thus, AAU-Net has a mechanism-driven and interpretable network architecture. In other words, it is ad hoc interpretable. Moreover, a multiscale feature visualization approach for AAU-Net is introduced to verify that meaningful features are encoded by AAU-Net, helping users to trust the detection results. The feature visualization approach enables the results of AAU-Net to be interpretable, i.e., post hoc interpretable. To verify AAU-Net’s capability of feature encoding and anomaly detection, we designed and performed simulations and experiments. The results show that AAU-Net can learn signal features that match the dynamic mechanism of the mechanical system. Considering the excellent feature learning ability, unsurprisingly, AAU-Net achieves the best overall anomaly detection performance compared with other algorithms.

Vibration signal monitoring and fault diagnosis are highly important and are of great significance for the stable operation of aeroengine rotor systems. However, the working conditions of aeroengine are complex, and its corresponding vibration during speed change often has obvious time-varying characteristics and contains a large amount of aerodynamic and combustion noise; therefore, the diagnosis of aeroengines is challenging. The conventional synchrosqueezing transform (SST) methods have several disadvantages for the processing of strong time-varying signals with low signal-to-noise ratios (SNRs). To solve this problem, this article proposes two kinds of time-frequency postprocessing methods based on the generalized S-transform (GST), namely, multisynchrosqueezing transform based on GST (GST-MSST) and second-order synchroextracting transform based on GST (GST-SET2). GST-MSST is less computationally burdensome but is not suitable for dealing with emphasized frequency modulation (FM) signals and can be applied to on-board online analysis; GST-SET2 is more computationally burdensome but is suitable for the analysis of strong FM signals and can be applied to offline fine analysis. The results of numerical simulation show that these two methods can provide clear and accurate time-frequency representation (TFR) with good noise robustness. Finally, a certain type of aeroengine pressurizer is taken as an example to verify the effectiveness of the method of this article in pressurizer fault diagnosis.

DNA methylation is an epigenetic modification that plays a crucial role in various biological processes. Aberrant DNA methylation is closely associated with the onset of diseases, and the specific localization of methylation sites in the genome offers further insight into the connection between methylation and diseases. Currently, there are numerous methods available for site-specific methylation detection. Electrochemical biosensors have garnered significant attention due to their distinct advantages, such as rapidity, simplicity, high sensitivity, low cost, and the potential for miniaturization. In this paper, we present a systematic review of the primary sensing strategies utilized in the past decade for analyzing site-specific methylation and their applications in electrochemical sensors, from a novel perspective focusing on the localization analysis of site-specific methylation. These strategies include bisulfite treatment, restriction endonuclease treatment, other sensing strategies, and deamination without direct bisulfite treatment. We hope that this paper can offer ideas and references for establishing site-specific methylation electrochemical analysis in clinical practice.