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The residual oil saturation of the matrix near the well zone of a tight reservoir is high due to the tight reservoir’s complex conditions, such as the small pore throat radius and low permeability of the matrix and the development of microfractures, which can result in serious water channeling, even after long-term water injection development. The aim of this paper is to improve the effects of depressurization and augmented injection for tight reservoir waterflooding development by reducing the tight matrix’s residual oil saturation, increasing and maintaining its water phase permeability near the well zone using a nano-SiO2 microemulsion system with a small particle size and high interfacial activity. Therefore, four nano-microemulsion systems were evaluated and screened for their temperature resistance, salt resistance, interfacial tension, solubilization, and dilution resistance. A microemulsion system of 13% A + 4% B + 4% C + 4% n-butanol + 6% oil phase + 69% NaCl solution (10%) + 1% OP-5 + 0.5% anti-temperature agent + 0.3% nanosilica material was preferred. According to the core displacement experiment, the depressurization rate can reach 28~60% when the injection concentration of the system is 1~10% and the injection volume is 2~5 PV. The results of the on-site test show that the water injection pressure dropped to 17.5 MPa, which was lower than the reservoir fracture re-opening pressure. The pressure reduction rate was approximately 20%. The validity period of the depressurization and augmented injection has reached 23 months to date.

High-resolution and thermal infrared hyperspectral data acquired from the Thermal Infrared Airborne Spectrographic Imager (TASI) have been recognized as efficient tools in geology, demonstrating significant potential for rock discernment. Deep learning (DL), as an advanced technology, has driven substantial advancements in lithological mapping by automatically extracting high-level semantic features from images to enhance recognition accuracy. However, gathering sufficient high-quality lithological samples for model training is challenging in many scenarios, posing limitations for data-driven DL approaches. Moreover, existing sample collection approaches are plagued by limited verifiability, subjective bias, and variation in the spectra of the same class at different locations. To tackle these challenges, a novel sample generation method called multi-lithology spectra sample selection (MLS3) is first employed. This method involves multiple steps: multiple spectra extraction, spectra combination and optimization, lithological type identification, and sample selection. In this study, the TASI hyperspectral data collected from the Liuyuan area in Gansu Province, China, were used as experimental data. Samples generated based on MLS3 were fed into five typical DL models, including two-dimensional convolutional neural network (2D-CNN), hybrid spectral CNN (HybridSN), multiscale residual network (MSRN), spectral-spatial residual network (SSRN), and spectral partitioning residual network (SPRN) for lithological mapping. Among these models, the accuracy of the SPRN reaches 84.03%, outperforming the other algorithms. Furthermore, MLS3 demonstrates superior performance, achieving an overall accuracy of 2.25–6.96% higher than other sample collection methods when SPRN is used as the DL framework. In general, MLS3 enables both the quantity and quality of samples, providing inspiration for the application of DL to hyperspectral lithological mapping.

Poplar (Populus L.) anthracnose is an infectious disease that seriously affects the growth and yields of poplar trees, and large-scale poplar infections have led to huge economic losses in the Chinese poplar industry. To efficiently and accurately detect poplar anthracnose for improved prevention and control, this study collected hyperspectral data from the leaves of four types of poplar trees, namely healthy trees and those with black spot disease, early-stage anthracnose, and late-stage anthracnose, and constructed a poplar anthracnose detection model based on machine learning and deep learning. We then comprehensively analyzed poplar anthracnose using advanced hyperspectral-based plant disease detection methodologies. Our research focused on establishing a detection model for poplar anthracnose based on small samples, employing the Design of Experiments (DoE)-based entropy weight method to obtain the best preprocessing combination to improve the detection model’s overall performance. We also analyzed the spectral characteristics of poplar anthracnose by comparing typical feature extraction methods (principal component analysis (PCA), variable combination population analysis (VCPA), and the successive projection algorithm (SPA)) with the vegetation index (VI) method (spectral disease indices (SDIs)) for data dimensionality reduction. The results showed notable improvements in the SDI-based model, which achieved 89.86% accuracy. However, this was inferior to the model based on typical feature extraction methods. Nevertheless, it achieved 100% accuracy for early-stage anthracnose and black spot disease in a controlled environment respectively. We conclude that the SDI-based model is suitable for low-cost detection tasks and is the best poplar anthracnose detection model. These findings contribute to the timely detection of poplar growth and will greatly facilitate the forestry sector’s development.

The sea cucumber is an essential nutrient source and a significant economic marine resource associated with successful aquaculture. However, sea cucumbers are highly susceptible to autolysis induced by endogenous protease after postmortem, and the phenomenon of body wall “melting” occurs, which seriously affects the food quality of products and the degree of acceptance by consumers. To satisfy the growing demand for fresh or processed sea cucumbers, we must clarify the autolysis mechanism of sea cucumbers and the methods to achieve autolysis regulation. In this paper, the factors leading to the quality deterioration and texture softening of sea cucumbers are reviewed, with emphasis on enzymatic characteristics, the autolysis mechanism, the effects of autolysis on the physicochemical properties of the body wall of the sea cucumber, and the development of potential natural protease inhibitors. We aim to provide some reference in future preservation and processing processes for sea cucumbers, promote new processing and preservation technologies, and advance the sea cucumber industry’s development.

Coral reef bleaching events have become more frequent all over the world and pose a serious threat to coral reef ecosystems. Therefore, there is an urgent need for better detection of coral reef bleaching in a time- and cost-saving manner. In recent years, remote sensing technology has often been utilized and gained recognition for coral reef bleaching detection. However, bleaching corals in the water always have weak spectral change signals, causing difficulties in using remote sensing data. Additionally, uneven change samples make it challenging to adequately capture the details of coral reef bleaching detection and produce thematic maps. To resolve these problems, a novel method named coral reef bleaching detection by positive-unlabeled bagging (CBD-PUB) is proposed in this paper. To test the capacity of the method, a series of multi-temporal Sentinel-2 remote sensing images are utilized, and Lizard Island in Australia is taken as a case study area. The pseudo-invariant feature atmospheric correction (PIF) algorithm is adopted to improve coral reef bleaching spectral signals. After that, CBD-PUB is employed to effectively explore coral reef bleaching variation and its corresponding influence relations. The experimental results show that the overall accuracy of bleaching detection by the proposed algorithm reaches 92.1% and outperforms the traditional method. It fully demonstrates the feasibility of the model for the field of coral reef bleaching detection and provides assistance in the monitoring and protection of coral environments.

Libration and precession are different body forces that are ubiquitous in many rapidly rotating systems, particularly in geophysical and astrophysical flows. Libration is a modulation of the background rotation magnitude, whereas precession is a modulation of the background rotation direction. Assessing the consequences of these body forces in large-scale flows is challenging. The Ekman number, the ratio of the rotation time scale to the viscous time scale quantifying the rotation speed, is extremely small, leading to extremely thin and intense shear layers in the flows even when the amplitudes of the body forces are very small. We consider the consequences of libration and precession numerically in a geometrically simple container, a cube, which lends itself to very efficient, accurate, and robust numerical treatment, with the axis of rotation passing through opposite vertices, so that all walls of the cube are at oblique angles to the rotation axis. This results in the geometric focusing of inertial wavebeams reflecting off the walls, whereby the energy density of the wavebeams increases along with the magnitude of their wavevector. The nature of this focusing depends on the forcing frequency but not on the body force. In the inviscid setting, wavebeams form infinitesimally thin vortex sheets, and their energy density becomes unbounded upon focusing. We present linear inviscid ray tracing to set the scene for the focusing of wavebeams and then consider viscous problems at an Ekman number that is typical of current state-of-the-art laboratory experiments. We begin by considering the linear responses, which are comprised of focusing viscous shear layers, of which their details are mostly captured via ray tracing, and particular solutions accounting for the body forces. These have complicated spatio-temporal structures, which differ for libration and precession. Increasing the forcing amplitude from zero introduces nonlinear interactions, enhances the focusing effects via vortex tilting and stretching when the shear layers reflect at the walls, and also introduces temporal superharmonics and a mean flow. When the magnitude of the mean flow is within a few percent of the magnitude of the instantaneous flow, instabilities breaking the spatio-temporal symmetries set in. These are localized in the oscillatory boundary layers where the reflections are concentrated and introduce broadband dynamics in the boundary layers, with additional inertial wavebeams emitted into the interior. The details again depend on the specifics of the body forces.

With the development of hyperspectral sensors, the availability of hyperspectral images (HSIs) has increased significantly, prompting advancements in deep learning-based hyperspectral image classification (HSIC) methods. Recently, graph convolutional networks (GCNs) have been proposed to process graph-structured data in non-Euclidean domains, and have been used for HSIC. The superpixel segmentation should be implemented first in the GCN-based methods, however, it is difficult to manually select the optimal superpixel segmentation sizes to obtain the useful information for classification. To solve this problem, we constructed a HSIC model based on a multiscale feature search-based graph convolutional network (MFSGCN) in this study. Firstly, pixel-level features of HSIs are extracted sequentially using 3D asymmetric decomposition convolution and 2D convolution. Then, superpixel-level features at different scales are extracted using multilayer GCNs. Finally, the neural architecture search (NAS) method is used to automatically assign different weights to different scales of superpixel features. Thus, a more discriminative feature map is obtained for classification. Compared with other GCN-based networks, the MFSGCN network can automatically capture features and obtain higher classification accuracy. The proposed MFSGCN model was implemented on three commonly used HSI datasets and compared to some state-of-the-art methods. The results confirm that MFSGCN effectively improves accuracy.

Human papillomavirus (HPV) is a prevalent cause of mucosal and cutaneous infections and underlying conditions ranging from benign warts to anogenital and oropharyngeal cancers affecting both males and females, notably cervical cancer. Cervical cancer is the fourth leading cause of cancer deaths among women globally and is the most impactful in low- and middle-income countries (LMICs), where the costs of screening and licensed L1-based HPV vaccines pose significant barriers to comprehensive administration. Additionally, the licensed L1-based HPV vaccines fail to protect against all oncogenic HPV types. This study generated three independent lots of an L2-based target antigen (LBTA), which was engineered from conserved linear L2-protective epitopes (aa11–88) from five human alphapapillomavirus genotypes in E. coli under cGMP conditions and adjuvanted with aluminum phosphate. Vaccination of rabbits with LBTA generated high neutralizing antibody titers against all 17 HPV types tested, surpassing the nine types covered by Gardasil®9. Passive transfer of naïve mice with LBTA antiserum revealed its capacity to confer protection against vaginal challenge with all 17 αHPV types tested. LBTA shows stability at room temperature over >1 month. Standard in vitro and in vivo toxicology studies suggest a promising safety profile. These findings suggest LBTA’s promise as a next-generation vaccine with comprehensive coverage aimed at reducing the economic and healthcare burden of cervical and other HPV+ cancers in LMICs, and it has received regulatory approval for a first-in-human clinical study (NCT05672966).

In this study, a method for the active shape control for carbon fiber-reinforced polymer (CFRC) reflectors using piezoelectric lead zirconate titanate (PZT) actuators is proposed. According to this method, a finite element model considering higher transverse shear deformation with independent voltage degrees of freedom is formulated by the Hamilton principle. An optimal shape controller that minimizes the discrete root mean square (RMS) error of a reflecting surface is applied. Then, the optimal arrangements of the PZT actuators are determined by numerical optimization methods, which are developed by modifying the classical Genetic Algorithm, with both single and multi-objective optimizations being studied. In the single optimization formulation, the number of actuators is considered as a constraint, and the RMS error of the reflector is regarded as the optimizing target. A hybrid method that combines the gradient projection method with an adaptive Genetic Algorithm is proposed to solve this problem. In the multi-objective optimization, the residual RMS errors and power consumption of the actuators are considered as the optimization targets. Pareto optimal solutions are obtained by an improved multi-objective Genetic Algorithm. Numerical examples are provided to show the effectiveness of the proposed methods.

The interplay between the immune system and cancer underscores the central role of immunotherapy in cancer treatment. In this context, the innate immune system plays a critical role in preventing tumor invasion. Myeloid differentiation factor 88 (MyD88) is crucial for innate immunity, and activation of MyD88 promotes the production of inflammatory cytokines and induces infiltration, polarization, and immune escape of immune cells in the tumor microenvironment. Additionally, abnormal MyD88 signaling induces tumor cell proliferation and metastasis, which are closely associated with poor prognosis. Therefore, MyD88 could serve as a novel tumor biomarker and is a promising target for cancer therapy. Current strategies targeting MyD88 including inhibition of signaling pathways and protein multimerization, have made substantial progress, especially in inflammatory diseases and chronic inflammation-induced cancers. However, the specific role of MyD88 in regulating tumor immunity and tumorigenic mechanisms remains unclear. Therefore, this review describes the involvement of MyD88 in tumor immune escape and disease therapy. In addition, classical and non-classical MyD88 inhibitors were collated to provide insights into potential cancer treatment strategies. Despite several challenges and complexities, targeting MyD88 is a promising avenue for improving cancer treatment and has the potential to revolutionize patient outcomes.

The molecular weight (MW) of an enzyme is a critical parameter in enzyme-constrained models (ecModels). It is determined by two factors: the presence of subunits and the abundance of each subunit. Although the number of subunits (NS) can potentially be obtained from UniProt, this information is not readily available for most proteins. In this study, we addressed this gap by extracting and curating subunit information from the UniProt database to establish a robust benchmark dataset. Subsequently, we propose a novel model named DeepSub, which leverages the protein language model and Bi-directional Gated Recurrent Unit (GRU), to predict NS in homo-oligomers solely based on protein sequences. DeepSub demonstrates remarkable accuracy, achieving an accuracy rate as high as 0.967, surpassing the performance of QUEEN. To validate the effectiveness of DeepSub, we performed predictions for protein homo-oligomers that have been reported in the literature but are not documented in the UniProt database. Examples include homoserine dehydrogenase from Corynebacterium glutamicum, Matrilin-4 from Mus musculus and Homo sapiens, and the Multimerins protein family from M. musculus and H. sapiens. The predicted results align closely with the reported findings in the literature, underscoring the reliability and utility of DeepSub.

In order to improve the simulation efficiency under the premise of ensuring the fidelity of synthetic aperture radar (SAR) simulation images, we propose a BVH-assisted fast SAR image simulation method based on spatial segmentation. The beam scanning model is established based on RD imaging geometric relation, and the bounding volume hierarchy (BVH) algorithm is used to assist in obtaining the time-varying latticed radiation and shadow areas within the radar beam, combining them with the real-time position of the sensors to complete the simulation of the electromagnetic (EM) wave transmission. The ray tracing algorithm is used to calculate the multiple backscatter fields of EM waves, including various material properties of the target surface. The SAR spatial traversal is adopted to spatially segment the latticed radiation area, and the compute unified device architecture (CUDA) kernel function is designed using the echo matrix cell method to make each cell of the target echo matrix as a subfield of the backscattering field, and the position of the echo matrix cell is traversed to obtain the target backscattering field. The target simulated echo is processed by the range Doppler (RD) imaging algorithm to obtain the SAR-simulated image. The simulation results show that compared with a CPU single-thread simulation, the simulation speed of the proposed method is significantly improved, and the SAR simulation image has high structural similarity with the real image, which fully verifies the effectiveness of the proposed method.

The aim of this study was to investigate if the supplementation of folic acid and taurine can relieve the adverse effects of different levels of heat stress (HS) on growth performance, physiological indices, antioxidative capacity, immunity, rumen fermentation and microbiota. A total of 24 Dorper × Hu crossbred lambs (27.51 ± 0.96 kg) were divided into four groups: control group (C, 25 °C), moderate HS group (MHS, 35 °C), severe HS group (SHS, 40 °C), and the treatment group, under severe HS (RHS, 40 °C, 4 and 40 mg/kg BW/d coated folic acid and taurine, respectively). Results showed that, compared with Group C, HS significantly decreased the ADG of lambs (p < 0.05), and the ADG in the RHS group was markedly higher than in the MHS and SHS group (p < 0.05). HS had significant detrimental effects on physiological indices, antioxidative indices and immune status on the 4th day (p < 0.05). The physiological indices, such as RR and ST, increased significantly (p < 0.05) with the HS level and were significantly decreased in the RHS group, compared to the SHS group (p < 0.05). HS induced the significant increase of MDA, TNF-α, and IL-β, and the decrease of T-AOC, SOD, GPx, IL-10, IL-13, IgA, IgG, and IgM (p < 0.05). However, there was a significant improvement in these indices after the supplementation of folic acid and taurine under HS. Moreover, there were a significant increase in Quinella and Succinivibrio, and an evident decrease of the genera Rikenellaceae_RC9_gut_group and Asteroleplasma under HS (p < 0.05). The LEfSe analysis showed that the genera Butyrivibrio, Eubacterium_ventriosum_group, and f_Bifidobacteriaceae were enriched in the MHS, SHS and RHS groups, respectively. Correlated analysis indicated that the genus Rikenellaceae_RC9_gut_group was positively associated with MDA, while it was negatively involved in IL-10, IgA, IgM, and SOD (p < 0.05); The genus Anaeroplasma was positively associated with the propionate and valerate, while the genus Succinivibrio was negatively involved in TNF-α (p < 0.05). In conclusion, folic acid and taurine may alleviate the adverse effects of HS on antioxidant capacity, immunomodulation, and rumen fermentation of lambs by inducing changes in the microbiome that improve animal growth performance.

The concrete of the main structure of subway stations is highly constrained, so the temperature effect and shrinkage effect generated during the construction process often cause large restraint stress, thus causing the problem of structural cracking. Therefore, low-heat-of-hydration cements are usually used in such construction work to reduce the temperature during the aging of concrete. Therefore, based on the hydration heat release subprogram of concrete, an effective numerical simulation was carried out, and compared with the semi-adiabatic temperature rise test, a method of numerical calculation and parameter fitting to solve the influence coefficient of the hydration heat model was proposed, and the sensitivity of the hydration heat release model and the influence coefficient for several concrete mixtures with additives was studied. The main conclusions are as follows: (1) Combined with the measured temperature curves of several types of mineral-admixed concrete, the composite exponential exothermic model of several types of mineral-admixed concrete can be obtained by adjusting the parameter fitting to solve the influence coefficient of the exothermic hydration model proposed in this paper. (2) One can adjust the influencing coefficient a to control the maximum temperature of concrete. Similarly, the temperature curve of concrete can be adjusted by modifying the influencing coefficient b.

Despite the notable achievements of programmed death 1 (PD-1) antibodies in treating various cancers, the overall efficacy remains limited in the majority of colorectal cancer (CRC) cases. Metabolism reprogramming of tumors inhibits the tricarboxylic acid (TCA) cycle, leading to down-regulation of fumarate hydratase (FH), which is related to poor prognosis in CRC patients. By establishing a tumor-bearing mouse model of CRC with Fh1 expression deficiency, we confirmed that the therapeutic effect of PD-1 antibodies alone was suboptimal in mice with low Fh1 expression, which was improved by combination with a protein invertase subtilisin/kexin 9 (PCSK9) inhibitor. Mechanistically, FH binds to Ras-related nucleoprotein (RAN), which inhibits the nuclear import of the PCSK9 transcription factor SREBF1/2, thus reducing the expression of PCSK9. This leads to increased clonal expansion of CD8+ T cells while the number of Tregs remains unchanged, and the expression of PD-L1 does not change significantly, thus enhancing the immunotherapy response. On the contrary, the expression of PCSK9 increased in CRC cells with low FH expression, which antagonized the effects of immunotherapy. Overall, CRC patients with low FH expression may benefit from combinatorial therapy with PD-1 antibodies and PCSK9 inhibitors to enhance the curative effect.

Human herpes simplex virus (HSV), a double-stranded DNA virus belonging to the Herpesviridae family and alpha herpesvirus subfamily, is one of the most epidemic pathogens in the population. Cell-to-cell spread is a special intercellular transmission mechanism of HSV that indicates the virulence of this virus. Through numerous studies on mutant HSV strains, many viral and host proteins involved in this process have been identified; however, the mechanisms remain poorly understood. Here, we evaluated the effect of the membrane protein genes US7 and UL56 on cell-to-cell spread in vitro between two HSV-1 (HB94 and HN19) strains using a plaque assay, syncytium formation assay, and the CRISPR/Cas9 technique. US7 knockout resulted in the inhibition of viral cell-to-cell spread; additionally, glycoprotein I (US7) of the HB94 strain was found to promote cell-to-cell spread compared to that of the HN19 strain. UL56 knockout did not affect plaque size and syncytium formation; however, the gene product of UL56 from the HN19 strain inhibited plaque formation and membrane infusion. This study presents preliminary evidence of the functions of US7 and UL56 in the cell-to-cell spread of HSV-1, which will provide important clues to reveal the mechanisms of cell-to-cell spread, and contributes to the clinical drugs development.

Current COVID-19 vaccines are effective countermeasures to control the SARS-CoV-2 virus pandemic by inducing systemic immune responses through intramuscular injection. However, respiratory mucosal immunization will be needed to elicit local sterilizing immunity to prevent virus replication in the nasopharynx, shedding, and transmission. In this study, we first compared the immunoprotective ability of a chimpanzee replication–deficient adenovirus–vectored COVID-19 vaccine expressing a stabilized pre–fusion spike glycoprotein from the ancestral SARS-CoV-2 strain Wuhan–Hu–1 (BV-AdCoV-1) administered through either aerosol inhalation, intranasal spray, or intramuscular injection in cynomolgus monkeys and rhesus macaques. Compared with intranasal administration, aerosol inhalation of BV-AdCoV-1 elicited stronger humoral and mucosal immunity that conferred excellent protection against SARS-CoV-2 infection in rhesus macaques. Importantly, aerosol inhalation induced immunity comparable to that obtained by intramuscular injection, although at a significantly lower dose. Furthermore, to address the problem of immune escape variants, we evaluated the merits of heterologous boosting with an adenovirus–based Omicron BA.1 vaccine (C68–COA04). Boosting rhesus macaques vaccinated with two doses of BV-AdCoV-1 with either the homologous or the heterologous C68–COA04 vector resulted in cross–neutralizing immunity against WT, Delta, and Omicron subvariants, including BA.4/5 stronger than that obtained by administering a bivalent BV-AdCoV-1/C68–COA04 vaccine. These results demonstrate that the administration of BV-AdCoV-1 or C68–COA04 via aerosol inhalation is a promising approach to prevent SARS-CoV-2 infection and transmission and curtail the pandemic spread.

Hu sheep, a locally bred species in China known for its high productivity, is currently suffering from pneumonia. Here, we combine high-throughput 16SrRNA gene sequencing and bacterial culturing to examine the bacterial community in pneumonic Hu Sheep lungs (p < 0.05). The results showed that the abundance and diversity of lung bacteria in healthy sheep were significantly higher than those in pneumonia sheep (p = 0.139), while there was no significant difference between moderate and severe pneumonia. Furthermore, the composition of the lung microbiota community underwent significant alterations between different levels of pneumonia severity. The application of LEfSe analysis revealed a notable enrichment of Mannheimiae within the lungs of sheep afflicted with moderate pneumonia (p < 0.01), surpassing the levels observed in their healthy counterparts. Additionally, Fusobacterium emerged as the prevailing bacterial group within the lungs of sheep suffering from severe pneumonia. Integrating the results of bacterial isolation and identification, we conclusively determined that Mannheimia haemolytica was the primary pathogenic bacterium within the lungs of sheep afflicted with moderate pneumonia. Furthermore, the exacerbation of pneumonia may be attributed to the synergistic interplay between Fusobacterium spp. and other bacterial species. Our results provide new insights for guiding preventive and therapeutic measures for pneumonia of different severities in sheep.

Sunscald in kiwifruit, an environmental stress caused by solar radiation during the summer, reduces fruit quality and yields and causes economic losses. The efficient and timely detection of sunscald and similar diseases is a challenging task but helps to implement measures to control stress. This study provides high-precision detection models and relevant spectral information on kiwifruit physiology for similar statuses, including early-stage sunscald, late-stage sunscald, anthracnose, and healthy. Primarily, in the laboratory, 429 groups of spectral reflectance data for leaves of four statuses were collected and analyzed using a hyperspectral reflection acquisition system. Then, multiple modeling approaches, including combined preprocessing methods, feature extraction algorithms, and classification algorithms, were designed to extract bands and evaluate the performance of the models to detect the statuses of kiwifruit. Finally, the detection of different stages of kiwifruit sunscald under anthracnose interference was accomplished. As influential bands, 694–713 nm, 758–777 nm, 780–799 nm, and 1303–1322 nm were extracted. The overall accuracy, precision, recall, and F1-score values of the models reached 100%, demonstrating an ability to detect all statuses with 100% accuracy. It was concluded that the combined processing of moving average and standard normal variable transformations (MS) could significantly improve the data; the near-infrared support vector machine and visible convolutional neural network with MS (NIR-MS-SVM and VIS-MS-CNN) were established as high-precision detection techniques for the classification of similar kiwifruit statuses, demonstrating 25.58% higher accuracy than the single support vector machine. The VIS-MS-CNN model reached convergence with a stable cross-entropy loss of 0.75 in training and 0.77 in validation. The techniques developed in this study will improve orchard management efficiency and yields and increase researchers’ understanding of kiwifruit physiology.

We present a novel fast synthetic aperture radar (SAR) image simulation method based on the echo matrix cell algorithm including multiple scattering. To improve the efficiency of SAR image simulation while ensuring the fidelity of the simulated results, we first discretized the target facets set in the SAR beams footprint into lattice targets using the range-Doppler (RD) imaging geometry model and provided the basis for simulating electromagnetic wave transmission. Based on the simulation of electromagnetic waves transmission, we used the ray tracing algorithm to calculate the multiple backscattering field including multi-polarimetric information and various material properties. Then, based on the echo matrix cell algorithm, we set the echo matrix cell as the subfield of the target backscattering field and designed the CUDA kernel function to implement a computation parallelization for SAR echo generation. All the echo matrix cells are traversed in parallel to obtain the total backscattering field of the target, reproducing the time-varying characteristic of the backscatter coefficient for each lattice target within the synthetic aperture time. The echo signal is processed using the RD imaging algorithm to obtain the simulated SAR image. Finally, we select some targets including aircraft carrier and airplane models for simulation tests. The computation efficiency is improved over 170-fold by comparing the computations of the proposed method and CPU single-thread. We also performed some qualitative and quantitative evaluations on the fidelity of the simulated SAR images. The experimental results verify the effectiveness of the proposed method.

Metamaterials (MTMs) based on a periodic array of resonant coils have been shown to behave as μ-negative (MNG), enabling the focusing of magnetic flux. The phenomenon has been deployed by designers to boost the efficiency of many inductively coupled systems, such as magnetic resonance imaging, underwater and underground communications, and charging base stations (CBS) for consumer electronics and implanted devices. However, due to their dependency on high-Q unit cells, linear MNG-like MTMs have limited bandwidth, restricting their use in many applications, notably in near-field simultaneous wireless information and power transmission (NF-SWIPT) systems. To improve the tight constraints of the amplitude-bandwidth trade-off of artificial magnetic lenses, this paper presents a theoretical analysis of nonlinear MTMs based on a lattice of Duffing resonators (DRs). Additionally, it introduces a criterium for the quantification and evaluation of the amplitude-bandwidth enhancement. The analytical results are based on a circuit model and further verified by numerical simulations using commercial software. The preliminary findings in this paper open up possibilities for nonlinear MTM lenses and can be applied to enhance the linear amplitude-bandwidth limit.

In this study, superhydrophobic ZnO/Polytetrafluoroethylene (ZnO/PTFE) films with water droplet contact angles (CA) observed as high as 165° and water droplet sliding angles of (SA) E. coli, the cell viability count of the ZnO/PTFE sample (3.5 log reduction, 99.96%) was conspicuously lower than that of the ZnO/HDTMS sample (1.2 log reduction, 93.87%) under 1 h illumination of UV light, which showed that the ZnO/PTFE sample has a better photocatalytic property than the ZnO/ HDTMS films. The ZnO/PTFE films also showed good mechanical robustness, which is an important consideration in their widespread real-world adoption.

This study aimed to investigate the distribution of microplastics (MPs) within the Shiwuli River in Hefei, a Chinese inland city. Water and sediment samples were collected during flood season (from May to September) and non-flood season (from October to April) at 10 representative points along the truck stream. The electron microscope, the laser direct infrared chemical imaging system (LDIR), and the scanning electron microscope (SEM) were used to observe and quantify the colour and shape of the MPs, to identify the number, size, and polymer composition of the MPs, and to observe the microstructures of typical MP particles, respectively. The polymer risk index (RI) model and the pollution load index (PLI) model were used to assess the polymer-related risks and the overall extent of MP pollution in the river, respectively. Analysis of MP abundance for different sampling points showed that the water of Shiwuli River had an average abundance of MPs of 8.4 ± 2.5 particles/L during the flood season and 5.8 ± 1.7 particles/L during the non-flood season; the sediment had an average abundance of MPs of 78.9 ± 8.3 particles/kg during the flood season and 63.9 ± 7.1 particles/kg during the non-flood season. The abundance of MPs of different points was investigated. Result show that the more abundances of MPs were found at confluences with tributaries (S4, S5, and S6), where they are also close to the residential and industrial development, while lower values were found in agricultural areas (S8) and wetland ecological regions (S9 and S10). In water, the maximum appeared at S5 with 21.7 ± 4.6 particles/L during the flood season and 15.9 ± 4.2 particles/L during the non-flood season, respectively; the minimum appeared at S9 with 1.8 ± 1.0 particles/L during the flood season and 2.2 ± 0.4 particles/L during the non-flood season, respectively. In sediment, the maximum appeared at S5 with 174.1 ± 10.1 particles/kg during the flood season and 143.6 ± 10.4 particles/kg during the non-flood season, respectively; the minimum appeared at S8 with 10.3 ± 2.8 particles/kg during the flood season and at S9 with 12.1 ± 3.2 particles/kg during the non-flood season, respectively. MP characteristics were also studied. Results show that the MPs mainly exhibited a fibroid morphology (27.90–34%), and red-coloured particles (19.10%) within the smaller size less than 500 μm (38.60%) were more prevalent. Additionally, the result of LDIR scanning shows that a total of eleven types of MP polymers were found in the river water and sediment, including acrylates (ACR), chlorinated polyethylene (CPE), ethylene vinyl acetate (EVA), polyethylene (PE), polyethylene terephthalate (PET), polypropylene (PP), polystyrene (PS), polyurethane (PU), polyvinylchloride (PVC), polyamide (PA), and silicon. The most common particle was PE (19.3–21.6%). Furthermore, the environmental risk assessment demonstrated that the PS polymer posed a Level-III risk in the water samples and a Level-II risk in the sediment samples from the Shiwuli River. The remaining polymer types exhibited Level-I risk. The PLIzone value for water was 2.24 during the flood season, indicating heavy pollution, and 1.66 during the non-flood season, indicating moderate pollution. Similarly, the PLIzone value for sediments was 2.34 during the flood season and 1.91 during the non-flood season, both suggesting a heavy pollution. These findings highlight the potential risk posed by MP pollution in the Shiwuli River to the quality of drinking water sources in Chaohu Lake in Hefei. They provide valuable insights into management, pollution control, and integrated management strategies pertaining to MPs in urban inland rivers in Hefei.

The increasing demand for fine and high-value-added chemicals has motivated the lasting exploration of high-performance catalysts with desirable activity, selectivity, and durability [...]

Due to the existing coupling loop between the motor drive system and the ground, when switching devices in the drive system operate at a high speed, common-mode (CM)-conducted electromagnetic interference (EMI) is generated in the loop. The electromagnetic environment quality of the motor and peripheral equipment is reduced. As a small and lightweight EMI suppression device, the active EMI filter (AEF) can be used in the motor drive system to suppress the CM-conducted EMI. As the unity gain bandwidth of the operational amplifier (OPAMP) in the AEF is fixed, the CM-conducted EMI suppression effect of the AEF is poor. To solve this problem, the multiple-amplification-channel method is used to spread the frequency range of the CM voltage that the AEF can sense; therefore, the frequency of the compensation current output using the AEF is more complete, and a better CM-conducted EMI attenuation effect is obtained. At the same time, a combination of the period frequency carrier modulation (PCFM) and the multi-amplification-channel AEF is adopted so that the CM-conducted EMI in the whole EMI frequency range of the motor drive system is effectively suppressed.

Triple-negative breast cancer (TNBC) lacks estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 expressions, making targeted therapies ineffective. Mesenchymal stem cells (MSCs) have emerged as a promising approach for TNBC treatment by modulating the tumor microenvironment (TME) and interacting with cancer cells. This review aims to comprehensively overview the role of MSCs in TNBC treatment, including their mechanisms of action and application strategies. We analyze the interactions between MSC and TNBC cells, including the impact of MSCs on TNBC cell proliferation, migration, invasion, metastasis, angiogenesis, and drug resistance, along with the signaling pathways and molecular mechanisms involved. We also explore the impact of MSCs on other components of the TME, such as immune and stromal cells, and the underlying mechanisms. The review discusses the application strategies of MSCs in TNBC treatment, including their use as cell or drug carriers and the advantages and limitations of different types and sources of MSCs in terms of safety and efficacy. Finally, we discuss the challenges and prospects of MSCs in TNBC treatment and propose potential solutions or improvement methods. Overall, this review provides valuable insights into the potential of MSCs as a novel therapeutic approach for TNBC treatment.

As one of the primary nodes in the flow of micro-plastics (MPs) in the environment, it is critical to examine and assess the Sewage Treatment, occurrence, and removal of MPs in waste treatment plant (WWTP). This research explored the shape, size, and composition of MPs at various stages of the WWTP process in the south of the city of Hefei, China, in dry and rainy weather conditions, as well as the removal effectiveness of MPs in a three-stage process. The collected MPs were quantitatively and qualitatively examined using an Osmosis electron microscope and micro-FTIR. The pollution risk of MPs in WWTP was assessed using the EU classification, labelling and packaging (CLP) standard and the pollution load index (PLI). The findings revealed that the average abundance of fibrous MPs was greatest in WWTP sewage and sludge, 49.3% and 39.7% in dry weather, and 50.1% and 43.2% in rainy weather, respectively. The average distribution of MPs in the 0–500 μm range was highest in WWTP wastewater and sludge, 64.9% and 60.4% in dry weather and 67.9% and 69.0% in rainy weather, respectively. Finally, the overall removal rate was 87.7% and 83.5%. At the same time, it has been demonstrated that MPs with varied compositions are strongly tied to human activities, and environmental conditions (such as rainy weather) also influence their source. In both dry and wet weather, the amount of polymers and the risk score were linked to the pollution risk of MPs in WWTP. In wet weather, the MPS pollution index was more variable. The pollution indices of MPs in row water and tail water were 2.40 and 2.46, respectively, which were heavily contaminated, and 1.0 and 1.2, which were moderately polluted. MPs in dewatered sludge had severely polluted indexes of 3.5 and 3.4, respectively. As a result, there is still MPs efflux or buildup in sludge during and after the WWTP process, which presents an ecological contamination concern.

In recent years, diabetes-associated breast cancer has become a significant clinical challenge. Diabetes is not only a risk factor for breast cancer but also worsens its prognosis. Patients with diabetes usually show hyperglycemia and hyperinsulinemia, which are accompanied by different glucose, protein, and lipid metabolism disorders. Metabolic abnormalities observed in diabetes can induce the occurrence and development of breast cancer. The changes in substrate availability and hormone environment not only create a favorable metabolic environment for tumorigenesis but also induce metabolic reprogramming events required for breast cancer cell transformation. Metabolic reprogramming is the basis for the development, swift proliferation, and survival of cancer cells. Metabolism must also be reprogrammed to support the energy requirements of the biosynthetic processes in cancer cells. In addition, metabolic reprogramming is essential to enable cancer cells to overcome apoptosis signals and promote invasion and metastasis. This review aims to describe the major metabolic changes in diabetes and outline how cancer cells can use cellular metabolic changes to drive abnormal growth and proliferation. We will specifically examine the mechanism of metabolic reprogramming by which diabetes may promote the development of breast cancer, focusing on the role of glucose metabolism, amino acid metabolism, and lipid metabolism in this process and potential therapeutic targets. Although diabetes-associated breast cancer has always been a common health problem, research focused on finding treatments suitable for the specific needs of patients with concurrent conditions is still limited. Most studies are still currently in the pre-clinical stage and mainly focus on reprogramming the glucose metabolism. More research targeting the amino acid and lipid metabolism is needed.

Genome-scale metabolic models (GEMs) play an important role in the phenotype prediction of microorganisms, and their accuracy can be further improved by integrating other types of biological data such as enzyme concentrations and kinetic coefficients. Enzyme-constrained models (ecModels) have been constructed for several species and were successfully applied to increase the production of commodity chemicals. However, there was still no genome-scale ecModel for the important model organism Bacillus subtilis prior to this study. Here, we integrated enzyme kinetic and proteomic data to construct the first genome-scale ecModel of B. subtilis (ecBSU1) using the ECMpy workflow. We first used ecBSU1 to simulate overflow metabolism and explore the trade-off between biomass yield and enzyme usage efficiency. Next, we simulated the growth rate on eight previously published substrates and found that the simulation results of ecBSU1 were in good agreement with the literature. Finally, we identified target genes that enhance the yield of commodity chemicals using ecBSU1, most of which were consistent with the experimental data, and some of which may be potential novel targets for metabolic engineering. This work demonstrates that the integration of enzymatic constraints is an effective method to improve the performance of GEMs. The ecModel can predict overflow metabolism more precisely and can be used for the identification of target genes to guide the rational design of microbial cell factories.

Nitrate is a prominent pollutant in water bodies around the world. The isotopes in nitrate provide an effective approach to trace the sources and transformations of nitrate in water bodies. However, determination of isotopic composition by conventional analytical techniques is time-consuming, laborious, and expensive, and alternative methods are urgently needed. In this study, the rapid determination of 15NO3− in water bodies using Fourier transform infrared attenuated total reflectance spectroscopy (FTIR-ATR) coupled with a deconvolution algorithm and a partial least squares regression (PLSR) model was explored. The results indicated that the characteristic peaks of 14NO3−/15NO3− mixtures with varied 14N/15N ratios were observed, and the proportion of 15NO3− was negatively correlated with the wavenumber of absorption peaks. The PLSR models for nitrate prediction of 14NO3−/15NO3− mixtures with different proportions were established based on deconvoluted spectra, which exhibited good performance with the ratio of prediction to deviation (RPD) values of more than 2.0 and the correlation coefficients (R2) of more than 0.84. Overall, the spectra pretreatment by the deconvolution algorithm dramatically improved the prediction models. Therefore, FTIR-ATR combined with deconvolution and PLSR provided a rapid, simple, and affordable method for determination of 15NO3− content in water bodies, which would facilitate and enhance the study of nitrate sources and water environment quality management.

Composite polymer electrolyte (CPE) is expected to have great prospects in solid-state batteries. However, their application is impeded due to the poor interfacial compatibility between CPE and electrodes that result in sluggish ionic transformation, especially at low temperatures. Here, on the basis of Poly (vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) polymer electrolyte, gel composite polymer electrolyte (GCPE) with fast Li+ transport channel is prepared by in-situ polymerization with poly (ethylene glycol) methyl ether acrylate (PEGMEA) monomer and FEC as additive. Compared with CPE, GCPE increases the ionic conductivity by 10 times. It also achieves more uniform lithium precipitation and significantly inhibits the growth of lithium dendrites. The LFP/GCPE/Li battery has a capacity retention of over 99% at both room temperature and 0 °C after 100 cycles. In addition, the coulombic efficiency is above 99% during cycling. Our work provides a new technology to prepare GCPE with high ionic conductivity at both room temperature and low temperatures that has great potential in the application of solid-state lithium batteries.

The use of optical sensing for in vivo applications is compelling, since it offers the advantages of non-invasiveness, non-ionizing radiation, and real-time monitoring. However, the signal-to-noise ratio (SNR) of the optical signal deteriorates dramatically as the biological tissue increases. Although increasing laser power can improve the SNR, intense lasers can severely disturb biological processes and viability. Quantum sensing with bright squeezed light can make the measurement sensitivity break through the quantum noise limit under weak laser conditions. A bright squeezed light source is demonstrated to avoid the deterioration of SNR and biological damage, which integrates an external cavity frequency-doubled laser, a semi-monolithic standing cavity with periodically poled titanyl phosphate (PPKTP), and a balanced homodyne detector (BHD) assembled on a dedicated breadboard. With the rational design of the mechanical elements, the optical layout, and the feedback control equipment, a maximum non-classical noise reduction of −10.7 ± 0.2 dB is observed. The average squeeze of −10 ± 0.2 dB in continuous operation for 60 min is demonstrated. Finally, the intracavity loss of degenerate optical parametric amplifier (DOPA) and the initial bright squeezed light can be calculated to be 0.0021 and −15.5 ± 0.2 dB, respectively. Through the above experimental and theoretical analysis, the direction of improving bright squeeze level is pointed out.

Humidity sensors are important in industrial fields and human activities. Metal−organic frameworks (MOFs) and their derivatives are a class of promising humidity−sensing materials with the characteristics of a large specific surface area, high porosity, modifiable frameworks, and high stability. The drawbacks of MOFs, such as poor film formation, low electrical conductivity, and limited hydrophilicity, have been gradually overcome with the development of material science. Currently, it is moving towards a critical development stage of MOF−based humidity sensors from usability to ease of use, of which great challenges remain unsolved. In order to better understand the related challenges and point out the direction for the future development of MOF−based humidity sensors, we reviewed the development of such sensors based on related published work, focusing on six primary types (impedance, capacitive, resistive, fluorescent, quartz crystal microbalance (QCM), and others) and analyzed the sensing mechanism, material design, and sensing performance involved, and presented our thoughts on the possible future research directions.

We present a novel echo-based synthetic aperture radar (SAR) image simulation method that comprehensively utilizes both SAR effective view and ray tracing algorithms. To improve the fidelity of simulated SAR images, we first design an SAR effective view algorithm to process the selected facet target model, with the purpose of discretizing the facets in the SAR effective view into lattice targets and ensuring that the interval of lattice targets is set strictly following the Nyquist sampling law. Then, we combine the ray tracing algorithm and SAR echo time-domain simulation and perform SAR echo time-domain simulation of lattice targets based on ray tracing. Various kinds of backscatter coefficient for each point target can be recorded, corresponding to the number of transmitted pulses within the synthetic aperture time. The echo matrixes of lattice targets are superimposed to generate the raw echo signal. Finally, the raw echo signal is processed by using the range-Doppler (RD) imaging algorithm to obtain the simulated SAR image. We conducted SAR image simulation tests on several facet target models, including car body, assault boat, and airplane, with different material properties. The simulated SAR images obtained by the proposed method were qualitatively and quantitatively evaluated. The experimental results verify the effectiveness of the proposed method.

Selective catalytic reduction of NOx by CO (CO-SCR) to both N2 and CO2 is a promising way to simultaneously remove two harmful gases, CO and NOx, in automobile and factory exhaust gases. The development of efficient catalysts is the key challenge for the technology to be commercialized. The low-cost Cu-based catalysts have shown promising performance in CO-SCR, but there are some technical problems that obstruct their practical implementation, such as high reduction temperature and low O2, H2O, and SO2 resistance. This paper provides a comprehensive overview and insights into CO-SCR under O2-containing conditions over the Cu-based catalysts, including catalytic performances of non-supported, supported mono-metallic, supported bimetallic, and supported multi-metallic Cu-based catalysts. In addition, the effects of O2 concentration, reaction temperature, H2O, and SO2 on the catalytic performance are discussed. Furthermore, the reaction mechanism of CO-SCR on Cu-based catalysts is briefly summarized. Lastly, challenges and perspectives with respect to this reaction are discussed. We hope this work can provide theoretical guidance for the rational design of efficient Cu-based catalysts in the CO-SCR reaction for commercial applications.

The genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only considers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing substrate uptake rate, which deviates from the experimentally measured values. Recently, the integration of enzymatic constraints into stoichiometry-based GEMs was proven to be effective in making novel discoveries and predicting new engineering targets. Here, we present the first genome-scale enzyme-constrained model (ecCGL1) for Corynebacterium glutamicum reconstructed by integrating enzyme kinetic data from various sources using a ECMpy workflow based on the high-quality GEM of C. glutamicum (obtained by modifying the iCW773 model). The enzyme-constrained model improved the prediction of phenotypes and simulated overflow metabolism, while also recapitulating the trade-off between biomass yield and enzyme usage efficiency. Finally, we used the ecCGL1 to identify several gene modification targets for l-lysine production, most of which agree with previously reported genes. This study shows that incorporating enzyme kinetic information into the GEM enhances the cellular phenotypes prediction of C. glutamicum, which can help identify key enzymes and thus provide reliable guidance for metabolic engineering.

Recent studies have shown that diaporine, a novel fungal metabolic product, has a strong in vitro and in vivo anticancer effect on human non-small-cell lung and breast cancers. In this study, three human hepatocarcinoma cell lines (HepG2, Hep3B, and Huh7) were used to evaluate the efficacy of diaporine alone and in combination with the standard cytotoxic drugs oxaliplatin and doxorubicin for the treatment of liver cancer. We demonstrated that diaporine, oxaliplatin, and doxorubicin triggered a concentration- and time-dependent decrease in the number of HepG2 cells. Diaporine at a concentration of 2.5 μM showed almost 100% inhibition of cell counts at 72 h. Similar effects were observed only with much higher concentrations (100 μM) of oxaliplatin or doxorubicin. Decreases in cell numbers after 48 h treatment with diaporine, oxaliplatin, and doxorubicin were also demonstrated in two additional hepatoma cell lines, Hep3B and Huh7. The combination of these drugs at low concentration for 48 h in vitro noticeably showed that diaporine improved the inhibitory effect on the number of cancer cells induced by oxaliplatin or doxorubicin. Additionally, this combination effectively inhibited colony growth in vitro. We found that inhibition of phosphorylation of ERK1/2 significantly increased when diaporine was used in combination with other agents. In addition, we also found that when diaporine was used in combination with doxorubicin or oxaliplatin, their proapoptotic effect greatly increased. We further revealed that the induction of apoptosis in hepatoma cells after treatment is due, at least in part, to the inhibition of phosphorylation of AKT, leading to the activation of caspase-3, inactivation of poly (ADP-ribose) polymerase (PARP), and subsequently to DNA damage, as indicated by the increased level of H2AX. Based on these findings, we suggest that diaporine in combination with the standard cytotoxic drugs oxaliplatin and doxorubicin may play a role in the treatment of liver cancer.

Although winter wheat has been mapped by remote sensing in several studies, such mapping efforts did not sufficiently utilize contextual information to reduce the noise and still depended heavily on optical imagery and exhausting classification approaches. Furthermore, the influence of similarity measures on winter wheat identification remains unclear. To overcome these limitations, this study developed an object-based automatic approach to map winter wheat using multitemporal Sentinel-1 (S1) and Sentinel-2 (S2) imagery. First, after S1 and S2 images were preprocessed, the Simple Non-Iterative Clustering (SNIC) algorithm was used to conduct image segmentation to obtain homogeneous spatial objects with a fusion of S1 and S2 bands. Second, the temporal phenology patterns (TPP) of winter wheat and other typical land covers were derived from object-level S1 and S2 imagery based on the collected ground truth samples, and two improved distance measures (i.e., a composite of Euclidean distance and Spectral Angle Distance, (ESD) and the difference–similarity factor distance (DSF)) were built to evaluate the similarity between two TPPs. Third, winter wheat objects were automatically identified from the segmented spatial objects by the maximum between-class variance method (OTSU) with distance measures based on the unique TPP of winter wheat. According to ground truth data, the DSF measure was superior to other distance measures in winter wheat mapping, since it achieved the best overall accuracy (OA), best kappa coefficient (Kappa) and more spatial details for each feasible band (i.e., NDVI, VV, and VH/VV), or it obtained results comparable to those for the best one (e.g., NDVI + VV). The resultant winter wheat maps derived from the NDVI band with the DSF measure achieved the best accuracy and more details, and had an average OA and Kappa of 92% and 84%, respectively. The VV polarization with the DSF measure produced the second best winter wheat maps with an average OA and Kappa of 91% and 80%, respectively. The results indicate the great potential of the proposed object-based approach for automatic winter wheat mapping for both optical and Synthetic Aperture Radar (SAR) imagery.

Single-crystal sapphire (α-Al2O3) is an important material and widely used in many advanced fields. The semi-fixed abrasive grain processing method based on solid-phase reaction theory is a prominent processing method for achieving ultra-precision damage-free surfaces. In order to develop the proposed method for polishing sapphire, the basic characteristics of the semi-fixed abrasive grains polishing tool for polishing sapphire were determined. Weight analysis was used to study the influence rules of parameters on surface roughness and material removal rates using an orthogonal experiment. Then, the optimized polishing tool was obtained through a mixture of abrasive particle sizes to reduce the difficulty in molding the polishing tool. Finally, polishing experiments using different polishing tools were carried out to investigate polishing performance by considering the surface roughness, material removal rate and the surface morphology during polishing. The results showed that (1) external load affects the surface roughness and material removal rate the most, followed by abrasive particle size, sand bond ratio, revolution speed of the workpiece and he polishing tool; (2) the difficulty in manufacturing the polishing tool could be reduced by mixing larger abrasive particles with small abrasive particles; (3) the polishing tool with 200 nm and 1 μm particle sizes performed best in the first 210 min polishing.

Hyperspectral remote sensing image (HSI) include rich spectral information that can be very beneficial for change detection (CD) technology. Due to the existence of many mixed pixels, pixel-wise approaches can lead to considerable errors in the resulting CD map. The spectral unmixing (SU) method is a potential solution to this problem, as it decomposes mixed pixels into a set of fractions of land cover. Subsequently, the CD map is created by comparing the abundance images. However, based only on the abundance images created through the SU method, they are unable to effectively provide detailed change information. Meanwhile, the features of change information cannot be sufficiently extracted by the traditional sub-pixel CD framework, which leads to a poor CD result. To address these problems, this paper presents an integrated CD method based on multi-endmember spectral unmixing, joint matrix and CNN (MSUJMC) for HSI. Three main steps are considered to accomplish this task. First, considering the endmember spectral variability, more reliable endmember abundance information is obtained by multi-endmember spectral unmixing (MSU). Second, the original image features are incorporated with the abundance images using a joint matrix (JM) algorithm to provide more temporal and spatial land cover change information characteristics. Third, to efficiently extract the change features and to better handle the fused multi-source information, the convolutional neural network (CNN) is introduced to realize a high-accuracy CD result. The proposed method has been verified on simulated and real multitemporal HSI datasets, which provide multiple changes. Experimental results verify the effectiveness of the proposed approach.

The reconstruction of moving objects based on phase shifting profilometry has attracted intensive interests. Most of the methods introduce the phase shift by projecting multiple fringe patterns, which is undesirable in moving object reconstruction as the errors caused by the motion will be intensified when the number of the fringe pattern is increased. This paper proposes the reconstruction of the isolated moving object by projecting two fringe patterns with different frequencies. The phase shift required by the phase shifting profilometry is generated by the object motion, and the model describing the motion-induced phase shift is presented. Then, the phase information in different frequencies is retrieved by analyzing the influence introduced by movement. Finally, the mismatch on the phase information between the two frequencies is compensated and the isolated moving object is reconstructed. Experiments are presented to verify the effectiveness of the proposed method.

Pentamode metamaterials have been receiving an increasing amount of interest due to their water-like properties. In this paper, a two-dimensional composite pentamode metamaterial of rectangular unit cell is proposed. The unit cells can be classified into two groups, one with uniform arms and the other with non-uniform arms. Phononic band structures of the unit cells were calculated to derive their properties. The unit cells can be pentamode metamaterials that permit acoustic wave travelling or have a total band gap that impedes acoustic wave propagation by varying the structures. The influences of geometric parameters and materials of the composed elements on the effective velocities and anisotropy were analyzed. The metamaterials can be used for acoustic wave control under water. Simulations of materials with different unit cells were conducted to verify the calculated properties of the unit cells. The research provides theoretical support for applications of the pentamode metamaterials.

Flow characteristics and pollutant dispersion characteristics of a mechanical upper vent of a tunnel under various discharge patterns and ambient wind were studied by using the computational fluid dynamics (CFD) numerical method and an experimental model. On this basis, variations in the environmental impact radius rcriti changing with the ambient wind velocity U for pollutants from vertical and horizontal outlets were also analyzed with the set discharge capacity. According to our findings, the pollutant emission of the upper vent under a vertical discharge pattern can be considered as free buoyant jet diffusion under the impact of the momentum of ambient wind, while the emission under the horizontal discharge pattern can be regarded as semi-confined buoyant jet diffusion under the impact of the momentum of ambient wind. Moreover, the rcriti of the vertical outlet increased with the increase of U when U ≤ 3 m/s and was minimally affected by U for U > 3 m/s. Additionally, the rcriti of the horizontal outlet decreased with the increase of U. When the discharge capacity was constant, the rcriti of the upper vent under both vertical and horizontal discharge patterns declined with the increase of the discharge velocity V0. Note that the rcriti of the vertical outlet was smaller than that of the horizontal one for the range of parameters considered. These results can provide a theoretical reference for optimizing the design of a vent on the top of an urban tunnel.

In this paper, statistical inference and prediction issue of left truncated and right censored dependent competing risk data are studied. When the latent lifetime is distributed by Marshall–Olkin bivariate Rayleigh distribution, the maximum likelihood estimates of unknown parameters are established, and corresponding approximate confidence intervals are also constructed by using a Fisher information matrix and asymptotic approximate theory. Furthermore, Bayesian estimates and associated high posterior density credible intervals of unknown parameters are provided based on general flexible priors. In addition, when there is an order restriction between unknown parameters, the point and interval estimates based on classical and Bayesian frameworks are discussed too. Besides, the prediction issue of a censored sample is addressed based on both likelihood and Bayesian methods. Finally, extensive simulation studies are conducted to investigate the performance of the proposed methods, and two real-life examples are presented for illustration purposes.

Lithium resources face risks of shortages owing to the rapid development of the lithium industry. This makes the efficient production and recycling of lithium an issue that should be addressed immediately. Lithium bromide is widely used as a water-absorbent material, a humidity regulator, and an absorption refrigerant in the industry. However, there are few studies on the recovery of lithium from lithium bromide after disposal. In this paper, a bipolar membrane electrodialysis (BMED) process is proposed to convert waste lithium bromide into lithium hydroxide, with the generation of valuable hydrobromic acid as a by-product. The effects of the current density, the feed salt concentration, and the initial salt chamber volume on the performance of the BMED process were studied. When the reaction conditions were optimized, it was concluded that an initial salt chamber volume of 200 mL and a salt concentration of 0.3 mol/L provided the maximum benefit. A high current density leads to high energy consumption but with high current efficiency; therefore, the optimum current density was identified as 30 mA/cm2. Under the optimized conditions, the total economic cost of the BMED process was calculated as 2.243 USD·kg−1LiOH. As well as solving the problem of recycling waste lithium bromide, the process also represents a novel production methodology for lithium hydroxide. Given the prices of lithium hydroxide and hydrobromic acid, the process is both environmentally friendly and economical.

Cryo-electron microscopy (Cryo-EM) has become a routine technology for resolving the structure of biological macromolecules due to the resolution revolution in recent years. The specimens are typically prepared in a very thin layer of vitrified ice suspending in the holes of the perforated amorphous carbon film. However, the samples prepared by directly applying to the conventional support membranes may suffer from partial or complete denaturation caused by sticking to the air–water interface (AWI). With the application in materials, graphene has also been used recently to improve frozen sample preparation instead of a suspended conventional amorphous thin carbon. It has been proven that graphene or graphene oxide and various chemical modifications on its surface can effectively prevent particles from adsorbing to the AWI, which improves the dispersion, adsorbed number, and orientation preference of frozen particles in the ice layer. Their excellent properties and thinner thickness can significantly reduce the background noise, allowing high-resolution three-dimensional reconstructions using a minimum data set.

In recent decades, lithological mapping techniques using hyperspectral remotely sensed imagery have developed rapidly. The processing chains using visible-near infrared (VNIR) and shortwave infrared (SWIR) hyperspectral data are proven to be available in practice. The thermal infrared (TIR) portion of the electromagnetic spectrum has considerable potential for mineral and lithology mapping. In particular, the abovementioned rocks at wavelengths of 8–12 μm were found to be discriminative, which can be seen as a characteristic to apply to lithology classification. Moreover, it was found that most of the lithology mapping and classification for hyperspectral thermal infrared data are still carried out by traditional spectral matching methods, which are not very reliable due to the complex diversity of geological lithology. In recent years, deep learning has made great achievements in hyperspectral imagery classification feature extraction. It usually captures abstract features through a multilayer network, especially convolutional neural networks (CNNs), which have received more attention due to their unique advantages. Hence, in this paper, lithology classification with CNNs was tested on thermal infrared hyperspectral data using a Thermal Airborne Spectrographic Imager (TASI) at three small sites in Liuyuan, Gansu Province, China. Three different CNN algorithms, including one-dimensional CNN (1-D CNN), two-dimensional CNN (2-D CNN) and three-dimensional CNN (3-D CNN), were implemented and compared to the six relevant state-of-the-art methods. At the three sites, the maximum overall accuracy (OA) based on CNNs was 94.70%, 96.47% and 98.56%, representing improvements of 22.58%, 25.93% and 16.88% over the worst OA. Meanwhile, the average accuracy of all classes (AA) and kappa coefficient (kappa) value were consistent with the OA, which confirmed that the focal method effectively improved accuracy and outperformed other methods.

Due to the high temporal repetition rates, median/low spatial resolution remote sensing images are the main data source of change detection (CD). It is worth noting that they contain a large number of mixed pixels, which makes adequately capturing the details in the resulting thematic map challenging. The spectral unmixing (SU) method is a potential solution to this problem, as it decomposes mixed pixels into a set of fractions of the land covers. However, there are accumulated errors in the fractional difference images, which lead to a poor change detection results. Meanwhile, the spectra variation of the endmember and the heterogeneity of the land cover materials cannot be fully considered in the traditional framework. In order to solve this problem, a novel change detection approach with image stacking and dividing based on spectral unmixing while considering the variability of endmembers (CD_SDSUVE) was proposed in this paper. Firstly, the remote sensing images at different times were stacked into a unified framework. After that, several patch images were produced by dividing the stacked images so that the similar endmembers according to each land cover can be completely extracted and compared. Finally, the multiple endmember spectral mixture analysis (MESMA) is performed, and the abundant images were combined to produce the entire change detection thematic map. This proposed algorithm was implemented and compared to four relevant state-of-the-art methods on three experimental data, whereby the results confirmed that it effectively improved the accuracy. In the simulated data, the overall accuracy (OA) and Kappa coefficient values were 99.61% and 0.99. In the two real data, the maximum of OA were acquired with 93.26% and 80.85%, which gained 14.88% and 13.42% over the worst results at most. Meanwhile, the Kappa coefficient value was consistent with the OA.

The ongoing coronavirus disease (COVID-19) pandemic is caused by a new coronavirus (severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2)) first reported in Wuhan City, China. From there, it has been rapidly spreading to many cities inside and outside China. Nowadays, more than 110 million cases with deaths surpassing 2 million have been recorded worldwide, thus representing a major health and economic issues. Rapid development of a protective vaccine against COVID-19 is therefore of paramount importance. Here, we demonstrated that the recombinantly expressed receptor-binding domain (RBD) of the spike protein can be coupled to immunologically optimized virus-like particles derived from cucumber mosaic virus (CuMVTT). The RBD displayed CuMVTT bound to ACE2, the viral receptor, demonstrating proper folding of RBD. Furthermore, a highly repetitive display of the RBD on CuMVTT resulted in a vaccine candidate that induced high levels of specific antibodies in mice, which were able to block binding of the spike protein to ACE2 and potently neutralize SARS-CoV-2 virus in vitro.

Lithium carbonate is an important chemical raw material that is widely used in many contexts. The preparation of lithium carbonate by acid roasting is limited due to the large amounts of low-value sodium sulfate waste salts that result. In this research, bipolar membrane electrodialysis (BMED) technology was developed to treat waste sodium sulfate containing lithium carbonate for conversion of low-value sodium sulfate into high-value sulfuric acid and sodium hydroxide. Both can be used as raw materials in upstream processes. In order to verify the feasibility of the method, the effects of the feed salt concentration, current density, flow rate, and volume ratio on the desalination performance were determined. The conversion rate of sodium sulfate was close to 100%. The energy consumption obtained under the best experimental conditions was 1.4 kWh·kg−1. The purity of the obtained sulfuric acid and sodium hydroxide products reached 98.32% and 98.23%, respectively. Calculated under the best process conditions, the total process cost of BMED was estimated to be USD 0.705 kg−1 Na2SO4, which is considered low and provides an indication of the potential economic and environmental benefits of using applying this technology.