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Anomaly detection in industrial control system (ICS) data is one of the key technologies for ensuring the security monitoring of ICSs. ICS data are characterized as complex, multi-dimensional, and long-sequence time-series data that embody ICS business logic. Due to its complex and varying periodic characteristics, as well as the presence of long-distance and misaligned temporal associations among features, current anomaly detection methods in ICS are insufficient for feature extraction. This paper proposes an anomaly detection method named TFANet, based on time–frequency fusion feature attention encoding. Considering that periodic variations are more concentrated in the frequency domain, this method first transforms the time-domain data into the frequency domain, obtaining both amplitude and phase data. Then, these data, together with the original time-series data, are used to extract features from two perspectives: long-term temporal changes and long-distance associations. Finally, the six features learned from both the time and frequency domains are fused, and the feature weights are calculated using an attention mechanism to complete the anomaly classification. In multi-classification tasks on three ICS datasets, the proposed method outperforms three popular time-series models—iTransformer, Crossformer, and TimesNet—across five metrics: accuracy, precision, recall, F1 score, and AUC-ROC, with average improvements of approximately 19%, 37%, 31%, 35%, and 22%, respectively. [ABSTRACT FROM AUTHOR]

The construction of new towns is one of the main measures to evacuate urban populations and promote regional coordination and urban–rural integration in China. Mining the spatio-temporal pattern of new town hot spots based on multivariate data and analyzing the influencing factors of new town construction hot spots can provide a strategic basis for new town construction, but few researchers have extracted and analyzed the influencing factors of new town internal hot spots and their classification. In order to define the key points of Wuhan's new town construction and promote the construction of new cities in an orderly and efficient manner, this paper first constructs a space-time cube based on the luminous remote sensing data from 2010 to 2019, extracts hot spots and emerging hot spots in Wuhan New City, selects 14 influencing factor indicators such as population density, and uses bivariate Moran's index to analyze the influencing factors of hot spots, indicating that the number of bus stops and vegetation coverage rate are the most significant. Secondly, the disorderly multivariate logistic regression model is used to analyze the influencing factors of emerging hot spots. The results show that population density, vegetation coverage, road density, distance to water bodies, and distance to train stations are the most significant factors. Finally, based on the analysis results, some relevant suggestions for the construction of Wuhan New City are proposed, providing theoretical support for the planning and policy guidance of new cities, and offering reference for the construction of new towns in other cities, promoting the construction of high-quality cities. [ABSTRACT FROM AUTHOR]

Elevated atmospheric reactive nitrogen (Nr) emissions and the subsequent nitrogen (N) deposition have negatively impacted the global environment, particularly in China. In order to assess the long-term trends in atmospheric N deposition in the south of China, Taojiang County in Hunan Province was selected as a representative rural area for study. We analyzed interannual variation in atmospheric Nr, including gaseous ammonia (NH3), nitrogen dioxide (NO2), nitrate acid (HNO3) vapor, particulate ammonium (NH4+), and nitrate (NO3−) in air and NH4+-N and NO3−-N in precipitation from 2011 to 2020. The 10-year average atmospheric wet-plus-dry N deposition was 41.9 kg N ha−1 yr−1, which decreased by approximately 24% after 2012, indicating that NH3 and NOx emissions were effectively reduced by emission controls introduced in 2013. Wet deposition accounted for approximately 74% of the total N deposition and was significantly influenced by annual precipitation amount. Reduced N (NH3, pNH4+, and NH4+ in rainwater) was the dominant form, comprising approximately 58% of the total N deposition, while oxidized N (pNO3−, NO2, HNO3, and NO3− in rainwater) accounted for 42% of the total N deposition. Atmospheric HNO3, NO2, and NH3 concentrations and deposition declined by 30–80% over the decade, while particulate NH4+ and NO3− concentrations and deposition remained at relatively stable levels, which suggests that ongoing research and policy should focus on rural particulate pollution. Future strategies must concentrate on the integrated control of NH3 and NOx emissions to mitigate air pollution and protect human health, particularly in rural areas because current abatement efforts are primarily directed toward urban areas and the industrial sector, whereas non-point source NH3 pollution, influenced mainly by agricultural activities, dominates in rural regions. [ABSTRACT FROM AUTHOR]

A modified parallel model for estimating the thermal conductivity of unsaturated sand was proposed in this study. The heat conduction in the solid phase of sand depends mainly on the form of contacts between solid particles, while water bridges at the particle contacts increase the contact areas and remarkably enlarge the transfer paths of heat conduction in sandy soils. However, the thermal conductivity of the solid particle itself (λs) cannot describe the influence of the form of contacts and water bridges on heat conduction through the solid phase. In this study, the equivalent thermal conductivity of the solid particle (λes) was presented which reflected the influence of the form of contacts and water bridges between particles under dry conditions or a low degree of saturation, respectively. The relationship between λes and degree of saturation was described by hyperbolic expression. The modified model was calibrated using measured values of the thermal conductivity from published datasets, including those for 41 types of sand from 15 studies. Numerical analyses of the temperature field of the energy pile were performed and validated against laboratory measurements. The results illustrated that the modified model was more applicable than the original model for predictions of sand thermal conductivity. [ABSTRACT FROM AUTHOR]

The urban fringe is the transitional area from rural form to urban form, and it is also the urban space reserve land in the Territorial Spatial Plan. However, few researchers predict its overall evolution and guide the implementation of the Territorial Spatial Plan. This study attempts to explore the dynamic evolution law of urban fringe, analyze its driving factors, predict its future development, and put forward management suggestions for the implementation of the Territorial Spatial Plan. In this paper, the land use data of Wuhan in 2000, 2010 and 2020 are applied to delimit the urban fringe area of Wuhan by means of a sliding t-test. Fifteen driving factors are selected from three dimensions, natural factors, socio-economic factors and traffic accessibility, and brought into the Logistic model to explore the driving factors of its spatial evolution. The CA–Markov model is used to predict the fringe area of Wuhan in 2035. The results show that the transformation of rural hinterland into urban fringe is obviously affected by the distance from railway stations, highways, commercial centers and urban main roads. It is predicted that the outer boundary of Wuhan's fringe area in 2035 will be basically the same as the planned urban development boundary. In order to realize the intention of land space planning, the development and construction of the northwest of the Huangpi District, the East Lake Scenic Area, and the west side of the Jiangxia District should be restricted. From the perspective of the evolution of the fringe area, this paper puts forward some management suggestions for the implementation of the Territorial Spatial Plan and makes a beneficial attempt in theory and method to understand the development characteristics of the fringe area and promote the implementation of the Territorial Spatial Plan. [ABSTRACT FROM AUTHOR]

Rationally relieving the population of urban centers in large cities, such as megacities and supercities, is one of the current goals of population development in China. The fringe area of a large city is a potential area to undertake the population of the central area. Studying the relationship between the population and the built environment in this area can help urban planners formulate targeted construction strategies to attract the population of the city center to move to the fringe areas. This paper takes the fringe areas of Wuhan in 2010 and 2020 as its specific research object and puts forward the "5D" index system of built environments that affects the spatial distribution of population based on population data and built environment data. The OLS model is used to screen the influencing factors. This paper analyzes the correlation between population and built environment using a multi-scale geographic weighted regression model as well. According to the results of the regression analysis combined with the development and construction of the fringe areas of remote urban areas in Wuhan over the past 20 years, some suggestions are put forward for the planning and construction of remote urban areas. The results show that the "5D" index system of the built environment covers the influencing factors of the spatial distribution of the population. MGWR reveals the correlation between the influencing factors and the spatial distribution of population in the marginal areas on the global scale and the local scale, respectively, which provides a clear direction for the development of planning and construction to improve the attractiveness of the non-central areas to the population. [ABSTRACT FROM AUTHOR]

The hydroisomerization of n-alkanes is an important step in the production of high-quality bio-aviation kerosene. A SAPO-11 molecular sieve was synthesized using the hydrothermal synthesis method, and a 0.5 wt% Pt/SAPO-11 catalyst was prepared using the impregnation method. The crystal phase, pore structure, acidity, and morphology of Pt/SAPO-11 were characterized via X-ray diffraction, N2 adsorption-desorption, NH3 temperature-programmed desorption, scanning electron microscopy, and transmission electron microscopy, respectively. The hydroisomerization performance of the catalyst was evaluated with bio-n-hexadecane as the model compound. The results showed that temperature and space velocity had significant effects. Under the conditions of 340 °C, 1.5 MPa, WHSV = 1.0 h−1, V(H2): V(n-hexadecane) = 1000:1, the conversion of n-hexadecane and the selectivity of i-hexadecane were 81.8% and 86.5%, respectively. [ABSTRACT FROM AUTHOR]

An important way to improve the resolution of electromagnetic exploration is by using known seismic and logging data. Based on previous work, 2D magnetotelluric (MT) parallel-constrained-inversion, based on an artificial-fish-swarm algorithm is further developed. The finite-difference (FD) method with paralleling frequency is used for 2D MT-forward-modeling, to improve computational efficiency. The results of the FD and finite-element (FE) methods show that the accuracy of FD is comparable to FE in the case of suitable mesh-generation; however, the calculation speed is ten times faster than that of the FE. The artificial-fish-swarm algorithm is introduced and applied to parallel-constrained-inversion of 2D MT data. The results of the synthetic-model test show that the artificial-fish-swarm-inversion based on paralleling forward can recover the model well and effectively improve the inversion speed. The processing and interpretation results of the field data are verified by drilling, which shows that the proposed inversion-method has good practicability. [ABSTRACT FROM AUTHOR]

In desert regions, aeolian sand is abundant, but it is not suitable to be used directly as the upper roadbed filler for highways. Generally, gravelly soil is mined around the desert as upper roadbed fill, resulting in high engineering expenses for road construction in the desert hinterland. Geocells have a significant reinforcing effect on aeolian sand. However, in the completed desert highway, the dynamic performance of geocell-reinforced aeolian sand as an upper layer of roadbed fill has not been studied. Using a field test method, the dynamic performance of geocell-reinforced aeolian sand as an upper roadbed fill is examined. The results show that the majority of the frequency distribution of road vibration is within 30 Hz. In the horizontal direction, the actual vibration amplitude decay on the side of geocell-reinforced aeolian sand is slower but smoother than on the side of gravelly soils. In vibration velocity, the work area depth of the geocell-reinforced aeolian sand side of the roadbed is less than that of the gravelly soil side. The maximum difference can reach 0.55 m. As far as vibration velocity is concerned, the 30 cm gravelly soils can be substituted with 15 cm geocell-reinforced aeolian sands as the upper roadbed. In summary, the dynamic attenuation characteristics of geocell-reinforced aeolian sand are superior to gravelly soils. The research results provide a reference for the design of the desert highway subgrade. [ABSTRACT FROM AUTHOR]

The Forest Resources Assets Departure Audit (FRADA) is an important measure for the Chinese government to protect and develop forest resources, as well as to promote carbon peaking and carbon neutrality. Explicit audit content and scientific audit method are the basic premises for conducting an accurate audit. In this study, landscape ecology methods were employed to learn the spatiotemporal properties of forest resources, with the support of spatial information technology such as Geography Information System (GIS) and Remote Sensing (RS). A FRADA system from the perspective of landscape pattern index and ecological vulnerability was constructed. A case study was conducted for the evaluation of the proposed FRADA System. Lichuan, a county-level city in Hubei Province, China that is rich in forest resources was selected as the audit sample area, and the tenure period from 2015 to 2019 was regarded as the tenure cycle. The results showed that: (1) Before and after the tenure cycle, the number of patches (NP), patch density (PD) and largest patch index (LPI) of forest landscape showed a downward trend. Meanwhile, the decreased quantity and quality of forest landscapes showed inadequate protection of large forest patches with significant ecological value. In general, it was not beneficial for the sustainable development of forest resources in the view of ecology. (2) The landscape shape index (LSI) and connectivity index (CONNECT) showed an upward trend, which indicates that the shape of the forest landscape is changing in a complex way, and the connectivity has been improved. While, fractal dimension (FD) and fragmentation index (FN) showed a downward trend, indicating that the fragmentation trend of the forest landscape has been alleviated and the forest resources have a continuous and concentrated distribution. (3) The vulnerability of the forest landscape was significantly aggravated. Regarding administrative areas under the jurisdictions of Nanping Township, Jiannan Town and Fobaoshan Development Zone were particularly disturbed, and required special attention. The research results enrich and develop the theories and methods of FRADA and provide suggestions for the sustainable development of forest resources in Lichuan. [ABSTRACT FROM AUTHOR]

Rainfall data have a profound significance for meteorology, climatology, hydrology, and environmental sciences. However, existing rainfall observation methods (including ground-based rain gauges and radar-/satellite-based remote sensing) are not efficient in terms of spatiotemporal resolution and cannot meet the needs of high-resolution application scenarios (urban waterlogging, emergency rescue, etc.). Widespread surveillance cameras have been regarded as alternative rain gauges in existing studies. Surveillance audio, through exploiting their nonstop use to record rainfall acoustic signals, should be considered a type of data source to obtain high-resolution and all-weather data. In this study, a method named parallel neural network based on attention mechanisms and multi-scale fusion (PNNAMMS) is proposed for automatically classifying rainfall levels by surveillance audio. The proposed model employs a parallel dual-channel network with spatial channel extracting the frequency domain correlation, and temporal channel capturing the time-domain continuity of the rainfall sound. Additionally, attention mechanisms are used on the two channels to obtain significant spatiotemporal elements. A multi-scale fusion method was adopted to fuse different scale features in the spatial channel for more robust performance in complex surveillance scenarios. In experiments showed that our method achieved an estimation accuracy of 84.64% for rainfall levels and outperformed previously proposed methods. [ABSTRACT FROM AUTHOR]

Substantial ammonia (NH3) losses from rice production result in poor nitrogen (N) use efficiency and environmental damage. A data synthesis using the published literature (127 studies with 700 paired observations), combined with an incubation experiment using 50 paddy soils from across China, were conducted to improve the current understanding of the NH3 loss from paddy rice and its drivers. The efficacy of the urease inhibitor Limus® for reducing NH3 losses was also evaluated. The mean loss of N, through NH3 volatilization, was 16.2% of the urea-N applied to paddy rice. The largest losses were from double rice cropping systems, and losses increased with the N application rate, surface application of N, unstable N types (ammonium bicarbonate and urea), and high floodwater pH. Under simulated flooded conditions, urea amended with Limus® reduced NH3 loss by 36.6%, compared to urea alone, but floodwater pH had a significant effect on inhibitor efficacy. Key driving factors were air temperature, N application rate, and floodwater pH. The effectiveness and limitations of the inhibitor in NH3 emission mitigation was examined, as well as its basis as one means of N pollution control in paddy rice cropping systems. [ABSTRACT FROM AUTHOR]

Improving the attractiveness of urban waterfronts has become an important objective to promote economic development and improve the environmental quality. However, few studies have focused on the inherent characteristics of urban waterfront attractiveness. In this study, mobile phone signaling data and the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) were used to construct the attractiveness evaluation system of the riverside in Wuhan. The OLS (ordinary least squares) regression model was used to analyze the relationship between the POI (point of interest) and the attractiveness of river waterfronts. Furthermore, the high-or-low-value aggregation classification of research units was performed according to attractiveness and the POI indicators to reveal the influencing factors of the attractiveness of the Wuhan urban riverside. Results showed the following. (1) The high-value distribution of attractiveness of the river waterfronts in Wuhan presented regional aggregation characteristics, and the attractiveness of economically developed areas was high. (2) Consumer POIs (CPOIs) and outdoor recreation POIs (RPOIs) had a positive effect on the attractiveness of the riverside in Wuhan, while housing POIs (HPOIs), public service POIs (OPOIs), and the high degree of POI mixing had a negative impact on the attractiveness of the urban riverside. (3) The high–high agglomeration spaces were mainly concentrated in the economically developed areas of the city center, which are mainly open spaces where urban cultural activities are held, while the low–low agglomeration spaces were mostly gathered in the suburban areas. The spatial distribution of the high–low agglomeration spaces, which are mainly green open spaces, was relatively fragmented, while the low–high agglomeration spaces, which are mainly freight terminals, linear walks, and residential areas, were near the city center. [ABSTRACT FROM AUTHOR]

The urban fringe area is a discontinuous spatial phenomenon that refers to the urban-rural interlacing zone which is undergoing urbanization on the fringe of the core built-up area of a large city after the emergence of industrialization. Dynamic, ambiguous, and complex interlacing of various types of lands make urban planners and managers fuzzy about the spatial scope of the urban fringe and it is difficult to control its evolution patterns scientifically. Based on remote sensing data from 2000 to 2020, the range of Wuhan's urban fringe was extracted from the surface impermeability ratio mutation points, landscape flocculation, and population density. On this basis, the dynamic evolution characteristics of land-use and landscape patterns in the urban fringe area of Wuhan City were analyzed by using dynamic change and landscape pattern index analysis. The results show that: Wuhan City shows a clear "urban core area-urban fringe area-rural hinterland" circle structure, and the urban fringe area continuously extends to the rural hinterland. Moreover, most of the rural hinterland, in the process of moving to the urban core area, has gone through the process of the urban fringe. By comparison with other cities, it is found that the expansion of large cities is generally influenced by policies, topography, and traffic arteries, and gradually shifts from expansion to infill, with the urban core of Wuhan continuously extending and the urban fringe rapidly expanding from 2000 to 2010, and gradually entering a stable development state from 2010 to 2020. The future urban construction of Wuhan should pay attention to the influences of these characteristics on the implementation of urban territorial spatial planning. [ABSTRACT FROM AUTHOR]

The scarcity of attack samples is the bottleneck problem of anomaly detection of underlying business data in the industrial control system. Predecessors have done a lot of research on temporal data generation, but most of them are not suitable for industrial control attack sample generation. The change patterns of the characteristics of the underlying business data attack samples can be divided into three types: oscillation type, step type, and pulse type. This paper proposes an adaptive industrial control attack sample expansion algorithm based on GAN, which expands the three types of features in different ways. The basic network structure of data expansion adopts GAN. According to the characteristics of oscillation type changes, momentum is selected as the optimizer. Aiming at the characteristics of step type changes, the Adam optimization method is improved. For pulse type features, attack samples are generated according to the location and length of the pulse. Compared with previous time-series data generation methods, this method is more targeted for each feature and has higher similarities. [ABSTRACT FROM AUTHOR]

The rolling bearing is a critical part of rotating machinery and its condition determines the performance of industrial equipment; it is necessary to detect rolling bearing faults as early as possible. The traditional methods of fault diagnosis are not efficient and are time-consuming. With the help of deep learning, the convolution neural network (CNN) plays a huge role in the data-driven methods of bearing fault diagnosis. However, the vibration signal is non-stationary, contains high noise, and is one-dimensional, which is difficult to analyze directly by the CNN model. Considering the multi-domain learning as an advantage of deep learning, this paper proposes a novel rolling bearing fault diagnosis approach using an improved one-dimensional (1D) and two-dimensional (2D) convolution neural network (CNN) of two-domain information learning. The constructed fault diagnosis model combining 1D and 2D CNN extracts the fault features from the two-domain information of bearing fault samples. The padding and dropout technology are utilized to fully extract features from the raw data and reduce over-fitting. To prove the validity of the proposed method, this paper performs two tests with two bearing datasets, the Case Western Reserve University (CWRU) bearing dataset and the Dalian University of Technology (DUT) vibration laboratory dataset. The experimental results show that our proposed method achieves high recognition accuracy of rolling bearing fault states via two-domain learning of monitoring data, and there is no manual experience necessary. Vibration data under strong noise were also used to test the method, and the results show the superiority and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Based on the equivalent factor table of terrestrial ESV in China in 2010, this paper proposes a regionalized and interannual ESV correction method. Taking southern Guangzhou as the study area, which is located in the geometric center of the Guangdong–Hong Kong–Macao Greater Bay Area (GHM GBA), this study evaluated the ecosystem service equivalent value (ESEV) and ecosystem service value (ESV) and their changes in 2004, 2010 and 2016 and carried out grid processing, trend analysis and global/local spatial autocorrelation of the ecosystem service density (ESD) in the study area by using the Natural Resources Integrated Database of Guangdong Province (NRIDB). The results of data processing and spatial analysis computed in this study were published as Web services through the NRIDB and displayed in the visualization front-end. The results showed that (1) during 2004–2016, the urbanization process in the study area led to the expansion of built-up land, which occupied a large amount of ecological land and resulted in a decreasing trend of ESV. (2) Benefiting from socioeconomic development, the standard value volume of the ESEV (D value) kept increasing, which resulted in an increasing trend for the total ESV. (3) The spatial autocorrelation analysis results of ESD indicated that the spatial distribution of ESD in the study area had strong spatial agglomeration and positive spatial correlation. With the progress of urbanization and increases in research scale and interval distance, the spatial difference in ESD became larger over time. The research results provide a reference for the coordinated allocation of natural resources and environmental protection in the GHM GBA. [ABSTRACT FROM AUTHOR]

Auricularia cornea var. Li. polysaccharide (ACP) has many important biological activities and has potential application value in food engineering, pharmaceutical science, and health care. The results were as follows: the extraction rate of ACP was 28.18% ± 1.41% and the purity of ACP was 86.92% ± 2.80%. ACP contains mannitol 32.41%, glucuronic acid 6.96%, rhamnose 0.32%, glucose 42.35%, galactose 0.77%, xylose 16.83%, and fucose 0.36%, without galacturonic acid and arabinose. In addition, the results of an animal test of diabetes mellitus II (DM II) with ACP showed that the total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and fasting blood glucose and water in the serum of mice with ACP were significantly lower than those in the model group; the serum SOD, hepatic glycogen, and insulin of mice added with ACP were significantly higher than those in the model group. More importantly, ACP had no significant adverse effects on organ index and liver and kidney tissue morphology in mice. These results suggest that ACP can be used as a potential functional food component for the prevention or treatment of diabetes. [ABSTRACT FROM AUTHOR]

The gas turbine engine is a widely used thermodynamic system for aircraft. The demand for quantifying the uncertainty of engine performance is increasing due to the expectation of reliable engine performance design. In this paper, a fast, accurate, and robust uncertainty quantification method is proposed to investigate the impact of component performance uncertainty on the performance of a classical turboshaft engine. The Gaussian process model is firstly utilized to accurately approximate the relationships between inputs and outputs of the engine performance simulation model. Latin hypercube sampling is subsequently employed to perform uncertainty analysis of the engine performance. The accuracy, robustness, and convergence rate of the proposed method are validated by comparing with the Monte Carlo sampling method. Two main scenarios are investigated, where uncertain parameters are considered to be mutually independent and partially correlated, respectively. Finally, the variance-based sensitivity analysis is used to determine the main contributors to the engine performance uncertainty. Both approximation and sampling errors are explained in the uncertainty quantification to give more accurate results. The final results yield new insights about the engine performance uncertainty and the important component performance parameters. [ABSTRACT FROM AUTHOR]

Knowledge of the sunshine requirements of landscape plants is important information for the adaptive selection and configuration of plants for urban greening, and is also a basic attribute of plant databases. In the existing studies, the light compensation point (LCP) and light saturation point (LSP) have been commonly used to indicate the shade tolerance for a specific plant; however, these values are difficult to adopt in practice because the landscape architect does not always know what range of solar radiation is the best for maintaining plant health, i.e., normal growth and reproduction. In this paper, to bridge the gap, we present a novel digital framework to predict the sunshine requirements of landscape plants. First, the research introduces the proposed framework, which is composed of a black-box model, solar radiation simulation, and a health standard system for plants. Then, the data fitting between solar radiation and plant growth response is used to obtain the value of solar radiation at different health levels. Finally, we adopt the LI-6400XT Portable Photosynthetic System (Li-Cor Inc., Lincoln, NE, USA) to verify the stability and accuracy of the digital framework through 15 landscape plant species of a residential area in the city of Wuhan, China, and also compared and analyzed the results of other researchers on the same plant species. The results show that the digital framework can robustly obtain the values of the healthy, sub-healthy, and unhealthy levels for the 15 landscape plant species. The purpose of this study is to provide an efficient forecasting tool for large-scale surveys of plant sunshine requirements. The proposed framework will be beneficial for the adaptive selection and configuration of urban plants and will facilitate the construction of landscape plant databases in future studies. [ABSTRACT FROM AUTHOR]

Rolling bearings are the vital components of large electromechanical equipment, thus it is of great significance to develop intelligent fault diagnoses for them to improve equipment operation reliability. In this paper, a fault diagnosis method based on refined composite multiscale reverse dispersion entropy (RCMRDE) and random forest is developed. Firstly, rolling bearing vibration signals are adaptively decomposed by variational mode decomposition (VMD), and then the RCMRDE values of 25 scales are calculated for original signal and each decomposed component as the initial feature set. Secondly, based on the joint mutual information maximization (JMIM) algorithm, the top 15 sensitive features are selected as a new feature set and feed into random forest model to identify bearing health status. Finally, to verify the effectiveness and superiority of the presented method, actual data acquisition and analysis are performed on the bearing fault diagnosis experimental platform. These results indicate that the presented method can precisely diagnose bearing fault types and damage degree, and the average identification accuracy rate is 97.33%. Compared with the refine composite multiscale dispersion entropy (RCMDE) and multiscale dispersion entropy (MDE), the fault diagnosis accuracy is improved by 2.67% and 8.67%, respectively. Furthermore, compared with the RCMRDE method without VMD decomposition, the fault diagnosis accuracy is improved by 3.67%. Research results prove that a better feature extraction technique is proposed, which can effectively overcome the deficiency of existing entropy and significantly enhance the ability of fault identification. [ABSTRACT FROM AUTHOR]

Trehalose is known for its effect of improving the stability of freeze-dried foods. In this work, vacuum freeze-drying (VFD) technology was employed to prepare northeast black bee royal jelly into lyophilized powder and a novel method mixing trehalose into royal jelly is successfully developed to enhance the free radical scavenging ability and the nutrition stability of royal jelly lyophilized powder. The effects of different trehalose content (0, 0.1, 0.3, 0.5, 0.7 and 0.9 wt.%) on the physicochemical properties of lyophilized royal jelly powder were studied. With systematic analysis, it was found that the incorporation of suitable trehalose content in lyophilized royal jelly powder can reduce the loss of the protein, total sugar, total flavone content during the VFD process and enhance the total phenolic antioxidant capacity, solubility, angle of repose, and bulk density of the royal jelly powder. Finally, lyophilized royal jelly with 0.5 wt.% trehalose is selected as the suitable addition content which exhibits the best radical scavenging ability as well as the lowest hygroscopicity. From the perspective of sensory evaluation, all royal jelly lyophilized powders with trehalose are acceptable. [ABSTRACT FROM AUTHOR]

The effect of Auriculariacornea (AC) as an alternative for pork fat on the physico-chemical properties and sensory characteristics of cooked sausage were evaluated. The results indicated that replacement of pork fat with AC led to a significant increase in the protein, ash, moisture, cooking loss, water holding capacity, springiness, and chewiness, especially isoleucine, leucine, proline, palmitic, palmitoleic, oleic, and arachidonic acids of the sausages. In contrast, AC reduced the level of fat (12.61%–87.56%) and energy (5.76%–56.40%) of the sausages. In addition, AC led to the mild lightness, yellowness, whiteness, and soft texture, while it did not affect the water activity of the sausages. From the sensory point of view, all sausages were judged acceptable, and the substitution of 75% of pork fat by AC exhibited best sensory characteristics. In a word, AC is a promising food to partially replace the pork fat in sausages. [ABSTRACT FROM AUTHOR]

Studies of soil Cd and Zn are often performed on sites that are contaminated or have deficient Zn conditions. Soil characteristics and crop management could impact the soil mobility and uptake of Cd and Zn, even when considering unpolluted Cd soils and adequate soil Zn levels. The concentrations of these two metals were assessed in soil and silage maize under five P fertilization treatments at two growth stages under low Cd and sufficient Zn conditions. Pearson correlation coefficients and stepwise linear regressions were calculated to investigate the soil characteristics influencing the bioavailable metal fraction in soil and the metal concentration in silage maize. P treatments did not impact Cd accumulation in maize; however, the Zn uptake was affected by P placement at the leaf development stage. From early development to maturity, the Cd level in maize decreased to 10% of the initial uptake, while the Zn level decreased to 50% of the initial uptake. This reduction in both metals may be attributed to a dilution effect derived from high biomass production. Silage maize could alleviate the initial Cd uptake while diminishing the depressant effect of P fertilizer on Zn concentration. Further research is required to understand the effect of P fertilizer on Cd uptake and its relation to Zn under field conditions at early and mature stages. [ABSTRACT FROM AUTHOR]

Monitoring the work cycles of earthmoving excavators is an important aspect of construction productivity assessment. Currently, the most advanced method for the recognition of work cycles is the "Stretching-Bending" Sequential Pattern (SBSP), which is based on fixed-carrier video monitoring (FC-SBSP). However, the application of this method presupposes the availability of preconstructed installation carriers to act as a surveillance camera as well as installed and commissioned surveillance systems that work in tandem with them. Obviously, this method is difficult to apply to projects with no conditions for a monitoring camera installation or which have a short construction time. This highlights the potential application of Unmanned Aerial Vehicle (UAV) remote sensing, which is flexible and mobile. Unfortunately, few studies have been conducted on the application of UAV remote sensing for the work cycle monitoring of earthmoving excavators. This research is necessary because the use of UAV remote sensing for monitoring the work cycles of earthmoving excavators can improve construction productivity and save time and costs, especially in post-disaster reconstruction projects involving harsh construction environments, and emergency projects with short construction periods. In addition, the challenges posed by UAV shaking may have to be taken into account when using the SBSP for UAV remote sensing. To this end, this study used application experiments in which stabilization processing of UAV video data was performed for UAV shaking. The application experimental results show that the work cycle performance of UAV remote-sensing-based SBSP (UAV-SBSP) for UAV video data was 2.45% and 5.36% lower in terms of precision and recall, respectively, without stabilization processing than after stabilization processing. Comparative experiments were also designed to investigate the applicability of the SBSP oriented toward UAV remote sensing. Comparative experimental results show that the same level of performance was obtained for the recognition of work cycles with the UAV-SBSP as compared with the FC-SBSP, demonstrating the good applicability of this method. Therefore, the results of this study show that UAV remote sensing enables effective monitoring of earthmoving excavator work cycles in construction sites where monitoring cameras are not available for installation, and it can be used as an alternative technology to fixed-carrier video monitoring for onsite proximity monitoring. [ABSTRACT FROM AUTHOR]

Predicting in vivo protein–DNA binding sites is a challenging but pressing task in a variety of fields like drug design and development. Most promoters contain a number of transcription factor (TF) binding sites, but only a small minority has been identified by biochemical experiments that are time-consuming and laborious. To tackle this challenge, many computational methods have been proposed to predict TF binding sites from DNA sequence. Although previous methods have achieved remarkable performance in the prediction of protein–DNA interactions, there is still considerable room for improvement. In this paper, we present a hybrid deep learning framework, termed DeepD2V, for transcription factor binding sites prediction. First, we construct the input matrix with an original DNA sequence and its three kinds of variant sequences, including its inverse, complementary, and complementary inverse sequence. A sliding window of size k with a specific stride is used to obtain its k-mer representation of input sequences. Next, we use word2vec to obtain a pre-trained k-mer word distributed representation model. Finally, the probability of protein–DNA binding is predicted by using the recurrent and convolutional neural network. The experiment results on 50 public ChIP-seq benchmark datasets demonstrate the superior performance and robustness of DeepD2V. Moreover, we verify that the performance of DeepD2V using word2vec-based k-mer distributed representation is better than one-hot encoding, and the integrated framework of both convolutional neural network (CNN) and bidirectional LSTM (bi-LSTM) outperforms CNN or the bi-LSTM model when used alone. The source code of DeepD2V is available at the github repository. [ABSTRACT FROM AUTHOR]

Counting the number of work cycles per unit of time of earthmoving excavators is essential in order to calculate their productivity in earthmoving projects. The existing methods based on computer vision (CV) find it difficult to recognize the work cycles of earthmoving excavators effectively in long video sequences. Even the most advanced sequential pattern-based approach finds recognition difficult because it has to discern many atomic actions with a similar visual appearance. In this paper, we combine atomic actions with a similar visual appearance to build a stretching–bending sequential pattern (SBSP) containing only "Stretching" and "Bending" atomic actions. These two atomic actions are recognized using a deep learning-based single-shot detector (SSD). The intersection over union (IOU) is used to associate atomic actions to recognize the work cycle. In addition, we consider the impact of reality factors (such as driver misoperation) on work cycle recognition, which has been neglected in existing studies. We propose to use the time required to transform "Stretching" to "Bending" in the work cycle to filter out abnormal work cycles caused by driver misoperation. A case study is used to evaluate the proposed method. The results show that SBSP can effectively recognize the work cycles of earthmoving excavators in real time in long video sequences and has the ability to calculate the productivity of earthmoving excavators accurately. [ABSTRACT FROM AUTHOR]

In data clustering, the measured data are usually regarded as uncertain data. As a probability-based clustering technique, possible world can easily cluster the uncertain data. However, the method of possible world needs to satisfy two conditions: determine the data of different possible worlds and determine the corresponding probability of occurrence. The existing methods mostly make multiple measurements and treat each measurement as deterministic data of a possible world. In this paper, a possible world-based fusion estimation model is proposed, which changes the deterministic data into probability distribution according to the estimation algorithm, and the corresponding probability can be confirmed naturally. Further, in the clustering stage, the Kullback–Leibler divergence is introduced to describe the relationships of probability distributions among different possible worlds. Then, an application in wearable body networks (WBNs) is given, and some interesting conclusions are shown. Finally, simulations show better performance when the relationships between features in measured data are more complex. [ABSTRACT FROM AUTHOR]

With atmospheric reactive nitrogen (Nr) emissions increasing globally, research into Nr deposition has attracted increasing attention, especially in remote environments. These ecosystems are very sensitive to global change, especially enhanced Nr deposition. Forest environments, in particular, are highlighted because of their important ecological function. We quantified atmospheric Nr concentrations and deposition over four years of continuous monitoring in a southeast Tibetan boreal forest ecosystem, an ecosystem in which forest biomass and carbon density are high around the world. Average annual bulk Nr deposition was 3.00 kg N ha−1 y−1, with those of reduced and oxidized species estimated at 1.60 and 1.40 kg N ha−1 y−1, respectively. Bulk deposition of both NH4+ and NO3− were controlled by precipitation amount: both Nr deposition and precipitation were highest in summer and lowest in winter. Dry deposition of NH3 and NO2 were 1.18 and 0.05 kg N ha−1 y−1, respectively. Atmospheric NH3 concentrations were in the range 1.15–3.53 mg N L−1, highest in summer and lowest in winter. In contrast, no clear trend in seasonal NO2 concentrations was observed. Monthly NO2 concentrations were 0.79–1.13 mg N L−1. Total Nr deposition (bulk plus dry) was 4.23 (3.00 + 1.23) kg N ha−1 y−1 in the forest. Reduced nitrogen was the dominant species. In conclusion, Nr deposition was in the range at which forest net productivity and carbon sequestration are sensitive to any variation in nitrogen input, so quantification of Nr deposition should continue and with greater detail. [ABSTRACT FROM AUTHOR]

Target Tracking (TT) is a fundamental application of wireless sensor networks. TT based on received signal strength indication (RSSI) is by far the cheapest and simplest approach, but suffers from a low stability and precision owing to multiple paths, occlusions, and decalibration effects. To address this problem, we propose an innovative TT algorithm, known as the SVM+KF method, which combines the support vector machine (SVM) and an improved Kalman filter (KF). We first use the SVM to obtain an initial estimate of the target's position based on the RSSI. This enhances the ability of our algorithm to process nonlinear data. We then apply an improved KF to modify this estimated position. Our improved KF adds the threshold value of the innovation update in the traditional KF. This value changes dynamically according to the target speed and network parameters to ensure the stability of the results. Simulations and real experiments in different scenarios demonstrate that our algorithm provides a superior tracking accuracy and stability compared to similar algorithms. [ABSTRACT FROM AUTHOR]

Matching points are the direct data sources of the fundamental matrix, camera parameters, and point cloud calculation. Thus, their uncertainty has a direct influence on the quality of image-based 3D reconstruction and is dependent on the number, accuracy, and distribution of the matching points. This study mainly focuses on the uncertainty of matching point distribution. First, horizontal dilution of precision (HDOP) is used to quantify the feature point distribution in the overlapping region of multiple images. Then, the quantization method is constructed. H D O P ∗ ¯ , the average of 2 × arctan (H D O P × n 5 − 1) / π on all images, is utilized to measure the uncertainty of matching point distribution on 3D reconstruction. Finally, simulated and real scene experiments were performed to describe and verify the rationality of the proposed method. We found that the relationship between H D O P ∗ ¯ and the matching point distribution in this study was consistent with that between matching point distribution and 3D reconstruction. Consequently, it may be a feasible method to predict the quality of 3D reconstruction by calculating the uncertainty of matching point distribution. [ABSTRACT FROM AUTHOR]

This work discusses the integration of multi-camera video moving objects (MCVO) and GIS. This integration was motivated by the characteristics of multi-camera videos distributed in the urban environment, namely, large data volume, sparse distribution and complex spatial–temporal correlation of MCVO, thereby resulting in low efficiency of manual browsing and retrieval of videos. To address the aforementioned drawbacks, on the basis of multi-camera video moving object extraction, this paper first analyzed the characteristics of different video-GIS Information fusion methods and investigated the integrated data organization of MCVO by constructing a spatial–temporal pipeline among different cameras. Then, the conceptual integration model of MCVO and GIS was proposed on the basis of spatial mapping, and the GIS-MCVO prototype system was constructed in this study. Finally, this study analyzed the applications and potential benefits of the GIS-MCVO system, including a GIS-based user interface on video moving object expression in the virtual geographic scene, video compression storage, blind zone trajectory deduction, retrieval of MCVO, and video synopsis. Examples have shown that the integration of MCVO and GIS can improve the efficiency of expressing video information, achieve the compression of video data, rapidly assisting the user in browsing video objects from multiple cameras. [ABSTRACT FROM AUTHOR]

In data-centric wireless sensor networks (WSNs), sensing data have a high time–space correlation. Most queries are spatial and used to obtain data in a defined region. Geographic routing (GR) protocols are the optimal choice for routing spatial queries. However, several drawbacks still exist in GRs, and these the include premature death of nodes and communication latency, which result in reduced network life and query efficiency. A new clustering GR protocol called quadtree grid (QTGrid) was proposed in this study to save energy and improve spatial query efficiency. First, the monitoring area was logically divided into clusters by a quadtree structure, and each grid's location was encoded to reduce the memory overhead. Second, cluster head (CH) nodes were selected based on several metrics, such as distance from the candidate node to the grid center and adjacent CHs and residual energy. Third, the next-hop routing node was selected depending on the residual energy of the candidate node and its distance to the sink node. Lastly, a lossless data aggregation algorithm and a flexible spatial query algorithm were adopted to reduce the transmission of redundant data and meet the application requirements, respectively. Simulation results showed that compared with three related protocols, QTGrid has lower energy consumption and higher spatial query efficiency and is more suitable for large-scale WSN spatial query application scenarios. [ABSTRACT FROM AUTHOR]

Anaerobic digestion (AD) is an important technology for the treatment of livestock and poultry manure. The optimal experimental conditions were studied, with deer manure as a fermentation material and mushroom residue as an inoculum. At the same time, methane production was increased by adding zeolite and changing the magnetic field conditions. The results showed that a 6% solid content was the best condition for producing methane. The optimal conditions for methane production were obtained by adding 35 g of mushroom residue to 80 g of deer manure at 35 °C. The addition of organic wastewater (OW) improved methane production. The result of improving the methane production factor showed that adding zeolite during the reaction process could increase the methane production rate. When the amount of zeolite was over 8% total solids (TSes), methane production could improve, but the rate decreased. Setting a different magnetic field strength in the AD environment showed that when the distance between the magnetic field and the reactor was 50 mm and the magnetic field strength was 10–50 mT, the methane production increment and the content of methane in the mixed gases increased. [ABSTRACT FROM AUTHOR]

Topographic factors such as slope and aspect are essential parameters in depicting the structure and morphology of a terrain surface. We study the effect of the number of points in the neighbourhood of a digital elevation model (DEM) interpolation method on mean slope, mean aspect, and RMSEs of slope and aspect from the interpolated DEM. As the moving least squares (MLS) method can maintain the inherent properties and other characteristics of a surface, this method is chosen for DEM interpolation. Three areas containing different types of topographic features are selected for study. Simulated data from a Gauss surface is also used for comparison. First, the impact of the number of points on the DEM root mean square error (RMSE) is analysed. The DEM RMSE in the three study areas decreases gradually with the number of points in the neighbourhood. In addition, the effect of the number of points in the neighbourhood on mean slope and mean aspect was studied across varying topographies through regression analysis. The two variables respond differently to changes in terrain. However, the RMSEs of the slope and aspect in all study areas are logarithmically related to the number of points in the neighbourhood and the values decrease uniformly as the number of points in the neighbourhood increases. With more points in the neighbourhood, the RMSEs of the slope and aspect are not sensitive to topography differences and the same trends are observed for the three studied quantities. Results for the Gauss surface are similar. Finally, this study analyses the spatial distribution of slope and aspect errors. The slope error is concentrated in ridges, valleys, steep-slope areas, and ditch edges while the aspect error is concentrated in ridges, valleys, and flat regions. With more points in the neighbourhood, the number of grid cells in which the slope error is greater than 15° is gradually reduced. With similar terrain types and data sources, if the calculation efficiency is not a concern, sufficient points in the spatial autocorrelation range should be analysed in the neighbourhood to maximize the accuracy of the slope and aspect. However, selecting between 10 and 12 points in the neighbourhood is economical. [ABSTRACT FROM AUTHOR]

Ecosystem balance is an important factor that affects healthy and sustainable urban development. The traditional cellular automata (CA) model considers only a few ecological factors, however, the MCR model can account for ecological factors. In previous studies, few ecological factors were added to the CA model. Thus, the minimal cumulative resistance (MCR) model is combined with the CA and Markov models for the simulation of urban expansion. To verify the reliability of the method, the Wuhan metropolitan area was selected as a representative urban area, and its expansion in the past and future was simulated. Firstly, seven influential factors were selected from the perspective of location theory. The transformation rules of the comprehensive resistance surface followed by the modified CA–Markov model were constructed on the basis of the MCR model. The expansion of the Wuhan metropolitan area in 2013 was simulated on the basis of the 1996 and 2006 maps of land-use status, and the kappa coefficient was used as an index to evaluate the accuracy of the proposed method. Then, the expansion of the Wuhan metropolitan area in 2020 was simulated. Finally, the simulation results obtained with and without the MCR model were compared and analysed from the macro- and micro levels. Results show that the prediction accuracy of the two models differed for ecological regions, such as woodlands and water bodies. The similarities between the regions that were overestimated and underestimated by the MCR-modified CA–Markov model and non-MCR model may be attributed to solution of the land-use transfer matrix with the Markov model. The accuracy of the MCR-modified CA–Markov model for predicting forests, water and other ecological regions was higher than that of the Markov model. Therefore, the proposed MCR-modified CA–Markov model has potential applications in environmentally-conscious urban expansion. [ABSTRACT FROM AUTHOR]

This paper presents an effective approach for depth reconstruction from a single image through the incorporation of semantic information and local details from the image. A unified framework for depth acquisition is constructed by joining a deep Convolutional Neural Network (CNN) and a continuous pairwise Conditional Random Field (CRF) model. Semantic information and relative depth trends of local regions inside the image are integrated into the framework. A deep CNN network is firstly used to automatically learn a hierarchical feature representation of the image. To get more local details in the image, the relative depth trends of local regions are incorporated into the network. Combined with semantic information of the image, a continuous pairwise CRF is then established and is used as the loss function of the unified model. Experiments on real scenes demonstrate that the proposed approach is effective and that the approach obtains satisfactory results. [ABSTRACT FROM AUTHOR]

Drug-target interactions provide insight into the drug-side effects and drug repositioning. However, wet-lab biochemical experiments are time-consuming and labor-intensive, and are insufficient to meet the pressing demand for drug research and development. With the rapid advancement of deep learning, computational methods are increasingly applied to screen drug-target interactions. Many methods consider this problem as a binary classification task (binding or not), but ignore the quantitative binding affinity. In this paper, we propose a new end-to-end deep learning method called DeepMHADTA, which uses the multi-head self-attention mechanism in a deep residual network to predict drug-target binding affinity. On two benchmark datasets, our method outperformed several current state-of-the-art methods in terms of multiple performance measures, including mean square error (MSE), consistency index (CI), rm2, and PR curve area (AUPR). The results demonstrated that our method achieved better performance in predicting the drug-target binding affinity.

The Momoge Nature Reserve is the research object of this study. Through field sampling, laboratory experiments and analysis, the contents, distribution characteristics, source identification, pollution levels and risk levels of polycyclic aromatic hydrocarbons (PAHs) in wetland soils were studied. The results show that the sum content of 16 types of PAHs (Σ16 PAH) in the wetland soil was within the range (0.029-0.4152) mg/kg. PAHs in wetland soil are primarily 2-3-rings PAHs. PAHs in the Momoge wetland soil have multiple sources: petroleum, combustion of petroleum and coal, and others, of which petroleum and the sum of combustion of petroleum and coal account for 38.0% and 59.3%, respectively. Research, using the standard index and pollution range methods, shows that the content of the PAH labelled Nap, found in the Momoge wetland soil, is excessive; some sampling sites exhibit a low level of pollution. The result of a biotoxicity assessment shows that there are two sampling sites that occasionally present an ecological toxicity hazard. The result of the organic carbon normalization process shows that an ecological risk exists only at sampling site No. 10., Competing Interests: The authors declare no conflict of interest.