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Intelligent machinery fault diagnosis system has been receiving increasing attention recently due to the potential large benefits of maintenance cost reduction, enhanced operation safety and reliability. This paper proposes a novel deep learning method for rotating machinery fault diagnosis. Since accurately labeled data are usually difficult to obtain in real industries, data augmentation techniques are proposed to artificially create additional valid samples for model training, and the proposed method manages to achieve high diagnosis accuracy with small original training dataset. Two augmentation methods are investigated including sample-based and dataset-based methods, and five augmentation techniques are considered in general, i.e. additional Gaussian noise, masking noise, signal translation, amplitude shifting and time stretching. The effectiveness of the proposed method is validated by carrying out experiments on two popular rolling bearing datasets. Fairly high diagnosis accuracy up to 99.9% can be obtained using limited training data. By comparing with the latest advanced researches on the same datasets, the superiority of the proposed method is demonstrated. Furthermore, the diagnostic performance of the deep neural network is extensively evaluated with respect to data augmentation strength, network depth and so forth. The results of this study suggest that the proposed intelligent fault diagnosis method offers a new and promising approach.

Reprogramming the immunosuppressive tumor microenvironment by modulating macrophages holds great promise in tumor immunotherapy. As a class of professional phagocytes and antigen‐presenting cells in the innate immune system, macrophages can not only directly engulf and clear tumor cells, but also play roles in presenting tumor‐specific antigen to initiate adaptive immunity. However, the tumor‐associated macrophages (TAMs) usually display tumor‐supportive M2 phenotype rather than anti‐tumor M1 phenotype. They can support tumor cells to escape immunological surveillance, aggravate tumor progression, and impede tumor‐specific T cell immunity. Although many TAMs‐modulating agents have shown great success in therapy of multiple tumors, they face enormous challenges including poor tumor accumulation and off‐target side effects. An alternative solution is the use of advanced nanostructures, which not only can deliver TAMs‐modulating agents to augment therapeutic efficacy, but also can directly serve as modulators of TAMs. Another important strategy is the exploitation of macrophages and macrophage‐derived components as tumor‐targeting delivery vehicles. Herein, we summarize the recent advances in targeting and engineering macrophages for tumor immunotherapy, including (1) direct and indirect effects of macrophages on the augmentation of immunotherapy and (2) strategies for engineering macrophage‐based drug carriers. The existing perspectives and challenges of macrophage‐based tumor immunotherapies are also highlighted. Modulating macrophages for tumor immunotherapy holds great promise to improve anti‐tumor efficacy. Inhibition of macrophage recruitment, depleting tumor‐associated macrophages (TAMs), repolarizing TAMs, and regulating macrophage‐mediated phagocytosis of tumor cells are the four major strategies for manipulating macrophage‐mediated tumor immunotherapy.

California is the world’s biggest producer and exporter of almonds. Currently, the sweeping of almonds during the harvest creates a significant amount of dust, causing air pollution in the neighboring urban areas. A low-dust sweeping system was designed to reduce the dust during the sweeping of almonds in the orchard. The system includes a feedback control system to control the sweeper brushes’ height and their angular velocity by adjusting the forward velocity of the harvester and the brushes’ rotational speeds to avoid any extra overlapping sweeping, which increases dust generation. The governing kinematic equations for sweepers’ angular velocity and vehicle forward speed were derived. The feedback controllers for synchronizing these speeds were designed to optimize brush/dust contact to minimize dust generation. The sweepers’ height controller was also designed to stabilize the gap between the brushes and the orchard floor and track the road trajectory. Controllers were simulated and tuned for a fast response for agricultural applications with less than a second response delay. Results showed that the designed system has acceptable performance and generates low amounts of dust within the acceptable range of California ambient air quality standards.

Squamous cell carcinomas (SCCs) are aggressive malignancies. Previous report demonstrated that master transcription factors (TFs) TP63 and SOX2 exhibited overlapping genomic occupancy in SCCs. However, functional consequence of their frequent co-localization at super-enhancers remains incompletely understood. Here, epigenomic profilings of different types of SCCs reveal that TP63 and SOX2 cooperatively and lineage-specifically regulate long non-coding RNA (lncRNA) CCAT1 expression, through activation of its super-enhancers and promoter. Silencing of CCAT1 substantially reduces cellular growth both in vitro and in vivo, phenotyping the effect of inhibiting either TP63 or SOX2. ChIRP analysis shows that CCAT1 forms a complex with TP63 and SOX2, which regulates EGFR expression by binding to the super-enhancers of EGFR , thereby activating both MEK/ERK1/2 and PI3K/AKT signaling pathways. These results together identify a SCC-specific DNA/RNA/protein complex which activates TP63/SOX2-CCAT1-EGFR cascade and promotes SCC tumorigenesis, advancing our understanding of transcription dysregulation in cancer biology mediated by master TFs and super-enhancers. Master regulator transcription factors TP63 and SOX2 have been reported to overlap in genomic occupancy in squamous cell carcinomas (SCCs). Here, the authors demonstrate that TP63 and SOX2 promote co-operatively long non-coding RNA CCAT1 expression through activating its super-enhancer, and CCAT1 forms a complex with TP63 and SOX2, which regulates EGFR super-enhancers and enhances both the MEK/ERK1/2 and PI3K/AKT signaling pathways in SCC.

Significance Autophagy is an essential homeostatic process that is critically important for maintaining health and that is dysregulated in multiple devastating diseases. The steps in the final stages of autophagy that culminate in autophagosome:lysosome fusion are not well understood. The γ-aminobutyric acid receptor-associated protein (GABARAP) family of Atg8 (autophagy-related 8) proteins has been implicated in autophagosome maturation. Here we report that phosphatidylinositol 4-kinase IIα (PI4KIIα), a lipid kinase that generates phosphatidylinositol 4-phosphate (PI4P) and binds GABARAPs, is recruited to autophagosomes by GABARAPs. Furthermore, PI4P generation by PI4KIIα, but not by PI4KIIIβ, another major mammalian PI4K, promotes autophagosome fusion with lysosomes. Our results establish for the first time to our knowledge that PI4KIIα is a specific downstream effector of GABARAP and that PI4P has a key role in the final stage of autophagy. The Atg8 autophagy proteins are essential for autophagosome biogenesis and maturation. The γ-aminobutyric acid receptor-associated protein (GABARAP) Atg8 family is much less understood than the LC3 Atg8 family, and the relationship between the GABARAPs’ previously identified roles as modulators of transmembrane protein trafficking and autophagy is not known. Here we report that GABARAPs recruit palmitoylated PI4KIIα, a lipid kinase that generates phosphatidylinositol 4-phosphate (PI4P) and binds GABARAPs, from the perinuclear Golgi region to autophagosomes to generate PI4P in situ. Depletion of either GABARAP or PI4KIIα, or overexpression of a dominant-negative kinase-dead PI4KIIα mutant, decreases autophagy flux by blocking autophagsome:lysosome fusion, resulting in the accumulation of abnormally large autophagosomes. The autophagosome defects are rescued by overexpressing PI4KIIα or by restoring intracellular PI4P through “PI4P shuttling.” Importantly, PI4KIIα’s role in autophagy is distinct from that of PI4KIIIβ and is independent of subsequent phosphatidylinositol 4,5 biphosphate (PIP ₂) generation. Thus, GABARAPs recruit PI4KIIα to autophagosomes, and PI4P generation on autophagosomes is critically important for fusion with lysosomes. Our results establish that PI4KIIα and PI4P are essential effectors of the GABARAP interactome’s fusion machinery.

The Gaussian-radial-basis function neural network (GRBFNN) has been a popular choice for interpolation and classification. However, it is computationally intensive when the dimension of the input vector is high. To address this issue, we propose a new feedforward network-separable Gaussian neural network (SGNN) by taking advantage of the separable property of Gaussian-radial-basis functions, which splits input data into multiple columns and sequentially feeds them into parallel layers formed by uni-variate Gaussian functions. This structure reduces the number of neurons from O(Nd) of GRBFNN to O(dN), which exponentially improves the computational speed of SGNN and makes it scale linearly as the input dimension increases. In addition, SGNN can preserve the dominant subspace of the Hessian matrix of GRBFNN in gradient descent training, leading to a similar level of accuracy to GRBFNN. It is experimentally demonstrated that SGNN can achieve an acceleration of 100 times with a similar level of accuracy over GRBFNN on tri-variate function approximations. The SGNN also has better trainability and is more tuning-friendly than DNNs with RuLU and Sigmoid functions. For approximating functions with a complex geometry, SGNN can lead to results that are three orders of magnitude more accurate than those of a RuLU-DNN with twice the number of layers and the number of neurons per layer.

This study introduces a neural network-based approach to predict dust emissions, specifically PM2.5 particles, during almond harvesting in California. Using a feedforward neural network (FNN), this research predicted PM2.5 emissions by analyzing key operational parameters of an advanced almond harvester. Preprocessing steps like outlier removal and normalization were employed to refine the dataset for training. The network’s architecture was designed with two hidden layers and optimized using tanh activation and MSE loss functions through the Adam algorithm, striking a balance between model complexity and predictive accuracy. The model was trained on extensive field data from an almond pickup system, including variables like brush speed, angular velocity, and harvester forward speed. The results demonstrate a notable predictive accuracy of the FNN model, with a mean squared error (MSE) of 0.02 and a mean absolute error (MAE) of 0.01, indicating high precision in forecasting PM2.5 levels. By integrating machine learning with agricultural practices, this research provides a significant tool for environmental management in almond production, offering a method to reduce harmful emissions while maintaining operational efficiency. This model presents a solution for the almond industry and sets a precedent for applying predictive analytics in sustainable agriculture.

In Northwest China, the highway infrastructure often faces challenges due to the widespread presence of subgrade soil. This soil undergoes significant changes in performance under cyclic loading and freeze-thaw cycles. To effectively design and construct highways in these regions, it is crucial to understand the impact of various factors on the deformation characteristics and mechanical properties of subgrade soil. This study aims to investigate the influence of freeze-thaw cycles, water content, confining pressure, and loading rate on the deformation behavior and mechanical properties of subgrade soil under cyclic loading conditions. Experimental tests were conducted to analyze the deformation characteristics and mechanical properties of the subgrade soil. The test results revealed the following: 1) Dynamic loading leads to a noticeable decrease in the strength of subgrade soil, resulting in a softening effect on the stress-strain curve. The cumulative strain of the soil is positively correlated with the number of freeze-thaw cycles and water content, while negatively correlated with confining pressure. The final cumulative strain remains below 1%. 2) The failure stress of subgrade soil decreases exponentially with an increase in freeze-thaw cycles, dropping from 224.52 kPa to 196.76 kPa. 3) An increase in water content linearly decreases the failure stress of subgrade soil, ranging from 377.1 kPa to 151.5 kPa. 4) Confining pressure exhibits a linearly increasing relationship with the failure stress of subgrade soil, ranging from 151.6 kPa to 274.5 kPa. 5) The failure stress of subgrade soil demonstrates a linear increase with the loading rate, ranging from 200.46 kPa to 210.62 kPa. These findings provide valuable insights for the design and construction of highways in seasonal frozen areas. They also offer guidance for preventing and mitigating subgrade freeze-thaw issues in the future.

A new type of responses called as periodic-chaotic motion is found by numerical simulations in a Duffing oscillator with a slowly periodically parametric excitation. The periodic-chaotic motion is an attractor, and simultaneously possesses the feature of periodic and chaotic oscillations, which is a new addition to the rich nonlinear motions of the Duffing system including equlibria, periodic responses, quasi-periodic oscillations and chaos. In the current slow-fast Duffing system, we find three new attractors in the form of periodic-chaotic motions. These are called the fixed-point chaotic attractor, the fixed-point strange nonchaotic attractor, and the critical behavior with the maximum Lyapunov exponent fluctuating around zero. The system periodically switches between one attractor with a fixed single-well potential and the other with time-varying two-well potentials in every period of excitation. This behavior is apparently the mechanism to generate the periodic-chaotic motion.