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Due to their high accuracy, excellent stability, minor size, and low cost, silicon piezoresistive pressure sensors are used to monitor downhole pressure under high-temperature, high-pressure conditions. However, due to silicon’s temperature sensitivity, high and very varied downhole temperatures cause a significant bias in pressure measurement by the pressure sensor. The temperature coefficients differ from manufacturer to manufacturer and even vary from batch to batch within the same manufacturer. To ensure high accuracy and long-term stability for downhole pressure monitoring at high temperatures, this study proposes a temperature compensation method based on bilinear interpolation for piezoresistive pressure sensors under downhole high-temperature and high-pressure environments. A number of calibrations were performed with high-temperature co-calibration equipment to obtain the individual temperature characteristics of each sensor. Through the calibration, it was found that the output of the tested pressure measurement system is positively linear with pressure at the same temperatures and nearly negatively linear with temperature at the same pressures, which serves as the bias correction for the subsequent bilinear interpolation temperature compensation method. Based on this result, after least squares fitting and interpolating, a bilinear interpolation approach was introduced to compensate for temperature-induced pressure bias, which is easier to implement in a microcontroller (MCU). The test results show that the proposed method significantly improves the overall measurement accuracy of the tested sensor from 21.2% F.S. to 0.1% F.S. In addition, it reduces the MCU computational complexity of the compensation model, meeting the high accuracy demand for downhole pressure monitoring at high temperatures and pressures.

Image inpainting is an active area of research in image processing that focuses on reconstructing damaged or missing parts of an image. The advent of deep learning has greatly advanced the field of image restoration in recent years. While there are many existing methods that can produce high-quality restoration results, they often struggle when dealing with images that have large missing areas, resulting in blurry and artifact-filled outcomes. This is primarily because of the presence of invalid information in the inpainting region, which interferes with the inpainting process. To tackle this challenge, the paper proposes a novel approach called separable mask update convolution. This technique automatically learns and updates the mask, which represents the missing area, to better control the influence of invalid information within the mask area on the restoration results. Furthermore, this convolution method reduces the number of network parameters and the size of the model. The paper also introduces a regional normalization technique that collaborates with separable mask update convolution layers for improved feature extraction, thereby enhancing the quality of the restored image. Experimental results demonstrate that the proposed method performs well in restoring images with large missing areas and outperforms state-of-the-art image inpainting methods significantly in terms of image quality.

Disulfidptosis, a recently identified form of programmed cell death, is initiated by depletion of endogenous nicotinamide adenine dinucleotide phosphate (NADPH) under glucose starvation. Tumor cells, owing to their heightened requirements of energy and nutrients, are more susceptible to disulfidptosis than normal cells. Here, we introduced an effective strategy to induce tumor disulfidptosis via interrupting cellular energy supply and reducing power by integrating a copper single-atom nanozyme (CuSAE) and glucose oxidase (GOx). GOx induces glucose starvation, impeding generation of NADPH through pentose phosphate pathway (PPP). CuSAE mimics NADPH oxidase, depleting existing NADPH, which intensifies the blockade of disulfide reduction and efficiently triggers disulfidptosis of tumor cells. Furthermore, CuSAE exhibits peroxidase- and glutathione oxidase-mimicking activities, catalyzing generation of •OH radical and depletion cellular GSH, which enhances oxidative stress and exacerbates cell damage. Disulfidptosis is confirmed as the predominant type of cell death induced by GOx/CuSAE. In vivo assays demonstrated the high antitumor potency of GOx/CuSAE in treating with female tumor-bearing mice, with minimal systemic toxicity observed. This work introduces a promising strategy for designing antitumor agents by inducing disulfidptosis. The enzyme hybrids that combine nanozymes and natural enzymes offer a feasible approach to achieve this multifaceted therapeutic goal. The therapeutic applications of disulfidptosis are promising but underexplored. Here, the authors report a strategy for inducing tumor disulfidptosis via interrupting cellular energy supply and reducing power by integrating a copper single-atom nanozyme and glucose oxidase.

The stochastic resonance (SR) phenomenon in high-dimensional nonlinear system and the mechanism of SR are presented in this paper. Based on theoretical analysis, the differences between traditional SR models and the high-dimensional SR model are discussed, and a 5-D SR model is constructed to explain the SR phenomenon in detail. The mechanism of SR is analyzed from the perspective of equilibrium point, and the expression of key parameters is derived. The results of numerical simulation and circuit implementation show that the characteristic of original signal can be well restored whether it is masked by white Gaussian noise or chaotic signal, which shows the great performance of high-dimensional SR system in signal processing. These investigations show the existence of SR in high-dimensional nonlinear system and also can be applied to practical application.

In this paper, a combination of theoretical modeling, finite element simulation, and experimental methods is employed to investigate the forming mechanism and evolutionary pattern of the stagnant region during mechanical scratching with a diamond wedge tool. The study is structured as follows: Firstly, a theoretical calculation model for the geometric parameters of the stagnant region on the formed groove surface is established based on the contact friction partition mechanism and slip-line field theory. The model indicates that the geometric parameters l B-sg , l V-sg , and ∆l sg of the stagnant region are determined by the length of the stagnant region l p-sg in the plastic flow plane and the transformation parameters. Secondly, the formation process of the stagnant region in mechanical scratching is investigated using an orthogonal cutting simulation model with a negative rake angle tool. The results reveal that the stagnant region is a plastic deformation region formed due to the geometrical characteristics of the negative front surface of the scratching tool and its excessive extrusion, which leads to the formation of adhesive friction within the material. Thirdly, the characteristics of the stagnant region are determined through scratching experiments. Compared to the material in the plastic flow region, the material within the stagnant region exhibits finer and denser microstructures, reduced surface hardening peaks and hardened layer depths, and significantly improved surface roughness. Finally, the evolutionary pattern of the stagnant region under the influence of scratching processing parameters is examined based on the theoretical calculation model of the geometric parameters and the scratching experiment. The findings indicate that as the wedge angle of the scratching tool decreases, the relief angle increases, the absolute value of the rotation angle around the Y-axis decreases, the scratching speed decreases, and the material’s plastic adherence improves, the P I/k value decreases, the l p-sg value increases, and consequently, the geometric parameters l B-sg , l V-sg , and ∆l sg of the stagnant region on the formed groove surface also increase. The deviation analysis of the geometric parameters of the stagnant region reveals a consistent trend between the theoretical and experimental values of l V-sg and ∆l sg , with maximum deviations of 15 μm and 4.13%, respectively. This study provides theoretical and experimental evidence for the establishment of the theoretical model of the stagnant region in mechanical scratching, the analysis of its forming mechanism, and the control of the stagnant region geometric parameters on the formed groove surface.

Familial dysautonomia (FD), a rare neurodevelopmental and neurodegenerative disorder affects the sympathetic and sensory nervous system. Although almost all patients harbor a mutation in ELP1, it remains unresolved exactly how function of sympathetic neurons (symNs) is affected; knowledge critical for understanding debilitating disease hallmarks, including cardiovascular instability or dysautonomic crises, that result from dysregulated sympathetic activity. Here, we employ the human pluripotent stem cell (hPSC) system to understand symN disease mechanisms and test candidate drugs. FD symNs are intrinsically hyperactive in vitro, in cardiomyocyte co-cultures, and in animal models. We report reduced norepinephrine transporter expression, decreased intracellular norepinephrine (NE), decreased NE re-uptake, and excessive extracellular NE in FD symNs. SymN hyperactivity is not a direct ELP1 mutation result, but may connect to NET via RAB proteins. We found that candidate drugs lowered hyperactivity independent of ELP1 modulation. Our findings may have implications for other symN disorders and may allow future drug testing and discovery. Sympathetic neurons are affected in familial dysautonomia, a rare disease associated with a mutation in ELP1, but the mechanisms are not fully understood. Here the authors show, using neurons derived from participants with familial dysauotnomia, that spontaneous sympathetic neuron hyperactivity is observed and is associated with norepinephrine transporter deficits.

In order to improve the performance of sliding mode observer in detecting the state of induction motor, a state detection method based on particle swarm optimization (PSO)-backstepping (BS)-sliding mode observer (SMO) is proposed in this paper. In this method, the controller is constructed and the parameters of the control rate are optimized, so that the tracking accuracy and robustness of the new observer are improved relative to conventional observer, exponential observer, PI and PID. Firstly, the state equation of the induction motor under stator and rotor winding fault and stator current sensor fault is established. Secondly, the new sliding mode observer is designed using the backstepping method based on the new reaching law. Then, the new fitness function and PSO is used to optimize the parameters of the new sliding mode observer. Finally, the simulation comparison experiment of stator current state detection is carried out under the simulated fault condition of induction motor. The feasibility of the method is verified by comparing the state tracking situation and the state detection error. The comparative experimental results show that the method has less jitter, stronger robustness, and higher state tracking accuracy when detecting stator current states under different faults.

Increasing immunotherapy response rate and durability can lead to significant improvements in cancer care. To address this challenge, a novel multivalent immune checkpoint therapeutic platform is constructed through site‐specific ligation of anti‐PD‐L1 nanobody (Nb) on ferritin (Ftn) nanocage. Nb‐Ftn blocks PD‐1/PD‐L1 interaction and downregulates PD‐L1 levels via endocytosis‐induced degradation. In addition, the cage structure of Ftn allows encapsulation of indocyanine green (ICG), an FDA‐approved dye. Photothermal treatment with Nb‐Ftn@ICG induces immunogenic death of tumor cells, which improves systemic immune response via maturation of dendritic cells and enhanced infiltration of T cells. Moreover, Nb‐Ftn encapsulation significantly enhances cellular uptake, tumor accumulation and retention of ICG. In vivo assays showed that this nanoplatform ablates the primary tumor, suppresses abscopal tumors and inhibits tumor metastasis, leading to a prolonged survival rate. This work presents a novel strategy for improving cancer immunotherapy using multivalent nanobody‐ferritin conjugates as immunological targeting and enhancing carriers. A multivalent PD‐L1 nanobody (Nb) is constructed via assembly of ferritin nanocage (Ftn). With encapsulation of photosensitizer and photothermal therapy, the Nb‐Ftn conjugate significantly enhances the immunotherapeutic response via DC maturation and enhanced T‐cell infiltration, resulting in efficient ablation of primary tumor, inhibition of distal tumor and suppression of metastasis, leading to a prolonged survival rate.

Current single-view image inpainting methods often suffer from low image information utilization and suboptimal repair outcomes. To address these challenges, this paper introduces a novel image inpainting framework that leverages a structure-guided multi-scale attention pyramid network. This network consists of a structural repair network and a multi-scale attention pyramid semantic repair network. The structural repair component utilizes a dual-branch U-Net network for robust structure prediction under strong constraints. The predicted structural view then serves as auxiliary information for the semantic repair network. This latter network exploits the pyramid structure to extract multi-scale features of the image, which are further refined through an attention feature fusion module. Additionally, a separable gated convolution strategy is employed during feature extraction to minimize the impact of invalid information from missing areas, thereby enhancing the restoration quality. Experiments conducted on standard datasets such as Paris Street View and CelebA demonstrate the superiority of our approach over existing methods through quantitative and qualitative comparisons. Further ablation studies, by incrementally integrating proposed mechanisms into a baseline model, substantiate the effectiveness of our multi-view restoration strategy, separable gated convolution, and multi-scale attention feature fusion.

When a single-phase grounding fault occurs in a resonant grounding system, the determination of the fault location remains a significant challenge due to the small fault current and the instability of the grounding arc. In order to solve the problem of low protection sensitivity when a high-resistance grounding fault occurs in a resonant grounding system, this paper proposes a fault location method based on the combination of dynamic time warping (DTW) distance and fuzzy C-means (FCM) clustering. By analyzing the characteristics of the zero-sequence current upstream and downstream of the fault point when a single-phase grounding fault occurs in the resonant grounding system, it is concluded that the waveform similarity on both sides of the fault point is low. DTW distance can be used to measure the similarity of two time series, and has the characteristics of good fault tolerance and synchronization error tolerance. According to the rule that the DTW value of faulty section is much larger than that of nonfaulty sections, FCM clustering is used to classify the DTW value of each section. The membership degree matrix and cluster centers are obtained. In the membership degree matrix, the section corresponding to the data in a class of their own is the faulty section, and all other data correspond to the nonfaulty section; otherwise, it is a fault occurring at the end of the line. The simulation results of MATLAB/Simulink and the field data test show that the method can accurately locate the faulty section.