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Anthropogenic heat is the heat generated by human activities such as industry, construction, transport, and metabolism. Accurate estimates of anthropogenic heat are essential for studying the impacts of human activities on the climate and atmospheric environment. Commonly applied methods for estimating anthropogenic heat include the inventory method, the energy balance equation method, and the building model simulation method. In recent years, the rapid development of computer technology and the availability of massive data have made machine learning a powerful tool for estimating anthropogenic heat fluxes and assessing its effects. Multi-source remote sensing data have also been widely used to obtain more details of the spatial and temporal distribution characteristics of anthropogenic heat. This paper reviews the main approaches for estimating anthropogenic heat emissions. The typical algorithms of the abovementioned three methods are introduced, and their advantages and limitations are also evaluated. Moreover, the recent progress in the application of remote sensing data and machine learning are discussed as well. Based on big data and machine learning techniques, the research on feature engineering and model fusion will bring about major changes in data analysis and modeling of anthropogenic heat. More in-depth research of this issue is recommended to provide important support for curbing global warming, mitigating air pollution, and achieving the national goals of carbon peak and a carbon neutrality strategy.

Investigating the flexural fatigue performance of rubberized concrete is essential for advancing its application in dynamic load environments, which aligns with the green and low-carbon objectives of modern building materials. In this study, comprehensive mechanical and fatigue tests—including assessments of static strength, fatigue life, and flexural fatigue strength—were conducted to clarify the mechanisms by which crumb rubber influences the fatigue resistance of concrete. Increasing the rubber content markedly improved fatigue life, although a reduction in compressive strength was observed. Specifically, compared to the reference concrete, concrete incorporating 10% pretreated crumb rubber by volume of fine aggregate exhibited a 21.2% increase in fatigue life. Furthermore, pretreatment of crumb rubber not only further enhanced the fatigue resistance of concrete but also partially mitigated the decline in strength. Additionally, the compressive strength of concrete with 7.5% pretreated crumb rubber was 15.0% higher than that of concrete with the same amount of untreated crumb rubber. Pretreated crumb rubber further augmented the energy dissipation capability of concrete, thereby enhancing its fatigue performance. Consequently, rubberized concrete emerges as a promising alternative material for fatigue-critical applications, and the pretreatment of crumb rubber is recommended as an effective auxiliary measure to optimize performance.

The microstructures and tensile properties were investigated for high-pressure die-cast Mg1.6La1.0Ce alloy with different Nd contents from 0 to 3 wt.%. The results showed that fine dendritic and segmented dendritic microstructures were observed in the as-cast alloys, but the eutectic morphology changed from lamellar for low Nd-containing alloys to continuous fibrous with dendritic α-Mg in eutectics for high Nd-containing alloys. The compositions of the eutectic phase were also transferred from Mg 12 (La, Ce) in the alloys without Nd to Mg 12 (La, Ce, Nd) in the alloys with Nd addition. When the concentration of Nd was greater than 2 wt.%, numerous nanoscale Mg-Nd intermetallic phases precipitated at the grain boundaries and eutectic area. The addition of Nd was also found to increase tensile strength significantly at ambient temperature and elevated temperatures. For the alloy without Nd, the yield strength and ultimate tensile strength (UTS) was 135.6 MPa and 189.9 MPa at ambient temperature and 56.3 MPa and 57.4 MPa at 300 °C, respectively. When adding 3 wt.% Nd, the yield strength increased to 161.9 MPa at ambient temperature and 75.4 MPa at 300 °C. The UTS increased to 181.9 MPa at ambient temperature and 90.4 MPa at 300 °C. The significant improvement in strength was attributed to the strengthening from the refined structure and the dispersion of second-phases of intermetallic compounds.

Invasive fungal infections (IFIs) have emerged as a significant health threat and cause approximately 3.75 million deaths per year globally. Understanding the detailed mechanism of the immune response to eliminate invasive fungal pathogens may help to provide new insights for the development of antifungal methods and drugs. Previously, we reported that the tyrosine kinase receptor EPH receptor B2 (EPHB2) is a coreceptor of β-glucan and phosphorylates Syk to activate the antifungal downstream signaling pathway. However, how EPHB2 is activated after fungal infection is still unknown. In this study, we show that EPHA5 plays a critical role in antifungal immunity by phosphorylating EPHB2 and Dectin-1 after fungal infection, which facilitates the recruitment and activation of Syk and subsequent activation of downstream antifungal signaling pathways. Additionally, we showed that EphA5 -deficient mice exhibited increased susceptibility to Candida albicans infection, with increased fungal burdens and impaired immune cell recruitment. This study provides not only a mechanistic explanation for EPHB2 and Dectin-1 activation after fungal infection but also new insights into potential therapeutic strategies for treating IFIs.

Pile foundations are commonly used to support structures subjected to complex loading conditions. In seismic-prone regions, understanding the soil–pile interaction under cyclic loading is essential for ensuring the stability and safety of these foundations. Numerical modeling is an effective tool for predicting the nonlinear behavior of soil under seismic excitation, but selecting an appropriate constitutive model remains a significant challenge. This study investigates the seismic behavior of pile groups embedded in soft clay using advanced finite element analysis. The piles are modeled as aluminum with a linear elastic response and are analyzed within a soil domain characterized by two kinematic hardening constitutive models based on the Von Mises failure criterion. Model parameters are calibrated using a combination of experimental and numerical data. The study also examines the influence of pile spacing within the group on seismic response, revealing notable differences in the response patterns. The results show that the nonlinear kinematic hardening model provides a more accurate correlation with experimental centrifuge test results compared to the multilinear model. These findings contribute to enhancing the understanding of soil–pile interaction under seismic loading and improving the design of pile foundations.

Transendothelial migration of malignant cells plays an essential role in tumor progression and metastasis. The present study revealed that treating human umbilical vein endothelial cells (HUVECs) with exosomes derived from metastatic breast cancer cells increased the number of cancer cells migrating through the endothelial cell layer and impaired the tube formation of HUVECs. Furthermore, the expression of intercellular junction proteins, including vascular endothelial cadherin (VE-cadherin) and zona occluden-1 (ZO-1), was reduced significantly in HUVECs treated with carcinoma-derived exosomes. Proteomic analyses revealed that thrombospondin-1 (TSP1) was highly expressed in breast cancer cell MDA-MB-231-derived exosomes. Treating HUVECs with TSP1-enriched exosomes similarly promoted the transendothelial migration of malignant cells and decreased the expression of intercellular junction proteins. TSP1-down regulation abolished the effects of exosomes on HUVECs. The migration of breast cancer cells was markedly increased in a zebrafish in vivo model injected with TSP1-overexpressing breast cancer cells. Taken together, these results suggest that carcinoma-derived exosomal TSP1 facilitated the transendothelial migration of breast cancer cells via disrupting the intercellular integrity of endothelial cells.

Machine learning and image processing have been combined to identify and detect defects in mature citrus fruit at night, which has great research and development significance. First, a multi-light vision system was used to collect citrus UV images, and from these, 1500 samples were obtained, 80% of which were training and 20% were experimental sets. For a support vector machine (SVM) model with “2*Cb-Cr”, “4*a-b-l”, and “H” as the training features, the accuracy of the final training model in the experimental set is 99.67%. Then, the SVM model was used to identify mature citrus regions, detect defects, and output the defective citrus regions label. The average running time of the detection algorithm was 0.84097 s, the accuracy of citrus region detection was 95.32%, the accuracy of citrus defect detection was 96.32%, the precision was 95.24%, and the recall rate was 87.91%. The results show that the algorithm had suitable accuracy and real-time performance in recognition and defect detection in citrus in a natural environment at night.

An intelligent production line design scheme for 3C products is introduced amid the current situation of low automation in assembly, testing, packaging and other production contacts in the 3C manufacturing industry, high demand for upgrades and fierce market competition, taking a 3C electronic product as an example to investigate its overall layout, workstation structure, architecture system and control system. In addition, according to the concept of lean production, a method is proposed to realize dynamic balance of production line. First of all, the dynamic balance mathematical model is established, then the genetic algorithm is used to solve the model parameters, and finally the production line can keep the balance by adjusting the running speed of field equipment according to the calculation results. The actual verification shows that the production line runs stably, completes the automatic and intelligent production of products, with a balance rate at an excellent level. This can serve as a reference for relevant enterprises to build intelligent production lines for 3C products and implement lean production.

Tumour cell metastasis can be genetically regulated by proteins contained in cancer cell-derived extracellular vesicles (EVs) released to the tumour microenvironment. Here, we found that the number of infiltrated macrophages was positively correlated with the expression of signal-induced proliferation-associated 1 (SIPA1) in invasive breast ductal carcinoma tissues and MDA-MB-231 xenograft tumours. EVs derived from MDA-MB-231 cells (231-EVs) significantly enhanced macrophage migration, compared with that from SIPA1-knockdown MDA-MB-231 cells (231/si-EVs) both in vitro and in vivo. We revealed that SIPA1 promoted the transcription of MYH9, which encodes myosin-9, and up-regulated the expression level of myosin-9 in breast cancer cells and their EVs. We also found that blocking myosin-9 by either down-regulating SIPA1 expression or blebbistatin treatment led to the suppression of macrophage infiltration. Survival analysis showed that breast cancer patients with high expression of SIPA1 and MYH9 molecules had worse relapse-free survival (p = 0.028). In summary, SIPA1high breast cancer can enhance macrophage infiltration through EVs enriched with myosin-9, which might aggravate the malignancy of breast cancer.

Increased dependence on aerobic glycolysis is characteristic of most cancer cells, whereas the mechanism underlying the promotion of aerobic glycolysis in metastatic breast cancer cells under ambient oxygen has not been well understood. Here, we demonstrated that aberrant expression of signal-induced proliferation-associated 1 (SIPA1) enhanced aerobic glycolysis and altered the main source of ATP production from oxidative phosphorylation to glycolysis in breast cancer cells. We revealed that SIPA1 promoted the transcription of EPAS1 , which is known as the gene encoding hypoxia-inducible factor-2α (HIF-2α) and up-regulated the expression of multiple glycolysis-related genes to increase aerobic glycolysis. We also found that blocking aerobic glycolysis by either knocking down SIPA1 expression or oxamate treatment led to the suppression of tumor metastasis of breast cancer cells both in vitro and in vivo . Taken together, aberrant expression of SIPA1 resulted in the alteration of glucose metabolism from oxidative phosphorylation to aerobic glycolysis even at ambient oxygen levels, which might aggravate the malignancy of breast cancer cells. The present findings indicate a potential target for the development of therapeutics against breast cancers with dysregulated SIPA1 expression.