μ\mueV-deep neutron bound states in nanocrystals

The nuclear strong force induces the widely studied neutron scattering states and MeV-energy nuclear bound states. Whether this same interaction could lead to low-energy bound states for a neutron in the nuclear force field of a cluster of nuclei is an open question. Here, we computationally demonstrate the existence of -$\mu$eV-level neutronic bound states originating from nuclear interaction in nanocrystals with a spatial extent of tens of nanometers. These negative-energy neutron wavefunctions depend on the size, dimension, and nuclear spin polarization of the nanoparticles, providing engineering degrees of freedom for the artificial neutronic "molecule"

Efficient Quantum Transduction Using Anti-Ferromagnetic Topological Insulators

Transduction of quantum information between distinct quantum systems is an essential step in various applications, including quantum networks and quantum computing. However, quantum transduction needs to mediate between photons with vastly different frequencies, making it challenging to design high-performance transducers, due to multifaceted and sometimes conflicting requirements. In this work, we first discuss some general principles for quantum transducer design, and then propose solid-state anti-ferromagnetic topological insulators to serve as highly effective transducers. First, topological insulators exhibit band-inversion, which can greatly enhance their optical responses. Coupled with their robust spin-orbit coupling and high spin density, this property leads to strong nonlinear interaction in topological insulators, thereby substantially improving transduction efficiency. Second, the anti-ferromagnetic order can minimize the detrimental influence on other neighboring quantum systems due to magnetic interactions. Using $\rm MnBi_2Te_4$ as an example, we showcase that unit transduction fidelity can be achieved with modest experimental requirements, while the transduction bandwidth can reach the GHz range. The strong nonlinear interaction in magnetic topological insulators can find diverse applications, including the generation of entanglement between photons of distinct frequencies.Comment: 15 pages, 3 figure

Study on the Cutting Temperature of the Textured Tool by 3D FEA Simulation

Temperature plays an important role in the life of cutting tools. However, it is sometimes difficult to observe instantaneously because of limited data acquisition, especially for the textured tool. In this paper, the performance of microgroove textured cutting tools for three-dimensional (3D) oblique dry turning of AISI 1045 steel was studied by finite element simulation. The effects of width, depth and spacing of strip micro-texture on cutting temperature and the effect of cutting speed on cutting temperature of strip micro-texture tool were studied. The results show that the maximum temperature of the tool and the tip temperature first increase and then decrease with the increase of micro-texture width. When the micro-texture depth increases, the maximum temperature and the tool tip temperature decrease first and then increase. The highest temperature and tip temperature of the tool gradually increase with the increase of micro-texture spacing. The highest temperature and tip temperature of the tool increase with the increase of cutting speed, feed speed and cutting depth. In addition, the effective mechanism of micro-texture parameter for the temperature was proposed. It provides profound guidance for optimizing the microstructure parameters and cutting process of cutting tools according to cutting temperature in this study. It also provides an effective and practical method for the design, innovation and development of micro-textured tools

Current Considerations in Direct Percutaneous Endoscopic Jejunostomy

BACKGROUND: Direct percutaneous endoscopic jejunostomy (DPEJ) is a well-known approach to deliver postpyloric enteral nutritional support to individuals who cannot tolerate gastric feeding. However, it is technically difficult, and some case series have reported significant procedural failure rates. The present article describes current indications, successes and complications of DPEJ placement METHODS: A MEDLINE database search was performed to identify relevant articles using the key words “direct percutaneous endoscopic jejunostomy”, “percutaneous endoscopic gastrostomy”, and “percutaneous endoscopic gastrostomy with a jejunal extension tube”. Additional articles were identified by a manual search of the references cited in the key articles obtained in the primary search. RESULTS: DPEJ is gradually becoming more common in the treatment of patients who cannot tolerate gastric feeding. Differences in patient selection and technique modifications may contribute to the various success rates reported. Failure is most often due to inadequate transillumination or gastroduodenal obstruction. Currently, there are limited data to evaluate the safety and effectiveness of DPEJ. CONCLUSION: The clinical use of DPEJ is increasing. With appropriate care and expertise, DPEJ may prove to be reliable and safe

Reinforcement learning-guided long-timescale simulation of hydrogen transport in metals

Atomic diffusion in solids is an important process in various phenomena. However, atomistic simulations of diffusion processes are confronted with the timescale problem: the accessible simulation time is usually far shorter than that of experimental interests. In this work, we developed a long-timescale method using reinforcement learning that simulates diffusion processes. As a testbed, we simulate hydrogen diffusion in pure metals and a medium entropy alloy, CrCoNi, getting hydrogen diffusivity reasonably consistent with previous experiments. We also demonstrate that our method can accelerate the sampling of low-energy configurations compared to the Metropolis-Hastings algorithm using hydrogen migration to copper (111) surface sites as an example

Scaling Multi-Camera 3D Object Detection through Weak-to-Strong Eliciting

The emergence of Multi-Camera 3D Object Detection (MC3D-Det), facilitated by bird's-eye view (BEV) representation, signifies a notable progression in 3D object detection. Scaling MC3D-Det training effectively accommodates varied camera parameters and urban landscapes, paving the way for the MC3D-Det foundation model. However, the multi-view fusion stage of the MC3D-Det method relies on the ill-posed monocular perception during training rather than surround refinement ability, leading to what we term "surround refinement degradation". To this end, our study presents a weak-to-strong eliciting framework aimed at enhancing surround refinement while maintaining robust monocular perception. Specifically, our framework employs weakly tuned experts trained on distinct subsets, and each is inherently biased toward specific camera configurations and scenarios. These biased experts can learn the perception of monocular degeneration, which can help the multi-view fusion stage to enhance surround refinement abilities. Moreover, a composite distillation strategy is proposed to integrate the universal knowledge of 2D foundation models and task-specific information. Finally, for MC3D-Det joint training, the elaborate dataset merge strategy is designed to solve the problem of inconsistent camera numbers and camera parameters. We set up a multiple dataset joint training benchmark for MC3D-Det and adequately evaluated existing methods. Further, we demonstrate the proposed framework brings a generalized and significant boost over multiple baselines. Our code is at \url{https://github.com/EnVision-Research/Scale-BEV}