Search

As global carbon reduction initiatives progress and the new energy sector rapidly develops, photovoltaic (PV) power generation is playing an increasingly significant role in renewable energy. Accurate PV output forecasting, influenced by meteorological factors, is essential for efficient energy management. This paper presents an optimal hybrid forecasting strategy, integrating bidirectional temporal convolutional networks (BiTCN), dynamic convolution (DC), bidirectional long short-term memory networks (BiLSTM), and a novel mixed-state space model (Mixed-SSM). The mixed-SSM combines the state space model (SSM), multilayer perceptron (MLP), and multi-head self-attention mechanism (MHSA) to capture complementary temporal, nonlinear, and long-term features. Pearson and Spearman correlation analyses are used to select features strongly correlated with PV output, improving the prediction correlation coefficient (R2) by at least 0.87%. The K-Means++ algorithm further enhances input data features, achieving a maximum R2 of 86.9% and a positive R2 gain of 6.62%. Compared with BiTCN variants such as BiTCN-BiGRU, BiTCN-transformer, and BiTCN-LSTM, the proposed method delivers a mean absolute error (MAE) of 1.1%, root mean squared error (RMSE) of 1.2%, and an R2 of 89.1%. These results demonstrate the model’s effectiveness in forecasting PV power and supporting low-carbon, safe grid operation.

Microbial communities are essential components of aquatic ecosystems and are widely employed for the detection, protection, and restoration of water ecosystems. The polyurethane foam unit (PFU) method, an effective and widely used environmental monitoring technique, has been improved with the eDNA-PFU method, offering efficiency, rapidity, and standardization advantages. This research aimed to explore the colonization process of microbial communities within PFUs using eDNA-PFU technology. To achieve this, we conducted ten-day monitoring and sequencing of microbial communities within PFUs in a stable and controlled artificial aquatic ecosystem, comparing them with water environmental samples (eDNA samples). Results showed 1065 genera in eDNA-PFU and 1059 in eDNA, with eDNA-PFU detecting 99.95% of eDNA-identified species. Additionally, the diversity indices of bacteria and eukaryotes in both methods showed similar trends over time in the colonization process; however, relative abundance differed. We further analyzed the colonization dynamics of microbes in eDNA-PFU and identified four clusters with varying colonization speeds. Notably, we found differences in colonization rates between bacteria and eukaryotes. Furthermore, the Molecular Ecological Networks (MEN) showed that the network in eDNA-PFU was more modular, forming a unique microbial community differentiated from the aquatic environment. In conclusion, this study, using eDNA-PFU, comprehensively explored microbial colonization and interrelationships in a controlled mesocosm system, providing foundational data and reference standards for its application in aquatic ecosystem monitoring and beyond.

To provide sufficient lift during takeoff and landing, large aircraft are equipped with complicated high-lift devices. The use of simple flaps coupled with active flow control (AFC) can achieve lift improvement while reducing mechanical structure and weight. The present study focuses on verifying the feasibility and effectiveness of simple flaps combined with sweeping jet flow control. An experimental study on the AFC of flaps, using sweeping jets, was carried out using a NASA SC(2)-0410 supercritical airfoil wind-tunnel model at Re = 2.0 × 105 (with velocity V = 10 m/s). In the experiment, the wing angle of attack (α) ranged from 3 to 18°, and the flap deflection angle (δ) ranged from 0 to 30°; the aerodynamic characteristics and surface pressure characteristics of the wing at typical working conditions were analyzed. Using sweeping jets to control the flow on the flaps, the momentum coefficients (for three actuator groups) of the jet are 0.8%, 3.6%, and 8.2%, respectively, and the maximum lift coefficient was increased by approximately 33%. The influence of the sweeping jet flow rate on the aerodynamic performance of the airfoil is analyzed. There are two main reasons for the lift coefficient increase caused by sweeping jet flow: an extra suction peak near the flap and a suction peak increase near the leading edge area caused by induced flow.

Salidroside has been widely used in anti-tumor, cardiovascular, and cerebrovascular protection. However, there are few reports of its use for wound repair. Herein, salidroside inflammation-targeted emulsion gel and non-targeted emulsion gel were developed for wound repair. The inflammation-targeted emulsion gels showed an overall trend of better transdermal penetration and lower potential than non-targeted emulsion gels (−58.7 mV and −1.6 mV, respectively). The apparent improvement of the trauma surface was significant in each administration group. There was a significant difference in the rate of wound healing of the rats between each administration group and the model group at days 7 and 14. Pathological tissue sections showed that inflammatory cells in the epidermis, dermis, and basal layer were significantly reduced, and the granulation tissue was proliferated in the inflammation-targeted emulsion gel group and the non-targeted emulsion gel group. Regarding the expressions of EGF and bFGF, the expressions of bFGF and EGF in the tissues of the inflammation-targeted group at days 7, 14, or 21 were significantly higher than that of the non-targeted emulsion gel group and the model group, both of which were statistically significant compared with the model group (p < 0.05). These results demonstrated that salidroside has the potential as an alternative drug for wound repair.

Hot springs are some of the most special environments on Earth. Many prokaryotic and eukaryotic microbes have been found to live in this environment. The Himalayan geothermal belt (HGB) has numerous hot springs spread across the area. Comprehensive research using molecular techniques to investigate eukaryotic microorganisms is still lacking; investigating the composition and diversity of eukaryotic microorganisms such as protists in the hot spring ecosystems will not only provide critical information on the adaptations of protists to extreme conditions, but could also give valuable contributions to the global knowledge of biogeographic diversity. In this study, we used high-throughput sequencing to illuminate the diversity and composition pattern of protist communities in 41 geothermal springs across the HGB on the Tibetan Plateau. A total of 1238 amplicon sequence variants (ASVs) of protists were identified in the hot springs of the HGB. In general, Cercozoa was the phylum with the highest richness, and Bacillariophyta was the phylum with the highest relative abundance in protists. Based on the occurrence of protist ASVs, most of them are rare. A high variation in protist diversity was found in the hot springs of the HGB. The high variation in protist diversity may be due to the different in environmental conditions of these hot springs. Temperature, salinity, and pH are the most important environmental factors that affect the protist communities in the surface sediments of the hot springs in the HGB. In summary, this study provides the first comprehensive study of the composition and diversity of protists in the hot springs of the HGB and facilitates our understanding of the adaptation of protists in these extreme habitats.

The development of the commercial multi global navigation satellite system (GNSS) dual (multi)-antenna common-clock receiver that uses time-synchronization technology has brought new opportunities for high-precision GNSS-based attitude determination. In this article, for the first time, we present a performance comparison of global positioning system (GPS) L1/Galileo navigation satellite system (Galileo) E1 attitude determination with a common-clock receiver using four different models, i.e., the loosely combined single-differenced (SD-LC) model, the tightly combined single-differenced (SD-TC) model, the loosely combined double-differenced (DD-LC) model, and the tightly combined double-differenced (DD-TC) model. We first introduce the SD-LC, SD-TC, DD-LC, and DD-TC relative positioning models with GPS L1/Galileo E1 observations from a common-clock receiver. Then, we present a performance comparison of the four models in both single-epoch and multi-epoch modes using static data collected with a Trimble BD992 common-clock receiver in terms of the ambiguity dilution of precision (ADOP), the ambiguity resolution (AR) success and failure rates, and the positioning and attitude determination accuracy. In the case of the single-epoch mode, the experimental results revealed that the results of the single-differenced (SD) models were identical to those of double-differenced (DD) models, i.e., the results of SD-LC and SD-TC models were identical to DD-LC and DD-TC models, respectively. Moreover, compared with the loosely combined model (SD-LC/DD-LC), the tightly combined model (SD-TC/DD-TC) delivered a much higher AR success rate and a lower AR failure rate, especially under a high elevation cutoff angle. The AR success rate increased by approximately 35.1% under a 40° elevation cutoff angle, while the AR failure rate decreased by approximately 4.3%. In the case of the multi-epoch mode, the experimental results confirmed the advantages of the tightly combined model over the loosely combined model as well as the SD model over the DD model. Compared with the DD-LC and SD-LC models, the AR success rates of the DD-TC and SD-TC models were improved by approximately 16.7% and 0.6% under a 45° elevation cutoff angle, respectively. The AR failure rates were reduced by approximately 12.4% and 0.3%, respectively. Moreover, compared with the DD-LC and DD-TC models, the AR success rates of the SD-LC and SD-TC models under a 45° elevation cutoff angle were improved by approximately 24.0% and 7.9%, respectively, and the AR failure rates were reduced by approximately 19.9% and 7.8%, respectively. Meanwhile, compared with the DD model, the SD model delivered comparable yaw accuracy and remarkably better pitch accuracy. The pitch accuracy was improved by approximately 65.2–75.0%.

Urban agglomeration is the strategic core area of social–economic high-quality development in the world. However, high-density agglomeration and high-speed expansion have caused dramatic changes in land space, leading to prominent eco-environmental problems and, thus, threatening human well-being. How to solve the contradiction between urban agglomeration land expansion and eco-environment protection has become an urgent scientific problem. In this paper, we constructed a framework of assessing land space change and its eco-environmental effects in the urban agglomeration by using the Geo-informatic Graphic, eco-environmental quality index (EQI), and eco-environmental contribution rate. We then quantitatively analyzed the characteristics of land space transition as well as its eco-environmental effects in the Guanzhong Plain urban Agglomeration (GPUA) based on the land use data in 1990, 2000, 2010 and 2020. The results indicated that from 1990 to 2020, the production space of the GPUA continued to shrink, the living space continued to expand, and the ecological space showed a fluctuating increasing trend. There were significant regional differences in the land space change of the GPUA, with 92.2% of the counties showing a significant contraction in agricultural production space, 93.3% and 91.1% showing a significant expansion in urban and rural living space, and 64.4% showing an increase in woodland ecological space. Agricultural production space is transformed into ecological space and living space, and living space occupies ecological space and agricultural production space, which is the main mode of land space transition in the GPUA. With the continuous expansion of low-quality and high-quality areas of the eco-environment and the continuous contraction of medium-quality areas, the improvement and deterioration of the eco-environmental quality of the GPUA have coexisted, first showing a trend of deterioration and then improvement. The transition of agricultural production space into grassland and woodland ecological space improved the eco-environmental quality, while the transition of grassland ecological space into agricultural production space, and the occupation of agricultural production space by urban and rural living space as well as industrial and mining production space resulted in the deterioration of eco-environmental quality. The findings of this study may provide a theoretical basis for optimizing the allocation of land space resources in ecologically fragile urban agglomeration.

In this paper, a new algorithm for extracting the laser fringe center is proposed. Based on a deep learning skeleton extraction network, the laser stripe center can be extracted quickly and accurately. Skeleton extraction is the process of reducing the shape image to its approximate central axis representation while maintaining the image’s topological and geometric shape. Skeleton extraction is an important step in topological and geometric shape analysis. According to the characteristics of the wheelset laser curve dataset, a new skeleton extraction network, a hierarchical skeleton network (LuoNet), is proposed. The proposed architecture has three levels of the encoder–decoder network, and YE Module interconnection is designed between each level of the encoder and decoder network. In the wheelset laser curve dataset, the F1_score can reach 0.714. Compared with the traditional laser curve center extraction algorithm, the proposed LuoNet algorithm has the advantages of short running time, high accuracy, and stable extraction results.

Global navigation satellite system (GNSS)-based attitude determination has been widely applied in a variety of fields due to its high precision, no error accumulation, low power consumption, and low cost. Recently, the emergence of common-clock receivers and construction of GNSS systems have brought new opportunities for high-precision GNSS-based attitude determination. In this contribution, we focus on evaluating the performance of the BeiDou regional navigation satellite system (BDS-2)/BeiDou global navigation satellite system (BDS-3)/Global Positioning System (GPS)/Galileo navigation satellite system (Galileo) attitude determination based on the single-differenced (SD) model with a common-clock receiver. We first investigate the time-varying characteristics of BDS-2/BDS-3/GPS/Galileo line bias (LB) with two different types of common-clock receivers. The results have confirmed that both the phase and code LBs are relatively stable in the time domain once the receivers have started. However, the phase LB is expected to change to an arbitrary value after each restart of the common-clock receivers. For the first time, it is also found that the phase LBs of overlapping frequencies shared by different GNSS systems are identical. Then, we primarily evaluated the performance of BDS-2/BDS-3/GPS/Galileo precise relative positioning and attitude determination based on the SD model with a common-clock receiver, using a static dataset collected at Wuhan. Experimental results demonstrated that, compared with the double-differenced (DD) model, the SD model can deliver a comparable root–mean–square (RMS) error of yaw but a significantly smaller RMS error of pitch, whether for BDS-2, BDS-3, GPS, or Galileo alone or a combination of them. The improvements of pitch accuracy are approximately 20.8–47.5% and 40.7–57.5% with single- and dual-frequency observations, respectively. Additionally, BDS-3 can deliver relatively superior positioning and attitude accuracy with respect to GPS and Galileo, due to its better geometry. The three-dimensional positioning and attitude (including yaw and pitch) accuracy for both the DD and SD models can be remarkably improved by the BDS-2, BDS-3, GPS, and Galileo combination with respect to a single system alone.

A model-free damage identification method for grid structures based on displacement difference is proposed. The inherent relationship between the displacement difference and the position of structural damage was deduced in detail by the Sherman−Morrison−Woodbury formula, and the basic principle of damage localization of the grid structure was obtained. That is, except for the tensile and compressive deformations of the damaged elements, the deformations of other elements were small, and only rigid body displacements occurred before and after the structural damage. According to this rule, a method for identifying the position of the damage was proposed for the space grid structure by using the rate of change of length for each element. Taking a space grid structure with a large number of elements as an example, the elastic modulus reduction method was used to simulate the damage to the elements, and the static and dynamic test parameters were simulated respectively to obtain the difference in displacement before and after the structural damage. The rate of change of length of each element was calculated based on the obtained displacement difference, and data noise was added to the simulation. The results indicated that the element with the larger length change rate in the structure was the most likely to be damaged, and the damaged element can be accurately evaluated even in the presence of noise in data.

Bioelectrochemical systems (BES) are promising technologies to convert organic compounds in wastewater to electrical energy through a series of complex physical-chemical, biological and electrochemical processes. Representative BES such as microbial fuel cells (MFCs) have been studied and advanced for energy recovery. Substantial experimental and modeling efforts have been made for investigating the processes involved in electricity generation toward the improvement of the BES performance for practical applications. However, there are many parameters that will potentially affect these processes, thereby making the optimization of system performance hard to be achieved. Mathematical models, including engineering models and statistical models, are powerful tools to help understand the interactions among the parameters in BES and perform optimization of BES configuration/operation. This review paper aims to introduce and discuss the recent developments of BES modeling from engineering and statistical aspects, including analysis on the model structure, description of application cases and sensitivity analysis of various parameters. It is expected to serves as a compass for integrating the engineering and statistical modeling strategies to improve model accuracy for BES development.