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To improve the convenience of life for the people with reduced mobility, a combined control method of wheelchair utilizing periocular electromyography (Per-EMG) and electroencephalography (EEG) is presented. Based on the Per-EMG and EEG signals obtained from the bioelectric sensors, a novel feature classification combined model is proposed by combining convolutional neural network (CNN) and long short-term memory (LSTM) neural network. These two deep learning architectures enable the comprehensive analysis and accurate classification of the acquired signals. Then the inferencing results can be converted to the corresponding driving command of the rehabilitation wheelchair. Furthermore, the important metrics such as accuracy, precision and recall are adopted to evaluate the performance of this combined model. These metrics provide a quantitative assessment of the model's classification capabilities. By practical experiments, the proposed combined control method for rehabilitation wheelchair demonstrates its reasonability and effectiveness. And the wheelchair with combined control method can enhance the mobility and independence of the people with reduced mobility. These findings contribute to the development of assistive technologies in the field of rehabilitation. • A combined control method of wheelchair utilizing periocular electromyography and electroencephalography signals is presented to improve the convenience of life for the people with reduced mobility. • A feature classification combination model is proposed by combining convolutional neural networks with long short-term memory neural networks. • A comprehensive method for extracting features of Per-EMG and EEG has been proposed. [ABSTRACT FROM AUTHOR]

Mechanical metamaterials composed of curvilinear elements (MM-CEs) offer better programmability and functionality than their rectilinear counterparts. They thus hold promising potential for diverse applications, such as flexible electronics, impact protection, and deformation control. However, existing research on MM-CEs has primarily focused on specific curve shapes, neglecting the vast potential of the wider curve design space. Based on data-driven methods, this work builds a dataset with a hierarchical data structure, which transforms the infinite and difficult-to-conceptualize curve data into a finite number of potentially researchable curve sub-types. Moreover, methods are developed based on data mining to better understand the broad design space, excavate hidden connections of different curve sub-types and discover new rules for designing MM-CEs. The outcomes of this study offer novel strategies to investigate other structures and metamaterials with complex geometries as well. [ABSTRACT FROM AUTHOR]

Previous studies have indicated that maternal exposure to particles with aerodynamic diameter <2.5 μm (PM 2.5) is associated with adverse birth outcomes. However, the critical exposure windows remain inconsistent. A retrospective cohort study was conducted in Huai River Basin, Henan, China during 2013–2018. Daily PM 2.5 concentration was collected using Chinese Air Quality Reanalysis datasets. We calculated exposures for each participant based on the residential address during pregnancy. Binary logistic regression was used to examine the trimester-specific association of PM 2.5 exposure with preterm birth (PTB), low birth weight (LBW) and term LBW (tLBW), and we further estimated monthly and weekly association using distributed lag models. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for each 10 μg/m3 increase in PM 2.5 exposure. Stratified analyses were performed by maternal age, infant gender, parity, and socioeconomic status (SES). In total, 196,780 eligible births were identified, including 4257 (2.2%) PTBs, 3483 (1.8%) LBWs and 1770 (0.9%) tLBWs. Maternal PM 2.5 exposure during the second trimester were associated with the risk of PTB and LBW. At the monthly level, the PTB and LBW risks were associated with PM 2.5 exposure mainly in the 4th -6th month. By estimating the weekly-specific association, we observed that critical exposure windows of PM 2.5 exposure and PTB were in the 18th- 27th gestational weeks. Stronger associations were found in younger, multiparous mothers and those with a female baby and in low SES. In conclusion, the results indicate that maternal PM 2.5 exposure during the second trimester was associated with PTB and LBW. Younger, multiparous mothers and those with female babies and in low SES were susceptible. [Display omitted] • Exposure to PM 2.5 showed adverse effects on PTB and LBW. • The susceptible windows mainly occurred in the second trimester of pregnancy. • The effects were modified by maternal age, parity, infant gender and SES. [ABSTRACT FROM AUTHOR]

In arid and semi-arid regions, water-quality problems are crucial to local social demand and human well-being. However, the conventional remote sensing-based direct detection of water quality parameters, especially using spectral reflectance of water, must satisfy certain preconditions (e.g., flat water surface and ideal radiation geometry). In this study, we hypothesized that drone-borne hyperspectral imagery of emergent plants could be better applied to retrieval total nitrogen (TN) concentration in water regardless of preconditions possibly due to the spectral responses of emergent plants on nitrogen removal and water purification. To test this hypothesis, a total of 200 groups of bootstrap samples were used to examine the relationship between the extracted TN concentrations from the drone-borne hyperspectral imagery of emergent plants and the experimentally measured TN concentrations in Ebinur Lake Oasis using four machine learning (ML) models (Partial Least Squares (PLS), Random Forest (RF), Extreme Learning Machine (ELM), and Gaussian Process (GP)). Through the introduction of the fractional order derivative (FOD), we build a decision-level fusion (DLF) model to minimize the regression results' biases of individual ML models. For individual ML model, GP performed the best. Still, the amount of uncertainty in individual ML models renders their performance to be subpar. The introduction of the DLF model greatly minimizes the regression results' biases. The DLF model allows to reduce potential uncertainties without sacrificing accuracy. In conclusion, the spectral response caused by nitrogen removal and water purification on emergent plants could be used to retrieve TN concentration in water with a DLF model framework. Our study offers a new perspective and a basic scientific support for water quality monitoring in arid regions. Image 1 • An indirect remote sensed method for retrieving total nitrogen concentration in water are proposed. • Fractional order derivative is an effective data mining technology for drone-borne hyperspectral data. • Decision-level fusion (DLF) model allows to reduce potential uncertainties without sacrificing accuracy. • Combined use of Bootstrap and DLF model is effective when dealing with small sample size. [ABSTRACT FROM AUTHOR]

[Display omitted] • The growth of exotic mangrove was significantly faster than that of native species. • The morphological patterns can be used to indicate the hotspots of mangroves. • Sonneratia species was found in the forest gap and forest edge of native mangroves. • The spread of Sonneratia species is fast in bare tidal flat and tidal creek zones. • Invasive potentials of Sonneratia mangrove in Shenzhen Bay should not be ignored. Mangroves are essential forest communities in tropical and subtropical coastal zones, providing unique ecological functions and significant social and economic value. Accurate and efficient monitoring of mangroves is crucial for their protection and management. The impact of exotic species on native species is still debated, especially for mangroves, the spatiotemporal dynamics of introduced species and native mangrove species need to be closely monitored. This study aimed to investigate how exotic mangrove species affects the spatial dynamics of mangroves in Shenzhen Bay, China. Yearly Landsat images from 2000 to 2011 and Chinese high spatial resolution images during 2012–2022 were obtained and the pixel-based and multiscale object-based methods were used to obtain mangrove distribution. The results showed that: (1) Overall, mangroves in the study area increased from 281.51 ha to 526.43 ha from 2000 to 2022, with different spatiotemporal patterns in Shenzhen (+5.14%) and Hong Kong (+2.38%); (2) rapid spread of Sonneratia species was one of the main contributions to the growth of mangrove hotspots, and growth hotspots were mainly concentrated in the estuary delta; and (3) Sonneratia mangrove species was dominant in interspecific competition, not only invading the habitat of local mangroves, but also preventing the expansion of native mangrove growth. The results provide a reference for fine-scale and accurate mangrove mapping with high spatial and temporal resolutions, and highlight the need to pay more attentions to the spread and invasive potential of exotic mangrove species in the study area, as well as cooperation in adjacent reserves. [ABSTRACT FROM AUTHOR]

[Display omitted] • For population modelling, remote sensing nighttime light data and nighttime LBS data were utilized. • Population maps of the Tibetan Plateau at the city level were modelled by geographically weighted regression. • The crowd exposure frequency characterized by nighttime LBS data contributed significantly to population modelling. • The model predicted more accurate population maps than three mainstream international population datasets. • The operational mechanism of the new demographic model was revealed. Population mapping is one of the fundamental materials for regional sustainability studies. Most scholars applied remote sensing data with excessive indicators to fit the population distribution. Nevertheless, over-complex models were lack of accuracy. This paper proposed a population model in the Qinghai-Tibet Plateau as a study area; the model has consisted of Human Activity Extent and Crowd Exposure Frequency. Performed remote sensing land cover data, nighttime light data, and LBS geographic big data as candidate indicators for exploratory regression experiments eventually developed an optimal population model assembled by nighttime LBS data and nighttime light data. The model fits significantly better at the city level (R2values of 0.9922) and reduces the error compared with other studies and publicly available datasets (%RMSE values of 6.83%). For the first time, the model proposes that Crowd Exposure Frequency based on nighttime LBS data can provide effective population simulation in the global, nighttime light data gain compensation for it. Nighttime light data plays a dominant role in densely populated areas; at the same time, nighttime LBS revised its overestimation. They modified each other to make the model accuracy significantly elevated. The modelling framework allows dynamic and low-cost population estimates of ecologically vulnerable areas and thus serves sustainable regional development. [ABSTRACT FROM AUTHOR]

The existing research on watershed ecological compensation pays less attention to the special basin (without significant relationship between upstream and downstream or the compensation subject and object are distributed on the left and right banks of the basin), which makes the previous research methods difficult to be effectively used. Therefore, it is necessary to explore a new method to solve the problem of ecological compensation in special basin. Taking Taihu Lake Basin (Jiangsu section) as an example, this paper first calculates the ecosystem service value (ESV) of the region from 2005 to 2018, and makes a spatial-temporal analysis; through the construction of land use intensity index system and coupling coordination degree model, the relationship between ESV and land use intensity is explored, and the reasons for its change are explored; finally, this paper constructs a compensation standard accounting model based on GDP, basin area and population, and applies it to the accounting of compensation standard and the identification of compensation subject and object. The results show that: from 2005 to 2018, the land use of this area changed greatly, mainly reflected in farmland and urban land; ESV decreased by 21.41%, which was mainly affected by the change of land use, the downturn of grain market, and farmers' going out to work; the coupling coordination degree of land use intensity and ESV in this area entered the stage of coordinated development from 2010, but the situation of each city was quite different; population, area and GDP have a great influence on compensation subject and object, and compensation standard. This study can not only provide a complete methodology for governments to implement ecological compensation in special basins, and improve the existing ecological compensation theory. [ABSTRACT FROM AUTHOR]

• Maternal exposure to temperature extremes and variability can increase the risk of GDM. • The susceptible time window for the effect of non-optimal temperature on GDM appeared in the second trimester. • No interaction between ambient temperature and air pollution on GDM were observed. Previous observational studies have shown that exposure to ambient temperature and air pollution were associated with the incidence of gestational diabetes mellitus (GDM). However, the susceptible time window of non-optimal temperature on GDM is still unknown, and the interaction with air pollution has not been examined. We conducted a prospective cohort study in Guangzhou, China to investigate the windows of susceptibility of temperature extremes and variability on the risk of GDM and to explore any interaction effect with air pollution. Daily maximum (T max), minimum temperature (T min) and diurnal temperature range (DTR) were obtained from Guangdong Meteorological Service. Distributed lag non-linear models with a logistic regression were applied to assess the effect of temperature extremes and DTR in different weeks of gestation on GDM. To examine the interaction effect, relative excess risk due to interaction index, attributable proportion and synergy index were calculated. There were 5,165 pregnant women enrolled, of which 604 were diagnosed with GDM (11.7%). Compared with a reference temperature (50th percentile of T max), we found that extreme high temperature (99th percentile of T max) exposure during 21st and 22nd gestational weeks was associated with an increased risk of GDM. Extreme low temperature (1st percentile of T max) exposure during 14th to 17th weeks increased the risk of GDM. We observed that per 1 °C increment of DTR during 21st to 24th weeks was associated with an elevated GDM risk. No interaction effect of temperature extremes or variability with air pollution on GDM were observed. Our results suggested that non-optimal temperature is an independent risk factor of GDM. The time window of susceptibility for extreme temperatures and DTR exposure on the risk of GDM generally occurred in second trimester of pregnancy. In the context of climate change, our study has important implications for reproductive health and justifies more research in different climate zones. [ABSTRACT FROM AUTHOR]

Aerosols are an important contributor to global atmospheric environmental changes and have critical effects on the global climate system and human health. Central Asia is one of the most important sources of dust aerosols in the world and produces a significant portion of global aerosols. Central Asia is also a scarce aerosol-data area, so it is of great significance to obtain and verify new aerosol data from this region. In this study, based on the aerosol optical depth (AOD) data from remote sensing (MYD04_L2) and ground-based observations (AERONET and Microtops II), the applicability of multiangle implementation of atmospheric correction (MAIAC) AOD in Central Asia was comprehensively analyzed, and the variation in AOD in Central Asia over the last 20 years was analyzed by the information entropy method. The results indicate that MAIAC AOD has good application prospects in Central Asia and can effectively compensate for the lack of observational data from Central Asia. MAIAC AOD exhibits excellent spatiotemporal consistency with MYD04 deep blue (DB) AOD and has a better ability than MYD04 DB AOD to describe local fine-scale features. Furthermore, MAIAC AOD demonstrates high consistency with ground-based AOD observations, showing high R (0.737) and low RMSE (0.067) values and having 65.2% of samples falling within the expected error (EE) envelope. When employing the ground-based AOD observations as a bridge, MAIAC exhibits superiority to MYD04 DB in both the richness number of valid high-quality retrievals and the retrieval accuracy of various evaluation indicators. The annual variation in AOD in Central Asia exhibits a unimodal distribution, with AOD being largest in April, followed by March and May, and comparable rangeability. Based on information entropy, interannual variation in AOD exists in most areas of Central Asia, with AOD in the Taklimakan Desert area being significantly increased and that in northern Central Asia (Kazakhstan) showing a downward trend. • MAIAC and MYD04 DB AODs exhibit good spatiotemporal consistency over Central Asia. • A good agreement between the AOD values from MAIAC AOD and AERONET was found. • Spatial and temporal variations are evaluated against MAIAC products. • The AOD annual variation in Central Asia is characterized by a unimodal distribution. [ABSTRACT FROM AUTHOR]

As an important system to solve cross regional water pollution, watershed ecological compensation has been widely used in the world. However, the existing studies mainly focus on the governments, while ignoring the important role of polluting enterprises in watershed ecological compensation. Thus, the established watershed ecological compensation mechanism is difficult to implement sustainably. Therefore, taking the local governments and polluting enterprises in the watershed as the research object, and studying the change process and influencing factors of their decision-making behavior is of great significance to attract polluting enterprises to join the watershed ecological compensation, and to formulate a sustainable watershed ecological compensation mechanism to solve the cross regional water pollution. Therefore, based on prospect theory and evolutionary game theory, this paper firstly establishes an evolutionary game model between local governments and polluting enterprises in Taihu Lake Basin; secondly, combined with simulation technology, their decision-making behaviors and influencing factors of watershed ecological compensation are studied. The results show that: (1) The initial probabilities will affect their decision-making behaviors; (2) The ecological compensation fee has little influence on the decision-making behaviors of polluting enterprises; (3) The increase of environmental tax rate has significant influence on the local governments' decision-making behaviors with low initial probabilities; (4) The improvement of supervision ability can promote local governments and polluting enterprises to reach a stable state faster; (5) The marginal decreasing degree of value function has a stronger influence on local governments than on polluting enterprises. This paper can provide suggestions for local governments to build a sustainable watershed ecological compensation mechanism including polluting enterprises, and provide the scientific basis for decision-makers of polluting enterprises whether to join watershed ecological compensation. Image 1 • The players will follow the prospect theory to make decisions in the short term, but the expectation theory in the long term. • Initial probabilities will affect decision-making behaviors of players. • Ecological compensation fee has little influence on polluting enterprises. • In the low probabilities, environmental tax rate has significant influence on the local government. • Marginal decreasing degree of value function has a stronger influence on local governments than on polluting enterprises. [ABSTRACT FROM AUTHOR]