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This study aims to evaluate and analyze the differences in sleep quality among epilepsy patients of different genders to provide a reference for determining the influencing factors of sleep quality based on logistic multiple regression analysis. A retrospective analysis was conducted on the clinical data of 220 epilepsy patients admitted to our hospital from May 2022 to November 2023. The patients were divided into low-quality and high-quality sleep groups. Data were obtained using a general information questionnaire survey and the Pittsburgh Sleep Quality Index (PSQI). The average PSQI score for male patients was 12.89 ± 3.13, while for female patients, it was 9.26 ± 2.60. The PSQI score for male patients was significantly higher than that for female patients (p < 0.05). Moreover, the influencing factors for sleep quality in epilepsy patients included gender, annual income, medical insurance, frequency of epilepsy onset and medication use. Male epilepsy patients suffer from poorer sleep quality compared to female patients. Factors such as gender, annual income, medical insurance, seizure frequency and medication use significantly affect sleep quality in epilepsy patients. It could be recommended that patients adhere to medical advice for managing epilepsy and that healthcare authorities enhance awareness and policy support to reduce seizures and improve patients’ quality of life.

Folate receptor autoantibody (FRAA) has caught increasing attention since its discovery in biological fluids of patients with autism spectrum disorder (ASD), but quantification and understanding of its function are still in their infancy. In this study, we aimed to quantify serum binding-FRAA and explore its relation with serum folate, vitamin B12 (VB12) and ferritin. We quantitated serum binding-FRAA in 132 ASD children and 132 typically-developing (TD) children, as well as serum levels of folate, VB12 and ferritin. The results showed that serum binding-FRAA in the ASD group was significantly lower than that in the TD group (p 0.05). However, there was significant difference in boys between these two groups, not girls. Additionally, the combination of nitrite and binding-FRAA showed potential diagnostic value in patients with ASD (AUC > 0.7). Moreover, in the ASD group, the level of folate was consistent with that of binding-FRAA, whereas in the TD group, the binding-FRAA level was high when the folate level was low. Altogether, these differences revealed that the low serum FRAA in autistic children was mediated by multiple factors, which deserves more comprehensive investigation with larger population and mechanistic studies.

The Huairou Solar Observing Station (HSOS) has conducted solar vector magnetic field observations for 40 years and developed multiple vector magnetographs (including one space magnetic field observation instrument). Using these accumulated magnetic field observation data, HSOS has achieved significant progress in solar physics research, including important advancements in the helicity sign rule of solar active regions, the helicity characteristics of strong and weak magnetic fields in active regions, the chromospheric magnetic field characteristics of the Sun, the evolution of magnetic fields in active regions, and the extraction of magnetic field characteristics for flare precursors. However, due to historical reasons, the calibration of vector magnetic field data in HSOS are not standardized. Therefore, this paper summarizes past historical experiences and introduces the standardized calibration procedure for vector magnetic field processing in detail. These calibration procedures are the basic steps of the calibration process for the space vector magnetograph (Full-Disk Vector MagnetoGraph, abbreviated as FMG) observation data, and are also applicable to the calibration of other instrument observation data at HSOS. They mainly include basic processing of polarization data and in-depth processing of vector magnetic fields. We believe that such calibration processing of the historical data collected by HSOS over the past 40 years will help us to accurately measure and analyze the solar magnetic field, further revealing the laws of solar activity and its impact on the Earth’s environment.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by diverse clinical features. EEG biomarkers such as spectral power and functional connectivity have emerged as potential tools for enhancing early diagnosis and understanding of the neural processes underlying ASD. However, existing studies yield conflicting results, necessitating a comprehensive, data-driven analysis. We conducted a retrospective cross-sectional study involving 246 children with ASD and 42 control children. EEG was collected, and diverse EEG features, including spectral power and spectral coherence were extracted. Statistical inference methods, coupled with machine learning models, were employed to identify differences in EEG features between ASD and control groups and develop classification models for diagnostic purposes. Our analysis revealed statistically significant differences in spectral coherence, particularly in gamma and beta frequency bands, indicating elevated long range functional connectivity between frontal and parietal regions in the ASD group. Machine learning models achieved modest classification performance of ROC-AUC at 0.65. While machine learning approaches offer some discriminative power classifying individuals with ASD from controls, they also indicate the need for further refinement.

It is debated whether the pervasive intergenic transcription from eukaryotic genomes has functional significance. Here, Xu et al. find that only 1–5% of yeast intergenic transcription is unattributable to chance promoter activity or neighboring gene expression.

The accurate, quantitative, and objective prediction of the brain age for premature infants will contribute to the exploration of brain maturity and catch-up growth. Traditional approaches rely heavily on a pediatrician’s clinical experience, which makes the whole process time-consuming and labor-intensive. To solve this problem, we propose a deep learning-based brain age prediction model for preterm infants via neonatal MRI for this purpose, and it is called as BAPNET for short. First of all, we collected a specific dataset including MR images of 281 preterm infants. Then, a pretraining model (DeepBrainNet) is applied as the main backbone, and transfer learning is utilized to enhance the baseline model by making knowledge transfer from the ImageNet dataset. The proposal can be viewed as a specific prediction model by absorbing knowledge enhancement from peripheral visual features. On a test set of 70 preterm infants held out from the original dataset, 2D-BPANET achieved results with an mean square error (MAE) of 1.15 and the 95% - 95% content tolerance interval for a difference (prediction and ground truth) of [−3.82, 3.39], whereas 3D-BPANET achieved better results with an MAE of 1.8 and a difference of [0.51, 3.09]. Meanwhile, we leverage heatmaps to verify the consistency between hindbrain regions and cortical fold regions outputted by our model and the latest studies of brain development in preterm infants. In conclusion, BPANET demonstrates that deep learning can estimate brain maturity in preterm infants and provides a reference standard for preterm infant brain development, which could be applied as a promising tool.

Semantic segmentation of high-resolution remote sensing urban images is widely used in many fields, such as environmental protection, urban management, and sustainable development. For many years, convolutional neural networks (CNNs) have been a prevalent method in the field, but the convolution operations are deficient in modeling global information due to their local nature. In recent years, the Transformer-based methods have demonstrated their advantages in many domains due to the powerful ability to model global information, such as semantic segmentation, instance segmentation, and object detection. Despite the above advantages, Transformer-based architectures tend to incur significant computational costs, limiting the model’s real-time application potential. To address this problem, we propose a U-shaped network with Transformer as the decoder and CNN as the encoder to segment remote sensing urban scene images. For efficient segmentation, we design a window-based, multi-head, focused linear self-attention (WMFSA) mechanism and further propose the global–local information modeling module (GLIM), which can capture both global and local contexts through a dual-branch structure. Experimenting on four challenging datasets, we demonstrate that our model not only achieves a higher segmentation accuracy compared with other methods but also can obtain competitive speeds to enhance the model’s real-time application potential. Specifically, the mIoU of our method is 68.2% and 52.8% on the UAVid and LoveDA datasets, respectively, while the speed is 114 FPS, with a 1024 × 1024 input on a single 3090 GPU.

Strengthening the construction of ecological civilization is an inevitable requirement for promoting high-quality economic and social development. It is of great significance to study the evolutionary trend and relationship between urban spatial structure and ecological efficiency to promote high-quality social development. Taking Shandong Province as an example, this paper obtains data on urban factors such as points of interest, night light, number of employed people at the end of the year and water supply; uses Anselin Local Moran’s I index to identify urban centers; analyzes the distribution form characteristics and agglomeration degree of urban space; and studies the spatial distribution characteristics and causes of differences in ecological efficiency based on the Super-SBM DEA model with undesirable output. The results show that all cities in Shandong Province show an inverse S-shaped circle decreasing trend, Laiwu city has the highest compactness (compactness index is 2.96), and Tai ‘an city has the lowest compactness index of 0.04. The level of eco-efficiency in Shandong Province is “low in the west and high in the east”, and the difference in eco-efficiency between regions is increasing year by year. Urban compactness has a “first increasing and then decreasing” effect on eco-efficiency. Technological innovation and industrial structure narrow the spatial difference in eco-efficiency, and the level of economic development expands it to a certain extent. This study aims to fill the gaps in existing research. By analyzing data on the evolution of urban spatial structure and resource consumption, it will reveal the trends of changes in the urban spatial structure of Shandong Province and study the impact of these changes on ecological benefits.

Rural building automatic extraction technology is of great significance for rural planning and disaster assessment; however, existing methods face the dilemma of scarce sample data and large regional differences in rural buildings. To solve this problem, this study constructed an image dataset of typical Chinese rural buildings, including nine typical geographical regions, such as the Northeast and North China Plains. Additionally, an improved remote sensing image rural building extraction network called AGSC-Net was designed. Based on an encoder–decoder structure, the model integrates multiple attention gate (AG) modules and a context collaboration network (CC-Net). The AG modules realize focused expression of building-related features through feature selection. The CC-Net module models the global dependency between different building instances, providing complementary localization and scale information to the decoder. By embedding AG and CC-Net modules between the encoder and decoder, the model can capture multiscale semantic information on building features. Experiments show that, compared with other models, AGSC-Net achieved the best quantitative metrics on two rural building datasets, verifying the accuracy of the extraction results. This study provides an effective example for automatic extraction in complex rural scenes and lays the foundation for related monitoring and planning applications.

Abstract Rice bacterial blight, caused by Xanthomonas oryzae pv. oryzae (Xoo), is one of the most serious diseases affecting rice production worldwide. Xa21 was the first disease resistance gene cloned in rice, which encodes a receptor kinase and confers broad resistance against Xoo stains. Dozens of components in the Xa21-mediated pathway have been identified in the past decades, however, the involvement of mitogen-activated protein kinase (MAPK) genes in the pathway has not been well described. To identify MAPK involved in Xa21-mediated resistance, the level of MAPK proteins was profiled using Western blot analysis. The abundance of OsMPK17 (MPK17) was found decreased during the rice-Xoo interaction in the background of Xa21. To investigate the function of MPK17, MPK17-RNAi and over-expression (OX) transgenic lines were generated. The RNAi lines showed an enhanced resistance, while OX lines had impaired resistance against Xoo, indicating that MPK17 plays negative role in Xa21-mediated resistance. Furthermore, the abundance of transcription factor WRKY62 and pathogenesis-related proteins PR1A were changed in the MPK17 transgenic lines when inoculated with Xoo. We also observed that the MPK17-RNAi and -OX rice plants showed altered agronomic traits, indicating that MPK17 also plays roles in the growth and development. On the basis of the current study and published results, we propose a “Xa21-MPK17-WRKY62-PR1A” signaling that functions in the Xa21-mediated disease resistance pathway. The identification of MPK17 advances our understanding of the mechanism underlying Xa21-mediated immunity, specifically in the mid- and late-stages.

To determine the composition and protective effect on Raw 264.7 cells of polysaccharide from Pseudostellaria heterophylla as a traditional Chinese medicinal, the main active ingredient-Pseudostellaria heterophylla polysaccharide (PHP) was extracted by water, precipitated by ethanol, and purified by anion exchange column chromatography. Gel permeation chromatography (GPC) and high performance liquid chromatography (HPLC) were used to determine molecular weight and monosaccharide composition of PHP. Ultraviolet spectroscopy (UV) and Fourier transform infrared spectroscopy (FT-IR) were used for primary structural characterization of PHP. Furthermore, the Raw 264.7 cell model was used to explore protective effect of PHP on Raw 264.7 cells induced by LPS. The results showed that the yield of crude polysaccharide of Pseudostellaria heterophylla was 8.23%, and the polysaccharide content was 51.47%. After purification by DEAE-52 column chromatography, the polysaccharide content increased to 80.52%, and the molecular weight distribution was between 500 to 92×103 Da. The monosaccharide components of PHP include glucose, galactose, arabinose, mannose and glucuronic acid. Cell experiments showed that the safe dose of PHP was 80~640 µg/mL and could increase the survival rate of cells induced by lipopolysaccharide (LPS). At a dose of 640 µg/mL, the survival rate increased by 7.21%, and it was found that PHP could improve the morphology of Raw 264.7 cells. In summary, in this study the polysaccharides from Pseudostellaria heterophylla were purified, the structure of PHP was characterized, and it was preliminarily confirmed that PHP had potential protective effect on LPS-induced damage to Raw 264.7 cells. This laid the foundation for the follow-up exploration of functional activity, structure-effect and dose-effect of PHP.

The wireless sensor networks (WSNs) are the core collecting network of the power internet of things (PIoT). With the development of PIoT, higher standards of timeliness and security are required for the data collection work done by the WSNs. In recent years, researchers have proposed a series of lightweight security protocols for identity authentication and data access in resource-constrained systems such as WSNs. But on one hand, these lightweight security protocols do not take into account of needs of the simultaneous access by multiple users, thus will not meet the requirements of PIoT; on the other hand, it does not take the entire process of remote accessing into account when calculating communication efficiency, and cannot fully reflect the advantage in efficiency. Therefore, this paper proposes a PIoT-oriented WSNs communication model based on PIoT’s characteristics such as multi-user, and higher standard of timeliness and security. It also designs a new identity authentication protocol for multi-gateway WSNs. Through the informal security analysis, the protocol proposed in this paper is proved to be more secure and efficient comparing to other multi-user access protocols.

Accurate and efficient classification maps of urban functional zones (UFZs) are crucial to urban planning, management, and decision making. Due to the complex socioeconomic UFZ properties, it is increasingly challenging to identify urban functional zones by using remote-sensing images (RSIs) alone. Point-of-interest (POI) data and remote-sensing image data play important roles in UFZ extraction. However, many existing methods only use a single type of data or simply combine the two, failing to take full advantage of the complementary advantages between them. Therefore, we designed a deep-learning framework that integrates the above two types of data to identify urban functional areas. In the first part of the complementary feature-learning and fusion module, we use a convolutional neural network (CNN) to extract visual features and social features. Specifically, we extract visual features from RSI data, while POI data are converted into a distance heatmap tensor that is input into the CNN with gated attention mechanisms to extract social features. Then, we use a feature fusion module (FFM) with adaptive weights to fuse the two types of features. The second part is the spatial-relationship-modeling module. We designed a new spatial-relationship-learning network based on a vision transformer model with long- and short-distance attention, which can simultaneously learn the global and local spatial relationships of the urban functional zones. Finally, a feature aggregation module (FGM) utilizes the two spatial relationships efficiently. The experimental results show that the proposed model can fully extract visual features, social features, and spatial relationship features from RSIs and POIs for more accurate UFZ recognition.

The traditional deep learning networks in remote sensing image building segmentation have problems such as incomplete internal extraction, low accuracy of edge segmentation, and difficult prediction of irregular targets. Based on this, this paper proposes the network SCA-Net, which fuses the dense connection feature pyramid (DCFP) module and the spatial and channel attention mechanisms (SCA) module, for high precision automatic extraction of building contours. First, the DCFP module is used to obtain a large range of multi-scale contextual information without sacrificing the spatial resolution of features, thus improving the recognition rate of small-sized buildings. Second, SCA module is used to enhance feature learning ability of building pixels and suppress irrelevant feature information input. Finally, a fully convolutional asymmetric encoder-decoder structure is designed to obtain the final building segmentation results by gradually fusing the features and decoding them. The results on the WHU dataset as well as the CHN dataset show that the extraction of buildings using this method has good extraction results and high accuracy performance, outperforming several comparable advanced networks.

Microalgae can effectively accumulate starch by using nutritional limitation methods in the context of bioalcohol fuel production. However, relatively few studies have focused on starch accumulation in microalgae and its molecular basis, especially under sulfur limitation conditions. In this study, the starch accumulation dynamics and physiological responses of Chlorella sorokiniana under sulfur starvation (SS) and sulfur replenishment (SR) conditions were investigated, and the genes involved in the transcriptional regulation were explored using RNA-seq. The starch content in C. sorokiniana cells significantly increased from 1.6% to 55.0% of dry weight within 24 h under SS conditions, and then, it decreased to 3.4% within 12 h after transition to SR conditions. However, cell growth was inhibited, and pigment content decreased under SS conditions. Using RNA-seq analysis, a total of 9720 differentially expressed genes (DEGs) induced by sulfur status were obtained. These genes were narrowed down to 454 starvation and replenishment cross-validated (SRV)-DEGs, among which 283 SRV-DEGs were significantly up-regulated and 171 SRV-DEGs were down-regulated under SS conditions, and returned to their previous state under SR conditions. The SRV-DEGs enriched in the sulfate metabolism pathway were all up-regulated under SS conditions after 6 h to speed up the sulfur metabolic cycle, and the transcriptional abundance of a sulfate transporter (SULTR4), cysteine synthase[O-acetylserine(thiol)-lyase] (OASTL), serine acetyltransferase (SAT), and methanethiol oxidase (SELENBP1) increased 8.6-fold, 12.6-fold, 8.7-fold, and 12.4-fold, respectively. Protein synthesis was correspondingly inhibited, which resulted in the reallocation of carbon and elevated the starch synthesis pathway, in which the expressions of glycogen branching enzyme (GBE) and starch synthase (SS) were up-regulated 12.0- and 3.0-fold, respectively. The fatty acid desaturase (FAD) and phosphatidic acid phosphatase (PAP) in the lipid synthesis pathway were strongly up-regulated 8.8- and 16.2-fold, respectively, indicating the competitive synthesis of lipids. The down-regulation of SRV-DEGs associated with carbon fixation, such as those in the Calvin cycle, possibly affected cell growth. The time-resolved transcriptional analysis identified the SRV-DEGs, revealing the underlying starch accumulation mechanism, as well as the relationship with cell growth and lipid synthesis.

Deep learning technology, such as fully convolutional networks (FCNs), have shown competitive performance in the automatic extraction of buildings from high-resolution aerial images (HRAIs). However, there are problems of over-segmentation and internal cavity in traditional FCNs used for building extraction. To address these issues, this paper proposes a new building graph convolutional network (BGC-Net), which optimizes the segmentation results by introducing the graph convolutional network (GCN). The core of BGC-Net includes two major modules. One is an atrous attention pyramid (AAP) module, obtained by fusing the attention mechanism and atrous convolution, which improves the performance of the model in extracting multi-scale buildings through multi-scale feature fusion; the other is a dual graph convolutional (DGN) module, the build of which is based on GCN, which improves the segmentation accuracy of object edges by adding long-range contextual information. The performance of BGC-Net is tested on two high spatial resolution datasets (Wuhan University building dataset and a Chinese typical city building dataset) and compared with several state-of-the-art networks. Experimental results demonstrate that the proposed method outperforms several state-of-the-art approaches (FCN8s, DANet, SegNet, U-Net, ARC-Net, BAR-Net) in both visual interpretation and quantitative evaluations. The BGC-Net proposed in this paper has better results when extracting the completeness of buildings, including boundary segmentation accuracy, and shows great potential in high-precision remote sensing mapping applications.

Consumers in electricity markets are becoming more proactive because of the rapid development of demand–response management and distributed energy resources, which boost the transformation of peer-to-peer (P2P) energy-trading mechanisms. However, in the P2P negotiation process, it is a challenging task to prevent private information from being attacked by malicious agents. In this paper, we propose a privacy-preserving, two-party, secure computation mechanism for consensus-based P2P energy trading. First, a novel P2P negotiation mechanism for energy trading is proposed based on the consensus + innovation (C + I) method and the power transfer distribution factor (PTDF), and this mechanism can simultaneously maximize social welfare and maintain physical network constraints. In addition, the C + I method only requires a minimum set of information to be exchanged. Then, we analyze the strategy of malicious neighboring agents colluding to attack in order to steal private information. To defend against this attack, we propose a two-party, secure computation mechanism in order to realize safe negotiation between each pair of prosumers based on Paillier homomorphic encryption (HE), a smart contract (SC), and zero-knowledge proof (ZKP). The energy price is updated in a safe way without leaking any private information. Finally, we simulate the functionality of the privacy-preserving mechanism in terms of convergence performance, computational efficiency, scalability, and SC operations.

New materials and technologies facilitate the design of thermochromic dynamic paintings. However, creating a thermochromic painting requires knowledge of electrical engineering and computer science, which is a barrier for artists and enthusiasts with non-technology backgrounds. Existing toolkits only support limited design space and fail to provide usable solutions for independent creation and for meeting the needs of the artists. We present ViviPaint, a toolkit that assists artists and enthusiasts in creating thermochromic paintings easily and conveniently. We summarized the pain points and challenges by observing a professional artist’s entire thermochromic painting creation process. We then designed ViviPaint consisting of a design tool and a set of hardware components. The design tool provides a GUI animation choreography interface, hardware assembly guidance, and assistance in assembly process. The hardware components comprise an augmented picture frame with a detachable structure and 24 temperature-changing units using Peltier elements. The results of our evaluation study (N = 8) indicate that our toolkit is easy to use and effectively assists users in creating thermochromic paintings.

The lumped hydrological model and empirical model have the problems of low accuracy and short forecasting period in real-time flood forecasting of small- and medium-sized rivers in a mountainous watershed. The sharing of underlying surface data such as high-resolution DEM, land use data, soil data, and the popularization and application of the Internet of Things, big data, cloud computing, and intelligent calculation methods makes distributed hydrological model an effective method for real-time runoff simulation and prediction. The topographic, kinematic, approximation, and integration (TOPKAPI) model is a distributed hydrological model whose physical mechanism developed gradually in the late 20th century. It has great advantages in real-time flood forecasting in small- and medium-sized watersheds. Based on the data required by the TOPKAPI model, in this study, 26 selected flood events were simulated from 2000 to 2013 at the outlet section of the upper reach of the Zhenjiang River in Guangdong Province, and the effect of application of the model in flood forecasting of small- and medium-sized rivers was evaluated. The results show that the pass rate (considering the peak discharge as the evaluation item) of 18 flood events in the calibration period was 66.67%, and that of 8 flood events in the validation period was 75%, while the mean Nash efficiency coefficient of the selected 26 flood events was 0.789. According to the simulation results, real-time flood forecasting should be closely combined with the dispatching of the small- and medium-sized reservoirs in the basin. The application of the TOPKAPI model can make a scientific and rapid analysis of the flood control situation in the whole basin and provide accurate information and maximum convenience for flood forecasting consultation and decision making. Additionally, it can improve the efficiency of disaster prevention and mitigation work in small- and medium-sized river basins, and has a major significance in enhancing the modernization level of flood forecasting.

Wetlands are dynamic landscapes and their spatial extent and types can change over time. Mapping wetland locations, types, and monitoring wetland typological changes have important ecological significance. The National Wetlands Inventory data suffer from two problems: the omission error that some wetlands are not mapped, and the out-of-date wetland types in many counties of the United States. To address these two problems, we proposed an automatic wetland classification model for newly mapped (or existing) wetland polygons lacking typological information. The research goals in this study were (1) to develop a nonparametric and automatic rule-based model to assign wetland types to palustrine wetlands using high-resolution remotely sensed data and (2) to quantify wetland typological changes based on the wetland types obtained from the previous step. The model is a direct application of the Cowardin et al. (1979) wetland classification system without modification. The input information for the proposed model includes Light Detection and Ranging (LiDAR)-derived vegetation height and color infrared aerial imagery-derived vegetation spectral information. We tested the model for the palustrine wetlands in Horry County, SC, and analyzed 29,090 palustrine wetland polygons (101,427 ha). The model achieved an overall agreement of 87% for wetland-type classification and showed the dynamics of wetland typological changes. This nonparametric model can be easily applied to other areas where wetland inventory needs updating.

Vegetation indices are widely used to derive land surface phenology (LSP). However, due to inconsistent illumination geometries, reflectance varies with solar zenith angles (SZA), which in turn affects the vegetation indices, and thus the derived LSP. To examine the SZA effect on LSP, the MODIS bidirectional reflectance distribution function (BRDF) product and a BRDF model were employed to derive LSPs under several constant SZAs (i.e., 0°, 15°, 30°, 45°, and 60°) in the Harvard Forest, Massachusetts, USA. The LSPs derived under varying SZAs from the MODIS nadir BRDF-adjusted reflectance (NBAR) and MODIS vegetation index products were used as baselines. The results show that with increasing SZA, NDVI increases but EVI decreases. The magnitude of SZA-induced NDVI/EVI changes suggests that EVI is more sensitive to varying SZAs than NDVI. NDVI and EVI are comparable in deriving the start of season (SOS), but EVI is more accurate when deriving the end of season (EOS). Specifically, NDVI/EVI-derived SOSs are relatively close to those derived from ground measurements, with an absolute mean difference of 8.01 days for NDVI-derived SOSs and 9.07 days for EVI-derived SOSs over ten years. However, a considerable lag exists for EOSs derived from vegetation indices, especially from the NDVI time series, with an absolute mean difference of 14.67 days relative to that derived from ground measurements. The SOSs derived from NDVI time series are generally earlier, while those from EVI time series are delayed. In contrast, the EOSs derived from NDVI time series are delayed; those derived from the simulated EVI time series under a fixed illumination geometry are also delayed, but those derived from the products with varying illumination geometries (i.e., MODIS NBAR product and MODIS vegetation index product) are advanced. LSPs derived from varying illumination geometries could lead to a difference spanning from a few days to a month in this case study, which highlights the importance of normalizing the illumination geometry when deriving LSP from NDVI/EVI time series.

Mn:0.15Pb(In1/2Nb1/2)O3-0.55Pb(Mg1/3Nb2/3)O3-0.30PbTiO3 (Mn:PIMNT) pyroelectric chips were prepared by a two-step annealing method. For the two steps, annealing temperatures dependence of microstructure, defects, surface stress, surface roughness, dielectric properties and pyroelectric properties were studied comprehensively. The controlling factors influencing the pyroelectric properties of the Mn:PIMNT crystals were analyzed and the optimum annealing temperature ranges for the two steps were determined: 600–700 °C for the first step and 500–600 °C for the second step. The pyroelectric properties of the thin Mn:PIMNT chips were significantly enhanced by the two-step annealing method via tuning oxygen vacancies and eliminating surface stress. Based on Mn:PIMNT pyroelectric chips annealed at the most favorable conditions (annealed at 600 °C for the first step and 500 °C for the second step), infrared detectors were prepared with specific detectivity D* = 1.63 × 109 cmHz1/2W−1, nearly three times higher than in commercial LiTaO3 detectors.

To reduce the workload of inspectors and improve the inspection efficiency of urban transmission towers, a new inspection method is proposed in this paper, in which an unmanned aerial vehicle (UAV) and vehicle cooperate with each other. We investigate the cooperative path planning problem of a UAV and a vehicle for transmission tower inspection and develop a new 0−1 integer programming model to address the problem. An odd-even layered genetic algorithm (O-ELGA) is proposed to efficiently solve the model. Finally, the effectiveness of the algorithm is further verified by simulation experiments.

BACKGROUND: Bone mineral density (BMD) increases progressively during childhood and adolescence and is affected by various genetic and environmental factors. The aim of this study was to establish reference values for lumbar BMD in healthy Chinese infants and young children and investigate its influencing factors. METHODS AND FINDINGS: Healthy children aged 0 to 3 years who underwent regular physical examinations at the Child Health Care Clinic of Hubei Maternal and Child Health Hospital (N = 11,898) were recruited for this study. We also chose 379 preterm infants aged 0 to 1 years to preliminarily explore the development of BMD in this special population. BMD (g/cm(2)) measurements of the lumbar spine (L2-L4) were carried out with dual-energy X-ray absorptiometry and a questionnaire was administered to full-term children's parents to gather information on various nutritional and lifestyle factors as well as mothers' nutritional supplement use during pregnancy. Lumbar BMD significantly increased with age among both boys and girls (p0.05), either among healthy reference children or preterm infants. However, BMD values in preterm infants were significantly lower than those in term infants 3 to 8 months old (p

We consider a model in which an outcome depends on two discrete treatment variables, where one treatment is given before the other. We formulate a three-equation triangular system with weak separability conditions. Without assuming assignment is random, we establish the identification of an average structural function using two-step matching. We also consider decomposing the effect of the first treatment into direct and indirect effects, which are shown to be identified by the proposed methodology. We allow for both of the treatment variables to be non-binary and do not appeal to an identification-at-infinity argument.