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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]

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]

In this article, an ambient mass spectrometric method with molecularly imprinted polymer (MIP)-coated wooden tips was developed for sensitive analysis of trace macrolide antibiotics in complex food samples. A novel solid-phase microextraction (SPME) probe was prepared, via the modification of a layer MIP coating (with roxithromycin as template molecule) on the surface of wooden tips. The obtained MIP-coated wooden-tip SPME probe can be applied directly to enrich trace macrolide antibiotics from complex food samples, with enrichment factors of 244–1604, 72–370, and 12–82 folds for analysis of five investigated macrolide antibiotics in drinking water, honey, and milk samples, respectively. After extraction, a high voltage and some spray solvent were applied on the loaded SPME probe to desorb and ionize analytes enriched on the probe surface for electrospray ionization mass spectrometric (ESI-MS) analysis under ambient and open-air conditions. The method showed good linearity, with correlation coefficient values (r 2) no less than 0.9904, and the calibration function was verified via Mandel's fitting test (p > 0.063). The limits of detection were in the range of 0.003–0.05, 1.1–5.1, and 1.9–15.8 ng/g for analysis of drinking water, honey, and milk samples, respectively. Recoveries of the five targeted macrolide antibiotics in honey and milk samples ranged from 73.4% to 98.1%, with the standard deviations no higher than 8.6%. As a result, MIP-coated wooden-tip ESI-MS method could be feasibly used as a sensitive method for determination of trace macrolide antibiotics in complex food samples. Image 1 • A novel SPME probe was prepared via molecularly imprinted polymer (MIP) technique. • The SPME probe showed high enrichment capacity toward macrolide antibiotics. • An ambient mass spectrometric method with MIP-coated wooden tips was developed. • The method was suitable to detect trace macrolide antibiotics sensitively. [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]

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]

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]

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]

Abstract Venom peptides are an excellent source of pharmacologically active molecules for ion channels that have been considered as promising drug targets. However, mining venoms that interact with ion channel remains challenging. Previously an autocrine based high throughput selection system was developed to screen venom peptide library but the method includes repetitious selection rounds that may cause loss of valuable hits. To simplify the selection process, next generation sequencing was employed to directly identify the positive hits after a single round of selection. The advantage of the improved system was demonstrated by the discovery of 3 novel Kv1.3 targeting venom peptides among which Kappa-thalatoxin-Tas2a is a potent Kv1.3 antagonist. Therefore, this simplified method is efficient to identify novel venom peptides that target ion channels. Highlights • Next generation sequencing simplify the selection of target specific venom peptides. • 3 novel Kv1.3 targeting venom peptides are identified by a single round of selection. • We found κ-thalatoxin-Tas2a is a potent antagonist of Kv1.3 by the improved method. [ABSTRACT FROM AUTHOR]

Highlights • ABA and NO improved tall fescue low-light tolerance. • ABA mediated antioxidant defense and photosynthesis under low-light stress. • ABA elevated NO level through enhancing NOS-like activity and up-regulating FaNOA1 expression. Abstract Abscisic acid (ABA) and nitric oxide (NO) are both extremely important signalling molecules employed by plants to mediate plant tolerance under abiotic stresses. In the present study, the role of NO in ABA-induced photosynthesis and antioxidant system in leaves of tall fescue ( Festuca arundinacea Schreb.) seedlings was investigated. Low-light (LL) stress increased intercellular CO 2 concentration ( Ci ), as well as decreased plant height, leaf width, tiller number, biomass, chlorophyll content, net photosynthetic rate ( Pn ), stomatal conductance ( Gs ), transpiration rate ( Tr ), the maximum quantum yield of photosystem photochemistry ( F v / F m ), actual photochemical efficiency of photosystem II ( Φ PSII ), photochemical quenching (qP), the apparent electron transport rate (ETR), enzymatic activity of ribulose-1,5-bisphosphate carboxylase (Rubisco) and glyceraldehyde phosphate dehydrogenase (GADPH) and phosphoribulokinase (PRK), which were substantially alleviated by exogenous ABA application. Moreover, application of these leaves with exogenous ABA resulted in reduced LL stress induced ion leakage and reduced contents of MDA, H 2 O 2 and superoxide radicals O 2 • - , as well as higher activities of peroxidase (POD), superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX). Expression of genes coding antioxidant enzymes increased in tall fescue in the presence of ABA under LL stress. These responses could be reversed by treatment with fluridone (the ABA biosynthesis inhibitor). The application of ABA elevated NO level, enhanced nitric oxide synthase (NOS)-like activity and up-regulated the tall fescue nitric oxide synthase-associated gene, NITRIC OXIDE ASSOCIATED 1 ( FaNOA1 ) expression level. In addition, neither exogenous NO nor specific NO scavenger 2-(4-carboxy-2-phenyl)-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide (PTIO) or NO synthase (NOS) specific inhibitor N ω-nitro- L -arginine (LNNA) treatments had no effect on endogenous ABA concentration and ABA receptors transcript levels ( FaPYR1 and FaPYL1 ), implying that the protective effect of ABA under LL stress might be mediated by NO. These results indicated that NO was involved in the ABA-mediated LL tolerance via regulating the antioxidant defense system and keeping a high level of photosynthetic rate in tall fescue. [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]

Abstract: Optogenetics employs light to manipulate cellular events such as activation of molecular channels, signaling molecules, and gene transcription, by transfection with engineered light‐sensitive proteins. In the last decade, great progresses in life science have been made by optogenetics, but the gene transfection limits its further medical applications. In this study, it is reported that a short flash of femtosecond‐laser stimulation can directly activate extracellular signal‐regulated kinase (ERK), an essential signaling pathway, in target cells, without any optogenetic technology. This all‐optical approach can be easily accomplished by a two‐photon or confocal microscope coupled with a femtosecond laser. The photostimulation can initiate immediate ERK translocation into nucleus and exit back to cytosol. The nuclear ERK level can be regulated by combination of multiple photostimulations. Subsequent up‐regulation of phosphorylated endogenous eIF4E indicates successful activation of the ERK signaling pathway. Occasionally, ERK activation in the surrounding cells of the stimulation is observed. Some key signaling cascades in the ERK2 pathway activated by photostimulation is further clarified. Altogether, this all‐optical method can activate ERK without any exogenous chemicals or proteins and thus be with promissing potential for related research. [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]

Ni nanoparticles (with average diameter 7 nm) capped with oleylamine and oleic acid were added to a DLC/DIOS solid-liquid synergetic lubricating system. The tribological behavior rubbed with steel balls in three lubricating regimes (boundary, mixed and elasto-hydrodynamic lubrication) has been systematically studied by surface analyses techniques such as SEM, EDX and XPS. It was found that, in all the lubricating regimes, the system's lubricating performance was substantially improved because of the addition of the Ni nanoparticles. For all the lubrication regimes, the COF decreased by 10.3-19.1%. In the boundary lubrication regime, the wear rate of DLC could be decreased by 50%. Ni nanocores facilitate the adequate oxidation of the steel surface and the transformation of C=C ( sp2) to C-C ( sp3) of the DLC sliding surface. The tribo-film containing ferrous oxides, Ni and its oxides, presents low shearing strength and good cohesion to the DLC coating, which greatly improves the lubricating behaviors of the DLC/DIOS solid-liquid synergetic system. In the elasto-hydrodynamic lubrication regime, the Ni nanoparticles cause the occurrence of obstructed or plug flow in the oil films, which in turn reduces friction by reducing the layers of lubricant molecules to slide on each other. [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: The influence of yttrium on the corrosion residual strength of an AZ91D magnesium alloy was investigated detailedly. Scanning electron microscope was employed to analyze the microstructure and the fractography of the studied alloys. The microstructure of AZ91D magnesium alloy is remarkably refined due to the addition of yttrium. The electrochemical potentiodynamic polarization curve of the studied alloy was performed with a CHI 660b electrochemical station in the three-electrode system. The result reveals that yttrium significantly promotes the overall corrosion resistance of AZ91D magnesium alloy by suppressing the cathodic reaction in corrosion process. However, the nucleation and propagation of corrosion pits on the surface of the 1.0wt.% Y modified AZ91D magnesium alloy indicate that pitting corrosion still emerges after the addition of yttrium. Furthermore, stress concentration caused by corrosion pits should be responsible for the drop of corrosion residual strength although the addition of yttrium remarkably weakens the effect of stress concentration at the tip of corrosion pits in loading process. [Copyright &y& Elsevier]

Abstract: The effect of corrosion on the tensile behaviour of the 1.0wt.% Ce modified AZ91 magnesium alloy was investigated by the immersion of the test bar in 3.5wt.% NaCl aqueous solution for 0, 12, 40, 108, 204, 372 and 468h with the subsequent tensile tests in this paper. The fractography was analyzed by scanning electron microscopy. The results show that pitting corrosion should be responsible for the drop of the corrosion residual strength within the testing time. The depth of the corrosion pits was statistically and quantitatively obtained by an optical microscopy and the maximal value was recorded as the extreme depth of the corrosion pit. Furthermore, the corrosion residual strength is linearly dependent on the extreme depth of the corrosion pit, which can be attributed to the loss of cross-sectional area and the emergence of stress concentration caused by the initiation and development of corrosion pits. [Copyright &y& Elsevier]

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: The corrosion residual strength of the 1.0wt.% Yttrium modified AZ91 magnesium alloy was investigated with the pre-corrosion of the samples in 3.5% NaCl aqueous solution at pH 7.0. In order to analyze the effect of corrosion on the tensile strength, scanning electronic microscope (SEM), optical microscope (OM) and camera were used to record the corrosion morphology and fracture texture. The tensile behavior of test sample was performed on an AG-10TA materials test machine. The results reveal that the initiation and development of corrosion pit, which results in the variation of the surface configuration of the test sample and thus leads to the stress concentration at the corrosion pit, is the main reason for the drop of the corrosion residual strength. The corrosion residual strength closely depends on the dimension of the extreme depth of corrosion pit other than the amount of corrosion pits. The variation of the corrosion residual strength follows the regulation of exponential decay from 0 to 204h. [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]

Abstract: Al2O3 and carbon short fibres reinforced AlSi12CuMgNi metal matrix hybrid composites were fabricated by preform squeeze-infiltrated route. The fibres distribution and microstructure of matrix in the hybrid composites were observed. Moreover, the effect of heat-treatment on the dry sliding friction and wear properties of the hybrid composites were determined by statistics method under the condition of 196N load and velocity of 0.837m/s. Alumina and carbon short fibres distributed hybrid uniformly in the hybrid composites. Metal matrix microstructure was fined after heat-treatment. The results of statistics analysis showed that four samples of wear rate and friction coefficient of both as-cast and as heat-treated hybrid composites accorded with normal distribution. The statistics average values of wear rate and friction coefficient of as-cast hybrid composites were larger than those of as heat-treated hybrid composites. Heat-treatment was beneficial to the improvement of dry sliding friction and wear property of the composites. Dominant wear mechanism of as-cast hybrid composites was ploughing grooves and delamination. Heat-treatment contributed to improving the resistivity of delamination of composites. Dominant wear mechanism of as heat-treated hybrid composites was slightly ploughing. [Copyright &y& Elsevier]

Shallow-buried rock mass/obstructions in a reclaimed sand site have seriously delayed a construction project in eastern Singapore, and seismic surveys were conducted to estimate the location and range of the rock. Active-source and passive-source seismic data were recorded using an array with 24 geophones at 1 m intervals. A 2D full waveform inversion (FWI) approach, multichannel analysis of surface waves (MASW), and cross-correlation seismic interferometry were used to process the collected seismic data. Results show that MASW and cross-correlation provide useful information, but limited to 1D S-wave velocity profiles and the upper surface of shallow buried rock. Using the full information of seismic waveform, the FWI approach can reflect even the range of rock. Sequential inversion with a low-pass frequency filter at incremental corner frequencies can increase the precision of the FWI approach. With higher precision, the FWI of surface waves is considered to be an attractive and effective method to detect underground obstruction. • FWI is used to process seismic data for underground obstruction survey. • An accurate 2D S-wave velocity structure can be acquired through FWI. • MASW can only reflect the upper surface of granite. • Accuracy of crosscorrelation appears lower than MASW and FWI at shallow depth. • Sequential application of low pass frequency filters can improve accuracy of FWI. [ABSTRACT FROM AUTHOR]

• SE of CNT composite grouts in inclined fracture with flowing water was investigated. • Inclination angle of fracture plays a crucial role in flowing water grouting. • CNT composite grouts are applicable for flowing water grouting in inclined fracture. • Silica fume improve SE of CNT composite grouts. A series of physical simulation tests were conducted to investigate performance of carbon nanotube (CNT) composite grouts in inclined fracture grouting with flowing water. The viscosity of grouts increases with an increase of CNTs content. Silica fume can improve the dispersion of CNTs and maximal fluid pressure of CNT composite grouts. Orthogonal experimental results shows that sealing efficiency (SE) of CNT composite grouts increases with an increase of CNTs and a decrease of initial flowing rate. Slight inclination can improve the SE of CNT composite grouts and superfluous silica fume has limited effects on SE. [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]

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]

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]

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]

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 simultaneous analysis of perfluoroalkyl substances (PFASs) and lipids in biological tissues is of importance, especially for in situ and microscale analysis, because it provides significant information to understand the relevance of content, composition, and distribution of lipids to the bioaccumulation of PFASs as well as lipid metabolism affected by the biotoxicity of PFASs. In this study, we report the development of a novel ambient mass spectrometry method for the rapid, in situ, and microscale analysis of PFASs and lipids simultaneously in biological tissues for the investigation of their biological correlation. A microscale solid-phase microextraction (SPME) probe with a probe-end diameter of several-μm was employed for in situ and microscale sampling of biological tissues after PFAS exposure. The SPME probe showed a desirable capacity for the enrichment of PFASs and lipid species simultaneously. After sampling and extraction, the loaded SPME probe was directly applied for nanoESI-MS analysis under ambient and open-air conditions. A high-resolution Fourier transform ion cyclotron resonance (FTICR) mass spectrometer operated in the field-induced mode was introduced to record mass spectra using fast polarity switching between positive and negative ion detection. Most of the lipid species were recorded in the positive ion mass spectrum, and PFASs were recorded in the negative ion mass spectrum. By using the developed method, the in situ analysis of PFASs and lipids in the muscle, brain, heart, kidney, liver, and intestine of zebrafish was realized. In addition, simultaneously imaging PFASs and lipids in individual Daphnia magna was successfully achieved for the investigation of their biological correlation. [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]