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Abstract We report observations of direct evidence of energetic protons being accelerated above ∼400 keV within the reconnection exhaust of a heliospheric current sheet (HCS) crossing by NASA’s Parker Solar Probe (PSP) at a distance of ∼16.25 solar radii (R_s) from the Sun. Inside the exhaust, both the reconnection-generated plasma jet and the accelerated protons up to ∼400 keV propagated toward the Sun, unambiguously establishing their origin from HCS reconnection sites located antisunward of PSP. Within the core of the exhaust, PSP detected stably trapped energetic protons up to ∼400 keV, which is ≈1000 times greater than the available magnetic energy per particle. The differential energy spectrum of the accelerated protons behaved as a pure power law with spectral index of ∼−5. Supporting simulations using thekglobalmodel suggest that the trapping and acceleration of protons up to ∼400 keV in the reconnection exhaust are likely facilitated by merging magnetic islands with a guide field between ∼0.2 and 0.3 of the reconnecting magnetic field, consistent with the observations. These new results, enabled by PSP’s proximity to the Sun, demonstrate that magnetic reconnection in the HCS is a significant new source of energetic particles in the near-Sun solar wind. Our findings of in situ particle acceleration via magnetic reconnection at the HCS provide valuable insights into this fundamental process, which frequently converts the large magnetic field energy density in the near-Sun plasma environment and may be responsible for heating the Sun’s atmosphere, accelerating the solar wind, and energizing charged particles to extremely high energies in solar flares. 

Abstract We report observations of multiple subscale reconnecting current sheets embedded inside a large-scale heliospheric current sheet (HCS) reconnection exhaust. The discovery was made possible by the unusual skimming trajectory of Parker Solar Probe through a sunward-directed HCS exhaust, sampling structures convecting with the exhaust outflows for more than 3 hr during Encounter 14, at a radial distance of ∼17 solar radii. A large number of subscale current sheets (SCSs) were detected inside the HCS exhaust. Remarkably, five SCSs showed direct evidence for reconnection, displaying near-Alfvénic outflow jets and bifurcated current sheets. The reconnecting SCSs all had small magnetic shears (27°–81°), i.e., strong guide fields. The thickness of the subscale reconnecting current sheets ranged from ∼60 km to ∼5000 km (∼20–2000 ion inertial lengths). The SCS exhausts were directed predominantly in the normal or out-of-plane direction of the HCS, i.e., nearly orthogonal to the HCS exhaust direction. The presence of multiple low-magnetic-shear reconnecting current sheets inside a large-scale exhaust could be associated with coalescence of multiple large flux ropes inside the HCS exhaust. The orientation of some SCS exhausts was partly in the ecliptic plane of the HCS, which may indicate that the coalescence process is highly three-dimensional. Since the coalescence process is likely short-lived, the detection of five such events inside a single HCS crossing could imply the common occurrence of flux rope coalescence in large-scale HCS reconnection exhausts. 

Siphons are often used for releasing water by gravity, especially from shallow ponds. This paper investigates the hydrodynamic characteristics of the flow in a siphon under leakage (e.g., pipe puncture) and no-leakage conditions using the OpenFOAM Computational Fluid Dynamics (CFD) package. The volume-of-fluid (VOF) method was used to solve the air-water two-phase flow, and the standard k-ε model was used to model the turbulence. The CFD model was assessed using experimental measurements for the no-leakage condition. The simulations for the leakage condition focused on investigating the siphon flow break phenomenon as a function of leakage location. The results show the impact of leak position on the siphon flow break and the suitability of OpenFOAM for simulating the complex two-phase flow dynamics that occurs in a siphon. 

Abstract Quantum Chromodynamics predicts a phase transition from hadronic matter to quark–gluon plasma (QGP) at high temperatures and energy densities, where quarks and gluons (partons) are no longer confined within hadrons. The QGP forms in ultrarelativistic heavy-ion collisions. Anisotropic flow coefficients, quantifying the azimuthal expansion of produced matter, probe QGP properties. Flow measurements in high-energy heavy-ion collisions show a distinctive grouping of anisotropic flow for baryons and mesons at intermediate transverse momentum – a feature associated with flow imparted at the quark level, confirming QGP existence. The observation of QGP-like features in proton–proton and proton–ion collisions has sparked debate about QGP formation in smaller systems. For the first time, we demonstrate the distinctive grouping of anisotropic flow for baryons and mesons in high-multiplicity proton–lead and proton–proton collisions at the Large Hadron Collider (LHC). These results are described by a model including hydrodynamic flow followed by hadron formation via quark coalescence, consistent with the formation of partonic flowing systems in these collisions. 

This study aims at sensing and understanding the worker’s activity in a human-centered intelligent manufacturing system. We propose a novel multi-modal approach for worker activity recognition by leveraging information from different sensors and in different modalities. Specifically, a smart armband and a visual camera are applied to capture Inertial Measurement Unit (IMU) signals and videos, respectively. For the IMU signals, we design two novel feature transform mechanisms, in both frequency and spatial domains, to assemble the captured IMU signals as images, which allow using convolutional neural networks to learn the most discriminative features. Along with the above two modalities, we propose two other modalities for the video data, i.e., at the video frame and video clip levels. Each of the four modalities returns a probability distribution on activity prediction. Then, these probability distributions are fused to output the worker activity classification result. A worker activity dataset is established, which at present contains 6 common activities in assembly tasks, i.e., grab a tool/part, hammer a nail, use a power-screwdriver, rest arms, turn a screwdriver, and use a wrench. The developed multi-modal approach is evaluated on this dataset and achieves recognition accuracies as high as 97% and 100% in the leave-one-out and half-half experiments, respectively.