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•A NiO/CNT/PEDOT nanocomposite was electrochemically synthesized in one step.•The composite is a coaxial-structured nanotube of CNT and NiO/PEDOT layer.•The formation mechanism of the ...composite and its electrochemical properties were analyzed.•Sensors based on the composite can simultaneously detect DA, 5-HT, and Trp in serum.•The composite sensors showed high sensitivity, selectivity, and stability.
A novel NiO/carbon nanotube (CNT)/poly(3,4-ethylenedioxythiophene) (PEDOT) composite with a coaxial tubular nanostructure was successfully deposited on a glassy carbon electrode (GCE) through electrochemical processes. The outer PEDOT matrix has a high affinity for biomolecules, while the CNT inner layer has a strong electron-transport capacity. Hence, the NiO/CNT/PEDOT/GCE was used for the simultaneous determination of dopamine (DA), serotonin (5-HT), and tryptophan (Trp). Electrochemical responses of the NiO/CNT/PEDOT/GCE sensor were investigated by cyclic voltammetry, electrochemical impedance spectroscopy, and differential pulse voltammetry. Three well-separated oxidation peaks were observed in a mixed system containing DA, 5-HT, and Trp; the 5-HT–DA and Trp–5-HT potential separations were 160 and 324 mV, respectively, owing to the synergistic effect of NiO, CNT, and PEDOT. Linear responses were obtained for DA, 5-HT, and Trp in the 0.03–20, 0.3–35, and 1–41 μM concentration ranges, with detection limits of 0.026, 0.063, and 0.210 μM, respectively. In addition, it was possible to simultaneously determine the three analytes using the NiO/CNT/PEDOT composite, which suggests that the sensor has promising applicability.
The high-density Industrial Internet of Things needs to meet the requirements of high-density device access and massive data transmission, which requires the support of multiple-input multiple-output ...(MIMO) antenna cognitive systems to keep high throughput. In such a system, spectral efficiency (SE) optimization based on dynamic power allocation is an effective way to enhance the network throughput as the channel quality variations significantly affect the spectral efficiency performance. Deep learning methods have illustrated the ability to efficiently solve the non-convexity of resource allocation problems induced by the channel multi-path and inter-user interference effects. However, current real-valued deep-learning-based power allocation methods have failed to utilize the representational capacity of complex-valued data as they regard the complex-valued channel data as two parts: real and imaginary data. In this paper, we propose a complex-valued power allocation network (AttCVNN) with cross-channel and in-channel attention mechanisms to improve the model performance where the former considers the relationship between cognitive users and the primary user, i.e., inter-network users, while the latter focuses on the relationship among cognitive users, i.e., intra-network users. Comparison experiments indicate that the proposed AttCVNN notably outperforms both the equal power allocation method (EPM) and the real-valued and the complex-valued fully connected network (FNN, CVFNN) and shows a better convergence rate in the training phase than the real-valued convolutional neural network (AttCNN).
Accurate inventories of grasslands are important for studies of greenhouse gas (GHG) dynamics, as grasslands store about one-third of the global terrestrial carbon stocks. This paper develops a ...framework for large-area grassland mapping based on the probability of grassland occurrence and the interactive pathways of fractional vegetation and soil-related endmember nexuses. In this study, grassland occurrence probability maps were produced based on data on bio-climate factors obtained from MODIS/Terra Land Surface Temperature (MOD11A2), MODIS/Terra Vegetation Indices (MOD13A3), and Tropical Rainfall Measuring Mission (TRMM 3B43) using the random forests (RF) method. Time series of 8-day fractional vegetation-related endmembers (green vegetation, non-photosynthetic vegetation, sand land, saline land, and dark surfaces) were generated using linear spectral mixture analysis (LSMA) based on MODIS/Terra Surface Reflectance data (MOD09A1). Time-series endmember fraction maps and grassland occurrence probabilities were employed to map grassland distribution using an RF model. This approach improved the accuracy by 5% compared to using endmember fractions alone. Additionally, based on the grassland occurrence probability maps, we identified extensive ecologically sensitive regions, encompassing 1.54 (104 km2) of desert-to-steppe (D-S) and 2.34 (104 km2) of steppe-to-meadow (S-M) transition regions. Among these, the D-S area is located near the threshold of 310 mm/yr in precipitation, an annual temperature of 10.16 °C, and a surface comprehensive drought index (TVPDI) of 0.59. The S-M area is situated close to the line of 437 mm/yr in precipitation, an annual temperature of 5.49 °C, and a TVPDI of 0.83.
Abstract
This study proposed a novel framework to identify critical water quality dynamics as early warning signals for diagnosing changes in the resilience of river basin ecosystems. We established ...empirical linkages between the theoretical background of three resilience capacities (robustness, adaptability and transformability) and water quality dynamics. Then, the processes of resilience degradation and their risk transfer or accumulation have been identified based on the shifts among different states. The methods of time-domain analysis and frequency-domain analysis were integrated into this framework, aiming to identify gradual and transient responses of water quality and its periodic fluctuation characteristics at multiple temporal scales. The time-domain analysis methods obtained the trend, cumulative periodic fluctuation of water quality by extracting the key characteristic parameters from the time-series data. The wavelet transform methods were introduced into the frequency-domain analysis to reveal the water quality fluctuation patterns at specific temporal scales. We tested the proposed framework in a typical agriculture-intensive watershed in eastern China. The results showed that this framework can be effectively used to identify three resilience states of river basin ecosystems. The degraded resilience regions were mainly distributed in the downstream area, which was influenced by their specific land use/cover and different agricultural soil health conditions. Particularly, urban sewage discharge was the main cause of periodic fluctuation in water quality time series at multiple high-frequency scales. The theoretical background of resilience capacities was elaborated in non-equilibrium dynamics before resilience degradation. Thus, this novel framework could reveal the pollution processes and driving mechanisms in different river reaches, and it also can provide adaptive management suggestions according to resilience dynamics traits.
The accurate mapping and analysis of coastal wetlands and their dynamics are crucial for local coastal wetland protection, sustainable social development, and biodiversity preservation. However, ...detailed mapping and comprehensive analysis of coastal wetlands remain scarce. In this study, we utilized Landsat-TM/OLI remote sensing data and employed the linear spectral mixture analysis (LSMA) method to map changes in coastal wetlands and analyze their dynamics in the Yellow River Delta (YRD) from 1991 to 2020. Our mapping results demonstrate high accuracy and are consistent with previous studies, boasting an overall accuracy exceeding 96%. During the period of 1991–2020, the YRD estuary expanded by approximately 8744.58 ha towards the east and north. The vegetation of P. australis and S. salsa underwent transformation due to agricultural practices or degradation to bare flats. Moreover, these areas saw extensive colonization by the invasive species S. alterniflora. Over the three decades, S. alterniflora expanded approximately 5 km along the coast, significantly impacting the local coastal wetland biodiversity. Furthermore, a considerable number of natural wetlands transitioned into human-made wetlands from 1991 to 2014. In particular, bare flats underwent substantial changes, transforming into aquaculture sites and salt exploitation areas. These dynamics in coastal wetlands had significant repercussions on local ecosystems, including wetland fragmentation, biodiversity depletion, and water pollution. However, post-2014, numerous wetland protection strategies were implemented, resulting in the restoration of natural wetlands. Detailed wetland mapping and dynamic analysis furnish valuable insights for the management, protection, and sustainable utilization of diverse coastal wetlands.
Developed here is an integrated framework for identifying production-living-ecological space (PLES) quantitatively at grid scale from the perspective of multifunction land use, and 25 compound space ...types are classified to highlight the multiple functions of PLES. As a typical mountainous city in northern Hebei province, Zhangjiakou is used as a case study, and the results show that more than 80% of the land space in Zhangjiakou has remarkable triple functionality. The living-dominated space and the production-dominated space are distributed mainly in the valleys of the Yanghe, Sanggan, and Huliu rivers and have obvious spatial consistency, while the ecological-dominated space is concentrated mostly at the eastern Yanshan Mountains and southern Taihang Mountains and complements the other two types of space. The former two are spatially fragmented, while the latter has been expanding to the periphery over time. From 1990 to 2015, the ecological-dominated space has increased the most by 1555.02 km
2
, while the living-dominated space has increased the least by 816.79 km
2
. The types of PLES are more diverse in the medium and low mountains and the areas with gentle slope, and the influencing factors include natural ecological environment, socioeconomic development, human consumption demand, and institutional policies. Therefore, these findings can mitigate conflicts among PLES in mountainous and similar areas, and promote the balanced development of land space.
There are millions of base stations distributed across China, each containing many support devices and monitoring sensors. Conventional base station management systems tend to be hosted in the cloud, ...but cloud-based systems are difficult to reprogram and performing tasks in real-time is sometimes problematic, for example, sounding a combination of alarms or executing linked tasks. To overcome these drawbacks, we propose a hybrid edge-cloud IoT base station system, called BSIS. This paper includes a theoretical mathematical model that demonstrates the dynamic characteristics of BSIS along with a formulation for implementing BSIS in practice. Embedded programmable logic controllers serve as the edge nodes; a dynamic programming method creates a seamless integration between the edge nodes and the cloud. The paper concludes with a series of comprehensive analyses on scalability, responsiveness, and reliability. These analyses indicate a possible 60% reduction in the number of alarms, an edge response time of less than 0.1s, and an average downtime ratio of 0.66%.
The number of intelligent applications available for IIoT environments is growing, but when the time-series data these applications rely on are incomplete, their performance suffers. Unfortunately, ...incomplete data are all too frequent to a phenomenon in the world of IIoT. A common workaround is to use imputation. However, the current methods are largely designed to reconstruct a single missing pattern, where a robust and flexible imputation framework would be able to handle many different missing patterns. Hence, the framework presented in this study, RAEF, is capable of processing multiple missing patterns. Based on a recurrent autoencoder, RAEF houses a novel neuron structure, called a gated regulator, which reduces the negative impact of different missing patterns. In a comparison of the state-of-the-art time-series imputation frameworks at a range of different missing rates, RAEF yielded fewer errors than all its counterparts.
Current non-invasive early detection of colorectal cancer (CRC) requires improvement. We aimed to identified a fecal Clostridium symbiosum-based biomarker for early and advanced colorectal cancer ...detection.
In the test stage, the relative abundance of Clostridium symbiosum (C. symbiosum) was measured by qPCR in 781 cases including 242 controls, 212 colorectal adenoma (CRA) patients, 109 early CRC (tumor restricted to the submucosa) patients, 218 advanced CRC patients. The prediction accuracy was compared to Fusobacterium nucleatum (F. nucleatum), fecal immunochemical test (FIT) and CEA (carcinoembryonic antigen) and validated in an independent cohort of 256 subjects. Current status of the trial:ongoing/still enrolling. Primary endpoint:June, 2017 (Clinicaltrials.gov Identifier NCT02845973).
Significant stepwise increase of C. symbiosum abundance was found in CRA, early CRC and advanced CRC (P<0.01). C. symbiosum outperformed all the other markers in early CRC prediction performance. The combination of C. symbiosum and FIT achieved better performance (0.803 for test cohort and 0.707 for validation cohort). For overall discrimination of CRCs, the combination of all above markers achieved the performance of 0.876.
Fecal C. symbiosum is a promising biomarker for early and noninvasive detection of colorectal cancer, being more effective than F. nucleatum, FIT and CEA. Combining C. symbiosum and FIT or CEA may improve the diagnosis power.
•The fecal abundance of Clostridium symbiosum was found increased in patients with colorectal neoplasia and it may serve as a potiential biomarker in non-invasive early differentiation of colorectal cancer from healthy controls.•The fecal abundance of Clostridium symbiosum was even more sensitive and efficient in diagnosis of both early and advanced colorectal cancer than reported markers like fecal immunochemical test, carcinoembryonic antigen and the abundance of Fusobacterium nucleatum.•Combining the abundance of Clostridium symbiosum and the other markers above may further enhance its predictive performance.
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