Search

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]

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]

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]

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]

Seismic vulnerability assessments play a significant role in comprehensive risk mitigation efforts and seismic emergency planning, especially for urban areas with a high population density and a complex construction environment. Traditional approaches such as in situ fieldwork are accurate for conducting seismic vulnerability assessments of buildings; however, they are too much time and cost-consuming, especially in moderate to low seismic hazard regions. To address this issue, an integrated approach for a macroseismic vulnerability assessment composed of data mining methods and GIScience technology was presented and applied to Urumqi, China. First, vulnerability proxies were established via in situ data of buildings in the Tianshan District with an EMS-98 vulnerability classification scheme and two data mining methods, namely, support vector machine and association rule learning methods. Then, vulnerability proxies were applied to the Urumqi database, and the accuracy was validated. Finally, seismic risk maps were constructed through data consisting of direct damage to buildings and human casualties. The results indicated that the two data mining methods could achieve desirable accuracies and stabilities when estimating the seismic vulnerability. The seismic risk of Urumqi was estimated as Slight with a predicted number of 61,380 homeless people for a seismic intensity scenario of VIII. [ABSTRACT FROM AUTHOR]

Clustering is widely used in data analysis, and density-based methods are developed rapidly in the recent 10 years. Although the state-of-art density peak clustering algorithms are efficient and can detect arbitrary shape clusters, they are nonsphere type of centroid-based methods essentially. In this paper, a novel local density hierarchical clustering algorithm based on reverse nearest neighbors, RNN-LDH, is proposed. By constructing and using a reverse nearest neighbor graph, the extended core regions are found out as initial clusters. Then, a new local density metric is defined to calculate the density of each object; meanwhile, the density hierarchical relationships among the objects are built according to their densities and neighbor relations. Finally, each unclustered object is classified to one of the initial clusters or noise. Results of experiments on synthetic and real data sets show that RNN-LDH outperforms the current clustering methods based on density peak or reverse nearest neighbors. [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 microstructure, tensile and fatigue behavior of a hot-rolled Ti50Zr alloy applied for dental implant were investigated. The commercial Ti16Zr alloy was taken as a reference material. The load vs. number of cycling curves of the one-piece type Ti16Zr and Ti50Zr dental implants were tested according to ISO 14801 standard testing methods. The results show that the yield strength and fatigue limit of Ti50Zr alloy are up to 1001±8 MPa and 500 ± 10 MPa, respectively, 23% and 32% higher than Ti16Zr alloy. The enhanced strength of Ti50Zr alloy is attributed to multiple mechanisms, including acicular martensite strengthening, Zr solid solution strengthening, nano-twins and dislocations strengthening. The maximum cycling loads of Ti50Zr implants in air, artificial saliva solution and with sand blasting plus acid etching (SLA) treated surface reach 400 N, 400 N and 350 N (5 × 106 cycles without failure), respectively, 33%∼75% higher than Ti16Zr implants. High surface roughness harms the fatigue performance of the Ti-Zr implants owing to the promotion role of notch-type defects to fatigue crack initiation. A corrosion-assisted fatigue process in artificial saliva solution under large load reduces the fatigue life of the Ti-Zr implants. Ti50Zr alloy is an ideal candidate material applied for dental implant with regards to its good biocompatibility, excellent mechanical properties and economic processing route. • Commercial Ti16Zr alloy and new Ti50Zr alloy used for dental implant. • Microstructures and strengthening mechanisms of Ti16Zr and Ti50Zr alloy. • Fatigue behavior of Ti16Zr and Ti50Zr alloy under different conditions. • Ti50Zr implant has better fatigue property than Ti16Zr implant. [ABSTRACT FROM AUTHOR]

Single-chain variable fragment (scFv) is the most common format for phage display antibody library. The isolated scFvs need to be reformatted to full-length IgGs for further characterization. High throughput reformatting of scFv to IgG without disrupting VH–VL pairing is of great demanding for exhaustive screening of all antibodies in IgG format. Herein, we developed a strategy based on the overlap extension PCR in emulsion to reformat scFv to IgG while maintain the accuracy and complexity of variable region pairing. Using CD40 as an example target, we reformatted phage display derived CD40 binding scFv library to IgG mammalian display library and isolated high affinity CD40 binding IgGs. This robust and reliable antibody reformatting approach could be integrated into any phage display based antibody drug discovery. [ABSTRACT FROM AUTHOR]

The Fe–Cr–C ternary steels containing different contents of Cr were successfully cast and further processed in the lab. Differential scanning calorimetry (DSC), optical microscope (OM), and scanning electron microscope (SEM) were employed to investigate the transformation of the Fe–Cr–C ternary steels from pearlite to austenite. It is found that the values of Ac1 and Ac3 are raised with increase in the content of Cr. In addition, the information on the transformation kinetics was obtained from experiment results and the Johnson-Mehl-Avrami-Kolmogorov model (JMAK). The obtained austenitic transformation kinetic process has been described in three overlapping steps: site saturation nucleation, diffusion-controlled growth, and impingement correction. The pre-exponential factor for diffusion decreases firstly and then increases. The activation energy for diffusion increases with the Cr content. In addition, Cr strongly affects the effective diffusion distance, the austenite nucleation sites, and the carbon activity gradient during the austenitic transformation kinetic process. [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]

Abstract: Viscosities and densities of d-glucose–water mixtures were measured, and conductivities in the NiSO4–d-glucose–water system were also measured at 278.15–308.15K in 5K intervals. Based on the Lee–Wheaton conductivity equation, values of limiting molar conductivities Λ 0 and association constants K A were obtained. Changes in the standard thermodynamic functions, Gibbs energy enthalpy and entropy for a process of ion-pair formation were calculated at different temperatures. The results show that (1) at given temperatures, the Λ 0 values for 1/2NiSO4 decrease with increasing concentrations of d-glucose; (2) at fixed concentrations of d-glucose, the Λ 0 values for 1/2NiSO4 increase with increasing temperature; and (3) the process of association of NiSO4 is controlled by the change in entropy . [Copyright &y& Elsevier]

Abstract: Densities have been measured for d-galactose+HCl+water at 10° intervals from 278.15 to 318.15 K. The apparent molar volumes (V Φ,Gal) and standard apparent molar volumes (V 0 Φ,Gal) for d-galactose in aqueous solutions of 0.2~2.1 mol kg−1 HCl have been calculated. Volumetric interaction parameters of galactose with HCl in water have also been obtained from transfer volumes. Results show that V Φ,Gal and V 0 Φ,Gal for galactose in aqueous HCl solutions increase with increasing molality of HCl. At a given temperature, the standard transfer apparent molar volumes (Δt V 0 Φ,Gal) for galactose from water to aqueous HCl solutions are positive and increase with increasing molality of HCl. In the investigated temperature range, the relation: V 0 Φ,Gal = b 0 + b 1(T −273.15 K)0.77 can be used to describe the temperature dependence of the standard apparent molar volumes. Values of standard partial molar isobaric expansion coefficient (∂V 0 Φ,Gal/∂T) p are positive and decrease with increasing temperature and molalities of HCl. [Copyright &y& Elsevier]

Abstract: The corrosion behavior of AZ91Ce alloy in the presence of sulfate-reducing bacteria (SRB) was investigated using a specially designed in-situ corrosion method. For comparison, AZ91 alloy was also studied under the same corrosion condition. It seemed that AZ91Ce alloy was susceptible to crystal boundary corrosion under SRB condition to some extent. A possible mechanism for the crystal boundary corrosion was proposed. The microstructure and corrosion morphologies of alloys were analyzed by optical microscope and SEM, and the corrosion products were detected by X-ray photoelectron spectrometer (XPS). The analysis results showed that Ce in AZ91 alloy can reduce the grain size, and impede the formation of oxide, and thereby improved the corrosion performance. The electrochemical test revealed that the formation of Ce compound can restrict the cathodic reaction, and thus improve the corrosion resistance significantly as observed under the sterile condition. However, it appeared that the improvement was unconspicuous in the presence of SRB. [Copyright &y& Elsevier]

The existing density clustering algorithms have high error rates on processing data sets with mixed density clusters. For overcoming shortcomings of these algorithms, a double-density clustering method based on Nearest-to-First-in strategy, DDNFC, is proposed, which calculates two densities for each point by using its reverse k nearest neighborhood and local spatial position deviation, respectively. Points whose densities are both greater than respective average densities of all points are core. By searching the strongly connected subgraph in the graph constructed by the core objects, the data set is clustered initially. Then each non-core object is classified to its nearest cluster by using a strategy dubbed as 'Nearest-to-First-in': the distance of each unclassified point to its nearest cluster calculated firstly; only the points with the minimum distance are placed to their nearest cluster; this procedure is repeated until all unclassified points are clustered or the minimum distance is infinite. To test the proposed method, experiments on several artificial and real-world data sets are carried out. The results show that DDNFC is superior to the state-of-art methods like DBSCAN, DPC, RNN-DBSCAN, and so on. [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 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]

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]

Highlights: [•] Conductivities Λ for MnSO4–saccharide–water solutions were measured. [•] Limiting molar conductivity for ½MnSO4 decreases with increasing saccharide molality. [•] Values of Λ 0 for ½MnSO4 at a given m s are in the order: fructose>galactose>glucose. [•] Values of Λ 0 for ½MnSO4 at given m s are larger than those for ½MnSO4. [Copyright &y& Elsevier]

Casting defects seriously affect the service life of a cold working die for the automobile industry. For the newly developed cold working die steel, its repairability is a very important scientific and technical issue, especially how to evaluate its repairability is of great engineering value. In this work, the repairability of the newly developed 5Cr5MoV cold working die steel is investigated in detail by a newly designed method, defined as the round pit evaluation method. Experimental results show that the dimension of the round pit significantly influences the microstructure evolution and microhardness distribution in the repaired zone, i.e., the control of round pit dimension can regulate and control the performance matching between the repaired zone and the matrix. When the diameter of the round pit is controlled at about 20 mm, a better matching performance can be obtained. Finally, the round pit evaluation method was established based on a newly developed model of which a microhardness fluctuation coefficient was proposed and used to evaluate the repair effect. [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]

• 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]

ZnO nanorods@nanoflowers (quantum dots) hybrid nanostructures have been successfully fabricated by the spin coating method. The transmission electron micrograph (TEM) and field-emission scanning electron microscopy (FE-SEM) images showed that ZnO quantum dots (QDs) and ZnO nanoflowers (NFs) had been assembled on the surfaces of ZnO nanorods (NRs) or in the upper parts of the gaps between the rods. The morphology of ZnO nanoflowers was formed through a general route of zero-dimensional (0D)→2D→3D, controlling by sodium dodecyl sulfate (SDS) during the experiment. The blue-shifts of Raman and PL spectra were observed for the hybrid nanostructures at room temperature, which were explained by the quantum confinement effects. Compared with the photocatalytic activities of the samples, ZnO NRs@NFs showed enhanced photocatalytic performance for the degradation of RhB under UV radiation, which could be attributed to the special structural feature with an open and porous nanostructured surface layer and the surface defects. ZnO NRs@NFs would show a wider application for its particular porous morphology. [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]

Abstract: The compressive property of Zn–22Al alloy (ZA22) foams was investigated for the strain rates ranging from 2.2×10−3 to 1.8×103 s−1. The dynamic compressive behaviors of the foams were studied utilizing Split Hopkinson Pressure Bar (SHPB) system. The experimental results indicate that the compressive strength and energy absorption capacity of the foams under dynamic loading condition are much greater than that under quasi-static loading condition for certain relative density, which demonstrates the remarkable strain rate sensitivity. The correlative factors of the strain rate sensitivity, the cell wall material and the gas pressure in close cell, were discussed. [Copyright &y& Elsevier]

Abstract: The compressive behaviors of Zn–22Al composite foams using SiC particles as reinforcement and stabilizing agent (ZA22/SiCp composite foams) were investigated in this study. To observe the deformation mechanisms, the deformation processes of the composite foams were recorded, and the cracks under the compressive load were observed by scanning electron microscope (SEM). Owing to the presence of the particles, ZA22/SiCp composite foams show more brittle compressive behavior with a large stress fluctuation than Zn–22Al alloy foams (ZA22 foams). The compressive processes of composite foams are characterized by the formation and propagation of the localized deformation band. The photomicrographs of the cracks indicate that a complicated effect of SiC particles on the compressive properties of the foams result in a high compressive strength. As a consequence, ZA22/SiCp composite foams exhibit slightly high energy absorption capacities, although low energy absorption efficiencies, as compared with ZA22 foams. [Copyright &y& Elsevier]

Abstract: Activity coefficients of CaCl2 in disaccharide {(maltose, lactose)+water} mixtures at 298.15K were determined by cell potentials. The molalities of CaCl2 ranged from about 0.01mol·kg−1 to 0.20mol·kg−1, the mass fractions of maltose from 0.05 to 0.25, and those of lactose from 0.025 to 0.125. The cell potentials were analyzed by using the Debye–Hückel extended equation and the Pitzer equation. The activity coefficients obtained from the two theoretical models are in good agreement with each other. Gibbs free energy interaction parameters (g ES) and salting constants (k S) were also obtained. These were discussed in terms of the stereo-chemistry of saccharide molecules and the structural interaction model. [Copyright &y& Elsevier]