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With the wide application of graphene oxide nanoparticles (GONPs), a great amount of GONP waste is discarded and concentrated in landfills. It has been proven that GONPs have strong toxicity and could gather toxic substances due to their high adsorption capacity. GONPs will seriously pollute the surrounding environment if they leak through the geosynthetic clay liner (GCL) in landfills. To investigate various factors (temperature, ionic strength (IS) and humic acid (HA)) on the transport and retention of GONPs in the GCL, a self-designed apparatus was created and column tests were carried out. The experimental results show that GONPs could be transported through the GCL. The mobility and sorption ratio of GONPs in GCL decreased with an increase in temperature and IS, and increased with an increase in HA. The temperature had little effect on the deposition ratio of GONPs in the GCL. The deposition ratio of GONPs in the GCL increased with IS, and decreased with an increase in HA. The transport of GONPs in GCL, glass beads and quartz sand was compared, and the results show that the retention ability of the GCL is much better than other porous materials. The experimental results could provide significant references for the pollution treatment in landfills. [ABSTRACT FROM AUTHOR]

This study focuses on the effect of Cu doping on the thermoelectric properties of BiSbTe3 (BST) alloys. Firstly, we successfully prepared high-quality BST alloys using the KCl solvent method and selected the most promising BST(KCl)3.5 sample from them, followed by Cu doping experiments. The experimental results show that Cu doping significantly improves the crystal quality and density of the alloy, thereby increasing the material's mobility and reducing the resistivity, leading to an improvement in the power factor. However, Cu doping also introduces a large number of dislocations and lattice distortions. Additionally, the highly doped samples contain Cu2Te and BST superlattice structures, which further enhance the scattering of heat transfer phonons, significantly reducing the lattice thermal conductivity. In the experiment, Cu-doped samples exhibited extremely low lattice thermal conductivity, as low as 0.32 W m−1 K−1 at 300 K. These improvements in structure and performance have greatly enhanced the thermoelectric efficiency of Cu-doped samples, leading to a significant increase in their ZT values. Particularly, in samples with a Cu doping level of x = 0.01, the maximum ZT value was 1.47 at 400 K, and the average ZT value reached 1.28 in the temperature range of 300 K to 450 K. These findings underscore the wide-ranging application potential of Cu-doped BiSbTe3 alloys in room temperature refrigeration and power generation. The study provides crucial insights into the characteristics of Cu-doped BiSbTe3 alloys, while also contributing to the optimization of thermoelectric material design and the advancement of sustainable energy conversion technologies. [ABSTRACT FROM AUTHOR]

A large number of non-degradable materials have severely damaged the ecological environment. Now, people are increasingly pursuing the use of environmentally friendly materials to replace traditional chemical materials. Polyhydroxyalkonates (PHAs) are receiving increasing attention because of the unique biodegradability and biocompatibility they offer. However, the applications of PHAs are still limited due to high production costs and insufficient study. This project examines the optimal electrospinning parameters for the production of PHA-based fibrous membranes for air filtration. A common biodegradable polyester, Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), was electrospun into a nanofibrous membrane with a well-controlled surface microstructure. In order to produce smooth, bead-free fibers with micron-scale diameters, the effect of the process parameters (applied electric field, solution flow rate, inner diameter of hollow needle, and polymer concentration) on the electrospun fiber microstructure was optimized. The well-defined fibrous structure was optimized at an applied electric field of 20 kV, flow rate of 0.5 mL/h, solution concentration of 12 wt.%, and needle inner diameter of 0.21 mm. The morphology of the electrospun PHBV fibrous membrane was observed by scanning electron microscopy (SEM). Fourier transform infrared (FTIR) and Raman spectroscopy were used to explore the chemical signatures and phases of the electrospun PHBV nanofiber. The ball burst strength (BBS) was measured to assess the mechanical strength of the membrane. The small pore size of the nanofiber membranes ensured they had good application prospects in the field of air filtration. The particle filtration efficiency (PFE) of the optimized electrospun PHBV fibrous membrane was above 98% at standard atmospheric pressure. [ABSTRACT FROM AUTHOR]

In this study, Bi2Se3 thermoelectric materials with high density, low thermal conductivity, and excellent thermoelectric performance are prepared using a Se slow-release method. It was found that the number of Se defects in the material could be effectively reduced by changing the content of NaCl solvent, and a certain number of Na atoms are introduced into the Bi2Se3 matrix. The carrier concentration of Bi2Se3 is greatly optimized due to the synergistic effect of the two, leading to a gradual increase in the Seebeck coefficient from −94 to −122 μV K−1 at room temperature. Simultaneously, we conducted an in-depth investigation into the effects of Na doping and Se vacancies on the electronic structure of the materials. Our findings indicate that the presence of these two defects gives rise to resonance energy levels and sharpens the density of state peaks near the Fermi level. This discovery further underscores the potential for enhancing thermoelectric performance. Finally, the Bi2Se3(NaCl)10 sample achieved the maximum ZT value of 0.69 at 550 K, and the average ZT within the temperature detection range reached 0.36, increasing the thermoelectric performance of the material. [ABSTRACT FROM AUTHOR]

Bismuth telluride (Bi 2 Te 3) is a widely used thermoelectric (TE) material in practical applications. However, it contains expensive and toxic tellurium, a promising TE material should include low-cost and non-toxic element. Bismuth selenide (Bi 2 Se 3) is similar to Bi 2 Te 3 in electronic and crystal structure. The electron effective mass of Bi 2 Se 3 is smaller than that of Bi 2 Te 3 , while its electrical transport performance is inferior to that of Bi 2 Te 3. Therefore, in this study, Cl and K co-doped Bi 2 Se 3 thermoelectric material with high density, low thermal conductivity, and excellent thermoelectric performance was prepared using KCl as the solvent. It is found that the effective electron mass and carrier concentration of the doped sample is effectively improved, which significantly increases the doped sample's electrical transport properties and ZT value. Finally, the Bi 2 Se 3 (KCl) 3.5 sample reached a maximum ZT value of 0.79 at 550 K, and the average ZT in the measured temperature range reached 0.45. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) target detection plays a crucial role in military surveillance, earth observation, and disaster monitoring. With the development of deep learning (DL) and SAR imaging technology, numerous SAR target detection methods have been proposed and achieved better detection results. However, detecting different categories of SAR vehicle targets is still challenging due to the influence of coherent speckle noises and background clutter. This article presents a novel attention feature fusion awareness network (AFFNet) for vehicle target detection in SAR images. Specifically, we propose a multi-scale semantic attention (MSSA) module to obtain multi-scale and semantic features of target region; the variable multi-scale feature fusion (VMSFF) module is introduced to effectively fuse different feature information and alleviate target deformation interference by establishing feature correlation; the part feature awareness (PFA) module is used to obtain unique attribute of different vehicle targets to generate accurate anchor boxes. In addition, we design a candidate boundary box selection scheme, which can effectively adapt to SAR targets with different scales and categories. Overall, AFFNet is designed based on the SAR imaging mechanism and target physical feature information. To evaluate the performance of the proposed method, extensive experiments are conducted on the MSTAR dataset. The experiment results show that the proposed AFFNet obtains the mAP of 98.36 % and 97.26 % on standard operating conditions (SOCs) and extended operating conditions (EOCs), which is more efficient than the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Many educational and psychological studies have shown that the development of students is generally step-by-step (i.e. ordinal development) to a specific level. This study proposed a novel longitudinal learning diagnosis model with polytomous attributes to track students' ordinal development in learning. Using the concept of polytomous attributes in the proposed model, the learning process of a specific skill, from non-mastery to mastery, can be divided into multiple ordinal steps in order to better characterize the learning trajectory. The results of an empirical study conducted to explore the performance of the proposed model indicated that it could adequately diagnose the ordinal development of skills in longitudinal assessments. A simulation study was also conducted to examine the estimation accuracy of general ability and the classification accuracy of attributes of the proposed model in different simulated conditions. [ABSTRACT FROM AUTHOR]

This study used Bayesian Regulated Neural Networks (BRNN) with 10-fold cross-validation to accurately forecast fading effects of plasma treatment on cotton fabrics for a given set of parameters. By training six independent BRNN models, a reduction in model complexity and an enhancement in generalizability to unknown datasets were achieved. The input comprises plasma treatment parameters and color measurements of the cotton fabric before fading, while the output comprises color measurements after fading. The plasma treatment parameters included color depth, air (oxygen) concentration, water content and treatment time. Color measurements included CIE L*a*b*C*h and K/S values. Furthermore, 162 datasets derived from two-color mixed-dye cotton fabrics were utilized for training and testing. The outcomes revealed superior prediction performance of the BRNN compared to the Levenberg-Marquardt Neural Networks, with R2 values approaching 1 and 82.35% to 94.12% of the sample predictions lying within the acceptable color difference range. Through global sensitivity analysis, the impact of treatment parameters on fading effects was quantified, providing a scientific basis for parameter adjustment. This study not only elucidated the mechanism of plasma treatment-induced fading but also offers effective prediction tools for the intelligent and digital development of the fashion clothing fading domain. [ABSTRACT FROM AUTHOR]

Dynamic monitoring of building environments is essential for observing rural land changes and socio-economic development, especially in agricultural countries, such as China. Rapid and accurate building extraction and floor area estimation at the village level are vital for the overall planning of rural development and intensive land use and the "beautiful countryside" construction policy in China. Traditional in situ field surveys are an effective way to collect building information but are time-consuming and labor-intensive. Moreover, rural buildings are usually covered by vegetation and trees, leading to incomplete boundaries. This paper proposes a comprehensive method to perform village-level homestead area estimation by combining unmanned aerial vehicle (UAV) photogrammetry and deep learning technology. First, to tackle the problem of complex surface feature scenes in remote sensing images, we proposed a novel Efficient Deep-wise Spatial Attention Network (EDSANet), which uses dual attention extraction and attention feature refinement to aggregate multi-level semantics and enhance the accuracy of building extraction, especially for high-spatial-resolution imagery. Qualitative and quantitative experiments were conducted with the newly built dataset (named the rural Weinan building dataset) with different deep learning networks to examine the performance of the EDSANet model in the task of rural building extraction. Then, the number of floors of each building was estimated using the normalized digital surface model (nDSM) generated from UAV oblique photogrammetry. The floor area of the entire village was rapidly calculated by multiplying the area of each building in the village by the number of floors. The case study was conducted in Helan village, Shannxi province, China. The results show that the overall accuracy of the building extraction from UAV images with the EDSANet model was 0.939 and that the precision reached 0.949. The buildings in Helan village primarily have two stories, and their total floor area is 3.1 × 105 m2. The field survey results verified that the accuracy of the nDSM model was 0.94; the RMSE was 0.243. The proposed workflow and experimental results highlight the potential of UAV oblique photogrammetry and deep learning for rapid and efficient village-level building extraction and floor area estimation in China, as well as worldwide. [ABSTRACT FROM AUTHOR]

Previous studies have found that participants use two cognitive strategies—constructive matching and response elimination—in responding to items in the Raven's Advanced Progressive Matrices (APM). This study proposed a multi-strategy psychometric model that builds on item responses and also incorporates eye-tracking measures, including but not limited to the proportional time on matrix area (PTM), the rate of toggling (ROT), and the rate of latency to first toggle (RLT). By jointly analyzing item responses and eye-tracking measures, this model can measure each participant's intelligence and identify the cognitive strategy used by each participant for each item in the APM. Several main findings were revealed from an eye-tracking-based APM study using the proposed model: (1) The effects of PTM and RLT on the constructive matching strategy selection probability were positive and higher for the former than the latter, while the effect of ROT was negligible. (2) The average intelligence of participants who used the constructive matching strategy was higher than that of participants who used the response elimination strategy, and participants with higher intelligence were more likely to use the constructive matching strategy. (3) High-intelligence participants increased their use of the constructive matching strategy as item difficulty increased, whereas low-intelligence participants decreased their use as item difficulty increased. (4) Participants took significantly less time using the constructive matching strategy than the response elimination strategy. Overall, the proposed model follows the theory-driven modeling logic and provides a new way of studying cognitive strategy in the APM by presenting quantitative results. • The multi-strategy eye-tracking model can measure intelligence and identify strategy. • High-intelligence people are likely to use constructive matching. • Low-intelligence people tend to use response elimination for difficult items. • The proportional time on matrix has the greatest effect on the strategy selection. [ABSTRACT FROM AUTHOR]

Moso bamboo plantations suffer from phosphorus (P) limitations. A better understanding of soil P cycling contributes to the sustainable management and development of the bamboo forests. In this study, different phosphate-solubilizing bacteria (PSB) were applied to soils to stimulate bamboo growth, and the metagenomics method was employed to investigate the changes in soil microbial communities and relative abundances of functional genes. Meanwhile, soil properties and bamboo biomass and physiological indices were also quantitatively analyzed. The PSB inoculations changed soil P fractions and significantly elevated available P (AP) content by stimulating phosphatase activity and functional genes involved in P-transformation. The PSB inoculations significantly stimulated soil acid phosphatase activities and enhanced the relative abundances of functional genes associated with inorganic P-solubilization and organic P-mineralization. Moreover, increased soil available nutrients were beneficial to the moso bamboo growth, and the bamboo biomass in the different treatments increased by from 32.43 % to 69.04 % compared with the control treatment. Significant increases in root activity, carotenoid, chlorophyll a and total chlorophyll contents after the PSB inoculations also contributed to moso bamboo growth. The PSB strain inoculations shaped soil microbial communities and increased microbial connections. Besides adjusting soil P supplies and functional genes, the PSB also promoted the moso bamboo growth by increasing soil mineral N, AK and bamboo chlorophyll contents and stimulating functional microorganisms. Our study could provide a theoretical basis to improve soil nutrient utilization for moso bamboo with the PSB inoculations. • The PSB accelerated soil P transformation by functional gene and phosphatase. • The PSB significantly shaped microbial community structures and connections. • Available nutrient contributed to bamboo growth and physiological indexes. • Chlorophyll and soil K increased by the PSB had positive effects on bamboo biomass. • The PSB significantly contributed to carotenoid and root activity of bamboo. [ABSTRACT FROM AUTHOR]

The variation in seasonal conditions causes an impact on the wood–water relations and dimensional stability like shrinkage and swelling with solar drying technology, leading to discrepancies in drying qualities. To address this processing problem, we studied the pattern of drying kinetics and dimensional changes in four seasons and Northeast China. The experimental analysis shows that the Page, the Aghbashlo et al., the two-term exponential, and the Weibull models may be applied to emulate solar drying kinetics during different seasons of poplar timber. The results also show that the diffusion coefficients fall below the range of 6.01 × 10–11 to 1.52 × 10–9 m2/s, the activation energy mean is 32.74 kJ mol−1, and the R2 is 0.84 and the wood dimensional changes during the drying are in descending order of summer, spring, winter, and fall. Conclusively, solar timber drying is seasonally affected, and related studies are needed to provide a theoretical basis to develop the numerical modeling, and a thermo-mass-mechanical multi-physics coupled model for the seasonality of solar drying. This research will eventually provide references for improving the application of solar drying technology in different regions of the world, such as the development of heat storage media in different seasons, the upgrade of drying equipment, and the standardization of solar intermittent drying schedules. [ABSTRACT FROM AUTHOR]

The impacts of changes in hydraulic retention time (HRT) on nitrogen elimination, the activity of sludge and the structure of microbial communities in a sequencing batch reactor under the joint stress of ibuprofen (IBP) and ciprofloxacin (CIP) were investigated. Under the combined stress of IBP (belonged to nonsteroidal anti-inflammatory drugs) and CIP (belonged to antibiotics), the elimination level of ammonia nitrogen, chemical oxygen demand, and the rate of specific ammonia oxidation, specific nitrite oxidation, specific nitrate reduction in activated sludge declined with the shortening of HRT. Under the joint stress of IBP and CIP, the content of polysaccharide (PS) and protein (PN) together with the ratio of PN to PS in the extracellular polymeric substances grew with the shortening of HRT, and the content of aromatic proteins increased significantly, which indicated that aromatic proteins were the major protective response substances to the shortening of HRT. Under the joint stress of IBP and CIP, with the reduction of HRT, and the reduction rate of relative abundance of nitrifying bacteria was more evident than that of denitrifying bacteria. This exploration gives novel thoughts for understanding the impacts of HRT on biological nitrogen removal of wastewater under combined stress of IBP and CIP. [ABSTRACT FROM AUTHOR]

Aerial Image Semantic segmentation based on convolution neural networks (CNNs) has made significant process in recent years. Nevertheless, their vulnerability to adversarial example attacks could not be neglected. Existing studies typically focus on adversarial attacks for image classification, ignoring the negative effect of adversarial examples on semantic segmentation. In this article, we systematically assess and verify the influence of adversarial attacks on aerial image semantic segmentation. Meanwhile, based on the robust characteristics of global features, we construct a novel global feature attention network (GFANet) for aerial image semantic segmentation to solve the threat of adversarial attacks. GFANet uses the global context encoder (GCE) to obtain the context dependencies of global features, introduces the global coordinate attention mechanism (GCAM) to enhance the global feature representation to suppress adversarial noise, and the feature consistency alignment (FCA) is used for feature calibration. In addition, we construct a universal adversarial training strategy to improve the robustness of the semantic segmentation model against adversarial example attacks. Extensive experiments on three aerial image datasets demonstrate that GFANet is more robust against adversarial attacks than existing state-of-the-art semantic segmentation models. [ABSTRACT FROM AUTHOR]

It is known that nested states composed of ring-shaped hidden vorticity quantum droplets (QDs) with topological charges (S 2 = − S 1 , referred to as hidden vorticity) are stable in binary Bose-Einstein condensates (BECs). However, this nested multiring states with hidden vorticity are very extreme cases. Hence, the nested patterns with different topological charges are worth studying. In this paper, we use the imaginary-time-integration method to obtain for the first time the ring-shaped QDs with high topological charge (up to S 2 = 15) in a radially periodic potential. We analyzed in detail the influence of the modulation depth on the stability of the ring-shaped QDs. The trapping capacity of the troughs is studied with fixed modulation depth. Finally, we obtain the nested patterns constructed by ring-shaped QDs with different topological charges, greatly expanding the encoding of topological states. The results provide reference for designing data storage devices using vortex topology charges. • We have studied the ring-shaped QDs with arbitrary topological charges in a radially periodic potential. • We have analyzed stability intervals for the ring-shaped QDs, and studied the trapping capacity of the circular troughs. • We have obtained the stable nested vortex modes constructed by ring-shaped QDs with different topological charges. [ABSTRACT FROM AUTHOR]

Assessing and responding to threats is vital in everyday life. Unfortunately, many mental illnesses involve impaired risk assessment, affecting patients, families, and society. The brain processes behind these behaviors are not well understood. We developed a transgenic mouse model (disrupted-in-schizophrenia 1 [DISC1]-N) with a disrupted avoidance response in risky settings. Our study utilized single-nucleus RNA sequencing and path-clamp coupling with real-time RT-PCR to uncover a previously undescribed group of glutamatergic neurons in the basolateral amygdala (BLA) marked by Wolfram syndrome 1 (WFS1) expression, whose activity is modulated by adjacent astrocytes. These neurons in DISC1-N mice exhibited diminished firing ability and impaired communication with the astrocytes. Remarkably, optogenetic activation of these astrocytes reinstated neuronal excitability via D-serine acting on BLAWFS1 neurons' NMDA receptors, leading to improved risk-assessment behavior in the DISC1-N mice. Our findings point to BLA astrocytes as a promising target for treating risk-assessment dysfunctions in mental disorders. [Display omitted] • DISC1-N mutation impacts risk assessment • BLAWFS1 neuron-astrocyte dysfunction in DISC1-N mice • Stimulating astrocytes revives BLAWFS1 neuron fire in DISC1-N • Astrocytes activation restores risk response via D-serine at NMDA receptors Zhou et al. discover that DISC1-N mice display impaired risk assessment. A specific group of glutamatergic neurons in the BLA exhibit diminished firing ability and impaired communication with astrocytes. Activation of these astrocytes reinstates neuronal excitability via these neurons' NMDA receptors, leading to improved risk-assessment behavior in these mice. [ABSTRACT FROM AUTHOR]

High dynamic range (HDR) images have recently drawn much attention in multimedia community. In this paper, we proposed an HDR image steganography method based on deep learning, which is for HDR images with OpenEXR format. To the best of our knowledge, this is the first steganography method that applies deep learning to HDR image steganography, and the first steganography method that hides images in HDR images. The LDR secret image is hidden in the mantissa of the HDR cover image of the same size through a hidden network, and recovered through an extraction network in the receiver. Experimental results show that the proposed algorithm has advantages in security, robustness and capacity compared with other “hiding image in image” algorithms. [ABSTRACT FROM AUTHOR]

Considering the significant influence of clouds on thermal infrared (TIR) data sea surface temperature (SST) retrieval, this study focuses on the reduction of cloud influence on Moderate Resolution Imaging Spectroradiometer (MODIS) longwave SST retrieval. First, the quality level (QL) 3 MODIS SSTs are classified into cloudy or clear-sky pixels based on MODIS cloud mask products. The cloudy SST pixels, flagged as confident cloudy or probably cloudy in the cloud masks, are further classified into three groups according to their associated daytime cloud-top and optical properties. Taking the SSTs measured by 11 buoys over one year as the reference data, those three groups of cloudy SSTs are significantly underestimated, with biases of −33.45 °C, −6.35 °C, and −4.72 °C. The QL 3 clear-sky SSTs are identified as probably and confident clear in the cloud masks and are not influenced by clouds, with a bias of nearly 0 °C. Then, three support vector regression (SVR) models are individually proposed for the three groups of cloudy SSTs. Taking the cloud-top and optical parameters as inputs of the proposed SVR models, we can obtain SSTs with a bias of 0 °C and a root-mean-square error (RMSE) of less than 1.6 °C for the three groups of cloudy SSTs. The method proposed in this study shows the potential for TIR SST estimation under some cloudy conditions using satellite remote sensing cloud products. Considering the RMSE of 0.4 °C in operational sea surface temperature and sea ice analysis, further study is needed before actual applying the method proposed in this study. [ABSTRACT FROM AUTHOR]

Head and neck squamous cell carcinoma (HNSCC) is a common malignant tumour. Despite advancements in surgery, radiotherapy and chemotherapy, which have improved the prognosis of most patients, a subset of patients with poor prognoses still exist due to loss of surgical opportunities, postoperative recurrence, and metastasis, among other reasons. The tumour microenvironment (TME) is a complex organization composed of tumour, stromal, and endothelial cells. Communication and interaction between tumours and immune cells within the TME are increasingly being recognized as pivotal in inhibiting or promoting tumour development. Previous studies on T cells in the TME of HNSCC have yielded novel therapeutic possibilities. However, the function of B cells, another adaptive immune cell type, in the TME of HNSCC patients has yet to be determined. Recent studies have revealed various distinct subtypes of B cells and tertiary lymphoid structures (TLSs) in the TME of HNSCC patients, which are believed to impact the efficacy of immune checkpoint inhibitors (ICIs). Therefore, this paper focuses on B cells in the TME to explore potential directions for future immunotherapy for HNSCC. [ABSTRACT FROM AUTHOR]

The occurrence of earthquakes has been recognized as a natural disaster capable of significantly impacting land use/cover change (LUCC) patterns. During the post-earthquake recovery and reconstruction phase, obtaining an accurate and scientific grounded understanding of the LUCC process in the affected area is essential for analyzing the subsequent changes in recovery and identifying the underlying driving forces. In this study, we selected the 2014 Ludian Ms6.5 earthquake as the focal point of our research. Through the utilization of Google Earth Engine and Landsat 8 OLI remote sensing image, we employed the Random Forest classification algorithm to analyze the structural and transformational changes of LUCC in Ludian County prior to and post the earthquake. In addition, we produced comprehensive post-earthquake damage map and recovery map through the LUCC transfer amount to analyze each township's damage and recovery degree quantitatively. Finally, we employed the geographic detector to investigate the driving factors contributing to the post-earthquake recovery degree. As is demonstrated by our results, the earthquake inflicted substantial damage on cropland, grassland, and built-up areas, with Longtoushan Town and Huodehong Town suffering the most severe damage. After three years, we found that cropland and built-up areas had been restored, while previously unused land had been successfully treated, yielding initial restoration progress. Upon reaching the six-year mark, built-up areas continued to expand, while cropland acreage exhibited a decline in comparison to the P1 and P2 phases. Concurrently, forest and grassland areas increased relative to the P1 and P2 phases, signifying Ludian County's successful restoration. The factor detection results show that socio-economic factors are potent factors that promote post-earthquake recovery, and the earthquake factors also have a continuous impact. The research results can serve as a valuable reference for informing local disaster area reconstruction planning as well as ensuring effective protection of ecological environment. [ABSTRACT FROM AUTHOR]

The electric field detector of the CSES satellite has captured a vast number of lightning whistler events. To recognize them effectively from the massive amount of electric field detector data, a recognition algorithm based on speech technology has attracted attention. However, this approach has failed to recognize the lightning whistler events which are contaminated by other low-frequency electromagnetic disturbances. To overcome this limitation, we apply the single-channel blind source separation method and audio recognition approach to develop a novel model, which consists of two stages. (1) The training stage: Firstly, we preprocess the electric field detector wave data into the audio fragment. Then, for each audio fragment, mel-frequency cepstral coefficients are extracted and input into the long short-term memory network for training the novel lightning whistler recognition model. (2) The inference stage: Firstly, we process each audio fragment with the single-channel blind source to generate two different sub-signals. Then, for each sub-signal, the mel-frequency cepstral coefficient features are extracted and input into the lightning whistler recognition model to recognize the lightning whistler. Finally, the two results above are processed by decision fusion to obtain the final recognition result. Experimental results based on the electric field detector data of the CSES satellite demonstrate the effectiveness of the algorithm. Compared with classical methods, the accuracy, recall, and F1-score of this algorithm can be increased by 17%, 62.2%, and 50%, respectively. However, the time cost only increases by 0.41 s. [ABSTRACT FROM AUTHOR]

The outstanding performance of deep neural networks (DNNs) in multiple computer vision in recent years has promoted its widespread use in aerial image semantic segmentation. Nonetheless, prior research has demonstrated the high susceptibility of DNNs to adversarial attacks. This poses significant security risks when applying DNNs to safety-critical earth observation missions. As an essential means of attacking DNNs, data poisoning attacks destroy model performance by contaminating model training data, allowing attackers to control prediction results by carefully crafting poisoning samples. Toward building a more robust DNNs-based aerial image semantic segmentation model, in this study, we proposed a robust invariant feature enhancement network (RIFENet) that can resist data poisoning attacks and has superior semantic segmentation performance. The constructed RIFENet improves the resistance to poisoning attacks by extracting and enhancing robust invariant features. Specifically, RIFENet uses a texture feature enhancement module (T-FEM), structural feature enhancement module (S-FEM), global feature enhancement module (G-FEM), and multi-resolution feature fusion module (MR-FFM) to enhance the representation of different robust features in the feature extraction process to suppress the interference of poisoning samples. Experiments on several benchmark aerial image datasets demonstrate that the proposed method is more robust and exhibits better generalization than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Profiting from the powerful feature extraction and representation capabilities of deep learning (DL), aerial image semantic segmentation based on deep neural networks (DNNs) has achieved remarkable success in recent years. Nevertheless, the security and robustness of DNNs deserve attention when dealing with safety-critical earth observation tasks. As a typical attack pattern in adversarial machine learning (AML), backdoor attacks intend to embed hidden triggers in DNNs by poisoning training data. The attacked DNNs behave normally on benign samples, but when the hidden trigger is activated, its prediction is modified to a specified target label. In this article, we systematically assess the threat of backdoor attacks to aerial image semantic segmentation tasks. To defend against backdoor attacks and maintain better semantic segmentation accuracy, we construct a novel robust generative adversarial network (RFGAN). Motivated by the sensitivity of human visual systems to global and edge information in images, RFGAN designs the robust global feature extractor (RobGF) and the robust edge feature extractor (RobEF) that force DNNs to learn global and edge features. Then, RFGAN uses robust global and edge features as guidance to obtain benign samples by the constructed generator, and the discriminator to obtain semantic segmentation results. Our method is the first attempt to address the backdoor threat to aerial image semantic segmentation by constructing the robust DNNs model architecture. Extensive experiments on real-world scenes aerial image benchmark datasets demonstrate that the constructed RFGAN can effectively defend against backdoor attacks and achieve better semantic segmentation results compared with the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Over the past two decades, the location and morphology of the coastline, as well as the land use/land cover (LULC) in the Circum-Bohai region in China, have undergone significant changes due to rapid industrialization and urbanization. Analyzing the temporal and spatial variation in coastal lines and LULC can provide a meaningful basis for the rational allocation of land resources. Using Landsat TM/OLI series dates from the Google Earth Engine (GEE) platform, this study applied the Linear Superposition Water Index (LSWI) and the Otsu threshold method (OTSU) algorithm to extract and analyze the coastline of the Circum-Bohai region. Additionally, the Random Forests (RF) method was employed to extract LULC information in the coastal zone. Using the geographical detector, we further explored the influence of social and economic factors, as well as natural factors, on spatial differentiation mechanisms of LULC change in the Circum-Bohai. Our results show that between 2000 and 2020, the Circum-Bohai coastline generally expanded towards the ocean by a total of 1062.99 km. The highest rate of change occurred during 2010 to 2015, and human activities were the primary cause of most of the changes, with the exception of the Yellow River Delta, where natural factors were dominant. The main types of LULC in the study area from 2000 to 2020 were farmland and construction land. The area of farmland proportion decreased by 1.75%, while the area of construction land proportion increased from 16.73% to 29.54%. Our findings indicate that the degree of land use in the Circum-Bohai is deepening. Based on our factor detection analysis, the added value of the secondary industry was the most critical influencing factor on LULC. Furthermore, the combined effect of the added value of the secondary industry and gross domestic product (GDP) has a significant driving impact on LULC. These findings can provide reference and data support for the sustainable development and comprehensive management of land resources. The relevant departments can use these results to prompt corresponding policies for the rational allocation of land resources. [ABSTRACT FROM AUTHOR]

Deep learning (DL) models have recently been widely used in UAV aerial image semantic segmentation tasks and have achieved excellent performance. However, DL models are vulnerable to adversarial examples, which bring significant security risks to safety-critical systems. Existing research mainly focuses on solving digital attacks for aerial image semantic segmentation, but adversarial patches with physical attack attributes are more threatening than digital attacks. In this article, we systematically evaluate the threat of adversarial patches on the aerial image semantic segmentation task for the first time. To defend against adversarial patch attacks and obtain accurate semantic segmentation results, we construct a novel robust feature extraction network (RFENet). Based on the characteristics of aerial images and adversarial patches, RFENet designs a limited receptive field mechanism (LRFM), a spatial semantic enhancement module (SSEM), a boundary feature perception module (BFPM) and a global correlation encoder module (GCEM), respectively, to solve adversarial patch attacks from the DL model architecture design level. We discover that semantic features, shape features and global features contained in aerial images can significantly enhance the robustness of the DL model against patch attacks. Extensive experiments on three aerial image benchmark datasets demonstrate that the proposed RFENet has strong resistance to adversarial patch attacks compared with the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The efficient and automatic detection of chest abnormalities is vital for the auxiliary diagnosis of medical images. Many studies utilize computer vision and deep learning approaches involving symmetry and asymmetry concepts to detect chest abnormalities, and achieve promising findings. However, an accurate instance-level and multi-label detection of abnormalities in chest X-rays remains a significant challenge. Here, a novel anomaly detection method for symmetric chest X-rays using dual-attention and multi-scale feature fusion is proposed. Three aspects of our method should be noted in comparison with the previous approaches. We improved the deep neural network with channel-dimensional and spatial-dimensional attention to capture the abundant contextual features. We then used an optimized multi-scale learning framework for feature fusion to adapt to the scale variation in the abnormalities. Considering the influence of the data imbalance and other factors, we introduced a seesaw loss function to flexibly adjust the sample weights and enhance the model learning efficiency. The rigorous experimental evaluation of a public chest X-ray dataset with fourteen different types of abnormalities demonstrates that our model has a mean average precision of 0.362 and outperforms existing methods. [ABSTRACT FROM AUTHOR]

Building contour extraction from high-resolution remote sensing images is a basic task for the reasonable planning of regional construction. Recently, building segmentation methods based on the U-Net network have become popular as they largely improve the segmentation accuracy by applying 'skip connection' to combine high-level and low-level feature information more effectively. Meanwhile, researchers have demonstrated that introducing an attention mechanism into U-Net can enhance local feature expression and improve the performance of building extraction in remote sensing images. In this paper, we intend to explore the effectiveness of the primeval attention gate module and propose the novel Attention Gate Module (AG) based on adjusting the position of 'Resampler' in an attention gate to Sigmoid function for a building extraction task, and a novel Attention Gates U network (AGs-Unet) is further proposed based on AG, which can automatically learn different forms of building structures in high-resolution remote sensing images and realize efficient extraction of building contour. AGs-Unet integrates attention gates with a single U-Net network, in which a series of attention gate modules are added into the 'skip connection' for suppressing the irrelevant and noisy feature responses in the input image to highlight the dominant features of the buildings in the image. AGs-Unet improves the feature selection of the attention map to enhance the ability of feature learning, as well as paying attention to the feature information of small-scale buildings. We conducted the experiments on the WHU building dataset and the INRIA Aerial Image Labeling dataset, in which the proposed AGs-Unet model is compared with several classic models (such as FCN8s, SegNet, U-Net, and DANet) and two state-of-the-art models (such as PISANet, and ARC-Net). The extraction accuracy of each model is evaluated by using three evaluation indexes, namely, overall accuracy, precision, and intersection over union. Experimental results show that the proposed AGs-Unet model can improve the quality of building extraction from high-resolution remote sensing images effectively in terms of prediction performance and result accuracy. [ABSTRACT FROM AUTHOR]

Objective: We aimed to compare the prognosis of laryngeal squamous cell carcinoma (LSCC) and nSCCs of the larynx.Then we established a nomogram for nSCCs of the larynx.Methods: Prognosis between the 529 pairs nSCCs of the larynx patients and LSCC patients were compared after propensity score matching (PSM). 591 nSCCs of the larynx patients were divided into the modeling and validation groups. Univariate and multivariate Cox analyses obtain independent prognostic factors, which were then included in the nomogram to predict the 3 and 5 year survival. Prognostic accuracy of the nomogram was evaluated using the consistency index (C-index) and the calibration curve.Results: Prognosis of nSCCs of the larynx was poorer than LSCC. Age, race, tumor location, tumor-node-metastasis stage, and method were independent prognostic factors of nSCCs of the larynx (P < 0.05). Internal and external validation proves the nomogram reliable CONCLUSION: The nomogram showed good prognostic accuracy and would assist clinicians in making more accurate evaluations for patients. [ABSTRACT FROM AUTHOR]

As the convenient outlet to the Bo Sea and the major region of economic development in the Yellow River Basin, Shandong Province in China has undergone large changes in land use/land cover (LULC) in the past two decades with rapid urbanization and population growth. The analysis of the LULC change patterns and its driving factors in the Shandong section of the Yellow River Basin can provide a scientific basis for rational planning and ecological protection of land resources in the Shandong section of the Yellow River Basin. In this manuscript, we analyzed the spatial pattern of LULC and its spatial and temporal changes in the Shandong section of the Yellow River Basin in 2000, 2010, and 2020 by using the random forest classification algorithm with the Google Earth Engine platform and multi-temporal Landsat TM/OLI data. The driving factors of LULC changes were also quantified by the factor detector and interaction detector in the geodetector. Results show that in the past two decades, the LULC types in the study area are mainly farmland and construction land, among which the proportion of farmland area has decreased and the proportion of construction land area has increased from 19.4% to 29.7%. Based on the results of factor detector, it can be concluded that elevation, slope, and soil type are the key factors affecting LULC change in the study area. The interaction between elevation and slope, slope and soil type, and temperature and precipitation has strong explanatory power for the spatial variation of LULC change in the study area. The research results can provide data support for ecological environmental protection, sustainable, and high-quality development of the Shandong section of the Yellow River Basin, and help local governments take corresponding measures to achieve coordinated and sustainable socioeconomic and environmental development. [ABSTRACT FROM AUTHOR]

[Display omitted] • Species richness hotspots were identified by multi-species distribution modeling. • The magnitude and direction of future biodiversity hotspots changes were discussed. • The MaxEnt and InVEST models were complementary in biodiversity conservation. • Suggestions for biodiversity conservation were proposed based on gap analysis. Mainstreaming biodiversity into protection planning and management is of great significance for biodiversity conservation and sustainable development. Species potential distribution modeling is an effective way for species diversity evaluation and biodiversity hotspots identification, which are crucial for biodiversity conservation. Taking the Central Urban Area of Chongqing Municipality as the study area, the main objectives of this study were to identify the potentially suitable habitats, species richness and biodiversity of key protected species in current and future, determine the relative contribution of environmental factors and assess the conservation effectiveness of protected areas (PAs) based on MaxEnt model and gap analysis. The results showed that the current potentially suitable habitats of total key protected species were mainly located in "two rivers and four mountains", with a total area of 1610.55 km2, of which forestland accounted for 59.78 %. Species suitable habitats demonstrated clear topographic heterogeneity, and the distribution index decreased at first and then increased with increasing terrain niche index (TNI). Meanwhile, it was observed that key protected plants and birds shared similar suitable habitats in mountainous areas, with an overlapping area of 753.53 km2, and the high species richness covered 182.83 km2. In 2050, the future biodiversity hotspots would remain stable and increase steadily. In terms of the direction of centroid shift, the biodiversity hotspots would migrate to low latitude, low altitude and southeast by 8.34 km. The jackknife tests indicated that the potential distribution of key protected species was mainly determined by land use, mean diurnal range and TNI. Additionally, the problems of high protection gaps and low protection effectiveness coexisted in the existing PAs, with the overlapping area of the comprehensive biodiversity hotspots and the existing PAs was only 446.96 km2. Finally, suggestions for natural PAs system optimization and ecological protection were proposed. This study provides scientific supports for biodiversity conservation and efficient management. [ABSTRACT FROM AUTHOR]

The impact of polytetrafluoroethylene-nanoplastics (PTFE-NPs) on biological sewage disposal was delved, containing nitrogen remotion, microbiological activity and composition of extracellular polymer (EPS). The addition of PTFE-NPs reduced the removal efficiencies of chemical oxygen demand (COD) and ammonia nitrogen (NH 4 +-N) by 3.43 % and 2.35 %, respectively. In comparison with no PTFE-NPs, the specific oxygen uptake rate (SOUR), specific ammonia oxidation rate (SAOR), specific nitrite oxidation rate (SNOR) and specific nitrate reduction rate (SNRR) decreased by 65.26 %, 65.24 %, 41.77 % and 54.56 %, respectively. The PTFE-NPs inhibited the activities of nitrobacteria and denitrobacteria. It was worth noting that, nitrite oxidized bacterium was more resistant to adverse environments than ammonia oxidizing bacterium. Compared with no PTFE-NPs, the reactive oxygen species (ROS) content and lactate dehydrogenase (LDH) grew by 130 % and 50 % under PTFE-NPs pressure. The appearance of PTFE-NPs affected the normal function of microorganisms by inducing endocellular oxidative stress and destroying the completeness of the cytomembrane. The protein (PN) and polysaccharide (PS) levels in loosely bound EPS (LB-EPS) and tightly bound EPS (TB -EPS) increased by 4.96, 0.70, 3.07 and 0.71 mg g−1 VSS, under PTFE-NPs. Meanwhile, the PN/PS ratios of LB-EPS and TB -EPS increased from 6.18 and 6.41–11.04 and 9.29, respectively. The LB-EPS might provide sufficient binding sites for PTFE-NPs adsorption due to its loose and porous structure. The defense mechanism of bacteria against PTFE-NPs was mainly the PN in loosely bound EPS. Moreover, the functional groups referred to the complexation of EPS with PTFE-NPs were mainly related to N-H, C O, and C-N in proteins and O-H in polysaccharides. • The PTFE-NPs inhibited the activities of nitrobacteria and denitrobacteria. • The PTFE-NPs caused the ROS content and LDH release to increase. • The PN levels in LB-EPS and TB-EPS were higher than those of PS at PTFE-NPs pressure. • The main functional groups of EPS bound with PTFE-NPs were N-H, C O, C-N and O-H. [ABSTRACT FROM AUTHOR]

[Display omitted] • PTFE-NPs weaken the Cu(II) and CIP antagonism on sludge activities. • Nitrifier was more alive to Cu(II) and CIP than denitrifier with or without PTFE-NPs. • Adsorption of CIP and Cu(II) by PTFE-NPs was synergistic. • PN level in EPS was decreased by PTFE-NPs at mixed Cu(II)/CIP. • PTFE-NPs abated the binding of functional groups in PN and PS to mixed Cu(II)/CIP. The work aimed to explore effects of polytetrafluoroethylene nanoplastics on joint inhibitions of ciprofloxacin and bivalent copper on the nitrogen removal in a sequencing batch reactor and its potential mechanisms. The addition of bivalent copper and/or ciprofloxacin reduced the ammonia nitrogen elimination rate with or without polytetrafluoroethylene nanoplastics. Adsorption kinetics and thermodynamics showed the binary bivalent copper and ciprofloxacin promoted their adsorptions by polytetrafluoroethylene nanoplastics. Polytetrafluoroethylene nanoplastics enhanced combined toxicities of ciprofloxacin and bivalent copper to sludge activities and microbial community involved into nitrification and denitrification due to the adsorption of ciprofloxacin and bivalent copper by polytetrafluoroethylene nanoplastics. With or without polytetrafluoroethylene nanoplastics, bivalent copper and/or ciprofloxacin caused more obvious level changes of protein than polysaccharide. This study provides novel insights for understanding the effect of combined heavy metals and antibiotics on the performance in a sequencing batch reactor with the nanoplastics stress. [ABSTRACT FROM AUTHOR]

• The time series reconstruction algorithm is applied to RSEI for the first time. • Corrected the correlation between RSEI and ecological components. • Alleviate the instability of RSEI time series. Remote Sensing Ecological Index (RSEI) is one of the most widely used ecological quality assessment indicators. Due to the noise caused by adverse atmoshperic conditions and other factors, the RSEI calculated from the original image usually has the phenomena of lack of information and unstable image quality. Therefore, based on Google Earth Engine (GEE) cloud platform, this study adopts three common data reconstruction algorithms firstly, namely: Savitory-Golay filter (SG), harmonic analysis of time series (HANTS), Whittaker Smoother (WS), which are used to reconstruct the original MODIS time series data in the Yangtze River Basin (YRB) from 2000 to 2020, in order to optimize the calculation process of RSEI. At the same time, three indicators (correlation coefficient (R), standard deviation (STD), root mean square error (RMSE)) are used for the accuracy evaluation. The results show that data reconstruction can fill gaps in RSEI, the reconstruction performance of WS and SG for four parameters is better than HANTS, and the four SG reconstructed sequences have the strongest correlation with the original sequences (R between 0.8 ∼ 1), while the WS reconstruction sequence has the lowest error value (both STD and RMSE are less than 1), both of them can correct the pixel value, which is conducive to maintaining the stability of RSEI in the temporal dimension; the RSEI produced by HANTS has the best accuracy, that is, R, STD, RMSE are respectively 0.898, 0.130, 0.104. As shown by the research, it is necessary to de-noise each parameter before synthesizing RSEI. This study can provide a theoretical basis for applying time-frequency algorithms to optimize the ecological monitoring performance of RSEI. [ABSTRACT FROM AUTHOR]

• The RSEI of the long time series is calculated by GEE. • Apply Theil-Sen-MK trend analysis to judge the long-term trend of RSEI. • Investigate the contribution of environment, human, and topography to RSEI changes. The Yangtze River Basin has a wide range and complicated topography. In recent years, under the background of climate and land cover change, the ecological response of the whole ecological quality of the Yangtze River Basin is still unknown. To reveal the spatiotemporal changes in ecological quality in the Yangtze River Basin from 2001 to 2019 and their relationship with environmental and topographical factors, this study used the Google Earth Engine (GEE) platform to calculate the remote sensing ecological index (RSEI) based on the Moderate-resolution Imaging Spectroradiometer (MODIS) product image set, combined with the digital elevation data set and statistical yearbook. The data evaluated the ecological quality of the Yangtze River Basin and analyzed its causes. The results showed that: 1) the average RSEI of the Yangtze River Basin showed an overall upward trend, the growth rate was 0.027 (year−1), and the variation ranged from 0.5 to 0.568. The overall ecological quality rank was mainly neutral and slightly good. 2) The ecological quality of 85.7% of the Yangtze River Basin remains stable. A total of 11.2% of the regional ecological quality is improving, and 3.1% of the regional ecological quality is declining. Areas with reduced ecological quality are concentrated in the Hengduan Mountains. The dominant LST factor drives the deterioration of its ecological quality at a rate of −1.06 (year−1). The areas with improved ecological quality are concentrated in the upper and middle reaches of the Yangtze River. The dominant WET factor drives its ecological quality to improve at a rate of 0.27 (year−1). 3) From the perspective of topography, the ecological quality of the Yangtze River basin shows a wave-like decline and first rises and then falls in elevation and slope (the elevation is bounded by 2000 m and 6000 m, and the slope is bounded by 15°.). The average RSEI of the Yangtze River Basin is the highest on the northwest slope (0.554), and the ecological quality of sunny slopes is generally higher than that of shady slopes. The research shows that from 2001 to 2019, the overall ecological quality of the Yangtze River Basin has improved and evolved, but the ecological quality of the Hengduan Mountains has declined. Therefore, implementing different ecological protection policies in different regions is an important strategy for enhancing the stability of the ecosystem. [ABSTRACT FROM AUTHOR]