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Solar thermal storage ceramic materials use photothermal power generation technology to store heat energy, which is an important way to use clean energy and reduce carbon emissions. In this paper, MgAl2O4 ceramics were prepared by pressureless sintering with fused magnesia and α‐Al2O3 as the primary raw materials and TiO2 as the additive. The phase composition, microstructure, physical properties, thermal shock resistance, and thermal physical properties of MgAl2O4 thermal storage ceramics were studied. The results show that the main crystalline phase of B4 is MgAl2O4 and the secondary crystalline phase is MgTi2O5. Ti4+ is dissolved into MgAl2O4 lattice, which distorts the lattice, reduces activation energy, and promotes sintering. The sample B4 (69.73 wt% α‐Al2O3, 30.27 wt% fused magnesia, and 7 wt% TiO2) sintered at 1510°C had the best comprehensive physical properties, and its bending strength and bulk density were 89.41 MPa and 3.27 g/cm3, respectively. After 30 thermal shock cycles, the bending strength of the sample B4 is slightly lost. The thermal stress generated during the expansion and contraction of the grain volume led to the decrease of the bending strength. From room temperature to 1000°C, the specific heat capacity of sample B4 is.44–1.25 J/(g·K). The heat capacity increases with the increase of temperature, and the heat storage density increases. The heat storage density is 1218.75 kJ/kg (1000°C). Heat capacity increases with temperature due to intensified lattice vibration at high temperatures. Therefore, magnesia alumina spinel ceramics show great potential in solar thermal energy storage. [ABSTRACT FROM AUTHOR]

Heterotopic ossification (HO), defined as the formation of extraskeletal bone in muscle and soft tissues, is a diverse pathological process caused by either genetic mutations or inciting trauma. Fibrodysplasia ossificans progressiva (FOP) is a genetic form of HO caused by mutations in the bone morphogenetic protein (BMP) type I receptor gene activin A receptor type 1 (ACVR1). These mutations make ACVR1 hypersensitive to BMP and responsive to activin A. Hedgehog (Hh) signaling also contributes to HO development. However, the exact pathophysiology of how skeletogenic cells contribute to endochondral ossification in FOP remains unknown. Here, we showed that the wild-type or FOP-mutant ACVR1 localized in the cilia of stem cells from human exfoliated deciduous teeth with key FOP signaling components, including activin A receptor type 2A/2B, SMAD family member 1/5, and FK506-binding protein 12kD. Cilia suppression by deletion of intraflagellar transport 88 or ADP ribosylation factor like GTPase 3 effectively inhibited pathological BMP and Hh signaling, subdued aberrant chondro-osteogenic differentiation in primary mouse or human FOP cells, and diminished in vivo extraskeletal ossification in Acvr1Q207D, Sox2-Cre; Acvr1R206H/+ FOP mice and in burn tenotomy–treated wild-type mice. Our results provide a rationale for early and localized suppression of cilia in affected tissues after injury as a therapeutic strategy against either genetic or acquired HO. Editor's summary: Heterotopic ossification (HO) is ectopic bone formation common in fibrodysplasia ossificans progressiva (FOP). Cellular mechanisms of HO are not fully understood. He et al. found that inhibiting cilia formation on stem cells prevented HO in vitro and in mice. Genetically inhibiting two regulators of cilia formation, ADP ribosylation factor like GTPase 3 (ARL3) or intraflagellar transport 88, blocked HO in stem cells from exfoliated deciduous teeth from patients with FOP. Inhibiting these two genes in different mouse models of FOP resulted in decreased HO. Inhibiting Arl3 with a small interfering RNA treatment also reduced HO in a burn tenotomy model in wild-type mice. These data suggest that suppressing stem cell cilia could be a therapeutic approach to treat HO. —Brandon Berry [ABSTRACT FROM AUTHOR]

Chinese Baijiu industry is undergoing industrial modernization. However, the key processes including Daqu processing are directly modified based on empirical knowledge, and the lack of engineering mechanisms lead to the inability of rational design. Here, this study quantitatively analyzed the heat generation and flow during Daqu solid-state fermentation, tracked and recorded the parameters during Daqu processing, simplified the biochemical reactions of the fermentation process, and built a Daqu heat balance model. Through the model, the heat generated during the whole fermentation process was transferred outside of the system through three ways: Water evaporation (54. 22% ), conduction of wall and air flow (42. 00% ), and air temperature rising ( < 1% ). The remaining heat (3. 77% ) was the cumulative heat, which was positively correlated with the temperature rise of the Daqu ( R² =0.85). It was verified that the average error of the model for the temperature change of dacquoise was 5. 05% with R² = 0. 94. Based on the model, the effect of regulating moisture parameters on the temperature of Daqu was explored, which showed the importance of moisture control for regulating the temperature during Daqu preparation. Through the preliminary exploration of the feasibility of regulating and optimizing the future production of Daqu from the perspective of mathematical modeling, the results of the study can provide theoretical support for the construction of modern fermentation process. [ABSTRACT FROM AUTHOR]

It is important to differentiate milk with different thermostabilities for diverse applications in food products and for the appropriate selection of processing and maintenance of manufacturing facilities. In this review, an overview of the chemical changes in milk subjected to high-temperature heating is given. An emphasis is given to the studies of traditional and state-of-the-art strategies for assessing the milk thermostability, as well as their influencing factors. Traditional subjective and objective techniques have been used extensively in many studies for evaluating thermostability, whereas recent research has been focused on novel approaches with greater objectivity and accuracy, including innovative physical, spectroscopic, and predictive tools. [ABSTRACT FROM AUTHOR]

Rubidium atomic clocks have been used extensively in various fields, with applications such as a core component of Global Navigation Satellite Systems (GNSS). However, they exhibit inherently poor long-term stability. This paper presents the development of a control system for rubidium atomic clocks. It introduces an adaptive Kalman filtering algorithm for the disciplining of a rubidium atomic clock, utilizing autocovariance least squares (ALS) to estimate the clock's noise parameters. The experimental results demonstrate that the proposed algorithm achieves a high estimation accuracy. The standard deviation of the clock error between the steered rubidium atomic clock 1 Pulse Per Second (1PPS) and Coordinated Universal Time (UTC) provided by the National Time Service Center (NTSC) is better than 2.568 nanoseconds(ns), with peak-to-peak values improving to within 11.358 ns. Notably, its frequency stability is reduced to 3.06 × 10−13 @100,000 s. The results for the rubidium atomic clock demonstrate that the adaptive Kalman filtering algorithm proposed herein constitutes an accurate and effective control strategy for the rubidium atomic clock discipline. [ABSTRACT FROM AUTHOR]

In the task of human behavior detection, video classification based on deep learning has become a prevalent technique. The existing models are limited due to an inadequate understanding of behavior characteristics, which restricts their ability to achieve more accurate recognition results. To address this issue, this paper proposes a new model, which is an improvement upon the existing PPTSM model. Specifically, our model employs a multi-scale dilated attention mechanism, which enables the model to integrate multi-scale semantic information and capture characteristic information of abnormal human behavior more effectively. Additionally, to enhance the characteristic information of human behavior, we propose a gradient flow feature information fusion module that integrates high-level semantic features with low-level detail features, enabling the network to extract more comprehensive features. Experiments conducted on an elevator passenger dataset containing four abnormal behaviors (door picking, jumping, kicking, and door blocking) show that the top-1 Acc of our model is improved by 10% compared to the PPTSM model, reaching 95%. Moreover, experiments with four publicly available datasets(UCF24, UCF101, HMDB51, and the Something-Something-v1 dataset) demonstrate that our method achieves results superior to PPTSM by 6.8%, 6.1%, 21.2%, and 3.96%, respectively. [ABSTRACT FROM AUTHOR]