Edge-Assisted V2X Motion Planning and Power Control Under Channel Uncertainty

Edge-assisted vehicle-to-everything (V2X) motion planning is an emerging paradigm to achieve safe and efficient autonomous driving, since it leverages the global position information shared among multiple vehicles. However, due to the imperfect channel state information (CSI), the position information of vehicles may become outdated and inaccurate. Conventional methods ignoring the communication delays could severely jeopardize driving safety. To fill this gap, this paper proposes a robust V2X motion planning policy that adapts between competitive driving under a low communication delay and conservative driving under a high communication delay, and guarantees small communication delays at key waypoints via power control. This is achieved by integrating the vehicle mobility and communication delay models and solving a joint design of motion planning and power control problem via the block coordinate descent framework. Simulation results show that the proposed driving policy achieves the smallest collision ratio compared with other benchmark policies

Privacy-preserving Anomaly Detection in Cloud Manufacturing via Federated Transformer

With the rapid development of cloud manufacturing, industrial production with edge computing as the core architecture has been greatly developed. However, edge devices often suffer from abnormalities and failures in industrial production. Therefore, detecting these abnormal situations timely and accurately is crucial for cloud manufacturing. As such, a straightforward solution is that the edge device uploads the data to the cloud for anomaly detection. However, Industry 4.0 puts forward higher requirements for data privacy and security so that it is unrealistic to upload data from edge devices directly to the cloud. Considering the above-mentioned severe challenges, this paper customizes a weakly-supervised edge computing anomaly detection framework, i.e., Federated Learning-based Transformer framework (\textit{FedAnomaly}), to deal with the anomaly detection problem in cloud manufacturing. Specifically, we introduce federated learning (FL) framework that allows edge devices to train an anomaly detection model in collaboration with the cloud without compromising privacy. To boost the privacy performance of the framework, we add differential privacy noise to the uploaded features. To further improve the ability of edge devices to extract abnormal features, we use the Transformer to extract the feature representation of abnormal data. In this context, we design a novel collaborative learning protocol to promote efficient collaboration between FL and Transformer. Furthermore, extensive case studies on four benchmark data sets verify the effectiveness of the proposed framework. To the best of our knowledge, this is the first time integrating FL and Transformer to deal with anomaly detection problems in cloud manufacturing

Numerical Simulation and Field Test of a PDC Bit with Mixed Cutter Arrangement to Break Non-Homogeneous Granite

As the depth of petroleum drilling increases, the strata environment becomes more complex. The efficiency and lifespan of Polycrystalline Diamond Compact (PDC) drill bits fail to meet current drilling demands. However, the structure and arrangement of PDC cutters are valuable determinants of drilling efficiency, although related research still has gaps and deficiencies. This study focuses on PDC cutters in axe, triangular prism, and circular forms. It establishes an inhomogeneous granite model based on the actual measurements of granite and verifies the accuracy of this model through uniaxial compression simulation. Finite element models of three types of cutters in various combination schemes are constructed to examine rock-breaking effects, with the best scheme optimized using Box-Behnken response surface methodology. The rock-breaking process of the optimal PDC drill bit layout has been compared to that of a single cutter bit. Field drilling has demonstrated the effectiveness of a mixed cutter arrangement. The results show that the inhomogeneous granite model can be trusted. The optimal arrangement involves axe cutters in the front row and an alternate arrangement of triangular prism cutters and axe cutters in the back row. The optimal lateral and longitudinal distances for the triangular cutters from the front row of axe cutters are 10 mm and 7 mm, respectively, while those for the back row of axe cutters from the front row are 10.06 mm and 7 mm, respectively. The ROP standard deviation in the drilling process of mixed cutter bits decreases by 53.06% and 43.08% compared to axe and triangular prism cutter bits, respectively. The drilling efficiency increases by 16.8% and 16.6%, respectively, demonstrating higher efficiency and stability. Field drilling results indicate that a mixed cutter bit increases efficiency by 23.5% compared to a bit with only triangular prism cutters. This study posits that research on the combination schemes and parameters of PDC cutters can significantly enhance drilling efficiency, thereby reducing the drilling cycle and costs

DataSheet_1_Decadal intensified and slantwise Subpolar Front in the Japan/East Sea.zip

The Subpolar Front in the Japan/East Sea (JES) could far-reaching influence the atmospheric processes over the downstream regions. However its variability on decadal timescale remains less understood. In this study, the decadal trends in the intensity and position of the SPF in the JES during the time period 1985−2020 are analyzed by using four categories of satellite observed high-resolution sea surface temperature products. The results show that there is a significant intensification trend of the SPF at a rate of 0.37°C/100km/decade. The SPF is further divided into three regions based on the meridional sea surface temperature gradient (MSSTG): the eastern (135−138°E), central (130−135°E) and western (128−130°E) regions, respectively. These three regions showed different meridional movements with the eastern SPF moving poleward by 0.08°/decade, the central SPF moving equatorward by −0.11°/decade and the western SPF showing no significant displacements. The reverse meridional movements between the central and eastern SPF increased its skewness. The frontogenesis rate equation is employed to identify the mechanisms of these decadal trends. Results show that the geostrophic advection term, especially its zonal component, had a crucial role in the decadal trends of the intensity and position of the central and eastern SPF. The decadal trend of the central SPF was mainly attributed to the zonal geostrophic advection of the MSSTG associated with the enhancement of the Subpolar Front Current (SFC) in the upstream region, whereas the decadal trend in the eastern SPF was mainly driven by the zonal geostrophic shear advection controlled by the shear of the SFC in the downstream region. Before 2002, the eastern SPF moved poleward at a rate of 0.27°/decade, whereas there was no obvious trend after 2002. Further decomposition showed that this shift was caused by meridional Ekman advection of the MSSTG.</p

Driver Distraction Detection Based on Cloud Computing Architecture and Lightweight Neural Network

Distracted behavior detection is an important task in computer-assisted driving. Although deep learning has made significant progress in this area, it is still difficult to meet the requirements of the real-time analysis and processing of massive data by relying solely on local computing power. To overcome these problems, this paper proposes a driving distraction detection method based on cloud–fog computing architecture, which introduces scalable modules and a model-driven optimization based on greedy pruning. Specifically, the proposed method makes full use of cloud–fog computing to process complex driving scene data, solves the problem of local computing resource limitations, and achieves the goal of detecting distracted driving behavior in real time. In terms of feature extraction, scalable modules are used to adapt to different levels of feature extraction to effectively capture the diversity of driving behaviors. Additionally, in order to improve the performance of the model, a model-driven optimization method based on greedy pruning is introduced to optimize the model structure to obtain a lighter and more efficient model. Through verification experiments on multiple driving scene datasets such as LDDB and Statefarm, the effectiveness of the proposed driving distraction detection method is proved

Application of Advanced Technologies—Nanotechnology, Genomics Technology, and 3D Printing Technology—In Precision Anesthesia: A Comprehensive Narrative Review

There has been increasing interest and rapid developments in precision medicine, which is a new medical concept and model based on individualized medicine with the joint application of genomics, bioinformatics engineering, and big data science. By applying numerous emerging medical frontier technologies, precision medicine could allow individualized and precise treatment for specific diseases and patients. This article reviews the application and progress of advanced technologies in the anesthesiology field, in which nanotechnology and genomics can provide more personalized anesthesia protocols, while 3D printing can yield more patient-friendly anesthesia supplies and technical training materials to improve the accuracy and efficiency of decision-making in anesthesiology. The objective of this manuscript is to analyze the recent scientific evidence on the application of nanotechnology in anesthesiology. It specifically focuses on nanomedicine, precision medicine, and clinical anesthesia. In addition, it also includes genomics and 3D printing. By studying the current research and advancements in these advanced technologies, this review aims to provide a deeper understanding of the potential impact of these advanced technologies on improving anesthesia techniques, personalized pain management, and advancing precision medicine in the field of anesthesia

Multi-dimensional single-cell characterization revealed suppressive immune microenvironment in AFP-positive hepatocellular carcinoma

Abstract Alpha-fetoprotein (AFP)-secreting hepatocellular carcinoma (HCC), which accounts for ~75% of HCCs, is more aggressive with a worse prognosis than those without AFP production. The mechanism through which the interaction between tumors and the microenvironment leads to distinct phenotypes is not yet clear. Therefore, our study aims to identify the characteristic features and potential treatment targets of AFP-negative HCC (ANHC) and AFP-positive HCC (APHC). We utilized single-cell RNA sequencing to analyze 6 ANHC, 6 APHC, and 4 adjacent normal tissues. Integrated multi-omics analysis together with survival analysis were also performed. Further validation was conducted via cytometry time-of-flight on 30 HCCs and multiplex immunohistochemistry on additional 59 HCCs. Our data showed that the genes related to antigen processing and interferon-γ response were abundant in tumor cells of APHC. Meanwhile, APHC was associated with multifaceted immune distortion, including exhaustion of diverse T cell subpopulations, and the accumulation of tumor-associated macrophages (TAMs). Notably, TAM-SPP1+ was highly enriched in APHC, as was its receptor CD44 on T cells and tumor cells. Targeting the Spp1-Cd44 axis restored T cell function in vitro and significantly reduced tumor burden when treated with either anti-Spp1 or anti-Cd44 antibody alone or in combination with anti-Pd-1 antibody in the mouse model. Furthermore, elevated IL6 and TGF-β1 signaling contributed to the enrichment of TAM-SPP1+ in APHC. In conclusion, this study uncovered a highly suppressive microenvironment in APHC and highlighted the role of TAM-SPP1+ in regulating the immune microenvironment, thereby revealing the SPP1-CD44 axis as a promising target for achieving a more favorable immune response in APHC treatment

High-Capacity and Self-Stabilized Manganese Carbonate Microspheres as Anode Material for Lithium-Ion Batteries

Manganese carbonate (MnCO₃) is an attractive anode material with high capacity based on conversion reaction for lithium-ion batteries (LIBs), but its application is mainly hindered by poor cycling performance. Building nanostructures/porous structures and nanocomposites has been demonstrated as an effective strategy to buffer the volume changes and maintain the electrode integrity for long-term cycling. It is widely believed that microsized MnCO₃ is not suitable for use as anode material for LIBs because of its poor conductivity and the absence of nanostructure. Herein, different from previous reports, spherical MnCO₃ with the mean diameters of 6.9 μm (MnCO₃–B), 4.0 μm (MnCO₃–M), and 2.6 μm (MnCO₃–S) were prepared via controllable precipitation and utilized as anode materials for LIBs. It is interesting that the as-prepared MnCO₃ microspheres demonstrate both high capacity and excellent cycling performance comparable to their reported nanosized counterparts. MnCO₃–B, MnCO₃–M, and MnCO₃–S deliver reversible specific capacities of 487.3, 573.9, and 656.8 mA h g^–1 after 100 cycles, respectively. All the MnCO₃ microspheres show capacity retention more than 90% after the initial stage. The advantages of MnCO₃ microspheres were investigated via constant-current charge/discharge, cyclic voltammetry and electrochemical impedance spectroscopy. The results indicate that there should be substantial structure transformation from microsized particle to self-stabilized nanostructured matrix for MnCO₃ at the initial charge/discharge stage. The evolution of EIS during charge/discharge clearly indicates the formation and stabilization of the nanostructured matrix. The self-stabilized porous matrix maintains the electrode structure to deliver excellent cycling performance, and contributes extra capacity beyond conversion reaction