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Nodal shape functions and their gradients are vital in transferring physical information within the material point method (MPM). Their continuity is related to numerical stability and accuracy, and their support domain size affects computational efficiency. In this paper, a scheme of aggregated and smoothed Bernstein functions is proposed to improve the MPM. In detail, the Bernstein polynomials are smoothed with a convolution reformation to eliminate the cell crossing error, and an aggregation strategy is implemented to cut down the node amount required for field probing. Hierarchical MPM variants are obtained with choices of original Bernstein polynomials and degrees of smoothing. Numerical examples show that mass, momentum, and energy conservations are all well met, and no cell crossing noise exists. In addition, solution accuracy and numerical stability are significantly improved in large deformation problems. [ABSTRACT FROM AUTHOR]

The Zinc-air battery (ZAB) has become a hot research topic for nearly a decade due to its high energy densities. As an important category of catalysts for ZAB, perovskites have attracted extensive interests because of their environmentally friendly properties, cheapness, and excellent electrocatalytic performances. This review article discusses the mechanistic analyses regarding the progress of perovskites for ZAB. In addition, electrode manipulation methods of perovskites for battery device are also emphasized. Finally, perspectives are given on the limitations of the current perovskite catalysts for ZABs. We hope that this review will provide new clues for promoting perovskites as catalysts for many energy-storage and conversion applications in the future. [ABSTRACT FROM AUTHOR]

In the process of production, processing, transportation, and storage of edible oils, the oils inevitably come into contact with plastic products. As a result, plasticizers migrate into edible oils, are harmful to human health, and can exhibit reproductive toxicity. Therefore, the determination of plasticizers in edible oils is very important, and a series of sample preparation methods and determination techniques have been developed for the determination of plasticizers in edible oils. Phthalic acid ester (PAE) plasticizers are the most widely used among all plasticizers. This review aims to provide a comprehensive overview of the sample preparation methods and detection techniques reported for the determination of PAEs in edible oils since 2010, focusing on sample preparation methods of edible oils combined with various separation-based analytical techniques, such as gas chromatography (GC) and liquid chromatography (LC) with different detectors. Furthermore, the advantages, disadvantages, and limitations of these techniques as well as the prospective future developments are also discussed. [ABSTRACT FROM AUTHOR]

Positioning the pixels of ground control points (GCPs) in drone images is an issue of great concern in the field of drone photogrammetry. The current mainstream automatic approaches are based on standardized markers, such as circular coded targets and point coded targets. There is no denying that introducing standardized markers improves the efficiency of positioning GCP pixels. However, the low flexibility leads to some drawbacks, such as the heavy logistical input in placing and maintaining GCP markers. Especially as drone photogrammetry steps into the era of large scenes, the logistical input in maintaining GCP markers becomes much more costly. This paper proposes a novel positioning method applicable for non-standardized GCPs. Firstly, regions of interest (ROIs) are extracted from drone images with stereovision technologies. Secondly, the quality of ROIs is evaluated using image entropy, and then the outliers are filtered by an adjusted boxplot. Thirdly, pixels of interest are searched with a corner detector, and the precise imagery coordinates are obtained by subpixel optimization. Finally, the verification was carried out in an urban scene, and the results show that this method has good applicability to the GCPs on road traffic signs, and the accuracy rate is over 95%. [ABSTRACT FROM AUTHOR]

In this paper, a multipath convolutional neural network (MP-CNN) is proposed for rehabilitation exercise recognition using sensor data. It consists of two novel components: a dynamic convolutional neural network (D-CNN) and a state transition probability CNN (S-CNN). In the D-CNN, Gaussian mixture models (GMMs) are exploited to capture the distribution of sensor data for the body movements of the physical rehabilitation exercises. Then, the input signals and the GMMs are screened into different segments. These form multiple paths in the CNN. The S-CNN uses a modified Lempel–Ziv–Welch (LZW) algorithm to extract the transition probabilities of hidden states as discriminate features of different movements. Then, the D-CNN and the S-CNN are combined to build the MP-CNN. To evaluate the rehabilitation exercise, a special evaluation matrix is proposed along with the deep learning classifier to learn the general feature representation for each class of rehabilitation exercise at different levels. Then, for any rehabilitation exercise, it can be classified by the deep learning model and compared to the learned best features. The distance to the best feature is used as the score for the evaluation. We demonstrate our method with our collected dataset and several activity recognition datasets. The classification results are superior when compared to those obtained using other deep learning models, and the evaluation scores are effective for practical applications. [ABSTRACT FROM AUTHOR]

Post-translational modifications of host or viral proteins are key strategies exploited by viruses to support virus replication and counteract host immune response. SUMOylation is a post-translational modification process mediated by a family of ubiquitin-like proteins called small ubiquitin-like modifier (SUMO) proteins. Multiple sequence alignment of 78 representative flaviviruses showed that most (72/78, 92.3%) have a putative SUMO-interacting motif (SIM) at their non-structural 5 (NS5) protein's N-terminal domain. The putative SIM was highly conserved among 414 pre-epidemic and epidemic Zika virus (ZIKV) strains, with all of them having a putative SIM core amino acid sequence of VIDL (327/414, 79.0%) or VVDL (87/414, 21.0%). Molecular docking predicted that the hydrophobic SIM core residues bind to the β2 strand of the SUMO-1 protein, and the acidic residues flanking the core strengthen the binding through interactions with the basic surface of the SUMO protein. The SUMO inhibitor 2-D08 significantly reduced replication of flaviviruses and protected cells against ZIKV-induced cytopathic effects in vitro. A SIM-mutated ZIKV NS5 failed to efficiently suppress type I interferon signaling. Overall, these findings may suggest SUMO modification of the viral NS5 protein to be an evolutionarily conserved post-translational modification process among flaviviruses to enhance virus replication and suppress host antiviral response. [ABSTRACT FROM AUTHOR]

False data injection attack (FDIA) is a deliberate modification of measurement data collected by the power grid using vulnerabilities in power grid state estimation, resulting in erroneous judgments made by the power grid control center. As a symmetrical defense scheme, FDIA detection usually uses machine learning methods to detect attack samples. However, existing detection models for FDIA typically require large-scale training samples, which are difficult to obtain in practical scenarios, making it difficult for detection models to achieve effective detection performance. In light of this, this paper proposes a novel FDIA sample generation method to construct large-scale attack samples by introducing a hybrid Laplacian model capable of accurately fitting the distribution of data changes. First, we analyze the large-scale power system sensing measurement data and establish the data distribution model of symmetric Laplace distribution. Furthermore, a hybrid Laplace-domain symmetric distribution model with multi-dimensional component parameters is constructed, which can induce a deliberate deviation in the state estimation from its safe value by injecting into the power system measurement. Due to the influence of the multivariate parameters of the hybrid Laplace-domain distribution model, the sample deviation generated by this model can not only obtain an efficient attack effect, but also effectively avoid the recognition of the FDIA detection model. Extensive experiments are carried out over IEEE 14-bus and IEEE 118-bus test systems. The corresponding results unequivocally demonstrate that our proposed attack method can quickly construct large-scale FDIA attack samples and exhibit significantly higher resistance to detection by state-of-the-art detection models, while also offering superior concealment capabilities compared to traditional FDIA approaches. [ABSTRACT FROM AUTHOR]

Adjusting the roll angle of a rover's body is a commonly used strategy to improve its traversability over sloped terrains. However, its range of adjustment is often limited, due to constraints imposed by the rover design and geometry factors such as suspension, chassis, size, and suspension travel. In order to improve the rover's traversability under these constraints, this paper proposes a reconfigurable rover design with a two-level (sliding and rolling) mechanism to adjust the body's roll angle. Specifically, the rolling mechanism is a bionic structure, akin to the human ankle joint which can change the contact pose between the wheel and the terrain. This novel adjustment mechanism can modulate the wheel–terrain contact pose, center-of-mass projection over the supporting polygon, wheel load, and the rover driving mode. Combining the wheel–load model and terramechanics-based wheel–terrain interaction model, we develop an integrated model to describe the system dynamics, especially the relationship between rover pose and wheel slippage parameters. Using this model, we develop an associated attitude control strategy to calculate the desired rover pose using particle swarm algorithm while considering the slip rate and angle constraints. We then adjust the sliding and rolling servo angles accordingly for slope traversing operations. To evaluate the proposed design and control strategies, we conduct extensive simulation and experimental studies. The results indicate that our proposed rover design and associated control strategy can double the maximum slope angles that the rover can negotiate, resulting in significantly improved traversability over soft sloped terrains. [ABSTRACT FROM AUTHOR]

Urologic cancers, particularly kidney, bladder, and prostate cancer, have a growing incidence and account for about a million annual deaths worldwide. Treatments, including surgery, chemotherapy, radiotherapy, hormone therapy, and immunotherapy are the main therapeutic options in urologic cancers. Immunotherapy is now a clinical reality with marked success in solid tumors. Immunological checkpoint blockade, non-specific activation of the immune system, adoptive cell therapy, and tumor vaccine are the main modalities of immunotherapy. Immunotherapy has long been used to treat urologic cancers; however, dose-limiting toxicities and low response rates remain major challenges in the clinic. Herein, nanomaterial-based platforms are utilized as the "savior". The combination of nanotechnology with immunotherapy can achieve precision medicine, enhance efficacy, and reduce toxicities. In this review, we highlight the principles of cancer immunotherapy in urology. Meanwhile, we summarize the nano-immune technology and platforms currently used for urologic cancer treatment. The ultimate goal is to help in the rational design of strategies for nanomedicine-based immunotherapy in urologic cancer. [ABSTRACT FROM AUTHOR]

Due to the complex structure of the angle grinder and the existence of multiple rotating parts, the coupling phenomenon of the data results in the complexity and chaos of the data. The market scale of angle grinder is huge. Manual diagnosis and traditional diagnosis are difficult to meet the requirements, so a fault diagnosis method of angle grinder that is based on adaptive parameters and chaos theory of dual-strategy differential evolution algorithm (ACD-DE) and SVM model hybrid algorithm is proposed by combining a chaos-mapping algorithm, dynamic and adaptive scale factor, and crossover factor. The effectiveness and robustness of the algorithm are proven by solving eight test functions. The acceleration signal is decomposed by wavelet packet decomposition and reconstruction, and a variety of sensor signals are processed and constructed as feature vectors. The training set and the test set of the fault diagnosis model are divided. SVM model is used as the fault diagnosis model and optimized by ACD-DE. Based on the fault data of the angle grinder, the hybrid algorithm is compared with other optimization algorithms and other machine learning models; the comparison results show that the performance of the improved differential evolution algorithm is improved, in which the precision rate is 98.81%, the recall rate is 98.74%, and the F1 score is 0.9877. Experiments show that the hybrid algorithm has strong diagnosis accuracy and robustness. [ABSTRACT FROM AUTHOR]

Wilms' tumor 1-associating protein (WTAP) is required for N6-methyladenosine (m6A) RNA methylation modifications, which regulate biological processes such as RNA splicing, cell proliferation, cell cycle, and embryonic development. m6A is the predominant form of mRNA modification in eukaryotes. WTAP exerts m6A modification by binding to methyltransferase-like 3 (METTL3) in the nucleus to form the METTL3-methyltransferase-like 14 (METTL14)-WTAP (MMW) complex, a core component of the methyltransferase complex (MTC), and localizing to the nuclear patches. Studies have demonstrated that WTAP plays a critical role in various cancers, both dependent and independent of its role in m6A modification of methyltransferases. Here, we describe the recent findings on the structural features of WTAP, the mechanisms by which WTAP regulates the biological functions, and the molecular mechanisms of its functions in various cancers. By summarizing the latest WTAP research, we expect to provide new directions and insights for oncology research and discover new targets for cancer treatment. [ABSTRACT FROM AUTHOR]

There are abundant sea-sand resources on the earth. Traditional sea-sand concrete faced various problems relating to insufficient anticorrosion ability. In this paper, artificial seawater, sea sand, industrial waste, steel fiber, and polycarboxylate superplasticizer were used to prepare ultra-high-performance polymer cement mortar (SSUHPC). At the same time, freshwater river-sand ultra-high-performance polymer cement mortar (FRUHPC) with the same mixing ratio was prepared for comparative study. The compressive strength of SSUHPC reached 162.1 MPa, while the that of FRUHPC reached 173.3 MPa, which was slightly higher. Meanwhile, SSUHPC showed excellent anticorrosion characteristics in terms of carbonization, frost resistance and chloride resistance, and especially for sulfate resistance. The composition of SSUHPC was separated into three parts: mortar, pore and steel fiber, and the performance difference mechanisms of SSUHPC and FRUHPC were investigated by X-ray computed tomography (X-CT), mercury intrusion porosimetry (MIP), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The hydration degree of mortar in SSUHPC was higher, with higher content of CSH and CH, and its better optimized gel pore characteristics gave SSUHPC better corrosion resistance. The mechanical properties of SSUHPC were slightly poor due to the uneven dispersion of steel fibers and air pores, with an- air pore porosity of 1.52% (above 200 μm) that was twice that of FRUHPC (0.6%). In this paper, the mechanics and anticorrosion performance of ultra-high-performance polymer cement mortar prepared with seawater sea sand were comprehensively evaluated, and the mechanism of performance difference between SSUHPC and FRUHPC was revealed, conducive to the targeted improvement of sea sand concrete. [ABSTRACT FROM AUTHOR]

Firefighting in high-rise buildings remains a difficult problem in the world because fire extinguishing equipment and tactics have many deficiencies in dealing with such building fires, especially for buildings higher than 50 m. In the present study, the LY100 fire extinguishing system is taken as an example to introduce the application of the fire drone in the fire control of high-rise buildings. The LY100 fire extinguishing system mainly contains the twin-rotor drone, high-pressure liquid fire extinguishing equipment, pressure fire extinguishing equipment, associated vehicle and extinguishing agent. The LY100 system can be deployed quickly and operated flexibly. Based on such advantages, the indoor fire, exterior thermal insulation layer fire and top platform fire of high-rise building can be extinguished in a timely manner with the LY100 system. In addition, four kinds of firefighting tactics are described in this paper, including one drone operation, double drone cooperative operation, three or more drone cooperative operations, and cooperating with the lifting fire truck. Finally, the experiments are presented to verify the spraying distance of the fire drone system. [ABSTRACT FROM AUTHOR]

Electric energy metering plays a crucial role in ensuring fair and equitable transactions between grid companies and power users. With the implementation of the State Grid Corporation's energy Internet strategy, higher requirements have been put forward for power grid companies to reduce costs and increase efficiency and user service capabilities. Meanwhile, the accuracy and real-time requirements for electric energy measurements have also increased. Electricity information collection systems are mainly used to collect the user-side energy metering data for the power users. Attributed to communication errors, communication delays, equipment failures and other reasons, some of the collected data is missed or confused, which seriously affects the refined management and service quality of power grid companies. How to deal with such data has been one of the important issues in the fields of machine learning and data mining. This paper proposes a collaborative fitting algorithm to solve the problem of missing collected data based on latent semantics. Firstly, a tree structure of user history data is established, and then the user groups adjacent to the user with missing data are obtained from this. Finally, the missing data are recovered using the alternating least-squares matrix factorization algorithm. Through numerical verification, this method has high reliability and accuracy in recoverying the missing data. [ABSTRACT FROM AUTHOR]

A polarization-insensitive broadband terahertz absorber based on single-layer graphene metasurface has been designed and simulated, in which the graphene metasurface is composed of isolated circular patches. After simulation and optimization, the absorption bandwidth of this absorber with more than 90% absorptance is up to 2 THz. The simulation results demonstrate that the broadband absorption can be achieved by combining the localized surface plasmon (LSP) resonances on the graphene patches and the resonances caused by the coupling between them. The absorption bandwidth can be changed by changing the chemical potential of graphene and the structural parameters. Due to the symmetrical configuration, the proposed absorber is completely insensitive to polarization and have the characteristics of wide angle oblique incidence that they can achieve broadband absorption with 70% absorptance in the range of incident angle from 0° to 50° for both TE and TM polarized waves. The flexible and simple design, polarization insensitive, wide-angle incident, broadband and high absorption properties make it possible for our proposed absorber to have promising applications in terahertz detection, imaging and cloaking objects. [ABSTRACT FROM AUTHOR]

Many computer-aided diagnosis methods, especially ones with deep learning strategies, of liver cancers based on medical images have been proposed. However, most of such methods analyze the images under only one scale, and the deep learning models are always unexplainable. In this paper, we propose a deep learning-based multi-scale and multi-level fusing approach of CNNs for liver lesion diagnosis on magnetic resonance images, termed as MMF-CNN. We introduce a multi-scale representation strategy to encode both the local and semi-local complementary information of the images. To take advantage of the complementary information of multi-scale representations, we propose a multi-level fusion method to combine the information of both the feature level and the decision level hierarchically and generate a robust diagnostic classifier based on deep learning. We further explore the explanation of the diagnosis decision of the deep neural network through visualizing the areas of interest of the network. A new scoring method is designed to evaluate whether the attention maps can highlight the relevant radiological features. The explanation and visualization make the decision-making process of the deep neural network transparent for the clinicians. We apply our proposed approach to various state-of-the-art deep learning architectures. The experimental results demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Persons living with HIV (PLWH) continuously experience symptom burdens. Their symptom prevalence and severity are also quite different. Mobile health (mHealth) applications (apps) offer exceptional opportunities for using personalized interventions when and where PLWH are needed. This study aimed to demonstrate the development process of the symptom management (SM) app and the structure and content of it. Our research team systematically searched for evidence-based resources and summarized up-to-date evidence for symptom management and health education. Our multidisciplinary research team that included physicians, nurses, software engineers, and nursing professors, evaluated the structure and content of the drafted app. Both quantitative data and qualitative results were collected at a group discussion meeting. Quantitative data were scores of sufficient evidence, situational suitability, practicability, cost-effectiveness, and understandability (ranged from one to four) for 119 items of the app contents, including the health tracking module, the self-assessment module, coping strategies for 18 symptoms (80 items), medication management, complementary therapy, diet management, exercise, relaxation techniques, and the obtaining support module. The SM app was comprised of eight modules and provided several personalized symptom management functions, including assessing symptoms and receiving different symptom management strategies, tracking health indicators, and communicating with medical staff. The SM app was a promising and flexible tool for HIV symptom management. It provided PLWH with personalized symptom management strategies and facilitated the case management for medical staff. Future studies are needed to further test the app's usability among PLWH users and its effects on symptom management. [ABSTRACT FROM AUTHOR]

High-grade serous carcinoma (HGSC), the most lethal subtype of epithelial ovarian cancer (EOC), is characterized by widespread TP53 mutations (>90%), most of which are missense mutations (>70%). The objective of this study was to investigate differential transcriptional targets affected by a common germline P72R SNP (rs1042522) in two p53 hotspot mutants, R248Q and R248W, and identify the mechanism through which the P72R SNP affects the neomorphic properties of these mutants. Using isogenic cell line models, transcriptomic analysis, xenografts, and patient data, we found that the P72R SNP modifies the effect of p53 hotspot mutants on cellular morphology and invasion properties. Most importantly, RNA sequencing studies identified CXCL1 a critical factor that is differentially affected by P72R SNP in R248Q and R248W mutants and is responsible for differences in cellular morphology and functional properties observed in these p53 mutants. We show that the mutants with the P72 SNP promote a reversion of the EMT phenotype to epithelial characteristics, whereas its R72 counterpart promotes a mesenchymal transition via the chemokine CXCL1. These studies reveal a new role of the P72R SNP in modulating the neomorphic properties of p53 mutants via CXCL1, which has significant implications for tumor invasion and metastasis. [ABSTRACT FROM AUTHOR]