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The fermionic Hubbard model (FHM) 1 describes a wide range of physical phenomena resulting from strong electron–electron correlations, including conjectured mechanisms for unconventional superconductivity. Resolving its low-temperature physics is, however, challenging theoretically or numerically. Ultracold fermions in optical lattices 2 , 3 provide a clean and well-controlled platform offering a path to simulate the FHM. Doping the antiferromagnetic ground state of a FHM simulator at half-filling is expected to yield various exotic phases, including stripe order 4 , pseudogap 5 , and d -wave superfluid 6 , offering valuable insights into high-temperature superconductivity 7 – 9 . Although the observation of antiferromagnetic correlations over short 10 and extended distances 11 has been obtained, the antiferromagnetic phase has yet to be realized as it requires sufficiently low temperatures in a large and uniform quantum simulator. Here we report the observation of the antiferromagnetic phase transition in a three-dimensional fermionic Hubbard system comprising lithium-6 atoms in a uniform optical lattice with approximately 800,000 sites. When the interaction strength, temperature and doping concentration are finely tuned to approach their respective critical values, a sharp increase in the spin structure factor is observed. These observations can be well described by a power-law divergence, with a critical exponent of 1.396 from the Heisenberg universality class 12 . At half-filling and with optimal interaction strength, the measured spin structure factor reaches 123(8), signifying the establishment of an antiferromagnetic phase. Our results provide opportunities for exploring the low-temperature phase diagram of the FHM. Antiferromagnetic phase transition is observed in a three-dimensional fermionic Hubbard system comprising lithium-6 atoms in a uniform optical lattice with approximately 800,000 sites.

Purpose To determine whether adjunctive blood flow restriction (BFR) training during postoperative weeks 13–20 is associated with differences in functional, strength, balance, muscle morphology, and neuromuscular outcomes compared with sham BFR following anterior cruciate ligament reconstruction (ACLR). Methods In this single-centre randomized controlled trial, 48 patients aged 18–35 years who underwent primary unilateral ACLR were randomly assigned to a BFR group or a Sham-BFR group. From postoperative weeks 13 to 20, both groups performed identical low-load resistance training (30% one-repetition maximum) twice weekly. The BFR group received individualized blood flow restriction at 40% arterial occlusion pressure, whereas the Sham-BFR group underwent the same protocol with minimal cuff pressure. Outcomes assessed at postoperative week 24 included the International Knee Documentation Committee (IKDC) score, Tegner Activity Scale, knee range of motion (ROM), isometric knee extensor and flexor strength, quadriceps muscle thickness, Y-Balance Test performance, and quadriceps surface electromyography (sEMG). Results Forty-three patients completed the study (BFR, n  = 21; Sham-BFR, n  = 22). At 24 weeks postoperatively, the BFR group demonstrated statistically significantly higher IKDC score and Tegner Activity Scale than the Sham-BFR group ( p  < 0.01). Knee extensor and flexor strength, Y-Balance Test composite scores, and quadriceps sEMG amplitudes were also significantly greater in the BFR group ( p  < 0.05). No significant between-group differences were observed in knee ROM or quadriceps muscle thickness. Conclusion Adjunctive BFR training during mid-stage ACLR rehabilitation was associated with more favourable functional, strength, balance, and neuromuscular outcomes than sham BFR at 24 weeks postoperatively. These findings should be interpreted cautiously because baseline outcome measurements and immediate post-intervention assessments were not available. Trial registration (Chinese Clinical Trial Registry ( https://www.chictr.org.cn ), No. ChiCTR2400087631, 31/07/2024)

The process of semen collection plays a key role in the quality of semen specimens. However, the association between semen collection time and semen quality is still unclear. In this study, ejaculates by masturbation from 746 subfertile men or healthy men who underwent semen analysis were examined. The median (interquartile range) semen collection time for all participants was 7.0 (5.0-11.0) min, and the median time taken for semen collection was lower in healthy men than that in subfertile men (6.0 min vs 7.0 min). An increase in the time required to produce semen samples was associated with poorer semen quality. Among those undergoing assisted reproductive technology (ART), the miscarriage rate was positively correlated with the semen collection time. After adjusting for confounders, the highest quartile (Q4) of collection time was negatively associated with semen volume and sperm concentration. A longer time to produce semen samples (Q3 and Q4) was negatively correlated with progressive and total sperm motility. In addition, there was a significant negative linear association between the semen collection time and the sperm morphology. Higher risks of asthenozoospermia (adjusted odds ratio [OR] = 2.06, 95% confidence interval [CI]: 1.31-3.25, P = 0.002) and teratozoospermia (adjusted OR = 1.98, 95% CI: 1.10-3.55, P = 0.02) were observed in Q3 than those in Q1. Our results indicate that a higher risk of abnormal semen parameter values was associated with an increase in time for semen collection, which may be related to male fertility through its association with semen quality.

The atomization process of liquid droplet is an important stage in the fluid movement process in the jet exhausting atomization nozzle. This stage is directly influenced on the diameter and desperation of water droplet. Today two-dimensional model is often used in the most common simulation framework. But the atomization process in nozzle is usually happened in the three-dimensional model, so the results are not quite agreed with the practice. In this paper, a new three-dimensional model was proposed to study the mechanism of the atomization process. After applying the VOF method and turbulent model in CFD software Fluent, a numerical simulation was performed to analyze the mechanism of atomization process and some related factors affecting the atomization. Results indicated that the shapes of atomization were accorded with experimental investigations. According to the results, the necessity of further characteristic parameters on the atomization process was analyzed.

Rotator cuff injury (RCI) is a common musculoskeletal disorder and is frequently accompanied by sleep disturbances. However, objective assessments of sleep architecture alterations in this context remain limited. Here, we established a mouse RCI model to systematically characterize RCI-induced changes in sleep architecture and to provide neurophysiological clues to the mechanisms underlying sleep disruption. Using a mouse RCI model, we analyzed sleep architecture based on 24-hour EEG/EMG recordings. Mice were assigned to RCI and sham groups, and we quantified vigilance-state durations, the sleep fragmentation index (SFI), episode numbers, and EEG power spectral density (PSD) features over 24 hours as well as across the light (12 h) and dark (12 h) phases. Data were analyzed using unpaired t-tests and two-way repeated-measures ANOVA. RCI mice exhibited marked disruptions in sleep architecture, characterized by reduced NREM sleep duration, increased wakefulness, and enhanced sleep fragmentation during the light phase, while total REM sleep duration remained largely unchanged. These alterations showed clear light-dark phase dependence, accompanied by significant alterations in stage-specific EEG spectral power. Compared with sham controls, the number of NREM episodes increased in RCI mice, whereas REM episode counts did not differ significantly. The SFI was significantly elevated in the RCI group, indicating impaired sleep continuity and reduced sleep quality. This study demonstrates that rotator cuff injury disrupts sleep architecture, with a pattern indicative of impaired sleep maintenance and increased sleep fragmentation. These findings provide new neurophysiological evidence linking rotator cuff injury to sleep disturbances and may inform future strategies to alleviate nocturnal sleep problems in patients with rotator cuff pathology.

The real-time detection of personnel is a significant component of the construction of intelligent mines, especially for the personnel safety pre-warning in underground excavation workface. The underground excavation environment faces many critical challenges, including noise interference, uneven illumination, and the occlusion of mechanical equipment. Due to traditional detection algorithms' low detection accuracy and significant resource consumption, this study proposes an accurate and lightweight RAD-YOLOv8s (You Only Look Once v8) personnel detection algorithm for personnel safety pre-warning in underground excavation workface. First, a parameterized backbone network based on HGNetv2 (Hierarchical Graph Network) is employed to reduce the algorithm’s complexity while collecting richer feature information. Second, according to the different targets, the C2f-AKConv (Alterable Kernel Convolution) module is introduced to the algorithm’s neck network to flexibly adjust the size and shape of the convolution kernel, enhancing the algorithm's capacity to adapt to target deformation. Finally, a novel DCNV4-Dyhead (Deformable Convolutional Network v4 -Dynamic Head)module is developed to compensate for the limitations of classic standard convolution in long-range modeling and adaptive spatial aggregation to improve model detection performance. The proposed RAD-YOLOv8s detection algorithm was verified on a dataset of underground excavation workface people. The results showed that the mAP@0.5 and GFlops achieved 91% and 21.1%, which were 2.1% and 25.7% higher than YOLOv8s, respectively. Thus, the algorithm demonstrates improved accuracy and efficiency for real-time personnel safety monitoring in smart mines.

Aerosol optical depth (AOD) has been widely used to estimate near-surface particulate matter (PM). In this study, ground-measured data from the Campaign on Atmospheric Aerosol Research network of China (CARE-China) and the Aerosol Robotic Network (AERONET) were used to evaluate the accuracy of Visible Infrared Imaging Radiometer Suite (VIIRS) AOD data for different aerosol types. These four aerosol types were from dust, smoke, urban, and uncertain and a fifth “type” was included for unclassified (i.e., total) aerosols. The correlation for dust aerosol was the worst (R2 = 0.15), whereas the correlations for smoke and urban types were better (R2 values of 0.69 and 0.55, respectively). The mixed-effects model was used to estimate the PM2.5 concentrations in Beijing–Tianjin–Hebei (BTH), Sichuan–Chongqing (SC), the Pearl River Delta (PRD), the Yangtze River Delta (YRD), and the Middle Yangtze River (MYR) using the classified aerosol type and unclassified aerosol type methods. The results suggest that the cross validation (CV) of different aerosol types has higher correlation coefficients than that of the unclassified aerosol type. For example, the R2 values for dust, smoke, urban, uncertain, and unclassified aerosol types BTH were 0.76, 0.85, 0.82, 0.82, and 0.78, respectively. Compared with the daily PM2.5 concentrations, the air quality levels estimated using the classified aerosol type method were consistent with ground-measured PM2.5, and the relative error was low (most RE was within ±20%). The classified aerosol type method improved the accuracy of the PM2.5 estimation compared to the unclassified method, although there was an overestimation or underestimation in some regions. The seasonal distribution of PM2.5 was analyzed and the PM2.5 concentrations were high during winter, low during summer, and moderate during spring and autumn. Spatially, the higher PM2.5 concentrations were predominantly distributed in areas of human activity and industrial areas.

In this study, amino thermoset plastic (ATP) particles with medium hardness and angular shape were selected as abrasive to remove the aircraft coating from the glass fiber reinforced polymer (GFRP) surface. According to the characteristics of the coating removal, it is found that ATP abrasive can be used in abrasive air jet (AAJ) to remove the coating from the GFRP surface. To illustrate the mechanism of coating removal by ATP-AAJ, a novel interaction model between ATP particle and coating material based on the law of momentum conservation was established; then an erosion model was further proposed to quantitatively estimate coating removal. It indicates that the above two models can be adequate to quantitatively evaluate the coating removal process. The results of this study showed that ATP abrasive can not only be used in AAJ (about 15 cycles of use) to remove the coating by delamination but also can avoid damage to the GFRP substrate surface. These discoveries are specially used for aircraft coating removal from the large aerospace monolithic components made of GFPR and other polymer composites, which can effectively avoid the degradation behavior of the composite substrate and, more importantly, benefit to economic improvement and environmental protection.

In this study, a two-dimensional model according to the microcutting mechanism of abrasive particle was developed to demonstrate the mechanics of surface formation of the abrasive air jet polishing (AAJP) of aluminum alloy using amino thermosetting plastic (ATP) abrasive. It is shown that due to the characteristics of medium hardness and angular shape of ATP particle, the impacted surface can be generated by particle sliding, ploughing, microcutting, and indentation with the impinging angle increasing from 0º to 90º. Moreover, the effects of particle impacting on the surface microstructural characteristics, especially residual stress, have been analyzed. It has been found that, compared with particle ploughing and sliding processes, particle microcutting and indentation processes have an obvious effect on the surface residual stress; furthermore, particle microcutting process that can cause the impacted surface with high material deformation and ductility is more beneficial for improving the compressive residual stress than particle indentation process. The results of the study are expected to be applied to improve the fatigue performance of integral and large aircraft structures made of aluminum alloy or other metal materials.

Aim： Huanglian-Jie-Du decoction （HUDD） is an important multiherb remedy in TCM, which is recently demonstrated to be effective to treat ischemic stroke. Here, we aimed to investigate the pharmacological mechanisms of HUDD in the treatment of ischemic stroke using systems biology approaches. Methods： Putative targets of HUDD were predicted using MetaDrug. An interaction network of putative HLIDD targets and known therapeutic targets for the treatment of ischemic stroke was then constructed, and candidate HUDD targets were identified by calculating topological features, including ＇Degree＇, ＇Node-betweenness＇, ＇Closeness＇, and ＇K-coreness＇. The binding efficiencies of the candidate HLJDD targets with the corresponding compositive compounds were further validated by a molecular docking simulation. Results： A total of 809 putative targets were obtained for 168 compositive compounds in HUDD. Additionally, 39 putative targets were common to all four herbs of HUDD. Next, 49 major nodes were identified as candidate HUDD targets due to their network topological importance. The enrichment analysis based on the Gene Ontology （GO） annotation system and the Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway demonstrated that candidate HLJDD targets were more frequently involved in G-protein-coupled receptor signaling pathways, neuroactive ligand-receptor interactions and gap junctions, which all played important roles in the progression of ischemic stroke. Finally, the molecular docking simulation showed that 170 pairs of chemical components and candidate HUDD targets had strong binding efficiencies. Conclusion： This study has developed for the first time a comprehensive systems approach integrating drug target prediction, network analysis and molecular docking simulation to reveal the relationships between the herbs contained in HUDD and their putative targets and ischemic stroke-related pathways.