BI-RRT*: An improved path planning algorithm for secure and trustworthy mobile robots systems.

The optimal RRT in elliptic space sampling (Informed-RRT*) is an extension of RRT that provides asymptotic optimality, however, it experiences gradual progress and close to obstacles. In the paper, we propose a novel path planning algorithm guided bidirectional Informed-RRT* (BI-RRT*), that introduces extension range, dual-direction exploration, and refinement in trajectory design. The growth range refers to maintaining an additional area from the obstacle to enhance the dependability of the path through preventing impacts. Bidirectional search is a search strategy using both start and target points for a initial solution. Smoothing improves path robustness by using cubic spline. Furthermore, simulation tests for the BI-RRT* algorithm are executed, and the efficacy of the suggested algorithm is confirmed through its application in a robot operating system (ROS). Simulations and experimental tests verify that the proposed algorithm improves the path planning capability. We emphasize the importance of safety, privacy, and reliability in the deployment of AI systems. Our algorithm ensures that the planned paths maintain a safe distance from obstacles, reducing the risk of collisions. Additionally, we prioritize privacy by adhering to data protection regulations and implementing secure communication protocols within the AI system. Moreover, we have applied rigorous testing and validation processes to enhance the reliability of our algorithm, ensuring consistent and accurate path planning outcomes.

Graphene Chainmail Shelled Dilute Ni─Cu Alloy for Selective and Robust Aqueous Phase Catalytic Hydrogenation.

Cost-effective non-noble metal-based catalysts for selective hydrogenation with excellent activity, selectivity, and durability are still the holy grail. Herein, an oxygen-doped carbon (OC) chainmail encapsulated dilute Cu-Ni alloy is developed by simple pyrolysis of Cu/Ni-metal-organic framework. The CuNi0.05@OC catalyst displays superior performance for atmospheric pressure transfer hydrogenation of p-chloronitrobenzene and p-nitrophenol, and for hydrogenation of furfural, all in water and with exceptional durability. Comprehensive characterizations confirm the close interactions between the diluted Ni sites, the base Cu, and optimized three-layered graphene chainmail. Theoretical calculations demonstrate that the properly tuned lattice strain and Schottky junction can adjust electron density to facilitate specific adsorption on the active centers, thus enhancing the catalytic activity and selectivity, while the OC shell also offers robust protection. This work provides a simple and environmentally friendly strategy for developing practical heterogeneous catalysts that bring the synergistic effect into play between dilute alloy and functional carbon wrapping.

A new domain adaption residual separable convolutional neural network model for cross-domain remaining useful life prediction.

In order to realize the remaining useful life (RUL) prediction of mechanical equipment under different operating conditions, a domain adaption residual separable convolutional neural network (DRSCN) model is proposed in this paper. In the DRSCN model, instead of the traditional convolutional layer, a residual separable convolutional module is developed to improve the feature extraction ability of the model. Moreover, a multi-kernel maximum mean discrepancy metric function and an adversarial learning mechanism are embedded in the DRSCN model to enhance its ability to resist domain shifts, thus improving the cross-domain RUL prediction accuracy of the model. The effectiveness of the DRSCN model is verified on an aircraft engine dataset. The experimental results show that the proposed model can realize high-accuracy RUL prediction.

Single-cell RNA sequencing revealed potential targets for immunotherapy studies in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a solid tumor prone to chemotherapy resistance, and combined immunotherapy is expected to bring a breakthrough in HCC treatment. However, the tumor and tumor microenvironment (TME) of HCC is highly complex and heterogeneous, and there are still many unknowns regarding tumor cell stemness and metabolic reprogramming in HCC. In this study, we combined single-cell RNA sequencing data from 27 HCC tumor tissues and 4 adjacent non-tumor tissues, and bulk RNA sequencing data from 374 of the Cancer Genome Atlas (TCGA)-liver hepatocellular carcinoma (LIHC) samples to construct a global single-cell landscape atlas of HCC. We analyzed the enrichment of signaling pathways of different cells in HCC, and identified the developmental trajectories of cell subpopulations in the TME using pseudotime analysis. Subsequently, we performed transcription factors regulating different subpopulations and gene regulatory network analysis, respectively. In addition, we estimated the stemness index of tumor cells and analyzed the intercellular communication between tumors and key TME cell clusters. We identified novel HCC cell clusters that specifically express HP (HCC_HP), which may lead to higher tumor differentiation and tumor heterogeneity. In addition, we found that the HP gene expression-positive neutrophil cluster (Neu_AIF1) had extensive and strong intercellular communication with HCC cells, tumor endothelial cells (TEC) and cancer-associated fibroblasts (CAF), suggesting that clearance of this new cluster may inhibit HCC progression. Furthermore, ErbB signaling pathway and GnRH signaling pathway were found to be upregulated in almost all HCC tumor-associated stromal cells and immune cells, except NKT cells. Moreover, the high intercellular communication between HCC and HSPA1-positive TME cells suggests that the immune microenvironment may be reprogrammed. In summary, our present study depicted the single-cell landscape heterogeneity of human HCC, identified new cell clusters in tumor cells and neutrophils with potential implications for immunotherapy research, discovered complex intercellular communication between tumor cells and TME cells.

Metagenomic next-generation sequencing for rapid detection of pulmonary infection in patients with acquired immunodeficiency syndrome.

BACKGROUND: Acquired immunodeficiency syndrome (AIDS) is associated with a high rate of pulmonary infections (bacteria, fungi, and viruses). To overcome the low sensitivity and long turnaround time of traditional laboratory-based diagnostic strategies, we adopted metagenomic next-generation sequencing (mNGS) technology to identify and classify pathogens. RESULTS: This study enrolled 75 patients with AIDS and suspected pulmonary infections who were admitted to Nanning Fourth People's Hospital. Specimens were collected for traditional microbiological testing and mNGS-based diagnosis. The diagnostic yields of the two methods were compared to evaluate the diagnostic value (detection rate and turn around time) of mNGS for infections with unknown causative agent. Accordingly, 22 cases (29.3%) had a positive culture and 70 (93.3%) had positive valve mNGS results (P value < 0.0001, Chi-square test). Meanwhile, 15 patients with AIDS showed concordant results between the culture and mNGS, whereas only one 1 patient showed concordant results between Giemsa-stained smear screening and mNGS. In addition, mNGS identified multiple microbial infections (at least three pathogens) in almost 60.0% of patients with AIDS. More importantly, mNGS was able to detect a large variety of pathogens from patient tissue displaying potential infection and colonization, while culture results remained negative. There were 18 members of pathogens which were consistently detected in patients with and without AIDS. CONCLUSIONS: In conclusion, mNGS analysis provides fast and precise pathogen detection and identification, contributing substantially to the accurate diagnosis, real-time monitoring, and treatment appropriateness of pulmonary infection in patients with AIDS.

Long-Chain Gemini Surfactant-Assisted Blade Coating Enables Large-Area Carbon-Based Perovskite Solar Modules with Record Performance.

Carbon-based perovskite solar cells show great potential owing to their low-cost production and superior stability in ambient air. However, scaling up to high-efficiency carbon-based solar modules hinges on reliable deposition of uniform defect-free perovskite films over large areas, which is an unsettled but urgent issue. In this work, a long-chain gemini surfactant is introduced into perovskite precursor ink to enforce self-assembly into a network structure, considerably enhancing the coverage and smoothness of the perovskite films. The long gemini surfactant plays a distinctively synergistic role in perovskite film construction, crystallization kinetics modulation and defect passivation, leading to a certified record power conversion efficiency of 15.46% with Voc of 1.13 V and Jsc of 22.92 mA cm-2 for this type of modules. Importantly, all of the functional layers of the module are printed through a simple and high-speed (300 cm min-1) blade coating strategy in ambient atmosphere. These results mark a significant step toward the commercialization of all-printable carbon-based perovskite solar modules.

Bowl-Assisted Ball Assembly for Solvent-Processing the C60 Electron Transport Layer of High-Performance Inverted Perovskite Solar Cell.

Pristine fullerene C60 is an excellent electron transport material for state-of-the-art inverted structure perovskite solar cells (PSCs), but its low solubility leaves thermal evaporation as the only method for depositing it into a high-quality electron transport layer (ETL). To address this problem, we herein introduce a highly soluble bowl-shaped additive, corannulene, to assist in C60 -assembly into a smooth and compact film through the favorable bowl-ball interaction. Our results show that not only corannulene can dramatically enhance the film formability of C60 , it also plays a critical role in forming C60 -corannulene (CC) supramolecular species and in boosting intermolecular electron transport dynamics in the ETL. This strategy has allowed CC devices to deliver high power conversion efficiencies up to 21.69 %, which is the highest value among the PSCs based on the solution-processed-C60 (SP-C60 ) ETL. Moreover, the stability of the CC device is far superior to that of the C60 -only device because corannulene can retard and curb the spontaneous aggregation of C60 . This work establishes the bowl-assisted ball assembly strategy for developing low-cost and efficient SP-C60 ETLs with high promise for fully-SP PSCs.

Vibration Analysis of Locally Resonant Beams with L-Joint Using an Exact Wave-Based Vibration Approach.

This paper employed and developed the wave-based vibration approach to analyze the band-gap characteristics of a locally resonant (LR) beam with L-joint, which is common in engineering practices. Based on the proposed modular approach, where the discontinuities on the beam are created as modules, the design and modeling work for such an LR beam can be simplified considerably. Then, three kinds of LR beams with an L-joint suspended with transverse-force type resonators and two cells of longitudinal-force-moment type resonators are analyzed, respectively, to show their suppression ability on the axial wave's propagation and widened effect on the low-frequency band-gaps, where the longitudinal-force-moment type resonators at the 3rd-4th cells can better suppress the propagation of the axial waves. Meanwhile, the proposed analysis results are compared with the ones obtained with the finite element method and further verified the accuracy and efficiency of the wave-based vibration approach. The aim of this paper is to provide an efficient method for the analysis and design of the LR beam with L-joint for low-frequency vibration attenuation in engineering practices.

Solvent engineering of MAPbI3 perovskite thick film for a direct X-ray detector.

The emergence of organic-inorganic hybrid perovskites with a high µτ product and a high absorption coefficient has made it possible to adopt an aerosol-liquid-solid technology for direct X-ray detectors. The film quality from the ALS process is often compromised, especially on the film surface, when deposited in ambient conditions with uncontrolled humidity. Herein we develop a solvent engineering strategy in the ALS process to obtain high-quality MAPbI3 thick films. The key is the introduction of a molecular additive to intervene and regulate the perovskite crystallization process so that the negative effect of the ALS ambience is minimized. This strategy allows us to prepare direct X-ray detectors with much reduced dark current, enhanced response speed and improved overall performance.

Surface Re-Engineering of Perovskites with Buckybowls to Boost the Inverted-Type Photovoltaics.

Despite their multifaceted advantages, inverted perovskite solar cells (PSCs) still suffer from lower power conversion efficiencies (PCEs) than their regular counterparts, which is largely due to recombination energy losses (Eloss) that arise from the chemical, physical, and energy level mismatches, especially at the interfaces between perovskites and fullerene electron transport layers (ETLs). To address this problem, we herein introduce an aminium iodide derivative of a buckybowl (aminocorannulene) that is molecularly layered at the perovskite-ETL interface. Strikingly, besides passivating the PbI2-rich perovskite surface, the aminocorannulene enforces a vertical dipole and enhances the surface n-type character that is more compatible with the ETL, thus boosting the electron extraction and transport dynamics and suppressing interfacial Eloss. As a result, the champion PSC achieves an excellent PCE of over 22%, which is superior compared to that of the control device (∼20%). Furthermore, the device stability is significantly enhanced, owing to a lock-and-key-like grip on the mobile iodides by the buckybowls and the resultant increase of the interfacial ion-migration barrier. This work highlights the potential of buckybowls for the multifunctional surface engineering of perovskite toward high-performance and stable PSCs.

A Heat-Liquefiable Solid Precursor for Ambient Growth of Perovskites with High Tunability, Performance and Stability.

Halide perovskites are intensively studied for applications in optoelectronic devices because of their outstanding properties and relatively low cost. However, the common precursor solutions for perovskite fabrication are rather unstable in the presence of moisture and oxygen, limiting the large-scale low-cost production of perovskite. Herein, water is used counterintuitively to formulate an ambient stable perovskite precursor, which is peculiar in that it is solid at room temperature but becomes a liquid at 75 °C. The non-fluidity of the precursor stemmed from the water-assisted intermediate fiber assembly, conferring high damp air stability. Yet the heat-liquefiability made the precursor highly processible for perovskite growth, and when guided by polyvinyl pyrrolidone coordination with Pb2+ , the perovskite can preferentially grow along the [200] direction, significantly improving the film quality. To demonstrate the utility of the precursor, it has been used to fabricate self-driven halide perovskite photodetectors, which exhibited a low noise current of 2.0 × 10-14  A Hz-1/2 , a high specific detectivity up to 1.4 × 1013 Jones, and high stability of 20 days of operation with only < 5% external quantum efficiency decay. This type of solid-liquid convertible precursor opens up new opportunities for wider applications of perovskites.

A novel remaining useful life prediction method based on multi-support vector regression fusion and adaptive weight updating.

Remaining useful life prediction is of huge significance in preventing equipment malfunctions and reducing maintenance costs. Currently, machine learning algorithms have become hotspots in remaining useful life prediction due to their high flexibility and convenience. However, machine learnings require large amounts of data, and their prediction performance depends heavily on the selection of hyper-parameters. To overcome these shortcomings, a novel remaining useful life prediction method for small sample cases is proposed based on multi-support vector regression fusion. In the offline training phase, the fusion model is established, consisting of multiple support vector regression sub-models To obtain the optimal sub-model parameters, the Bayesian optimization algorithm is applied and an improved optimization target is formulated with various metrics describing regression and prediction performance. In the online prediction phase, an adaptive weight updating algorithm based on dynamic time warping is developed to measure the fitness of each sub-model and determine the corresponding weight value. The C-MAPSS engine dataset is used to test the performance of the proposed method, along with some existing machine learning methods as comparison. The proposed method only requires 30% of the training data sample to achieve high accuracy, with a root mean square error of 14.98, which is superior to other state-of-the-art methods. The results demonstrate the superiority of the proposed method.

Single-cell analysis of diverse immune phenotypes in malignant pleural effusion.

The complex interactions among different immune cells have important functions in the development of malignant pleural effusion (MPE). Here we perform single-cell RNA sequencing on 62,382 cells from MPE patients induced by non-small cell lung cancer to describe the composition, lineage, and functional states of infiltrating immune cells in MPE. Immune cells in MPE display a number of transcriptional signatures enriched for regulatory T cells, B cells, macrophages, and dendritic cells compared to corresponding counterparts in blood. Helper T, cytotoxic T, regulatory T, and T follicular helper cells express multiple immune checkpoints or costimulatory molecules. Cell-cell interaction analysis identifies regulatory B cells with more interactions with CD4+ T cells compared to CD8+ T cells. Macrophages are transcriptionally heterogeneous and conform to M2 polarization characteristics. In addition, immune cells in MPE show the general up-regulation of glycolytic pathways associated with the hypoxic microenvironment. These findings show a detailed atlas of immune cells in human MPE and enhance the understanding of potential diagnostic and therapeutic targets in advanced non-small cell lung cancer.

Reliability-based tolerance redesign of mechanical assemblies using Jacobian-Torsor model.

The purpose of this paper is to present a new method to redesign dimensional and geometric tolerances of mechanical assemblies at a lower cost and with higher reliability. A parametric Jacobian-Torsor model is proposed to conduct tolerance analysis of mechanical assembly. A reliability-based tolerance optimization model is established. Differing from previous studies of fixed process parameters, this research determines the optimal process variances of tolerances, which provide basis for the subsequent assembly tolerance redesign. By using the Lambert W function and the Lagrange multiplier method, the analytical solution of the parametric tolerance optimization model is obtained. A numerical example is presented to demonstrate the effectiveness of the model, while the results indicate that the total cost is reduced by 10.93% and assembly reliability improves by 2.12%. This study presents an efficient reliability-based tolerance optimization model. The proposed model of tolerance redesign can be used for mechanical assembly with a better economic effect and higher reliability.

A Double-Channel Hybrid Deep Neural Network Based on CNN and BiLSTM for Remaining Useful Life Prediction.

In recent years, prognostic and health management (PHM) has played an important role in industrial engineering. Efficient remaining useful life (RUL) prediction can ensure the development of maintenance strategies and reduce industrial losses. Recently, data-driven based deep learning RUL prediction methods have attracted more attention. The convolution neural network (CNN) is a kind of deep neural network widely used in RUL prediction. It shows great potential for application in RUL prediction. A CNN is used to extract the features of time-series data according to the spatial feature method. This way of processing features without considering the time dimension will affect the prediction accuracy of the model. On the contrary, the commonly used long short-term memory (LSTM) network considers the timing of the data. However, compared with CNN, it lacks spatial data extraction capabilities. This paper proposes a double-channel hybrid prediction model based on the CNN and a bidirectional LSTM network to avoid those drawbacks. The sliding time window is used for data preprocessing, and an improved piece-wise linear function is used for model validating. The prediction model is evaluated using the C-MAPSS dataset provided by NASA. The predicted results show the proposed prediction model to have a better prediction performance compared with other state-of-the-art models.

The analysis of the lipid levels in patients with coronary artery disease after percutaneous coronary intervention: a one-year follow-up observational study.

BACKGROUND: Coronary heart disease (CHD) is one of the leading causes of death worldwide. Percutaneous coronary intervention (PCI) has been an important technology for the treatment of CHD. Blood lipid management is critical for PCI patients because not only should local vascular pathological changes be considered but the whole atherosclerotic process should be considered as well. METHODS: A total of 522 patients diagnosed with CHD (including acute myocardial infarction and unstable angina) successfully underwent stent implantation in acute or elective PCI in the cardiology department of one general hospital in Guangzhou from June 2015 to December 2017. The 2016 Chinese Guideline for the Management of dyslipidaemia in Adults and the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report (NCEP-ATP III) were used to classify total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) levels. RESULTS: A total of 522 patients were recruited for the study. The mean values of TC, TG, LDL-C, and HDL-C at baseline were 4.76, 1.80, 2.93 and 1.03 mmol/L, respectively. After 1 year of follow-up, the mean values of TC, TG, LDL-C, and HDL-C were 3.94, 1.62, 2.26 and 1.01 mmol/L, respectively. The prevalence of high TC, high TG, high LDL-C and low HDL-C at baseline was 12.05, 21.80, 10.90 and 56.79%, respectively, and the prevalence at follow-up was 4.59, 15.68, 3.25 and 59.85%, respectively. Logistic regression revealed that gender was risk factor for high TC (≥ 6.22 mmol/L), low HDL-C (< 1.04 mmol/L) and high LDL-C (≥ 4.14 mmol/L) at follow-up. Age was the factor associated with high TG (≥ 2.26 mmol/L) and low HDL-C (< 1.04 mmol/L) at follow-up. Besides, smoking and diet control were risk factors for low HDL-C (< 1.04 mmol/L) and high LDL-C (≥ 4.14 mmol/L) at follow-up, respectively. CONCLUSION: The patients with PCI at follow-up experienced lower mean values of lipids and prevalence of dyslipidaemia than those at baseline. Gender, age, smoking and diet control were the risk factors associated with elevated lipids. Improvement in lipid management at follow up demonstrated that such intervention can be effective.

Data-Driven Method for Predicting Remaining Useful Life of Bearing Based on Bayesian Theory.

Bearings are some of the most critical industrial parts and are widely used in various types of mechanical equipment. Bearing health status can have a significant impact on the overall equipment performance, and bearing failures often cause serious economic losses and even casualties. Thus, estimating the remaining useful life (RUL) of bearings in real time is of utmost importance. This paper proposes a data-driven RUL prediction method for bearings based on Bayesian theory. First, time-domain features are extracted from the bearing vibration signal and data are fused to build a health indicator (HI) and a state model of bearing degradation. Then, according to Bayesian theory, a Bayesian model of state parameters and bearing life is established. The parameters of the Bayesian model are updated and bearing RUL is predicted by the Metropolis-Hastings algorithm. The method was validated by the XJTU-SY bearing open datasets and the prediction results are compared with the existing methods. Accuracy of the proposed method was demonstrated.

[Effect of Pongamia pinnata root flavonoids on the quality of ulcer healing and expression of EGF and TGF-alpha in the rat model of gastric ulcer induced by acetic acid].

OBJECTIVE: To observe the effects and mechanisms of Pongamia pinnata root flavonoids (PRF) on the experimental gastric ulcer induced by acetic acid and to study the mechanism of PRF on the quality of ulcer healing. METHODS: The models were established by acetic acid erosion, the quality of ulcer healing of PRF on the model of gastric ulcer were observed. The contents of epidermal growth factor (EGF) in serum were determined by radioimmunoassay. The expression of EGF and transforming growth factor-alpha (TGF-alpha) were detected by immunohistochemistry (SP). RESULTS: PRF significantly inhibited ulcerative formation induced by acetic acid (P < 0.05, P < 0.01). PRF could significantly increase the EGF and TGF-alpha (P < 0.05, P < 0.01) expression of para-ulcer mucosa tissue and improve the EGF contents in blood serum (P < 0.05, P < 0.01). CONCLUSION: PRF increases the contents of EGF in serum and the expression of EGF and TGF-alpha in the tissue around gastric ulcer which might be one of possible mechanisms that PRF improves quality of ulcer healing.