Tech Pro (China)

Green manuring enhances multiple agroecosystem services functions, yet its impact on net greenhouse gas mitigation remains controversial, primarily due to a limited number of long-term experiments. To address these challenges, this study investigated the long-term effects of green manure (GM) rotations on 100-cm-depth soil organic carbon (SOC) sequestration, N 2 O emissions, and crop yields based on eight long-term experimental sites (7–16 years) in China’s upland cropping systems combined with process-based modeling. The results demonstrated that green manuring significantly increased SOC concentration by 8.8%–14.4% ( p <0.001) across 0–100 cm soil profiles compared to fallow system, with annual SOC sequestration rates reaching 0.95–1.16 Mg C ha -1 yr -1 ( p <0.001). Notably, topsoil layer (0–40 cm) contributed 67.5% of total profile SOC accumulation. Green manuring can replace approximately 40% of synthetic fertilizers of N while maintaining long-term yield stability, though with potential trade-offs in elevated N 2 O emissions. The optimal net global warming potential (NGWP) reached -16.47 Mg CO 2 -eq ha -1 yr -1 under GM-based system with 30% reduction in fertilizer N. Meanwhile, under the condition that GM substitution for fertilizer N achieved no yield reduction, the greenhouse gas intensity (GHGI) was optimized within the substitution rate range of 20%–40%. The results from process-based modeling demonstrate that substituting 30% of N fertilizer with GM achieves optimal soil C sequestration while maintaining stable crop yields. These findings provide direct evidence that GM rotation increases C sequestration, addressing previous knowledge gaps in understanding the C sequestration and emission reduction effects of GM-based rotation. • GM increases SOC by 8.8%–14.4% in the 0–100 cm soil layer with a C sequestration rate of 0.95–1.16 Mg C ha -1 yr -1 . • Topsoil (0-40 cm) contributes 67.5% of total SOC accumulation under GM-based systems. • 30% N fertilizer substitution with GM optimizes yield stability and reduces NGWP by 16.47 Mg CO 2 -eq ha -1 yr -1 . • GM-based systems lower the greenhouse gas emission intensity at 20%-40% N substitution.

To address the “secondary dispatch” problem in alpine virtual power plants caused by uncertainties in decentralized resource allocation, we develop an algorithm for aggregating dispatch parameters of distributed resources to achieve real-time load-demand matching. Based on alpine power generation resources, we design a specialized virtual power plant structure and analyze its market trading applications. For the actual operation of the decentralized resources in the alpine virtual power plant, we determined the power provided by the alpine virtual power plant to the electric power system as well as the adjustable power capacity and other scheduling parameters, and then designed the dispatch model objective with the decentralized resource power and scheduling parameters based on the imitator dynamic algorithm. The model incorporates constraints based on these parameters to enable effective aggregation of adjustable power ranges for both individual resources and the entire virtual power plant, while ensuring compliance with all power constraints. This approach enhances scheduling flexibility and resolves the grid-side secondary dispatch issue. An improved ant colony algorithm based on continuous optimization was used to solve the aggregation parameters. Experimental results demonstrate superior solution performance, with the aggregated parameters increasing wind farm planned output by over 12 MW across different periods. This significantly boosts power delivery to the main grid, provides more stable supply, and improves virtual power plant revenue.

Global energy consumption and carbon emissions are heavily influenced by the building sector, particularly due to the substantial energy demands of heating, ventilation, and air conditioning (HVAC) systems. Many of these systems also rely on energy-intensive ultraviolet germicidal irradiation (UVGI) for air disinfection, a practice that has become more widespread since the COVID-19 pandemic. This study examines an alternative solution: replacing conventional filters and UVGI with energy-efficient air filtration system combined with antimicrobial air filters to enhance sustainability. Testing conducted on a commercial building's air handling unit (AHU), following an internationally recognized measurement protocol, revealed an average 57.77 % reduction in total power consumption while maintaining airflow, alongside a 93.3% improvement in airborne bacteria reduction. For that single AHU, this equates to annual energy savings of 27,500 kWh and a reduction of 10.7 tonnes in carbon emissions. Scaling these results globally suggests a potential annual carbon reduction of 0.299-0.432 gigatonnes through widespread adoption of this retrofit strategy. The findings highlight the considerable energy efficiency and environmental benefits of implementing energyefficient air filtration technology combined with antimicrobial air filters in ventilation systems.

Dementia is a condition that affects a very large population, and an effective clinical diagnosis can lead to early intervention to delay progressive cognitive decline. A relatively low-cost, noninvasive, and highly accessible biomarker measurement, electroencephalography (EEG), may be used for this purpose. Many EEG processing and classification methods, which are based on classical techniques, have been explored by researchers over the years. On the other hand, quantum machine learning (QML), which combines the concepts of quantum computing and machine learning, has recently gained much attention with the rising anticipation that this emerging research field may provide significant advantages over classical methods. This paper presents a healthy-dementia classification framework using synchrony features of CS-EMD decomposed EEGs and QML. In this study, three QML models, a variable quantum classifier (VQC), a quantum support vector machine (QSVM), and a quantum neural network (QNN) were explored. Their performance was evaluated through classification experiments using real EEG data. The performances of these QML methods, along with two popular classical classifiers (support vector machine and neural network), were compared. In our experiments, the quantum neural network outperformed all the other tested classifiers, reaching 96.58% accuracy, 97.11% specificity, 96.83% precision, 96.34% sensitivity, 96.28% F1 score, 97.69% AUC, an MCC of 0.9320, and a Kappa of 0.9268. Compared to the runner-up, a classical neural network, the QNN classifier shows an improvement by 8.92% in accuracy, 12.03% in specificity, 13.49% in precision, 5.57% in sensitivity, 10.29% in F1, 3.75% in AUC, 17.83% in MCC, and 18.90% in Kappa. The proposed framework, utilizing a quantum neural network, demonstrates effective classification of healthy individuals and dementia patients with high accuracy, showing good potential as a screening tool for dementia. Similar techniques may also be adapted for other biosignal analyses.

With the rapid advancement of computer virtual reality technology, the demand for simulation-based intelligent driving systems in the domains of roads, traffic, vehicles, and safety has become increasingly prominent. However, the closed nature of current technologies, high costs, limited application scenarios, and significant discrepancies between simulation results and real-world outcomes have constrained the progress and application of driving simulation technology. In response to these challenges, this paper presents the development of virtual reality intelligent driving simulation software using the open-source Unity3D physics engine. By integrating engineering design and 3D modeling, we have constructed an open-source driving simulation system that offers realistic simulation effects, flexible application scenarios, and the potential for further development.

Sleep monitoring typically requires the uncomfortable and expensive polysomnography (PSG) test to determine the sleep stages. Body movement and cardiopulmonary signals provide an alternative way to perform sleep staging. In recent years, long-short term memory (LSTM) networks and convolutional neural networks (CNN) have dominated automatic sleep staging due to their better learning ability than machine learning classifiers. However, LSTM may lose information when dealing with long sequences, while CNN is not good at sequence modeling. As an improvement, we develop a hierarchical attention-based deep learning method for sleep staging using body movement, electrocardiogram (ECG), and abdominal breathing signals. We apply the multi-head self-attention to model the global context of feature sequences and coupled it with CNN to achieve a hierarchical self-attention weight assignment. We evaluate the performance of the method using two public datasets. Our method outperforms other baselines in the three sleep stages, achieving an accuracy of 84.3%, an F1 score of 0.8038, and a Cohen's Kappa coefficient of 0.7036. The result demonstrates the effectiveness of the hierarchical self-attention mechanism when processing feature sequences in the sleep stage classification problem. This paper provides new possibilities for long-term sleep monitoring using movement and cardiopulmonary signals obtained from non-invasive devices. The code can be found at: https://github.com/scutrd/attention-sleep-staging.

Based on Indra's NOMRRC 7000B series Instrument Landing System (ILS) equipment manual, a mathematical model of the antenna array of the 20-Element Localizer Antenna System was established. The horizontal radiation signal and pattern of the antenna array were simulated and analyzed to verify the accuracy of the model. Then the End-around Taxiway screen was modeled and the type of interference of the End-around Taxiway screen to the Localizer was analyzed. Finally, the received signal's Difference in the Depth of Modulation (DDM) was simulated, and the influence of the End-around Taxiway screen on the course structure of the Localizer signal is compared and analyzed. The results show that the setting of End-around Taxiway screen has little effect on the Localizer signal, and it also provides a certain reference and reference for the setting and application of End-around Taxiway screen.

Partial discharge (PD) synchronous detection is an effective means to improve the quality of the handover test when the withstand voltage test is carried out on the high-voltage cable line, because it is easier to find the hidden defects when the voltage is higher than the operating voltage. However, PD detection of cable lines requires that PD sensors and signal processing devices must be installed at each cable junction or termination. The detection signals distributed in each joint of the cable must be transmitted and collected to the central processing unit for centralized analysis and display. For those several kilometers to more than ten kilometers of buried cable line, if there is no pre-wired communication network, the only way to rely on wireless communication. Due to the complex conditions of the test site, the speed and the stability of the wireless communication network are difficult to guarantee the data transmission of more than 3M bytes/s required by PD measurement. A new distributed PD detection system with wireless communication has been successfully developed and used in recent voltage tests of several 110 kV and 220 kV cable lines. A hybrid wireless communication system was used at the trial site, utilizing 4G/5G communication between the local distributed signal processors and Wi-Fi communication between the central signal processing unit. This communication method can not only meet the data needs of PRPD and PRPS graphs for displaying PD detection signals distributed in the whole line synchronously, but also realize the display of point-to-point signal waveform, and ensure the trend observation effect of each distributed signal and the synchronous comparison function of multi-point signal actograms. This paper attempts to briefly describe the composition and technical parameters of the PD detection system, the form of field application and the system method through a 110kV cable circuit withstand voltage test case.

The operational status of the instrument landing system directly affects aircraft landing and airport operational standards. Whose changeable status cannot be determined during the flight inspection period of the ILS. Proposes an innovative manner by analyze the QAR data from the routine flights of domestic airports. In order to improve the accuracy of recorded QAR data, this paper used the integrated track latitude and longitude correction model and the integral sliding track model. The signal in space change trend of the airport instrument landing system was also analyzed, to monitor hen the quality and operation status of the instrument landing system signal.

In the article titled “Miniaturized Printed Inverted-F Antenna for Internet of Things: A Design on PCB with a Meandering Line and Shorting Strip” [1], there were errors in Figures 3, 4, and 6. In Figure 3, the “W” and “L&lt;sub&gt;2&lt;/sub&gt;” were missing, in Figure 4, the “I” was missing, and in Figure 6, the numbers −30, −25, −20, −15, and −10 were mistakenly written as −35. These errors occurred during the production process. The correct figures are as follows.