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The organ-on-a-chip (OOAC) is in the list of top 10 emerging technologies and refers to a physiological organ biomimetic system built on a microfluidic chip. Through a combination of cell biology, engineering, and biomaterial technology, the microenvironment of the chip simulates that of the organ in terms of tissue interfaces and mechanical stimulation. This reflects the structural and functional characteristics of human tissue and can predict response to an array of stimuli including drug responses and environmental effects. OOAC has broad applications in precision medicine and biological defense strategies. Here, we introduce the concepts of OOAC and review its application to the construction of physiological models, drug development, and toxicology from the perspective of different organs. We further discuss existing challenges and provide future perspectives for its application.

The axial vibration of transformer windings under short-circuit impact is a critical challenge affecting their mechanical stability and operational reliability. Traditional dynamic analysis methods often overlook the prestressed equilibrium state, resulting in significant prediction deviations. To address this issue, this study proposes a coupled static–dynamic framework that integrates nonlinear pad modeling with experimental validation. Static analysis of the axial pressing process revealed that the actual pad stress (1.92 MPa) was significantly lower than the design target (3 MPa) due to the self-weight of the winding, resulting in a non-uniform preload distribution. Dynamic simulations incorporating time-varying electromagnetic forces revealed that the axial displacement amplitude exhibited end-constrained and upper-middle and lower-middle region-enhanced characteristics: the bottom showed the most constraint, while the upper-middle and lower-middle regions exhibited the largest amplitudes. A comparative analysis further demonstrated that insufficient preload caused momentary pad decompression, whereas adequate preload ensured continuous contact and maintained structural stability. The experimental monitoring using fiber Bragg grating sensors qualitatively confirmed the simulated vibration trends, capturing a rapid increase during the initial short-circuit and a secondary surge upon reclosing, despite sensor calibration limitations. Moreover, successive short-circuit events with short intervals led to stress accumulation, highlighting the importance of protection coordination. Overall, the proposed framework enhances vibration prediction accuracy and offers practical guidance for optimizing preload levels, pad layout, and protection strategies to improve transformer short-circuit withstand capability.

Speckle reduction remains a critical issue for ultrasound image processing and analysis. The nonlocal means (NLM) filter has recently attached much attention due to its competitive despeckling performance. However, the existing NLM methods usually determine the similarity between two patches by directly utilizing the gray-level information of the noisy image, which renders it difficult to represent the structural similarity of ultrasound images effectively. To address this problem, the NLM method based on the simple deep learning baseline named PCANet is proposed by introducing the intrinsic features of image patches extracted by this network rather than the pixel intensities into the pixel similarity computation. In this approach, the improved two-stage PCANet is proposed by using Parametric Rectified Linear Unit (PReLU) activation function instead of the binary hashing and block histograms in the original PCANet. This model is firstly trained on the ultrasound database to learn the convolution kernels. Then, the trained PCANet is utilized to extract the intrinsic features from the image patches in the pre-denoised version of the noisy image to be despeckled. These obtained features are concatenated together to determine the structural similarity between image patches in the NLM method, based on which the weighted mean of all pixels in a search window is computed to produce the final despeckled image. Extensive experiments have been conducted on a variety of images to demonstrate the superiority of the proposed method over several well-known despeckling algorithm and the PCANet based NLM method using ReLU function and sigmoid function. Visual inspection indicates that the proposed method outperforms the compared methods in reducing speckle noise and preserving image details. The quantitative comparisons show that among all the evaluated methods, our method produces the best structural similarity index metrics (SSIM) values for the synthetic image, as well as the highest equivalent number of looks (ENL) value for the simulated image and the clinical ultrasound images.

The power transformer is a critical primary device in the power grid, and the verification of its winding mechanical stability is of paramount importance in ensuring the safe and stable operation of the power grid. In the conventional numerical calculation methods for verifying the mechanical stability of power transformer windings, the influence of temperature variations at the winding hot spots on winding mechanical stability has not been taken into account. In reality, factors such as the transformer’s operating load rate, ambient temperature, and the duration of short-circuit fault currents passing through will affect the mechanical stability margin of the transformer windings. Under conditions such as winding aging, deformation, or other reasons, the transformer windings may become unstable due to material parameter degradation, leading to insufficient mechanical stability margin. This paper analyzes the mechanical stability of power transformer windings considering the impact of the temperature field. Initially, a numerical model for calculating short-circuit currents in transformers was established to compute the short-circuit current under three-phase short-circuit-to-ground conditions as an excitation. Subsequently, a 3D electromagnetic force finite element calculation model was developed to determine the electromagnetic forces experienced under this condition. The results of the calculated electromagnetic forces were then used in a numerical calculation method to assess the mechanical stability of the windings. Furthermore, a 3D transformer electromagnetic–thermal flow finite element model was created to calculate the steady-state temperature rise under various operating conditions of the transformer. This model is validated through transformer temperature rise tests, and transient temperature rises under different operating conditions are calculated. The obtained data are fitted using the nonlinear least squares method to derive a fitting function for the winding hot spot temperature concerning load rate, ambient temperature, and short-circuit time. Taking into consideration the influence of temperature on the yield strength and modulus of elasticity of transformer winding materials, the variation in mechanical stability margin of transformer windings due to temperature effects is analyzed. Additionally, the operating domain for preventing the transformer from becoming unstable under three-phase short-circuit impacts is calculated for different degrees of material parameter degradation. This method provides an effective reference for transformer design and operation, demonstrating clear practical value.

HighlightsThe split-ring topography is studied systematically from wettability, evaporation assembly to optoelectronic devices.An efficient dual-function laser etching scheme has been developed to fabricate the split-ring lyophilic pattern and the lateral electrode array simultaneously.A non-contact human-machine interface based on CsPbBr3 perovskite photodetector arrays has been successfully applied to wearable devices, automobile displays, robot remote control.Photodetectors with long detection distances and fast response are important media in constructing a non-contact human–machine interface for the Masterly Internet of Things (MIT). All-inorganic perovskites have excellent optoelectronic performance with high moisture and oxygen resistance, making them one of the promising candidates for high-performance photodetectors, but a simple, low-cost and reliable fabrication technology is urgently needed. Here, a dual-function laser etching method is developed to complete both the lyophilic split-ring structure and electrode patterning. This novel split-ring structure can capture the perovskite precursor droplet efficiently and achieve the uniform and compact deposition of CsPbBr3 films. Furthermore, our devices based on laterally conducting split-ring structured photodetectors possess outstanding performance, including the maximum responsivity of 1.44 × 105 mA W−1, a response time of 150 μs in 1.5 kHz and one-unit area < 4 × 10–2 mm2. Based on these split-ring photodetector arrays, we realized three-dimensional gesture detection with up to 100 mm distance detection and up to 600 mm s−1 speed detection, for low-cost, integrative, and non-contact human–machine interfaces. Finally, we applied this MIT to wearable and flexible digital gesture recognition watch panel, safe and comfortable central controller integrated on the car screen, and remote control of the robot, demonstrating the broad potential applications.

Background Breast cancer has remained the most common malignancy in women over the past two decades. As lifestyle and living environments have changed, alterations to the disease spectrum have inevitably occurred in this time. As molecular profiling has become a routine diagnostic and objective indicator of breast cancer etiology, we analyzed changes in gene expression in breast cancer populations over two decades using The Cancer Genome Atlas database. Methods We performed Heatmap and Venn diagram analyses to identify constantly up- and down-regulated genes in breast cancer patients of this cohort. We used Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to visualize associated functional pathways. Results We determined that three oncogenes, PD-L2 , ETV5 , and MTOR and 113 long intergenic non-coding RNAs (lincRNAs) were constantly up-regulated, whereas two oncogenes, BCR and GTF2I , one tumor suppression gene MEN1 , and 30 lincRNAs were constantly down-regulated. Up-regulated genes were enriched in “focal adhesion” and “PI3K-Akt signaling” pathways, etc., and down-regulated genes were significantly enriched in “metabolic pathways” and “viral myocarditis”. Eight up-regulated genes exhibited doubled or higher expression and the expression of three down-regulated genes was halved or lowered and correlated with long-term survival. Conclusions In this study, we found that gene expression and molecular pathway enrichments are constantly changing with time, importantly, some altered genes were associated with prognostics and are potential therapeutic targets, suggesting that the current molecular subtyping system must be updated to keep pace with this dynamic change.

Rock grain size parameter is the key parameter of reservoir rock physics analysis. The study found that the relationship between the NMRT2spectrum and rock grain size distribution curve is directly related to NMRT2distribution and grain size distribution of rock, so you can use T2 to retrieve the size distribution of rock NMR spectral data. Based on the calculation of the rock core experiment results of grain size distribution by NMR T2 distribution using the conversion method of piecewise nonlinear calibration for each core, adjusting the calibration parameters to make the grain size distribution calculated with the core analysis of grain size distribution approximation, the error is minimum to obtain each core scale parameter conversion. In the end, the core parameters are classified according to the parameters of pore and permeability. Finally, according to the actual NMR T2 distribution curve inversion of underground rock granularity, the inversion results contrast with the core analysis results, and the reliability of the method is verified to provide accurate, continuous rock size distribution profile analysis of geological reservoir rock physics.

The randomness of power grid has been greatly increased as the new energy power proportion increases due to the uncertainty of wind turbine (WT) and photovoltaic (PV) power, posing significant challenges to grid security and economic efficiency. In this paper, the typical‐day WT and PV power outputs were obtained by the Latin hypercube sampling method. A multiobjective dual‐layer optimization model has the goal of reducing network loss and voltage deviation. The Whale Optimization Algorithm (WOA) was employed to solve the model. Based on the optimization results, the dispatch schemes for reactive power compensation devices, energy storage systems, and on‐load voltage regulation devices are formulated to improve system stability and smooth the output fluctuations of new energy sources. Finally, the proposed method is verified in the improved AC/DC hybrid grid based on IEEE 39‐notes system. The results indicated that the method can effectively reduce the network loss and smooth voltage fluctuations. It provides a theoretical basis for the stable and economical of grids with a high proportion of new energy power.

Background Germline mutations in cancer-predisposition genes are critical for clinical risk assessment and therapeutic decisions in breast cancer, yet large-scale genomic studies and population-specific tools remain limited for Asian populations. Methods In this prospective clinic-based cohort study, 2700 unselected Chinese breast cancer patients underwent germline sequencing for single nucleotide variations, insertion/deletions, and large genomic rearrangements using a clinically validated 32-gene panel. Multiplex ligation-dependent probe amplification was used for confirmation of copy number variations. A multivariate logistic regression model (PEEKABOO) was developed to predict mutation probability. Therapeutic impact was assessed in 632 patients receiving neoadjuvant therapy. Results The overall prevalence of deleterious germline variants was 11.4%, predominantly BRCA2 (3.7%) and BRCA1 (3.1%) with protein-truncating variants accounting for 81.8% of alterations. Mutation prevalence progressively increased across hereditary risk tiers: 4.9% for the NeoFHS-zero group, 13.1% for the NeoFHS-low group, and 19.9% for the NeoFHS-high group ( p  < 0.001). In HER2-negative breast cancer, germline homologous recombination repair gene mutations (gHRRm) independently predicted higher pathological complete response (pCR) rates (OR, 2.24; 95% CI, 1.09–4.66; p  = 0.028). A numerically higher pCR rate was observed in gHRR-mutant TNBC patients receiving neoadjuvant immunotherapy combined with chemotherapy (80.0% vs 55.6%, p  = 0.6). The PEEKABOO model exhibited strong performance in predicting mutation probability in both panel genes (area under curve [AUC], 0.73; accuracy, 57%; sensitivity/specificity, 76%/54%; PPV/NPV, 17%/95%) and BRCA1/2 (AUC, 0.80; accuracy, 62%; sensitivity/specificity, 81%/61%; PPV/NPV, 13%/98%). Conclusions Our study establishes a unique germline mutation profile of Chinese breast cancer in a large-scale targeted sequencing cohort. Germline HRR gene mutation status is a potential biomarker for response to neoadjuvant treatment with DNA-damaging chemotherapeutics for HER2-negative breast cancer. The population-specific PEEKABOO model improves the predictive efficiency of germline mutations and represents a clinically applicable tool for risk stratification in Chinese patients.

Simulating wind turbine blade icing quickly is important for wind farms to issue early warnings and effectively deal with the adverse effects of cold weather. However, current numerical simulation methods suffer from high computational costs and lack straightforward acceleration techniques for practical ice prediction. Here, we developed a fast and simple blade icing simulation method via an integrated physical simulation and statistical analysis method. This method consists of two steps: firstly, numerical simulation with CFD, and secondly, table look-up calculations. Over 10,000 sets of wind turbine blade icing simulations based on FENSAP-ICE and an NACA64-A17 wing were conducted to develop this method and analyze the influences of environmental factors on blade icing. The results show that ice thickness generally increases with an increase in wind speed, a decrease in temperature, and an increase in liquid water content (LWC), but there is a nonlinear relationship between them. For example, ice thickness has a linear relationship with the LWC within a certain range but hardly changes with a LWC beyond that range. The validation results show that the fast simulation method established in this paper has good consistency with the original numerical simulation method. It can greatly improve the computational efficiency of icing simulations while retaining the accuracy of numerical simulations. It takes less than 1 s to complete over 1000 sets of icing simulations, which offers potential for the fast prediction of wind turbine blade icing in the future.