Articular cartilage features a distinct biphasic structure that integrates soft and hard components, thereby enabling high load-bearing capacity and low friction within joints. Herein, a dual-layer coating combining an Al2O3-reinforced glass-ceramic base with a polytetrafluoroethylene (PTFE) top layer was applied to 304 stainless steel. A critical innovation is the in-situ texturing via controlled crystallization, which creates gradientdistributed tapered pores in the glass-ceramic layer, enabling mechanical interlocking with PTFE. This bioinspired design simultaneously achieves ultralow friction (COF 0.093), high load-bearing capacity (1.14 GPa), and long-term wear resistance under dry sliding-overcoming the inherent conflict between hardness and lubrication in conventional single-phase coatings. The conical pores anchor PTFE to maintain continuous lubrication, while Al2O3 reinforcement toughens the glass matrix prevent delamination. This study establishes a scalable strategy for high-performance tribological coatings, addressing unmet needs in heavy-load mechanical systems.

Porosity, while often regarded as a defect of coatings, paradoxically facilitates the creation of surface texture. An innovative composite coating featuring a distinctive bilayer structure, consisting of a thin PTFE layer over a porous glass-SiC coating, has been created for application on 304 stainless steel. The self-lubricating PTFE layer minimizes friction, while the underlying glass-ceramic layer provides superior wear resistance. The amalgamation of soft and hard layers results in a composite coating that exhibits remarkable properties, including extremely low friction coefficient (below 0.1), exceptional load-bearing capacity (1.1 GPa), and an extended service life under dry sliding conditions. The findings introduce a novel strategy for designing self-lubricating materials with enhanced mechanical properties, thereby broadening the potential applications of glass-ceramic coatings to other domains.

The path complexity generated by the Voronoi diagram in the existing algorithms is usually high, which leads to low efficiency in mobile robot navigation. This work proposes a Voronoi diagram optimization method based on the ray model. By re-connecting the key nodes in the map skeleton using the ray model principle, a more complete and concise Voronoi diagram is generated, and navigation paths that better conform to the principle of mobile robot motion are found. The path optimization effect of the algorithm is verified by simulation and real experiments, reducing the speed loss of the robot motion and improving navigation efficiency. This optimization method helps to reduce time cost and energy consumption, enabling mobile robots to integrate more efficiently and economically into people’s daily life.

T-cell acute lymphoblastic leukemia (T-ALL) is a highly aggressive hematologic malignancy driven by uncontrolled proliferation of immature T-cell precursors. Despite recent therapeutic advances, relapse remains frequent, underscoring the need for novel targeted strategies. Our previous work revealed that T-ALL cells exhibit a profound reliance on oxidative phosphorylation (OXPHOS), which supports energy production necessary for rapid proliferation and confers resistance to conventional therapies (Baran et al., Nat Commun 2022). While blockade of OXPHOS via inhibition of mitochondrial Complex I shows initial efficacy, T-ALL adapts by upregulating glutaminolysis and glycolysis, exporting lactate via monocarboxylate transporters (MCTs), and acidifying the microenvironment, thereby limiting durable responses. Understanding these intricate metabolic dependencies is crucial for identifying new intervention points aimed at disrupting energy pathways and overcoming therapy resistance. To systematically uncover these adaptive vulnerabilities, we performed genome-wide CRISPR-Cas9-based synthetic lethality screens in PF382 T-ALL cells treated with OXPHOS and MCT1 inhibitors. These screens particularly following MCT1 inhibition, identified key mitochondrial dependencies, including electron transport chain components (NDUF, UQCRC, COX), mitochondrial ribosomal proteins, mitochondrial translation factors, TCA cycle enzymes, mitochondrial genome regulators, and cofactors critical for mitochondrial function. Additionally, significant hits involved stress response pathways: sensors of apoptosis, chromatin- and nuclear membrane regulators, as well as lipid metabolism, lipid biosynthesis, and membrane trafficking genes. These findings suggest that dual OXPHOS/MCT1 inhibition triggers extensive metabolic reprogramming involving mitochondrial dysfunction, oxidative stress, and chromatin remodeling, which collectively enable cell survival. Combined OXPHOS/MCT1 targeting resulted in potent synthetic lethality (SL) by disrupting critical mitochondrial energy generation and lactate export. Consistent with these findings, Seahorse and GEA analyses indicated that MCT1 blockade increases OXPHOS activity, unveiling SL relationships involving mitochondrial biosynthesis and bioenergetics pathways, highlighting OXPHOS inhibition or downstream targeting as a promising potent therapeutic approach. We further validated these mechanisms utilizing multi-omics (GEA, targeted and untargeted metabolomics, in-silico METAFlux), functional assays (Seahorse, flow cytometry (FL), western blotting (WB)), and advanced imaging (confocal-, electron (EM)-, high-resolution (HRM)- microscopy in-vitro, hyperpolarized MRI in-vitro and in-vivo). In vitro, MCT1/OXPHOS inhibition caused irreversible mitochondrial damage, disrupted fusion/fission dynamics (EM, HRM), impaired enzymatic activity of mitochondrial complexes, perturbed transmembrane traffic of metabolites (Mass spectrometry, METAFlux), perturbed oxidative and anaerobic respiration (Seahorse), depleted ATP, disrupted redox homeostasis (Mass spectrometry), elevated ROS leading to DNA damage, and induced apoptosis (FL, WB), while sparing healthy hematopoietic cells. MCT1/OXPHOS blockade, in line with results of our screen, induced intracellular acidification and triggered lipophagy, rendering cells additionally vulnerable to inhibitors of lipid metabolism, as indicated in our in vitro screen. In vivo, hyperpolarized MRI in T-ALL PDX models, supported by an ex-vivo metabolites analysis (HPLC), confirmed the therapeutic effect, demonstrated by on-target reduced pyruvate-to-lactate ratios and increased lactate trapping post-MCT1 and MCT1/OXPHOS inhibitors treatment, with the latter leading to disease eradication and significantly prolonged overall survival. In summary, our CRISPR-Cas9 screens reveal critical mitochondrial dependencies and adaptive metabolic pathways in T-ALL. Targeting OXPHOS and MCT1, or their downstream signaling, simultaneously induces synthetic lethality toward T-ALL cells, offering a promising therapeutic strategy to eradicate T-ALL cells, providing therapeutic window to spare healthy hematopoietic cells, and ultimately warranting further in vitro and in vivo investigations.

To enhance the mechanical properties and biocompatibility of 316L stainless steel, multilayer titanium-doped diamond-like nanocomposite (Ti-DLC) films were deposited using a hybrid physical vapor deposition (PVD)/ plasma-enhanced chemical vapor deposition (PECVD) technique. The Ti-DLC films were deposited with different Ti target power ranging from 1 to 3 kW. The multilayer film consisted of three distinct layers: a TiN layer, a Ti (C, N) layer, and a Ti-DLC layer. As the Ti target power increased from 1 to 3 kW, the Ti content in the film gradually increased. The resulting films exhibited a dense and smooth surface with relatively low roughness. Notably, the Ti-DLC films deposited at a power of 1.5 kW exhibited the highest nanohardness and the lowest friction coefficient. Moreover, the film deposited at a power of 2 kW demonstrated a high corrosion potential, a low corrosion current density, and a wide passive zone, indicating excellent corrosion resistance. Furthermore, compared with the substrate and DLC film, the Ti-DLC films exhibited superior cell activity and proliferation. This suggests a reduced exchange of harmful ions from the substrate to the external environment, minimizing cytotoxicity. Overall, these results highlight that the chemically inert fabricated films possess dense and smooth properties along with exceptional mechanical strength, low friction, corrosion resistance, and biocompatibility. Consequently, they offer a viable alternative for long-term implantation purposes.

The garnet-type Li6.4La3Zr1.4Ta0.6O12 (LLZTO) is a widely studied solid-state electrolytes (SSEs) due to its high ionic conductivity and electrochemical stability. However, the conventional preparation process of LLZTO electrolyte tablets has encountered several issues such as powder embedment in the mold gaps and inadequate bonding between powders, leading to fragile electrolyte tablets that are prone to cracking. In this study, the effects of LLZTO electrolyte tablet pressing process parameters, including mixed powder content, polyvinyl alcohol (PVA) content and cold pressing pressure, on the electrochemical properties of button cells were investigated. The results indicated that the produced solid-state electrolyte tablets with high quality, low interfacial resistance and low surface passivation layer (Li2CO3) content was obtained by using 0.8 g of mixed powder content, 0.7 g of PVA content and 20Mpa pressure. The interface resistance decreased from 2927.1 omega to 1069.7 omega under these conditions. Additionally, the Li symmetric cell treated with adhesive exhibited stable cycling for over 400 hours at 25 degrees C under a current density of 0.1 mA & sdot; cm-2. This paper presents a straightforward preparation method for LLZTO electrolyte tablets with minimal surface passivation layer content. Through experimentation, the optimal conditions for process parameters including mixed powder content, PVA content, and cold pressing pressure are identified. Under these optimized conditions, the LLZTO electrolyte tablets demonstrate a notable enhancement in quality and a reduction in interface resistance, leading to improved electrochemical performance. image

The low hardness and limited anti-wear of aluminum alloys pose obstacles to their stability and service life, especially in corrosive environments. Here, the amorphous CrAlN coating was first deposited on aluminum alloy by DC magnetron sputtering. The CrAlN coating with 30.2% nitrogen content was dense and prevented the intrusion of oxidation and corrosive fluids, thus greatly improving the anti-corrosion of the coating. The wear rate of the coated aluminum alloy in 3.5% NaCl solution decreased by three orders of magnitude. This work paves the way for constructing amorphous protective coatings on light alloys.

Traditional control methods of robotic peg-in-hole assembly rely on complex contact state analysis. Reinforcement learning (RL) is gradually becoming a preferred method of controlling robotic peg-in-hole assembly tasks. However, the training process of RL is quite time-consuming because RL methods are always globally connected, which means all state components are assumed to be the input of policies for all action components, thus increasing action space and state space to be explored. In this paper, we first define continuous space serialized Shapley value (CS3) and construct a connection graph to clarify the correlativity of action components on state components. Then we propose a local connection reinforcement learning (LCRL) method based on the connection graph, which eliminates the influence of irrelevant state components on the selection of action components. The simulation and experiment results demonstrate that the control strategy obtained through LCRL method improves the stability and rapidity of the control process. LCRL method will enhance the data-efficiency and increase the final reward of the training process.

Peptide-specific expansion of CD8+ T cells. PBMCs from healthy donors were isolated and expanded as described in the Materials and Methods. Tetramer staining was performed pre- and post-expansion to quantify frequencies of peptide-specific CD8+ T cells. A, Wildtype-1, E380Q. B, Wildtype-2, Y537S, D538G. Data represents three independent experiments, from 3 separate donors, performed in triplicate. Statistical significance was determined via comparison of pre-expansion versus post-expansion CD8+tetramer+ population using unpaired Student t test. *, P < 0.05.

On-ground emulation is crucial to cutting-edge space technology involving the rendezvous and maintenance of noncooperative objects. However, existing systems are restricted to two objects and have a limited range of motion, and they cannot emulate some real space missions. The introduction of mobile robots is a potential solution, but on the one hand, their relatively low precision may ruin the emulation; on the other hand, how to take full advantage of mobile robots’ larger range of motion in on-ground emulation is still to be solved. This paper presents a novel emulation platform for noncooperative object missions in space that can complete high-precision kinetic emulations of large-scale and multiobject motion. We use different kinds of mobile robots in our system and overcome the poor kinetic accuracy of mobile platforms. First, the composition and kinetics of the whole system are characterized. We simplify the hyper-redundant system and propose a trajectory mapping method based on the workspace of mobile manipulators. Then, a dynamic programming method is proposed to plan the joint trajectories of mobile manipulators. We propose a feasibility function based on manipulability and the singularity avoidance coefficient, which ensures that the mobile base moves smoothly and that high-precision manipulators have enough space to compensate for the movement errors of mobile bases. Finally, experimental results verify the feasibility and effectiveness of the system and the planning methods.

Robotic force-based compliance control is a preferred approach to achieve high-precision assembly tasks. When the geometric features of assembly objects are asymmetric or irregular, reinforcement learning (RL) agents are gradually incorporated into the compliance controller to adapt to complex force-pose mapping which is hard to model analytically. Since force-pose mapping is strongly dependent on geometric features, a compliance controller is only optimal for current geometric features. To reduce the learning cost of assembly objects with different geometric features, this paper is devoted to answering how to reconfigure existing controllers for new assembly objects with different geometric features. In this paper, model-based parameters are first reconfigured based on the proposed Equivalent Theory of Compliance Law (ETCL). Then the RL agent is transferred based on the proposed Weighted Dimensional Policy Distillation (WDPD) method. The experiment results demonstrate that the control reconfiguration method costs less time and achieves better control performance, which confirms the validity of proposed methods.

Radar signal has been shown as a promising source for human identification. In daily home sleep-monitoring scenarios, large-scale motion features may not always be practical, and the heart motion or respiration data may not be as ideal as they are in a controlled laboratory setting. Human identification from radar sequences is still a challenging task. Furthermore, there is a need to address the open-set recognition problem for radar sequences, which has not been sufficiently studied. In this paper, we propose a deep learning-based approach for human identification using radar sequences captured during sleep in a daily home-monitoring setup. To enhance robustness, we preprocess the sequences to mitigate environmental interference before employing a deep convolution neural network for human identification. We introduce a Principal Component Space feature representation to detect unknown sequences. Our method is rigorously evaluated using both a public data set and a set of experimentally acquired radar sequences. We report a labeling accuracy of 98.2% and 96.8% on average for the two data sets, respectively, which outperforms the state-of-the-art techniques. Our method excels at accurately distinguishing unknown sequences from labeled ones, with nearly 100% detection of unknown samples and minimal misclassification of labeled samples as unknown.

Traffic time series anomaly detection has been intensively studied for years because of its potential applications in intelligent transportation. However, classical traffic anomaly detection methods often overlook the evolving dynamic associations between road network nodes, which leads to challenges in capturing the long-term temporal correlations, spatial characteristics, and abnormal node behaviors in datasets with high periodicity and trends, such as morning peak travel periods. In this paper, we propose a mirror temporal graph autoencoder (MTGAE) framework to explore anomalies and capture unseen nodes and the spatiotemporal correlation between nodes in the traffic network. Specifically, we propose the mirror temporal convolutional module to enhance feature extraction capabilities and capture hidden node-to-node features in the traffic network. Morever, we propose the graph convolutional gate recurrent unit cell (GCGRU CELL) module. This module uses Gaussian kernel functions to map data into a high-dimensional space, and enables the identification of anomalous information and potential anomalies within the complex interdependencies of the traffic network, based on prior knowledge and input data. We compared our work with several other advanced deep-learning anomaly detection models. Experimental results on the NYC dataset illustrate that our model works best compared to other models for traffic anomaly detection.