Research on indoor dynamic temperature based on circulating water heating

During the heating season, cities in northeast China primarily emphasize the implementation of large-scale CHP central heating systems. However, the heat-electricity constraints associated with these heating units hinder the integration of clean energy sources such as wind power, photovoltaic, and other renewable energies. Despite efforts to reduce carbon emissions and address concerns related to urban haze formation, energy conservation, and emission reduction, coal remains the predominant energy source for heating.In light of these challenges, this paper aims to investigate the heat transfer characteristics of buildings during the heating season. It also examines the feasibility of implementing heating regulations and provides a theoretical foundation for establishing a regional heat model that incorporates low-carbon energy islands. The proposed model combines wind power, photovoltaic power generation, and other renewable energy sources supplemented with coal power.By employing the concentrated heat capacity method, this study develops a mathematical model to simulate the dynamic thermal process involved in heating buildings. The model encompasses various components, including the dynamic heat transfer model of the building’s envelope structure, which considers its heat storage characteristics, as well as the radiator model and the indoor air heat balance equation. Furthermore, the model comprehensively accounts for the influence of different indoor and outdoor disturbances on the heat transfer process, including the effects of solar radiation.To validate the model, a simulation program is implemented using Matlab. This program is capable of hourly calculations to determine the indoor temperature. By comparing the simulated temperature values with the measured ones, the rationality and accuracy of the model are verified

A classification model for power corridors based on the improved PointNet++ network

AbstractAiming at the existing deep learning classification model for power corridor point cloud still need to improve the classification efficiency and the robustness of the classification model to meet the requirements of practical applications. An improved classification model based on PointNet++ is proposed. Based on the fact that the main features of the power corridor scene are power lines, poles, and vegetation, the initial data are first optimally filtered, and then the ensemble abstraction module of the classical PointNet++ is modified to better adapt to the power corridor scene. Finally, h-Swish is used as the activation function to realize the accurate classification of the features of the power corridor scene, and the training time of deep learning is also greatly reduced. The experimental results show that the improved algorithm achieves an average F1 value of 97.58%, which is 3.62 percentage points higher than the classical PointNet++. Therefore, the algorithm has great potential in point cloud classification

A 3D Scene Information Enhancement Method Applied in Augmented Reality

Aiming at the problem that the detection of small planes with unobvious texture is easy to be missed in augmented reality scene, a 3D scene information enhancement method to grab the planes for augmented reality scene is proposed based on a series of images of a real scene taken by a monocular camera. Firstly, we extract the feature points from the images. Secondly, we match the feature points from different images, and build the three-dimensional sparse point cloud data of the scene based on the feature points and the camera internal parameters. Thirdly, we estimate the position and size of the planes based on the sparse point cloud. The planes can be used to provide extra structural information for augmented reality. In this paper, an optimized feature points extraction and matching algorithm based on Scale Invariant Feature Transform (SIFT) is proposed, and a fast spatial planes recognition method based on a RANdom SAmple Consensus (RANSAC) is established. Experiments show that the method can achieve higher accuracy compared to the Oriented Fast and Rotated Brief (ORB), Binary Robust Invariant Scalable Keypoints (BRISK) and Super Point. The proposed method can effectively solve the problem of missing detection of faces in ARCore, and improve the integration effect between virtual objects and real scenes

Expanding flavone and flavonol production capabilities in Escherichia coli

Flavones and flavonols are important classes of flavonoids with nutraceutical and pharmacological value, and their production by fermentation with recombinant microorganisms promises to be a scalable and economically favorable alternative to extraction from plant sources. Flavones and flavonols have been produced recombinantly in a number of microorganisms, with Saccharomyces cerevisiae typically being a preferred production host for these compounds due to higher yields and titers of precursor compounds, as well as generally improved ability to functionally express cytochrome P450 enzymes without requiring modification to improve their solubility. Recently, a rapid prototyping platform has been developed for high-value compounds in E. coli, and a number of gatekeeper (2S)-flavanones, from which flavones and flavonols can be derived, have been produced to high titers in E. coli using this platform. In this study, we extended these metabolic pathways using the previously reported platform to produce apigenin, chrysin, luteolin and kaempferol from the gatekeeper flavonoids naringenin, pinocembrin and eriodictyol by the expression of either type-I flavone synthases (FNS-I) or type-II flavone synthases (FNS-II) for flavone biosynthesis, and by the expression of flavanone 3-dioxygenases (F3H) and flavonol synthases (FLS) for the production of the flavonol kaempferol. In our best-performing strains, titers of apigenin and kaempferol reached 128 mg L−1 and 151 mg L−1 in 96-DeepWell plates in cultures supplemented with an additional 3 mM tyrosine, though titers for chrysin (6.8 mg L−1) from phenylalanine, and luteolin (5.0 mg L−1) from caffeic acid were considerably lower. In strains with upregulated tyrosine production, apigenin and kaempferol titers reached 80.2 mg L−1 and 42.4 mg L−1 respectively, without the further supplementation of tyrosine beyond the amount present in the rich medium. Notably, the highest apigenin, chrysin and luteolin titers were achieved with FNS-II enzymes, suggesting that cytochrome P450s can show competitive performance compared with non-cytochrome P450 enzymes in prokaryotes for the production of flavones

D14–SCFD3-dependent degradation of D53 regulates strigolactone signalling

Strigolactones (SLs), a newly discovered class of carotenoid-derived phytohormones, are essential for developmental processes that shape plant architecture and interactions with parasitic weeds and symbiotic arbuscular mycorrhizal fungi. Despite the rapid progress in elucidating the SL biosynthetic pathway, the perception and signalling mechanisms of SL remain poorly understood. Here we show that DWARF 53 (D53) acts as a repressor of SL signalling and that SLs induce its degradation. We find that the rice (Oryza sativa) d53 mutant, which produces an exaggerated number of tillers compared to wild-type plants, is caused by a gain-of-function mutation and is insensitive to exogenous SL treatment. The D53 gene product shares predicted features with the class I Clp ATPase proteins and can form a complex with the α/β hydrolase protein DWARF 14 (D14) and the F-box protein DWARF 3 (D3), two previously identified signalling components potentially responsible for SL perception. We demonstrate that, in a D14- and D3-dependent manner, SLs induce D53 degradation by the proteasome and abrogate its activity in promoting axillary bud outgrowth. Our combined genetic and biochemical data reveal that D53 acts as a repressor of the SL signalling pathway, whose hormone-induced degradation represents a key molecular link between SL perception and responses