ALBERT

Description: Subtropical forests have great potential as carbon sinks; however, the relationship between net ecosystem productivity (NEP) and climate change is still unclear. This study took Zhejiang Province, a subtropical region, as an example. Based on remote sensing classification data of forest resources, the integrated terrestrial ecosystem carbon cycle (InTEC) model was used to simulate the spatiotemporal dynamics of the forest NEP in Zhejiang Province during 1985–2015 and analyze its response to meteorological factors such as temperature, precipitation, relative humidity, and radiation. Three patterns emerged: (1) The optimized InTEC model can better simulate the forest NEP in Zhejiang Province, and the correlation coefficient between the simulated NEP and observed NEP was up to 0.75. (2) From 1985 to 2015, the increase in the total NEP was rapid, with an average annual growth rate of 1.52 Tg·C·yr−1. During 1985–1988, the forests in Zhejiang Province were carbon sources. After 1988, the forests turned into carbon sinks and this continued to increase. During 2000–2015, more than 97% of the forests in Zhejiang Province were carbon sinks. The total NEP reached 32.02 Tg·C·yr−1, and the annual mean NEP increased to 441.91 gC·m−2·yr−1. The carbon sequestration capacity of forests in the east and southwest of Zhejiang Province is higher than that in the northeast of Zhejiang Province. (3) From 2000 to 2015, there was an extremely significant correlation between forest NEP and precipitation, with a correlation coefficient of 0.85. Simultaneously, the forest NEP showed a negative correlation with temperature and radiation, with a correlation coefficient of −0.56 for both, and the forest NEP was slightly negatively correlated with relative humidity. The relative contribution rates of temperature, precipitation, relative humidity, and radiation data to NEP showed that the contribution of precipitation to NEP is the largest, reaching 61%, followed by temperature and radiation at 18% and 17%, respectively. The relative contribution rate of relative humidity is the smallest at only 4%. During the period of 1985–1999, due to significant man-made disturbances, the NEP had a weak correlation with temperature, precipitation, relative humidity, and radiation. The results of this study are important for addressing climate change and illustrating the response mechanism between subtropical forest NEP and climate change.

Description: The purpose of this study is to examine whether for-profit firms make opportunistic use of their corporate foundations to pursue self-serving earnings objectives in China. Using data on corporate foundations and a sample of firms listed on the A-share market from 2010 to 2016, we first use the propensity score matching method to explore the effect of corporate foundations on earnings management of their founder firms. We find that the overall discretionary accruals of firms with corporate foundations are significantly higher than for those without corporate foundations. Given the ownership property with Chinese characteristics, we further find that the significant difference is driven by privately-owned firms. Then we develop a model of discretionary donation expenditures to measure the magnitude of earnings management associated with corporate foundations. We observe that firms with small profits and consecutive earnings increase record income-increasing discretionary donation expenditures. While firms that record income-decreasing discretionary donation expenditures create earnings reserves that they can use in subsequent periods to report consecutive earnings increases. The results demonstrate that the visibly ethical behavior of establishing corporate foundations does not necessarily represent the consistent embodiment of corporate social responsibility (CSR), but can be regarded as corporate hypocrisy with self-interest embedded in benevolence.

Description: In the era of the Internet of Things and Artificial Intelligence, the Wi-Fi fingerprinting-based indoor positioning system (IPS) has been recognized as the most promising IPS for various applications. Fingerprinting-based algorithms critically rely on a fingerprint database built from machine learning methods. However, currently methods are based on single-feature Received Signal Strength (RSS), which is extremely unstable in performance in terms of precision and robustness. The reason for this is that single feature machines cannot capture the complete channel characteristics and are susceptible to interference. The objective of this paper is to exploit the Time of Arrival (TOA) feature and propose a heterogeneous features fusion model to enhance the precision and robustness of indoor positioning. Several challenges are addressed: (1) machine learning models based on heterogeneous features, (2) the optimization of algorithms for high precision and robustness, and (3) computational complexity. This paper provides several heterogeneous features fusion-based localization models. Their effectiveness and efficiency are thoroughly compared with state-of-the-art methods.

Description: The highly accurate multiresolution leaf area index (LAI) is an important parameter for carbon cycle simulation for bamboo forests at different scales. However, current LAI products have discontinuous resolution with 1 km mostly, that makes it difficult to accurately quantify the spatiotemporal evolution of carbon cycle at different resolutions. Thus, this study used MODIS LAI product (MOD15A2) and MODIS reflectance data (MOD09Q1) of Moso bamboo forest (MBF) from 2015, and it adopted a hierarchical Bayesian network (HBN) algorithm coupled with a dynamic LAI model and the PROSAIL model to obtain high-precision LAI data at multiresolution (i.e., 1000, 500, and 250 m). The results showed the LAIs assimilated using the HBN at the three resolutions corresponded with the actual growth trend of the MBF and correlated significantly with the observed LAI with a determination coefficient (R2) value of 〉0.80. The highest-precision assimilated LAI was obtained at 1000-m resolution with R2 values of 0.91. The LAI assimilated using the HBN algorithm achieved better accuracy than the MODIS LAI with increases in the R2 value of 2.7 times and decreases in the root mean square error of 87.8%. Therefore, the HBN algorithm applied in this study can effectively obtain highly accurate multiresolution LAI time series data for bamboo forest.

Description: A better understanding of soil fungal communities is very useful in revealing the effects of an agroforestry system and would also help us to understand the fungi-mediated effects of agricultural practices on the processes of soil nutrient cycling and crop productivity. Compared to conventional monoculture farming, agroforestry systems have obvious advantages in improving land use efficiency and maintaining soil physicochemical properties, reducing losses of water, soil material, organic matter, and nutrients, as well as ensuring the stability of yields. In this study, we attempted to investigate the impact of a mulberry/alfalfa intercropping system on the soil physicochemical properties and the rhizosphere fungal characteristics (such as the diversity and structure of the fungal community), and to analyze possible correlations among the planting pattern, the soil physicochemical factors, and the fungal community structure. In the intercropping and monoculture systems, we determined the soil physicochemical properties using chemical analysis and the fungal community structure with MiSeq sequencing of the fungal ITS1 region. The results showed that intercropping significantly improved the soil physicochemical properties of alfalfa (total nitrogen, alkaline hydrolysable nitrogen, available potassium, and total carbon contents). Sequencing results showed that the dominant taxonomic groups were Ascomycota, Basidiomycota, and Mucoromycota. Intercropping increased the fungal richness of mulberry and alfalfa rhizosphere soils and improved the fungal diversity of mulberry. The diversity and structure of the fungal community were predominantly influenced by both the planting pattern and soil environmental factors (total nitrogen, total phosphate, and total carbon). Variance partitioning analysis showed that the planting pattern explained 25.9% of the variation of the fungal community structure, and soil environmental factors explained 63.1% of the variation. Planting patterns and soil physicochemical properties conjointly resulted in changes of the soil fungal community structure in proportion.

Description: Land use and land cover change (LUCC) is an important driver of ecosystem function and services. Thus, LUCC analysis may lay foundation for landscape planning, conservation and management. It is especially true for alpine landscapes, which are more susceptible to climate changes and human activities. However, the information on LUCC in sacred landscape is limited, which will hinder the landscape conservation and development. We chose Kailash Sacred Landscape in China (KSL-China) to investigate the patterns and dynamics of LUCC and the driving forces using remote sensing data and meteorological data from 1990 to 2008. A supervised classification of land use and land cover was established based on field survey. Rangelands presented marked fluctuations due to climatic warming and its induced drought, for example, dramatic decreases were found in high- and medium-cover rangelands over the period 2000–2008. And recession of most glaciers was also observed in the study period. Instead, an increase of anthropogenic activities accelerated intensive alteration of land use, such as conversion of cropland to built-up land. We found that the change of vegetation cover was positively correlated with growing season precipitation (GSP). In addition, vegetation cover was substantially reduced along the pilgrimage routes particularly within 5 km of the routes. The findings of the study suggest that climatic warming and human disturbance are interacted to cause remarkable LUCC. Tourism development was responsible land use change in urban and pilgrimage routes. This study has important implications for landscape conservation and ecosystem management. The reduction of rangeland cover may decrease the rangeland quality and pose pressure for the carrying capacity of rangelands in the KSL-China. With the increasing risk of climate warming, rangeland conservation is imperative. The future development should shift from livestock-focus animal husbandry to service-based ecotourism in the sacred landscape.

Description: In recent years, the GPS wave buoy has been developed for in situ wave monitoring based on satellite GPS signals. Many research works have been completed on the GPS-based wave measurement technology and great progress has been achieved. The basic principle of the GPS wave buoy is to calculate the movement velocity of the buoy using the Doppler frequency shift of satellite GPS signals, and then to calculate the wave parameters from the movement velocity according to ocean wave theory. The shortage of the GPS wave buoy is the occasional occurrence of some unusual values in the movement velocity. This is mainly due to the fact that the GPS antenna is occasionally covered by sea water and cannot normally receive high-quality satellite GPS signals. The traditional solution is to remove these unusual movement velocity values from the records, which requires furthering extend the acquisition time of satellite GPS signals to ensure there is a large enough quantity of effective movement velocity values. Based on the traditional GPS wave measurement technology, this paper presents the algorithmic flow and proposes two improvement measures. On the one hand, the neural network algorithm is used to correct the unusual movement velocity data so that extending the acquisition time of satellite GPS signals is not necessary and battery power is saved. On the other hand, the Gaussian low-pass filter is used to correct the raw directional wave spectrum, which can further eliminate the influence of noise spectrum energy and improve the measurement accuracy. The on-site sea test of the SBF7-1A GPS wave buoy, developed by the National Ocean Technology Center in China, and the gravity-acceleration-type DWR-MKIII Waverider buoy are highlighted in this article. The wave data acquired by the two buoys are analyzed and processed. It can be seen from the processed results that the ocean wave parameters from the two kinds of wave buoys, such as wave height, wave period, wave direction, wave frequency spectrum, and directional wave spectrum, are in good consistency, indicating that the SBF7-1A GPS wave buoy is comparable to the traditional gravity-acceleration-type wave buoy in terms of its accuracy. Therefore, the feasibility and validity of the two improvement measures proposed in this paper are confirmed.

Description: F-containing wastewater does great harm to human health and the ecological environment and thus needs to be treated efficiently. In this paper, the new calcium-containing precipitant calcite and aided precipitant fluorite were adopted to purify F-containing wastewater. Relevant reaction conditions, such as reaction time, oscillation rate, dosage of hydrochloric acid, calcite dosage and the assisting sedimentation performance of fluorite, and action mechanism are analyzed. The experiment showed that the removal rate of fluoride in simulated wastewater reached 96.20%, when the reaction time, the dosage of calcite, the dosage of 5% dilute hydrochloric acid, and the oscillation rate was 30 min, 2 g/L, 21.76 g/L, and 160 r/min, respectively. Moreover, the removal rate of fluoride in the actual F-containing smelting wastewater reaches approximately 95% under the optimum condition of calcite dosage of 12 g/L, reaction time of 30 min, and oscillation rate of 160 r/min. The addition of fluorite significantly improves the sedimentation performance of the reactive precipitates. The experimental results showed that calcite and fluorite can effectively reduce the concentration of fluoride ions in F-containing wastewater and solve the problem of slow sedimentation of reactive precipitates.

Description: Degradation analysis has been widely used in reliability modeling problems of complex systems. A system with complex structure and various functions may have multiple degradation features, and any of them may be a cause of product failure. Typically, these features are not independent of each other, and the dependence of multiple degradation processes in a system cannot be ignored. Therefore, the premise of multivariate degradation modeling is to capture and measure the dependence among multiple features. To address this problem, this paper adopts copula entropy, which is a combination of the copula function and information entropy theory, to measure the dependence among different degradation processes. The copula function was employed to identify the complex dependence structure of performance features, and information entropy theory was used to quantify the degree of dependence. An engineering case was utilized to illustrate the effectiveness of the proposed method. The results show that this method is valid for the dependence measurement of multiple degradation processes.

Description: For fast and accurate faulty line selection (FLS) in single phase-to-ground (SPG) faults for resonant grounded distribution networks (RGDNs), this paper proposes a FLS method based on zero-sequence power characteristics. Through analysis of the zero-sequence current of SPG faults, it is found that the transient zero-sequence current in the faulty line contains abundant attenuated DC components, while that in the sound line is weak, which could be utilized as a technically practical way of judging faulty lines. Thus a FLS scheme using a transient zero-sequence DC component energy integral is put forward, which highlights the difference of the DC components in the faulty line and the sound line. A series of simulation test results indicate that the proposed scheme shows favorable performance under various SPG fault conditions, and should be helpful to improve the success rate of FLS significantly for RGDN in the future.