The development of the modernization process has resulted in the consumption of natural resources and the accumulation of construction waste, and the environmental bearing capacity faces severe challenges to a certain...
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The interplay among topology, crystal symmetry, magnetic order, and strong electron correlation can give rise to a plethora of exotic physical phenomena. The ZrSiS family is known as typical topological Dirac semimeta...
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The interplay among topology, crystal symmetry, magnetic order, and strong electron correlation can give rise to a plethora of exotic physical phenomena. The ZrSiS family is known as typical topological Dirac semimetals, among them LnSbTe (Ln denotes lanthanide) compounds exhibit intriguing characteristics due to the presence of Ln 4f electrons, resulting in quantum states and unique properties. In this paper, the topological electronic structure of PrSbTe is systematically studied by angle-resolved photoemission spectroscopy (ARPES), combined with magnetic, specific heat measurements, and band structure calculations. The detailed three-dimensional electronic structure of PrSbTe has been obtained, and a diamond-shaped Fermi surface and multiple Dirac nodal lines have been observed, which are in remarkable agreement with theoretical calculations. Moreover, the 4f electrons in PrSbTe are rather localized, which can be revealed by on-resonant ARPES data and further confirmed by the rather small Sommerfeld coefficient of γ=2.6231mJ/molK2. Our results provide more detailed information about the LnSbTe family, which gives a deeper understanding of the interaction between Ln 4f electrons and the topological states.
Synaptic transistors are pivotal for hardware-level neuromorphic computing. However, the lack of switching behavior diversity has limited the implementation of advanced computing tasks, which are constrained by tradit...
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Synaptic transistors are pivotal for hardware-level neuromorphic computing. However, the lack of switching behavior diversity has limited the implementation of advanced computing tasks, which are constrained by traditional interfacial or uncontrollable materials engineering. Here, a universal planar p-n junction structure is devised, with rational alignment of energy levels between crosslinkable p-type poly(indacenodithiophene-alt-benzothiadizole)-based conjugated polymer with hydroxyl groups at the ends of its side chains (OH-IDTBT-10%) and different n-type conjugated polymers, fabricated through efficient solution processing. This structure enables reconfigurable switching of p-type and n-type carrier transport by modifying the transistor architecture, along with significant non-volatile memory and synaptic plasticity. By strategically adjusting crosslinkers, a large memory window up to 48.5 V is achieved, sustained performance over 500 cycles, and a diverse array of synaptic behaviors modulated by electrical pulses. The underlying mechanism involves quantum well-like structures and discrete physical charge traps at the bilayer interface. The versatility of the strategy is proven across different n-type polymer systems. An artificial neural network (ANN) constructed by these devices affords a remarkably high facial recognition accuracy of 97.58% using the Yale Face Database with minimized training epochs of 200. This design provides an opportunity for high performance hardware with diverse synaptic behaviors in advanced neuromorphic computing.
The 2021 Mw 7.4 Maduo (Madoi) earthquake that struck the northern Tibetan Plateau resulted in widespread coseismic deformation features, such as surface ruptures and soil liquefaction. By utilizing the unmanned aerial...
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The 2021 Mw 7.4 Maduo (Madoi) earthquake that struck the northern Tibetan Plateau resulted in widespread coseismic deformation features, such as surface ruptures and soil liquefaction. By utilizing the unmanned aerial vehicle (UAV) photogrammetry technology, we accurately recognize and map 39,286 liquefaction sites within a 1.5 km wide zone along the coseismic surface rupture. We then systematically analyze the coseismic liquefaction distribution characteristics and the possible influencing factors. The coseismic liquefaction density remains on a higher level within 250 m from the surface rupture and decreases in a power law with the increasing distance. The amplification of the seismic waves in the vicinity of the rupture zone enhances the liquefaction effects near it. More than 90% of coseismic liquefaction occurs in the peak ground acceleration (PGA) > 0.50 g, and the liquefaction density is significantly higher in the region with seismic intensity > VIII. Combined with the sedimentary distribution along-strike of the surface rupture, the mapped liquefaction sites indicate that the differences in the sedimentary environments could cause more intense liquefaction on the western side of the epicenter, where loose Quaternary deposits are widely spread. The stronger coseismic liquefaction sites correspond to the Eling Lake section, the Yellow River floodplain, and the Heihe River floodplain, where the soil is mostly saturated with loose fine-grained sand and the groundwater level is high. Our results show that the massive liquefaction caused by the strong ground shaking during the Maduo (Madoi) earthquake was distributed as the specific local sedimentary environment and the groundwater level changed.
With the advances in new-generation information technologies, smart process planning is becoming the focus for smart process planning with less time and lower cost. Big data-based reusing and evaluating the multi-dime...
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With the advances in new-generation information technologies, smart process planning is becoming the focus for smart process planning with less time and lower cost. Big data-based reusing and evaluating the multi-dimensional process knowledge is widely accepted as an effective strategy for improving competitiveness of enterprises. However, there was little research on how to reuse and evaluate process knowledge with dynamical changing machining status. In this paper, we propose a novel digital twin-based approach for reusing and evaluating process knowledge. First, the digital twin-based process knowledge model which contains the geometric information and real-time process equipment status is introduced to represent the purpose and requirement of machining planning. Second, the process big data is constructed based on the three-layer and its association rules for accumulating process knowledge. Moreover, the similarity calculation algorithm of the scene model is proposed to filter the unmatched process knowledge. For accurately reusing the process knowledge, the process reusability evaluation approach of the candidate knowledge set is presented based on the real-time machining status and the calculated confidence. Finally, the diesel engine parts are applied in the developed prototype module to verify the effectiveness of the proposed method. The proposed method can promote the development and application of the smart process planning.
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