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In this paper, adaptive proportional derivative (APD) parameter control was proposed to solve the problem of high power consumption caused by the unclear mechanism of liquid disturbance during the lifting-up of magnetic bearing in left ventricular assist devices. A mathematical model was derived that describes how the rotor operates in liquid filling. The disturbance caused by the liquid in the lifting-up process was analyzed, and an adaptive control system was developed to improve dynamic performance and reduce power consumption. The experimental results show that APD control requires a shorter rise time without overshoot of rotor displacement compared to traditional fixed configurations. When using the APD controller, the peak current dropped by 8%. The duration in which the current is greater than 1A was reduced by 10.2 ms, and the average current also dropped by 34%. [ABSTRACT FROM AUTHOR]

To address the potential threat to the power grid industry posed by quantum computers and ensure the security of bidirectional communication in smart grids, it is imperative to develop quantum-safe authentication protocols. This paper proposes a semi-quantum bidirectional authentication protocol between a control center (CC) and a neighboring gateway (NG). This method uses single photons to facilitate communication between the CC and the NG. Security analysis demonstrates that the protocol can effectively resist common attack methods, including double CNOT attacks, impersonation attacks, interception-measurement-retransmission attacks, and entanglement-measurement attacks. Comparisons with other protocols reveal that this protocol has significant advantages, making it more appealing and practical for real-world applications. Finally, by simulating the protocol on the IBM quantum simulator, this protocol not only validates the theoretical framework but also confirms the practical feasibility of the protocol. [ABSTRACT FROM AUTHOR]

Small-sized microplastics (MPs) pose concerns about potential risks to both the environment and human health. However, research on MP pollution is hampered by limitations in the detection techniques. Also, few studies have provided insight into the release of small-sized MPs from disposable polyethylene terephthalate (PET)-bottled drinks for outdoor usage. Thus, PET bottles' potential to release small MPs sized 1–100 μm outdoors was studied in relation to physical and chemical parameters (temperature, sunlight irradiation, and drink characteristics) using flow cytometry and Nile Red dye. The results showed that temperatures below 80 °C had little effect on the release of MPs from PET bottles. Sunlight irradiation and alkalinity were prone to promote the generation of MPs, mostly 1–5 μm in size. Moreover, the combined impact test implied that two pairings—acidity with temperature and alkalinity with sunlight—positively affected MP release, with maximum releases of 21,622 ± 2477 particles/L and 31,081 ± 7173 particles/L, respectively. Based on the rapid quantification of small-sized MPs using flow cytometry after Nile Red selection, the results hereby presented will assist researchers in reducing MP release and aid them in the evaluation of MPs' contamination of aquatic environments. [ABSTRACT FROM AUTHOR]

The cast thin sections of tight oil reservoirs contain important parameters such as rock mineral composition and content, porosity, permeability and stratigraphic characteristics, which are of great significance for reservoir evaluation. The use of deep learning technology for intelligent identification of thin section images is a development trend of mineral identification. However, the difficulty of making cast thin sections, the complexity of the making process and the high cost of thin section annotation have led to a lack of cast thin section images, which cannot meet the training requirements of deep learning image recognition models. In order to increase the sample size and improve the training effect of deep learning model, we proposed a generation and annotation method of thin section images of tight oil reservoir based on deep learning, by taking Fuyu reservoir in Sanzhao Sag as the target area. Firstly, the Augmentor strategy space was used to preliminarily augment the original images while preserving the original image features to meet the requirements of the model. Secondly, the category attention mechanism was added to the original StyleGAN network to avoid the influence of the uneven number of components in thin sections on the quality of the generated images. Then, the SALM annotation module was designed to achieve semi-automatic annotation of the generated images. Finally, experiments on image sharpness, distortion, standard accuracy and annotation efficiency were designed to verify the advantages of the method in image quality and annotation efficiency. [ABSTRACT FROM AUTHOR]

That certain preconceptual paternal exposures reprogram the developmental phenotypic plasticity in future generation(s) has conceptualized the "paternal programming of offspring health" hypothesis. This transgenerational effect is transmitted primarily through sperm epigenetic mechanisms—DNA methylation, non-coding RNAs (ncRNAs) and associated RNA modifications, and histone modifications—and potentially through non-sperm-specific mechanisms—seminal plasma and circulating factors—that create 'imprinted' memory of ancestral information. The epigenetic landscape in sperm is highly responsive to environmental cues, due to, in part, the soma-to-germline communication mediated by epididymosomes. While human epidemiological studies and experimental animal studies have provided solid evidences in support of transgenerational epigenetic inheritance, how ancestral information is memorized as epigenetic codes for germline transmission is poorly understood. Particular elusive is what the downstream effector pathways that decode those epigenetic codes into persistent phenotypes. In this review, we discuss the paternal reprogramming of offspring phenotype and the possible underlying epigenetic mechanisms. Cracking these epigenetic mechanisms will lead to a better appreciation of "Paternal Origins of Health and Disease" and guide innovation of intervention algorithms to achieve 'healthier' outcomes in future generations. All this will revolutionize our understanding of human disease etiology. Highlight: Transgenerational epigenetic inheritance defines phenotypic plasticity in future generations. Paternally acquired phenotype is primarily transmitted through sperm epigenetic mechanisms. Epigenetic information in sperm is highly responsive to environmental cues. Transgenerational epigenetic inheritance revolutionizes our understanding of human disease etiology. [ABSTRACT FROM AUTHOR]

Secure multiparty quantum summation, as a critical part of secure multiparty quantum computation, plays a pivotal role in the field of quantum cryptography. In this paper, a hybrid protocol is proposed to implement secure multiparty quantum summation and anonymous ranking simultaneously, which uses d$d$‐level two‐particle entangled states. In the designed protocol, the particles are conveyed in the way of the circle‐type transmission mode, which can compute the sum of the secret and the ranking by analyzing the distribution of participants associated with different secret values. Moreover, random number sequences can be obtained by constructing a fundamental system of solutions, which avoids the transmission of random numbers and reduces the use of quantum resources. According to the analysis, it can be seen that the protocol is secure against known attacks, and the secrets remain undisclosed throughout the protocol's implementation. [ABSTRACT FROM AUTHOR]

Objectives: Existing evidence exhibits that obstructive sleep apnea (OSA) is a potential consequence of Parkinson's disease (PD) or a contributor to PD progression. This investigation aimed to detect potential critical genes and molecular mechanisms underlying interactions between PD and OSA through bioinformatics analyses. Methods: The Gene Expression Omnibus (GEO) database was employed to obtain the expression profiles GSE20163 and GSE135917. The identification of common genes connected to PD and OSA was performed utilizing weighted gene co-expression network analysis and the R 4.0.4 program. The Cytoscape program was utilized to generate a network of protein-protein interactions (PPI), and the CytoHubba plugin was utilized to detect hub genes. Subsequently, functional enrichment analyses of the hub genes were conducted. Markers with increased diagnostic values for PD and OSA were confirmed using the GEO datasets GSE8397 and GSE38792. Results: Typically, 57 genes that are common were identified in PD and OSA. Among these common genes, the top 10 hub genes in the PPI network were chosen. The verified datasets confirmed the presence of three important genes: CADPS, CHGA, and SCG3. Functional enrichment analysis revealed that these hub genes mostly participate in GABAergic synapses. Conclusion: Our findings suggest that CADPS, CHGA, and SCG3 are key genes involved in molecular mechanisms underlying interactions between OSA and PD. Functional enrichment of hub genes indicated a link between GABAergic synapses and the shared pathogenesis of PD and OSA. These candidate genes and corresponding pathways offer novel insights regarding biological targets that underlie the transcriptional connection between OSA and PD. [ABSTRACT FROM AUTHOR]

The diagenetic facies of tight oil reservoirs reflect the diagenetic characteristics and micro-pore structure of reservoirs, determining the formation and distribution of sweet spot zones. By establishing the correlation between diagenetic facies and logging curves, we can effectively identify the vertical variation of diagenetic facies types and predict the spatial variation of reservoir quality. However, it is still challenging work to establish the correlation between logging and diagenetic facies, and there are some problems such as low accuracy, high time consumption and high cost. To this end, we propose a lithofacies identification method for tight oil reservoirs based on hybrid intelligence using the Fuyu oil layer of the Sanzhao depression in Songliao Basin as the target area. Firstly, the geological characteristics of the selected area were analyzed, the definition and classification scheme of diagenetic facies and the dominant diagenetic facies were discussed, and the logging response characteristics of various diagenetic facies were summarized. Secondly, based on the standardization of logging curves, the logging image data set of various diagenetic facies was built, and the imbalanced data set processing was performed. Thirdly, by integrating CNN (Convolutional Neural Networks) and ViT (Visual Transformer), the C-ViTM hybrid intelligent model was constructed to identify the diagenetic facies of tight oil reservoirs. Finally, the effectiveness of the method is demonstrated through experiments with different thicknesses, accuracy and single-well identification. The experimental results show that the C-ViTM method has the best identification effect at the sample thickness of 0.5 m, with Precision of above 86%, Recall of above 90% and F1 score of above 89%. The calculation result of the Jaccard index in the identification of a single well was 0.79, and the diagenetic facies of tight reservoirs can be identified efficiently and accurately. At the same time, it also provides a new idea for the identification of the diagenetic facies of old oilfields with only logging image data sets. [ABSTRACT FROM AUTHOR]

In recent years, UNET (Ronneberger et al. 2015) and its derivative models have been widely used in medical image segmentation with more superficial structures and excellent segmentation results. Due to the lack of modeling for the overall characteristics of the target, the division tasks of minor marks will produce some discrete noise points, resulting in a decline in model accuracy and application effects. We propose a multi-tasking medical image analysis model UoloNet, a YOLO-based (Redmon et al. 2016; Shafiee et al. 2017) object detection branch is added based on UNET. The shared learning of the two tasks through semantic segmentation and object detection has promoted the model’s mastery of the overall characteristics of the target. In the reasoning stage, merging the two functions of object detection and semantic segmentation can effectively remove discrete noise points in the division and enhance the accuracy of semantic segmentation. In the future, the object detection task will be the problem of excessive convergence of semantic segmentation tasks. The model uses CIOU (Zheng et al. 2020) losses instead of IOU losses in YOLO, which further improves the model’s overall accuracy. The effectiveness of the proposed model is verified both in the MRI dataset SEHPI, which we posted and in the public dataset LITS (Christ 2017). [ABSTRACT FROM AUTHOR]

Medical data are an intangible asset and an important resource for the entire society. The mining and application of medical data can generate enormous value. Currently, medical data management is mostly centralized and heavily relies on central servers, which are prone to malfunctions or malicious attacks, making it difficult to form a consensus among multiple parties and achieve secure sharing. Blockchain technology offers a solution to enhance medical data security. However, in medical data security sharing schemes based on blockchain, the widely adopted Practical Byzantine Fault-Tolerant (PBFT) algorithm encounters challenges, including intricate communication, limited scalability, and the inability to dynamically add or remove nodes. These issues make it challenging to address practical requirements effectively. In this paper, we implement an efficient and scalable consensus algorithm based on the PBFT consensus algorithm, referred to as Me-PBFT, which is more suitable for the field of medical data security. First, we design a reputation evaluation model to select more trusted nodes to participate in the system consensus, which is implemented based on a sigmoid function with adjustable difficulty. Second, we implement the division of node roles to construct a dual consensus layer structure. Finally, we design a node dynamic join and exit mechanism on the overall framework of the algorithm. Analysis shows that compared to PBFT and RAFT, ME-PBFT can reduce communication complexity, improve fault tolerance, and have good scalability. It can meet the need for consensus and secure sharing of medical data among multiple parties. [ABSTRACT FROM AUTHOR]

Renal tubular injury is a key factor in the progression of diabetic kidney disease to end‐stage renal disease. Hyperoside, a natural flavonol glycoside in various plants, is a potentially effective drug for the clinical treatment of diabetic kidney disease. However, the specific mechanisms remain unknown. Therefore, this study will explore the effect and mechanism of hyperoside on renal tubulointerstitium in diabetic kidney disease. db/db mouse (C57BL/KsJ) is a model of type 2 diabetes resulting from Leptin receptor point mutations, with the appearance of diabetic kidney disease. Therefore, db/db mice were used for in vivo experimental studies. In vitro, human renal tubular epithelial cells were incubated with bovine serum albumin to simulate the injury of renal tubular epithelial cells caused by excessive albumin in primary urine. The experimental results showed that hyperoside could improve kidney function and reduce kidney tissue damage in mice, and could inhibit oxidative stress, extracellularly regulated protein kinases 1/2 signaling activation, and pyroptosis in human renal tubular epithelial cells. Therefore, hyperoside inhibited oxidative stress by regulating the activation of the extracellularly regulated protein kinases 1/2/mitogen‐activated protein kinase signaling pathway, thereby alleviating proteinuria‐induced pyroptosis in renal tubular epithelial cells. This study provides novel evidence that could facilitate the clinical application of hyperoside in diabetic kidney disease treatment. [ABSTRACT FROM AUTHOR]

With the rapid growth of the global population and the increasing urbanization, the urban landscape in China is gradually enriched, and the scale of the landscape that plays a healing role is expanding. However, curing the problem of landscape ecological security is an important part of Homeland security, economic and social sustainable development. We must deal with the relationship between high-quality social development and ecological environment protection on the basis of scientific evaluation. To address this issue, research has provided better data support for feature extraction through image preprocessing. Then the Convolutional neural network in deep learning is trained through a large number of collected measured data. Finally, the pressure state response model is used to evaluate the ecological security of the healing landscape. The results show that the average error of the ground class in 2010 was 13.65%, and the fitting accuracy reached 86.35%, indicating that this method has high accuracy and can be effectively applied in evaluation. Meanwhile, in 2010 and 2019, the average landscape ecological security levels of City A were 7.27 and 6.65, both at a "safe" level, but the overall security level showed a downward trend. It is recommended to optimize the land use pattern in future urban planning and construction, improve the urban landscape ecological security index value, and maintain consistency with the actual situation of the city. This can provide reference for the evaluation model of urban landscape ecological security, and further provide scientific basis and guidance for the ecological civilization construction of urban agglomerations. In subsequent research, the evolution trend of urban landscape ecological security can be taken as the research goal, and finally, guidance on optimizing urban landscape ecological security can be provided. [ABSTRACT FROM AUTHOR]

A sustainable and cost-effective energy storage system is the research focus at present and also the development direction of the future. A green and cost-effective cathode material for high-voltage dual-ion batteries (DIBs) is presented in this work through the artificial implantation of unsaturated organic sulfonates on the surface of natural graphite (NG) particles via a low-cost and green synthesis route. It is interesting to discover and validate that π–π stacking interactions exist between the π-bonds of unsaturated sulfonates and graphite layers through both practical experiments and density functional theory simulations. The C≡C bond-containing propyne sulfonate (PyS) exhibits stronger π–π stacking interactions on graphite layers than the C＝C bond-containing allyl sulfonate and C–C bond-containing propane sulfonate. The PyS layer works as an artificial and highly stable cathode/electrolyte interphase (CEI), which effectively precludes electrolyte decomposition, reduces interfacial resistance and protects the NG cathode from structural degradation. The as-prepared NG@PyS exhibits outstanding cycling performance as the cathode material of DIBs by virtue of the high adhesion capability of PyS on graphite. [ABSTRACT FROM AUTHOR]

Key message: The ClACO gene encoding 1-aminocyclopropane-1-carboxylate oxidase enabled highly efficient 15N uptake in watermelon. Nitrogen is one of the most essential nutrient elements that play a pivotal role in regulating plant growth and development for crop productivity. Elucidating the genetic basis of high nitrogen uptake is the key to improve nitrogen use efficiency for sustainable agricultural productivity. Whereas previous researches on nitrogen absorption process are mainly focused on a few model plants or crops. To date, the causal genes that determine the efficient nitrogen uptake of watermelon have not been mapped and remains largely unknown. Here, we fine-mapped the 1-aminocyclopropane-1-carboxylate oxidase (ClACO) gene associated with nitrogen uptake efficiency in watermelon via bulked segregant analysis (BSA). The variations in the ClACO gene led to the changes of gene expression levels between two watermelon accessions with different nitrogen uptake efficiencies. Intriguingly, in terms of the transcript abundance of ClACO, it was concomitant with significant differences in ethylene evolutions in roots and root architectures between the two accessions and among the different genotypic offsprings of the recombinant BC2F1(ZJU132)-18. These findings suggest that ethylene as a negative regulator altered nitrogen uptake efficiency in watermelon by controlling root development. In conclusion, our current study will provide valuable target gene for precise breeding of ‘green’ watermelon varieties with high-nitrogen uptake efficiencies. [ABSTRACT FROM AUTHOR]

Skin cancer is one of the deadliest cancers, and it has been widely developed worldwide since the last decade. Malignant melanoma is currently the most deadly skin cancer. If malignant melanoma is diagnosed at an early stage, the probability of patients being cured will be greatly improved. At present, most existing skin lesion image classification methods only use deep learning. However, the multi‐modal features of skin lesions in the medical domain are not well utilized and integrated. To reduce the classification error of the skin lesion images caused by the complexity and subjectivity of visual interpretation, a malignant melanoma dermoscopy image classification method based on multi‐modal medical features is proposed in this paper which is inspired by the fuzzy decision‐making process of doctors. It can reduce the subjective difference in the image classification process and assist dermatologists to analyze the skin lesion area. Firstly, the feature detection method based on the extension theory can effectively quantify the difference between different colour features. Then, an interpretable segmentation edge of the skin lesion is established by using the neutrosophic theory which can convert the image into the neutrosophic space. The edge of the skin lesion is captured by applying the Hierarchical Gaussian Mixture Model (HGMM) method. Next, the edge sequence is established by segmenting the edge, and the contour regularity, symmetry, and uniformity of the edge of the skin lesion are analyzed. Finally, the extracted multi‐feature sets are used for dermoscopy image classification. Experiments are carried out on real datasets, and the classification accuracy of four kernel functions is verified. The experimental results show that the authors' method can effectively improve the classification accuracy of benign dermoscopy images and malignant dermoscopy images. [ABSTRACT FROM AUTHOR]

Dual‐ion batteries (DIBs) with graphite as cathode material, show superiority in terms of sustainability, affordability, and environmental impact over Li‐ion batteries that rely on transition‐metal based cathodes. However, graphite cathodes severely suffer from poor structural stability during anion storage at high potentials because of the co‐intercalation and oxidative decomposition of electrolytes. This work presents an in situ electrochemistry‐driven route to create a bifunctional interphase through implantation of diethylenetriaminepenta(methylene‐phosphonic acid) (DTPMP) on the surface of graphite particles. The reaction mechanisms and functions of DTPMP are investigated both experimentally and theoretically. The DTPMP‐derived interphase not only improves the antioxidative stability of electrolytes but also benefits the desolvation of PF6− anions, which doubly protect the graphitic structure and give rise to fast‐charge and ultralong cycling performance of graphite cathodes in DIBs. [ABSTRACT FROM AUTHOR]

Gas turbine blades normally operate under high‐temperatures, extreme pressure, and high‐speed conditions, which leads to complex failure mechanisms and difficulties in fatigue life assessment. Moreover, due to the randomness of manufacturing errors, working loads, and material properties, the low cycle fatigue (LCF) life normally presents unavoidable stochastic behavior. Therefore, accurate and efficient fatigue life prediction is critical for the design of gas turbine blades. Accordingly, this paper analyzes the effects of mean stress, anisotropy, and uncertainties on the LCF life of nickel‐based single‐crystal turbine blades. The modified Hill yield criterion is employed to deal with the anisotropic damage characteristics of nickel‐based single‐crystal superalloy (NSCS) under a multiaxial stress state. Meanwhile, the effects of the temperature and crystal orientation are introduced into the walker mean stress correction term. A multiaxial LCF life prediction model is developed based on the energy criterion‐based fatigue life estimation method. Moreover, multi‐source uncertainties originating from rotation speeds, material properties, and temperature are analyzed and quantified. Comparison between the predicted and tested life indicates that the proposed method produces good accuracy and robustness, which can provide a reference for the reliability design of NSCS turbine blades. [ABSTRACT FROM AUTHOR]

Compared to X-rays, electrons have stronger interactions with matter. In electron diffraction, the low-order structure factors are sensitive to subtle changes in the arrangement of valence electrons around atoms when the scattering vector is smaller than the critical scattering vector. Therefore, electron diffraction is more advantageous for studying the distribution of atoms in the structure with atomic numbers smaller than that of sulfur. In this work, the crystal structure of Sr1.2Ca0.8Nb2O7 (SCNO-0.8) was analyzed using single-crystal X-ray diffraction (SC-XRD) and three-dimensional electron diffraction (3D-ED) techniques, respectively. Interestingly, the superstructure could only be identified by the 3D-ED technique, while no signal corresponding to the superstructure was detected from the SC-XRD data. The superstructure in SCNO-0.8 was disclosed to be caused by different tilting of NbO6 octahedra and the displacements of Sr/Ca atoms in the different atomic layers perpendicular to the a-axis. Therefore, the application of 3D-ED provides an effective method for studying superstructures caused by ordered arrangements of light atoms. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to evaluate the accuracy of automatic cephalometric landmark localization and measurements using cephalometric analysis via artificial intelligence (AI) compared with computer-assisted manual analysis. Methods: Reconstructed lateral cephalograms (RLCs) from cone-beam computed tomography (CBCT) in 85 patients were selected. Computer-assisted manual analysis (Dolphin Imaging 11.9) and AI automatic analysis (Planmeca Romexis 6.2) were used to locate 19 landmarks and obtain 23 measurements. Mean radial error (MRE) and successful detection rate (SDR) values were calculated to assess the accuracy of automatic landmark digitization. Paired t tests and Bland‒Altman plots were used to compare the differences and consistencies in cephalometric measurements between manual and automatic analysis programs. Results: The MRE for 19 cephalometric landmarks was 2.07 ± 1.35 mm with the automatic program. The average SDR within 1 mm, 2 mm, 2.5 mm, 3 and 4 mm were 18.82%, 58.58%, 71.70%, 82.04% and 91.39%, respectively. Soft tissue landmarks (1.54 ± 0.85 mm) had the most consistency, while dental landmarks (2.37 ± 1.55 mm) had the most variation. In total, 15 out of 23 measurements were within the clinically acceptable level of accuracy, 2 mm or 2°. The rates of consistency within the 95% limits of agreement were all above 90% for all measurement parameters. Conclusion: Automatic analysis software collects cephalometric measurements almost effectively enough to be acceptable in clinical work. Nevertheless, automatic cephalometry is not capable of completely replacing manual tracing. Additional manual supervision and adjustment for automatic programs can increase accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Background: Orthodontic tooth movement (OTM), a process of alveolar bone remodelling, is induced by mechanical force and regulated by local inflammation. Bone marrow-derived mesenchymal stem cells (BMSCs) play a fundamental role in osteogenesis during OTM. Macrophages are mechanosensitive cells that can regulate local inflammatory microenvironment and promote BMSCs osteogenesis by secreting diverse mediators. However, whether and how mechanical force regulates osteogenesis during OTM via macrophage-derived exosomes remains elusive. Results: Mechanical stimulation (MS) promoted bone marrow-derived macrophage (BMDM)-mediated BMSCs osteogenesis. Importantly, when exosomes from mechanically stimulated BMDMs (MS-BMDM-EXOs) were blocked, the pro-osteogenic effect was suppressed. Additionally, compared with exosomes derived from BMDMs (BMDM-EXOs), MS-BMDM-EXOs exhibited a stronger ability to enhance BMSCs osteogenesis. At in vivo, mechanical force-induced alveolar bone formation was impaired during OTM when exosomes were blocked, and MS-BMDM-EXOs were more effective in promoting alveolar bone formation than BMDM-EXOs. Further proteomic analysis revealed that ubiquitin carboxyl-terminal hydrolase isozyme L3 (UCHL3) was enriched in MS-BMDM-EXOs compared with BMDM-EXOs. We went on to show that BMSCs osteogenesis and mechanical force-induced bone formation were impaired when UCHL3 was inhibited. Furthermore, mothers against decapentaplegic homologue 1 (SMAD1) was identified as the target protein of UCHL3. At the mechanistic level, we showed that SMAD1 interacted with UCHL3 in BMSCs and was downregulated when UCHL3 was suppressed. Consistently, overexpression of SMAD1 rescued the adverse effect of inhibiting UCHL3 on BMSCs osteogenesis. Conclusions: This study suggests that mechanical force-induced macrophage-derived exosomal UCHL3 promotes BMSCs osteogenesis by targeting SMAD1, thereby promoting alveolar bone formation during OTM. [ABSTRACT FROM AUTHOR]

The existence of odors in drainage pipelines is one of the most prominent environmental problems that urban residents complain about nowadays. Odorous substances in sewage can cause corrosion and erosion in drainage pipelines, and even lead to great harm to the human body and environments. Ideas for in situ odor control can be divided into two main categories: the elimination of odorous substances and the inhibition of the production of odorous substances. However, there is a lack of comprehensive summary of in situ overall deodorization techniques, which has limited the wide application of these methods. We conducted a systematic review to summarize recent advances in in situ overall deodorization. Firstly, the main odorous substances in drainage pipelines and their basic characteristics are concluded. Special attention has been paid to volatile sulfur compounds (VSCs) and nitrogen-containing compounds, as the main odorous substances. Subsequently, typical sources of these odorous substances are summarized based on their formation mechanisms. Then, in situ deodorization techniques (including pipeline condition optimization techniques, odor source control techniques, chemical control techniques, and biological control techniques) are introduced. Finally, upcoming research efforts on deodorization mechanism improvement, research gap supplementation, and economic efficiency enhancement to meet practical conditions are proposed. [ABSTRACT FROM AUTHOR]

Affected by factors such as complex production operation data, high dimensions, and weak regularity, the existing ultra-short-term working condition prediction method struggles to guarantee the prediction accuracy and operation speed. Therefore, we propose an ultra-short-term working condition prediction method based on a convolutional neural network (CNN) and a long short-term memory network (LSTM). Firstly, we use sliding window and normalized processing methods to carry out data processing, and use CNN to extract the characteristics of processed production operation data. Secondly, we then improve the LSTM gated structure and introduce L2 norm, learning the change law of the production operation data by means of the LSTM prediction layer, and then obtain the predicted value of the working condition. We use the Bayesian method to select the parameters of the CNN-LSTM model to improve the prediction accuracy. Finally, we apply our method to a real-world application to demonstrate that our ultra-short-term working condition prediction method achieves superior results for prediction accuracy and running speed when compared with other methods. [ABSTRACT FROM AUTHOR]

Research in the field of industrial heritage regeneration suffers from high subjectivity and low reliability in design and evaluation. The study takes seven regeneration projects of the Kailuan family of industrial heritage as examples, designs four optimisation and improvement strategies and constructs an evaluation system containing four intermediate layers and 23 indicator layers based on the evaluation factor method to realise the evaluation of design solutions. The average evaluation value of the four intermediate layers was approximately 0.65, and the average evaluation value of the 23 evaluation indicators was approximately 0.68. The evaluation values of the four intermediate layers for the seven Kailuan projects showed that the evaluation of the park and the evaluation of the participants' perceptions were roughly higher than the standard values, while the evaluation of the buildings and the evaluation of the environmental image had lower evaluation values. The correlations between the park evaluation and the other three intermediate level evaluation indicators were all over 0.500, and all had positive correlations, while the correlations between any two of the remaining indicators were weak and not statistically significant. The regenerative landscape design and evaluation of the Kailuan system of factory heritage enriches the current landscape design evaluation system and provides corresponding optimisation strategies for landscape optimisation design. [ABSTRACT FROM AUTHOR]

This study explores the impact of capital market openness on the profitability premium by using the Shanghai‐Hong Kong Stock Connect (SHKSC) programme as an exogenous shock. The empirical results show that (1) we can basically rule out the mispricing mechanism driving the significant profitability premium in China; (2) following SHKSC, the expected stock returns increase more across less profitable firms, and the less profitable firms have more increases in illiquidity, capital structure and information disclosure quality; and (3) the results are still robust after using propensity score matching analysis, deleting the highly volatile periods and conducting a firm‐level test with the triple‐differencing design. [ABSTRACT FROM AUTHOR]

Parametric design, with its unique scientific and logical nature, is gradually applied in the field of landscape design. Therefore, the GIS (geographic information systems) technology of parametric software was applied to the optimization of traditional landscape architecture, and its practical application quality was discussed. The actual analysis results showed that the evaluation result of parametric design had the highest score of 7.71 in behavioral perception. The overall score was 7.28, showing a high scientific nature. In the evaluation of landscape environmental benefits, after the optimization of landscape greening by parametric design, the air cleanliness and living comfort were generally improved, compared with those before optimization, and the highest values were 11.97 ± 6.01 and 5.86 ± 2.11 respectively. In the evaluation of the economic benefits of gardens, the economic income of gardens in the past 8 years generally increased, reaching the highest of 3.5795 billion yuan, with a growth rate of 78.92%. At the same time, the return on investment reached 26.17%, far exceeding the expected 20%. Among the social benefits, the weight of increasing employment opportunities was the largest at 0.36. In summary, parameterized optimization of traditional landscape design can effectively improve its social, environmental, and economic benefits and has good practical value. [ABSTRACT FROM AUTHOR]

In vitro digestion methods that can accurately predict the estimated GI (eGI) values of complex carbohydrate foods, including biscuits, are worth exploring. In the current study, standard commercial biscuits with varied clinical GI values between 9~30 were digested using both the INFOGEST and single-enzyme digestion protocols. The digestion kinetic parameters were acquired through mathematical fitting by mathematical kinetics models. The results showed that compared with the INFOGEST protocol, the A U R 180 deduced from digesting using either porcine pancreatin or α-amylase showed the best potential in predicting the eGI values. Accordingly, mathematical equations were established based on the relations between the A U R 180 and the GI values. When digesting using porcine pancreatin, G I = 1.834 + 0.009 × A U C R 180 ( R 2 = 0.952), and when digesting using only α-amylase, G I = 6.101 + 0.009 × A U C R 180   (R 2 = 0.902). The A U R 180 represents the area under the curve of the reducing-sugar content normalized to the total carbohydrates versus the digestion time in 180 min. The in vitro method presented enabled the rapid and accurate prediction of the eGI values of biscuits, and the validity of the formula was verified by another batch of biscuits with a known GI, and the error rate of most samples was less than 30%. [ABSTRACT FROM AUTHOR]

The blind millionaires’ problem is an expansion of the millionaires’ problem, comparing the secrets belonging to two different sets. When each set has only one participant, it corresponds to the original millionaires’ problem. In this paper, we propose a new solution to quantum blind millionaires’ (QBM) problem based on d-level n-particle entangled states | χ ⟩ n for the first time. In our solution, the summation of the secrets held by the participants from two sets can be compared successfully; here, each participant may have one or multiple secrets. Specially two different cases for overall and local comparison are discussed, respectively. During the QBM solution for overall comparison, it can be seen that the summation of all the secrets in each set can compare its value with the other set. Accordingly, during the QBM solution for local comparison, any one participant in one set can compare his secret with the summation of all the secrets in the other set. Moreover, the security analysis shows that the present solutions can prevent secrets leakage even if participants collude. Finally, the efficiency and some further discussions of our solutions are also proposed. As a new attempt, we hope these work may make positive effects to the development of quantum secure multiparty computation in future. [ABSTRACT FROM AUTHOR]

The Allium genus is cultivated globally as vegetables, condiments, or medicinal plants and is characterized by large genomes and strong pungency. However, the genome evolution and genomic basis underlying their unique flavor formation remain poorly understood. Herein, we report an 11.27-Gb chromosome-scale genome assembly for bunching onion (A. fistulosum). The uneven bursts of long-terminal repeats contribute to diversity in genome constituents, and dispersed duplication events largely account for gene expansion in Allium genomes. The extensive duplication and differentiation of alliinase and lachrymatory factor synthase manifest as important evolutionary events during flavor formation in Allium crops. Furthermore, differential selective preference for flavor-related genes likely lead to the variations in isoalliin content in bunching onions. Moreover, we reveal that China is the origin and domestication center for bunching onions. Our findings provide insights into Allium genome evolution, flavor formation and domestication history and enable future genome-assisted breeding of important traits in these crops. Genome evolution of Allium genus and genomic basis underlying the pungency flavor formation remain poorly understood. Here, the authors assemble the genome of bunching onion and conduct population genetics analyses to reveal Allium genome evolution, flavor formation and domestication history. [ABSTRACT FROM AUTHOR]

Objective : To investigate the longitudinal influence of alveolar bone grafting on the oral microbiota of children with cleft lip and palate (CLP). Materials and methods : Twenty-eight children with nonsyndromic CLP were recruited and underwent secondary alveolar bone grafting at the first time. Unstimulated saliva and plaque samples were collected from the subjects preoperatively and at 2 days, 1 month, and 3 months postoperatively. The v3–v4 hypervariable regions of the 16S rRNA gene from bacterial DNA were sequenced using the Illumina MiSeq sequencing platform. Results : The alpha diversity of the saliva and plaque microbiota was significantly decreased at 2 days postoperatively and then increased at 1 and 3 months postoperatively. The saliva and plaque microbiota compositions at 2 days postoperatively differed from those at the other time points, and the microbiota compositions at 1 and 3 months postoperatively showed a gradual shift toward the preoperative composition. The saliva, but not plaque, microbiota composition 3 months postoperatively was similar to that preoperatively. Conclusion : The effect of secondary alveolar bone grafting on the plaque microbiota in children with CLP lasted longer than the saliva microbiota. Alveolar bone grafting altered the saliva microbiota in children with CLP within 3 months postoperatively. [ABSTRACT FROM AUTHOR]

Spermatogenesis is a highly complex developmental process that typically consists of mitosis, meiosis, and spermiogenesis. DNA/RNA helicase DHX36, a unique guanine-quadruplex (G4) resolvase, plays crucial roles in a variety of biological processes. We previously showed that DHX36 is highly expressed in male germ cells with the highest level in zygotene spermatocytes. Here, we deleted Dhx36 in advanced germ cells with Stra8 -GFPCre and found that a Dhx36 deficiency in the differentiated spermatogonia leads to meiotic defects and abnormal spermiogenesis. These defects in late stages of spermatogenesis arise from dysregulated transcription of G4-harboring genes, which are required for meiosis. Thus, this study reveals that Dhx36 plays crucial roles in late stages of spermatogenesis. [ABSTRACT FROM AUTHOR]

To meet the ever-increasing demands of sustainable energy storage devices, exploration of high-performance anode materials for sodium-ion batteries (SIBs) remains an urgent task. Currently, the preparation of MoSe2/carbon nanocompsites, one promising category of anode materials for SIBs, is always based on the stepwise generation of MoSe2 and carbon, which complicates the synthesis procedure and also faces the probability that MoSe2 may detach from carbon during repeated sodiation/desodiation reactions. This work presents a metal–organic framework (MOF)-derived strategy to synthesize embedding-type and chemically bonded MoSe2/nitrogen-doped carbon (NC) nanocomposites via the simultaneous formation of MoSe2 and carbon in one step. Two series of nanocomposites, binary MoSe2/NC and ternary MoSe2/MoO2/NC, are obtained by adjusting the pyrolysis atmospheres and temperatures of MOFs. The microstrip-like MoSe2/NC nanocomposites obtained at a temperature of 400 °C exhibit the optimal Na+-storage capacity at various discharge/charge rates, the reasons for which are illustrated from the aspects of hierarchical microstrip-like morphology, ultrasmall crystallite size and expanded interlayer spacing of MoSe2, porous structure, high carbon content, and the strong chemical bonding interactions between MoSe2 and NC. [ABSTRACT FROM AUTHOR]

Key message: Gene mining in a C. lanatus × C. amarus population revealed one gene, ACS7, linked to primary root elongation in watermelon. Watermelon is a xerophytic crop characterized by a long primary root and robust lateral roots. Therefore, watermelon serves as an excellent model for studying root elongation and development. However, the genetic mechanism underlying the primary root elongation in watermelon remains unknown. Herein, through bulk segregant analysis we identified a genetic locus, qPRL.Chr03, controlling primary root length (PRL) using two different watermelon species (Citrullus lanatus and Citrullus amarus) that differ in their root architecture. Fine mapping revealed that xaa-Pro dipeptidase and 1-aminocyclopropane-1-carboxylate synthase 7 (ACS7) are candidate regulators of the primary root growth. Allelic variation in the delimited region among 193 watermelon accessions indicated that the long-root alleles might only exist in C. amarus. Interestingly, the discrepancy in PRL among the C. amarus accessions was clearly associated with a nonsynonymous single nucleotide polymorphism variant within the ACS7 gene. The ACS7 expression and ethylene levels in the primary root tips suggested that ethylene is a negative regulator of root elongation in watermelon, as supported by the application of 1-aminocyclopropane-1-carboxylate (ACC, the ethylene precursor) or 2-aminoethoxyvinyl glycine (AVG, an ACS inhibitor). To the best of our knowledge, these findings provide the first description of the genetic basis of root elongation in watermelon. The detected markers of the ACS7 gene will facilitate marker-assisted selection for the PRL trait to improve water and nutrient use efficacy in watermelon and beyond. [ABSTRACT FROM AUTHOR]

During the last years, object-based image segmentation (OBIA) has seen a considerable increase in the image segmentation. OBIA is generally based on superpixel methods, in which the clustering-based method plays an increasingly important role. Most clustering methods for generating superpixels suffer from inaccurate classification points with inappropriate cluster centers. To solve the problem, we propose a competitive mechanism-based superpixel generation (CMSuG) method, which both accelerates convergence and promotes robustness for noise sensitivity. Then, image segmentation results will be obtained by a region adjacent graph (RAG)-based merging algorithm after constructing an RAG. However, high segmentation accuracy is customarily accompanied by expensive time-consuming costs. To improve computational efficiency, we address a parallel CMSuG algorithm, the time of which is much less than the CMSuG method. In addition, we present a parallel RAG method to decrease the expensive time-consuming cost in serial RAG construction. By leveraging parallel techniques, the running time of the whole image segmentation method decline with the time complexity from O (N) + O (K2) to O (N/K) or O (K2), in which N is the size of an input image and K is the given number of the superpixel. In the experiments, both nature image and remote sensing image segmentation results demonstrate that our CMSuG method outperforms the state-of-the-art superpixel generation methods, and then performs well for image segmentation in turn. Compared with the serial segmentation method, our parallel techniques gain more than four times acceleration in both remote sensing image dataset and nature image dataset. [ABSTRACT FROM AUTHOR]

The oil concentration is a key parameter of great significance to evaluate the oilfield re-injection water (it generally shortened to re-injection water). However, the detection of the oil concentration for the re-injection water is limited due to the lack of the rapid and efficient detection technology for the evaluation of the organics concentration. The present study proposed a rapid analytical method for oil concentration of the re-injection water based on its optical properties, which has been similarly used in rapid detection of materials content, such as gas mixture and water steam quality. The transmittance spectra of the re-injection water were measured by the ultraviolet and visible (UV–Vis) spectrometer, and the effect on the spectra by the oil concentration was also investigated. A fitting relationship between the optical properties and the oil concentration was established. The results showed that the oil concentration has a great impact on the transmittance spectra of the re-injection water. The accuracy of fitting relationship between the optical properties and the oil concentration developed by the double-thickness inversion model and spectroscopy method is good, in which the minimum fitting error is 1.57% and the best fitting accuracy is 99.96%. The oil concentration also plays an important role in the optical properties of re-injection water, which greatly influence on the fitting accuracy. [ABSTRACT FROM AUTHOR]

Terrestrial tight oil has extremely strong diagenesis heterogeneity, so a large number of rock thin slices are needed to reveal the real microscopic pore-throat structure characteristics. In addition, difficult identification, high cost, long time, strong subjectivity and other problems exist in the identification of tight oil rock thin slices, and it is difficult to meet the needs of fine description and quantitative characterization of the reservoir. In this paper, a method for identifying the characteristics of rock thin slices in tight oil reservoirs based on the deep learning technique was proposed. The present work has the following steps: first, the image preprocessing technique was studied. The original image noise was removed by filtering, and the image pixel size was unified by a normalization technique to ensure the quality of samples; second, the self-labeling image data augmentation technique was constructed to solve the problem of sparse samples; third, the Mask R-CNN algorithm was introduced and improved to synchronize the segmentation and recognition of rock thin slice components in tight oil reservoirs; Finally, it was demonstrated through experiments that the SMR method has significant advantages in accuracy, execution speed and migration. [ABSTRACT FROM AUTHOR]

Water quality deterioration of drinking water distribution systems (DWDSs) caused by water source switching has been reported previously. However, systematic investigation of the biostability of DWDS under water source switching is limited. Aged pipes, including three commonly used pipe materials dug out from a practical DWDS, were used to systematically investigate the biofilm stability mechanism of DWDS under water source switching to quality-improved water. An increase in adenosine triphosphate (ATP) concentration in the bulk water during the initial stage of the switching period was observed, indicating the risk of biofilm release through aged pipe surfaces after water source switching. Sloughing of biofilms might contribute to temporary instability. From day 35, the ATP concentration in the polyethylene (PE) and plastic stainless steel composite (PS) pipes were maintained at approximately 2.40 and 2.56 ng/L, respectively. In contrast, the ATP concentration in the ductile iron (DI) pipes was higher, at approximately 3.43 ng/L from day 42. The water quality variation could cause areas of the biofilm to slough and reduce the biomass of biofilm, causing partial alteration of the microbial community. 16S rRNA gene amplicon sequencing–based functional prediction revealed that the biofilm could increase the abundance of chlorine-resistant bacteria attributed to the increase in Pseudomonas and Methylobacterium after switching to quality-improved water. Moreover, the profiles of specific pathways linked to human diseases, antibiotic resistance, and antibiotic biosynthesis revealed that the safety of the biofilm could improve after switching to quality-improved water. Key points: • The PE and PS biofilm showed improved resistance to water quality perturbation. • Greater number of Methylobacterium was found in the biofilm after water source switching. • 3.16S gene–based metagenomics prediction revealed that the safety of the biofilm under water source switching. [ABSTRACT FROM AUTHOR]

Skin lesion segmentation is the first and indispensable step of malignant melanoma recognition and diagnosis. At present, most of the existing skin lesions segmentation techniques often used traditional methods like optimum thresholding, etc., and deep learning methods like U-net, etc. However, the edges of skin lesions in malignant melanoma images are gradually changed in color, and this change is nonlinear. The existing methods can not effectively distinguish banded edges between lesion areas and healthy skin areas well. Aiming at the uncertainty and fuzziness of banded edges, the neutrosophic set theory is used in this paper which is better than fuzzy theory to deal with banded edge segmentation. Therefore, we proposed a neutrosophy domain-based segmentation method that contains six steps. Firstly, an image is converted into three channels and the pixel matrix of each channel is obtained. Secondly, the pixel matrixes are converted into Neutrosophic Set domain by using the neutrosophic set conversion method to express the uncertainty and fuzziness of banded edges of malignant melanoma images. Thirdly, a new Neutrosophic Entropy model is proposed to combine the three memberships according to some rules by using the transformations in the neutrosophic space to comprehensively express three memberships and highlight the banded edges of the images. Fourthly, the feature augment method is established by the difference of three components. Fifthly, the dilation is used on the neutrosophic entropy matrixes to fill in the noise region. Finally, the image that is represented by transformed matrix is segmented by the Hierarchical Gaussian Mixture Model clustering method to obtain the banded edge of the image. Qualitative and quantitative experiments are performed on malignant melanoma image dataset to evaluate the performance of the NeDSeM method. Compared with some state-of-the-art methods, our method has achieved good results in terms of performance and accuracy. [ABSTRACT FROM AUTHOR]

U-Net++ is one of the most prominent deep convolutional neural networks in the field of medical image segmentation after U-Net. However, the semantic gaps between the encoder and decoder subnets are still large, which will lead to fuzzy feature maps and even target regions of segmentation. To solve this problem, we propose an improved semantic segmentation model utilizing channel attention mechanism and Laplacian sharpening filter, SCU-Net++: dense skip connections are redesigned with sharpening filters to ease the semantic gaps, and channel attention modules are used to make the model pay more attention on the feature maps that are useful for our pixel-level classification task. Compared with U-Net++, the proposed model obtains a more competitive performance on the Pancreas Segmentation dataset and Liver Tumor Segmentation dataset, while increases a very small number of learnable parameters and thus almost does not make additional training and reasoning costs. The training of the proposed method is carried out in deep supervision mode, which alleviates the problem of gradient disappearance, and pruning mechanism can be activated to accelerate the reasoning speed. [ABSTRACT FROM AUTHOR]

A skin lesion is a part of the skin that has abnormal growth on body parts. Early detection of the lesion is necessary, especially malignant melanoma, which is the deadliest form of skin cancer. It can be more readily treated successfully if detected and classified accurately in its early stages. At present, most of the existing skin lesion image classification methods only use deep learning. However, medical domain features are not well integrated into deep learning methods. In this paper, for skin diseases in Asians, a two-phase classification method for skin lesion images is proposed to solve the above problems. First, a classification framework integrated with medical domain knowledge, deep learning, and a refined strategy is proposed. Then, a skin-dependent feature is introduced to efficiently distinguish malignant melanoma. An extension theory-based method is presented to detect the existence of this feature. Finally, a classification method based on deep learning (YoDyCK: YOLOv3 optimized by Dynamic Convolution Kernel) is proposed to classify them into three classes: pigmented nevi, nail matrix nevi and malignant melanomas. We conducted a variety of experiments to evaluate the performance of the proposed method in skin lesion images. Compared with three state-of-the-art methods, our method significantly improves the classification accuracy of skin diseases. [ABSTRACT FROM AUTHOR]

Data are distributed between different parties. Collecting data from multiple parties for analysis and mining will serve people better. However, it also brings unprecedented privacy threats to the participants. Therefore, safe and reliable data publishing among multiple data owners is an urgent problem to be solved. We mainly study the problem of privacy protection in data publishing. For a centralized scenario, we propose the LDA-DP algorithm. First, the within-class mean vectors and the pooled within-class scatter matrix are perturbed by the Gaussian noise. Second, the optimal projection direction vector with differential privacy is obtained by the Fisher criterion. Finally, the low-dimensional projection data of the original data are obtained. For distributed scenarios, we propose the Mul-LDA-DP algorithm based on a blockchain and differential privacy technology. First, the within-class mean vectors and within-class scatter matrices of local data are perturbed by the Gaussian noise and uploaded to the blockchain network. Second, the projection direction vector is calculated in the blockchain network and returned to the data owner. Finally, the data owner uses the projection direction vector to generate low-dimensional projection data of the original data and upload it to the blockchain network for publishing. Furthermore, in a distributed scenario, we propose a correlated noise generation scheme that uses the additivity of the Gaussian distribution to mitigate the effects of noise and can achieve the same noise level as the centralized scenario. We measure the utility of the published data by the SVM misclassification rate. We conduct comparative experiments with similar algorithms on different real data sets. The experimental results show that the data released by the two algorithms can maintain good utility in SVM classification. [ABSTRACT FROM AUTHOR]

To guarantee information security in communication, quantum identity authentication plays a key role in politics, economy, finance, daily life and other fields. In this paper, a new quantum multiparty simultaneous identity authentication protocol with Greenberger–Home–Zeilinger (GHZ) state is presented. In this protocol, the authenticator and the certified parties are the participants with quantum ability, whereas the third party is a classical participant. Here, the third-party is honest and the other two parties may be dishonest. With the help of a classical third-party, a quantum authenticator and the multiple certified parties can implement two-way identity authentication at the same time. It reduces the quantum burden of participants and lowers down the trustworthiness, which makes the protocol be feasible in practice. Through further security analysis, the protocol can effectively prevent an illegal dishonest participant from obtaining a legitimate identity. It shows that the protocol is against impersonation attack, intercept-measure-resend attack and entangle-measure attack, etc. In all, the paper provides positive efforts for the subsequent security identity authentication in quantum network. [ABSTRACT FROM AUTHOR]

Memristors and memcapacitors with threshold voltages have a great potential for chaotic circuit design, memory, and neuromorphic systems. Numerous physical experiments have shown the existence of resistance and capacitance coupling changes phenomenon in some voltage-controlled memristors. In this brief, a voltage threshold adaptive memcapacitive-coupled memristor (VTEAM-M) model is proposed to describe the behavior of resistance-capacitance coupling changes and multiple memristive states of voltage-controlled memristors. The core idea of the model is to divide the device into multiple states, each state corresponds to an equivalent dielectric constant function and an equivalent resistivity function, and then use state variables to represent the superposition and transition between states. The VTEAM-M model retaining the accuracy of Voltage ThrEshold Adaptive Memristor (VTEAM) model in resistance simulation, additionally, adds the modeling of multiple memristive states and capacitance changes of devices. The simulation results are in good agreement with the variation of resistance and capacitance with voltage in real devices. [ABSTRACT FROM AUTHOR]

Blockchain technology has been widely used in digital currency, Internet of Things, and other important fields because of its decentralization, nontampering, and anonymity. The vigorous development of blockchain cannot be separated from the security guarantee. However, there are various security threats within the blockchain that have shown in the past to cause huge financial losses. This paper aims at studying the multi-level security threats existing in the Ethereum blockchain, and exploring the security protection schemes under multiple attack scenarios. There are ten attack scenarios studied in this paper, which are replay attack, short url attack, false top-up attack, transaction order dependence attack, integer overflow attack, re-entrancy attack, honeypot attack, airdrop hunting attack, writing of arbitrary storage address attack, and gas exhaustion denial of service attack. This paper also proposes protection schemes. Finally, these schemes are evaluated by experiments. Experimental results show that our approach is efficient and does not bring too much extra cost and that the time cost has doubled at most. [ABSTRACT FROM AUTHOR]

Anonymous entanglement refers to the construction of entangled channel between two parties among the quantum network, while keeping the identities of these two parties unknown to other parties. A recent work of [A. Unnikrishnan et al., Phys. Rev. Lett. 122, 240501 (2019)] proposed the first anonymous entanglement protocol in the presence of malicious parties and untrusted source. However, the correctness of the original protocol will be threatened, considering that the generated entangled channel can be destroyed by malicious parties without being detected. In this paper, we propose an improved protocol, which ensures the anonymity of the parties and the correctness of shared entanglement channel simultaneously. Furthermore, we consider the noisy measurement in the security analysis, which makes our protocol practical and rigorous. Based on the security analysis, genuine multipartite entanglement could be certified in the untrusted quantum network with our protocol. [ABSTRACT FROM AUTHOR]

This study attempts to widen the conceptual space of resilience in (Western) psychology in order to better capture the resilience landscape of an ethnic minority group ravaged by the HIV/AIDS pandemic—the Nuosu-Yi in Southwest China. Without decolonizing the construct of resilience, non-Western versions of coping with adversities cannot be properly understood. Our process of decolonization of resilience involved two steps: First, we conducted semistructured interviews with the target population (N = 21) to take inventory of their Indigenous notions of resilience. Second, for conceptual comparison, we mapped the themes and categories, derived from thematic analysis, of the interview data onto the conceptual space of the Resilience Scale for Adults (RSA), which we used as proxy for mainstream conceptualizations of resilience. This mapping revealed multiple lacunae in the theoretical framework of RSA, and unique properties in the Indigenous approach to adversities in contrast. Far reaching theoretical and practical implications of this investigation are discussed. [ABSTRACT FROM AUTHOR]

The inverse synthetic aperture radar (ISAR) imaging of the target above the rough surface based on the time-domain (TD) electromagnetic scattering algorithms is focused in this letter. It effectively combines the TD ray tracing (TDRT) method for the composite scene and radar imaging model. The scattering echoes for ISAR imaging are solved by TDRT that the TD mutual coupling between the target and rough surface can be considered by the ray bounce strategy. Compared with the frequency-domain ray tracing and multilevel fast multipole method, the TDRT algorithm not only ensures the calculation accuracy but also greatly reduces the calculation cost. Then, we generate the ISAR image of the target above the rough surface according to the echo matrix based on TDRT. In order to improve the imaging quality of the target, the nonlinear mapping theory is presented to suppress the virtual image points and noisy points and it is beneficial to the future research of target recognition in the composite scene. [ABSTRACT FROM AUTHOR]

The World Health Organization has recognized that testicular function is temperature dependent. Testicular heat exposure caused by occupational factors, lifestyle, and clinical diseases can lead to different degrees of reproductive problems. The aim of this study was to reveal the transcriptional regulatory network and its potential crucial roles in mediating the effects of testicular heat exposure. Testicular tissue was collected from a group of mice subjected to scrotal heat exposure as well as a control group. RNA was isolated from both groups and used for high-throughput sequencing. Using differential transcriptome expression analysis, 172 circRNAs, 279 miRNAs, 465 lncRNAs, and 2721 mRNAs were identified as significantly differentially expressed in mouse testicular tissue after heat exposure compared with the control group. Through Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, differentially expressed lncRNAs and mRNAs were found to have potentially important functions in meiotic cell cycle (GO:0051321), cytoplasm (GO:0005737), membrane raft (GO:0045121), MAPK signaling (mmu04010), purine metabolism (mmu00230), and homologous recombination (mmu03440). Some of the most upregulated and downregulated lncRNAs and circRNAs were predicted to be associated with numerous miRNAs and mRNAs through competing endogenous RNA regulatory network analysis, which were validated with molecular biology experiments. This research provides high-throughput sequencing data of a testicular heat exposure model and lays the foundation for further study on circRNAs, miRNAs, and lncRNAs that are involved in male reproductive diseases related to elevated testicular temperature. [ABSTRACT FROM AUTHOR]