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Abstract Background and objectives Lung cancer is one of the prevailing malignancies worldwide. Surgical interventions hold an important position in the treatment framework for lung cancer. Pleural metastasis is often assumed to be a surgical contraindication, but not all instances of pleural metastasis can be accurately identified before surgery. The question of how to address pleural metastasis detected intraoperatively is still undecided. Methods This retrospective study included 187 lung cancer patients who underwent surgery from 2005 to 2017 in whom pleural metastasis was discovered incidentally during the operation. Data on demographic, surgical, pathological, postoperative treatment, and survival information were collected for further analysis. Results For patients with intraoperatively detected pleural metastasis, two independent protective prognostic factors were receiving primary tumor resection (compared to only receiving pleural nodule biopsy, HR = 0.079, p = 0.022) and receiving postoperative adjuvant chemotherapy (HR = 0.081, p

Abstract Background Evidence on the prevalence of smoking in China remains insufficient, with most previous studies focusing on a single region. However, smoking prevalence exhibits significant inequalities across the entire country. This study aimed to evaluate the risk of tobacco prevalence across the country, taking into account spatial inequalities. Methods The data used in this study were collected in 23 provinces, 5 autonomous regions, and 4 municipalities directly under the central government in 2022. Large population survey data were used, and a Bayesian geostatistical model was employed to investigate smoking prevalence rates across multiple spatial domains. Findings Significant spatial variations were observed in smokers and exposure to secondhand smoke across China. Higher levels of smokers and secondhand smoke exposure were observed in western and northeastern regions. Additionally, the autonomous region of Tibet, Shanghai municipality, and Yunnan province had the highest prevalence of smokers, while Tibet, Qinghai province, and Yunnan province had the highest prevalence of exposure to secondhand smoke. Conclusion We have developed a model-based, high-resolution nationwide assessment of smoking risks and employed rigorous Bayesian geostatistical models to help visualize smoking prevalence predictions. These prediction maps provide estimates of the geographical distribution of smoking, which will serve as strong evidence for the formulation and implementation of smoking cessation policies. Highlights Our study investigated the prevalence of smokers and exposure to secondhand smoke in different spatial areas of China and explored various factors influencing the smoking prevalence. For the first time, our study applied Bayesian geostatistical modeling to generate a risk prediction map of smoking prevalence, which provides a more intuitive and clear understanding of the spatial disparities in smoking prevalence across different geographical regions, economic levels, and development status. We found significant spatial variations in smokers and secondhand smoke exposure in China, with higher rates in the western and northeastern regions.

Owing to the significant development in graphene, an increasing number of studies have been conducted to identify novel two-dimensional (2D) organic materials with Dirac cones and topological properties. Although a series of toy models based on specific lattice patterns has been proposed and demonstrated to possess a Dirac cone, realistic materials corresponding to the lattice models must be identified to achieve excellent properties for practical applications. To understand factors contributing to the rarity of 2D organic Dirac materials and provide guidance for identifying novel organic Dirac systems, we review recent theoretical studies pertaining to various 2D Dirac models and their corresponding organic Dirac materials, including the Haldane, Kagome, Libe, line-centered honeycomb, and Cairo pentagonal models. Subsequently, the corresponding structural and topological electronic properties are summarized. Additionally, we investigate the relationship between the existence of Dirac cones and their structural features, as well as the manner by which Dirac points emerge and propagate in these systems.

IntroductionAcute myeloid leukemia(AML) is a diverse malignancy with a prognosis that varies, being especially unfavorable in older patients and those with high-risk characteristics. Metabolic reprogramming has become a significant factor in AML development , presenting new opportunities for prognostic assessment and therapeutic intervention.MethodsMetabolism-related differentially expressed genes (mDEGs) were identified by integrating KEGG metabolic gene lists with AML gene expression data from GSE63270. Using TCGA data, we performed consensus clustering and survival analysis to investigate the prognostic significance of mDEGs. A metabolic risk model was constructed using LASSO Cox reg ression and enhanced by a nomogram incorporated clinical characteristics. The model was validated through receiver operating characteristic (ROC) curves and survival statistics. Gene network analysis was conducted to identify critical prognostic factors. The tumor immune microenvironment was evaluated using CIBERSORT and ESTIMATE algorithms, followed by correlation analysis between immune checkpoint gene expression and risk scores. Drug sensitivity predictions and in vitro assays were performed to explore the effects of mDEGs on cell proliferation and chemoresistance.ResultsAn 11-gene metabolic prognostic model was established and validated. High-risk patients had worse overall survival in both training and validation cohorts (p < 0.05). The risk score was an independent prognostic factor. High-risk patients showed increased immune cell infiltration and potential response to checkpoint inhibitors but decreased drug sensitivity. The model correlated with sensitivity to drugs such as venetoclax. Carbonic anhydrase 13 (CA13) was identified as a key gene related to prognosis and doxorubicin resistance. Knocking down CA13 reduced proliferation and increased cell death with doxorubicin treatment.ConclusionA novel metabolic gene signature was developed to stratify risk and predict prognosis in AML, serving as an independent prognostic factor. CA13 was identified as a potential therapeutic target. This study provides new insights into the prognostic and therapeutic implications of metabolic genes in AML.

ABSTRACTZero watermarking constructs the watermark information according to the characteristics of the original data, without changing the data structure and data accuracy. Maintaining high data accuracy is the premise of building information modelling (BIM) usability, so zero watermarking is a hotspot in the research of BIM data security protection. BIM model is a type of 3D model, however, most of the existing zero watermarking algorithms for 3D models are difficult to be better applied to BIM data due to data structure differences. To solve this problem, a zero-watermarking algorithm for BIM data based on distance partitioning and skewness measure is proposed. Firstly, after spatial partitioning based on element paradigm value, the mapping relationship between different partitions and watermarking bits is established. Then, the skewness of elements is calculated, and the skewness measure sign is used as the feature to obtain the binary sequence. Finally, the dissimilarity operation is performed on the binary sequence and the original watermarking sequence which was disordered to construct the zero watermark of the BIM data. The experimental results show that the zero watermarks constructed from different BIM data are unique and robust to translation, rotation, element deletion, element addition, and format conversion attacks. In addition, the superiority of this paper’s algorithm over the comparison algorithm in terms of robustness is compared. Therefore, the proposed algorithm can effectively provide technical support for BIM data copyright protection.

Abstract Background There may be unexplored interactions between family health, personality, and smoking that could help provide new perspectives on tobacco control. Objective To examine the relationship between the health of one’s family and their smoking habits, as well as investigate the potential influence of personality on this relationship. Methods For this cross-sectional investigation, a national survey conducted in China in 2022 recruited a total of 21,916 individuals. The Family Health Scale was utilized to assess the health of the family. The 10-item Big Five Inventory scale was utilized to assess the Big five personality traits. The relationship between big five personality, family health, and smoking were investigated using binary and linear logistic regression. The indirect effects mediated by Big five personality were analyzed using mediation analysis with Sobel tests, and the indirect effects were composited using the Karlson-Holm-Breen method. Results The overall prevalence of smoking in the study population was 14.87%, 26.19% for males and 3.54% for females. Urban and rural smoking prevalence was 13.81% and 16.10% respectively. Binary logistic regression analysis revealed a significant negative relationship between smoking and family health (odds ratio 0.964, 95% CI 0.959, 0.970, P

Nanozymes, a category of nanomaterials endowed with enzyme-mimicking capabilities, have exhibited considerable potential across diverse application domains. This comprehensive review delves into the intricacies of regulating nanozymes through N elements, elucidating the mechanisms governing N element control in the design and application of these nanomaterials. The initial sections introduce the foundational background and significance of nanozymes. Subsequent exploration delves into the detailed discussion of N element regulation mechanisms on nanozymes, encompassing N vacancies, N doping, N coordination, and nitride. These regulatory pathways play an instrumental role in fine-tuning the catalytic activity and specificity of nanozymes. The review further scrutinizes practical applications of N element regulation on nanozymes, spanning sensing detection, infection therapy, tumor therapy, and pollutant degradation. In conclusion, it succinctly summarizes the current research findings and proposes future directions for development. This thorough investigation into the regulation of nanozymes by N elements anticipates precise control over their performance, thereby advancing the extensive utilization of nanozymes in the realms of biomedical and environmental applications.

Alzheimer’s disease (Alzheimer’s disease, AD) is a progressive neurological disorder characterized by memory loss and cognitive impairment. It is characterized by the formation of tau protein neurofibrillary tangles and β-amyloid plaques. Recent studies have found that mitochondria in neuronal cells of AD patients exhibit various dysfunctions, including reduced numbers, ultrastructural changes, reduced enzyme activity, and abnormal kinetics. These abnormal mitochondria not only lead to the loss of normal neuronal cell function, but are also a major driver of AD progression. In this review, we will focus on the advances of mitochondria and their multi-omics in AD research, with particular emphasis on how mitochondrial dysfunction in AD drives disease progression. At the same time, we will focus on summarizing how mitochondrial genomics technologies have revealed specific details of these dysfunctions and how therapeutic strategies targeting mitochondria may provide new directions for future AD treatments. By delving into the key mechanisms of mitochondria in AD related to energy metabolism, altered kinetics, regulation of cell death, and dysregulation of calcium-ion homeostasis, and how mitochondrial multi-omics technologies can be utilized to provide us with a better understanding of these processes. In the future, mitochondria-centered therapeutic strategies will be a key idea in the treatment of AD.

Existing zero-watermarking algorithms for remote sensing images heavily rely on traditional feature extraction techniques, which are vulnerable to targeted attacks and lack discriminability for images captured by different sensors or at different time periods in the same geographical area. To address these limitations, this article proposes a novel watermarking algorithm based on blockchain and stacked denoising autoencoder (SDAE) to achieve lossless copyright protection for high-resolution remote sensing images. The algorithm utilizes SDAE to extract deep and robust features from local square feature regions for watermark construction. Moreover, the algorithm incorporates a watermark registration scheme designed with Hyperledger Fabric and InterPlanetary file system to ensure secure and trustworthy registration of watermarks and associated parameter information, enhancing the algorithm's uniqueness. Experimental results demonstrate the effectiveness of the proposed algorithm against various watermark attacks and its high discriminability for similar images. This algorithm holds significant potential for wide-ranging applications in the field of lossless copyright protection for high resolution remote sensing images (HRRS), effectively safeguarding the commercial interests of data providers.

Identifying template watermarks under severe geometric distortions is a significant scientific problem in the current watermarking research for remote sensing images. We propose a novel watermarking algorithm that integrates the ring-shaped template watermark with the multiscale local contrast measure (LCM) method. In the embedding stage, the ring-shaped template watermark is embedded into the discrete Fourier transform (DFT) magnitude coefficients, converting the watermark into small targets in the DFT domain. During the detection stage, the multiscale LCM, a classic infrared small target detection method, enhances these small targets and generates a contrast map. Peak detection is then performed on the contrast map to determine the radius of the template watermark. Finally, circular edge local binarization is applied to extract the watermark information. The proposed method enables synchronization recovery of watermarks under blind conditions. The experimental results demonstrate that the method possesses strong robustness against various geometric attacks such as rotation, scaling, translation, and cropping. It outperforms comparative algorithms in terms of robustness and also exhibits good imperceptibility.

Diabetes mellitus is a prevalent chronic disease characterized by hyperglycemia. Diabetic peripheral neuropathy (DPN) is one of the complications of diabetes mellitus and is caused by neuron injury induced by hyperglycemic circumstances. The incidence of DPN varies among different countries and regions, ranging from nearly 20% to over 70%. Patients with DPN may encounter symmetric pain or discomfort of the extremes, leading to reduced quality of life and even amputation. The pharmacological management for painful DPN mainly includes antidepressants due to their analgesic effects. Nevertheless, effective therapies to impact the pathogenesis and progression of DPN are lacking. Glucagon-like peptide-1 receptor (GLP-1R) agonists show efficacy in controlling blood glucose and serve as a treatment modality for diabetes mellitus. In recent years, evidence has been proposed that GLP-1R agonists exert neuroprotective effects through modulating inflammation, oxidative stress, and mitochondrial dysfunction. On the other hand, clinical evidence on the potential of GLP-1R agonists for treating DPN is still controversial and limited. This narrative review summarizes the preclinical and clinical studies investigating the capacity of GLP-1R agonists as therapeutic agents for DPN.

Renal fibrosis is increasingly recognized as a global public health problem. Acute kidney injury (AKI) and chronic kidney disease (CKD) both result in renal fibrosis. Oxidative stress and inflammation play central roles in progressive renal fibrosis. Oxidative stress and inflammation are closely linked and form a vicious cycle in which oxidative stress induces inflammation through various molecular mechanisms. Ample evidence has indicated that a hyperactive nuclear factor kappa B (NF-ƙB) signaling pathway plays a pivotal role in renal fibrosis. Hyperactive NF-ƙB causes the activation and recruitment of immune cells. Inflammation, in turn, triggers oxidative stress through the production of reactive oxygen species and nitrogen species by activating leukocytes and resident cells. These events mediate organ injury through apoptosis, necrosis, and fibrosis. Therefore, developing a strategy to target the NF-ƙB signaling pathway is important for the effective treatment of renal fibrosis. This Review summarizes the effect of the NF-ƙB signaling pathway on renal fibrosis in the context of AKI and CKD (immunoglobulin A nephropathy, membranous nephropathy, diabetic nephropathy, hypertensive nephropathy, and kidney transplantation). Therapies targeting the NF-ƙB signaling pathway, including natural products, are also discussed. In addition, NF-ƙB-dependent non-coding RNAs are involved in renal inflammation and fibrosis and are crucial targets in the development of effective treatments for kidney disease. This Review provides a clear pathophysiological rationale and specific concept-driven therapeutic strategy for the treatment of renal fibrosis by targeting the NF-ƙB signaling pathway.

In order to safeguard image copyrights, zero-watermarking technology extracts robust features and generates watermarks without altering the original image. Traditional zero-watermarking methods rely on handcrafted feature descriptors to enhance their performance. With the advancement of deep learning, this paper introduces “ZWNet”, an end-to-end zero-watermarking scheme that obviates the necessity for specialized knowledge in image features and is exclusively composed of artificial neural networks. The architecture of ZWNet synergistically incorporates ConvNeXt and LK-PAN to augment the extraction of local features while accounting for the global context. A key aspect of ZWNet is its watermark block, as the network head part, which fulfills functions such as feature optimization, identifier output, encryption, and copyright fusion. The training strategy addresses the challenge of simultaneously enhancing robustness and discriminability by producing the same identifier for attacked images and distinct identifiers for different images. Experimental validation of ZWNet’s performance has been conducted, demonstrating its robustness with the normalized coefficient of the zero-watermark consistently exceeding 0.97 against rotation, noise, crop, and blur attacks. Regarding discriminability, the Hamming distance of the generated watermarks exceeds 88 for images with the same copyright but different content. Furthermore, the efficiency of watermark generation is affirmed, with an average processing time of 96 ms. These experimental results substantiate the superiority of the proposed scheme over existing zero-watermarking methods.

We propose a novel silicon carbide (SiC) self-aligned N-type ion implanted trench MOSFET (NITMOS) device. The maximum electric field in the gate oxide could be effectively reduced to below 3 MV/cm with the introduction of the P-epi layer below the trench. The P-epi layer is partially counter-doped by a self-aligned N-type ion implantation process, resulting in a relatively low specific on-resistance (Ron,sp). The lateral spacing between the trench sidewall and N-implanted region (Wsp) plays a crucial role in determining the performance of the SiC NITMOS device, which is comprehensively studied through the numerical simulation. With the Wsp increasing, the SiC NITMOS device demonstrates a better short-circuit capability owing to the reduced saturation current. The gate-to-drain capacitance (Cgd) and gate-to-drain charge (Qgd) are also investigated. It is observed that both Cgd and Qgd decrease as the Wsp increases, owing to the enhanced screen effect. Compared to the SiC double-trench MOSFET device, the optimal SiC NITMOS device exhibits a 79% reduction in Cgd, a 38% decrease in Qgd, and a 41% reduction in Qgd × Ron,sp. A higher switching speed and a lower switching loss can be achieved using the proposed structure.

With the increasing ease of building information modeling data usage, digital watermarking technology has become increasingly crucial for BIM data copyright protection. In response to the problem that existing robust watermarking methods mainly focus on BIM exchange formats and cannot adapt to BIM data, a novel watermarking algorithm specifically designed for BIM data, which combines element perturbation and invisible character embedding, is proposed. The proposed algorithm first calculates the centroid of the enclosing box to locate the elements, and establishes a synchronous relationship between the element coordinates and the watermarked bits using a mapping mechanism, by which the watermarking robustness is effectively enhanced. Taking into consideration both data availability and the need for watermark invisibility, the algorithm classifies the BIM elements based on their mobility, and perturbs the movable elements while embedding invisible characters within the attributes of the immovable elements. Then, the watermark information after dislocation is embedded into the data. We use building model and structural model BIM data to carry out the experiments, and the results demonstrate that the signal-to-noise ratio and peak signal-to-noise ratio before and after watermark embedding are both greater than 100 dB. In addition, the increased information redundancy accounts for less than 0.15% of the original data., which means watermark embedding has very little impact on the original data. Additionally, the NC coefficient of watermark extraction is higher than 0.85 when facing attacks such as translation, element addition, element deletion, and geometry–property separation. These findings indicate a high level of imperceptibility and robustness offered by the algorithm. In conclusion, the robust watermarking algorithm for BIM data fulfills the practical requirements and provides a feasible solution for protecting the copyright of BIM data.

Zoledronic acid (ZOL) is a potent antiresorptive agent that increases bone mineral density (BMD) and reduces fracture risk in postmenopausal osteoporosis (PMOP). The anti-osteoporotic effect of ZOL is determined by annual BMD measurement. In most cases, bone turnover markers function as early indicators of therapeutic response, but they fail to reflect long-term effects. We used untargeted metabolomics to characterize time-dependent metabolic shifts in response to ZOL and to screen potential therapeutic markers. In addition, bone marrow RNA-seq was performed to support plasma metabolic profiling. Sixty rats were assigned to sham-operated group (SHAM, n = 21) and ovariectomy group (OVX, n = 39) and received sham operation or bilateral ovariectomy, respectively. After modeling and verification, rats in the OVX group were further divided into normal saline group (NS, n = 15) and ZOL group (ZA, n = 18). Three doses of 100 μg/kg ZOL were administrated to the ZA group every 2 weeks to simulate 3-year ZOL therapy in PMOP. An equal volume of saline was administered to the SHAM and NS groups. Plasma samples were collected at five time points for metabolic profiling. At the end of the study, selected rats were euthanatized for bone marrow RNA-seq. A total number of 163 compound were identified as differential metabolites between the ZA and NS groups, including mevalonate, a critical molecule in target pathway of ZOL. In addition, prolyl hydroxyproline (PHP), leucyl hydroxyproline (LHP), 4-vinylphenol sulfate (4-VPS) were identified as differential metabolites throughout the study. Moreover, 4-VPS negatively correlated with increased vertebral BMD after ZOL administration as time-series analysis revealed. Bone marrow RNA-seq showed that the PI3K-AKT signaling pathway was significantly associated with ZOL-mediated changes in expression (adjusted-p = 0.018). In conclusion, mevalonate, PHP, LHP, and 4-VPS are candidate therapeutic markers of ZOL. The pharmacological effect of ZOL likely occurs through inhibition of the PI3K-AKT signaling pathway.

Abstract Background Extracellular vesicle (EV) biomarkers have promising diagnosis and screening capacity for several cancers, but the diagnostic value for pancreatic cancer (PC) is controversial. The aim of our study was to review the diagnostic performance of EV biomarkers for PC. Methods We performed a systematic review of PubMed, Medline, and Web Of Science databases from inception to 18 Feb 2022. We identified studies reporting the diagnostic performance of EV biomarkers for PC and summarized the information of sensitivity, specificity, area under the curve (AUC), or receiver operator characteristic (ROC) curve) in according to a pre-designed data collection form. Pooled sensitivity and specificity was calculated using a random-effect model. Results We identified 39 studies, including 2037 PC patients and 1632 noncancerous, seven of which were conducted independent validation tests. Seventeen studies emphasized on EV RNAs, sixteen on EV proteins, and sixteen on biomarker panels. MiR-10b, miR-21, and GPC1 were the most frequently reported RNA and protein for PC diagnosis. For individual RNAs and proteins, the pooled sensitivity and specificity were 79% (95% CI: 77–81%) and 87% (95% CI: 85–89%), 72% (95% CI: 69–74%) and 77% (95% CI: 74–80%), respectively. the pooled sensitivity and specificity of EV RNA combined with protein panels were 84% (95% CI: 81–86%) and 89% (95% CI: 86–91%), respectively. Surprisingly, for early stage (stage I and II) PC EV biomarkers showed excellent diagnostic performance with the sensitivity of 90% (95% CI: 87–93%) and the specificity of 94% (95% CI: 92–95%). Both in sensitivity and subgroup analyses, we did not observe notable difference in pooled sensitivity and specificity. Studies might be limited by the isolation and detection techniques of EVs to a certain extent. Conclusions EV biomarkers showed appealing diagnostic preference for PC, especially for early stage PC. Solving the deficiency of technologies of isolation and detection EVs has important implications for application these novel noninvasive biomarkers in clinical practice.

Magnetism has revolutionized important technologies, and continues to bring forth new phenomena in emergent materials and reduced dimensions. Here, using first-principles calculations, we demonstrate that the already-synthesized two-dimensional (2D) Ni-tetracyanoquinodimethane (Ni2(TCNQ)2) lattice is a stable ferromagnetism material with multiple spin-polarized Dirac cones. The conical bands in proximity of the Fermi level can be tuned by external tensile strain and show the fourfold degenerate electronic states at the critical tensile strain of ∼2.35 %, whose energy dispersion is consistent with 2D Cairo pentagonal lattice. In addition, spin-orbital coupling can open a band gap at the Dirac point of A, leading to topologically nontrivial electronic states characterized by the non-zero Chern number and the edge states of nanoribbon. Our results offer versatile platforms for the realization of massless spintronics with full-spin polarization in 2D Cairo pentagonal Ni2(TCNQ)2 Lattice.

The integrity of remote-sensing image data is susceptible to corruption during storage and transmission. Perceptual hashing is a non-destructive data integrity-protection technique suitable for high-accuracy requirements of remote-sensing image data. However, the existing remote-sensing image perceptual hash-authentication algorithms face security issues in storing and transmitting the original perceptual hash value. This paper proposes a remote-sensing image integrity authentication method based on blockchain and perceptual hash to address this problem. The proposed method comprises three parts: perceptual hash value generation, secure blockchain storage and transmission, and remote-sensing image integrity authentication. An NSCT-based perceptual hashing algorithm that considers the multi-band characteristics of remote-sensing images is proposed. A Perceptual Hash Secure Storage and Transmission Framework (PH-SSTF) is designed by combining Hyperledger Fabric and InterPlanetary File System (IPFS). The experimental results show that the method can effectively verify remote-sensing image integrity and tamper with the location. The perceptual hashing algorithm exhibits strong robustness and sensitivity. Meanwhile, the comparison results of data-tampering identification for multiple landscape types show that the algorithm has stronger stability and broader applicability compared with existing perceptual hash algorithms. Additionally, the proposed method provides secure storage, transmission, and privacy protection for the perceptual hash value.

In this paper, we study a single-machine green scheduling problem with learning effects and past-sequence-dependent delivery times. The problem can be properly applied to tackle green manufacturing where production and delivery time are variable and highly subject to process-reengineering. Our goal is to determine the optimal sequence such that total weighted completion time and maximum tardiness are minimized. For the general case, we provide the analysis procedure of lower bound, and also propose the heuristic and branch-and-bound algorithms. Furthermore, computational experiments are conducted to demonstrate the effectiveness of our algorithms.

Abstract Background N6-methyladenosine (m6A) is the most prevalent modification in mRNA in biological processes and associated with various malignant tumor initiation and progression. The present study aimed to construct a prognostic risk model based on m6A-related genes (the downstream genes influenced by m6A modulators) for LUSC. Methods Based on TCGA, we stratified LUSC patients with and without genetic alteration of m6A modulators into altered and unaltered groups. Using univariate Cox and Lasso regression analyses, we identified prognostic m6A-related genes to construct a prognostic risk model. We then applied a multivariate Cox proportional regression model and the survival analysis to evaluate the risk model. Moreover, we performed the Receiver operating characteristic curve to assess the efficiency of the prognostic model based on TCGA and GSE43131. We analyzed the characteristics of tumor-associated immune cell infiltration in LUSC through the CIBERSORT method. Results Three m6A-related genes (FAM71F1, MT1E, and MYEOV) were identified as prognostic genes for LUSC. A novel prognostic risk model based on the three m6A-related genes was constructed. The multivariate Cox analysis showed that the prognostic risk model was an independent risk factor (HR = 2.44, 95% CI = 1.21~3.56, p = 0.029). Patients with a high-risk group had worse overall survival both in TCGA (p = 0.018) and GSE43131 (p = 0.00017). The 1, 2, and 3-year AUC value in TCGA was 0.662, 0.662, and 0.655, respectively; The 1, 2, and 3-year AUC value in GSE43131 was 0.724, 0.724, and 0.722, respectively. The proportion of infiltrated neutrophils in the high-risk group was higher than that in the low-risk group (p = 0.028), whereas that of resting NK cells (p = 0.002) was lower. Conclusion A novel prognostic risk model based on three m6A-related genes for LUSC was generated in this study.

The dried leaves of Eucommia ulmoides Oliv., which have a high nutritional value, are mainly used in both medicine and food. In this study, we used Eucommia ulmoides leaf superfine powder as an additive in the fermentation of glutinous rice (Semen Oryzae Glutinosae) to develop a new healthcare product, Eucommia leaf sweet rice wine. The fermentation conditions were optimized, and the nutrient value was evaluated through analyses of metabolites, functional compositions, antioxidant capacity, and antihyperglycemic, antihyperlipidemic, and antihypertensive abilities. The metabolic analysis demonstrated that Eucommia leaf sweet rice wine contained a large number of flavonoids and other metabolites. Eucommia leaf sweet rice wine had higher contents of flavonoid (729.0 ± 0.11 μg/g), free amino acids (55.0 ± 0.37 μg/g), polyphenol (150.0 ± 0.43 μg/g), and polysaccharide (0.25 ± 0.03 μg/g) than traditional sweet rice wine, with increases of 14.7, 2.6, 6.8, and 6.3 times, respectively. In addition, an analysis of antioxidant capacity in vitro revealed that Eucommia leaf sweet rice wine had a high level of activity in scavenging 2, 2-diphenyl-1-picrylhydrazyl (DPPH), superoxide anion, and hydroxyl radicals, as well as in reducing iron, indicating that it was a strong antioxidant. Furthermore, Eucommia leaf sweet rice wine had a high cholate binding capacity and could significantly inhibit α-amylase, α-glucosidase, and angiotensin-converting enzyme (ACE) activity. In conclusion, this study developed a new application of Eucommia leaf in sweet rice wine fermentation and brewed Eucommia leaf sweet rice wine with strong antioxidant activity and positive antihypertensive, antihyperglycemic, and antihyperlipidemic effects in vitro. This study suggests new opportunities for the wider use of Eucommia ulmoides leaves and adds variety to sweet rice wine.

With the increasing demand for high-precision and difficult-to-obtain geospatial point cloud data copyright protection in military, scientific research, and other fields, research on lossless watermarking is receiving more and more attention. However, most of the current geospatial point cloud data watermarking algorithms embed copyright information by modifying vertex coordinate values, which not only damages the data accuracy and quality but may also cause incalculable losses to data users. To maintain data fidelity and protect its copyright, in this paper, we propose a lossless embedded watermarking algorithm based on vertical stability. First, the watermark information is generated based on the binary encoding of the copyright information and the code of the traceability information. Second, the watermark index is calculated based on the length of the watermark information after compression and the vertical distribution characteristics of the data. Finally, watermark embedding is completed by modifying the relative storage order of the corresponding data according to the index and watermark value. The experimental results show that the proposed algorithm has good invisibility without damaging the data accuracy. In addition, compared with existing algorithms, this method has a higher robustness under operations such as projection transformation, precision perturbation, and vertex deletion of geospatial point cloud data.

Junction barrier Schottky (JBS) structures are extensively used in silicon carbide devices; however, the complex surface composition makes the direct barrier evaluation difficult. To exclude the field-dependent distortion on a barrier for a direct unbiased evaluation in JBS structures, this work proposes a new evaluation method with a physics-based derivation and experimental demonstration, where a batch of JBS diodes are fabricated with a quasi-continuous spacing variation distribution achieved by the spreading etching technique. In addition, a detailed analysis based on the field-dependent barrier is provided. The result illustrates the chain-like activation and its saturation limit with a quantitative estimate in the straggle region. With the capability of the high linearity to exploit the statistical information for analysis stability, the proposed indicator could be a quantitative and versatile reference for designers dealing with nonideal surfaces.

Abstract Although adipose‐derived mesenchymal stem cells (ADMSCs) isolated from patients’ fat are considered as the most important autologous stem cells for tissue repair, significant difficulties in the neural differentiation of ADMSCs still impede stem cell therapy for neurodegenerative diseases. Herein, a wireless‐electrical stimulation method is proposed to direct the neural differentiation of ADMSCs based on the electromagnetic effect using a graphene film as a conductive scaffold. By placing a rotating magnet on the top of a culture system without any inducer, the ADMSCs cultured on graphene differentiate into functional neurons within 15 days. As a conductive biodegradable nanomaterial, graphene film acts as a wireless electrical signal generator driven by the electromagnetic induction, and millivolt‐level voltage generated in situ provokes ADMSCs to differentiate into neurons, proved by morphological variation, extremely high levels of neuron‐specific genes, and proteins. Most importantly, Ca2+ intracellular influx is observed in these ADMSC‐derived neurons once exposure to neurotransmitters, indicating that these cells are functional neurons. This research enhances stem cell therapy for neurodegenerative diseases using autologous ADMSCs and overcomes the lack of neural stem cells. This nanostructure‐mediated physical‐signal simulation method is inexpensive, safe, and localized, and has a significant impact on neural regeneration.

It is necessary to develop novel nanocomposite films for reducing soil pollution caused by the use of petroleum-based materials. In this paper, a simple solvent-cast method was used to fabricate gutta percha films with carbon nanotubes (CNTs) by homogeneously dispersing different amount of CNTs (0%, 0.5%, 1%, 1.5% and 2%, wt/%) through gutta percha matrix. The obtained nanocomposite films were characterized by Raman spectroscopy, atomic force microscopy (AFM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). Meanwhile, the wettability, mechanical properties, water vapor barrier properties, electromagnetic absorption of films as well as their photo-thermal conversion of the soil were investigated in detail. The results showed that the 1% loading of CNTs film significantly improved the mechanical properties of the nanocomposite films compared to the control: the tensile strength, elongation at break and Young's modulus were increased by 44.0%, 30.5% and 26.1%, respectively. In addition, the CNTs/gutta percha films demonstrated lower thermal stability, glass transition temperature (Tg), melting enthalpy (ΔHm) crystallinity and water vapor permeability (WVP) than the pristine gutta percha film. Especially, the WVP value of the film with 1% CNTs was the lowest. And finally, the soil warming experiments showed that the 1% CNTs film had the highest 83.73% photo-thermal conversion efficiency, compared to the 18.61% for control. This nanocomposite film has the potential for application in agriculture in arid regions because of its high stretchability and photo-thermal conversion efficiency, which could be potential application in agricultural mulch.

To achieve watermark synchronization from an image with perspective distortion is a challenge. This paper proposes a screen-cam robust and blind watermarking scheme for tile satellite images, which means we do not need any user interaction or additional information in watermark detection. To achieve this, at the watermark embedding side, we divide tiles into synchronization tiles and message tiles, and propose a discrete Fourier transform (DFT) based embedding method to cope with the quality degradation caused by screen-cam process. At the extraction side, based on the idea of template matching, we first use a synchronization response index to estimate an appropriate scale level and positions of embedded synchronization watermarks from the noise component, which is estimated by Wiener filter. Then, we propose verification, selection, and precise locations methods to ensure the effectiveness and accuracy of synchronization detection results. After that, we can extract the regions of message tiles. Finally, we extract watermark message based on the local max value from the DFT domain of the noise component. Experimental results demonstrate the validity of the proposed scheme against common attacks and screen-cam attack with a tripod as well as handhold shooting.

With the increasing demand for copyright protection of high-precision and sensitive vector data in Geographical Information System (GIS), research on lossless watermarking has attracted more and more attention. In this paper, a new lossless watermarking method based on line pairs is proposed. Firstly, the points and polygons are unified into the polylines, and then every two adjacent polylines are combined into a line pair. Besides, the storage direction and the interior angle of the line pairs are analyzed. Secondly, the watermark bit is determined by the interior angle of each line pair. Then, the watermark information is embedded by judging whether the storage direction of each line pair and the watermark bit is the same. Finally, experimental results verify that the proposed method works effectively for the data of the points, polylines and polygons, and has good invisibility without damaging the vector data. In addition, compared with the existing algorithm, the proposed method achieves higher robustness against geometric attacks, such as translating, rotating, and scaling the vector data.

The existing commutative encryption and watermarking (CEW) methods based on feature invariants can achieve both the robustness of the watermarking algorithm and the security of the encryption algorithm. However, they are only applicable to the raster data such as images, videos, etc. In particular, the organization structure and storage structure of vector map have not been considered in these methods. Therefore, they cannot be used for vector map. This paper derives two feature invariants to solve this problem, which are the sum of inner angles and the storage direction of two adjacent objects according to the inherent characteristics of vector map. Based on these two feature invariants, a new CEW method is proposed in this paper, which includes the feature invariants based watermarking algorithm and the perceptual stream cipher based encryption algorithm on coordinates. Since the coordinate values used in encryption and the feature invariants used in watermarking are independent of each other, the commutativity is achieved for the proposed CEW method. The experiments are given to verify that the proposed CEW method can achieve the commutativity between encryption and watermarking without deteriorating accuracy of data. Besides, it has been verified that the proposed method is more robust to rotate, scaling, translation, and projection transformation compared with the existing CEW methods and has high security. The proposed algorithm has good scalability of encryption, and arbitrary encryption methods based on encrypting the coordinate values can be applied without affecting the extracted feature invariants.

The study evaluated the influences of riboflavin (RF) supply on the growth performance, nutrient digestibility and ruminal fermentation in lambs. Forty-eight Hu lambs were randomly assigned into four groups receiving RF of 0, 15, 30 and 45 mg/kg dry mater (DM), respectively. Increasing RF supply did not affect the DM intake, but quadratically increased the average daily gain and linearly decreased feed conversion ratio. Total-tract DM, neutral detergent fibre, acid detergent fibre and crude protein digestibility increased quadratically. Rumen pH and propionate molar percentage decreased linearly, total volatile fatty acids concentration, acetate proportion and the ratio of acetate to propionate increased linearly, but ammonia nitrogen concentration was unchanged with increasing RF supply. Linear increases were observed on the activities of carboxymethyl-cellulase, xylanase, pectinase and protease, and the populations of bacteria, fungi, protozoa, dominant cellulolytic bacteria, Ruminobacter amylophilus and Prevotella ruminicola. Methanogens population was not affected by RF supplementation. The microbial protein amount and urinary total purine derivatives excretion increased quadratically. The results indicated that 30 mg/kg DM RF supply improved growth performance, rumen fermentation and nutrient digestion in lambs.

Background: The frequency coupling characteristics in electroencephalogram (EEG) induced by anesthetics have been well studied in adults, but the investigation of the age-dependent cross frequency coupling features from children to adults is still lacking. Methods: We analyzed EEG signals recorded from pediatric to adult patients (n = 131), separated into six age groups:

As an emerging strategy for oncotherapy, Fenton chemistry can efficiently improve the conversion from endogenous H2O2 into highly toxic ·OH in the whole high-performance therapeutic process. Although promising, the efficiency of Fenton reaction in tumor regions is highly limited by the inefficient delivery of Fenton reagents and the restrictive conditions of tumor microenvironment. One promising strategy against the above limitations is to specifically increase the temperature around the tumor regions. In this study, a novel NIR light-mediated tumor-specific nanoplatform based on magnetic iron oxide nanoclusters (MNCs) was rationally designed and well developed for photothermally enhanced Fenton reaction-assisted oncotherapy. MNCs could accumulate into the tumor regions with the help of an external magnet field to enable T2-weighted magnetic resonance (MR) imaging of tumors and MR imaging-guided combined antitumor therapy. Our well-prepared MNCs also revealed excellent photothermal effect upon a NIR light irradiation, promising their further important role as a photothermal therapy (PTT) agent. More importantly, heat induced by the PTT of MNCs could accelerate the release of Fe from MNCs and enhance the efficiency of Fenton reaction under H2O2-enriched acidic tumor microenvironment. Results based on long-term toxicity investigations demonstrated the overall safety of MNCs after intravenous injection. This work therefore introduced a novel nanoplatform based on MNCs that exerted a great antitumor effect via photothermally enhanced tumor-specific Fenton chemistry.

This paper studies a single-machine problem with resource allocation (RA) and deteriorating effect (DE). Under group technology (GT) and limited resource availability, our goal is to determine the schedules of groups and jobs within each group such that the total completion time is minimized. For three special cases, polynomial time algorithms are given. For a general case, a heuristic, a tabu search algorithm, and an exact (i.e., branch-and-bound) algorithm are proposed to solve this problem.

The synergistic therapeutic benefits of resveratrol (RES) and piperine (PIP) have been proven for the treatment of various diseases. This study reports, for the first time, spray-drying of hydroxypropyl β-cyclodextrin (HP-β-CD) microcapsules for combined delivery of resveratrol and piperine. Phase solubility studies indicated that there was a strong interaction between the active ingredients and HP-β-CD, and both active ingredients can bind stably to HP-β-CD. The results of FTIR, XRD, and DSC demonstrated that RES-PIP/HP-β-CD inclusion complexes were successfully formed, with the RES and PIP encapsulated into the hollow spherical cavity of HP-β-CD. The results of SEM showed that the spray-dried microcapsules displayed a smooth surface and uniform particle size. Upon the formation of the spray-dried microcapsules, both RES and PIP presented significantly enhanced solubility. The results of DPPH and ABTS+ scavenging activity assays showed that the spray-drying process did not adversely influence the antioxidant activity of the bioactives, and the addition of PIP increased the antioxidation performance of RES.

Abstract Background Our case describe a rare recurrence case of Unicentric Castleman’s disease (UCD) with hyaline vascular type 14 years after surgery. Case presentation A 35-year-old Chinese female was admitted to hospital with one and half months history chest distress and chest pain. Patient reports a history of thoracic operation for mediastinal mass 14 years ago, and it was diagnosed UCD with hyaline vascular type after postoperative pathological examination. At this time, the imaging examination showed a mediastinal mass once again. Combining the medical history, postoperative microscopically examination and immunoperoxidase staining, patient was again diagnosed UCD with hyaline vascular type again. The hyaline vascular type is the most common type and usually presents as a UCD. Most patients with UCD have no clinical symptoms. The diagnosis of UCD is generally achieved with histological and immunohidtochemical findings postoperatively. Currently, the standard treatment of UCD is the complete surgical resection, with almost no relapse postoperative. The recurrence in UCD with hyaline vascular type postoperative have not previously been reported. Therefore, herein we describe a recurrence case of UCD with hyaline vascular type 14 years after surgery. Conclusion Our case is the first case which reports the relapse of UCD with hyaline vascular type after completely surgery. It indicates that long term follow-up is necessary for patient who is diagnosed UCD after surgery.

How to keep the fidelity of the digital elevation model (DEM) data is a crucial problem in the current watermarking research, as the watermarked DEM data need to preserve their accuracy. We proposed a zero watermarking method for the triangulated irregular network (TIN) DEM data. It takes full advantage of the characteristics of the edge length in the TIN DEM data. First, the radio of the edge lengths is quantified to the watermark index, and then the comparison of the edge lengths is quantified to the watermark bit. Finally, the watermark is constructed by combing the watermark bits according to the watermark indices with the help of the majority voting mechanism. In the method, the TIN DEM data are only used to construct the watermark, not to be embedded by the watermark. Therefore, the data quality is preserved to the greatest extent. Experiments verify the theoretical achievements of this method and demonstrate the method is lossless to the TIN DEM data. Simulation results also show that the method has good robustness on translation, rotation, scaling, and cropping attacks.

Zero watermarking does not alter the original information contained in vector map data and provides perfect imperceptibility. The use of zero watermarking for data copyright protection has become a significant trend in digital watermarking research. However, zero watermarking encounters tremendous obstacles to its development and application because of its requirement to store copyright information with a third party and its difficulty in confirming copyright ownership. Aiming at the shortcomings of the existing zero watermarking technology, this paper proposes a new zero watermarking construction method based on the angular features of vector data that store the zero watermarking and copyright information on the blockchain after an XOR operation. When the watermark is being extracted, the copyright information can be extracted with the XOR operation to obtain the information stored on the blockchain. Experimental results show that the combination of zero watermarking and blockchain proposed in this paper gives full play to the advantages of the two technologies and protects the copyright of data in a lossless fashion. Compared with the traditional zero watermarking algorithms, the proposed zero watermarking algorithm exhibits stronger robustness. Moreover, the proposed data copyright protection framework with a combination of zero watermarking and blockchain can also be applied to other data types, such as images, audio, video, and remote sensing images.

Zero watermarking is an important part of copyright protection of vector geographic data. However, how to improve the robustness of zero watermarking is still a critical challenge, especially in resisting attacks with significant distortion. We proposed a zero watermarking method for vector geographic data based on the number of neighboring features. The method makes full use of spatial characteristics of vector geographic data, including topological characteristics and statistical characteristics. First, the number of first-order neighboring features (NFNF) and the number of second-order neighboring features (NSNF) of every feature in vector geographic data are counted. Then, the watermark bit is determined by the NFNF value, and the watermark index is determined by the NSNF value. Finally, combine the watermark bits and the watermark indices to construct a watermark. Experiments verify the theoretical achievements and good robustness of this method. Simulation results also demonstrate that the normalized coefficient of the method is always kept at 1.00 under the attacks that distort data significantly, which has the superior performance in comparison to other methods.

This paper proposes a joint encryption and screen-cam robust watermarking scheme. This method combines the advantages of smartphone, encryption and watermarking technologies, thereby achieving watermark extraction with a smartphone, partial decryption and tracking leakage from sneak shots. We design a dual watermarking algorithm to achieve watermark detection from both encrypted and decrypted images. First, a watermark is embedded in the discrete Fourier transform (DFT) domain to enable leakage tracking. Then, a second watermark is generated based on QR (Quick response) code encoding and inverse DFT to achieve high watermark capacity and error correction ability, where the secret key for decryption is included in the watermark message. By hiding this message carrying the watermark for the encrypted image in the changes caused by embedding the first watermark, we can improve imperceptibility and will not affect the effectiveness of the proposed scheme. Finally, to enhance the robustness of watermark after encryption, a chaotic mapping-based segment encryption algorithm is proposed. In the process of watermark detection, to cope with perspective correction, a frame locating based algorithm is employed to achieve watermark synchronization from a recaptured picture of the encrypted image. Considering the severe quality degradation, we use a noise component and local statistic feature-based method to extract the message bits. The experimental results show that the proposed scheme is secure, and highly robust, to screen-cam the process for both before and after decryption. Additionally, after decryption, the proposed scheme also has high robustness against common image processing attacks.

Projection transformation is an important part of geographic analysis in geographic information systems, which are particularly common for vector geographic data. However, achieving resistance to projection transformation attacks on watermarking for vector geographic data is still a challenging task. We proposed a digital watermarking against projection transformation based on feature invariants for vector geographic data in this paper. Firstly, the features of projection transformation are analyzed, and the number of vertices, the storage order, and the storage direction of two adjacent objects are designed and used as the feature invariant to projection transformation. Then, the watermark index is calculated by the number of vertices of two adjacent objects, and the embedding rule is determined by the storage direction of two adjacent objects. Finally, the proposed scheme performs blind detection through the storage direction of adjacent features. Experimental results demonstrate that the method can effectively resist arbitrary projection transformation, which indicates the superior performance of the proposed method in comparison to the previous methods.

The screen-cam process, which is taking pictures of the content displayed on a screen with mobile phones or cameras, is one of the main ways that image information is leaked. However, traditional image watermarking methods are not resilient to screen-cam processes with severe distortion. In this paper, a screen-cam robust watermarking scheme with a feature-based synchronization method is proposed. First, the distortions caused by the screen-cam process are investigated. These distortions can be summarized into the five categories of linear distortion, gamma tweaking, geometric distortion, noise attack, and low-pass filtering attack. Then, a local square feature region (LSFR) construction method based on a Gaussian function, modified Harris–Laplace detector, and speeded-up robust feature (SURF) orientation descriptor is developed for watermark synchronization. Next, the message is repeatedly embedded in each selected LSFR by an improved embedding algorithm, which employs a non-rotating embedding method and a preprocessing method, to modulate the discrete Fourier transform (DFT) coefficients. In the process of watermark detection, we fully utilize the captured information and extract the message based on a local statistical feature. Finally, the experimental results are presented to illustrate the effectiveness of the method against common attacks and screen-cam attacks. Compared to the previous schemes, our scheme has not only good robustness against screen-cam attack, but is also effective against screen-cam with additional common desynchronization attacks.

The SiC (silicon carbide) high-power module has great potential to replace the IGBT (insulated gate bipolar transistor) power module in high-frequency and high-power applications, due to the superior properties of fast switching and low power loss, however, when the SiC high-power module operates under inappropriate conditions, the advantages of the SiC high-power module will be probably eliminated. In this paper, four kinds of SiC high-power modules are fabricated to investigate fast switching performance. The variations in characteristics of drain-source voltage at turn-on transient under the combined conditions of multiple factors are studied. A characteristic of voltage plateau is observed from the drain-source voltage waveform at turn-on transient in the experiments, and the characteristic is reproduced by simulation. The mechanism behind the voltage plateau is studied, and it is revealed that the characteristic of drain-source voltage plateau is a reflection of the miller plateau effect of gate-source voltage on drain-source voltage under the combined conditions of fast turn-on speed and low DC bus voltage, while the different values of drain-source voltage plateau are attributed to the discrepancy of structure between upper-side and lower-side in the corresponding partial path of the drain circuit loop inside the module, with the standard 62 mm package outline.

The first reported case of Phoma macdonaldii Boerema that caused sunflower black stem disease in China was from Xinjiang Uygur Autonomous Region in 2008. Black spindle lesions were observed on sunflower stems in the fields of Inner Mongolia region in China. The causal agents were isolated from the infected stems. Cultures on potato dextrose agar (PDA) plates were whitish with black particles underneath. Dark and pear-like pycnidia with yellowish or dirty whitish exudates were also observed on the surface of the cultures. The cultures produce primarily non-septate conidia. The cultures could infect sunflowers stems by artificial inoculation and developed symptoms consistent with those in the field. Morphological characteristics and molecular identification indicated the isolated causal agents were P. macdonaldii. Phylogenetic analysis based on ITS1, 5.8 s RNA and ITS2 region confirmed the identification. Disease resistance assessments were performed on twenty sunflower varieties and the results suggested that the resistance of seven sunflower varieties to P. macdonaldii was above the average; SC89 and Longkuiza1 were susceptible to P. macdonaldii.

The unique luminescent properties exhibited by rare earth ion-doped upconversion nanocrystals (UCNPs), such as long lifetime, narrow emission line, high color purity, and high resistance to photobleaching, have made them widely used in many areas, including but not limited to high-resolution displays, new-generation information technology, optical communication, bioimaging, and therapy. However, the inherent upconversion luminescent properties of UCNPs are influenced by various parameters, including the size, shape, crystal structure, and chemical composition of the UCNPs, and even the chosen synthesis process and the surfactant molecules used. This review will provide a complete summary on the synthesis methods and the surface modification strategies of UCNPs reported so far. Firstly, we summarize the synthesis methodologies developed in the past decades, such as thermal decomposition, thermal coprecipitation, hydro/solvothermal, sol-gel, combustion, and microwave synthesis. In the second part, five main streams of surface modification strategies for converting hydrophobic UCNPs into hydrophilic ones are elaborated. Finally, we consider the likely directions of the future development and challenges of the synthesis and surface modification, such as the large-scale production and actual applications, stability, and so on, of the UCNPs.

In this work, the surge reliability of 1200 V SiC metal-oxide-semiconductor field-effect transistors (MOSFETs) from various manufactures has been investigated in the reverse conduction mode. The surge current tests have been carried out in the channel conduction and non-conduction modes. The experimental results show that the maximum surge currents that the devices can withstand are similar for both cases. It is found that short circuits occurred between the gate and the source in the failed devices. The characteristics of the body diode have also changed after the tests. By measuring the device characteristics after each surge current is applied, it can be concluded that the damages to the gate oxide layer and the body diode occurred only when the maximum surge current is applied. By decapping the failed devices and observing the cross section of the damaged cell, it is found that high temperature caused by excessive current flow through the devices during the surge tests is the main reason for the device failure. Finally, the TCAD simulation of the devices has been carried out to bring insight into the operation of the devices during the surge events.

Various watermarking algorithms have been studied to better enable the copyright protection of remote sensing images. The robustness of such algorithms against image cropping attacks has subsequently been verified mainly by various experiments. However, to date, the experimental results are subject to the differences in experimental factors and computational resource costs. Hence, the study presented in this paper proposes a robustness computation model of watermarking remote sensing images in terms of the image cropping attack. The robustness computation model consists of three parts: analysis principles, an evaluation index, and a computation method. The robustness analysis principles are provided based on the salient features of watermarking remote sensing images and attacking properties. A probability-based evaluation index is then defined to more comprehensively measure the robustness of different algorithms. The computation method developed in this study is based on permutations and the inclusion-exclusion principle to theoretically calculate robustness. The experiments conducted to verify the effectiveness of the computation model, revealed true closeness between both the calculated and experimental results. Finally, the relationships between the robustness and the different parameters used in the watermarking algorithms are investigated by using the proposed computation model.