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The evaporation of suspension droplets is the underlying mechanism in many surface-coating and surface-patterning applications. However, the uniformity of the final deposit suffers from the coffee-stain effect caused by contact line pinning. Here, we show that control over particle deposition can be achieved through droplet evaporation on oil-wetted hydrophilic surfaces. We demonstrate by flow visualization, theory, and numerics that the final deposit of the particles is governed by the coupling of the flow field in the evaporating droplet, the movement of its contact line, and the wetting state of the thin film surrounding the droplet. We show that the dynamics of the contact line can be tuned through the addition of a surfactant, thereby controlling the surface energies, which then leads to control over the final particle deposit. We also obtain an analytical expression for the radial velocity profile which reflects the hindering of the evaporation at the rim of the droplet by the nonvolatile oil meniscus, preventing flow toward the contact line, thus suppressing the coffee-stain effect. Finally, we confirm our physical interpretation by numerical simulations that are in qualitative agreement with the experiment.

In railway surface defect detection applications, supervised deep learning methods suffer from the problems of insufficient defect samples and an imbalance between positive and negative samples. To overcome these problems, we propose a lightweight two-stage architecture including the railway cropping network (RC-Net) and defects removal variational autoencoder (DR-VAE), which requires only normal samples for training to achieve defect detection. First, we design a simple and effective RC-Net to extract railway surfaces accurately from railway inspection images. Second, the DR-VAE is proposed for background reconstruction of railway surface images to detect defects by self-supervised learning. Specifically, during the training process, DR-VAE contains a defect random mask module (D-RM) to generate self-supervised signals and uses a structural similarity index measure (SSIM) as pixel loss. In addition, the decoder of DR-VAE also acts as a discriminator to implement introspective adversarial training. In the inference stage, we reduce the random error of reconstruction by introducing a distribution capacity attenuation factor, and finally use the residuals of the original and reconstructed images to achieve segmentation of the defects. The experiments, including core parameter exploration and comparison with other models, indicate that the model can achieve a high detection accuracy.

With the rapid development of technology today, artificial intelligence is inevitably involved in numerous fields including the film production industry, and helps the industry from all periods of the production cycle. This paper focuses on three areas: script writing, special effects production and video restoration, and highlights the application of technologies such as Benjamin, flux system and ESRGAN in these three areas. By analyzing the positive impacts and potential risks, this paper points out that AI technology and human artistic creation are indispensable for the healthy development of the film industry only when they complement to each other, hoping to offer some references for the future research of the the application of artificial intelligence in the film industry.

This study presents an Internet of Things (IoT)-enabled Deep Learning Monitoring (IoT-E-DLM) model for real-time Athletic Performance (AP) tracking and feedback in collegiate sports. The proposed work integrates advanced wearable sensor technologies with a hybrid neural network combining Temporal Convolutional Networks, Bidirectional Long Short-Term Memory (TCN + BiLSTM) + Attention mechanisms. It is designed to overcome key challenges in processing heterogeneous, high-frequency sensor data and delivering low-latency, sport-specific feedback. The system deployed edge computing for real-time local processing and cloud setup for high-complexity analytics, achieving a balance between responsiveness and accuracy. Extensive research was tested with 147 student-athletes across numerous sports, including track and field, basketball, soccer, and swimming, over 12 months at Shangqiu University. The proposed model achieved a prediction accuracy of 93.45% with an average processing latency of 12.34 ms , outperforming conventional and state-of-the-art approaches. The system also demonstrated efficient resource usage (CPU: 68.34%, GPU: 72.56%), high data capture reliability (98.37%), and precise temporal synchronization. These results confirm the model’s effectiveness in enabling real-time performance monitoring and feedback delivery, establishing a robust groundwork for future developments in Artificial Intelligence (AI)-driven sports analytics.

Cryptochromes are blue light receptors that regulate plant growth and development. They also act as the core components of the central clock oscillator in animals. Although plant cryptochromes have been reported to regulate the circadian clock in blue light, how they do so is unclear. Here we show that Arabidopsis cryptochrome 2 (CRY2) forms photobodies with the TCP22 transcription factor in response to blue light in plant cells. We provide evidence that PPK kinases influence the characteristics of these photobodies and that together these components, along with LWD transcriptional regulators, can positively regulate the expression of CCA1 encoding a central component of the circadian oscillator. Cryptochrome signaling has been reported to regulate circadian oscillations in plants. Here the authors show that CRY2 and the TCP22 transcription factors can form photobodies in a blue light dependent manner and induce expression of CCA1 , a core component of the circadian oscillator.

The evaporation of multicomponent droplets is relevant to various applications but challenging to study due to the complex physicochemical dynamics. Recently, Li etal. (Phys. Rev. Lett., vol. 120, 2018, 224501) reported evaporation-triggered segregation in 1,2-hexanediol–water binary droplets. In this present work, we added 0.5 wt % silicone oil to the 1,2-hexanediol–water binary solution. This minute silicone oil concentration dramatically modifies the evaporation process, as it triggers an early extraction of the 1,2-hexanediol from the mixture. Surprisingly, we observe that the segregation of 1,2-hexanediol forms plumes, rising up from the rim of the sessile droplet towards the apex during droplet evaporation. By orientating the droplet upside down, i.e. by studying a pendent droplet, the absence of the plumes indicates that the flow structure is induced by buoyancy, which drives a Rayleigh–Taylor instability (i.e. driven by density differences and gravitational acceleration). From micro particle image velocimetry measurement, we further prove that the segregation of the non-volatile component (1,2-hexanediol) hinders the evaporation near the contact line, which leads to a suppression of the Marangoni flow in this region. Hence, on long time scales, gravitational effects, rather than Marangoni flows, play the dominant role in the flow structure. We compare the measurement of the evaporation rate with the diffusion model of Popov (Phys. Rev., vol. 71, 2005, 036313), coupled with Raoult's law and the activity coefficient. This comparison indeed confirms that the silicone-oil-triggered segregation of the non-volatile 1,2-hexanediol significantly delays the evaporation. With an extended diffusion model, in which the influence of the segregation has been implemented, the evaporation can be well described.

Plant cryptochromes undergo blue light-dependent phosphorylation to regulate their activity and abundance, but the protein kinases that phosphorylate plant cryptochromes have remained unclear. Here we show that photoexcited Arabidopsis cryptochrome 2 (CRY2) is phosphorylated in vivo on as many as 24 different residues, including 7 major phosphoserines. We demonstrate that four closely related Photoregulatory Protein Kinases (previously referred to as MUT9-like kinases) interact with and phosphorylate photoexcited CRY2. Analyses of the ppk123 and ppk124 triple mutants and amiR 4k artificial microRNA-expressing lines demonstrate that PPKs catalyse blue light-dependent CRY2 phosphorylation to both activate and destabilize the photoreceptor. Phenotypic analyses of these mutant lines indicate that PPKs may have additional substrates, including those involved in the phytochrome signal transduction pathway. These results reveal a mechanism underlying the co-action of cryptochromes and phytochromes to coordinate plant growth and development in response to different wavelengths of solar radiation in nature. Plant cryptochromes are regulated by blue-light dependent phosphorylation. Here the authors map the in vivo phosphorylation sites of Arabidopsis cryptochrome 2 and identify four closely related kinases that act to both activate and destabilize the receptor in response to blue light.

Tirofiban is a glycoprotein IIb/IIIa receptor blocker mainly used alongside other blood thinners in patients with acute coronary syndrome (ACS) undergoing procedures like angioplasty (PCI). Emerging clinical evidence suggests it may also be beneficial during endovascular treatment for ischemic stroke (IS). Because Tirofiban prevents blood clotting, it is important to assess how often it causes serious bleeding or other side effects when used in real-life settings. We used disproportionality analysis to review all FDA-reported adverse events linked to Tirofiban (from 2004 onward), focusing on cases where the drug was the primary suspect. The study results confirmed known AEs such as bleeding and thrombocytopenia. We observed both commonly reported bleeding events (e.g., brain and gut bleeding) and less commonly recognized types, such as hemothorax and hemorrhagic shock. Furthermore, other relatively rare but statistically significant AEs were identified, including acute respiratory failure, hemolysis, prolonged activated partial thromboplastin time (APTT), coagulopathy, and disseminated intravascular coagulation (DIC). The temporal analysis showed the median onset time of AEs was 1 day, which urges immediate monitoring after drug administration, such as symptom surveillance and complete blood count evaluation.

Background Open pilon fractures combined with sizeable segmental bone defects are rare, difficult to treat, and often result in the loss of ankle joint function. The purpose of this study was to determine clinical outcomes in patients with open pilon fractures and sizeable segmental bone defects treated by limited ORIF combined with an Ilizarov external fixator. Methods We conducted a retrospective analysis of open pilon fractures with sizeable segmental bone defects treated by limited ORIF combined with the Ilizarov external fixator strategy between July 2014 and August 2019. All patients were included for assessments of fracture healing and infection rates. Ankle functional outcomes were assessed in all patients according to the Paley criteria and American Orthopedic Foot and Ankle Society Score (AOFAS) at least 24 months post-injury. Results All patients were followed up for a mean of 41.09 months. The mean bone defect size was 5.64 ± 1.21 cm. The average EFI and BTI were 1.56 ± 0.28 months/cm and 11.12 ± 0.74 days/cm, respectively. According to the Paley evaluation system, the success rate of ankle joint reconstruction was 64% (7/11). The mean score based on the AOFAS functional assessment was 77.73 ± 8.87. Five patients showed posttraumatic arthritis, one of whom required ankle arthrodesis. Three patients developed pin site infections, and one patient developed a deep infection after bone grafting. Conclusion The strategy of limited ORIF combined with an Ilizarov external fixator can restore ankle function in most patients with complex open tibial pilon fractures. Ankle stiffness, pin tract infection, and traumatic arthritis were the most common complications associated with this therapy.

The existing research on security risk often focuses on specific types of crime, overlooking an integrated assessment of security risk by leveraging existing police resources. Thus, we draw on crime geography theories, integrating public security business data, socioeconomic data, and spatial analysis techniques, to identify integrated risk points and areas by examining the distribution of police resources and related factors and their influence on security risk. The findings indicate that security risk areas encompass high-incidence areas of public security issues, locations with concentrations of dangerous individuals and key facilities, and regions with a limited police presence, characterized by dense populations, diverse urban functions, high crime probabilities, and inadequate supervision. While both police resources and security risk are concentrated in urban areas, the latter exhibits a more scattered distribution on the urban periphery, suggesting opportunities to optimize resource allocation by extending police coverage to risk hotspots lacking patrol stations. Notably, Level 1 security risk areas often coincide with areas lacking a police presence, underscoring the need for strategic resource allocation. By comprehensively assessing the impact of police resources and public security data on spatial risk distribution, this study provides valuable insights for public security management and police operations.