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In the face of complex and changeable world, the smooth development of civilization needs the guidance of Karl Marx's thought. The era Marx faced was not fundamentally different from the times we live in today, and Marx's theory, which was born in that era and directly used to analyze it, is not outdated yet. Some of Marx's basic standpoints and views about civilization, including his basic theoretical propositions on the understanding of civilization itself, the goal of civilization, this historical process and requirement of the development of civilization, the conditions and environment for the progress of civilization, the laws and roads of the development of civilization, and the exchanges between and spread of civilizations, are still of immense value today.

Semantic segmentation of target objects in power transmission line corridor point cloud scenes is a crucial step in powerline tree barrier detection. The massive quantity, disordered distribution, and non-uniformity of point clouds in power transmission line corridor scenes pose significant challenges for feature extraction. Previous studies have often overlooked the core utilization of spatial information, limiting the network's ability to understand complex geometric shapes. To overcome this limitation, this paper focuses on enhancing the deep expression of spatial geometric information in segmentation networks and proposes a method called BDF-Net to improve RandLA-Net. For each input 3D point cloud data, BDF-Net first encodes the relative coordinates and relative distance information into spatial geometric feature representations through the Spatial Information Encoding block to capture the local spatial structure of the point cloud data. Subsequently, the Bilinear Pooling block effectively combines the feature information of the point cloud with the spatial geometric representation by leveraging its bilinear interaction capability thus learning more discriminative local feature descriptors. The Global Feature Extraction block captures the global structure information in the point cloud data by using the ratio between the point position and the relative position, so as to enhance the semantic understanding ability of the network. In order to verify the performance of BDF-Net, this paper constructs a dataset, PPCD, for the point cloud scenario of transmission line corridors and conducts detailed experiments on it. The experimental results show that BDF-Net achieves significant performance improvements in various evaluation metrics, specifically achieving an OA of 97.16%, a mIoU of 77.48%, and a mAcc of 87.6%, which are 3.03%, 16.23%, and 18.44% higher than RandLA-Net, respectively. Moreover, comparisons with other state-of-the-art methods also verify the superiority of BDF-Net in point cloud semantic segmentation tasks. [ABSTRACT FROM AUTHOR]

Organic memristors with uniform resistive switching behavior can mimic the functions of biological synapses, making them promising candidates for brain-inspired neuromorphic computing. Among these, organic memristors based on ion migration mechanisms have been widely studied because of the directional movement of anions such as metal cations and oxygen ions under electric fields, which is more similar to the biological process of ion release in synapses. However, controlling these ion dynamic processes is often challenging, with rapid ion migration leading to unstable device performance and reduced accuracy. Here, we present the fabrication of a memristor utilizing an organic heterojunction. The built-in electric field generated by the heterojunction slows down and orders the ion migration process, enabling the device to exhibit a wide range of precise multiple conductive states (10−6–10−3 A). This provides an advantage in simulating synaptic behavior. The continuously adjustable conductance in organic heterojunction memristors successfully mimics various forms of synaptic plasticity, including paired-pulse facilitation (PPF), post-tetanic potentiation (PTP), short-term plasticity (STP) and long-term plasticity (LTP), as well as higher-order functionalities such as simulating Pavlovian classical conditioning reflex experiment and experiential learning function. Furthermore, by utilizing X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary-ion mass spectrometry (ToF-SIMS) depth profiling, we have gained clear insights into the distribution and movement of ions of different compositions within the device, elucidating the underlying reasons for resistance changes. These findings provide reliable references and demonstrations for the design and control of future ion migration-type memristors. [ABSTRACT FROM AUTHOR]

Designing composite catalysts that harness the strengths of individual components while mitigating their limitations is a fascinating yet challenging task in catalyst engineering. In this study, we aimed to enhance the catalytic performance by anchoring ZIF-67 nanoparticles of precise sizes onto lamella Si-MWW zeolite surfaces through a stepwise regrowth process. Co ions were initially grafted onto the zeolite surface using ultrasonication, followed by a seed-assisted secondary growth method. Si-MWW proved to be the ideal zeolite support due to its thin layered structure, large external surface area and substantial lateral dimensions. The abundant Si–OH groups on its surface played a crucial role in securely binding Co ions, limiting size growth and preventing undesirable ZIF-67 aggregation. The resulting ZIF-67/MWW composite with finely dispersed nano-scale ZIF-67 particles exhibited a remarkable catalytic performance and stability in the aldol condensation reactions involving acetone and various aldehydes. This approach holds promise for designing MOF/zeolite composite catalysts. [ABSTRACT FROM AUTHOR]